From 08a7bb5343e3b45522a5c6f62acba247ff488fd7 Mon Sep 17 00:00:00 2001 From: Aaron Stannard Date: Sun, 5 Jul 2026 12:39:34 -0500 Subject: [PATCH 01/37] =?UTF-8?q?feat(memory):=20embedding=20foundation=20?= =?UTF-8?q?=E2=80=94=20ONNX=20runtime,=20provisioning,=20embed-on-write,?= =?UTF-8?q?=20backfill=20(memory-core-redesign=20slice=202)=20(#1577)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * feat(embeddings): standalone ONNX embedding runtime + model provisioner Adds the src/Netclaw.Embeddings project (Microsoft.ML.OnnxRuntime CPU EP, FastBertTokenizer, System.Numerics.Tensors), referenced by nothing yet — daemon/CLI wiring is Stage B. - OnnxMemoryEmbedder: single InferenceSession (IntraOpNumThreads=4), BoundedConcurrencyGate (default max 2 concurrent inferences, peak concurrency observable for tests), FastBertTokenizer WordPiece tokenization truncated to 512 tokens. Feeds only the input names the loaded ONNX graph declares rather than hardcoding the 3-input BERT signature. CLS-token pooling (last_hidden_state[:, 0, :]) + L2 normalization — verified against both allowlisted models' model cards (snowflake-arctic-embed-m: "use the CLS token"; mxbai-embed-large-v1: "works really well with cls pooling (default)"). - EmbeddingModelProvisioner: pinned in-code allowlist (model id -> URL, SHA-256, byte size, dimensions) injected as a required dependency (not a hardcoded internal) so tests can supply a localhost-pointed allowlist instead of ever reaching the real HuggingFace URLs. Atomic temp-file-then-rename download, byte-size + SHA-256 verification before the destination file is ever created, unknown-id rejection listing the allowlist. Allowlist entries (URLs pinned to a specific upstream commit, not `main`): snowflake-arctic-embed-m (768 dims, plain fp32 model.onnx, ~416 MB) and mxbai-embed-large-v1 fallback (1024 dims, ~1.27 GB fp32). Tests (Netclaw.Embeddings.Tests, no network): - Tiny fixture ONNX graph + WordPiece vocab (generated by Fixtures/generate_fixture_model.py, committed with a header comment explaining the graph shape and regeneration steps) exercise OnnxMemoryEmbedder end-to-end: deterministic output, L2-normalized, content-sensitive (attention-masked mean pooling reported at the CLS position so a content-blind bug would be caught), batch order preservation. - BoundedConcurrencyGate tested in isolation against a controlled fake delayed workload (Task.Delay lives in the fake, not in test orchestration) proving the concurrency bound is actually enforced. - EmbeddingModelProvisioner tested against a local HttpListener fixture: hash-mismatch and byte-size-mismatch rejection with no leftover temp files, unknown-id rejection listing the allowlist, successful provision leaves exactly the two expected files. opsx: memory-core-redesign slice 2 * feat(memory): IMemoryEmbedder seam, content hasher, vector index, embeddings schema - IMemoryEmbedder (+ UnavailableMemoryEmbedder degraded stub) in Netclaw.Actors/Memory so actor code carries no OnnxRuntime dependency; Netclaw.Embeddings implements the interface, never the reverse. UnavailableMemoryEmbedder throws InvalidOperationException with remediation text on Embed*Async rather than returning a garbage vector. - MemoryContentHasher: SHA-256 over normalized title+body, reusing CurationRulesEvaluator.NormalizeForContainment (promoted from private to internal) rather than a second hand-rolled normalizer, so curation's destructive-update guard and the embedding re-embed skip can't quietly disagree about what counts as changed content. - MemoryVectorIndex: per-model flat float[] + parallel id/kind arrays bundled into an immutable snapshot (no torn reads), TopK via System.Numerics.Tensors.TensorPrimitives.CosineSimilarity with a minCosine floor, reload gated on SQLiteMemoryStore.EmbeddingDataVersion so unchanged turns pay no reload cost. - SQLiteMemoryStore: memory_embeddings(item_id, item_kind, model_id, content_hash, dims, vector BLOB, created_at) DDL in the existing idempotent InitializeAsync; UpsertEmbeddingAsync (hash-skip: no write and no EmbeddingDataVersion bump when the content hash is unchanged, float32 LE blob); GetEmbeddingsForModelAsync (thin query for the vector index to consume — the design's FindNearestByEmbeddingAsync, renamed per plan); GetEmbeddingCoverageAsync (total recallable docs, embedded-current-hash count, other-model count) for the coverage diagnostics spec requirement; TombstoneDocumentAsync extended to delete the tombstoned document's embedding rows in the same transaction and bump the version counter, since vectors are derived data that must not keep surfacing a dead document as a kNN neighbor. No production code path calls any of this yet (embed-on-write, the vector index's runtime wiring, and the doctor/status degradation surfaces are Stage B) — this slice writes vectors, nothing reads them, zero behavior risk per the design's migration plan. opsx: memory-core-redesign slice 2 * feat(memory): mark tasks 2.1-2.6 complete (opsx: memory-core-redesign slice 2) Task 2.12 (tests) stays unchecked — its warmup/gap-repair/doctor-facing scenarios land with Stage B daemon wiring; the store/index/hasher/ provisioner/embedder subset testable at this layer is covered. * feat(memory): embed-on-write foundation — holder, coordinator, store seams, config (opsx: memory-core-redesign slice 2, tasks 2.7/2.8/2.11) - MemoryEmbedderHolder: mutable holder the warmup hosted service populates (hosted-service startup order vs construction-time DI documented on the type) - MemoryEmbedOnWriteCoordinator: single embed-on-write hook both curation pipelines call post-commit; embedding failures never fail the memory write (vectors are derived data, D3) - SQLiteMemoryStore: ApplyInlineCurationBatchAsync/ApplyCurationBatchAsync now return the written document rows (the post-commit ids+content the coordinator needs); GetDocumentsNeedingEmbeddingAsync derives gap-repair/backfill state (never a progress table); UpsertEmbeddingAsync reports wrote-vs-skipped - MemoryCurationActor + MemoryCurationWorkerService callers embed after commit - Memory.Embeddings config { Enabled=false (deliberate staging, flipped in Slice 3/4), ModelId=snowflake-arctic-embed-m, AutoDownload=true } + schema sync with defaults; NetclawPaths.ModelsDirectory + EmbeddingModelDirectory - DaemonRuntimeStatus.Embeddings wire type (ok/degraded/disabled) - EmbeddingModelProvisioner: skip-if-valid local copy (no network on restart) + TryLoadVerifiedAsync for AutoDownload=false paths * feat(daemon): embedding warmup service, gap repair, degraded status surface (opsx: memory-core-redesign slice 2, tasks 2.7/2.8/2.10) - EmbeddingWarmupHostedService: provision-or-degrade at startup (AutoDownload gates the network path entirely — even to repair a corrupt local copy), one warm-up inference, then a batched (16, yielding) gap-repair sweep over documents missing a current-model/current-hash embedding - ANY failure => UnavailableMemoryEmbedder + error-level memory_embedding_unavailable log; daemon NEVER fails startup on embeddings - DI: holder starts as Unavailable stub; warmup populates it; SessionMemoryServices carries it to the inline curation actor - MemoryCurationWorkerService embeds written docs post-commit (task 2.8's second pipeline call site) - DaemonRuntimeStatusService reports embeddings: ok/degraded/disabled with modelId under the Memory status block * feat(cli): netclaw memory backfill-embeddings + embedding doctor check (opsx: memory-core-redesign slice 2, tasks 2.9/2.10) - New 'netclaw memory' command group (offline, direct SQLite/model-file access) with backfill-embeddings [--force]: provisions if needed (clear error when AutoDownload=false and model missing), embeds in batches of 16 with progress output, final embedded/skipped-hash-unchanged/failed summary; safe against a live daemon (WAL + per-item upserts whose hash check re-queries at call time) - MemoryEmbeddingDoctorCheck: Error when Enabled but model missing/hash-invalid; Warning on missing current-model embeddings (count) or mixed-model corpus (recommends --force backfill); Pass with coverage summary; Pass when disabled - Allowlist is an injected dependency on both (same seam as the provisioner) so tests use the tiny fixture model — no network in tests - Schema round-trip tests for the Memory.Embeddings config section * docs(opsx): correct D2 model line to shipped reality; mark tasks 2.7-2.12 complete (memory-core-redesign slice 2) design.md D2 said 'snowflake-arctic-embed 137M int8' — Stage A shipped the ~110M-param arctic-embed-m fp32 ONNX artifact pinned by hash; int8 is noted as a future optimization, not what the allowlist points at. * ci+skills: arm64 onnxruntime smoke leg, memory/operations skill sync (opsx: memory-core-redesign slice 2) * feat(tools): ONNX embedding latency bench + measured numbers, docs(opsx) design.md Add tools/embed-latency-bench (standalone console, kept out of Netclaw.slnx): loads the production OnnxMemoryEmbedder path (hash-verified snowflake-arctic-embed-m, same pooling/threading/concurrency-gate config the daemon uses) and times batch=1 EmbedAsync calls across short-query/medium/doc-length corpora (20 warmup + 200 timed iterations each), plus cold-load and a concurrency=2 pass. Measured on the i9-9900K reference box (8 logical cores, contended: load avg 2.0-3.6, ~11/15 GiB RAM in use, live daemon running): short-query p50 281ms / p95 315ms - statistically indistinguishable from doc-length (p50 275ms / p95 294ms) because OnnxMemoryEmbedder pads every input to a fixed 512 tokens regardless of actual length, so the fixed-size fp32 forward pass dominates latency, not tokenization. Resolves memory-core-redesign task 2.13 and its design.md open question: the 150ms query-embedding sub-budget does NOT hold on this hardware (p95 ~2.1x over budget, margin ~-165ms). Updates D6, the Risks/Trade-offs entry, and the Open Questions table with the measured numbers and verdict; corrects stale "ONNX int8" wording to match the D2-shipped fp32 reality (int8 remains a deferred optimization). Highest-leverage unexplored mitigation: a query-specific max-length well below 512, not int8 quantization. * chore(bench): dynamic sequence length experiment Extends tools/embed-latency-bench with a bench-only parallel code path (OnnxMemoryEmbedder production code untouched) that: - inspects InferenceSession.InputMetadata to confirm the ONNX graph's sequence axis is symbolic (dynamic), not fixed - runs the same short/medium/doc corpora padded to actual tokenized length (bucket-of-8 rounding) instead of fixed 512, same 20 warmup / 200 timed / batch=1 / Release protocol - cross-checks correctness: cosine similarity between fixed-512 and dynamic-length embeddings for 10 fixed sentences - records load average before/after for honest contention context Measured on the reference box: short-query p50 19.0ms / p95 20.9ms (vs 281.9ms / 310.5ms fixed-512) with 1.000000 cosine parity across all 10 sentences. Well under the 150ms Slice 4 sub-budget. Updates openspec/changes/memory-core-redesign/design.md (D6, Risks, Open Questions) with the measured numbers and the decision to adopt dynamic sequence length as the Slice 4 mitigation. --- .../workflows/publish_release_binaries.yml | 29 ++ Directory.Packages.props | 13 + Netclaw.slnx | 2 + .../.system/files/netclaw-memory/SKILL.md | 15 +- .../.system/files/netclaw-operations/SKILL.md | 6 +- .../changes/memory-core-redesign/design.md | 107 ++++- .../changes/memory-core-redesign/tasks.md | 30 +- .../Memory/MemoryContentHasherTests.cs | 69 +++ .../MemoryEmbedOnWriteCoordinatorTests.cs | 153 +++++++ .../Memory/MemoryVectorIndexTests.cs | 142 ++++++ .../Memory/SQLiteMemoryStoreEmbeddingTests.cs | 427 +++++++++++++++++ .../Memory/UnavailableMemoryEmbedderTests.cs | 44 ++ .../Memory/CurationRulesEvaluator.cs | 13 +- src/Netclaw.Actors/Memory/IMemoryEmbedder.cs | 100 ++++ .../Memory/MemoryContentHasher.cs | 35 ++ .../Memory/MemoryCurationActor.cs | 32 +- .../Memory/MemoryEmbedOnWriteCoordinator.cs | 104 +++++ .../Memory/MemoryEmbedderHolder.cs | 55 +++ .../Memory/MemoryVectorIndex.cs | 143 ++++++ .../Memory/SQLiteMemoryStore.cs | 292 +++++++++++- src/Netclaw.Actors/Netclaw.Actors.csproj | 3 + .../Sessions/LlmSessionActor.cs | 4 +- .../Sessions/SessionDependencies.cs | 7 +- .../Doctor/ConfigSchemaDoctorCheckTests.cs | 54 +++ .../Doctor/MemoryEmbeddingDoctorCheckTests.cs | 192 ++++++++ .../Memory/MemoryCommandTests.cs | 197 ++++++++ .../Netclaw.Cli.Tests.csproj | 8 + .../Doctor/DoctorRegistrationExtensions.cs | 5 + .../Doctor/MemoryEmbeddingDoctorCheck.cs | 99 ++++ src/Netclaw.Cli/Memory/MemoryCommand.cs | 174 +++++++ src/Netclaw.Cli/Netclaw.Cli.csproj | 1 + src/Netclaw.Cli/Program.cs | 12 + .../MemoryConfigDefaultsTests.cs | 46 ++ .../DaemonRuntimeStatus.cs | 18 + src/Netclaw.Configuration/MemoryConfig.cs | 40 ++ src/Netclaw.Configuration/NetclawPaths.cs | 17 + .../Schemas/netclaw-config.v1.schema.json | 22 + .../DaemonRuntimeStatusServiceTests.cs | 82 +++- .../Netclaw.Daemon.Tests.csproj | 7 + .../EmbeddingWarmupHostedServiceTests.cs | 185 ++++++++ .../Gateway/DaemonRuntimeStatusService.cs | 36 +- src/Netclaw.Daemon/Netclaw.Daemon.csproj | 1 + src/Netclaw.Daemon/Program.cs | 18 +- .../Services/EmbeddingWarmupHostedService.cs | 185 ++++++++ .../Services/MemoryCurationWorkerService.cs | 12 +- .../BoundedConcurrencyGateTests.cs | 73 +++ .../EmbeddingModelProvisionerTests.cs | 239 ++++++++++ .../Fixtures/generate_fixture_model.py | 108 +++++ .../Fixtures/tiny-embedder.onnx | Bin 0 -> 1459 bytes .../Fixtures/tiny-vocab.txt | 18 + .../LocalArtifactServer.cs | 99 ++++ .../Netclaw.Embeddings.Tests.csproj | 26 ++ .../OnnxMemoryEmbedderTests.cs | 102 +++++ .../EmbeddingModelProvisioner.cs | 238 ++++++++++ .../Netclaw.Embeddings.csproj | 27 ++ src/Netclaw.Embeddings/OnnxMemoryEmbedder.cs | 263 +++++++++++ tools/embed-latency-bench/Program.cs | 433 ++++++++++++++++++ .../embed-latency-bench.csproj | 21 + 58 files changed, 4830 insertions(+), 53 deletions(-) create mode 100644 src/Netclaw.Actors.Tests/Memory/MemoryContentHasherTests.cs create mode 100644 src/Netclaw.Actors.Tests/Memory/MemoryEmbedOnWriteCoordinatorTests.cs create mode 100644 src/Netclaw.Actors.Tests/Memory/MemoryVectorIndexTests.cs create mode 100644 src/Netclaw.Actors.Tests/Memory/SQLiteMemoryStoreEmbeddingTests.cs create mode 100644 src/Netclaw.Actors.Tests/Memory/UnavailableMemoryEmbedderTests.cs create mode 100644 src/Netclaw.Actors/Memory/IMemoryEmbedder.cs create mode 100644 src/Netclaw.Actors/Memory/MemoryContentHasher.cs create mode 100644 src/Netclaw.Actors/Memory/MemoryEmbedOnWriteCoordinator.cs create mode 100644 src/Netclaw.Actors/Memory/MemoryEmbedderHolder.cs create mode 100644 src/Netclaw.Actors/Memory/MemoryVectorIndex.cs create mode 100644 src/Netclaw.Cli.Tests/Doctor/MemoryEmbeddingDoctorCheckTests.cs create mode 100644 src/Netclaw.Cli.Tests/Memory/MemoryCommandTests.cs create mode 100644 src/Netclaw.Cli/Doctor/MemoryEmbeddingDoctorCheck.cs create mode 100644 src/Netclaw.Cli/Memory/MemoryCommand.cs create mode 100644 src/Netclaw.Configuration.Tests/MemoryConfigDefaultsTests.cs create mode 100644 src/Netclaw.Daemon.Tests/Services/EmbeddingWarmupHostedServiceTests.cs create mode 100644 src/Netclaw.Daemon/Services/EmbeddingWarmupHostedService.cs create mode 100644 src/Netclaw.Embeddings.Tests/BoundedConcurrencyGateTests.cs create mode 100644 src/Netclaw.Embeddings.Tests/EmbeddingModelProvisionerTests.cs create mode 100644 src/Netclaw.Embeddings.Tests/Fixtures/generate_fixture_model.py create mode 100644 src/Netclaw.Embeddings.Tests/Fixtures/tiny-embedder.onnx create mode 100644 src/Netclaw.Embeddings.Tests/Fixtures/tiny-vocab.txt create mode 100644 src/Netclaw.Embeddings.Tests/LocalArtifactServer.cs create mode 100644 src/Netclaw.Embeddings.Tests/Netclaw.Embeddings.Tests.csproj create mode 100644 src/Netclaw.Embeddings.Tests/OnnxMemoryEmbedderTests.cs create mode 100644 src/Netclaw.Embeddings/EmbeddingModelProvisioner.cs create mode 100644 src/Netclaw.Embeddings/Netclaw.Embeddings.csproj create mode 100644 src/Netclaw.Embeddings/OnnxMemoryEmbedder.cs create mode 100644 tools/embed-latency-bench/Program.cs create mode 100644 tools/embed-latency-bench/embed-latency-bench.csproj diff --git a/.github/workflows/publish_release_binaries.yml b/.github/workflows/publish_release_binaries.yml index cabdaf7b9..55d7d2a29 100644 --- a/.github/workflows/publish_release_binaries.yml +++ b/.github/workflows/publish_release_binaries.yml @@ -143,6 +143,35 @@ jobs: --output-dir ./publish --version ${{ github.ref_name }} + # ARM64 cross-compile verification: since ARM64 binaries are built on x64 runners, + # we cannot execute them. Instead, verify the build actually produced ARM64 ELF + # files (not x64) using the `file` command to detect architecture mismatch. This + # catches silent cross-compile failures. See CONTRIBUTING.md § Cross-Platform + # Publishing for context. + - name: Verify ARM64 binaries are actually ARM64 (not x64) + if: matrix.rid == 'linux-arm64' + shell: bash + run: | + set -euo pipefail + CLI="./publish/cli/netclaw" + DAEMON="./publish/daemon/netclawd" + + for binary in "$CLI" "$DAEMON"; do + if [ ! -f "$binary" ]; then + echo "ERROR: Expected binary not found: $binary" >&2 + exit 1 + fi + + # Check architecture with `file` command + file_output=$(file "$binary") + if ! echo "$file_output" | grep -q "ARM aarch64"; then + echo "ERROR: Binary $binary is not ARM64:" >&2 + echo " $file_output" >&2 + exit 1 + fi + echo "✓ $binary is ARM64" + done + - name: Package archives (Unix) if: runner.os != 'Windows' run: | diff --git a/Directory.Packages.props b/Directory.Packages.props index 441104334..bd52f917b 100644 --- a/Directory.Packages.props +++ b/Directory.Packages.props @@ -109,6 +109,19 @@ + + + + + + + diff --git a/Netclaw.slnx b/Netclaw.slnx index a7cf41e75..4b0dba2ec 100644 --- a/Netclaw.slnx +++ b/Netclaw.slnx @@ -8,6 +8,8 @@ + + diff --git a/feeds/skills/.system/files/netclaw-memory/SKILL.md b/feeds/skills/.system/files/netclaw-memory/SKILL.md index 9157c1008..2f0973821 100644 --- a/feeds/skills/.system/files/netclaw-memory/SKILL.md +++ b/feeds/skills/.system/files/netclaw-memory/SKILL.md @@ -3,7 +3,7 @@ name: netclaw-memory description: "REQUIRED when the user asks what you remember, recall, or know from past conversations, previous sessions, cross-session memory, memory classes, or memory types. Also before using memory tools: find_memories, get_memories, store_memory, update_memory." metadata: author: netclaw - version: "1.7.0" + version: "1.8.0" --- # Netclaw Memory @@ -153,6 +153,19 @@ Useful log events: - `memory_observation_sidecar_completed` - `memory_observation_gate_result` +### Embeddings + +Embeddings are provisioned at daemon start when `Memory.Embeddings.Enabled` is +`true` (default `false` for now). When unavailable: +- Log: `memory_embedding_unavailable` +- Daemon status shows: `embeddings: degraded` +- Lexical recall continues to work normally + +To repopulate existing memory vectors after enabling embeddings: +``` +netclaw memory backfill-embeddings [--force] +``` + ## Eval Gate Before rollout, run the redesigned provider-independent eval suites first, diff --git a/feeds/skills/.system/files/netclaw-operations/SKILL.md b/feeds/skills/.system/files/netclaw-operations/SKILL.md index a514dbd60..863c76be2 100644 --- a/feeds/skills/.system/files/netclaw-operations/SKILL.md +++ b/feeds/skills/.system/files/netclaw-operations/SKILL.md @@ -3,7 +3,7 @@ name: netclaw-operations description: "REQUIRED when the user asks about scheduling, reminders, cron jobs, timers, background jobs, diagnostics, troubleshooting, MCP tools, daemon health, identity updates, or Netclaw capabilities and self-maintenance." metadata: author: netclaw - version: "2.24.0" + version: "2.25.0" --- # Netclaw Operations @@ -298,7 +298,9 @@ Add or switch model providers (including OAuth login) and configure search backe ## Diagnostics, Kill Switches & Self-Maintenance When something is broken, start with `netclaw status`, then `netclaw doctor`. Feature -kill switches and self-update/health are covered in the reference. Full guidance: +kill switches and self-update/health are covered in the reference. Memory embeddings +can be backfilled with `netclaw memory backfill-embeddings [--force]`; doctor checks +memory embedding availability. Full guidance: `skill_read_resource('netclaw-operations', 'references/diagnostics.md')`. ## Identity diff --git a/openspec/changes/memory-core-redesign/design.md b/openspec/changes/memory-core-redesign/design.md index 86b14695f..482b6967b 100644 --- a/openspec/changes/memory-core-redesign/design.md +++ b/openspec/changes/memory-core-redesign/design.md @@ -91,9 +91,10 @@ daemon start when `AutoDownload=true` (atomic temp+rename download, hash verify, then one warm-up inference), or the operator runs `netclaw memory backfill-embeddings`. The ~90–140 MB artifact is never an embedded resource (would bloat every RID publish). Default model: -snowflake-arctic-embed 137M int8 (May-ratified; mxbai-embed-large 335M is the -allowlisted fallback). Post-PoC decision deferred: mirroring artifacts into -the existing R2 feeds channel vs pinned upstream URLs. +snowflake-arctic-embed-m (~110M params, fp32 ONNX, pinned by hash — int8 is a +future optimization, not what Stage A shipped; May-ratified), mxbai-embed-large +335M is the allowlisted fallback. Post-PoC decision deferred: mirroring +artifacts into the existing R2 feeds channel vs pinned upstream URLs. ### D3. Vector storage: separate `memory_embeddings` table, owned by the store @@ -172,11 +173,31 @@ the outer bounds. *Alternative considered*: RRF fusion — rejected: rank-only fusion always admits the top item even when nothing is relevant; the zero-injection -behavior requires an absolute score. *Latency risk is explicit*: Ollama -measurements ran far above the 10–50 ms/query assumption; the ONNX int8 -short-query latency MUST be measured before this slice ships (mitigations: -raise `RecallTimeoutMs`, pre-warmed session, or skip-vector-under-pressure — -all loud, none silent). +behavior requires an absolute score. *Latency measured, not assumed*: Ollama +measurements ran far above the 10–50 ms/query assumption, and the in-process +ONNX fp32 measurement (Slice 2 task 2.13; full numbers in Open Questions) +shows the same problem persists — p95 ≈ 315 ms on the i9-9900K reference box, +~2× over the 150 ms sub-budget, because the embedder pads every input to a +fixed 512 tokens regardless of actual length. + +**Mitigation, measured (`tools/embed-latency-bench` dynamic-length +extension)**: the ONNX graph's sequence axis is symbolic +(`input_ids`/`attention_mask`/`token_type_ids` all declare +`[batch_size, sequence_length]`, no fixed shape), so padding to the actual +tokenized length (rounded up to a multiple of 8) instead of a fixed 512 is a +drop-in change — no re-export needed. On the same reference box: short-query +p50 **19.0 ms**, p95 **20.9 ms** (was p50 281.9 ms / p95 310.5 ms fixed-512 — +~15× faster, ~7× under the 150 ms sub-budget); medium (~178 tok) p50 +**84.1 ms** (was 281.7 ms); doc-length (~442 tok) p50 **235.5 ms** (was +280.3 ms — smaller gain because 442 tokens is already close to 512). +Correctness parity across 10 fixed sentences (short queries + longer bank +sentences), fixed-512 vs dynamic-length, cosine similarity: **1.000000 on +every sentence** (min = mean = 1.000000) — the attention mask fully absorbs +the padding difference, so this is a pure performance change with no +retrieval-quality risk. **Decision: Slice 4 adopts dynamic sequence length +(bucket-of-8 rounding) as the query-embedding mitigation**, not int8 +quantization and not a relaxed budget — the 150 ms sub-budget holds with +large headroom once padding is length-aware. ### D7. Taxonomy rebalance: recall modes mean what they say @@ -247,10 +268,17 @@ compatibility; only dead *behavior* is deleted. - [Model download unavailable offline at first run] → loud degraded mode: doctor Error, daemon status `embeddings: degraded`, rate-limited logs; lexical recall keeps serving. Never silent. -- [Query-embedding latency blows the 300 ms recall budget on CPU] → measured - gate before Slice 4 ships; warmup inference at start; per-turn vector - sub-budget with logged lexical fallback; `RecallTimeoutMs` already - operator-tunable. +- [Query-embedding latency blows the 300 ms recall budget on CPU] → + **confirmed with fixed-512 padding, then resolved by measurement** (Slice 2 + task 2.13: p95 ≈ 315 ms, ~2× over the 150 ms sub-budget on the reference + box). The dynamic-sequence-length experiment (see D6 and Open Questions) + confirmed the ONNX graph's sequence axis is symbolic (not a fixed shape) + and measured short-query p95 at 20.9 ms once padding matches actual token + length — ~7× under budget, with 1.000000 cosine parity against fixed-512 + across 10 test sentences. Slice 4 ships dynamic-length padding + (bucket-of-8) as the mitigation; warmup inference at start and + `RecallTimeoutMs` remain in place as defense-in-depth, not as the primary + fix. - [LLM merge synthesis loses information] → MergeGuard token-retention check + structural-append fallback; consolidation applies only via human-ratified plan files with a backup taken first. @@ -288,8 +316,59 @@ compatibility; only dead *behavior* is deleted. ## Open Questions -- ONNX int8 query-embedding latency on reference hardware (measure in Slice 2; - gates Slice 4's sub-budget design). +- ~~ONNX int8 query-embedding latency on reference hardware (measure in + Slice 2; gates Slice 4's sub-budget design)~~ **MEASURED (Slice 2 task + 2.13, `tools/embed-latency-bench`, batch=1, 200 timed iterations/corpus + after 20 warmups)**. Production path is fp32, not int8 (int8 remains a + deferred D2 optimization). Reference box: i9-9900K, 8 logical cores, + contended condition (load avg 2.0–3.6, ~11/15 GiB RAM in use, live daemon + running): + + | corpus | tokens (mean) | p50 | p95 | + |------------------------------|---------------|---------|--------| + | short query | 13.8 | 281 ms | 315 ms | + | medium (~180 tok) | 178.2 | 274 ms | 298 ms | + | doc-length (~440 tok) | 442.1 | 275 ms | 294 ms | + | short, concurrency=2 | 13.8 | 274 ms | 291 ms | + | cold load (model load + 1st embed) | — | 1069 ms | — | + + All three corpora cost nearly the same regardless of length, because + `OnnxMemoryEmbedder` always runs a fixed 512-token forward pass (no + length-based truncation) — the fp32 matmul, not tokenization, dominates. + Concurrency=2 gave no throughput benefit on this contended box (two + parallel 100-call loops took as long in aggregate as one sequential + 200-call stream). **Verdict: the 150 ms query-embedding sub-budget does + not hold on this hardware — p95 is ~2.1× over budget (margin ≈ −165 ms)**; + the highest-leverage unexplored mitigation is a query-specific max-length + (e.g. 64 tokens, not int8 quantization) before Slice 4 ships. +- ~~Does dynamic (query-specific) sequence length actually work on this ONNX + graph, and is it a drop-in change?~~ **MEASURED AND RESOLVED** (same + `tools/embed-latency-bench`, dynamic-length extension, same box, same + batch=1/200-iteration/20-warmup protocol). Step 1: `InferenceSession + .InputMetadata` shows all three inputs (`input_ids`, `attention_mask`, + `token_type_ids`) declare shape `[batch_size, sequence_length]` — both + dimensions symbolic, not fixed — so the graph accepts any sequence length; + no re-export required. Step 2: padding each input to its actual tokenized + length (rounded up to a multiple of 8) instead of fixed 512: + + | corpus | tokens (mean) | fixed-512 p50 | fixed-512 p95 | dynamic-len p50 | dynamic-len p95 | + |------------------------|---------------|---------------|---------------|------------------|------------------| + | short query | 13.8 | 281.9 ms | 310.5 ms | **19.0 ms** | **20.9 ms** | + | medium (~178 tok) | 178.2 | 281.7 ms | 312.2 ms | **84.1 ms** | **93.3 ms** | + | doc-length (~442 tok) | 442.1 | 280.3 ms | 304.6 ms | **235.5 ms** | **250.1 ms** | + + Step 3, correctness (not just speed): 10 fixed sentences (5 short queries + + 5 longer bank sentences), embedded both ways, cosine similarity fixed-512 + vs dynamic-length — **1.000000 on all 10 (min = mean = 1.000000)**: the + attention mask fully accounts for the padding difference, so this is a + correctness-neutral, pure-performance change. Contention context: load + average 1.40/1.44/2.36 before the ~6-minute run, 4.76/3.63/3.08 after (the + run's own CPU load, not external contention). **Verdict: dynamic sequence + length is adopted as the Slice 4 mitigation** — short-query p95 lands at + ~14% of the 150 ms sub-budget (huge margin), medium and doc-length both + drop meaningfully too. Int8 quantization and relaxing the sub-budget are no + longer necessary; both remain available as future levers if traffic shifts + toward longer queries. - Final `MinCosineSimilarity` default (calibrate against `gold-prod-2026-07` during Slice 4; 0.55 is the working hypothesis). - Whether the R2 feeds channel should mirror model artifacts (post-PoC diff --git a/openspec/changes/memory-core-redesign/tasks.md b/openspec/changes/memory-core-redesign/tasks.md index a5d6f7281..6ac5eaaee 100644 --- a/openspec/changes/memory-core-redesign/tasks.md +++ b/openspec/changes/memory-core-redesign/tasks.md @@ -12,21 +12,21 @@ constitution gates (tests, evals where mapped, schema/skill sync, slopwatch). ## 2. Embedding foundation -- [ ] 2.1 Create `src/Netclaw.Embeddings` project (Microsoft.ML.OnnxRuntime CPU, FastBertTokenizer, System.Numerics.Tensors) and `IMemoryEmbedder` seam in `Netclaw.Actors/Memory` -- [ ] 2.2 Implement `OnnxMemoryEmbedder` (single InferenceSession, bounded intra-op threads, concurrency semaphore) + `UnavailableMemoryEmbedder` -- [ ] 2.3 Implement `EmbeddingModelProvisioner`: pinned allowlist (id → URL, size, SHA-256), atomic download, hash verification, rejection of unknown ids -- [ ] 2.4 Add `memory_embeddings` table + `UpsertEmbeddingAsync`/`FindNearestByEmbeddingAsync`/coverage queries to `SQLiteMemoryStore.InitializeAsync` (idempotent DDL) -- [ ] 2.5 Implement `MemoryContentHasher` (normalized title+body SHA-256) and hash-skip on re-embed -- [ ] 2.6 Implement `MemoryVectorIndex` (per-model flat float[] brute-force cosine, store-version invalidation) -- [ ] 2.7 `EmbeddingWarmupHostedService`: provision-or-degrade at startup, warm-up inference, gap-repair sweep; register `IMemoryEmbedder` in daemon DI -- [ ] 2.8 Embed-on-write after both curation batch commit paths -- [ ] 2.9 `netclaw memory backfill-embeddings [--force]` CLI command -- [ ] 2.10 `MemoryEmbeddingDoctorCheck` (model presence/hash, coverage, mixed-model warning) + daemon status `embeddings: degraded` surface + rate-limited degradation logs -- [ ] 2.11 Config: `Memory.Embeddings { Enabled, ModelId, AutoDownload }` + schema sync with defaults -- [ ] 2.12 Tests: provisioner hash-rejection/unknown-id, hash-skip, gap repair, vector index invalidation, degraded stub; CI uses a tiny fixture ONNX model (no downloads in tests) -- [ ] 2.13 **Measure ONNX int8 short-query embedding latency on reference hardware; record the number in design.md and gate Slice 4's sub-budget on it** -- [ ] 2.14 ARM64 publish smoke leg exercising OnnxRuntime load -- [ ] 2.15 Update `netclaw-memory` + `netclaw-operations` skills (backfill command, degraded mode); eval suite run +- [x] 2.1 Create `src/Netclaw.Embeddings` project (Microsoft.ML.OnnxRuntime CPU, FastBertTokenizer, System.Numerics.Tensors) and `IMemoryEmbedder` seam in `Netclaw.Actors/Memory` +- [x] 2.2 Implement `OnnxMemoryEmbedder` (single InferenceSession, bounded intra-op threads, concurrency semaphore) + `UnavailableMemoryEmbedder` +- [x] 2.3 Implement `EmbeddingModelProvisioner`: pinned allowlist (id → URL, size, SHA-256), atomic download, hash verification, rejection of unknown ids +- [x] 2.4 Add `memory_embeddings` table + `UpsertEmbeddingAsync`/`FindNearestByEmbeddingAsync`/coverage queries to `SQLiteMemoryStore.InitializeAsync` (idempotent DDL) +- [x] 2.5 Implement `MemoryContentHasher` (normalized title+body SHA-256) and hash-skip on re-embed +- [x] 2.6 Implement `MemoryVectorIndex` (per-model flat float[] brute-force cosine, store-version invalidation) +- [x] 2.7 `EmbeddingWarmupHostedService`: provision-or-degrade at startup, warm-up inference, gap-repair sweep; register `IMemoryEmbedder` in daemon DI +- [x] 2.8 Embed-on-write after both curation batch commit paths +- [x] 2.9 `netclaw memory backfill-embeddings [--force]` CLI command +- [x] 2.10 `MemoryEmbeddingDoctorCheck` (model presence/hash, coverage, mixed-model warning) + daemon status `embeddings: degraded` surface + rate-limited degradation logs +- [x] 2.11 Config: `Memory.Embeddings { Enabled, ModelId, AutoDownload }` + schema sync with defaults +- [x] 2.12 Tests: provisioner hash-rejection/unknown-id, hash-skip, gap repair, vector index invalidation, degraded stub; CI uses a tiny fixture ONNX model (no downloads in tests) +- [x] 2.13 **Measure ONNX int8 short-query embedding latency on reference hardware; record the number in design.md and gate Slice 4's sub-budget on it** +- [x] 2.14 ARM64 publish smoke leg exercising OnnxRuntime load +- [x] 2.15 Update `netclaw-memory` + `netclaw-operations` skills (backfill command, degraded mode); eval suite run ## 3. Write-side nominate→decide + lossless merge diff --git a/src/Netclaw.Actors.Tests/Memory/MemoryContentHasherTests.cs b/src/Netclaw.Actors.Tests/Memory/MemoryContentHasherTests.cs new file mode 100644 index 000000000..292ab91c8 --- /dev/null +++ b/src/Netclaw.Actors.Tests/Memory/MemoryContentHasherTests.cs @@ -0,0 +1,69 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using Netclaw.Actors.Memory; +using Xunit; + +namespace Netclaw.Actors.Tests.Memory; + +public sealed class MemoryContentHasherTests +{ + [Fact] + public void ComputeHash_is_case_insensitive() + { + var lower = MemoryContentHasher.ComputeHash("netclaw source location", "the repo lives on github"); + var upper = MemoryContentHasher.ComputeHash("NETCLAW SOURCE LOCATION", "THE REPO LIVES ON GITHUB"); + + Assert.Equal(lower, upper); + } + + [Fact] + public void ComputeHash_collapses_whitespace_differences() + { + var tight = MemoryContentHasher.ComputeHash("title", "one two three"); + var loose = MemoryContentHasher.ComputeHash("title", "one two\tthree\n"); + + Assert.Equal(tight, loose); + } + + [Fact] + public void ComputeHash_is_deterministic() + { + var h1 = MemoryContentHasher.ComputeHash("Netclaw memory redesign", "Use sqlite-backed automatic recall."); + var h2 = MemoryContentHasher.ComputeHash("Netclaw memory redesign", "Use sqlite-backed automatic recall."); + + Assert.Equal(h1, h2); + } + + [Fact] + public void ComputeHash_distinguishes_different_content() + { + var a = MemoryContentHasher.ComputeHash("title", "body one"); + var b = MemoryContentHasher.ComputeHash("title", "body two"); + + Assert.NotEqual(a, b); + } + + [Fact] + public void ComputeHash_distinguishes_title_from_body_content() + { + // Swapping title/body content must not collide, even though the normalized + // concatenation contains the same tokens overall. + var a = MemoryContentHasher.ComputeHash("alpha", "beta"); + var b = MemoryContentHasher.ComputeHash("beta", "alpha"); + + Assert.NotEqual(a, b); + } + + [Fact] + public void ComputeHash_produces_lowercase_hex_sha256() + { + var hash = MemoryContentHasher.ComputeHash("t", "b"); + + Assert.Equal(64, hash.Length); + Assert.Equal(hash, hash.ToLowerInvariant(), StringComparer.Ordinal); + Assert.True(hash.All(c => Uri.IsHexDigit(c))); + } +} diff --git a/src/Netclaw.Actors.Tests/Memory/MemoryEmbedOnWriteCoordinatorTests.cs b/src/Netclaw.Actors.Tests/Memory/MemoryEmbedOnWriteCoordinatorTests.cs new file mode 100644 index 000000000..226e4dbcb --- /dev/null +++ b/src/Netclaw.Actors.Tests/Memory/MemoryEmbedOnWriteCoordinatorTests.cs @@ -0,0 +1,153 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using Microsoft.Extensions.Logging.Abstractions; +using Netclaw.Actors.Memory; +using Xunit; + +namespace Netclaw.Actors.Tests.Memory; + +/// +/// Covers (memory-core-redesign Slice 2, task +/// 2.8): the single embed-on-write hook both curation write pipelines call after their store +/// batch-apply commits. +/// +public sealed class MemoryEmbedOnWriteCoordinatorTests : IAsyncLifetime +{ + private readonly string _baseDir = Path.Combine(Path.GetTempPath(), "netclaw-embed-on-write-tests", Guid.NewGuid().ToString("N")); + private readonly string _dbPath; + private readonly SQLiteMemoryStore _store; + + public MemoryEmbedOnWriteCoordinatorTests() + { + Directory.CreateDirectory(_baseDir); + _dbPath = Path.Combine(_baseDir, "netclaw.db"); + _store = new SQLiteMemoryStore(_dbPath, TimeProvider.System); + } + + public async ValueTask InitializeAsync() => await _store.InitializeAsync(TestContext.Current.CancellationToken); + + public async ValueTask DisposeAsync() => await SqliteTempDirectoryCleanup.TryDeleteDirectoryAsync(_baseDir); + + [Fact] + public async Task Available_embedder_embeds_written_documents_with_the_correct_content_hash() + { + var anchor = _store.CreateDefaultAnchor("coordinator-test"); + var now = DateTimeOffset.UtcNow.ToUnixTimeMilliseconds(); + await _store.UpsertDocumentAsync(new SQLiteMemoryDocument( + DocumentId: "doc-1", + Anchor: anchor, + MemoryClass: "durable_fact", + Title: "Title", + MarkdownBody: "Body", + AliasesJson: null, + FacetsJson: null, + SlotsJson: null, + UpdateSemantics: "merge-document", + Sensitivity: "normal", + RecallMode: "auto", + Confidence: 0.9, + FreshnessAtMs: now, + ExpiresAtMs: null, + CreatedAtMs: now, + UpdatedAtMs: now), TestContext.Current.CancellationToken); + + var holder = new MemoryEmbedderHolder(new FakeMemoryEmbedder("model-a", dimensions: 3)); + var written = new[] { new MemoryDocumentWriteResult("doc-1", "Title", "Body") }; + + await MemoryEmbedOnWriteCoordinator.EmbedWrittenDocumentsAsync( + holder, _store, written, NullLogger.Instance, TestContext.Current.CancellationToken); + + var rows = await _store.GetEmbeddingsForModelAsync("model-a", TestContext.Current.CancellationToken); + var row = Assert.Single(rows); + Assert.Equal("doc-1", row.ItemId); + Assert.Equal("document", row.ItemKind); + + // The coverage query recomputes MemoryContentHasher over memory_documents and compares + // against the stored content_hash — a non-zero EmbeddedCurrentHashCount here proves the + // coordinator wrote the correct hash, not just some hash. + var coverage = await _store.GetEmbeddingCoverageAsync("model-a", TestContext.Current.CancellationToken); + Assert.Equal(1, coverage.EmbeddedCurrentHashCount); + } + + [Fact] + public async Task Null_holder_skips_embedding_without_throwing() + { + var written = new[] { new MemoryDocumentWriteResult("doc-1", "Title", "Body") }; + + await MemoryEmbedOnWriteCoordinator.EmbedWrittenDocumentsAsync( + holder: null, _store, written, NullLogger.Instance, TestContext.Current.CancellationToken); + + Assert.Empty(await _store.GetEmbeddingsForModelAsync("model-a", TestContext.Current.CancellationToken)); + } + + [Fact] + public async Task Unavailable_embedder_skips_embedding_without_throwing() + { + var holder = new MemoryEmbedderHolder(new UnavailableMemoryEmbedder("model-a", "not provisioned")); + var written = new[] { new MemoryDocumentWriteResult("doc-1", "Title", "Body") }; + + await MemoryEmbedOnWriteCoordinator.EmbedWrittenDocumentsAsync( + holder, _store, written, NullLogger.Instance, TestContext.Current.CancellationToken); + + Assert.Empty(await _store.GetEmbeddingsForModelAsync("model-a", TestContext.Current.CancellationToken)); + } + + [Fact] + public async Task Embed_failure_on_one_item_is_isolated_and_does_not_throw_or_block_others() + { + var holder = new MemoryEmbedderHolder(new FakeMemoryEmbedder("model-a", dimensions: 2, failOnText: "Bad\nBody")); + var written = new[] + { + new MemoryDocumentWriteResult("doc-bad", "Bad", "Body"), + new MemoryDocumentWriteResult("doc-good", "Good", "Body"), + }; + + // Must not throw: an embedding failure must never propagate out of the coordinator and + // fail/retry the memory write that already committed (design D3: vectors are derived data). + await MemoryEmbedOnWriteCoordinator.EmbedWrittenDocumentsAsync( + holder, _store, written, NullLogger.Instance, TestContext.Current.CancellationToken); + + var rows = await _store.GetEmbeddingsForModelAsync("model-a", TestContext.Current.CancellationToken); + var row = Assert.Single(rows); + Assert.Equal("doc-good", row.ItemId); + } + + [Fact] + public async Task Empty_written_list_is_a_no_op() + { + var holder = new MemoryEmbedderHolder(new FakeMemoryEmbedder("model-a", dimensions: 2)); + + await MemoryEmbedOnWriteCoordinator.EmbedWrittenDocumentsAsync( + holder, _store, [], NullLogger.Instance, TestContext.Current.CancellationToken); + + Assert.Empty(await _store.GetEmbeddingsForModelAsync("model-a", TestContext.Current.CancellationToken)); + } + + private sealed class FakeMemoryEmbedder(string modelId, int dimensions, string? failOnText = null) : IMemoryEmbedder + { + public string ModelId => modelId; + + public int Dimensions => dimensions; + + public bool IsAvailable => true; + + public ValueTask> EmbedAsync(string text, CancellationToken ct) + { + if (failOnText is not null && string.Equals(text, failOnText, StringComparison.Ordinal)) + throw new InvalidOperationException("simulated embed failure"); + + return ValueTask.FromResult>(new float[dimensions]); + } + + public async ValueTask>> EmbedBatchAsync(IReadOnlyList texts, CancellationToken ct) + { + var results = new List>(texts.Count); + foreach (var text in texts) + results.Add(await EmbedAsync(text, ct)); + return results; + } + } +} diff --git a/src/Netclaw.Actors.Tests/Memory/MemoryVectorIndexTests.cs b/src/Netclaw.Actors.Tests/Memory/MemoryVectorIndexTests.cs new file mode 100644 index 000000000..1f47362a4 --- /dev/null +++ b/src/Netclaw.Actors.Tests/Memory/MemoryVectorIndexTests.cs @@ -0,0 +1,142 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using Netclaw.Actors.Memory; +using Xunit; + +namespace Netclaw.Actors.Tests.Memory; + +public sealed class MemoryVectorIndexTests : IAsyncLifetime +{ + private const string ModelId = "test-model"; + private const int Dimensions = 3; + + private readonly string _baseDir = Path.Combine(Path.GetTempPath(), "netclaw-vector-index-tests", Guid.NewGuid().ToString("N")); + private SQLiteMemoryStore _store = null!; + private MemoryVectorIndex _index = null!; + + public async ValueTask InitializeAsync() + { + Directory.CreateDirectory(_baseDir); + _store = new SQLiteMemoryStore(Path.Combine(_baseDir, "netclaw.db"), TimeProvider.System); + await _store.InitializeAsync(TestContext.Current.CancellationToken); + _index = new MemoryVectorIndex(_store, ModelId, Dimensions); + } + + public async ValueTask DisposeAsync() => await SqliteTempDirectoryCleanup.TryDeleteDirectoryAsync(_baseDir); + + private async Task SeedAsync(string itemId, float[] vector) + { + await _store.UpsertEmbeddingAsync(itemId, "document", ModelId, contentHash: $"hash-{itemId}", vector, TestContext.Current.CancellationToken); + } + + [Fact] + public async Task TopK_orders_by_descending_cosine_and_applies_the_minCosine_floor() + { + await SeedAsync("doc-exact", [1f, 0f, 0f]); + await SeedAsync("doc-close", [0.95f, 0.05f, 0f]); + await SeedAsync("doc-orthogonal", [0f, 1f, 0f]); + await SeedAsync("doc-opposite", [-1f, 0f, 0f]); + + await _index.ReloadIfStaleAsync(TestContext.Current.CancellationToken); + + var results = _index.TopK([1f, 0f, 0f], k: 10, minCosine: 0.5); + + Assert.Equal(["doc-exact", "doc-close"], results.Select(r => r.ItemId)); + Assert.True(results[0].Cosine >= results[1].Cosine); + } + + [Fact] + public async Task TopK_limits_results_to_k() + { + await SeedAsync("doc-1", [1f, 0f, 0f]); + await SeedAsync("doc-2", [0.99f, 0.01f, 0f]); + await SeedAsync("doc-3", [0.98f, 0.02f, 0f]); + + await _index.ReloadIfStaleAsync(TestContext.Current.CancellationToken); + + var results = _index.TopK([1f, 0f, 0f], k: 2, minCosine: -1.0); + + Assert.Equal(2, results.Count); + } + + [Fact] + public async Task TopK_returns_empty_before_any_reload() + { + await SeedAsync("doc-1", [1f, 0f, 0f]); + + // No ReloadIfStaleAsync call yet — the index has never loaded anything. + var results = _index.TopK([1f, 0f, 0f], k: 10, minCosine: -1.0); + + Assert.Empty(results); + } + + [Fact] + public async Task ReloadIfStaleAsync_is_a_no_op_when_the_store_version_has_not_changed() + { + await SeedAsync("doc-1", [1f, 0f, 0f]); + + var firstReload = await _index.ReloadIfStaleAsync(TestContext.Current.CancellationToken); + var secondReload = await _index.ReloadIfStaleAsync(TestContext.Current.CancellationToken); + + Assert.True(firstReload); + Assert.False(secondReload); + } + + [Fact] + public async Task ReloadIfStaleAsync_picks_up_new_rows_after_a_version_bump() + { + await SeedAsync("doc-1", [1f, 0f, 0f]); + await _index.ReloadIfStaleAsync(TestContext.Current.CancellationToken); + Assert.Single(_index.TopK([1f, 0f, 0f], k: 10, minCosine: -1.0)); + + await SeedAsync("doc-2", [0f, 1f, 0f]); + var reloaded = await _index.ReloadIfStaleAsync(TestContext.Current.CancellationToken); + + Assert.True(reloaded); + Assert.Equal(2, _index.Count); + } + + [Fact] + public async Task ReloadIfStaleAsync_reflects_deletion_via_document_tombstone() + { + var anchor = _store.CreateDefaultAnchor("vector-index-tombstone-test"); + var now = DateTimeOffset.UtcNow.ToUnixTimeMilliseconds(); + await _store.UpsertDocumentAsync(new SQLiteMemoryDocument( + DocumentId: "doc-to-delete", + Anchor: anchor, + MemoryClass: "durable_fact", + Title: "t", + MarkdownBody: "b", + AliasesJson: null, + FacetsJson: null, + SlotsJson: null, + UpdateSemantics: "merge-document", + Sensitivity: "normal", + RecallMode: "auto", + Confidence: 0.9, + FreshnessAtMs: now, + ExpiresAtMs: null, + CreatedAtMs: now, + UpdatedAtMs: now), TestContext.Current.CancellationToken); + await SeedAsync("doc-to-delete", [1f, 0f, 0f]); + await _index.ReloadIfStaleAsync(TestContext.Current.CancellationToken); + Assert.Equal(1, _index.Count); + + await _store.TombstoneDocumentAsync("doc-to-delete", TestContext.Current.CancellationToken); + await _index.ReloadIfStaleAsync(TestContext.Current.CancellationToken); + + Assert.Equal(0, _index.Count); + } + + [Fact] + public async Task TopK_rejects_a_query_of_the_wrong_dimension() + { + await SeedAsync("doc-1", [1f, 0f, 0f]); + await _index.ReloadIfStaleAsync(TestContext.Current.CancellationToken); + + Assert.Throws(() => _index.TopK([1f, 0f], k: 5, minCosine: 0.0)); + } +} diff --git a/src/Netclaw.Actors.Tests/Memory/SQLiteMemoryStoreEmbeddingTests.cs b/src/Netclaw.Actors.Tests/Memory/SQLiteMemoryStoreEmbeddingTests.cs new file mode 100644 index 000000000..271578785 --- /dev/null +++ b/src/Netclaw.Actors.Tests/Memory/SQLiteMemoryStoreEmbeddingTests.cs @@ -0,0 +1,427 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using Netclaw.Actors.Memory; +using Netclaw.Configuration; +using Xunit; + +namespace Netclaw.Actors.Tests.Memory; + +/// +/// Covers the memory_embeddings table added in memory-core-redesign Slice 2: +/// upsert/coverage/hash-skip round-trips, deletion via document tombstone, and +/// bump semantics. +/// +public sealed class SQLiteMemoryStoreEmbeddingTests : IAsyncLifetime +{ + private readonly string _baseDir = Path.Combine(Path.GetTempPath(), "netclaw-sqlite-embedding-tests", Guid.NewGuid().ToString("N")); + private readonly string _dbPath; + private readonly SQLiteMemoryStore _store; + + public SQLiteMemoryStoreEmbeddingTests() + { + Directory.CreateDirectory(_baseDir); + _dbPath = Path.Combine(_baseDir, "netclaw.db"); + _store = new SQLiteMemoryStore(_dbPath, TimeProvider.System); + } + + public async ValueTask InitializeAsync() => await _store.InitializeAsync(TestContext.Current.CancellationToken); + + public async ValueTask DisposeAsync() => await SqliteTempDirectoryCleanup.TryDeleteDirectoryAsync(_baseDir); + + [Fact] + public async Task UpsertEmbeddingAsync_round_trips_the_vector() + { + float[] vector = [0.1f, 0.2f, 0.3f, 0.4f]; + + await _store.UpsertEmbeddingAsync("doc-1", "document", "model-a", "hash-1", vector, TestContext.Current.CancellationToken); + + var rows = await _store.GetEmbeddingsForModelAsync("model-a", TestContext.Current.CancellationToken); + + var row = Assert.Single(rows); + Assert.Equal("doc-1", row.ItemId); + Assert.Equal("document", row.ItemKind); + Assert.Equal(vector, row.Vector.ToArray()); + } + + [Fact] + public async Task UpsertEmbeddingAsync_with_unchanged_hash_is_a_no_op_and_does_not_bump_the_version() + { + float[] vector = [1f, 2f, 3f]; + await _store.UpsertEmbeddingAsync("doc-1", "document", "model-a", "hash-1", vector, TestContext.Current.CancellationToken); + var versionAfterFirstWrite = _store.EmbeddingDataVersion; + + // Same hash, even with a different (bogus) vector — must be skipped entirely: the + // stored vector is untouched and the version counter does not move. + await _store.UpsertEmbeddingAsync("doc-1", "document", "model-a", "hash-1", new float[] { 9f, 9f, 9f }, TestContext.Current.CancellationToken); + + var rows = await _store.GetEmbeddingsForModelAsync("model-a", TestContext.Current.CancellationToken); + Assert.Equal(vector, Assert.Single(rows).Vector.ToArray()); + Assert.Equal(versionAfterFirstWrite, _store.EmbeddingDataVersion); + } + + [Fact] + public async Task UpsertEmbeddingAsync_with_changed_hash_overwrites_and_bumps_the_version() + { + await _store.UpsertEmbeddingAsync("doc-1", "document", "model-a", "hash-1", new float[] { 1f, 2f, 3f }, TestContext.Current.CancellationToken); + var versionAfterFirstWrite = _store.EmbeddingDataVersion; + + await _store.UpsertEmbeddingAsync("doc-1", "document", "model-a", "hash-2", new float[] { 4f, 5f, 6f }, TestContext.Current.CancellationToken); + + var rows = await _store.GetEmbeddingsForModelAsync("model-a", TestContext.Current.CancellationToken); + Assert.Equal(new float[] { 4f, 5f, 6f }, Assert.Single(rows).Vector.ToArray()); + Assert.True(_store.EmbeddingDataVersion > versionAfterFirstWrite); + } + + [Fact] + public async Task UpsertEmbeddingAsync_keys_rows_by_item_and_model_independently() + { + await _store.UpsertEmbeddingAsync("doc-1", "document", "model-a", "hash-1", new float[] { 1f }, TestContext.Current.CancellationToken); + await _store.UpsertEmbeddingAsync("doc-1", "document", "model-b", "hash-1", new float[] { 2f }, TestContext.Current.CancellationToken); + + var modelARows = await _store.GetEmbeddingsForModelAsync("model-a", TestContext.Current.CancellationToken); + var modelBRows = await _store.GetEmbeddingsForModelAsync("model-b", TestContext.Current.CancellationToken); + + Assert.Equal(new float[] { 1f }, Assert.Single(modelARows).Vector.ToArray()); + Assert.Equal(new float[] { 2f }, Assert.Single(modelBRows).Vector.ToArray()); + } + + [Fact] + public async Task TombstoneDocumentAsync_deletes_the_document_embedding_and_bumps_the_version() + { + var anchor = _store.CreateDefaultAnchor("embedding-tombstone-test"); + var now = DateTimeOffset.UtcNow.ToUnixTimeMilliseconds(); + await _store.UpsertDocumentAsync(new SQLiteMemoryDocument( + DocumentId: "doc-1", + Anchor: anchor, + MemoryClass: "durable_fact", + Title: "t", + MarkdownBody: "b", + AliasesJson: null, + FacetsJson: null, + SlotsJson: null, + UpdateSemantics: "merge-document", + Sensitivity: "normal", + RecallMode: "auto", + Confidence: 0.9, + FreshnessAtMs: now, + ExpiresAtMs: null, + CreatedAtMs: now, + UpdatedAtMs: now), TestContext.Current.CancellationToken); + await _store.UpsertEmbeddingAsync("doc-1", "document", "model-a", "hash-1", new float[] { 1f, 2f }, TestContext.Current.CancellationToken); + var versionBeforeTombstone = _store.EmbeddingDataVersion; + + var tombstoned = await _store.TombstoneDocumentAsync("doc-1", TestContext.Current.CancellationToken); + + Assert.True(tombstoned); + Assert.Empty(await _store.GetEmbeddingsForModelAsync("model-a", TestContext.Current.CancellationToken)); + Assert.True(_store.EmbeddingDataVersion > versionBeforeTombstone); + } + + [Fact] + public async Task TombstoneDocumentAsync_with_no_embedding_row_does_not_bump_the_version() + { + var anchor = _store.CreateDefaultAnchor("no-embedding-tombstone-test"); + var now = DateTimeOffset.UtcNow.ToUnixTimeMilliseconds(); + await _store.UpsertDocumentAsync(new SQLiteMemoryDocument( + DocumentId: "doc-no-embedding", + Anchor: anchor, + MemoryClass: "durable_fact", + Title: "t", + MarkdownBody: "b", + AliasesJson: null, + FacetsJson: null, + SlotsJson: null, + UpdateSemantics: "merge-document", + Sensitivity: "normal", + RecallMode: "auto", + Confidence: 0.9, + FreshnessAtMs: now, + ExpiresAtMs: null, + CreatedAtMs: now, + UpdatedAtMs: now), TestContext.Current.CancellationToken); + var versionBefore = _store.EmbeddingDataVersion; + + var tombstoned = await _store.TombstoneDocumentAsync("doc-no-embedding", TestContext.Current.CancellationToken); + + Assert.True(tombstoned); + Assert.Equal(versionBefore, _store.EmbeddingDataVersion); + } + + [Fact] + public async Task GetEmbeddingCoverageAsync_reports_total_current_and_other_model_counts() + { + var anchor = _store.CreateDefaultAnchor("coverage-test"); + var now = DateTimeOffset.UtcNow.ToUnixTimeMilliseconds(); + + async Task SeedDocAsync(string id, string title, string body) + { + await _store.UpsertDocumentAsync(new SQLiteMemoryDocument( + DocumentId: id, + Anchor: anchor, + MemoryClass: "durable_fact", + Title: title, + MarkdownBody: body, + AliasesJson: null, + FacetsJson: null, + SlotsJson: null, + UpdateSemantics: "merge-document", + Sensitivity: "normal", + RecallMode: "auto", + Confidence: 0.9, + FreshnessAtMs: now, + ExpiresAtMs: null, + CreatedAtMs: now, + UpdatedAtMs: now), TestContext.Current.CancellationToken); + } + + // doc-current: embedded under model-a with the hash matching its current content. + await SeedDocAsync("doc-current", "Current", "up to date body"); + var currentHash = MemoryContentHasher.ComputeHash("Current", "up to date body"); + await _store.UpsertEmbeddingAsync("doc-current", "document", "model-a", currentHash, new float[] { 1f }, TestContext.Current.CancellationToken); + + // doc-stale: has a model-a row, but its stored hash no longer matches (content edited + // since the embedding was written) — should NOT count toward EmbeddedCurrentHashCount. + await SeedDocAsync("doc-stale", "Stale", "edited body"); + await _store.UpsertEmbeddingAsync("doc-stale", "document", "model-a", "stale-hash-from-before-the-edit", new float[] { 2f }, TestContext.Current.CancellationToken); + + // doc-other-model: only has a row under model-b. + await SeedDocAsync("doc-other-model", "Other", "other model body"); + await _store.UpsertEmbeddingAsync("doc-other-model", "document", "model-b", "whatever", new float[] { 3f }, TestContext.Current.CancellationToken); + + // doc-unembedded: no embedding row at all. + await SeedDocAsync("doc-unembedded", "Unembedded", "never embedded"); + + var coverage = await _store.GetEmbeddingCoverageAsync("model-a", TestContext.Current.CancellationToken); + + Assert.Equal(4, coverage.TotalRecallableDocuments); + Assert.Equal(1, coverage.EmbeddedCurrentHashCount); + Assert.Equal(1, coverage.OtherModelCount); + } + + [Fact] + public async Task GetDocumentsNeedingEmbeddingAsync_returns_only_missing_or_stale_documents() + { + var anchor = _store.CreateDefaultAnchor("gap-repair-test"); + var now = DateTimeOffset.UtcNow.ToUnixTimeMilliseconds(); + + async Task SeedDocAsync(string id, string title, string body) + { + await _store.UpsertDocumentAsync(new SQLiteMemoryDocument( + DocumentId: id, + Anchor: anchor, + MemoryClass: "durable_fact", + Title: title, + MarkdownBody: body, + AliasesJson: null, + FacetsJson: null, + SlotsJson: null, + UpdateSemantics: "merge-document", + Sensitivity: "normal", + RecallMode: "auto", + Confidence: 0.9, + FreshnessAtMs: now, + ExpiresAtMs: null, + CreatedAtMs: now, + UpdatedAtMs: now), TestContext.Current.CancellationToken); + } + + await SeedDocAsync("doc-current", "Current", "up to date body"); + var currentHash = MemoryContentHasher.ComputeHash("Current", "up to date body"); + await _store.UpsertEmbeddingAsync("doc-current", "document", "model-a", currentHash, new float[] { 1f }, TestContext.Current.CancellationToken); + + await SeedDocAsync("doc-stale", "Stale", "edited body"); + await _store.UpsertEmbeddingAsync("doc-stale", "document", "model-a", "stale-hash-from-before-the-edit", new float[] { 2f }, TestContext.Current.CancellationToken); + + await SeedDocAsync("doc-unembedded", "Unembedded", "never embedded"); + + var missing = await _store.GetDocumentsNeedingEmbeddingAsync("model-a", force: false, TestContext.Current.CancellationToken); + + Assert.Equal( + new[] { "doc-stale", "doc-unembedded" }, + missing.Select(m => m.DocumentId).Order(StringComparer.Ordinal)); + } + + [Fact] + public async Task GetDocumentsNeedingEmbeddingAsync_with_force_returns_every_recallable_document() + { + var anchor = _store.CreateDefaultAnchor("gap-repair-force-test"); + var now = DateTimeOffset.UtcNow.ToUnixTimeMilliseconds(); + await _store.UpsertDocumentAsync(new SQLiteMemoryDocument( + DocumentId: "doc-current", + Anchor: anchor, + MemoryClass: "durable_fact", + Title: "Current", + MarkdownBody: "up to date body", + AliasesJson: null, + FacetsJson: null, + SlotsJson: null, + UpdateSemantics: "merge-document", + Sensitivity: "normal", + RecallMode: "auto", + Confidence: 0.9, + FreshnessAtMs: now, + ExpiresAtMs: null, + CreatedAtMs: now, + UpdatedAtMs: now), TestContext.Current.CancellationToken); + var currentHash = MemoryContentHasher.ComputeHash("Current", "up to date body"); + await _store.UpsertEmbeddingAsync("doc-current", "document", "model-a", currentHash, new float[] { 1f }, TestContext.Current.CancellationToken); + + var forced = await _store.GetDocumentsNeedingEmbeddingAsync("model-a", force: true, TestContext.Current.CancellationToken); + + // Already fully current, but --force means "every recallable document" regardless. + var doc = Assert.Single(forced); + Assert.Equal("doc-current", doc.DocumentId); + } + + [Fact] + public async Task GetEmbeddingCoverageAsync_excludes_tombstoned_documents_from_the_total() + { + var anchor = _store.CreateDefaultAnchor("coverage-tombstone-test"); + var now = DateTimeOffset.UtcNow.ToUnixTimeMilliseconds(); + await _store.UpsertDocumentAsync(new SQLiteMemoryDocument( + DocumentId: "doc-live", + Anchor: anchor, + MemoryClass: "durable_fact", + Title: "t", + MarkdownBody: "b", + AliasesJson: null, + FacetsJson: null, + SlotsJson: null, + UpdateSemantics: "merge-document", + Sensitivity: "normal", + RecallMode: "auto", + Confidence: 0.9, + FreshnessAtMs: now, + ExpiresAtMs: null, + CreatedAtMs: now, + UpdatedAtMs: now), TestContext.Current.CancellationToken); + await _store.UpsertDocumentAsync(new SQLiteMemoryDocument( + DocumentId: "doc-tombstoned", + Anchor: anchor, + MemoryClass: "durable_fact", + Title: "t2", + MarkdownBody: "b2", + AliasesJson: null, + FacetsJson: null, + SlotsJson: null, + UpdateSemantics: "merge-document", + Sensitivity: "normal", + RecallMode: "auto", + Confidence: 0.9, + FreshnessAtMs: now, + ExpiresAtMs: null, + CreatedAtMs: now, + UpdatedAtMs: now), TestContext.Current.CancellationToken); + await _store.TombstoneDocumentAsync("doc-tombstoned", TestContext.Current.CancellationToken); + + var coverage = await _store.GetEmbeddingCoverageAsync("model-a", TestContext.Current.CancellationToken); + + Assert.Equal(1, coverage.TotalRecallableDocuments); + } + + // ── Batch-apply write results (memory-core-redesign Slice 2, task 2.8: the seam + // MemoryEmbedOnWriteCoordinator needs post-commit document ids+content) ── + + [Fact] + public async Task ApplyInlineCurationBatchAsync_returns_written_documents_but_not_records() + { + var operations = new[] + { + DocumentOperation(memoryId: null, title: "New Doc", content: "doc body"), + RecordOperation(memoryId: "rec-1", title: "Evidence", content: "evidence body"), + }; + + var written = await _store.ApplyInlineCurationBatchAsync(operations, TestContext.Current.CancellationToken); + + var doc = Assert.Single(written); + Assert.Equal("New Doc", doc.Title); + Assert.Equal("doc body", doc.Body); + Assert.False(string.IsNullOrWhiteSpace(doc.DocumentId)); + } + + [Fact] + public async Task ApplyInlineCurationBatchAsync_reports_the_final_document_id_for_an_update() + { + var written = await _store.ApplyInlineCurationBatchAsync( + [DocumentOperation(memoryId: "doc-explicit-id", title: "Updated", content: "updated body")], + TestContext.Current.CancellationToken); + + var doc = Assert.Single(written); + Assert.Equal("doc-explicit-id", doc.DocumentId); + } + + [Fact] + public async Task ApplyCurationBatchAsync_returns_written_documents_but_not_records() + { + await _store.EnqueueCheckpointAsync(new SQLiteMemoryCheckpoint( + CheckpointId: "cp-embed-1", + SessionId: "chan/thread", + TurnId: "turn-1", + TriggerType: "turn-complete", + Priority: 10, + Status: "pending", + PayloadJson: "{}", + RetryCount: 0, + CreatedAtMs: DateTimeOffset.UtcNow.ToUnixTimeMilliseconds(), + UpdatedAtMs: DateTimeOffset.UtcNow.ToUnixTimeMilliseconds()), TestContext.Current.CancellationToken); + + var operations = new[] + { + DocumentOperation(memoryId: null, title: "Worker Doc", content: "worker body"), + RecordOperation(memoryId: "rec-2", title: "Worker Evidence", content: "worker evidence body"), + }; + + var written = await _store.ApplyCurationBatchAsync("cp-embed-1", operations, TestContext.Current.CancellationToken); + + var doc = Assert.Single(written); + Assert.Equal("Worker Doc", doc.Title); + Assert.Equal("worker body", doc.Body); + } + + private static SQLiteMemoryCurationOperation DocumentOperation(string? memoryId, string title, string content) + => new( + Kind: "document", + MemoryClass: "durable_fact", + MemoryId: memoryId, + AnchorCanonicalName: title, + AnchorType: "topic", + Title: title, + Content: content, + AliasesJson: null, + FacetsJson: null, + SlotsJson: null, + Relations: null, + UpdateSemantics: "merge-document", + Boundary: TrustBoundary.TrustedInstanceValue, + Audience: TrustAudience.Team, + Sensitivity: "normal", + RecallMode: "auto", + Confidence: 0.9, + FreshnessAtMs: DateTimeOffset.UtcNow.ToUnixTimeMilliseconds(), + ExpiresAtMs: null); + + private static SQLiteMemoryCurationOperation RecordOperation(string memoryId, string title, string content) + => new( + Kind: "record", + MemoryClass: "evidence", + MemoryId: memoryId, + AnchorCanonicalName: title, + AnchorType: "topic", + Title: title, + Content: content, + AliasesJson: null, + FacetsJson: null, + SlotsJson: null, + Relations: null, + UpdateSemantics: "immutable-record", + Boundary: TrustBoundary.TrustedInstanceValue, + Audience: TrustAudience.Team, + Sensitivity: "normal", + RecallMode: "searchable", + Confidence: 0.8, + FreshnessAtMs: DateTimeOffset.UtcNow.ToUnixTimeMilliseconds(), + ExpiresAtMs: null); +} diff --git a/src/Netclaw.Actors.Tests/Memory/UnavailableMemoryEmbedderTests.cs b/src/Netclaw.Actors.Tests/Memory/UnavailableMemoryEmbedderTests.cs new file mode 100644 index 000000000..d456bb3d8 --- /dev/null +++ b/src/Netclaw.Actors.Tests/Memory/UnavailableMemoryEmbedderTests.cs @@ -0,0 +1,44 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using Netclaw.Actors.Memory; +using Xunit; + +namespace Netclaw.Actors.Tests.Memory; + +public sealed class UnavailableMemoryEmbedderTests +{ + [Fact] + public void IsAvailable_is_always_false() + { + IMemoryEmbedder embedder = new UnavailableMemoryEmbedder("snowflake-arctic-embed-m", "model not provisioned"); + + Assert.False(embedder.IsAvailable); + Assert.Equal(0, embedder.Dimensions); + Assert.Equal("snowflake-arctic-embed-m", embedder.ModelId); + } + + [Fact] + public async Task EmbedAsync_throws_with_remediation_text_instead_of_returning_a_vector() + { + IMemoryEmbedder embedder = new UnavailableMemoryEmbedder("snowflake-arctic-embed-m", "hash verification failed"); + + var ex = await Assert.ThrowsAsync( + async () => await embedder.EmbedAsync("some text", CancellationToken.None)); + + Assert.Contains("hash verification failed", ex.Message, StringComparison.Ordinal); + Assert.Contains("snowflake-arctic-embed-m", ex.Message, StringComparison.Ordinal); + Assert.Contains("IsAvailable", ex.Message, StringComparison.Ordinal); + } + + [Fact] + public async Task EmbedBatchAsync_throws_instead_of_returning_garbage_vectors() + { + IMemoryEmbedder embedder = new UnavailableMemoryEmbedder("snowflake-arctic-embed-m", "runtime load error"); + + await Assert.ThrowsAsync( + async () => await embedder.EmbedBatchAsync(["a", "b"], CancellationToken.None)); + } +} diff --git a/src/Netclaw.Actors/Memory/CurationRulesEvaluator.cs b/src/Netclaw.Actors/Memory/CurationRulesEvaluator.cs index c9b60c9e8..4a7fb15ee 100644 --- a/src/Netclaw.Actors/Memory/CurationRulesEvaluator.cs +++ b/src/Netclaw.Actors/Memory/CurationRulesEvaluator.cs @@ -265,11 +265,16 @@ private static bool PreservesContent(string proposed, string existing) return NormalizeForContainment(proposed).Contains(existingNorm, StringComparison.Ordinal); } - private static string NormalizeForContainment(string value) + /// + /// Lowercase and collapse all whitespace runs to single spaces so formatting differences + /// don't hide a genuine containment. Case folding happens here so the Contains + /// check above can stay Ordinal. Internal (not private) because + /// reuses the exact same normalization for its content + /// hash — the two "does this content actually differ" judgments in the memory subsystem + /// must agree, so this is the one place either can drift from the other. + /// + internal static string NormalizeForContainment(string value) { - // Lowercase and collapse all whitespace runs to single spaces so formatting - // differences don't hide a genuine containment. Case folding happens here so - // the Contains check can stay Ordinal. return string.Join(' ', (value ?? string.Empty) .ToLowerInvariant() .Split((char[]?)null, StringSplitOptions.RemoveEmptyEntries)); diff --git a/src/Netclaw.Actors/Memory/IMemoryEmbedder.cs b/src/Netclaw.Actors/Memory/IMemoryEmbedder.cs new file mode 100644 index 000000000..197eb3569 --- /dev/null +++ b/src/Netclaw.Actors/Memory/IMemoryEmbedder.cs @@ -0,0 +1,100 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +namespace Netclaw.Actors.Memory; + +/// +/// Consumer-defined seam for computing memory embeddings (memory-core-redesign D1). Owned by +/// the memory subsystem, not the embedding runtime, so actor code never references OnnxRuntime +/// or any other inference library: Netclaw.Embeddings's OnnxMemoryEmbedder +/// implements this interface and is wired in by the daemon; Netclaw.Actors never +/// references that project. +/// +/// +/// is the degraded-mode contract. When false, every write and +/// recall path that would otherwise consult embeddings MUST fall back to its lexical path +/// instead — loudly (a logged degradation event and a doctor/status surface land in later +/// slices), never silently. and are +/// only ever meant to be called when is true; an implementation +/// whose model failed to load () throws rather than +/// returning a zero or garbage vector, because a garbage vector would silently corrupt +/// cosine-similarity scoring instead of visibly failing the caller that skipped the check. +/// +/// +public interface IMemoryEmbedder +{ + /// + /// The allowlisted model id this embedder was provisioned with (e.g. + /// snowflake-arctic-embed-m). Vectors are keyed by (item id, model id) in + /// storage so a model change never silently compares vectors across incompatible spaces. + /// + string ModelId { get; } + + /// Embedding vector width produced by . + int Dimensions { get; } + + /// + /// True when this embedder can actually compute embeddings right now. False is a real, + /// expected operating mode (model not yet provisioned, hash verification failed, runtime + /// load error) — not a condition for the embedder itself to throw on; only calling + /// or while unavailable throws. + /// + bool IsAvailable { get; } + + /// + /// Embed a single piece of text. Callers MUST check first; + /// calling this while unavailable throws rather than degrading silently. + /// + ValueTask> EmbedAsync(string text, CancellationToken ct); + + /// + /// Embed a batch of texts, preserving input order in the output list. Batching lets + /// callers (backfill, gap-repair) amortize per-call overhead that the single-item path + /// pays every time. + /// + ValueTask>> EmbedBatchAsync(IReadOnlyList texts, CancellationToken ct); +} + +/// +/// Degraded-mode stub used when no embedding model is provisioned, hash verification failed, +/// or the runtime failed to load. is permanently false for an +/// instance of this type. It intentionally lives in Netclaw.Actors rather than +/// Netclaw.Embeddings — it needs no OnnxRuntime dependency, and keeping it beside +/// means a caller can always construct a safe default without +/// referencing the embeddings project at all (e.g. in tests, or a config path that disables +/// embeddings entirely). +/// +/// +/// This type does not log on its own: it does not know whether it is degrading a write or a +/// recall path, and logging here would double-count against the caller's own degradation log +/// (memory_recall_vector_degraded and friends, added in later slices). Calling +/// or anyway is a caller bug — code that +/// didn't check first — so both throw rather than returning a zero +/// vector that would silently poison cosine-similarity scoring. +/// +/// +public sealed class UnavailableMemoryEmbedder(string modelId, string reason) : IMemoryEmbedder +{ + public string ModelId { get; } = modelId; + + /// + /// No model is loaded, so there is no real vector width; 0 is the sentinel value for + /// "produces no vectors." + /// + public int Dimensions => 0; + + public bool IsAvailable => false; + + public ValueTask> EmbedAsync(string text, CancellationToken ct) + => throw new InvalidOperationException(BuildMessage(nameof(EmbedAsync))); + + public ValueTask>> EmbedBatchAsync(IReadOnlyList texts, CancellationToken ct) + => throw new InvalidOperationException(BuildMessage(nameof(EmbedBatchAsync))); + + private string BuildMessage(string calledMethod) + => $"Embedding model '{ModelId}' is unavailable ({reason}). Provision it (auto-download " + + "or `netclaw memory backfill-embeddings`) and check `netclaw doctor` for remediation. " + + $"Callers must check IsAvailable before calling {calledMethod}."; +} diff --git a/src/Netclaw.Actors/Memory/MemoryContentHasher.cs b/src/Netclaw.Actors/Memory/MemoryContentHasher.cs new file mode 100644 index 000000000..a2b9518c1 --- /dev/null +++ b/src/Netclaw.Actors/Memory/MemoryContentHasher.cs @@ -0,0 +1,35 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using System.Security.Cryptography; +using System.Text; + +namespace Netclaw.Actors.Memory; + +/// +/// Computes the content hash stored in memory_embeddings.content_hash (memory-core- +/// redesign D3). An embedding is only ever recomputed when this hash changes for the item, so +/// re-running backfill on an unchanged corpus is free. Normalization intentionally reuses +/// (lowercase, whitespace-collapse) +/// rather than a second hand-rolled normalizer, so the two "does this content actually differ" +/// judgments in the memory subsystem — curation's destructive-update guard and the embedding +/// re-embed skip — can never quietly disagree about what counts as a change. +/// +public static class MemoryContentHasher +{ + /// + /// SHA-256 hex digest (lowercase) of the normalized "{title}\n{body}" + /// representation of a memory item. + /// + public static string ComputeHash(string title, string body) + { + var normalized = CurationRulesEvaluator.NormalizeForContainment(title) + + "\n" + + CurationRulesEvaluator.NormalizeForContainment(body); + var bytes = Encoding.UTF8.GetBytes(normalized); + var hash = SHA256.HashData(bytes); + return Convert.ToHexStringLower(hash); + } +} diff --git a/src/Netclaw.Actors/Memory/MemoryCurationActor.cs b/src/Netclaw.Actors/Memory/MemoryCurationActor.cs index 517f02d62..59beb8101 100644 --- a/src/Netclaw.Actors/Memory/MemoryCurationActor.cs +++ b/src/Netclaw.Actors/Memory/MemoryCurationActor.cs @@ -55,16 +55,29 @@ public sealed class MemoryCurationActor : ReceiveActor, IWithUnboundedStash private readonly SessionId _sessionId; private readonly ILoggingAdapter _log; private readonly MemoryCurationEvaluator _evaluator; + private readonly MemoryEmbedderHolder? _embedderHolder; private IActorRef? _currentRequester; public IStash Stash { get; set; } = null!; - public MemoryCurationActor(SQLiteMemoryStore store, SessionId sessionId, IChatClientProvider? clientProvider = null) + /// + /// Resolves the process's at write time (memory-core-redesign + /// Slice 2, task 2.8). Optional like above: a null holder + /// is a genuine operating mode (a test harness or a session wired without the embedding + /// subsystem), not a placeholder — treats a null + /// holder identically to an unavailable embedder and skips embedding with a debug log. + /// + public MemoryCurationActor( + SQLiteMemoryStore store, + SessionId sessionId, + IChatClientProvider? clientProvider = null, + MemoryEmbedderHolder? embedderHolder = null) { _store = store; _sessionId = sessionId; _log = Context.GetLogger(); + _embedderHolder = embedderHolder; var llmClient = clientProvider != null ? clientProvider.GetClient(ModelRole.Compaction) @@ -77,8 +90,12 @@ public MemoryCurationActor(SQLiteMemoryStore store, SessionId sessionId, IChatCl /// /// Create Props for the MemoryCurationActor. /// - public static Props CreateProps(SQLiteMemoryStore store, SessionId sessionId, IChatClientProvider? clientProvider = null) - => Props.Create(() => new MemoryCurationActor(store, sessionId, clientProvider)); + public static Props CreateProps( + SQLiteMemoryStore store, + SessionId sessionId, + IChatClientProvider? clientProvider = null, + MemoryEmbedderHolder? embedderHolder = null) + => Props.Create(() => new MemoryCurationActor(store, sessionId, clientProvider, embedderHolder)); // ── Idle behavior ─────────────────────────────────────────────── @@ -241,7 +258,14 @@ private void StartWriting(IReadOnlyList<(SQLiteMemoryCurationOperation Operation // Write all accepted operations in a single batch if (toWrite.Count > 0) { - await _store.ApplyInlineCurationBatchAsync(toWrite); + var writtenDocs = await _store.ApplyInlineCurationBatchAsync(toWrite); + + // Embed-on-write (memory-core-redesign Slice 2, task 2.8): runs after the + // write above has already committed. Vectors are derived data — a failure + // here must never fail this write; MemoryEmbedOnWriteCoordinator isolates + // and logs per-item failures instead of propagating them. + await MemoryEmbedOnWriteCoordinator.EmbedWrittenDocumentsAsync( + _embedderHolder, _store, writtenDocs, _log); } self.Tell(new WriteBatchResult(new CurationCompleted( diff --git a/src/Netclaw.Actors/Memory/MemoryEmbedOnWriteCoordinator.cs b/src/Netclaw.Actors/Memory/MemoryEmbedOnWriteCoordinator.cs new file mode 100644 index 000000000..4b2cab419 --- /dev/null +++ b/src/Netclaw.Actors/Memory/MemoryEmbedOnWriteCoordinator.cs @@ -0,0 +1,104 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using Akka.Event; +using Microsoft.Extensions.Logging; + +namespace Netclaw.Actors.Memory; + +/// +/// One memory_documents row written by a curation batch-apply +/// ( or +/// ), carrying exactly what +/// needs to embed it: the final (post-anchor- +/// resolution) document id and the text that was persisted. Immutable memory_records +/// (Evidence) are never included — they bypass curation evaluation entirely (see +/// 's "immutable record bypass") and are +/// excluded from embedding coverage by the same scope +/// already uses (its coverage query only reads memory_documents). +/// +public sealed record MemoryDocumentWriteResult(string DocumentId, string Title, string Body); + +/// +/// Embed-on-write hook for memory-core-redesign Slice 2 (task 2.8), called once per commit by +/// both curation write pipelines after their store batch-apply call returns: +/// (inline per-session path, after +/// ) and +/// Netclaw.Daemon.Services.MemoryCurationWorkerService (checkpoint-worker path, after +/// ). This is the one place embed-on- +/// write logic lives — the two call sites exist because two physically separate store commit +/// methods exist by design (D3: the store's standalone-initialization contract is preserved +/// per-pipeline), not because the logic itself is duplicated. +/// +/// +/// Failure isolation: by the time this runs, the memory write has already committed. +/// Vectors are derived data (design D3) — an embedding failure here must never fail, retry, or +/// roll back the write it followed. Each item's hash+embed+upsert is wrapped individually so +/// one bad item does not block the rest of the batch; a failure logs a warning and is left for +/// the startup gap-repair sweep (EmbeddingWarmupHostedService) or +/// netclaw memory backfill-embeddings to self-heal. There is no per-write degradation +/// log when the embedder is simply unavailable — that condition already gets a loud signal once +/// (the warmup failure log + doctor + daemon status), so logging it again on every write would +/// be spam, not signal; a debug-level line is enough for local troubleshooting. +/// +/// +public static class MemoryEmbedOnWriteCoordinator +{ + /// item_kind value written for every embedded memory_documents row. + public const string DocumentItemKind = "document"; + + /// Entry point for the inline per-session actor (Akka logging). + public static Task EmbedWrittenDocumentsAsync( + MemoryEmbedderHolder? holder, + SQLiteMemoryStore store, + IReadOnlyList written, + ILoggingAdapter log, + CancellationToken ct = default) + => EmbedWrittenDocumentsCoreAsync(holder, store, written, new AkkaCurationLog(log), ct); + + /// Entry point for the daemon checkpoint worker (Microsoft.Extensions.Logging). + public static Task EmbedWrittenDocumentsAsync( + MemoryEmbedderHolder? holder, + SQLiteMemoryStore store, + IReadOnlyList written, + ILogger log, + CancellationToken ct = default) + => EmbedWrittenDocumentsCoreAsync(holder, store, written, new MicrosoftCurationLog(log), ct); + + private static async Task EmbedWrittenDocumentsCoreAsync( + MemoryEmbedderHolder? holder, + SQLiteMemoryStore store, + IReadOnlyList written, + ICurationLog log, + CancellationToken ct) + { + if (written.Count == 0) + return; + + var embedder = holder?.Current; + if (embedder is null || !embedder.IsAvailable) + { + // Not the loud signal — the warmup failure log + doctor + daemon status already + // cover that. This is local troubleshooting detail only. + log.Debug("memory_embed_on_write_skipped reason=embedder_unavailable count={0}", written.Count); + return; + } + + foreach (var doc in written) + { + try + { + var hash = MemoryContentHasher.ComputeHash(doc.Title, doc.Body); + var vector = await embedder.EmbedAsync($"{doc.Title}\n{doc.Body}", ct).ConfigureAwait(false); + await store.UpsertEmbeddingAsync( + doc.DocumentId, DocumentItemKind, embedder.ModelId, hash, vector, ct).ConfigureAwait(false); + } + catch (Exception ex) + { + log.Warning(ex, "memory_embed_on_write_failed documentId={0}", doc.DocumentId); + } + } + } +} diff --git a/src/Netclaw.Actors/Memory/MemoryEmbedderHolder.cs b/src/Netclaw.Actors/Memory/MemoryEmbedderHolder.cs new file mode 100644 index 000000000..0a0c71b23 --- /dev/null +++ b/src/Netclaw.Actors/Memory/MemoryEmbedderHolder.cs @@ -0,0 +1,55 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +namespace Netclaw.Actors.Memory; + +/// +/// Mutable holder for the process's singleton +/// (memory-core-redesign Slice 2, task 2.7). +/// +/// +/// Why a holder, not a plain DI singleton: the real embedder is only known once +/// EmbeddingWarmupHostedService (Netclaw.Daemon) finishes provisioning and loading the +/// model — an step that +/// necessarily runs after the DI container has already been built and every other singleton +/// (the curation actor's session, the checkpoint worker) has already resolved its constructor +/// dependencies. A container builds its singleton graph once; there is no way to inject "the +/// embedder after warmup completes" into a constructor, only a slot that gets filled in later. +/// Consumers MUST read at the time they actually need to embed (never +/// cache the value they read), so the transition from unavailable to available — or the +/// reverse, if a future re-provision fails — surfaces without a process restart. +/// +/// +/// +/// Every reader always sees a valid (construction requires an +/// initial value, typically an stub while warmup is +/// still running) — the holder itself is never null-valued, only whatever it currently holds +/// may report as false. +/// +/// +public sealed class MemoryEmbedderHolder +{ + private volatile IMemoryEmbedder _current; + + public MemoryEmbedderHolder(IMemoryEmbedder initial) + { + ArgumentNullException.ThrowIfNull(initial); + _current = initial; + } + + /// The embedder to use right now. Always non-null. + public IMemoryEmbedder Current => _current; + + /// + /// Replaces the current embedder. Called only by EmbeddingWarmupHostedService once + /// provisioning completes — successfully (an OnnxMemoryEmbedder) or not (a fresh + /// carrying the failure reason). + /// + public void Set(IMemoryEmbedder embedder) + { + ArgumentNullException.ThrowIfNull(embedder); + _current = embedder; + } +} diff --git a/src/Netclaw.Actors/Memory/MemoryVectorIndex.cs b/src/Netclaw.Actors/Memory/MemoryVectorIndex.cs new file mode 100644 index 000000000..b35b4df4b --- /dev/null +++ b/src/Netclaw.Actors/Memory/MemoryVectorIndex.cs @@ -0,0 +1,143 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using System.Numerics.Tensors; + +namespace Netclaw.Actors.Memory; + +/// +/// A single nearest-neighbor match returned by . +/// +public sealed record MemoryVectorMatch(string ItemId, string ItemKind, double Cosine); + +/// +/// In-memory brute-force kNN index over one embedding model's vectors (memory-core-redesign +/// D3). Brute force is deliberate, not a placeholder: at the audited corpus scale (~1,200 +/// documents, ~1.8 MB of float32 vectors) a full scan is sub-millisecond, and an ANN index +/// would add a dependency (native or otherwise) for zero measured benefit — revisit only if +/// the corpus grows past roughly 50k items. +/// +/// +/// The index snapshots into a flat +/// float[] (row-major, one -wide slice per item) plus parallel +/// id/kind arrays, bundled into an immutable so a reader never observes +/// a torn combination of old ids with new vectors. Reloading is keyed on +/// — a process-local monotonic counter +/// bumped by every embedding upsert/delete — so is a cheap +/// no-op on every call except the ones that raced a real data change. Cross-process +/// invalidation (multiple daemons against one SQLite file) is out of scope for the +/// single-process MVP; if that ever changes, the version counter would need to move to a +/// persisted data_version column instead of an in-process field. +/// +/// +public sealed class MemoryVectorIndex +{ + private readonly SQLiteMemoryStore _store; + private readonly object _reloadGate = new(); + private Snapshot _snapshot = Snapshot.Empty; + + public MemoryVectorIndex(SQLiteMemoryStore store, string modelId, int dimensions) + { + ArgumentNullException.ThrowIfNull(store); + if (string.IsNullOrWhiteSpace(modelId)) + throw new ArgumentException("Model id is required.", nameof(modelId)); + if (dimensions <= 0) + throw new ArgumentOutOfRangeException(nameof(dimensions), dimensions, "Dimensions must be positive."); + + _store = store; + ModelId = modelId; + Dimensions = dimensions; + } + + /// The embedding model this index serves vectors for. + public string ModelId { get; } + + /// Vector width for ; every loaded row must match this. + public int Dimensions { get; } + + /// Number of vectors currently loaded into the index. + public int Count => Volatile.Read(ref _snapshot).Ids.Length; + + /// + /// Reloads from the store when has + /// advanced past the version this index last loaded. Returns true when a reload was + /// attempted (the store had newer data at the time this call started) — not necessarily + /// that this call's snapshot is the one that ended up installed, since a concurrent faster + /// reload for an even newer version is allowed to win instead (see + /// install below). Safe to call from multiple callers concurrently. + /// + public async Task ReloadIfStaleAsync(CancellationToken ct) + { + var currentVersion = _store.EmbeddingDataVersion; + if (Volatile.Read(ref _snapshot).Version == currentVersion) + return false; + + var rows = await _store.GetEmbeddingsForModelAsync(ModelId, ct).ConfigureAwait(false); + var vectors = new float[rows.Count * Dimensions]; + var ids = new string[rows.Count]; + var itemKinds = new string[rows.Count]; + for (var i = 0; i < rows.Count; i++) + { + if (rows[i].Vector.Length != Dimensions) + throw new InvalidOperationException( + $"Embedding row for item '{rows[i].ItemId}' has {rows[i].Vector.Length} dimensions; " + + $"index '{ModelId}' expects {Dimensions}. Mixed-model rows must not share a model id."); + + ids[i] = rows[i].ItemId; + itemKinds[i] = rows[i].ItemKind; + rows[i].Vector.Span.CopyTo(vectors.AsSpan(i * Dimensions, Dimensions)); + } + + var candidate = new Snapshot(currentVersion, vectors, ids, itemKinds); + + lock (_reloadGate) + { + // Only install if nothing fresher has already landed — a slower reload racing a + // faster one must not clobber newer data with stale data. + if (candidate.Version > Volatile.Read(ref _snapshot).Version) + Volatile.Write(ref _snapshot, candidate); + } + + return true; + } + + /// + /// Returns up to items whose cosine similarity to + /// is at least , ordered by + /// descending similarity. Operates on the last snapshot installed by + /// — callers that need current data must reload first. + /// + public IReadOnlyList TopK(ReadOnlySpan query, int k, double minCosine) + { + if (k <= 0) + return []; + if (query.Length != Dimensions) + throw new ArgumentException($"Query vector has {query.Length} dimensions; index '{ModelId}' expects {Dimensions}.", nameof(query)); + + var snapshot = Volatile.Read(ref _snapshot); + if (snapshot.Ids.Length == 0) + return []; + + // Full scan + sort: at corpus scale (D3: brute force is sub-ms up to ~50k items) this + // is simpler and fast enough. A partial-selection heap is an optimization to reach for + // only if profiling ever shows this method as hot. + var matches = new List(); + for (var i = 0; i < snapshot.Ids.Length; i++) + { + var candidate = snapshot.Vectors.AsSpan(i * Dimensions, Dimensions); + var cosine = TensorPrimitives.CosineSimilarity(query, candidate); + if (cosine >= minCosine) + matches.Add(new MemoryVectorMatch(snapshot.Ids[i], snapshot.ItemKinds[i], cosine)); + } + + matches.Sort((a, b) => b.Cosine.CompareTo(a.Cosine)); + return matches.Count <= k ? matches : matches.GetRange(0, k); + } + + private sealed record Snapshot(long Version, float[] Vectors, string[] Ids, string[] ItemKinds) + { + public static readonly Snapshot Empty = new(-1, [], [], []); + } +} diff --git a/src/Netclaw.Actors/Memory/SQLiteMemoryStore.cs b/src/Netclaw.Actors/Memory/SQLiteMemoryStore.cs index 0a0044dbe..9120c1e01 100644 --- a/src/Netclaw.Actors/Memory/SQLiteMemoryStore.cs +++ b/src/Netclaw.Actors/Memory/SQLiteMemoryStore.cs @@ -3,6 +3,7 @@ // Copyright (C) 2026 - 2026 Petabridge, LLC // // ----------------------------------------------------------------------- +using System.Runtime.InteropServices; using Microsoft.Data.Sqlite; using Microsoft.Extensions.Logging; using Microsoft.Extensions.Logging.Abstractions; @@ -19,6 +20,7 @@ public sealed class SQLiteMemoryStore private readonly string _connectionString; private readonly TimeProvider _timeProvider; private readonly ILogger _logger; + private long _embeddingDataVersion; public SQLiteMemoryStore(string sqlitePath, TimeProvider timeProvider, ILogger? logger = null) { @@ -27,6 +29,16 @@ public SQLiteMemoryStore(string sqlitePath, TimeProvider timeProvider, ILogger.Instance; } + /// + /// Process-local monotonic counter bumped whenever memory_embeddings rows change + /// (a real write in , or a deletion via + /// ). uses this to + /// decide when its in-memory snapshot is stale without round-tripping to SQLite. Restarts + /// reset it to 0, which is safe: a fresh always reloads on + /// its first call regardless of the counter's absolute value. + /// + public long EmbeddingDataVersion => Interlocked.Read(ref _embeddingDataVersion); + public async Task InitializeAsync(CancellationToken ct = default) { await WithConnectionAsync(async (conn, ct) => @@ -140,6 +152,20 @@ updated_at INTEGER NOT NULL CREATE INDEX IF NOT EXISTS idx_memory_checkpoints_pending ON memory_checkpoints(status, priority DESC, created_at ASC); + + CREATE TABLE IF NOT EXISTS memory_embeddings( + item_id TEXT NOT NULL, + item_kind TEXT NOT NULL, + model_id TEXT NOT NULL, + content_hash TEXT NOT NULL, + dims INTEGER NOT NULL, + vector BLOB NOT NULL, + created_at INTEGER NOT NULL, + PRIMARY KEY(item_id, model_id) + ); + + CREATE INDEX IF NOT EXISTS idx_memory_embeddings_model + ON memory_embeddings(model_id); """; await using var cmd = conn.CreateCommand(); @@ -1056,7 +1082,7 @@ UPDATE memory_documents public async Task TombstoneDocumentAsync(string documentId, CancellationToken ct = default) { - return await WithConnectionAsync(async (conn, ct) => + var (tombstoned, embeddingsDeleted) = await WithConnectionAsync(async (conn, ct) => { await using var tx = (SqliteTransaction)await conn.BeginTransactionAsync(ct); @@ -1073,14 +1099,232 @@ UPDATE memory_documents cmd.Parameters.AddWithValue("$updatedAt", _timeProvider.GetUtcNow().ToUnixTimeMilliseconds()); var affected = await cmd.ExecuteNonQueryAsync(ct); + var embeddingsDeleted = 0; if (affected > 0) + { await DeleteDocumentFtsAsync(conn, tx, documentId, ct); + // Vectors are derived data (design D3): a tombstoned document must not keep + // surfacing as a kNN neighbor, so its embedding rows are removed in the same + // transaction as the tombstone itself rather than left to rot. + await using var deleteEmbeddings = conn.CreateCommand(); + deleteEmbeddings.Transaction = tx; + deleteEmbeddings.CommandText = "DELETE FROM memory_embeddings WHERE item_id = $id;"; + deleteEmbeddings.Parameters.AddWithValue("$id", documentId); + embeddingsDeleted = await deleteEmbeddings.ExecuteNonQueryAsync(ct); + } + await tx.CommitAsync(ct); - return affected > 0; + return (affected > 0, embeddingsDeleted); + }, ct); + + if (embeddingsDeleted > 0) + Interlocked.Increment(ref _embeddingDataVersion); + + return tombstoned; + } + + /// + /// Upserts an embedding row keyed by (item_id, model_id). Skips the write entirely — + /// no row change, no bump — when the stored + /// already matches, so a naive caller that re-embeds on + /// every write (or a backfill re-run) pays no cost when nothing changed (design D3). + /// Returns whether a row was actually written (false for the hash-unchanged skip) so + /// callers like netclaw memory backfill-embeddings can report accurate + /// embedded/skipped counts, including when a concurrent live daemon has already embedded + /// the same item between the caller's candidate scan and this call. + /// is written as a little-endian float32 blob; every supported + /// deployment target (linux-x64, linux-arm64) is little-endian, so no byte-order handling + /// is needed on read. + /// + public async Task UpsertEmbeddingAsync( + string itemId, + string itemKind, + string modelId, + string contentHash, + ReadOnlyMemory vector, + CancellationToken ct = default) + { + var wrote = await WithConnectionAsync(async (conn, ct) => + { + await using var existing = conn.CreateCommand(); + existing.CommandText = "SELECT content_hash FROM memory_embeddings WHERE item_id = $itemId AND model_id = $modelId;"; + existing.Parameters.AddWithValue("$itemId", itemId); + existing.Parameters.AddWithValue("$modelId", modelId); + var existingHash = (string?)await existing.ExecuteScalarAsync(ct); + + if (existingHash is not null && string.Equals(existingHash, contentHash, StringComparison.Ordinal)) + return false; + + await using var cmd = conn.CreateCommand(); + cmd.CommandText = """ + INSERT INTO memory_embeddings(item_id, item_kind, model_id, content_hash, dims, vector, created_at) + VALUES($itemId, $itemKind, $modelId, $contentHash, $dims, $vector, $createdAt) + ON CONFLICT(item_id, model_id) DO UPDATE SET + item_kind=excluded.item_kind, + content_hash=excluded.content_hash, + dims=excluded.dims, + vector=excluded.vector, + created_at=excluded.created_at; + """; + cmd.Parameters.AddWithValue("$itemId", itemId); + cmd.Parameters.AddWithValue("$itemKind", itemKind); + cmd.Parameters.AddWithValue("$modelId", modelId); + cmd.Parameters.AddWithValue("$contentHash", contentHash); + cmd.Parameters.AddWithValue("$dims", vector.Length); + cmd.Parameters.AddWithValue("$vector", VectorToBlob(vector.Span)); + cmd.Parameters.AddWithValue("$createdAt", _timeProvider.GetUtcNow().ToUnixTimeMilliseconds()); + await cmd.ExecuteNonQueryAsync(ct); + return true; + }, ct); + + if (wrote) + Interlocked.Increment(ref _embeddingDataVersion); + + return wrote; + } + + /// + /// All embedding rows for — the raw material + /// loads into its flat in-memory snapshot. A thin store + /// query rather than a similarity search: kNN math belongs in the index, not the store. + /// + public async Task> GetEmbeddingsForModelAsync( + string modelId, + CancellationToken ct = default) + { + return await WithConnectionAsync(async (conn, ct) => + { + await using var cmd = conn.CreateCommand(); + cmd.CommandText = "SELECT item_id, item_kind, vector FROM memory_embeddings WHERE model_id = $modelId;"; + cmd.Parameters.AddWithValue("$modelId", modelId); + + var results = new List(); + await using var reader = await cmd.ExecuteReaderAsync(ct); + while (await reader.ReadAsync(ct)) + { + var itemId = reader.GetString(0); + var itemKind = reader.GetString(1); + var blob = reader.GetFieldValue(2); + results.Add(new SQLiteMemoryEmbeddingRow(itemId, itemKind, BlobToVector(blob))); + } + + return (IReadOnlyList)results; + }, ct); + } + + /// + /// Coverage diagnostics for (memory-embeddings spec: "Embedding + /// coverage diagnostics"). + /// requires recomputing per document in + /// application code — SQLite has no native SHA-256 — so this method loads full document + /// bodies. That is an acceptable cost for a diagnostic query (doctor/status), never a + /// per-turn hot path, at the audited corpus scale (~1,200 documents). + /// + public async Task GetEmbeddingCoverageAsync(string modelId, CancellationToken ct = default) + { + return await WithConnectionAsync(async (conn, ct) => + { + var documents = await LoadNonTombstonedDocumentsAsync(conn, ct); + var currentModelHashes = await LoadCurrentModelHashesAsync(conn, modelId, ct); + + var embeddedCurrentHash = 0; + foreach (var doc in documents) + { + if (currentModelHashes.TryGetValue(doc.Id, out var storedHash) + && string.Equals(storedHash, MemoryContentHasher.ComputeHash(doc.Title, doc.Body), StringComparison.Ordinal)) + { + embeddedCurrentHash++; + } + } + + await using var otherModelCmd = conn.CreateCommand(); + otherModelCmd.CommandText = "SELECT COUNT(DISTINCT item_id) FROM memory_embeddings WHERE model_id != $modelId;"; + otherModelCmd.Parameters.AddWithValue("$modelId", modelId); + var otherModelCount = Convert.ToInt32(await otherModelCmd.ExecuteScalarAsync(ct)); + + return new MemoryEmbeddingCoverage(documents.Count, embeddedCurrentHash, otherModelCount); + }, ct); + } + + /// + /// Documents lacking a current-model, current-hash embedding — the same "derived backfill + /// state" counts, but returning the actual rows so + /// callers (the daemon warmup service's gap-repair sweep, netclaw memory + /// backfill-embeddings) can embed them. Backfill state is never tracked in a separate + /// progress table (design D3) — this is always a fresh LEFT-JOIN-shaped comparison against + /// the current model id and content hash. When is true, every + /// non-tombstoned document is returned regardless of its current embedding state (used by + /// --force backfill after a model change). + /// + public async Task> GetDocumentsNeedingEmbeddingAsync( + string modelId, + bool force = false, + CancellationToken ct = default) + { + return await WithConnectionAsync(async (conn, ct) => + { + var documents = await LoadNonTombstonedDocumentsAsync(conn, ct); + + if (force) + { + return (IReadOnlyList)documents + .Select(d => new MemoryDocumentWriteResult(d.Id, d.Title, d.Body)) + .ToList(); + } + + var currentModelHashes = await LoadCurrentModelHashesAsync(conn, modelId, ct); + var missing = new List(); + foreach (var doc in documents) + { + var currentHash = MemoryContentHasher.ComputeHash(doc.Title, doc.Body); + if (!currentModelHashes.TryGetValue(doc.Id, out var storedHash) + || !string.Equals(storedHash, currentHash, StringComparison.Ordinal)) + { + missing.Add(new MemoryDocumentWriteResult(doc.Id, doc.Title, doc.Body)); + } + } + + return (IReadOnlyList)missing; }, ct); } + private static async Task> LoadNonTombstonedDocumentsAsync( + SqliteConnection conn, CancellationToken ct) + { + await using var docsCmd = conn.CreateCommand(); + docsCmd.CommandText = $""" + SELECT document_id, title, markdown_body FROM memory_documents + WHERE update_semantics != '{MemoryUpdateSemantics.Tombstone.ToWireValue()}'; + """; + + var documents = new List<(string Id, string Title, string Body)>(); + await using var reader = await docsCmd.ExecuteReaderAsync(ct); + while (await reader.ReadAsync(ct)) + documents.Add((reader.GetString(0), reader.GetString(1), reader.GetString(2))); + return documents; + } + + private static async Task> LoadCurrentModelHashesAsync( + SqliteConnection conn, string modelId, CancellationToken ct) + { + await using var embCmd = conn.CreateCommand(); + embCmd.CommandText = "SELECT item_id, content_hash FROM memory_embeddings WHERE model_id = $modelId;"; + embCmd.Parameters.AddWithValue("$modelId", modelId); + + var hashes = new Dictionary(StringComparer.Ordinal); + await using var reader = await embCmd.ExecuteReaderAsync(ct); + while (await reader.ReadAsync(ct)) + hashes[reader.GetString(0)] = reader.GetString(1); + return hashes; + } + + private static byte[] VectorToBlob(ReadOnlySpan vector) + => MemoryMarshal.AsBytes(vector).ToArray(); + + private static float[] BlobToVector(byte[] blob) + => MemoryMarshal.Cast(blob).ToArray(); + public async Task SupersedeRecordAsync(string recordId, string payloadJson, CancellationToken ct = default) { return await WithConnectionAsync(async (conn, ct) => @@ -1344,11 +1588,17 @@ UPDATE memory_documents /// Write a batch of curation operations without an associated checkpoint. /// Used by the inline curation actor path where proposals are sent directly /// from the session actor rather than through the checkpoint queue. + /// Returns the memory_documents rows written in this batch (never immutable + /// memory_records) so the caller can embed them post-commit + /// (, memory-core-redesign Slice 2) knowing the + /// final document id — which for a Create decision is only assigned inside this method. /// - public async Task ApplyInlineCurationBatchAsync( + public async Task> ApplyInlineCurationBatchAsync( IReadOnlyList operations, CancellationToken ct = default) { + var written = new List(); + await WithConnectionAsync(async (conn, ct) => { await using var tx = (SqliteTransaction)await conn.BeginTransactionAsync(ct); @@ -1490,6 +1740,7 @@ ON CONFLICT(document_id) DO UPDATE SET documentCmd.Parameters.AddWithValue("$createdAt", now); documentCmd.Parameters.AddWithValue("$updatedAt", now); await documentCmd.ExecuteNonQueryAsync(ct); + written.Add(new MemoryDocumentWriteResult(documentId, operation.Title, operation.Content)); if (IsSearchableRecallMode(resolvedRecallMode)) await UpsertDocumentFtsAsync(conn, tx, documentId, operation.Title, operation.Content, operation.AliasesJson, operation.FacetsJson, ct); @@ -1497,13 +1748,22 @@ ON CONFLICT(document_id) DO UPDATE SET await tx.CommitAsync(ct); }, ct); + + return written; } - public async Task ApplyCurationBatchAsync( + /// + /// Returns the memory_documents rows written in this batch — see + /// 's remarks for why the caller needs this to + /// embed post-commit. + /// + public async Task> ApplyCurationBatchAsync( string checkpointId, IReadOnlyList operations, CancellationToken ct = default) { + var written = new List(); + await WithConnectionAsync(async (conn, ct) => { await using var tx = (SqliteTransaction)await conn.BeginTransactionAsync(ct); @@ -1648,6 +1908,7 @@ ON CONFLICT(document_id) DO UPDATE SET documentCmd.Parameters.AddWithValue("$createdAt", now); documentCmd.Parameters.AddWithValue("$updatedAt", now); await documentCmd.ExecuteNonQueryAsync(ct); + written.Add(new MemoryDocumentWriteResult(documentId, operation.Title, operation.Content)); if (IsSearchableRecallMode(resolvedRecallMode)) await UpsertDocumentFtsAsync(conn, tx, documentId, operation.Title, operation.Content, operation.AliasesJson, operation.FacetsJson, ct); @@ -1667,6 +1928,8 @@ UPDATE memory_checkpoints await tx.CommitAsync(ct); }, ct); + + return written; } private async Task WithConnectionAsync( @@ -2025,3 +2288,24 @@ public sealed record SQLiteMemoryRelationOperation( string TargetCanonicalName, string TargetAnchorType, double Confidence); + +/// One memory_embeddings row, as loaded by . +public sealed record SQLiteMemoryEmbeddingRow(string ItemId, string ItemKind, ReadOnlyMemory Vector); + +/// +/// Coverage diagnostics for one embedding model, as returned by +/// . +/// +/// Non-tombstoned documents in the corpus. +/// +/// Of those, how many have an embedding row for the queried model whose stored content hash +/// matches the document's current title/body. +/// +/// +/// Distinct items with an embedding row under a model id other than the one queried — a +/// non-zero count means the corpus mixes similarity spaces and thresholds are miscalibrated. +/// +public sealed record MemoryEmbeddingCoverage( + int TotalRecallableDocuments, + int EmbeddedCurrentHashCount, + int OtherModelCount); diff --git a/src/Netclaw.Actors/Netclaw.Actors.csproj b/src/Netclaw.Actors/Netclaw.Actors.csproj index e50b6c8df..84755b8b2 100644 --- a/src/Netclaw.Actors/Netclaw.Actors.csproj +++ b/src/Netclaw.Actors/Netclaw.Actors.csproj @@ -26,6 +26,9 @@ + + diff --git a/src/Netclaw.Actors/Sessions/LlmSessionActor.cs b/src/Netclaw.Actors/Sessions/LlmSessionActor.cs index fa705f950..0bc6afae8 100644 --- a/src/Netclaw.Actors/Sessions/LlmSessionActor.cs +++ b/src/Netclaw.Actors/Sessions/LlmSessionActor.cs @@ -69,6 +69,7 @@ public sealed class LlmSessionActor : ReceivePersistentActor, IWithTimers private readonly string _sessionsBasePath; private readonly ISessionLifecycleObserver? _lifecycleObserver; private readonly Memory.SQLiteMemoryStore? _memoryStore; + private readonly Memory.MemoryEmbedderHolder? _memoryEmbedderHolder; private readonly IChatClientProvider _clientProvider; private readonly ILoggingAdapter _log; @@ -239,6 +240,7 @@ public LlmSessionActor( _memoryRecallCoordinator = memory?.RecallCoordinator ?? NullMemoryRecallCoordinator.Instance; _memoryCheckpointSink = memory?.CheckpointSink ?? NullMemoryCheckpointSink.Instance; _memoryStore = memory?.MemoryStore; + _memoryEmbedderHolder = memory?.EmbedderHolder; _memoryConfig = memory?.MemoryConfig ?? new MemoryConfig(); _timeProvider = services.TimeProvider; _sessionsBasePath = services.Paths.SessionsDirectory; @@ -312,7 +314,7 @@ public LlmSessionActor( if (_memoryStore is not null) { _curationActor = Context.ActorOf( - Memory.MemoryCurationActor.CreateProps(_memoryStore, _sessionId, _clientProvider), + Memory.MemoryCurationActor.CreateProps(_memoryStore, _sessionId, _clientProvider, _memoryEmbedderHolder), "memory-curation"); // Distillation processes a full transcript — allow 5x normal sidecar timeout diff --git a/src/Netclaw.Actors/Sessions/SessionDependencies.cs b/src/Netclaw.Actors/Sessions/SessionDependencies.cs index acf0ed8db..1578a30bf 100644 --- a/src/Netclaw.Actors/Sessions/SessionDependencies.cs +++ b/src/Netclaw.Actors/Sessions/SessionDependencies.cs @@ -40,13 +40,18 @@ public sealed record SessionToolServices( /// /// Memory infrastructure for recall, checkpoint, and curation. +/// resolves the process's embedder for embed-on-write +/// (memory-core-redesign Slice 2). Null is a genuine state — same as +/// being null — for any session/test harness that has not wired +/// up the embedding subsystem at all. /// public sealed record SessionMemoryServices( IMemoryExtractor MemoryExtractor, IMemoryRecallCoordinator RecallCoordinator, IMemoryCheckpointSink CheckpointSink, SQLiteMemoryStore? MemoryStore, - MemoryConfig? MemoryConfig = null); + MemoryConfig? MemoryConfig = null, + MemoryEmbedderHolder? EmbedderHolder = null); /// /// Metrics and lifecycle observation. diff --git a/src/Netclaw.Cli.Tests/Doctor/ConfigSchemaDoctorCheckTests.cs b/src/Netclaw.Cli.Tests/Doctor/ConfigSchemaDoctorCheckTests.cs index b3418b7f3..9773789a8 100644 --- a/src/Netclaw.Cli.Tests/Doctor/ConfigSchemaDoctorCheckTests.cs +++ b/src/Netclaw.Cli.Tests/Doctor/ConfigSchemaDoctorCheckTests.cs @@ -97,6 +97,60 @@ await File.WriteAllTextAsync(paths.NetclawConfigPath, Assert.Equal(DoctorSeverity.Pass, result.Severity); } + [Fact] + public async Task ReturnsPass_WhenMemoryEmbeddingsConfigMatchesSchemaV1() + { + var basePath = CreateTempBasePath(); + var paths = new NetclawPaths(basePath); + paths.EnsureDirectoriesExist(); + + await File.WriteAllTextAsync(paths.NetclawConfigPath, + """ + { + "configVersion": 1, + "Memory": { + "Enabled": true, + "Embeddings": { + "Enabled": true, + "ModelId": "snowflake-arctic-embed-m", + "AutoDownload": false + } + } + } + """, TestContext.Current.CancellationToken); + + var check = new ConfigSchemaDoctorCheck(paths); + var result = await check.RunAsync(TestContext.Current.CancellationToken); + + Assert.Equal(DoctorSeverity.Pass, result.Severity); + } + + [Fact] + public async Task ReturnsError_WhenMemoryEmbeddingsHasAnUnknownProperty() + { + var basePath = CreateTempBasePath(); + var paths = new NetclawPaths(basePath); + paths.EnsureDirectoriesExist(); + + await File.WriteAllTextAsync(paths.NetclawConfigPath, + """ + { + "configVersion": 1, + "Memory": { + "Embeddings": { + "Enabled": true, + "NotARealProperty": "oops" + } + } + } + """, TestContext.Current.CancellationToken); + + var check = new ConfigSchemaDoctorCheck(paths); + var result = await check.RunAsync(TestContext.Current.CancellationToken); + + Assert.Equal(DoctorSeverity.Error, result.Severity); + } + [Fact] public async Task ReturnsPass_WhenReverseProxyTrustedProxiesLookValid() { diff --git a/src/Netclaw.Cli.Tests/Doctor/MemoryEmbeddingDoctorCheckTests.cs b/src/Netclaw.Cli.Tests/Doctor/MemoryEmbeddingDoctorCheckTests.cs new file mode 100644 index 000000000..0cc7f2ff7 --- /dev/null +++ b/src/Netclaw.Cli.Tests/Doctor/MemoryEmbeddingDoctorCheckTests.cs @@ -0,0 +1,192 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using System.Security.Cryptography; +using System.Text.Json; +using Microsoft.Extensions.Configuration; +using Netclaw.Actors.Memory; +using Netclaw.Cli.Doctor; +using Netclaw.Configuration; +using Netclaw.Embeddings; +using Xunit; + +namespace Netclaw.Cli.Tests.Doctor; + +/// +/// Covers every severity branch of +/// (memory-core-redesign spec: "Embedding coverage diagnostics"), using the tiny fixture ONNX +/// graph (linked from Netclaw.Embeddings.Tests/Fixtures) instead of the real allowlist — +/// no network access anywhere in these tests. +/// +public sealed class MemoryEmbeddingDoctorCheckTests +{ + private const string ModelId = "tiny-fixture"; + private static string FixturesDir => Path.Combine(AppContext.BaseDirectory, "Fixtures"); + + [Fact] + public async Task Passes_with_embeddings_disabled_message_when_config_off() + { + var paths = CreateTempPaths(); + var config = WriteConfig(paths, enabled: false); + var check = new MemoryEmbeddingDoctorCheck(paths, config, FixtureAllowlist()); + + var result = await check.RunAsync(TestContext.Current.CancellationToken); + + Assert.Equal(DoctorSeverity.Pass, result.Severity); + Assert.Contains("disabled", result.Message, StringComparison.OrdinalIgnoreCase); + } + + [Fact] + public async Task Errors_when_enabled_but_model_is_missing() + { + var paths = CreateTempPaths(); + var config = WriteConfig(paths, enabled: true); + // No model files placed at paths.EmbeddingModelDirectory(ModelId). + var check = new MemoryEmbeddingDoctorCheck(paths, config, FixtureAllowlist()); + + var result = await check.RunAsync(TestContext.Current.CancellationToken); + + Assert.Equal(DoctorSeverity.Error, result.Severity); + Assert.Contains(ModelId, result.Message, StringComparison.Ordinal); + } + + [Fact] + public async Task Warns_when_items_lack_a_current_model_embedding() + { + var paths = CreateTempPaths(); + var config = WriteConfig(paths, enabled: true); + PrePlaceValidModelFiles(paths); + + var store = new SQLiteMemoryStore(paths.MemorySqliteDbPath, TimeProvider.System); + await store.InitializeAsync(TestContext.Current.CancellationToken); + await SeedDocumentAsync(store, "doc-unembedded", "Unembedded", "never embedded"); + + var check = new MemoryEmbeddingDoctorCheck(paths, config, FixtureAllowlist()); + var result = await check.RunAsync(TestContext.Current.CancellationToken); + + Assert.Equal(DoctorSeverity.Warning, result.Severity); + Assert.Contains("lack a current-model embedding", result.Message, StringComparison.OrdinalIgnoreCase); + } + + [Fact] + public async Task Warns_on_mixed_model_corpus() + { + var paths = CreateTempPaths(); + var config = WriteConfig(paths, enabled: true); + PrePlaceValidModelFiles(paths); + + var store = new SQLiteMemoryStore(paths.MemorySqliteDbPath, TimeProvider.System); + await store.InitializeAsync(TestContext.Current.CancellationToken); + await SeedDocumentAsync(store, "doc-1", "Doc", "body"); + var hash = MemoryContentHasher.ComputeHash("Doc", "body"); + await store.UpsertEmbeddingAsync("doc-1", "document", ModelId, hash, new float[] { 1f }, TestContext.Current.CancellationToken); + await store.UpsertEmbeddingAsync("doc-1", "document", "some-other-model", hash, new float[] { 2f }, TestContext.Current.CancellationToken); + + var check = new MemoryEmbeddingDoctorCheck(paths, config, FixtureAllowlist()); + var result = await check.RunAsync(TestContext.Current.CancellationToken); + + Assert.Equal(DoctorSeverity.Warning, result.Severity); + Assert.Contains("another model id", result.Message, StringComparison.OrdinalIgnoreCase); + } + + [Fact] + public async Task Passes_with_coverage_summary_when_fully_embedded() + { + var paths = CreateTempPaths(); + var config = WriteConfig(paths, enabled: true); + PrePlaceValidModelFiles(paths); + + var store = new SQLiteMemoryStore(paths.MemorySqliteDbPath, TimeProvider.System); + await store.InitializeAsync(TestContext.Current.CancellationToken); + await SeedDocumentAsync(store, "doc-1", "Doc", "body"); + var hash = MemoryContentHasher.ComputeHash("Doc", "body"); + await store.UpsertEmbeddingAsync("doc-1", "document", ModelId, hash, new float[] { 1f }, TestContext.Current.CancellationToken); + + var check = new MemoryEmbeddingDoctorCheck(paths, config, FixtureAllowlist()); + var result = await check.RunAsync(TestContext.Current.CancellationToken); + + Assert.Equal(DoctorSeverity.Pass, result.Severity); + Assert.Contains("healthy", result.Message, StringComparison.OrdinalIgnoreCase); + } + + private static NetclawPaths CreateTempPaths() + { + var basePath = Path.Combine(Path.GetTempPath(), "netclaw-embedding-doctor-tests", Guid.NewGuid().ToString("N")); + var paths = new NetclawPaths(basePath); + paths.EnsureDirectoriesExist(); + return paths; + } + + private static IConfiguration WriteConfig(NetclawPaths paths, bool enabled) + { + var config = new Dictionary + { + ["Memory"] = new Dictionary + { + ["Embeddings"] = new Dictionary + { + ["Enabled"] = enabled, + ["ModelId"] = ModelId, + ["AutoDownload"] = true, + } + } + }; + + File.WriteAllText(paths.NetclawConfigPath, JsonSerializer.Serialize(config)); + + return new ConfigurationBuilder() + .AddJsonFile(paths.NetclawConfigPath, optional: false) + .Build(); + } + + private static void PrePlaceValidModelFiles(NetclawPaths paths) + { + var dir = paths.EmbeddingModelDirectory(ModelId); + Directory.CreateDirectory(dir); + File.Copy(Path.Combine(FixturesDir, "tiny-embedder.onnx"), Path.Combine(dir, "model.onnx"), overwrite: true); + File.Copy(Path.Combine(FixturesDir, "tiny-vocab.txt"), Path.Combine(dir, "vocab.txt"), overwrite: true); + } + + private static async Task SeedDocumentAsync(SQLiteMemoryStore store, string id, string title, string body) + { + var anchor = store.CreateDefaultAnchor(id); + var now = DateTimeOffset.UtcNow.ToUnixTimeMilliseconds(); + await store.UpsertDocumentAsync(new SQLiteMemoryDocument( + DocumentId: id, + Anchor: anchor, + MemoryClass: "durable_fact", + Title: title, + MarkdownBody: body, + AliasesJson: null, + FacetsJson: null, + SlotsJson: null, + UpdateSemantics: "merge-document", + Sensitivity: "normal", + RecallMode: "auto", + Confidence: 0.9, + FreshnessAtMs: now, + ExpiresAtMs: null, + CreatedAtMs: now, + UpdatedAtMs: now)); + } + + internal static IReadOnlyDictionary FixtureAllowlist() + { + var modelBytes = File.ReadAllBytes(Path.Combine(FixturesDir, "tiny-embedder.onnx")); + var vocabBytes = File.ReadAllBytes(Path.Combine(FixturesDir, "tiny-vocab.txt")); + + return new Dictionary + { + [ModelId] = new( + ModelId, + ModelUrl: new Uri("http://127.0.0.1:1/unused-model.onnx"), + TokenizerUrl: new Uri("http://127.0.0.1:1/unused-vocab.txt"), + ModelSha256: Convert.ToHexStringLower(SHA256.HashData(modelBytes)), + TokenizerSha256: Convert.ToHexStringLower(SHA256.HashData(vocabBytes)), + Dimensions: 8, + ModelByteSize: modelBytes.Length), + }; + } +} diff --git a/src/Netclaw.Cli.Tests/Memory/MemoryCommandTests.cs b/src/Netclaw.Cli.Tests/Memory/MemoryCommandTests.cs new file mode 100644 index 000000000..14c7c9882 --- /dev/null +++ b/src/Netclaw.Cli.Tests/Memory/MemoryCommandTests.cs @@ -0,0 +1,197 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using System.Security.Cryptography; +using Microsoft.Extensions.Configuration; +using Netclaw.Actors.Memory; +using Netclaw.Cli.Memory; +using Netclaw.Configuration; +using Netclaw.Embeddings; +using Xunit; + +namespace Netclaw.Cli.Tests.Memory; + +/// +/// Covers the core loop of netclaw memory backfill-embeddings +/// (memory-core-redesign Slice 2, task 2.9): provisioning, embedding, and the final +/// embedded/skipped-hash-unchanged/failed summary. Uses the internal allowlist-injectable +/// overload of +/// pointed at the tiny fixture ONNX graph — no network access. +/// +public sealed class MemoryCommandTests +{ + private const string ModelId = "tiny-fixture"; + private static string FixturesDir => Path.Combine(AppContext.BaseDirectory, "Fixtures"); + + [Fact] + public async Task BackfillEmbeddings_embeds_missing_documents_and_reports_a_summary() + { + var paths = CreateTempPaths(prePlaceValidModel: true); + var config = BuildConfig(autoDownload: true); + + var store = new SQLiteMemoryStore(paths.MemorySqliteDbPath, TimeProvider.System); + await store.InitializeAsync(TestContext.Current.CancellationToken); + await SeedDocumentAsync(store, "doc-1", "Doc One", "first body"); + await SeedDocumentAsync(store, "doc-2", "Doc Two", "second body"); + + var (exitCode, stdout) = await RunCapturedAsync(["memory", "backfill-embeddings"], paths, config); + + Assert.Equal(0, exitCode); + Assert.Contains("embedded=2 skipped-hash-unchanged=0 failed=0", stdout); + + var rows = await store.GetEmbeddingsForModelAsync(ModelId, TestContext.Current.CancellationToken); + Assert.Equal(2, rows.Count); + } + + [Fact] + public async Task BackfillEmbeddings_is_a_no_op_when_nothing_is_missing() + { + var paths = CreateTempPaths(prePlaceValidModel: true); + var config = BuildConfig(autoDownload: true); + + var store = new SQLiteMemoryStore(paths.MemorySqliteDbPath, TimeProvider.System); + await store.InitializeAsync(TestContext.Current.CancellationToken); + await SeedDocumentAsync(store, "doc-1", "Doc One", "first body"); + var hash = MemoryContentHasher.ComputeHash("Doc One", "first body"); + await store.UpsertEmbeddingAsync("doc-1", "document", ModelId, hash, new float[] { 1f }, TestContext.Current.CancellationToken); + + var (exitCode, stdout) = await RunCapturedAsync(["memory", "backfill-embeddings"], paths, config); + + Assert.Equal(0, exitCode); + Assert.Contains("Nothing to backfill", stdout); + } + + [Fact] + public async Task BackfillEmbeddings_fails_clearly_when_autodownload_is_false_and_model_is_missing() + { + var paths = CreateTempPaths(prePlaceValidModel: false); + // AutoDownload=false and no pre-placed model files: the CLI must refuse, not download. + var config = BuildConfig(autoDownload: false); + + var (exitCode, _, stderr) = await RunCapturedWithStderrAsync(["memory", "backfill-embeddings"], paths, config); + + Assert.Equal(1, exitCode); + Assert.Contains("AutoDownload", stderr); + } + + [Fact] + public async Task BackfillEmbeddings_with_force_re_embeds_every_recallable_document() + { + var paths = CreateTempPaths(prePlaceValidModel: true); + var config = BuildConfig(autoDownload: true); + + var store = new SQLiteMemoryStore(paths.MemorySqliteDbPath, TimeProvider.System); + await store.InitializeAsync(TestContext.Current.CancellationToken); + await SeedDocumentAsync(store, "doc-1", "Doc One", "first body"); + var hash = MemoryContentHasher.ComputeHash("Doc One", "first body"); + await store.UpsertEmbeddingAsync("doc-1", "document", ModelId, hash, new float[] { 1f }, TestContext.Current.CancellationToken); + + var (exitCode, stdout) = await RunCapturedAsync(["memory", "backfill-embeddings", "--force"], paths, config); + + Assert.Equal(0, exitCode); + // Already current-hash-embedded, so --force's candidate set still resolves to a no-op + // write (UpsertEmbeddingAsync's own hash check), reported as skipped, not embedded. + Assert.Contains("embedded=0 skipped-hash-unchanged=1 failed=0", stdout); + } + + private static async Task<(int ExitCode, string Stdout)> RunCapturedAsync(string[] args, NetclawPaths paths, IConfiguration config) + { + var (exitCode, stdout, _) = await RunCapturedWithStderrAsync(args, paths, config); + return (exitCode, stdout); + } + + private static async Task<(int ExitCode, string Stdout, string Stderr)> RunCapturedWithStderrAsync( + string[] args, NetclawPaths paths, IConfiguration config) + { + var originalOut = Console.Out; + var originalError = Console.Error; + using var stdout = new StringWriter(); + using var stderr = new StringWriter(); + Console.SetOut(stdout); + Console.SetError(stderr); + try + { + var exitCode = await MemoryCommand.RunAsync(args, paths, config, FixtureAllowlist()); + return (exitCode, stdout.ToString(), stderr.ToString()); + } + finally + { + Console.SetOut(originalOut); + Console.SetError(originalError); + } + } + + private static NetclawPaths CreateTempPaths(bool prePlaceValidModel) + { + var basePath = Path.Combine(Path.GetTempPath(), "netclaw-memory-command-tests", Guid.NewGuid().ToString("N")); + var paths = new NetclawPaths(basePath); + paths.EnsureDirectoriesExist(); + + if (prePlaceValidModel) + { + // Pre-place a valid local copy so ProvisionAsync's skip-if-valid path never reaches + // the network (the fixture allowlist's URLs are unreachable dummies). + var dir = paths.EmbeddingModelDirectory(ModelId); + Directory.CreateDirectory(dir); + File.Copy(Path.Combine(FixturesDir, "tiny-embedder.onnx"), Path.Combine(dir, "model.onnx"), overwrite: true); + File.Copy(Path.Combine(FixturesDir, "tiny-vocab.txt"), Path.Combine(dir, "vocab.txt"), overwrite: true); + } + + return paths; + } + + private static IConfiguration BuildConfig(bool autoDownload) + { + var settings = new Dictionary + { + ["Memory:Embeddings:Enabled"] = "true", + ["Memory:Embeddings:ModelId"] = ModelId, + ["Memory:Embeddings:AutoDownload"] = autoDownload ? "true" : "false", + }; + + return new ConfigurationBuilder().AddInMemoryCollection(settings).Build(); + } + + private static async Task SeedDocumentAsync(SQLiteMemoryStore store, string id, string title, string body) + { + var anchor = store.CreateDefaultAnchor(id); + var now = DateTimeOffset.UtcNow.ToUnixTimeMilliseconds(); + await store.UpsertDocumentAsync(new SQLiteMemoryDocument( + DocumentId: id, + Anchor: anchor, + MemoryClass: "durable_fact", + Title: title, + MarkdownBody: body, + AliasesJson: null, + FacetsJson: null, + SlotsJson: null, + UpdateSemantics: "merge-document", + Sensitivity: "normal", + RecallMode: "auto", + Confidence: 0.9, + FreshnessAtMs: now, + ExpiresAtMs: null, + CreatedAtMs: now, + UpdatedAtMs: now)); + } + + private static IReadOnlyDictionary FixtureAllowlist() + { + var modelBytes = File.ReadAllBytes(Path.Combine(FixturesDir, "tiny-embedder.onnx")); + var vocabBytes = File.ReadAllBytes(Path.Combine(FixturesDir, "tiny-vocab.txt")); + + return new Dictionary + { + [ModelId] = new( + ModelId, + ModelUrl: new Uri("http://127.0.0.1:1/unused-model.onnx"), + TokenizerUrl: new Uri("http://127.0.0.1:1/unused-vocab.txt"), + ModelSha256: Convert.ToHexStringLower(SHA256.HashData(modelBytes)), + TokenizerSha256: Convert.ToHexStringLower(SHA256.HashData(vocabBytes)), + Dimensions: 8, + ModelByteSize: modelBytes.Length), + }; + } +} diff --git a/src/Netclaw.Cli.Tests/Netclaw.Cli.Tests.csproj b/src/Netclaw.Cli.Tests/Netclaw.Cli.Tests.csproj index 3e1746d5b..f82cc6572 100644 --- a/src/Netclaw.Cli.Tests/Netclaw.Cli.Tests.csproj +++ b/src/Netclaw.Cli.Tests/Netclaw.Cli.Tests.csproj @@ -27,4 +27,12 @@ + + + + + + diff --git a/src/Netclaw.Cli/Doctor/DoctorRegistrationExtensions.cs b/src/Netclaw.Cli/Doctor/DoctorRegistrationExtensions.cs index 61da74a93..6bd265991 100644 --- a/src/Netclaw.Cli/Doctor/DoctorRegistrationExtensions.cs +++ b/src/Netclaw.Cli/Doctor/DoctorRegistrationExtensions.cs @@ -4,6 +4,7 @@ // // ----------------------------------------------------------------------- using Microsoft.Extensions.DependencyInjection; +using Netclaw.Embeddings; using Netclaw.Providers; namespace Netclaw.Cli.Doctor; @@ -15,6 +16,9 @@ public static void AddDoctorChecks(this IServiceCollection services) services.AddProviderDescriptors(); services.AddSingleton(); services.AddSingleton(); + // Real allowlist for production; MemoryEmbeddingDoctorCheckTests supplies a small + // fixture-pointed allowlist directly to the type instead of using this registration. + services.AddSingleton>(EmbeddingModelProvisioner.Allowlist); services.AddSingleton(); services.AddSingleton(); services.AddSingleton(); @@ -27,6 +31,7 @@ public static void AddDoctorChecks(this IServiceCollection services) services.AddSingleton(); services.AddSingleton(); services.AddSingleton(); + services.AddSingleton(); services.AddSingleton(); services.AddSingleton(); services.AddSingleton(); diff --git a/src/Netclaw.Cli/Doctor/MemoryEmbeddingDoctorCheck.cs b/src/Netclaw.Cli/Doctor/MemoryEmbeddingDoctorCheck.cs new file mode 100644 index 000000000..1b8b45c97 --- /dev/null +++ b/src/Netclaw.Cli/Doctor/MemoryEmbeddingDoctorCheck.cs @@ -0,0 +1,99 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using Microsoft.Extensions.Configuration; +using Netclaw.Actors.Memory; +using Netclaw.Configuration; +using Netclaw.Embeddings; + +namespace Netclaw.Cli.Doctor; + +/// +/// Embedding coverage diagnostics (memory-core-redesign spec: "Embedding coverage +/// diagnostics"). Reports model provisioning state and corpus coverage so a degraded or +/// partially-embedded corpus surfaces in netclaw doctor instead of only in a daemon log +/// line (design D2/D3, spec "Loud degradation without silent fallback"). Mirrors +/// 's pattern of constructing its own +/// directly against the same on-disk database rather than +/// sharing the daemon process's DI-resolved instance. +/// +/// +/// The embedding model allowlist to verify against — an explicit, required dependency (same +/// seam itself uses) rather than always reading the +/// static internally, so tests can supply a +/// small allowlist pointed at a local fixture instead of ever reaching the real ~100-300 MB +/// HuggingFace artifacts. Production wiring () passes +/// itself. +/// +public sealed class MemoryEmbeddingDoctorCheck( + NetclawPaths paths, + IConfiguration configuration, + IReadOnlyDictionary allowlist) : IDoctorCheck +{ + private const string CheckName = "Memory Embeddings"; + + public async Task RunAsync(CancellationToken cancellationToken = default) + { + var memoryConfig = configuration.GetSection("Memory").Get() ?? new MemoryConfig(); + + if (!memoryConfig.Embeddings.Enabled) + { + return DoctorCheckResult.Pass( + CheckName, + "Embeddings disabled (Memory.Embeddings.Enabled is false)."); + } + + var modelId = memoryConfig.Embeddings.ModelId; + var modelDirectory = paths.EmbeddingModelDirectory(modelId); + + try + { + var provisioner = new EmbeddingModelProvisioner(new HttpClient(), allowlist); + var verified = await provisioner.TryLoadVerifiedAsync(modelId, modelDirectory, cancellationToken); + if (verified is null) + { + return DoctorCheckResult.Error( + CheckName, + $"Embedding model '{modelId}' is missing or fails hash verification at {modelDirectory}.", + memoryConfig.Embeddings.AutoDownload + ? "Restart the daemon to re-provision, or run `netclaw memory backfill-embeddings`." + : "Memory.Embeddings.AutoDownload is false — provision the model manually, or enable AutoDownload and restart the daemon."); + } + + var store = new SQLiteMemoryStore(paths.MemorySqliteDbPath, TimeProvider.System); + await store.InitializeAsync(cancellationToken); + var coverage = await store.GetEmbeddingCoverageAsync(modelId, cancellationToken); + + if (coverage.OtherModelCount > 0) + { + return DoctorCheckResult.Warning( + CheckName, + $"Embeddings exist under another model id in addition to '{modelId}' ({coverage.OtherModelCount} items) — " + + "similarity thresholds are calibrated per model.", + "Run `netclaw memory backfill-embeddings --force` to re-embed the full corpus under the active model."); + } + + var missing = coverage.TotalRecallableDocuments - coverage.EmbeddedCurrentHashCount; + if (missing > 0) + { + return DoctorCheckResult.Warning( + CheckName, + $"{missing} of {coverage.TotalRecallableDocuments} recallable documents lack a current-model embedding.", + "The daemon's gap-repair sweep heals this at next startup, or run `netclaw memory backfill-embeddings` now."); + } + + return DoctorCheckResult.Pass( + CheckName, + $"Embeddings healthy: {coverage.EmbeddedCurrentHashCount}/{coverage.TotalRecallableDocuments} documents embedded under '{modelId}'."); + } + catch (Exception ex) + { + return DoctorCheckResult.Error( + CheckName, + $"Unable to inspect embedding health: {ex.Message}", + "Verify the models directory and SQLite memory database are readable."); + } + } +} diff --git a/src/Netclaw.Cli/Memory/MemoryCommand.cs b/src/Netclaw.Cli/Memory/MemoryCommand.cs new file mode 100644 index 000000000..8a5858e9b --- /dev/null +++ b/src/Netclaw.Cli/Memory/MemoryCommand.cs @@ -0,0 +1,174 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using Microsoft.Extensions.Configuration; +using Netclaw.Actors.Memory; +using Netclaw.Configuration; +using Netclaw.Embeddings; + +namespace Netclaw.Cli.Memory; + +/// +/// Handles netclaw memory <subcommand> CLI subcommands +/// (memory-core-redesign Slice 2, task 2.9). All commands are offline — they operate directly +/// on the SQLite memory database and the embedding model files, no daemon required, following +/// the same direct-store-access convention as MemoryCheckpointHealthDoctorCheck. +/// +internal static class MemoryCommand +{ + public static Task RunAsync(string[] args, NetclawPaths paths, IConfiguration configuration) + => RunAsync(args, paths, configuration, EmbeddingModelProvisioner.Allowlist); + + /// + /// Test-visible entry point: is the same explicit, required + /// dependency and MemoryEmbeddingDoctorCheck + /// take, so tests can point this command at a small fixture allowlist instead of the real + /// ~100-300 MB HuggingFace artifacts. Production callers use the single-argument overload, + /// which always passes . + /// + internal static Task RunAsync( + string[] args, + NetclawPaths paths, + IConfiguration configuration, + IReadOnlyDictionary allowlist) + { + var subcommand = args.Length > 1 ? args[1] : "help"; + + if (subcommand is "help" or "-h" or "--help") + return Task.FromResult(WriteHelp()); + + return subcommand switch + { + "backfill-embeddings" => RunBackfillEmbeddingsAsync(args, paths, configuration, allowlist), + _ => Task.FromResult(WriteHelp()) + }; + } + + private static int WriteHelp() + { + Console.WriteLine("Usage: netclaw memory "); + Console.WriteLine(); + Console.WriteLine("Subcommands:"); + Console.WriteLine(" backfill-embeddings [--force] Provision the embedding model (if needed) and"); + Console.WriteLine(" embed memories missing a current-model embedding."); + Console.WriteLine(" --force re-scans every recallable document instead"); + Console.WriteLine(" of only ones missing a current-model embedding."); + return 0; + } + + private static async Task RunBackfillEmbeddingsAsync( + string[] args, + NetclawPaths paths, + IConfiguration configuration, + IReadOnlyDictionary allowlist) + { + var force = args.Contains("--force", StringComparer.OrdinalIgnoreCase); + var memoryConfig = configuration.GetSection("Memory").Get() ?? new MemoryConfig(); + var modelId = memoryConfig.Embeddings.ModelId; + var modelDirectory = paths.EmbeddingModelDirectory(modelId); + + ProvisionedEmbeddingModel provisioned; + using (var httpClient = new HttpClient()) + { + var provisioner = new EmbeddingModelProvisioner(httpClient, allowlist); + try + { + if (memoryConfig.Embeddings.AutoDownload) + { + Console.WriteLine($"Provisioning embedding model '{modelId}'..."); + provisioned = await provisioner.ProvisionAsync(modelId, modelDirectory); + } + else + { + provisioned = await provisioner.TryLoadVerifiedAsync(modelId, modelDirectory) + ?? throw new InvalidOperationException( + $"Embedding model '{modelId}' is not provisioned (or fails hash verification) at " + + $"{modelDirectory}, and Memory.Embeddings.AutoDownload is false. Provision the model " + + "manually, or enable AutoDownload and re-run this command."); + } + } + catch (Exception ex) + { + Console.Error.WriteLine($"[FAIL] unable to provision embedding model '{modelId}': {ex.Message}"); + return 1; + } + } + + Console.WriteLine($"Loading embedder '{provisioned.ModelId}' ({provisioned.Dimensions} dims)..."); + using var embedder = await OnnxMemoryEmbedder.LoadAsync( + provisioned.ModelPath, provisioned.VocabPath, provisioned.ModelId, provisioned.Dimensions); + + // Direct SQLite access, same as the doctor checks: WAL mode (set by InitializeAsync's + // idempotent DDL) plus Microsoft.Data.Sqlite's default busy-timeout keep each small + // per-item upsert transaction below safe to interleave with a live daemon's own writes + // (curation commits, embed-on-write) against the same database file. + var store = new SQLiteMemoryStore(paths.MemorySqliteDbPath, TimeProvider.System); + await store.InitializeAsync(); + + var candidates = await store.GetDocumentsNeedingEmbeddingAsync(embedder.ModelId, force); + if (candidates.Count == 0) + { + Console.WriteLine("Nothing to backfill: all recallable documents already have a current-model embedding."); + return 0; + } + + Console.WriteLine($"Embedding {candidates.Count} document(s){(force ? " (--force)" : "")}..."); + + const int batchSize = 16; + var embedded = 0; + var skippedUnchanged = 0; + var failed = 0; + + for (var offset = 0; offset < candidates.Count; offset += batchSize) + { + var batch = candidates.Skip(offset).Take(batchSize).ToArray(); + var texts = batch.Select(d => $"{d.Title}\n{d.Body}").ToArray(); + + IReadOnlyList> vectors; + try + { + vectors = await embedder.EmbedBatchAsync(texts, CancellationToken.None); + } + catch (Exception ex) + { + failed += batch.Length; + Console.Error.WriteLine($"[WARN] batch at offset {offset} failed to embed: {ex.Message}"); + continue; + } + + for (var i = 0; i < batch.Length; i++) + { + try + { + var hash = MemoryContentHasher.ComputeHash(batch[i].Title, batch[i].Body); + + // UpsertEmbeddingAsync's own hash check (re-queried at call time) is what + // makes this safe against a concurrent live daemon: if the daemon's own + // embed-on-write already embedded this item between our candidate scan and + // now, this call correctly no-ops instead of double-writing. + var wrote = await store.UpsertEmbeddingAsync( + batch[i].DocumentId, MemoryEmbedOnWriteCoordinator.DocumentItemKind, + embedder.ModelId, hash, vectors[i]); + + if (wrote) + embedded++; + else + skippedUnchanged++; + } + catch (Exception ex) + { + failed++; + Console.Error.WriteLine($"[WARN] failed to store embedding for {batch[i].DocumentId}: {ex.Message}"); + } + } + + Console.WriteLine($" ...{Math.Min(offset + batch.Length, candidates.Count)}/{candidates.Count}"); + } + + Console.WriteLine(); + Console.WriteLine($"Done: embedded={embedded} skipped-hash-unchanged={skippedUnchanged} failed={failed}"); + return failed > 0 ? 1 : 0; + } +} diff --git a/src/Netclaw.Cli/Netclaw.Cli.csproj b/src/Netclaw.Cli/Netclaw.Cli.csproj index 3197e3f3f..6fa91cbc0 100644 --- a/src/Netclaw.Cli/Netclaw.Cli.csproj +++ b/src/Netclaw.Cli/Netclaw.Cli.csproj @@ -32,6 +32,7 @@ + diff --git a/src/Netclaw.Cli/Program.cs b/src/Netclaw.Cli/Program.cs index a00234bf9..d95c21f0d 100644 --- a/src/Netclaw.Cli/Program.cs +++ b/src/Netclaw.Cli/Program.cs @@ -22,6 +22,7 @@ using Netclaw.Cli.Doctor; using Netclaw.Cli.Mcp; using Netclaw.Cli.Mattermost; +using Netclaw.Cli.Memory; using Netclaw.Cli.Reminder; using Netclaw.Cli.Secrets; using Netclaw.Cli.Model; @@ -852,6 +853,16 @@ static async Task RunAsync(string[] args) return; } + // ── Memory management (memory-core-redesign Slice 2) ── + if (mode is "memory") + { + var paths = new NetclawPaths(); + paths.EnsureDirectoriesExist(); + // All memory subcommands are offline — direct SQLite/model-file access, no daemon needed + Environment.ExitCode = await MemoryCommand.RunAsync(args, paths, BuildCliConfig()); + return; + } + // ── Webhook management ── if (mode is "webhooks") { @@ -1243,6 +1254,7 @@ static void WriteGeneralHelp() Console.WriteLine(" provider Manage LLM providers (TUI) or use subcommands"); Console.WriteLine(" model Manage model assignments (TUI) or use subcommands"); Console.WriteLine(" reminder Manage scheduled reminders (daemon-required)"); + Console.WriteLine(" memory Manage cross-session memory (embeddings backfill, offline)"); Console.WriteLine(" skill Manage skills and skill sources"); Console.WriteLine(" webhooks Manage inbound webhook routes"); Console.WriteLine(" secrets Manage encrypted secrets (set key/value pairs)"); diff --git a/src/Netclaw.Configuration.Tests/MemoryConfigDefaultsTests.cs b/src/Netclaw.Configuration.Tests/MemoryConfigDefaultsTests.cs new file mode 100644 index 000000000..3cfc3e479 --- /dev/null +++ b/src/Netclaw.Configuration.Tests/MemoryConfigDefaultsTests.cs @@ -0,0 +1,46 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using Xunit; + +namespace Netclaw.Configuration.Tests; + +/// +/// Bear-trap tests for defaults (memory-core-redesign +/// Slice 2, task 2.11). If you change a default, you must update these assertions — forcing a +/// deliberate decision rather than an accidental drift. +/// defaults to false in particular: flipping it is a deliberate Slice 3/4 decision, not something +/// that should silently change because a refactor touched the property initializer. +/// +public sealed class MemoryConfigDefaultsTests +{ + [Fact] + public void Embeddings_disabled_by_default() + { + var config = new MemoryConfig(); + Assert.False(config.Embeddings.Enabled); + } + + [Fact] + public void Embeddings_model_id_defaults_to_snowflake_arctic_embed_m() + { + var config = new MemoryConfig(); + Assert.Equal("snowflake-arctic-embed-m", config.Embeddings.ModelId); + } + + [Fact] + public void Embeddings_auto_download_defaults_to_true() + { + var config = new MemoryConfig(); + Assert.True(config.Embeddings.AutoDownload); + } + + [Fact] + public void Memory_subsystem_remains_enabled_by_default() + { + var config = new MemoryConfig(); + Assert.True(config.Enabled); + } +} diff --git a/src/Netclaw.Configuration/DaemonRuntimeStatus.cs b/src/Netclaw.Configuration/DaemonRuntimeStatus.cs index f5f0e90b9..79a327b2a 100644 --- a/src/Netclaw.Configuration/DaemonRuntimeStatus.cs +++ b/src/Netclaw.Configuration/DaemonRuntimeStatus.cs @@ -147,6 +147,24 @@ public sealed class Memory : IWireType public string? DatabasePath { get; init; } public int? PendingCheckpoints { get; init; } + + public Embeddings? Embeddings { get; init; } + } + + /// + /// Embedding subsystem status (memory-core-redesign D2/Requirement "Loud degradation + /// without silent fallback"). is one of "ok" (embedder loaded + /// and warmed up), "degraded" (provisioning/load failed — memory falls back to + /// lexical-only paths), or "disabled" (Memory.Embeddings.Enabled is false). + /// + public sealed class Embeddings : IWireType + { + public required string Status { get; init; } + + public string? ModelId { get; init; } + + /// Human-readable cause when is "degraded". + public string? DegradedReason { get; init; } } public sealed class Reminders : IWireType diff --git a/src/Netclaw.Configuration/MemoryConfig.cs b/src/Netclaw.Configuration/MemoryConfig.cs index 6002345ed..9c8195222 100644 --- a/src/Netclaw.Configuration/MemoryConfig.cs +++ b/src/Netclaw.Configuration/MemoryConfig.cs @@ -26,4 +26,44 @@ public sealed class MemoryConfig /// Maximum number of items injected into the automatic recall bundle. /// public int AutoRecallMaxItems { get; set; } = 3; + + /// + /// Embedding-based semantic memory settings (memory-core-redesign Slice 2: embedding + /// foundation). See for why this defaults off. + /// + public MemoryEmbeddingsConfig Embeddings { get; set; } = new(); +} + +/// +/// Configuration for the in-process ONNX embedding runtime (memory-core-redesign D1/D2). +/// +public sealed class MemoryEmbeddingsConfig +{ + /// + /// When true, the daemon provisions/loads the embedding model at startup + /// (EmbeddingWarmupHostedService) and computes embeddings on memory writes. + /// Defaults to false for Slice 2 ("embedding foundation"): this slice only writes + /// vectors — nothing in the write or read path consumes them yet (nominate/decide dedup is + /// Slice 3, hybrid recall is Slice 4). Flipping this default to true is a deliberate + /// decision left to whichever of those slices ships first, not an oversight here. + /// + public bool Enabled { get; set; } + + /// + /// Allowlisted embedding model id (see EmbeddingModelProvisioner.Allowlist in + /// Netclaw.Embeddings). An id absent from the allowlist is a configuration error, + /// surfaced by the doctor check and warmup service — never a silently-accepted arbitrary + /// model source (supply-chain boundary, design D2). + /// + public string ModelId { get; set; } = "snowflake-arctic-embed-m"; + + /// + /// When true, the daemon downloads the model artifact at startup if not already + /// provisioned. When false, a missing or invalid model is a loud degraded-mode condition + /// (doctor error, daemon status embeddings: degraded) rather than a silent network + /// fetch — operators can pre-provision the model file (or run + /// netclaw memory backfill-embeddings after manually placing it) to stay fully + /// offline. + /// + public bool AutoDownload { get; set; } = true; } diff --git a/src/Netclaw.Configuration/NetclawPaths.cs b/src/Netclaw.Configuration/NetclawPaths.cs index 02f434c2a..efc121913 100644 --- a/src/Netclaw.Configuration/NetclawPaths.cs +++ b/src/Netclaw.Configuration/NetclawPaths.cs @@ -126,6 +126,22 @@ public string ServerFeedAgentSyncStatePath(string feedName) public string McpOAuthMetadataPath => Path.Combine(ConfigDirectory, "mcp-oauth-metadata.json"); public string KeysDirectory => Path.Combine(BasePath, "keys"); + // ── Downloaded model artifacts (memory-core-redesign D2: embedding models) ── + /// + /// Root directory for downloaded/provisioned model artifacts (currently embedding models; + /// is the per-model subdirectory). Kept separate from + /// because these artifacts are large (tens to hundreds of MB), + /// hash-verified, and intentionally never embedded in the application binary. + /// + public string ModelsDirectory => Path.Combine(BasePath, "models"); + + /// + /// Directory for one embedding model's provisioned files (model.onnx, + /// vocab.txt), keyed by allowlist model id so switching + /// Memory.Embeddings.ModelId never collides with a previously provisioned model. + /// + public string EmbeddingModelDirectory(string modelId) => Path.Combine(ModelsDirectory, modelId); + public NetclawPaths(string? basePath = null, string? workspacesDirectory = null) { BasePath = PathExpansion.ExpandHome(basePath) @@ -188,6 +204,7 @@ private IEnumerable StandardDirectories() yield return KeysDirectory; yield return CacheDirectory; yield return WorkspacesDirectory; + yield return ModelsDirectory; } } diff --git a/src/Netclaw.Configuration/Schemas/netclaw-config.v1.schema.json b/src/Netclaw.Configuration/Schemas/netclaw-config.v1.schema.json index a183c0bb2..efddfb071 100644 --- a/src/Netclaw.Configuration/Schemas/netclaw-config.v1.schema.json +++ b/src/Netclaw.Configuration/Schemas/netclaw-config.v1.schema.json @@ -364,6 +364,28 @@ "maximum": 10, "default": 3, "description": "Maximum number of memory items auto-injected per turn." + }, + "Embeddings": { + "type": "object", + "description": "In-process ONNX embedding runtime settings (memory-core-redesign).", + "properties": { + "Enabled": { + "type": "boolean", + "default": false, + "description": "When true, the daemon provisions the embedding model at startup and computes embeddings on memory writes. Defaults to false: this slice only writes vectors, nothing consumes them yet." + }, + "ModelId": { + "type": "string", + "default": "snowflake-arctic-embed-m", + "description": "Allowlisted embedding model id. An id absent from the in-code allowlist is a configuration error." + }, + "AutoDownload": { + "type": "boolean", + "default": true, + "description": "When true, downloads the model artifact at daemon startup if not already provisioned. When false, a missing model degrades loudly instead of fetching over the network." + } + }, + "additionalProperties": false } }, "additionalProperties": false diff --git a/src/Netclaw.Daemon.Tests/Gateway/DaemonRuntimeStatusServiceTests.cs b/src/Netclaw.Daemon.Tests/Gateway/DaemonRuntimeStatusServiceTests.cs index 028adce84..3ffa898bf 100644 --- a/src/Netclaw.Daemon.Tests/Gateway/DaemonRuntimeStatusServiceTests.cs +++ b/src/Netclaw.Daemon.Tests/Gateway/DaemonRuntimeStatusServiceTests.cs @@ -54,7 +54,9 @@ private DaemonRuntimeStatusService CreateService( McpClientManager? mcpClientManager = null, SQLiteMemoryStore? sqliteMemoryStore = null, IChatClientProvider? chatClientProvider = null, - ProviderRuntimeValidation? providerValidation = null) + ProviderRuntimeValidation? providerValidation = null, + MemoryEmbedderHolder? memoryEmbedderHolder = null, + MemoryConfig? memoryConfig = null) { return new DaemonRuntimeStatusService( new DaemonStartClock(TimeProvider.System), @@ -69,7 +71,9 @@ private DaemonRuntimeStatusService CreateService( chatClientProvider ?? new TestChatClientProvider(), providerValidation ?? new ProviderRuntimeValidation(ProviderRuntimeStatus.Valid, null, []), mcpClientManager, - sqliteMemoryStore); + sqliteMemoryStore, + memoryEmbedderHolder, + memoryConfig); } private static IChannelRegistry CreateRegistry( @@ -368,6 +372,65 @@ public async Task StatusIncludesMemory_SqliteBackend() Assert.Equal(0, status.Memory.PendingCheckpoints); } + [Fact] + public async Task StatusReportsEmbeddingsDisabled_WhenConfigOff() + { + var paths = CreatePaths(); + paths.EnsureDirectoriesExist(); + var sqliteStore = new SQLiteMemoryStore(paths.MemorySqliteDbPath, TimeProvider.System); + await sqliteStore.InitializeAsync(TestContext.Current.CancellationToken); + + var service = CreateService( + paths: paths, + sqliteMemoryStore: sqliteStore, + memoryConfig: new MemoryConfig { Embeddings = { Enabled = false } }); + + var status = await service.GetStatusAsync(TestContext.Current.CancellationToken); + + Assert.Equal("disabled", status.Memory!.Embeddings!.Status); + } + + [Fact] + public async Task StatusReportsEmbeddingsOk_WhenHolderIsAvailable() + { + var paths = CreatePaths(); + paths.EnsureDirectoriesExist(); + var sqliteStore = new SQLiteMemoryStore(paths.MemorySqliteDbPath, TimeProvider.System); + await sqliteStore.InitializeAsync(TestContext.Current.CancellationToken); + + var holder = new MemoryEmbedderHolder(new FakeAvailableEmbedder("tiny-fixture")); + var service = CreateService( + paths: paths, + sqliteMemoryStore: sqliteStore, + memoryEmbedderHolder: holder, + memoryConfig: new MemoryConfig { Embeddings = { Enabled = true, ModelId = "tiny-fixture" } }); + + var status = await service.GetStatusAsync(TestContext.Current.CancellationToken); + + Assert.Equal("ok", status.Memory!.Embeddings!.Status); + Assert.Equal("tiny-fixture", status.Memory.Embeddings.ModelId); + } + + [Fact] + public async Task StatusReportsEmbeddingsDegraded_WhenEnabledButHolderIsUnavailable() + { + var paths = CreatePaths(); + paths.EnsureDirectoriesExist(); + var sqliteStore = new SQLiteMemoryStore(paths.MemorySqliteDbPath, TimeProvider.System); + await sqliteStore.InitializeAsync(TestContext.Current.CancellationToken); + + var holder = new MemoryEmbedderHolder(new UnavailableMemoryEmbedder("tiny-fixture", "model missing")); + var service = CreateService( + paths: paths, + sqliteMemoryStore: sqliteStore, + memoryEmbedderHolder: holder, + memoryConfig: new MemoryConfig { Embeddings = { Enabled = true, ModelId = "tiny-fixture" } }); + + var status = await service.GetStatusAsync(TestContext.Current.CancellationToken); + + Assert.Equal("degraded", status.Memory!.Embeddings!.Status); + } + [Fact] public async Task StatusIncludesChannelCountersForEnabledChannels() { @@ -430,4 +493,19 @@ private sealed class TestChatClientProvider : IChatClientProvider { public IChatClient GetClient(ModelRole role) => throw new NotSupportedException(); } + + private sealed class FakeAvailableEmbedder(string modelId) : IMemoryEmbedder + { + public string ModelId => modelId; + + public int Dimensions => 8; + + public bool IsAvailable => true; + + public ValueTask> EmbedAsync(string text, CancellationToken ct) + => ValueTask.FromResult>(new float[Dimensions]); + + public ValueTask>> EmbedBatchAsync(IReadOnlyList texts, CancellationToken ct) + => ValueTask.FromResult>>(texts.Select(_ => (ReadOnlyMemory)new float[Dimensions]).ToList()); + } } diff --git a/src/Netclaw.Daemon.Tests/Netclaw.Daemon.Tests.csproj b/src/Netclaw.Daemon.Tests/Netclaw.Daemon.Tests.csproj index 37f0164d5..3d5b6ad50 100644 --- a/src/Netclaw.Daemon.Tests/Netclaw.Daemon.Tests.csproj +++ b/src/Netclaw.Daemon.Tests/Netclaw.Daemon.Tests.csproj @@ -37,4 +37,11 @@ ReferenceOutputAssembly="false" /> + + + + + + diff --git a/src/Netclaw.Daemon.Tests/Services/EmbeddingWarmupHostedServiceTests.cs b/src/Netclaw.Daemon.Tests/Services/EmbeddingWarmupHostedServiceTests.cs new file mode 100644 index 000000000..83e2da71c --- /dev/null +++ b/src/Netclaw.Daemon.Tests/Services/EmbeddingWarmupHostedServiceTests.cs @@ -0,0 +1,185 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using System.Security.Cryptography; +using Microsoft.Data.Sqlite; +using Microsoft.Extensions.Logging.Abstractions; +using Netclaw.Actors.Memory; +using Netclaw.Configuration; +using Netclaw.Daemon.Services; +using Netclaw.Embeddings; +using Xunit; + +namespace Netclaw.Daemon.Tests.Services; + +/// +/// Covers (memory-core-redesign Slice 2, task 2.7): +/// degraded path, success path, and gap repair. Uses the tiny fixture ONNX graph committed at +/// Netclaw.Embeddings.Tests/Fixtures (linked into this project's output) — no network +/// access anywhere in these tests. The allowlist is an injected, required dependency of +/// (see its remarks), so pointing it at the fixture +/// instead of the real HuggingFace allowlist requires no test-only seam beyond that. +/// +public sealed class EmbeddingWarmupHostedServiceTests : IAsyncLifetime +{ + private const string ModelId = "tiny-fixture"; + private const int Dimensions = 8; + + private readonly string _baseDir = Path.Combine(Path.GetTempPath(), $"netclaw-embedding-warmup-tests-{Guid.NewGuid():N}"); + private NetclawPaths _paths = null!; + private SQLiteMemoryStore _store = null!; + private EmbeddingModelProvisioner _provisioner = null!; + + private static string FixturesDir => Path.Combine(AppContext.BaseDirectory, "Fixtures"); + + public async ValueTask InitializeAsync() + { + _paths = new NetclawPaths(_baseDir); + _paths.EnsureDirectoriesExist(); + _store = new SQLiteMemoryStore(_paths.MemorySqliteDbPath, TimeProvider.System); + await _store.InitializeAsync(); + + var modelBytes = await File.ReadAllBytesAsync(Path.Combine(FixturesDir, "tiny-embedder.onnx")); + var vocabBytes = await File.ReadAllBytesAsync(Path.Combine(FixturesDir, "tiny-vocab.txt")); + var allowlist = new Dictionary + { + [ModelId] = new( + ModelId, + // Never actually fetched in these tests: the fixture files are pre-placed as an + // already-valid local copy, so ProvisionAsync's skip-if-valid path never reaches + // the network. A live URL is not required for that path to work. + ModelUrl: new Uri("http://127.0.0.1:1/unused-model.onnx"), + TokenizerUrl: new Uri("http://127.0.0.1:1/unused-vocab.txt"), + ModelSha256: Sha256Hex(modelBytes), + TokenizerSha256: Sha256Hex(vocabBytes), + Dimensions: Dimensions, + ModelByteSize: modelBytes.Length), + }; + _provisioner = new EmbeddingModelProvisioner(new HttpClient(), allowlist); + } + + public async ValueTask DisposeAsync() => await TryDeleteDirectoryAsync(_baseDir); + + [Fact] + public async Task Success_path_loads_the_fixture_model_with_no_network_and_populates_the_holder() + { + PrePlaceValidModelFiles(); + var holder = new MemoryEmbedderHolder(new UnavailableMemoryEmbedder(ModelId, "warmup not yet run")); + var memoryConfig = new MemoryConfig { Embeddings = { Enabled = true, ModelId = ModelId, AutoDownload = true } }; + var service = CreateService(holder, memoryConfig); + + await service.WarmUpAsync(TestContext.Current.CancellationToken); + + Assert.True(holder.Current.IsAvailable); + Assert.Equal(ModelId, holder.Current.ModelId); + Assert.Equal(Dimensions, holder.Current.Dimensions); + } + + [Fact] + public async Task Degraded_path_sets_an_unavailable_embedder_when_the_model_is_missing_and_autodownload_is_false() + { + // No PrePlaceValidModelFiles() call — the model directory is empty. + var holder = new MemoryEmbedderHolder(new UnavailableMemoryEmbedder(ModelId, "warmup not yet run")); + var memoryConfig = new MemoryConfig { Embeddings = { Enabled = true, ModelId = ModelId, AutoDownload = false } }; + var service = CreateService(holder, memoryConfig); + + await service.WarmUpAsync(TestContext.Current.CancellationToken); + + Assert.False(holder.Current.IsAvailable); + Assert.IsType(holder.Current); + } + + [Fact] + public async Task Disabled_config_leaves_the_holder_at_its_initial_value() + { + var initial = new UnavailableMemoryEmbedder(ModelId, "embeddings disabled"); + var holder = new MemoryEmbedderHolder(initial); + var memoryConfig = new MemoryConfig { Embeddings = { Enabled = false, ModelId = ModelId } }; + var service = CreateService(holder, memoryConfig); + + await service.WarmUpAsync(TestContext.Current.CancellationToken); + + Assert.Same(initial, holder.Current); + } + + [Fact] + public async Task Gap_repair_embeds_documents_missing_a_current_model_embedding() + { + PrePlaceValidModelFiles(); + + var anchor = _store.CreateDefaultAnchor("gap-repair-warmup-test"); + var now = DateTimeOffset.UtcNow.ToUnixTimeMilliseconds(); + await _store.UpsertDocumentAsync(new SQLiteMemoryDocument( + DocumentId: "doc-needs-embedding", + Anchor: anchor, + MemoryClass: "durable_fact", + Title: "Needs Embedding", + MarkdownBody: "this document has never been embedded", + AliasesJson: null, + FacetsJson: null, + SlotsJson: null, + UpdateSemantics: "merge-document", + Sensitivity: "normal", + RecallMode: "auto", + Confidence: 0.9, + FreshnessAtMs: now, + ExpiresAtMs: null, + CreatedAtMs: now, + UpdatedAtMs: now), TestContext.Current.CancellationToken); + + var holder = new MemoryEmbedderHolder(new UnavailableMemoryEmbedder(ModelId, "warmup not yet run")); + var memoryConfig = new MemoryConfig { Embeddings = { Enabled = true, ModelId = ModelId, AutoDownload = true } }; + var service = CreateService(holder, memoryConfig); + + await service.WarmUpAsync(TestContext.Current.CancellationToken); + + var rows = await _store.GetEmbeddingsForModelAsync(ModelId, TestContext.Current.CancellationToken); + var row = Assert.Single(rows); + Assert.Equal("doc-needs-embedding", row.ItemId); + } + + private EmbeddingWarmupHostedService CreateService(MemoryEmbedderHolder holder, MemoryConfig memoryConfig) + => new(_provisioner, _store, holder, memoryConfig, _paths, NullLogger.Instance); + + private void PrePlaceValidModelFiles() + { + var dir = _paths.EmbeddingModelDirectory(ModelId); + Directory.CreateDirectory(dir); + File.Copy(Path.Combine(FixturesDir, "tiny-embedder.onnx"), Path.Combine(dir, "model.onnx"), overwrite: true); + File.Copy(Path.Combine(FixturesDir, "tiny-vocab.txt"), Path.Combine(dir, "vocab.txt"), overwrite: true); + } + + private static string Sha256Hex(byte[] bytes) => Convert.ToHexStringLower(SHA256.HashData(bytes)); + + private static async Task TryDeleteDirectoryAsync(string path) + { + if (!Directory.Exists(path)) + return; + + var dbPath = Path.Combine(path, "netclaw.db"); + if (File.Exists(dbPath)) + { + var connectionString = new SqliteConnectionStringBuilder { DataSource = dbPath }.ToString(); + SqliteConnection.ClearPool(new SqliteConnection(connectionString)); + } + + for (var i = 0; i < 8; i++) + { + try + { + Directory.Delete(path, recursive: true); + return; + } + catch (IOException) when (i < 7) + { + await Task.Delay(25 * (i + 1)); + } + catch (UnauthorizedAccessException) when (i < 7) + { + await Task.Delay(25 * (i + 1)); + } + } + } +} diff --git a/src/Netclaw.Daemon/Gateway/DaemonRuntimeStatusService.cs b/src/Netclaw.Daemon/Gateway/DaemonRuntimeStatusService.cs index c1ea97aa1..1787938f1 100644 --- a/src/Netclaw.Daemon/Gateway/DaemonRuntimeStatusService.cs +++ b/src/Netclaw.Daemon/Gateway/DaemonRuntimeStatusService.cs @@ -36,6 +36,8 @@ internal sealed class DaemonRuntimeStatusService( ProviderRuntimeValidation providerValidation, McpClientManager? mcpClientManager = null, SQLiteMemoryStore? sqliteMemoryStore = null, + MemoryEmbedderHolder? memoryEmbedderHolder = null, + MemoryConfig? memoryConfig = null, IRequiredActor? reminderManagerActor = null) { public async Task GetStatusAsync(CancellationToken cancellationToken = default) @@ -292,7 +294,8 @@ private DaemonRuntimeStatus.Update BuildUpdateStatus() Provider = "sqlite", Status = "healthy", DatabasePath = paths.MemorySqliteDbPath, - PendingCheckpoints = pending + PendingCheckpoints = pending, + Embeddings = BuildEmbeddingsStatus() }; } catch @@ -301,11 +304,40 @@ private DaemonRuntimeStatus.Update BuildUpdateStatus() { Provider = "sqlite", Status = "degraded", - DatabasePath = paths.MemorySqliteDbPath + DatabasePath = paths.MemorySqliteDbPath, + Embeddings = BuildEmbeddingsStatus() }; } } + /// + /// Embeddings status (memory-core-redesign D2/Requirement "Loud degradation without silent + /// fallback"): "disabled" when Memory.Embeddings.Enabled is false, "ok" + /// when the resolved is available, otherwise "degraded". + /// + private DaemonRuntimeStatus.Embeddings BuildEmbeddingsStatus() + { + if (memoryConfig?.Embeddings.Enabled != true) + { + return new DaemonRuntimeStatus.Embeddings { Status = "disabled" }; + } + + var embedder = memoryEmbedderHolder?.Current; + if (embedder is { IsAvailable: true }) + { + return new DaemonRuntimeStatus.Embeddings { Status = "ok", ModelId = embedder.ModelId }; + } + + return new DaemonRuntimeStatus.Embeddings + { + Status = "degraded", + ModelId = embedder?.ModelId ?? memoryConfig.Embeddings.ModelId, + DegradedReason = memoryEmbedderHolder is null + ? "embedding subsystem not wired up" + : "embedding model unavailable — see daemon logs for memory_embedding_unavailable" + }; + } + private async Task BuildReminderHealthAsync(CancellationToken ct) { if (reminderManagerActor is null) diff --git a/src/Netclaw.Daemon/Netclaw.Daemon.csproj b/src/Netclaw.Daemon/Netclaw.Daemon.csproj index ea1aa0065..79653b8c1 100644 --- a/src/Netclaw.Daemon/Netclaw.Daemon.csproj +++ b/src/Netclaw.Daemon/Netclaw.Daemon.csproj @@ -61,6 +61,7 @@ + diff --git a/src/Netclaw.Daemon/Program.cs b/src/Netclaw.Daemon/Program.cs index 31414178d..8ac180192 100644 --- a/src/Netclaw.Daemon/Program.cs +++ b/src/Netclaw.Daemon/Program.cs @@ -47,6 +47,7 @@ using Netclaw.Daemon.Lifecycle; using Netclaw.Daemon.Reminders; using Netclaw.Daemon.Webhooks; +using Netclaw.Embeddings; using Netclaw.Search; using Netclaw.Tools; using Netclaw.Security; @@ -732,6 +733,20 @@ static void ConfigureDaemonServices( toolRegistry.Register(new SqliteGetMemoriesTool(memoryStore)); toolRegistry.Register(new SqliteStoreMemoryTool(new SQLiteMemoryCheckpointSink(memoryStore, TimeProvider.System))); toolRegistry.Register(new SqliteUpdateMemoryTool(memoryStore)); + + // Embedding foundation (memory-core-redesign Slice 2). The holder always exists — + // starts pointed at an Unavailable stub so any consumer resolving it before warmup + // completes gets a safe, explicit degraded value rather than a null reference — and + // EmbeddingWarmupHostedService populates it at startup (see that type's remarks for why + // a mutable holder is required instead of constructor injection). + services.AddHttpClient("EmbeddingModelProvisioner").AddNetclawHeaders("embedding-provisioner"); + services.AddSingleton(sp => new EmbeddingModelProvisioner( + sp.GetRequiredService().CreateClient("EmbeddingModelProvisioner"), + EmbeddingModelProvisioner.Allowlist)); + services.AddSingleton(new MemoryEmbedderHolder( + new UnavailableMemoryEmbedder(memoryConfig.Embeddings.ModelId, "embedding warmup has not completed yet"))); + services.AddSingleton(); + services.AddSingleton(sp => sp.GetRequiredService()); } services.AddSingleton(NullMemoryExtractor.Instance); @@ -979,7 +994,8 @@ static void ConfigureDaemonServices( sp.GetService() ?? NullMemoryRecallCoordinator.Instance, sp.GetService() ?? NullMemoryCheckpointSink.Instance, sp.GetService(), - sp.GetService())); + sp.GetService(), + sp.GetService())); services.AddSingleton(sp => new SessionObservability( sp.GetService(), diff --git a/src/Netclaw.Daemon/Services/EmbeddingWarmupHostedService.cs b/src/Netclaw.Daemon/Services/EmbeddingWarmupHostedService.cs new file mode 100644 index 000000000..a95744b41 --- /dev/null +++ b/src/Netclaw.Daemon/Services/EmbeddingWarmupHostedService.cs @@ -0,0 +1,185 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using Microsoft.Extensions.Hosting; +using Microsoft.Extensions.Logging; +using Netclaw.Actors.Memory; +using Netclaw.Configuration; +using Netclaw.Embeddings; + +namespace Netclaw.Daemon.Services; + +/// +/// Provisions/loads the embedding model at daemon startup, warms it up with one inference call, +/// then runs a gap-repair sweep over documents missing a current-model embedding +/// (memory-core-redesign Slice 2, task 2.7). Populates , which +/// every embed-on-write and (in later slices) recall consumer resolves at time of use. +/// +/// +/// Never fails startup: ANY failure here (missing model with AutoDownload=false, +/// download/hash failure, ONNX load failure) leaves the holder pointed at an +/// carrying the failure reason, logs +/// memory_embedding_unavailable at error level, and returns normally — degraded is a +/// running state, not a startup fault (design D2, spec "Loud degradation without silent +/// fallback"). This runs on a background thread pool task rather than blocking +/// so a slow/hanging download can never delay the rest of the host's +/// startup sequence either. +/// +/// +internal sealed class EmbeddingWarmupHostedService( + EmbeddingModelProvisioner provisioner, + SQLiteMemoryStore store, + MemoryEmbedderHolder holder, + MemoryConfig memoryConfig, + NetclawPaths paths, + ILogger logger) : IHostedService +{ + /// + /// Gap-repair batch size. Kept small and yielding between batches (task 2.7) so a large + /// backlog on a fresh Enabled=true flip does not monopolize the CPU the daemon needs + /// for everything else at startup. + /// + internal const int GapRepairBatchSize = 16; + + public Task StartAsync(CancellationToken cancellationToken) + { + _ = Task.Run(() => WarmUpAsync(CancellationToken.None), CancellationToken.None); + return Task.CompletedTask; + } + + public Task StopAsync(CancellationToken cancellationToken) => Task.CompletedTask; + + /// Internal entry point so tests can await warmup to completion deterministically. + internal async Task WarmUpAsync(CancellationToken ct) + { + if (!memoryConfig.Embeddings.Enabled) + { + logger.LogInformation( + "memory_embedding_disabled reason={Reason}", + "Memory.Embeddings.Enabled is false"); + return; + } + + var modelId = memoryConfig.Embeddings.ModelId; + IMemoryEmbedder embedder; + try + { + embedder = await LoadEmbedderAsync(modelId, ct).ConfigureAwait(false); + } + catch (Exception ex) + { + logger.LogError(ex, "memory_embedding_unavailable model={ModelId} reason={Reason}", modelId, ex.Message); + holder.Set(new UnavailableMemoryEmbedder(modelId, ex.Message)); + return; + } + + holder.Set(embedder); + logger.LogInformation( + "memory_embedding_ready model={ModelId} dims={Dimensions}", + embedder.ModelId, + embedder.Dimensions); + + try + { + await GapRepairAsync(embedder, ct).ConfigureAwait(false); + } + catch (Exception ex) + { + // The embedder itself is already loaded and the holder is already populated — a + // gap-repair failure (e.g. a transient store error) must not undo that or leave an + // unobserved exception on this fire-and-forget warmup task. The doctor check and + // the next daemon restart's sweep both retry whatever remains unembedded. + logger.LogWarning(ex, "memory_embedding_gap_repair_failed model={ModelId}", embedder.ModelId); + } + } + + private async Task LoadEmbedderAsync(string modelId, CancellationToken ct) + { + var modelDirectory = paths.EmbeddingModelDirectory(modelId); + + ProvisionedEmbeddingModel provisioned; + if (memoryConfig.Embeddings.AutoDownload) + { + provisioned = await provisioner.ProvisionAsync(modelId, modelDirectory, ct).ConfigureAwait(false); + } + else + { + // AutoDownload=false gates the network path entirely — even to repair a corrupted + // local copy. A missing/invalid model here is a loud degraded-mode condition, not a + // fallback to fetching it anyway. + provisioned = await provisioner.TryLoadVerifiedAsync(modelId, modelDirectory, ct).ConfigureAwait(false) + ?? throw new InvalidOperationException( + $"Embedding model '{modelId}' is not provisioned (or failed hash verification) at " + + $"{modelDirectory}, and Memory.Embeddings.AutoDownload is false. Provision it manually " + + "or enable AutoDownload, then restart the daemon or run `netclaw memory backfill-embeddings`."); + } + + var embedder = await OnnxMemoryEmbedder.LoadAsync( + provisioned.ModelPath, + provisioned.VocabPath, + provisioned.ModelId, + provisioned.Dimensions, + ct: ct).ConfigureAwait(false); + + // Warm-up inference (design D1/D2): pays first-call ONNX session / JIT cost here rather + // than on the first real memory write or recall query. + await embedder.EmbedAsync("netclaw embedding warmup", ct).ConfigureAwait(false); + + return embedder; + } + + /// + /// Embeds every recallable document missing a current-model/current-hash embedding, in + /// small batches, yielding between batches (task 2.7). This is what self-heals the gap + /// described in design D3's failure/recovery note: a crash between a document commit and + /// its embedding upsert leaves a missing-embedding row, which this sweep (and the embedding + /// doctor check) both detect and repair. + /// + private async Task GapRepairAsync(IMemoryEmbedder embedder, CancellationToken ct) + { + var missing = await store.GetDocumentsNeedingEmbeddingAsync(embedder.ModelId, force: false, ct).ConfigureAwait(false); + if (missing.Count == 0) + { + logger.LogInformation("memory_embedding_gap_repair_complete embedded=0 model={ModelId}", embedder.ModelId); + return; + } + + var embedded = 0; + var failed = 0; + for (var offset = 0; offset < missing.Count; offset += GapRepairBatchSize) + { + var batch = missing.Skip(offset).Take(GapRepairBatchSize).ToArray(); + var texts = batch.Select(d => $"{d.Title}\n{d.Body}").ToArray(); + + try + { + var vectors = await embedder.EmbedBatchAsync(texts, ct).ConfigureAwait(false); + for (var i = 0; i < batch.Length; i++) + { + var hash = MemoryContentHasher.ComputeHash(batch[i].Title, batch[i].Body); + await store.UpsertEmbeddingAsync( + batch[i].DocumentId, MemoryEmbedOnWriteCoordinator.DocumentItemKind, + embedder.ModelId, hash, vectors[i], ct).ConfigureAwait(false); + embedded++; + } + } + catch (Exception ex) + { + // One bad batch must not abort the sweep — the doctor check and the next + // restart's sweep will retry whatever remains missing. + failed += batch.Length; + logger.LogWarning(ex, "memory_embedding_gap_repair_batch_failed count={Count}", batch.Length); + } + + // Yield between batches so gap-repair on a large backlog does not monopolize the + // CPU the daemon needs for everything else at startup. + await Task.Yield(); + } + + logger.LogInformation( + "memory_embedding_gap_repair_complete embedded={Embedded} failed={Failed} model={ModelId}", + embedded, failed, embedder.ModelId); + } +} diff --git a/src/Netclaw.Daemon/Services/MemoryCurationWorkerService.cs b/src/Netclaw.Daemon/Services/MemoryCurationWorkerService.cs index 2e6b9bea1..346acff40 100644 --- a/src/Netclaw.Daemon/Services/MemoryCurationWorkerService.cs +++ b/src/Netclaw.Daemon/Services/MemoryCurationWorkerService.cs @@ -15,7 +15,8 @@ internal sealed class MemoryCurationWorkerService( MemoryCurationEngine engine, TimeProvider timeProvider, ILogger logger, - ISessionMetrics? metrics = null) : IHostedService, IDisposable + ISessionMetrics? metrics = null, + MemoryEmbedderHolder? embedderHolder = null) : IHostedService, IDisposable { private readonly CancellationTokenSource _cts = new(); private Task? _worker; @@ -56,7 +57,14 @@ private async Task RunAsync(CancellationToken ct) { var started = timeProvider.GetUtcNow().ToUnixTimeMilliseconds(); var operations = await engine.CurateAsync(leased, ct); - await store.ApplyCurationBatchAsync(leased.CheckpointId, operations, ct); + var writtenDocs = await store.ApplyCurationBatchAsync(leased.CheckpointId, operations, ct); + + // Embed-on-write (memory-core-redesign Slice 2, task 2.8): runs after the + // checkpoint's write has already committed. Vectors are derived data — a + // failure here must never fail or retry this checkpoint; + // MemoryEmbedOnWriteCoordinator isolates and logs per-item failures. + await MemoryEmbedOnWriteCoordinator.EmbedWrittenDocumentsAsync( + embedderHolder, store, writtenDocs, logger, ct); var ended = timeProvider.GetUtcNow().ToUnixTimeMilliseconds(); logger.LogInformation( diff --git a/src/Netclaw.Embeddings.Tests/BoundedConcurrencyGateTests.cs b/src/Netclaw.Embeddings.Tests/BoundedConcurrencyGateTests.cs new file mode 100644 index 000000000..43f3e52b6 --- /dev/null +++ b/src/Netclaw.Embeddings.Tests/BoundedConcurrencyGateTests.cs @@ -0,0 +1,73 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using Xunit; + +namespace Netclaw.Embeddings.Tests; + +/// +/// Proves the concurrency bound relies on +/// (BoundedConcurrencyGate) is actually enforced under real contention, without racing +/// on wall-clock sleeps in test orchestration. The tiny fixture ONNX model runs in +/// microseconds, so a test that fired concurrent real inferences could never reliably observe +/// overlap; testing the gate in isolation with a controlled fake unit of work (a +/// Task.Delay inside the fake work item — legitimate per the constitution's testing +/// guidelines, since the delay lives in the fake, not in test orchestration logic) is the +/// deterministic way to prove the bound holds. +/// +public sealed class BoundedConcurrencyGateTests +{ + [Fact] + public async Task RunAsync_never_exceeds_the_configured_max_concurrency() + { + var gate = new BoundedConcurrencyGate(maxConcurrency: 2); + var tasks = new Task[6]; + + for (var i = 0; i < tasks.Length; i++) + { + tasks[i] = gate.RunAsync(async ct => + { + await Task.Delay(20, ct); + return 0; + }, TestContext.Current.CancellationToken); + } + + await Task.WhenAll(tasks); + + Assert.True(gate.PeakObservedConcurrency <= 2, $"expected peak <= 2, observed {gate.PeakObservedConcurrency}"); + // With 6 tasks racing for 2 slots and a real (non-zero) delay inside each, contention + // is all but guaranteed — assert it actually happened so this test cannot pass + // vacuously (e.g. if the gate silently stopped gating and everything just ran serially + // one at a time, peak would still be 1 and the <= 2 assertion above would be + // meaningless on its own). + Assert.True(gate.PeakObservedConcurrency >= 2, $"expected genuine contention (peak >= 2), observed {gate.PeakObservedConcurrency}"); + } + + [Fact] + public async Task RunAsync_lets_all_queued_work_complete() + { + var gate = new BoundedConcurrencyGate(maxConcurrency: 2); + var completed = 0; + + var tasks = Enumerable.Range(0, 10) + .Select(_ => gate.RunAsync(async ct => + { + await Task.Delay(5, ct); + return Interlocked.Increment(ref completed); + }, TestContext.Current.CancellationToken)) + .ToArray(); + + await Task.WhenAll(tasks); + + Assert.Equal(10, completed); + } + + [Fact] + public void Constructor_rejects_non_positive_concurrency() + { + Assert.Throws(() => new BoundedConcurrencyGate(0)); + Assert.Throws(() => new BoundedConcurrencyGate(-1)); + } +} diff --git a/src/Netclaw.Embeddings.Tests/EmbeddingModelProvisionerTests.cs b/src/Netclaw.Embeddings.Tests/EmbeddingModelProvisionerTests.cs new file mode 100644 index 000000000..5deb1384d --- /dev/null +++ b/src/Netclaw.Embeddings.Tests/EmbeddingModelProvisionerTests.cs @@ -0,0 +1,239 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using System.Security.Cryptography; +using System.Text; +using Xunit; + +namespace Netclaw.Embeddings.Tests; + +/// +/// Exercises against a local +/// fixture — no network access, and never touches the real +/// production (tests build their own small +/// allowlist pointed at the local server, since the allowlist is an injected, required +/// dependency rather than a hardcoded internal). +/// +public sealed class EmbeddingModelProvisionerTests : IAsyncLifetime +{ + private LocalArtifactServer _server = null!; + private HttpClient _httpClient = null!; + private string _destinationDirectory = null!; + + public ValueTask InitializeAsync() + { + _server = new LocalArtifactServer(); + _httpClient = new HttpClient(); + _destinationDirectory = Path.Combine(Path.GetTempPath(), "netclaw-embedding-provisioner-tests", Guid.NewGuid().ToString("N")); + return ValueTask.CompletedTask; + } + + public ValueTask DisposeAsync() + { + _httpClient.Dispose(); + _server.Dispose(); + if (Directory.Exists(_destinationDirectory)) + Directory.Delete(_destinationDirectory, recursive: true); + return ValueTask.CompletedTask; + } + + private static string Sha256Hex(byte[] bytes) => Convert.ToHexStringLower(SHA256.HashData(bytes)); + + [Fact] + public async Task ProvisionAsync_downloads_and_verifies_matching_artifacts() + { + var modelBytes = Encoding.UTF8.GetBytes("fake-onnx-model-bytes"); + var vocabBytes = Encoding.UTF8.GetBytes("[PAD]\n[UNK]\n[CLS]\n[SEP]\n"); + + var modelUrl = _server.AddRoute("/model.onnx", modelBytes); + var vocabUrl = _server.AddRoute("/vocab.txt", vocabBytes); + + var allowlist = new Dictionary + { + ["test-model"] = new EmbeddingModelManifestEntry( + "test-model", modelUrl, vocabUrl, + Sha256Hex(modelBytes), Sha256Hex(vocabBytes), + Dimensions: 8, ModelByteSize: modelBytes.Length), + }; + + var provisioner = new EmbeddingModelProvisioner(_httpClient, allowlist); + var result = await provisioner.ProvisionAsync("test-model", _destinationDirectory, TestContext.Current.CancellationToken); + + Assert.Equal("test-model", result.ModelId); + Assert.Equal(8, result.Dimensions); + Assert.Equal(modelBytes, await File.ReadAllBytesAsync(result.ModelPath, TestContext.Current.CancellationToken)); + Assert.Equal(vocabBytes, await File.ReadAllBytesAsync(result.VocabPath, TestContext.Current.CancellationToken)); + + // Nothing but the two final artifacts remains — no leftover temp files. + var leftoverFiles = Directory.GetFiles(_destinationDirectory).Select(Path.GetFileName).ToArray(); + Assert.Equal(["model.onnx", "vocab.txt"], leftoverFiles.OrderBy(x => x, StringComparer.Ordinal)); + } + + [Fact] + public async Task ProvisionAsync_skips_the_network_entirely_when_a_valid_local_copy_already_exists() + { + var modelBytes = Encoding.UTF8.GetBytes("fake-onnx-model-bytes"); + var vocabBytes = Encoding.UTF8.GetBytes("[PAD]\n[UNK]\n[CLS]\n[SEP]\n"); + + var modelUrl = _server.AddRoute("/model.onnx", modelBytes); + var vocabUrl = _server.AddRoute("/vocab.txt", vocabBytes); + + var allowlist = new Dictionary + { + ["test-model"] = new EmbeddingModelManifestEntry( + "test-model", modelUrl, vocabUrl, + Sha256Hex(modelBytes), Sha256Hex(vocabBytes), + Dimensions: 8, ModelByteSize: modelBytes.Length), + }; + var provisioner = new EmbeddingModelProvisioner(_httpClient, allowlist); + await provisioner.ProvisionAsync("test-model", _destinationDirectory, TestContext.Current.CancellationToken); + + // Tear down the server: any further attempt to reach the network would now throw. + _server.Dispose(); + + // Task 2.7: "already-provisioned+hash-valid loads without network" — this call must + // succeed even though the server is gone, proving it never re-downloaded. + var result = await provisioner.ProvisionAsync("test-model", _destinationDirectory, TestContext.Current.CancellationToken); + + Assert.Equal("test-model", result.ModelId); + Assert.Equal(modelBytes, await File.ReadAllBytesAsync(result.ModelPath, TestContext.Current.CancellationToken)); + } + + [Fact] + public async Task TryLoadVerifiedAsync_returns_null_when_no_local_copy_exists() + { + var allowlist = new Dictionary + { + ["test-model"] = DummyEntry("test-model"), + }; + var provisioner = new EmbeddingModelProvisioner(_httpClient, allowlist); + + var result = await provisioner.TryLoadVerifiedAsync("test-model", _destinationDirectory, TestContext.Current.CancellationToken); + + Assert.Null(result); + } + + [Fact] + public async Task TryLoadVerifiedAsync_returns_null_for_an_unknown_model_id_without_touching_the_network() + { + var provisioner = new EmbeddingModelProvisioner(_httpClient, new Dictionary()); + + var result = await provisioner.TryLoadVerifiedAsync("nonexistent-model", _destinationDirectory, TestContext.Current.CancellationToken); + + Assert.Null(result); + } + + [Fact] + public async Task TryLoadVerifiedAsync_returns_the_provisioned_model_without_network_when_the_local_copy_is_valid() + { + var modelBytes = Encoding.UTF8.GetBytes("fake-onnx-model-bytes"); + var vocabBytes = Encoding.UTF8.GetBytes("[PAD]\n[UNK]\n[CLS]\n[SEP]\n"); + var modelUrl = _server.AddRoute("/model.onnx", modelBytes); + var vocabUrl = _server.AddRoute("/vocab.txt", vocabBytes); + + var allowlist = new Dictionary + { + ["test-model"] = new EmbeddingModelManifestEntry( + "test-model", modelUrl, vocabUrl, + Sha256Hex(modelBytes), Sha256Hex(vocabBytes), + Dimensions: 8, ModelByteSize: modelBytes.Length), + }; + var provisioner = new EmbeddingModelProvisioner(_httpClient, allowlist); + await provisioner.ProvisionAsync("test-model", _destinationDirectory, TestContext.Current.CancellationToken); + _server.Dispose(); + + var result = await provisioner.TryLoadVerifiedAsync("test-model", _destinationDirectory, TestContext.Current.CancellationToken); + + Assert.NotNull(result); + Assert.Equal(8, result!.Dimensions); + } + + [Fact] + public async Task ProvisionAsync_rejects_unknown_model_id_listing_the_allowlist() + { + var allowlist = new Dictionary + { + ["known-a"] = DummyEntry("known-a"), + ["known-b"] = DummyEntry("known-b"), + }; + var provisioner = new EmbeddingModelProvisioner(_httpClient, allowlist); + + var ex = await Assert.ThrowsAsync( + () => provisioner.ProvisionAsync("nonexistent-model", _destinationDirectory, TestContext.Current.CancellationToken)); + + Assert.Contains("nonexistent-model", ex.Message, StringComparison.Ordinal); + Assert.Contains("known-a", ex.Message, StringComparison.Ordinal); + Assert.Contains("known-b", ex.Message, StringComparison.Ordinal); + } + + [Fact] + public async Task ProvisionAsync_rejects_sha256_mismatch_and_leaves_nothing_behind() + { + var modelBytes = Encoding.UTF8.GetBytes("real-content"); + var vocabBytes = Encoding.UTF8.GetBytes("vocab-content"); + var modelUrl = _server.AddRoute("/model.onnx", modelBytes); + var vocabUrl = _server.AddRoute("/vocab.txt", vocabBytes); + + var allowlist = new Dictionary + { + ["tampered"] = new EmbeddingModelManifestEntry( + "tampered", modelUrl, vocabUrl, + ModelSha256: Sha256Hex(Encoding.UTF8.GetBytes("this-does-not-match-the-served-bytes")), + TokenizerSha256: Sha256Hex(vocabBytes), + Dimensions: 8, ModelByteSize: modelBytes.Length), + }; + + var provisioner = new EmbeddingModelProvisioner(_httpClient, allowlist); + + var ex = await Assert.ThrowsAsync( + () => provisioner.ProvisionAsync("tampered", _destinationDirectory, TestContext.Current.CancellationToken)); + + Assert.Contains("SHA-256", ex.Message, StringComparison.Ordinal); + + // The artifact was discarded, not loaded — no final file and no leftover temp file. + if (Directory.Exists(_destinationDirectory)) + Assert.Empty(Directory.GetFiles(_destinationDirectory)); + } + + [Fact] + public async Task ProvisionAsync_rejects_byte_size_mismatch_before_hashing() + { + var modelBytes = Encoding.UTF8.GetBytes("some content of a certain length"); + var vocabBytes = Encoding.UTF8.GetBytes("vocab"); + var modelUrl = _server.AddRoute("/model.onnx", modelBytes); + var vocabUrl = _server.AddRoute("/vocab.txt", vocabBytes); + + var allowlist = new Dictionary + { + ["wrong-size"] = new EmbeddingModelManifestEntry( + "wrong-size", modelUrl, vocabUrl, + Sha256Hex(modelBytes), Sha256Hex(vocabBytes), + Dimensions: 8, ModelByteSize: modelBytes.Length + 1), + }; + + var provisioner = new EmbeddingModelProvisioner(_httpClient, allowlist); + + var ex = await Assert.ThrowsAsync( + () => provisioner.ProvisionAsync("wrong-size", _destinationDirectory, TestContext.Current.CancellationToken)); + + Assert.Contains("bytes", ex.Message, StringComparison.Ordinal); + if (Directory.Exists(_destinationDirectory)) + Assert.Empty(Directory.GetFiles(_destinationDirectory)); + } + + [Fact] + public void ProductionAllowlist_has_the_two_ratified_models_with_distinct_ids() + { + Assert.True(EmbeddingModelProvisioner.Allowlist.ContainsKey("snowflake-arctic-embed-m")); + Assert.True(EmbeddingModelProvisioner.Allowlist.ContainsKey("mxbai-embed-large-v1")); + Assert.Equal(768, EmbeddingModelProvisioner.Allowlist["snowflake-arctic-embed-m"].Dimensions); + Assert.Equal(1024, EmbeddingModelProvisioner.Allowlist["mxbai-embed-large-v1"].Dimensions); + Assert.All(EmbeddingModelProvisioner.Allowlist.Values, e => Assert.Equal(64, e.ModelSha256.Length)); + Assert.All(EmbeddingModelProvisioner.Allowlist.Values, e => Assert.Equal(64, e.TokenizerSha256.Length)); + } + + private static EmbeddingModelManifestEntry DummyEntry(string id) + => new(id, new Uri("http://127.0.0.1:1/model.onnx"), new Uri("http://127.0.0.1:1/vocab.txt"), new string('0', 64), new string('0', 64), 8, 1); +} diff --git a/src/Netclaw.Embeddings.Tests/Fixtures/generate_fixture_model.py b/src/Netclaw.Embeddings.Tests/Fixtures/generate_fixture_model.py new file mode 100644 index 000000000..84f4b7c4f --- /dev/null +++ b/src/Netclaw.Embeddings.Tests/Fixtures/generate_fixture_model.py @@ -0,0 +1,108 @@ +#!/usr/bin/env python3 +"""Generates the tiny fixture ONNX model + WordPiece vocab used by +Netclaw.Embeddings.Tests (OnnxMemoryEmbedderTests). + +Regeneration: + python3 -m venv /tmp/onnxgen && source /tmp/onnxgen/bin/activate + pip install onnx==1.22.0 numpy + python3 generate_fixture_model.py + +Graph shape (deliberately NOT a real BERT export — see below for why): + + input_ids int64 [batch, seq] --Gather(embedding_matrix)--> token_embeddings [batch, seq, dims] + attention_mask int64 [batch, seq] --Cast/Unsqueeze--> mask [batch, seq, 1] + token_embeddings * mask --ReduceSum(axis=1)--> sum_embeddings [batch, 1, dims] + mask --ReduceSum(axis=1)--> sum_mask [batch, 1, 1] --Clip(min=1e-9)--> + last_hidden_state = sum_embeddings / sum_mask [batch, 1, dims] + +Why mean-pooling instead of a plain Gather + CLS passthrough: OnnxMemoryEmbedder +always reads position 0 along the sequence axis of `last_hidden_state` (CLS-token +selection — matches both allowlisted production models per their model cards). +A plain Gather has no cross-token mixing, so a fixture that just emits per-token +rows would make position 0 *always* equal the fixed [CLS]-token embedding row +regardless of the rest of the input — every text would embed identically, and a +bug that dropped the input text entirely would go uncaught. Attention-masked mean +pooling over all real (non-padding) tokens, reported as the graph's only sequence +position, makes the fixture's output genuinely depend on input content — exactly +like a real model's contextualized CLS output does — while keeping +OnnxMemoryEmbedder's "always read index 0" logic identical for fixture and +production graphs. The graph declares no token_type_ids input (unlike the real +BERT exports) on purpose: OnnxMemoryEmbedder must feed only the inputs a loaded +session actually declares (session.InputMetadata.Keys), never a hardcoded +assumption of the 3-input production signature. +""" +import sys +import numpy as np +import onnx +from onnx import helper, TensorProto, numpy_helper + +VOCAB = [ + "[PAD]", "[UNK]", "[CLS]", "[SEP]", + "the", "cat", "sat", "on", "mat", + "dog", "run", "##ning", + "hello", "world", + "quarterly", "revenue", "grew", "percent", +] +DIMS = 8 + + +def main(out_dir: str) -> None: + vocab_size = len(VOCAB) + + # Fixed, deterministic embedding matrix: row i = [i*0.1, i*0.1+0.01, ...]. + # No randomness so the fixture (and its expected test vectors) never drifts + # across regenerations. + rows = [] + for i in range(vocab_size): + rows.append([round(i * 0.1 + j * 0.01, 4) for j in range(DIMS)]) + embedding_matrix = np.array(rows, dtype=np.float32) + + input_ids = helper.make_tensor_value_info("input_ids", TensorProto.INT64, ["batch", "seq"]) + attention_mask = helper.make_tensor_value_info("attention_mask", TensorProto.INT64, ["batch", "seq"]) + last_hidden_state = helper.make_tensor_value_info( + "last_hidden_state", TensorProto.FLOAT, ["batch", 1, DIMS] + ) + + initializers = [ + numpy_helper.from_array(embedding_matrix, name="embedding_matrix"), + numpy_helper.from_array(np.array([1], dtype=np.int64), name="axis_1"), + numpy_helper.from_array(np.array([-1], dtype=np.int64), name="axis_neg1"), + numpy_helper.from_array(np.array(1e-9, dtype=np.float32), name="mask_floor"), + ] + + nodes = [ + helper.make_node("Gather", ["embedding_matrix", "input_ids"], ["token_embeddings"], axis=0, name="gather_token_embeddings"), + helper.make_node("Cast", ["attention_mask"], ["mask_float"], to=TensorProto.FLOAT, name="cast_mask"), + helper.make_node("Unsqueeze", ["mask_float", "axis_neg1"], ["mask_expanded"], name="unsqueeze_mask"), + helper.make_node("Mul", ["token_embeddings", "mask_expanded"], ["masked_embeddings"], name="apply_mask"), + helper.make_node("ReduceSum", ["masked_embeddings", "axis_1"], ["sum_embeddings"], keepdims=1, name="sum_embeddings"), + helper.make_node("ReduceSum", ["mask_expanded", "axis_1"], ["sum_mask"], keepdims=1, name="sum_mask"), + helper.make_node("Clip", ["sum_mask", "mask_floor"], ["sum_mask_clipped"], name="clip_sum_mask"), + helper.make_node("Div", ["sum_embeddings", "sum_mask_clipped"], ["last_hidden_state"], name="mean_pool"), + ] + + graph = helper.make_graph( + nodes=nodes, + name="tiny_memory_embedder_fixture", + inputs=[input_ids, attention_mask], + outputs=[last_hidden_state], + initializer=initializers, + ) + + model = helper.make_model(graph, producer_name="netclaw-fixture-generator", opset_imports=[helper.make_opsetid("", 18)]) + model.ir_version = 9 + onnx.checker.check_model(model) + + model_path = f"{out_dir}/tiny-embedder.onnx" + onnx.save(model, model_path) + + vocab_path = f"{out_dir}/tiny-vocab.txt" + with open(vocab_path, "w", encoding="utf-8") as f: + f.write("\n".join(VOCAB) + "\n") + + print(f"wrote {model_path} ({vocab_size} vocab rows x {DIMS} dims)") + print(f"wrote {vocab_path}") + + +if __name__ == "__main__": + main(sys.argv[1] if len(sys.argv) > 1 else ".") diff --git a/src/Netclaw.Embeddings.Tests/Fixtures/tiny-embedder.onnx b/src/Netclaw.Embeddings.Tests/Fixtures/tiny-embedder.onnx new file mode 100644 index 0000000000000000000000000000000000000000..63c64230c4603413a5bd3ddac13a1b1229615178 GIT binary patch literal 1459 zcmaLXZ)_7~90%~W>)7={Mt7;e8ZzRRkWs)u8WTde=g}#tSukcpLnOn?t@m`hw7t9a zp3?$vP*eX!tps^7f&?8=f~LGcLLr#I7XG{_C{fe!H`W)v883V@-s<VAgnchuk!`6sFH*{QZ@O=R(VAo;CWj!b*@L- zUDFwJIX}Q>q0sB|*JLMYickLCEq8br*B3t^(QG@je->+b#m$gCq`8VLs|VUDw>xfj zarctz?$99D-6Y@bW@9Ufl;+&ljjS{Kh>0a>8mQ!wWtUQ%bd?Q5 zMVwcZQlf_I8KvCSN;eFxq{qfPDeCCK$g>o+R3Uq4DWTDIg)~JU_4y9ba#w+lDIcSD zoDP;xpnif*&74M0iN@pS=;MJhJ!Suh{t7Kj-+@u3PkJA~snJyGPgv8mI`J1Q9~&wL z7-q9bsf~>(gx#t<#gKy)raKt2uqVB1*swf-@$6@i<2#t&!z{r(3+o)LA7GtF_6x{VhItX@C773C zU4eBK)+F*xA>TEa*J0j(c@tIz)@@inA>Yr)cL(M#Fn@*l8?3vq?!kJ1d=HWD5zOCV z)?og@Vyh&7xUFyQN7*m=y$yvU)>*&5@b>7J{+>Hx5$wgrE5^$82UYu{Eqi+wzx`H` zh}0&BNt@g{tcm0(X_Z?XKDM{F>8oK2ko+VdNqs@$3(Nli DzWWpg literal 0 HcmV?d00001 diff --git a/src/Netclaw.Embeddings.Tests/Fixtures/tiny-vocab.txt b/src/Netclaw.Embeddings.Tests/Fixtures/tiny-vocab.txt new file mode 100644 index 000000000..7813fee34 --- /dev/null +++ b/src/Netclaw.Embeddings.Tests/Fixtures/tiny-vocab.txt @@ -0,0 +1,18 @@ +[PAD] +[UNK] +[CLS] +[SEP] +the +cat +sat +on +mat +dog +run +##ning +hello +world +quarterly +revenue +grew +percent diff --git a/src/Netclaw.Embeddings.Tests/LocalArtifactServer.cs b/src/Netclaw.Embeddings.Tests/LocalArtifactServer.cs new file mode 100644 index 000000000..404867281 --- /dev/null +++ b/src/Netclaw.Embeddings.Tests/LocalArtifactServer.cs @@ -0,0 +1,99 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using System.Net; +using System.Net.Sockets; + +namespace Netclaw.Embeddings.Tests; + +/// +/// Minimal localhost HTTP server used only by so +/// those tests exercise real HTTP download behavior (streaming, byte-exact transfer) without +/// ever reaching the internet or the real HuggingFace allowlist URLs. +/// +internal sealed class LocalArtifactServer : IDisposable +{ + private readonly HttpListener _listener; + private readonly Dictionary _routes = new(StringComparer.Ordinal); + private readonly Task _serveLoop; + private bool _disposed; + + public LocalArtifactServer() + { + Port = GetFreePort(); + _listener = new HttpListener(); + _listener.Prefixes.Add($"http://127.0.0.1:{Port}/"); + _listener.Start(); + _serveLoop = Task.Run(ServeLoopAsync); + } + + public int Port { get; } + + /// Registers content to serve at and returns its full URI. + public Uri AddRoute(string path, byte[] content) + { + _routes[path] = content; + return new Uri($"http://127.0.0.1:{Port}{path}"); + } + + private async Task ServeLoopAsync() + { + while (true) + { + HttpListenerContext ctx; + try + { + ctx = await _listener.GetContextAsync().ConfigureAwait(false); + } + catch + { + return; // listener stopped/disposed — end the loop + } + + _ = HandleAsync(ctx); + } + } + + private async Task HandleAsync(HttpListenerContext ctx) + { + try + { + if (_routes.TryGetValue(ctx.Request.Url!.AbsolutePath, out var bytes)) + { + ctx.Response.ContentLength64 = bytes.Length; + await ctx.Response.OutputStream.WriteAsync(bytes).ConfigureAwait(false); + } + else + { + ctx.Response.StatusCode = 404; + } + } + finally + { + ctx.Response.OutputStream.Close(); + } + } + + private static int GetFreePort() + { + using var probe = new TcpListener(IPAddress.Loopback, 0); + probe.Start(); + var port = ((IPEndPoint)probe.LocalEndpoint).Port; + probe.Stop(); + return port; + } + + public void Dispose() + { + // Idempotent: some tests dispose the server early (mid-test) to prove a later call + // makes no network access, then the test class's own DisposeAsync disposes it again. + if (_disposed) + return; + _disposed = true; + + _listener.Stop(); + _listener.Close(); + } +} diff --git a/src/Netclaw.Embeddings.Tests/Netclaw.Embeddings.Tests.csproj b/src/Netclaw.Embeddings.Tests/Netclaw.Embeddings.Tests.csproj new file mode 100644 index 000000000..0178938e8 --- /dev/null +++ b/src/Netclaw.Embeddings.Tests/Netclaw.Embeddings.Tests.csproj @@ -0,0 +1,26 @@ + + + + net10.0 + enable + enable + false + true + + + + + + + + + + + + + + + + + + diff --git a/src/Netclaw.Embeddings.Tests/OnnxMemoryEmbedderTests.cs b/src/Netclaw.Embeddings.Tests/OnnxMemoryEmbedderTests.cs new file mode 100644 index 000000000..fcf90d97a --- /dev/null +++ b/src/Netclaw.Embeddings.Tests/OnnxMemoryEmbedderTests.cs @@ -0,0 +1,102 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using Xunit; + +namespace Netclaw.Embeddings.Tests; + +/// +/// Exercises against the tiny fixture graph committed at +/// Fixtures/tiny-embedder.onnx / Fixtures/tiny-vocab.txt (generated by +/// Fixtures/generate_fixture_model.py — see that file's header comment for the graph +/// shape and why it mean-pools instead of doing a plain CLS passthrough). No network access; +/// this is the CI-safe substitute for the real ~110M/~335M-parameter allowlisted models. +/// +public sealed class OnnxMemoryEmbedderTests : IAsyncLifetime +{ + private const string ModelId = "tiny-fixture"; + private const int Dimensions = 8; + + private OnnxMemoryEmbedder _embedder = null!; + + public async ValueTask InitializeAsync() + { + var fixturesDir = Path.Combine(AppContext.BaseDirectory, "Fixtures"); + _embedder = await OnnxMemoryEmbedder.LoadAsync( + modelPath: Path.Combine(fixturesDir, "tiny-embedder.onnx"), + vocabPath: Path.Combine(fixturesDir, "tiny-vocab.txt"), + modelId: ModelId, + dimensions: Dimensions, + maxConcurrency: 2); + } + + public ValueTask DisposeAsync() + { + _embedder.Dispose(); + return ValueTask.CompletedTask; + } + + [Fact] + public void Loaded_embedder_reports_its_identity() + { + Assert.Equal(ModelId, _embedder.ModelId); + Assert.Equal(Dimensions, _embedder.Dimensions); + Assert.True(_embedder.IsAvailable); + } + + [Fact] + public async Task EmbedAsync_is_deterministic_for_the_same_text() + { + var v1 = await _embedder.EmbedAsync("cat sat on the mat", TestContext.Current.CancellationToken); + var v2 = await _embedder.EmbedAsync("cat sat on the mat", TestContext.Current.CancellationToken); + + Assert.Equal(v1.ToArray(), v2.ToArray()); + } + + [Fact] + public async Task EmbedAsync_produces_L2_normalized_vectors_of_the_declared_dimension() + { + var vector = await _embedder.EmbedAsync("hello world", TestContext.Current.CancellationToken); + + Assert.Equal(Dimensions, vector.Length); + var normSquared = vector.ToArray().Sum(x => (double)x * x); + Assert.True(Math.Abs(normSquared - 1.0) < 1e-4, $"expected unit-length vector, got ||v||^2={normSquared}"); + } + + [Fact] + public async Task EmbedAsync_reflects_the_input_text_not_just_the_CLS_token() + { + // The fixture's mean-pooling graph (see its header comment) makes the output depend on + // every real token, not just position 0 — so different inputs must not collapse to + // the same vector the way a naive CLS-only passthrough over an un-contextualized + // Gather would. + var v1 = await _embedder.EmbedAsync("cat sat on the mat", TestContext.Current.CancellationToken); + var v2 = await _embedder.EmbedAsync("quarterly revenue grew", TestContext.Current.CancellationToken); + + Assert.NotEqual(v1.ToArray(), v2.ToArray()); + } + + [Fact] + public async Task EmbedBatchAsync_preserves_input_order() + { + string[] texts = ["hello world", "cat sat", "dog running", "quarterly revenue grew by percent"]; + + var batch = await _embedder.EmbedBatchAsync(texts, TestContext.Current.CancellationToken); + + Assert.Equal(texts.Length, batch.Count); + for (var i = 0; i < texts.Length; i++) + { + var single = await _embedder.EmbedAsync(texts[i], TestContext.Current.CancellationToken); + Assert.Equal(single.ToArray(), batch[i].ToArray()); + } + } + + [Fact] + public async Task EmbedBatchAsync_of_empty_input_returns_empty() + { + var batch = await _embedder.EmbedBatchAsync([], TestContext.Current.CancellationToken); + Assert.Empty(batch); + } +} diff --git a/src/Netclaw.Embeddings/EmbeddingModelProvisioner.cs b/src/Netclaw.Embeddings/EmbeddingModelProvisioner.cs new file mode 100644 index 000000000..083c1a2ee --- /dev/null +++ b/src/Netclaw.Embeddings/EmbeddingModelProvisioner.cs @@ -0,0 +1,238 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using System.Security.Cryptography; + +namespace Netclaw.Embeddings; + +/// +/// One entry in : everything needed to fetch +/// and verify one embedding model's artifacts. / +/// are pinned to a specific upstream commit (not a mutable branch) so the pinned SHA-256 values +/// can never silently stop matching what the URL serves. +/// +/// Allowlist key, e.g. snowflake-arctic-embed-m. +/// Download location for model.onnx. +/// Download location for the WordPiece vocab.txt. +/// Expected SHA-256 (lowercase hex) of the model artifact. +/// Expected SHA-256 (lowercase hex) of the vocab artifact. +/// Embedding vector width this model produces. +/// Expected byte size of the model artifact — a cheap first check before hashing. +public sealed record EmbeddingModelManifestEntry( + string ModelId, + Uri ModelUrl, + Uri TokenizerUrl, + string ModelSha256, + string TokenizerSha256, + int Dimensions, + long ModelByteSize); + +/// Files placed on disk by , ready for . +public sealed record ProvisionedEmbeddingModel(string ModelId, string ModelPath, string VocabPath, int Dimensions); + +/// +/// Thrown when a requested model id is not on the allowlist, or a downloaded artifact fails +/// byte-size or SHA-256 verification. Never wraps a partially-written file — callers can treat +/// this as "nothing was provisioned." +/// +public sealed class EmbeddingModelProvisioningException(string message) : Exception(message); + +/// +/// Downloads and verifies embedding model artifacts against a pinned in-code allowlist +/// (memory-core-redesign D2) — a supply-chain boundary. Arbitrary model URLs are rejected by +/// construction: there is no code path that accepts a caller-supplied URL, only a caller- +/// supplied looked up in +/// . This type performs no daemon wiring, no +/// construction, and no warm-up inference — it only gets verified files onto disk. +/// +public sealed class EmbeddingModelProvisioner +{ + /// + /// Pinned allowlist: model id → download locations, expected hashes, and dimensions. + /// Primary is snowflake-arctic-embed-m (May-2026-ratified nominator model); + /// mxbai-embed-large-v1 is the allowlisted fallback. Both entries point at the + /// plain fp32 onnx/model.onnx artifact (not the int8/fp16/quantized variants also + /// published on HuggingFace) for correctness; a quantized variant is a future optimization, + /// not this stage's concern. URLs are pinned to a specific HuggingFace repo commit sha + /// (not main) so the pinned hash can never silently drift out of sync with what the + /// URL serves. + /// + public static IReadOnlyDictionary Allowlist { get; } = + new Dictionary(StringComparer.Ordinal) + { + ["snowflake-arctic-embed-m"] = new EmbeddingModelManifestEntry( + ModelId: "snowflake-arctic-embed-m", + ModelUrl: new Uri("https://huggingface.co/Snowflake/snowflake-arctic-embed-m/resolve/fc74610d18462d218e312aa986ec5c8a75a98152/onnx/model.onnx"), + TokenizerUrl: new Uri("https://huggingface.co/Snowflake/snowflake-arctic-embed-m/resolve/fc74610d18462d218e312aa986ec5c8a75a98152/vocab.txt"), + ModelSha256: "564e6c65ee0c739a486702e9e3e9b33c3f697c19c34dbe886bce9eec497ce971", + TokenizerSha256: "07eced375cec144d27c900241f3e339478dec958f92fddbc551f295c992038a3", + Dimensions: 768, + ModelByteSize: 435_811_541), + + ["mxbai-embed-large-v1"] = new EmbeddingModelManifestEntry( + ModelId: "mxbai-embed-large-v1", + ModelUrl: new Uri("https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1/resolve/b33106f585b9ce46904ad7443a3b52b7a63e231c/onnx/model.onnx"), + TokenizerUrl: new Uri("https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1/resolve/b33106f585b9ce46904ad7443a3b52b7a63e231c/vocab.txt"), + ModelSha256: "adb53ed475faa339bfad3bd2bdb7e6a30b4f47280ade9811f81bef7953f9ab77", + TokenizerSha256: "07eced375cec144d27c900241f3e339478dec958f92fddbc551f295c992038a3", + Dimensions: 1024, + ModelByteSize: 1_336_854_282), + }; + + private readonly HttpClient _httpClient; + private readonly IReadOnlyDictionary _allowlist; + + /// Used for all artifact downloads. + /// + /// The allowlist to resolve model ids against — an explicit, required dependency rather + /// than always reading the static internally, so tests can supply + /// a small allowlist pointed at a local HTTP fixture instead of ever reaching the real + /// HuggingFace URLs. Production wiring passes itself. + /// + public EmbeddingModelProvisioner(HttpClient httpClient, IReadOnlyDictionary allowlist) + { + ArgumentNullException.ThrowIfNull(httpClient); + ArgumentNullException.ThrowIfNull(allowlist); + _httpClient = httpClient; + _allowlist = allowlist; + } + + /// + /// Downloads and verifies 's artifacts into + /// as model.onnx and vocab.txt. Each + /// download lands in a temp file first and is only renamed into place (atomic on the same + /// filesystem) after its SHA-256 (and, for the model file, byte size) matches the allowlist + /// entry — a hash mismatch discards the temp file and throws + /// without ever creating or replacing the + /// destination file. + /// + /// + /// When both destination files already exist and hash-verify against the allowlist entry, + /// this method returns immediately without any network access (memory-core-redesign task + /// 2.7: "already-provisioned+hash-valid loads without network"). This makes repeated calls + /// — e.g. the daemon's warmup service running on every restart — idempotent and safe to run + /// with AutoDownload=false once a model has been provisioned at least once. + /// + /// + public async Task ProvisionAsync( + string modelId, + string destinationDirectory, + CancellationToken ct = default) + { + if (!_allowlist.TryGetValue(modelId, out var entry)) + { + throw new EmbeddingModelProvisioningException( + $"Unknown embedding model id '{modelId}'. Allowlisted ids: {string.Join(", ", _allowlist.Keys.Order(StringComparer.Ordinal))}."); + } + + Directory.CreateDirectory(destinationDirectory); + var modelPath = Path.Combine(destinationDirectory, "model.onnx"); + var vocabPath = Path.Combine(destinationDirectory, "vocab.txt"); + + if (await IsValidAsync(modelPath, entry.ModelSha256, entry.ModelByteSize, ct).ConfigureAwait(false) + && await IsValidAsync(vocabPath, entry.TokenizerSha256, expectedByteSize: null, ct).ConfigureAwait(false)) + { + return new ProvisionedEmbeddingModel(modelId, modelPath, vocabPath, entry.Dimensions); + } + + await DownloadAndVerifyAsync(entry.ModelUrl, modelPath, entry.ModelSha256, entry.ModelByteSize, ct).ConfigureAwait(false); + await DownloadAndVerifyAsync(entry.TokenizerUrl, vocabPath, entry.TokenizerSha256, expectedByteSize: null, ct).ConfigureAwait(false); + + return new ProvisionedEmbeddingModel(modelId, modelPath, vocabPath, entry.Dimensions); + } + + /// + /// Verifies whether 's artifacts are already present and + /// hash-valid at , without ever accessing the + /// network. Returns null when the model id is unknown to the allowlist, or either file is + /// missing or fails verification (including a corrupted local copy) — callers that must + /// never trigger a download use this instead of + /// (memory-core-redesign task 2.7: Memory.Embeddings.AutoDownload=false gates the + /// network path entirely, even to repair a bad local copy). + /// + public async Task TryLoadVerifiedAsync( + string modelId, + string destinationDirectory, + CancellationToken ct = default) + { + if (!_allowlist.TryGetValue(modelId, out var entry)) + return null; + + var modelPath = Path.Combine(destinationDirectory, "model.onnx"); + var vocabPath = Path.Combine(destinationDirectory, "vocab.txt"); + + if (!await IsValidAsync(modelPath, entry.ModelSha256, entry.ModelByteSize, ct).ConfigureAwait(false)) + return null; + if (!await IsValidAsync(vocabPath, entry.TokenizerSha256, expectedByteSize: null, ct).ConfigureAwait(false)) + return null; + + return new ProvisionedEmbeddingModel(modelId, modelPath, vocabPath, entry.Dimensions); + } + + private static async Task IsValidAsync(string path, string expectedSha256, long? expectedByteSize, CancellationToken ct) + { + if (!File.Exists(path)) + return false; + + if (expectedByteSize is { } expected && new FileInfo(path).Length != expected) + return false; + + var actualSha256 = await ComputeSha256Async(path, ct).ConfigureAwait(false); + return string.Equals(actualSha256, expectedSha256, StringComparison.OrdinalIgnoreCase); + } + + private async Task DownloadAndVerifyAsync( + Uri source, + string destinationPath, + string expectedSha256, + long? expectedByteSize, + CancellationToken ct) + { + var tempPath = $"{destinationPath}.tmp-{Guid.NewGuid():N}"; + try + { + await using (var responseStream = await _httpClient.GetStreamAsync(source, ct).ConfigureAwait(false)) + await using (var fileStream = new FileStream(tempPath, FileMode.Create, FileAccess.Write, FileShare.None)) + { + await responseStream.CopyToAsync(fileStream, ct).ConfigureAwait(false); + } + + // Cheap fail-fast before hashing a potentially large file: a truncated or swapped + // artifact almost always has the wrong size. + var actualByteSize = new FileInfo(tempPath).Length; + if (expectedByteSize is { } expected && actualByteSize != expected) + { + throw new EmbeddingModelProvisioningException( + $"Downloaded artifact from {source} is {actualByteSize} bytes; the allowlist for this entry expects {expected} bytes. " + + "Discarding — this is a supply-chain integrity boundary, never loaded."); + } + + var actualSha256 = await ComputeSha256Async(tempPath, ct).ConfigureAwait(false); + if (!string.Equals(actualSha256, expectedSha256, StringComparison.OrdinalIgnoreCase)) + { + throw new EmbeddingModelProvisioningException( + $"Downloaded artifact from {source} does not match the pinned SHA-256 (expected {expectedSha256}, got {actualSha256}). " + + "Discarding — this is a supply-chain integrity boundary, never loaded."); + } + + File.Move(tempPath, destinationPath, overwrite: true); + } + finally + { + // No-op once Move above has succeeded (the file no longer exists at tempPath); + // cleans up the partial download on any failure path, including a hash/size + // mismatch or a cancelled/faulted copy. + if (File.Exists(tempPath)) + File.Delete(tempPath); + } + } + + private static async Task ComputeSha256Async(string path, CancellationToken ct) + { + await using var stream = new FileStream(path, FileMode.Open, FileAccess.Read, FileShare.Read); + var hash = await SHA256.HashDataAsync(stream, ct).ConfigureAwait(false); + return Convert.ToHexStringLower(hash); + } +} diff --git a/src/Netclaw.Embeddings/Netclaw.Embeddings.csproj b/src/Netclaw.Embeddings/Netclaw.Embeddings.csproj new file mode 100644 index 000000000..0f8c940a8 --- /dev/null +++ b/src/Netclaw.Embeddings/Netclaw.Embeddings.csproj @@ -0,0 +1,27 @@ + + + + net10.0 + enable + enable + + + + + + + + + + + + + + + + + + diff --git a/src/Netclaw.Embeddings/OnnxMemoryEmbedder.cs b/src/Netclaw.Embeddings/OnnxMemoryEmbedder.cs new file mode 100644 index 000000000..4c87cd6be --- /dev/null +++ b/src/Netclaw.Embeddings/OnnxMemoryEmbedder.cs @@ -0,0 +1,263 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using System.Numerics.Tensors; +using FastBertTokenizer; +using Microsoft.ML.OnnxRuntime; +using Microsoft.ML.OnnxRuntime.Tensors; +using Netclaw.Actors.Memory; + +namespace Netclaw.Embeddings; + +/// +/// In-process ONNX-backed (memory-core-redesign D1). Owns +/// exactly one and one for its +/// lifetime — construction loads both once; there is no re-provisioning without constructing a +/// new instance (daemon wiring for that is Stage B). +/// +/// +/// Pooling: both allowlisted models ('s +/// snowflake-arctic-embed-m and mxbai-embed-large-v1) are BERT-class encoders +/// exported with add_pooling_layer=False — their ONNX graphs return only +/// last_hidden_state (per-token hidden states), never a pre-pooled vector. Both model +/// cards document CLS-token pooling as the correct/default strategy for retrieval embeddings +/// (arctic-embed-m: "use the CLS token to embed each text portion"; mxbai-embed-large-v1: +/// "works really well with cls pooling (default)"), so this embedder always reads +/// last_hidden_state[:, 0, :] — position 0 along the sequence axis — rather than mean- +/// pooling across tokens. The result is then L2-normalized so stored cosine similarity needs +/// no further scaling. +/// +/// +/// +/// Inputs: this embedder feeds only the input names the loaded ONNX graph actually +/// declares (), rather than hardcoding the +/// production models' 3-input BERT signature (input_ids, attention_mask, +/// token_type_ids) — the test fixture graph declares a different, smaller input set, and +/// this embedder must work against either without a fixture-only code path. +/// +/// +/// +/// Concurrency: a single supports concurrent +/// calls, but an +/// unbounded number of them would oversubscribe the CPU beyond what +/// assumes. +/// caps concurrent inference calls (default 2) so embedding work shares the machine +/// predictably with everything else the daemon is doing — this matters because query +/// embedding sits on the recall latency budget in a later slice. +/// +/// +public sealed class OnnxMemoryEmbedder : IMemoryEmbedder, IDisposable +{ + // Both allowlisted models cap at 512 (their tokenizer_config.json model_max_length). + private const int MaxTokens = 512; + + private readonly InferenceSession _session; + private readonly BertTokenizer _tokenizer; + private readonly BoundedConcurrencyGate _gate; + private readonly string _outputName; + + private OnnxMemoryEmbedder( + string modelId, + int dimensions, + InferenceSession session, + BertTokenizer tokenizer, + int maxConcurrency) + { + if (session.OutputMetadata.Count != 1) + throw new InvalidOperationException( + $"Embedding model '{modelId}' declares {session.OutputMetadata.Count} outputs; " + + "OnnxMemoryEmbedder expects exactly one (the per-token hidden-state tensor)."); + + ModelId = modelId; + Dimensions = dimensions; + _session = session; + _tokenizer = tokenizer; + _gate = new BoundedConcurrencyGate(maxConcurrency); + _outputName = session.OutputMetadata.Keys.Single(); + } + + /// + public string ModelId { get; } + + /// + public int Dimensions { get; } + + /// + public bool IsAvailable => true; + + /// + /// Loads the ONNX model and WordPiece vocabulary from disk. Both files are expected to + /// already be provisioned and hash-verified () — + /// this constructor does no downloading or verification of its own. + /// + /// Path to the model.onnx file. + /// Path to the WordPiece vocab.txt file. + /// The allowlisted model id these files correspond to. + /// Expected output vector width, from the allowlist manifest. + /// Maximum concurrent inference calls (default 2). + /// Threads ONNX Runtime uses per inference call (default 4). + public static async Task LoadAsync( + string modelPath, + string vocabPath, + string modelId, + int dimensions, + int maxConcurrency = 2, + int intraOpNumThreads = 4, + CancellationToken ct = default) + { + ct.ThrowIfCancellationRequested(); + + using var sessionOptions = new SessionOptions { IntraOpNumThreads = intraOpNumThreads }; + var session = new InferenceSession(modelPath, sessionOptions); + + var tokenizer = new BertTokenizer(); + // Both allowlisted models (Snowflake/snowflake-arctic-embed-m, + // mixedbread-ai/mxbai-embed-large-v1) publish do_lower_case=true in their + // tokenizer_config.json — a standard BERT-base-uncased vocabulary. + await tokenizer.LoadVocabularyAsync(vocabPath, convertInputToLowercase: true); + + return new OnnxMemoryEmbedder(modelId, dimensions, session, tokenizer, maxConcurrency); + } + + /// + public async ValueTask> EmbedAsync(string text, CancellationToken ct) + => await _gate.RunAsync(_ => Task.FromResult(EmbedOne(text)), ct).ConfigureAwait(false); + + /// + public async ValueTask>> EmbedBatchAsync(IReadOnlyList texts, CancellationToken ct) + { + if (texts.Count == 0) + return []; + + // Each item acquires the gate independently (rather than holding one slot for the + // whole batch) so a large batch call and a concurrent single EmbedAsync call from the + // live write path interleave fairly instead of one blocking behind the other for the + // batch's full duration. + var tasks = new Task>[texts.Count]; + for (var i = 0; i < texts.Count; i++) + { + var text = texts[i]; + tasks[i] = _gate.RunAsync(_ => Task.FromResult(EmbedOne(text)), ct); + } + + return await Task.WhenAll(tasks).ConfigureAwait(false); + } + + private ReadOnlyMemory EmbedOne(string text) + { + var inputIds = new long[MaxTokens]; + var attentionMask = new long[MaxTokens]; + var tokenTypeIds = new long[MaxTokens]; + + // This overload writes into the caller-supplied spans instead of BertTokenizer's + // internal reused buffers, so calling it from multiple gate-scheduled tasks + // concurrently against the one shared _tokenizer instance is safe. + _tokenizer.Encode(text, inputIds, attentionMask, tokenTypeIds, MaxTokens); + + var inputIdsTensor = new DenseTensor(inputIds, [1, MaxTokens]); + var attentionMaskTensor = new DenseTensor(attentionMask, [1, MaxTokens]); + var tokenTypeIdsTensor = new DenseTensor(tokenTypeIds, [1, MaxTokens]); + + var available = new Dictionary(StringComparer.Ordinal) + { + ["input_ids"] = NamedOnnxValue.CreateFromTensor("input_ids", inputIdsTensor), + ["attention_mask"] = NamedOnnxValue.CreateFromTensor("attention_mask", attentionMaskTensor), + ["token_type_ids"] = NamedOnnxValue.CreateFromTensor("token_type_ids", tokenTypeIdsTensor), + }; + + var feed = new List(_session.InputMetadata.Count); + foreach (var inputName in _session.InputMetadata.Keys) + { + if (!available.TryGetValue(inputName, out var value)) + throw new InvalidOperationException( + $"Embedding model '{ModelId}' declares input '{inputName}', which this embedder does not know how to produce."); + feed.Add(value); + } + + using var outputs = _session.Run(feed); + var lastHiddenState = outputs.First(o => o.Name == _outputName).AsTensor(); + + var dims = lastHiddenState.Dimensions[^1]; + if (dims != Dimensions) + throw new InvalidOperationException( + $"Embedding model '{ModelId}' produced a {dims}-dimensional vector; allowlist declares {Dimensions}."); + + var vector = new float[dims]; + for (var d = 0; d < dims; d++) + vector[d] = lastHiddenState[0, 0, d]; // CLS token: position 0 along the sequence axis + + NormalizeL2(vector); + return vector; + } + + private static void NormalizeL2(float[] vector) + { + var norm = TensorPrimitives.Norm((ReadOnlySpan)vector); + if (norm > 0f) + TensorPrimitives.Divide(vector, norm, vector); + } + + public void Dispose() => _session.Dispose(); +} + +/// +/// Bounds concurrent execution of a unit of async work and reports the peak concurrency +/// actually observed, so tests can prove the bound is enforced under real contention without +/// racing on wall-clock sleeps. Used by to keep concurrent +/// ONNX inference calls within a predictable share of the CPU. +/// +internal sealed class BoundedConcurrencyGate +{ + private readonly SemaphoreSlim _semaphore; + private int _active; + private int _peakObserved; + + public BoundedConcurrencyGate(int maxConcurrency) + { + if (maxConcurrency <= 0) + throw new ArgumentOutOfRangeException(nameof(maxConcurrency), maxConcurrency, "Must be positive."); + + MaxConcurrency = maxConcurrency; + _semaphore = new SemaphoreSlim(maxConcurrency, maxConcurrency); + } + + public int MaxConcurrency { get; } + + /// Highest number of calls ever observed executing inside concurrently. + public int PeakObservedConcurrency => Volatile.Read(ref _peakObserved); + + public async Task RunAsync(Func> work, CancellationToken ct) + { + await _semaphore.WaitAsync(ct).ConfigureAwait(false); + try + { + var current = Interlocked.Increment(ref _active); + InterlockedMax(ref _peakObserved, current); + try + { + return await work(ct).ConfigureAwait(false); + } + finally + { + Interlocked.Decrement(ref _active); + } + } + finally + { + _semaphore.Release(); + } + } + + private static void InterlockedMax(ref int target, int value) + { + int initial; + do + { + initial = Volatile.Read(ref target); + if (value <= initial) + return; + } while (Interlocked.CompareExchange(ref target, value, initial) != initial); + } +} diff --git a/tools/embed-latency-bench/Program.cs b/tools/embed-latency-bench/Program.cs new file mode 100644 index 000000000..ba3998aaf --- /dev/null +++ b/tools/embed-latency-bench/Program.cs @@ -0,0 +1,433 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- + +// Honest one-shot latency bench for OnnxMemoryEmbedder (memory-core-redesign task 2.13). +// +// Loads the production embedder exactly the way the daemon would (provisioner hash-verify +// against the pinned allowlist, then OnnxMemoryEmbedder.LoadAsync — same pooling, same +// IntraOpNumThreads=4, same BoundedConcurrencyGate(2)), then times batch=1 EmbedAsync calls +// across three hardcoded corpora (short query / medium / doc-length), a cold-load measurement, +// and a concurrency-2 pass. This is a Stopwatch harness, not BenchmarkDotNet — the goal is one +// honest percentile table on the reference box, not microbenchmark rigor. +// +// Usage: dotnet run -c Release --project tools/embed-latency-bench [modelDirectory] +// Default modelDirectory: ~/recall-research-local/models/snowflake-arctic-embed-m +// +// Never downloads anything: if the model directory is missing or fails SHA-256 verification +// against EmbeddingModelProvisioner.Allowlist, this exits with an error instead of fetching it. + +using System.Diagnostics; +using System.Numerics.Tensors; +using FastBertTokenizer; +using Microsoft.ML.OnnxRuntime; +using Microsoft.ML.OnnxRuntime.Tensors; +using Netclaw.Embeddings; + +// Captured before any other work so the cold-load number can include .NET host/runtime +// startup — the literal "process start -> first embed complete" the task asked for. +var processStartUtc = Process.GetCurrentProcess().StartTime.ToUniversalTime(); + +const int WarmupIterations = 20; +const int TimedIterations = 200; +const int ConcurrencyIterationsPerLoop = 100; +const int MaxTokens = 512; +const int DynamicLengthBucket = 8; + +// Honest contention context: load average is one line in /proc/loadavg (1m 5m 15m ...). +// Read once here, and again at the very end, so the report shows what the box looked like +// before this ~5-6 minute run started and what it drifted to by the time it finished. +string ReadLoadAverage() => File.Exists("/proc/loadavg") + ? string.Join(' ', File.ReadAllText("/proc/loadavg").Split(' ').Take(3)) + : "unavailable (non-Linux host)"; + +var loadAverageBefore = ReadLoadAverage(); + +var modelDir = args.Length > 0 + ? args[0] + : Path.Combine(Environment.GetFolderPath(Environment.SpecialFolder.UserProfile), "recall-research-local", "models", "snowflake-arctic-embed-m"); + +Console.WriteLine($"Model directory: {modelDir}"); + +using var httpClient = new HttpClient(); // required by EmbeddingModelProvisioner's constructor; never used for I/O here — TryLoadVerifiedAsync is disk-only. +var provisioner = new EmbeddingModelProvisioner(httpClient, EmbeddingModelProvisioner.Allowlist); + +var verified = await provisioner.TryLoadVerifiedAsync("snowflake-arctic-embed-m", modelDir); +if (verified is null) +{ + Console.Error.WriteLine( + $"STOP: '{modelDir}' does not contain a hash-verified snowflake-arctic-embed-m " + + "(model.onnx + vocab.txt) matching EmbeddingModelProvisioner.Allowlist. Refusing to " + + "proceed — this tool never downloads."); + return 1; +} + +Console.WriteLine($"Verified model: {verified.ModelId} ({verified.Dimensions} dims) at {verified.ModelPath}"); + +// --- Dynamic-sequence-length feasibility check (Slice 4 design experiment) ------------------ +// +// A -1 (or named symbolic) dimension on the sequence axis means the exported ONNX graph +// accepts any sequence length at inference time — the padding to a fixed MaxTokens=512 in +// OnnxMemoryEmbedder is an application choice, not something the graph requires. A concrete +// positive dimension there means the graph was exported with a static shape and rejects +// anything else; dynamic length would need a re-export, not just a code change. +bool sequenceAxisIsDynamic; +using (var diagSession = new InferenceSession(verified.ModelPath)) +{ + Console.WriteLine(); + Console.WriteLine("ONNX graph input metadata (dynamic-sequence-length feasibility check):"); + var seqDims = new List(); + foreach (var (name, meta) in diagSession.InputMetadata) + { + var dims = string.Join(", ", meta.Dimensions); + var symbolic = string.Join(", ", meta.SymbolicDimensions.Select(s => string.IsNullOrEmpty(s) ? "" : s)); + Console.WriteLine($" {name}: dims=[{dims}] symbolic=[{symbolic}]"); + + // Sequence axis is conventionally dimension index 1 (dim 0 is batch) for a + // [batch, sequence] BERT input tensor. + if (meta.Dimensions.Length > 1) + seqDims.Add(meta.Dimensions[1] < 0 || !string.IsNullOrEmpty(meta.SymbolicDimensions[1])); + } + + sequenceAxisIsDynamic = seqDims.Count > 0 && seqDims.All(d => d); + Console.WriteLine(sequenceAxisIsDynamic + ? " Verdict: sequence axis is DYNAMIC on every input — graph accepts variable-length sequences." + : " Verdict: sequence axis is FIXED on at least one input — graph requires the exported shape."); +} + +// --- Corpora (deterministic, hardcoded) --------------------------------------------------- + +string[] shortQueries = +[ + "what's our grafana dashboard convention?", + "how do I restart the daemon safely?", + "where do we store the slack webhook secret?", + "what does MinCosineSimilarity default to in production?", + "did we ever decide on mirroring model artifacts into R2?", + "what version is pinned in Directory.Build.props right now?", + "how many logical cores does the reference box have?", + "which model is the allowlist's default embedder?", + "what's the checkpoint worker's idle loop actually for?", + "can you summarize yesterday's release notes for me?", + "who owns the memory_embeddings table schema change?", + "what's the config key for the recall timeout?", + "is the semaphore capped at two concurrent inference calls?", + "what tokenizer library are we using for BERT models?", + "when did we last run the full eval suite?", + "what's the vector weight in the hybrid fusion score?", + "how do I run the light smoke test suite locally?", + "what's currently blocking slice four from shipping?", + "which subreddit rule blocks self-promotional posts?", + "what does netclaw doctor --fix actually repair?", +]; + +// Medium/doc-length corpora are built from a fixed sentence bank (thematically real content +// about this codebase) rather than hand-authored essays, so their length is deterministically +// controllable; actual token counts are measured below rather than assumed. +string[] sentenceBank = +[ + "The daemon persists session state under the Slack thread identity of channelId and threadTs, so every conversation maps to exactly one actor.", + "Query embedding runs in-process through OnnxRuntime with CLS-token pooling and L2 normalization before the vector is compared against stored memories.", + "The recall coordinator merges FTS5 lexical candidates with vector nearest-neighbor candidates before applying the policy gates uniformly across both sources.", + "Consolidation only executes from a human-ratified plan file, never automatically, and always takes a VACUUM INTO backup before touching the live database.", + "The expiry sweep runs inside the checkpoint worker's idle loop and deletes rows whose expires_at timestamp has already passed the grace window.", + "MinCosineSimilarity acts as an absolute floor rather than a relative rank cutoff, so a mediocre top candidate can still be suppressed entirely.", + "The embedding model allowlist pins a specific HuggingFace commit SHA for both the model weights and the tokenizer vocabulary file.", + "SchemaFixResolver can only repair validation errors it recognizes, so new enum properties must ship as strings with named values from day one.", + "Akka.Hosting wires the actor system through dependency injection, keeping the constructor signature explicit about every collaborator the actor needs.", + "The bounded concurrency gate caps simultaneous ONNX inference calls at two by default, sharing the CPU predictably with the rest of the daemon.", + "TimeProvider is injected everywhere instead of DateTimeOffset.UtcNow so that tests can advance a virtual clock without any wall-clock sleeping.", + "The nominator model and the fallback model both export fp32 ONNX graphs with add_pooling_layer disabled, so pooling always happens in application code.", + "Backfill re-embeds only rows whose content hash no longer matches the stored hash, making repeated runs of the same backfill essentially free.", + "The doctor command surfaces embedding coverage gaps, model hash mismatches, and mixed-model rows as loud warnings rather than silent degradation.", + "Slopwatch flags disabled tests, suppressed warnings, and empty catch blocks as reward-hacking signals that must be fixed or explicitly baselined.", + "The observer sidecar proposes a recall mode for each distilled memory, and the policy gate honors that proposal for durable facts by default.", + "A crash between the document commit and the embedding upsert leaves a coverage gap that the next backfill pass repairs automatically.", + "The vector index is a flat in-memory array per model, invalidated by a store version counter whenever the underlying table changes.", + "Structural append is the fallback path whenever the merge guard rejects a synthesized body for losing too many load-bearing tokens.", + "Trace-class memories are short-lived operational state with a seventy-two hour time-to-live, weighted below durable facts during recall scoring.", + "The tool-lessons block is injected once per tool per session as an exact anchor-id lookup, entirely outside the pre-turn recall budget.", + "Recency decay multiplies the fused score by a floor-bounded factor derived from a configurable half-life measured in days.", + "Every configuration schema uses additionalProperties false, so an unlisted property on any Config type is rejected at doctor time.", + "The release version gate checks that the pushed tag matches VersionPrefix and VersionSuffix exactly, rejecting any other tag shape.", + "Prerelease tags always use the dotted beta.N form, because a mixed identifier like beta1 sorts lexically in the wrong order.", + "The memory store's InitializeAsync method creates the embeddings table idempotently, independent of the daemon's own migration pipeline.", + "Evidence records are policy-forced into an immutable, searchable class, which is why lessons needed their own dedicated memory class instead.", + "The 22 legacy compaction rows were repaired directly during the quick-win slice, ahead of the taxonomy rebalance that formalized the invariant.", + "Content hash is computed over the normalized title and body concatenation, using SHA-256 the same way the provisioner verifies model artifacts.", + "A rate-limited log line fires whenever vector recall degrades to lexical-only, so operators see the condition without being flooded by it.", +]; + +string BuildFromBank(int startIndex, int count) +{ + var parts = new string[count]; + for (var i = 0; i < count; i++) + parts[i] = sentenceBank[(startIndex + i) % sentenceBank.Length]; + return string.Join(' ', parts); +} + +string[] mediumCorpus = Enumerable.Range(0, 20) + .Select(i => BuildFromBank(startIndex: i * 3, count: 6)) + .ToArray(); + +string[] docCorpus = Enumerable.Range(0, 20) + .Select(i => BuildFromBank(startIndex: i * 7, count: 15)) + .ToArray(); + +// Fixed 10-sentence correctness set spanning short queries and longer bank sentences, so the +// fixed-512-vs-dynamic-length parity check isn't only exercised at one length. +string[] correctnessSentences = +[ + .. shortQueries.Take(5), + .. sentenceBank.Take(5), +]; + +// --- Token-count diagnostic: measure the corpora shape claim rather than assume it ---------- + +var diagTokenizer = new BertTokenizer(); +await diagTokenizer.LoadVocabularyAsync(verified.VocabPath, convertInputToLowercase: true); + +(int Min, int Max, double Mean) TokenStats(string[] corpus) +{ + var counts = new int[corpus.Length]; + for (var i = 0; i < corpus.Length; i++) + { + var ids = new long[MaxTokens]; + var mask = new long[MaxTokens]; + var types = new long[MaxTokens]; + diagTokenizer.Encode(corpus[i], ids, mask, types, MaxTokens); + counts[i] = (int)mask.Sum(); + } + return (counts.Min(), counts.Max(), counts.Average()); +} + +var shortStats = TokenStats(shortQueries); +var mediumStats = TokenStats(mediumCorpus); +var docStats = TokenStats(docCorpus); + +Console.WriteLine(); +Console.WriteLine("Corpus token counts (actual, via production tokenizer):"); +Console.WriteLine($" short : min={shortStats.Min} max={shortStats.Max} mean={shortStats.Mean:F1}"); +Console.WriteLine($" medium: min={mediumStats.Min} max={mediumStats.Max} mean={mediumStats.Mean:F1}"); +Console.WriteLine($" doc : min={docStats.Min} max={docStats.Max} mean={docStats.Mean:F1}"); + +// --- Cold load ------------------------------------------------------------------------------- + +var loadOnlySw = Stopwatch.StartNew(); +var embedder = await OnnxMemoryEmbedder.LoadAsync(verified.ModelPath, verified.VocabPath, verified.ModelId, verified.Dimensions); +_ = await embedder.EmbedAsync(shortQueries[0], CancellationToken.None); +loadOnlySw.Stop(); +var processToFirstEmbedMs = (DateTime.UtcNow - processStartUtc).TotalMilliseconds; + +Console.WriteLine(); +Console.WriteLine($"Cold load — process start -> first embed complete: {processToFirstEmbedMs:F1} ms (includes .NET host/runtime startup)"); +Console.WriteLine($"Cold load — LoadAsync + first embed only: {loadOnlySw.Elapsed.TotalMilliseconds:F1} ms"); + +// --- Percentile helper ----------------------------------------------------------------------- + +Row Percentiles(string label, List samplesMs) +{ + var sorted = samplesMs.Order().ToArray(); + double Pct(double p) + { + var rank = (int)Math.Ceiling(p / 100.0 * sorted.Length) - 1; + return sorted[Math.Clamp(rank, 0, sorted.Length - 1)]; + } + + return new Row(label, sorted.Length, Pct(50), Pct(90), Pct(95), Pct(99), sorted[^1], sorted.Average()); +} + +async Task> RunCorpus(string[] corpus, int warmup, int timed) +{ + for (var i = 0; i < warmup; i++) + _ = await embedder.EmbedAsync(corpus[i % corpus.Length], CancellationToken.None); + + var samples = new List(timed); + for (var i = 0; i < timed; i++) + { + var sw = Stopwatch.StartNew(); + _ = await embedder.EmbedAsync(corpus[i % corpus.Length], CancellationToken.None); + sw.Stop(); + samples.Add(sw.Elapsed.TotalMilliseconds); + } + + return samples; +} + +var rows = new List +{ + Percentiles("short", await RunCorpus(shortQueries, WarmupIterations, TimedIterations)), + Percentiles("medium", await RunCorpus(mediumCorpus, WarmupIterations, TimedIterations)), + Percentiles("doc", await RunCorpus(docCorpus, WarmupIterations, TimedIterations)), +}; + +// --- Concurrency-2 short-query pass (two parallel loops share the SemaphoreSlim(2) gate) --- + +async Task> RunConcurrentLoop(int iterations) +{ + var samples = new List(iterations); + for (var i = 0; i < iterations; i++) + { + var sw = Stopwatch.StartNew(); + _ = await embedder.EmbedAsync(shortQueries[i % shortQueries.Length], CancellationToken.None); + sw.Stop(); + samples.Add(sw.Elapsed.TotalMilliseconds); + } + + return samples; +} + +var concurrencySw = Stopwatch.StartNew(); +var concurrentResults = await Task.WhenAll( + RunConcurrentLoop(ConcurrencyIterationsPerLoop), + RunConcurrentLoop(ConcurrencyIterationsPerLoop)); +concurrencySw.Stop(); +var concurrentSamples = concurrentResults[0].Concat(concurrentResults[1]).ToList(); +rows.Add(Percentiles("short (concurrency=2)", concurrentSamples)); + +Console.WriteLine(); +Console.WriteLine($"Concurrency-2 pass total wall time: {concurrencySw.Elapsed.TotalMilliseconds:F1} ms for {concurrentSamples.Count} total calls (2x{ConcurrencyIterationsPerLoop})"); + +// Capture fixed-512 embeddings for the correctness set before disposing the fixed embedder — +// these are compared against the dynamic-length variant below (bitwise-different padding, same +// semantic content, should cosine-agree near 1.0 if the attention mask does its job). +var fixedCorrectnessEmbeddings = new ReadOnlyMemory[correctnessSentences.Length]; +for (var i = 0; i < correctnessSentences.Length; i++) + fixedCorrectnessEmbeddings[i] = await embedder.EmbedAsync(correctnessSentences[i], CancellationToken.None); + +embedder.Dispose(); + +// --- Dynamic sequence length experiment (Slice 4 design decision) -------------------------- +// +// Bench-only parallel code path: OnnxMemoryEmbedder is not touched. This loads its own +// InferenceSession + BertTokenizer and pads each input only to its actual tokenized length, +// rounded up to a multiple of DynamicLengthBucket, instead of the fixed MaxTokens=512. +List<(string Sentence, float Cosine)>? correctnessResults = null; + +if (sequenceAxisIsDynamic) +{ + using var dynamicSessionOptions = new SessionOptions { IntraOpNumThreads = 4 }; + using var dynamicSession = new InferenceSession(verified.ModelPath, dynamicSessionOptions); + var dynamicTokenizer = new BertTokenizer(); + await dynamicTokenizer.LoadVocabularyAsync(verified.VocabPath, convertInputToLowercase: true); + var outputName = dynamicSession.OutputMetadata.Keys.Single(); + + ReadOnlyMemory EmbedOneDynamic(string text) + { + var scratchIds = new long[MaxTokens]; + var scratchMask = new long[MaxTokens]; + var scratchTypes = new long[MaxTokens]; + dynamicTokenizer.Encode(text, scratchIds, scratchMask, scratchTypes, MaxTokens); + + var actualLen = (int)scratchMask.Sum(); + var bucketLen = Math.Max(DynamicLengthBucket, ((actualLen + DynamicLengthBucket - 1) / DynamicLengthBucket) * DynamicLengthBucket); + + var inputIds = scratchIds[..bucketLen]; + var attentionMask = scratchMask[..bucketLen]; + var tokenTypeIds = scratchTypes[..bucketLen]; + + var available = new Dictionary(StringComparer.Ordinal) + { + ["input_ids"] = NamedOnnxValue.CreateFromTensor("input_ids", new DenseTensor(inputIds, [1, bucketLen])), + ["attention_mask"] = NamedOnnxValue.CreateFromTensor("attention_mask", new DenseTensor(attentionMask, [1, bucketLen])), + ["token_type_ids"] = NamedOnnxValue.CreateFromTensor("token_type_ids", new DenseTensor(tokenTypeIds, [1, bucketLen])), + }; + + var feed = new List(dynamicSession.InputMetadata.Count); + foreach (var inputName in dynamicSession.InputMetadata.Keys) + feed.Add(available[inputName]); + + using var outputs = dynamicSession.Run(feed); + var lastHiddenState = outputs.First(o => o.Name == outputName).AsTensor(); + var dims = lastHiddenState.Dimensions[^1]; + + var vector = new float[dims]; + for (var d = 0; d < dims; d++) + vector[d] = lastHiddenState[0, 0, d]; // CLS token + + var norm = TensorPrimitives.Norm((ReadOnlySpan)vector); + if (norm > 0f) + TensorPrimitives.Divide(vector, norm, vector); + + return vector; + } + + List RunCorpusDynamic(string[] corpus, int warmup, int timed) + { + for (var i = 0; i < warmup; i++) + _ = EmbedOneDynamic(corpus[i % corpus.Length]); + + var samples = new List(timed); + for (var i = 0; i < timed; i++) + { + var sw = Stopwatch.StartNew(); + _ = EmbedOneDynamic(corpus[i % corpus.Length]); + sw.Stop(); + samples.Add(sw.Elapsed.TotalMilliseconds); + } + + return samples; + } + + rows.Add(Percentiles("short (dynamic-len)", RunCorpusDynamic(shortQueries, WarmupIterations, TimedIterations))); + rows.Add(Percentiles("medium (dynamic-len)", RunCorpusDynamic(mediumCorpus, WarmupIterations, TimedIterations))); + rows.Add(Percentiles("doc (dynamic-len)", RunCorpusDynamic(docCorpus, WarmupIterations, TimedIterations))); + + // Correctness: same 10 sentences, dynamic-length path, cosine-compared to the fixed-512 + // embeddings captured above. Both vectors are already L2-normalized, so cosine similarity + // reduces to a plain dot product. + correctnessResults = new List<(string, float)>(correctnessSentences.Length); + for (var i = 0; i < correctnessSentences.Length; i++) + { + var dynamicVec = EmbedOneDynamic(correctnessSentences[i]); + var cosine = TensorPrimitives.Dot(fixedCorrectnessEmbeddings[i].Span, dynamicVec.Span); + correctnessResults.Add((correctnessSentences[i], cosine)); + } +} +else +{ + Console.WriteLine(); + Console.WriteLine( + "Dynamic-length pass SKIPPED: the ONNX graph's sequence axis is fixed on at least one " + + "input, so it rejects any shape other than the exported one. Padding to a different " + + "fixed size (e.g. 64) is not an option either — a statically-shaped graph has exactly " + + "one legal input shape, not a small set of them. Verdict: dynamic sequence length is " + + "NOT a drop-in change here; it would require re-exporting the ONNX graph with dynamic " + + "axes on the sequence dimension, or pursuing int8 quantization (the deferred D2 lever) " + + "instead."); +} + +// --- Report ------------------------------------------------------------------------------ + +Console.WriteLine(); +Console.WriteLine($"{"corpus",-24}{"n",5}{"p50",8}{"p90",8}{"p95",8}{"p99",8}{"max",8}{"mean",8} (ms, batch=1)"); +foreach (var row in rows) +{ + Console.WriteLine( + $"{row.Label,-24}{row.N,5}{row.P50,8:F1}{row.P90,8:F1}{row.P95,8:F1}{row.P99,8:F1}{row.Max,8:F1}{row.Mean,8:F1}"); +} + +if (correctnessResults is not null) +{ + Console.WriteLine(); + Console.WriteLine("Fixed-512 vs dynamic-length correctness check (cosine similarity, 10 fixed sentences):"); + foreach (var (sentence, cosine) in correctnessResults) + { + var preview = sentence.Length > 60 ? sentence[..60] + "..." : sentence; + Console.WriteLine($" {cosine:F6} \"{preview}\""); + } + + var minCosine = correctnessResults.Min(r => r.Cosine); + var meanCosine = correctnessResults.Average(r => r.Cosine); + Console.WriteLine($" min={minCosine:F6} mean={meanCosine:F6}"); +} + +Console.WriteLine(); +Console.WriteLine($"Load average before run (1m 5m 15m): {loadAverageBefore}"); +Console.WriteLine($"Load average after run (1m 5m 15m): {ReadLoadAverage()}"); + +return 0; + +internal readonly record struct Row(string Label, int N, double P50, double P90, double P95, double P99, double Max, double Mean); diff --git a/tools/embed-latency-bench/embed-latency-bench.csproj b/tools/embed-latency-bench/embed-latency-bench.csproj new file mode 100644 index 000000000..a8aa1e17a --- /dev/null +++ b/tools/embed-latency-bench/embed-latency-bench.csproj @@ -0,0 +1,21 @@ + + + + Exe + net10.0 + enable + enable + + false + embed-latency-bench + Netclaw.Tools.EmbedLatencyBench + + + + + + + From 88cb85ad2798d2048f3ef00d0f18960e0b4383aa Mon Sep 17 00:00:00 2001 From: Aaron Stannard Date: Sun, 5 Jul 2026 15:54:25 -0500 Subject: [PATCH 02/37] feat(memory): kNN-nominate/LLM-decide dedup + lossless merges (memory-core-redesign slice 3) (#1585) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * feat(memory): config surface for write-side curation (opsx: memory-core-redesign slice 3) MemoryConfig gains Curation { NominatorSimilarityThreshold, NominatorK, LlmMaxOutputTokens, LlmTimeoutSeconds } with schema sync (defaults, additionalProperties: false). Nominator threshold/K are defined now with doc comments noting they are consumed by Slice 3 Stage B (task 3.1), not this change. Task 3.5. * feat(memory): merged-body response protocol for UPDATE/CONSOLIDATE (opsx: memory-core-redesign slice 3) CurationPromptBuilder's system prompt now instructs the curator to emit a '---'-delimited lossless merged body after UPDATE/CONSOLIDATE keyword lines; SKIP/CREATE remain keyword-only. ParseResponse extracts the optional body into the new CurationDecision.MergedBody (absent/malformed body treated as null, keyword-only responses remain valid). CurationDecision also gains FromLlmTier, distinguishing LLM-synthesized decisions from the deterministic rules tier for write-routing purposes (wired in the next commit). BuildUserMessage gains a useFullCandidateContent parameter (default false, current 700-char preview behavior) for Stage B's full-content nominated candidates — not consumed yet. Task 3.2. * feat(memory): MergeGuard load-bearing token retention validator (opsx: memory-core-redesign slice 3) New deterministic MergeGuard.Validate(sourceBodies, mergedBody) -> pure function checking (1) retention: >=95% of the union of load-bearing tokens (URLs, numbers/versions/quantities/dates, camelCase/snake_case/kebab-case/ dotted.path/ALL_CAPS identifiers, file paths) extracted from every source body must survive case-insensitively in the merged body, and (2) collapse: merged length must be >=60% of the longest single source. Converts an LLM merge error from silent data loss into a recoverable append-fallback signal (design D5); wired into the write path in the next commit. Task 3.3. * feat(memory): guard-validated write routing, close raw-overwrite paths (opsx: memory-core-redesign slice 3) MemoryCurationEvaluator.ApplyDecisionAsync now routes every LLM-tier UPDATE/CONSOLIDATE decision through MergeGuard-validated merge or a structural append fallback (existing body + dated separator + proposal, AppendDocument semantics) instead of a raw overwrite — this is what makes AppendDocument a real, reachable write path for the first time. EvaluateAsync returns a new CurationEvaluation (Decision + Candidates) so ApplyDecisionAsync can validate against the same candidate bodies the decision was made against without re-querying the store; both callers (MemoryCurationActor, MemoryCurationEngine) and the parity tests are updated for the new signature. GuardDestructiveUpdate is no longer applied to LLM-tier decisions in EvaluateAsync: its raw-proposal containment check would reject a legitimate reworded merge, and the new write-time guard supersedes it for that tier. The deterministic tier's exact-anchor UPDATE keeps its pre-Slice-3 behavior unchanged (GuardDestructiveUpdate's containment proof already makes that raw overwrite non-lossy on its own terms) — this is the one decision shape explicitly exempted per design D5. Deterministic-tier CONSOLIDATE (fuzzy match >=80% overlap, no LLM call) previously reached the store with no guard at all; it now flows through the same append-fallback path as an LLM decision with no merged body, closing that gap too. MemoryCurationConfig threads through MemoryCurationActor/MemoryCurationEngine to TryLlmEvaluationAsync, replacing the hardcoded 10s timeout and 4096 max output tokens. SQLiteMemoryStore exposes its TimeProvider so the append fallback's date separator stays consistent with the store's own persisted timestamps. New MemoryCurationMergeRoutingTests exercise this end-to-end through the real evaluator + store: guard-fail and body-absent LLM Update/Consolidate produce append semantics with the target's original body intact as a prefix; a guard- passing merge writes the merged body; the deterministic exact-anchor Update and fuzzy-match Consolidate paths are covered as regression/closed-gap proof. Task 3.4. Marks tasks 3.2-3.5 complete in tasks.md (NOT 3.1/3.6/3.7 — Stage B, a later dispatch). * feat(memory): embedding kNN nominator — cosine nominates, LLM decides (opsx: memory-core-redesign slice 3, task 3.1) Adds the nominate→decide dedup step to the shared MemoryCurationEvaluator (design D4): when the embedder is available and there is no exact anchor match, the proposal is embedded (same title\ncontent concatenation as embed-on-write) and MemoryVectorIndex.TopK shortlists up to Memory.Curation.NominatorK existing documents at or above Memory.Curation.NominatorSimilarityThreshold. Nominees are hydrated into full-content candidates (SQLiteMemoryStore.GetCandidatesByIdsAsync) tagged with their cosine (ExistingMemoryCandidate.CosineSimilarity; anchor/lexical candidates carry null). Invariants (May 2026 measurement: no cosine threshold separates duplicates from siblings — siblings live at 0.905–0.941 inside the duplicate band): - Any nominee FORCES the LLM tier with full-content candidate previews; cosine never auto-merges and never auto-skips. - Nominee present + no LLM (daemon checkpoint worker today) or LLM failure → conservative Create, deliberately bypassing TryAutoResolveAmbiguous: semantic-near content is exactly the ambiguity Jaccard heuristics cannot adjudicate, and a duplicate is recoverable where a wrong merge is not. - No nominee + no anchor match → Create with zero LLM calls (the cheap common case — median nominee count on a random write is 0). - Embedder unavailable/absent → pre-slice lexical content-term search runs unchanged as the degraded path (curation_nominator_degraded marker). Wiring: new MemoryVectorIndexHolder (defers index construction until the warmed-up embedder's model id/dimensions are known, mirrors MemoryEmbedderHolder's holder-not-singleton rationale) is registered in the daemon DI and threaded into BOTH write pipelines — the inline actor via MemoryCurationActor.CreateProps/SessionMemoryServices, and the daemon worker via MemoryCurationEngine's constructor. New log markers: curation_nominated count/topCosine, curation_nominator_degraded, curation_nominee_no_llm_decision. Known limitation (documented on NominateAsync): proposals evaluated in one batch cannot nominate each other — neither is committed/indexed while the other is evaluated. Cross-batch and steady-state dedup are unaffected. * test(memory): nominator matrix — forced-LLM, sibling never-auto-merge, degraded path, parity, actor e2e (opsx: memory-core-redesign slice 3, task 3.6) All fixtures synthetic; cosine geometry is hand-crafted (unit vectors at exactly 0.93 — inside the measured sibling band) rather than model-derived, so every scenario is deterministic. - MemoryCurationNominatorTests (evaluator level): * paraphrase pair at cosine 0.93 with word-Jaccard <0.4 forces the LLM tier (recording IChatClient proves the call) even though the lexical tier finds zero candidates and would have said Create silently * nominee + scripted LLM CREATE → two separate documents persist * nominee + NO LLM → conservative Create (reason cites no-auto-merge on cosine alone); still two documents — never a merge without the LLM * novel proposal (no nominee, no anchor) → Create with zero LLM calls * embedder unavailable → lexical candidates still produced + curation_nominator_degraded fires; null holder behaves identically - MemoryCurationEvaluatorParityTests: nominee-present construction parity — actor-style (ILoggingAdapter) and engine-style (ILogger) evaluators sharing one store/embedder/index reach the identical forced-LLM decision - MemoryCurationActorNominatorTests (Akka.TestKit): proposal driven through the real MemoryCurationActor end-to-end to a committed store write; AwaitAssertAsync polls for the forced LLM call (no sleeps) * docs(memory): netclaw-memory skill 1.9.0 — semantic dedup + lossless merges; check tasks 3.1/3.6/3.7 (opsx: memory-core-redesign slice 3, task 3.7) Skill addition (How Memory Works): with Memory.Embeddings.Enabled, a near-duplicate proposal is nominated by embedding similarity and adjudicated by the curator LLM (skip/update/consolidate/create) — similarity alone never merges or skips — and merges are lossless-or-append (merged body keeps every source fact; a deterministic guard falls back to appending instead of overwriting when that check fails). Memory eval gate (category=Memory, Qwen3.6-27B-NVFP4 @ spark-acad, 5 runs/case): 5/5 cases passed (100.0%), GREEN. Embeddings default OFF, so the nominator idles in the eval daemon — the gate proves the shared curation paths did not regress. --- .../.system/files/netclaw-memory/SKILL.md | 9 +- .../changes/memory-core-redesign/tasks.md | 14 +- .../Memory/CurationPromptBuilderTests.cs | 154 +++++ .../MemoryCurationActorNominatorTests.cs | 217 +++++++ .../MemoryCurationEvaluatorParityTests.cs | 97 ++- .../Memory/MemoryCurationMergeRoutingTests.cs | 314 ++++++++++ .../Memory/MemoryCurationNominatorTests.cs | 400 ++++++++++++ .../Memory/MergeGuardTests.cs | 250 ++++++++ ...onMemoryObserverStorageIntegrationTests.cs | 6 +- .../SidecarSessionCorrelationTests.cs | 3 +- .../Memory/CurationPromptBuilder.cs | 90 ++- .../Memory/CurationRulesEvaluator.cs | 41 +- .../Memory/MemoryCurationActor.cs | 67 +- .../Memory/MemoryCurationEvaluator.cs | 576 +++++++++++++++--- .../Memory/MemoryCurationPipeline.cs | 18 +- .../Memory/MemoryVectorIndexHolder.cs | 69 +++ src/Netclaw.Actors/Memory/MergeGuard.cs | 183 ++++++ .../Memory/SQLiteMemoryStore.cs | 66 ++ .../Sessions/LlmSessionActor.cs | 6 +- .../Sessions/SessionDependencies.cs | 10 +- .../Doctor/ConfigSchemaDoctorCheckTests.cs | 55 ++ .../MemoryConfigDefaultsTests.cs | 30 + src/Netclaw.Configuration/MemoryConfig.cs | 50 ++ .../Schemas/netclaw-config.v1.schema.json | 34 ++ src/Netclaw.Daemon/Program.cs | 10 +- 25 files changed, 2628 insertions(+), 141 deletions(-) create mode 100644 src/Netclaw.Actors.Tests/Memory/MemoryCurationActorNominatorTests.cs create mode 100644 src/Netclaw.Actors.Tests/Memory/MemoryCurationMergeRoutingTests.cs create mode 100644 src/Netclaw.Actors.Tests/Memory/MemoryCurationNominatorTests.cs create mode 100644 src/Netclaw.Actors.Tests/Memory/MergeGuardTests.cs create mode 100644 src/Netclaw.Actors/Memory/MemoryVectorIndexHolder.cs create mode 100644 src/Netclaw.Actors/Memory/MergeGuard.cs diff --git a/feeds/skills/.system/files/netclaw-memory/SKILL.md b/feeds/skills/.system/files/netclaw-memory/SKILL.md index 2f0973821..3bf49d781 100644 --- a/feeds/skills/.system/files/netclaw-memory/SKILL.md +++ b/feeds/skills/.system/files/netclaw-memory/SKILL.md @@ -3,7 +3,7 @@ name: netclaw-memory description: "REQUIRED when the user asks what you remember, recall, or know from past conversations, previous sessions, cross-session memory, memory classes, or memory types. Also before using memory tools: find_memories, get_memories, store_memory, update_memory." metadata: author: netclaw - version: "1.8.0" + version: "1.9.0" --- # Netclaw Memory @@ -45,6 +45,13 @@ Both gates must pass for memory to function. - **Explicit tools** are a manual-control layer on top of automatic recall. - Memory is SQLite-backed and cross-session only within the active domain/boundary policy envelope. +- **Duplicate detection is semantic when embeddings are enabled** + (`Memory.Embeddings.Enabled`): a near-duplicate proposal is nominated by + embedding similarity and adjudicated by the curator LLM (skip, update, + consolidate, or create) — similarity alone never merges or skips anything. + Merges are lossless-or-append: the curator writes a merged body that keeps + every source fact, and a deterministic guard falls back to appending the + proposal instead of overwriting when that check fails. - Memory IDs shown by automatic recall, `find_memories`, and `get_memories` (e.g. `doc-…` / `rec-…`) are stable, opaque handles. Copy them **verbatim** into `get_memories` or `update_memory` — do not rewrite or reformat them. diff --git a/openspec/changes/memory-core-redesign/tasks.md b/openspec/changes/memory-core-redesign/tasks.md index 6ac5eaaee..02456d98b 100644 --- a/openspec/changes/memory-core-redesign/tasks.md +++ b/openspec/changes/memory-core-redesign/tasks.md @@ -30,13 +30,13 @@ constitution gates (tests, evals where mapped, schema/skill sync, slopwatch). ## 3. Write-side nominate→decide + lossless merge -- [ ] 3.1 Nominator in the shared evaluator: kNN shortlist at `Memory.Curation.NominatorSimilarityThreshold`/`NominatorK`; any nominee forces the LLM tier; no-nominee-no-anchor creates without LLM; lexical candidate search becomes the logged degraded path -- [ ] 3.2 Extend `CurationPromptBuilder` response protocol: CONSOLIDATE/UPDATE emit a merged body; `CurationDecision.MergedBody`; full-content previews for nominated candidates -- [ ] 3.3 Implement `MergeGuard` (load-bearing-token retention ≥95%, length collapse check) with structural-append fallback producing `AppendDocument` semantics -- [ ] 3.4 Route all curation UPDATE/CONSOLIDATE writes through guard-validated merged bodies; make raw whole-body overwrite unreachable from curation decisions -- [ ] 3.5 Config: `Memory.Curation { NominatorSimilarityThreshold, NominatorK, LlmMaxOutputTokens, LlmTimeoutSeconds }` (replacing hardcoded constants) + schema sync -- [ ] 3.6 Tests: paraphrase-dupe nomination (fixture pairs from the audit corpus shape), sibling pairs never auto-merge, MergeGuard property tests, append fallback, both-pipelines parity -- [ ] 3.7 Eval suite (memory category) + skill sync; update decision-mix expectations (consolidate share should rise from ~0.1%) +- [x] 3.1 Nominator in the shared evaluator: kNN shortlist at `Memory.Curation.NominatorSimilarityThreshold`/`NominatorK`; any nominee forces the LLM tier; no-nominee-no-anchor creates without LLM; lexical candidate search becomes the logged degraded path +- [x] 3.2 Extend `CurationPromptBuilder` response protocol: CONSOLIDATE/UPDATE emit a merged body; `CurationDecision.MergedBody`; full-content previews for nominated candidates +- [x] 3.3 Implement `MergeGuard` (load-bearing-token retention ≥95%, length collapse check) with structural-append fallback producing `AppendDocument` semantics +- [x] 3.4 Route all curation UPDATE/CONSOLIDATE writes through guard-validated merged bodies; make raw whole-body overwrite unreachable from curation decisions +- [x] 3.5 Config: `Memory.Curation { NominatorSimilarityThreshold, NominatorK, LlmMaxOutputTokens, LlmTimeoutSeconds }` (replacing hardcoded constants) + schema sync +- [x] 3.6 Tests: paraphrase-dupe nomination (fixture pairs from the audit corpus shape), sibling pairs never auto-merge, MergeGuard property tests, append fallback, both-pipelines parity +- [x] 3.7 Eval suite (memory category) + skill sync; update decision-mix expectations (consolidate share should rise from ~0.1%) ## 4. Read-side hybrid recall + absolute floor diff --git a/src/Netclaw.Actors.Tests/Memory/CurationPromptBuilderTests.cs b/src/Netclaw.Actors.Tests/Memory/CurationPromptBuilderTests.cs index f2cb5c69c..53fc45d12 100644 --- a/src/Netclaw.Actors.Tests/Memory/CurationPromptBuilderTests.cs +++ b/src/Netclaw.Actors.Tests/Memory/CurationPromptBuilderTests.cs @@ -101,6 +101,104 @@ public void ParseResponse_returns_null_for_unclosed_think_block() Assert.Null(CurationPromptBuilder.ParseResponse("reasoning with no closing tag and no answer")); } + // ── ParseResponse: merged-body protocol (memory-core-redesign Slice 3 task 3.2) ── + + [Fact] + public void ParseResponse_parses_UPDATE_with_merged_body() + { + var response = "UPDATE doc-abc123\n---\nConfig path is /etc/app/config.yaml (previously /etc/app/config.json)."; + + var decision = CurationPromptBuilder.ParseResponse(response); + + Assert.NotNull(decision); + Assert.Equal(CurationDecisionKind.Update, decision.Kind); + Assert.Equal("doc-abc123", decision.TargetDocumentId); + Assert.Equal( + "Config path is /etc/app/config.yaml (previously /etc/app/config.json).", + decision.MergedBody); + Assert.True(decision.FromLlmTier); + } + + [Fact] + public void ParseResponse_parses_CONSOLIDATE_with_merged_body() + { + var response = + "CONSOLIDATE doc-abc123 doc-def456\n---\n" + + "Akka.NET GitHub repository: https://github.com/akkadotnet/akka.net.\n" + + "Latest stable release is 1.5.62 (previously 1.5.60)."; + + var decision = CurationPromptBuilder.ParseResponse(response); + + Assert.NotNull(decision); + Assert.Equal(CurationDecisionKind.Consolidate, decision.Kind); + Assert.Equal(2, decision.ConsolidationTargetIds!.Count); + Assert.NotNull(decision.MergedBody); + Assert.Contains("1.5.62", decision.MergedBody); + Assert.Contains("1.5.60", decision.MergedBody); + Assert.True(decision.FromLlmTier); + } + + [Fact] + public void ParseResponse_UPDATE_keyword_only_still_valid_with_no_body() + { + var decision = CurationPromptBuilder.ParseResponse("UPDATE doc-42"); + + Assert.NotNull(decision); + Assert.Equal(CurationDecisionKind.Update, decision.Kind); + Assert.Equal("doc-42", decision.TargetDocumentId); + Assert.Null(decision.MergedBody); + } + + [Fact] + public void ParseResponse_CONSOLIDATE_keyword_only_still_valid_with_no_body() + { + var decision = CurationPromptBuilder.ParseResponse("CONSOLIDATE doc-1 doc-2"); + + Assert.NotNull(decision); + Assert.Equal(CurationDecisionKind.Consolidate, decision.Kind); + Assert.Equal(2, decision.ConsolidationTargetIds!.Count); + Assert.Null(decision.MergedBody); + } + + [Fact] + public void ParseResponse_UPDATE_with_malformed_empty_body_treats_body_as_absent() + { + // Separator present but nothing meaningful follows it (just whitespace). + var decision = CurationPromptBuilder.ParseResponse("UPDATE doc-42\n---\n \n "); + + Assert.NotNull(decision); + Assert.Equal(CurationDecisionKind.Update, decision.Kind); + Assert.Null(decision.MergedBody); + } + + [Fact] + public void ParseResponse_SKIP_and_CREATE_never_carry_a_merged_body_even_with_a_separator() + { + // SKIP/CREATE are keyword-only per protocol; a stray "---" after them should not be + // misread as introducing a body for a decision kind that never carries one. + var skip = CurationPromptBuilder.ParseResponse("SKIP\n---\nirrelevant trailing text"); + var create = CurationPromptBuilder.ParseResponse("CREATE\n---\nirrelevant trailing text"); + + Assert.NotNull(skip); + Assert.Null(skip.MergedBody); + Assert.NotNull(create); + Assert.Null(create.MergedBody); + } + + [Fact] + public void ParseResponse_strips_think_block_before_parsing_merged_body() + { + var response = + "These are the same fact, worded differently.\n" + + "UPDATE doc-abc123\n---\nMerged content preserving both sources."; + + var decision = CurationPromptBuilder.ParseResponse(response); + + Assert.NotNull(decision); + Assert.Equal(CurationDecisionKind.Update, decision.Kind); + Assert.Equal("Merged content preserving both sources.", decision.MergedBody); + } + // ── BuildUserMessage ──────────────────────────────────────────── [Fact] @@ -214,6 +312,62 @@ public void BuildUserMessage_truncates_long_content() Assert.DoesNotContain(longContent, message); } + [Fact] + public void BuildUserMessage_truncates_candidate_content_by_default() + { + // Legacy default (task 3.2): candidates shown as a 700-char preview, same as today, + // until Stage B passes useFullCandidateContent: true for nominated candidates. + var longCandidateContent = new string('y', 1_000); + var proposal = MakeMinimalProposal(); + var candidates = new[] { MakeCandidate(longCandidateContent) }; + + var message = CurationPromptBuilder.BuildUserMessage(proposal, candidates); + + Assert.DoesNotContain(longCandidateContent, message); + } + + [Fact] + public void BuildUserMessage_shows_full_candidate_content_when_requested() + { + var longCandidateContent = new string('y', 1_000); + var proposal = MakeMinimalProposal(); + var candidates = new[] { MakeCandidate(longCandidateContent) }; + + var message = CurationPromptBuilder.BuildUserMessage(proposal, candidates, useFullCandidateContent: true); + + Assert.Contains(longCandidateContent, message); + } + + private static SQLiteMemoryCurationOperation MakeMinimalProposal() => new( + Kind: "document", + MemoryClass: "durable_fact", + MemoryId: null, + AnchorCanonicalName: "test", + AnchorType: "concept", + Title: "Test", + Content: "proposal content", + AliasesJson: null, + FacetsJson: null, + SlotsJson: null, + Relations: null, + UpdateSemantics: "merge-document", + Boundary: TrustBoundary.TrustedInstanceValue, + Audience: TrustAudience.Team, + Sensitivity: "normal", + RecallMode: "auto", + Confidence: 0.9, + FreshnessAtMs: 1000, + ExpiresAtMs: null); + + private static ExistingMemoryCandidate MakeCandidate(string content) => new( + DocumentId: "doc-abc123", + AnchorId: "anchor:existing", + AnchorCanonicalName: "existing", + Content: content, + FreshnessAtMs: 900, + Confidence: 0.85, + IsExactAnchorMatch: false); + // ── SystemPrompt ──────────────────────────────────────────────── [Fact] diff --git a/src/Netclaw.Actors.Tests/Memory/MemoryCurationActorNominatorTests.cs b/src/Netclaw.Actors.Tests/Memory/MemoryCurationActorNominatorTests.cs new file mode 100644 index 000000000..e5c3c13f6 --- /dev/null +++ b/src/Netclaw.Actors.Tests/Memory/MemoryCurationActorNominatorTests.cs @@ -0,0 +1,217 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using System.Runtime.CompilerServices; +using Akka.Hosting; +using Akka.Hosting.TestKit; +using Microsoft.Data.Sqlite; +using Microsoft.Extensions.AI; +using Netclaw.Actors.Memory; +using Netclaw.Actors.Protocol; +using Netclaw.Configuration; +using Xunit; +using AiChatMessage = Microsoft.Extensions.AI.ChatMessage; +using AiChatRole = Microsoft.Extensions.AI.ChatRole; + +namespace Netclaw.Actors.Tests.Memory; + +/// +/// Actor-level end-to-end coverage for the embedding kNN nominator (memory-core-redesign +/// Slice 3 Stage B, task 3.6): drives a proposal through the REAL +/// with a fake embedder + scripted LLM, all the way to a committed store write. Complements +/// 's evaluator-level coverage by proving the same +/// contract holds through the actor's full Idle -> Evaluating -> Writing state machine, using +/// AwaitAssertAsync to poll for the LLM call rather than a sleep. Mirrors +/// 's +/// temp-store/try-finally-cleanup shape for driving directly. +/// +public sealed class MemoryCurationActorNominatorTests : TestKit +{ + private const string ModelId = "test-nominator-model"; + private const int Dimensions = 2; + + // Same hand-crafted 0.93-cosine pair as MemoryCurationNominatorTests. + private static readonly float[] ExistingVector = [1f, 0f]; + private static readonly float[] QueryVectorAt093 = [0.93f, 0.367623f]; + + private readonly string _dbDir = Path.Combine( + Path.GetTempPath(), "netclaw-curation-actor-nominator-tests", Guid.NewGuid().ToString("N")); + + public MemoryCurationActorNominatorTests(ITestOutputHelper output) : base(output: output) + { + } + + protected override void ConfigureAkka(AkkaConfigurationBuilder builder, IServiceProvider provider) + { + // No persistence or hosting needed — MemoryCurationActor is a plain ReceiveActor. + } + + [Fact] + public async Task Proposal_with_a_forced_nominee_reaches_the_LLM_and_commits_two_documents_end_to_end() + { + var ct = TestContext.Current.CancellationToken; + var (store, dbPath) = await CreateStoreAsync(); + + try + { + const string existingBody = "The build pipeline stores intermediate render artifacts in a graphite-backed cache layer."; + var anchor = store.CreateDefaultAnchor("graphite-render-cache"); + var now = DateTimeOffset.UtcNow.ToUnixTimeMilliseconds(); + await store.UpsertDocumentAsync(new SQLiteMemoryDocument( + DocumentId: "doc-existing", + Anchor: anchor, + MemoryClass: "durable_fact", + Title: "Existing", + MarkdownBody: existingBody, + AliasesJson: null, + FacetsJson: null, + SlotsJson: null, + UpdateSemantics: "merge-document", + Sensitivity: "normal", + RecallMode: "auto", + Confidence: 0.9, + FreshnessAtMs: now, + ExpiresAtMs: null, + CreatedAtMs: now, + UpdatedAtMs: now), ct); + await store.UpsertEmbeddingAsync( + "doc-existing", MemoryEmbedOnWriteCoordinator.DocumentItemKind, ModelId, "hash-existing", ExistingVector, ct); + + var embedderHolder = new MemoryEmbedderHolder(new ScriptedEmbedder(ModelId, Dimensions, QueryVectorAt093)); + var vectorIndexHolder = new MemoryVectorIndexHolder(store); + var chatClient = new RecordingCurationChatClient("CREATE"); + var clientProvider = new SingleClientProvider(chatClient); + + var curationActor = Sys.ActorOf( + MemoryCurationActor.CreateProps( + store, new SessionId("test-session"), new MemoryCurationConfig(), + clientProvider, embedderHolder, vectorIndexHolder), + "curation-nominator"); + + var probe = CreateTestProbe("curation-nominator-probe"); + var operation = MakeOperation( + "sunfish-deploy-queue", "Deployment jobs wait in a queue before promotion to production."); + + curationActor.Tell(new EvaluateProposals([operation]), probe.Ref); + + // No sleeps: poll until the scripted LLM has actually been reached, proving the + // nominee forced the LLM tier, before asserting the final reply/store state. + await AwaitAssertAsync( + () => Assert.True(chatClient.CallCount >= 1, + $"Expected the nominee to force an LLM call, but CallCount={chatClient.CallCount}"), + cancellationToken: ct); + + var completed = await probe.ExpectMsgAsync(TimeSpan.FromSeconds(10), cancellationToken: ct); + Assert.Equal(1, completed.Evaluated); + Assert.Equal(1, completed.Created); + Assert.Equal(0, completed.Skipped); + Assert.Equal(0, completed.Updated); + Assert.Equal(0, completed.Consolidated); + + // By the time CurationCompleted is sent, ApplyInlineCurationBatchAsync has already + // committed (StartWriting awaits it before replying) — both the pre-existing + // sibling and the newly created proposal survive as separate documents, never merged. + await using var conn = new SqliteConnection($"Data Source={dbPath}"); + await conn.OpenAsync(ct); + await using var cmd = conn.CreateCommand(); + cmd.CommandText = "SELECT COUNT(*) FROM memory_documents WHERE update_semantics != 'tombstone';"; + var count = Convert.ToInt32(await cmd.ExecuteScalarAsync(ct)); + Assert.Equal(2, count); + } + finally + { + await CleanupAsync(); + } + } + + // ── Helpers ────────────────────────────────────────────────────────── + + private async Task<(SQLiteMemoryStore Store, string DbPath)> CreateStoreAsync() + { + Directory.CreateDirectory(_dbDir); + var dbPath = Path.Combine(_dbDir, "test.db"); + var store = new SQLiteMemoryStore(dbPath, TimeProvider.System); + await store.InitializeAsync(TestContext.Current.CancellationToken); + return (store, dbPath); + } + + private Task CleanupAsync() => SqliteTempDirectoryCleanup.TryDeleteDirectoryAsync(_dbDir); + + private static SQLiteMemoryCurationOperation MakeOperation(string anchor, string content) => + new( + Kind: "document", + MemoryClass: "durable_fact", + MemoryId: null, + AnchorCanonicalName: anchor, + AnchorType: "concept", + Title: $"Title for {anchor}", + Content: content, + AliasesJson: null, + FacetsJson: null, + SlotsJson: null, + Relations: null, + UpdateSemantics: "merge-document", + Boundary: TrustBoundary.TrustedInstanceValue, + Audience: TrustAudience.Public, + Sensitivity: "normal", + RecallMode: "auto", + Confidence: 0.9, + FreshnessAtMs: DateTimeOffset.UtcNow.ToUnixTimeMilliseconds(), + ExpiresAtMs: null); + + private sealed class SingleClientProvider(IChatClient client) : IChatClientProvider + { + public IChatClient GetClient(ModelRole role) => client; + } + + private sealed class ScriptedEmbedder(string modelId, int dimensions, float[] queryVector) : IMemoryEmbedder + { + public string ModelId => modelId; + + public int Dimensions => dimensions; + + public bool IsAvailable => true; + + public ValueTask> EmbedAsync(string text, CancellationToken ct) + => ValueTask.FromResult>(queryVector); + + public ValueTask>> EmbedBatchAsync(IReadOnlyList texts, CancellationToken ct) + => ValueTask.FromResult>>( + texts.Select(_ => (ReadOnlyMemory)queryVector).ToList()); + } + + private sealed class RecordingCurationChatClient(string? responseText) : IChatClient + { + public int CallCount { get; private set; } + + public Task GetResponseAsync( + IEnumerable messages, ChatOptions? options = null, CancellationToken cancellationToken = default) + { + CallCount++; + return Task.FromResult(new ChatResponse(new AiChatMessage(AiChatRole.Assistant, responseText ?? string.Empty))); + } + + public IAsyncEnumerable GetStreamingResponseAsync( + IEnumerable messages, ChatOptions? options = null, CancellationToken cancellationToken = default) + { + CallCount++; + return StreamAsync(cancellationToken); + } + + private async IAsyncEnumerable StreamAsync([EnumeratorCancellation] CancellationToken cancellationToken) + { + if (responseText is not null) + yield return new ChatResponseUpdate(AiChatRole.Assistant, responseText); + + await Task.CompletedTask; + } + + public object? GetService(Type serviceType, object? serviceKey = null) => null; + + public void Dispose() + { + } + } +} diff --git a/src/Netclaw.Actors.Tests/Memory/MemoryCurationEvaluatorParityTests.cs b/src/Netclaw.Actors.Tests/Memory/MemoryCurationEvaluatorParityTests.cs index c428f0241..b0f00fcb1 100644 --- a/src/Netclaw.Actors.Tests/Memory/MemoryCurationEvaluatorParityTests.cs +++ b/src/Netclaw.Actors.Tests/Memory/MemoryCurationEvaluatorParityTests.cs @@ -222,9 +222,9 @@ await SeedDocumentAsync( freshnessAtMs: 2000); var evaluator = new MemoryCurationEvaluator( - _store, (ILoggingAdapter)NoLogger.Instance, new ScriptedCurationChatClient("SKIP")); + _store, (ILoggingAdapter)NoLogger.Instance, new MemoryCurationConfig(), new ScriptedCurationChatClient("SKIP")); - var decision = await evaluator.EvaluateAsync(operation, TestSessionId, ct); + var decision = (await evaluator.EvaluateAsync(operation, TestSessionId, ct)).Decision; Assert.Equal(CurationDecisionKind.Skip, decision.Kind); Assert.Contains("LLM decision", decision.Reason); @@ -253,14 +253,74 @@ await SeedDocumentAsync( // TryLlmEvaluationAsync must surface curation_llm_no_decision and fall through to // the same deterministic auto-resolve path the no-LLM matrix case exercises. var evaluator = new MemoryCurationEvaluator( - _store, (ILoggingAdapter)NoLogger.Instance, new ScriptedCurationChatClient(responseText: null)); + _store, (ILoggingAdapter)NoLogger.Instance, new MemoryCurationConfig(), new ScriptedCurationChatClient(responseText: null)); - var decision = await evaluator.EvaluateAsync(operation, TestSessionId, ct); + var decision = (await evaluator.EvaluateAsync(operation, TestSessionId, ct)).Decision; Assert.Equal(CurationDecisionKind.Skip, decision.Kind); Assert.Contains("auto-resolved", decision.Reason); } + // ── Nominator-present parity (memory-core-redesign Slice 3 Stage B) ── + + /// + /// Extends the parity contract to the embedding kNN nominator (task 3.1): both evaluator + /// constructions — actor-style () and engine-style + /// () — must reach the SAME forced-LLM decision when a nominee fires, + /// sharing one and + /// exactly as shares one . + /// + [Fact] + public async Task NomineePresent_returns_identical_forced_LLM_decision_on_both_paths() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + + const string existingBody = "The build pipeline stores intermediate render artifacts in a graphite-backed cache layer."; + var anchor = _store.CreateDefaultAnchor("graphite-render-cache"); + await _store.UpsertDocumentAsync(new SQLiteMemoryDocument( + DocumentId: "doc-existing", + Anchor: anchor, + MemoryClass: "durable_fact", + Title: "Existing", + MarkdownBody: existingBody, + AliasesJson: null, + FacetsJson: null, + SlotsJson: null, + UpdateSemantics: "merge-document", + Sensitivity: "normal", + RecallMode: "auto", + Confidence: 0.9, + FreshnessAtMs: 1000, + ExpiresAtMs: null, + CreatedAtMs: 1000, + UpdatedAtMs: 1000), ct); + await _store.UpsertEmbeddingAsync( + "doc-existing", MemoryEmbedOnWriteCoordinator.DocumentItemKind, "test-nominator-model", "hash-existing", + new float[] { 1f, 0f }, ct); + + var operation = MakeOperation( + "sunfish-deploy-queue", "Deployment jobs wait in a queue before promotion to production.", freshnessAtMs: 2000); + + var embedderHolder = new MemoryEmbedderHolder( + new ScriptedEmbedder("test-nominator-model", dimensions: 2, [0.93f, 0.367623f])); + var vectorIndexHolder = new MemoryVectorIndexHolder(_store); + + var actorLike = new MemoryCurationEvaluator( + _store, (ILoggingAdapter)NoLogger.Instance, new MemoryCurationConfig(), + new ScriptedCurationChatClient("SKIP"), embedderHolder, vectorIndexHolder); + var engineLike = new MemoryCurationEvaluator( + _store, (ILogger)NullLogger.Instance, new MemoryCurationConfig(), + new ScriptedCurationChatClient("SKIP"), embedderHolder, vectorIndexHolder); + + var fromActor = (await actorLike.EvaluateAsync(operation, TestSessionId, ct)).Decision; + var fromEngine = (await engineLike.EvaluateAsync(operation, TestSessionId, ct)).Decision; + + AssertSameDecision(fromActor, fromEngine); + Assert.True(fromActor.FromLlmTier); + Assert.Equal(CurationDecisionKind.Skip, fromActor.Kind); + } + // ── helpers ────────────────────────────────────────────────────── private async Task<(CurationDecision FromActor, CurationDecision FromEngine)> EvaluateOnBothAsync( @@ -269,11 +329,11 @@ await SeedDocumentAsync( // Constructed exactly as MemoryCurationActor and MemoryCurationEngine construct // their evaluators today: no LLM client, differing only in which logger stack // they log through. - var actorLike = new MemoryCurationEvaluator(_store, (ILoggingAdapter)NoLogger.Instance); - var engineLike = new MemoryCurationEvaluator(_store, (ILogger)NullLogger.Instance); + var actorLike = new MemoryCurationEvaluator(_store, (ILoggingAdapter)NoLogger.Instance, new MemoryCurationConfig()); + var engineLike = new MemoryCurationEvaluator(_store, (ILogger)NullLogger.Instance, new MemoryCurationConfig()); - var fromActor = await actorLike.EvaluateAsync(operation, TestSessionId, ct); - var fromEngine = await engineLike.EvaluateAsync(operation, TestSessionId, ct); + var fromActor = (await actorLike.EvaluateAsync(operation, TestSessionId, ct)).Decision; + var fromEngine = (await engineLike.EvaluateAsync(operation, TestSessionId, ct)).Decision; return (fromActor, fromEngine); } @@ -369,4 +429,25 @@ public void Dispose() { } } + + /// + /// Fake embedder that ignores its input text and always returns the same hand-crafted query + /// vector — sufficient for , + /// which embeds at most one proposal per evaluator. + /// + private sealed class ScriptedEmbedder(string modelId, int dimensions, float[] queryVector) : IMemoryEmbedder + { + public string ModelId => modelId; + + public int Dimensions => dimensions; + + public bool IsAvailable => true; + + public ValueTask> EmbedAsync(string text, CancellationToken ct) + => ValueTask.FromResult>(queryVector); + + public ValueTask>> EmbedBatchAsync(IReadOnlyList texts, CancellationToken ct) + => ValueTask.FromResult>>( + texts.Select(_ => (ReadOnlyMemory)queryVector).ToList()); + } } diff --git a/src/Netclaw.Actors.Tests/Memory/MemoryCurationMergeRoutingTests.cs b/src/Netclaw.Actors.Tests/Memory/MemoryCurationMergeRoutingTests.cs new file mode 100644 index 000000000..d66a05db7 --- /dev/null +++ b/src/Netclaw.Actors.Tests/Memory/MemoryCurationMergeRoutingTests.cs @@ -0,0 +1,314 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using System.Runtime.CompilerServices; +using Akka.Event; +using Microsoft.Extensions.AI; +using Netclaw.Actors.Memory; +using Netclaw.Actors.Protocol; +using Netclaw.Configuration; +using Xunit; +using AiChatMessage = Microsoft.Extensions.AI.ChatMessage; +using AiChatRole = Microsoft.Extensions.AI.ChatRole; + +namespace Netclaw.Actors.Tests.Memory; + +/// +/// End-to-end coverage for memory-core-redesign Slice 3 task 3.4's guard-validated write +/// routing, exercised through the REAL + +/// (not just the pure decision layer): a guard-failing or +/// body-absent LLM UPDATE/CONSOLIDATE decision must land as a structural append with +/// AppendDocument semantics, never a raw overwrite of the target's body, and the target's +/// original content must survive intact as a prefix. The deterministic tier's exact-anchor +/// UPDATE keeps its pre-Slice-3 raw-overwrite behavior — a regression guard proves that. +/// +public sealed class MemoryCurationMergeRoutingTests : IAsyncDisposable +{ + private static readonly SessionId TestSessionId = new("test-channel/merge-routing"); + + private readonly string _baseDir = Path.Combine(Path.GetTempPath(), "netclaw-curation-merge-routing-tests", Guid.NewGuid().ToString("N")); + private readonly string _dbPath; + private readonly SQLiteMemoryStore _store; + + public MemoryCurationMergeRoutingTests() + { + Directory.CreateDirectory(_baseDir); + _dbPath = Path.Combine(_baseDir, "netclaw.db"); + _store = new SQLiteMemoryStore(_dbPath, TimeProvider.System); + } + + public async ValueTask DisposeAsync() => await SqliteTempDirectoryCleanup.TryDeleteDirectoryAsync(_baseDir); + + // ── Guard failure -> append fallback (overwrite-unreachable) ────── + + [Fact] + public async Task LlmUpdate_withLossyMergedBody_appendsInsteadOfOverwriting() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + + const string originalBody = + "Netclaw GitHub repository: https://github.com/netclaw-dev/netclaw. The repository is private."; + await SeedDocumentAsync("netclaw-github-repository", "doc-repo", originalBody, freshnessAtMs: 1000, ct); + + var operation = MakeOperation( + "netclaw-github-repo", + "Netclaw GitHub repository at https://github.com/netclaw-dev/netclaw, private repo", + freshnessAtMs: 2000); + + // Lossy: drops the URL entirely — MergeGuard must reject this. + var evaluator = new MemoryCurationEvaluator( + _store, (ILoggingAdapter)NoLogger.Instance, new MemoryCurationConfig(), + new ScriptedCurationChatClient("UPDATE doc-repo\n---\nRepository details updated.")); + + var evaluation = await evaluator.EvaluateAsync(operation, TestSessionId, ct); + Assert.Equal(CurationDecisionKind.Update, evaluation.Decision.Kind); + Assert.True(evaluation.Decision.FromLlmTier); + Assert.NotNull(evaluation.Decision.MergedBody); + + var writeOp = await evaluator.ApplyDecisionAsync(operation, evaluation.Decision, evaluation.Candidates, ct); + Assert.NotNull(writeOp); + await _store.ApplyInlineCurationBatchAsync([writeOp!], ct); + + var stored = await GetDocumentAsync("doc-repo", ct); + Assert.NotNull(stored); + + // Overwrite-unreachable: the original body is NOT replaced by the lossy merge — the + // rejected merged body ("Repository details updated.") is discarded entirely, and the + // append fallback appends the ORIGINAL PROPOSAL content instead (never the untrusted + // merged text), on top of the target's untouched original body. + Assert.StartsWith(originalBody, stored!.Value.Body, StringComparison.Ordinal); + Assert.Contains(operation.Content, stored.Value.Body); + Assert.DoesNotContain("Repository details updated.", stored.Value.Body); + Assert.Contains("---", stored.Value.Body); + Assert.Equal("append-document", stored.Value.UpdateSemantics); + } + + [Fact] + public async Task LlmUpdate_withNoMergedBody_appendsInsteadOfOverwriting() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + + const string originalBody = + "Netclaw GitHub repository: https://github.com/netclaw-dev/netclaw. The repository is private."; + await SeedDocumentAsync("netclaw-github-repository", "doc-repo", originalBody, freshnessAtMs: 1000, ct); + + var operation = MakeOperation( + "netclaw-github-repo", + "Netclaw GitHub repository at https://github.com/netclaw-dev/netclaw, private repo", + freshnessAtMs: 2000); + + // Keyword-only LLM response — no "---" body at all. + var evaluator = new MemoryCurationEvaluator( + _store, (ILoggingAdapter)NoLogger.Instance, new MemoryCurationConfig(), + new ScriptedCurationChatClient("UPDATE doc-repo")); + + var evaluation = await evaluator.EvaluateAsync(operation, TestSessionId, ct); + Assert.Equal(CurationDecisionKind.Update, evaluation.Decision.Kind); + Assert.True(evaluation.Decision.FromLlmTier); + Assert.Null(evaluation.Decision.MergedBody); + + var writeOp = await evaluator.ApplyDecisionAsync(operation, evaluation.Decision, evaluation.Candidates, ct); + await _store.ApplyInlineCurationBatchAsync([writeOp!], ct); + + var stored = await GetDocumentAsync("doc-repo", ct); + Assert.NotNull(stored); + Assert.StartsWith(originalBody, stored!.Value.Body, StringComparison.Ordinal); + Assert.Contains(operation.Content, stored.Value.Body); + Assert.Equal("append-document", stored.Value.UpdateSemantics); + } + + // ── Guard pass -> merged body written ───────────────────────────── + + [Fact] + public async Task LlmUpdate_withFaithfulMergedBody_writesMergedBody() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + + const string originalBody = + "Netclaw GitHub repository: https://github.com/netclaw-dev/netclaw. The repository is private."; + await SeedDocumentAsync("netclaw-github-repository", "doc-repo", originalBody, freshnessAtMs: 1000, ct); + + var operation = MakeOperation( + "netclaw-github-repo", + "Netclaw GitHub repository at https://github.com/netclaw-dev/netclaw, private repo", + freshnessAtMs: 2000); + + const string mergedBody = + "Netclaw GitHub repository: https://github.com/netclaw-dev/netclaw. The repository remains private."; + var evaluator = new MemoryCurationEvaluator( + _store, (ILoggingAdapter)NoLogger.Instance, new MemoryCurationConfig(), + new ScriptedCurationChatClient($"UPDATE doc-repo\n---\n{mergedBody}")); + + var evaluation = await evaluator.EvaluateAsync(operation, TestSessionId, ct); + var writeOp = await evaluator.ApplyDecisionAsync(operation, evaluation.Decision, evaluation.Candidates, ct); + await _store.ApplyInlineCurationBatchAsync([writeOp!], ct); + + var stored = await GetDocumentAsync("doc-repo", ct); + Assert.NotNull(stored); + Assert.Equal(mergedBody, stored!.Value.Body); + Assert.Equal("merge-document", stored.Value.UpdateSemantics); + } + + // ── Deterministic tier regression: exact-anchor Update keeps raw overwrite ─ + + [Fact] + public async Task DeterministicUpdate_exactAnchorSuperset_stillOverwritesRawly() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + await SeedDocumentAsync("latest-version", "doc-version", "Latest version is 1.5.62.", freshnessAtMs: 1000, ct); + + var operation = MakeOperation( + "latest-version", + "Latest version is 1.5.62. Released with the new serializer.", + freshnessAtMs: 2000); + + // No LLM client — this exercises the deterministic exact-anchor path only. + var evaluator = new MemoryCurationEvaluator(_store, (ILoggingAdapter)NoLogger.Instance, new MemoryCurationConfig()); + + var evaluation = await evaluator.EvaluateAsync(operation, TestSessionId, ct); + Assert.Equal(CurationDecisionKind.Update, evaluation.Decision.Kind); + Assert.False(evaluation.Decision.FromLlmTier); + + var writeOp = await evaluator.ApplyDecisionAsync(operation, evaluation.Decision, evaluation.Candidates, ct); + await _store.ApplyInlineCurationBatchAsync([writeOp!], ct); + + var stored = await GetDocumentAsync("doc-version", ct); + Assert.NotNull(stored); + // Raw overwrite: the stored body IS the proposal's content verbatim, not appended. + Assert.Equal(operation.Content, stored!.Value.Body); + Assert.Equal("merge-document", stored.Value.UpdateSemantics); + } + + // ── Consolidate: deterministic tier (no LLM, no merged body) also appends ─ + + [Fact] + public async Task DeterministicConsolidate_appendsRatherThanOverwriting() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + + const string originalBody = "Akka.NET latest release version is 1.5.62"; + await SeedDocumentAsync("akka-net-latest-release", "doc-akka", originalBody, freshnessAtMs: 1000, ct); + + var operation = MakeOperation( + "akka-net-release", "Akka.NET latest release version is 1.5.62", freshnessAtMs: 2000); + + // No LLM client — fuzzy match >80% overlap resolves to Consolidate deterministically. + var evaluator = new MemoryCurationEvaluator(_store, (ILoggingAdapter)NoLogger.Instance, new MemoryCurationConfig()); + + var evaluation = await evaluator.EvaluateAsync(operation, TestSessionId, ct); + Assert.Equal(CurationDecisionKind.Consolidate, evaluation.Decision.Kind); + Assert.False(evaluation.Decision.FromLlmTier); + Assert.Null(evaluation.Decision.MergedBody); + + var writeOp = await evaluator.ApplyDecisionAsync(operation, evaluation.Decision, evaluation.Candidates, ct); + Assert.NotNull(writeOp); + Assert.Equal("doc-akka", writeOp!.MemoryId); + await _store.ApplyInlineCurationBatchAsync([writeOp], ct); + + var stored = await GetDocumentAsync("doc-akka", ct); + Assert.NotNull(stored); + Assert.StartsWith(originalBody, stored!.Value.Body, StringComparison.Ordinal); + Assert.Equal("append-document", stored.Value.UpdateSemantics); + } + + // ── helpers ────────────────────────────────────────────────────── + + private async Task SeedDocumentAsync( + string anchorName, string docId, string content, long freshnessAtMs, CancellationToken ct) + { + var anchor = _store.CreateDefaultAnchor(anchorName); + await _store.UpsertDocumentAsync(new SQLiteMemoryDocument( + DocumentId: docId, + Anchor: anchor, + MemoryClass: "durable_fact", + Title: $"Existing {anchorName}", + MarkdownBody: content, + AliasesJson: null, + FacetsJson: null, + SlotsJson: null, + UpdateSemantics: "merge-document", + Sensitivity: "normal", + RecallMode: "auto", + Confidence: 0.9, + FreshnessAtMs: freshnessAtMs, + ExpiresAtMs: null, + CreatedAtMs: freshnessAtMs, + UpdatedAtMs: freshnessAtMs), ct); + } + + private async Task<(string Body, string UpdateSemantics)?> GetDocumentAsync(string documentId, CancellationToken ct) + { + var handles = await _store.ResolveMemoryHandlesAsync( + [documentId], TrustBoundary.TrustedInstanceValue, TrustAudience.Public, ct); + var resolved = handles.FirstOrDefault(h => h.Resolved); + if (resolved is null) + return null; + + var hydrated = await _store.GetMemoriesByResolvedHandlesAsync( + [resolved], TrustBoundary.TrustedInstanceValue, TrustAudience.Public, ct); + var item = hydrated.FirstOrDefault(); + return item is null ? null : (item.Content, item.UpdateSemantics); + } + + private static SQLiteMemoryCurationOperation MakeOperation( + string anchor, + string content, + string kind = "document", + string updateSemantics = "merge-document", + long freshnessAtMs = 2000) => + new( + Kind: kind, + MemoryClass: "durable_fact", + MemoryId: null, + AnchorCanonicalName: anchor, + AnchorType: "concept", + Title: $"Title for {anchor}", + Content: content, + AliasesJson: null, + FacetsJson: null, + SlotsJson: null, + Relations: null, + UpdateSemantics: updateSemantics, + Boundary: TrustBoundary.TrustedInstanceValue, + Audience: TrustAudience.Public, + Sensitivity: "normal", + RecallMode: "auto", + Confidence: 0.9, + FreshnessAtMs: freshnessAtMs, + ExpiresAtMs: null); + + /// + /// Minimal scripted : streams as a + /// single update. Mirrors MemoryCurationEvaluatorParityTests.ScriptedCurationChatClient + /// (kept as a separate private copy rather than shared test infra — small and self-contained). + /// + private sealed class ScriptedCurationChatClient(string responseText) : IChatClient + { + public Task GetResponseAsync( + IEnumerable messages, ChatOptions? options = null, CancellationToken cancellationToken = default) + => Task.FromResult(new ChatResponse(new AiChatMessage(AiChatRole.Assistant, responseText))); + + public IAsyncEnumerable GetStreamingResponseAsync( + IEnumerable messages, ChatOptions? options = null, CancellationToken cancellationToken = default) + => StreamAsync(cancellationToken); + + private async IAsyncEnumerable StreamAsync([EnumeratorCancellation] CancellationToken cancellationToken) + { + yield return new ChatResponseUpdate(AiChatRole.Assistant, responseText); + await Task.CompletedTask; + } + + public object? GetService(Type serviceType, object? serviceKey = null) => null; + + public void Dispose() + { + } + } +} diff --git a/src/Netclaw.Actors.Tests/Memory/MemoryCurationNominatorTests.cs b/src/Netclaw.Actors.Tests/Memory/MemoryCurationNominatorTests.cs new file mode 100644 index 000000000..1fdd1e0da --- /dev/null +++ b/src/Netclaw.Actors.Tests/Memory/MemoryCurationNominatorTests.cs @@ -0,0 +1,400 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using System.Runtime.CompilerServices; +using Akka.Event; +using Microsoft.Data.Sqlite; +using Microsoft.Extensions.AI; +using Microsoft.Extensions.Logging; +using Netclaw.Actors.Memory; +using Netclaw.Actors.Protocol; +using Netclaw.Configuration; +using Xunit; +using AiChatMessage = Microsoft.Extensions.AI.ChatMessage; +using AiChatRole = Microsoft.Extensions.AI.ChatRole; + +namespace Netclaw.Actors.Tests.Memory; + +/// +/// Covers the embedding kNN nominator in +/// (memory-core-redesign Slice 3 Stage B, tasks 3.1/3.6). All fixtures are synthetic — no +/// operator corpus content — and cosine similarity is engineered directly via hand-crafted +/// unit vectors rather than a real embedding model, so every scenario is exact and +/// deterministic rather than dependent on a specific model's output. +/// +/// +/// The central invariant under test throughout this file (design D4, corroborated by +/// docs/research/memory-recall-findings-2026-05.md and +/// docs/research/memory-audit-2026-07.md §5): cosine similarity NOMINATES ONLY. It +/// forces a decision to the LLM tier; it never itself decides skip, merge, or create. +/// +/// +public sealed class MemoryCurationNominatorTests : IAsyncDisposable +{ + private const string ModelId = "test-nominator-model"; + private const int Dimensions = 2; + + // Hand-crafted unit vectors with cosine similarity == 0.93 exactly (0.93^2 + 0.367623^2 == + // 0.999999...): the paraphrase-pair cosine the May 2026 measurement places inside the band + // where duplicates and merely-related siblings are indistinguishable by threshold alone + // (siblings measured at 0.905-0.941, design D4/proposal.md). + private static readonly float[] ExistingVector = [1f, 0f]; + private static readonly float[] QueryVectorAt093 = [0.93f, 0.367623f]; + + private static readonly SessionId TestSessionId = new("test-channel/nominator"); + + private readonly string _baseDir = Path.Combine(Path.GetTempPath(), "netclaw-curation-nominator-tests", Guid.NewGuid().ToString("N")); + private readonly string _dbPath; + private readonly SQLiteMemoryStore _store; + + public MemoryCurationNominatorTests() + { + Directory.CreateDirectory(_baseDir); + _dbPath = Path.Combine(_baseDir, "netclaw.db"); + _store = new SQLiteMemoryStore(_dbPath, TimeProvider.System); + } + + public async ValueTask DisposeAsync() => await SqliteTempDirectoryCleanup.TryDeleteDirectoryAsync(_baseDir); + + // ── Nomination forcing ────────────────────────────────────────────── + + [Fact] + public async Task Paraphrase_pair_at_cosine_0_93_forces_LLM_tier_even_though_Jaccard_band_would_have_said_Create() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + + const string existingBody = "The build pipeline stores intermediate render artifacts in a graphite-backed cache layer."; + const string proposalContent = "Deployment jobs wait in a queue before promotion to production."; + + // Unrelated anchor names, near-zero word overlap: the pre-Slice-3 lexical/anchor tier + // finds NO candidates at all here (CurationRulesEvaluator: "no existing candidates" -> + // Create, zero LLM calls) — cosine nomination is the ONLY signal that finds this pair. + Assert.True(WordJaccard(existingBody, proposalContent) < 0.4, "fixture must have low word overlap"); + + await SeedDocumentWithEmbeddingAsync("graphite-render-cache", "doc-existing", existingBody, freshnessAtMs: 1000, ct); + var operation = MakeOperation("sunfish-deploy-queue", proposalContent, freshnessAtMs: 2000); + + var embedderHolder = new MemoryEmbedderHolder(new ScriptedEmbedder(ModelId, Dimensions, QueryVectorAt093)); + var vectorIndexHolder = new MemoryVectorIndexHolder(_store); + var chatClient = new RecordingCurationChatClient("CREATE"); + + var evaluator = new MemoryCurationEvaluator( + _store, (ILoggingAdapter)NoLogger.Instance, new MemoryCurationConfig(), chatClient, embedderHolder, vectorIndexHolder); + + var evaluation = await evaluator.EvaluateAsync(operation, TestSessionId, ct); + + Assert.Equal(1, chatClient.CallCount); + Assert.True(evaluation.Decision.FromLlmTier); + Assert.Contains(evaluation.Candidates, c => c.DocumentId == "doc-existing" && c.CosineSimilarity is not null); + } + + // ── Sibling never-auto-merge ───────────────────────────────────────── + + [Fact] + public async Task Nominee_present_LLM_says_Create_persists_two_separate_documents_not_a_merge() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + + const string existingBody = "The build pipeline stores intermediate render artifacts in a graphite-backed cache layer."; + await SeedDocumentWithEmbeddingAsync("graphite-render-cache", "doc-existing", existingBody, freshnessAtMs: 1000, ct); + + var operation = MakeOperation( + "sunfish-deploy-queue", "Deployment jobs wait in a queue before promotion to production.", freshnessAtMs: 2000); + + var embedderHolder = new MemoryEmbedderHolder(new ScriptedEmbedder(ModelId, Dimensions, QueryVectorAt093)); + var vectorIndexHolder = new MemoryVectorIndexHolder(_store); + var chatClient = new RecordingCurationChatClient("CREATE"); + + var evaluator = new MemoryCurationEvaluator( + _store, (ILoggingAdapter)NoLogger.Instance, new MemoryCurationConfig(), chatClient, embedderHolder, vectorIndexHolder); + + var evaluation = await evaluator.EvaluateAsync(operation, TestSessionId, ct); + Assert.Equal(CurationDecisionKind.Create, evaluation.Decision.Kind); + Assert.Equal(1, chatClient.CallCount); + + var writeOp = await evaluator.ApplyDecisionAsync(operation, evaluation.Decision, evaluation.Candidates, ct); + Assert.NotNull(writeOp); + await _store.ApplyInlineCurationBatchAsync([writeOp!], ct); + + // Two documents survive — the nominee (a cosine-adjacent sibling in this fixture, per + // the LLM's own CREATE call) was never auto-merged into the existing one. + Assert.Equal(2, await CountNonTombstonedDocumentsAsync(ct)); + } + + [Fact] + public async Task Nominee_present_with_no_LLM_available_conservatively_creates_never_auto_merges() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + + const string existingBody = "The build pipeline stores intermediate render artifacts in a graphite-backed cache layer."; + await SeedDocumentWithEmbeddingAsync("graphite-render-cache", "doc-existing", existingBody, freshnessAtMs: 1000, ct); + + var operation = MakeOperation( + "sunfish-deploy-queue", "Deployment jobs wait in a queue before promotion to production.", freshnessAtMs: 2000); + + var embedderHolder = new MemoryEmbedderHolder(new ScriptedEmbedder(ModelId, Dimensions, QueryVectorAt093)); + var vectorIndexHolder = new MemoryVectorIndexHolder(_store); + + // No LLM client at all — the daemon-checkpoint-worker shape today. A nominee here must + // NOT fall to TryAutoResolveAmbiguous (which could return Skip); the outcome must be + // Create, never a merge decided by cosine alone. + var evaluator = new MemoryCurationEvaluator( + _store, (ILoggingAdapter)NoLogger.Instance, new MemoryCurationConfig(), llmClient: null, + embedderHolder: embedderHolder, vectorIndexHolder: vectorIndexHolder); + + var evaluation = await evaluator.EvaluateAsync(operation, TestSessionId, ct); + Assert.Equal(CurationDecisionKind.Create, evaluation.Decision.Kind); + Assert.False(evaluation.Decision.FromLlmTier); + Assert.Contains("conservative create", evaluation.Decision.Reason); + Assert.Contains("no auto-merge on cosine alone", evaluation.Decision.Reason); + + var writeOp = await evaluator.ApplyDecisionAsync(operation, evaluation.Decision, evaluation.Candidates, ct); + Assert.NotNull(writeOp); + await _store.ApplyInlineCurationBatchAsync([writeOp!], ct); + + Assert.Equal(2, await CountNonTombstonedDocumentsAsync(ct)); + } + + // ── Novel proposal skips the curator ───────────────────────────────── + + [Fact] + public async Task Novel_proposal_with_no_nominee_and_no_anchor_match_skips_the_curator_entirely() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + + // Empty store: no anchor to match, and the vector index has nothing to nominate — the + // "median nominee count on a random write is 0" common case (design D4 point 3). + var operation = MakeOperation( + "brand-new-topic", "Completely novel content nobody has proposed before.", freshnessAtMs: 1000); + + var embedderHolder = new MemoryEmbedderHolder(new ScriptedEmbedder(ModelId, Dimensions, QueryVectorAt093)); + var vectorIndexHolder = new MemoryVectorIndexHolder(_store); + var chatClient = new RecordingCurationChatClient("CREATE"); + + var evaluator = new MemoryCurationEvaluator( + _store, (ILoggingAdapter)NoLogger.Instance, new MemoryCurationConfig(), chatClient, embedderHolder, vectorIndexHolder); + + var evaluation = await evaluator.EvaluateAsync(operation, TestSessionId, ct); + + Assert.Equal(CurationDecisionKind.Create, evaluation.Decision.Kind); + Assert.False(evaluation.Decision.FromLlmTier); + Assert.Equal(0, chatClient.CallCount); + } + + // ── Degraded path ───────────────────────────────────────────────────── + + [Fact] + public async Task Embedder_unavailable_falls_back_to_lexical_search_and_logs_the_degraded_marker() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + + const string existingBody = "Deployment jobs wait in a queue before promotion to production servers."; + const string proposalContent = "Deployment jobs wait in a queue before reaching production."; + + // Substantial lexical overlap (unlike the nomination-forcing fixture above) so the + // degraded path's lexical content-term search actually surfaces this document. + Assert.True(WordJaccard(existingBody, proposalContent) > 0.4, "fixture must have high word overlap"); + + await SeedDocumentWithEmbeddingAsync("totally-different-topic", "doc-existing", existingBody, freshnessAtMs: 1000, ct); + var operation = MakeOperation("another-unrelated-subject", proposalContent, freshnessAtMs: 2000); + + var recordingLogger = new RecordingLogger(); + var embedderHolder = new MemoryEmbedderHolder(new UnavailableMemoryEmbedder(ModelId, "not provisioned")); + var vectorIndexHolder = new MemoryVectorIndexHolder(_store); + + var evaluator = new MemoryCurationEvaluator( + _store, (ILogger)recordingLogger, new MemoryCurationConfig(), llmClient: null, embedderHolder, vectorIndexHolder); + + var evaluation = await evaluator.EvaluateAsync(operation, TestSessionId, ct); + + Assert.Contains(recordingLogger.Entries, e => e.Contains("curation_nominator_degraded", StringComparison.Ordinal)); + Assert.Contains(evaluation.Candidates, c => c.DocumentId == "doc-existing"); + // Lexical-path candidates never carry cosine evidence. + Assert.All(evaluation.Candidates, c => Assert.Null(c.CosineSimilarity)); + } + + [Fact] + public async Task Null_embedder_holder_is_treated_identically_to_unavailable_and_degrades() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + + const string existingBody = "Deployment jobs wait in a queue before promotion to production servers."; + const string proposalContent = "Deployment jobs wait in a queue before reaching production."; + + await SeedDocumentWithEmbeddingAsync("totally-different-topic", "doc-existing", existingBody, freshnessAtMs: 1000, ct); + var operation = MakeOperation("another-unrelated-subject", proposalContent, freshnessAtMs: 2000); + + var recordingLogger = new RecordingLogger(); + + // No embedder holder AND no vector index holder at all — a test harness / build that + // never wired up the embedding subsystem, same as the pre-Slice-3 constructor shape. + var evaluator = new MemoryCurationEvaluator( + _store, (ILogger)recordingLogger, new MemoryCurationConfig()); + + var evaluation = await evaluator.EvaluateAsync(operation, TestSessionId, ct); + + Assert.Contains(recordingLogger.Entries, e => e.Contains("curation_nominator_degraded", StringComparison.Ordinal)); + Assert.Contains(evaluation.Candidates, c => c.DocumentId == "doc-existing"); + } + + // ── helpers ────────────────────────────────────────────────────────── + + private async Task SeedDocumentWithEmbeddingAsync( + string anchorName, string docId, string content, long freshnessAtMs, CancellationToken ct) + { + var anchor = _store.CreateDefaultAnchor(anchorName); + await _store.UpsertDocumentAsync(new SQLiteMemoryDocument( + DocumentId: docId, + Anchor: anchor, + MemoryClass: "durable_fact", + Title: $"Existing {anchorName}", + MarkdownBody: content, + AliasesJson: null, + FacetsJson: null, + SlotsJson: null, + UpdateSemantics: "merge-document", + Sensitivity: "normal", + RecallMode: "auto", + Confidence: 0.9, + FreshnessAtMs: freshnessAtMs, + ExpiresAtMs: null, + CreatedAtMs: freshnessAtMs, + UpdatedAtMs: freshnessAtMs), ct); + + await _store.UpsertEmbeddingAsync( + docId, MemoryEmbedOnWriteCoordinator.DocumentItemKind, ModelId, contentHash: $"hash-{docId}", ExistingVector, ct); + } + + private async Task CountNonTombstonedDocumentsAsync(CancellationToken ct) + { + await using var conn = new SqliteConnection($"Data Source={_dbPath}"); + await conn.OpenAsync(ct); + await using var cmd = conn.CreateCommand(); + cmd.CommandText = "SELECT COUNT(*) FROM memory_documents WHERE update_semantics != 'tombstone';"; + return Convert.ToInt32(await cmd.ExecuteScalarAsync(ct)); + } + + private static double WordJaccard(string a, string b) + { + var wordsA = Tokenize(a); + var wordsB = Tokenize(b); + var union = wordsA.Union(wordsB).Count(); + return union == 0 ? 0 : (double)wordsA.Intersect(wordsB).Count() / union; + + static HashSet Tokenize(string text) => + text.Split([' ', '.', ',', ':', ';', '!', '?'], StringSplitOptions.RemoveEmptyEntries) + .Select(w => w.Trim().ToLowerInvariant()) + .Where(w => w.Length > 0) + .ToHashSet(); + } + + private static SQLiteMemoryCurationOperation MakeOperation( + string anchor, + string content, + string kind = "document", + string updateSemantics = "merge-document", + long freshnessAtMs = 2000) => + new( + Kind: kind, + MemoryClass: "durable_fact", + MemoryId: null, + AnchorCanonicalName: anchor, + AnchorType: "concept", + Title: $"Title for {anchor}", + Content: content, + AliasesJson: null, + FacetsJson: null, + SlotsJson: null, + Relations: null, + UpdateSemantics: updateSemantics, + Boundary: TrustBoundary.TrustedInstanceValue, + Audience: TrustAudience.Public, + Sensitivity: "normal", + RecallMode: "auto", + Confidence: 0.9, + FreshnessAtMs: freshnessAtMs, + ExpiresAtMs: null); + + /// + /// Fake embedder that ignores its input text and always returns the same, hand-crafted + /// query vector — sufficient here because every test in this file embeds at most one + /// proposal, and the geometry (not the input text) is what needs to be controlled. + /// + private sealed class ScriptedEmbedder(string modelId, int dimensions, float[] queryVector) : IMemoryEmbedder + { + public string ModelId => modelId; + + public int Dimensions => dimensions; + + public bool IsAvailable => true; + + public ValueTask> EmbedAsync(string text, CancellationToken ct) + => ValueTask.FromResult>(queryVector); + + public ValueTask>> EmbedBatchAsync(IReadOnlyList texts, CancellationToken ct) + => ValueTask.FromResult>>( + texts.Select(_ => (ReadOnlyMemory)queryVector).ToList()); + } + + /// + /// Scripted that records how many times it was invoked, so tests + /// can assert the LLM tier was (or was never) reached — the nomination-forcing contract's + /// core observable. Mirrors MemoryCurationEvaluatorParityTests.ScriptedCurationChatClient + /// plus a call counter (kept as a separate private copy per that file's own convention). + /// + private sealed class RecordingCurationChatClient(string? responseText) : IChatClient + { + public int CallCount { get; private set; } + + public Task GetResponseAsync( + IEnumerable messages, ChatOptions? options = null, CancellationToken cancellationToken = default) + { + CallCount++; + return Task.FromResult(new ChatResponse(new AiChatMessage(AiChatRole.Assistant, responseText ?? string.Empty))); + } + + public IAsyncEnumerable GetStreamingResponseAsync( + IEnumerable messages, ChatOptions? options = null, CancellationToken cancellationToken = default) + { + CallCount++; + return StreamAsync(cancellationToken); + } + + private async IAsyncEnumerable StreamAsync([EnumeratorCancellation] CancellationToken cancellationToken) + { + if (responseText is not null) + yield return new ChatResponseUpdate(AiChatRole.Assistant, responseText); + + await Task.CompletedTask; + } + + public object? GetService(Type serviceType, object? serviceKey = null) => null; + + public void Dispose() + { + } + } + + /// Records every log line emitted through the Microsoft.Extensions.Logging ctor. + private sealed class RecordingLogger : ILogger + { + public List Entries { get; } = []; + + public IDisposable? BeginScope(TState state) where TState : notnull => null; + + public bool IsEnabled(Microsoft.Extensions.Logging.LogLevel logLevel) => true; + + public void Log( + Microsoft.Extensions.Logging.LogLevel logLevel, EventId eventId, TState state, Exception? exception, + Func formatter) + => Entries.Add(formatter(state, exception)); + } +} diff --git a/src/Netclaw.Actors.Tests/Memory/MergeGuardTests.cs b/src/Netclaw.Actors.Tests/Memory/MergeGuardTests.cs new file mode 100644 index 000000000..41a64b36f --- /dev/null +++ b/src/Netclaw.Actors.Tests/Memory/MergeGuardTests.cs @@ -0,0 +1,250 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using Netclaw.Actors.Memory; +using Xunit; + +namespace Netclaw.Actors.Tests.Memory; + +/// +/// Table/property tests for (memory-core-redesign Slice 3 task 3.3): +/// load-bearing token extraction (URLs, numbers/versions/dates, identifiers, file paths), the +/// 95% retention boundary, the 60% length-collapse floor, and pass-through on faithful unions. +/// +public sealed class MergeGuardTests +{ + // ── Faithful merges pass ───────────────────────────────────────── + + [Fact] + public void Validate_passes_when_merged_body_is_a_faithful_union() + { + var sources = new[] + { + "Widget specs: 16 cores, 64GB RAM, 2 NICs.", + "Widget pricing is TBD as of 2026-05-13." + }; + var merged = "Widget specs: 16 cores, 64GB RAM, 2 NICs. Pricing is TBD as of 2026-05-13."; + + var result = MergeGuard.Validate(sources, merged); + + Assert.True(result.Passed); + Assert.Empty(result.MissingTokens); + } + + [Fact] + public void Validate_passes_when_merged_body_reorders_and_rewords_but_keeps_every_token() + { + var sources = new[] + { + "Akka.NET GitHub repository: https://github.com/akkadotnet/akka.net. Latest stable release is 1.5.60 as of 2026-04-02.", + "Akka.NET release version is now 1.5.62." + }; + var merged = + "Akka.NET GitHub repository: https://github.com/akkadotnet/akka.net. " + + "Latest stable release is 1.5.62 (previously 1.5.60 as of 2026-04-02)."; + + var result = MergeGuard.Validate(sources, merged); + + Assert.True(result.Passed); + } + + [Fact] + public void Validate_passes_trivially_when_there_are_no_source_bodies() + { + var result = MergeGuard.Validate([], "anything"); + + Assert.True(result.Passed); + Assert.Contains("no source bodies", result.Reason); + } + + // ── Token-category retention ───────────────────────────────────── + + [Fact] + public void Validate_fails_when_a_url_is_dropped() + { + var sources = new[] { "Repo lives at https://github.com/netclaw-dev/netclaw." }; + var merged = "Repo lives at the usual place."; + + var result = MergeGuard.Validate(sources, merged); + + Assert.False(result.Passed); + Assert.Contains(result.MissingTokens, t => t.Contains("github.com", StringComparison.OrdinalIgnoreCase)); + } + + [Fact] + public void Validate_fails_when_a_version_number_is_dropped() + { + var sources = new[] { "Latest version is 1.5.62, released with the new serializer." }; + var merged = "Latest version was released with the new serializer."; + + var result = MergeGuard.Validate(sources, merged); + + Assert.False(result.Passed); + Assert.Contains("1.5.62", result.MissingTokens); + } + + [Fact] + public void Validate_fails_when_a_date_is_dropped() + { + var sources = new[] { "Config path moved to /etc/app/config.yaml on 2026-06-01." }; + var merged = "Config path moved to /etc/app/config.yaml."; + + var result = MergeGuard.Validate(sources, merged); + + Assert.False(result.Passed); + Assert.Contains("2026-06-01", result.MissingTokens); + } + + [Fact] + public void Validate_fails_when_a_written_date_is_dropped() + { + var sources = new[] { "Release shipped on May 13, 2026 after code freeze." }; + var merged = "Release shipped after code freeze."; + + var result = MergeGuard.Validate(sources, merged); + + Assert.False(result.Passed); + } + + [Fact] + public void Validate_fails_when_a_camelCase_identifier_is_dropped() + { + var sources = new[] { "The knob is called maxOutputTokens and defaults to 4096." }; + var merged = "The token cap defaults to 4096."; + + var result = MergeGuard.Validate(sources, merged); + + Assert.False(result.Passed); + Assert.Contains("maxOutputTokens", result.MissingTokens); + } + + [Fact] + public void Validate_fails_when_a_file_path_is_dropped() + { + var sources = new[] { "The guard lives in src/Netclaw.Actors/Memory/MergeGuard.cs." }; + var merged = "The guard lives in the memory module."; + + var result = MergeGuard.Validate(sources, merged); + + Assert.False(result.Passed); + Assert.Contains(result.MissingTokens, t => t.Contains("MergeGuard.cs", StringComparison.OrdinalIgnoreCase)); + } + + [Fact] + public void Validate_retention_is_case_insensitive() + { + var sources = new[] { "Endpoint is HTTPS://EXAMPLE.COM/api." }; + var merged = "Endpoint is https://example.com/api and it is stable."; + + var result = MergeGuard.Validate(sources, merged); + + Assert.True(result.Passed); + } + + // ── 95% retention boundary ──────────────────────────────────────── + + [Fact] + public void Validate_passes_at_exactly_the_95_percent_retention_boundary() + { + // 20 distinct load-bearing integers; merged keeps 19/20 = 95% exactly. + var tokens = Enumerable.Range(100, 20).Select(n => n.ToString()).ToArray(); + var source = "Values: " + string.Join(", ", tokens) + "."; + var merged = "Values: " + string.Join(", ", tokens.Take(19)) + "."; + + var result = MergeGuard.Validate([source], merged); + + Assert.True(result.Passed); + Assert.Single(result.MissingTokens); + } + + [Fact] + public void Validate_fails_just_below_the_95_percent_retention_boundary() + { + // Same 20 tokens; merged keeps 18/20 = 90%, below the floor. + var tokens = Enumerable.Range(100, 20).Select(n => n.ToString()).ToArray(); + var source = "Values: " + string.Join(", ", tokens) + "."; + var merged = "Values: " + string.Join(", ", tokens.Take(18)) + "."; + + var result = MergeGuard.Validate([source], merged); + + Assert.False(result.Passed); + Assert.Equal(2, result.MissingTokens.Count); + } + + [Fact] + public void Validate_counts_the_union_across_multiple_sources_not_per_source() + { + var sourceA = "Value alpha is 111."; + var sourceB = "Value beta is 222."; + // Merged keeps only one of the two distinct tokens across the union of both sources. + var merged = "Value alpha is 111 and beta was updated."; + + var result = MergeGuard.Validate([sourceA, sourceB], merged); + + Assert.False(result.Passed); + Assert.Contains("222", result.MissingTokens); + } + + // ── Length-collapse floor ───────────────────────────────────────── + + [Fact] + public void Validate_fails_on_length_collapse_even_when_tokens_are_retained() + { + // Merged repeats every load-bearing token but discards all surrounding prose, + // collapsing well below 60% of the longest source's length. + var longSource = "Config value is 42. " + new string('x', 200); + var merged = "42"; + + var result = MergeGuard.Validate([longSource], merged); + + Assert.False(result.Passed); + Assert.Contains("collapse", result.Reason, StringComparison.OrdinalIgnoreCase); + } + + [Fact] + public void Validate_passes_at_exactly_the_60_percent_length_boundary() + { + var longSource = new string('a', 100); + var merged = new string('a', 60); + + var result = MergeGuard.Validate([longSource], merged); + + Assert.True(result.Passed); + } + + [Fact] + public void Validate_fails_just_below_the_60_percent_length_boundary() + { + var longSource = new string('a', 100); + var merged = new string('a', 59); + + var result = MergeGuard.Validate([longSource], merged); + + Assert.False(result.Passed); + } + + [Fact] + public void Validate_uses_the_longest_source_for_the_length_floor() + { + var shortSource = "Short note."; + var longSource = new string('b', 200); + // Merged is well above 60% of the SHORT source but not the long one. + var merged = new string('b', 100); + + var result = MergeGuard.Validate([shortSource, longSource], merged); + + Assert.False(result.Passed); + } + + // ── Empty/null handling ──────────────────────────────────────────── + + [Fact] + public void Validate_treats_empty_source_bodies_as_contributing_nothing() + { + var result = MergeGuard.Validate(["", " ", "real content here"], "real content here, unchanged"); + + Assert.True(result.Passed); + } +} diff --git a/src/Netclaw.Actors.Tests/Sessions/SessionMemoryObserverStorageIntegrationTests.cs b/src/Netclaw.Actors.Tests/Sessions/SessionMemoryObserverStorageIntegrationTests.cs index 635a77e49..913eb7d5c 100644 --- a/src/Netclaw.Actors.Tests/Sessions/SessionMemoryObserverStorageIntegrationTests.cs +++ b/src/Netclaw.Actors.Tests/Sessions/SessionMemoryObserverStorageIntegrationTests.cs @@ -60,7 +60,7 @@ public async Task Curation_actor_persists_create_decision_to_memory_documents() try { var curationActor = Sys.ActorOf( - MemoryCurationActor.CreateProps(store, new SessionId("test-session")), + MemoryCurationActor.CreateProps(store, new SessionId("test-session"), new MemoryCurationConfig()), "curation-create"); var probe = CreateTestProbe("curation-create-probe"); @@ -106,7 +106,7 @@ public async Task Curation_actor_persists_multiple_proposals_in_one_batch() try { var curationActor = Sys.ActorOf( - MemoryCurationActor.CreateProps(store, new SessionId("test-session")), + MemoryCurationActor.CreateProps(store, new SessionId("test-session"), new MemoryCurationConfig()), "curation-batch"); var probe = CreateTestProbe("curation-batch-probe"); @@ -163,7 +163,7 @@ public async Task Curation_actor_replies_with_zero_evaluated_for_empty_batch() try { var curationActor = Sys.ActorOf( - MemoryCurationActor.CreateProps(store, new SessionId("test-session")), + MemoryCurationActor.CreateProps(store, new SessionId("test-session"), new MemoryCurationConfig()), "curation-empty"); var probe = CreateTestProbe("curation-empty-probe"); diff --git a/src/Netclaw.Actors.Tests/Sessions/SidecarSessionCorrelationTests.cs b/src/Netclaw.Actors.Tests/Sessions/SidecarSessionCorrelationTests.cs index 075443a11..8860b23c0 100644 --- a/src/Netclaw.Actors.Tests/Sessions/SidecarSessionCorrelationTests.cs +++ b/src/Netclaw.Actors.Tests/Sessions/SidecarSessionCorrelationTests.cs @@ -121,7 +121,8 @@ public async Task MemoryCuration_carries_session_scoped_options() ExpiresAtMs: null); await MemoryCurationEvaluator.TryLlmEvaluationAsync( - captor, sessionId, operation, candidates: [], log: new AkkaCurationLog(NoLogger.Instance)); + captor, sessionId, operation, candidates: [], log: new AkkaCurationLog(NoLogger.Instance), + curationConfig: new MemoryCurationConfig()); AssertScopedTo(sessionId, captor); } diff --git a/src/Netclaw.Actors/Memory/CurationPromptBuilder.cs b/src/Netclaw.Actors/Memory/CurationPromptBuilder.cs index 17c686c00..7e1aef5d4 100644 --- a/src/Netclaw.Actors/Memory/CurationPromptBuilder.cs +++ b/src/Netclaw.Actors/Memory/CurationPromptBuilder.cs @@ -70,26 +70,57 @@ living value (a current setting/status/canonical fact) and the old value CREATE — Genuinely new, OR distinct from the candidates in what it is ABOUT (different date, entity, event, or reading). + For UPDATE and CONSOLIDATE only: after the keyword line, write a line + containing only "---", then the complete merged document body — a + LOSSLESS union of the proposal and every candidate you named. You are + combining, not summarizing: every fact, identifier, number, URL, and date + from every source must still appear somewhere in the merged body. State + the newest value first; keep a superseded dated value inline rather than + deleting it, e.g. "current value is 42 (previously 30 as of 2026-05-13)". + SKIP and CREATE never include a body — respond with the keyword alone. + SAME fact, merge: "DB pool size is 20" and "the database connection pool max is set to 20". DISTINCT, create: a CPU temperature logged at 14:00 vs the same metric at 15:00; a staging-server config vs a production-server config. - Respond with ONLY the decision keyword and any required IDs. No explanation. + Respond with ONLY the decision keyword, any required IDs, and — for + UPDATE/CONSOLIDATE — the merged body after the "---" separator. No other + explanation. Examples: SKIP + UPDATE doc-abc123 + --- + Config path is /etc/app/config.yaml (previously /etc/app/config.json as + of 2026-06-01). Default timeout is 30s. + CONSOLIDATE doc-abc123 doc-def456 + --- + Akka.NET GitHub repository: https://github.com/akkadotnet/akka.net. + Latest stable release is 1.5.62 (previously 1.5.60 as of 2026-04-02). + CREATE """; /// /// Build the user message for a curation evaluation request. /// + /// + /// When false (the legacy default, used by the content-search/fuzzy-anchor candidate + /// path), each candidate's content is truncated to + /// like the proposal's own content always is. When true, candidates are shown in full — + /// the decider needs complete bodies to synthesize a lossless merge (memory-core-redesign + /// Slice 3 task 3.2). Nothing in this change sets it true yet: the embedding kNN + /// nominator that will pass full-content nominated candidates is Stage B (task 3.1), + /// still to come — this parameter exists now so that work does not need to touch the + /// prompt-building signature again. + /// public static string BuildUserMessage( SQLiteMemoryCurationOperation proposal, - IReadOnlyList candidates) + IReadOnlyList candidates, + bool useFullCandidateContent = false) { var sb = new StringBuilder(); @@ -111,7 +142,7 @@ public static string BuildUserMessage( { var c = candidates[i]; sb.AppendLine($"[{i + 1}] id={c.DocumentId} anchor={c.AnchorCanonicalName}"); - sb.AppendLine($" content: {TruncateContent(c.Content)}"); + sb.AppendLine($" content: {(useFullCandidateContent ? c.Content : TruncateContent(c.Content))}"); sb.AppendLine($" timestamp: {c.FreshnessAtMs}"); } } @@ -120,8 +151,10 @@ public static string BuildUserMessage( } /// - /// Parse a single-keyword LLM response into a curation decision. - /// Returns null if the response cannot be parsed. + /// Parse an LLM response into a curation decision: a keyword line, optionally (for + /// UPDATE/CONSOLIDATE) followed by a "---" separator line and a merged markdown body + /// (memory-core-redesign Slice 3 task 3.2). Returns null if the response cannot be + /// parsed. /// public static CurationDecision? ParseResponse(string response) { @@ -131,7 +164,7 @@ public static string BuildUserMessage( // Reasoning models may inline hidden chain-of-thought wrapped in // ...; strip it so the bare decision keyword is what we parse. // When the serving stack emits reasoning on a separate channel, the text is - // already just the keyword and this is a no-op. + // already just the keyword (and optional body) and this is a no-op. var trimmed = StripThinkBlocks(response).Trim(); if (trimmed.Length == 0) return null; @@ -139,25 +172,35 @@ public static string BuildUserMessage( // SKIP if (trimmed.StartsWith("SKIP", StringComparison.OrdinalIgnoreCase)) { - return new CurationDecision(CurationDecisionKind.Skip, null, null, null, "LLM decision: SKIP"); + return new CurationDecision(CurationDecisionKind.Skip, null, null, null, "LLM decision: SKIP", FromLlmTier: true); } // CREATE if (trimmed.StartsWith("CREATE", StringComparison.OrdinalIgnoreCase)) { - return new CurationDecision(CurationDecisionKind.Create, null, null, null, "LLM decision: CREATE"); + return new CurationDecision(CurationDecisionKind.Create, null, null, null, "LLM decision: CREATE", FromLlmTier: true); } - // UPDATE + // UPDATE [---\n] var updateMatch = Regex.Match(trimmed, @"^UPDATE\s+(\S+)", RegexOptions.IgnoreCase); if (updateMatch.Success) { var targetId = updateMatch.Groups[1].Value; - return new CurationDecision(CurationDecisionKind.Update, targetId, null, null, $"LLM decision: UPDATE {targetId}"); + return new CurationDecision( + CurationDecisionKind.Update, + targetId, + null, + null, + $"LLM decision: UPDATE {targetId}", + MergedBody: ExtractMergedBody(trimmed), + FromLlmTier: true); } - // CONSOLIDATE [...] - var consolidateMatch = Regex.Match(trimmed, @"^CONSOLIDATE\s+(.+)$", RegexOptions.IgnoreCase); + // CONSOLIDATE [...] [---\n] + // Captures only the rest of the FIRST line: unlike the pre-Slice-3 shape (always a + // single keyword line), a merged body may follow on subsequent lines, and `.` + // without RegexOptions.Singleline cannot cross the newline before it. + var consolidateMatch = Regex.Match(trimmed, @"^CONSOLIDATE\s+([^\r\n]+)", RegexOptions.IgnoreCase); if (consolidateMatch.Success) { var ids = consolidateMatch.Groups[1].Value @@ -170,13 +213,34 @@ public static string BuildUserMessage( null, ids, null, - $"LLM decision: CONSOLIDATE {string.Join(" ", ids)}"); + $"LLM decision: CONSOLIDATE {string.Join(" ", ids)}", + MergedBody: ExtractMergedBody(trimmed), + FromLlmTier: true); } } return null; } + private static readonly Regex MergedBodySeparatorPattern = new(@"(?m)^-{3,}\s*$", RegexOptions.Compiled); + + /// + /// Finds the first "---" separator line and returns everything after it, trimmed. + /// Returns null when there is no separator, or when the text after it is empty once + /// trimmed (a malformed/empty body is treated as absent, per task 3.2) — the caller + /// then falls back to the keyword-only decision semantics (task 3.4's append-fallback + /// routing for a body-absent LLM UPDATE/CONSOLIDATE). + /// + private static string? ExtractMergedBody(string trimmedResponse) + { + var separator = MergedBodySeparatorPattern.Match(trimmedResponse); + if (!separator.Success) + return null; + + var body = trimmedResponse[(separator.Index + separator.Length)..].Trim(); + return body.Length == 0 ? null : body; + } + private static string StripThinkBlocks(string text) { // Remove complete ... spans (case-insensitive, across newlines), diff --git a/src/Netclaw.Actors/Memory/CurationRulesEvaluator.cs b/src/Netclaw.Actors/Memory/CurationRulesEvaluator.cs index 4a7fb15ee..f0679ec51 100644 --- a/src/Netclaw.Actors/Memory/CurationRulesEvaluator.cs +++ b/src/Netclaw.Actors/Memory/CurationRulesEvaluator.cs @@ -29,6 +29,16 @@ public enum CurationDecisionKind /// /// An existing memory document that is a candidate for matching against a proposal. /// +/// +/// The embedding cosine similarity that nominated this candidate via +/// (memory-core-redesign Slice 3 Stage B, task 3.1). Null +/// for candidates sourced only from anchor-name matching or lexical content search — those +/// carry no embedding evidence. A non-null value here is what +/// uses to force the decision to the LLM +/// tier: per design D4, cosine similarity is nomination evidence only and must never itself +/// decide skip/merge/create, so this field is read for "is a nominee present" and then handed +/// to the curator LLM as context — never compared against a threshold to auto-decide. +/// public sealed record ExistingMemoryCandidate( string DocumentId, string AnchorId, @@ -36,17 +46,44 @@ public sealed record ExistingMemoryCandidate( string Content, long? FreshnessAtMs, double Confidence, - bool IsExactAnchorMatch); + bool IsExactAnchorMatch, + double? CosineSimilarity = null); /// /// Result of curation evaluation for a single proposal. /// +/// +/// The complete, lossless-union markdown body synthesized by the curation LLM for an +/// UPDATE/CONSOLIDATE decision (memory-core-redesign Slice 3, design D5; +/// is the only producer). Null for +/// SKIP/CREATE, for any decision produced by the deterministic rules tier (which never +/// synthesizes a body), and for keyword-only LLM UPDATE/CONSOLIDATE responses. +/// validates this against every +/// source body via before writing it; on guard failure or when +/// this is null for an LLM-tier decision, the write degrades to a structural append +/// instead of the raw overwrite this field's absence would otherwise imply. +/// +/// +/// True when produced this decision, as +/// opposed to the deterministic rules tier (). Governs +/// write routing in : the +/// deterministic tier's UPDATE (exact-anchor path) keeps its pre-Slice-3 guarantee — a raw +/// overwrite that has already +/// verified is a proposal-preserves-existing-content superset — while every LLM-tier +/// UPDATE/CONSOLIDATE routes through -validated merge or structural +/// append instead. Deterministic-tier CONSOLIDATE never sets this either, but it flows +/// through the same guarded path anyway because it never carries a +/// (the rules tier does not synthesize one) — see 's +/// remarks for why that unification is safe. +/// public sealed record CurationDecision( CurationDecisionKind Kind, string? TargetDocumentId, IReadOnlyList? ConsolidationTargetIds, string? CanonicalAnchorName, - string Reason); + string Reason, + string? MergedBody = null, + bool FromLlmTier = false); /// /// Deterministic rules-based evaluator for memory curation decisions. diff --git a/src/Netclaw.Actors/Memory/MemoryCurationActor.cs b/src/Netclaw.Actors/Memory/MemoryCurationActor.cs index 59beb8101..6b24354e2 100644 --- a/src/Netclaw.Actors/Memory/MemoryCurationActor.cs +++ b/src/Netclaw.Actors/Memory/MemoryCurationActor.cs @@ -36,7 +36,7 @@ public sealed record CurationFailed(string Reason); // ── Internal messages ─────────────────────────────────────────────── internal sealed record EvaluationBatchResult( - IReadOnlyList<(SQLiteMemoryCurationOperation Operation, CurationDecision Decision)> Decisions); + IReadOnlyList<(SQLiteMemoryCurationOperation Operation, CurationEvaluation Evaluation)> Evaluations); internal sealed record WriteBatchResult(CurationCompleted Summary); @@ -61,18 +61,32 @@ public sealed class MemoryCurationActor : ReceiveActor, IWithUnboundedStash public IStash Stash { get; set; } = null!; + /// + /// Write-side curation settings (memory-core-redesign Slice 3): nominator threshold/K and + /// the curation LLM's timeout/token-cap, threaded to + /// in place of the hardcoded constants Slice 1 shipped with. + /// /// - /// Resolves the process's at write time (memory-core-redesign - /// Slice 2, task 2.8). Optional like above: a null holder - /// is a genuine operating mode (a test harness or a session wired without the embedding - /// subsystem), not a placeholder — treats a null - /// holder identically to an unavailable embedder and skips embedding with a debug log. + /// Resolves the process's for embed-on-write (memory-core- + /// redesign Slice 2, task 2.8) AND for the evaluator's embedding kNN nominator (Slice 3 + /// Stage B, task 3.1) — the same holder serves both. Optional like + /// above: a null holder is a genuine operating mode (a + /// test harness or a session wired without the embedding subsystem), not a placeholder — + /// both and + /// treat a null holder identically to an unavailable embedder and degrade accordingly. + /// + /// + /// Resolves the process's for the nominator (Slice 3 Stage + /// B). Provided alongside in production; independently + /// nullable for the same test-harness reason. /// public MemoryCurationActor( SQLiteMemoryStore store, SessionId sessionId, + MemoryCurationConfig curationConfig, IChatClientProvider? clientProvider = null, - MemoryEmbedderHolder? embedderHolder = null) + MemoryEmbedderHolder? embedderHolder = null, + MemoryVectorIndexHolder? vectorIndexHolder = null) { _store = store; _sessionId = sessionId; @@ -82,7 +96,8 @@ public MemoryCurationActor( var llmClient = clientProvider != null ? clientProvider.GetClient(ModelRole.Compaction) : null; - _evaluator = new MemoryCurationEvaluator(_store, _log, llmClient); + _evaluator = new MemoryCurationEvaluator( + _store, _log, curationConfig, llmClient, embedderHolder, vectorIndexHolder); Become(Idle); } @@ -93,9 +108,12 @@ public MemoryCurationActor( public static Props CreateProps( SQLiteMemoryStore store, SessionId sessionId, + MemoryCurationConfig curationConfig, IChatClientProvider? clientProvider = null, - MemoryEmbedderHolder? embedderHolder = null) - => Props.Create(() => new MemoryCurationActor(store, sessionId, clientProvider, embedderHolder)); + MemoryEmbedderHolder? embedderHolder = null, + MemoryVectorIndexHolder? vectorIndexHolder = null) + => Props.Create(() => new MemoryCurationActor( + store, sessionId, curationConfig, clientProvider, embedderHolder, vectorIndexHolder)); // ── Idle behavior ─────────────────────────────────────────────── @@ -121,8 +139,8 @@ private void Evaluating() { Receive(msg => { - _log.Info("curation_actor_evaluated decisionCount={0}", msg.Decisions.Count); - StartWriting(msg.Decisions); + _log.Info("curation_actor_evaluated decisionCount={0}", msg.Evaluations.Count); + StartWriting(msg.Evaluations); }); // Stash incoming proposals while evaluating @@ -184,15 +202,15 @@ private void StartEvaluation(IReadOnlyList operat { try { - var decisions = new List<(SQLiteMemoryCurationOperation, CurationDecision)>(); + var evaluations = new List<(SQLiteMemoryCurationOperation, CurationEvaluation)>(); foreach (var operation in operations) { - var decision = await EvaluateSingleAsync(operation); - decisions.Add((operation, decision)); + var evaluation = await EvaluateSingleAsync(operation); + evaluations.Add((operation, evaluation)); } - self.Tell(new EvaluationBatchResult(decisions)); + self.Tell(new EvaluationBatchResult(evaluations)); } catch (Exception ex) { @@ -203,12 +221,12 @@ private void StartEvaluation(IReadOnlyList operat // Decision logic lives in MemoryCurationEvaluator (shared with the daemon checkpoint // worker — memory-core-redesign Slice 1) so the two write pipelines cannot diverge. - private Task EvaluateSingleAsync(SQLiteMemoryCurationOperation operation) + private Task EvaluateSingleAsync(SQLiteMemoryCurationOperation operation) => _evaluator.EvaluateAsync(operation, _sessionId); // ── Write pipeline ────────────────────────────────────────────── - private void StartWriting(IReadOnlyList<(SQLiteMemoryCurationOperation Operation, CurationDecision Decision)> decisions) + private void StartWriting(IReadOnlyList<(SQLiteMemoryCurationOperation Operation, CurationEvaluation Evaluation)> evaluations) { Become(Writing); var self = Self; @@ -223,12 +241,15 @@ private void StartWriting(IReadOnlyList<(SQLiteMemoryCurationOperation Operation var created = 0; var toWrite = new List(); - foreach (var (operation, decision) in decisions) + foreach (var (operation, evaluation) in evaluations) { + var decision = evaluation.Decision; + // Decision -> write-operation mapping (including Consolidate's - // re-anchor/tombstone side effects) lives in MemoryCurationEvaluator, - // shared with the daemon checkpoint worker. - var writeOp = await _evaluator.ApplyDecisionAsync(operation, decision); + // re-anchor/tombstone side effects and Slice 3's guard-validated + // merge/append routing) lives in MemoryCurationEvaluator, shared with + // the daemon checkpoint worker. + var writeOp = await _evaluator.ApplyDecisionAsync(operation, decision, evaluation.Candidates); switch (decision.Kind) { @@ -269,7 +290,7 @@ await MemoryEmbedOnWriteCoordinator.EmbedWrittenDocumentsAsync( } self.Tell(new WriteBatchResult(new CurationCompleted( - Evaluated: decisions.Count, + Evaluated: evaluations.Count, Skipped: skipped, Updated: updated, Consolidated: consolidated, diff --git a/src/Netclaw.Actors/Memory/MemoryCurationEvaluator.cs b/src/Netclaw.Actors/Memory/MemoryCurationEvaluator.cs index a7ebc3a4f..cf3e393a8 100644 --- a/src/Netclaw.Actors/Memory/MemoryCurationEvaluator.cs +++ b/src/Netclaw.Actors/Memory/MemoryCurationEvaluator.cs @@ -17,9 +17,11 @@ namespace Netclaw.Actors.Memory; /// /// Minimal logging seam so emits one set of log -/// markers (curation_dual_search, curation_llm_decision, -/// curation_llm_no_decision, curation_llm_timeout, curation_llm_error, -/// curation_ambiguous_auto_resolved, curation_ambiguous_create_fallback, +/// markers (curation_dual_search, curation_nominated, +/// curation_nominator_degraded, curation_nominee_no_llm_decision, +/// curation_llm_decision, curation_llm_no_decision, curation_llm_timeout, +/// curation_llm_error, curation_ambiguous_auto_resolved, +/// curation_ambiguous_create_fallback, /// curation_skip/_update/_consolidate/_create, /// curation_reanchor, curation_tombstone_anchor) regardless of which of the /// two callers is driving the evaluator: the inline per-session actor @@ -75,23 +77,52 @@ internal sealed class MicrosoftCurationLog(ILogger log) : ICurationLog /// slice (finding D14) and the daemon path performed no relationship evaluation at all. /// /// Decision flow (): immutable records bypass evaluation; fuzzy -/// anchor candidates are queried, then content-term candidates are added when there is no -/// exact anchor match; runs the deterministic -/// tier; an Ambiguous result escalates to the LLM tier (when available) followed by -/// , or else falls back to +/// anchor candidates are queried; on an exact anchor match, the deterministic fast path +/// ('s EvaluateExactMatch) decides Skip/Update +/// with no further evidence gathering. Otherwise, the embedding kNN nominator (memory-core- +/// redesign Slice 3 Stage B, task 3.1, design D4) queries +/// when the embedder is available: any nominee forces the decision to the LLM tier, +/// regardless of what the lexical rules tier would have decided — the May 2026 measurement +/// (docs/research/memory-recall-findings-2026-05.md; corroborated at corpus scale in +/// docs/research/memory-audit-2026-07.md §5) found no cosine threshold that separates true +/// duplicates from merely-related siblings, so cosine similarity is nomination evidence ONLY — +/// it never auto-merges and never auto-skips. When no nominee fires (or the embedder is +/// unavailable, in which case the pre-Slice-3 lexical content-term search runs instead as an +/// explicitly-logged degraded path), runs the +/// deterministic tier as before; an Ambiguous result escalates to the LLM tier (when available) +/// followed by , or else falls back to /// and finally a Create default. /// maps the resulting decision to the operation that should /// be written (or nothing, for Skip), executing Consolidate's re-anchor/tombstone side /// effects — this mapping is unified too, since a second, hand-copied switch statement per /// caller is exactly the kind of divergence this slice removes. +/// +/// Guard-validated write routing (memory-core-redesign Slice 3, design D5): an LLM-tier +/// UPDATE/CONSOLIDATE decision () never overwrites +/// a target's raw body. When it carries a synthesized , +/// validates it with against every +/// source body and writes it only on a pass; on guard failure, or when no merged body was +/// produced, the write degrades to a structural append () +/// so information is never silently dropped. The deterministic tier's UPDATE (the exact-anchor +/// path in ) is the one decision shape that keeps its +/// pre-Slice-3 behavior unchanged: +/// already proves the proposal is a content superset of the target before that decision can +/// reach Update, so its raw overwrite is provably non-lossy on its own terms — appending there +/// too would just bloat documents that are legitimately single-value replacements (e.g. a +/// version bump) for no safety benefit. GuardDestructiveUpdate is therefore no longer applied +/// to LLM-tier decisions in : its raw-proposal containment check +/// would reject a legitimate reworded merge (the unmerged proposal rarely contains the +/// target's exact wording verbatim), and the write-time guard above supersedes it for that +/// tier anyway. /// public sealed class MemoryCurationEvaluator { - private static readonly TimeSpan LlmTimeout = TimeSpan.FromSeconds(10); - private readonly SQLiteMemoryStore _store; private readonly IChatClient? _llmClient; private readonly ICurationLog _log; + private readonly MemoryCurationConfig _curationConfig; + private readonly MemoryEmbedderHolder? _embedderHolder; + private readonly MemoryVectorIndexHolder? _vectorIndexHolder; /// /// Constructs an evaluator that logs through Akka's actor logging (the inline @@ -101,8 +132,27 @@ public sealed class MemoryCurationEvaluator /// which case Ambiguous decisions resolve via /// only. /// - public MemoryCurationEvaluator(SQLiteMemoryStore store, ILoggingAdapter log, IChatClient? llmClient = null) - : this(store, (ICurationLog)new AkkaCurationLog(log), llmClient) + /// + /// Resolves the process's for the kNN nominator + /// (memory-core-redesign Slice 3 Stage B). Optional like : a + /// null holder is a genuine operating mode (a test harness, or a build with the embedding + /// subsystem not wired up at all) — treats it identically to an + /// unavailable embedder and runs the lexical degraded path. + /// + /// + /// Resolves the process's for the same nominator. Optional + /// for the same reason as — the two are provided together + /// in production (see Netclaw.Daemon.Program), but each is independently nullable so + /// a caller missing one still degrades safely rather than throwing. + /// + public MemoryCurationEvaluator( + SQLiteMemoryStore store, + ILoggingAdapter log, + MemoryCurationConfig curationConfig, + IChatClient? llmClient = null, + MemoryEmbedderHolder? embedderHolder = null, + MemoryVectorIndexHolder? vectorIndexHolder = null) + : this(store, (ICurationLog)new AkkaCurationLog(log), curationConfig, llmClient, embedderHolder, vectorIndexHolder) { } @@ -110,24 +160,48 @@ public MemoryCurationEvaluator(SQLiteMemoryStore store, ILoggingAdapter log, ICh /// Constructs an evaluator that logs through Microsoft.Extensions.Logging (the daemon /// checkpoint-worker path). The daemon worker has no LLM client to give this evaluator /// today — that absence is intentional and permanent for this call site, not a - /// placeholder to be filled in later in this slice. + /// placeholder to be filled in later in this slice. and + /// ARE wired here (memory-core-redesign Slice 3 Stage + /// B): the nominator runs on both write pipelines even though only the inline pipeline has + /// an LLM client — a nominee found with no LLM available still forces the conservative + /// no-auto-merge Create outcome documented on . /// - public MemoryCurationEvaluator(SQLiteMemoryStore store, ILogger log, IChatClient? llmClient = null) - : this(store, (ICurationLog)new MicrosoftCurationLog(log), llmClient) + public MemoryCurationEvaluator( + SQLiteMemoryStore store, + ILogger log, + MemoryCurationConfig curationConfig, + IChatClient? llmClient = null, + MemoryEmbedderHolder? embedderHolder = null, + MemoryVectorIndexHolder? vectorIndexHolder = null) + : this(store, (ICurationLog)new MicrosoftCurationLog(log), curationConfig, llmClient, embedderHolder, vectorIndexHolder) { } - private MemoryCurationEvaluator(SQLiteMemoryStore store, ICurationLog log, IChatClient? llmClient) + private MemoryCurationEvaluator( + SQLiteMemoryStore store, + ICurationLog log, + MemoryCurationConfig curationConfig, + IChatClient? llmClient, + MemoryEmbedderHolder? embedderHolder, + MemoryVectorIndexHolder? vectorIndexHolder) { _store = store; _log = log; + _curationConfig = curationConfig; _llmClient = llmClient; + _embedderHolder = embedderHolder; + _vectorIndexHolder = vectorIndexHolder; } /// - /// Evaluate a single curation proposal against existing memories and return a decision. + /// Evaluate a single curation proposal against existing memories and return a decision + /// together with the candidates it was evaluated against — + /// needs those same candidate bodies (memory-core-redesign Slice 3) to validate or build a + /// merged/appended write, and re-querying the store at apply time could see a different + /// (possibly stale-in-the-other-direction) snapshot than the one the decision was actually + /// made against. /// - public async Task EvaluateAsync( + public async Task EvaluateAsync( SQLiteMemoryCurationOperation operation, SessionId sessionId, CancellationToken ct = default) @@ -136,7 +210,9 @@ public async Task EvaluateAsync( if (MemoryDomainEnumExtensions.TryFromWireValue(operation.Kind, out MemoryKind kind) && kind == MemoryKind.Record) { - return new CurationDecision(CurationDecisionKind.Create, null, null, null, "immutable record bypass"); + return new CurationEvaluation( + new CurationDecision(CurationDecisionKind.Create, null, null, null, "immutable record bypass"), + []); } // Query existing anchors for matches (by name) @@ -145,44 +221,144 @@ public async Task EvaluateAsync( // Build a mutable candidate list — content search may add more candidates below. var candidates = new List(anchorCandidates); - // Run content-based search when there is no exact anchor match. - // This catches semantically identical content under very different anchor names - // (e.g., "netclaw-github-repo" vs "netclaw-source-location" — different names, same info). + // Embedding kNN nomination (memory-core-redesign Slice 3 Stage B, task 3.1, design D4) + // vs. the pre-Slice-3 lexical content-term search: these are alternatives, not additive. + // An exact anchor match already resolves deterministically below with no further + // evidence gathering (the "existing exact-anchor deterministic fast path" design D4 + // calls out as unchanged), so neither runs in that case. var hasExactAnchorMatch = anchorCandidates.Any(c => c.IsExactAnchorMatch); - if (!hasExactAnchorMatch && !string.IsNullOrWhiteSpace(operation.Content)) + if (!hasExactAnchorMatch) { - var contentTerms = operation.Content - .Split([' ', '\t', '\n', '\r', '.', ',', ':', ';', '!', '?', '"', '\''], - StringSplitOptions.RemoveEmptyEntries | StringSplitOptions.TrimEntries) - .Where(t => t.Length >= 3) - .Select(t => t.ToLowerInvariant()) - .Distinct(StringComparer.OrdinalIgnoreCase) - .Take(8) - .ToArray(); + var embedder = _embedderHolder?.Current; + if (embedder is not null && embedder.IsAvailable && _vectorIndexHolder is not null) + { + var (nominees, topCosine) = await NominateAsync(embedder, operation, ct); + if (nominees.Count > 0) + { + // Merge like the content-term path below: dedup by DocumentId, but a nominee + // that is ALSO already present (e.g. it also fuzzy-matched by anchor name) + // must keep its cosine tag rather than being dropped as a duplicate. + var byDocId = new Dictionary(StringComparer.OrdinalIgnoreCase); + for (var i = 0; i < candidates.Count; i++) + byDocId[candidates[i].DocumentId] = i; + + foreach (var nominee in nominees) + { + if (byDocId.TryGetValue(nominee.DocumentId, out var existingIndex)) + candidates[existingIndex] = candidates[existingIndex] with { CosineSimilarity = nominee.CosineSimilarity }; + else + candidates.Add(nominee); + } - if (contentTerms.Length > 0) + _log.Info( + "curation_nominated anchor={0} count={1} topCosine={2}", + operation.AnchorCanonicalName, + nominees.Count, + topCosine.ToString("F4", System.Globalization.CultureInfo.InvariantCulture)); + } + } + else { - var contentCandidates = await _store.FindCandidatesByContentAsync(contentTerms, ct: ct); + // Degraded path (design D4 point 4): no healthy embedder/index, so fall back to + // the lexical content-term search exactly as before Slice 3 — this catches + // semantically identical content under very different anchor names (e.g., + // "netclaw-github-repo" vs "netclaw-source-location") when there is no embedding + // evidence available to do better. Logged unconditionally in this branch (not + // gated on content being non-blank) so the degraded condition itself is always + // observable, independent of whether a search ends up running. Debug level, not + // Warning: "embedder unavailable" is the default operating mode while + // Memory.Embeddings.Enabled is false (task 3.1's target default), so this would + // otherwise be Warning-level spam on every single curation evaluation in the + // common case — the loud, once-per-transition signal already exists at startup + // (EmbeddingWarmupHostedService's memory_embedding_unavailable / + // memory_embedding_disabled) and in doctor/status, matching + // MemoryEmbedOnWriteCoordinator's identical reasoning for its own + // embedder-unavailable skip log. + _log.Debug( + "curation_nominator_degraded anchor={0} reason={1}", + operation.AnchorCanonicalName, + embedder is null ? "no_embedder_configured" : !embedder.IsAvailable ? "embedder_unavailable" : "vector_index_unavailable"); - // Merge content candidates with anchor candidates, deduplicating by DocumentId. - if (contentCandidates.Count > 0) + if (!string.IsNullOrWhiteSpace(operation.Content)) { - var existingDocIds = new HashSet( - candidates.Select(c => c.DocumentId), StringComparer.OrdinalIgnoreCase); - foreach (var cc in contentCandidates) + var contentTerms = operation.Content + .Split([' ', '\t', '\n', '\r', '.', ',', ':', ';', '!', '?', '"', '\''], + StringSplitOptions.RemoveEmptyEntries | StringSplitOptions.TrimEntries) + .Where(t => t.Length >= 3) + .Select(t => t.ToLowerInvariant()) + .Distinct(StringComparer.OrdinalIgnoreCase) + .Take(8) + .ToArray(); + + if (contentTerms.Length > 0) { - if (!existingDocIds.Contains(cc.DocumentId)) - candidates.Add(cc); + var contentCandidates = await _store.FindCandidatesByContentAsync(contentTerms, ct: ct); + + // Merge content candidates with anchor candidates, deduplicating by DocumentId. + if (contentCandidates.Count > 0) + { + var existingDocIds = new HashSet( + candidates.Select(c => c.DocumentId), StringComparer.OrdinalIgnoreCase); + foreach (var cc in contentCandidates) + { + if (!existingDocIds.Contains(cc.DocumentId)) + candidates.Add(cc); + } + + _log.Debug( + "curation_dual_search anchor={0} anchor_hits={1} content_hits={2} merged={3}", + operation.AnchorCanonicalName, + anchorCandidates.Count, + contentCandidates.Count, + candidates.Count); + } } + } + } + } - _log.Debug( - "curation_dual_search anchor={0} anchor_hits={1} content_hits={2} merged={3}", + // Any nominee forces the LLM tier, regardless of what the lexical rules tier below would + // have decided (design D4: "no cosine threshold separates duplicates from siblings," so + // similarity nominates only — it never auto-merges or auto-skips on its own). + var hasNominee = candidates.Any(c => c.CosineSimilarity.HasValue); + if (hasNominee) + { + if (_llmClient is not null) + { + var nomineeLlmDecision = await TryLlmEvaluationAsync( + _llmClient, sessionId, operation, candidates, _log, _curationConfig, useFullCandidateContent: true); + if (nomineeLlmDecision is not null) + { + // GuardDestructiveUpdate is deliberately NOT applied here — see this class's + // remarks; write-time MergeGuard/structural-append routing supersedes it for + // every LLM-tier decision. + _log.Info( + "curation_llm_decision anchor={0} decision={1} reason={2}", operation.AnchorCanonicalName, - anchorCandidates.Count, - contentCandidates.Count, - candidates.Count); + nomineeLlmDecision.Kind, + nomineeLlmDecision.Reason); + return new CurationEvaluation(nomineeLlmDecision, candidates); } + + // LLM failed to produce a parseable decision — fall through to the conservative + // no-LLM handling below rather than a second (Jaccard-based) attempt. } + + // No LLM available, or the LLM call failed: a nominee is semantic-near evidence that + // TryAutoResolveAmbiguous's Jaccard heuristics cannot safely adjudicate (that is + // exactly the ambiguity the May 2026 measurement showed deterministic logic cannot + // resolve), so this does NOT call TryAutoResolveAmbiguous — doing so risks an + // auto-Skip driven by cosine-adjacent content that is actually a distinct sibling. + // The conservative outcome is Create: the cost is a possible duplicate document, + // recoverable by a future curation pass; a wrong auto-merge is not. + _log.Warning( + "curation_nominee_no_llm_decision anchor={0} llm_available={1} — conservative create", + operation.AnchorCanonicalName, + _llmClient is not null); + return new CurationEvaluation( + new CurationDecision(CurationDecisionKind.Create, null, null, null, + "nominee present but no LLM decision available: conservative create (no auto-merge on cosine alone)"), + candidates); } // Apply rules tier @@ -191,16 +367,21 @@ public async Task EvaluateAsync( // If rules tier is ambiguous and LLM is available, escalate if (rulesDecision.Kind == CurationDecisionKind.Ambiguous && _llmClient is not null) { - var llmDecision = await TryLlmEvaluationAsync(_llmClient, sessionId, operation, candidates, _log); + var llmDecision = await TryLlmEvaluationAsync( + _llmClient, sessionId, operation, candidates, _log, _curationConfig); if (llmDecision is not null) { - var guarded = CurationRulesEvaluator.GuardDestructiveUpdate(llmDecision, operation, candidates); + // GuardDestructiveUpdate is deliberately NOT applied here — see this class's + // remarks. LLM-tier UPDATE/CONSOLIDATE write safety is now the write-time + // MergeGuard/structural-append routing in ApplyDecisionAsync, which handles + // both the has-a-merged-body and no-merged-body cases without needing this + // decision downgraded first. _log.Info( "curation_llm_decision anchor={0} decision={1} reason={2}", operation.AnchorCanonicalName, - guarded.Kind, - guarded.Reason); - return guarded; + llmDecision.Kind, + llmDecision.Reason); + return new CurationEvaluation(llmDecision, candidates); } // LLM failed — fall through to deterministic auto-resolution below @@ -217,16 +398,92 @@ public async Task EvaluateAsync( operation.AnchorCanonicalName, autoResolved.Kind, autoResolved.Reason); - return autoResolved; + return new CurationEvaluation(autoResolved, candidates); } _log.Warning("curation_ambiguous_create_fallback anchor={0} llm_available={1}", operation.AnchorCanonicalName, _llmClient is not null); - return new CurationDecision(CurationDecisionKind.Create, null, null, null, - "ambiguous: auto-resolve insufficient, defaulting to create"); + return new CurationEvaluation( + new CurationDecision(CurationDecisionKind.Create, null, null, null, + "ambiguous: auto-resolve insufficient, defaulting to create"), + candidates); } - return CurationRulesEvaluator.GuardDestructiveUpdate(rulesDecision, operation, candidates); + return new CurationEvaluation( + CurationRulesEvaluator.GuardDestructiveUpdate(rulesDecision, operation, candidates), + candidates); + } + + /// + /// Embedding kNN nomination step (memory-core-redesign Slice 3 Stage B, task 3.1). Embeds + /// the proposal's "{title}\n{content}" text — the exact concatenation + /// embeds a written document with, so a proposal + /// and its eventual stored form land in the same region of embedding space — queries + /// 's current for up to + /// neighbors at or above + /// , then hydrates the + /// matched document ids into full-content candidates tagged with their cosine. + /// + /// + /// Cost: curation runs off the interactive turn path (checkpoint/session-boundary + /// triggered, via the daemon worker or the inline actor's post-turn write phase) — unlike + /// the sub-150ms recall-query budget (design D6), there is no per-turn latency budget here, + /// so the ~210ms median / ~280ms mean single-embed cost measured for the nominator model + /// (docs/research/memory-audit-2026-07.md §4, snowflake-arctic-embed 137M) is + /// acceptable — it does not block a user-visible response. + /// + /// + /// + /// Known limitation (intra-batch nomination): a proposal is nominated against the + /// store's already-committed embeddings only — it cannot nominate itself (it has not been + /// written yet), which is correct. But when a caller evaluates several proposals as one + /// batch (both write pipelines evaluate a checkpoint's candidates in a loop before any of + /// them commits), two mutually-near-duplicate proposals within that SAME batch will not see + /// each other, because neither is in the index yet when the other is nominated. This is an + /// accepted gap for this slice, not a design goal: cross-batch and steady-state dedup (the + /// overwhelming majority of write traffic) both work correctly, and closing the intra-batch + /// case would require either serializing writes mid-batch or a second batch-local similarity + /// pass — deferred until evidence shows same-batch near-duplicates are common enough to + /// justify the added complexity. + /// + /// + private async Task<(IReadOnlyList Nominees, double TopCosine)> NominateAsync( + IMemoryEmbedder embedder, + SQLiteMemoryCurationOperation operation, + CancellationToken ct) + { + var vectorIndex = await _vectorIndexHolder!.GetCurrentAsync(embedder, ct); + if (vectorIndex is null) + return ([], 0); + + var queryVector = await embedder.EmbedAsync($"{operation.Title}\n{operation.Content}", ct); + var matches = vectorIndex.TopK( + queryVector.Span, _curationConfig.NominatorK, _curationConfig.NominatorSimilarityThreshold); + if (matches.Count == 0) + return ([], 0); + + // Only "document" items are ever embedded (MemoryEmbedOnWriteCoordinator.DocumentItemKind) + // — immutable records bypass curation and are never embedded — but filter defensively + // rather than assume, since a future item kind sharing this model's embedding table + // would otherwise be silently mis-hydrated as a document candidate. + var documentIds = matches + .Where(m => string.Equals(m.ItemKind, MemoryEmbedOnWriteCoordinator.DocumentItemKind, StringComparison.Ordinal)) + .Select(m => m.ItemId) + .ToArray(); + if (documentIds.Length == 0) + return ([], 0); + + var hydrated = await _store.GetCandidatesByIdsAsync(documentIds, ct); + if (hydrated.Count == 0) + return ([], 0); + + var cosineByDocId = matches.ToDictionary(m => m.ItemId, m => m.Cosine, StringComparer.Ordinal); + var tagged = hydrated + .Select(c => cosineByDocId.TryGetValue(c.DocumentId, out var cosine) ? c with { CosineSimilarity = cosine } : c) + .ToArray(); + + // matches is already sorted descending by cosine (MemoryVectorIndex.TopK's contract). + return (tagged, matches[0].Cosine); } internal static async Task TryLlmEvaluationAsync( @@ -234,16 +491,18 @@ public async Task EvaluateAsync( SessionId sessionId, SQLiteMemoryCurationOperation operation, IReadOnlyList candidates, - ICurationLog log) + ICurationLog log, + MemoryCurationConfig curationConfig, + bool useFullCandidateContent = false) { try { - using var cts = new CancellationTokenSource(LlmTimeout); + using var cts = new CancellationTokenSource(TimeSpan.FromSeconds(curationConfig.LlmTimeoutSeconds)); var messages = new List { new(ChatRole.System, CurationPromptBuilder.SystemPrompt), - new(ChatRole.User, CurationPromptBuilder.BuildUserMessage(operation, candidates)) + new(ChatRole.User, CurationPromptBuilder.BuildUserMessage(operation, candidates, useFullCandidateContent)) }; // SessionScopedChatOptions carries the session id so this sidecar's chat-client @@ -255,17 +514,16 @@ public async Task EvaluateAsync( // Token cap is the THIRD line of defense, so it must never be the // binding constraint. Layering: (1) reasoning suppression below is // the primary fix — suppressed/non-reasoning models emit just the - // keyword and never approach any cap; (2) the 10s call timeout + // keyword and never approach any cap; (2) the call timeout above // bounds wall-clock when a model ignores suppression and thinks at // length. The cap only matters in the remaining window — suppression // ignored but thinking finishes inside the timeout — where a tight // cap truncates mid-think and reproduces the measured // responseLength=0 empty-reply failure (July 2026 audit: at 512, a // Qwen3.6-class model produced 0 successful curation decisions - // ever). Unemitted tokens cost nothing, so size this generously; - // it becomes the Memory.Curation.LlmMaxOutputTokens config knob in - // the memory-core-redesign change. - MaxOutputTokens = 4096, + // ever). Unemitted tokens cost nothing, so this is sized generously by + // default — see Memory.Curation.LlmMaxOutputTokens (MemoryCurationConfig). + MaxOutputTokens = curationConfig.LlmMaxOutputTokens, // Belt: ask the serving stack not to think at all for this // keyword-classification call. This expresses intent only — the raw // provider-dialect field name (vLLM/llama.cpp/SGLang's @@ -327,9 +585,16 @@ public async Task EvaluateAsync( /// mapping is identical from both the actor's write phase and the daemon's /// checkpoint-apply phase — before this slice only the actor performed it at all. /// + /// + /// The exact candidate set was evaluated against + /// ('s ) — the + /// source of the target/consolidation-target bodies that guard-validated write routing + /// (memory-core-redesign Slice 3) merges or appends against. + /// public async Task ApplyDecisionAsync( SQLiteMemoryCurationOperation operation, CurationDecision decision, + IReadOnlyList candidates, CancellationToken ct = default) { switch (decision.Kind) @@ -342,15 +607,7 @@ public async Task EvaluateAsync( return null; case CurationDecisionKind.Update: - _log.Info( - "curation_update anchor={0} targetDoc={1} reason={2}", - operation.AnchorCanonicalName, - decision.TargetDocumentId!, - decision.Reason); - - // Set the operation's MemoryId to the existing document ID - // so the ON CONFLICT UPDATE fires - return operation with { MemoryId = decision.TargetDocumentId }; + return ApplyUpdate(operation, decision, candidates); case CurationDecisionKind.Consolidate: _log.Info( @@ -361,9 +618,7 @@ public async Task EvaluateAsync( decision.Reason); await ExecuteConsolidationAsync(operation, decision, ct); - // After consolidation, write the new proposal under the canonical anchor - var canonicalAnchor = decision.CanonicalAnchorName ?? operation.AnchorCanonicalName; - return operation with { AnchorCanonicalName = canonicalAnchor }; + return ApplyConsolidate(operation, decision, candidates); case CurationDecisionKind.Create: _log.Info( @@ -380,6 +635,171 @@ public async Task EvaluateAsync( } } + /// + /// Write routing for an Update decision. The deterministic tier (exact-anchor path, + /// false) keeps its pre-Slice-3 raw overwrite — + /// see this class's remarks for why that is still provably non-lossy. Every LLM-tier + /// Update routes through instead. + /// + private SQLiteMemoryCurationOperation ApplyUpdate( + SQLiteMemoryCurationOperation operation, + CurationDecision decision, + IReadOnlyList candidates) + { + _log.Info( + "curation_update anchor={0} targetDoc={1} reason={2}", + operation.AnchorCanonicalName, + decision.TargetDocumentId!, + decision.Reason); + + if (!decision.FromLlmTier) + { + // Deterministic exact-anchor path: GuardDestructiveUpdate has already verified + // (in EvaluateAsync) that the proposal preserves the target's content, so this + // overwrite cannot drop information. Set MemoryId so the ON CONFLICT UPDATE fires + // against the existing document. + return operation with { MemoryId = decision.TargetDocumentId }; + } + + var target = candidates.FirstOrDefault( + c => string.Equals(c.DocumentId, decision.TargetDocumentId, StringComparison.Ordinal)); + if (target is null) + { + // The LLM named a document id outside the evaluated candidate set — there is no + // known existing body to merge with or safely append to. Rather than trust an + // unverified id for an overwrite, fall through as a plain create. + _log.Warning( + "curation_update_target_unknown anchor={0} targetId={1} — creating instead", + operation.AnchorCanonicalName, + decision.TargetDocumentId ?? "(null)"); + return operation; + } + + return ApplyGuardedMergeOrAppend( + operation, decision.MergedBody, target.DocumentId, target.Content, [target.Content, operation.Content]); + } + + /// + /// Write routing for a Consolidate decision, after 's + /// re-anchor/tombstone side effects. Both tiers flow through + /// uniformly: the deterministic tier (fuzzy match + /// ≥80% overlap, no LLM call) never produces a , + /// so it always takes that method's append branch — the only lossless option available + /// without an LLM-synthesized merge. This also fixes the pre-Slice-3 gap where a + /// deterministic Consolidate reached the store with no guard at all + /// ( is a no-op for Consolidate). + /// + private SQLiteMemoryCurationOperation ApplyConsolidate( + SQLiteMemoryCurationOperation operation, + CurationDecision decision, + IReadOnlyList candidates) + { + var canonicalAnchor = decision.CanonicalAnchorName ?? operation.AnchorCanonicalName; + var operationWithAnchor = operation with { AnchorCanonicalName = canonicalAnchor }; + + var consolidationTargets = decision.ConsolidationTargetIds is null + ? [] + : candidates + .Where(c => decision.ConsolidationTargetIds.Contains(c.DocumentId, StringComparer.Ordinal)) + .ToArray(); + + if (consolidationTargets.Length == 0) + { + // No known candidate content to merge/append against (e.g. an id the LLM invented, + // or a decision with no target ids at all) — nothing to preserve, so the store's + // own anchor-based lookup resolves the target document as it did before this slice. + return operationWithAnchor; + } + + // Deterministic pick of the primary consolidation target: same ordering + // CurationRulesEvaluator uses for "best" (confidence, then freshness). Pinning the + // write to this SAME document — rather than trusting the store's separate + // updated_at-based anchor lookup — guarantees the write lands on the document + // MergeGuard actually validated content against. + var primary = consolidationTargets + .OrderByDescending(c => c.Confidence) + .ThenByDescending(c => c.FreshnessAtMs ?? 0) + .First(); + + var allSourceBodies = consolidationTargets.Select(c => c.Content).Append(operation.Content).ToArray(); + + return ApplyGuardedMergeOrAppend( + operationWithAnchor, decision.MergedBody, primary.DocumentId, primary.Content, allSourceBodies); + } + + /// + /// Shared write routing for LLM-tier Update and deterministic/LLM-tier Consolidate + /// (memory-core-redesign Slice 3, design D5): validates a synthesized + /// against every source body via ; + /// on pass, writes the merged body with MergeDocument semantics (the existing merge path). + /// On guard failure, or when no merged body was produced at all, falls back to a + /// structural append (existing target body + dated separator + proposal) with + /// AppendDocument semantics — unconditionally lossless because it is concatenation, unlike + /// an overwrite. This is what makes a + /// real, reachable write path for the first time. + /// + private SQLiteMemoryCurationOperation ApplyGuardedMergeOrAppend( + SQLiteMemoryCurationOperation operation, + string? mergedBody, + string targetDocumentId, + string targetBody, + IReadOnlyList allSourceBodies) + { + if (!string.IsNullOrWhiteSpace(mergedBody)) + { + var guardResult = MergeGuard.Validate(allSourceBodies, mergedBody); + if (guardResult.Passed) + { + _log.Info( + "curation_merge_guard_passed anchor={0} targetDoc={1} reason={2}", + operation.AnchorCanonicalName, + targetDocumentId, + guardResult.Reason); + + return operation with + { + MemoryId = targetDocumentId, + Content = mergedBody, + UpdateSemantics = MemoryUpdateSemantics.MergeDocument.ToWireValue() + }; + } + + _log.Warning( + "curation_merge_guard_failed anchor={0} targetDoc={1} missingTokens=[{2}] reason={3}", + operation.AnchorCanonicalName, + targetDocumentId, + string.Join(",", guardResult.MissingTokens), + guardResult.Reason); + } + + var appendedBody = BuildAppendedBody(targetBody, operation.Content); + _log.Info( + "curation_append_fallback anchor={0} targetDoc={1} hadMergedBody={2}", + operation.AnchorCanonicalName, + targetDocumentId, + !string.IsNullOrWhiteSpace(mergedBody)); + + return operation with + { + MemoryId = targetDocumentId, + Content = appendedBody, + UpdateSemantics = MemoryUpdateSemantics.AppendDocument.ToWireValue() + }; + } + + /// + /// Builds the structural-append body: the existing content, a dated provenance separator, + /// then the proposal — plain concatenation, so no source content can be lost. The date + /// comes from the store's own () + /// rather than a second injected clock, so it stays consistent with the row's own + /// persisted timestamps and stays virtualizable in tests via the same seam. + /// + private string BuildAppendedBody(string existingBody, string proposalContent) + { + var isoDate = _store.TimeProvider.GetUtcNow().ToString("yyyy-MM-dd", System.Globalization.CultureInfo.InvariantCulture); + return $"{existingBody}\n\n---\n_[merged {isoDate}]_\n{proposalContent}"; + } + private async Task ExecuteConsolidationAsync( SQLiteMemoryCurationOperation operation, CurationDecision decision, @@ -426,3 +846,13 @@ private async Task ExecuteConsolidationAsync( } } } + +/// +/// A curation decision paired with the exact candidate set it was evaluated against — see +/// 's remarks for why +/// needs the same candidates rather +/// than re-querying the store. +/// +public sealed record CurationEvaluation( + CurationDecision Decision, + IReadOnlyList Candidates); diff --git a/src/Netclaw.Actors/Memory/MemoryCurationPipeline.cs b/src/Netclaw.Actors/Memory/MemoryCurationPipeline.cs index 76f7bb39c..f3f0b6f45 100644 --- a/src/Netclaw.Actors/Memory/MemoryCurationPipeline.cs +++ b/src/Netclaw.Actors/Memory/MemoryCurationPipeline.cs @@ -509,7 +509,10 @@ private static string Slugify(string value) public sealed class MemoryCurationEngine( SQLiteMemoryStore store, MemoryRulesFirstExtractor rules, - ILogger? logger = null) + MemoryConfig memoryConfig, + ILogger? logger = null, + MemoryEmbedderHolder? embedderHolder = null, + MemoryVectorIndexHolder? vectorIndexHolder = null) { private const string CheckpointDroppedEvent = "memory_checkpoint_dropped_before_curation"; private const string CheckpointDroppedTemplate = @@ -528,8 +531,15 @@ public sealed class MemoryCurationEngine( // all beyond the fingerprint check below — routing through the shared evaluator is // what makes GuardDestructiveUpdate (previously inline-actor-only; audit finding D14) // apply here too. + // + // embedderHolder/vectorIndexHolder ARE wired here (memory-core-redesign Slice 3 Stage B, + // task 3.1): the embedding kNN nominator runs on this pipeline too, even with no LLM + // client — a nominee found with no LLM available forces the conservative no-auto-merge + // Create outcome documented on MemoryCurationEvaluator.EvaluateAsync, never a silent + // auto-skip/auto-merge on cosine alone. private readonly MemoryCurationEvaluator _evaluator = - new(store, (ILogger)(logger ?? NullLogger.Instance), llmClient: null); + new(store, (ILogger)(logger ?? NullLogger.Instance), memoryConfig.Curation, + llmClient: null, embedderHolder, vectorIndexHolder); public async Task> CurateAsync( SQLiteMemoryCheckpoint checkpoint, @@ -625,8 +635,8 @@ private async Task> EvaluateAndAppl foreach (var operation in operations) { - var decision = await _evaluator.EvaluateAsync(operation, sessionId, ct); - var writeOp = await _evaluator.ApplyDecisionAsync(operation, decision, ct); + var evaluation = await _evaluator.EvaluateAsync(operation, sessionId, ct); + var writeOp = await _evaluator.ApplyDecisionAsync(operation, evaluation.Decision, evaluation.Candidates, ct); if (writeOp is not null) results.Add(writeOp); } diff --git a/src/Netclaw.Actors/Memory/MemoryVectorIndexHolder.cs b/src/Netclaw.Actors/Memory/MemoryVectorIndexHolder.cs new file mode 100644 index 000000000..14ffc5049 --- /dev/null +++ b/src/Netclaw.Actors/Memory/MemoryVectorIndexHolder.cs @@ -0,0 +1,69 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +namespace Netclaw.Actors.Memory; + +/// +/// Process-singleton owner of the the embedding kNN nominator +/// queries (memory-core-redesign Slice 3 Stage B, task 3.1). Mirrors +/// 's reason for existing as a mutable holder rather than a +/// plain DI singleton: requires a known, positive +/// at construction, but the real embedder (and its +/// dimensions) is only known once EmbeddingWarmupHostedService finishes provisioning — +/// which runs after every other singleton has already resolved its constructor dependencies. +/// This holder defers index construction to first use, and rebuilds it if the active embedder's +/// model id ever changes (an operator flipping Memory.Embeddings.ModelId and restarting). +/// +/// +/// Callers must call at the time they actually need to query +/// — never cache the returned index across calls — so a model change or the transition from +/// unavailable to available surfaces without a process restart, exactly like +/// . +/// +/// +public sealed class MemoryVectorIndexHolder +{ + private readonly SQLiteMemoryStore _store; + private readonly object _gate = new(); + private MemoryVectorIndex? _index; + + public MemoryVectorIndexHolder(SQLiteMemoryStore store) + { + ArgumentNullException.ThrowIfNull(store); + _store = store; + } + + /// + /// Returns the vector index for 's current model, reloaded to the + /// store's latest committed embeddings (memory-core-redesign Slice 3: cheap when nothing + /// changed — only does real work when + /// has advanced). Returns null when + /// cannot currently produce vectors — there is nothing to index, + /// and callers should treat this identically to "no vector evidence available" (the + /// degraded/lexical path). + /// + public async Task GetCurrentAsync(IMemoryEmbedder embedder, CancellationToken ct) + { + if (!embedder.IsAvailable) + return null; + + var index = Volatile.Read(ref _index); + if (index is null || !string.Equals(index.ModelId, embedder.ModelId, StringComparison.Ordinal)) + { + lock (_gate) + { + index = _index; + if (index is null || !string.Equals(index.ModelId, embedder.ModelId, StringComparison.Ordinal)) + { + index = new MemoryVectorIndex(_store, embedder.ModelId, embedder.Dimensions); + Volatile.Write(ref _index, index); + } + } + } + + await index.ReloadIfStaleAsync(ct).ConfigureAwait(false); + return index; + } +} diff --git a/src/Netclaw.Actors/Memory/MergeGuard.cs b/src/Netclaw.Actors/Memory/MergeGuard.cs new file mode 100644 index 000000000..4772e0a24 --- /dev/null +++ b/src/Netclaw.Actors/Memory/MergeGuard.cs @@ -0,0 +1,183 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using System.Text.RegularExpressions; + +namespace Netclaw.Actors.Memory; + +/// +/// Result of a single call. +/// +public sealed record MergeGuardResult(bool Passed, IReadOnlyList MissingTokens, string Reason); + +/// +/// Deterministic validator for LLM-synthesized merge bodies (memory-core-redesign design +/// D5). The curation LLM occasionally drops information when combining several source +/// documents into one — the May 2026 decider eval measured ~27% wrong-merge on hard +/// near-duplicates. Trusting the merge blindly risks silent, unrecoverable data loss; +/// refusing to merge at all just recreates the duplicate-accumulation problem curation +/// exists to fix. This guard turns a bad merge into a recoverable state instead: on +/// failure, falls back to a +/// structural append, so every source's content survives even though the synthesis didn't +/// land — over-appending is the acceptable failure mode, silent loss is not. +/// +/// Two independent checks, both must pass: +/// 1. Retention — every load-bearing token (URL; number/version/quantity/date; +/// camelCase/snake_case/kebab-case/dotted.path/ALL_CAPS identifier; file path) extracted +/// from ANY source body must be case-insensitively present in the merged body, for at +/// least 95% of the token union across all sources. The 5% slack tolerates trivial LLM +/// rewording of genuinely incidental tokens (e.g. a URL repeated verbatim in two sources). +/// 2. Collapse — the merged body must be at least 60% as long as the longest single +/// source. Catches an LLM that "merges" by discarding everything but a short summary, +/// which could otherwise pass the retention check if the summary happens to repeat every +/// load-bearing token without preserving the surrounding prose. +/// +/// Pure function, no I/O — safe to property-test with generated source/merged bodies. +/// +public static class MergeGuard +{ + private const double RetentionThreshold = 0.95; + private const double LengthCollapseThreshold = 0.60; + + private const string MonthNames = + "Jan(?:uary)?|Feb(?:ruary)?|Mar(?:ch)?|Apr(?:il)?|May|Jun(?:e)?|Jul(?:y)?|Aug(?:ust)?|" + + "Sep(?:tember)?|Oct(?:ober)?|Nov(?:ember)?|Dec(?:ember)?"; + + // URLs (trailing sentence punctuation is trimmed in ExtractLoadBearingTokens below). + private static readonly Regex UrlPattern = new( + @"https?://[^\s""'<>\)\]]+", RegexOptions.Compiled | RegexOptions.IgnoreCase); + + // ISO-8601 dates, with or without a time component: 2026-05-13, 2026-05-13T10:00:00Z. + private static readonly Regex IsoDatePattern = new( + @"\b\d{4}-\d{2}-\d{2}(?:T\d{2}:\d{2}(?::\d{2})?Z?)?\b", RegexOptions.Compiled); + + // Slash-separated dates: 05/13/2026, 5/13/26. + private static readonly Regex SlashDatePattern = new( + @"\b\d{1,2}/\d{1,2}/\d{2,4}\b", RegexOptions.Compiled); + + // Written dates in either order: "May 13, 2026" / "13 May 2026". + private static readonly Regex WrittenDatePattern = new( + $@"\b(?:{MonthNames})\.?\s+\d{{1,2}}(?:st|nd|rd|th)?,?\s+\d{{4}}\b", + RegexOptions.Compiled | RegexOptions.IgnoreCase); + + private static readonly Regex ReverseWrittenDatePattern = new( + $@"\b\d{{1,2}}\s+(?:{MonthNames})\.?,?\s+\d{{4}}\b", + RegexOptions.Compiled | RegexOptions.IgnoreCase); + + // Dotted/multi-segment versions: 1.2.3, 1.5.62, 10.0. + private static readonly Regex VersionPattern = new( + @"\b\d+(?:\.\d+){1,3}\b", RegexOptions.Compiled); + + // Quantities: a number immediately followed by a short unit — 64GB, 300ms, 72h, 10s. + private static readonly Regex QuantityPattern = new( + @"\b\d+(?:\.\d+)?[a-zA-Z]{1,6}\b", RegexOptions.Compiled); + + // Bare integers not already part of a version, quantity, or identifier: the lookarounds + // exclude a digit preceded by "." (the "62" inside "1.5.62") or a letter/underscore + // (mid-identifier), and a digit followed by a letter/underscore (the "20" inside "20GB") + // or "." (the "1" inside "1.5"). Ordinary sentence punctuation immediately after a + // number — "cost is 111." — is deliberately NOT excluded here, unlike a naive + // "no dot allowed after" rule would: a trailing period with no digit after it is not part + // of a version/decimal, so the number is still load-bearing and must be captured. + private static readonly Regex BareIntegerPattern = new( + @"(? + /// Validates a synthesized merge body against every source body it claims to combine. + /// + /// + /// Every body the merge is supposed to losslessly union — for an UPDATE decision, the + /// target document's current content plus the proposal; for CONSOLIDATE, every + /// consolidation target's content plus the proposal. + /// + /// The LLM-synthesized merged body to validate. + public static MergeGuardResult Validate(IReadOnlyList sourceBodies, string mergedBody) + { + ArgumentNullException.ThrowIfNull(sourceBodies); + mergedBody ??= string.Empty; + + if (sourceBodies.Count == 0) + return new MergeGuardResult(true, [], "no source bodies to validate against"); + + var union = new HashSet(StringComparer.OrdinalIgnoreCase); + var longestSourceLength = 0; + foreach (var source in sourceBodies) + { + if (string.IsNullOrEmpty(source)) + continue; + + longestSourceLength = Math.Max(longestSourceLength, source.Length); + foreach (var token in ExtractLoadBearingTokens(source)) + union.Add(token); + } + + var missing = union + .Where(token => !mergedBody.Contains(token, StringComparison.OrdinalIgnoreCase)) + .ToArray(); + var retainedCount = union.Count - missing.Length; + var retentionRatio = union.Count == 0 ? 1.0 : (double)retainedCount / union.Count; + var retentionOk = retentionRatio >= RetentionThreshold; + + var lengthRatio = longestSourceLength == 0 ? 1.0 : (double)mergedBody.Length / longestSourceLength; + var lengthOk = lengthRatio >= LengthCollapseThreshold; + + var passed = retentionOk && lengthOk; + var reason = !retentionOk + ? $"retention {retentionRatio:P0} below {RetentionThreshold:P0} floor — missing {missing.Length}/{union.Count} load-bearing tokens" + : !lengthOk + ? $"merged length {mergedBody.Length} is only {lengthRatio:P0} of longest source ({longestSourceLength} chars), below the {LengthCollapseThreshold:P0} collapse floor" + : $"retained {retainedCount}/{union.Count} load-bearing tokens ({retentionRatio:P0}); merged length {mergedBody.Length} is {lengthRatio:P0} of longest source ({longestSourceLength} chars)"; + + return new MergeGuardResult(passed, missing, reason); + } + + private static IEnumerable ExtractLoadBearingTokens(string text) + { + foreach (var pattern in TokenPatterns) + { + foreach (Match match in pattern.Matches(text)) + { + var token = match.Value.TrimEnd('.', ',', ':', ';', ')', ']'); + if (token.Length > 0) + yield return token; + } + } + } +} diff --git a/src/Netclaw.Actors/Memory/SQLiteMemoryStore.cs b/src/Netclaw.Actors/Memory/SQLiteMemoryStore.cs index 9120c1e01..9cc49afba 100644 --- a/src/Netclaw.Actors/Memory/SQLiteMemoryStore.cs +++ b/src/Netclaw.Actors/Memory/SQLiteMemoryStore.cs @@ -29,6 +29,15 @@ public SQLiteMemoryStore(string sqlitePath, TimeProvider timeProvider, ILogger.Instance; } + /// + /// The clock this store persists timestamps with. Exposed so callers that need dates + /// consistent with the store's own persisted timestamps (e.g. + /// 's structural-append fallback separator, + /// memory-core-redesign Slice 3) reuse this instance instead of threading a second + /// dependency through the same call chain. + /// + public TimeProvider TimeProvider => _timeProvider; + /// /// Process-local monotonic counter bumped whenever memory_embeddings rows change /// (a real write in , or a deletion via @@ -1213,6 +1222,63 @@ public async Task> GetEmbeddingsForModel }, ct); } + /// + /// Hydrates documents by id into full-content rows — + /// how the embedding kNN nominator (memory-core-redesign Slice 3 Stage B, task 3.1) turns + /// nominee ids into candidates the curation evaluator + /// can reason about (and hand full-content to the curator LLM, + /// 's useFullCandidateContent). Non-tombstoned + /// documents under active anchors only, mirroring 's + /// filters. is always false here — + /// cosine nomination is a distinct signal from anchor-name matching, tagged onto the result + /// by the caller (which already has each id's cosine from ). + /// One id per query, over a single connection, mirroring + /// 's per-id loop rather than a dynamic SQL + /// IN clause — the nominee count is bounded by Memory.Curation.NominatorK (default 5), + /// so this is never a large batch. + /// + public async Task> GetCandidatesByIdsAsync( + IReadOnlyList documentIds, + CancellationToken ct = default) + { + if (documentIds.Count == 0) + return []; + + return await WithConnectionAsync(async (conn, ct) => + { + var results = new List(); + foreach (var documentId in documentIds) + { + await using var cmd = conn.CreateCommand(); + cmd.CommandText = $""" + SELECT a.anchor_id, a.canonical_name, + d.document_id, d.markdown_body, d.freshness_at, d.confidence + FROM memory_documents d + JOIN memory_anchors a ON d.anchor_id = a.anchor_id + WHERE d.document_id = $id + AND a.status = 'active' + AND d.update_semantics != '{MemoryUpdateSemantics.Tombstone.ToWireValue()}'; + """; + cmd.Parameters.AddWithValue("$id", documentId); + + await using var reader = await cmd.ExecuteReaderAsync(ct); + if (await reader.ReadAsync(ct)) + { + results.Add(new ExistingMemoryCandidate( + DocumentId: reader.GetString(2), + AnchorId: reader.GetString(0), + AnchorCanonicalName: reader.GetString(1), + Content: reader.GetString(3), + FreshnessAtMs: reader.IsDBNull(4) ? null : reader.GetInt64(4), + Confidence: reader.IsDBNull(5) ? 0.0 : reader.GetDouble(5), + IsExactAnchorMatch: false)); + } + } + + return (IReadOnlyList)results; + }, ct); + } + /// /// Coverage diagnostics for (memory-embeddings spec: "Embedding /// coverage diagnostics"). diff --git a/src/Netclaw.Actors/Sessions/LlmSessionActor.cs b/src/Netclaw.Actors/Sessions/LlmSessionActor.cs index 0bc6afae8..172a2fd06 100644 --- a/src/Netclaw.Actors/Sessions/LlmSessionActor.cs +++ b/src/Netclaw.Actors/Sessions/LlmSessionActor.cs @@ -70,6 +70,7 @@ public sealed class LlmSessionActor : ReceivePersistentActor, IWithTimers private readonly ISessionLifecycleObserver? _lifecycleObserver; private readonly Memory.SQLiteMemoryStore? _memoryStore; private readonly Memory.MemoryEmbedderHolder? _memoryEmbedderHolder; + private readonly Memory.MemoryVectorIndexHolder? _memoryVectorIndexHolder; private readonly IChatClientProvider _clientProvider; private readonly ILoggingAdapter _log; @@ -241,6 +242,7 @@ public LlmSessionActor( _memoryCheckpointSink = memory?.CheckpointSink ?? NullMemoryCheckpointSink.Instance; _memoryStore = memory?.MemoryStore; _memoryEmbedderHolder = memory?.EmbedderHolder; + _memoryVectorIndexHolder = memory?.VectorIndexHolder; _memoryConfig = memory?.MemoryConfig ?? new MemoryConfig(); _timeProvider = services.TimeProvider; _sessionsBasePath = services.Paths.SessionsDirectory; @@ -314,7 +316,9 @@ public LlmSessionActor( if (_memoryStore is not null) { _curationActor = Context.ActorOf( - Memory.MemoryCurationActor.CreateProps(_memoryStore, _sessionId, _clientProvider, _memoryEmbedderHolder), + Memory.MemoryCurationActor.CreateProps( + _memoryStore, _sessionId, _memoryConfig.Curation, _clientProvider, + _memoryEmbedderHolder, _memoryVectorIndexHolder), "memory-curation"); // Distillation processes a full transcript — allow 5x normal sidecar timeout diff --git a/src/Netclaw.Actors/Sessions/SessionDependencies.cs b/src/Netclaw.Actors/Sessions/SessionDependencies.cs index 1578a30bf..09cb0d6c9 100644 --- a/src/Netclaw.Actors/Sessions/SessionDependencies.cs +++ b/src/Netclaw.Actors/Sessions/SessionDependencies.cs @@ -41,9 +41,10 @@ public sealed record SessionToolServices( /// /// Memory infrastructure for recall, checkpoint, and curation. /// resolves the process's embedder for embed-on-write -/// (memory-core-redesign Slice 2). Null is a genuine state — same as -/// being null — for any session/test harness that has not wired -/// up the embedding subsystem at all. +/// (memory-core-redesign Slice 2) and for the curation evaluator's embedding kNN nominator +/// (Slice 3 Stage B, task 3.1); resolves the nominator's +/// vector index. Null is a genuine state — same as being null — +/// for any session/test harness that has not wired up the embedding subsystem at all. /// public sealed record SessionMemoryServices( IMemoryExtractor MemoryExtractor, @@ -51,7 +52,8 @@ public sealed record SessionMemoryServices( IMemoryCheckpointSink CheckpointSink, SQLiteMemoryStore? MemoryStore, MemoryConfig? MemoryConfig = null, - MemoryEmbedderHolder? EmbedderHolder = null); + MemoryEmbedderHolder? EmbedderHolder = null, + MemoryVectorIndexHolder? VectorIndexHolder = null); /// /// Metrics and lifecycle observation. diff --git a/src/Netclaw.Cli.Tests/Doctor/ConfigSchemaDoctorCheckTests.cs b/src/Netclaw.Cli.Tests/Doctor/ConfigSchemaDoctorCheckTests.cs index 9773789a8..1584bb716 100644 --- a/src/Netclaw.Cli.Tests/Doctor/ConfigSchemaDoctorCheckTests.cs +++ b/src/Netclaw.Cli.Tests/Doctor/ConfigSchemaDoctorCheckTests.cs @@ -151,6 +151,61 @@ await File.WriteAllTextAsync(paths.NetclawConfigPath, Assert.Equal(DoctorSeverity.Error, result.Severity); } + [Fact] + public async Task ReturnsPass_WhenMemoryCurationConfigMatchesSchemaV1() + { + var basePath = CreateTempBasePath(); + var paths = new NetclawPaths(basePath); + paths.EnsureDirectoriesExist(); + + await File.WriteAllTextAsync(paths.NetclawConfigPath, + """ + { + "configVersion": 1, + "Memory": { + "Enabled": true, + "Curation": { + "NominatorSimilarityThreshold": 0.9, + "NominatorK": 3, + "LlmMaxOutputTokens": 2048, + "LlmTimeoutSeconds": 15 + } + } + } + """, TestContext.Current.CancellationToken); + + var check = new ConfigSchemaDoctorCheck(paths); + var result = await check.RunAsync(TestContext.Current.CancellationToken); + + Assert.Equal(DoctorSeverity.Pass, result.Severity); + } + + [Fact] + public async Task ReturnsError_WhenMemoryCurationHasAnUnknownProperty() + { + var basePath = CreateTempBasePath(); + var paths = new NetclawPaths(basePath); + paths.EnsureDirectoriesExist(); + + await File.WriteAllTextAsync(paths.NetclawConfigPath, + """ + { + "configVersion": 1, + "Memory": { + "Curation": { + "NominatorK": 3, + "NotARealProperty": "oops" + } + } + } + """, TestContext.Current.CancellationToken); + + var check = new ConfigSchemaDoctorCheck(paths); + var result = await check.RunAsync(TestContext.Current.CancellationToken); + + Assert.Equal(DoctorSeverity.Error, result.Severity); + } + [Fact] public async Task ReturnsPass_WhenReverseProxyTrustedProxiesLookValid() { diff --git a/src/Netclaw.Configuration.Tests/MemoryConfigDefaultsTests.cs b/src/Netclaw.Configuration.Tests/MemoryConfigDefaultsTests.cs index 3cfc3e479..eef9d1dd4 100644 --- a/src/Netclaw.Configuration.Tests/MemoryConfigDefaultsTests.cs +++ b/src/Netclaw.Configuration.Tests/MemoryConfigDefaultsTests.cs @@ -43,4 +43,34 @@ public void Memory_subsystem_remains_enabled_by_default() var config = new MemoryConfig(); Assert.True(config.Enabled); } + + // ── MemoryCurationConfig (memory-core-redesign Slice 3, task 3.5) ── + + [Fact] + public void Curation_nominator_similarity_threshold_defaults_to_0_86() + { + var config = new MemoryConfig(); + Assert.Equal(0.86, config.Curation.NominatorSimilarityThreshold); + } + + [Fact] + public void Curation_nominator_k_defaults_to_5() + { + var config = new MemoryConfig(); + Assert.Equal(5, config.Curation.NominatorK); + } + + [Fact] + public void Curation_llm_max_output_tokens_defaults_to_4096() + { + var config = new MemoryConfig(); + Assert.Equal(4096, config.Curation.LlmMaxOutputTokens); + } + + [Fact] + public void Curation_llm_timeout_seconds_defaults_to_10() + { + var config = new MemoryConfig(); + Assert.Equal(10, config.Curation.LlmTimeoutSeconds); + } } diff --git a/src/Netclaw.Configuration/MemoryConfig.cs b/src/Netclaw.Configuration/MemoryConfig.cs index 9c8195222..9de84e81f 100644 --- a/src/Netclaw.Configuration/MemoryConfig.cs +++ b/src/Netclaw.Configuration/MemoryConfig.cs @@ -32,6 +32,12 @@ public sealed class MemoryConfig /// foundation). See for why this defaults off. /// public MemoryEmbeddingsConfig Embeddings { get; set; } = new(); + + /// + /// Write-side curation settings (memory-core-redesign Slice 3: nominate→decide + + /// lossless merge). See . + /// + public MemoryCurationConfig Curation { get; set; } = new(); } /// @@ -67,3 +73,47 @@ public sealed class MemoryEmbeddingsConfig /// public bool AutoDownload { get; set; } = true; } + +/// +/// Configuration for write-side curation: the embedding kNN nominator and the curation LLM +/// call (memory-core-redesign Slice 3, design D4/D5). +/// +public sealed class MemoryCurationConfig +{ + /// + /// Embedding cosine similarity threshold above which an existing memory is nominated as a + /// dedup candidate, forcing the curator LLM to adjudicate the relationship (design D4: "no + /// cosine threshold separates duplicates from siblings," so similarity only nominates — + /// it never auto-merges or auto-skips). Consumed by + /// 's embedding kNN nominator + /// (memory-core-redesign Slice 3 Stage B, task 3.1) via + /// Netclaw.Actors.Memory.MemoryVectorIndex.TopK. + /// + public double NominatorSimilarityThreshold { get; set; } = 0.86; + + /// + /// Maximum number of nearest-neighbor nominees the kNN nominator shortlists per proposal. + /// See 's remarks — same Slice 3 Stage B consumer. + /// + public int NominatorK { get; set; } = 5; + + /// + /// Maximum output tokens for the curation LLM call + /// ('s + /// TryLlmEvaluationAsync). Sized generously by default: the token cap is the third + /// line of defense against a truncated reply (after reasoning suppression and the call + /// timeout below), so it must never be the binding constraint — the July 2026 audit found + /// a 512-token cap produced zero successful curation decisions ever, because a + /// reasoning-capable model was truncated mid-think before emitting its answer. Raising + /// this further is nearly free (unemitted tokens cost nothing); lowering it below what a + /// verbose merged body needs risks reproducing that failure with the new merged-body + /// protocol (task 3.2). + /// + public int LlmMaxOutputTokens { get; set; } = 4096; + + /// + /// Wall-clock timeout, in seconds, for the curation LLM call. Bounds latency when a model + /// ignores reasoning suppression and thinks at length regardless of the token cap above. + /// + public int LlmTimeoutSeconds { get; set; } = 10; +} diff --git a/src/Netclaw.Configuration/Schemas/netclaw-config.v1.schema.json b/src/Netclaw.Configuration/Schemas/netclaw-config.v1.schema.json index efddfb071..18e454463 100644 --- a/src/Netclaw.Configuration/Schemas/netclaw-config.v1.schema.json +++ b/src/Netclaw.Configuration/Schemas/netclaw-config.v1.schema.json @@ -386,6 +386,40 @@ } }, "additionalProperties": false + }, + "Curation": { + "type": "object", + "description": "Write-side curation settings: embedding kNN nominator and curation LLM call (memory-core-redesign Slice 3).", + "properties": { + "NominatorSimilarityThreshold": { + "type": "number", + "minimum": 0, + "maximum": 1, + "default": 0.86, + "description": "Embedding cosine similarity threshold above which an existing memory is nominated for the curator LLM to adjudicate. Not yet consumed (Slice 3 Stage B)." + }, + "NominatorK": { + "type": "integer", + "minimum": 1, + "maximum": 50, + "default": 5, + "description": "Maximum number of nearest-neighbor nominees the kNN nominator shortlists per proposal. Not yet consumed (Slice 3 Stage B)." + }, + "LlmMaxOutputTokens": { + "type": "integer", + "minimum": 1, + "default": 4096, + "description": "Maximum output tokens for the curation LLM call. Sized generously — the token cap is a defense-in-depth bound, not the primary control." + }, + "LlmTimeoutSeconds": { + "type": "integer", + "minimum": 1, + "maximum": 300, + "default": 10, + "description": "Wall-clock timeout in seconds for the curation LLM call." + } + }, + "additionalProperties": false } }, "additionalProperties": false diff --git a/src/Netclaw.Daemon/Program.cs b/src/Netclaw.Daemon/Program.cs index 3b9b66900..8bfbafac3 100644 --- a/src/Netclaw.Daemon/Program.cs +++ b/src/Netclaw.Daemon/Program.cs @@ -752,6 +752,13 @@ static void ConfigureDaemonServices( EmbeddingModelProvisioner.Allowlist)); services.AddSingleton(new MemoryEmbedderHolder( new UnavailableMemoryEmbedder(memoryConfig.Embeddings.ModelId, "embedding warmup has not completed yet"))); + + // Vector index for the curation evaluator's embedding kNN nominator (memory-core- + // redesign Slice 3 Stage B, task 3.1). Registered alongside MemoryEmbedderHolder above: + // both are optional dependencies of MemoryCurationActor/MemoryCurationEngine that + // degrade to the lexical content-term search when either is absent or the embedder is + // unavailable. + services.AddSingleton(new MemoryVectorIndexHolder(memoryStore)); services.AddSingleton(); services.AddSingleton(sp => sp.GetRequiredService()); } @@ -1002,7 +1009,8 @@ static void ConfigureDaemonServices( sp.GetService() ?? NullMemoryCheckpointSink.Instance, sp.GetService(), sp.GetService(), - sp.GetService())); + sp.GetService(), + sp.GetService())); services.AddSingleton(sp => new SessionObservability( sp.GetService(), From ec15629475e42d753a883de78f9e2509b9db51e4 Mon Sep 17 00:00:00 2001 From: Aaron Stannard Date: Sun, 5 Jul 2026 20:57:09 -0500 Subject: [PATCH 03/37] fix(memory): guard-rejected anchor updates fall through to nomination instead of silently dropping (opsx: memory-core-redesign) (#1587) Eval run ad9a2312 (daemon log lines 856/903/943/1036) surfaced a silent-fallback bug in MemoryCurationEvaluator.EvaluateAsync: when an anchor exact-match's deterministic Update decision is downgraded by GuardDestructiveUpdate (the proposal would not preserve the target's content), the resulting Skip was returned as the FINAL decision - terminating evaluation before the embedding kNN nominator or lexical content-term search ever ran. An LLM-emitted junk anchor (the bare stopword "the") collided with an unrelated existing document sharing that same junk anchor text; the guard correctly refused to clobber the unrelated doc, but nothing then created the requested fact - an explicit store_memory proposal ended as a no-op (operations=0) with only a debug-level skip marker, in 4/5 repro runs. Fix: when GuardDestructiveUpdate downgrades an anchor-matched Update to Skip, demote every exact-anchor-matched candidate to an ordinary fuzzy candidate and re-run the evaluation chain as if there had been no exact anchor match at all - embedding nomination (when available), then lexical content search, then the rules/LLM/auto-resolve tiers, with Create as the terminal default. The demotion is what prevents re-looping into the same Update/guard-reject pair. The guard's protective effect is unchanged: the mismatched target is never overwritten. Emits a new curation_guard_fallthrough anchor={name} rejectedTarget={id} marker so the path is observable; existing skip/degraded markers are untouched. Explicitly out of scope: anchor-name hygiene/stopword filtering (junk anchors like "the" shouldn't be stored or fuzzy-matched at all) - that's a broader change to anchor matching behavior, left as a follow-up with a code comment. Tests (MemoryCurationEvaluatorParityTests, both Akka/ILogger construction paths for parity): - guard-rejected anchor Update, no other candidates -> Create (was Skip) - guard-rejected anchor Update + scripted embedder/index with a real near-dupe above the nominator threshold -> nominator runs, LLM tier invoked - regression: guard-rejected Update whose content is close enough to clear TryAutoResolveAmbiguous's thresholds once re-evaluated as fuzzy -> genuine auto-resolved Skip (fall-through does not force Create over a real dupe) - curation_guard_fallthrough marker fires with anchor + rejected target id Suites green: Actors 2617, Daemon 832, Cli 1231, Configuration 461, Embeddings 18. Release build clean (0 warnings/errors). Slopwatch 0 new issues. Header verification clean. --- .../MemoryCurationEvaluatorParityTests.cs | 192 +++++++++++++++++- .../Memory/MemoryCurationEvaluator.cs | 123 +++++++++-- 2 files changed, 287 insertions(+), 28 deletions(-) diff --git a/src/Netclaw.Actors.Tests/Memory/MemoryCurationEvaluatorParityTests.cs b/src/Netclaw.Actors.Tests/Memory/MemoryCurationEvaluatorParityTests.cs index b0f00fcb1..6510f3867 100644 --- a/src/Netclaw.Actors.Tests/Memory/MemoryCurationEvaluatorParityTests.cs +++ b/src/Netclaw.Actors.Tests/Memory/MemoryCurationEvaluatorParityTests.cs @@ -175,10 +175,19 @@ await SeedDocumentAsync( Assert.Equal(CurationDecisionKind.Create, fromActor.Kind); } - // ── guard downgrade: Update whose proposal drops existing body -> Skip ── + // ── guard downgrade: Update whose proposal drops existing body -> falls through ── + /// + /// Guard-fallthrough fix (July 2026 audit, eval run ad9a2312): before this fix, a + /// guard-rejected anchor-matched Update terminated as Skip — an explicit proposal silently + /// becoming a no-op with zero writes. No other candidate exists in this store for the + /// fallen-through content search or embedding nominator (both unavailable/empty here) to + /// find, so the terminal decision is the Create default the flow's remarks document — NOT + /// the old Skip. This is the same fixture GuardDowngrade_narrowerProposal_downgradesUpdateToSkip_identically + /// used pre-fix (renamed here since Skip was exactly the bug). + /// [Fact] - public async Task GuardDowngrade_narrowerProposal_downgradesUpdateToSkip_identically() + public async Task GuardDowngrade_narrowerProposal_noOtherCandidates_fallsThroughToCreate_identically() { var ct = TestContext.Current.CancellationToken; await _store.InitializeAsync(ct); @@ -189,19 +198,169 @@ await SeedDocumentAsync( freshnessAtMs: 1000, ct); - // Newer, but narrower — the rules tier would pick Update (exact anchor, low - // overlap, fresher), and GuardDestructiveUpdate must downgrade it on BOTH paths - // now (audit finding D14: this guard used to run only on the inline actor path). + // Newer, but narrower — the rules tier would pick Update (exact anchor, low overlap, + // fresher), and GuardDestructiveUpdate downgrades it on BOTH paths (audit finding D14: + // this guard used to run only on the inline actor path). Pre-fix, that downgrade was + // returned as the terminal Skip decision — the silent-fallback bug. Post-fix, the guard + // rejection triggers a fall-through re-evaluation (no exact anchor match, nomination/ + // content search run for the first time, rules tier re-runs as pure fuzzy) which here + // finds nothing else to match against and lands on Create. var operation = MakeOperation("widget-specs", "Widget pricing is TBD as of Q2.", freshnessAtMs: 2000); var (fromActor, fromEngine) = await EvaluateOnBothAsync(operation, ct); + AssertSameDecision(fromActor, fromEngine); + Assert.Equal(CurationDecisionKind.Create, fromActor.Kind); + Assert.Contains("fuzzy anchor match but low content overlap", fromActor.Reason); + } + + /// + /// Regression companion to the Create case above: when the guard-rejected proposal IS close + /// enough in content to the anchor target to clear the deterministic auto-resolve thresholds + /// ('s 60% content-overlap / 50% + /// anchor-Jaccard bars) once re-evaluated as an ordinary fuzzy candidate, the fall-through + /// must NOT force Create — it must let the normal ambiguous/auto-resolve machinery decide, + /// which correctly lands on Skip here. This proves the fix doesn't trade "always drops the + /// fact" for "never skips a real duplicate" — the fall-through defers to whatever the rest of + /// the flow genuinely produces. + /// + [Fact] + public async Task GuardDowngrade_contentCloseEnoughToAutoResolve_fallsThroughToLegitimateSkip_identically() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + await SeedDocumentAsync( + "widget-specs", + "doc-widget", + "Widget specs: 16 cores, 64GB RAM, 2 NICs. Warranty is 3 years from Acme Corp in Denver.", + freshnessAtMs: 1000, + ct); + + // Reworded/reordered restatement of the SAME facts: word-level overlap is ~62% (inside + // the exact-match tier's Update band, ≤80%) but GuardDestructiveUpdate's stricter + // substring-containment check fails (the words are reordered, not a literal superset), + // so the guard still rejects. Re-evaluated as a fuzzy candidate after fall-through, that + // same ~62% overlap clears TryAutoResolveAmbiguous's 60% content / 100% anchor-Jaccard + // (identical anchor name) thresholds, so this is genuine skip territory rather than the + // guard's blunt termination. + var operation = MakeOperation( + "widget-specs", + "Acme Corp widget in Denver: 2 NICs, 64GB RAM, 16 cores, and a 3 year warranty included.", + freshnessAtMs: 2000); + + var (fromActor, fromEngine) = await EvaluateOnBothAsync(operation, ct); + AssertSameDecision(fromActor, fromEngine); Assert.Equal(CurationDecisionKind.Skip, fromActor.Kind); - Assert.Contains("update guarded", fromActor.Reason); + Assert.Contains("auto-resolved", fromActor.Reason); Assert.Equal("doc-widget", fromActor.TargetDocumentId); } + /// + /// Asserts the structured curation_guard_fallthrough marker fires with the rejected + /// anchor and target — the July 2026 audit tooling greps daemon logs for this exact string + /// (per this class's remarks), so the marker itself is a load-bearing observability + /// contract, not incidental. + /// + [Fact] + public async Task GuardDowngrade_logsStructuredFallthroughMarker() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + await SeedDocumentAsync( + "widget-specs", + "doc-widget", + "Widget specs: 16 cores, 64GB RAM, 2 NICs. Pricing on file. Vendor contacts listed.", + freshnessAtMs: 1000, + ct); + + var operation = MakeOperation("widget-specs", "Widget pricing is TBD as of Q2.", freshnessAtMs: 2000); + + var recordingLogger = new RecordingLogger(); + var evaluator = new MemoryCurationEvaluator(_store, (ILogger)recordingLogger, new MemoryCurationConfig()); + + var evaluation = await evaluator.EvaluateAsync(operation, TestSessionId, ct); + + Assert.Equal(CurationDecisionKind.Create, evaluation.Decision.Kind); + Assert.Contains( + recordingLogger.Entries, + e => e.Contains("curation_guard_fallthrough", StringComparison.Ordinal) + && e.Contains("widget-specs", StringComparison.Ordinal) + && e.Contains("doc-widget", StringComparison.Ordinal)); + } + + // ── guard downgrade + nominator: near-dupe elsewhere still forces the LLM tier ── + + /// + /// A guard-rejected anchor Update must not merely fall through to Create/auto-resolve when a + /// real embedding nominee is available — the fall-through re-runs nomination (this proposal's + /// exact anchor match previously short-circuited it entirely, so it had never run at all), and + /// a nominee at or above must + /// still force the LLM tier exactly as it would for a proposal with no anchor match in the + /// first place (design D4: cosine nominates, it never auto-decides). + /// + [Fact] + public async Task GuardDowngrade_withNominatorNearDupe_forcesLlmTier_identically() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + + // Same anchor-collision shape as the eval-run repro: an exact anchor match whose content + // is unrelated to the proposal (guard will reject the Update), PLUS a real near-duplicate + // elsewhere in the store that only the embedding nominator — never run on the first pass + // because the exact anchor match short-circuited it — can find. + await SeedDocumentAsync( + "the", + "doc-unrelated-junk-anchor", + "Unrelated content that happens to share the same junk anchor name.", + freshnessAtMs: 1000, + ct); + + const string nearDupeBody = "The build pipeline stores intermediate render artifacts in a graphite-backed cache layer."; + var nearDupeAnchor = _store.CreateDefaultAnchor("graphite-render-cache"); + await _store.UpsertDocumentAsync(new SQLiteMemoryDocument( + DocumentId: "doc-near-dupe", + Anchor: nearDupeAnchor, + MemoryClass: "durable_fact", + Title: "Existing near-dupe", + MarkdownBody: nearDupeBody, + AliasesJson: null, + FacetsJson: null, + SlotsJson: null, + UpdateSemantics: "merge-document", + Sensitivity: "normal", + RecallMode: "auto", + Confidence: 0.9, + FreshnessAtMs: 1000, + ExpiresAtMs: null, + CreatedAtMs: 1000, + UpdatedAtMs: 1000), ct); + await _store.UpsertEmbeddingAsync( + "doc-near-dupe", MemoryEmbedOnWriteCoordinator.DocumentItemKind, "test-nominator-model", "hash-near-dupe", + new float[] { 1f, 0f }, ct); + + var operation = MakeOperation( + "the", "Deployment jobs wait in a queue before promotion to production.", freshnessAtMs: 2000); + + var embedderHolder = new MemoryEmbedderHolder( + new ScriptedEmbedder("test-nominator-model", dimensions: 2, [0.93f, 0.367623f])); + var vectorIndexHolder = new MemoryVectorIndexHolder(_store); + + var actorLike = new MemoryCurationEvaluator( + _store, (ILoggingAdapter)NoLogger.Instance, new MemoryCurationConfig(), + new ScriptedCurationChatClient("SKIP"), embedderHolder, vectorIndexHolder); + var engineLike = new MemoryCurationEvaluator( + _store, (ILogger)NullLogger.Instance, new MemoryCurationConfig(), + new ScriptedCurationChatClient("SKIP"), embedderHolder, vectorIndexHolder); + + var fromActor = (await actorLike.EvaluateAsync(operation, TestSessionId, ct)).Decision; + var fromEngine = (await engineLike.EvaluateAsync(operation, TestSessionId, ct)).Decision; + + AssertSameDecision(fromActor, fromEngine); + Assert.True(fromActor.FromLlmTier); + Assert.Equal(CurationDecisionKind.Skip, fromActor.Kind); + } + // ── LLM tier: parseable decision ───────────────────────────────── [Fact] @@ -450,4 +609,25 @@ public ValueTask>> EmbedBatchAsync(IReadOnly => ValueTask.FromResult>>( texts.Select(_ => (ReadOnlyMemory)queryVector).ToList()); } + + /// + /// Records every log line emitted through the Microsoft.Extensions.Logging ctor path, so the + /// curation_guard_fallthrough marker can be asserted directly rather than only + /// inferred from the resulting decision shape. Mirrors + /// MemoryCurationNominatorTests.RecordingLogger (kept as a separate private copy per + /// that file's own convention for test-only doubles). + /// + private sealed class RecordingLogger : ILogger + { + public List Entries { get; } = []; + + public IDisposable? BeginScope(TState state) where TState : notnull => null; + + public bool IsEnabled(Microsoft.Extensions.Logging.LogLevel logLevel) => true; + + public void Log( + Microsoft.Extensions.Logging.LogLevel logLevel, EventId eventId, TState state, Exception? exception, + Func formatter) + => Entries.Add(formatter(state, exception)); + } } diff --git a/src/Netclaw.Actors/Memory/MemoryCurationEvaluator.cs b/src/Netclaw.Actors/Memory/MemoryCurationEvaluator.cs index cf3e393a8..ee31e2402 100644 --- a/src/Netclaw.Actors/Memory/MemoryCurationEvaluator.cs +++ b/src/Netclaw.Actors/Memory/MemoryCurationEvaluator.cs @@ -21,7 +21,7 @@ namespace Netclaw.Actors.Memory; /// curation_nominator_degraded, curation_nominee_no_llm_decision, /// curation_llm_decision, curation_llm_no_decision, curation_llm_timeout, /// curation_llm_error, curation_ambiguous_auto_resolved, -/// curation_ambiguous_create_fallback, +/// curation_ambiguous_create_fallback, curation_guard_fallthrough, /// curation_skip/_update/_consolidate/_create, /// curation_reanchor, curation_tombstone_anchor) regardless of which of the /// two callers is driving the evaluator: the inline per-session actor @@ -79,24 +79,48 @@ internal sealed class MicrosoftCurationLog(ILogger log) : ICurationLog /// Decision flow (): immutable records bypass evaluation; fuzzy /// anchor candidates are queried; on an exact anchor match, the deterministic fast path /// ('s EvaluateExactMatch) decides Skip/Update -/// with no further evidence gathering. Otherwise, the embedding kNN nominator (memory-core- -/// redesign Slice 3 Stage B, task 3.1, design D4) queries -/// when the embedder is available: any nominee forces the decision to the LLM tier, -/// regardless of what the lexical rules tier would have decided — the May 2026 measurement -/// (docs/research/memory-recall-findings-2026-05.md; corroborated at corpus scale in -/// docs/research/memory-audit-2026-07.md §5) found no cosine threshold that separates true -/// duplicates from merely-related siblings, so cosine similarity is nomination evidence ONLY — -/// it never auto-merges and never auto-skips. When no nominee fires (or the embedder is -/// unavailable, in which case the pre-Slice-3 lexical content-term search runs instead as an -/// explicitly-logged degraded path), runs the -/// deterministic tier as before; an Ambiguous result escalates to the LLM tier (when available) -/// followed by , or else falls back to -/// and finally a Create default. -/// maps the resulting decision to the operation that should -/// be written (or nothing, for Skip), executing Consolidate's re-anchor/tombstone side +/// with no further evidence gathering — UNLESS that Update is downgraded by +/// (see "Guard fall-through" below), +/// in which case evidence gathering resumes rather than terminating. Otherwise, the embedding +/// kNN nominator (memory-core-redesign Slice 3 Stage B, task 3.1, design D4) queries +/// when the embedder is available: any nominee forces the +/// decision to the LLM tier, regardless of what the lexical rules tier would have decided — +/// the May 2026 measurement (docs/research/memory-recall-findings-2026-05.md; corroborated +/// at corpus scale in docs/research/memory-audit-2026-07.md §5) found no cosine threshold +/// that separates true duplicates from merely-related siblings, so cosine similarity is +/// nomination evidence ONLY — it never auto-merges and never auto-skips. When no nominee fires +/// (or the embedder is unavailable, in which case the pre-Slice-3 lexical content-term search +/// runs instead as an explicitly-logged degraded path), +/// runs the deterministic tier as before; an Ambiguous result escalates to the LLM tier (when +/// available) followed by , or else +/// falls back to and finally a Create +/// default. maps the resulting decision to the operation that +/// should be written (or nothing, for Skip), executing Consolidate's re-anchor/tombstone side /// effects — this mapping is unified too, since a second, hand-copied switch statement per /// caller is exactly the kind of divergence this slice removes. /// +/// +/// Guard fall-through (memory-core-redesign, July 2026 audit finding, eval run +/// ad9a2312): when the exact-anchor deterministic path picks Update and +/// downgrades it to Skip because the +/// proposal would not preserve the target's content, that Skip must NOT be returned as the final +/// decision — an explicit store_memory proposal silently ending as a no-op (no write, no +/// further evidence gathering) is a silent-fallback violation, observed when an LLM-emitted junk +/// anchor (e.g. the bare stopword the) collided with an unrelated existing document sharing +/// the same junk anchor text. The guard's protective effect is correct and must be kept — the +/// mismatched target must never be overwritten — but the correct response to "this anchor match +/// doesn't apply" is to re-run evaluation exactly as if there had been no exact anchor match at +/// all: every candidate that carried IsExactAnchorMatch is demoted to an ordinary fuzzy +/// candidate (so the rules tier cannot re-derive the same Update/guard-reject pair and loop), then +/// the embedding nominator / lexical content-term search that the exact-match fast path had +/// short-circuited runs for the first time, followed by the usual rules-tier/LLM-tier/auto-resolve +/// chain with a Create default. This is deliberately NOT a fix to anchor-name hygiene — junk +/// anchors like the should arguably never be stored or fuzzy-matched at all, but filtering +/// them is a broader behavior change to anchor matching left as a follow-up; this fall-through +/// fixes the narrower "explicit store becomes a silent no-op" failure regardless of why the guard +/// rejected the match. +/// +/// /// Guard-validated write routing (memory-core-redesign Slice 3, design D5): an LLM-tier /// UPDATE/CONSOLIDATE decision () never overwrites /// a target's raw body. When it carries a synthesized , @@ -221,12 +245,38 @@ public async Task EvaluateAsync( // Build a mutable candidate list — content search may add more candidates below. var candidates = new List(anchorCandidates); + return await EvaluateCandidatesAsync( + operation, sessionId, candidates, anchorCandidates.Any(c => c.IsExactAnchorMatch), ct); + } + + /// + /// The evaluation body proper, factored out of so the guard + /// fall-through case (see this class's remarks) can re-run the same evidence-gathering and + /// decision chain a second time with forced false — + /// exactly as if the anchor query at the top of had found no + /// exact match — without re-querying the store for anchors a second time. + /// + private async Task EvaluateCandidatesAsync( + SQLiteMemoryCurationOperation operation, + SessionId sessionId, + List candidates, + bool hasExactAnchorMatch, + CancellationToken ct) + { // Embedding kNN nomination (memory-core-redesign Slice 3 Stage B, task 3.1, design D4) // vs. the pre-Slice-3 lexical content-term search: these are alternatives, not additive. // An exact anchor match already resolves deterministically below with no further // evidence gathering (the "existing exact-anchor deterministic fast path" design D4 - // calls out as unchanged), so neither runs in that case. - var hasExactAnchorMatch = anchorCandidates.Any(c => c.IsExactAnchorMatch); + // calls out as unchanged), so neither runs in that case — unless the guard fall-through + // below re-invokes this method with hasExactAnchorMatch forced false, in which case this + // runs for the first time for this proposal. + // + // Captured before any mutation below: on the first (normal) call this is exactly the + // anchor-name-fuzzy-match hit count `curation_dual_search` below reports; on a guard + // fall-through re-entry it is that same anchor-hit count with the rejected match(es) + // demoted rather than removed, which is the correct "anchor_hits" figure for this pass + // either way (no nomination/content-search candidates have been merged in yet). + var anchorHitCount = candidates.Count; if (!hasExactAnchorMatch) { var embedder = _embedderHolder?.Current; @@ -308,7 +358,7 @@ public async Task EvaluateAsync( _log.Debug( "curation_dual_search anchor={0} anchor_hits={1} content_hits={2} merged={3}", operation.AnchorCanonicalName, - anchorCandidates.Count, + anchorHitCount, contentCandidates.Count, candidates.Count); } @@ -409,9 +459,38 @@ public async Task EvaluateAsync( candidates); } - return new CurationEvaluation( - CurationRulesEvaluator.GuardDestructiveUpdate(rulesDecision, operation, candidates), - candidates); + var guardedDecision = CurationRulesEvaluator.GuardDestructiveUpdate(rulesDecision, operation, candidates); + + // Guard fall-through (see this class's remarks): the ONLY decision shape + // GuardDestructiveUpdate ever changes is Update -> Skip, and Update can only be + // produced by the exact-anchor deterministic fast path (CurationRulesEvaluator's fuzzy + // tier never returns Update) — so this downgrade is only reachable when + // hasExactAnchorMatch was true for this call. Terminating on that Skip would silently + // drop an explicit proposal with no further evidence gathering (the July 2026 audit + // bug); instead, demote every exact-anchor-matched candidate to an ordinary fuzzy + // candidate and re-run the full evaluation chain as if there had been no exact anchor + // match at all. The demotion is what keeps this from looping: with no candidate left + // claiming IsExactAnchorMatch, CurationRulesEvaluator.Evaluate cannot re-derive the same + // Update decision on the re-run, so this branch cannot fire twice for one proposal. + if (hasExactAnchorMatch + && rulesDecision.Kind == CurationDecisionKind.Update + && guardedDecision.Kind == CurationDecisionKind.Skip) + { + _log.Warning( + "curation_guard_fallthrough anchor={0} rejectedTarget={1}", + operation.AnchorCanonicalName, + guardedDecision.TargetDocumentId ?? "(unknown)"); + + for (var i = 0; i < candidates.Count; i++) + { + if (candidates[i].IsExactAnchorMatch) + candidates[i] = candidates[i] with { IsExactAnchorMatch = false }; + } + + return await EvaluateCandidatesAsync(operation, sessionId, candidates, hasExactAnchorMatch: false, ct); + } + + return new CurationEvaluation(guardedDecision, candidates); } /// From 12e447686a5b21146eb922d7ab51525d73f1e8ef Mon Sep 17 00:00:00 2001 From: Aaron Stannard Date: Wed, 8 Jul 2026 12:48:45 -0500 Subject: [PATCH 04/37] fix(smoke): update help screenshot baseline for netclaw memory command (#1603) The memory-core-redesign slice 2 (#1577) added the `netclaw memory` CLI command but never updated the approved `help` screenshot baseline, so Screenshot Regression (Linux) has been red on every push to feature/memory-embeddings since (verified via gh run list against feature/memory-embeddings: 3/3 recent pushes failed this check). Reviewed the captured diff locally: the only change is the new memory Manage cross-session memory (embeddings backfill, offline) line shifting everything below it down one row. 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acac13975424f1309f7ffe2804cfd46bf49ad7ec Mon Sep 17 00:00:00 2001 From: Aaron Stannard Date: Sun, 5 Jul 2026 21:17:36 -0500 Subject: [PATCH 05/37] opsx: memory-relevance-gate change artifacts (proposal, design, specs, tasks) (#1588) --- .../memory-relevance-gate/.openspec.yaml | 2 + .../changes/memory-relevance-gate/design.md | 387 ++++++++++++++++++ .../changes/memory-relevance-gate/proposal.md | 167 ++++++++ .../specs/memory-embeddings/spec.md | 43 ++ .../specs/memory-relevance-gate/spec.md | 167 ++++++++ .../specs/netclaw-agent-memory/spec.md | 84 ++++ .../changes/memory-relevance-gate/tasks.md | 77 ++++ 7 files changed, 927 insertions(+) create mode 100644 openspec/changes/memory-relevance-gate/.openspec.yaml create mode 100644 openspec/changes/memory-relevance-gate/design.md create mode 100644 openspec/changes/memory-relevance-gate/proposal.md create mode 100644 openspec/changes/memory-relevance-gate/specs/memory-embeddings/spec.md create mode 100644 openspec/changes/memory-relevance-gate/specs/memory-relevance-gate/spec.md create mode 100644 openspec/changes/memory-relevance-gate/specs/netclaw-agent-memory/spec.md create mode 100644 openspec/changes/memory-relevance-gate/tasks.md diff --git a/openspec/changes/memory-relevance-gate/.openspec.yaml b/openspec/changes/memory-relevance-gate/.openspec.yaml new file mode 100644 index 000000000..dd9a1d92e --- /dev/null +++ b/openspec/changes/memory-relevance-gate/.openspec.yaml @@ -0,0 +1,2 @@ +schema: spec-driven +created: 2026-07-06 diff --git a/openspec/changes/memory-relevance-gate/design.md b/openspec/changes/memory-relevance-gate/design.md new file mode 100644 index 000000000..5ae6e76ea --- /dev/null +++ b/openspec/changes/memory-relevance-gate/design.md @@ -0,0 +1,387 @@ +# Design: memory-relevance-gate + +## Context + +memory-core-redesign Slice 4 (`openspec/changes/memory-core-redesign/`, design +D6) shipped hybrid recall with an absolute cosine floor +(`Memory.Recall.MinCosineSimilarity`, calibrated per embedding model against +`gold-prod-2026-07`): a query embeds once per turn, FTS5 and vector top-k +candidates are unioned and fused, and any candidate below the floor is +dropped before ranking — zero survivors means zero injection. That floor is +real and measured (τ=0.67 for the current uint8-quantized embedding +variant), but a floor sweep across the gate-shootout's checksum run shows it +still injects *something* for the great majority of nothing-relevant queries: +**16.7% zero-injection accuracy** on `gold-prod-2026-07` (93 queries, in-sample +calibration set) and **7.3%** on a 450-query out-of-sample expansion +(`~/recall-research-local/2026-07/gold-expansion/`, disjoint from the +calibration set by normalized-text exclusion). The reason is structural, not +a mistuned constant: cosine similarity measures topical "aboutness" between a +query and a candidate, not "does this candidate help answer the question" — +a memory can be comfortably on-topic (cosine 0.74, well above a 0.67 floor) +and still be useless for the turn (e.g. an unrelated project fact that +happens to share vocabulary with the query). + +Four designs were measured head-to-head against this residual, then the +winner was re-validated out-of-sample on a gold set 4.8x larger than the one +used to pick it (`~/recall-research-local/2026-07/gate-shootout/` and +`gold-expansion/` respectively — both operator-local research stores holding +real, PII-bearing traffic; never committed, per the convention in +`docs/research/memory-audit-2026-07.md`). This design records that shoot-out, +the winning architecture, and the residuals that remain. + +**Actor/persistence context** (unchanged from memory-core-redesign): recall +runs on the session actor's turn path under `Memory.RecallTimeoutMs` (default +300 ms), executed by `SQLiteMemoryRecallCoordinator` +(`Netclaw.Actors/Sessions`). The embedding runtime lives behind the +consumer-defined `IMemoryEmbedder` seam (`Netclaw.Actors/Memory`), implemented +by `OnnxMemoryEmbedder` in `Netclaw.Embeddings`, resolved at call time through +the mutable `MemoryEmbedderHolder` (a plain DI singleton cannot hold a value +that is only known after `EmbeddingWarmupHostedService` finishes +provisioning, which necessarily runs after the DI container is built). +`EmbeddingModelProvisioner`'s pinned in-code allowlist (model id → URL, byte +size, SHA-256) is the supply-chain boundary: arbitrary URLs are never +accepted, only ids present in the allowlist. + +**Layering note**: this change's implementation targets the +`feature/memory-embeddings` branch, which carries memory-core-redesign's +embedding foundation, write-side nominate→decide, and read-side hybrid +recall slices ahead of `dev`. Because memory-core-redesign has not yet been +archived, the `memory-embeddings` capability does not yet exist under +`openspec/specs/`; this change's `specs/memory-embeddings/spec.md` delta is +therefore written against memory-core-redesign's own proposed spec +(`openspec/changes/memory-core-redesign/specs/memory-embeddings/spec.md`) as +its base, not against a synced main spec. If memory-core-redesign archives +(and syncs `memory-embeddings` into `openspec/specs/`) before this change +does, `opsx-sync` will need both deltas applied in dependency order — +memory-core-redesign's first, then this one. + +## Goals / Non-Goals + +**Goals** + +1. Close the measured residual: most nothing-relevant queries should inject + nothing, not "something topically adjacent." Target the validated + operating point (86.8% zero-injection accuracy out-of-sample), not just + the in-sample number. +2. Preserve recall: a query that has something genuinely relevant to say + should keep getting it. 98.3% recall retention out-of-sample is the + accepted cost, not zero cost — record this honestly. +3. Reuse memory-core-redesign's machinery wholesale — provisioning, + holder-and-warmup lifecycle, degradation contract, doctor/status surfaces + — so this change is a new manifest entry and a new scoring stage, not a + parallel subsystem. +4. Loud degradation: gate unavailability must never silently change recall + behavior without a marker. + +**Non-Goals** + +- Recalibrating the cosine floor, the fusion weights, or swapping the + embedding model — this change adds a stage strictly after that pipeline's + existing output. +- Domain-calibrated or class-conditional thresholds for the measured MS + MARCO under-scoring of procedural/command-style memories — recorded as a + residual, deferred. +- Re-running or expanding the judged gold sets further; the shoot-out and + gold-expansion results are consumed as already-ratified inputs. +- Ensembling multiple relevance models or scoring schemes. +- Collapsing `MemoryEmbedderHolder` and the new relevance-scorer holder into + a single combined holder — noted as an optional simplification (D4), not + required for this change. + +## Decisions + +### D1. Scorer seam + ONNX cross-encoder implementation, mirroring `IMemoryEmbedder` exactly + +`IRelevanceScorer` lives in `Netclaw.Actors/Memory` — a consumer-defined seam +in the same spirit as `IMemoryEmbedder`, so actor code never references +OnnxRuntime. Shape: + +- `string ModelId` — the allowlisted relevance-model id (vectors and scores + are never compared across models, same rule as embeddings). +- `bool IsAvailable` — real, expected false state (not provisioned, hash + failure, runtime load error); only calling the scoring method while + unavailable throws (matches `IMemoryEmbedder`'s contract exactly — no + garbage score silently corrupting the gate). +- `ValueTask> ScoreAsync(string query, IReadOnlyList candidates, CancellationToken ct)` + — batch, order-preserving, one call per turn for the ≤`AutoRecallMaxItems` + (3) floor survivors, mirroring `EmbedBatchAsync`'s batching rationale. + +`OnnxCrossEncoderScorer` (`Netclaw.Embeddings`) implements it: pair encoding +`[CLS] query [SEP] candidate [SEP]` with correct `token_type_ids` (0 for +query+CLS+SEP, 1 for candidate+final SEP), truncation strategy `only_second` +(caps the total at the model's max length by truncating only the candidate +side — a query is never truncated), dynamic sequence length bucketed to +multiples of 8 (the same bucketing convention `OnnxMemoryEmbedder` already +uses, avoiding a proliferation of ORT graph re-optimizations for arbitrary +lengths). The model's single `logits` output (shape `[batch,1]`) is passed +through a sigmoid host-side — the upstream model ships +`sbert_ce_default_activation_function: Identity`, so the activation is +explicitly not baked into the graph and must be applied by the caller. +`UnavailableRelevanceScorer` is the degraded-mode stub, matching +`UnavailableMemoryEmbedder`'s throw-on-call contract byte for byte. + +*Alternative considered*: extend `OnnxMemoryEmbedder`'s existing +`InferenceSession` to also serve cross-encoder inference — rejected: the +cross-encoder is a materially different model (a `BertForSequenceClassification` +pair-input head, not the bi-encoder's single-input pooling graph) with its +own tokenizer vocabulary; sharing a session would couple two independently +lifecycled models for no benefit. A second dedicated session, following the +exact same holder/warmup pattern, is simpler to reason about. + +### D2. Model selection: `Xenova/ms-marco-MiniLM-L-6-v2`, int8, chosen from a 4-design measured shoot-out + +| design | mechanism | in-sample verdict | out-of-sample verdict | +|---|---|---|---| +| A — distribution-shape | `z_top50 ≥ 2.80` (local-neighborhood outlier score) | 70.0% zero-inj, 100% retention — looked like a clean win | **Fails**: 65.2% zero-inj, **86.5% retention (below the ≥90% constraint)**, F0.5 0.089 < 0.100 floor-only | +| **B — cross-encoder (winner)** | `Xenova/ms-marco-MiniLM-L-6-v2`, pair scoring | 91.7% zero-inj (S*=0.08), 100% retention | **86.8% zero-inj (S*=0.02, 95% CI 82.3–90.3), 98.3% retention**, F0.5 0.130 vs 0.100 | +| C — learned feature gate | logistic/GBM over cosine/margin/z/length/age | candidate-level: 86.7% zero-inj across 10 CV folds (8 positive instances) | **Not viable**: query-level OOF AUC 0.545 (chance); candidate-level positives grew only 8→39 across the expansion, still insufficient, 80%-relative recall collapse on a differently-composed transfer set | +| D — per-memory offender priors | `pollution_count/injection_count` per docId | coverage ceiling measured directly, no separate OOS pass needed | **Structurally dead**: only 1.1% of top-3 candidates have 3+ injection history to build a prior from (5.3% even at a relaxed 2+ threshold); 80.6% of top-3 candidates are cold-start | + +Gate B is the only design whose out-of-sample result both replicates its +in-sample claim *and* clears the ≥90% recall-retention constraint. Its +in-sample recommended threshold (S*=0.08, chosen because gold-prod showed a +flat 100%-retention plateau from 0.02–0.08) turned out to be an artifact of +having only 8 floor-surviving true positives to calibrate against — the +450-query expansion grew that count to 39, and retention at S*=0.08 dropped +to 90.1% (exactly on the constraint boundary, zero margin). **The frozen +operating point for this change is S*=0.02**, which trades 1.8 points of +zero-injection accuracy (88.6%→86.8%) for 8.2 points of recall retention +(90.1%→98.3%) versus the in-sample-optimal S*=0.08 — the right side of that +trade given goal #2 above. + +Model artifact (frozen, quantized int8, the standard HuggingFace dynamic-INT8 +export — same family of artifact as the embedder's own quantization +options): + +- File: `model_quantized.onnx` +- Size: 23,143,499 bytes (22.07 MB) +- SHA-256: `e9d8ebf845c413e981c175bfe49a3bfa9b3dcce2a3ba54875ee5df5a58639fbe` + +The fp32 reference variant (`model.onnx`, 90,992,115 bytes / 86.78 MB, SHA-256 +`c623d0bcb99f4622beb413eaef00cfbe5db20df9f1dd982da4b4f26022881870`) was +measured bit-for-bit quality-identical to the quantized variant on both gold +sets and materially heavier on RAM (161–211 MB vs 48–103 MB incremental, +depending on measurement convention) for zero quality benefit — ruled out. + +*Alternative considered*: shipping Gate A (distribution-shape) as a cheap +first-pass filter ahead of Gate B — rejected: Gate A's out-of-sample failure +(recall retention below its own promised floor, F0.5 *worse* than doing +nothing) means it would need its own re-validation and threshold governance +for no measured benefit once Gate B is in place; not worth the added +moving part. + +### D3. Provisioning: a manifest *entry kind*, not a parallel allowlist + +The relevance model is provisioned through the exact same pinned-allowlist +mechanism `EmbeddingModelProvisioner` already implements for embedding +models — the allowlist gains a `RelevanceModelManifestEntry` alongside the +existing `EmbeddingModelManifestEntry`: `ModelId`, `ModelUrl`, `ModelSha256`, +`ModelByteSize`, and — the one field embedding manifests don't need — +`CalibratedThreshold` (S*=0.02). This is memory-core-redesign's +**manifest-carried operating point** pattern (the same zero-config mechanism +that let `MinCosineSimilarity` ship without requiring every operator to +calibrate their own floor): the threshold travels with the model id it was +measured against, so a future model swap cannot silently reuse a threshold +calibrated for a different model's score distribution. Download, atomic +temp+rename, and SHA-256 verification reuse the provisioner's existing code +path unchanged — this is a new manifest row and entry type, not new +download/verify logic. + +*Alternative considered*: a fully separate `RelevanceModelProvisioner` +class — rejected: the download/verify/reject-unknown-id logic has zero +model-kind-specific behavior; duplicating it would just be two copies of the +same supply-chain boundary to keep in sync. + +### D4. Warmup and holder: extend the existing warmup service; a third holder, not a forced merge + +`EmbeddingWarmupHostedService` gains a second provisioning step: when +`Memory.Embeddings.Enabled`, it provisions and warms the relevance model the +same way it does the embedding model (provision-or-degrade, one warm-up +inference call, gap-repair is not applicable here since there's no per-item +derived state to repair). The scorer is exposed through a new +`RelevanceScorerHolder`, following `MemoryEmbedderHolder`'s exact shape +(mutable holder, always non-null, initial value an `UnavailableRelevanceScorer` +stub, replaced once by the warmup service, read fresh on every use — never +cached by a consumer). + +Keeping three holders (`MemoryEmbedderHolder`, `MemoryVectorIndexHolder`, +`RelevanceScorerHolder`) rather than merging them keeps each concern +independently swappable and testable, consistent with what already exists. +**Consolidating the two model-runtime holders (embedder + relevance scorer) +into a single combined "embedding runtime holder"** is noted here as an +optional future simplification — both models are provisioned by the same +warmup step and share the same availability semantics, so a combined holder +would remove one moving part — but it is not required for this change and is +left as a follow-up decision rather than blocking this slice on a refactor +of already-shipped code. + +### D5. Recall wiring: a post-floor scoring stage under its own sub-budget + +In `SQLiteMemoryRecallCoordinator`, the gate applies strictly after the +existing hybrid-recall floor stage, and only in `hybrid` mode (a query +vector was available): the floor already reduced the candidate set to +`aboveFloor` (≤`AutoRecallMaxItems` = 3, per the shoot-out's exact +candidate-generation protocol — the gate never sees a candidate the floor +would not already have admitted). Each survivor is paired with the query and +scored via `RelevanceScorerHolder.Current.ScoreAsync`, under a CE sub-budget +(~60 ms) nested inside the overall `RecallTimeoutMs` via a linked +`CancellationTokenSource` — the same pattern the query-embedding sub-budget +already uses (measured p95 35 ms for 3 pairs leaves roughly 1.7x headroom +before the sub-budget itself is hit). Candidates scoring below the +manifest/config threshold are dropped; **zero survivors after the gate is a +"nothing injected" outcome**, identical in kind to zero survivors at the +floor — the `[memory-recall]` block continues to be omitted entirely, not +emitted empty. + +When the gate is unavailable, over its sub-budget, or recall is running in +`lexical` (degraded, no query vector) mode, the gate step is skipped +entirely and the floor's own output proceeds to injection unfiltered — this +is the same floor-only behavior that shipped in Slice 4, now reachable via +two independent degradation paths (embedder degraded → lexical mode already +skips the floor's cosine gate too; relevance-scorer degraded → floor's +cosine gate still applies, but no CE gate on top). + +*Alternative considered*: applying the gate to the full vector top-k (10 +candidates, before the floor) instead of just the ≤3 floor survivors — +rejected: this is exactly what the shoot-out measured and what the +out-of-sample validation certifies (candidates = floor-passing top-3); +scoring a wider candidate pool the gate was never validated against would +invalidate the calibrated threshold and roughly 3x the per-turn CE cost for +no measured benefit. + +### D6. Activation: one mental switch, explicit override only + +`Memory.Recall.RelevanceGate { Enabled, Threshold }`, both nullable: + +- `Enabled = null` (default) → follows `Memory.Embeddings.Enabled`. An + operator who turned on embeddings gets the gate; there is no second switch + to discover or forget to flip. +- `Enabled = true/false` → explicit override, independent of the embeddings + switch (e.g. an operator who wants embeddings for dedup/hybrid-recall but + not the extra CE latency per turn). +- `Threshold = null` (default) → follows the manifest's calibrated S* + (0.02) for whichever relevance model id is active. +- `Threshold = ` → explicit override, for an operator who re-runs the + shoot-out's threshold sweep against their own corpus and wants a different + operating point. + +This mirrors `MinCosineSimilarity`'s existing "config default, manifest +provides the calibrated number" relationship — no new configuration +philosophy, just one more nullable pair. + +### D7. Logging and eval coverage + +`memory_retrieval_final` gains two fields: `gateScores` (the CE score per +surviving-then-gated candidate, for post-hoc threshold analysis without +needing a fresh eval run) and `droppedByGate` (count, mirroring the existing +`filteredByFloor` field's shape). A new eval case seeds a corpus with +unrelated memories, asks an off-topic question, and asserts both that no +`[memory-recall]` block appears in the assembled prompt and that a gate +marker appears in the logs — the automated analogue of the shoot-out's +"zero-injection accuracy" metric, pinned as a regression gate rather than +left as a one-time measurement. + +### D8. Degradation semantics + +Model unavailable (not provisioned, hash failure, runtime load error) or CE +sub-budget exceeded ⇒ floor-only behavior (identical to pre-this-change +Slice 4 output) plus a rate-limited `memory_recall_gate_degraded` log +(matching the existing `memory_recall_vector_degraded` cooldown pattern — +loud on the first occurrence of a reason, not spammy on every subsequent +turn) and doctor visibility (extending the existing embedding doctor check +or adding a sibling relevance-gate doctor check — implementation detail for +tasks, not a design fork). The system never silently changes recall +selectivity without one of these signals firing. + +## Risks / Trade-offs + +- [MS MARCO domain mismatch under-scores procedural/command-style memories] + → measured, not hypothetical: of the 39 floor-surviving true positives in + the expanded gold set, 6 scored below S*=0.08 (2 below the frozen S*=0.02), + concentrated in release-workflow/procedural-context memories that are + useful-as-context but don't read as "the answer" to a cross-encoder trained + on MS MARCO's answer-passage judgments. At the frozen S*=0.02 this costs + ~1.7% of retained recall. Mitigation: recorded as a residual, not silently + absorbed; future work is domain calibration or a class-conditional + threshold for procedural/tool-lesson-adjacent memory classes. +- [Judge-agreement caveat on the validation set] → the 450-query expansion's + inter-rater agreement (κ=0.435, pooling 11 candidates/query, mostly + sub-floor and deliberately ambiguous) is materially below the original + July gold set's agreement (κ=0.754, judging only the 3 actually-injected + items/query — an easier, less skewed task). Mitigated by harsher-wins + aggregation (a doc counts as `relevant` only if both judging passes agreed) + which biases the expanded gold set conservative — the right bias for + validating a precision-oriented gate, but it means per-query labels in the + expansion are noisier than July's and the aggregate tables should be + trusted over any single query's label. +- [Threshold is model-conditional, like every other threshold in this + system] → S*=0.02 is calibrated specifically against + `Xenova/ms-marco-MiniLM-L-6-v2`'s score distribution; swapping the + relevance model without re-running the threshold sweep would silently + invalidate it. Mitigated the same way `MinCosineSimilarity` is: the + threshold travels in the manifest keyed to the model id (D3), not as a + bare config default disconnected from which model produced it. +- [Combined resource envelope is real but not free] → quantized CE adds + ~103 MB incremental RSS and ~11 ms p50 / ~35 ms p95 for 3 pairs on the + reference CPU; combined with int8 embeddings (263 MB) and daemon peak + (397 MB), the operator's measured total is ≈763 MB against a 1 GB K8s pod + limit — inside budget, but the margin (≈260 MB) is not so large that a + future addition to the memory runtime gets it for free. Mitigated by + measuring rather than assuming, and by keeping the CE sub-budget (~60 ms) + small relative to the overall 300 ms recall timeout so a degraded gate + never risks the turn itself. +- [Nested sub-budgets: query-embedding (~150 ms) + gate (~60 ms) inside one + 300 ms `RecallTimeoutMs`] → worst case both sub-budgets fully elapse + (210 ms) before any lexical/ranking work runs, leaving less slack than + Slice 4 alone had. Not yet measured end-to-end under production + contention. Flagged as an open question (below), not silently assumed + safe. +- [Two-holders-become-three] → `MemoryEmbedderHolder` + + `MemoryVectorIndexHolder` + the new `RelevanceScorerHolder` is more moving + parts than a consolidated holder would be. Accepted for this change (D4) + as consistent with the existing pattern; flagged as an optional future + consolidation rather than deferred silently. + +## Migration Plan + +1. Ships as an independent slice on top of `feature/memory-embeddings`'s + already-landed hybrid-recall stage (memory-core-redesign Slice 4). No + slice ordering dependency on any *other* part of memory-core-redesign + beyond what Slice 4 already requires. +2. Config-gated end to end: `Memory.Embeddings.Enabled = false` (the current + `dev` default) means the gate's provisioning step never runs and the + coordinator never attempts to resolve a `RelevanceScorerHolder` — zero + behavior change for any operator who hasn't already opted into + embeddings. `Memory.Recall.RelevanceGate.Enabled = false` is a second, + independent escape hatch for an operator who wants embeddings without the + gate's added per-turn latency. +3. Rollback: disabling either switch returns to exactly the prior Slice-4 + floor-only behavior; the relevance model artifact is derived/cacheable + data like the embedding model, safe to delete. +4. Schema: new `Memory.Recall.RelevanceGate` node added to + `netclaw-config.v1.schema.json`, all-nullable, migration-friendly per the + constitution's schema rules — no existing config document needs edits to + remain valid. +5. Calibration-verification harness: because the threshold is + model-conditional (Risk above), tasks include a short operator-facing note + (alongside the runbook, not a new production code path) describing how to + re-run the shoot-out's threshold-sweep protocol against a different + relevance model or a different corpus, so re-calibration is a documented + procedure rather than tribal knowledge trapped in a local research + directory. + +## Open Questions + +- Combined worst-case latency of the query-embedding sub-budget (~150 ms) + plus the new CE sub-budget (~60 ms) inside the single 300 ms + `RecallTimeoutMs`, measured end-to-end under realistic contention rather + than each sub-budget's own isolated measurement — gates this change's + sub-budget sizing the same way Slice 4 gated its own latency assumption + before shipping. +- Whether the deferred R2-mirroring decision for the embedding model artifact + (memory-core-redesign, post-PoC) should extend to this second (relevance) + model artifact once that decision is made. +- Whether to consolidate `MemoryEmbedderHolder` and `RelevanceScorerHolder` + into one combined embedding-runtime holder (D4) — left open rather than + decided, since both shapes are viable and the choice has no behavioral + consequence. diff --git a/openspec/changes/memory-relevance-gate/proposal.md b/openspec/changes/memory-relevance-gate/proposal.md new file mode 100644 index 000000000..f56cc536d --- /dev/null +++ b/openspec/changes/memory-relevance-gate/proposal.md @@ -0,0 +1,167 @@ +# Proposal: memory-relevance-gate + +Source PRD: PRD-007 (agent personality and local memory), continuing +`memory-core-redesign` (`openspec/changes/memory-core-redesign/`, PR #1570; +Slice 4 shipped the hybrid recall + calibrated cosine floor this change builds +on). Evidence base: `~/recall-research-local/2026-07/gate-shootout/` (4-design +gate shoot-out, 2026-07-06) and `~/recall-research-local/2026-07/gold-expansion/` +(450-query out-of-sample gold expansion + gate re-validation, 2026-07-06) — +operator-local research stores holding real (PII) traffic data, never +committed, per the same convention documented in +`docs/research/memory-audit-2026-07.md`. + +## Why + +Even with hybrid recall and the calibrated per-model cosine floor +(memory-core-redesign Slice 4), most nothing-relevant queries still cause an +injection: floor-only zero-injection accuracy measured **16.7%** on the July +gold set (`gold-prod-2026-07`, 93 queries) and **7.3%** on the 450-query +out-of-sample expanded gold set. Cosine similarity measures topical +"aboutness," not usefulness-for-answering — a candidate can clear the floor +and still be the wrong thing to inject. This is the dominant remaining +recall-quality defect because **60–65% of real queries have nothing relevant** +to recall at all (replicated across 543 labeled real-traffic queries: 93 July ++ 450 expansion), so the floor's residual miss rate lands on the majority +case, not the tail. + +## What Changes + +- **New relevance-gate stage after the cosine floor.** A tiny cross-encoder + scores `(query, candidate)` jointly for each of the (≤`AutoRecallMaxItems` + = 3) floor-surviving candidates; anything below a calibrated threshold S* is + dropped. Zero survivors after the gate ⇒ inject nothing, same as zero + survivors at the floor today. +- **Winner of a 4-design measured shoot-out, out-of-sample validated**: + `Xenova/ms-marco-MiniLM-L-6-v2`, `model_quantized.onnx` (int8, 22.07 MB, + SHA-256 `e9d8ebf845c413e981c175bfe49a3bfa9b3dcce2a3ba54875ee5df5a58639fbe`). + Out-of-sample (450-query expanded gold set, disjoint from the calibration + set) at S*=0.02: zero-injection accuracy **86.8%** (95% CI 82.3–90.3) vs + 7.3% floor-only, recall retention **98.3%**, F0.5 **0.130** vs 0.100 + floor-only, mean injected **0.251** vs 2.538 floor-only. +- **Reuses memory-core-redesign's infrastructure wholesale** — this is the + change's selling point, not an afterthought: the same consumer-defined-seam + pattern (`IMemoryEmbedder` → `IRelevanceScorer`), the same + allowlist-manifest provisioning pattern (`EmbeddingModelProvisioner` gains a + relevance-model manifest entry kind carrying pinned URL/SHA-256/size *and* + the calibrated operating threshold), the same warmup hosted service, and the + same loud-degradation contract (rate-limited log marker + doctor + visibility) — no new machinery class, only a new manifest entry and a new + scoring step in an existing pipeline. +- **One mental switch.** Gate activation is tied to + `Memory.Embeddings.Enabled` — there is no separate "turn semantic recall + quality on" knob. `Memory.Recall.RelevanceGate { Enabled (nullable, follows + Embeddings), Threshold (nullable, follows the manifest's calibrated S*) }` + exists only for an explicit operator override. +- **Logging.** `memory_retrieval_final` gains `gateScores` and `droppedByGate` + fields. A new eval case asserts the zero-injection behavior end-to-end: + seeded corpus, off-topic question, assert no `[memory-recall]` block and a + gate marker in the logs. +- **Rejected alternatives** (recorded for provenance; not shipped): + - *Distribution-shape statistical gate* (`z_top50 ≥ 2.80`): looked viable + in-sample (70% zero-injection) but failed out-of-sample — 65.2% + zero-injection accuracy, **86.5% recall retention (below the ≥90% + constraint)**, F0.5 0.089, *worse* than the 0.100 floor-only baseline. + - *Learned feature gate* (candidate-/query-level logistic regression and + GBM over cosine/margin/z-score/length/age features): query-level variant + measured out-of-fold AUC 0.545 (chance = 0.500, i.e. no signal); + candidate-level variant's positive-class support grew only 8→39 across + the gold expansion — still not enough to certify signal over + small-sample luck, and it showed an 80%-relative recall collapse on a + differently-composed transfer set. + - *Per-memory offender priors* (`pollution_count`/`injection_count` per + `docId`): structurally cold-start-bound — only 1.1% of top-3 recall + candidates carry 3+ injection observations to build a prior from, 5.3% + even at a relaxed 2+ threshold; 80.6% of top-3 candidates are cold-start + with no addressable history at all. + +## Capabilities + +### New Capabilities + +- `memory-relevance-gate`: the `IRelevanceScorer` seam and + `OnnxCrossEncoderScorer` implementation, the relevance-model provisioning + manifest kind (pinned URL/SHA-256/size + calibrated threshold), and the + post-floor gate stage wired into automatic recall. + +### Modified Capabilities + +- `netclaw-agent-memory`: the automatic pre-turn recall requirement gains a + post-floor relevance-gate stage — floor-surviving candidates are scored and + filtered before injection; zero survivors after the gate is a "nothing + injected" outcome exactly like zero survivors at the floor; gate + unavailability or sub-budget timeout degrades to floor-only behavior with a + loud marker. +- `memory-embeddings`: the pinned-allowlist provisioning requirement is + generalized to a manifest entry *kind* so it can provision relevance + (cross-encoder) models alongside embedding models, and the warmup hosted + service provisions/warms both. + +## Impact + +- **Code**: new `IRelevanceScorer` seam (`Netclaw.Actors/Memory`), new + `OnnxCrossEncoderScorer` (`Netclaw.Embeddings`, pair encoding `[CLS] q [SEP] + d [SEP]` with `token_type_ids`, sigmoid over the single-logit head, dynamic + sequence length bucket-of-8 matching the embedder's convention); + `EmbeddingModelProvisioner`'s allowlist gains a relevance-model manifest + kind; `SQLiteMemoryRecallCoordinator` gains the post-floor gate stage under + a CE sub-budget; `Netclaw.Configuration` gains + `Memory.Recall.RelevanceGate`; doctor/status surfaces extend to cover the + relevance model; `netclaw-memory` skill update. +- **Dependencies**: none new — reuses the `Microsoft.ML.OnnxRuntime` + + managed-tokenizer stack memory-core-redesign Slice 2 already adopted. One + new pinned model artifact (~22 MB int8), never embedded in the binary, + downloaded and hash-verified at provisioning time exactly like the + embedding model is today. +- **Data/config**: `netclaw-config.v1.schema.json` gains the new nodes, all + nullable with manifest-derived defaults — additive, non-breaking. +- **Evals**: new zero-injection gate eval case; `memory_retrieval_final`'s + log schema gains two additive fields (`gateScores`, `droppedByGate`). +- **Target branch**: implementation lands on `feature/memory-embeddings` (the + in-flight branch carrying memory-core-redesign's embedding and recall + slices), not directly on `dev` — this change's tasks assume that branch's + `IMemoryEmbedder`/`MemoryEmbedderHolder`/`SQLiteMemoryRecallCoordinator` + hybrid-recall code as their starting point. + +### In scope (MVP) + +- The cross-encoder scorer, its provisioning manifest entry, the coordinator + wiring (score → threshold → drop), the config surface, degradation + semantics, logging fields, and the zero-injection eval case. +- Recording the shoot-out's rejected alternatives and residual failure modes + in `design.md` for provenance. + +### Out of scope + +- Domain-calibrated or class-conditional thresholds for the measured MS + MARCO under-scoring of procedural/command-style memories (residual, ~1.7% + of retained recall at S*=0.02) — future work, not this change. +- Consolidating `MemoryEmbedderHolder` and a prospective relevance-scorer + holder into one combined embedding-runtime holder — noted as an optional + simplification in `design.md`, not required for this change to ship. +- Any change to the cosine floor itself, the embedding model, or the fusion + weights (memory-core-redesign Slice 4 territory; this change only adds a + stage after that pipeline's existing output). +- Re-running or expanding the judged gold sets further; this change consumes + the existing gate-shootout and gold-expansion results as already-ratified + inputs. + +## Security and Operational Impact + +- **Model supply chain**: the relevance model is provisioned through the + same pinned-allowlist mechanism as the embedding model — id → URL + byte + size + SHA-256, arbitrary URLs rejected, atomic download (temp + rename), + hash-verified before load. No new supply-chain surface, only a new + manifest entry kind on the existing one. +- **Resource envelope**: measured on the reference CPU — ~11 ms p50 / ~35 ms + p95 to score 3 pairs (quantized int8), ~103 MB incremental RSS. Combined + with int8 embeddings (263 MB) and daemon peak (397 MB), the operator's + measured total is ≈763 MB — inside the 1 GB K8s pod limit, with headroom + noted rather than assumed. +- **Degradation**: gate unavailability (model not provisioned) or exceeding + its CE sub-budget (~60 ms, linked CTS) degrades to floor-only behavior — the + pre-existing, already-shipped recall path — plus a rate-limited + `memory_recall_gate_degraded` log marker and doctor visibility. Never a + silent fallback, matching memory-core-redesign's degradation contract. +- **Operations**: no new operator action required — gate activation follows + `Memory.Embeddings.Enabled`; the existing warmup hosted service and doctor + checks extend to cover the new model without a new CLI verb. diff --git a/openspec/changes/memory-relevance-gate/specs/memory-embeddings/spec.md b/openspec/changes/memory-relevance-gate/specs/memory-embeddings/spec.md new file mode 100644 index 000000000..1e451615d --- /dev/null +++ b/openspec/changes/memory-relevance-gate/specs/memory-embeddings/spec.md @@ -0,0 +1,43 @@ +# Delta: memory-embeddings (memory-relevance-gate) + +## MODIFIED Requirements + +### Requirement: Pinned model provisioning + +Memory-subsystem models SHALL be selected by id from a pinned in-code +allowlist mapping model id to download URL, byte size, and SHA-256, covering +more than one kind of model artifact (embedding models and relevance-scoring +models share the same allowlist mechanism). A relevance-model manifest entry +SHALL additionally carry a calibrated similarity threshold alongside its +download and verification fields, so a model's operating point travels with +its id rather than living as a disconnected configuration default. Arbitrary +model URLs SHALL be rejected for every manifest kind. Provisioning SHALL +download atomically (temporary file then rename), verify the hash before +load, and run at daemon initialization when auto-download is enabled or on +explicit operator command. No model artifact SHALL be embedded in the +application binary. + +#### Scenario: Hash mismatch refuses the model + +- **GIVEN** a downloaded model artifact whose SHA-256 does not match the + allowlist entry +- **WHEN** provisioning verifies the artifact +- **THEN** the artifact is discarded and not loaded +- **AND** the failure is surfaced as a doctor-visible error + +#### Scenario: Unknown model id is rejected + +- **GIVEN** configuration naming a model id absent from the allowlist +- **WHEN** the daemon initializes embeddings +- **THEN** provisioning refuses with a configuration error identifying the + allowlisted ids + +#### Scenario: Relevance manifest entry's threshold travels with its model id + +- **GIVEN** an allowlisted relevance-model manifest entry carrying a + calibrated threshold +- **WHEN** that model id is provisioned and becomes active +- **THEN** the calibrated threshold from that same manifest entry is what + governs gating, not a threshold associated with any other model id +- **AND** switching to a different allowlisted relevance-model id switches + the effective threshold to that id's own calibrated value diff --git a/openspec/changes/memory-relevance-gate/specs/memory-relevance-gate/spec.md b/openspec/changes/memory-relevance-gate/specs/memory-relevance-gate/spec.md new file mode 100644 index 000000000..d0ed1bb26 --- /dev/null +++ b/openspec/changes/memory-relevance-gate/specs/memory-relevance-gate/spec.md @@ -0,0 +1,167 @@ +# Spec: memory-relevance-gate (new capability) + +## ADDED Requirements + +### Requirement: In-process cross-encoder relevance scoring + +The system SHALL score floor-surviving recall candidates against the query +with an in-process CPU ONNX cross-encoder — no sidecar processes, no network +inference hop, mirroring the embedding runtime's execution model. The scorer +SHALL sit behind a narrow interface owned by the memory subsystem so actor +code carries no ONNX dependency, SHALL preserve candidate order across a +batch call, and SHALL encode each `(query, candidate)` pair jointly (not as +two independently embedded vectors) so the score reflects usefulness for +answering the query rather than topical similarity alone. + +#### Scenario: Candidates score without external services + +- **GIVEN** a healthy daemon with the relevance model provisioned +- **WHEN** automatic recall has floor-surviving candidates to gate +- **THEN** each candidate is scored in-process against the query +- **AND** no network call or child process is involved in scoring + +#### Scenario: Query text is never truncated to fit a candidate + +- **GIVEN** a floor-surviving candidate whose combined length with the query + exceeds the model's maximum sequence length +- **WHEN** the pair is encoded for scoring +- **THEN** the candidate side is truncated to fit +- **AND** the query side is preserved in full + +### Requirement: Relevance model provisioning carries a calibrated operating point + +The relevance model SHALL be provisioned through the same pinned-allowlist +mechanism as other memory-subsystem models (id → download URL, byte size, +SHA-256, arbitrary URLs rejected), and its manifest entry SHALL additionally +carry a calibrated similarity threshold that travels with the model id. A +relevance model SHALL NOT be usable with a threshold calibrated for a +different model id. + +#### Scenario: Calibrated threshold ships with the model id + +- **GIVEN** the relevance model manifest entry for the active model id +- **WHEN** the recall coordinator applies the gate +- **THEN** it uses the threshold carried by that manifest entry unless the + operator has configured an explicit override +- **AND** no separate operator calibration step is required to get a + working default + +#### Scenario: Hash mismatch refuses the relevance model + +- **GIVEN** a downloaded relevance model artifact whose SHA-256 does not + match the allowlist entry +- **WHEN** provisioning verifies the artifact +- **THEN** the artifact is discarded and not loaded +- **AND** the gate reports unavailable rather than scoring with an unverified + artifact + +### Requirement: Post-floor relevance gate on automatic recall + +After the existing absolute cosine floor admits candidates, the system SHALL +score each surviving candidate (bounded to the automatic recall item limit) +against the query and SHALL drop any candidate whose score falls below the +active threshold. When every floor-surviving candidate is dropped by the +gate, the turn SHALL inject nothing, identical in kind to the existing +zero-survivors-at-the-floor outcome. The gate SHALL run under its own latency +sub-budget nested inside the overall recall timeout. + +#### Scenario: Topically-adjacent but unhelpful candidate is rejected + +- **GIVEN** a floor-surviving candidate whose cosine similarity to the query + clears the absolute floor but whose content does not help answer the query +- **WHEN** the relevance gate scores the candidate +- **THEN** the candidate scores below the active threshold +- **AND** the candidate is dropped before injection + +#### Scenario: Genuinely relevant candidate survives the gate + +- **GIVEN** a floor-surviving candidate that directly answers the query +- **WHEN** the relevance gate scores the candidate +- **THEN** the candidate scores above the active threshold +- **AND** the candidate remains eligible for injection + +#### Scenario: All candidates gated out means nothing injected + +- **GIVEN** every floor-surviving candidate for a turn scores below the + active threshold +- **WHEN** automatic recall completes for that turn +- **THEN** no memory items are injected +- **AND** the recall context block is omitted entirely from the prompt + +### Requirement: Loud degradation without silent fallback + +Automatic recall SHALL degrade to the floor-only result, unfiltered by the +relevance gate, when the relevance model is unavailable (not provisioned, +hash verification failed, runtime load error) or the gate exceeds its +per-turn sub-budget. The degraded state SHALL be loud: a doctor check +reports the cause, and a rate-limited structured log event records the +degradation reason. The system SHALL NOT silently apply or silently skip +gating without one of these signals. + +#### Scenario: Missing relevance model degrades to floor-only, loudly + +- **GIVEN** the relevance model is not provisioned +- **WHEN** a turn triggers automatic recall with floor-surviving candidates +- **THEN** recall injects the floor's own result unfiltered by any gate +- **AND** a rate-limited degradation event is logged +- **AND** `netclaw doctor` reports the missing relevance model with + remediation + +#### Scenario: Gate sub-budget timeout degrades to floor-only + +- **GIVEN** the relevance model is available but scoring exceeds its + configured sub-budget for a turn +- **WHEN** the sub-budget elapses +- **THEN** the gate stops waiting and recall injects the floor's own result + unfiltered for that turn +- **AND** the degradation is logged at a rate-limited interval, not on every + occurrence + +### Requirement: Gate activation follows embedding enablement + +The relevance gate SHALL be active whenever automatic embeddings are +enabled, without requiring a separate operator decision, while still +allowing an explicit override in either direction. The active similarity +threshold SHALL default to the value carried by the provisioned model's +manifest entry, while allowing an explicit operator override. + +#### Scenario: Enabling embeddings enables the gate with no extra configuration + +- **GIVEN** an operator enables automatic memory embeddings with no gate + configuration present +- **WHEN** the daemon starts +- **THEN** the relevance gate is active using the manifest-provided + threshold for the provisioned relevance model + +#### Scenario: Explicit override disables the gate independent of embeddings + +- **GIVEN** automatic memory embeddings are enabled +- **AND** the operator has explicitly disabled the relevance gate +- **WHEN** automatic recall runs +- **THEN** hybrid recall with the absolute cosine floor still applies +- **AND** no candidate is scored or dropped by the relevance gate + +### Requirement: Gate decisions are observable in retrieval logging and evals + +The final retrieval log record for a turn SHALL include the relevance score +computed for each gated candidate and the count of candidates dropped by the +gate. The eval suite SHALL include a case that seeds a memory corpus, poses +an off-topic query, and asserts both that no recall context block is added +to the prompt and that a gate-degradation-or-decision marker is present in +the logs for that turn. + +#### Scenario: Retrieval log records gate scores and drop count + +- **GIVEN** a turn where the relevance gate scored and dropped at least one + floor-surviving candidate +- **WHEN** the final retrieval log line is written +- **THEN** it includes the score computed for each gated candidate +- **AND** it includes the count of candidates the gate dropped + +#### Scenario: Zero-injection eval case passes on an off-topic query + +- **GIVEN** a seeded memory corpus with no content relevant to a specific + off-topic question +- **WHEN** the eval case asks that question +- **THEN** the assembled prompt contains no `[memory-recall]` block +- **AND** the turn's logs contain a relevance-gate marker for the decision diff --git a/openspec/changes/memory-relevance-gate/specs/netclaw-agent-memory/spec.md b/openspec/changes/memory-relevance-gate/specs/netclaw-agent-memory/spec.md new file mode 100644 index 000000000..91947f46b --- /dev/null +++ b/openspec/changes/memory-relevance-gate/specs/netclaw-agent-memory/spec.md @@ -0,0 +1,84 @@ +# Delta: netclaw-agent-memory (memory-relevance-gate) + +## MODIFIED Requirements + +### Requirement: Automatic pre-turn recall + +The system SHALL execute automatic recall before each user-facing model turn +using the latest user message, recent session context, active anchors, and +policy scope. Recall SHALL be hybrid: lexical (FTS5) and semantic (embedding +cosine) candidates are merged, and every candidate SHALL pass the identical +audience/boundary/sensitivity/recall-mode policy gates regardless of which +retriever surfaced it. Injection SHALL be gated by an absolute relevance +floor: when no candidate clears the configured minimum semantic similarity, +the turn SHALL inject nothing and the recall context block SHALL be omitted +entirely. Floor-surviving candidates SHALL additionally pass a relevance +gate — a cross-encoder scoring of each candidate jointly with the query — +before injection; when the gate is active and every floor-surviving +candidate scores below the active threshold, the turn SHALL inject nothing, +identical in kind to the floor's own zero-survivors outcome. Automatic +recall SHALL be bounded by a latency budget and SHALL degrade safely — to +lexical-only scoring with a structured degradation log when the embedder is +unavailable or over its sub-budget, to floor-only scoring with a structured +degradation log when the relevance gate is unavailable or over its +sub-budget, and to no injection when the memory substrate is unavailable. + +#### Scenario: Recall completes within budget + +- **GIVEN** the memory substrate is healthy +- **WHEN** a new turn begins +- **THEN** the session retrieves and injects a bounded recall bundle before the + model call +- **AND** the recall operation completes within the configured time budget or + degrades safely + +#### Scenario: Nothing relevant means nothing injected + +- **GIVEN** the memory store contains no memory semantically related to the + user's message +- **WHEN** automatic recall runs for the turn +- **THEN** no memory items are injected +- **AND** no recall context block is added to the prompt +- **AND** the retrieval log records zero injected items with the applied floor + +#### Scenario: Vector-sourced candidates obey policy gates + +- **GIVEN** a memory item excluded by the session's audience or sensitivity + policy +- **WHEN** the semantic retriever surfaces that item as a top cosine candidate +- **THEN** the item is filtered before scoring exactly as a lexical candidate + would be + +#### Scenario: Embedder degradation is loud, not silent + +- **GIVEN** the embedding runtime is unavailable or exceeds its per-turn + sub-budget +- **WHEN** automatic recall runs +- **THEN** recall proceeds lexical-only within the same latency budget +- **AND** a structured vector-degradation event is logged for diagnostics + +#### Scenario: Recall failure degrades without blocking the turn + +- **GIVEN** the memory database is temporarily unavailable +- **WHEN** the session starts automatic recall for a turn +- **THEN** the user-facing turn continues without durable recall injection +- **AND** the session records degraded memory status for diagnostics + +#### Scenario: Floor-surviving candidate that is not useful is gated out + +- **GIVEN** a candidate clears the absolute cosine floor but does not help + answer the user's message +- **WHEN** the relevance gate scores that candidate +- **THEN** the candidate is dropped before injection +- **AND** no recall context block is added for that candidate alone if it + was the only floor survivor + +#### Scenario: Relevance gate degradation is loud, not silent + +- **GIVEN** the relevance gate is unavailable or exceeds its per-turn + sub-budget +- **WHEN** automatic recall runs with candidates that survived the absolute + cosine floor +- **THEN** those candidates are injected unfiltered by the gate, within the + same latency budget +- **AND** a structured gate-degradation event is logged for diagnostics diff --git a/openspec/changes/memory-relevance-gate/tasks.md b/openspec/changes/memory-relevance-gate/tasks.md new file mode 100644 index 000000000..d2de8867e --- /dev/null +++ b/openspec/changes/memory-relevance-gate/tasks.md @@ -0,0 +1,77 @@ +# Tasks: memory-relevance-gate + +Implementation targets `feature/memory-embeddings` (memory-core-redesign +Slices 2–4 are this change's starting point, not `dev`). Slices are +independently shippable in order. + +## 1. Scorer, provisioning, manifest/config + +- [ ] 1.1 `IRelevanceScorer` seam in `Netclaw.Actors/Memory` (`ModelId`, + `IsAvailable`, order-preserving batch `ScoreAsync`) + + `UnavailableRelevanceScorer` stub, matching `IMemoryEmbedder`'s + throw-on-call-while-unavailable contract +- [ ] 1.2 `OnnxCrossEncoderScorer` in `Netclaw.Embeddings`: pair encoding + (`[CLS] query [SEP] candidate [SEP]`, correct `token_type_ids`, + `only_second` truncation so the query is never truncated), dynamic + sequence length bucketed to multiples of 8, sigmoid applied host-side + over the single-logit output +- [ ] 1.3 `RelevanceModelManifestEntry` (`ModelId`, `ModelUrl`, + `ModelSha256`, `ModelByteSize`, `CalibratedThreshold`) added to + `EmbeddingModelProvisioner`'s allowlist alongside the existing + embedding-model entries; pin `Xenova/ms-marco-MiniLM-L-6-v2` + `model_quantized.onnx` (22.07 MB, + SHA-256 `e9d8ebf845c413e981c175bfe49a3bfa9b3dcce2a3ba54875ee5df5a58639fbe`, + `CalibratedThreshold = 0.02`) +- [ ] 1.4 `RelevanceScorerHolder` (mirrors `MemoryEmbedderHolder`: mutable, + always non-null, initial `UnavailableRelevanceScorer`, replaced once by + the warmup service); `EmbeddingWarmupHostedService` gains a second + provision-or-degrade step (provision, hash-verify, one warm-up + inference) for the relevance model when `Memory.Embeddings.Enabled` +- [ ] 1.5 Config: `Memory.Recall.RelevanceGate { Enabled (nullable, follows + Embeddings.Enabled), Threshold (nullable, follows manifest + `CalibratedThreshold`) }` + `netclaw-config.v1.schema.json` sync with + defaults (additive, nullable, non-breaking) + +## 2. Coordinator wiring, degradation, tests, eval + +- [ ] 2.1 `SQLiteMemoryRecallCoordinator`: post-floor gate stage — score each + of the ≤`AutoRecallMaxItems` floor survivors under a ~60 ms CE + sub-budget (linked CTS nested inside `RecallTimeoutMs`, same pattern as + the existing query-embedding sub-budget); drop candidates below the + active threshold; zero survivors after the gate ⇒ inject nothing + (reuse the existing zero-injection path, don't fork it) +- [ ] 2.2 Degradation: relevance model unavailable, sub-budget exceeded, or + recall running in lexical (non-hybrid) mode ⇒ skip the gate entirely + and inject the floor's own result unfiltered; rate-limited + `memory_recall_gate_degraded` log (same cooldown pattern as + `memory_recall_vector_degraded`) +- [ ] 2.3 Doctor visibility for the relevance model (extend the existing + embedding doctor check or add a sibling check): model presence/hash, + provisioning failure, degraded-mode reason +- [ ] 2.4 Logging: `memory_retrieval_final` gains `gateScores` (per-candidate + score for every gated candidate) and `droppedByGate` (count) +- [ ] 2.5 Tests: pair-encoding correctness (token_type_ids, truncation-only- + second, dynamic length bucketing) against fixture pairs; threshold + admit/reject boundary; degraded-scorer fallback to floor-only; + sub-budget-timeout fallback; zero-survivors-after-gate produces the + same result shape as zero-survivors-at-the-floor; config + nullable-follows-manifest resolution (both `Enabled` and `Threshold`) +- [ ] 2.6 Eval case: seed a corpus with unrelated memories, ask an off-topic + question, assert no `[memory-recall]` block in the assembled prompt + and a gate marker present in the logs for that turn (the zero- + injection regression the gate exists to enforce) + +## 3. Docs, skill sync, scorecard, calibration note + +- [ ] 3.1 Update `netclaw-memory` skill: relevance gate exists, follows + `Memory.Embeddings.Enabled`, explicit override knobs, degraded-mode + behavior (floor-only fallback) +- [ ] 3.2 Runbook (`docs/runbooks/memory-health-and-evals.md`): relevance + gate section — doctor check, degradation log line, how to read + `gateScores`/`droppedByGate` in `memory_retrieval_final` +- [ ] 3.3 Record a scorecard in `design.md` (already drafted from the + shoot-out; keep in sync if any number changes before merge) and add a + short calibration-verification harness note (how to re-run the + threshold sweep against a different relevance model or corpus, so + re-calibration is a documented procedure, not tribal knowledge in a + local research directory) alongside the runbook From f507fdfeaf8e0f6f95c93e4882d4a08fab42d5f1 Mon Sep 17 00:00:00 2001 From: Aaron Stannard Date: Mon, 6 Jul 2026 12:38:01 -0500 Subject: [PATCH 06/37] test: add failing tests for BOM frontmatter and missing SkillName (#1583) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * test: add failing tests for BOM frontmatter parsing and missing SkillName in issues * fix: handle UTF-8 BOM in ExtractFrontmatter and populate SkillName on all issues - Strip UTF-8 BOM (\uFEFF) before checking for YAML frontmatter delimiter - Populate SkillName on all SkillScanIssue records created in SkillScanner - Fix BOM check in ParseSkillFile and ParseFlatSkillFile for issue kind determination Fixes #1582 * fix(skills): prevent scan-aborting crash on degenerate frontmatter; normalize issue SkillNames Review follow-ups on the BOM/SkillName change: - ExtractFrontmatter threw ArgumentOutOfRangeException on a degenerate "---\n---" block (empty YAML body): the opening line's newline is also the closing delimiter, so the slice computed a negative-length range. Because Scan's parse calls are unguarded, this propagated out and aborted the entire skill-discovery pass (no skills loaded at all). Since File.ReadAllText strips the BOM, a plain "---\n---" SKILL.md on disk hit this too. Now guarded to return null (reported as invalid frontmatter). - ResourceEnumerationFailed derived SkillName from the parent directory (Path.GetDirectoryName(skillDirectory)), yielding the container name ("files") instead of the skill name. Fixed to use the leaf directory. - Normalized all error-path SkillName derivations via NormalizeSkillName so errored rows render the canonical lowercased name, consistent with accepted skills. - Removed dead caller-side content.TrimStart('') no-ops: content comes from File.ReadAllText which already strips the BOM, so BOM tolerance lives solely in ExtractFrontmatter. Adds regression tests: degenerate-block returns null (does not throw), Scan survives a degenerate SKILL.md and keeps discovering healthy siblings, and issue SkillNames are normalized from mixed-case directories. --- .../Skills/SkillScannerTests.cs | 111 ++++++++++++++++++ src/Netclaw.Actors/Skills/SkillScanner.cs | 70 ++++++++--- 2 files changed, 163 insertions(+), 18 deletions(-) diff --git a/src/Netclaw.Actors.Tests/Skills/SkillScannerTests.cs b/src/Netclaw.Actors.Tests/Skills/SkillScannerTests.cs index b6440bcf9..c287bbc8c 100644 --- a/src/Netclaw.Actors.Tests/Skills/SkillScannerTests.cs +++ b/src/Netclaw.Actors.Tests/Skills/SkillScannerTests.cs @@ -5,6 +5,7 @@ // ----------------------------------------------------------------------- using Netclaw.Actors.Skills; using Netclaw.Configuration; +using System.Linq; using Xunit; namespace Netclaw.Actors.Tests.Skills; @@ -813,4 +814,114 @@ private void WriteNestedSkill(string category, string skillName, string descript # {skillName} """); } + + [Fact] + public void ExtractFrontmatter_handles_utf8_bom() + { + // SKILL.md files saved by some editors (e.g., Notepad on Windows) include + // a UTF-8 BOM (\uFEFF) at the start of the file. ExtractFrontmatter should + // strip the BOM and still parse the frontmatter correctly. + var content = "\uFEFF---\nname: bom-skill\ndescription: \"A skill with BOM\"\n---\n\n# Content\n"; + + var result = SkillScanner.ExtractFrontmatter(content); + + Assert.NotNull(result); + Assert.Equal("bom-skill", result.Name); + Assert.Equal("A skill with BOM", result.Description); + } + + [Theory] + [InlineData("---\n---\n")] // empty frontmatter body + [InlineData("---\n---\n")] // BOM-prefixed empty frontmatter body + [InlineData("---\n---")] // no trailing newline + public void ExtractFrontmatter_returns_null_for_degenerate_block_without_throwing(string content) + { + // A degenerate block like "---\n---" has an empty YAML body: the opening line's + // newline IS the closing delimiter's newline. The slice must not compute a + // negative-length range (ArgumentOutOfRangeException) — it must return null so the + // file is reported as invalid frontmatter rather than crashing the scan. + var result = SkillScanner.ExtractFrontmatter(content); + + Assert.Null(result); + } + + [Fact] + public void Scan_does_not_abort_on_skill_with_degenerate_frontmatter() + { + // Regression: a SKILL.md whose frontmatter is an empty "---\n---" block previously + // threw ArgumentOutOfRangeException out of the unguarded parse call, aborting the + // entire discovery pass so that no skills loaded at all. Scan must instead skip the + // bad skill (recording an issue) and continue discovering healthy siblings. + WriteSkill("degenerate", "---\n---\n\n# Body\n"); + WriteSkill("healthy", """ + --- + name: healthy + description: A perfectly good skill. + --- + + # Healthy + """); + + var result = SkillScanner.Scan(_skillsDir); + + Assert.Contains(result.AcceptedSkills, s => s.Name == "healthy"); + Assert.Contains(result.Issues, i => + i.Path.EndsWith(Path.Combine("degenerate", "SKILL.md"), StringComparison.OrdinalIgnoreCase)); + } + + [Fact] + public void SkillScanIssue_populates_skill_name_for_broken_frontmatter() + { + // When a SKILL.md has invalid frontmatter, the resulting SkillScanIssue + // should include the SkillName (derived from the parent directory name) + // so that issue reporting can identify the skill by name. + WriteSkill("broken-frontmatter", """ + --- + name: broken-frontmatter + description: [invalid yaml {{{ + --- + + # Broken + """); + + var result = SkillScanner.Scan(_skillsDir); + + Assert.Empty(result.AcceptedSkills); // broken frontmatter => skill rejected + var issuesForSkill = result.Issues + .Where(i => i.Path.EndsWith(Path.Combine("broken-frontmatter", "SKILL.md"), StringComparison.OrdinalIgnoreCase)) + .ToList(); + + Assert.NotEmpty(issuesForSkill); + Assert.All(issuesForSkill, i => + { + Assert.NotNull(i.SkillName); + Assert.Equal("broken-frontmatter", i.SkillName); + }); + } + + [Fact] + public void SkillScanIssue_normalizes_skill_name_from_mixed_case_directory() + { + // Issue SkillNames must be the canonical (lowercased) skill name — the same + // representation accepted skills use — so that errored and accepted rows render + // consistently regardless of the on-disk directory casing. + WriteSkill("Mixed-Case", """ + --- + name: Mixed-Case + description: [invalid yaml {{{ + --- + + # Broken + """); + + var result = SkillScanner.Scan(_skillsDir); + + var issuesForSkill = result.Issues + .Where(i => i.Path.EndsWith(Path.Combine("Mixed-Case", "SKILL.md"), StringComparison.OrdinalIgnoreCase)) + .ToList(); + + Assert.NotEmpty(issuesForSkill); + Assert.All(issuesForSkill, i => Assert.Equal("mixed-case", i.SkillName)); + } + } diff --git a/src/Netclaw.Actors/Skills/SkillScanner.cs b/src/Netclaw.Actors/Skills/SkillScanner.cs index 0b78b456e..3fa5fc3fd 100644 --- a/src/Netclaw.Actors/Skills/SkillScanner.cs +++ b/src/Netclaw.Actors/Skills/SkillScanner.cs @@ -1,4 +1,4 @@ -// ----------------------------------------------------------------------- +// ----------------------------------------------------------------------- // // Copyright (C) 2026 - 2026 Petabridge, LLC // @@ -280,24 +280,31 @@ private static void MergeSources( } catch (Exception ex) when (ex is IOException or UnauthorizedAccessException) { + var skillName = NormalizeSkillName(Path.GetFileName(Path.GetDirectoryName(canonicalSkillFilePath)!)); issues.Add(new SkillScanIssue( Path: canonicalSkillFilePath, Kind: SkillScanIssueKind.UnreadableFile, - Message: $"Failed to read skill file: {ex.Message}")); + Message: $"Failed to read skill file: {ex.Message}", + SkillName: skillName)); return null; } var frontmatter = ExtractFrontmatter(content); if (frontmatter is null) { + var skillName = NormalizeSkillName(Path.GetFileName(Path.GetDirectoryName(canonicalSkillFilePath)!)); + // content is from File.ReadAllText, which already strips any UTF-8 BOM, so no TrimStart + // is needed here; BOM tolerance for direct-string callers lives in ExtractFrontmatter. + var hasFrontmatterStart = content.StartsWith("---", StringComparison.Ordinal); issues.Add(new SkillScanIssue( Path: canonicalSkillFilePath, - Kind: content.StartsWith("---", StringComparison.Ordinal) + Kind: hasFrontmatterStart ? SkillScanIssueKind.InvalidFrontmatter : SkillScanIssueKind.MissingFrontmatter, - Message: content.StartsWith("---", StringComparison.Ordinal) + Message: hasFrontmatterStart ? "Skill frontmatter is invalid or unparseable." - : "Skill file must start with YAML frontmatter.")); + : "Skill file must start with YAML frontmatter.", + SkillName: skillName)); return null; } @@ -329,41 +336,47 @@ private static void MergeSources( } catch (Exception ex) when (ex is IOException or UnauthorizedAccessException) { + var skillName = NormalizeSkillName(Path.GetFileNameWithoutExtension(canonicalPath)); issues.Add(new SkillScanIssue( Path: canonicalPath, Kind: SkillScanIssueKind.UnreadableFile, - Message: $"Failed to read flat skill file: {ex.Message}")); + Message: $"Failed to read flat skill file: {ex.Message}", + SkillName: skillName)); return null; } var frontmatter = ExtractFrontmatter(content); if (frontmatter is null) { + // content is from File.ReadAllText (BOM already stripped), so no TrimStart needed. if (allowFrontmatterlessFlatFiles && !content.StartsWith("---", StringComparison.Ordinal)) return BuildFlatSkillEntryWithoutFrontmatter(canonicalPath, canonicalRoot, content, issues); + var skillName = NormalizeSkillName(Path.GetFileNameWithoutExtension(canonicalPath)); issues.Add(new SkillScanIssue( Path: canonicalPath, Kind: SkillScanIssueKind.FlatFileMissingFrontmatter, - Message: "Flat .md file found but lacks valid YAML frontmatter. Add frontmatter with name and description, or move into a skill-name/SKILL.md directory.")); + Message: "Flat .md file found but lacks valid YAML frontmatter. Add frontmatter with name and description, or move into a skill-name/SKILL.md directory.", + SkillName: skillName)); return null; } + // Derive skill name from frontmatter or filename + var fileNameWithoutExt = Path.GetFileNameWithoutExtension(canonicalPath); + var name = !string.IsNullOrWhiteSpace(frontmatter.Name) + ? NormalizeSkillName(frontmatter.Name) + : NormalizeSkillName(fileNameWithoutExt); + if (string.IsNullOrWhiteSpace(frontmatter.Description)) { issues.Add(new SkillScanIssue( Path: canonicalPath, Kind: SkillScanIssueKind.FlatFileNoDescription, - Message: "Flat .md file has frontmatter but missing description field.")); + Message: "Flat .md file has frontmatter but missing description field.", + SkillName: name)); return null; } - // Derive skill name from frontmatter or filename - var fileNameWithoutExt = Path.GetFileNameWithoutExtension(canonicalPath); - var name = !string.IsNullOrWhiteSpace(frontmatter.Name) - ? NormalizeSkillName(frontmatter.Name) - : NormalizeSkillName(fileNameWithoutExt); - if (strictNameMatch && !string.IsNullOrWhiteSpace(frontmatter.Name)) { var expectedName = NormalizeSkillName(fileNameWithoutExt); @@ -405,6 +418,8 @@ private static void MergeSources( ///

public static SkillFrontmatter? ExtractFrontmatter(string content) { + // Strip UTF-8 BOM — some editors (e.g., Notepad on Windows) prepend \uFEFF + content = content.TrimStart('\uFEFF'); if (!content.StartsWith("---", StringComparison.Ordinal)) return null; @@ -413,7 +428,16 @@ private static void MergeSources( if (closingIndex < 0) return null; - var yamlBlock = content[(content.IndexOf('\n', 0) + 1)..closingIndex]; + // Guard degenerate blocks like "---\n---" where the opening line's newline IS the + // closing delimiter: the YAML body is empty, so there is nothing to deserialize. + // Without this, content[(firstNewline+1)..closingIndex] slices a negative-length + // range and throws ArgumentOutOfRangeException, which propagates out of Scan (the + // parse calls are unguarded) and aborts the entire skill-discovery pass. + var firstNewline = content.IndexOf('\n', StringComparison.Ordinal); + if (firstNewline < 0 || firstNewline >= closingIndex) + return null; + + var yamlBlock = content[(firstNewline + 1)..closingIndex]; try { @@ -451,10 +475,14 @@ internal static string ExtractBody(string content) // Description is required per AgentSkills.io spec if (string.IsNullOrWhiteSpace(fm.Description)) { + var skillName = !string.IsNullOrWhiteSpace(fm.Name) + ? NormalizeSkillName(fm.Name) + : NormalizeSkillName(Path.GetFileName(skillDirectory)); issues.Add(new SkillScanIssue( Path: filePath, Kind: SkillScanIssueKind.MissingDescription, - Message: "Skill frontmatter must include a non-empty description.")); + Message: "Skill frontmatter must include a non-empty description.", + SkillName: skillName)); return null; } @@ -581,10 +609,14 @@ private static (bool HasSubagentMetadata, string? Subagent, string? Error) Parse } catch (Exception ex) when (ex is IOException or UnauthorizedAccessException) { + // skillDirectory is already the skill's own directory, so its leaf name IS the + // skill name — do not climb to the parent (that yields the container dir, e.g. "files"). + var skillName = NormalizeSkillName(Path.GetFileName(skillDirectory)); issues.Add(new SkillScanIssue( Path: skillDirectory, Kind: SkillScanIssueKind.ResourceEnumerationFailed, - Message: $"Failed to enumerate resources: {ex.Message}")); + Message: $"Failed to enumerate resources: {ex.Message}", + SkillName: skillName)); return null; } @@ -632,10 +664,12 @@ private static string Truncate(string value, int maxLength) var description = ExtractFirstNonEmptyMarkdownLine(content); if (string.IsNullOrWhiteSpace(description)) { + var skillName = NormalizeSkillName(Path.GetFileNameWithoutExtension(canonicalPath)); issues.Add(new SkillScanIssue( Path: canonicalPath, Kind: SkillScanIssueKind.FlatFileNoDescription, - Message: "Flat .md file without frontmatter must contain at least one non-empty line to infer a description.")); + Message: "Flat .md file without frontmatter must contain at least one non-empty line to infer a description.", + SkillName: skillName)); return null; } From d2d07aa8a8f5d75fbdf9f8948f326d50b094c122 Mon Sep 17 00:00:00 2001 From: Aaron Stannard Date: Tue, 7 Jul 2026 13:41:51 -0500 Subject: [PATCH 07/37] chore: bump SkillServer to 0.4.0-beta.3 (#1593) * chore: bump SkillServer to 0.4.0-beta.3 and adapt to API changes * fix: update test mocks to use v1 API paths * test: align sidecar mock with v1 API --- Directory.Packages.props | 2 +- ...killServerNativeSidecarIntegrationTests.cs | 2 +- .../ServerFeedSkillSyncServiceTests.cs | 40 ++++++------------- .../Services/ServerFeedSkillSyncService.cs | 13 +----- 4 files changed, 17 insertions(+), 40 deletions(-) diff --git a/Directory.Packages.props b/Directory.Packages.props index bd52f917b..9a10b312b 100644 --- a/Directory.Packages.props +++ b/Directory.Packages.props @@ -67,7 +67,7 @@ - + diff --git a/src/Netclaw.Daemon.IntegrationTests/SkillServerNativeSidecarIntegrationTests.cs b/src/Netclaw.Daemon.IntegrationTests/SkillServerNativeSidecarIntegrationTests.cs index 1d5c6812a..e64ad7298 100644 --- a/src/Netclaw.Daemon.IntegrationTests/SkillServerNativeSidecarIntegrationTests.cs +++ b/src/Netclaw.Daemon.IntegrationTests/SkillServerNativeSidecarIntegrationTests.cs @@ -25,7 +25,7 @@ namespace Netclaw.Daemon.IntegrationTests; [Trait("Category", "Integration")] public sealed class SkillServerNativeSidecarIntegrationTests : IAsyncLifetime { - private const string Image = "ghcr.io/netclaw-dev/skillserver:0.4.0-beta.1"; + private const string Image = "ghcr.io/netclaw-dev/skillserver:0.4.0-beta.3"; private const string ApiKey = "sk-test-native-sidecar-sync"; private const int ContainerPort = 8080; private const int HostPort = 18080; diff --git a/src/Netclaw.Daemon.Tests/Services/ServerFeedSkillSyncServiceTests.cs b/src/Netclaw.Daemon.Tests/Services/ServerFeedSkillSyncServiceTests.cs index b2a910026..76aa6b353 100644 --- a/src/Netclaw.Daemon.Tests/Services/ServerFeedSkillSyncServiceTests.cs +++ b/src/Netclaw.Daemon.Tests/Services/ServerFeedSkillSyncServiceTests.cs @@ -146,7 +146,7 @@ public async Task SyncOnce_missing_native_sidecar_preserves_rfc_skill_sync() """, "application/json"); handler.AddStringResponse(BaseUrl + "skills/feed-skill/1.0.0/SKILL.md", skillContent, "text/markdown"); - handler.AddErrorResponse(BaseUrl + "manifest.json", HttpStatusCode.NotFound); + handler.AddErrorResponse(BaseUrl + "subagents/v1/index.json", HttpStatusCode.NotFound); var service = CreateService(handler); await service.SyncOnceAsync(CancellationToken.None); @@ -412,72 +412,58 @@ private static void AddNativeSubAgentResponses( byte[] artifactContent, string expectedDigest) { + // Use absolute hrefs so the client resolves direct native index traversal correctly. handler.AddStringResponse( - BaseUrl + "manifest.json", - """ - { - "$schema": "https://schemas.netclaw.dev/skillserver/native-manifest/v1.json", - "generatedAt": "2026-06-30T00:00:00Z", - "links": { - "self": { "href": "manifest.json" }, - "rfcSkills": { "href": ".well-known/agent-skills/index.json" }, - "skills": { "href": "manifest/skills/index.json" }, - "subagents": { "href": "manifest/subagents/index.json" } - } - } - """, - "application/json"); - handler.AddStringResponse( - BaseUrl + "manifest/subagents/index.json", + BaseUrl + "subagents/v1/index.json", """ { "kind": "subagent-collection-index", - "links": { "self": { "href": "manifest/subagents/index.json" } }, + "links": { "self": { "href": "/subagents/v1/index.json" } }, "pages": [ - { "range": "a-z", "href": "manifest/subagents/pages/a-z.json" } + { "range": "a-z", "href": "/subagents/v1/pages/a-z.json" } ] } """, "application/json"); handler.AddStringResponse( - BaseUrl + "manifest/subagents/pages/a-z.json", + BaseUrl + "subagents/v1/pages/a-z.json", $$""" { "kind": "subagent-collection-page", "range": "a-z", - "links": { "self": { "href": "manifest/subagents/pages/a-z.json" } }, + "links": { "self": { "href": "/subagents/v1/pages/a-z.json" } }, "items": [ { "name": "{{name}}", "latestVersion": "{{version}}", "versionRange": { "min": "{{version}}", "max": "{{version}}", "count": 1 }, - "href": "manifest/subagents/{{name}}/index.json" + "href": "/subagents/v1/{{name}}/index.json" } ] } """, "application/json"); handler.AddStringResponse( - BaseUrl + $"manifest/subagents/{name}/index.json", + BaseUrl + $"subagents/v1/{name}/index.json", $$""" { "kind": "subagent-identity-index", "name": "{{name}}", "latestVersion": "{{version}}", - "links": { "self": { "href": "manifest/subagents/{{name}}/index.json" } }, + "links": { "self": { "href": "/subagents/v1/{{name}}/index.json" } }, "versions": [ { "version": "{{version}}", "publishedAt": "2026-06-30T00:00:00Z", "digest": "sha256:{{expectedDigest}}", - "href": "manifest/subagents/{{name}}/versions/{{version}}.json" + "href": "/subagents/v1/{{name}}/versions/{{version}}.json" } ] } """, "application/json"); handler.AddStringResponse( - BaseUrl + $"manifest/subagents/{name}/versions/{version}.json", + BaseUrl + $"subagents/v1/{name}/versions/{version}.json", $$""" { "kind": "subagent-version-detail", @@ -487,7 +473,7 @@ private static void AddNativeSubAgentResponses( "description": "Test sub-agent", "url": "{{BaseUrl}}subagents/{{name}}/{{version}}/agent.md", "digest": "sha256:{{expectedDigest}}", - "links": { "self": { "href": "manifest/subagents/{{name}}/versions/{{version}}.json" } } + "links": { "self": { "href": "/subagents/v1/{{name}}/versions/{{version}}.json" } } } """, "application/json"); diff --git a/src/Netclaw.Daemon/Services/ServerFeedSkillSyncService.cs b/src/Netclaw.Daemon/Services/ServerFeedSkillSyncService.cs index 8b2026b8a..b850e93d5 100644 --- a/src/Netclaw.Daemon/Services/ServerFeedSkillSyncService.cs +++ b/src/Netclaw.Daemon/Services/ServerFeedSkillSyncService.cs @@ -1,4 +1,4 @@ -// ----------------------------------------------------------------------- +// ----------------------------------------------------------------------- // // Copyright (C) 2026 - 2026 Petabridge, LLC // @@ -373,16 +373,7 @@ private async Task SyncNativeSubAgentsAsync( using var cts = CancellationTokenSource.CreateLinkedTokenSource(cancellationToken); cts.CancelAfter(TimeSpan.FromSeconds(feed.TimeoutSeconds)); - var manifest = await client.GetManifestAsync(cts.Token); - if (manifest?.Links?.SubAgents is not { Href.Length: > 0 } subAgentsLink) - { - _logger.LogDebug( - "Server feed '{FeedName}' native sidecar is unavailable or does not advertise sub-agents", - feed.Name); - return; - } - - subAgentIndex = await client.GetNativeSubAgentIndexAsync(subAgentsLink, cts.Token); + subAgentIndex = await client.GetNativeSubAgentIndexAsync(cts.Token); } catch (OperationCanceledException) when (!cancellationToken.IsCancellationRequested) { From 26b88be9e881f8c69bf96768acf428219463b4a8 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 7 Jul 2026 19:25:52 +0000 Subject: [PATCH 08/37] Bump Grpc.Tools from 2.81.1 to 2.82.0 (#1591) --- updated-dependencies: - dependency-name: Grpc.Tools dependency-version: 2.82.0 dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- Directory.Packages.props | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Directory.Packages.props b/Directory.Packages.props index 9a10b312b..b326cb248 100644 --- a/Directory.Packages.props +++ b/Directory.Packages.props @@ -74,7 +74,7 @@ - + From dec40d8ac0b7959144d64772602658b2ef1fabd6 Mon Sep 17 00:00:00 2001 From: Aaron Stannard Date: Tue, 7 Jul 2026 14:43:01 -0500 Subject: [PATCH 09/37] Bump version to 0.25.0-beta.2 and update release notes (#1594) --- Directory.Build.props | 2 +- RELEASE_NOTES.md | 9 +++++++++ 2 files changed, 10 insertions(+), 1 deletion(-) diff --git a/Directory.Build.props b/Directory.Build.props index be6b2ec4f..3a3f1e3e1 100644 --- a/Directory.Build.props +++ b/Directory.Build.props @@ -9,7 +9,7 @@ enable true 0.25.0 - beta.1 + beta.2 Netclaw v0.25.0-beta.1 — SkillServer native sub-agent sync, memory curation unification, systemd PATH fix **Features** diff --git a/RELEASE_NOTES.md b/RELEASE_NOTES.md index 2e5e293d9..0f4a5cffb 100644 --- a/RELEASE_NOTES.md +++ b/RELEASE_NOTES.md @@ -1,5 +1,14 @@ # NetClaw Release Notes +## 0.25.0-beta.2 (2026-07-07) + +### Bug Fixes +- **UTF-8 BOM in skill frontmatter** — Fixed: skill scanner now strips UTF-8 BOM (`\uFEFF`) before parsing YAML frontmatter, and populates `SkillName` on all `SkillScanIssue` records so degenerate frontmatter no longer crashes the scan ([#1583](https://github.com/netclaw-dev/netclaw/pull/1583)) +- **Model capability provenance logging** — Fixed: daemon now logs effective model capabilities with their provenance source, improving diagnostic visibility for model configuration issues ([#1584](https://github.com/netclaw-dev/netclaw/pull/1584)) + +### Dependency Updates +- **Bump SkillServer** — `Netclaw.SkillClient` 0.4.0-beta.1 → 0.4.0-beta.3 and adapt to API changes ([#1593](https://github.com/netclaw-dev/netclaw/pull/1593)) + ## 0.25.0-beta.1 (2026-07-05) ### Features From e1dea9d8bc6045883fec996cba180a5384d51627 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 8 Jul 2026 11:33:40 -0500 Subject: [PATCH 10/37] Bump Netclaw.SkillClient from 0.4.0-beta.3 to 0.4.0-beta.4 (#1596) --- updated-dependencies: - dependency-name: Netclaw.SkillClient dependency-version: 0.4.0-beta.4 dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- Directory.Packages.props | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Directory.Packages.props b/Directory.Packages.props index b326cb248..88d7af217 100644 --- a/Directory.Packages.props +++ b/Directory.Packages.props @@ -67,7 +67,7 @@ - + From 4d6f365a11708906c103f78b0a2d0e0d36cb6a15 Mon Sep 17 00:00:00 2001 From: Aaron Stannard Date: Wed, 8 Jul 2026 12:03:31 -0500 Subject: [PATCH 11/37] fix(subagents): record sub-agent LLM token usage to daily stats (#1597) (#1600) Sub-agent LLM calls never recorded token usage, so every sub-agent's input/output tokens were invisible to `netclaw stats`. This was a pre-existing gap, not a regression: SubAgentActor never had an ISessionMetrics dependency and discarded the ChatResponse.Usage it already receives from StreamingResponseReader. The recent observability PRs (#1428, #1468, #1472/#1499) only added logs and pruned dead OTel Activities; none ever touched token tracking. Fix records at the source, mirroring the main session: - Inject ISessionMetrics into SubAgentActor (via SubAgentSpawner/CreateProps). DI already registers it as a process-wide singleton, so no Program.cs change. - Record response.Usage on every LlmResponseReceived (one per LLM turn: tool-call turns, retries, the forced-no-tools final turn, repair turns). - Add cumulative input/output token totals to the completion summary log. Recorded in the child, not propagated to the parent: ISessionMetrics is the SAME process-wide singleton both share, so re-recording in the parent would double-count, and folding sub-agent tokens into the parent's UsageOutput would corrupt its context-window percentage (the sub-agent has its own context window). Regression coverage (all four fail if the recording is removed): - SubAgentActor bills usage per LLM call and sums across the turn loop. - Completion summary log carries token totals. - Full spawner->CreateProps->actor wiring bills tokens to the spawner's metrics. Adds a UsageOverride hook to the sub-agent test FakeChatClient and extracts a shared RecordingSessionMetrics test helper. --- .../SubAgents/RecordingSessionMetrics.cs | 49 ++++++++++++ .../SubAgents/SubAgentActorTests.cs | 12 ++- .../SubAgents/SubAgentObservabilityTests.cs | 75 +++++++++++++++++++ .../SubAgents/SubAgentSpawnerTests.cs | 63 ++++++++++++++++ src/Netclaw.Actors/SubAgents/SubAgentActor.cs | 63 ++++++++++++++-- .../SubAgents/SubAgentSpawner.cs | 13 +++- 6 files changed, 263 insertions(+), 12 deletions(-) create mode 100644 src/Netclaw.Actors.Tests/SubAgents/RecordingSessionMetrics.cs diff --git a/src/Netclaw.Actors.Tests/SubAgents/RecordingSessionMetrics.cs b/src/Netclaw.Actors.Tests/SubAgents/RecordingSessionMetrics.cs new file mode 100644 index 000000000..8f2ff1c56 --- /dev/null +++ b/src/Netclaw.Actors.Tests/SubAgents/RecordingSessionMetrics.cs @@ -0,0 +1,49 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using Netclaw.Actors.Telemetry; +using Netclaw.Configuration; + +namespace Netclaw.Actors.Tests.SubAgents; + +/// +/// Records every call so a test can +/// assert a sub-agent bills each LLM call to the daily-stats sink. Thread-safe: the +/// actor records on its mailbox thread while the test reads after the Ask +/// completes. Regression support for issue #1597. +/// +internal sealed class RecordingSessionMetrics : ISessionMetrics +{ + private readonly object _gate = new(); + private readonly List<(long Input, long Output)> _tokenUsageCalls = []; + private long _totalInput; + private long _totalOutput; + + public IReadOnlyList<(long Input, long Output)> TokenUsageCalls + { + get { lock (_gate) { return _tokenUsageCalls.ToArray(); } } + } + + public long TotalInputTokens { get { lock (_gate) { return _totalInput; } } } + + public long TotalOutputTokens { get { lock (_gate) { return _totalOutput; } } } + + public void RecordTokenUsage(long inputTokens, long outputTokens) + { + lock (_gate) + { + _tokenUsageCalls.Add((inputTokens, outputTokens)); + _totalInput += inputTokens; + _totalOutput += outputTokens; + } + } + + public void RecordTurnCompleted() { } + public void RecordSessionCreated() { } + public void RecordMemoriesFormed(int count) { } + public void RecordMemoriesRecalled(int count) { } + public void RecordSkillsLoaded(int count) { } + public void RecordSkillLoaded(string skillName, SkillLoadMethod method) { } +} diff --git a/src/Netclaw.Actors.Tests/SubAgents/SubAgentActorTests.cs b/src/Netclaw.Actors.Tests/SubAgents/SubAgentActorTests.cs index 7b1f58b55..0a6ffd8f2 100644 --- a/src/Netclaw.Actors.Tests/SubAgents/SubAgentActorTests.cs +++ b/src/Netclaw.Actors.Tests/SubAgents/SubAgentActorTests.cs @@ -1415,6 +1415,14 @@ internal sealed class FakeChatClient : IChatClient public IReadOnlyList? ResponseTextsByCall { get; set; } + /// + /// When set, every returned response carries these token counts as + /// . The streaming reader coalesces that back into + /// response.Usage, so a test can prove the sub-agent bills each LLM call's + /// tokens to . + /// + public UsageDetails? UsageOverride { get; set; } + public async Task GetResponseAsync( IEnumerable messages, ChatOptions? options = null, @@ -1437,7 +1445,7 @@ public async Task GetResponseAsync( var toolCallContents = new List(ToolCallsOnFirstCall); var toolCallMessage = new ChatMessage( ChatRole.Assistant, toolCallContents); - return new ChatResponse(toolCallMessage); + return new ChatResponse(toolCallMessage) { Usage = UsageOverride }; } } @@ -1448,7 +1456,7 @@ public async Task GetResponseAsync( var responseMessage = new ChatMessage( ChatRole.Assistant, [new TextContent(responseText)]); - return new ChatResponse(responseMessage); + return new ChatResponse(responseMessage) { Usage = UsageOverride }; } public IAsyncEnumerable GetStreamingResponseAsync( diff --git a/src/Netclaw.Actors.Tests/SubAgents/SubAgentObservabilityTests.cs b/src/Netclaw.Actors.Tests/SubAgents/SubAgentObservabilityTests.cs index c93cc824e..c37161dfc 100644 --- a/src/Netclaw.Actors.Tests/SubAgents/SubAgentObservabilityTests.cs +++ b/src/Netclaw.Actors.Tests/SubAgents/SubAgentObservabilityTests.cs @@ -107,4 +107,79 @@ await agent.Ask( NewRun("Greet the user"), TimeSpan.FromSeconds(5), TestContext.Current.CancellationToken); }, cancellationToken: TestContext.Current.CancellationToken); } + + // Regression coverage for issue #1597: sub-agent LLM calls used to discard + // ChatResponse.Usage entirely — the actor had no ISessionMetrics and never read + // response.Usage — so every sub-agent's token consumption was invisible to + // `netclaw stats`. These tests pin the sub-agent to the shared daily-stats sink. + + [Fact] + public async Task Records_token_usage_to_session_metrics_on_text_response() + { + var metrics = new RecordingSessionMetrics(); + var fakeClient = new FakeChatClient + { + UsageOverride = new UsageDetails { InputTokenCount = 120, OutputTokenCount = 45 } + }; + var agent = Sys.ActorOf(SubAgentActor.CreateProps( + CreateDefinition(), fakeClient, sessionMetrics: metrics)); + + var result = await agent.Ask( + NewRun("Say hello"), TimeSpan.FromSeconds(5), TestContext.Current.CancellationToken); + + Assert.True(result.Success); + // A single LLM call → exactly one usage record billed to the shared + // process-wide daily-stats sink (the same singleton the parent session uses). + var call = Assert.Single(metrics.TokenUsageCalls); + Assert.Equal((120L, 45L), call); + } + + [Fact] + public async Task Records_token_usage_for_every_llm_call_across_the_turn_loop() + { + // A tool-call turn followed by a final-text turn = two LLM calls. Both must be + // billed. This is the crux of #1597: the sub-agent's INTERNAL calls (not just + // its single final output) have to reach `netclaw stats`, so the recorded total + // is the per-call usage summed — not one call's worth. + var metrics = new RecordingSessionMetrics(); + var fakeTool = new FakeNetclawTool("greet", "Hello from tool!"); + var fakeClient = new FakeChatClient + { + ToolCallsOnFirstCall = + [ + new FunctionCallContent("call-1", "greet", + new Dictionary { ["name"] = "World" }) + ], + UsageOverride = new UsageDetails { InputTokenCount = 120, OutputTokenCount = 45 } + }; + var agent = Sys.ActorOf(SubAgentActor.CreateProps( + CreateDefinition([fakeTool]), fakeClient, sessionMetrics: metrics)); + + var result = await agent.Ask( + NewRun("Greet the user"), TimeSpan.FromSeconds(5), TestContext.Current.CancellationToken); + + Assert.True(result.Success); + Assert.Equal(2, fakeClient.CallCount); + Assert.Equal(2, metrics.TokenUsageCalls.Count); + Assert.Equal(240L, metrics.TotalInputTokens); + Assert.Equal(90L, metrics.TotalOutputTokens); + } + + [Fact] + public async Task Completion_summary_reports_cumulative_token_totals() + { + var fakeClient = new FakeChatClient + { + UsageOverride = new UsageDetails { InputTokenCount = 120, OutputTokenCount = 45 } + }; + var agent = Sys.ActorOf(SubAgentActor.CreateProps(CreateDefinition(), fakeClient)); + + // The completion summary now carries token totals so sub-agent cost is visible + // in the logs (and Seq), not just tool/iteration/duration counts. + await EventFilter.Info(contains: "inputTokens=120, outputTokens=45").ExpectAsync(1, async () => + { + await agent.Ask( + NewRun("Say hello"), TimeSpan.FromSeconds(5), TestContext.Current.CancellationToken); + }, cancellationToken: TestContext.Current.CancellationToken); + } } diff --git a/src/Netclaw.Actors.Tests/SubAgents/SubAgentSpawnerTests.cs b/src/Netclaw.Actors.Tests/SubAgents/SubAgentSpawnerTests.cs index 21293c31c..cca1518b6 100644 --- a/src/Netclaw.Actors.Tests/SubAgents/SubAgentSpawnerTests.cs +++ b/src/Netclaw.Actors.Tests/SubAgents/SubAgentSpawnerTests.cs @@ -150,6 +150,69 @@ public async Task Spawn_async_ignores_definition_tool_metadata_for_runtime_tool_ Assert.Equal(1, started.ToolCount); } + [Fact] + public async Task Spawned_sub_agent_bills_its_llm_calls_to_session_metrics() + { + // Full-wiring regression guard for #1597: a sub-agent spawned through the real + // SubAgentSpawner must record its LLM-call tokens to the ISessionMetrics handed + // to the spawner. Unlike the actor-level tests, this exercises the + // spawner -> CreateProps -> actor pass-through, so dropping the metrics argument + // anywhere along that chain fails here. The SpawnChildActor factory materializes + // the spawner-built Props into a real SubAgentActor (a probe stand-in would + // bypass CreateProps entirely and hide a broken pass-through). + var toolRegistry = new ToolRegistry(); + toolRegistry.Register(new FakeNetclawTool("inspect_context", "ok")); + + var metrics = new RecordingSessionMetrics(); + var chatClient = new FakeChatClient + { + UsageOverride = new UsageDetails { InputTokenCount = 175, OutputTokenCount = 60 } + }; + + var spawner = new SubAgentSpawner( + new SingleClientProvider(chatClient), + toolRegistry, + new ToolAccessPolicy( + new ToolConfig(), + new EffectivePolicyDefaults( + DeploymentPosture.Personal, + TrustAudience.Personal, + ShellExecutionMode.HostAllowed, + UsedStrictFallback: false), + new ShellCommandPolicy()), + approvalService: null, + new StaticSystemPromptProvider("You are a summarizer."), + NullLogger.Instance, + sessionMetrics: metrics); + + var context = new ToolExecutionContext("console/subagent-parent", "/tmp/netclaw/sessions/parent") + { + Audience = TrustAudience.Personal + }; + context.SpawnChildActor = (props, name, _) => Task.FromResult(Sys.ActorOf((Props)props, name)); + + var profile = new SubAgentProfile + { + Name = "summarizer", + Description = "Summarize content", + SystemPrompt = "You are a summarizer.", + ToolNames = ["inspect_context"], + Visibility = SubAgentVisibility.UserFacing + }; + + var result = await spawner.SpawnAsync( + profile, + "Summarize the repo.", + runtimeContext: null, + context, + TestContext.Current.CancellationToken); + + Assert.True(result.Success, $"Expected success but got: {result.Output}"); + // One text-only LLM call → exactly one usage record, carrying the fake's tokens. + var call = Assert.Single(metrics.TokenUsageCalls); + Assert.Equal((175L, 60L), call); + } + private sealed class NoOpChatClient : IChatClient { public Task GetResponseAsync( diff --git a/src/Netclaw.Actors/SubAgents/SubAgentActor.cs b/src/Netclaw.Actors/SubAgents/SubAgentActor.cs index d28f62512..7dde15929 100644 --- a/src/Netclaw.Actors/SubAgents/SubAgentActor.cs +++ b/src/Netclaw.Actors/SubAgents/SubAgentActor.cs @@ -63,6 +63,13 @@ [Subagent Execution Contract] private readonly ToolAccessPolicy _toolAccessPolicy; private readonly IToolApprovalService? _approvalService; private readonly int _maxToolIterations; + + // Process-wide daily-stats sink (the same singleton the parent session records + // to). Nullable because a hosting configuration without the daemon stats backend + // is a real runtime state — mirrors LlmSessionActor._sessionMetrics. When present, + // every LLM call this sub-agent makes is billed here so its tokens show up in + // `netclaw stats` instead of vanishing. + private readonly Telemetry.ISessionMetrics? _sessionMetrics; private readonly ToolRegistry _toolRegistry; private IReadOnlyList _aiTools = []; private ILoggingAdapter _log; @@ -74,6 +81,12 @@ [Subagent Execution Contract] // timer scheduler — it doesn't track elapsed time itself). private readonly Stopwatch _runStopwatch = Stopwatch.StartNew(); + // Cumulative token usage across every LLM call this sub-agent makes. Summed for + // the completion summary log; per-call usage is also recorded to _sessionMetrics + // as each call returns (see RecordUsage). + private long _runInputTokens; + private long _runOutputTokens; + // Conversation state (not persisted — ephemeral) private readonly List _history = []; private long _llmCallId; @@ -137,7 +150,8 @@ public SubAgentActor( IChatClient chatClient, ToolAccessPolicy? toolAccessPolicy = null, IToolApprovalService? approvalService = null, - int maxToolIterations = DefaultMaxToolIterations) + int maxToolIterations = DefaultMaxToolIterations, + Telemetry.ISessionMetrics? sessionMetrics = null) { if (maxToolIterations <= 0) throw new ArgumentOutOfRangeException(nameof(maxToolIterations), maxToolIterations, @@ -145,6 +159,7 @@ public SubAgentActor( _definition = definition; _chatClient = chatClient; + _sessionMetrics = sessionMetrics; _toolAccessPolicy = toolAccessPolicy ?? new ToolAccessPolicy( new ToolConfig { ShellMode = ShellExecutionMode.HostAllowed }, new EffectivePolicyDefaults( @@ -175,14 +190,16 @@ public static Props CreateProps( IChatClient chatClient, ToolAccessPolicy? toolAccessPolicy = null, IToolApprovalService? approvalService = null, - int maxToolIterations = DefaultMaxToolIterations) + int maxToolIterations = DefaultMaxToolIterations, + Telemetry.ISessionMetrics? sessionMetrics = null) { return Props.Create(() => new SubAgentActor( definition, chatClient, toolAccessPolicy, approvalService, - maxToolIterations)); + maxToolIterations, + sessionMetrics)); } /// @@ -320,6 +337,14 @@ private void Processing() // the synchronous processing that follows (tool dispatch or completion). RestartWatchdog(_interDeltaBudget); var response = msg.Response; + + // Record this call's token usage before branching so EVERY call is billed — + // tool-call turns, retries, the forced-no-tools final turn, and repair turns + // all flow through here exactly once. Mirrors the main session, which records + // its own per-call usage; without this the sub-agent's tokens never reach the + // daily-stats pipeline and `netclaw stats` under-counts by the whole sub-run. + RecordUsage(response.Usage); + var lastMessage = response.Messages[^1]; var analysis = LlmResponseClassifier.Analyze(lastMessage); @@ -630,6 +655,24 @@ private void Processing() }); } + // Bill one LLM call's token usage to the shared daily-stats sink and accumulate + // the run totals for the completion summary log. We record at the source (here in + // the child) rather than propagating totals up to the parent: the parent's + // ISessionMetrics is the SAME process-wide singleton, so re-recording there would + // double-count, and folding sub-agent tokens into the parent's UsageOutput would + // corrupt its context-window percentage (the sub-agent has its own context window). + private void RecordUsage(UsageDetails? usage) + { + if (usage is null) + return; + + var input = usage.InputTokenCount ?? 0; + var output = usage.OutputTokenCount ?? 0; + _runInputTokens += input; + _runOutputTokens += output; + _sessionMetrics?.RecordTokenUsage(input, output); + } + private void HandleToolCalls(AiChatMessage assistantMessage, List toolCalls) { _turnState.ResetEmptyResponseGuards(); @@ -752,13 +795,17 @@ private void Complete( _log.Info("SubAgent [{AgentName}] completed (success={Success}, outcome={Outcome}, reason={Reason}, output={OutputLength} chars, iterations={Iterations})", _definition.Name, success, resolvedOutcome, outcomeReason?.Value ?? "-", output.Length, _turnState.ToolIterationCount); - // Log cumulative stats for observability — total LLM calls, tool usage, etc. - // This gives operators a single summary line for sub-agent duration analysis. + // Log cumulative stats for observability — total LLM calls, tool usage, tokens. + // This gives operators a single summary line for sub-agent cost/duration analysis. + // (success is already on the "completed" line above; omitted here to stay within + // ILoggingAdapter's 6-argument ceiling.) _log.Info( - "SubAgent [{AgentName}] summary: success={Success}, totalToolCalls={TotalToolCalls}, " - + "iterations={Iterations}, duration={Duration}s", - _definition.Name, success, _turnState.ToolCallCount, + "SubAgent [{AgentName}] summary: totalToolCalls={TotalToolCalls}, " + + "iterations={Iterations}, inputTokens={InputTokens}, outputTokens={OutputTokens}, " + + "duration={Duration}s", + _definition.Name, _turnState.ToolCallCount, _turnState.ToolIterationCount, + _runInputTokens, _runOutputTokens, _runStopwatch.Elapsed.TotalSeconds); var findings = success && _definition.EmitStructuredFindings diff --git a/src/Netclaw.Actors/SubAgents/SubAgentSpawner.cs b/src/Netclaw.Actors/SubAgents/SubAgentSpawner.cs index de2b673ea..530cda8b1 100644 --- a/src/Netclaw.Actors/SubAgents/SubAgentSpawner.cs +++ b/src/Netclaw.Actors/SubAgents/SubAgentSpawner.cs @@ -33,6 +33,12 @@ public sealed class SubAgentSpawner private readonly SubAgentConfig _subAgentConfig; private readonly ILogger _logger; + // The process-wide daily-stats sink, handed to each spawned SubAgentActor so its + // LLM calls are billed to `netclaw stats`. Nullable to match the rest of the stats + // wiring (a host without the daemon stats backend is a real runtime state); DI + // injects the registered singleton in production. + private readonly Telemetry.ISessionMetrics? _sessionMetrics; + public SubAgentSpawner( IChatClientProvider chatClientProvider, ToolRegistry toolRegistry, @@ -40,7 +46,8 @@ public SubAgentSpawner( IToolApprovalService? approvalService, ISystemPromptProvider promptProvider, ILogger logger, - SubAgentConfig? subAgentConfig = null) + SubAgentConfig? subAgentConfig = null, + Telemetry.ISessionMetrics? sessionMetrics = null) { _chatClientProvider = chatClientProvider; _toolRegistry = toolRegistry; @@ -49,6 +56,7 @@ public SubAgentSpawner( _promptProvider = promptProvider; _subAgentConfig = subAgentConfig ?? new SubAgentConfig(); _logger = logger; + _sessionMetrics = sessionMetrics; } /// @@ -139,7 +147,8 @@ public async Task SpawnAsync( chatClient, _toolAccessPolicy, _approvalService, - SubAgentMaxToolIterations); + SubAgentMaxToolIterations, + _sessionMetrics); var actorName = $"subagent-{definition.Name}-{runId}"; IActorRef subAgent; try From 03723de3f90caf34008636eb55b22d6a74acbbfb Mon Sep 17 00:00:00 2001 From: Aaron Stannard Date: Wed, 8 Jul 2026 18:54:10 +0000 Subject: [PATCH 12/37] fix(release): extract only the latest RELEASE_NOTES section for the GitHub release body The 'Extract latest release notes' step in publish_release_binaries.yml matched on '####' headings, but RELEASE_NOTES.md actually uses '## X.Y.Z (YYYY-MM-DD)' headings. The regex never matched, so the entire RELEASE_NOTES.md file was dumped verbatim as the GitHub release body for every tagged release (including the 0.25.0-beta.1/beta.2 prereleases). Fix the regex to match '## ...' sections instead, so only the section for the tag being released is extracted. Also correct CONTRIBUTING.md, which documented the stale '#### X.Y.Z YYYY-MM-DD ####' heading format in the Releasing section. --- .github/workflows/publish_release_binaries.yml | 2 +- CONTRIBUTING.md | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/publish_release_binaries.yml b/.github/workflows/publish_release_binaries.yml index 55d7d2a29..085d329f8 100644 --- a/.github/workflows/publish_release_binaries.yml +++ b/.github/workflows/publish_release_binaries.yml @@ -74,7 +74,7 @@ jobs: run: | $content = Get-Content RELEASE_NOTES.md -Raw # Match from the first #### heading to just before the second one - if ($content -match '(?s)(####.+?)(?=\n####|\z)') { + if ($content -match '(?s)(## \d.+?)(?=\n## \d|\z)') { $Matches[1].Trim() | Set-Content RELEASE_NOTES_LATEST.md } else { $content | Set-Content RELEASE_NOTES_LATEST.md diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 6b3fe711c..fed5d25f9 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -190,7 +190,7 @@ prerelease — or the workflow's version gate fails the release. 1. Bump `` in `Directory.Build.props` (e.g. `0.22.1` → `0.22.2`); leave `` empty. -2. Add a release-notes section to `RELEASE_NOTES.md` (`#### X.Y.Z YYYY-MM-DD ####`). +2. Add a release-notes section to `RELEASE_NOTES.md` (`## X.Y.Z (YYYY-MM-DD)`). 3. Commit, then tag and push the bare version: ```bash git tag 0.22.2 && git push origin 0.22.2 @@ -210,7 +210,7 @@ Prereleases ship to opt-in testers without touching any stable surface. Use the **dotted** `beta.N` form (`beta.1`, `beta.2`, … `beta.10`) — never `beta1`. A non-dotted identifier compares lexically (so `beta10` would rank below `beta2`), and the release version gate rejects it. -2. Add a `RELEASE_NOTES.md` section for `0.23.0-beta.1`. +2. Add a `RELEASE_NOTES.md` section for `0.23.0-beta.1` (`## 0.23.0-beta.1 (YYYY-MM-DD)`). 3. Commit, then tag and push the full version (prefix `-` suffix): ```bash git tag 0.23.0-beta.1 && git push origin 0.23.0-beta.1 From 8c27daa606dcc84ef70e2d4d3f29a0578acb66a6 Mon Sep 17 00:00:00 2001 From: Aaron Stannard Date: Wed, 8 Jul 2026 19:01:31 +0000 Subject: [PATCH 13/37] chore(release): prepare 0.25.0-alpha.onnx.1 experimental prerelease MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit This is the named experimental memory-embeddings prerelease, cut from feature/memory-embeddings. It is a superset of 0.25.0-beta.2 and is channel-neutral (alpha.* sorts below beta.* in SemVer precedence), so it will not advance the beta or stable release channels — install only by exact version pin. --- Directory.Build.props | 2 +- RELEASE_NOTES.md | 15 +++++++++++++++ 2 files changed, 16 insertions(+), 1 deletion(-) diff --git a/Directory.Build.props b/Directory.Build.props index 3a3f1e3e1..b759b0476 100644 --- a/Directory.Build.props +++ b/Directory.Build.props @@ -9,7 +9,7 @@ enable true 0.25.0 - beta.2 + alpha.onnx.1 Netclaw v0.25.0-beta.1 — SkillServer native sub-agent sync, memory curation unification, systemd PATH fix **Features** diff --git a/RELEASE_NOTES.md b/RELEASE_NOTES.md index 0f4a5cffb..d5023e303 100644 --- a/RELEASE_NOTES.md +++ b/RELEASE_NOTES.md @@ -1,5 +1,20 @@ # NetClaw Release Notes +## 0.25.0-alpha.onnx.1 (2026-07-08) + +> **Experimental feature build.** This is a named experimental prerelease of the semantic +> memory-embeddings foundation, gated behind a config flag that is **off by default** +> (`Memory.Embeddings.Enabled`). Without opting in, runtime behavior is identical to +> 0.25.0-beta.2, which this build fully contains. It is not published to the beta channel — +> install only by exact pin: `NETCLAW_VERSION=0.25.0-alpha.onnx.1`. Embeddings live in a +> new additive table; disabling the flag or downgrading afterward is safe — vectors are +> derived data and original memory content is never touched. + +### Memory (Experimental) +- **Semantic memory embeddings (opt-in)** — In-process ONNX embedding runtime (snowflake-arctic-embed-m, CPU-only, hash-pinned model downloaded at daemon startup), embed-on-write with startup gap repair, a `netclaw memory backfill-embeddings [--force]` CLI command, and a doctor check for model/coverage status ([#1577](https://github.com/netclaw-dev/netclaw/pull/1577)) +- **Semantic dedup: kNN-nominate / LLM-decide with lossless merges** — With embeddings enabled, near-duplicate memories are nominated by vector similarity and merged by the curation LLM under a deterministic MergeGuard; guard failures fall back to a lossless structural append ([#1585](https://github.com/netclaw-dev/netclaw/pull/1585)) +- **Guard-rejected anchor updates fall through to nomination** — instead of being silently dropped ([#1587](https://github.com/netclaw-dev/netclaw/pull/1587)) + ## 0.25.0-beta.2 (2026-07-07) ### Bug Fixes From a6d0a2f0dca40976ace61e5fd9df31c74bb5e37a Mon Sep 17 00:00:00 2001 From: Aaron Stannard Date: Wed, 8 Jul 2026 20:07:31 +0000 Subject: [PATCH 14/37] feat(memory): read-side hybrid recall with absolute cosine floor (memory-core-redesign slice 4, tasks 4.1-4.5) Read-side hybrid recall (design D6): SQLiteMemoryRecallCoordinator now embeds the query once per turn under a fixed 150ms sub-budget (linked CTS nested inside the caller's Memory.RecallTimeoutMs) via the same MemoryEmbedderHolder/MemoryVectorIndexHolder Slice 3's curation evaluator established, and unions FTS5 lexical candidates with the vector index's top-50 cosine matches (MemoryVectorIndex.TopK(minCosine: MinCosineSimilarity) applies the absolute floor at the source). Vector-only hits hydrate through the new SQLiteMemoryStore.GetRecallCandidatesByIdsAsync, sharing its WHERE predicate (recall-mode allowlist, boundary, audience, sensitivity, memory-class, expiry) with SearchByPlanAsync's document branch via a new DocumentRecallPolicyPredicateSql helper, so the two queries cannot drift and a vector hit can never bypass a gate a lexical hit would have to clear. DeterministicCandidateSelector.Score is now public so a vector-only candidate is scored against plan terms with the identical lexical weights lexical hits use. Fusion: fused = VectorWeight*cosine + LexicalWeight*squash(selectorScore) + (RecallRank/100/10), squash(s) = s/(s+8) mapping the unbounded selector score into [0,1). Recency decay (task 4.4) multiplies the fused score by 0.85 + 0.15*2^(-ageDays/RecencyHalfLifeDays), structurally floor-bounded at 0.85 so it only breaks ties. Absolute floor: only candidates with cosine >= MinCosineSimilarity are injectable; zero survivors returns a healthy empty result (not degraded) so the [memory-recall] block is omitted - design D6's "nothing relevant means nothing injected." Degraded path (embedder/index unavailable, sub-budget exceeded, or no holders wired) runs the pre-Slice-4 lexical pipeline verbatim - same selector+composite formula, same 14.0 floor - which is exactly what the untouched MemoryRecallScenarioTests suite (including P09) still pins. A rate-limited memory_recall_vector_degraded log fires on every fallback reason: Debug when Memory.Embeddings.Enabled is false (the default, intentional state - mirrors curation_nominator_degraded's level choice), Warning when embeddings are enabled but the turn still degraded. Dynamic-length embedding (part of task 4.1's latency budget): ported the bucket-of-8 padding from tools/embed-latency-bench into OnnxMemoryEmbedder.EmbedOne, extracting ComputeBucketedLength as a directly-unit-tested helper. This is the measured mitigation design D6 requires to keep the 150ms sub-budget from being blown by the previous fixed-512-token padding. Config: Memory.Recall { VectorWeight=0.7, LexicalWeight=0.3, MinCosineSimilarity=0.55, RecencyHalfLifeDays=30 }, schema-synced with per-field defaults/bounds under Memory (additionalProperties: false). Design note: the coordinator's ctor now takes MemoryConfig (not just MemoryRecallConfig) so it can read Embeddings.Enabled for the Debug/Warning log-level split without threading a second config dependency alongside it; TimeProvider is a new required ctor parameter (DI already registers TimeProvider.System as a singleton, so Program.cs needed no new registrations for either). Tests: fusion floor rejects a lexically-strong/low-cosine candidate, zero-survivors is healthy-empty, recency decay bounds (ratio to the 0.85 floor), degraded-path parity (unavailable embedder == no holders), Debug-vs-Warning log level split, gated-hydration exclusions for recall_mode/boundary/audience/sensitivity/memory_class (extends SQLiteMemoryStoreEmbeddingTests), dynamic-length padding determinism/ dimension/norm + ComputeBucketedLength unit tests. MemoryRecallScenarioTests (the pre-Slice-4 lexical gold suite) passes unchanged. Gates: dotnet build (Debug+Release) clean, 0 warnings/errors. Netclaw.Actors.Tests 2641, Netclaw.Embeddings.Tests 29, Netclaw.Configuration.Tests 465, Netclaw.Cli.Tests 1233, Netclaw.Daemon.Tests 832 - all green. slopwatch: 0 new violations (5 pre-existing SW004 warnings in untouched files). Header verification clean. Tasks 4.1-4.5 marked done in tasks.md. Tasks 4.6 (calibration), 4.7 (gold-set regression suite), 4.8 (flip P09), 4.9 (eval/skill sync) are explicitly out of scope for this slice. --- .../changes/memory-core-redesign/tasks.md | 10 +- .../Memory/MemoryEvalSeedSuiteTests.cs | 6 +- .../Memory/MemoryRedesignedEvalSuiteTests.cs | 10 + .../Memory/SQLiteMemoryStoreEmbeddingTests.cs | 134 +++++++ .../DeterministicRetrievalPlanningTests.cs | 16 + .../Sessions/LlmSessionIntegrationTests.cs | 4 +- .../Sessions/MemoryRecallScenarioTests.cs | 2 + .../Sessions/SQLiteMemoryRecallHybridTests.cs | 351 ++++++++++++++++ .../Memory/SQLiteMemoryStore.cs | 128 +++++- .../DeterministicCandidateSelector.cs | 14 +- .../Sessions/SQLiteMemoryRecallCoordinator.cs | 375 +++++++++++++++++- .../Doctor/ConfigSchemaDoctorCheckTests.cs | 55 +++ .../MemoryConfigDefaultsTests.cs | 30 ++ src/Netclaw.Configuration/MemoryConfig.cs | 52 +++ .../Schemas/netclaw-config.v1.schema.json | 35 ++ .../OnnxMemoryEmbedderTests.cs | 43 ++ src/Netclaw.Embeddings/OnnxMemoryEmbedder.cs | 54 ++- 17 files changed, 1279 insertions(+), 40 deletions(-) create mode 100644 src/Netclaw.Actors.Tests/Sessions/SQLiteMemoryRecallHybridTests.cs diff --git a/openspec/changes/memory-core-redesign/tasks.md b/openspec/changes/memory-core-redesign/tasks.md index 02456d98b..94eeb2656 100644 --- a/openspec/changes/memory-core-redesign/tasks.md +++ b/openspec/changes/memory-core-redesign/tasks.md @@ -40,11 +40,11 @@ constitution gates (tests, evals where mapped, schema/skill sync, slopwatch). ## 4. Read-side hybrid recall + absolute floor -- [ ] 4.1 Query embedding per turn with a vector sub-budget inside `RecallTimeoutMs`; lexical-only fallback + `memory_recall_vector_degraded` log on miss -- [ ] 4.2 Candidate union (FTS5 ∪ vector top-k) with policy-gate parity for vector-sourced hits -- [ ] 4.3 Weighted fusion scoring + `MinCosineSimilarity` absolute floor; omit the `[memory-recall]` block entirely on zero injections -- [ ] 4.4 Recency half-life decay (floor-bounded multiplier) on composite scores -- [ ] 4.5 Config: `Memory.Recall { VectorWeight, LexicalWeight, MinCosineSimilarity, RecencyHalfLifeDays }` + schema sync +- [x] 4.1 Query embedding per turn with a vector sub-budget inside `RecallTimeoutMs`; lexical-only fallback + `memory_recall_vector_degraded` log on miss +- [x] 4.2 Candidate union (FTS5 ∪ vector top-k) with policy-gate parity for vector-sourced hits +- [x] 4.3 Weighted fusion scoring + `MinCosineSimilarity` absolute floor; omit the `[memory-recall]` block entirely on zero injections +- [x] 4.4 Recency half-life decay (floor-bounded multiplier) on composite scores +- [x] 4.5 Config: `Memory.Recall { VectorWeight, LexicalWeight, MinCosineSimilarity, RecencyHalfLifeDays }` + schema sync - [ ] 4.6 Calibrate the floor against `gold-prod-2026-07` (local gold set); record calibration numbers in design.md - [ ] 4.7 Gold-set recall regression suite (fixture corpus + labeled queries asserting injected/withheld ids, MRR/precision floors, zero-injection cases) - [ ] 4.8 Flip scenario P09 (paraphrase-gap) back to expected-recall; policy-parity scenario test; latency budget test with warm embedder diff --git a/src/Netclaw.Actors.Tests/Memory/MemoryEvalSeedSuiteTests.cs b/src/Netclaw.Actors.Tests/Memory/MemoryEvalSeedSuiteTests.cs index c9f342ffa..a36ea966e 100644 --- a/src/Netclaw.Actors.Tests/Memory/MemoryEvalSeedSuiteTests.cs +++ b/src/Netclaw.Actors.Tests/Memory/MemoryEvalSeedSuiteTests.cs @@ -50,7 +50,7 @@ await _store.UpsertDocumentAsync(new SQLiteMemoryDocument( CreatedAtMs: now, UpdatedAtMs: now), TestContext.Current.CancellationToken); - var coordinator = new SQLiteMemoryRecallCoordinator(_store, NullLogger.Instance, sessionTuning: new SessionTuning()); + var coordinator = new SQLiteMemoryRecallCoordinator(_store, NullLogger.Instance, new MemoryConfig(), TimeProvider.System, sessionTuning: new SessionTuning()); var result = await coordinator.RecallAsync(new AutomaticRecallRequest( SessionId: (SessionId)"ops/thread-1", Query: "router failover", @@ -87,7 +87,7 @@ await _store.UpsertDocumentAsync(new SQLiteMemoryDocument( CreatedAtMs: now, UpdatedAtMs: now), TestContext.Current.CancellationToken); - var coordinator = new SQLiteMemoryRecallCoordinator(_store, NullLogger.Instance, sessionTuning: new SessionTuning()); + var coordinator = new SQLiteMemoryRecallCoordinator(_store, NullLogger.Instance, new MemoryConfig(), TimeProvider.System, sessionTuning: new SessionTuning()); var result = await coordinator.RecallAsync(new AutomaticRecallRequest( SessionId: (SessionId)"ops/thread-1", Query: "token", @@ -310,7 +310,7 @@ await _store.UpsertDocumentAsync(new SQLiteMemoryDocument( UpdatedAtMs: now), TestContext.Current.CancellationToken); } - var coordinator = new SQLiteMemoryRecallCoordinator(_store, NullLogger.Instance, sessionTuning: new SessionTuning()); + var coordinator = new SQLiteMemoryRecallCoordinator(_store, NullLogger.Instance, new MemoryConfig(), TimeProvider.System, sessionTuning: new SessionTuning()); var start = TimeProvider.System.GetTimestamp(); var result = await coordinator.RecallAsync(new AutomaticRecallRequest( SessionId: (SessionId)"latency/thread-1", diff --git a/src/Netclaw.Actors.Tests/Memory/MemoryRedesignedEvalSuiteTests.cs b/src/Netclaw.Actors.Tests/Memory/MemoryRedesignedEvalSuiteTests.cs index 874b73560..0efe69353 100644 --- a/src/Netclaw.Actors.Tests/Memory/MemoryRedesignedEvalSuiteTests.cs +++ b/src/Netclaw.Actors.Tests/Memory/MemoryRedesignedEvalSuiteTests.cs @@ -65,6 +65,8 @@ public async Task Formation_then_auto_recall_surfaces_durable_fact() var recall = new SQLiteMemoryRecallCoordinator( _store, NullLogger.Instance, + new MemoryConfig(), + _timeProvider, sessionTuning: new SessionTuning()); var result = await recall.RecallAsync(new AutomaticRecallRequest( @@ -125,6 +127,8 @@ public async Task Formation_then_recall_surfaces_travel_origin_and_persists_meta var recall = new SQLiteMemoryRecallCoordinator( _store, NullLogger.Instance, + new MemoryConfig(), + _timeProvider, sessionTuning: new SessionTuning { DeterministicRetrievalEnabled = true }); var result = await recall.RecallAsync(new AutomaticRecallRequest( @@ -185,6 +189,8 @@ public async Task Formation_then_recall_surfaces_preferred_airline_and_persists_ var recall = new SQLiteMemoryRecallCoordinator( _store, NullLogger.Instance, + new MemoryConfig(), + _timeProvider, sessionTuning: new SessionTuning { DeterministicRetrievalEnabled = true }); var result = await recall.RecallAsync(new AutomaticRecallRequest( @@ -253,6 +259,8 @@ await _store.ApplyCurationBatchAsync( var recall = new SQLiteMemoryRecallCoordinator( _store, NullLogger.Instance, + new MemoryConfig(), + _timeProvider, sessionTuning: new SessionTuning()); var auto = await recall.RecallAsync(new AutomaticRecallRequest( @@ -471,6 +479,8 @@ public async Task Eval_reporting_thresholds_meet_smoke_targets_for_current_fixtu var recall = new SQLiteMemoryRecallCoordinator( _store, NullLogger.Instance, + new MemoryConfig(), + _timeProvider, sessionTuning: new SessionTuning()); var acceptedFact = proposalGate.Accept( diff --git a/src/Netclaw.Actors.Tests/Memory/SQLiteMemoryStoreEmbeddingTests.cs b/src/Netclaw.Actors.Tests/Memory/SQLiteMemoryStoreEmbeddingTests.cs index 271578785..ce561966a 100644 --- a/src/Netclaw.Actors.Tests/Memory/SQLiteMemoryStoreEmbeddingTests.cs +++ b/src/Netclaw.Actors.Tests/Memory/SQLiteMemoryStoreEmbeddingTests.cs @@ -381,6 +381,140 @@ await _store.EnqueueCheckpointAsync(new SQLiteMemoryCheckpoint( Assert.Equal("worker body", doc.Body); } + // ── GetRecallCandidatesByIdsAsync gated hydration (memory-core-redesign Slice 4, task 4.2) ── + // + // These prove SQLiteMemoryRecallCoordinator's hybrid path cannot use a vector-sourced hit to + // bypass a policy gate a lexically-discovered hit (SearchByPlanAsync) would have to clear: + // every scenario here mirrors one of SearchByPlanAsync's document-branch predicates + // (recall_mode allowlist, boundary match, audience membership, sensitivity exclusion, + // memory-class allowlist) via the shared DocumentRecallPolicyPredicateSql helper. + + [Fact] + public async Task GetRecallCandidatesByIdsAsync_returns_a_document_that_clears_every_gate() + { + await SeedGatedDocumentAsync("doc-gated-ok"); + + var result = await _store.GetRecallCandidatesByIdsAsync( + ["doc-gated-ok"], + [MemoryClass.DurableFact.ToWireValue()], + TrustBoundary.TrustedInstanceValue, + TrustAudience.Public, + allowExpiredEvidence: false, + TestContext.Current.CancellationToken); + + Assert.Single(result, x => x.Id == "doc-gated-ok"); + } + + [Fact] + public async Task GetRecallCandidatesByIdsAsync_excludes_a_manual_recall_mode_document() + { + await SeedGatedDocumentAsync("doc-gated-manual", recallMode: "manual"); + + var result = await _store.GetRecallCandidatesByIdsAsync( + ["doc-gated-manual"], + [MemoryClass.DurableFact.ToWireValue()], + TrustBoundary.TrustedInstanceValue, + TrustAudience.Public, + allowExpiredEvidence: false, + TestContext.Current.CancellationToken); + + Assert.Empty(result); + } + + [Fact] + public async Task GetRecallCandidatesByIdsAsync_excludes_a_secret_sensitivity_document() + { + await SeedGatedDocumentAsync("doc-gated-secret", sensitivity: "secret"); + + var result = await _store.GetRecallCandidatesByIdsAsync( + ["doc-gated-secret"], + [MemoryClass.DurableFact.ToWireValue()], + TrustBoundary.TrustedInstanceValue, + TrustAudience.Public, + allowExpiredEvidence: false, + TestContext.Current.CancellationToken); + + Assert.Empty(result); + } + + [Fact] + public async Task GetRecallCandidatesByIdsAsync_excludes_a_document_outside_the_requested_boundary() + { + await SeedGatedDocumentAsync("doc-gated-boundary"); + + var result = await _store.GetRecallCandidatesByIdsAsync( + ["doc-gated-boundary"], + [MemoryClass.DurableFact.ToWireValue()], + "some-other-boundary", + TrustAudience.Public, + allowExpiredEvidence: false, + TestContext.Current.CancellationToken); + + Assert.Empty(result); + } + + [Fact] + public async Task GetRecallCandidatesByIdsAsync_excludes_a_document_outside_the_requested_audience() + { + await SeedGatedDocumentAsync("doc-gated-audience", audience: TrustAudience.Team.ToWireValue()); + + // Public's allowed-audience set (MemoryPolicyEvaluator.AllowedAudienceWireValues) is + // [Public] only -- Team is not visible to a Public-scoped request. + var result = await _store.GetRecallCandidatesByIdsAsync( + ["doc-gated-audience"], + [MemoryClass.DurableFact.ToWireValue()], + TrustBoundary.TrustedInstanceValue, + TrustAudience.Public, + allowExpiredEvidence: false, + TestContext.Current.CancellationToken); + + Assert.Empty(result); + } + + [Fact] + public async Task GetRecallCandidatesByIdsAsync_excludes_a_document_outside_the_requested_memory_class() + { + await SeedGatedDocumentAsync("doc-gated-class"); + + var result = await _store.GetRecallCandidatesByIdsAsync( + ["doc-gated-class"], + [MemoryClass.Evidence.ToWireValue()], + TrustBoundary.TrustedInstanceValue, + TrustAudience.Public, + allowExpiredEvidence: false, + TestContext.Current.CancellationToken); + + Assert.Empty(result); + } + + private async Task SeedGatedDocumentAsync( + string documentId, + string recallMode = "auto", + string sensitivity = "normal", + string audience = "public") + { + var anchor = _store.CreateDefaultAnchor(documentId); + var now = DateTimeOffset.UtcNow.ToUnixTimeMilliseconds(); + await _store.UpsertDocumentAsync(new SQLiteMemoryDocument( + DocumentId: documentId, + Anchor: anchor, + MemoryClass: "durable_fact", + Title: "Gated hydration fixture", + MarkdownBody: "Gated hydration fixture body.", + AliasesJson: null, + FacetsJson: null, + SlotsJson: null, + UpdateSemantics: "merge-document", + Sensitivity: sensitivity, + RecallMode: recallMode, + Confidence: 0.9, + FreshnessAtMs: now, + ExpiresAtMs: null, + CreatedAtMs: now, + UpdatedAtMs: now, + Audience: audience), TestContext.Current.CancellationToken); + } + private static SQLiteMemoryCurationOperation DocumentOperation(string? memoryId, string title, string content) => new( Kind: "document", diff --git a/src/Netclaw.Actors.Tests/Sessions/DeterministicRetrievalPlanningTests.cs b/src/Netclaw.Actors.Tests/Sessions/DeterministicRetrievalPlanningTests.cs index 19ecc0c25..322266ab0 100644 --- a/src/Netclaw.Actors.Tests/Sessions/DeterministicRetrievalPlanningTests.cs +++ b/src/Netclaw.Actors.Tests/Sessions/DeterministicRetrievalPlanningTests.cs @@ -58,6 +58,8 @@ public async Task Coordinator_keeps_stage_empty_when_deterministic_planning_succ var coordinator = new SQLiteMemoryRecallCoordinator( store, NullLogger.Instance, + new MemoryConfig(), + TimeProvider.System, sessionTuning: new SessionTuning { DeterministicRetrievalEnabled = true }); var result = await coordinator.RecallAsync(new AutomaticRecallRequest( @@ -103,6 +105,8 @@ await store.UpsertDocumentAsync(new SQLiteMemoryDocument( var coordinator = new SQLiteMemoryRecallCoordinator( store, NullLogger.Instance, + new MemoryConfig(), + TimeProvider.System, sessionTuning: new SessionTuning { DeterministicRetrievalEnabled = true }); var result = await coordinator.RecallAsync(new AutomaticRecallRequest( @@ -148,6 +152,8 @@ await store.UpsertDocumentAsync(new SQLiteMemoryDocument( var coordinator = new SQLiteMemoryRecallCoordinator( store, NullLogger.Instance, + new MemoryConfig(), + TimeProvider.System, sessionTuning: new SessionTuning { DeterministicRetrievalEnabled = true }); var result = await coordinator.RecallAsync(new AutomaticRecallRequest( @@ -231,6 +237,8 @@ await store.UpsertDocumentAsync(new SQLiteMemoryDocument( var coordinator = new SQLiteMemoryRecallCoordinator( store, NullLogger.Instance, + new MemoryConfig(), + TimeProvider.System, sessionTuning: new SessionTuning { DeterministicRetrievalEnabled = true, @@ -289,6 +297,8 @@ await store.UpsertDocumentAsync(new SQLiteMemoryDocument( var budgeted = new SQLiteMemoryRecallCoordinator( store, NullLogger.Instance, + new MemoryConfig(), + TimeProvider.System, sessionTuning: new SessionTuning { MaxRecallInjectedChars = 700 }); var budgetedResult = await budgeted.RecallAsync(request, TestContext.Current.CancellationToken); @@ -300,6 +310,8 @@ await store.UpsertDocumentAsync(new SQLiteMemoryDocument( var unbounded = new SQLiteMemoryRecallCoordinator( store, NullLogger.Instance, + new MemoryConfig(), + TimeProvider.System, sessionTuning: new SessionTuning { MaxRecallInjectedChars = 0 }); var unboundedResult = await unbounded.RecallAsync(request, TestContext.Current.CancellationToken); Assert.Equal(3, unboundedResult.Items.Count); @@ -337,6 +349,8 @@ await store.UpsertDocumentAsync(new SQLiteMemoryDocument( var coordinator = new SQLiteMemoryRecallCoordinator( store, NullLogger.Instance, + new MemoryConfig(), + TimeProvider.System, sessionTuning: new SessionTuning { DeterministicRetrievalEnabled = true }); var result = await coordinator.RecallAsync(new AutomaticRecallRequest( @@ -381,6 +395,8 @@ await store.UpsertDocumentAsync(new SQLiteMemoryDocument( var coordinator = new SQLiteMemoryRecallCoordinator( store, NullLogger.Instance, + new MemoryConfig(), + TimeProvider.System, sessionTuning: new SessionTuning { DeterministicRetrievalEnabled = true }); var result = await coordinator.RecallAsync(new AutomaticRecallRequest( diff --git a/src/Netclaw.Actors.Tests/Sessions/LlmSessionIntegrationTests.cs b/src/Netclaw.Actors.Tests/Sessions/LlmSessionIntegrationTests.cs index 64aa8abb9..56f02dbb2 100644 --- a/src/Netclaw.Actors.Tests/Sessions/LlmSessionIntegrationTests.cs +++ b/src/Netclaw.Actors.Tests/Sessions/LlmSessionIntegrationTests.cs @@ -68,7 +68,9 @@ protected override void ConfigureSessionServices(IServiceCollection services) services.AddSingleton(sp => new SQLiteMemoryStore(Path.Combine(Path.GetTempPath(), $"netclaw-sidecar-tests-{Guid.NewGuid():N}.db"), TimeProvider.System)); services.AddSingleton(sp => new SQLiteMemoryRecallCoordinator( sp.GetRequiredService(), - Microsoft.Extensions.Logging.Abstractions.NullLogger.Instance)); + Microsoft.Extensions.Logging.Abstractions.NullLogger.Instance, + new MemoryConfig(), + sp.GetRequiredService())); var registry = new ToolRegistry(); registry.Register(new McpToolAdapter( diff --git a/src/Netclaw.Actors.Tests/Sessions/MemoryRecallScenarioTests.cs b/src/Netclaw.Actors.Tests/Sessions/MemoryRecallScenarioTests.cs index 1c0c5df21..f3b76edc7 100644 --- a/src/Netclaw.Actors.Tests/Sessions/MemoryRecallScenarioTests.cs +++ b/src/Netclaw.Actors.Tests/Sessions/MemoryRecallScenarioTests.cs @@ -183,6 +183,8 @@ public async Task Scenario_matches_expected_and_rejects_forbidden( var coordinator = new SQLiteMemoryRecallCoordinator( _store, NullLogger.Instance, + new MemoryConfig(), + TimeProvider.System, sessionTuning: new SessionTuning()); var request = new AutomaticRecallRequest( diff --git a/src/Netclaw.Actors.Tests/Sessions/SQLiteMemoryRecallHybridTests.cs b/src/Netclaw.Actors.Tests/Sessions/SQLiteMemoryRecallHybridTests.cs new file mode 100644 index 000000000..8c9d324b4 --- /dev/null +++ b/src/Netclaw.Actors.Tests/Sessions/SQLiteMemoryRecallHybridTests.cs @@ -0,0 +1,351 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using Microsoft.Extensions.Logging; +using Microsoft.Extensions.Logging.Abstractions; +using Microsoft.Extensions.Time.Testing; +using Netclaw.Actors.Memory; +using Netclaw.Actors.Protocol; +using Netclaw.Actors.Sessions; +using Netclaw.Actors.Tests.Memory; +using Netclaw.Configuration; +using Xunit; + +namespace Netclaw.Actors.Tests.Sessions; + +/// +/// Covers 's hybrid recall path +/// (memory-core-redesign Slice 4, design D6, tasks 4.1-4.4): the absolute cosine floor, the +/// zero-injection contract, recency decay bounds, and degraded-path parity with the pre-Slice-4 +/// lexical-only coordinator. Fixture geometry is engineered directly via hand-crafted 2D unit +/// vectors (same technique as MemoryCurationNominatorTests) rather than a real embedding +/// model, so every scenario is exact and deterministic. +/// +/// +/// Gated-hydration policy-gate exclusions (recall_mode/boundary/audience/sensitivity/ +/// memory_class) live in SQLiteMemoryStoreEmbeddingTests — those exercise +/// directly, which this class does +/// not need to re-prove. +/// +/// +public sealed class SQLiteMemoryRecallHybridTests : IAsyncDisposable +{ + private const string ModelId = "hybrid-recall-test-model"; + private const int Dimensions = 2; + + // A unit vector and its exact opposite: cosine(QueryVector, QueryVector) == 1.0, + // cosine(QueryVector, OrthogonalVector) == 0.0. Sufficient geometry for every scenario here + // (either "matches the query" or "shares no direction with it at all"). + private static readonly float[] QueryVector = [1f, 0f]; + private static readonly float[] OrthogonalVector = [0f, 1f]; + + private readonly string _baseDir = Path.Combine(Path.GetTempPath(), "netclaw-recall-hybrid-tests", Guid.NewGuid().ToString("N")); + private readonly string _dbPath; + private readonly SQLiteMemoryStore _store; + + public SQLiteMemoryRecallHybridTests() + { + Directory.CreateDirectory(_baseDir); + _dbPath = Path.Combine(_baseDir, "netclaw.db"); + _store = new SQLiteMemoryStore(_dbPath, TimeProvider.System); + } + + public async ValueTask DisposeAsync() => await SqliteTempDirectoryCleanup.TryDeleteDirectoryAsync(_baseDir); + + // ── Absolute cosine floor (task 4.3) ──────────────────────────────── + + [Fact] + public async Task Absolute_floor_excludes_a_lexically_strong_candidate_whose_cosine_is_below_threshold() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + + // Strong lexical match: title+content share every query term, so the pre-Slice-4 + // selector score alone clears the old lexical floor comfortably. Its embedding is the + // exact opposite direction of the query vector (cosine 0.0) -- well below + // MinCosineSimilarity's default 0.55. The absolute floor must reject it regardless of + // how strong the lexical match is. + await SeedDocumentAsync("doc-lexical-strong", "Grafana dashboard provisioning convention", + "Grafana dashboard provisioning convention details for the ops team.", ct); + await _store.UpsertEmbeddingAsync( + "doc-lexical-strong", MemoryEmbedOnWriteCoordinator.DocumentItemKind, ModelId, "hash-orthogonal", OrthogonalVector, ct); + + var coordinator = BuildHybridCoordinator(TimeProvider.System, NullLogger.Instance); + + var result = await coordinator.RecallAsync(new AutomaticRecallRequest( + SessionId: (SessionId)"hybrid/floor-1", + Query: "what is our grafana dashboard provisioning convention?", + RecentUserMessages: ["what is our grafana dashboard provisioning convention?"], + MaxItems: 3), ct); + + Assert.False(result.Degraded); + Assert.DoesNotContain(result.Items, i => i.Id.Value == "doc-lexical-strong"); + } + + [Fact] + public async Task Absolute_floor_admits_a_candidate_at_or_above_the_configured_cosine_threshold() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + + await SeedDocumentAsync("doc-cosine-match", "Grafana dashboard provisioning convention", + "Grafana dashboard provisioning convention details for the ops team.", ct); + await _store.UpsertEmbeddingAsync( + "doc-cosine-match", MemoryEmbedOnWriteCoordinator.DocumentItemKind, ModelId, "hash-match", QueryVector, ct); + + var coordinator = BuildHybridCoordinator(TimeProvider.System, NullLogger.Instance); + + var result = await coordinator.RecallAsync(new AutomaticRecallRequest( + SessionId: (SessionId)"hybrid/floor-2", + Query: "what is our grafana dashboard provisioning convention?", + RecentUserMessages: ["what is our grafana dashboard provisioning convention?"], + MaxItems: 3), ct); + + Assert.False(result.Degraded); + Assert.Contains(result.Items, i => i.Id.Value == "doc-cosine-match"); + } + + // ── Zero-injection contract (task 4.3) ────────────────────────────── + + [Fact] + public async Task Zero_survivors_returns_a_healthy_empty_result_not_a_degraded_one() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + + // Lexically matchable, but never embedded at all -- the vector index has zero rows for + // it, so its cosine defaults to 0.0 and the absolute floor excludes it. This is the + // "nothing relevant exists" case design D6 requires to surface as healthy-empty, not a + // degraded/error result. + await SeedDocumentAsync("doc-no-embedding", "Grafana dashboard provisioning convention", + "Grafana dashboard provisioning convention details for the ops team.", ct); + + var coordinator = BuildHybridCoordinator(TimeProvider.System, NullLogger.Instance); + + var result = await coordinator.RecallAsync(new AutomaticRecallRequest( + SessionId: (SessionId)"hybrid/zero-survivors", + Query: "what is our grafana dashboard provisioning convention?", + RecentUserMessages: ["what is our grafana dashboard provisioning convention?"], + MaxItems: 3), ct); + + Assert.False(result.Degraded); + Assert.Empty(result.Items); + } + + // ── Recency decay bounds (task 4.4) ───────────────────────────────── + + [Fact] + public async Task Recency_decay_downweights_an_old_candidate_toward_the_085_floor_without_zeroing_it() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + + var fakeTime = new FakeTimeProvider(DateTimeOffset.Parse("2026-07-08T00:00:00Z")); + var nowMs = fakeTime.GetUtcNow().ToUnixTimeMilliseconds(); + // Default RecencyHalfLifeDays is 30; 3650 days (10 years) drives the decay term to + // effectively zero, isolating the 0.85 floor. + var ancientMs = fakeTime.GetUtcNow().AddDays(-3650).ToUnixTimeMilliseconds(); + + // Identical title/content/class/semantics/embedding -- every fusion component except + // recency is equal, so any score difference is attributable to the decay multiplier + // alone. + await SeedDocumentAsync("doc-fresh", "Widget rollout plan", "Widget rollout plan details for the release team.", ct, updatedAtMs: nowMs); + await SeedDocumentAsync("doc-ancient", "Widget rollout plan", "Widget rollout plan details for the release team.", ct, updatedAtMs: ancientMs); + await _store.UpsertEmbeddingAsync("doc-fresh", MemoryEmbedOnWriteCoordinator.DocumentItemKind, ModelId, "hash-fresh", QueryVector, ct); + await _store.UpsertEmbeddingAsync("doc-ancient", MemoryEmbedOnWriteCoordinator.DocumentItemKind, ModelId, "hash-ancient", QueryVector, ct); + + var coordinator = BuildHybridCoordinator(fakeTime, NullLogger.Instance); + + var result = await coordinator.RecallAsync(new AutomaticRecallRequest( + SessionId: (SessionId)"hybrid/recency", + Query: "widget rollout plan", + RecentUserMessages: ["widget rollout plan"], + MaxItems: 5), ct); + + Assert.False(result.Degraded); + var fresh = Assert.Single(result.Items, i => i.Id.Value == "doc-fresh"); + var ancient = Assert.Single(result.Items, i => i.Id.Value == "doc-ancient"); + + Assert.True(fresh.Score > ancient.Score, $"expected fresh ({fresh.Score:F6}) > ancient ({ancient.Score:F6})"); + + // Fresh multiplier == 1.0 (age 0), ancient multiplier -> 0.85 floor (age >> half-life), + // so the ratio should land within a tight tolerance of 1.0/0.85, never below it (the + // floor guarantees the ancient candidate is downweighted by at most ~15%). + var ratio = fresh.Score / ancient.Score; + Assert.True(Math.Abs(ratio - (1.0 / 0.85)) < 0.01, $"expected ratio near {1.0 / 0.85:F6}, got {ratio:F6}"); + } + + // ── Degraded-path parity (task 4.1) ───────────────────────────────── + + [Fact] + public async Task Unavailable_embedder_produces_identical_results_to_a_coordinator_built_without_holders() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + + await SeedDocumentAsync("doc-degraded-parity", "TextForge Pricing Model", + "TextForge uses a monthly subscription with a discounted annual plan.", ct, + aliasesJson: "[\"textforge\",\"pricing model\"]"); + + var request = new AutomaticRecallRequest( + SessionId: (SessionId)"hybrid/degraded-parity", + Query: "What's the pricing model for TextForge?", + RecentUserMessages: ["What's the pricing model for TextForge?"], + MaxItems: 3); + + var withoutHolders = new SQLiteMemoryRecallCoordinator( + _store, + NullLogger.Instance, + new MemoryConfig(), + TimeProvider.System, + sessionTuning: new SessionTuning { DeterministicRetrievalEnabled = true }); + + var withUnavailableEmbedder = new SQLiteMemoryRecallCoordinator( + _store, + NullLogger.Instance, + new MemoryConfig(), + TimeProvider.System, + sessionTuning: new SessionTuning { DeterministicRetrievalEnabled = true }, + embedderHolder: new MemoryEmbedderHolder(new UnavailableMemoryEmbedder(ModelId, "test: never provisioned")), + vectorIndexHolder: new MemoryVectorIndexHolder(_store)); + + var baselineResult = await withoutHolders.RecallAsync(request, ct); + var degradedResult = await withUnavailableEmbedder.RecallAsync(request, ct); + + Assert.False(baselineResult.Degraded); + Assert.False(degradedResult.Degraded); + Assert.Equal( + baselineResult.Items.Select(i => (i.Id.Value, i.Title, i.Content, i.Sensitivity, i.Score)), + degradedResult.Items.Select(i => (i.Id.Value, i.Title, i.Content, i.Sensitivity, i.Score))); + } + + // ── Rate-limited degraded log, Debug vs Warning (task 4.1) ────────── + + [Fact] + public async Task Vector_degraded_log_is_debug_when_embeddings_are_disabled_by_config() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + + var recordingLogger = new RecordingLogger(); + var coordinator = new SQLiteMemoryRecallCoordinator( + _store, + recordingLogger, + new MemoryConfig { Embeddings = new MemoryEmbeddingsConfig { Enabled = false } }, + TimeProvider.System, + sessionTuning: new SessionTuning { DeterministicRetrievalEnabled = true }, + embedderHolder: new MemoryEmbedderHolder(new UnavailableMemoryEmbedder(ModelId, "warmup has not completed yet")), + vectorIndexHolder: new MemoryVectorIndexHolder(_store)); + + await coordinator.RecallAsync(new AutomaticRecallRequest( + SessionId: (SessionId)"hybrid/loglevel-debug", + Query: "anything", + RecentUserMessages: ["anything"], + MaxItems: 3), ct); + + Assert.Contains(recordingLogger.Entries, e => e.Level == LogLevel.Debug && e.Message.Contains("memory_recall_vector_degraded")); + Assert.DoesNotContain(recordingLogger.Entries, e => e.Level == LogLevel.Warning && e.Message.Contains("memory_recall_vector_degraded")); + } + + [Fact] + public async Task Vector_degraded_log_is_warning_when_embeddings_are_enabled_but_the_turn_still_degraded() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + + var recordingLogger = new RecordingLogger(); + var coordinator = new SQLiteMemoryRecallCoordinator( + _store, + recordingLogger, + new MemoryConfig { Embeddings = new MemoryEmbeddingsConfig { Enabled = true } }, + TimeProvider.System, + sessionTuning: new SessionTuning { DeterministicRetrievalEnabled = true }, + embedderHolder: new MemoryEmbedderHolder(new UnavailableMemoryEmbedder(ModelId, "model load failed")), + vectorIndexHolder: new MemoryVectorIndexHolder(_store)); + + await coordinator.RecallAsync(new AutomaticRecallRequest( + SessionId: (SessionId)"hybrid/loglevel-warning", + Query: "anything", + RecentUserMessages: ["anything"], + MaxItems: 3), ct); + + Assert.Contains(recordingLogger.Entries, e => e.Level == LogLevel.Warning && e.Message.Contains("memory_recall_vector_degraded")); + } + + // ── Fixtures ───────────────────────────────────────────────────────── + + private SQLiteMemoryRecallCoordinator BuildHybridCoordinator(TimeProvider timeProvider, ILogger logger) + => new( + _store, + logger, + new MemoryConfig(), + timeProvider, + sessionTuning: new SessionTuning { DeterministicRetrievalEnabled = true }, + embedderHolder: new MemoryEmbedderHolder(new ScriptedEmbedder(ModelId, Dimensions, QueryVector)), + vectorIndexHolder: new MemoryVectorIndexHolder(_store)); + + private async Task SeedDocumentAsync( + string documentId, string title, string content, CancellationToken ct, + long? updatedAtMs = null, string? aliasesJson = null) + { + var anchor = _store.CreateDefaultAnchor(documentId); + var now = updatedAtMs ?? TimeProvider.System.GetUtcNow().ToUnixTimeMilliseconds(); + await _store.UpsertDocumentAsync(new SQLiteMemoryDocument( + DocumentId: documentId, + Anchor: anchor, + MemoryClass: "durable_fact", + Title: title, + MarkdownBody: content, + AliasesJson: aliasesJson, + FacetsJson: null, + SlotsJson: null, + UpdateSemantics: "merge-document", + Sensitivity: "normal", + RecallMode: "auto", + Confidence: 0.9, + FreshnessAtMs: now, + ExpiresAtMs: null, + CreatedAtMs: now, + UpdatedAtMs: now), ct); + } + + /// + /// Fake embedder that ignores its input text and always returns the same, hand-crafted query + /// vector -- sufficient here because every test in this file embeds at most one query and + /// the geometry (not the input text) is what needs to be controlled. Mirrors + /// MemoryCurationNominatorTests.ScriptedEmbedder (kept as a separate private copy per + /// that file's own convention). + /// + private sealed class ScriptedEmbedder(string modelId, int dimensions, float[] queryVector) : IMemoryEmbedder + { + public string ModelId => modelId; + + public int Dimensions => dimensions; + + public bool IsAvailable => true; + + public ValueTask> EmbedAsync(string text, CancellationToken ct) + => ValueTask.FromResult>(queryVector); + + public ValueTask>> EmbedBatchAsync(IReadOnlyList texts, CancellationToken ct) + => ValueTask.FromResult>>( + texts.Select(_ => (ReadOnlyMemory)queryVector).ToList()); + } + + /// Records every (level, message) pair logged through the generic ILogger ctor seam. + private sealed class RecordingLogger : ILogger + { + public List<(LogLevel Level, string Message)> Entries { get; } = []; + + public IDisposable? BeginScope(TState state) where TState : notnull => null; + + public bool IsEnabled(LogLevel logLevel) => true; + + public void Log( + LogLevel logLevel, EventId eventId, TState state, Exception? exception, + Func formatter) + => Entries.Add((logLevel, formatter(state, exception))); + } +} diff --git a/src/Netclaw.Actors/Memory/SQLiteMemoryStore.cs b/src/Netclaw.Actors/Memory/SQLiteMemoryStore.cs index 9cc49afba..4fe608b07 100644 --- a/src/Netclaw.Actors/Memory/SQLiteMemoryStore.cs +++ b/src/Netclaw.Actors/Memory/SQLiteMemoryStore.cs @@ -845,6 +845,32 @@ private static async Task ResolveHandleOnConnectionAsync( : ResolvedMemoryHandle.Failed(rawId, parsed.Kind, $"Memory \"{rawId}\" was not found or is not accessible from this session."); } + /// + /// Shared read-side policy predicate for the memory_documents table (memory-core- + /// redesign Slice 4, design D6): recall-mode allowlist (auto/searchable), boundary COALESCE + /// match, audience allowed-set, sensitivity exclusion (never secret), memory-class allowlist, + /// and expiry. Both 's document branch and + /// build their WHERE clause from this single + /// string so the two queries cannot drift apart — a vector-sourced recall candidate must + /// clear the EXACT gates a lexically-discovered one would, not an independently-maintained + /// copy of them (spec scenario "Vector-sourced candidates obey policy gates"). + /// + /// + /// The parameter names baked into the returned SQL ($boundary, $planLegacyBoundary, + /// $planFallbackAudience, $now, $allowExpiredEvidence) are part of this contract: every + /// caller MUST bind them under these exact names, in addition to whatever produced + /// and . + /// + /// + private static string DocumentRecallPolicyPredicateSql(string tableAlias, string classInClause, string audienceInClause) => $""" + {tableAlias}.recall_mode IN ('{MemoryRecallMode.Auto.ToWireValue()}', '{MemoryRecallMode.Searchable.ToWireValue()}') + AND COALESCE({tableAlias}.boundary, $planLegacyBoundary) = $boundary + AND COALESCE({tableAlias}.audience, $planFallbackAudience) IN ({audienceInClause}) + AND {tableAlias}.sensitivity != '{MemorySensitivity.Secret.ToWireValue()}' + AND {tableAlias}.memory_class IN ({classInClause}) + AND ({tableAlias}.expires_at IS NULL OR {tableAlias}.expires_at > $now OR $allowExpiredEvidence = 1) + """; + public async Task> SearchByPlanAsync( IReadOnlyList queryTerms, IReadOnlyList memoryClasses, @@ -917,12 +943,7 @@ ORDER BY fts_rank dh.fts_rank AS score FROM doc_hits dh JOIN memory_documents d ON d.document_id = dh.document_id - WHERE d.recall_mode IN ('{MemoryRecallMode.Auto.ToWireValue()}', '{MemoryRecallMode.Searchable.ToWireValue()}') - AND COALESCE(d.boundary, $planLegacyBoundary) = $boundary - AND COALESCE(d.audience, $planFallbackAudience) IN ({whereAudiences}) - AND d.sensitivity != '{MemorySensitivity.Secret.ToWireValue()}' - AND d.memory_class IN ({whereClasses}) - AND (d.expires_at IS NULL OR d.expires_at > $now OR $allowExpiredEvidence = 1) + WHERE {DocumentRecallPolicyPredicateSql("d", whereClasses, whereAudiences)} UNION ALL @@ -990,6 +1011,101 @@ AND r.memory_class IN ({whereClasses}) }, ct); } + /// + /// Hydrates documents by id through the IDENTICAL policy predicates + /// applies to its document branch — + /// — so a vector-sourced recall candidate + /// (memory-core-redesign Slice 4, design D6) can never bypass a gate a lexically-discovered + /// one would have to clear. This is a SECURITY requirement, not a convenience method: + /// returns bare ids and cosine similarities with no + /// policy fields at all, so MUST + /// hydrate vector-only hits through this method — never through + /// , which applies no policy gates at all and exists + /// for the write-side curation nominator's already-trusted internal comparisons — before a + /// vector hit is allowed to reach recall scoring. + /// + /// + /// Documents only: only document items are ever embedded + /// ( — immutable records bypass + /// curation and are never embedded), so every id passed in is expected to be a document id. + /// + /// + public async Task> GetRecallCandidatesByIdsAsync( + IReadOnlyList documentIds, + IReadOnlyList memoryClasses, + string boundary, + TrustAudience audience, + bool allowExpiredEvidence, + CancellationToken ct = default) + { + if (documentIds.Count == 0 || memoryClasses.Count == 0) + return []; + + return await WithConnectionAsync(async (conn, ct) => + { + var now = _timeProvider.GetUtcNow().ToUnixTimeMilliseconds(); + await using var cmd = conn.CreateCommand(); + + var idClauses = new List(); + for (var i = 0; i < documentIds.Count; i++) + { + idClauses.Add($"$id{i}"); + cmd.Parameters.AddWithValue($"$id{i}", documentIds[i]); + } + + var classClauses = new List(); + for (var i = 0; i < memoryClasses.Count; i++) + { + classClauses.Add($"$c{i}"); + cmd.Parameters.AddWithValue($"$c{i}", memoryClasses[i]); + } + + var allowedAudiences = MemoryPolicyEvaluator.AllowedAudienceWireValues(audience); + var audienceClauses = new List(); + for (var i = 0; i < allowedAudiences.Count; i++) + { + audienceClauses.Add($"$a{i}"); + cmd.Parameters.AddWithValue($"$a{i}", allowedAudiences[i]); + } + + cmd.CommandText = $""" + SELECT d.document_id, d.memory_class, d.title, d.markdown_body, d.aliases_json, d.facets_json, d.slots_json, d.boundary, d.audience, d.sensitivity, d.recall_mode, d.update_semantics, d.expires_at, d.updated_at + FROM memory_documents d + WHERE d.document_id IN ({string.Join(",", idClauses)}) + AND {DocumentRecallPolicyPredicateSql("d", string.Join(",", classClauses), string.Join(",", audienceClauses))} + """; + cmd.Parameters.AddWithValue("$boundary", boundary); + cmd.Parameters.AddWithValue("$planLegacyBoundary", TrustBoundary.LegacyRestrictedValue); + cmd.Parameters.AddWithValue("$planFallbackAudience", TrustAudience.Personal.ToWireValue()); + cmd.Parameters.AddWithValue("$now", now); + cmd.Parameters.AddWithValue("$allowExpiredEvidence", allowExpiredEvidence ? 1 : 0); + + var output = new List(); + await using var reader = await cmd.ExecuteReaderAsync(ct); + while (await reader.ReadAsync(ct)) + { + output.Add(new SQLiteMemoryHydratedItem( + Id: reader.GetString(0), + Kind: "document", + MemoryClass: reader.GetString(1), + Title: reader.GetString(2), + Content: reader.GetString(3), + AliasesJson: reader.IsDBNull(4) ? null : reader.GetString(4), + FacetsJson: reader.IsDBNull(5) ? null : reader.GetString(5), + SlotsJson: reader.IsDBNull(6) ? null : reader.GetString(6), + Boundary: reader.IsDBNull(7) ? TrustBoundary.LegacyRestrictedValue : reader.GetString(7), + Audience: reader.IsDBNull(8) ? TrustAudience.Personal.ToWireValue() : reader.GetString(8), + Sensitivity: reader.GetString(9), + RecallMode: reader.GetString(10), + UpdateSemantics: reader.GetString(11), + ExpiresAtMs: reader.IsDBNull(12) ? null : reader.GetInt64(12), + UpdatedAtMs: reader.GetInt64(13))); + } + + return (IReadOnlyList)output; + }, ct); + } + public async Task UpdateDocumentTextAsync(string documentId, string oldText, string newText, CancellationToken ct = default) { return await WithConnectionAsync(async (conn, ct) => diff --git a/src/Netclaw.Actors/Sessions/DeterministicCandidateSelector.cs b/src/Netclaw.Actors/Sessions/DeterministicCandidateSelector.cs index 311c532a1..30254d43f 100644 --- a/src/Netclaw.Actors/Sessions/DeterministicCandidateSelector.cs +++ b/src/Netclaw.Actors/Sessions/DeterministicCandidateSelector.cs @@ -47,7 +47,19 @@ public IReadOnlyList SelectWithScores( public sealed record ScoredCandidate(SQLiteMemoryHydratedItem Item, double SelectorScore); - private static double Score(DeterministicRetrievalRequestPlan plan, SQLiteMemoryHydratedItem document) + /// + /// Scores a single candidate against 's lexical/facet/anchor/soft-scope + /// terms, without 's class/sensitivity filtering or + /// gate. Exposed (rather than kept private) so + /// memory-core-redesign Slice 4's hybrid recall coordinator can score a vector-sourced + /// candidate that never went through the FTS5 lexical search — using the SAME weights this + /// class applies to lexical hits, not an independently-maintained approximation — before + /// squashing that score into the fusion formula. A candidate with none of the plan's terms + /// still returns (not zero); callers relying on "no lexical + /// evidence" should treat that baseline as effectively negligible after + /// squash(s) = s / (s + 8.0), not literally zero. + /// + public static double Score(DeterministicRetrievalRequestPlan plan, SQLiteMemoryHydratedItem document) { // Baseline: candidates survived SQL pre-filtering (FTS match), so they // deserve a non-zero score. Lexical/facet/anchor matches boost above this. diff --git a/src/Netclaw.Actors/Sessions/SQLiteMemoryRecallCoordinator.cs b/src/Netclaw.Actors/Sessions/SQLiteMemoryRecallCoordinator.cs index 4453d39be..e9fa5ef9b 100644 --- a/src/Netclaw.Actors/Sessions/SQLiteMemoryRecallCoordinator.cs +++ b/src/Netclaw.Actors/Sessions/SQLiteMemoryRecallCoordinator.cs @@ -1,8 +1,9 @@ -// ----------------------------------------------------------------------- +// ----------------------------------------------------------------------- // // Copyright (C) 2026 - 2026 Petabridge, LLC // // ----------------------------------------------------------------------- +using System.Collections.Concurrent; using Netclaw.Actors.Memory; using Microsoft.Extensions.Logging; using Netclaw.Configuration; @@ -11,15 +12,57 @@ namespace Netclaw.Actors.Sessions; /// /// Automatic recall coordinator over SQLite-backed durable memory. +/// +/// +/// Hybrid recall (memory-core-redesign Slice 4, design D6): when +/// embedderHolder's current embedder is available and vectorIndexHolder is +/// wired, each turn embeds the query once — under a fixed +/// sub-budget nested inside the caller's overall Memory.RecallTimeoutMs via a linked +/// CTS — and unions FTS5 lexical candidates with the vector index's top-k cosine matches. +/// Vector-only hits are hydrated through , +/// which applies the IDENTICAL policy predicates +/// applies to lexical hits — a vector hit can never bypass a gate a lexical one would have to +/// clear. Scoring fuses a weighted cosine + squashed lexical-selector-score + dampened +/// class-prior composite, recency-decayed, then applies an ABSOLUTE floor: any candidate +/// (regardless of source) whose cosine falls below +/// is dropped before ranking. Zero survivors means zero injection and a HEALTHY (non-degraded) +/// empty result — the caller () +/// already omits the [memory-recall] block entirely for that shape. +/// +/// +/// +/// Degraded path (embedder unavailable, over its sub-budget, or no holder wired): recall +/// falls back to the pre-Slice-4 lexical-only pipeline VERBATIM — same selector scoring, same +/// composite formula, same floor — which is +/// exactly what MemoryRecallScenarioTests exercises and pins (constructed without either +/// holder). A rate-limited memory_recall_vector_degraded log fires on every fallback +/// reason: Debug when embeddings are disabled by config (the default, intentional state — +/// mirrors MemoryCurationEvaluator's curation_nominator_degraded level choice, so +/// this is not Warning-level spam on every turn of a deployment that simply hasn't turned +/// embeddings on), Warning when embeddings are enabled but the turn still degraded (a genuine +/// runtime anomaly worth noticing: timeout, embed failure, missing index). +/// /// public sealed class SQLiteMemoryRecallCoordinator( SQLiteMemoryStore store, ILogger logger, - SessionTuning? sessionTuning = null) : IMemoryRecallCoordinator + MemoryConfig memoryConfig, + TimeProvider timeProvider, + SessionTuning? sessionTuning = null, + MemoryEmbedderHolder? embedderHolder = null, + MemoryVectorIndexHolder? vectorIndexHolder = null) : IMemoryRecallCoordinator { private readonly SessionTuning _sessionTuning = sessionTuning ?? new SessionTuning(); + private readonly MemoryRecallConfig _recallConfig = memoryConfig.Recall; + + // Read once at construction (DI-resolved MemoryConfig is effectively immutable for the + // process's lifetime — an operator flip requires a restart, same as every other Memory.* + // setting). Drives the Debug-vs-Warning split on the degraded log: see this class's summary. + private readonly bool _embeddingsEnabledByConfig = memoryConfig.Embeddings.Enabled; + private readonly DeterministicRetrievalRequestPlanner _deterministicPlanner = new(); private readonly DeterministicCandidateSelector _candidateSelector = new(); + private readonly ConcurrentDictionary _lastVectorDegradedLogMs = new(StringComparer.Ordinal); /// /// Default minimum composite score a candidate must reach to survive @@ -36,9 +79,84 @@ public sealed class SQLiteMemoryRecallCoordinator( /// audit floor sweep pins the reject side. Override via /// . See issue /// #582 and docs/research/memory-audit-2026-07.md. + /// + /// + /// This floor governs the DEGRADED (lexical-only) path exclusively + /// (memory-core-redesign Slice 4). When a query vector is available the absolute cosine + /// floor () governs admission instead — + /// the two floors are never both applied to the same candidate set. + /// /// private const double DefaultMinimumRecallCompositeScore = 14.0; + // RecallRank dampened by 100x so it acts as a tiebreaker (~2 points + // for DurableFact+MergeDocument) rather than overriding SelectorScore + // (~4 points per lexical match). Unchanged by Slice 4 — this constant governs the + // degraded/lexical composite exclusively; hybrid fusion applies its own further-dampened + // variant (see HybridClassPriorDampeningFactor) sized for a [0,1]-scale formula. + private const double RecallRankDampeningFactor = 100.0; + + /// + /// Sub-budget, in milliseconds, for the per-turn query embedding call + /// (memory-core-redesign Slice 4, design D6), applied via a CTS linked to (nested inside) + /// the caller's overall recall ct (Memory.RecallTimeoutMs, default 300ms). + /// Not a config knob: design D6 measured dynamic-length embedding (Slice 4 Stage A, + /// tools/embed-latency-bench) at short-query p50 ≈ 19ms / p95 ≈ 21ms on the + /// reference box, so 150ms leaves roughly 7x headroom over that measurement before a + /// moderately loaded host would flap into the degraded path on every turn — a deliberately + /// generous, fixed ceiling rather than a value operators should be tempted to tune per + /// environment. + /// + private const int VectorEmbedSubBudgetMs = 150; + + /// + /// Number of nearest-neighbor vector candidates fetched per recall turn (design D6). Sized + /// well above Memory.AutoRecallMaxItems since the union with lexical candidates and + /// the absolute cosine floor both shrink the pool before the outer MaxItems/char-budget + /// bounds apply. + /// + private const int VectorTopK = 50; + + /// + /// Minimum interval between two memory_recall_vector_degraded log lines for the SAME + /// degradation reason, so a long-lived degraded condition (embeddings disabled, model + /// unprovisioned) does not log on every single turn. + /// + private static readonly TimeSpan VectorDegradedLogCooldown = TimeSpan.FromMinutes(5); + + /// + /// Hybrid fusion dampens the class prior further than the lexical/degraded path: cosine + /// (0..1) and squash(selectorScore) (0..~1) are both already bounded fusion terms, so + /// applying only the lexical path's /100 dampening (max ≈ 4.8 for DurableFact+MergeDocument) + /// would let the class prior swamp both fusion terms instead of acting as a tiebreaker the + /// way it does against an unbounded SelectorScore. Dividing the already-/100-dampened prior + /// by a further 10x caps it at ≈0.48 — comparable in magnitude to, but never dominant over, + /// VectorWeight*cosine or LexicalWeight*squash(selectorScore). + /// + private const double HybridClassPriorDampeningFactor = 10.0; + + /// + /// Half-saturation constant for squash(s) = s / (s + SquashHalfSaturation), which maps + /// 's unbounded selector score (baseline 1.0, + /// +4/lexical term, +6/facet, +2/anchor) into [0, 1) for hybrid fusion. At 8.0: a single + /// lexical-term collision (score ≈5) squashes to ≈0.38, two independent matches (score ≈9) + /// to ≈0.53, and a facet-boosted match (score ≈15) to ≈0.65 — so lexical evidence + /// meaningfully moves the fused score without a bare baseline (score 1.0 → squash ≈0.11, + /// i.e. no real lexical evidence at all) competing with genuine vector similarity. + /// + private const double SquashHalfSaturation = 8.0; + + /// + /// Recency-decay floor for the hybrid fusion multiplier (task 4.4): + /// 0.85 + 0.15 * 2^(-ageDays/RecencyHalfLifeDays). Structurally bounded in + /// (0.85, 1.0] for any non-negative age (the decay term is always in (0, 1]), so recency can + /// only break a tie between otherwise-similar matches, never suppress an old-but-strong + /// match by more than 15%. + /// + private const double RecencyDecayFloor = 0.85; + + private const double RecencyDecayRange = 0.15; + public async Task RecallAsync(AutomaticRecallRequest request, CancellationToken ct = default) { try @@ -85,19 +203,41 @@ public async Task RecallAsync(AutomaticRecallRequest requ scoredCandidates.Count, string.Join("|", scoredCandidates.Select(x => $"{x.Item.Id}={x.SelectorScore:F1}"))); - // RecallRank dampened by 100x so it acts as a tiebreaker (~2 points - // for DurableFact+MergeDocument) rather than overriding SelectorScore - // (~4 points per lexical match). - const double RecallRankDampeningFactor = 100.0; var deterministicMaxItems = request.MaxItems <= 0 ? 3 : request.MaxItems; var minimumCompositeScore = _sessionTuning.MinimumRecallCompositeScore ?? DefaultMinimumRecallCompositeScore; - var rankedCandidates = scoredCandidates - .Select(x => (x.Item, x.SelectorScore, Composite: x.SelectorScore + (RecallRank(x.Item) / RecallRankDampeningFactor))) - .OrderByDescending(x => x.Composite) - .ToArray(); - var aboveFloor = rankedCandidates - .Where(x => x.Composite >= minimumCompositeScore) - .ToArray(); + + string mode; + RankedCandidate[] aboveFloor; + int totalConsidered; + + // ── Vector query embedding (memory-core-redesign Slice 4, task 4.1) ── + // Attempted once per turn, sub-budgeted inside the caller's overall ct. ANY + // failure here (unavailable, missing index, sub-budget timeout, embed error) + // degrades to the lexical-only path below, logged but never throws. + var embedded = await TryEmbedQueryAsync(request, ct); + + if (embedded is { } hybridInput) + { + mode = "hybrid"; + (aboveFloor, totalConsidered) = await ScoreHybrid( + request, deterministicPlan, effectiveBoundary, scoredCandidates, hybridInput, ct); + } + else + { + mode = "lexical"; + var rankedCandidates = scoredCandidates + .Select(x => new RankedCandidate( + x.Item, + x.SelectorScore + (RecallRank(x.Item) / RecallRankDampeningFactor), + Cosine: null)) + .OrderByDescending(x => x.Composite) + .ToArray(); + + totalConsidered = rankedCandidates.Length; + aboveFloor = rankedCandidates + .Where(x => x.Composite >= minimumCompositeScore) + .ToArray(); + } // Char budget: admit items in rank order until the next item's // content would blow the per-turn budget. Whole items are @@ -130,14 +270,15 @@ public async Task RecallAsync(AutomaticRecallRequest requ var deterministicItems = budgeted.ToArray(); logger.LogInformation( - "memory_retrieval_final session={SessionId} injectedCount={InjectedCount} filteredByFloor={FilteredByFloor} appliedFloor={AppliedFloor:F1} injectedChars={InjectedChars} droppedByBudget={DroppedByBudget} items={Items}", + "memory_retrieval_final session={SessionId} mode={Mode} injectedCount={InjectedCount} filteredByFloor={FilteredByFloor} appliedFloor={AppliedFloor:F3} injectedChars={InjectedChars} droppedByBudget={DroppedByBudget} items={Items}", request.SessionId, + mode, deterministicItems.Length, - rankedCandidates.Length - aboveFloor.Length, - minimumCompositeScore, + totalConsidered - aboveFloor.Length, + mode == "hybrid" ? _recallConfig.MinCosineSimilarity : minimumCompositeScore, injectedChars, droppedByBudget, - string.Join("|", deterministicItems.Select(i => $"{i.Id.Value}=score{i.Score:F1}"))); + string.Join("|", deterministicItems.Select(i => $"{i.Id.Value}=score{i.Score:F3}"))); logger.LogDebug( "memory_retrieval_final_detail session={SessionId} items={Items}", @@ -160,6 +301,199 @@ public async Task RecallAsync(AutomaticRecallRequest requ } } + /// + /// Attempts to embed 's query for hybrid recall + /// (memory-core-redesign Slice 4, task 4.1). Returns null — logging the specific + /// degradation reason via — for every failure mode: + /// no embedder wired, embedder unavailable, no vector index wired, index reload failure, + /// sub-budget timeout, or an embedding call exception. Never throws; callers treat null as + /// "run the lexical-only path," identically regardless of which reason produced it. + /// + private async Task<(ReadOnlyMemory QueryVector, MemoryVectorIndex Index)?> TryEmbedQueryAsync( + AutomaticRecallRequest request, CancellationToken ct) + { + var embedder = embedderHolder?.Current; + if (embedder is null) + { + LogVectorDegraded(request.SessionId.Value, "no_embedder_configured"); + return null; + } + + if (!embedder.IsAvailable) + { + LogVectorDegraded(request.SessionId.Value, "embedder_unavailable"); + return null; + } + + if (vectorIndexHolder is null) + { + LogVectorDegraded(request.SessionId.Value, "no_vector_index_configured"); + return null; + } + + MemoryVectorIndex? index; + try + { + index = await vectorIndexHolder.GetCurrentAsync(embedder, ct); + } + catch (Exception ex) + { + LogVectorDegraded(request.SessionId.Value, $"vector_index_reload_failed:{ex.GetType().Name}"); + return null; + } + + if (index is null) + { + LogVectorDegraded(request.SessionId.Value, "vector_index_unavailable"); + return null; + } + + try + { + using var vectorCts = CancellationTokenSource.CreateLinkedTokenSource(ct); + vectorCts.CancelAfter(VectorEmbedSubBudgetMs); + var vector = await embedder.EmbedAsync(request.Query, vectorCts.Token); + return (vector, index); + } + catch (OperationCanceledException) when (!ct.IsCancellationRequested) + { + // The sub-budget's own timer fired, not the caller's outer recall ct — degrade to + // lexical rather than propagating a cancellation that would fail the whole turn. + LogVectorDegraded(request.SessionId.Value, "sub_budget_exceeded"); + return null; + } + catch (Exception ex) + { + LogVectorDegraded(request.SessionId.Value, $"embed_failed:{ex.GetType().Name}"); + return null; + } + } + + /// + /// Builds the hybrid-mode ranked candidate pool (memory-core-redesign Slice 4, tasks + /// 4.2-4.4): vector top-k unioned with the lexical candidates already selected against the + /// plan, fused per design D6's weighted formula, recency-decayed, then filtered to the + /// absolute cosine floor. Vector-only ids are hydrated through + /// — the SAME policy gates + /// applied to the lexical candidates — and + /// scored via so a vector hit that also + /// happens to match plan terms is not scored as if it had none. + /// + private async Task<(RankedCandidate[] AboveFloor, int TotalConsidered)> ScoreHybrid( + AutomaticRecallRequest request, + DeterministicRetrievalRequestPlan deterministicPlan, + string effectiveBoundary, + IReadOnlyList scoredCandidates, + (ReadOnlyMemory QueryVector, MemoryVectorIndex Index) hybridInput, + CancellationToken ct) + { + var (queryVector, vectorIndex) = hybridInput; + + // The absolute floor (design D6) is applied HERE, at the TopK call itself: only matches + // at or above MinCosineSimilarity are ever candidates for injection, regardless of + // source. A lexical candidate absent from this map (never embedded, or embedded but not + // similar enough) defaults to cosine 0.0 below and is excluded by the same floor check. + var vectorMatches = vectorIndex.TopK(queryVector.Span, VectorTopK, minCosine: _recallConfig.MinCosineSimilarity) + .Where(m => string.Equals(m.ItemKind, MemoryEmbedOnWriteCoordinator.DocumentItemKind, StringComparison.Ordinal)) + .ToArray(); + var cosineByItemId = vectorMatches.ToDictionary(m => m.ItemId, m => m.Cosine, StringComparer.Ordinal); + + var lexicalIds = new HashSet(scoredCandidates.Select(x => x.Item.Id), StringComparer.Ordinal); + var vectorOnlyIds = vectorMatches + .Select(m => m.ItemId) + .Where(id => !lexicalIds.Contains(id)) + .ToArray(); + + IReadOnlyList vectorOnlyHydrated = vectorOnlyIds.Length == 0 + ? [] + : await store.GetRecallCandidatesByIdsAsync( + vectorOnlyIds, + deterministicPlan.AllowedMemoryClasses, + effectiveBoundary, + request.Audience, + allowExpiredEvidence: false, + ct); + + var pool = new List<(SQLiteMemoryHydratedItem Item, double SelectorScore)>(scoredCandidates.Count + vectorOnlyHydrated.Count); + foreach (var x in scoredCandidates) + pool.Add((x.Item, x.SelectorScore)); + foreach (var item in vectorOnlyHydrated) + pool.Add((item, DeterministicCandidateSelector.Score(deterministicPlan, item))); + + var nowMs = timeProvider.GetUtcNow().ToUnixTimeMilliseconds(); + var fused = pool + .Select(x => + { + var cosine = cosineByItemId.GetValueOrDefault(x.Item.Id, 0.0); + var squash = x.SelectorScore / (x.SelectorScore + SquashHalfSaturation); + var classPrior = (RecallRank(x.Item) / RecallRankDampeningFactor) / HybridClassPriorDampeningFactor; + var fusedScore = (_recallConfig.VectorWeight * cosine) + (_recallConfig.LexicalWeight * squash) + classPrior; + var recencyMultiplier = RecencyMultiplier(x.Item, nowMs); + return new RankedCandidate(x.Item, fusedScore * recencyMultiplier, cosine); + }) + .OrderByDescending(x => x.Composite) + .ToArray(); + + // THE absolute floor (design D6): cosine alone gates admission once a query vector + // exists — a high lexical/fused score cannot compensate for low semantic similarity. + // Zero survivors is intended, not an error: the "Nothing relevant means nothing + // injected" spec scenario, returned as a healthy empty result by the caller. + var aboveFloor = fused + .Where(x => x.Cosine is { } cosine && cosine >= _recallConfig.MinCosineSimilarity) + .ToArray(); + + return (aboveFloor, fused.Length); + } + + /// + /// Recency-decay multiplier applied to a candidate's fused score in hybrid mode only + /// (memory-core-redesign Slice 4, task 4.4) — see / + /// 's remarks for the formula and its bounds. A + /// non-positive disables decay entirely + /// (multiplier always 1.0) — the schema floors this at 1, but an operator-edited raw config + /// bypassing the doctor check should degrade to "no decay," not divide by zero. + /// + private double RecencyMultiplier(SQLiteMemoryHydratedItem item, long nowMs) + { + var halfLifeDays = _recallConfig.RecencyHalfLifeDays; + if (halfLifeDays <= 0) + return 1.0; + + var ageDays = Math.Max(0.0, (nowMs - item.UpdatedAtMs) / 86_400_000.0); + return RecencyDecayFloor + (RecencyDecayRange * Math.Pow(2.0, -ageDays / halfLifeDays)); + } + + /// + /// Rate-limited memory_recall_vector_degraded log (memory-core-redesign Slice 4, + /// task 4.1): at most one line per per + /// . Debug when embeddings are disabled by config — + /// the default, intentional operating mode, so this must not be Warning-level spam on every + /// turn of a deployment that has simply never turned embeddings on (mirrors + /// MemoryCurationEvaluator's curation_nominator_degraded reasoning). Warning + /// when embeddings are enabled but the turn still degraded — a genuine runtime condition an + /// operator should notice (loud, not silent, per the spec's degradation contract). + /// + /// + /// Best-effort throttle: a race between two concurrent recall calls hitting the same reason + /// at the same instant could both pass the check and both log once. Acceptable for a + /// diagnostic throttle, not a correctness gate, so no lock is taken here. + /// + /// + private void LogVectorDegraded(string sessionId, string reason) + { + var nowMs = timeProvider.GetUtcNow().ToUnixTimeMilliseconds(); + if (_lastVectorDegradedLogMs.TryGetValue(reason, out var lastMs) + && nowMs - lastMs < VectorDegradedLogCooldown.TotalMilliseconds) + return; + + _lastVectorDegradedLogMs[reason] = nowMs; + + if (_embeddingsEnabledByConfig) + logger.LogWarning("memory_recall_vector_degraded session={SessionId} reason={Reason}", sessionId, reason); + else + logger.LogDebug("memory_recall_vector_degraded session={SessionId} reason={Reason}", sessionId, reason); + } + private static int RecallRank(SQLiteMemoryHydratedItem document) { var score = 0; @@ -193,4 +527,11 @@ private static int RecallRank(SQLiteMemoryHydratedItem document) return score; } + + /// + /// A candidate after fusion scoring, in either mode. is null in the + /// degraded/lexical path (no query vector existed to compute one against) and non-null in + /// hybrid mode (0.0 for a candidate with no recorded embedding, its true cosine otherwise). + /// + private readonly record struct RankedCandidate(SQLiteMemoryHydratedItem Item, double Composite, double? Cosine); } diff --git a/src/Netclaw.Cli.Tests/Doctor/ConfigSchemaDoctorCheckTests.cs b/src/Netclaw.Cli.Tests/Doctor/ConfigSchemaDoctorCheckTests.cs index 1584bb716..bc2592689 100644 --- a/src/Netclaw.Cli.Tests/Doctor/ConfigSchemaDoctorCheckTests.cs +++ b/src/Netclaw.Cli.Tests/Doctor/ConfigSchemaDoctorCheckTests.cs @@ -206,6 +206,61 @@ await File.WriteAllTextAsync(paths.NetclawConfigPath, Assert.Equal(DoctorSeverity.Error, result.Severity); } + [Fact] + public async Task ReturnsPass_WhenMemoryRecallConfigMatchesSchemaV1() + { + var basePath = CreateTempBasePath(); + var paths = new NetclawPaths(basePath); + paths.EnsureDirectoriesExist(); + + await File.WriteAllTextAsync(paths.NetclawConfigPath, + """ + { + "configVersion": 1, + "Memory": { + "Enabled": true, + "Recall": { + "VectorWeight": 0.6, + "LexicalWeight": 0.4, + "MinCosineSimilarity": 0.5, + "RecencyHalfLifeDays": 45 + } + } + } + """, TestContext.Current.CancellationToken); + + var check = new ConfigSchemaDoctorCheck(paths); + var result = await check.RunAsync(TestContext.Current.CancellationToken); + + Assert.Equal(DoctorSeverity.Pass, result.Severity); + } + + [Fact] + public async Task ReturnsError_WhenMemoryRecallHasAnUnknownProperty() + { + var basePath = CreateTempBasePath(); + var paths = new NetclawPaths(basePath); + paths.EnsureDirectoriesExist(); + + await File.WriteAllTextAsync(paths.NetclawConfigPath, + """ + { + "configVersion": 1, + "Memory": { + "Recall": { + "VectorWeight": 0.6, + "NotARealProperty": "oops" + } + } + } + """, TestContext.Current.CancellationToken); + + var check = new ConfigSchemaDoctorCheck(paths); + var result = await check.RunAsync(TestContext.Current.CancellationToken); + + Assert.Equal(DoctorSeverity.Error, result.Severity); + } + [Fact] public async Task ReturnsPass_WhenReverseProxyTrustedProxiesLookValid() { diff --git a/src/Netclaw.Configuration.Tests/MemoryConfigDefaultsTests.cs b/src/Netclaw.Configuration.Tests/MemoryConfigDefaultsTests.cs index eef9d1dd4..0dfabc8c5 100644 --- a/src/Netclaw.Configuration.Tests/MemoryConfigDefaultsTests.cs +++ b/src/Netclaw.Configuration.Tests/MemoryConfigDefaultsTests.cs @@ -73,4 +73,34 @@ public void Curation_llm_timeout_seconds_defaults_to_10() var config = new MemoryConfig(); Assert.Equal(10, config.Curation.LlmTimeoutSeconds); } + + // ── MemoryRecallConfig (memory-core-redesign Slice 4, task 4.5) ── + + [Fact] + public void Recall_vector_weight_defaults_to_0_7() + { + var config = new MemoryConfig(); + Assert.Equal(0.7, config.Recall.VectorWeight); + } + + [Fact] + public void Recall_lexical_weight_defaults_to_0_3() + { + var config = new MemoryConfig(); + Assert.Equal(0.3, config.Recall.LexicalWeight); + } + + [Fact] + public void Recall_min_cosine_similarity_defaults_to_0_55() + { + var config = new MemoryConfig(); + Assert.Equal(0.55, config.Recall.MinCosineSimilarity); + } + + [Fact] + public void Recall_recency_half_life_days_defaults_to_30() + { + var config = new MemoryConfig(); + Assert.Equal(30, config.Recall.RecencyHalfLifeDays); + } } diff --git a/src/Netclaw.Configuration/MemoryConfig.cs b/src/Netclaw.Configuration/MemoryConfig.cs index 9de84e81f..a0a1c1c5d 100644 --- a/src/Netclaw.Configuration/MemoryConfig.cs +++ b/src/Netclaw.Configuration/MemoryConfig.cs @@ -38,6 +38,12 @@ public sealed class MemoryConfig /// lossless merge). See . /// public MemoryCurationConfig Curation { get; set; } = new(); + + /// + /// Read-side hybrid recall settings (memory-core-redesign Slice 4: weighted lexical/vector + /// fusion + absolute cosine floor). See . + /// + public MemoryRecallConfig Recall { get; set; } = new(); } /// @@ -117,3 +123,49 @@ public sealed class MemoryCurationConfig /// public int LlmTimeoutSeconds { get; set; } = 10; } + +/// +/// Configuration for read-side hybrid recall: weighted lexical/vector fusion and the absolute +/// cosine floor (memory-core-redesign Slice 4, design D6). Consumed by +/// . Every property is +/// defaulted, so no operator configuration is required once +/// is also on — a turn with no query vector +/// available (embedder unavailable, over its sub-budget, or embeddings disabled) degrades to +/// the pre-Slice-4 lexical-only composite floor unchanged, regardless of these values. +/// +public sealed class MemoryRecallConfig +{ + /// + /// Weight applied to a candidate's cosine similarity in the hybrid fusion score + /// (fused = VectorWeight*cosine + LexicalWeight*squash(selectorScore) + classPrior, + /// then recency-decayed). Only used in hybrid mode (a query vector was produced); ignored by + /// the lexical-only degraded path. + /// + public double VectorWeight { get; set; } = 0.7; + + /// + /// Weight applied to a candidate's squashed lexical selector score in the hybrid fusion + /// score. See for the full formula. + /// + public double LexicalWeight { get; set; } = 0.3; + + /// + /// Absolute relevance floor (design D6): when a query vector is available, any candidate — + /// vector- or lexical-sourced — whose cosine similarity to the query falls below this value + /// is dropped before ranking, regardless of fused score. Nothing surviving means nothing is + /// injected and the [memory-recall] block is omitted entirely — a healthy empty + /// result, not a degraded one. Calibrated against the real-traffic gold set + /// (gold-prod-2026-07); see design D6. + /// + public double MinCosineSimilarity { get; set; } = 0.55; + + /// + /// Half-life, in days, for the recency-decay multiplier applied to a candidate's fused score + /// in hybrid mode (0.85 + 0.15 * 2^(-ageDays/RecencyHalfLifeDays)). Floor-bounded at + /// 0.85 by construction (the decay term is always in (0, 1] for non-negative age), so an + /// old-but-otherwise-strong match is downweighted only enough to break ties toward fresher + /// knowledge, never zeroed by age alone. Age is measured from the item's + /// updated_at timestamp against . + /// + public double RecencyHalfLifeDays { get; set; } = 30; +} diff --git a/src/Netclaw.Configuration/Schemas/netclaw-config.v1.schema.json b/src/Netclaw.Configuration/Schemas/netclaw-config.v1.schema.json index 18e454463..00d03d8ef 100644 --- a/src/Netclaw.Configuration/Schemas/netclaw-config.v1.schema.json +++ b/src/Netclaw.Configuration/Schemas/netclaw-config.v1.schema.json @@ -420,6 +420,41 @@ } }, "additionalProperties": false + }, + "Recall": { + "type": "object", + "description": "Read-side hybrid recall settings: weighted lexical/vector fusion and the absolute cosine floor (memory-core-redesign Slice 4).", + "properties": { + "VectorWeight": { + "type": "number", + "minimum": 0, + "maximum": 1, + "default": 0.7, + "description": "Weight applied to a candidate's cosine similarity in the hybrid fusion score." + }, + "LexicalWeight": { + "type": "number", + "minimum": 0, + "maximum": 1, + "default": 0.3, + "description": "Weight applied to a candidate's squashed lexical selector score in the hybrid fusion score." + }, + "MinCosineSimilarity": { + "type": "number", + "minimum": 0, + "maximum": 1, + "default": 0.55, + "description": "Absolute relevance floor: when a query vector is available, any candidate below this cosine similarity is dropped before ranking, regardless of source." + }, + "RecencyHalfLifeDays": { + "type": "number", + "minimum": 1, + "maximum": 3650, + "default": 30, + "description": "Half-life in days for the recency-decay multiplier applied to a candidate's fused score in hybrid mode, floor-bounded at 0.85." + } + }, + "additionalProperties": false } }, "additionalProperties": false diff --git a/src/Netclaw.Embeddings.Tests/OnnxMemoryEmbedderTests.cs b/src/Netclaw.Embeddings.Tests/OnnxMemoryEmbedderTests.cs index fcf90d97a..2dffa5613 100644 --- a/src/Netclaw.Embeddings.Tests/OnnxMemoryEmbedderTests.cs +++ b/src/Netclaw.Embeddings.Tests/OnnxMemoryEmbedderTests.cs @@ -99,4 +99,47 @@ public async Task EmbedBatchAsync_of_empty_input_returns_empty() var batch = await _embedder.EmbedBatchAsync([], TestContext.Current.CancellationToken); Assert.Empty(batch); } + + // ── Dynamic-length padding (memory-core-redesign Slice 4, design D6 mitigation) ── + // + // OnnxMemoryEmbedder.EmbedOne now pads each input to bucket-of-8(actual token count) + // instead of always the fixed 512-token scratch buffers (tools/embed-latency-bench measured + // 1.000000 cosine parity vs fixed-512 across 10 fixed sentences on the real allowlisted + // model). There is no production hook to force the OLD fixed-512 behavior for a literal + // side-by-side cosine comparison here (adding one purely for this test would be exactly the + // kind of test-only production surface the constitution's "no optional params for test + // convenience" rule warns against), so these tests instead pin the properties that a broken + // bucketing implementation (wrong slice length, stale mask, truncation bug) would violate: + // determinism, correct/declared dimensionality, and a valid unit-length vector, across + // several distinctly-lengthed inputs so short, medium, and near-full-bucket lengths are all + // exercised through the real bucketing path. + [Theory] + [InlineData("cat")] + [InlineData("cat sat on the mat")] + [InlineData("the quarterly revenue report shows strong growth across every regional market segment this year")] + public async Task EmbedAsync_with_dynamic_length_padding_is_deterministic_and_normalized(string text) + { + var v1 = await _embedder.EmbedAsync(text, TestContext.Current.CancellationToken); + var v2 = await _embedder.EmbedAsync(text, TestContext.Current.CancellationToken); + + Assert.Equal(v1.ToArray(), v2.ToArray()); + Assert.Equal(Dimensions, v1.Length); + + var normSquared = v1.ToArray().Sum(x => (double)x * x); + Assert.True(Math.Abs(normSquared - 1.0) < 1e-4, $"expected unit-length vector for \"{text}\", got ||v||^2={normSquared}"); + } + + [Theory] + [InlineData(0, 8)] + [InlineData(1, 8)] + [InlineData(8, 8)] + [InlineData(9, 16)] + [InlineData(16, 16)] + [InlineData(17, 24)] + [InlineData(511, 512)] + [InlineData(512, 512)] + public void ComputeBucketedLength_rounds_up_to_the_nearest_bucket_of_8(int actualTokenCount, int expectedBucketLength) + { + Assert.Equal(expectedBucketLength, OnnxMemoryEmbedder.ComputeBucketedLength(actualTokenCount)); + } } diff --git a/src/Netclaw.Embeddings/OnnxMemoryEmbedder.cs b/src/Netclaw.Embeddings/OnnxMemoryEmbedder.cs index 4c87cd6be..94caeeada 100644 --- a/src/Netclaw.Embeddings/OnnxMemoryEmbedder.cs +++ b/src/Netclaw.Embeddings/OnnxMemoryEmbedder.cs @@ -53,6 +53,17 @@ public sealed class OnnxMemoryEmbedder : IMemoryEmbedder, IDisposable // Both allowlisted models cap at 512 (their tokenizer_config.json model_max_length). private const int MaxTokens = 512; + // Dynamic-length padding (memory-core-redesign Slice 4, design D6 mitigation): the ONNX + // graph's sequence axis is symbolic (input_ids/attention_mask/token_type_ids all declare + // [batch_size, sequence_length], no fixed shape), so padding to the actual tokenized length + // -- rounded up to a multiple of this bucket -- instead of always MaxTokens is a drop-in + // performance change with no retrieval-quality risk (measured cosine parity vs fixed-512: + // 1.000000 on every sentence in the Slice 2/4 correctness set, + // tools/embed-latency-bench). Reference-box short-query latency: p50 19.0ms / p95 20.9ms, + // vs p50 281.9ms / p95 310.5ms fixed-512 -- ~15x faster, leaving large headroom under the + // 150ms recall sub-budget (SQLiteMemoryRecallCoordinator.VectorEmbedSubBudgetMs). + private const int DynamicLengthBucket = 8; + private readonly InferenceSession _session; private readonly BertTokenizer _tokenizer; private readonly BoundedConcurrencyGate _gate; @@ -147,18 +158,28 @@ public async ValueTask>> EmbedBatchAsync(IRe private ReadOnlyMemory EmbedOne(string text) { - var inputIds = new long[MaxTokens]; - var attentionMask = new long[MaxTokens]; - var tokenTypeIds = new long[MaxTokens]; + var scratchIds = new long[MaxTokens]; + var scratchMask = new long[MaxTokens]; + var scratchTypes = new long[MaxTokens]; // This overload writes into the caller-supplied spans instead of BertTokenizer's // internal reused buffers, so calling it from multiple gate-scheduled tasks // concurrently against the one shared _tokenizer instance is safe. - _tokenizer.Encode(text, inputIds, attentionMask, tokenTypeIds, MaxTokens); + _tokenizer.Encode(text, scratchIds, scratchMask, scratchTypes, MaxTokens); + + // Dynamic-length padding: only feed the ONNX graph the actual tokenized length + // (rounded up to DynamicLengthBucket), not the full fixed-512 scratch buffers -- see + // DynamicLengthBucket's remarks. + var actualLen = (int)scratchMask.Sum(); + var bucketLen = ComputeBucketedLength(actualLen); + + var inputIds = scratchIds[..bucketLen]; + var attentionMask = scratchMask[..bucketLen]; + var tokenTypeIds = scratchTypes[..bucketLen]; - var inputIdsTensor = new DenseTensor(inputIds, [1, MaxTokens]); - var attentionMaskTensor = new DenseTensor(attentionMask, [1, MaxTokens]); - var tokenTypeIdsTensor = new DenseTensor(tokenTypeIds, [1, MaxTokens]); + var inputIdsTensor = new DenseTensor(inputIds, [1, bucketLen]); + var attentionMaskTensor = new DenseTensor(attentionMask, [1, bucketLen]); + var tokenTypeIdsTensor = new DenseTensor(tokenTypeIds, [1, bucketLen]); var available = new Dictionary(StringComparer.Ordinal) { @@ -192,6 +213,25 @@ private ReadOnlyMemory EmbedOne(string text) return vector; } + /// + /// Rounds up to the nearest multiple of + /// (minimum one bucket). A pure, directly-unit-tested + /// helper so the rounding rule itself has coverage independent of a live ONNX session. + /// Never exceeds in practice: + /// comes from 's attention-mask sum, + /// which already truncates to , and + /// (512) is itself a multiple of . + /// + internal static int ComputeBucketedLength(int actualTokenCount) + { + if (actualTokenCount <= 0) + return DynamicLengthBucket; + + return Math.Max( + DynamicLengthBucket, + ((actualTokenCount + DynamicLengthBucket - 1) / DynamicLengthBucket) * DynamicLengthBucket); + } + private static void NormalizeL2(float[] vector) { var norm = TensorPrimitives.Norm((ReadOnlySpan)vector); From b797f4b08d4b1faebd20482e47b5bb670ef7540e Mon Sep 17 00:00:00 2001 From: Aaron Stannard Date: Wed, 8 Jul 2026 20:15:17 +0000 Subject: [PATCH 15/37] feat(memory): calibrated MinCosineSimilarity default 0.68 from gold-prod-2026-07 sweep (memory-core-redesign task 4.6) Local floor-calibration sweep (2026-07-05, floor_calibration.py, in ~/recall-research-local/2026-07/quant-eval/) swept MinCosineSimilarity 0.30-0.75 against the 1,216-doc production snapshot (2026-07-03) and the 93-query gold-prod-2026-07 gold set (33 positive / 60 zero-relevant), using the fp32 ONNX production-faithful embedder replica. Result: fp32 (snowflake-arctic-embed-m, the shipped model per D2) optimum is 0.68 -- F0.5 0.141 vs 0.106 at the old 0.55 placeholder (+33% relative), zero-injection accuracy 13.3% (8/60, up from 0%), mean injected 2.53 (down from 3.00). Moderate symmetric plateau: robust to +/-0.01 drift, ~29% relative F0.5 drop by +/-0.03. uint8 variant optimum is 0.67 (F0.5 0.153, zero-injection 16.7%) but remains informational only since int8 is not shipped. Caveat carried into design.md and the config doc comment: even at the optimum, 83-87% of genuinely nothing-relevant queries still get something injected. An absolute cosine floor alone cannot close the zero-injection gap -- that residual is tracked under the separate memory-relevance-gate change. Changes: - design.md D6: replace the 0.55-placeholder phrasing with the calibrated 0.68 default, a compact per-variant calibration table, and the residual caveat with a pointer to memory-relevance-gate. - design.md Open Questions: mark the MinCosineSimilarity default question resolved with the measured answer. - MemoryConfig.cs: MinCosineSimilarity default 0.55 -> 0.68; doc comment now cites the calibration instead of calling it a placeholder. - netclaw-config.v1.schema.json: default 0.55 -> 0.68, description updated. - MemoryConfigDefaultsTests.cs: pinned default assertion updated to 0.68. - SQLiteMemoryRecallHybridTests.cs: stale comment referencing the old 0.55 default corrected to 0.68 (test behavior unaffected -- fixture uses cosine 0.0/1.0, both well clear of either threshold). - tasks.md: task 4.6 ticked. Gates: dotnet build Netclaw.slnx, dotnet test src/Netclaw.Configuration.Tests, dotnet test src/Netclaw.Actors.Tests --filter FullyQualifiedName~Recall, dotnet slopwatch analyze, and Add-FileHeaders.ps1 -Verify all green. --- .../changes/memory-core-redesign/design.md | 54 ++++++++++++++++--- .../changes/memory-core-redesign/tasks.md | 2 +- .../Sessions/SQLiteMemoryRecallHybridTests.cs | 2 +- .../MemoryConfigDefaultsTests.cs | 4 +- src/Netclaw.Configuration/MemoryConfig.cs | 7 +-- .../Schemas/netclaw-config.v1.schema.json | 4 +- 6 files changed, 57 insertions(+), 16 deletions(-) diff --git a/openspec/changes/memory-core-redesign/design.md b/openspec/changes/memory-core-redesign/design.md index 482b6967b..896d94d57 100644 --- a/openspec/changes/memory-core-redesign/design.md +++ b/openspec/changes/memory-core-redesign/design.md @@ -164,11 +164,12 @@ deduplicated, **all candidates passing the identical policy gates** correctness requirement with its own scenario. Scoring = weighted fusion (`VectorWeight` 0.7 × cosine + `LexicalWeight` 0.3 × squashed selector score + dampened class prior), then an **absolute floor**: `MinCosineSimilarity` -(default 0.55, calibrated against the real-traffic gold set -`gold-prod-2026-07`). Nothing above the floor → inject nothing, and the -volatile `[memory-recall]` block is omitted entirely (zero tokens). Recency -decay (`RecencyHalfLifeDays`, floor-bounded multiplier) breaks ties toward -fresh knowledge. The quick-win char budget and `AutoRecallMaxItems` remain +(default **0.68**, calibrated 2026-07-05 against the real-traffic gold set +`gold-prod-2026-07` — calibration summary below). Nothing above the floor → +inject nothing, and the volatile `[memory-recall]` block is omitted entirely +(zero tokens). Recency decay (`RecencyHalfLifeDays`, floor-bounded +multiplier) breaks ties toward fresh knowledge. The quick-win char budget and +`AutoRecallMaxItems` remain the outer bounds. *Alternative considered*: RRF fusion — rejected: rank-only fusion always @@ -199,6 +200,37 @@ retrieval-quality risk. **Decision: Slice 4 adopts dynamic sequence length quantization and not a relaxed budget — the 150 ms sub-budget holds with large headroom once padding is length-aware. +**Floor calibration, measured (2026-07-05, `floor_calibration.py`, task +4.6)**: swept `MinCosineSimilarity` from 0.30 to 0.75 (step 0.01) over the +same 1,216-doc production snapshot (2026-07-03) and the 93-query +`gold-prod-2026-07` gold set (33 positive / 60 zero-relevant queries), using +the fp32 ONNX production-faithful embedder replica and the cached doc/query +vectors already validated for the quantization eval. LOAD DECISION = inject +any of the top-3-by-cosine docs that clear the floor; objective = macro F0.5 +against `relevantDocIds`. + +| model | optimal τ | F0.5 @ optimum | F0.5 @ 0.55 (old default) | zero-injection acc. @ optimum | plateau shape | +|---|---:|---:|---:|---:|---| +| fp32 `snowflake-arctic-embed-m` (**shipped**) | **0.68** | 0.141 | 0.106 | 13.3% (8/60) | moderate, symmetric — robust to ±0.01 drift, ~29% relative F0.5 drop by ±0.03 | +| uint8 `snowflake-arctic-embed-m-int8` (not shipped, D2) | 0.67 | 0.153 | 0.106 | 16.7% (10/60) | asymmetric knife-edge — flat below the optimum, +0.01 above it costs 35% relative F0.5 | + +Production ships fp32 (D2), so **0.68** is the shipped default: +33% +relative F0.5 over the 0.55 placeholder, while mean injected count *drops* +(3.00 → 2.53 — fewer, more pertinent items). uint8's optimum sitting 0.01 +lower matches the compression shift already measured for +`NominatorSimilarityThreshold` (D2/D4); it remains informational only, since +int8 is not shipped. + +**Caveat that must not be dropped**: even at the F0.5 optimum, **83–87% of +genuinely nothing-relevant `gold-prod-2026-07` queries still get something +injected** (zero-injection accuracy tops out at 13.3–16.7%). Pushing the +floor further right buys more zero-accuracy but costs recall steeply on the +35% of queries where something is relevant (a property of this corpus's +embedding geometry, not a bug in the calibration). An absolute cosine floor +alone cannot close the zero-injection gap within the F0.5-preserving range — +that residual is tracked as the separate `memory-relevance-gate` change, not +solved here. + ### D7. Taxonomy rebalance: recall modes mean what they say - **BREAKING (semantic fix)**: `Searchable` leaves the automatic recall pool @@ -369,8 +401,16 @@ compatibility; only dead *behavior* is deleted. drop meaningfully too. Int8 quantization and relaxing the sub-budget are no longer necessary; both remain available as future levers if traffic shifts toward longer queries. -- Final `MinCosineSimilarity` default (calibrate against `gold-prod-2026-07` - during Slice 4; 0.55 is the working hypothesis). +- ~~Final `MinCosineSimilarity` default (calibrate against + `gold-prod-2026-07` during Slice 4; 0.55 is the working hypothesis)~~ + **MEASURED (Slice 4 task 4.6, `floor_calibration.py`, 2026-07-05)**: fp32 + optimum is **0.68** (F0.5 0.141 vs 0.106 at 0.55; zero-injection accuracy + 13.3%, up from 0%; moderate, symmetric plateau, robust to ±0.01 drift). + Production ships fp32 (D2), so 0.68 is the shipped default — full + calibration summary in D6. uint8's optimum (0.67, knife-edge above the + peak) stays informational until int8 ships. The residual 83–87% + zero-injection miss rate at the optimum is not solved by the floor alone; + tracked under the separate `memory-relevance-gate` change. - Whether the R2 feeds channel should mirror model artifacts (post-PoC operational decision; allowlist design is unaffected). - Trace auto-recall weighting while fresh (small prior vs durable-fact parity) diff --git a/openspec/changes/memory-core-redesign/tasks.md b/openspec/changes/memory-core-redesign/tasks.md index 94eeb2656..b9bb042d0 100644 --- a/openspec/changes/memory-core-redesign/tasks.md +++ b/openspec/changes/memory-core-redesign/tasks.md @@ -45,7 +45,7 @@ constitution gates (tests, evals where mapped, schema/skill sync, slopwatch). - [x] 4.3 Weighted fusion scoring + `MinCosineSimilarity` absolute floor; omit the `[memory-recall]` block entirely on zero injections - [x] 4.4 Recency half-life decay (floor-bounded multiplier) on composite scores - [x] 4.5 Config: `Memory.Recall { VectorWeight, LexicalWeight, MinCosineSimilarity, RecencyHalfLifeDays }` + schema sync -- [ ] 4.6 Calibrate the floor against `gold-prod-2026-07` (local gold set); record calibration numbers in design.md +- [x] 4.6 Calibrate the floor against `gold-prod-2026-07` (local gold set); record calibration numbers in design.md - [ ] 4.7 Gold-set recall regression suite (fixture corpus + labeled queries asserting injected/withheld ids, MRR/precision floors, zero-injection cases) - [ ] 4.8 Flip scenario P09 (paraphrase-gap) back to expected-recall; policy-parity scenario test; latency budget test with warm embedder - [ ] 4.9 Eval suite + `netclaw-memory` skill update (hybrid recall, zero-injection normality) diff --git a/src/Netclaw.Actors.Tests/Sessions/SQLiteMemoryRecallHybridTests.cs b/src/Netclaw.Actors.Tests/Sessions/SQLiteMemoryRecallHybridTests.cs index 8c9d324b4..40a7ba233 100644 --- a/src/Netclaw.Actors.Tests/Sessions/SQLiteMemoryRecallHybridTests.cs +++ b/src/Netclaw.Actors.Tests/Sessions/SQLiteMemoryRecallHybridTests.cs @@ -65,7 +65,7 @@ public async Task Absolute_floor_excludes_a_lexically_strong_candidate_whose_cos // Strong lexical match: title+content share every query term, so the pre-Slice-4 // selector score alone clears the old lexical floor comfortably. Its embedding is the // exact opposite direction of the query vector (cosine 0.0) -- well below - // MinCosineSimilarity's default 0.55. The absolute floor must reject it regardless of + // MinCosineSimilarity's default 0.68. The absolute floor must reject it regardless of // how strong the lexical match is. await SeedDocumentAsync("doc-lexical-strong", "Grafana dashboard provisioning convention", "Grafana dashboard provisioning convention details for the ops team.", ct); diff --git a/src/Netclaw.Configuration.Tests/MemoryConfigDefaultsTests.cs b/src/Netclaw.Configuration.Tests/MemoryConfigDefaultsTests.cs index 0dfabc8c5..939843680 100644 --- a/src/Netclaw.Configuration.Tests/MemoryConfigDefaultsTests.cs +++ b/src/Netclaw.Configuration.Tests/MemoryConfigDefaultsTests.cs @@ -91,10 +91,10 @@ public void Recall_lexical_weight_defaults_to_0_3() } [Fact] - public void Recall_min_cosine_similarity_defaults_to_0_55() + public void Recall_min_cosine_similarity_defaults_to_0_68() { var config = new MemoryConfig(); - Assert.Equal(0.55, config.Recall.MinCosineSimilarity); + Assert.Equal(0.68, config.Recall.MinCosineSimilarity); } [Fact] diff --git a/src/Netclaw.Configuration/MemoryConfig.cs b/src/Netclaw.Configuration/MemoryConfig.cs index a0a1c1c5d..d34f4c8a9 100644 --- a/src/Netclaw.Configuration/MemoryConfig.cs +++ b/src/Netclaw.Configuration/MemoryConfig.cs @@ -154,10 +154,11 @@ public sealed class MemoryRecallConfig /// vector- or lexical-sourced — whose cosine similarity to the query falls below this value /// is dropped before ranking, regardless of fused score. Nothing surviving means nothing is /// injected and the [memory-recall] block is omitted entirely — a healthy empty - /// result, not a degraded one. Calibrated against the real-traffic gold set - /// (gold-prod-2026-07); see design D6. + /// result, not a degraded one. Calibrated (not a placeholder) against the real-traffic gold + /// set (gold-prod-2026-07, 2026-07-05): maximizes F0.5 for the shipped fp32 + /// snowflake-arctic-embed-m embedder; see design D6 for the full sweep. /// - public double MinCosineSimilarity { get; set; } = 0.55; + public double MinCosineSimilarity { get; set; } = 0.68; /// /// Half-life, in days, for the recency-decay multiplier applied to a candidate's fused score diff --git a/src/Netclaw.Configuration/Schemas/netclaw-config.v1.schema.json b/src/Netclaw.Configuration/Schemas/netclaw-config.v1.schema.json index 00d03d8ef..2a68e103c 100644 --- a/src/Netclaw.Configuration/Schemas/netclaw-config.v1.schema.json +++ b/src/Netclaw.Configuration/Schemas/netclaw-config.v1.schema.json @@ -443,8 +443,8 @@ "type": "number", "minimum": 0, "maximum": 1, - "default": 0.55, - "description": "Absolute relevance floor: when a query vector is available, any candidate below this cosine similarity is dropped before ranking, regardless of source." + "default": 0.68, + "description": "Absolute relevance floor: when a query vector is available, any candidate below this cosine similarity is dropped before ranking, regardless of source. Calibrated against the gold-prod-2026-07 gold set for the shipped fp32 snowflake-arctic-embed-m embedder." }, "RecencyHalfLifeDays": { "type": "number", From 99a58e70b8863eab1e53376dad2a385f5cee6f4e Mon Sep 17 00:00:00 2001 From: Aaron Stannard Date: Wed, 8 Jul 2026 20:33:53 +0000 Subject: [PATCH 16/37] test(memory): gold-set recall regression suite, P09 paraphrase-gap flip, policy parity + latency budget (memory-core-redesign tasks 4.7-4.8) - Flip P09 back to expected-recall: wire a ScriptedEmbedder + real MemoryVectorIndex into the scenario coordinator for just this row (cosine 0.85 to M16's seeded embedding), keeping every other scenario on the pre-existing lexical-only coordinator. - Gold-set MRR/precision@3 test over the labeled scenario table (positive scenarios only): measured MRR 1.000, precision@3 0.849; floors set at 0.90/0.75 (~10% headroom). - Two zero-injection facts under a healthy embedder, including a lexically-strong-but-unembedded candidate (M07 vs the P01 query) to demonstrate the absolute cosine floor gates every candidate once a query vector exists, not just vector-sourced ones. - Two policy-parity facts: a high-cosine (~0.9) secret-sensitivity document and a high-cosine wrong-audience document are both withheld end-to-end through RecallAsync. - New latency budget test in Netclaw.Embeddings.Tests (D1 seam: Actors must not reference Embeddings) using the tiny fixture ONNX model: warms up once, asserts median of 10 short-query EmbedAsync calls is under the 150ms sub-budget. Real finding: SQLiteMemoryRecallCoordinator.ScoreHybrid applies MinCosineSimilarity to EVERY candidate once a query vector exists (missing embedding defaults to cosine 0.0), so a pure lexical hit lacking an embedding row is unrecallable whenever the embedder is healthy -- this is documented as deliberate in the coordinator's own docstring, not a bug, but it means wiring the embedder across the whole gold-set table (rather than per-scenario) would have broken every lexical-only scenario. Documented in the test file's class summary. --- .../changes/memory-core-redesign/tasks.md | 4 +- .../Sessions/MemoryRecallScenarioTests.cs | 336 +++++++++++++++++- .../EmbedQueryLatencyBudgetTests.cs | 86 +++++ 3 files changed, 407 insertions(+), 19 deletions(-) create mode 100644 src/Netclaw.Embeddings.Tests/EmbedQueryLatencyBudgetTests.cs diff --git a/openspec/changes/memory-core-redesign/tasks.md b/openspec/changes/memory-core-redesign/tasks.md index b9bb042d0..528924379 100644 --- a/openspec/changes/memory-core-redesign/tasks.md +++ b/openspec/changes/memory-core-redesign/tasks.md @@ -46,8 +46,8 @@ constitution gates (tests, evals where mapped, schema/skill sync, slopwatch). - [x] 4.4 Recency half-life decay (floor-bounded multiplier) on composite scores - [x] 4.5 Config: `Memory.Recall { VectorWeight, LexicalWeight, MinCosineSimilarity, RecencyHalfLifeDays }` + schema sync - [x] 4.6 Calibrate the floor against `gold-prod-2026-07` (local gold set); record calibration numbers in design.md -- [ ] 4.7 Gold-set recall regression suite (fixture corpus + labeled queries asserting injected/withheld ids, MRR/precision floors, zero-injection cases) -- [ ] 4.8 Flip scenario P09 (paraphrase-gap) back to expected-recall; policy-parity scenario test; latency budget test with warm embedder +- [x] 4.7 Gold-set recall regression suite (fixture corpus + labeled queries asserting injected/withheld ids, MRR/precision floors, zero-injection cases) +- [x] 4.8 Flip scenario P09 (paraphrase-gap) back to expected-recall; policy-parity scenario test; latency budget test with warm embedder - [ ] 4.9 Eval suite + `netclaw-memory` skill update (hybrid recall, zero-injection normality) ## 5. Taxonomy rebalance, trace revival, tool lessons diff --git a/src/Netclaw.Actors.Tests/Sessions/MemoryRecallScenarioTests.cs b/src/Netclaw.Actors.Tests/Sessions/MemoryRecallScenarioTests.cs index f3b76edc7..92700c19f 100644 --- a/src/Netclaw.Actors.Tests/Sessions/MemoryRecallScenarioTests.cs +++ b/src/Netclaw.Actors.Tests/Sessions/MemoryRecallScenarioTests.cs @@ -14,7 +14,10 @@ namespace Netclaw.Actors.Tests.Sessions; /// -/// Scenario suite for the memory recall composite-score floor (issue #582). +/// Scenario suite for the memory recall composite-score floor (issue #582), extended in +/// memory-core-redesign Slice 4 (tasks 4.7/4.8) into a gold-set regression suite covering hybrid +/// recall: the P09 paraphrase-gap flip, MRR/precision floors, zero-injection cases, and +/// policy-parity under a healthy embedder. /// /// Seeds a 16-document corpus mirroring the production DB shape that caused /// the pollution bug (a cluster of ops/eval trivia plus two topical clusters @@ -32,11 +35,41 @@ namespace Netclaw.Actors.Tests.Sessions; /// Document-vs-record priority is a separate concern handled by RecallRank /// weights, not the composite floor, and is deliberately out of scope here. /// The corpus contains only durable-fact documents. +/// +/// +/// Hybrid wiring (task 4.8): every scenario in still runs the +/// pre-Slice-4 lexical-only coordinator (no embedder/vector-index holders) EXCEPT P09, which +/// wires + a real loaded from the +/// store's memory_embeddings table (only M16 is embedded — see ). +/// This is deliberate, not incidental: +/// applies its absolute cosine floor to EVERY candidate once a query vector exists, including +/// lexical-only ones (a missing embedding defaults to cosine 0.0, which is always below the +/// floor) — so wiring the embedder across the whole table would silently zero out every +/// lexical-only scenario (P01–P08, P10, P15) instead of proving the paraphrase-gap fix. See the +/// zero-injection facts below for a direct demonstration of that behavior. +/// /// public sealed class MemoryRecallScenarioTests : IAsyncLifetime { private const string TestSessionId = "test/thread-1"; + // Hybrid fixture geometry (task 4.8): a 2D unit-vector space, same technique as + // MemoryCurationNominatorTests/SQLiteMemoryRecallHybridTests. Only M16 ever gets an + // embedding row (in SeedCorpusAsync) under this model id, so any coordinator wired with a + // ScriptedEmbedder under HybridModelId only ever has M16 as a possible vector candidate. + private const string HybridModelId = "recall-scenario-hybrid-test-model"; + private const int HybridDimensions = 2; + + // cosine(P09QueryVector, M16EmbeddingVector) == 0.85 -- comfortably above MinCosineSimilarity + // (default 0.68), the semantic bridge across the paraphrase gap the lexical path can't cross. + private static readonly float[] P09QueryVector = [1f, 0f]; + private static readonly float[] M16EmbeddingVector = [0.85f, 0.5267828f]; + + // cosine(NonMatchingQueryVector, M16EmbeddingVector) == 0.5267828 -- comfortably below the + // 0.68 floor. Used by the zero-injection facts to prove a healthy embedder with no + // qualifying candidate yields a healthy empty result, never a degraded one. + private static readonly float[] NonMatchingQueryVector = [0f, 1f]; + private readonly string _baseDir = Path.Combine( Path.GetTempPath(), "netclaw-recall-scenarios", @@ -109,15 +142,16 @@ public static IEnumerable Scenarios() // tokens with M16 ("versions ... cover" vs "runs net8.0 net9.0 ... // runners"), so under lexical recall M16 only ever surfaced via a // single weak token match — the exact signature of the measured - // pollution vector (docs/research/memory-audit-2026-07.md). With the calibrated floor the - // correct deterministic behavior is to inject NOTHING here rather - // than admit single-token matches corpus-wide. Semantic (embedding) - // recall is what serves this query; when hybrid recall lands, flip - // this back to expected: ["M16"]. + // pollution vector (docs/research/memory-audit-2026-07.md). Flipped back to + // expected-recall (memory-core-redesign Slice 4, task 4.8): the fixture wires a + // ScriptedEmbedder + real vector index (see the class summary and BuildCoordinator) + // whose query vector sits at cosine 0.85 to M16's seeded embedding, the semantic bridge + // lexical recall alone can't cross. yield return Row("P09", "Which .NET versions does our CI cover?", - expected: [], - forbidden: NoiseBand); + expected: ["M16"], + forbidden: NoiseBand, + useHybridRecall: true); yield return Row("P10", "Are our NuGet packages signed?", expected: ["M15"], @@ -177,15 +211,11 @@ public async Task Scenario_matches_expected_and_rejects_forbidden( string scenarioId, string prompt, string[] expectedIds, - string[] forbiddenIds) + string[] forbiddenIds, + bool useHybridRecall) { _ = scenarioId; // carried for failure diagnostics - var coordinator = new SQLiteMemoryRecallCoordinator( - _store, - NullLogger.Instance, - new MemoryConfig(), - TimeProvider.System, - sessionTuning: new SessionTuning()); + var coordinator = BuildCoordinator(useHybridRecall); var request = new AutomaticRecallRequest( SessionId: (SessionId)TestSessionId, @@ -218,9 +248,253 @@ public async Task Scenario_matches_expected_and_rejects_forbidden( } } - private static object[] Row(string id, string prompt, string[] expected, string[] forbidden) - => [id, prompt, expected, forbidden]; + // ── Gold-set MRR / precision floors (memory-core-redesign Slice 4, task 4.7) ─────────── + + /// + /// Computes MRR and precision@3 across every scenario in that has at + /// least one expected id (the standard IR definition needs a known-relevant item to rank) — + /// P01-P10/P15 lexical, P09 hybrid. Zero-expected scenarios (P11/P12/P14/P16/P19-21) are + /// covered by their own pass/fail assertions above and by the dedicated zero-injection facts + /// below; folding them into precision@3 here would either reward vacuous "returned nothing" + /// results or conflate two different failure modes into one number. + /// + /// + /// Floors are ~10% headroom below the values measured against this fixture corpus (as of + /// this test's authoring): MRR 1.000 (every positive scenario's expected id ranks first) and + /// precision@3 0.849 (P01/P15 each admit one extra non-forbidden, non-expected item alongside + /// the expected one — see their diagnostics in a failure message — and P08 admits two; every + /// other positive scenario returns exactly its expected id and nothing else). Tight enough to + /// catch a real regression, loose enough not to flake on an incidental single-candidate rank + /// change. + /// + /// + [Fact] + public async Task Gold_set_MRR_and_precision_at_3_meet_the_calibrated_floor() + { + const double MrrFloor = 0.90; + const double PrecisionAt3Floor = 0.75; + + var ct = TestContext.Current.CancellationToken; + var reciprocalRanks = new List(); + var precisions = new List(); + var diagnostics = new List(); + + foreach (var row in Scenarios()) + { + var scenarioId = (string)row[0]; + var prompt = (string)row[1]; + var expectedIds = (string[])row[2]; + var useHybridRecall = (bool)row[4]; + if (expectedIds.Length == 0) + continue; + + var coordinator = BuildCoordinator(useHybridRecall); + var result = await coordinator.RecallAsync(new AutomaticRecallRequest( + SessionId: (SessionId)TestSessionId, + Query: prompt, + RecentUserMessages: [prompt], + MaxItems: 3, + Audience: TrustAudience.Public), ct); + + Assert.False(result.Degraded, $"[{scenarioId}] recall degraded: {result.DegradeStage}/{result.DegradeReason}"); + + var items = result.Items; + var rankIndex = items.Select(i => i.Id.Value).ToList().FindIndex(id => expectedIds.Contains(id)); + var reciprocalRank = rankIndex >= 0 ? 1.0 / (rankIndex + 1) : 0.0; + reciprocalRanks.Add(reciprocalRank); + + var hitCount = items.Count(i => expectedIds.Contains(i.Id.Value)); + var precision = items.Count > 0 ? hitCount / (double)items.Count : 0.0; + precisions.Add(precision); + + diagnostics.Add( + $"{scenarioId}: rr={reciprocalRank:F3} precision={precision:F3} items=[{string.Join(",", items.Select(i => $"{i.Id.Value}={i.Score:F3}"))}]"); + } + + var mrr = reciprocalRanks.Average(); + var precisionAt3 = precisions.Average(); + var report = string.Join("\n", diagnostics); + + Assert.True(mrr >= MrrFloor, $"MRR {mrr:F3} fell below floor {MrrFloor:F3}\n{report}"); + Assert.True(precisionAt3 >= PrecisionAt3Floor, $"precision@3 {precisionAt3:F3} fell below floor {PrecisionAt3Floor:F3}\n{report}"); + } + + // ── Zero-injection cases under a healthy embedder (memory-core-redesign Slice 4, task 4.7) ── + // + // The Scenarios() theory's zero-expected rows (P11/P12/P14/P16/P19-21) all run the + // lexical-only coordinator. These two facts instead prove the zero-injection contract holds + // once a query vector exists: a candidate whose cosine falls below the floor (or has no + // embedding at all) never gets injected, and — this is the important, non-obvious part per + // this class's summary — that holds EVEN WHEN the candidate is the strongest possible + // lexical match, because SQLiteMemoryRecallCoordinator's absolute floor gates every + // candidate once hybrid mode is active, not just vector-sourced ones. + + [Fact] + public async Task ZeroInjection_novel_query_with_no_qualifying_candidate_is_empty_and_not_degraded() + { + var ct = TestContext.Current.CancellationToken; + var coordinator = BuildHybridCoordinator(NonMatchingQueryVector); + + var result = await coordinator.RecallAsync(new AutomaticRecallRequest( + SessionId: (SessionId)TestSessionId, + Query: "orchestra kayak zeppelin", + RecentUserMessages: ["orchestra kayak zeppelin"], + MaxItems: 3, + Audience: TrustAudience.Public), ct); + + Assert.False(result.Degraded); + Assert.Empty(result.Items); + } + + [Fact] + public async Task ZeroInjection_strong_lexical_match_without_an_embedding_is_excluded_when_the_embedder_is_healthy() + { + var ct = TestContext.Current.CancellationToken; + + // Reuses P01's exact query text: under the lexical-only coordinator (see the P01 row + // above) this clears the composite floor comfortably and recalls M07. M07 has no + // embedding row, so under a healthy embedder its cosine defaults to 0.0 -- below the + // 0.68 floor regardless of how strong the lexical match is. NonMatchingQueryVector also + // keeps M16 (the corpus's only embedded doc) below the floor, so nothing at all survives. + var coordinator = BuildHybridCoordinator(NonMatchingQueryVector); + + var result = await coordinator.RecallAsync(new AutomaticRecallRequest( + SessionId: (SessionId)TestSessionId, + Query: "How does backpressure work in Akka Streams?", + RecentUserMessages: ["How does backpressure work in Akka Streams?"], + MaxItems: 3, + Audience: TrustAudience.Public), ct); + + Assert.False(result.Degraded); + Assert.Empty(result.Items); + } + + // ── Policy parity under a healthy embedder (memory-core-redesign Slice 4, task 4.8) ──── + // + // SQLiteMemoryStoreEmbeddingTests already proves GetRecallCandidatesByIdsAsync itself applies + // every SearchByPlanAsync gate to a vector-sourced id. These two facts close the loop at the + // full RecallAsync level: a document with a HIGH cosine match (well above the floor) must + // still be withheld end-to-end when a policy gate says no — a strong vector signal can never + // stand in for a policy violation. + + [Fact] + public async Task PolicyParity_high_cosine_secret_document_is_withheld() + { + var ct = TestContext.Current.CancellationToken; + const string modelId = "policy-parity-secret-test-model"; + float[] queryVector = [0f, 1f]; + float[] secretDocVector = [0.4359f, 0.9f]; // cosine ~0.9 to queryVector + await SeedPolicyParityDocumentAsync( + "M17-secret", "Confidential Executive Compensation Review", + "Confidential executive compensation review figures withheld from automatic recall.", + modelId, secretDocVector, sensitivity: "secret", ct: ct); + + var coordinator = BuildHybridCoordinator(modelId, queryVector); + + var result = await coordinator.RecallAsync(new AutomaticRecallRequest( + SessionId: (SessionId)TestSessionId, + Query: "confidential executive compensation review", + RecentUserMessages: ["confidential executive compensation review"], + MaxItems: 3, + Audience: TrustAudience.Public), ct); + + Assert.False(result.Degraded); + Assert.DoesNotContain(result.Items, i => i.Id.Value == "M17-secret"); + } + + [Fact] + public async Task PolicyParity_high_cosine_wrong_audience_document_is_withheld() + { + var ct = TestContext.Current.CancellationToken; + const string modelId = "policy-parity-audience-test-model"; + float[] queryVector = [0f, 1f]; + float[] teamDocVector = [0.4359f, 0.9f]; // cosine ~0.9 to queryVector + + await SeedPolicyParityDocumentAsync( + "M18-team-only", "Team Roadmap Planning Notes", + "Team roadmap planning notes scoped to the team audience only.", + modelId, teamDocVector, audience: TrustAudience.Team.ToWireValue(), ct: ct); + + // Request audience is the default Public -- Public's allowed-audience set + // (MemoryPolicyEvaluator.AllowedAudienceWireValues) is [Public] only, so a Team-scoped + // document must never surface regardless of its cosine similarity. + var coordinator = BuildHybridCoordinator(modelId, queryVector); + + var result = await coordinator.RecallAsync(new AutomaticRecallRequest( + SessionId: (SessionId)TestSessionId, + Query: "team roadmap planning notes", + RecentUserMessages: ["team roadmap planning notes"], + MaxItems: 3, + Audience: TrustAudience.Public), ct); + + Assert.False(result.Degraded); + Assert.DoesNotContain(result.Items, i => i.Id.Value == "M18-team-only"); + } + + private async Task SeedPolicyParityDocumentAsync( + string documentId, string title, string body, string modelId, float[] vector, + CancellationToken ct, string sensitivity = "normal", string audience = "public") + { + var anchor = _store.CreateDefaultAnchor(documentId); + var now = TimeProvider.System.GetUtcNow().ToUnixTimeMilliseconds(); + await _store.UpsertDocumentAsync(new SQLiteMemoryDocument( + DocumentId: documentId, + Anchor: anchor, + MemoryClass: "durable_fact", + Title: title, + MarkdownBody: body, + AliasesJson: null, + FacetsJson: null, + SlotsJson: null, + UpdateSemantics: "merge-document", + Sensitivity: sensitivity, + RecallMode: "auto", + Confidence: 0.9, + FreshnessAtMs: now, + ExpiresAtMs: null, + CreatedAtMs: now, + UpdatedAtMs: now, + Audience: audience), ct); + await _store.UpsertEmbeddingAsync( + documentId, MemoryEmbedOnWriteCoordinator.DocumentItemKind, modelId, $"hash-{documentId}", vector, ct); + } + + /// Hybrid coordinator over HybridModelId/HybridDimensions (only M16 is embedded there). + private SQLiteMemoryRecallCoordinator BuildHybridCoordinator(float[] queryVector) + => BuildHybridCoordinator(HybridModelId, queryVector, HybridDimensions); + + private SQLiteMemoryRecallCoordinator BuildHybridCoordinator(string modelId, float[] queryVector, int dimensions = 2) + => new( + _store, + NullLogger.Instance, + new MemoryConfig(), + TimeProvider.System, + sessionTuning: new SessionTuning(), + embedderHolder: new MemoryEmbedderHolder(new ScriptedEmbedder(modelId, dimensions, queryVector)), + vectorIndexHolder: new MemoryVectorIndexHolder(_store)); + + private static object[] Row(string id, string prompt, string[] expected, string[] forbidden, bool useHybridRecall = false) + => [id, prompt, expected, forbidden, useHybridRecall]; + + /// + /// Builds the coordinator a scenario runs against (task 4.8). Every scenario except P09 gets + /// the pre-Slice-4 lexical-only coordinator (no holders wired) — this is the path + /// already pins as behaviorally identical to + /// "no embedder configured" (see SQLiteMemoryRecallHybridTests's degraded-parity + /// test), so it is not a lesser or stale code path, just the one every scenario here other + /// than P09 is designed to exercise. See the class summary for why the whole table can't + /// share a single hybrid-wired coordinator. + /// + private SQLiteMemoryRecallCoordinator BuildCoordinator(bool useHybridRecall) + => useHybridRecall + ? BuildHybridCoordinator(P09QueryVector) + : new SQLiteMemoryRecallCoordinator( + _store, + NullLogger.Instance, + new MemoryConfig(), + TimeProvider.System, + sessionTuning: new SessionTuning()); // The noise band — ops/eval trivia mirroring the polluting docs from #582. // Most scenarios assert that none of these leak into the recall result. @@ -291,6 +565,11 @@ await UpsertDoc("M16", "CI Build Matrix", "CI runs net8.0 and net9.0 on Linux and Windows runners for every pull request.", facets: "[\"ci\",\"build\"]", now: now, ct: ct); + // The ONLY embedded document in this corpus (task 4.8) -- every other scenario runs a + // coordinator without embedder/vector-index holders wired, so this row is inert for them + // (TryEmbedQueryAsync returns null before ever touching the store's embedding table). + await _store.UpsertEmbeddingAsync( + "M16", MemoryEmbedOnWriteCoordinator.DocumentItemKind, HybridModelId, "hash-m16", M16EmbeddingVector, ct); } private async Task UpsertDoc( @@ -345,4 +624,27 @@ private static async Task TryDeleteDirectoryAsync(string path) } } } + + /// + /// Fake embedder that ignores its input text and always returns the same, hand-crafted query + /// vector — sufficient here because every coordinator built against one instance embeds at + /// most one distinct query per test, and the geometry (not the input text) is what needs to + /// be controlled. Mirrors SQLiteMemoryRecallHybridTests.ScriptedEmbedder (kept as a + /// separate private copy per that file's own convention). + /// + private sealed class ScriptedEmbedder(string modelId, int dimensions, float[] queryVector) : IMemoryEmbedder + { + public string ModelId => modelId; + + public int Dimensions => dimensions; + + public bool IsAvailable => true; + + public ValueTask> EmbedAsync(string text, CancellationToken ct) + => ValueTask.FromResult>(queryVector); + + public ValueTask>> EmbedBatchAsync(IReadOnlyList texts, CancellationToken ct) + => ValueTask.FromResult>>( + texts.Select(_ => (ReadOnlyMemory)queryVector).ToList()); + } } diff --git a/src/Netclaw.Embeddings.Tests/EmbedQueryLatencyBudgetTests.cs b/src/Netclaw.Embeddings.Tests/EmbedQueryLatencyBudgetTests.cs new file mode 100644 index 000000000..489900112 --- /dev/null +++ b/src/Netclaw.Embeddings.Tests/EmbedQueryLatencyBudgetTests.cs @@ -0,0 +1,86 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using System.Diagnostics; +using Xunit; + +namespace Netclaw.Embeddings.Tests; + +/// +/// Latency budget test for the per-turn query-embedding sub-budget (memory-core-redesign Slice +/// 4, task 4.8): SQLiteMemoryRecallCoordinator's VectorEmbedSubBudgetMs gives each +/// turn's query-embedding call 150ms before degrading to the lexical-only path. This lives in +/// Netclaw.Embeddings.Tests, not Netclaw.Actors.Tests, because +/// Netclaw.Actors must never reference Netclaw.Embeddings (design D1 seam rule) — +/// is only visible from this project. +/// +/// +/// Uses the same tiny fixture ONNX model as (no network +/// access, no real allowlisted model download). A tiny fixture graph is drastically faster than +/// any real embedding model, so this is NOT a measurement of the sub-budget's real-world margin +/// (that measurement lives in design.md, task 2.13, tools/embed-latency-bench, against the +/// real allowlisted model: p50 19.0ms / p95 20.9ms on the reference box). It is a regression +/// guard against something making even a trivial model's EmbedAsync call pathologically +/// slow (a synchronization bug serializing every call through a lock, a leaked debug delay, a +/// broken bucketing path re-padding to the full 512-token scratch buffer). The 150ms bound is +/// intentionally generous for a model this tiny; median (not max) across repeated calls avoids +/// flaking on one slow first-call cost, which the explicit warm-up call below already absorbs. +/// +/// +public sealed class EmbedQueryLatencyBudgetTests : IAsyncLifetime +{ + private const string ModelId = "tiny-fixture"; + private const int Dimensions = 8; + private const int SampleCount = 10; + private const double MedianBudgetMs = 150.0; + + private OnnxMemoryEmbedder _embedder = null!; + + public async ValueTask InitializeAsync() + { + var fixturesDir = Path.Combine(AppContext.BaseDirectory, "Fixtures"); + _embedder = await OnnxMemoryEmbedder.LoadAsync( + modelPath: Path.Combine(fixturesDir, "tiny-embedder.onnx"), + vocabPath: Path.Combine(fixturesDir, "tiny-vocab.txt"), + modelId: ModelId, + dimensions: Dimensions, + maxConcurrency: 2); + } + + public ValueTask DisposeAsync() + { + _embedder.Dispose(); + return ValueTask.CompletedTask; + } + + [Fact] + public async Task Median_short_query_embed_latency_is_within_the_150ms_sub_budget_once_warm() + { + var ct = TestContext.Current.CancellationToken; + + // Warm-up call: absorbs first-call session/JIT costs the real + // EmbeddingWarmupHostedService pays once at startup, outside the per-turn budget. + await _embedder.EmbedAsync("warm up the inference session", ct); + + var samples = new double[SampleCount]; + for (var i = 0; i < SampleCount; i++) + { + var sw = Stopwatch.StartNew(); + await _embedder.EmbedAsync("What's our Sev2 response time for commercial support?", ct); + sw.Stop(); + samples[i] = sw.Elapsed.TotalMilliseconds; + } + + Array.Sort(samples); + var median = SampleCount % 2 == 0 + ? (samples[(SampleCount / 2) - 1] + samples[SampleCount / 2]) / 2.0 + : samples[SampleCount / 2]; + + Assert.True( + median < MedianBudgetMs, + $"median short-query embed latency {median:F2}ms exceeded the {MedianBudgetMs}ms sub-budget " + + $"across samples [{string.Join(", ", samples.Select(s => s.ToString("F2")))}]"); + } +} From 7564384dfc1fc67413d2616209cdf67cc30563fe Mon Sep 17 00:00:00 2001 From: Aaron Stannard Date: Wed, 8 Jul 2026 20:48:26 +0000 Subject: [PATCH 17/37] fix(memory): coverage-gap candidates degrade to lexical instead of blacking out recall (memory-core-redesign slice 4) Slice 4's ScoreHybrid applied the absolute MinCosineSimilarity floor to every candidate once a query vector existed, defaulting an unembedded candidate's cosine to 0.0 and then rejecting it via that same floor. That made any unembedded document structurally unrecallable while the embedder was healthy -- enabling embeddings on an un-backfilled corpus blacked out ALL recall until gap repair completed, contradicting design.md's migration plan ("both paths degrade loudly to lexical when coverage is incomplete rather than misbehaving"). Fix: distinguish three cases per candidate. 1. Embedded + cosine >= floor -> admitted (unchanged). 2. Embedded + cosine < floor -> excluded (unchanged; the calibrated absolute floor still gates every candidate the index actually holds a vector for). 3. No embedding row at all (a coverage gap) -> bypasses the floor, competes on fused/lexical score alone (cosine term 0). Emits a rate-limited memory_recall_coverage_gap warning (Debug when embeddings are disabled by config), following the same rate-limiting pattern as the existing memory_recall_vector_degraded log. MemoryVectorIndex.TopK gains an out-parameter overload reporting every item id the index holds an embedding for (regardless of cosine), computed from the identical snapshot the returned matches were scored against so case 2 vs. case 3 can never straddle a concurrent index reload. Test updates: inverted the MemoryRecallScenarioTests fact that pinned the old (wrong) behavior, renaming it to reflect that an unembedded strong lexical match IS now recalled; added SQLiteMemoryRecallHybridTests coverage for case 2 exclusion, case 3 admission + log, and no-log on a fully-covered corpus; fixed a zero-survivors test whose premise relied on the old defaulting-to-0.0 behavior. P09 and the MRR/precision floors are unaffected. Docs: corrected the coordinator's docstring (previously claimed the old behavior was deliberate) and added the three-case semantics to design.md D6, plus a netclaw-memory skill update documenting the new log event. --- .../.system/files/netclaw-memory/SKILL.md | 9 +- .../changes/memory-core-redesign/design.md | 17 ++- .../Sessions/MemoryRecallScenarioTests.cs | 42 +++--- .../Sessions/SQLiteMemoryRecallHybridTests.cs | 133 ++++++++++++++++-- .../Memory/MemoryVectorIndex.cs | 20 ++- .../Sessions/SQLiteMemoryRecallCoordinator.cs | 132 +++++++++++++---- 6 files changed, 289 insertions(+), 64 deletions(-) diff --git a/feeds/skills/.system/files/netclaw-memory/SKILL.md b/feeds/skills/.system/files/netclaw-memory/SKILL.md index 3bf49d781..8efb724ec 100644 --- a/feeds/skills/.system/files/netclaw-memory/SKILL.md +++ b/feeds/skills/.system/files/netclaw-memory/SKILL.md @@ -3,7 +3,7 @@ name: netclaw-memory description: "REQUIRED when the user asks what you remember, recall, or know from past conversations, previous sessions, cross-session memory, memory classes, or memory types. Also before using memory tools: find_memories, get_memories, store_memory, update_memory." metadata: author: netclaw - version: "1.9.0" + version: "1.9.1" --- # Netclaw Memory @@ -150,11 +150,16 @@ When memory behavior looks wrong: Useful log events: -**Recall pipeline** (grep for `memory_retrieval`): +**Recall pipeline** (grep for `memory_retrieval` / `memory_recall`): - `memory_retrieval_request_plan` — query tokenization, facets, soft scopes, anchor hints - `memory_retrieval_candidate_selection` — all candidates with selector scores - `memory_retrieval_final` — floor filtering results, final injected items - `turn_memory_recall` — summary event with item count and duration +- `memory_recall_vector_degraded` — turn fell back to lexical-only recall (embedder + unavailable, no vector index, or the query-embedding sub-budget was exceeded) +- `memory_recall_coverage_gap` — one or more candidates had no embedding row for the + current model; they degrade to lexical scoring rather than being excluded, and the + gap self-heals via embed-on-write plus `netclaw memory backfill-embeddings` **Formation pipeline** (grep for `memory_observation`): - `memory_observation_sidecar_completed` diff --git a/openspec/changes/memory-core-redesign/design.md b/openspec/changes/memory-core-redesign/design.md index 896d94d57..79b4fdff7 100644 --- a/openspec/changes/memory-core-redesign/design.md +++ b/openspec/changes/memory-core-redesign/design.md @@ -167,9 +167,20 @@ correctness requirement with its own scenario. Scoring = weighted fusion (default **0.68**, calibrated 2026-07-05 against the real-traffic gold set `gold-prod-2026-07` — calibration summary below). Nothing above the floor → inject nothing, and the volatile `[memory-recall]` block is omitted entirely -(zero tokens). Recency decay (`RecencyHalfLifeDays`, floor-bounded -multiplier) breaks ties toward fresh knowledge. The quick-win char budget and -`AutoRecallMaxItems` remain +(zero tokens). The floor applies only to a candidate the vector index +actually holds an embedding for; a candidate with no embedding row at all +(a coverage gap — not yet backfilled, or written before embeddings were +enabled) has no cosine for the floor to gate, so it degrades to lexical +scoring instead (its cosine term is 0, ranked purely on the fused +lexical/class-prior score) with a rate-limited `memory_recall_coverage_gap` +warning — honoring the Migration Plan's "both paths degrade loudly to +lexical when coverage is incomplete rather than misbehaving" rather than +blacking out recall for an un-backfilled corpus while the embedder is +otherwise healthy. Coverage self-heals via embed-on-write (new/updated +documents) plus gap repair (backfilling pre-existing ones), so the warning +is expected to fall off after both complete. Recency decay +(`RecencyHalfLifeDays`, floor-bounded multiplier) breaks ties toward fresh +knowledge. The quick-win char budget and `AutoRecallMaxItems` remain the outer bounds. *Alternative considered*: RRF fusion — rejected: rank-only fusion always diff --git a/src/Netclaw.Actors.Tests/Sessions/MemoryRecallScenarioTests.cs b/src/Netclaw.Actors.Tests/Sessions/MemoryRecallScenarioTests.cs index 92700c19f..c00d1b00f 100644 --- a/src/Netclaw.Actors.Tests/Sessions/MemoryRecallScenarioTests.cs +++ b/src/Netclaw.Actors.Tests/Sessions/MemoryRecallScenarioTests.cs @@ -41,12 +41,14 @@ namespace Netclaw.Actors.Tests.Sessions; /// pre-Slice-4 lexical-only coordinator (no embedder/vector-index holders) EXCEPT P09, which /// wires + a real loaded from the /// store's memory_embeddings table (only M16 is embedded — see ). -/// This is deliberate, not incidental: -/// applies its absolute cosine floor to EVERY candidate once a query vector exists, including -/// lexical-only ones (a missing embedding defaults to cosine 0.0, which is always below the -/// floor) — so wiring the embedder across the whole table would silently zero out every -/// lexical-only scenario (P01–P08, P10, P15) instead of proving the paraphrase-gap fix. See the -/// zero-injection facts below for a direct demonstration of that behavior. +/// This is deliberate, not incidental: 's +/// absolute cosine floor only ever gates a candidate the index actually holds a vector for — an +/// unembedded candidate (a coverage gap) bypasses the floor and competes on fused/lexical score +/// alone (gap-repair fix; see the class's own summary and design.md D6) — so wiring the embedder +/// across the whole table would change every lexical-only scenario's ranking geometry (coverage +/// gaps no longer defaulting to a rejected cosine of 0.0, but to an admitted one) instead of +/// isolating the paraphrase-gap fix P09 exists to prove. See the zero-injection facts below for a +/// direct demonstration of the coverage-gap-bypasses-the-floor behavior. /// /// public sealed class MemoryRecallScenarioTests : IAsyncLifetime @@ -319,15 +321,16 @@ public async Task Gold_set_MRR_and_precision_at_3_meet_the_calibrated_floor() Assert.True(precisionAt3 >= PrecisionAt3Floor, $"precision@3 {precisionAt3:F3} fell below floor {PrecisionAt3Floor:F3}\n{report}"); } - // ── Zero-injection cases under a healthy embedder (memory-core-redesign Slice 4, task 4.7) ── + // ── Zero-injection / coverage-gap cases under a healthy embedder (memory-core-redesign + // Slice 4, task 4.7; gap-repair fix) ── // // The Scenarios() theory's zero-expected rows (P11/P12/P14/P16/P19-21) all run the - // lexical-only coordinator. These two facts instead prove the zero-injection contract holds - // once a query vector exists: a candidate whose cosine falls below the floor (or has no - // embedding at all) never gets injected, and — this is the important, non-obvious part per - // this class's summary — that holds EVEN WHEN the candidate is the strongest possible - // lexical match, because SQLiteMemoryRecallCoordinator's absolute floor gates every - // candidate once hybrid mode is active, not just vector-sourced ones. + // lexical-only coordinator. These facts instead exercise the hybrid path directly: the first + // proves the zero-injection contract holds once a query vector exists and truly nothing + // qualifies (no lexical candidates, no vector matches). The second proves the gap-repair fix + // — a candidate with NO embedding row at all is a coverage gap, not a floor violation, so it + // is recalled on its lexical/fused score exactly as it would be pre-Slice-4, even though a + // healthy query vector exists this turn. [Fact] public async Task ZeroInjection_novel_query_with_no_qualifying_candidate_is_empty_and_not_degraded() @@ -347,15 +350,17 @@ public async Task ZeroInjection_novel_query_with_no_qualifying_candidate_is_empt } [Fact] - public async Task ZeroInjection_strong_lexical_match_without_an_embedding_is_excluded_when_the_embedder_is_healthy() + public async Task CoverageGap_strong_lexical_match_without_an_embedding_is_recalled_when_the_embedder_is_healthy() { var ct = TestContext.Current.CancellationToken; // Reuses P01's exact query text: under the lexical-only coordinator (see the P01 row // above) this clears the composite floor comfortably and recalls M07. M07 has no - // embedding row, so under a healthy embedder its cosine defaults to 0.0 -- below the - // 0.68 floor regardless of how strong the lexical match is. NonMatchingQueryVector also - // keeps M16 (the corpus's only embedded doc) below the floor, so nothing at all survives. + // embedding row at all under HybridModelId -- a coverage gap, not a candidate the index + // scored and rejected -- so the gap-repair fix bypasses the absolute floor for it + // entirely and it competes on fused score alone. NonMatchingQueryVector keeps M16 (the + // corpus's only embedded doc under this model) below the floor throughout, so M07's + // recall here is attributable ONLY to the coverage-gap bypass, not to any cosine signal. var coordinator = BuildHybridCoordinator(NonMatchingQueryVector); var result = await coordinator.RecallAsync(new AutomaticRecallRequest( @@ -366,7 +371,8 @@ public async Task ZeroInjection_strong_lexical_match_without_an_embedding_is_exc Audience: TrustAudience.Public), ct); Assert.False(result.Degraded); - Assert.Empty(result.Items); + Assert.Contains(result.Items, i => i.Id.Value == "M07"); + Assert.DoesNotContain(result.Items, i => i.Id.Value == "M16"); } // ── Policy parity under a healthy embedder (memory-core-redesign Slice 4, task 4.8) ──── diff --git a/src/Netclaw.Actors.Tests/Sessions/SQLiteMemoryRecallHybridTests.cs b/src/Netclaw.Actors.Tests/Sessions/SQLiteMemoryRecallHybridTests.cs index 40a7ba233..ad83d0769 100644 --- a/src/Netclaw.Actors.Tests/Sessions/SQLiteMemoryRecallHybridTests.cs +++ b/src/Netclaw.Actors.Tests/Sessions/SQLiteMemoryRecallHybridTests.cs @@ -17,11 +17,12 @@ namespace Netclaw.Actors.Tests.Sessions; /// /// Covers 's hybrid recall path -/// (memory-core-redesign Slice 4, design D6, tasks 4.1-4.4): the absolute cosine floor, the -/// zero-injection contract, recency decay bounds, and degraded-path parity with the pre-Slice-4 -/// lexical-only coordinator. Fixture geometry is engineered directly via hand-crafted 2D unit -/// vectors (same technique as MemoryCurationNominatorTests) rather than a real embedding -/// model, so every scenario is exact and deterministic. +/// (memory-core-redesign Slice 4, design D6, tasks 4.1-4.4, gap-repair fix): the absolute cosine +/// floor (embedded candidates only), the coverage-gap bypass for candidates with no embedding +/// row at all, the zero-injection contract, recency decay bounds, and degraded-path parity with +/// the pre-Slice-4 lexical-only coordinator. Fixture geometry is engineered directly via +/// hand-crafted 2D unit vectors (same technique as MemoryCurationNominatorTests) rather +/// than a real embedding model, so every scenario is exact and deterministic. /// /// /// Gated-hydration policy-gate exclusions (recall_mode/boundary/audience/sensitivity/ @@ -54,7 +55,7 @@ public SQLiteMemoryRecallHybridTests() public async ValueTask DisposeAsync() => await SqliteTempDirectoryCleanup.TryDeleteDirectoryAsync(_baseDir); - // ── Absolute cosine floor (task 4.3) ──────────────────────────────── + // ── Absolute cosine floor (task 4.3; gap-repair fix case 2) ───────── [Fact] public async Task Absolute_floor_excludes_a_lexically_strong_candidate_whose_cosine_is_below_threshold() @@ -63,10 +64,12 @@ public async Task Absolute_floor_excludes_a_lexically_strong_candidate_whose_cos await _store.InitializeAsync(ct); // Strong lexical match: title+content share every query term, so the pre-Slice-4 - // selector score alone clears the old lexical floor comfortably. Its embedding is the - // exact opposite direction of the query vector (cosine 0.0) -- well below - // MinCosineSimilarity's default 0.68. The absolute floor must reject it regardless of - // how strong the lexical match is. + // selector score alone clears the old lexical floor comfortably. Its embedding IS + // present (case 2, not a coverage gap) but points the exact opposite direction of the + // query vector (cosine 0.0) -- well below MinCosineSimilarity's default 0.68. The + // absolute floor must reject an embedded-but-dissimilar candidate regardless of how + // strong the lexical match is; only a genuine coverage gap (no embedding row at all) + // bypasses the floor -- see the coverage-gap facts below. await SeedDocumentAsync("doc-lexical-strong", "Grafana dashboard provisioning convention", "Grafana dashboard provisioning convention details for the ops team.", ct); await _store.UpsertEmbeddingAsync( @@ -107,6 +110,103 @@ await _store.UpsertEmbeddingAsync( Assert.Contains(result.Items, i => i.Id.Value == "doc-cosine-match"); } + // ── Coverage gap (gap-repair fix, cases 3 and its logging) ────────── + + [Fact] + public async Task CoverageGap_unembedded_candidate_with_a_strong_lexical_match_is_recalled() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + + // No UpsertEmbeddingAsync call at all for this document -- a genuine coverage gap, not a + // candidate the index scored and rejected. The absolute floor cannot apply to a + // similarity that was never computed, so the gap-repair fix admits it on its lexical/ + // fused score alone, exactly as the pre-Slice-4 lexical-only path would have. + await SeedDocumentAsync("doc-coverage-gap", "Grafana dashboard provisioning convention", + "Grafana dashboard provisioning convention details for the ops team.", ct); + + var coordinator = BuildHybridCoordinator(TimeProvider.System, NullLogger.Instance); + + var result = await coordinator.RecallAsync(new AutomaticRecallRequest( + SessionId: (SessionId)"hybrid/coverage-gap", + Query: "what is our grafana dashboard provisioning convention?", + RecentUserMessages: ["what is our grafana dashboard provisioning convention?"], + MaxItems: 3), ct); + + Assert.False(result.Degraded); + Assert.Contains(result.Items, i => i.Id.Value == "doc-coverage-gap"); + } + + [Fact] + public async Task CoverageGap_emits_a_rate_limited_warning_log_when_embeddings_are_enabled() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + + await SeedDocumentAsync("doc-coverage-gap-log", "Grafana dashboard provisioning convention", + "Grafana dashboard provisioning convention details for the ops team.", ct); + + var recordingLogger = new RecordingLogger(); + var coordinator = new SQLiteMemoryRecallCoordinator( + _store, + recordingLogger, + new MemoryConfig { Embeddings = new MemoryEmbeddingsConfig { Enabled = true } }, + TimeProvider.System, + sessionTuning: new SessionTuning { DeterministicRetrievalEnabled = true }, + embedderHolder: new MemoryEmbedderHolder(new ScriptedEmbedder(ModelId, Dimensions, QueryVector)), + vectorIndexHolder: new MemoryVectorIndexHolder(_store)); + + var result = await coordinator.RecallAsync(new AutomaticRecallRequest( + SessionId: (SessionId)"hybrid/coverage-gap-log", + Query: "what is our grafana dashboard provisioning convention?", + RecentUserMessages: ["what is our grafana dashboard provisioning convention?"], + MaxItems: 3), ct); + + Assert.False(result.Degraded); + Assert.Contains(result.Items, i => i.Id.Value == "doc-coverage-gap-log"); + Assert.Contains(recordingLogger.Entries, e => e.Level == LogLevel.Warning && e.Message.Contains("memory_recall_coverage_gap")); + } + + [Fact] + public async Task CoverageGap_no_log_is_emitted_when_the_corpus_is_fully_embedded() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + + // Every candidate this query can surface has an embedding row (whether or not its + // cosine clears the floor) -- no coverage gap exists, so the coverage-gap log must never + // fire, only the ordinary absolute-floor admit/reject logic. + await SeedDocumentAsync("doc-fully-covered-admit", "Grafana dashboard provisioning convention", + "Grafana dashboard provisioning convention details for the ops team.", ct); + await _store.UpsertEmbeddingAsync( + "doc-fully-covered-admit", MemoryEmbedOnWriteCoordinator.DocumentItemKind, ModelId, "hash-admit", QueryVector, ct); + await SeedDocumentAsync("doc-fully-covered-reject", "Grafana dashboard provisioning convention", + "Grafana dashboard provisioning convention details for the ops team, second copy.", ct); + await _store.UpsertEmbeddingAsync( + "doc-fully-covered-reject", MemoryEmbedOnWriteCoordinator.DocumentItemKind, ModelId, "hash-reject", OrthogonalVector, ct); + + var recordingLogger = new RecordingLogger(); + var coordinator = new SQLiteMemoryRecallCoordinator( + _store, + recordingLogger, + new MemoryConfig { Embeddings = new MemoryEmbeddingsConfig { Enabled = true } }, + TimeProvider.System, + sessionTuning: new SessionTuning { DeterministicRetrievalEnabled = true }, + embedderHolder: new MemoryEmbedderHolder(new ScriptedEmbedder(ModelId, Dimensions, QueryVector)), + vectorIndexHolder: new MemoryVectorIndexHolder(_store)); + + var result = await coordinator.RecallAsync(new AutomaticRecallRequest( + SessionId: (SessionId)"hybrid/no-coverage-gap", + Query: "what is our grafana dashboard provisioning convention?", + RecentUserMessages: ["what is our grafana dashboard provisioning convention?"], + MaxItems: 3), ct); + + Assert.False(result.Degraded); + Assert.Contains(result.Items, i => i.Id.Value == "doc-fully-covered-admit"); + Assert.DoesNotContain(result.Items, i => i.Id.Value == "doc-fully-covered-reject"); + Assert.DoesNotContain(recordingLogger.Entries, e => e.Message.Contains("memory_recall_coverage_gap")); + } + // ── Zero-injection contract (task 4.3) ────────────────────────────── [Fact] @@ -115,12 +215,15 @@ public async Task Zero_survivors_returns_a_healthy_empty_result_not_a_degraded_o var ct = TestContext.Current.CancellationToken; await _store.InitializeAsync(ct); - // Lexically matchable, but never embedded at all -- the vector index has zero rows for - // it, so its cosine defaults to 0.0 and the absolute floor excludes it. This is the - // "nothing relevant exists" case design D6 requires to surface as healthy-empty, not a - // degraded/error result. - await SeedDocumentAsync("doc-no-embedding", "Grafana dashboard provisioning convention", + // Lexically matchable AND embedded (case 2, not a coverage gap), but pointing the exact + // opposite direction of the query vector -- cosine 0.0, well below the floor. Since the + // gap-repair fix only bypasses the floor for a genuine coverage gap, this candidate is + // still excluded, so this remains the "nothing relevant exists" case design D6 requires + // to surface as healthy-empty, not a degraded/error result. + await SeedDocumentAsync("doc-embedded-below-floor", "Grafana dashboard provisioning convention", "Grafana dashboard provisioning convention details for the ops team.", ct); + await _store.UpsertEmbeddingAsync( + "doc-embedded-below-floor", MemoryEmbedOnWriteCoordinator.DocumentItemKind, ModelId, "hash-zero-survivors", OrthogonalVector, ct); var coordinator = BuildHybridCoordinator(TimeProvider.System, NullLogger.Instance); diff --git a/src/Netclaw.Actors/Memory/MemoryVectorIndex.cs b/src/Netclaw.Actors/Memory/MemoryVectorIndex.cs index b35b4df4b..4a2454d29 100644 --- a/src/Netclaw.Actors/Memory/MemoryVectorIndex.cs +++ b/src/Netclaw.Actors/Memory/MemoryVectorIndex.cs @@ -110,13 +110,29 @@ public async Task ReloadIfStaleAsync(CancellationToken ct) /// — callers that need current data must reload first. /// public IReadOnlyList TopK(ReadOnlySpan query, int k, double minCosine) + => TopK(query, k, minCosine, out _); + + /// + /// Overload of that additionally + /// reports, via , every item id that has ANY embedding row + /// in this model's index — regardless of whether its cosine cleared + /// — computed from the IDENTICAL snapshot the returned matches + /// were scored against (memory-core-redesign Slice 4 gap-repair fix, design D6). Callers that + /// need to tell "embedded but below the absolute floor" apart from "never embedded" (a + /// coverage gap the floor cannot apply to) must use this overload rather than a second, + /// independent call: two separate snapshot reads could straddle a concurrent + /// and observe a torn combination — matches from one + /// snapshot, membership from another. + /// + public IReadOnlyList TopK(ReadOnlySpan query, int k, double minCosine, out IReadOnlySet embeddedItemIds) { + var snapshot = Volatile.Read(ref _snapshot); + embeddedItemIds = new HashSet(snapshot.Ids, StringComparer.Ordinal); + if (k <= 0) return []; if (query.Length != Dimensions) throw new ArgumentException($"Query vector has {query.Length} dimensions; index '{ModelId}' expects {Dimensions}.", nameof(query)); - - var snapshot = Volatile.Read(ref _snapshot); if (snapshot.Ids.Length == 0) return []; diff --git a/src/Netclaw.Actors/Sessions/SQLiteMemoryRecallCoordinator.cs b/src/Netclaw.Actors/Sessions/SQLiteMemoryRecallCoordinator.cs index e9fa5ef9b..a02354fba 100644 --- a/src/Netclaw.Actors/Sessions/SQLiteMemoryRecallCoordinator.cs +++ b/src/Netclaw.Actors/Sessions/SQLiteMemoryRecallCoordinator.cs @@ -23,11 +23,25 @@ namespace Netclaw.Actors.Sessions; /// which applies the IDENTICAL policy predicates /// applies to lexical hits — a vector hit can never bypass a gate a lexical one would have to /// clear. Scoring fuses a weighted cosine + squashed lexical-selector-score + dampened -/// class-prior composite, recency-decayed, then applies an ABSOLUTE floor: any candidate -/// (regardless of source) whose cosine falls below -/// is dropped before ranking. Zero survivors means zero injection and a HEALTHY (non-degraded) -/// empty result — the caller () -/// already omits the [memory-recall] block entirely for that shape. +/// class-prior composite, recency-decayed, then admits by one of THREE cases per candidate +/// (gap-repair fix, corrects the original Slice 4 landing): +/// +/// Embedded for the current model AND cosine at or above +/// — admitted, ranked by fused score. +/// Embedded AND cosine below the floor — excluded; the calibrated absolute floor gates +/// admission for every candidate the index actually holds a vector for. +/// No embedding row at all for the current model (a coverage gap — not yet backfilled, or +/// written before embeddings were enabled) — the floor cannot apply to a similarity that was +/// never computed, so the candidate bypasses it and competes on fused score alone (cosine term +/// 0). A rate-limited memory_recall_coverage_gap log fires whenever this happens. +/// +/// Zero survivors across all three cases still means zero injection and a HEALTHY +/// (non-degraded) empty result — the caller +/// () already omits the +/// [memory-recall] block entirely for that shape. See for the +/// implementation and openspec/changes/memory-core-redesign/design.md D6 for the +/// migration-plan rationale (coverage gaps degrade loudly to lexical scoring rather than +/// silently blacking out recall while a corpus backfills). /// /// /// @@ -63,6 +77,7 @@ public sealed class SQLiteMemoryRecallCoordinator( private readonly DeterministicRetrievalRequestPlanner _deterministicPlanner = new(); private readonly DeterministicCandidateSelector _candidateSelector = new(); private readonly ConcurrentDictionary _lastVectorDegradedLogMs = new(StringComparer.Ordinal); + private readonly ConcurrentDictionary _lastCoverageGapLogMs = new(StringComparer.Ordinal); /// /// Default minimum composite score a candidate must reach to survive @@ -83,8 +98,10 @@ public sealed class SQLiteMemoryRecallCoordinator( /// /// This floor governs the DEGRADED (lexical-only) path exclusively /// (memory-core-redesign Slice 4). When a query vector is available the absolute cosine - /// floor () governs admission instead — - /// the two floors are never both applied to the same candidate set. + /// floor () governs admission for + /// EMBEDDED candidates instead — the two floors are never both applied to the same + /// candidate. A candidate with no embedding row at all (a coverage gap) is gated by + /// neither floor: see . /// /// private const double DefaultMinimumRecallCompositeScore = 14.0; @@ -371,9 +388,10 @@ public async Task RecallAsync(AutomaticRecallRequest requ /// /// Builds the hybrid-mode ranked candidate pool (memory-core-redesign Slice 4, tasks - /// 4.2-4.4): vector top-k unioned with the lexical candidates already selected against the - /// plan, fused per design D6's weighted formula, recency-decayed, then filtered to the - /// absolute cosine floor. Vector-only ids are hydrated through + /// 4.2-4.4; gap-repair fix corrects the floor semantics below): vector top-k unioned with the + /// lexical candidates already selected against the plan, fused per design D6's weighted + /// formula, recency-decayed, then admitted per the three-case semantics documented on this + /// class's summary. Vector-only ids are hydrated through /// — the SAME policy gates /// applied to the lexical candidates — and /// scored via so a vector hit that also @@ -389,11 +407,15 @@ public async Task RecallAsync(AutomaticRecallRequest requ { var (queryVector, vectorIndex) = hybridInput; - // The absolute floor (design D6) is applied HERE, at the TopK call itself: only matches - // at or above MinCosineSimilarity are ever candidates for injection, regardless of - // source. A lexical candidate absent from this map (never embedded, or embedded but not - // similar enough) defaults to cosine 0.0 below and is excluded by the same floor check. - var vectorMatches = vectorIndex.TopK(queryVector.Span, VectorTopK, minCosine: _recallConfig.MinCosineSimilarity) + // embeddedItemIds is read from the IDENTICAL snapshot vectorMatches was scored against + // (MemoryVectorIndex.TopK's out-parameter overload) so the case-2-vs-case-3 distinction + // below can never straddle a concurrent index reload. Only matches at or above + // MinCosineSimilarity are ever returned as vectorMatches, but embeddedItemIds reports + // EVERY item the index holds a vector for regardless of cosine — that's what lets a + // candidate embedded-but-below-floor (case 2, excluded) be told apart from a candidate + // never embedded at all (case 3, a coverage gap that bypasses the floor). + var vectorMatches = vectorIndex.TopK( + queryVector.Span, VectorTopK, minCosine: _recallConfig.MinCosineSimilarity, out var embeddedItemIds) .Where(m => string.Equals(m.ItemKind, MemoryEmbedOnWriteCoordinator.DocumentItemKind, StringComparison.Ordinal)) .ToArray(); var cosineByItemId = vectorMatches.ToDictionary(m => m.ItemId, m => m.Cosine, StringComparer.Ordinal); @@ -421,25 +443,49 @@ public async Task RecallAsync(AutomaticRecallRequest requ pool.Add((item, DeterministicCandidateSelector.Score(deterministicPlan, item))); var nowMs = timeProvider.GetUtcNow().ToUnixTimeMilliseconds(); + var gapCandidateCount = 0; var fused = pool .Select(x => { + // Only "document" items are ever embedded (MemoryEmbedOnWriteCoordinator. + // DocumentItemKind), so embeddedItemIds needs no further kind filtering here. + var isCoverageGap = !embeddedItemIds.Contains(x.Item.Id); + if (isCoverageGap) + gapCandidateCount++; + + // GetValueOrDefault is exact for case 1 (cleared the TopK floor) and a harmless + // placeholder for case 2 (embedded but below the floor, so TopK never returned a + // cosine for it) -- 0.0 is guaranteed below any positive floor, so case 2 is + // rejected below regardless of its true, unrecorded cosine. For case 3 the + // fusedScore's cosine term is legitimately 0: there is no vector to score. var cosine = cosineByItemId.GetValueOrDefault(x.Item.Id, 0.0); var squash = x.SelectorScore / (x.SelectorScore + SquashHalfSaturation); var classPrior = (RecallRank(x.Item) / RecallRankDampeningFactor) / HybridClassPriorDampeningFactor; var fusedScore = (_recallConfig.VectorWeight * cosine) + (_recallConfig.LexicalWeight * squash) + classPrior; var recencyMultiplier = RecencyMultiplier(x.Item, nowMs); - return new RankedCandidate(x.Item, fusedScore * recencyMultiplier, cosine); + // Cosine is null ONLY for a genuine coverage gap (case 3) -- that null is the + // floor check's signal below to admit on fused score alone. Case 1 and case 2 + // both carry a non-null cosine (real or the case-2 placeholder above). + return new RankedCandidate(x.Item, fusedScore * recencyMultiplier, isCoverageGap ? null : cosine); }) .OrderByDescending(x => x.Composite) .ToArray(); - // THE absolute floor (design D6): cosine alone gates admission once a query vector - // exists — a high lexical/fused score cannot compensate for low semantic similarity. - // Zero survivors is intended, not an error: the "Nothing relevant means nothing - // injected" spec scenario, returned as a healthy empty result by the caller. + if (gapCandidateCount > 0) + LogCoverageGap(request.SessionId.Value, gapCandidateCount, fused.Length); + + // THE absolute floor (design D6, corrected by the gap-repair fix): cosine gates + // admission only for a candidate the index actually holds a vector for. A coverage-gap + // candidate (Cosine null) has no similarity signal to gate on, so it degrades to + // competing on fused score alone (its cosine term already 0) instead of being dropped + // outright -- this is the fix: the original Slice 4 landing applied this same floor to + // EVERY candidate, including ones with no embedding row, which made an unembedded + // document structurally unrecallable while the embedder was healthy and blacked out + // recall on any un-backfilled corpus. See this class's summary and design.md D6. Zero + // survivors overall is still intended, not an error: the "nothing relevant" spec + // scenario, returned as a healthy empty result by the caller. var aboveFloor = fused - .Where(x => x.Cosine is { } cosine && cosine >= _recallConfig.MinCosineSimilarity) + .Where(x => x.Cosine is not { } cosine || cosine >= _recallConfig.MinCosineSimilarity) .ToArray(); return (aboveFloor, fused.Length); @@ -494,6 +540,40 @@ private void LogVectorDegraded(string sessionId, string reason) logger.LogDebug("memory_recall_vector_degraded session={SessionId} reason={Reason}", sessionId, reason); } + /// + /// Rate-limited memory_recall_coverage_gap log (gap-repair fix to + /// memory-core-redesign Slice 4, design D6): fires whenever admits + /// one or more candidates with no embedding row for the current model, following the exact + /// same rate-limiting pattern as — at most one line per + /// , tracked in its own dictionary since this is a + /// distinct condition (a coverage gap in an otherwise-healthy hybrid turn, not a fallback to + /// the degraded path). Warning when embeddings are enabled — an operator running hybrid + /// recall should know a corpus gap is being carried by lexical scoring alone until gap + /// repair / embed-on-write catches up. Debug when embeddings are disabled by config: this + /// path should be unreachable in that state (no query vector means + /// itself never runs), but the level split is kept consistent with + /// rather than asserting unreachability here. + /// + private void LogCoverageGap(string sessionId, int gapCandidateCount, int totalCandidateCount) + { + const string reason = "coverage_gap"; + var nowMs = timeProvider.GetUtcNow().ToUnixTimeMilliseconds(); + if (_lastCoverageGapLogMs.TryGetValue(reason, out var lastMs) + && nowMs - lastMs < VectorDegradedLogCooldown.TotalMilliseconds) + return; + + _lastCoverageGapLogMs[reason] = nowMs; + + if (_embeddingsEnabledByConfig) + logger.LogWarning( + "memory_recall_coverage_gap session={SessionId} gapCandidates={GapCandidates} totalCandidates={TotalCandidates}", + sessionId, gapCandidateCount, totalCandidateCount); + else + logger.LogDebug( + "memory_recall_coverage_gap session={SessionId} gapCandidates={GapCandidates} totalCandidates={TotalCandidates}", + sessionId, gapCandidateCount, totalCandidateCount); + } + private static int RecallRank(SQLiteMemoryHydratedItem document) { var score = 0; @@ -529,9 +609,13 @@ private static int RecallRank(SQLiteMemoryHydratedItem document) } /// - /// A candidate after fusion scoring, in either mode. is null in the - /// degraded/lexical path (no query vector existed to compute one against) and non-null in - /// hybrid mode (0.0 for a candidate with no recorded embedding, its true cosine otherwise). + /// A candidate after fusion scoring, in either mode. In the degraded/lexical path + /// is always null (no query vector existed to compute one against). In + /// hybrid mode it is null ONLY for a genuine coverage gap (no embedding row at all for the + /// current model — case 3 on this class's summary, bypasses the absolute floor) and non-null + /// otherwise: the real cosine when it cleared + /// (case 1), or a placeholder 0.0 when it did not (case 2 — the exact below-floor value was + /// never recorded, but any value below a positive floor rejects identically). /// private readonly record struct RankedCandidate(SQLiteMemoryHydratedItem Item, double Composite, double? Cosine); } From 22121d1ab4f33a82503232cb7fccc77cc89e9d29 Mon Sep 17 00:00:00 2001 From: Aaron Stannard Date: Wed, 8 Jul 2026 21:15:08 +0000 Subject: [PATCH 18/37] docs(memory): hybrid recall skill guidance + eval suite run (memory-core-redesign task 4.9) netclaw-memory skill 1.9.1 -> 1.10.0: - new Hybrid Recall section: FTS union vector candidates, weighted fusion (VectorWeight 0.7 / LexicalWeight 0.3), absolute MinCosineSimilarity floor (0.68, calibrated on gold-prod-2026-07), RecencyHalfLifeDays 30 - zero-injection turns documented as normal and healthy (agent must not report absent [memory-recall] blocks as memory failure) - explicit degradation guidance: memory_recall_vector_degraded (per-turn lexical fallback) and memory_recall_coverage_gap (per-candidate lexical fallback), both self-healing - operator guidance: run 'netclaw memory backfill-embeddings' right after enabling embeddings on an existing corpus Eval suite (Memory Pipeline category, qwen3:8b via old-gpu Ollama, 5 runs/case, threshold 0.80, image built from 7564384df): all 5 cases GREEN at 5/5 - memory_recall_active, memory_identity_preference_routing, memory_explicit_store, memory_checkpoint_enqueue, memory_recall_filters (run baa87c10-4dc6-4de5-b117-87ad076e52e5, archived under evals/runs/). No eval case changes needed: no case asserts an always-present [memory-recall] block, and the new vector-degraded/coverage-gap event names do not collide with the memory_recall_degraded assertion regex. --- .../.system/files/netclaw-memory/SKILL.md | 45 +++++++++++++++++-- .../changes/memory-core-redesign/tasks.md | 2 +- 2 files changed, 43 insertions(+), 4 deletions(-) diff --git a/feeds/skills/.system/files/netclaw-memory/SKILL.md b/feeds/skills/.system/files/netclaw-memory/SKILL.md index 8efb724ec..9faed6b69 100644 --- a/feeds/skills/.system/files/netclaw-memory/SKILL.md +++ b/feeds/skills/.system/files/netclaw-memory/SKILL.md @@ -3,7 +3,7 @@ name: netclaw-memory description: "REQUIRED when the user asks what you remember, recall, or know from past conversations, previous sessions, cross-session memory, memory classes, or memory types. Also before using memory tools: find_memories, get_memories, store_memory, update_memory." metadata: author: netclaw - version: "1.9.1" + version: "1.10.0" --- # Netclaw Memory @@ -33,8 +33,11 @@ Both gates must pass for memory to function. - Recall is **selective by design**: candidates must clear a relevance floor and a per-turn character budget, so **many turns inject nothing at all**. An absent `[memory-recall]` block means nothing relevant cleared the bar — - it is not a malfunction. Use `find_memories` when you believe relevant - memories exist that automatic recall did not surface. + this is the normal, healthy outcome for most turns, not a malfunction and + not evidence that memory is broken. Never tell the user "my memory isn't + working" just because a turn had no `[memory-recall]` block. Use + `find_memories` when you believe relevant memories exist that automatic + recall did not surface. - Recall is **policy-aware**: `audience` and `boundary` still govern what can be surfaced for the current turn. - Recall resolves once at turn start and the same bundle is reused during @@ -56,6 +59,42 @@ Both gates must pass for memory to function. (e.g. `doc-…` / `rec-…`) are stable, opaque handles. Copy them **verbatim** into `get_memories` or `update_memory` — do not rewrite or reformat them. +### Hybrid Recall (semantic + lexical) + +When `Memory.Embeddings.Enabled` is `true`, automatic recall is **hybrid**: +candidates come from the union of full-text search (FTS5) and vector +nearest-neighbor search, then a single fused ranking decides what (if +anything) gets injected. When embeddings are disabled, recall is +lexical-only — same candidate pool, no vector term or cosine floor. + +- **Fusion**: each candidate's score is `VectorWeight × cosine similarity + + LexicalWeight × squashed lexical score`, class-prior adjusted, then + **recency-decayed** (a half-life multiplier that favors fresher memories + among otherwise similar candidates but never zeroes out an old one on age + alone). +- **Absolute floor**: independent of the fused score, any candidate whose raw + cosine similarity falls below `MinCosineSimilarity` is dropped before + ranking. If nothing clears the floor, nothing is injected — this is a + correct, healthy outcome, not degraded recall. See the zero-injection note + above: don't editorialize about memory being broken when this happens. +- **Defaults** (`Memory.Recall` in `netclaw.json`): `VectorWeight` 0.7, + `LexicalWeight` 0.3, `MinCosineSimilarity` 0.68 (calibrated against a + real-traffic gold set, not a placeholder), `RecencyHalfLifeDays` 30. +- **Degradation is explicit and logged, not silent**: a turn whose + query-embedding step misses its latency sub-budget (or has no embedder + available) falls back to lexical-only scoring for that turn and logs + `memory_recall_vector_degraded`. A candidate with no embedding row for the + current model degrades to lexical-only scoring for that candidate alone + (rather than being excluded) and logs `memory_recall_coverage_gap`. Both + are self-healing, not persistent failures — see Diagnostics below. +- **Backfilling an existing corpus**: enabling `Memory.Embeddings.Enabled` + on a deployment that already has memories does not retroactively embed + them. Until they're embedded, recall for those documents degrades to + lexical scoring and `memory_recall_coverage_gap` keeps firing. Operators + should run `netclaw memory backfill-embeddings` right after turning + embeddings on so the gap closes immediately instead of waiting for + embed-on-write to catch up opportunistically. + ## When to Use Explicit Tools ### `find_memories` + `get_memories` diff --git a/openspec/changes/memory-core-redesign/tasks.md b/openspec/changes/memory-core-redesign/tasks.md index 528924379..81dc650c1 100644 --- a/openspec/changes/memory-core-redesign/tasks.md +++ b/openspec/changes/memory-core-redesign/tasks.md @@ -48,7 +48,7 @@ constitution gates (tests, evals where mapped, schema/skill sync, slopwatch). - [x] 4.6 Calibrate the floor against `gold-prod-2026-07` (local gold set); record calibration numbers in design.md - [x] 4.7 Gold-set recall regression suite (fixture corpus + labeled queries asserting injected/withheld ids, MRR/precision floors, zero-injection cases) - [x] 4.8 Flip scenario P09 (paraphrase-gap) back to expected-recall; policy-parity scenario test; latency budget test with warm embedder -- [ ] 4.9 Eval suite + `netclaw-memory` skill update (hybrid recall, zero-injection normality) +- [x] 4.9 Eval suite + `netclaw-memory` skill update (hybrid recall, zero-injection normality) ## 5. Taxonomy rebalance, trace revival, tool lessons From 76c326599f6fdf47b0ea16f483d3122630557c4b Mon Sep 17 00:00:00 2001 From: Aaron Stannard Date: Wed, 8 Jul 2026 22:12:15 +0000 Subject: [PATCH 19/37] feat(memory): cross-encoder relevance gate on recall (memory-relevance-gate sections 1-2) Implements sections 1-2 of the memory-relevance-gate OpenSpec change: a post-floor cross-encoder relevance gate on automatic recall, on top of memory-core-redesign Slice 4's hybrid recall + cosine floor. Section 1 (scorer, provisioning, config): - IRelevanceScorer seam + UnavailableRelevanceScorer stub (Netclaw.Actors/Memory), mirroring IMemoryEmbedder's contract exactly. - OnnxCrossEncoderScorer (Netclaw.Embeddings): manual [CLS] query [SEP] candidate [SEP] pair encoding (FastBertTokenizer has no native pair-encoding support), correct token_type_ids, only_second truncation with a proven never-overflow invariant, dynamic bucket-of-8 padding (reuses OnnxMemoryEmbedder.ComputeBucketedLength), host-side sigmoid. - RelevanceModelManifestEntry + EmbeddingModelProvisioner.RelevanceAllowlist, pinned to Xenova/ms-marco-MiniLM-L-6-v2 model_quantized.onnx - verified byte-for-byte against the live HuggingFace artifact (sha256, byte size, and tokenizer_config.json max length all confirmed) before committing the pin. - RelevanceScorerHolder (mirrors MemoryEmbedderHolder) that also carries the active model's manifest-calibrated threshold, since Netclaw.Actors cannot reference Netclaw.Embeddings' manifest type directly. - Memory.Recall.RelevanceGate { Enabled, Threshold } (both nullable, follows-manifest/follows-embeddings semantics) + schema sync. Section 2 (coordinator wiring, degradation, tests, eval): - SQLiteMemoryRecallCoordinator: post-floor gate stage under a 60ms sub-budget, scores the top AutoRecallMaxItems floor survivors, drops below-threshold candidates, reuses the existing zero-injection path for an all-dropped turn. - Loud degradation (gate disabled/no scorer/unavailable/sub-budget-exceeded) to floor-only unfiltered, rate-limited memory_recall_gate_degraded log matching memory_recall_vector_degraded's Debug/Warning split. - MemoryRelevanceGateDoctorCheck (sibling to MemoryEmbeddingDoctorCheck). - memory_retrieval_final gains droppedByGate + gateScores fields. - Tiny fixture cross-encoder ONNX graph + generator script (multiplicative per-segment type scale, not additive, so the fixture actually proves token_type_ids assignment rather than merely their presence). - Eval case (memory_relevance_gate_zero_injection): off-topic query against the seeded corpus asserts injectedCount=0 and the always-present droppedByGate marker (robust to whether the eval container has embeddings enabled). Tests: 2666 (Actors) + 41 (Embeddings) + 467 (Configuration) + 835 (Daemon) + 1237 (Cli) all green. slopwatch: 0 new issues (baseline refreshed - 2 stale pre-existing entries from unrelated prior file changes dropped, 1 new justified entry added for a fake service's Task.Delay(InfiniteTimeSpan) used to deterministically test the gate's sub-budget cancellation). --- .slopwatch/baseline.json | 65 ++- evals/run-evals.sh | 21 + .../changes/memory-relevance-gate/tasks.md | 22 +- .../Memory/UnavailableRelevanceScorerTests.cs | 34 ++ .../Sessions/SQLiteMemoryRecallGateTests.cs | 475 ++++++++++++++++++ src/Netclaw.Actors/Memory/IRelevanceScorer.cs | 88 ++++ .../Memory/RelevanceScorerHolder.cs | 67 +++ .../Sessions/SQLiteMemoryRecallCoordinator.cs | 182 ++++++- .../MemoryRelevanceGateDoctorCheckTests.cs | 143 ++++++ .../Doctor/DoctorRegistrationExtensions.cs | 3 + .../Doctor/MemoryRelevanceGateDoctorCheck.cs | 86 ++++ .../MemoryConfigDefaultsTests.cs | 16 + src/Netclaw.Configuration/MemoryConfig.cs | 37 ++ .../Schemas/netclaw-config.v1.schema.json | 17 + .../EmbeddingWarmupHostedServiceTests.cs | 106 +++- src/Netclaw.Daemon/Program.cs | 12 + .../Services/EmbeddingWarmupHostedService.cs | 93 +++- .../generate_fixture_cross_encoder.py | 159 ++++++ .../Fixtures/tiny-cross-encoder-vocab.txt | 13 + .../Fixtures/tiny-cross-encoder.onnx | Bin 0 -> 1459 bytes .../OnnxCrossEncoderScorerTests.cs | 207 ++++++++ .../EmbeddingModelProvisioner.cs | 127 +++++ .../OnnxCrossEncoderScorer.cs | 271 ++++++++++ 23 files changed, 2203 insertions(+), 41 deletions(-) create mode 100644 src/Netclaw.Actors.Tests/Memory/UnavailableRelevanceScorerTests.cs create mode 100644 src/Netclaw.Actors.Tests/Sessions/SQLiteMemoryRecallGateTests.cs create mode 100644 src/Netclaw.Actors/Memory/IRelevanceScorer.cs create mode 100644 src/Netclaw.Actors/Memory/RelevanceScorerHolder.cs create mode 100644 src/Netclaw.Cli.Tests/Doctor/MemoryRelevanceGateDoctorCheckTests.cs create mode 100644 src/Netclaw.Cli/Doctor/MemoryRelevanceGateDoctorCheck.cs create mode 100644 src/Netclaw.Embeddings.Tests/Fixtures/generate_fixture_cross_encoder.py create mode 100644 src/Netclaw.Embeddings.Tests/Fixtures/tiny-cross-encoder-vocab.txt create mode 100644 src/Netclaw.Embeddings.Tests/Fixtures/tiny-cross-encoder.onnx create mode 100644 src/Netclaw.Embeddings.Tests/OnnxCrossEncoderScorerTests.cs create mode 100644 src/Netclaw.Embeddings/OnnxCrossEncoderScorer.cs diff --git a/.slopwatch/baseline.json b/.slopwatch/baseline.json index 73ed51960..bb375ea27 100644 --- a/.slopwatch/baseline.json +++ b/.slopwatch/baseline.json @@ -1,7 +1,7 @@ { "version": 1, "createdAt": "2026-05-12T17:20:55.7365203+00:00", - "updatedAt": "2026-06-10T19:44:08.3092383+00:00", + "updatedAt": "2026-07-08T22:05:54.9570245+00:00", "description": "Initial baseline created by 'slopwatch init' on 2026-05-12 17:20:55 UTC", "entries": [ { @@ -31,24 +31,6 @@ "message": "Adding warnings to NoWarn: OPENAI001", "baselinedAt": "2026-05-12T17:20:55.7420301+00:00" }, - { - "hash": "1a29ed65e4ed3efb", - "ruleId": "SW004", - "filePath": "src/Netclaw.Daemon.Tests/Services/ConfigWatcherServiceTests.cs", - "lineNumber": 139, - "codeSnippet": "Task.Delay(50, ct)", - "message": "Test uses Task.Delay(50) which may indicate a timing-dependent test", - "baselinedAt": "2026-05-12T17:20:55.7420338+00:00" - }, - { - "hash": "fcb5e461d7f70a7c", - "ruleId": "SW004", - "filePath": "src/Netclaw.Daemon.Tests/Services/ConfigWatcherServiceTests.cs", - "lineNumber": 154, - "codeSnippet": "Task.Delay(100, ct)", - "message": "Test uses Task.Delay(100) which may indicate a timing-dependent test", - "baselinedAt": "2026-05-12T17:20:55.7420454+00:00" - }, { "hash": "6ea5c8bbead4b59c", "ruleId": "SW004", @@ -174,6 +156,51 @@ "codeSnippet": "Fact(SkipUnless = nameof(IsPosix), Skip = \"POSIX-only — matcher routes through BashParser on POSIX\")", "message": "Test method 'IsApproved_git_tag_grant_matches_both_version_forms' is disabled: POSIX-only — matcher routes through BashParser on POSIX", "baselinedAt": "2026-06-10T19:44:08.3092375+00:00" + }, + { + "hash": "687db840a8ff6f35", + "ruleId": "SW004", + "filePath": "src/Netclaw.Embeddings.Tests/BoundedConcurrencyGateTests.cs", + "lineNumber": 32, + "codeSnippet": "Task.Delay(20, ct)", + "message": "Test uses Task.Delay(20) which may indicate a timing-dependent test", + "baselinedAt": "2026-07-08T22:05:54.9567742+00:00" + }, + { + "hash": "06c8b73e9ee96bc1", + "ruleId": "SW004", + "filePath": "src/Netclaw.Embeddings.Tests/BoundedConcurrencyGateTests.cs", + "lineNumber": 54, + "codeSnippet": "Enumerable.Range(0, 10)\n .Select(_ => gate.RunAsync(async ct =>\n {\n await Task.Delay(5, ct);\n return Interlocked.Increment(ref completed);\n }, TestContext.Current.CancellationToken))\n .ToArray()", + "message": "Test uses Task.Delay(?) which may indicate a timing-dependent test", + "baselinedAt": "2026-07-08T22:05:54.9568105+00:00" + }, + { + "hash": "3d71bddf21a28fee", + "ruleId": "SW004", + "filePath": "src/Netclaw.Embeddings.Tests/BoundedConcurrencyGateTests.cs", + "lineNumber": 57, + "codeSnippet": "Task.Delay(5, ct)", + "message": "Test uses Task.Delay(5) which may indicate a timing-dependent test", + "baselinedAt": "2026-07-08T22:05:54.9568168+00:00" + }, + { + "hash": "be802d7249cc2884", + "ruleId": "SW004", + "filePath": "src/Netclaw.Daemon.Tests/Services/EmbeddingWarmupHostedServiceTests.cs", + "lineNumber": 281, + "codeSnippet": "Task.Delay(25 * (i + 1))", + "message": "Test uses Task.Delay(25 * (i + 1)) which may indicate a timing-dependent test", + "baselinedAt": "2026-07-08T22:05:54.9568311+00:00" + }, + { + "hash": "233d9d75b339f3f3", + "ruleId": "SW004", + "filePath": "src/Netclaw.Actors.Tests/Sessions/SQLiteMemoryRecallGateTests.cs", + "lineNumber": 456, + "codeSnippet": "Task.Delay(Timeout.InfiniteTimeSpan, ct)", + "message": "Test uses Task.Delay(Timeout.InfiniteTimeSpan) which may indicate a timing-dependent test", + "baselinedAt": "2026-07-08T22:05:54.957+00:00" } ] } \ No newline at end of file diff --git a/evals/run-evals.sh b/evals/run-evals.sh index 6d4b1f8aa..86650160c 100755 --- a/evals/run-evals.sh +++ b/evals/run-evals.sh @@ -1039,6 +1039,24 @@ assert_memory_recall_filters() { ' } +# memory-relevance-gate task 2.6: the automated analogue of the shoot-out's "zero-injection +# accuracy" metric. Off-topic query against the seeded corpus (travel/color/project-alpha/ +# secret-token fixtures) must inject nothing. +# +# The eval container does not set Memory.Embeddings.Enabled (default false), so this case +# exercises the pre-existing lexical-only floor, not the cross-encoder gate itself (that +# requires an out-of-process download+provisioning step outside this harness's scope — see +# openspec/changes/memory-relevance-gate/tasks.md task 2.6: "authoring the case is [required]; +# the eval RUN is not required here"). Asserting injectedCount=0 plus the unconditional +# droppedByGate= field (present on every memory_retrieval_final line regardless of mode -- +# droppedByGate=0 accurately reports "nothing was dropped because nothing reached the gate") +# keeps this case correct and meaningful in both today's lexical-only default and a future run +# with embeddings enabled, without needing to touch the eval container's global config. +assert_memory_relevance_gate_zero_injection() { + daemon_log_contains 'memory_retrieval_final.*injectedCount=0' \ + && daemon_log_contains 'droppedByGate=' +} + # Category 4: Tool Discovery & Use assert_tool_discovery() { stdout_contains '\[tool:call\] search_tools' @@ -1529,6 +1547,9 @@ run_all() { run_case memory_recall_filters "candidate selection with score filtering" \ "Tell me about my travel preferences" + run_case memory_relevance_gate_zero_injection "off-topic query injects nothing, gate marker logged" \ + "What is the boiling point of tungsten in degrees Celsius?" + end_category # ── Category 4: Tool Discovery & Use ── diff --git a/openspec/changes/memory-relevance-gate/tasks.md b/openspec/changes/memory-relevance-gate/tasks.md index d2de8867e..739b2402d 100644 --- a/openspec/changes/memory-relevance-gate/tasks.md +++ b/openspec/changes/memory-relevance-gate/tasks.md @@ -6,57 +6,57 @@ independently shippable in order. ## 1. Scorer, provisioning, manifest/config -- [ ] 1.1 `IRelevanceScorer` seam in `Netclaw.Actors/Memory` (`ModelId`, +- [x] 1.1 `IRelevanceScorer` seam in `Netclaw.Actors/Memory` (`ModelId`, `IsAvailable`, order-preserving batch `ScoreAsync`) + `UnavailableRelevanceScorer` stub, matching `IMemoryEmbedder`'s throw-on-call-while-unavailable contract -- [ ] 1.2 `OnnxCrossEncoderScorer` in `Netclaw.Embeddings`: pair encoding +- [x] 1.2 `OnnxCrossEncoderScorer` in `Netclaw.Embeddings`: pair encoding (`[CLS] query [SEP] candidate [SEP]`, correct `token_type_ids`, `only_second` truncation so the query is never truncated), dynamic sequence length bucketed to multiples of 8, sigmoid applied host-side over the single-logit output -- [ ] 1.3 `RelevanceModelManifestEntry` (`ModelId`, `ModelUrl`, +- [x] 1.3 `RelevanceModelManifestEntry` (`ModelId`, `ModelUrl`, `ModelSha256`, `ModelByteSize`, `CalibratedThreshold`) added to `EmbeddingModelProvisioner`'s allowlist alongside the existing embedding-model entries; pin `Xenova/ms-marco-MiniLM-L-6-v2` `model_quantized.onnx` (22.07 MB, SHA-256 `e9d8ebf845c413e981c175bfe49a3bfa9b3dcce2a3ba54875ee5df5a58639fbe`, `CalibratedThreshold = 0.02`) -- [ ] 1.4 `RelevanceScorerHolder` (mirrors `MemoryEmbedderHolder`: mutable, +- [x] 1.4 `RelevanceScorerHolder` (mirrors `MemoryEmbedderHolder`: mutable, always non-null, initial `UnavailableRelevanceScorer`, replaced once by the warmup service); `EmbeddingWarmupHostedService` gains a second provision-or-degrade step (provision, hash-verify, one warm-up inference) for the relevance model when `Memory.Embeddings.Enabled` -- [ ] 1.5 Config: `Memory.Recall.RelevanceGate { Enabled (nullable, follows +- [x] 1.5 Config: `Memory.Recall.RelevanceGate { Enabled (nullable, follows Embeddings.Enabled), Threshold (nullable, follows manifest `CalibratedThreshold`) }` + `netclaw-config.v1.schema.json` sync with defaults (additive, nullable, non-breaking) ## 2. Coordinator wiring, degradation, tests, eval -- [ ] 2.1 `SQLiteMemoryRecallCoordinator`: post-floor gate stage — score each +- [x] 2.1 `SQLiteMemoryRecallCoordinator`: post-floor gate stage — score each of the ≤`AutoRecallMaxItems` floor survivors under a ~60 ms CE sub-budget (linked CTS nested inside `RecallTimeoutMs`, same pattern as the existing query-embedding sub-budget); drop candidates below the active threshold; zero survivors after the gate ⇒ inject nothing (reuse the existing zero-injection path, don't fork it) -- [ ] 2.2 Degradation: relevance model unavailable, sub-budget exceeded, or +- [x] 2.2 Degradation: relevance model unavailable, sub-budget exceeded, or recall running in lexical (non-hybrid) mode ⇒ skip the gate entirely and inject the floor's own result unfiltered; rate-limited `memory_recall_gate_degraded` log (same cooldown pattern as `memory_recall_vector_degraded`) -- [ ] 2.3 Doctor visibility for the relevance model (extend the existing +- [x] 2.3 Doctor visibility for the relevance model (extend the existing embedding doctor check or add a sibling check): model presence/hash, provisioning failure, degraded-mode reason -- [ ] 2.4 Logging: `memory_retrieval_final` gains `gateScores` (per-candidate +- [x] 2.4 Logging: `memory_retrieval_final` gains `gateScores` (per-candidate score for every gated candidate) and `droppedByGate` (count) -- [ ] 2.5 Tests: pair-encoding correctness (token_type_ids, truncation-only- +- [x] 2.5 Tests: pair-encoding correctness (token_type_ids, truncation-only- second, dynamic length bucketing) against fixture pairs; threshold admit/reject boundary; degraded-scorer fallback to floor-only; sub-budget-timeout fallback; zero-survivors-after-gate produces the same result shape as zero-survivors-at-the-floor; config nullable-follows-manifest resolution (both `Enabled` and `Threshold`) -- [ ] 2.6 Eval case: seed a corpus with unrelated memories, ask an off-topic +- [x] 2.6 Eval case: seed a corpus with unrelated memories, ask an off-topic question, assert no `[memory-recall]` block in the assembled prompt and a gate marker present in the logs for that turn (the zero- injection regression the gate exists to enforce) diff --git a/src/Netclaw.Actors.Tests/Memory/UnavailableRelevanceScorerTests.cs b/src/Netclaw.Actors.Tests/Memory/UnavailableRelevanceScorerTests.cs new file mode 100644 index 000000000..d97997b57 --- /dev/null +++ b/src/Netclaw.Actors.Tests/Memory/UnavailableRelevanceScorerTests.cs @@ -0,0 +1,34 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using Netclaw.Actors.Memory; +using Xunit; + +namespace Netclaw.Actors.Tests.Memory; + +public sealed class UnavailableRelevanceScorerTests +{ + [Fact] + public void IsAvailable_is_always_false() + { + IRelevanceScorer scorer = new UnavailableRelevanceScorer("ms-marco-minilm-l-6-v2", "model not provisioned"); + + Assert.False(scorer.IsAvailable); + Assert.Equal("ms-marco-minilm-l-6-v2", scorer.ModelId); + } + + [Fact] + public async Task ScoreAsync_throws_with_remediation_text_instead_of_returning_a_score() + { + IRelevanceScorer scorer = new UnavailableRelevanceScorer("ms-marco-minilm-l-6-v2", "hash verification failed"); + + var ex = await Assert.ThrowsAsync( + async () => await scorer.ScoreAsync("query", ["candidate"], CancellationToken.None)); + + Assert.Contains("hash verification failed", ex.Message, StringComparison.Ordinal); + Assert.Contains("ms-marco-minilm-l-6-v2", ex.Message, StringComparison.Ordinal); + Assert.Contains("IsAvailable", ex.Message, StringComparison.Ordinal); + } +} diff --git a/src/Netclaw.Actors.Tests/Sessions/SQLiteMemoryRecallGateTests.cs b/src/Netclaw.Actors.Tests/Sessions/SQLiteMemoryRecallGateTests.cs new file mode 100644 index 000000000..cd6d844ba --- /dev/null +++ b/src/Netclaw.Actors.Tests/Sessions/SQLiteMemoryRecallGateTests.cs @@ -0,0 +1,475 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using Microsoft.Extensions.Logging; +using Microsoft.Extensions.Logging.Abstractions; +using Netclaw.Actors.Memory; +using Netclaw.Actors.Protocol; +using Netclaw.Actors.Sessions; +using Netclaw.Actors.Tests.Memory; +using Netclaw.Configuration; +using Xunit; + +namespace Netclaw.Actors.Tests.Sessions; + +/// +/// Covers 's post-floor relevance-gate stage +/// (memory-relevance-gate, design D5/D6/D8, tasks 2.1/2.2/2.5): threshold admit/reject, the +/// zero-survivors-after-gate contract, every degradation path (scorer unavailable, no scorer +/// configured, gate disabled by config, sub-budget timeout), and the config nullable-follows- +/// manifest resolution for both Enabled and Threshold. Uses the same hand-crafted +/// 2D unit-vector geometry as so every floor-survival +/// scenario here is exact and deterministic, and a fake +/// (this file's own copy, mirroring that file's ScriptedEmbedder convention) so gate +/// scores are exact and deterministic too, without needing the real ONNX model. +/// +public sealed class SQLiteMemoryRecallGateTests : IAsyncDisposable +{ + private const string EmbedderModelId = "gate-test-embedder"; + private const string RelevanceModelId = "gate-test-relevance-model"; + private const int Dimensions = 2; + private const double ManifestCalibratedThreshold = 0.5; + + private static readonly float[] QueryVector = [1f, 0f]; + + private readonly string _baseDir = Path.Combine(Path.GetTempPath(), "netclaw-recall-gate-tests", Guid.NewGuid().ToString("N")); + private readonly string _dbPath; + private readonly SQLiteMemoryStore _store; + + public SQLiteMemoryRecallGateTests() + { + Directory.CreateDirectory(_baseDir); + _dbPath = Path.Combine(_baseDir, "netclaw.db"); + _store = new SQLiteMemoryStore(_dbPath, TimeProvider.System); + } + + public async ValueTask DisposeAsync() => await SqliteTempDirectoryCleanup.TryDeleteDirectoryAsync(_baseDir); + + // ── Threshold admit/reject boundary (task 2.5) ────────────────────── + + [Fact] + public async Task Candidate_scoring_below_the_active_threshold_is_dropped() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + await SeedFloorSurvivingDocumentAsync("doc-below-threshold", ct); + + var coordinator = BuildCoordinator( + relevanceScorerHolder: BuildHolder(ScriptedRelevanceScorer.ReturningConstant(ManifestCalibratedThreshold - 0.1))); + + var result = await coordinator.RecallAsync(BuildRequest("gate/below-threshold"), ct); + + Assert.False(result.Degraded); + Assert.DoesNotContain(result.Items, i => i.Id.Value == "doc-below-threshold"); + } + + [Fact] + public async Task Candidate_scoring_at_or_above_the_active_threshold_survives() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + await SeedFloorSurvivingDocumentAsync("doc-above-threshold", ct); + + var coordinator = BuildCoordinator( + relevanceScorerHolder: BuildHolder(ScriptedRelevanceScorer.ReturningConstant(ManifestCalibratedThreshold + 0.1))); + + var result = await coordinator.RecallAsync(BuildRequest("gate/above-threshold"), ct); + + Assert.False(result.Degraded); + Assert.Contains(result.Items, i => i.Id.Value == "doc-above-threshold"); + } + + // ── Zero-survivors-after-gate contract (task 2.1, spec scenario) ──── + + [Fact] + public async Task Zero_survivors_after_the_gate_returns_a_healthy_empty_result_not_degraded() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + await SeedFloorSurvivingDocumentAsync("doc-gated-out", ct); + + var coordinator = BuildCoordinator( + relevanceScorerHolder: BuildHolder(ScriptedRelevanceScorer.ReturningConstant(0.0))); + + var result = await coordinator.RecallAsync(BuildRequest("gate/zero-survivors"), ct); + + Assert.False(result.Degraded); + Assert.Empty(result.Items); + } + + // ── Degradation paths (task 2.2, spec "loud degradation without silent fallback") ── + + [Fact] + public async Task Unavailable_scorer_degrades_to_floor_only_unfiltered() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + await SeedFloorSurvivingDocumentAsync("doc-scorer-unavailable", ct); + + // The scorer would reject everything if it ran -- proving the floor's own result reaches + // injection UNFILTERED requires a scorer whose score, if honored, would exclude the item. + var scorer = new ScriptedRelevanceScorer(RelevanceModelId, isAvailable: false, scoreFn: (_, candidates) => candidates.Select(_ => 0.0).ToArray()); + var coordinator = BuildCoordinator(relevanceScorerHolder: BuildHolder(scorer)); + + var result = await coordinator.RecallAsync(BuildRequest("gate/scorer-unavailable"), ct); + + Assert.False(result.Degraded); + Assert.Contains(result.Items, i => i.Id.Value == "doc-scorer-unavailable"); + } + + [Fact] + public async Task No_scorer_configured_degrades_to_floor_only_unfiltered() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + await SeedFloorSurvivingDocumentAsync("doc-no-scorer", ct); + + var coordinator = BuildCoordinator(relevanceScorerHolder: null); + + var result = await coordinator.RecallAsync(BuildRequest("gate/no-scorer"), ct); + + Assert.False(result.Degraded); + Assert.Contains(result.Items, i => i.Id.Value == "doc-no-scorer"); + } + + [Fact] + public async Task Gate_explicitly_disabled_degrades_to_floor_only_unfiltered() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + await SeedFloorSurvivingDocumentAsync("doc-gate-disabled", ct); + + var scorer = ScriptedRelevanceScorer.ReturningConstant(0.0); // would reject if it ran + var coordinator = BuildCoordinator( + relevanceScorerHolder: BuildHolder(scorer), + embeddingsEnabled: true, + relevanceGateEnabled: false); + + var result = await coordinator.RecallAsync(BuildRequest("gate/explicitly-disabled"), ct); + + Assert.False(result.Degraded); + Assert.Contains(result.Items, i => i.Id.Value == "doc-gate-disabled"); + } + + [Fact] + public async Task Sub_budget_timeout_degrades_to_floor_only_unfiltered() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + await SeedFloorSurvivingDocumentAsync("doc-timeout", ct); + + // Never completes on its own; only the coordinator's ~60ms sub-budget CTS can cancel it. + // Task.Delay inside a fake is the sanctioned way to simulate latency deterministically — + // no Thread.Sleep/Task.Delay appears in this test's own orchestration. + var scorer = new HangingRelevanceScorer(RelevanceModelId); + var coordinator = BuildCoordinator(relevanceScorerHolder: BuildHolder(scorer)); + + var result = await coordinator.RecallAsync(BuildRequest("gate/sub-budget-timeout"), ct); + + Assert.False(result.Degraded); + Assert.Contains(result.Items, i => i.Id.Value == "doc-timeout"); + } + + [Fact] + public async Task Gate_degraded_log_is_debug_when_disabled_by_config() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + await SeedFloorSurvivingDocumentAsync("doc-log-debug", ct); + + var recordingLogger = new RecordingLogger(); + var coordinator = BuildCoordinator( + relevanceScorerHolder: BuildHolder(ScriptedRelevanceScorer.ReturningConstant(1.0)), + embeddingsEnabled: false, + relevanceGateEnabled: null, + logger: recordingLogger); + + await coordinator.RecallAsync(BuildRequest("gate/log-debug"), ct); + + Assert.Contains(recordingLogger.Entries, e => e.Level == LogLevel.Debug && e.Message.Contains("memory_recall_gate_degraded")); + Assert.DoesNotContain(recordingLogger.Entries, e => e.Level == LogLevel.Warning && e.Message.Contains("memory_recall_gate_degraded")); + } + + [Fact] + public async Task Gate_degraded_log_is_warning_when_enabled_but_the_turn_still_degraded() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + await SeedFloorSurvivingDocumentAsync("doc-log-warning", ct); + + var recordingLogger = new RecordingLogger(); + var coordinator = BuildCoordinator( + relevanceScorerHolder: null, + embeddingsEnabled: true, + relevanceGateEnabled: null, + logger: recordingLogger); + + await coordinator.RecallAsync(BuildRequest("gate/log-warning"), ct); + + Assert.Contains(recordingLogger.Entries, e => e.Level == LogLevel.Warning && e.Message.Contains("memory_recall_gate_degraded")); + } + + // ── Logging: gateScores / droppedByGate fields (task 2.4) ─────────── + + [Fact] + public async Task Final_retrieval_log_carries_droppedByGate_and_gateScores() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + await SeedFloorSurvivingDocumentAsync("doc-logged", ct); + + var recordingLogger = new RecordingLogger(); + var coordinator = BuildCoordinator( + relevanceScorerHolder: BuildHolder(ScriptedRelevanceScorer.ReturningConstant(ManifestCalibratedThreshold - 0.1)), + logger: recordingLogger); + + await coordinator.RecallAsync(BuildRequest("gate/logged"), ct); + + Assert.Contains(recordingLogger.Entries, e => + e.Level == LogLevel.Information + && e.Message.Contains("memory_retrieval_final") + && e.Message.Contains("droppedByGate=1") + && e.Message.Contains("doc-logged=")); + } + + // ── Config nullable-follows-manifest resolution (task 1.5, 2.5) ───── + + [Fact] + public async Task Enabled_null_follows_embeddings_enabled_true() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + await SeedFloorSurvivingDocumentAsync("doc-follows-embeddings-on", ct); + + var coordinator = BuildCoordinator( + relevanceScorerHolder: BuildHolder(ScriptedRelevanceScorer.ReturningConstant(0.0)), + embeddingsEnabled: true, + relevanceGateEnabled: null); + + var result = await coordinator.RecallAsync(BuildRequest("gate/follows-on"), ct); + + // Embeddings enabled + gate follows (null) => gate is ACTIVE, so the below-threshold + // score actually drops the candidate. + Assert.False(result.Degraded); + Assert.DoesNotContain(result.Items, i => i.Id.Value == "doc-follows-embeddings-on"); + } + + [Fact] + public async Task Enabled_null_follows_embeddings_enabled_false() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + await SeedFloorSurvivingDocumentAsync("doc-follows-embeddings-off", ct); + + var coordinator = BuildCoordinator( + relevanceScorerHolder: BuildHolder(ScriptedRelevanceScorer.ReturningConstant(0.0)), + embeddingsEnabled: false, + relevanceGateEnabled: null); + + var result = await coordinator.RecallAsync(BuildRequest("gate/follows-off"), ct); + + // Embeddings disabled + gate follows (null) => gate is INACTIVE, so the below-threshold + // score never applies and the candidate survives unfiltered. + Assert.False(result.Degraded); + Assert.Contains(result.Items, i => i.Id.Value == "doc-follows-embeddings-off"); + } + + [Fact] + public async Task Enabled_explicit_true_overrides_embeddings_disabled() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + await SeedFloorSurvivingDocumentAsync("doc-explicit-override", ct); + + var coordinator = BuildCoordinator( + relevanceScorerHolder: BuildHolder(ScriptedRelevanceScorer.ReturningConstant(0.0)), + embeddingsEnabled: false, + relevanceGateEnabled: true); + + var result = await coordinator.RecallAsync(BuildRequest("gate/explicit-override"), ct); + + Assert.False(result.Degraded); + Assert.DoesNotContain(result.Items, i => i.Id.Value == "doc-explicit-override"); + } + + [Fact] + public async Task Threshold_null_follows_the_scorers_manifest_calibrated_value() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + await SeedFloorSurvivingDocumentAsync("doc-manifest-threshold", ct); + + var coordinator = BuildCoordinator( + relevanceScorerHolder: BuildHolder(ScriptedRelevanceScorer.ReturningConstant(ManifestCalibratedThreshold), calibratedThreshold: ManifestCalibratedThreshold), + thresholdOverride: null); + + var result = await coordinator.RecallAsync(BuildRequest("gate/manifest-threshold"), ct); + + // Score exactly equals the manifest threshold -- admitted (>=), proving the manifest + // value (not some other default) is what was actually compared against. + Assert.False(result.Degraded); + Assert.Contains(result.Items, i => i.Id.Value == "doc-manifest-threshold"); + } + + [Fact] + public async Task Threshold_explicit_override_takes_precedence_over_the_manifest_value() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + await SeedFloorSurvivingDocumentAsync("doc-threshold-override", ct); + + // Score clears the manifest's calibrated threshold (0.5) but not the operator's explicit + // override (0.9) -- if the override were ignored, this candidate would wrongly survive. + var coordinator = BuildCoordinator( + relevanceScorerHolder: BuildHolder(ScriptedRelevanceScorer.ReturningConstant(0.6), calibratedThreshold: ManifestCalibratedThreshold), + thresholdOverride: 0.9); + + var result = await coordinator.RecallAsync(BuildRequest("gate/threshold-override"), ct); + + Assert.False(result.Degraded); + Assert.DoesNotContain(result.Items, i => i.Id.Value == "doc-threshold-override"); + } + + // ── Fixtures ───────────────────────────────────────────────────────── + + private static RelevanceScorerHolder BuildHolder(IRelevanceScorer scorer, double calibratedThreshold = ManifestCalibratedThreshold) + => new(scorer, calibratedThreshold); + + private SQLiteMemoryRecallCoordinator BuildCoordinator( + RelevanceScorerHolder? relevanceScorerHolder, + bool embeddingsEnabled = true, + bool? relevanceGateEnabled = null, + double? thresholdOverride = null, + ILogger? logger = null) + => new( + _store, + logger ?? NullLogger.Instance, + new MemoryConfig + { + Embeddings = new MemoryEmbeddingsConfig { Enabled = embeddingsEnabled }, + Recall = new MemoryRecallConfig + { + RelevanceGate = new MemoryRelevanceGateConfig { Enabled = relevanceGateEnabled, Threshold = thresholdOverride }, + }, + }, + TimeProvider.System, + sessionTuning: new SessionTuning { DeterministicRetrievalEnabled = true }, + embedderHolder: new MemoryEmbedderHolder(new ScriptedEmbedder(EmbedderModelId, Dimensions, QueryVector)), + vectorIndexHolder: new MemoryVectorIndexHolder(_store), + relevanceScorerHolder: relevanceScorerHolder); + + private static AutomaticRecallRequest BuildRequest(string sessionId) + => new( + SessionId: (SessionId)sessionId, + Query: "what is our grafana dashboard provisioning convention?", + RecentUserMessages: ["what is our grafana dashboard provisioning convention?"], + MaxItems: 3); + + private async Task SeedFloorSurvivingDocumentAsync(string documentId, CancellationToken ct) + { + var anchor = _store.CreateDefaultAnchor(documentId); + var now = TimeProvider.System.GetUtcNow().ToUnixTimeMilliseconds(); + await _store.UpsertDocumentAsync(new SQLiteMemoryDocument( + DocumentId: documentId, + Anchor: anchor, + MemoryClass: "durable_fact", + Title: "Grafana dashboard provisioning convention", + MarkdownBody: "Grafana dashboard provisioning convention details for the ops team.", + AliasesJson: null, + FacetsJson: null, + SlotsJson: null, + UpdateSemantics: "merge-document", + Sensitivity: "normal", + RecallMode: "auto", + Confidence: 0.9, + FreshnessAtMs: now, + ExpiresAtMs: null, + CreatedAtMs: now, + UpdatedAtMs: now), ct); + + // Clears the absolute cosine floor (QueryVector against itself, cosine 1.0) so this + // candidate reaches the gate stage exactly like SQLiteMemoryRecallHybridTests's own + // floor-admission fixtures. + await _store.UpsertEmbeddingAsync( + documentId, MemoryEmbedOnWriteCoordinator.DocumentItemKind, EmbedderModelId, $"hash-{documentId}", QueryVector, ct); + } + + /// + /// Fake embedder that ignores its input text and always returns the same, hand-crafted query + /// vector. This file's own copy of the identical fake used by + /// SQLiteMemoryRecallHybridTests and MemoryCurationNominatorTests — kept + /// separate per those files' own stated convention. + /// + private sealed class ScriptedEmbedder(string modelId, int dimensions, float[] queryVector) : IMemoryEmbedder + { + public string ModelId => modelId; + + public int Dimensions => dimensions; + + public bool IsAvailable => true; + + public ValueTask> EmbedAsync(string text, CancellationToken ct) + => ValueTask.FromResult>(queryVector); + + public ValueTask>> EmbedBatchAsync(IReadOnlyList texts, CancellationToken ct) + => ValueTask.FromResult>>( + texts.Select(_ => (ReadOnlyMemory)queryVector).ToList()); + } + + /// + /// Fake relevance scorer whose score is fully controlled by the test — no ONNX involved, so + /// threshold-boundary scenarios can use exact values instead of a real model's opaque score + /// distribution. + /// + private sealed class ScriptedRelevanceScorer( + string modelId, + Func, IReadOnlyList> scoreFn, + bool isAvailable = true) : IRelevanceScorer + { + public static ScriptedRelevanceScorer ReturningConstant(double score) + => new(RelevanceModelId, (_, candidates) => candidates.Select(_ => score).ToArray()); + + public string ModelId => modelId; + + public bool IsAvailable => isAvailable; + + public ValueTask> ScoreAsync(string query, IReadOnlyList candidates, CancellationToken ct) + => ValueTask.FromResult(scoreFn(query, candidates)); + } + + /// + /// Fake relevance scorer that never completes on its own — only the coordinator's own + /// sub-budget-linked can end the call, so the sub- + /// budget-timeout test is deterministic rather than racing a wall-clock delay against the + /// coordinator's timer. + /// + private sealed class HangingRelevanceScorer(string modelId) : IRelevanceScorer + { + public string ModelId => modelId; + + public bool IsAvailable => true; + + public async ValueTask> ScoreAsync(string query, IReadOnlyList candidates, CancellationToken ct) + { + await Task.Delay(Timeout.InfiniteTimeSpan, ct); + return []; + } + } + + /// Records every (level, message) pair logged through the generic ILogger ctor seam. + private sealed class RecordingLogger : ILogger + { + public List<(LogLevel Level, string Message)> Entries { get; } = []; + + public IDisposable? BeginScope(TState state) where TState : notnull => null; + + public bool IsEnabled(LogLevel logLevel) => true; + + public void Log( + LogLevel logLevel, EventId eventId, TState state, Exception? exception, + Func formatter) + => Entries.Add((logLevel, formatter(state, exception))); + } +} diff --git a/src/Netclaw.Actors/Memory/IRelevanceScorer.cs b/src/Netclaw.Actors/Memory/IRelevanceScorer.cs new file mode 100644 index 000000000..e27eb2d22 --- /dev/null +++ b/src/Netclaw.Actors/Memory/IRelevanceScorer.cs @@ -0,0 +1,88 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +namespace Netclaw.Actors.Memory; + +/// +/// Consumer-defined seam for cross-encoder relevance scoring (memory-relevance-gate D1) — +/// mirrors 's exact shape and contract so the memory subsystem +/// gains a second in-process model runtime without a second design vocabulary. Owned by the +/// memory subsystem, not the inference runtime: Netclaw.Embeddings's +/// OnnxCrossEncoderScorer implements this interface and is wired in by the daemon; +/// Netclaw.Actors never references OnnxRuntime. +/// +/// +/// Unlike , a relevance scorer encodes the query and each +/// candidate jointly (one forward pass per pair) rather than independently — this is +/// what lets the score reflect "does this candidate help answer the query" rather than mere +/// topical similarity, and is the entire reason this seam exists alongside the embedder rather +/// than being folded into it. +/// +/// +/// +/// is the same degraded-mode contract as +/// : false is a real, expected operating state (not +/// provisioned, hash verification failed, runtime load error), and every recall path that would +/// otherwise consult the gate MUST fall back to floor-only behavior instead — loudly (a rate- +/// limited degradation log and doctor visibility), never silently. is +/// only ever meant to be called when is true; an implementation whose +/// model failed to load () throws rather than returning +/// a fabricated score, because a fabricated score would silently corrupt threshold gating +/// instead of visibly failing the caller that skipped the check. +/// +/// +public interface IRelevanceScorer +{ + /// + /// The allowlisted relevance-model id this scorer was provisioned with. Scores are never + /// compared across models — same rule as for + /// embedding vectors — because the calibrated operating threshold is calibrated against one + /// specific model's score distribution (memory-relevance-gate D3). + /// + string ModelId { get; } + + /// + /// True when this scorer can actually score right now. False is a real, expected operating + /// mode (model not yet provisioned, hash verification failed, runtime load error) — not a + /// condition for the scorer itself to throw on; only calling while + /// unavailable throws. + /// + bool IsAvailable { get; } + + /// + /// Scores each of jointly against , + /// preserving input order in the output list — one call per turn for the floor-surviving + /// candidates (bounded to Memory.AutoRecallMaxItems), mirroring + /// 's batching rationale. Callers MUST check + /// first; calling this while unavailable throws rather than + /// degrading silently. Scores are raw sigmoid-activated probabilities in [0, 1]; the caller + /// compares them against the active threshold, this method has no opinion on what "passes." + /// + ValueTask> ScoreAsync(string query, IReadOnlyList candidates, CancellationToken ct); +} + +/// +/// Degraded-mode stub used when no relevance model is provisioned, hash verification failed, or +/// the runtime failed to load. is permanently false for an instance of +/// this type. Mirrors byte for byte: it lives beside +/// in Netclaw.Actors (no OnnxRuntime dependency) so any +/// caller can always construct a safe default, and it does not log on its own — the caller's +/// own rate-limited degradation log (memory_recall_gate_degraded) is the single place +/// that decision is recorded, so this stub logging too would double-count it. +/// +public sealed class UnavailableRelevanceScorer(string modelId, string reason) : IRelevanceScorer +{ + public string ModelId { get; } = modelId; + + public bool IsAvailable => false; + + public ValueTask> ScoreAsync(string query, IReadOnlyList candidates, CancellationToken ct) + => throw new InvalidOperationException(BuildMessage(nameof(ScoreAsync))); + + private string BuildMessage(string calledMethod) + => $"Relevance model '{ModelId}' is unavailable ({reason}). Provision it (auto-download " + + "at daemon startup) and check `netclaw doctor` for remediation. " + + $"Callers must check IsAvailable before calling {calledMethod}."; +} diff --git a/src/Netclaw.Actors/Memory/RelevanceScorerHolder.cs b/src/Netclaw.Actors/Memory/RelevanceScorerHolder.cs new file mode 100644 index 000000000..12005bbd8 --- /dev/null +++ b/src/Netclaw.Actors/Memory/RelevanceScorerHolder.cs @@ -0,0 +1,67 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +namespace Netclaw.Actors.Memory; + +/// +/// Mutable holder for the process's singleton +/// (memory-relevance-gate D4) — mirrors 's exact reason for +/// existing as a mutable holder rather than a plain DI singleton: the real scorer is only known +/// once EmbeddingWarmupHostedService (Netclaw.Daemon) finishes provisioning and loading +/// the relevance model, which necessarily runs after the DI container has already been built. +/// Consumers MUST read at the time they actually need to score (never +/// cache the value they read), so the transition from unavailable to available surfaces without +/// a process restart. +/// +/// +/// Why the holder also carries , not just the scorer: +/// design D3's "the threshold travels with the model id" rule means a config default of +/// null for Memory.Recall.RelevanceGate.Threshold must resolve to whichever +/// threshold was calibrated for the model id currently loaded — a value Netclaw.Actors +/// otherwise has no way to learn, since the manifest entry that carries it +/// (RelevanceModelManifestEntry) lives in Netclaw.Embeddings, which +/// Netclaw.Actors never references. Keeping the threshold on this holder — set in the +/// same call that sets the scorer — keeps itself pure (matching +/// D1's exact interface shape) while still letting the coordinator resolve "the active model's +/// calibrated threshold" through a seam it already depends on. +/// +/// +public sealed class RelevanceScorerHolder +{ + private volatile IRelevanceScorer _current; + private double _calibratedThreshold; + + public RelevanceScorerHolder(IRelevanceScorer initial, double initialCalibratedThreshold) + { + ArgumentNullException.ThrowIfNull(initial); + _current = initial; + _calibratedThreshold = initialCalibratedThreshold; + } + + /// The scorer to use right now. Always non-null. + public IRelevanceScorer Current => _current; + + /// + /// The calibrated operating threshold for whichever model id is + /// currently scoring with — set atomically alongside the scorer by , so a + /// reader can never observe a scorer paired with a stale (different model's) threshold. + /// + public double CalibratedThreshold => Volatile.Read(ref _calibratedThreshold); + + /// + /// Replaces the current scorer and its calibrated threshold together. Called only by + /// EmbeddingWarmupHostedService once provisioning completes — successfully (an + /// OnnxCrossEncoderScorer paired with its manifest entry's + /// CalibratedThreshold) or not (a fresh + /// carrying the failure reason, paired with the same manifest threshold since that value + /// describes the model id, not whether it loaded). + /// + public void Set(IRelevanceScorer scorer, double calibratedThreshold) + { + ArgumentNullException.ThrowIfNull(scorer); + _current = scorer; + Volatile.Write(ref _calibratedThreshold, calibratedThreshold); + } +} diff --git a/src/Netclaw.Actors/Sessions/SQLiteMemoryRecallCoordinator.cs b/src/Netclaw.Actors/Sessions/SQLiteMemoryRecallCoordinator.cs index a02354fba..2ca40a8dd 100644 --- a/src/Netclaw.Actors/Sessions/SQLiteMemoryRecallCoordinator.cs +++ b/src/Netclaw.Actors/Sessions/SQLiteMemoryRecallCoordinator.cs @@ -56,6 +56,25 @@ namespace Netclaw.Actors.Sessions; /// embeddings on), Warning when embeddings are enabled but the turn still degraded (a genuine /// runtime anomaly worth noticing: timeout, embed failure, missing index). /// +/// +/// +/// Post-floor relevance gate (memory-relevance-gate, design D5/D6/D8): in hybrid mode +/// only, once produces its floor survivors, a tiny cross-encoder +/// (relevanceScorerHolder) scores each of the top AutoRecallMaxItems survivors +/// jointly against the query — under its own sub-budget, +/// linked-CTS-nested exactly like the query-embedding sub-budget above — and drops anything +/// below the active threshold ( if set, +/// otherwise the scorer's manifest-carried ). +/// Zero survivors after the gate reuses the SAME zero-injection path as zero survivors at the +/// floor (a healthy empty result, not degraded) — see . +/// Gate activation follows unless +/// explicitly overrides it (design D6, "one +/// mental switch"). Every degradation reason (gate disabled, no scorer configured, scorer +/// unavailable, sub-budget exceeded) degrades to the floor's own result unfiltered, logged via +/// the rate-limited memory_recall_gate_degraded — Debug/Warning split mirrors +/// memory_recall_vector_degraded's exact reasoning, keyed off the gate's OWN resolved +/// enablement rather than the embeddings flag directly. +/// /// public sealed class SQLiteMemoryRecallCoordinator( SQLiteMemoryStore store, @@ -64,7 +83,8 @@ public sealed class SQLiteMemoryRecallCoordinator( TimeProvider timeProvider, SessionTuning? sessionTuning = null, MemoryEmbedderHolder? embedderHolder = null, - MemoryVectorIndexHolder? vectorIndexHolder = null) : IMemoryRecallCoordinator + MemoryVectorIndexHolder? vectorIndexHolder = null, + RelevanceScorerHolder? relevanceScorerHolder = null) : IMemoryRecallCoordinator { private readonly SessionTuning _sessionTuning = sessionTuning ?? new SessionTuning(); private readonly MemoryRecallConfig _recallConfig = memoryConfig.Recall; @@ -74,10 +94,20 @@ public sealed class SQLiteMemoryRecallCoordinator( // setting). Drives the Debug-vs-Warning split on the degraded log: see this class's summary. private readonly bool _embeddingsEnabledByConfig = memoryConfig.Embeddings.Enabled; + // memory-relevance-gate D6: "one mental switch" — Enabled=null follows Embeddings.Enabled + // exactly (an operator who turns on embeddings gets the gate with nothing else to flip); + // Enabled=true/false is an explicit override independent of the embeddings switch. Threshold + // resolution (config override vs. the active scorer's manifest-carried calibrated value) + // happens per-turn in TryApplyRelevanceGateAsync, since it depends on which model is loaded. + private readonly bool _relevanceGateEnabledByConfig = + memoryConfig.Recall.RelevanceGate.Enabled ?? memoryConfig.Embeddings.Enabled; + private readonly double? _relevanceGateThresholdOverride = memoryConfig.Recall.RelevanceGate.Threshold; + private readonly DeterministicRetrievalRequestPlanner _deterministicPlanner = new(); private readonly DeterministicCandidateSelector _candidateSelector = new(); private readonly ConcurrentDictionary _lastVectorDegradedLogMs = new(StringComparer.Ordinal); private readonly ConcurrentDictionary _lastCoverageGapLogMs = new(StringComparer.Ordinal); + private readonly ConcurrentDictionary _lastGateDegradedLogMs = new(StringComparer.Ordinal); /// /// Default minimum composite score a candidate must reach to survive @@ -126,6 +156,19 @@ public sealed class SQLiteMemoryRecallCoordinator( /// private const int VectorEmbedSubBudgetMs = 150; + /// + /// Sub-budget, in milliseconds, for the per-turn cross-encoder relevance-gate scoring call + /// (memory-relevance-gate design D5), applied via a CTS linked to (nested inside) the + /// caller's overall recall ct — the same nesting pattern as + /// . Not a config knob: design D5 measured ~11ms p50 / + /// ~35ms p95 to score 3 pairs (quantized int8) on the reference CPU, so 60ms leaves roughly + /// 1.7x headroom before the sub-budget itself is hit. + /// + private const int RelevanceGateSubBudgetMs = 60; + + /// Shared empty instance for turns where the gate never ran (disabled, degraded, or lexical mode). + private static readonly IReadOnlyDictionary EmptyGateScores = new Dictionary(0, StringComparer.Ordinal); + /// /// Number of nearest-neighbor vector candidates fetched per recall turn (design D6). Sized /// well above Memory.AutoRecallMaxItems since the union with lexical candidates and @@ -256,6 +299,29 @@ public async Task RecallAsync(AutomaticRecallRequest requ .ToArray(); } + // ── Post-floor relevance gate (memory-relevance-gate, design D5, tasks 2.1/2.2) ── + // Only ever attempted in hybrid mode — the floor's absolute cosine gate is what + // the gate's calibrated threshold was validated against (shoot-out protocol: + // "candidates = floor-passing top-3"); lexical mode has no query vector, so it + // already degrades the floor itself, and that degradation is what + // memory_recall_vector_degraded already reports — a separate gate-specific log + // for "we're in lexical mode" would just restate the same root cause. `gated` (not + // `aboveFloor`) feeds the char-budget loop below so `filteredByFloor` in the final + // log line keeps meaning exactly what it always has: floor-only accounting. + var gated = aboveFloor; + var droppedByGate = 0; + IReadOnlyDictionary gateScores = EmptyGateScores; + if (mode == "hybrid" && aboveFloor.Length > 0) + { + var gateOutcome = await TryApplyRelevanceGateAsync(request, aboveFloor, deterministicMaxItems, ct); + if (gateOutcome is { } outcome) + { + gated = outcome.Survivors; + gateScores = outcome.Scores; + droppedByGate = outcome.Dropped; + } + } + // Char budget: admit items in rank order until the next item's // content would blow the per-turn budget. Whole items are // dropped, never truncated — a truncated memory reads as @@ -264,7 +330,7 @@ public async Task RecallAsync(AutomaticRecallRequest requ var injectedChars = 0; var droppedByBudget = 0; var budgeted = new List(deterministicMaxItems); - foreach (var x in aboveFloor) + foreach (var x in gated) { if (budgeted.Count >= deterministicMaxItems) break; @@ -287,7 +353,7 @@ public async Task RecallAsync(AutomaticRecallRequest requ var deterministicItems = budgeted.ToArray(); logger.LogInformation( - "memory_retrieval_final session={SessionId} mode={Mode} injectedCount={InjectedCount} filteredByFloor={FilteredByFloor} appliedFloor={AppliedFloor:F3} injectedChars={InjectedChars} droppedByBudget={DroppedByBudget} items={Items}", + "memory_retrieval_final session={SessionId} mode={Mode} injectedCount={InjectedCount} filteredByFloor={FilteredByFloor} appliedFloor={AppliedFloor:F3} injectedChars={InjectedChars} droppedByBudget={DroppedByBudget} droppedByGate={DroppedByGate} gateScores={GateScores} items={Items}", request.SessionId, mode, deterministicItems.Length, @@ -295,6 +361,8 @@ public async Task RecallAsync(AutomaticRecallRequest requ mode == "hybrid" ? _recallConfig.MinCosineSimilarity : minimumCompositeScore, injectedChars, droppedByBudget, + droppedByGate, + string.Join("|", gateScores.Select(kv => $"{kv.Key}={kv.Value:F3}")), string.Join("|", deterministicItems.Select(i => $"{i.Id.Value}=score{i.Score:F3}"))); logger.LogDebug( @@ -386,6 +454,86 @@ public async Task RecallAsync(AutomaticRecallRequest requ } } + /// + /// Applies the post-floor cross-encoder relevance gate (memory-relevance-gate, design D5, + /// tasks 2.1/2.2) to the top of — + /// the floor already ordered candidates by composite score descending, so this is exactly + /// "the ≤AutoRecallMaxItems floor survivors" the shoot-out validated the threshold against. + /// Candidates ranked below that cut never reach the gate at all (they were never going to be + /// injected either way, since the char-budget loop already bounds injection to the same + /// ). + /// + /// + /// Returns null for every degradation reason — gate disabled by config, no scorer configured, + /// scorer unavailable, sub-budget exceeded, or the scoring call itself throwing — mirroring + /// 's "never throws, null means skip" contract exactly. + /// Callers treat null as "inject the floor's own result unfiltered," identically regardless of + /// which reason produced it. + /// + /// + private async Task<(RankedCandidate[] Survivors, IReadOnlyDictionary Scores, int Dropped)?> TryApplyRelevanceGateAsync( + AutomaticRecallRequest request, RankedCandidate[] aboveFloor, int maxItems, CancellationToken ct) + { + if (!_relevanceGateEnabledByConfig) + { + LogGateDegraded(request.SessionId.Value, "gate_disabled_by_config"); + return null; + } + + var scorer = relevanceScorerHolder?.Current; + if (scorer is null) + { + LogGateDegraded(request.SessionId.Value, "no_scorer_configured"); + return null; + } + + if (!scorer.IsAvailable) + { + LogGateDegraded(request.SessionId.Value, "scorer_unavailable"); + return null; + } + + var candidatesToScore = aboveFloor.Length > maxItems ? aboveFloor[..maxItems] : aboveFloor; + var texts = candidatesToScore.Select(x => x.Item.Content ?? string.Empty).ToArray(); + + IReadOnlyList scores; + try + { + using var gateCts = CancellationTokenSource.CreateLinkedTokenSource(ct); + gateCts.CancelAfter(RelevanceGateSubBudgetMs); + scores = await scorer.ScoreAsync(request.Query, texts, gateCts.Token); + } + catch (OperationCanceledException) when (!ct.IsCancellationRequested) + { + // The sub-budget's own timer fired, not the caller's outer recall ct — degrade to + // floor-only rather than propagating a cancellation that would fail the whole turn. + LogGateDegraded(request.SessionId.Value, "sub_budget_exceeded"); + return null; + } + catch (Exception ex) + { + LogGateDegraded(request.SessionId.Value, $"score_failed:{ex.GetType().Name}"); + return null; + } + + var threshold = _relevanceGateThresholdOverride ?? relevanceScorerHolder!.CalibratedThreshold; + var scoreByItemId = new Dictionary(candidatesToScore.Length, StringComparer.Ordinal); + var survivors = new List(candidatesToScore.Length); + var dropped = 0; + for (var i = 0; i < candidatesToScore.Length; i++) + { + var candidate = candidatesToScore[i]; + var score = scores[i]; + scoreByItemId[candidate.Item.Id] = score; + if (score >= threshold) + survivors.Add(candidate); + else + dropped++; + } + + return (survivors.ToArray(), scoreByItemId, dropped); + } + /// /// Builds the hybrid-mode ranked candidate pool (memory-core-redesign Slice 4, tasks /// 4.2-4.4; gap-repair fix corrects the floor semantics below): vector top-k unioned with the @@ -574,6 +722,34 @@ private void LogCoverageGap(string sessionId, int gapCandidateCount, int totalCa sessionId, gapCandidateCount, totalCandidateCount); } + /// + /// Rate-limited memory_recall_gate_degraded log (memory-relevance-gate, design D8, + /// task 2.2): at most one line per per + /// — the exact same cooldown pattern as + /// , tracked in its own dictionary since gate degradation is a + /// distinct condition from vector degradation. Debug when the gate is off by config + /// (following 's resolved + /// Enabled — either it follows a disabled Memory.Embeddings.Enabled, or an + /// explicit override) — the default, intentional state, so this must not be Warning-level + /// spam on every turn. Warning when the gate is enabled but the turn still degraded (scorer + /// unavailable, sub-budget exceeded, scoring threw) — a genuine runtime condition an operator + /// should notice. + /// + private void LogGateDegraded(string sessionId, string reason) + { + var nowMs = timeProvider.GetUtcNow().ToUnixTimeMilliseconds(); + if (_lastGateDegradedLogMs.TryGetValue(reason, out var lastMs) + && nowMs - lastMs < VectorDegradedLogCooldown.TotalMilliseconds) + return; + + _lastGateDegradedLogMs[reason] = nowMs; + + if (_relevanceGateEnabledByConfig) + logger.LogWarning("memory_recall_gate_degraded session={SessionId} reason={Reason}", sessionId, reason); + else + logger.LogDebug("memory_recall_gate_degraded session={SessionId} reason={Reason}", sessionId, reason); + } + private static int RecallRank(SQLiteMemoryHydratedItem document) { var score = 0; diff --git a/src/Netclaw.Cli.Tests/Doctor/MemoryRelevanceGateDoctorCheckTests.cs b/src/Netclaw.Cli.Tests/Doctor/MemoryRelevanceGateDoctorCheckTests.cs new file mode 100644 index 000000000..db58a430c --- /dev/null +++ b/src/Netclaw.Cli.Tests/Doctor/MemoryRelevanceGateDoctorCheckTests.cs @@ -0,0 +1,143 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using System.Security.Cryptography; +using System.Text.Json; +using Microsoft.Extensions.Configuration; +using Netclaw.Cli.Doctor; +using Netclaw.Configuration; +using Netclaw.Embeddings; +using Xunit; + +namespace Netclaw.Cli.Tests.Doctor; + +/// +/// Covers every severity branch of (memory- +/// relevance-gate task 2.3), using the tiny fixture cross-encoder ONNX graph (linked from +/// Netclaw.Embeddings.Tests/Fixtures) instead of the real allowlist — no network access +/// anywhere in these tests. Mirrors 's structure. +/// +public sealed class MemoryRelevanceGateDoctorCheckTests +{ + private static string FixturesDir => Path.Combine(AppContext.BaseDirectory, "Fixtures"); + + [Fact] + public async Task Passes_with_disabled_message_when_embeddings_off_and_gate_not_overridden() + { + var paths = CreateTempPaths(); + var config = WriteConfig(paths, embeddingsEnabled: false, gateEnabled: null); + var check = new MemoryRelevanceGateDoctorCheck(paths, config, FixtureAllowlist()); + + var result = await check.RunAsync(TestContext.Current.CancellationToken); + + Assert.Equal(DoctorSeverity.Pass, result.Severity); + Assert.Contains("disabled", result.Message, StringComparison.OrdinalIgnoreCase); + Assert.Contains("follows", result.Message, StringComparison.OrdinalIgnoreCase); + } + + [Fact] + public async Task Passes_with_disabled_message_when_explicitly_disabled_despite_embeddings_on() + { + var paths = CreateTempPaths(); + var config = WriteConfig(paths, embeddingsEnabled: true, gateEnabled: false); + var check = new MemoryRelevanceGateDoctorCheck(paths, config, FixtureAllowlist()); + + var result = await check.RunAsync(TestContext.Current.CancellationToken); + + Assert.Equal(DoctorSeverity.Pass, result.Severity); + Assert.Contains("explicitly false", result.Message, StringComparison.OrdinalIgnoreCase); + } + + [Fact] + public async Task Errors_when_gate_active_but_model_is_missing() + { + var paths = CreateTempPaths(); + var config = WriteConfig(paths, embeddingsEnabled: true, gateEnabled: null); + // No model files placed at paths.EmbeddingModelDirectory(DefaultRelevanceModelId). + var check = new MemoryRelevanceGateDoctorCheck(paths, config, FixtureAllowlist()); + + var result = await check.RunAsync(TestContext.Current.CancellationToken); + + Assert.Equal(DoctorSeverity.Error, result.Severity); + Assert.Contains(EmbeddingModelProvisioner.DefaultRelevanceModelId, result.Message, StringComparison.Ordinal); + } + + [Fact] + public async Task Passes_with_healthy_message_when_model_is_provisioned() + { + var paths = CreateTempPaths(); + var config = WriteConfig(paths, embeddingsEnabled: true, gateEnabled: null); + PrePlaceValidModelFiles(paths); + + var check = new MemoryRelevanceGateDoctorCheck(paths, config, FixtureAllowlist()); + var result = await check.RunAsync(TestContext.Current.CancellationToken); + + Assert.Equal(DoctorSeverity.Pass, result.Severity); + Assert.Contains("healthy", result.Message, StringComparison.OrdinalIgnoreCase); + } + + private static NetclawPaths CreateTempPaths() + { + var basePath = Path.Combine(Path.GetTempPath(), "netclaw-relevance-gate-doctor-tests", Guid.NewGuid().ToString("N")); + var paths = new NetclawPaths(basePath); + paths.EnsureDirectoriesExist(); + return paths; + } + + private static IConfiguration WriteConfig(NetclawPaths paths, bool embeddingsEnabled, bool? gateEnabled) + { + var recall = new Dictionary + { + ["RelevanceGate"] = gateEnabled is { } enabled + ? new Dictionary { ["Enabled"] = enabled } + : new Dictionary(), + }; + + var config = new Dictionary + { + ["Memory"] = new Dictionary + { + ["Embeddings"] = new Dictionary + { + ["Enabled"] = embeddingsEnabled, + ["AutoDownload"] = true, + }, + ["Recall"] = recall, + } + }; + + File.WriteAllText(paths.NetclawConfigPath, JsonSerializer.Serialize(config)); + + return new ConfigurationBuilder() + .AddJsonFile(paths.NetclawConfigPath, optional: false) + .Build(); + } + + private static void PrePlaceValidModelFiles(NetclawPaths paths) + { + var dir = paths.EmbeddingModelDirectory(EmbeddingModelProvisioner.DefaultRelevanceModelId); + Directory.CreateDirectory(dir); + File.Copy(Path.Combine(FixturesDir, "tiny-cross-encoder.onnx"), Path.Combine(dir, "model.onnx"), overwrite: true); + File.Copy(Path.Combine(FixturesDir, "tiny-cross-encoder-vocab.txt"), Path.Combine(dir, "vocab.txt"), overwrite: true); + } + + private static IReadOnlyDictionary FixtureAllowlist() + { + var modelBytes = File.ReadAllBytes(Path.Combine(FixturesDir, "tiny-cross-encoder.onnx")); + var vocabBytes = File.ReadAllBytes(Path.Combine(FixturesDir, "tiny-cross-encoder-vocab.txt")); + + return new Dictionary + { + [EmbeddingModelProvisioner.DefaultRelevanceModelId] = new( + EmbeddingModelProvisioner.DefaultRelevanceModelId, + ModelUrl: new Uri("http://127.0.0.1:1/unused-model.onnx"), + TokenizerUrl: new Uri("http://127.0.0.1:1/unused-vocab.txt"), + ModelSha256: Convert.ToHexStringLower(SHA256.HashData(modelBytes)), + TokenizerSha256: Convert.ToHexStringLower(SHA256.HashData(vocabBytes)), + ModelByteSize: modelBytes.Length, + CalibratedThreshold: 0.02), + }; + } +} diff --git a/src/Netclaw.Cli/Doctor/DoctorRegistrationExtensions.cs b/src/Netclaw.Cli/Doctor/DoctorRegistrationExtensions.cs index 6bd265991..2fa8183d8 100644 --- a/src/Netclaw.Cli/Doctor/DoctorRegistrationExtensions.cs +++ b/src/Netclaw.Cli/Doctor/DoctorRegistrationExtensions.cs @@ -19,6 +19,8 @@ public static void AddDoctorChecks(this IServiceCollection services) // Real allowlist for production; MemoryEmbeddingDoctorCheckTests supplies a small // fixture-pointed allowlist directly to the type instead of using this registration. services.AddSingleton>(EmbeddingModelProvisioner.Allowlist); + // Same pattern for the relevance-model manifest kind (memory-relevance-gate D3). + services.AddSingleton>(EmbeddingModelProvisioner.RelevanceAllowlist); services.AddSingleton(); services.AddSingleton(); services.AddSingleton(); @@ -32,6 +34,7 @@ public static void AddDoctorChecks(this IServiceCollection services) services.AddSingleton(); services.AddSingleton(); services.AddSingleton(); + services.AddSingleton(); services.AddSingleton(); services.AddSingleton(); services.AddSingleton(); diff --git a/src/Netclaw.Cli/Doctor/MemoryRelevanceGateDoctorCheck.cs b/src/Netclaw.Cli/Doctor/MemoryRelevanceGateDoctorCheck.cs new file mode 100644 index 000000000..edc5e0bd7 --- /dev/null +++ b/src/Netclaw.Cli/Doctor/MemoryRelevanceGateDoctorCheck.cs @@ -0,0 +1,86 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using Microsoft.Extensions.Configuration; +using Netclaw.Configuration; +using Netclaw.Embeddings; + +namespace Netclaw.Cli.Doctor; + +/// +/// Relevance-gate model diagnostics (memory-relevance-gate spec: "Loud degradation without +/// silent fallback" — doctor visibility half of that contract; the other half is the coordinator's +/// rate-limited memory_recall_gate_degraded log). Added as a sibling to +/// rather than folded into it (design D8: "extending the +/// existing embedding doctor check or adding a sibling relevance-gate doctor check — +/// implementation detail... not a design fork") since the relevance model has no +/// corpus-coverage concept to report, only presence/hash/degraded-mode-reason. +/// +/// +/// The relevance-model allowlist to verify against — an explicit, required dependency (same +/// seam itself uses for both manifest kinds) rather than +/// always reading the static +/// internally, so tests can supply a small allowlist pointed at a local fixture instead of ever +/// reaching the real ~22 MB HuggingFace artifact. Production wiring +/// () passes +/// itself. Tests key their fixture +/// entry under — the same +/// constant this check looks up — since there is no config knob selecting which relevance model +/// id is active (design D2/D6: one ratified model, not an operator choice). +/// +public sealed class MemoryRelevanceGateDoctorCheck( + NetclawPaths paths, + IConfiguration configuration, + IReadOnlyDictionary allowlist) : IDoctorCheck +{ + private const string CheckName = "Memory Relevance Gate"; + + public async Task RunAsync(CancellationToken cancellationToken = default) + { + var memoryConfig = configuration.GetSection("Memory").Get() ?? new MemoryConfig(); + + // "One mental switch" (design D6): the gate follows Memory.Embeddings.Enabled unless + // explicitly overridden — identical resolution to what SQLiteMemoryRecallCoordinator + // applies at runtime. + var gateEnabled = memoryConfig.Recall.RelevanceGate.Enabled ?? memoryConfig.Embeddings.Enabled; + if (!gateEnabled) + { + return DoctorCheckResult.Pass( + CheckName, + memoryConfig.Recall.RelevanceGate.Enabled == false + ? "Relevance gate disabled (Memory.Recall.RelevanceGate.Enabled is explicitly false)." + : "Relevance gate disabled (follows Memory.Embeddings.Enabled, which is false)."); + } + + var modelId = EmbeddingModelProvisioner.DefaultRelevanceModelId; + var modelDirectory = paths.EmbeddingModelDirectory(modelId); + + try + { + var provisioner = new EmbeddingModelProvisioner(new HttpClient(), new Dictionary()); + var verified = await provisioner.TryLoadVerifiedRelevanceModelAsync(modelId, allowlist, modelDirectory, cancellationToken); + if (verified is null) + { + return DoctorCheckResult.Error( + CheckName, + $"Relevance model '{modelId}' is missing or fails hash verification at {modelDirectory}.", + memoryConfig.Embeddings.AutoDownload + ? "Restart the daemon to re-provision the relevance model." + : "Memory.Embeddings.AutoDownload is false — provision the model manually, or enable AutoDownload and restart the daemon."); + } + + return DoctorCheckResult.Pass( + CheckName, + $"Relevance gate healthy: model '{modelId}' provisioned (threshold {verified.CalibratedThreshold:F3})."); + } + catch (Exception ex) + { + return DoctorCheckResult.Error( + CheckName, + $"Unable to inspect relevance model health: {ex.Message}", + "Verify the models directory is readable."); + } + } +} diff --git a/src/Netclaw.Configuration.Tests/MemoryConfigDefaultsTests.cs b/src/Netclaw.Configuration.Tests/MemoryConfigDefaultsTests.cs index 939843680..7a5db33a0 100644 --- a/src/Netclaw.Configuration.Tests/MemoryConfigDefaultsTests.cs +++ b/src/Netclaw.Configuration.Tests/MemoryConfigDefaultsTests.cs @@ -103,4 +103,20 @@ public void Recall_recency_half_life_days_defaults_to_30() var config = new MemoryConfig(); Assert.Equal(30, config.Recall.RecencyHalfLifeDays); } + + // ── MemoryRelevanceGateConfig (memory-relevance-gate, design D6) ──── + + [Fact] + public void RelevanceGate_enabled_defaults_to_null_and_follows_embeddings_enabled() + { + var config = new MemoryConfig(); + Assert.Null(config.Recall.RelevanceGate.Enabled); + } + + [Fact] + public void RelevanceGate_threshold_defaults_to_null_and_follows_the_manifest_calibrated_value() + { + var config = new MemoryConfig(); + Assert.Null(config.Recall.RelevanceGate.Threshold); + } } diff --git a/src/Netclaw.Configuration/MemoryConfig.cs b/src/Netclaw.Configuration/MemoryConfig.cs index d34f4c8a9..7c5d750c8 100644 --- a/src/Netclaw.Configuration/MemoryConfig.cs +++ b/src/Netclaw.Configuration/MemoryConfig.cs @@ -169,4 +169,41 @@ public sealed class MemoryRecallConfig /// updated_at timestamp against . /// public double RecencyHalfLifeDays { get; set; } = 30; + + /// + /// Post-floor cross-encoder relevance gate settings (memory-relevance-gate, design D6). See + /// . + /// + public MemoryRelevanceGateConfig RelevanceGate { get; set; } = new(); +} + +/// +/// Configuration for the post-floor relevance gate (memory-relevance-gate D6): a tiny +/// cross-encoder scores each floor-surviving candidate jointly against the query and drops +/// anything below the active threshold. Both properties are genuinely-optional nullables (not a +/// backward-compatibility shim) — their absence is a real, intended runtime state: "follow +/// whatever the embeddings switch / the model's calibrated manifest value already say," so an +/// operator who only wants "on/off" never has to discover or set a second knob. +/// +public sealed class MemoryRelevanceGateConfig +{ + /// + /// null (default) — the gate follows : + /// an operator who turns on embeddings gets the gate with no second switch to flip. + /// true/false — explicit override, independent of the embeddings switch (e.g. + /// an operator who wants embeddings for dedup/hybrid-recall but not the extra per-turn + /// cross-encoder latency). + /// + public bool? Enabled { get; set; } + + /// + /// null (default) — the active threshold follows the provisioned relevance model's + /// manifest-carried CalibratedThreshold (RelevanceModelManifestEntry in + /// Netclaw.Embeddings; S*=0.02 for the shipped ms-marco-minilm-l-6-v2) — the + /// same "config default, manifest provides the calibrated number" relationship + /// already established. A concrete + /// value is an explicit operator override, e.g. after re-running the threshold-sweep + /// protocol against a different corpus or relevance model. + /// + public double? Threshold { get; set; } } diff --git a/src/Netclaw.Configuration/Schemas/netclaw-config.v1.schema.json b/src/Netclaw.Configuration/Schemas/netclaw-config.v1.schema.json index 2a68e103c..a91534ec8 100644 --- a/src/Netclaw.Configuration/Schemas/netclaw-config.v1.schema.json +++ b/src/Netclaw.Configuration/Schemas/netclaw-config.v1.schema.json @@ -452,6 +452,23 @@ "maximum": 3650, "default": 30, "description": "Half-life in days for the recency-decay multiplier applied to a candidate's fused score in hybrid mode, floor-bounded at 0.85." + }, + "RelevanceGate": { + "type": "object", + "description": "Post-floor cross-encoder relevance gate settings (memory-relevance-gate).", + "properties": { + "Enabled": { + "type": ["boolean", "null"], + "description": "When null (default), the gate follows Memory.Embeddings.Enabled. An explicit true/false overrides that, independent of the embeddings switch." + }, + "Threshold": { + "type": ["number", "null"], + "minimum": 0, + "maximum": 1, + "description": "When null (default), the active threshold follows the provisioned relevance model's manifest-carried calibrated threshold (S*=0.02 for ms-marco-minilm-l-6-v2). An explicit value overrides it." + } + }, + "additionalProperties": false } }, "additionalProperties": false diff --git a/src/Netclaw.Daemon.Tests/Services/EmbeddingWarmupHostedServiceTests.cs b/src/Netclaw.Daemon.Tests/Services/EmbeddingWarmupHostedServiceTests.cs index 83e2da71c..0896bdfe7 100644 --- a/src/Netclaw.Daemon.Tests/Services/EmbeddingWarmupHostedServiceTests.cs +++ b/src/Netclaw.Daemon.Tests/Services/EmbeddingWarmupHostedServiceTests.cs @@ -27,6 +27,12 @@ public sealed class EmbeddingWarmupHostedServiceTests : IAsyncLifetime private const string ModelId = "tiny-fixture"; private const int Dimensions = 8; + // WarmUpRelevanceGateAsync hardcodes this constant as the relevance model id to provision + // (memory-relevance-gate: there is no config knob selecting which relevance model is + // active), so any fixture allowlist a test supplies must be keyed under the SAME id. + private const string RelevanceModelId = EmbeddingModelProvisioner.DefaultRelevanceModelId; + private const double RelevanceCalibratedThreshold = 0.02; + private readonly string _baseDir = Path.Combine(Path.GetTempPath(), $"netclaw-embedding-warmup-tests-{Guid.NewGuid():N}"); private NetclawPaths _paths = null!; private SQLiteMemoryStore _store = null!; @@ -140,8 +146,80 @@ await _store.UpsertDocumentAsync(new SQLiteMemoryDocument( Assert.Equal("doc-needs-embedding", row.ItemId); } + // ── Relevance gate provisioning (memory-relevance-gate, design D4, task 1.4) ── + + [Fact] + public async Task Relevance_gate_success_path_loads_the_fixture_scorer_and_pairs_the_manifest_threshold() + { + PrePlaceValidModelFiles(); + PrePlaceValidRelevanceModelFiles(); + + var holder = new MemoryEmbedderHolder(new UnavailableMemoryEmbedder(ModelId, "warmup not yet run")); + var relevanceHolder = CreateRelevanceScorerHolder(); + var memoryConfig = new MemoryConfig { Embeddings = { Enabled = true, ModelId = ModelId, AutoDownload = true } }; + var service = CreateService(holder, memoryConfig, relevanceHolder, RelevanceFixtureAllowlist()); + + await service.WarmUpAsync(TestContext.Current.CancellationToken); + + Assert.True(relevanceHolder.Current.IsAvailable); + Assert.Equal(RelevanceModelId, relevanceHolder.Current.ModelId); + Assert.Equal(RelevanceCalibratedThreshold, relevanceHolder.CalibratedThreshold); + } + + [Fact] + public async Task Relevance_gate_degraded_path_sets_an_unavailable_scorer_when_the_model_is_missing() + { + PrePlaceValidModelFiles(); + // No PrePlaceValidRelevanceModelFiles() call -- the relevance model directory is empty. + + var holder = new MemoryEmbedderHolder(new UnavailableMemoryEmbedder(ModelId, "warmup not yet run")); + var relevanceHolder = CreateRelevanceScorerHolder(); + var memoryConfig = new MemoryConfig { Embeddings = { Enabled = true, ModelId = ModelId, AutoDownload = false } }; + var service = CreateService(holder, memoryConfig, relevanceHolder, RelevanceFixtureAllowlist()); + + await service.WarmUpAsync(TestContext.Current.CancellationToken); + + // The embedder itself still succeeds -- the two models are independently lifecycled. + Assert.True(holder.Current.IsAvailable); + Assert.False(relevanceHolder.Current.IsAvailable); + Assert.IsType(relevanceHolder.Current); + // The manifest's calibrated threshold is still known even though the model failed to + // load -- it describes the model id, not whether provisioning succeeded. + Assert.Equal(RelevanceCalibratedThreshold, relevanceHolder.CalibratedThreshold); + } + + [Fact] + public async Task Relevance_gate_disabled_config_leaves_the_relevance_holder_at_its_initial_value() + { + var holder = new MemoryEmbedderHolder(new UnavailableMemoryEmbedder(ModelId, "embeddings disabled")); + var initialRelevance = new UnavailableRelevanceScorer(RelevanceModelId, "embeddings disabled"); + var relevanceHolder = new RelevanceScorerHolder(initialRelevance, initialCalibratedThreshold: 0.0); + var memoryConfig = new MemoryConfig { Embeddings = { Enabled = false, ModelId = ModelId } }; + var service = CreateService(holder, memoryConfig, relevanceHolder, RelevanceFixtureAllowlist()); + + await service.WarmUpAsync(TestContext.Current.CancellationToken); + + // Memory.Embeddings.Enabled=false short-circuits WarmUpAsync entirely -- neither model's + // provisioning step ever runs. + Assert.Same(initialRelevance, relevanceHolder.Current); + } + private EmbeddingWarmupHostedService CreateService(MemoryEmbedderHolder holder, MemoryConfig memoryConfig) - => new(_provisioner, _store, holder, memoryConfig, _paths, NullLogger.Instance); + => CreateService(holder, memoryConfig, CreateRelevanceScorerHolder(), EmptyRelevanceAllowlist); + + private EmbeddingWarmupHostedService CreateService( + MemoryEmbedderHolder holder, + MemoryConfig memoryConfig, + RelevanceScorerHolder relevanceScorerHolder, + IReadOnlyDictionary relevanceAllowlist) + => new(_provisioner, _store, holder, relevanceScorerHolder, relevanceAllowlist, memoryConfig, _paths, + NullLogger.Instance); + + private static RelevanceScorerHolder CreateRelevanceScorerHolder() + => new(new UnavailableRelevanceScorer(RelevanceModelId, "warmup not yet run"), initialCalibratedThreshold: 0.0); + + private static readonly IReadOnlyDictionary EmptyRelevanceAllowlist = + new Dictionary(); private void PrePlaceValidModelFiles() { @@ -151,6 +229,32 @@ private void PrePlaceValidModelFiles() File.Copy(Path.Combine(FixturesDir, "tiny-vocab.txt"), Path.Combine(dir, "vocab.txt"), overwrite: true); } + private void PrePlaceValidRelevanceModelFiles() + { + var dir = _paths.EmbeddingModelDirectory(RelevanceModelId); + Directory.CreateDirectory(dir); + File.Copy(Path.Combine(FixturesDir, "tiny-cross-encoder.onnx"), Path.Combine(dir, "model.onnx"), overwrite: true); + File.Copy(Path.Combine(FixturesDir, "tiny-cross-encoder-vocab.txt"), Path.Combine(dir, "vocab.txt"), overwrite: true); + } + + private IReadOnlyDictionary RelevanceFixtureAllowlist() + { + var modelBytes = File.ReadAllBytes(Path.Combine(FixturesDir, "tiny-cross-encoder.onnx")); + var vocabBytes = File.ReadAllBytes(Path.Combine(FixturesDir, "tiny-cross-encoder-vocab.txt")); + + return new Dictionary + { + [RelevanceModelId] = new( + RelevanceModelId, + ModelUrl: new Uri("http://127.0.0.1:1/unused-model.onnx"), + TokenizerUrl: new Uri("http://127.0.0.1:1/unused-vocab.txt"), + ModelSha256: Sha256Hex(modelBytes), + TokenizerSha256: Sha256Hex(vocabBytes), + ModelByteSize: modelBytes.Length, + CalibratedThreshold: RelevanceCalibratedThreshold), + }; + } + private static string Sha256Hex(byte[] bytes) => Convert.ToHexStringLower(SHA256.HashData(bytes)); private static async Task TryDeleteDirectoryAsync(string path) diff --git a/src/Netclaw.Daemon/Program.cs b/src/Netclaw.Daemon/Program.cs index 8bfbafac3..a31ecb39a 100644 --- a/src/Netclaw.Daemon/Program.cs +++ b/src/Netclaw.Daemon/Program.cs @@ -759,6 +759,18 @@ static void ConfigureDaemonServices( // degrade to the lexical content-term search when either is absent or the embedder is // unavailable. services.AddSingleton(new MemoryVectorIndexHolder(memoryStore)); + + // Post-floor relevance gate (memory-relevance-gate D4). Same holder-and-warmup pattern + // as MemoryEmbedderHolder above; also an optional dependency of + // SQLiteMemoryRecallCoordinator, which degrades to floor-only behavior when this holder's + // current scorer is unavailable. + services.AddSingleton>( + EmbeddingModelProvisioner.RelevanceAllowlist); + services.AddSingleton(new RelevanceScorerHolder( + new UnavailableRelevanceScorer( + EmbeddingModelProvisioner.DefaultRelevanceModelId, "relevance gate warmup has not completed yet"), + initialCalibratedThreshold: EmbeddingModelProvisioner.RelevanceAllowlist[EmbeddingModelProvisioner.DefaultRelevanceModelId].CalibratedThreshold)); + services.AddSingleton(); services.AddSingleton(sp => sp.GetRequiredService()); } diff --git a/src/Netclaw.Daemon/Services/EmbeddingWarmupHostedService.cs b/src/Netclaw.Daemon/Services/EmbeddingWarmupHostedService.cs index a95744b41..4531fc699 100644 --- a/src/Netclaw.Daemon/Services/EmbeddingWarmupHostedService.cs +++ b/src/Netclaw.Daemon/Services/EmbeddingWarmupHostedService.cs @@ -15,23 +15,31 @@ namespace Netclaw.Daemon.Services; /// Provisions/loads the embedding model at daemon startup, warms it up with one inference call, /// then runs a gap-repair sweep over documents missing a current-model embedding /// (memory-core-redesign Slice 2, task 2.7). Populates , which -/// every embed-on-write and (in later slices) recall consumer resolves at time of use. +/// every embed-on-write and (in later slices) recall consumer resolves at time of use. Also +/// provisions/warms the post-floor relevance-gate's cross-encoder model +/// (memory-relevance-gate D4, task 1.4), populating — a +/// second, independent provision-or-degrade step gated by the same +/// Memory.Embeddings.Enabled switch, with no gap-repair analogue (there is no per-item +/// derived state for a scoring-only model to repair). /// /// /// Never fails startup: ANY failure here (missing model with AutoDownload=false, /// download/hash failure, ONNX load failure) leaves the holder pointed at an -/// carrying the failure reason, logs -/// memory_embedding_unavailable at error level, and returns normally — degraded is a -/// running state, not a startup fault (design D2, spec "Loud degradation without silent -/// fallback"). This runs on a background thread pool task rather than blocking -/// so a slow/hanging download can never delay the rest of the host's -/// startup sequence either. +/// (or, for the relevance gate, +/// ) carrying the failure reason, logs +/// memory_embedding_unavailable (or memory_relevance_gate_unavailable) at error +/// level, and returns normally — degraded is a running state, not a startup fault (design D2, +/// spec "Loud degradation without silent fallback"). This runs on a background thread pool task +/// rather than blocking so a slow/hanging download can never delay the +/// rest of the host's startup sequence either. /// /// internal sealed class EmbeddingWarmupHostedService( EmbeddingModelProvisioner provisioner, SQLiteMemoryStore store, MemoryEmbedderHolder holder, + RelevanceScorerHolder relevanceScorerHolder, + IReadOnlyDictionary relevanceAllowlist, MemoryConfig memoryConfig, NetclawPaths paths, ILogger logger) : IHostedService @@ -93,6 +101,77 @@ internal async Task WarmUpAsync(CancellationToken ct) // the next daemon restart's sweep both retry whatever remains unembedded. logger.LogWarning(ex, "memory_embedding_gap_repair_failed model={ModelId}", embedder.ModelId); } + + // Relevance gate (memory-relevance-gate, design D4, task 1.4): a second, independent + // provision-or-degrade step gated by the same Memory.Embeddings.Enabled switch (D6's + // "one mental switch" — there is no separate RelevanceGate.AutoDownload/ModelId knob). + // Runs regardless of whether the embedder itself just degraded above: the two models are + // separately lifecycled artifacts, so an embedder failure should not also prevent an + // attempt to provision the relevance model. No gap-repair analogue exists here — there is + // no per-item derived state to repair for a scoring-only model. + await WarmUpRelevanceGateAsync(ct).ConfigureAwait(false); + } + + /// + /// Provisions and warms the relevance (cross-encoder) model, mirroring + /// 's provision-or-degrade shape exactly. The manifest's + /// CalibratedThreshold is looked up unconditionally (success or failure) since it + /// describes the model id, not whether the model actually loaded — + /// always pairs a scorer (available or not) with the correct threshold for its model id. + /// + private async Task WarmUpRelevanceGateAsync(CancellationToken ct) + { + var modelId = EmbeddingModelProvisioner.DefaultRelevanceModelId; + var calibratedThreshold = relevanceAllowlist.TryGetValue(modelId, out var entry) + ? entry.CalibratedThreshold + : 0.0; + + try + { + var scorer = await LoadRelevanceScorerAsync(modelId, ct).ConfigureAwait(false); + relevanceScorerHolder.Set(scorer, calibratedThreshold); + logger.LogInformation("memory_relevance_gate_ready model={ModelId}", scorer.ModelId); + } + catch (Exception ex) + { + logger.LogError(ex, "memory_relevance_gate_unavailable model={ModelId} reason={Reason}", modelId, ex.Message); + relevanceScorerHolder.Set(new UnavailableRelevanceScorer(modelId, ex.Message), calibratedThreshold); + } + } + + private async Task LoadRelevanceScorerAsync(string modelId, CancellationToken ct) + { + // Keyed under the same ModelsDirectory root as embedding models (NetclawPaths. + // EmbeddingModelDirectory is already generalized by model id) — a distinct id string is + // all that's needed to avoid collisions, so no dedicated relevance-model path helper + // exists. + var modelDirectory = paths.EmbeddingModelDirectory(modelId); + + ProvisionedRelevanceModel provisioned; + if (memoryConfig.Embeddings.AutoDownload) + { + provisioned = await provisioner.ProvisionRelevanceModelAsync(modelId, relevanceAllowlist, modelDirectory, ct) + .ConfigureAwait(false); + } + else + { + provisioned = await provisioner.TryLoadVerifiedRelevanceModelAsync(modelId, relevanceAllowlist, modelDirectory, ct) + .ConfigureAwait(false) + ?? throw new InvalidOperationException( + $"Relevance model '{modelId}' is not provisioned (or failed hash verification) at " + + $"{modelDirectory}, and Memory.Embeddings.AutoDownload is false. Provision it manually " + + "or enable AutoDownload, then restart the daemon."); + } + + var scorer = await OnnxCrossEncoderScorer.LoadAsync(provisioned.ModelPath, provisioned.VocabPath, provisioned.ModelId, ct: ct) + .ConfigureAwait(false); + + // Warm-up inference (mirrors the embedder's own warm-up call): pays first-call ONNX + // session / JIT cost here rather than on the first real recall turn. + await scorer.ScoreAsync("netclaw relevance gate warmup query", ["netclaw relevance gate warmup candidate"], ct) + .ConfigureAwait(false); + + return scorer; } private async Task LoadEmbedderAsync(string modelId, CancellationToken ct) diff --git a/src/Netclaw.Embeddings.Tests/Fixtures/generate_fixture_cross_encoder.py b/src/Netclaw.Embeddings.Tests/Fixtures/generate_fixture_cross_encoder.py new file mode 100644 index 000000000..84570605c --- /dev/null +++ b/src/Netclaw.Embeddings.Tests/Fixtures/generate_fixture_cross_encoder.py @@ -0,0 +1,159 @@ +#!/usr/bin/env python3 +"""Generates the tiny fixture cross-encoder ONNX model + WordPiece vocab used by +Netclaw.Embeddings.Tests (OnnxCrossEncoderScorerTests). Sibling to +generate_fixture_model.py (the bi-encoder embedder fixture) - same conventions, +different graph shape. + +Regeneration: + python3 -m venv /tmp/onnxgen && source /tmp/onnxgen/bin/activate + pip install onnx==1.22.0 numpy + python3 generate_fixture_cross_encoder.py + +Graph shape (deliberately NOT a real BertForSequenceClassification export - see +below for why): + + input_ids int64 [batch, seq] --Gather(embedding_matrix)------> word_embeddings [batch, seq, 1] + token_type_ids int64 [batch, seq] --Gather(type_scale_matrix)-----> type_scale [batch, seq, 1] + word_embeddings * type_scale ------------------------------------------> combined [batch, seq, 1] + attention_mask int64 [batch, seq] --Cast/Unsqueeze------------------> mask [batch, seq, 1] + combined * mask --ReduceSum(axis=1)------------------------------------> sum_embeddings [batch, 1, 1] + mask --ReduceSum(axis=1)--> sum_mask [batch, 1, 1] --Clip(min=1e-9)--> + pooled = sum_embeddings / sum_mask [batch, 1, 1] + pooled_2d = Reshape(pooled, [-1, 1]) [batch, 1] + logits = MatMul(pooled_2d, classifier_weight[1,1]) + classifier_bias[1] [batch, 1] + +Why this shape: OnnxCrossEncoderScorer feeds three inputs (input_ids, +attention_mask, token_type_ids) and reads a single [batch, 1] "logits" output, +matching the real BertForSequenceClassification cross-encoder's declared +signature exactly (verified against the pinned Xenova/ms-marco-MiniLM-L-6-v2 +export: 3 inputs, one [batch,1] float output). Unlike the bi-encoder fixture +(generate_fixture_model.py), this graph actually CONSUMES token_type_ids - and +it MULTIPLIES the per-position type scale into the word embedding rather than +adding a separate type term. Addition would not work as a test fixture: +sum_i(word_i) + sum_i(type_i) is invariant to WHICH position holds which word +(addition commutes), so swapping a word between the query and candidate +segments would not change the pooled total at all - the fixture would then +"pass" even if OnnxCrossEncoderScorer fed all-zero token_type_ids by mistake. +Multiplying ties each word's OWN contribution to its OWN segment, so swapping +a nonzero-valued word between segments changes the total whenever the two +segments' scales differ - exactly the property OnnxCrossEncoderScorerTests +needs to prove pair encoding assigns token_type_ids to the correct positions, +not just "some type-1 positions exist somewhere." + +Single-dimension embeddings (DIMS=1) are a deliberate simplification versus the +embedder fixture's 8 dimensions: every test scenario in +OnnxCrossEncoderScorerTests computes its own expected sigmoid(logit) by hand +from the token counts and per-token scalar values below, so keeping the model +to one dimension keeps that hand computation tractable and exact rather than a +second matrix multiply to reason through. +""" +import sys +import numpy as np +import onnx +from onnx import helper, TensorProto, numpy_helper + +# Special tokens carry a zero embedding so every "signal" scenario in the test +# suite can reason purely about which content words are present, without special +# tokens perturbing the mean. Content words: +# "relevant" / "answer" -- strong positive signal (used as the pair's +# "topic" word so a query/candidate sharing it scores high) +# "irrelevant" -- strong negative signal +# "filler"/"the"/"cat"/"sat"/"on"/"mat" -- neutral filler, contributes 0, used +# to pad candidates past the truncation budget without affecting the score +VOCAB = [ + "[PAD]", "[UNK]", "[CLS]", "[SEP]", + "the", "cat", "sat", "on", "mat", "filler", + "relevant", "answer", "irrelevant", +] +DIMS = 1 + +# index -> scalar embedding value. Special tokens and neutral filler are 0.0; +# see module docstring for why a single dimension keeps every test's expected +# value hand-computable. +WORD_VALUES = { + "[PAD]": 0.0, "[UNK]": 0.0, "[CLS]": 0.0, "[SEP]": 0.0, + "the": 0.0, "cat": 0.0, "sat": 0.0, "on": 0.0, "mat": 0.0, "filler": 0.0, + "relevant": 10.0, + "answer": 10.0, + "irrelevant": -10.0, +} + +# Per-segment multiplicative scale: query segment (type 0) leaves a word's own +# value unchanged; candidate segment (type 1) doubles it. See the module +# docstring for why multiplication (not addition) is required for this fixture +# to actually prove token_type_ids assignment rather than merely their presence. +TYPE_SCALES = [1.0, 2.0] + +# Classifier weight/bias chosen so sigmoid(logit) lands well above 0.5 for a +# candidate containing "relevant"/"answer" paired with a matching query, and +# well below 0.5 otherwise -- see OnnxCrossEncoderScorerTests for the exact +# hand-computed expected values per scenario. +CLASSIFIER_WEIGHT = 1.0 +CLASSIFIER_BIAS = 0.0 + + +def main(out_dir: str) -> None: + vocab_size = len(VOCAB) + + embedding_rows = np.array([[WORD_VALUES[tok]] for tok in VOCAB], dtype=np.float32) + type_scale_rows = np.array([[v] for v in TYPE_SCALES], dtype=np.float32) + classifier_weight = np.array([[CLASSIFIER_WEIGHT]], dtype=np.float32) + classifier_bias = np.array([CLASSIFIER_BIAS], dtype=np.float32) + + input_ids = helper.make_tensor_value_info("input_ids", TensorProto.INT64, ["batch", "seq"]) + attention_mask = helper.make_tensor_value_info("attention_mask", TensorProto.INT64, ["batch", "seq"]) + token_type_ids = helper.make_tensor_value_info("token_type_ids", TensorProto.INT64, ["batch", "seq"]) + logits = helper.make_tensor_value_info("logits", TensorProto.FLOAT, ["batch", 1]) + + initializers = [ + numpy_helper.from_array(embedding_rows, name="embedding_matrix"), + numpy_helper.from_array(type_scale_rows, name="type_scale_matrix"), + numpy_helper.from_array(classifier_weight, name="classifier_weight"), + numpy_helper.from_array(classifier_bias, name="classifier_bias"), + numpy_helper.from_array(np.array([1], dtype=np.int64), name="axis_1"), + numpy_helper.from_array(np.array([-1], dtype=np.int64), name="axis_neg1"), + numpy_helper.from_array(np.array(1e-9, dtype=np.float32), name="mask_floor"), + numpy_helper.from_array(np.array([-1, DIMS], dtype=np.int64), name="reshape_2d_shape"), + ] + + nodes = [ + helper.make_node("Gather", ["embedding_matrix", "input_ids"], ["word_embeddings"], axis=0, name="gather_word_embeddings"), + helper.make_node("Gather", ["type_scale_matrix", "token_type_ids"], ["type_scale"], axis=0, name="gather_type_scale"), + helper.make_node("Mul", ["word_embeddings", "type_scale"], ["combined_embeddings"], name="apply_type_scale"), + helper.make_node("Cast", ["attention_mask"], ["mask_float"], to=TensorProto.FLOAT, name="cast_mask"), + helper.make_node("Unsqueeze", ["mask_float", "axis_neg1"], ["mask_expanded"], name="unsqueeze_mask"), + helper.make_node("Mul", ["combined_embeddings", "mask_expanded"], ["masked_embeddings"], name="apply_mask"), + helper.make_node("ReduceSum", ["masked_embeddings", "axis_1"], ["sum_embeddings"], keepdims=1, name="sum_embeddings"), + helper.make_node("ReduceSum", ["mask_expanded", "axis_1"], ["sum_mask"], keepdims=1, name="sum_mask"), + helper.make_node("Clip", ["sum_mask", "mask_floor"], ["sum_mask_clipped"], name="clip_sum_mask"), + helper.make_node("Div", ["sum_embeddings", "sum_mask_clipped"], ["pooled"], name="mean_pool"), + helper.make_node("Reshape", ["pooled", "reshape_2d_shape"], ["pooled_2d"], name="reshape_pooled"), + helper.make_node("MatMul", ["pooled_2d", "classifier_weight"], ["logits_matmul"], name="classifier_matmul"), + helper.make_node("Add", ["logits_matmul", "classifier_bias"], ["logits"], name="classifier_bias_add"), + ] + + graph = helper.make_graph( + nodes=nodes, + name="tiny_cross_encoder_fixture", + inputs=[input_ids, attention_mask, token_type_ids], + outputs=[logits], + initializer=initializers, + ) + + model = helper.make_model(graph, producer_name="netclaw-fixture-generator", opset_imports=[helper.make_opsetid("", 18)]) + model.ir_version = 9 + onnx.checker.check_model(model) + + model_path = f"{out_dir}/tiny-cross-encoder.onnx" + onnx.save(model, model_path) + + vocab_path = f"{out_dir}/tiny-cross-encoder-vocab.txt" + with open(vocab_path, "w", encoding="utf-8") as f: + f.write("\n".join(VOCAB) + "\n") + + print(f"wrote {model_path} ({vocab_size} vocab rows x {DIMS} dims)") + print(f"wrote {vocab_path}") + + +if __name__ == "__main__": + main(sys.argv[1] if len(sys.argv) > 1 else ".") diff --git a/src/Netclaw.Embeddings.Tests/Fixtures/tiny-cross-encoder-vocab.txt b/src/Netclaw.Embeddings.Tests/Fixtures/tiny-cross-encoder-vocab.txt new file mode 100644 index 000000000..e15ef6d20 --- /dev/null +++ b/src/Netclaw.Embeddings.Tests/Fixtures/tiny-cross-encoder-vocab.txt @@ -0,0 +1,13 @@ +[PAD] +[UNK] +[CLS] +[SEP] +the +cat +sat +on +mat +filler +relevant +answer +irrelevant diff --git a/src/Netclaw.Embeddings.Tests/Fixtures/tiny-cross-encoder.onnx b/src/Netclaw.Embeddings.Tests/Fixtures/tiny-cross-encoder.onnx new file mode 100644 index 0000000000000000000000000000000000000000..630db5cab736d2df195db73bed3bc9d54b8735f9 GIT binary patch literal 1459 zcma)6U2oGc6iwQ$P43h#^`>bHLZIwriZqEK9?&E{R*(jf#={Uo8V{D6c&*iZEp}MP z6R$k*ANVW$GH|}yG#wL>TG{6wAD?^eYfVS{Ar-DqZjP?lT%-v-n$VCYL`2ES&rSFO z1`Wp4^H?~+0TBtCgU-S@6_|M(ZQVqPhZhWP?oNo9(gZhft9zE?_9k@5obf&7m)e5Q zFch~j#oQ%6t<(o1n$ZyFfouy{Qp{ngzJ%@;eh*D!P?JI1ZWN4JNLQR2B#!;tV&B}5 z)jm&scn$+11Puj?LTQoD5M=-O%8!UJb(e6F!&c{w9NIdxM6{=7*_}d-33`?`4(a3= z^>YC_k4flJ&m5#7zfLLrmX(0dEd5h8hj(z7Rag~3!))1VGEgLw>*Z<4Qm?U~n|nG& z1D*!;Lu||~{epU_OFyQ8y#?Bg(%54GFS0yqxLJs5nP|4BxftCm^a2*VW#OU(8I=xp zeHO>EVEt@>D_9m@PT>R^u3>E<>c&yzO94HgB*fXoYQJS);U$z2FcQkAL|)0K9?l0y zFV;xF98|#qvU(Q;iC7V#OdJp#}oTdYe}KC6LyShT_t^aP=DffVMpsB1A1s< sOoTgy4bvD96yBec+oZtH5K)jyUIF`I-nTnFM|HYIsgCw literal 0 HcmV?d00001 diff --git a/src/Netclaw.Embeddings.Tests/OnnxCrossEncoderScorerTests.cs b/src/Netclaw.Embeddings.Tests/OnnxCrossEncoderScorerTests.cs new file mode 100644 index 000000000..26a30ddf7 --- /dev/null +++ b/src/Netclaw.Embeddings.Tests/OnnxCrossEncoderScorerTests.cs @@ -0,0 +1,207 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using Xunit; + +namespace Netclaw.Embeddings.Tests; + +/// +/// Exercises against the tiny fixture graph committed at +/// Fixtures/tiny-cross-encoder.onnx / Fixtures/tiny-cross-encoder-vocab.txt +/// (generated by Fixtures/generate_fixture_cross_encoder.py — see that file's header +/// comment for the graph shape, why it multiplies a per-segment scale into each word embedding +/// rather than adding one, and why every hand-computed expected value below is exact for a +/// single-dimension embedding). No network access; this is the CI-safe substitute for the real +/// 22 MB allowlisted Xenova/ms-marco-MiniLM-L-6-v2 model (memory-relevance-gate task 2.5). +/// +public sealed class OnnxCrossEncoderScorerTests : IAsyncLifetime +{ + private const string ModelId = "tiny-cross-encoder-fixture"; + + private OnnxCrossEncoderScorer _scorer = null!; + private long _relevantTokenId; + + public async ValueTask InitializeAsync() + { + var fixturesDir = Path.Combine(AppContext.BaseDirectory, "Fixtures"); + var vocabPath = Path.Combine(fixturesDir, "tiny-cross-encoder-vocab.txt"); + _scorer = await OnnxCrossEncoderScorer.LoadAsync( + modelPath: Path.Combine(fixturesDir, "tiny-cross-encoder.onnx"), + vocabPath: vocabPath, + modelId: ModelId, + maxConcurrency: 2); + + // FastBertTokenizer assigns ids by vocab.txt line number, so this is a robust, direct + // way to know "relevant"'s id without relying on any encode side-channel. + var vocabLines = File.ReadAllLines(vocabPath); + _relevantTokenId = Array.IndexOf(vocabLines, "relevant"); + Assert.True(_relevantTokenId >= 0, "fixture vocab must contain 'relevant'"); + } + + public ValueTask DisposeAsync() + { + _scorer.Dispose(); + return ValueTask.CompletedTask; + } + + [Fact] + public void Loaded_scorer_reports_its_identity() + { + Assert.Equal(ModelId, _scorer.ModelId); + Assert.True(_scorer.IsAvailable); + } + + // ── Sigmoid + batch scoring (task 1.2, 2.5) ──────────────────────────── + // + // The fixture's single-dimension embeddings make every logit hand-computable: "relevant" and + // "answer" both embed to 10.0, "irrelevant" to -10.0, everything else (fillers, special + // tokens) to 0.0. Query-segment (type 0) positions keep a word's value unscaled; candidate- + // segment (type 1) positions double it. Pooling is an attention-masked mean over every + // position (CLS + query + SEP + candidate + SEP), so e.g. "[CLS] relevant [SEP] relevant + // [SEP]" (5 tokens) sums to 10*1 (query "relevant") + 10*2 (candidate "relevant") = 30, + // mean 30/5 = 6.0, sigmoid(6.0) ≈ 0.9975. + + [Fact] + public async Task ScoreAsync_matches_the_hand_computed_sigmoid_for_a_matching_pair() + { + var scores = await _scorer.ScoreAsync("relevant", ["relevant"], TestContext.Current.CancellationToken); + + var expected = Sigmoid(6.0); // (10*1 + 10*2) / 5 + Assert.Equal(expected, scores[0], precision: 5); + } + + [Fact] + public async Task ScoreAsync_matches_the_hand_computed_sigmoid_for_an_unhelpful_pair() + { + var scores = await _scorer.ScoreAsync("relevant", ["irrelevant"], TestContext.Current.CancellationToken); + + var expected = Sigmoid(-2.0); // (10*1 + (-10)*2) / 5 + Assert.Equal(expected, scores[0], precision: 5); + } + + [Fact] + public async Task ScoreAsync_preserves_candidate_order_in_a_batch() + { + string[] candidates = ["irrelevant", "relevant", "filler"]; + + var scores = await _scorer.ScoreAsync("relevant", candidates, TestContext.Current.CancellationToken); + + Assert.Equal(3, scores.Count); + var single0 = await _scorer.ScoreAsync("relevant", [candidates[0]], TestContext.Current.CancellationToken); + var single1 = await _scorer.ScoreAsync("relevant", [candidates[1]], TestContext.Current.CancellationToken); + var single2 = await _scorer.ScoreAsync("relevant", [candidates[2]], TestContext.Current.CancellationToken); + Assert.Equal(single0[0], scores[0], precision: 6); + Assert.Equal(single1[0], scores[1], precision: 6); + Assert.Equal(single2[0], scores[2], precision: 6); + } + + [Fact] + public async Task ScoreAsync_of_empty_candidates_returns_empty_without_scoring() + { + var scores = await _scorer.ScoreAsync("relevant", [], TestContext.Current.CancellationToken); + Assert.Empty(scores); + } + + // ── Pair-encoding correctness: token_type_ids (task 1.2, 2.5) ────────── + // + // EncodePair is internal (InternalsVisibleTo) specifically so these scenarios can assert on + // the exact assembled arrays rather than needing a live ONNX Run per case. + + [Fact] + public void EncodePair_assembles_CLS_query_SEP_candidate_SEP_with_correct_token_type_ids() + { + var (ids, mask, types, length) = _scorer.EncodePair("relevant", "filler"); + + // [CLS] relevant [SEP] filler [SEP] -- 5 real tokens, bucketed to 8. + Assert.Equal(8, length); + Assert.Equal([1, 1, 1, 1, 1, 0, 0, 0], mask); + Assert.Equal([0, 0, 0, 1, 1, 0, 0, 0], types); + + // ids[2] is [SEP] closing the query segment; ids[4] is the SAME [SEP] id reused to close + // the candidate segment (one shared special-token id, two positions). + Assert.Equal(ids[2], ids[4]); + Assert.NotEqual(ids[0], ids[2]); // [CLS] and [SEP] are different vocab ids + Assert.NotEqual(ids[1], ids[3]); // "relevant" vs "filler" are different vocab ids + } + + [Fact] + public void EncodePair_swapping_query_and_candidate_changes_which_position_carries_the_word() + { + var forward = _scorer.EncodePair("relevant", "filler"); + var swapped = _scorer.EncodePair("filler", "relevant"); + + // Same structural shape (both single-word/single-word pairs)... + Assert.Equal(forward.Length, swapped.Length); + Assert.Equal(forward.Types, swapped.Types); + + // ...but the token id sequence differs: "relevant" sits at the query position (index 1) + // in the first pair and at the candidate position (index 3) in the second. + Assert.Equal(forward.Ids[1], swapped.Ids[3]); + Assert.Equal(forward.Ids[3], swapped.Ids[1]); + Assert.NotEqual(forward.Ids, swapped.Ids); + } + + // ── Pair-encoding correctness: only_second truncation (task 1.2, 2.5) ── + + [Fact] + public void EncodePair_truncates_the_candidate_never_the_query() + { + // Query alone would need every position under a plain single-sequence encode if it were + // long enough to matter here; what this test actually pins is the shoot-out's contract: + // an over-budget candidate loses tokens, the query never does. + var query = "the cat sat on the mat"; // 6 content tokens, comfortably short + var longCandidate = string.Join(' ', Enumerable.Repeat("filler", 600)); + + var (_, mask, _, length) = _scorer.EncodePair(query, longCandidate); + + Assert.True(length <= 512, $"expected the pair to respect MaxTokens, got {length}"); + // 1x[CLS] + 6 query tokens + 1x[SEP] fit entirely -- none of the query's own tokens are + // ever dropped, regardless of how long the candidate is. + Assert.Equal(6, mask.Skip(1).Take(6).Count(m => m == 1)); + } + + [Fact] + public void EncodePair_drops_a_candidate_marker_word_placed_at_the_end_of_an_over_budget_candidate() + { + var query = "the"; + var candidateMarkerAtEnd = string.Join(' ', Enumerable.Repeat("filler", 600)) + " relevant"; + + var (ids, _, _, _) = _scorer.EncodePair(query, candidateMarkerAtEnd); + + // only_second truncation keeps the candidate's PREFIX and drops its suffix -- "relevant" + // was the very last content token, so it must not survive into the assembled pair. + Assert.DoesNotContain(_relevantTokenId, ids); + } + + [Fact] + public void EncodePair_keeps_a_candidate_marker_word_placed_at_the_start_of_an_over_budget_candidate() + { + var query = "the"; + var candidateMarkerAtStart = "relevant " + string.Join(' ', Enumerable.Repeat("filler", 600)); + + var (ids, _, _, _) = _scorer.EncodePair(query, candidateMarkerAtStart); + + // The marker was the candidate's FIRST content token, so only_second truncation (which + // keeps the prefix) must retain it even though the overall candidate was truncated. + Assert.Contains(_relevantTokenId, ids); + } + + // ── Pair-encoding correctness: dynamic length bucketing (task 1.2, 2.5) ─ + + [Fact] + public void EncodePair_pads_the_assembled_length_up_to_a_bucket_of_8() + { + // "the cat" (2 content) + "sat on mat" (3 content): 1+2+1+3+1 = 8 raw tokens exactly -- + // must NOT be bumped to the next bucket (16). + var exact = _scorer.EncodePair("the cat", "sat on mat"); + Assert.Equal(8, exact.Length); + + // "relevant" (1) + "filler" (1): 1+1+1+1+1 = 5 raw tokens -- rounds up to 8. + var shortPair = _scorer.EncodePair("relevant", "filler"); + Assert.Equal(8, shortPair.Length); + } + + private static double Sigmoid(double logit) => 1.0 / (1.0 + Math.Exp(-logit)); +} diff --git a/src/Netclaw.Embeddings/EmbeddingModelProvisioner.cs b/src/Netclaw.Embeddings/EmbeddingModelProvisioner.cs index 083c1a2ee..c5a41dcf2 100644 --- a/src/Netclaw.Embeddings/EmbeddingModelProvisioner.cs +++ b/src/Netclaw.Embeddings/EmbeddingModelProvisioner.cs @@ -32,6 +32,37 @@ public sealed record EmbeddingModelManifestEntry( /// Files placed on disk by , ready for . public sealed record ProvisionedEmbeddingModel(string ModelId, string ModelPath, string VocabPath, int Dimensions); +/// +/// One entry in (memory-relevance-gate +/// D3): the same download/verification fields as , minus +/// Dimensions (a cross-encoder produces a single logit, not a fixed-width vector) and plus +/// — the one field embedding manifests don't need. The +/// threshold travels with the model id it was measured against so a future model swap can never +/// silently reuse a threshold calibrated for a different model's score distribution. +/// +/// Allowlist key, e.g. ms-marco-minilm-l-6-v2. +/// Download location for model.onnx. +/// Download location for the WordPiece vocab.txt. +/// Expected SHA-256 (lowercase hex) of the model artifact. +/// Expected SHA-256 (lowercase hex) of the vocab artifact. +/// Expected byte size of the model artifact — a cheap first check before hashing. +/// +/// The similarity threshold calibrated for this model id's score distribution (memory-relevance-gate +/// D2: S*=0.02 for the shipped ms-marco-minilm-l-6-v2). Governs gating unless the operator +/// configures an explicit Memory.Recall.RelevanceGate.Threshold override. +/// +public sealed record RelevanceModelManifestEntry( + string ModelId, + Uri ModelUrl, + Uri TokenizerUrl, + string ModelSha256, + string TokenizerSha256, + long ModelByteSize, + double CalibratedThreshold); + +/// Files placed on disk by , ready for OnnxCrossEncoderScorer.LoadAsync. +public sealed record ProvisionedRelevanceModel(string ModelId, string ModelPath, string VocabPath, double CalibratedThreshold); + /// /// Thrown when a requested model id is not on the allowlist, or a downloaded artifact fails /// byte-size or SHA-256 verification. Never wraps a partially-written file — callers can treat @@ -81,6 +112,38 @@ public sealed class EmbeddingModelProvisioner ModelByteSize: 1_336_854_282), }; + /// + /// Allowlist key for the single ratified relevance (cross-encoder) model + /// (memory-relevance-gate D2). Unlike embeddings, there is no operator-facing model-choice + /// knob for the relevance gate — the shoot-out ratified exactly one design/model pair, so + /// this id is a fixed constant rather than a Memory.Recall.RelevanceGate config + /// property. + /// + public const string DefaultRelevanceModelId = "ms-marco-minilm-l-6-v2"; + + /// + /// Pinned allowlist for relevance (cross-encoder) models — the same supply-chain mechanism + /// as , generalized to a manifest entry kind that additionally carries + /// a calibrated operating threshold (memory-relevance-gate D2/D3). Xenova/ms-marco-MiniLM-L-6-v2 + /// is the winner of a 4-design measured shoot-out, re-validated out-of-sample (see + /// openspec/changes/memory-relevance-gate/design.md D2): quantized int8, + /// bit-for-bit quality-identical to the fp32 variant on both gold sets at a fraction of the + /// RAM. URL is pinned to the repo's HEAD commit sha at the time this artifact was verified + /// (not main), matching 's own pinning convention. + /// + public static IReadOnlyDictionary RelevanceAllowlist { get; } = + new Dictionary(StringComparer.Ordinal) + { + [DefaultRelevanceModelId] = new RelevanceModelManifestEntry( + ModelId: DefaultRelevanceModelId, + ModelUrl: new Uri("https://huggingface.co/Xenova/ms-marco-MiniLM-L-6-v2/resolve/a09144355adeed5f58c8ed011d209bf8ee5a1fec/onnx/model_quantized.onnx"), + TokenizerUrl: new Uri("https://huggingface.co/Xenova/ms-marco-MiniLM-L-6-v2/resolve/a09144355adeed5f58c8ed011d209bf8ee5a1fec/vocab.txt"), + ModelSha256: "e9d8ebf845c413e981c175bfe49a3bfa9b3dcce2a3ba54875ee5df5a58639fbe", + TokenizerSha256: "07eced375cec144d27c900241f3e339478dec958f92fddbc551f295c992038a3", + ModelByteSize: 23_143_499, + CalibratedThreshold: 0.02), + }; + private readonly HttpClient _httpClient; private readonly IReadOnlyDictionary _allowlist; @@ -171,6 +234,70 @@ public async Task ProvisionAsync( return new ProvisionedEmbeddingModel(modelId, modelPath, vocabPath, entry.Dimensions); } + /// + /// Downloads and verifies 's relevance-model artifacts (memory- + /// relevance-gate D3) — identical download/atomic-rename/hash-verify code path as + /// , reused unchanged; only the manifest entry type differs. + /// The allowlist is a method parameter rather than a constructor-injected field (unlike + /// ) so this and can + /// be added without perturbing every existing embedding-only call site's constructor call — + /// callers pass in production, or a small fixture-pointed + /// dictionary in tests. + /// + public async Task ProvisionRelevanceModelAsync( + string modelId, + IReadOnlyDictionary allowlist, + string destinationDirectory, + CancellationToken ct = default) + { + if (!allowlist.TryGetValue(modelId, out var entry)) + { + throw new EmbeddingModelProvisioningException( + $"Unknown relevance model id '{modelId}'. Allowlisted ids: {string.Join(", ", allowlist.Keys.Order(StringComparer.Ordinal))}."); + } + + Directory.CreateDirectory(destinationDirectory); + var modelPath = Path.Combine(destinationDirectory, "model.onnx"); + var vocabPath = Path.Combine(destinationDirectory, "vocab.txt"); + + if (await IsValidAsync(modelPath, entry.ModelSha256, entry.ModelByteSize, ct).ConfigureAwait(false) + && await IsValidAsync(vocabPath, entry.TokenizerSha256, expectedByteSize: null, ct).ConfigureAwait(false)) + { + return new ProvisionedRelevanceModel(modelId, modelPath, vocabPath, entry.CalibratedThreshold); + } + + await DownloadAndVerifyAsync(entry.ModelUrl, modelPath, entry.ModelSha256, entry.ModelByteSize, ct).ConfigureAwait(false); + await DownloadAndVerifyAsync(entry.TokenizerUrl, vocabPath, entry.TokenizerSha256, expectedByteSize: null, ct).ConfigureAwait(false); + + return new ProvisionedRelevanceModel(modelId, modelPath, vocabPath, entry.CalibratedThreshold); + } + + /// + /// Verifies whether 's relevance-model artifacts are already + /// present and hash-valid at , without ever accessing + /// the network — the relevance-model analogue of , used + /// when Memory.Embeddings.AutoDownload=false gates the network path entirely. + /// + public async Task TryLoadVerifiedRelevanceModelAsync( + string modelId, + IReadOnlyDictionary allowlist, + string destinationDirectory, + CancellationToken ct = default) + { + if (!allowlist.TryGetValue(modelId, out var entry)) + return null; + + var modelPath = Path.Combine(destinationDirectory, "model.onnx"); + var vocabPath = Path.Combine(destinationDirectory, "vocab.txt"); + + if (!await IsValidAsync(modelPath, entry.ModelSha256, entry.ModelByteSize, ct).ConfigureAwait(false)) + return null; + if (!await IsValidAsync(vocabPath, entry.TokenizerSha256, expectedByteSize: null, ct).ConfigureAwait(false)) + return null; + + return new ProvisionedRelevanceModel(modelId, modelPath, vocabPath, entry.CalibratedThreshold); + } + private static async Task IsValidAsync(string path, string expectedSha256, long? expectedByteSize, CancellationToken ct) { if (!File.Exists(path)) diff --git a/src/Netclaw.Embeddings/OnnxCrossEncoderScorer.cs b/src/Netclaw.Embeddings/OnnxCrossEncoderScorer.cs new file mode 100644 index 000000000..91719d78a --- /dev/null +++ b/src/Netclaw.Embeddings/OnnxCrossEncoderScorer.cs @@ -0,0 +1,271 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using Microsoft.ML.OnnxRuntime; +using Microsoft.ML.OnnxRuntime.Tensors; +using Netclaw.Actors.Memory; + +namespace Netclaw.Embeddings; + +/// +/// In-process ONNX-backed (memory-relevance-gate D1). Owns +/// exactly one and one +/// for its lifetime, mirroring 's exact lifecycle shape — a +/// second, independently lifecycled session rather than an extension of the embedder's, because +/// the allowlisted relevance model (Xenova/ms-marco-MiniLM-L-6-v2) is a materially +/// different graph (BertForSequenceClassification pair-input head, not the bi-encoder's +/// single-input pooling graph) with its own tokenizer vocabulary (design D1's "alternative +/// considered"). +/// +/// +/// Pair encoding: has no built-in support +/// for two-segment (query, candidate) encoding with distinct token_type_ids — its +/// Encode overloads always wrap a single input in [CLS] ... [SEP] with +/// token_type_ids fixed at all-zero (see its XML docs: "Some models which can take +/// multiple sequences as input might need this but this is currently not supported by +/// FastBertTokenizer"). assembles the pair manually: it encodes the +/// query and candidate independently (each already wrapped in its own [CLS] ... [SEP]), +/// strips each segment's [CLS]/trailing [SEP] via the attention-mask sum (exactly +/// like reads its own actual-vs-padded length), then +/// splices [CLS] query [SEP] candidate [SEP] back together with the correct +/// token_type_ids (0 for [CLS]+query+first [SEP], 1 for candidate+final +/// [SEP] — verified against the real model's tokenizer.json pair post-processing +/// template, which encodes exactly this convention). +/// +/// +/// +/// Truncation (only_second): the query is encoded into a buffer one token shorter +/// than (see 's remarks) so that, +/// even in the extreme case where the query alone would consume the entire sequence budget, the +/// pair assembly can never exceed — the candidate side always absorbs +/// the truncation, down to zero candidate tokens in that extreme case, and the query is never +/// truncated for the pair's sake. +/// +/// +/// +/// Sigmoid: the model's single logits output (shape [batch, 1]) ships with +/// sbert_ce_default_activation_function: Identity in its config.json — the +/// activation is deliberately not baked into the graph, so it is applied host-side here. +/// +/// +public sealed class OnnxCrossEncoderScorer : IRelevanceScorer, IDisposable +{ + // The allowlisted model's tokenizer_config.json declares model_max_length: 512 (standard + // BERT position-embedding cap) — verified directly against the pinned artifact at the time + // this scorer was authored, the same way OnnxMemoryEmbedder's two allowlisted models both + // cap at 512. + private const int MaxTokens = 512; + + // The query is encoded into a buffer ONE token shorter than MaxTokens so that, even when the + // query alone would consume every position under a plain single-sequence encode (queryLen up + // to MaxTokens), the resulting query CONTENT length can never exceed MaxTokens-3. That + // invariant is what guarantees EncodePair's assembled pair -- 1x[CLS] + query + 1x[SEP] + + // candidate + 1x[SEP] -- never exceeds MaxTokens even in the pathological case where the + // candidate is truncated to zero tokens. Without this one-token reservation, a query that + // maxed out a full MaxTokens-sized single-sequence encode would leave no room for the pair's + // second [SEP], overflowing the model's position-embedding table by one. + private const int QueryEncodeBufferLength = MaxTokens - 1; + + private readonly InferenceSession _session; + private readonly FastBertTokenizer.BertTokenizer _tokenizer; + private readonly BoundedConcurrencyGate _gate; + private readonly string _outputName; + + private OnnxCrossEncoderScorer( + string modelId, + InferenceSession session, + FastBertTokenizer.BertTokenizer tokenizer, + int maxConcurrency) + { + if (session.OutputMetadata.Count != 1) + throw new InvalidOperationException( + $"Relevance model '{modelId}' declares {session.OutputMetadata.Count} outputs; " + + "OnnxCrossEncoderScorer expects exactly one (the single-logit classification head)."); + + ModelId = modelId; + _session = session; + _tokenizer = tokenizer; + _gate = new BoundedConcurrencyGate(maxConcurrency); + _outputName = session.OutputMetadata.Keys.Single(); + } + + /// + public string ModelId { get; } + + /// + public bool IsAvailable => true; + + /// + /// Loads the ONNX model and WordPiece vocabulary from disk. Both files are expected to + /// already be provisioned and hash-verified () — this + /// constructor does no downloading or verification of its own. + /// + /// Path to the model.onnx file. + /// Path to the WordPiece vocab.txt file. + /// The allowlisted model id these files correspond to. + /// Maximum concurrent inference calls (default 2). + /// Threads ONNX Runtime uses per inference call (default 4). + public static async Task LoadAsync( + string modelPath, + string vocabPath, + string modelId, + int maxConcurrency = 2, + int intraOpNumThreads = 4, + CancellationToken ct = default) + { + ct.ThrowIfCancellationRequested(); + + using var sessionOptions = new SessionOptions { IntraOpNumThreads = intraOpNumThreads }; + var session = new InferenceSession(modelPath, sessionOptions); + + var tokenizer = new FastBertTokenizer.BertTokenizer(); + // The allowlisted model (Xenova/ms-marco-MiniLM-L-6-v2) publishes do_lower_case=true in + // its tokenizer_config.json — a standard BERT-base-uncased vocabulary. + await tokenizer.LoadVocabularyAsync(vocabPath, convertInputToLowercase: true); + + return new OnnxCrossEncoderScorer(modelId, session, tokenizer, maxConcurrency); + } + + /// + public async ValueTask> ScoreAsync(string query, IReadOnlyList candidates, CancellationToken ct) + { + if (candidates.Count == 0) + return []; + + // Each candidate acquires the gate independently (mirrors + // OnnxMemoryEmbedder.EmbedBatchAsync) rather than holding one slot for the whole call — + // in practice this is at most AutoRecallMaxItems (3) pairs per turn, so the difference is + // academic, but it keeps this call path consistent with the embedder's own convention. + var tasks = new Task[candidates.Count]; + for (var i = 0; i < candidates.Count; i++) + { + var candidate = candidates[i]; + tasks[i] = _gate.RunAsync(_ => Task.FromResult(ScoreOne(query, candidate)), ct); + } + + return await Task.WhenAll(tasks).ConfigureAwait(false); + } + + private double ScoreOne(string query, string candidate) + { + var (ids, mask, types, length) = EncodePair(query, candidate); + + var inputIdsTensor = new DenseTensor(ids, [1, length]); + var attentionMaskTensor = new DenseTensor(mask, [1, length]); + var tokenTypeIdsTensor = new DenseTensor(types, [1, length]); + + var available = new Dictionary(StringComparer.Ordinal) + { + ["input_ids"] = NamedOnnxValue.CreateFromTensor("input_ids", inputIdsTensor), + ["attention_mask"] = NamedOnnxValue.CreateFromTensor("attention_mask", attentionMaskTensor), + ["token_type_ids"] = NamedOnnxValue.CreateFromTensor("token_type_ids", tokenTypeIdsTensor), + }; + + // Feed only the inputs the loaded graph actually declares (same defensive pattern as + // OnnxMemoryEmbedder.EmbedOne) rather than hardcoding the production model's 3-input + // signature — the test fixture graph declares the same three inputs, but this keeps the + // two code paths structurally identical rather than by coincidence. + var feed = new List(_session.InputMetadata.Count); + foreach (var inputName in _session.InputMetadata.Keys) + { + if (!available.TryGetValue(inputName, out var value)) + throw new InvalidOperationException( + $"Relevance model '{ModelId}' declares input '{inputName}', which this scorer does not know how to produce."); + feed.Add(value); + } + + using var outputs = _session.Run(feed); + var logits = outputs.First(o => o.Name == _outputName).AsTensor(); + var logit = logits[0, 0]; + + return Sigmoid(logit); + } + + /// + /// Assembles [CLS] query [SEP] candidate [SEP] with correct token_type_ids + /// (0 for the query segment including both flanking special tokens laid out below, 1 for the + /// candidate segment and its closing [SEP]) and only_second truncation (the + /// candidate is truncated to fit; the query never is — see 's + /// remarks for the invariant that makes this safe), then pads the assembled length up to a + /// bucket-of-8 boundary via — the same + /// dynamic-length convention already uses, reused directly + /// rather than duplicated. Internal (not private) so OnnxCrossEncoderScorerTests can + /// assert on the exact assembled arrays without needing a live ONNX Run for every + /// encoding-correctness scenario. + /// + internal (long[] Ids, long[] Mask, long[] Types, int Length) EncodePair(string query, string candidate) + { + // Buffers sized so Encode's own padTo argument fully populates them; only the + // non-padded prefix (found via the attention-mask sum) is meaningful, exactly like + // OnnxMemoryEmbedder.EmbedOne's own actualLen/scratch-buffer pattern. + var queryIds = new long[QueryEncodeBufferLength]; + var queryMask = new long[QueryEncodeBufferLength]; + var queryTypes = new long[QueryEncodeBufferLength]; + _tokenizer.Encode(query, queryIds, queryMask, queryTypes, QueryEncodeBufferLength); + var queryLen = (int)queryMask.Sum(); + var queryContentLen = queryLen - 2; // drop [CLS] and the single-sequence encode's own [SEP] + + var candidateIds = new long[MaxTokens]; + var candidateMask = new long[MaxTokens]; + var candidateTypes = new long[MaxTokens]; + _tokenizer.Encode(candidate, candidateIds, candidateMask, candidateTypes, MaxTokens); + var candidateLen = (int)candidateMask.Sum(); + var candidateContentLen = candidateLen - 2; + + // CLS/SEP ids are read off the query's own encode rather than hardcoded: FastBertTokenizer + // assigns special-token ids from vocab.txt line numbers, so they vary across vocabularies + // even though the tokens are always named [CLS]/[SEP] by convention. + var clsId = queryIds[0]; + var sepId = queryIds[queryLen - 1]; + + // only_second truncation: the candidate absorbs whatever budget remains after + // [CLS] + query + 2x[SEP]. QueryEncodeBufferLength guarantees this is never negative. + var availableForCandidate = Math.Max(0, MaxTokens - queryContentLen - 3); + var truncatedCandidateLen = Math.Min(candidateContentLen, availableForCandidate); + + var rawLength = 1 + queryContentLen + 1 + truncatedCandidateLen + 1; + var bucketLength = OnnxMemoryEmbedder.ComputeBucketedLength(rawLength); + + var ids = new long[bucketLength]; + var mask = new long[bucketLength]; + var types = new long[bucketLength]; + + var pos = 0; + ids[pos] = clsId; + mask[pos] = 1; + pos++; + + Array.Copy(queryIds, 1, ids, pos, queryContentLen); + for (var i = 0; i < queryContentLen; i++) + mask[pos + i] = 1; + pos += queryContentLen; + + ids[pos] = sepId; + mask[pos] = 1; + pos++; + + Array.Copy(candidateIds, 1, ids, pos, truncatedCandidateLen); + for (var i = 0; i < truncatedCandidateLen; i++) + { + types[pos + i] = 1; + mask[pos + i] = 1; + } + pos += truncatedCandidateLen; + + ids[pos] = sepId; + types[pos] = 1; + mask[pos] = 1; + + // Positions [rawLength, bucketLength) are left at their default 0/0/0 (id/mask/type) — + // the WordPiece convention every vocab.txt this repo touches follows ([PAD] is always + // line 1, i.e. id 0) plus the model's own attention-masked self-attention makes the + // padded id's exact value irrelevant to the score regardless. + return (ids, mask, types, bucketLength); + } + + private static double Sigmoid(float logit) => 1.0 / (1.0 + Math.Exp(-logit)); + + public void Dispose() => _session.Dispose(); +} From 177617536b0514c460f17dd4e50c08d297b1bc73 Mon Sep 17 00:00:00 2001 From: Aaron Stannard Date: Wed, 8 Jul 2026 22:16:22 +0000 Subject: [PATCH 20/37] docs(memory): relevance gate skill, runbook, and calibration procedure (memory-relevance-gate section 3) - netclaw-memory skill (1.10.0 -> 1.11.0): relevance-gate guidance, gate log event - runbook: relevance gate health section (doctor check, degradation log, gateScores/droppedByGate reading) - design.md: calibration verification procedure (harness location, inputs, outputs) - tasks.md: tick 3.1-3.3 (14/14 complete) --- docs/runbooks/memory-health-and-evals.md | 75 +++++++++++++++++++ .../.system/files/netclaw-memory/SKILL.md | 42 ++++++++++- .../changes/memory-relevance-gate/design.md | 61 ++++++++++++++- .../changes/memory-relevance-gate/tasks.md | 6 +- 4 files changed, 178 insertions(+), 6 deletions(-) diff --git a/docs/runbooks/memory-health-and-evals.md b/docs/runbooks/memory-health-and-evals.md index f320e2ce2..dfca725a8 100644 --- a/docs/runbooks/memory-health-and-evals.md +++ b/docs/runbooks/memory-health-and-evals.md @@ -113,6 +113,81 @@ PY Then restart the daemon from local binaries before running evals. +## Relevance Gate Health + +The relevance gate (`memory-relevance-gate`) is a post-floor cross-encoder +stage: for each of the (≤3) candidates that already cleared the cosine +floor, a small ONNX model (`ms-marco-minilm-l-6-v2`) scores `(query, +candidate)` jointly and drops anything below the calibrated threshold. +Activation follows `Memory.Embeddings.Enabled` unless +`Memory.Recall.RelevanceGate.Enabled`/`Threshold` explicitly override it. + +1. Run offline diagnostics and review the `Memory Relevance Gate` check: + +```bash +netclaw doctor +``` + + - `PASS` + "disabled (follows Memory.Embeddings.Enabled...)" or "disabled + (Memory.Recall.RelevanceGate.Enabled is explicitly false)" — expected, + healthy state for any deployment that hasn't opted into embeddings, or + that opted out of the gate specifically. Not an error. + - `PASS` + "Relevance gate healthy: model '...' provisioned (threshold + ...)" — the model is present, hash-verified, and its manifest-carried + (or config-overridden) threshold is reported. + - `ERROR` + "missing or fails hash verification at ``" — the model + was never provisioned or the on-disk artifact doesn't match the pinned + SHA-256. Restart the daemon to re-provision if `AutoDownload` is + enabled; otherwise provision manually and restart. + +2. Check the degradation log line. When the gate is skipped for a turn + (model unavailable, its ~60 ms sub-budget exceeded, or recall running in + lexical mode because there's no query vector), the coordinator logs a + rate-limited marker instead of silently changing what gets injected: + +``` +memory_recall_gate_degraded session= reason= +``` + + `reason` is one of `gate_disabled_by_config`, `no_scorer_configured`, + `scorer_unavailable`, `sub_budget_exceeded`, or `score_failed:`. + Logged at `Warning` when the gate is enabled but a turn still degraded (a + genuine runtime condition worth noticing); logged at `Debug` when the gate + is off by config (the default, intentional state — not spam). Rate-limited + per-reason with the same cooldown as `memory_recall_vector_degraded`, so + expect at most one `Warning` line per reason per cooldown window even + under sustained degradation, not one per turn. + +3. Read `gateScores`/`droppedByGate` on `memory_retrieval_final` when + diagnosing over- or under-injection: + +```bash +grep memory_retrieval_final "$HOME/.netclaw/logs/daemon-$(date +%F).log" | tail -20 +``` + + - `droppedByGate` — how many of the floor's survivors the gate rejected + this turn. `0` on a turn that also injected nothing means the floor + itself already filtered everything (or the gate didn't run); a nonzero + `droppedByGate` with zero final `injectedCount` means the gate is the + reason nothing was injected, not the floor. + - `gateScores` — the cross-encoder score for every candidate the gate + scored (`id=score`, e.g. `doc-abc123=0.014`), regardless of whether it + survived. Compare against the active threshold (config override, or the + manifest's calibrated default reported by the doctor check) to see how + close a dropped candidate came, or how comfortably a survivor cleared + the bar. Absent `gateScores` (empty) on a hybrid-mode turn is itself a + signal the gate didn't run for that turn — check for a paired + `memory_recall_gate_degraded` line first before assuming a config + problem. + - Zero `gateScores` and zero `droppedByGate` on a turn is normal whenever + the floor itself already produced zero survivors — the gate never runs + against an empty candidate set. This is not a gate failure. + +See `openspec/changes/memory-relevance-gate/design.md` for the calibration +procedure (threshold-sweep protocol, model shoot-out, and out-of-sample +validation numbers) if the operating point ever needs to be re-verified +against a different relevance model or corpus. + ## Reproducible Memory Score (Non-LLM Judge) Run the deterministic memory score script: diff --git a/feeds/skills/.system/files/netclaw-memory/SKILL.md b/feeds/skills/.system/files/netclaw-memory/SKILL.md index 9faed6b69..abc7c5cca 100644 --- a/feeds/skills/.system/files/netclaw-memory/SKILL.md +++ b/feeds/skills/.system/files/netclaw-memory/SKILL.md @@ -3,7 +3,7 @@ name: netclaw-memory description: "REQUIRED when the user asks what you remember, recall, or know from past conversations, previous sessions, cross-session memory, memory classes, or memory types. Also before using memory tools: find_memories, get_memories, store_memory, update_memory." metadata: author: netclaw - version: "1.10.0" + version: "1.11.0" --- # Netclaw Memory @@ -95,6 +95,39 @@ lexical-only — same candidate pool, no vector term or cosine floor. embeddings on so the gap closes immediately instead of waiting for embed-on-write to catch up opportunistically. +### Relevance Gate (cross-encoder) + +The cosine floor above answers "is this candidate on-topic?" — it does not +answer "does this candidate actually help answer the question?" A second +stage, the **relevance gate**, runs after the floor for exactly this reason: +a tiny cross-encoder (`ms-marco-minilm-l-6-v2`) jointly scores `(query, +candidate)` for each of the (≤3) floor survivors and drops anything below +its calibrated threshold. + +- **Activation follows `Memory.Embeddings.Enabled`** — one mental switch, no + second thing to discover. `Memory.Recall.RelevanceGate.Enabled` (nullable) + is an explicit override for an operator who wants embeddings for + dedup/hybrid-recall but not the extra per-turn cross-encoder latency; + `Memory.Recall.RelevanceGate.Threshold` (nullable) is an explicit override + of the manifest's calibrated operating point. Leave both `null` unless you + have a specific reason to diverge — the manifest-carried default is what + was validated out-of-sample. +- **Only ever runs in hybrid mode**, on the floor's own survivors — it never + sees a wider candidate pool and never runs when recall has already + degraded to lexical-only. +- **Zero survivors after the gate is a healthy outcome**, identical in kind + to zero survivors at the floor: the `[memory-recall]` block is omitted + entirely, not emitted empty. Do not treat an absent recall block as + evidence the gate (or memory generally) is broken — see the zero-injection + note above. +- **Degradation is explicit and logged, not silent**: when the relevance + model is unavailable, its sub-budget is exceeded, or recall is running in + lexical mode, the gate step is skipped and the floor's own result is + injected unfiltered — the exact pre-gate behavior. This fires + `memory_recall_gate_degraded` (rate-limited, same cooldown pattern as + `memory_recall_vector_degraded`). A degraded gate never silently changes + what gets injected without this marker. + ## When to Use Explicit Tools ### `find_memories` + `get_memories` @@ -192,13 +225,18 @@ Useful log events: **Recall pipeline** (grep for `memory_retrieval` / `memory_recall`): - `memory_retrieval_request_plan` — query tokenization, facets, soft scopes, anchor hints - `memory_retrieval_candidate_selection` — all candidates with selector scores -- `memory_retrieval_final` — floor filtering results, final injected items +- `memory_retrieval_final` — floor filtering results, final injected items; also carries + `gateScores` (the cross-encoder score for every candidate the relevance gate scored) and + `droppedByGate` (count the gate dropped) when the gate ran - `turn_memory_recall` — summary event with item count and duration - `memory_recall_vector_degraded` — turn fell back to lexical-only recall (embedder unavailable, no vector index, or the query-embedding sub-budget was exceeded) - `memory_recall_coverage_gap` — one or more candidates had no embedding row for the current model; they degrade to lexical scoring rather than being excluded, and the gap self-heals via embed-on-write plus `netclaw memory backfill-embeddings` +- `memory_recall_gate_degraded` — the relevance gate was skipped for this turn (model + unavailable, sub-budget exceeded, or recall in lexical mode); the floor's own result was + injected unfiltered **Formation pipeline** (grep for `memory_observation`): - `memory_observation_sidecar_completed` diff --git a/openspec/changes/memory-relevance-gate/design.md b/openspec/changes/memory-relevance-gate/design.md index 5ae6e76ea..a4fae17fd 100644 --- a/openspec/changes/memory-relevance-gate/design.md +++ b/openspec/changes/memory-relevance-gate/design.md @@ -368,7 +368,66 @@ selectivity without one of these signals firing. re-run the shoot-out's threshold-sweep protocol against a different relevance model or a different corpus, so re-calibration is a documented procedure rather than tribal knowledge trapped in a local research - directory. + directory. See "Calibration Verification Procedure" below. + +## Calibration Verification Procedure + +S*=0.02 is calibrated specifically against `ms-marco-minilm-l-6-v2`'s score +distribution (D3, D6) — it is not a universal constant. This section +documents the re-run procedure so a future model swap or corpus-specific +recalibration is a repeatable exercise, not tribal knowledge that only +exists in the shoot-out's own history. + +**Where the harness lives**: the gate-shootout scripts (the 4-design +comparison and the out-of-sample re-validation this design's scorecard +reports) and the floor-calibration/quantization-eval harness this change's +threshold sweep reuses both live in the operator's local research directory, +never in this repo: + +- `~/recall-research-local/2026-07/gate-shootout/` — the 4-design shoot-out + (distribution-shape, cross-encoder, learned feature gate, per-memory + offender priors) and the threshold-sweep driver used to pick S* for a + given model's score distribution. +- `~/recall-research-local/2026-07/quant-eval/` — the quantization/floor + harness (`floor_calibration.py` and related tooling) this change's sweep + protocol follows the same shape as: sweep a threshold over a fixed + corpus/gold-set pair, score every candidate, report zero-injection + accuracy, recall retention, and F0.5 at each candidate threshold. + +**Inputs required to re-run a sweep**: + +- A corpus snapshot as a SQLite file (`VACUUM INTO` clone of + `memory_documents`, same shape the July audit and the shoot-out both used) + — this is what candidates are drawn from. +- A judged gold-set JSONL (query, candidate doc ids, relevance labels) — + either an existing ratified set (`gold-prod-2026-07`, the 450-query + expansion) or a freshly judged set for a new corpus/domain. The judging + protocol (dual-pass judging, harsher-wins aggregation for ambiguous + candidates) is documented in the shoot-out's own history, not repeated here. +- The candidate relevance model as an ONNX artifact (the currently shipped + `model_quantized.onnx`, or a different cross-encoder being evaluated as a + replacement) — whatever model the new threshold will be calibrated *for*, + since the threshold is meaningless detached from the model that produced + the scores. + +**Outputs**: a per-threshold table (zero-injection accuracy, recall +retention, F0.5, mean injected count) — the same shape as the D2 scorecard +above — from which an operating point is chosen the same way S*=0.02 was: +prefer the highest zero-injection accuracy whose recall retention still +clears the ≥90% constraint, and re-validate the chosen point out-of-sample +(a disjoint gold-set expansion) before treating it as calibrated, not just +in-sample-optimal. The resulting `(ModelId, CalibratedThreshold)` pair is +what gets hand-carried into a new `RelevanceModelManifestEntry` in +`EmbeddingModelProvisioner`'s allowlist (D3) — recalibration never edits a +bare config default disconnected from which model produced it. + +**Why this stays repo-external**: both harness directories operate on real, +PII-bearing production traffic (query text, memory content) — the same +constraint documented in `docs/research/memory-audit-2026-07.md` for every +other research artifact referenced by this change and by +memory-core-redesign. The scripts and their outputs are operator-local by +design, never committed; only the *ratified, redacted results* (this +scorecard, the frozen threshold, the pinned model SHA-256) enter the repo. ## Open Questions diff --git a/openspec/changes/memory-relevance-gate/tasks.md b/openspec/changes/memory-relevance-gate/tasks.md index 739b2402d..c1d47e269 100644 --- a/openspec/changes/memory-relevance-gate/tasks.md +++ b/openspec/changes/memory-relevance-gate/tasks.md @@ -63,13 +63,13 @@ independently shippable in order. ## 3. Docs, skill sync, scorecard, calibration note -- [ ] 3.1 Update `netclaw-memory` skill: relevance gate exists, follows +- [x] 3.1 Update `netclaw-memory` skill: relevance gate exists, follows `Memory.Embeddings.Enabled`, explicit override knobs, degraded-mode behavior (floor-only fallback) -- [ ] 3.2 Runbook (`docs/runbooks/memory-health-and-evals.md`): relevance +- [x] 3.2 Runbook (`docs/runbooks/memory-health-and-evals.md`): relevance gate section — doctor check, degradation log line, how to read `gateScores`/`droppedByGate` in `memory_retrieval_final` -- [ ] 3.3 Record a scorecard in `design.md` (already drafted from the +- [x] 3.3 Record a scorecard in `design.md` (already drafted from the shoot-out; keep in sync if any number changes before merge) and add a short calibration-verification harness note (how to re-run the threshold sweep against a different relevance model or corpus, so From 9b9d78ba14bbf5d09d662a27b8d6fed448e23dd5 Mon Sep 17 00:00:00 2001 From: Aaron Stannard Date: Wed, 8 Jul 2026 22:21:45 +0000 Subject: [PATCH 21/37] opsx: memory-query-prefix change artifacts (proposal, design, specs, tasks) Arctic-embed-m's documented retrieval query prefix is unapplied in production, forfeiting measured recall quality (F0.5 0.141 -> 0.239 achievable). Change adds a query-vs-passage purpose seam, manifest-carried per-model retrieval calibration, and the atomic floor recalibration (0.68 -> 0.24 prefixed). Evidence: ~/recall-research-local/2026-07/arctic-prefix-eval/RESULTS.md --- .../memory-query-prefix/.openspec.yaml | 2 + .../changes/memory-query-prefix/design.md | 129 ++++++++++++++++++ .../changes/memory-query-prefix/proposal.md | 105 ++++++++++++++ .../specs/memory-embeddings/spec.md | 60 ++++++++ .../specs/netclaw-agent-memory/spec.md | 74 ++++++++++ openspec/changes/memory-query-prefix/tasks.md | 58 ++++++++ 6 files changed, 428 insertions(+) create mode 100644 openspec/changes/memory-query-prefix/.openspec.yaml create mode 100644 openspec/changes/memory-query-prefix/design.md create mode 100644 openspec/changes/memory-query-prefix/proposal.md create mode 100644 openspec/changes/memory-query-prefix/specs/memory-embeddings/spec.md create mode 100644 openspec/changes/memory-query-prefix/specs/netclaw-agent-memory/spec.md create mode 100644 openspec/changes/memory-query-prefix/tasks.md diff --git a/openspec/changes/memory-query-prefix/.openspec.yaml b/openspec/changes/memory-query-prefix/.openspec.yaml new file mode 100644 index 000000000..8cceb8d51 --- /dev/null +++ b/openspec/changes/memory-query-prefix/.openspec.yaml @@ -0,0 +1,2 @@ +schema: spec-driven +created: 2026-07-08 diff --git a/openspec/changes/memory-query-prefix/design.md b/openspec/changes/memory-query-prefix/design.md new file mode 100644 index 000000000..6b8c0459a --- /dev/null +++ b/openspec/changes/memory-query-prefix/design.md @@ -0,0 +1,129 @@ +# Design: memory-query-prefix + +## Context + +`OnnxMemoryEmbedder` runs every input — recall queries, embed-on-write +documents, backfill documents, dedup-nominator proposals — through one +`EmbedAsync(text)` path with no notion of purpose. `snowflake-arctic-embed-m` +is an asymmetric retrieval model: its model card (verified at the pinned HF +commit) instructs prefixing **queries** with +`Represent this sentence for searching relevant passages: ` and embedding +**documents** raw. Production has never applied the prefix, so all shipped +retrieval calibration (`MinCosineSimilarity` 0.68, memory-core-redesign D6) +measures the model off its intended operating mode. + +Measured on `gold-prod-2026-07` (93 real-traffic queries, 1,216-doc +production snapshot, production-faithful fp32 ONNX replica — +`~/recall-research-local/2026-07/arctic-prefix-eval/`): + +| configuration | optimal τ | F0.5 | recall@3 | zero-injection | +|---|:---:|---:|---:|---:| +| no prefix (shipped) | 0.68 | 0.141 | 0.146 | 13.3% | +| with prefix | 0.24 | 0.239 | 0.318 | 26.7% | + +The prefix compresses arctic's cosine distribution downward (top-1 median +0.789 → 0.392). At the shipped 0.68 floor, prefixed queries measure +**F0.5 = 0.0** — the two changes are inseparable. + +The relevance gate (memory-relevance-gate) already established the pattern +this change needs: model-specific calibration travels in the provisioner's +pinned manifest (`CalibratedThreshold`), and config exposes a nullable +override that follows the manifest when null. + +## Goals / Non-Goals + +**Goals:** + +- Recall queries embed with the active model's documented prefix; all + document-side embedding is byte-identical to today (no re-embed, no + migration). +- Floor and prefix change atomically and cannot be recombined incorrectly by + configuration alone. +- Per-model-variant calibration lives in the allowlist manifest so the int8 + variant (different floor) is an allowlist entry, not a code change. + +**Non-Goals:** + +- Model swaps (e5 declined; see proposal), symmetric/dual prefixes, reranker + threshold recalibration, any change to `memory_embeddings` rows at rest. + +## Decisions + +### D1. Purpose enum on the seam, not a second interface + +`IMemoryEmbedder.EmbedAsync` gains an `EmbeddingPurpose` parameter +(`Passage` | `RetrievalQuery`); `EmbedBatchAsync` likewise (a batch has one +purpose). All existing callers pass `Passage` explicitly — embed-on-write, +backfill CLI, gap repair, and the dedup nominator (proposal↔document +comparison is document-space by design; the audit's nominator calibration +τ=0.86 was measured unprefixed and stays valid). Only +`SQLiteMemoryRecallCoordinator`'s turn-query embedding passes +`RetrievalQuery`. No optional parameter: call sites are updated, per the +constitution's required-dependency rule. `UnavailableMemoryEmbedder` and all +test fakes implement the same signature; the fixture fake treats purposes +identically unless a test opts into distinct maps. + +*Rejected*: separate `EmbedQueryAsync` method — duplicates the batch/ +concurrency plumbing for one string concat; a mis-typed call reads the same +either way. Rejected: prefixing inside the coordinator — the prefix is a +property of the model, not of recall; the embedder owns model semantics. + +### D2. Prefix is manifest data, applied inside `OnnxMemoryEmbedder` + +`EmbeddingModelManifestEntry` gains `QueryPrefix` (string, may be empty) and +`CalibratedMinCosineSimilarity` (double). `OnnxMemoryEmbedder` prepends +`QueryPrefix` when purpose is `RetrievalQuery` before tokenization; token +budget unchanged (`only_first`-style truncation still applies to the combined +string — the 12-token prefix is negligible against 512). Arctic fp32 entry: +prefix as documented, `CalibratedMinCosineSimilarity = 0.24`. The mxbai +fallback entry gets its documented prefix +(`Represent this sentence for searching relevant passages: ` is +arctic-specific; mxbai documents its own retrieval prompt) and a floor +calibrated before that entry is ever flipped to — until calibrated, the +fallback entry carries no retrieval calibration and the coordinator treats a +missing calibration as "hybrid recall unavailable, lexical-only + degraded +log" rather than guessing (no silent fallback). + +### D3. Floor resolution: config-nullable follows manifest + +`MemoryRecallConfig.MinCosineSimilarity` becomes `double?`, default null → +resolve from the active model's `CalibratedMinCosineSimilarity` at scorer +load (carried on `MemoryVectorIndexHolder`/embedder holder the same way +`RelevanceScorerHolder` carries `CalibratedThreshold`). Explicit config value +overrides (operator experimentation), with the schema description warning +that the meaning is model-and-prefix-specific. This makes +prefix-without-recalibration unrepresentable by default: both ride the same +manifest entry. + +### D4. Calibration of record + +This change supersedes the 0.68 no-prefix calibration. The prefixed fp32 +sweep (`floor-calibration-prefix.json`, 116-point τ∈[−0.20, 0.95]) is the +calibration of record; design lineage: memory-core-redesign D6 records the +no-prefix history, this doc records the prefixed result, and the +calibration-verification procedure in memory-relevance-gate's design.md is +the documented re-run path (same harness family). Zero-injection residual +(73–87% of nothing-relevant queries still inject at the floor alone) remains +the relevance gate's job — gate numbers are cosine-independent and unaffected. + +## Risks / Trade-offs + +- **[Floor semantics change under operators' feet]** → nullable-follows- + manifest default means only operators who explicitly pinned 0.68 are + affected; schema description + skill guidance call it out; doctor and + `memory_retrieval_final` log the active floor and whether it came from + config or manifest. +- **[Actor/persistence boundaries]** → none moved: the purpose enum lives on + the existing seam interface in `Netclaw.Actors/Memory`; no persistence + shape changes; no new actor messages. Recall stays inside the existing + coordinator timeout envelope; failure modes and recovery are the Slice 4 + ones (sub-budget miss → lexical-only + `memory_recall_vector_degraded`), + now plus missing-calibration → lexical-only + the same degraded log with a + distinct reason. +- **[Prefix drift vs model]** → prefix is pinned next to the model hash in + the same allowlist entry; a model bump forces the author past the prefix + field. The fixture cross-check test asserts the arctic entry's prefix + matches the model-card string verbatim. +- **[Gold-set overfit]** → same risk profile as the 0.68 calibration it + replaces; mitigated identically (gold-set regression suite, documented + re-calibration procedure, config override escape hatch). diff --git a/openspec/changes/memory-query-prefix/proposal.md b/openspec/changes/memory-query-prefix/proposal.md new file mode 100644 index 000000000..dc95de1a7 --- /dev/null +++ b/openspec/changes/memory-query-prefix/proposal.md @@ -0,0 +1,105 @@ +# Proposal: memory-query-prefix + +## Why + +Production embeds recall queries with `snowflake-arctic-embed-m` but omits the +model's documented retrieval query prefix, silently forfeiting most of the +model's retrieval quality: measured on `gold-prod-2026-07`, the prefix lifts +F0.5 at the optimal floor from 0.141 to 0.239 (+69% relative), recall@3 from +0.146 to 0.318, and zero-injection accuracy from 13.3% to 26.7% +(`~/recall-research-local/2026-07/arctic-prefix-eval/RESULTS.md`, model card +confirmed for the exact pinned HF commit). The fix is query-side only — no +stored document vectors change — but it is **not drop-in**: the prefix +compresses arctic's cosine distribution downward (optimal floor shifts +0.68 → ~0.24), and prefixed queries against the current 0.68 floor measure +F0.5 = 0.0. Prefix and floor recalibration must ship atomically. + +Source PRD: PRD-007 (agent personality and local memory) — same lineage as +memory-core-redesign, which this change amends at the embedding-foundation +seam. + +## What Changes + +- `IMemoryEmbedder` gains a query-vs-passage distinction (embedding purpose) + so retrieval queries can carry a model-specific prefix while embed-on-write, + backfill, and the dedup nominator (document↔document comparisons) remain + unprefixed. Stored vectors are unaffected; no re-embed or backfill is + required. +- `EmbeddingModelProvisioner`'s allowlist entries carry per-model retrieval + metadata: the documented `QueryPrefix` (empty for models that use none) and + a `CalibratedMinCosineSimilarity` for the prefixed configuration — the same + manifest-carries-calibration pattern the relevance gate established with + `CalibratedThreshold`. This prepares the int8 variant, whose floor differs. +- `Memory.Recall.MinCosineSimilarity` becomes nullable: null (default) follows + the active model's manifest calibration; an explicit value overrides it. + **BREAKING** for configs that pinned the old 0.68 default explicitly — the + numeric meaning of the floor changes under the prefixed embedder, and the + schema description must say so. +- The recall coordinator applies the active model's query prefix when + embedding the turn query; the floor it enforces resolves from config-or- + manifest. +- Recalibration recorded: the prefixed fp32 sweep becomes the calibration of + record in memory-core-redesign's design.md D6 lineage (superseding the + no-prefix 0.68 calibration) via this change's own design doc; the + calibration-verification procedure documented by memory-relevance-gate + covers re-running it. + +## Capabilities + +### New Capabilities + +(none) + +### Modified Capabilities + +- `memory-embeddings`: embedding runtime requirement gains + query-vs-passage purpose semantics and manifest-carried retrieval + calibration (query prefix + calibrated floor) per allowlisted model. + Note: this capability's base spec is currently the pending delta in + `openspec/changes/memory-core-redesign/specs/memory-embeddings/spec.md` + (not yet archived to main specs); this change's delta applies on top of it. +- `netclaw-agent-memory`: the automatic pre-turn recall requirement's + absolute relevance floor becomes calibration-carried per model variant + (config override optional) instead of a single static default. + +## Impact + +- **Code**: `Netclaw.Actors/Memory` (`IMemoryEmbedder`, holder), `Netclaw. + Embeddings` (`OnnxMemoryEmbedder`, `EmbeddingModelProvisioner` allowlist), + `Netclaw.Actors/Sessions` (`SQLiteMemoryRecallCoordinator`), + `Netclaw.Configuration` (`MemoryRecallConfig`, schema), warmup service + unchanged except plumb-through; doctor output gains the active + prefix/floor. +- **Data**: none at rest — document vectors unchanged; query embeddings are + never persisted. No migration, no backfill. +- **Config**: `MinCosineSimilarity` default changes semantics + (nullable-follows-manifest); schema sync in the same PR per the + configuration schema sync rule. +- **Security/operational impact**: no new network or supply-chain surface + (prefix is a string constant in the pinned allowlist; no new artifacts). + Recall behavior changes for embeddings-enabled deployments only — + currently experimental/opt-in installs of the 0.25.0-alpha.onnx line; the + floor-follows-manifest default prevents the catastrophic + prefix-without-recalibration combination by construction. Doctor and + `memory_retrieval_final` logging expose the active prefix + floor so a + mismatch is diagnosable. +- **Evals**: recall-affecting change → eval suite run required (memory + category); gold-set regression suite thresholds unaffected (fixture + embedder is prefix-agnostic), but the fixture fake embedder must implement + the new seam. + +### In scope (MVP) + +- Prefix + purpose seam + manifest-carried floor for the two allowlisted + arctic variants (fp32 now; int8 entry lands with its own calibration in the + int8 productionization task). +- Atomic floor recalibration and config nullability. + +### Out of scope + +- Switching embedding models (e5-small-v2 evaluated and declined — + `~/recall-research-local/2026-07/e5-eval/RESULTS.md`). +- Re-running the relevance-gate threshold calibration (S* is measured on + gate scores, not cosine floors; unchanged). +- Symmetric-task prefixes (arctic documents none; e5-style dual prefixes are + a model-swap concern). diff --git a/openspec/changes/memory-query-prefix/specs/memory-embeddings/spec.md b/openspec/changes/memory-query-prefix/specs/memory-embeddings/spec.md new file mode 100644 index 000000000..b623e0f90 --- /dev/null +++ b/openspec/changes/memory-query-prefix/specs/memory-embeddings/spec.md @@ -0,0 +1,60 @@ +# Delta: memory-embeddings (memory-query-prefix) + +Base: this capability's base spec is the pending delta in +`openspec/changes/memory-core-redesign/specs/memory-embeddings/spec.md` +(not yet archived to main specs); the requirements below are ADDED on top +of it. + +## ADDED Requirements + +### Requirement: Purpose-distinguished embedding + +The embedding runtime SHALL distinguish retrieval-query embedding from +passage (document) embedding at its interface, and SHALL apply the active +model's documented retrieval query encoding — including any model-documented +query prefix — only to retrieval-query inputs. Document-side embedding +(embed-on-write, backfill, gap repair, and duplicate nomination) SHALL remain +in the model's document mode, byte-compatible with vectors already stored, so +adopting a query prefix SHALL NOT require re-embedding any stored content. + +#### Scenario: Recall query is embedded with the model's documented prefix + +- **GIVEN** the active embedding model's manifest documents a retrieval query + prefix +- **WHEN** the recall pipeline embeds a turn query +- **THEN** the embedded text is the documented prefix followed by the query +- **AND** the resulting vector is produced by the same session, pooling, and + normalization as document embeddings + +#### Scenario: Document-side embedding is unaffected by the prefix + +- **GIVEN** a corpus embedded before query-prefix support existed +- **WHEN** embed-on-write, backfill, or duplicate nomination embeds a + document or proposal +- **THEN** no prefix is applied +- **AND** the produced vectors are interchangeable with the pre-existing + stored vectors (no re-embed required) + +### Requirement: Manifest-carried retrieval calibration + +Each allowlisted embedding model entry SHALL carry its retrieval-mode +metadata: the documented query prefix (empty when the model documents none) +and the retrieval floor calibrated for that model in that encoding mode. +Runtime floor resolution SHALL prefer an explicit configuration override and +otherwise use the manifest calibration; components SHALL NOT hardcode floors +calibrated for a specific model or encoding mode outside the manifest. + +#### Scenario: Calibration travels with the model entry + +- **GIVEN** two allowlisted model variants with different calibrated floors +- **WHEN** the configured model id switches between them +- **THEN** the effective default floor changes to the newly active entry's + calibration without any code or configuration change + +#### Scenario: Prefix and floor cannot be recombined incorrectly by default + +- **GIVEN** a model entry whose calibration was measured with its documented + query prefix +- **WHEN** the runtime activates that entry with no explicit floor override +- **THEN** the prefixed encoding and its matching calibrated floor are applied + together diff --git a/openspec/changes/memory-query-prefix/specs/netclaw-agent-memory/spec.md b/openspec/changes/memory-query-prefix/specs/netclaw-agent-memory/spec.md new file mode 100644 index 000000000..3e450a5c1 --- /dev/null +++ b/openspec/changes/memory-query-prefix/specs/netclaw-agent-memory/spec.md @@ -0,0 +1,74 @@ +# Delta: netclaw-agent-memory (memory-query-prefix) + +Base: this delta applies on top of memory-core-redesign's pending delta for +the same requirement (hybrid recall + absolute floor), which is the current +authoritative text pre-archive. + +## MODIFIED Requirements + +### Requirement: Automatic pre-turn recall + +The system SHALL execute automatic recall before each user-facing model turn +using the latest user message, recent session context, active anchors, and +policy scope. Recall SHALL be hybrid: lexical (FTS5) and semantic (embedding +cosine) candidates are merged, and every candidate SHALL pass the identical +audience/boundary/sensitivity/recall-mode policy gates regardless of which +retriever surfaced it. The turn query SHALL be embedded in the active +embedding model's documented retrieval mode (including any model-documented +query prefix); document-side embeddings SHALL remain in the model's document +mode. Injection SHALL be gated by an absolute relevance floor whose value +SHALL resolve from the active model's manifest-carried retrieval calibration +unless explicitly overridden in configuration; a floor calibrated for one +model or encoding mode SHALL NOT be silently applied to another — when the +active model carries no retrieval calibration and no explicit override is +configured, recall SHALL run lexical-only with a structured degradation log. +When no candidate clears the effective floor, the turn SHALL inject nothing +and the recall context block SHALL be omitted entirely. Automatic recall +SHALL be bounded by a latency budget and SHALL degrade safely — to +lexical-only scoring with a structured degradation log when the embedder is +unavailable or over its sub-budget, and to no injection when the memory +substrate is unavailable. + +#### Scenario: Recall completes within budget + +- **GIVEN** the memory substrate is healthy +- **WHEN** a new turn begins +- **THEN** the session retrieves and injects a bounded recall bundle before the + model call +- **AND** the recall operation completes within the configured time budget or + degrades safely + +#### Scenario: Nothing relevant means nothing injected + +- **GIVEN** the memory store contains no memory semantically related to the + user's message +- **WHEN** automatic recall runs for the turn +- **THEN** no memory items are injected +- **AND** no recall context block is added to the prompt +- **AND** the retrieval log records zero injected items with the applied floor + +#### Scenario: Vector-sourced candidates obey policy gates + +- **GIVEN** a memory item excluded by the session's audience or sensitivity + policy +- **WHEN** the semantic retriever surfaces that item as a top cosine candidate +- **THEN** the item is filtered before scoring exactly as a lexical candidate + would be + +#### Scenario: Floor follows the active model's calibration + +- **GIVEN** `Memory.Recall.MinCosineSimilarity` is not explicitly configured +- **WHEN** recall runs with an embedding model whose manifest carries a + calibrated retrieval floor +- **THEN** that manifest value is the effective floor for the turn +- **AND** the retrieval log records the effective floor and its source + (manifest or config override) + +#### Scenario: Missing calibration degrades to lexical-only + +- **GIVEN** the active embedding model's manifest carries no retrieval + calibration and no explicit floor override is configured +- **WHEN** a turn begins with the embedder healthy +- **THEN** recall runs lexical-only for the turn +- **AND** a rate-limited structured degradation log records the missing + calibration as the reason diff --git a/openspec/changes/memory-query-prefix/tasks.md b/openspec/changes/memory-query-prefix/tasks.md new file mode 100644 index 000000000..ff8b2887c --- /dev/null +++ b/openspec/changes/memory-query-prefix/tasks.md @@ -0,0 +1,58 @@ +# Tasks: memory-query-prefix + +Implementation targets `feature/memory-embeddings` (on top of +memory-core-redesign Slices 2–4 and memory-relevance-gate). The prefix and +the floor recalibration ship in the same slice — they are not independently +safe. + +## 1. Seam, manifest, and embedder + +- [ ] 1.1 `EmbeddingPurpose` enum (`Passage`, `RetrievalQuery`) and purpose + parameter on `IMemoryEmbedder.EmbedAsync`/`EmbedBatchAsync`; update ALL + call sites explicitly (embed-on-write, backfill CLI, gap repair, dedup + nominator → `Passage`; recall coordinator → `RetrievalQuery`); no + optional parameter. `UnavailableMemoryEmbedder` + all test fakes updated. +- [ ] 1.2 `EmbeddingModelManifestEntry` gains `QueryPrefix` (string) and + `CalibratedMinCosineSimilarity` (double?); arctic fp32 entry pins the + model-card prefix verbatim and `CalibratedMinCosineSimilarity = 0.24`; + mxbai fallback entry carries its own documented prefix and a null + calibration (uncalibrated → recall treats as hybrid-unavailable) +- [ ] 1.3 `OnnxMemoryEmbedder` prepends the manifest `QueryPrefix` for + `RetrievalQuery` purpose before tokenization; passage path + byte-identical to today (regression-guarded by an exact-vector test + against the fixture model) +- [ ] 1.4 Holder plumbing: active entry's `QueryPrefix`/ + `CalibratedMinCosineSimilarity` reachable at the recall seam (same + pattern as `RelevanceScorerHolder.CalibratedThreshold`) + +## 2. Floor resolution, coordinator, config + +- [ ] 2.1 `MemoryRecallConfig.MinCosineSimilarity` becomes nullable; null → + manifest calibration, explicit value → override; schema sync + (nullable, description warns the value is model+encoding specific) + + defaults tests updated +- [ ] 2.2 `SQLiteMemoryRecallCoordinator`: effective floor resolved + config-or-manifest per turn; missing calibration + no override ⇒ + lexical-only + rate-limited degraded log (distinct reason, same + cooldown pattern); `memory_retrieval_final` logs effective floor and + its source +- [ ] 2.3 Doctor: embedding check reports active model's prefix presence and + effective floor source +- [ ] 2.4 Tests: prefix applied only to `RetrievalQuery` (fixture-model + vector inequality query-vs-passage for same text); passage-path + byte-compat regression; floor resolution matrix (manifest / override / + missing-calibration degrade); coordinator lexical-only on + missing-calibration; scenario suite (P01–P21 incl. P09) green with the + new seam signature + +## 3. Calibration record, evals, docs + +- [ ] 3.1 Record the prefixed fp32 calibration as the calibration of record: + design.md of THIS change already carries the table; add a superseding + note to memory-core-redesign design.md D6 (0.68 remains the no-prefix + historical record) — keep both changes' docs consistent +- [ ] 3.2 Eval suite run (memory category) on the final behavior +- [ ] 3.3 `netclaw-memory` skill sync: prefix is automatic, floor now + follows the model manifest by default, override knob semantics; bump + metadata.version +- [ ] 3.4 Full gates: build, all affected test suites, slopwatch, headers From a646aab8a0dd032c814b5a9dabcb5b6ffda1b879 Mon Sep 17 00:00:00 2001 From: Aaron Stannard Date: Wed, 8 Jul 2026 23:08:56 +0000 Subject: [PATCH 22/37] feat(memory): model-documented query prefix + manifest-carried floor calibration (memory-query-prefix) Implements all tasks (sections 1-3) of the memory-query-prefix OpenSpec change on top of memory-core-redesign Slices 2-4 and memory-relevance-gate. - EmbeddingPurpose (Passage | RetrievalQuery) added to the IMemoryEmbedder seam; every call site updated explicitly (embed-on-write, backfill CLI, gap repair, dedup nominator -> Passage; recall coordinator -> RetrievalQuery). No optional parameter. - EmbeddingModelManifestEntry gains QueryPrefix + CalibratedMinCosineSimilarity. Arctic fp32 entry pins the verified model-card prefix ("Represent this sentence for searching relevant passages: ") and CalibratedMinCosineSimilarity = 0.24. mxbai fallback entry's query prefix independently verified against its own HF model card (identical string, not copy-paste drift) with CalibratedMinCosineSimilarity left null (uncalibrated). - OnnxMemoryEmbedder applies the manifest prefix only for RetrievalQuery, before tokenization; passage path is byte-identical, pinned by a regression test against the pre-change vector. - MemoryEmbedderHolder now carries QueryPrefix/CalibratedMinCosineSimilarity alongside the embedder, mirroring RelevanceScorerHolder.CalibratedThreshold. - MemoryRecallConfig.MinCosineSimilarity is now nullable; null follows the active model's manifest calibration, explicit value overrides it. Schema updated to type ["number","null"] with a description warning the value is model/encoding-specific. - SQLiteMemoryRecallCoordinator resolves the effective floor per turn (override ?? manifest); missing calibration + no override degrades to lexical-only with a distinct "missing_calibration" reason via the existing rate-limited degraded-log mechanism. memory_retrieval_final now logs appliedFloor + floorSource (manifest|override|n/a). - MemoryEmbeddingDoctorCheck reports active-model query-prefix presence and effective floor + source. - memory-core-redesign design.md D6 gets a superseding note: 0.68 is the no-prefix historical record, 0.24 (prefixed) is the calibration of record. - netclaw-memory skill bumped to 1.12.0: prefix is automatic per-model, floor follows the manifest by default, override semantics documented. Gates: dotnet build clean; Actors (2669), Embeddings (47), Configuration (467), Daemon (835), Cli (1240) test suites all green; slopwatch 0 issues; file headers verified; eval suite (NETCLAW_EVAL_CATEGORY=Memory, qwen3:8b @ old-gpu:11434) 6/6 cases passed (100%). --- .../.system/files/netclaw-memory/SKILL.md | 62 ++++++-- .../changes/memory-core-redesign/design.md | 13 ++ openspec/changes/memory-query-prefix/tasks.md | 24 +-- .../MemoryCurationActorNominatorTests.cs | 6 +- .../MemoryCurationEvaluatorParityTests.cs | 12 +- .../Memory/MemoryCurationNominatorTests.cs | 14 +- .../MemoryEmbedOnWriteCoordinatorTests.cs | 14 +- .../Memory/UnavailableMemoryEmbedderTests.cs | 4 +- .../Sessions/MemoryRecallScenarioTests.cs | 11 +- .../Sessions/SQLiteMemoryRecallGateTests.cs | 11 +- .../Sessions/SQLiteMemoryRecallHybridTests.cs | 148 +++++++++++++++++- src/Netclaw.Actors/Memory/IMemoryEmbedder.cs | 48 ++++-- .../Memory/MemoryCurationEvaluator.cs | 2 +- .../Memory/MemoryEmbedOnWriteCoordinator.cs | 2 +- .../Memory/MemoryEmbedderHolder.cs | 52 +++++- .../Sessions/SQLiteMemoryRecallCoordinator.cs | 89 +++++++++-- .../Doctor/ConfigSchemaDoctorCheckTests.cs | 29 ++++ .../Doctor/MemoryEmbeddingDoctorCheckTests.cs | 51 +++++- .../Memory/MemoryCommandTests.cs | 4 +- .../Doctor/MemoryEmbeddingDoctorCheck.cs | 34 +++- src/Netclaw.Cli/Memory/MemoryCommand.cs | 4 +- .../MemoryConfigDefaultsTests.cs | 7 +- src/Netclaw.Configuration/MemoryConfig.cs | 37 +++-- .../Schemas/netclaw-config.v1.schema.json | 5 +- .../DaemonRuntimeStatusServiceTests.cs | 8 +- .../EmbeddingWarmupHostedServiceTests.cs | 41 +++-- src/Netclaw.Daemon/Program.cs | 13 +- .../Services/EmbeddingWarmupHostedService.cs | 33 ++-- .../EmbedQueryLatencyBudgetTests.cs | 6 +- .../EmbeddingModelProvisionerTests.cs | 43 ++++- .../OnnxMemoryEmbedderTests.cs | 101 ++++++++++-- .../EmbeddingModelProvisioner.cs | 72 +++++++-- src/Netclaw.Embeddings/OnnxMemoryEmbedder.cs | 50 +++++- tools/embed-latency-bench/Program.cs | 27 ++-- 34 files changed, 892 insertions(+), 185 deletions(-) diff --git a/feeds/skills/.system/files/netclaw-memory/SKILL.md b/feeds/skills/.system/files/netclaw-memory/SKILL.md index abc7c5cca..cdbb0ff1c 100644 --- a/feeds/skills/.system/files/netclaw-memory/SKILL.md +++ b/feeds/skills/.system/files/netclaw-memory/SKILL.md @@ -3,7 +3,7 @@ name: netclaw-memory description: "REQUIRED when the user asks what you remember, recall, or know from past conversations, previous sessions, cross-session memory, memory classes, or memory types. Also before using memory tools: find_memories, get_memories, store_memory, update_memory." metadata: author: netclaw - version: "1.11.0" + version: "1.12.0" --- # Netclaw Memory @@ -72,21 +72,44 @@ lexical-only — same candidate pool, no vector term or cosine floor. **recency-decayed** (a half-life multiplier that favors fresher memories among otherwise similar candidates but never zeroes out an old one on age alone). +- **Query prefix is automatic, per-model**: the turn query is embedded using + whatever retrieval-query encoding the active embedding model documents — + for the shipped `snowflake-arctic-embed-m`, that means a fixed instruction + string is prepended before the query text. This is a property of the + model, not something you configure; document-side embeddings (stored + memories) are never prefixed, so this never requires re-embedding existing + content. - **Absolute floor**: independent of the fused score, any candidate whose raw - cosine similarity falls below `MinCosineSimilarity` is dropped before - ranking. If nothing clears the floor, nothing is injected — this is a - correct, healthy outcome, not degraded recall. See the zero-injection note - above: don't editorialize about memory being broken when this happens. -- **Defaults** (`Memory.Recall` in `netclaw.json`): `VectorWeight` 0.7, - `LexicalWeight` 0.3, `MinCosineSimilarity` 0.68 (calibrated against a - real-traffic gold set, not a placeholder), `RecencyHalfLifeDays` 30. + cosine similarity falls below the effective `MinCosineSimilarity` is + dropped before ranking. If nothing clears the floor, nothing is injected — + this is a correct, healthy outcome, not degraded recall. See the + zero-injection note above: don't editorialize about memory being broken + when this happens. +- **The floor follows the active model's manifest by default.** + `Memory.Recall.MinCosineSimilarity` (nullable) is `null` unless an operator + explicitly overrides it — when `null`, the effective floor is whichever + calibration is pinned to the currently active embedding model (0.24 for + the shipped, prefixed `snowflake-arctic-embed-m` encoding). **The numeric + meaning of this value is model- and encoding-specific**: cosine + distributions shift materially between models, and even for the same + model between a prefixed and unprefixed encoding — an old value copied + from a different model/encoding combination can silently break recall + (measured: F0.5 = 0.0 when the pre-prefix 0.68 floor was applied to + prefixed queries). Only set an explicit override after re-running the + calibration-verification procedure against the model and encoding actually + active. `Memory.Recall.VectorWeight` defaults 0.7, `LexicalWeight` 0.3, + `RecencyHalfLifeDays` 30. - **Degradation is explicit and logged, not silent**: a turn whose query-embedding step misses its latency sub-budget (or has no embedder available) falls back to lexical-only scoring for that turn and logs - `memory_recall_vector_degraded`. A candidate with no embedding row for the - current model degrades to lexical-only scoring for that candidate alone - (rather than being excluded) and logs `memory_recall_coverage_gap`. Both - are self-healing, not persistent failures — see Diagnostics below. + `memory_recall_vector_degraded`. A model with no manifest-carried + retrieval calibration and no explicit `MinCosineSimilarity` override + degrades the same way, with reason `missing_calibration` — this is + expected for a newly-added or not-yet-calibrated model variant, not a + bug. A candidate with no embedding row for the current model degrades to + lexical-only scoring for that candidate alone (rather than being excluded) + and logs `memory_recall_coverage_gap`. All of these are self-healing or + intentional, not persistent failures — see Diagnostics below. - **Backfilling an existing corpus**: enabling `Memory.Embeddings.Enabled` on a deployment that already has memories does not retroactively embed them. Until they're embedded, recall for those documents degrades to @@ -225,9 +248,11 @@ Useful log events: **Recall pipeline** (grep for `memory_retrieval` / `memory_recall`): - `memory_retrieval_request_plan` — query tokenization, facets, soft scopes, anchor hints - `memory_retrieval_candidate_selection` — all candidates with selector scores -- `memory_retrieval_final` — floor filtering results, final injected items; also carries - `gateScores` (the cross-encoder score for every candidate the relevance gate scored) and - `droppedByGate` (count the gate dropped) when the gate ran +- `memory_retrieval_final` — floor filtering results, final injected items; carries + `appliedFloor` and `floorSource` (`manifest` or `override`) so a floor mismatch is + diagnosable without reading config; also carries `gateScores` (the cross-encoder score for + every candidate the relevance gate scored) and `droppedByGate` (count the gate dropped) when + the gate ran - `turn_memory_recall` — summary event with item count and duration - `memory_recall_vector_degraded` — turn fell back to lexical-only recall (embedder unavailable, no vector index, or the query-embedding sub-budget was exceeded) @@ -250,6 +275,13 @@ Embeddings are provisioned at daemon start when `Memory.Embeddings.Enabled` is - Daemon status shows: `embeddings: degraded` - Lexical recall continues to work normally +`netclaw doctor`'s Memory Embeddings check reports whether the active model +has a query prefix (`queryPrefix=True/False`) and the effective retrieval +floor plus its source (`floor=0.240 (source=manifest)`, or `floor=none ...` +when the active model carries no retrieval calibration and no override is +configured) — check this first when recall quality looks off after a model +or config change. + To repopulate existing memory vectors after enabling embeddings: ``` netclaw memory backfill-embeddings [--force] diff --git a/openspec/changes/memory-core-redesign/design.md b/openspec/changes/memory-core-redesign/design.md index 79b4fdff7..5aaa08e2b 100644 --- a/openspec/changes/memory-core-redesign/design.md +++ b/openspec/changes/memory-core-redesign/design.md @@ -242,6 +242,19 @@ alone cannot close the zero-injection gap within the F0.5-preserving range — that residual is tracked as the separate `memory-relevance-gate` change, not solved here. +**Superseded by `memory-query-prefix` (2026-07-08):** the 0.68 figure above +was measured with the query embedded RAW — `snowflake-arctic-embed-m`'s +documented retrieval-query prefix (`Represent this sentence for searching +relevant passages: `) was never applied. That is a no-prefix historical +record, kept here for the archive, not the calibration of record. Applying +the prefix compresses the model's cosine distribution downward (optimal τ +0.68 → 0.24) and lifts F0.5 at the optimum from 0.141 to 0.239 (+69% +relative) — see `openspec/changes/memory-query-prefix/design.md` D4 for the +full prefixed sweep and the atomic prefix+floor recalibration rationale. +`MemoryRecallConfig.MinCosineSimilarity` no longer defaults to 0.68; it +defaults to null and follows the active model's manifest-carried +calibration (0.24 for the shipped prefixed encoding). + ### D7. Taxonomy rebalance: recall modes mean what they say - **BREAKING (semantic fix)**: `Searchable` leaves the automatic recall pool diff --git a/openspec/changes/memory-query-prefix/tasks.md b/openspec/changes/memory-query-prefix/tasks.md index ff8b2887c..0396b6cfe 100644 --- a/openspec/changes/memory-query-prefix/tasks.md +++ b/openspec/changes/memory-query-prefix/tasks.md @@ -7,38 +7,38 @@ safe. ## 1. Seam, manifest, and embedder -- [ ] 1.1 `EmbeddingPurpose` enum (`Passage`, `RetrievalQuery`) and purpose +- [x] 1.1 `EmbeddingPurpose` enum (`Passage`, `RetrievalQuery`) and purpose parameter on `IMemoryEmbedder.EmbedAsync`/`EmbedBatchAsync`; update ALL call sites explicitly (embed-on-write, backfill CLI, gap repair, dedup nominator → `Passage`; recall coordinator → `RetrievalQuery`); no optional parameter. `UnavailableMemoryEmbedder` + all test fakes updated. -- [ ] 1.2 `EmbeddingModelManifestEntry` gains `QueryPrefix` (string) and +- [x] 1.2 `EmbeddingModelManifestEntry` gains `QueryPrefix` (string) and `CalibratedMinCosineSimilarity` (double?); arctic fp32 entry pins the model-card prefix verbatim and `CalibratedMinCosineSimilarity = 0.24`; mxbai fallback entry carries its own documented prefix and a null calibration (uncalibrated → recall treats as hybrid-unavailable) -- [ ] 1.3 `OnnxMemoryEmbedder` prepends the manifest `QueryPrefix` for +- [x] 1.3 `OnnxMemoryEmbedder` prepends the manifest `QueryPrefix` for `RetrievalQuery` purpose before tokenization; passage path byte-identical to today (regression-guarded by an exact-vector test against the fixture model) -- [ ] 1.4 Holder plumbing: active entry's `QueryPrefix`/ +- [x] 1.4 Holder plumbing: active entry's `QueryPrefix`/ `CalibratedMinCosineSimilarity` reachable at the recall seam (same pattern as `RelevanceScorerHolder.CalibratedThreshold`) ## 2. Floor resolution, coordinator, config -- [ ] 2.1 `MemoryRecallConfig.MinCosineSimilarity` becomes nullable; null → +- [x] 2.1 `MemoryRecallConfig.MinCosineSimilarity` becomes nullable; null → manifest calibration, explicit value → override; schema sync (nullable, description warns the value is model+encoding specific) + defaults tests updated -- [ ] 2.2 `SQLiteMemoryRecallCoordinator`: effective floor resolved +- [x] 2.2 `SQLiteMemoryRecallCoordinator`: effective floor resolved config-or-manifest per turn; missing calibration + no override ⇒ lexical-only + rate-limited degraded log (distinct reason, same cooldown pattern); `memory_retrieval_final` logs effective floor and its source -- [ ] 2.3 Doctor: embedding check reports active model's prefix presence and +- [x] 2.3 Doctor: embedding check reports active model's prefix presence and effective floor source -- [ ] 2.4 Tests: prefix applied only to `RetrievalQuery` (fixture-model +- [x] 2.4 Tests: prefix applied only to `RetrievalQuery` (fixture-model vector inequality query-vs-passage for same text); passage-path byte-compat regression; floor resolution matrix (manifest / override / missing-calibration degrade); coordinator lexical-only on @@ -47,12 +47,12 @@ safe. ## 3. Calibration record, evals, docs -- [ ] 3.1 Record the prefixed fp32 calibration as the calibration of record: +- [x] 3.1 Record the prefixed fp32 calibration as the calibration of record: design.md of THIS change already carries the table; add a superseding note to memory-core-redesign design.md D6 (0.68 remains the no-prefix historical record) — keep both changes' docs consistent -- [ ] 3.2 Eval suite run (memory category) on the final behavior -- [ ] 3.3 `netclaw-memory` skill sync: prefix is automatic, floor now +- [x] 3.2 Eval suite run (memory category) on the final behavior +- [x] 3.3 `netclaw-memory` skill sync: prefix is automatic, floor now follows the model manifest by default, override knob semantics; bump metadata.version -- [ ] 3.4 Full gates: build, all affected test suites, slopwatch, headers +- [x] 3.4 Full gates: build, all affected test suites, slopwatch, headers diff --git a/src/Netclaw.Actors.Tests/Memory/MemoryCurationActorNominatorTests.cs b/src/Netclaw.Actors.Tests/Memory/MemoryCurationActorNominatorTests.cs index e5c3c13f6..b3dc6d11b 100644 --- a/src/Netclaw.Actors.Tests/Memory/MemoryCurationActorNominatorTests.cs +++ b/src/Netclaw.Actors.Tests/Memory/MemoryCurationActorNominatorTests.cs @@ -79,7 +79,7 @@ await store.UpsertDocumentAsync(new SQLiteMemoryDocument( await store.UpsertEmbeddingAsync( "doc-existing", MemoryEmbedOnWriteCoordinator.DocumentItemKind, ModelId, "hash-existing", ExistingVector, ct); - var embedderHolder = new MemoryEmbedderHolder(new ScriptedEmbedder(ModelId, Dimensions, QueryVectorAt093)); + var embedderHolder = new MemoryEmbedderHolder(new ScriptedEmbedder(ModelId, Dimensions, QueryVectorAt093), initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: null); var vectorIndexHolder = new MemoryVectorIndexHolder(store); var chatClient = new RecordingCurationChatClient("CREATE"); var clientProvider = new SingleClientProvider(chatClient); @@ -174,10 +174,10 @@ private sealed class ScriptedEmbedder(string modelId, int dimensions, float[] qu public bool IsAvailable => true; - public ValueTask> EmbedAsync(string text, CancellationToken ct) + public ValueTask> EmbedAsync(string text, EmbeddingPurpose purpose, CancellationToken ct) => ValueTask.FromResult>(queryVector); - public ValueTask>> EmbedBatchAsync(IReadOnlyList texts, CancellationToken ct) + public ValueTask>> EmbedBatchAsync(IReadOnlyList texts, EmbeddingPurpose purpose, CancellationToken ct) => ValueTask.FromResult>>( texts.Select(_ => (ReadOnlyMemory)queryVector).ToList()); } diff --git a/src/Netclaw.Actors.Tests/Memory/MemoryCurationEvaluatorParityTests.cs b/src/Netclaw.Actors.Tests/Memory/MemoryCurationEvaluatorParityTests.cs index 6510f3867..81029e183 100644 --- a/src/Netclaw.Actors.Tests/Memory/MemoryCurationEvaluatorParityTests.cs +++ b/src/Netclaw.Actors.Tests/Memory/MemoryCurationEvaluatorParityTests.cs @@ -343,7 +343,9 @@ await _store.UpsertEmbeddingAsync( "the", "Deployment jobs wait in a queue before promotion to production.", freshnessAtMs: 2000); var embedderHolder = new MemoryEmbedderHolder( - new ScriptedEmbedder("test-nominator-model", dimensions: 2, [0.93f, 0.367623f])); + new ScriptedEmbedder("test-nominator-model", dimensions: 2, [0.93f, 0.367623f]), + initialQueryPrefix: "", + initialCalibratedMinCosineSimilarity: null); var vectorIndexHolder = new MemoryVectorIndexHolder(_store); var actorLike = new MemoryCurationEvaluator( @@ -462,7 +464,9 @@ await _store.UpsertEmbeddingAsync( "sunfish-deploy-queue", "Deployment jobs wait in a queue before promotion to production.", freshnessAtMs: 2000); var embedderHolder = new MemoryEmbedderHolder( - new ScriptedEmbedder("test-nominator-model", dimensions: 2, [0.93f, 0.367623f])); + new ScriptedEmbedder("test-nominator-model", dimensions: 2, [0.93f, 0.367623f]), + initialQueryPrefix: "", + initialCalibratedMinCosineSimilarity: null); var vectorIndexHolder = new MemoryVectorIndexHolder(_store); var actorLike = new MemoryCurationEvaluator( @@ -602,10 +606,10 @@ private sealed class ScriptedEmbedder(string modelId, int dimensions, float[] qu public bool IsAvailable => true; - public ValueTask> EmbedAsync(string text, CancellationToken ct) + public ValueTask> EmbedAsync(string text, EmbeddingPurpose purpose, CancellationToken ct) => ValueTask.FromResult>(queryVector); - public ValueTask>> EmbedBatchAsync(IReadOnlyList texts, CancellationToken ct) + public ValueTask>> EmbedBatchAsync(IReadOnlyList texts, EmbeddingPurpose purpose, CancellationToken ct) => ValueTask.FromResult>>( texts.Select(_ => (ReadOnlyMemory)queryVector).ToList()); } diff --git a/src/Netclaw.Actors.Tests/Memory/MemoryCurationNominatorTests.cs b/src/Netclaw.Actors.Tests/Memory/MemoryCurationNominatorTests.cs index 1fdd1e0da..8e383ee5a 100644 --- a/src/Netclaw.Actors.Tests/Memory/MemoryCurationNominatorTests.cs +++ b/src/Netclaw.Actors.Tests/Memory/MemoryCurationNominatorTests.cs @@ -77,7 +77,7 @@ public async Task Paraphrase_pair_at_cosine_0_93_forces_LLM_tier_even_though_Jac await SeedDocumentWithEmbeddingAsync("graphite-render-cache", "doc-existing", existingBody, freshnessAtMs: 1000, ct); var operation = MakeOperation("sunfish-deploy-queue", proposalContent, freshnessAtMs: 2000); - var embedderHolder = new MemoryEmbedderHolder(new ScriptedEmbedder(ModelId, Dimensions, QueryVectorAt093)); + var embedderHolder = new MemoryEmbedderHolder(new ScriptedEmbedder(ModelId, Dimensions, QueryVectorAt093), initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: null); var vectorIndexHolder = new MemoryVectorIndexHolder(_store); var chatClient = new RecordingCurationChatClient("CREATE"); @@ -105,7 +105,7 @@ public async Task Nominee_present_LLM_says_Create_persists_two_separate_document var operation = MakeOperation( "sunfish-deploy-queue", "Deployment jobs wait in a queue before promotion to production.", freshnessAtMs: 2000); - var embedderHolder = new MemoryEmbedderHolder(new ScriptedEmbedder(ModelId, Dimensions, QueryVectorAt093)); + var embedderHolder = new MemoryEmbedderHolder(new ScriptedEmbedder(ModelId, Dimensions, QueryVectorAt093), initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: null); var vectorIndexHolder = new MemoryVectorIndexHolder(_store); var chatClient = new RecordingCurationChatClient("CREATE"); @@ -137,7 +137,7 @@ public async Task Nominee_present_with_no_LLM_available_conservatively_creates_n var operation = MakeOperation( "sunfish-deploy-queue", "Deployment jobs wait in a queue before promotion to production.", freshnessAtMs: 2000); - var embedderHolder = new MemoryEmbedderHolder(new ScriptedEmbedder(ModelId, Dimensions, QueryVectorAt093)); + var embedderHolder = new MemoryEmbedderHolder(new ScriptedEmbedder(ModelId, Dimensions, QueryVectorAt093), initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: null); var vectorIndexHolder = new MemoryVectorIndexHolder(_store); // No LLM client at all — the daemon-checkpoint-worker shape today. A nominee here must @@ -173,7 +173,7 @@ public async Task Novel_proposal_with_no_nominee_and_no_anchor_match_skips_the_c var operation = MakeOperation( "brand-new-topic", "Completely novel content nobody has proposed before.", freshnessAtMs: 1000); - var embedderHolder = new MemoryEmbedderHolder(new ScriptedEmbedder(ModelId, Dimensions, QueryVectorAt093)); + var embedderHolder = new MemoryEmbedderHolder(new ScriptedEmbedder(ModelId, Dimensions, QueryVectorAt093), initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: null); var vectorIndexHolder = new MemoryVectorIndexHolder(_store); var chatClient = new RecordingCurationChatClient("CREATE"); @@ -206,7 +206,7 @@ public async Task Embedder_unavailable_falls_back_to_lexical_search_and_logs_the var operation = MakeOperation("another-unrelated-subject", proposalContent, freshnessAtMs: 2000); var recordingLogger = new RecordingLogger(); - var embedderHolder = new MemoryEmbedderHolder(new UnavailableMemoryEmbedder(ModelId, "not provisioned")); + var embedderHolder = new MemoryEmbedderHolder(new UnavailableMemoryEmbedder(ModelId, "not provisioned"), initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: null); var vectorIndexHolder = new MemoryVectorIndexHolder(_store); var evaluator = new MemoryCurationEvaluator( @@ -336,10 +336,10 @@ private sealed class ScriptedEmbedder(string modelId, int dimensions, float[] qu public bool IsAvailable => true; - public ValueTask> EmbedAsync(string text, CancellationToken ct) + public ValueTask> EmbedAsync(string text, EmbeddingPurpose purpose, CancellationToken ct) => ValueTask.FromResult>(queryVector); - public ValueTask>> EmbedBatchAsync(IReadOnlyList texts, CancellationToken ct) + public ValueTask>> EmbedBatchAsync(IReadOnlyList texts, EmbeddingPurpose purpose, CancellationToken ct) => ValueTask.FromResult>>( texts.Select(_ => (ReadOnlyMemory)queryVector).ToList()); } diff --git a/src/Netclaw.Actors.Tests/Memory/MemoryEmbedOnWriteCoordinatorTests.cs b/src/Netclaw.Actors.Tests/Memory/MemoryEmbedOnWriteCoordinatorTests.cs index 226e4dbcb..8792ceaaf 100644 --- a/src/Netclaw.Actors.Tests/Memory/MemoryEmbedOnWriteCoordinatorTests.cs +++ b/src/Netclaw.Actors.Tests/Memory/MemoryEmbedOnWriteCoordinatorTests.cs @@ -54,7 +54,7 @@ await _store.UpsertDocumentAsync(new SQLiteMemoryDocument( CreatedAtMs: now, UpdatedAtMs: now), TestContext.Current.CancellationToken); - var holder = new MemoryEmbedderHolder(new FakeMemoryEmbedder("model-a", dimensions: 3)); + var holder = new MemoryEmbedderHolder(new FakeMemoryEmbedder("model-a", dimensions: 3), initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: null); var written = new[] { new MemoryDocumentWriteResult("doc-1", "Title", "Body") }; await MemoryEmbedOnWriteCoordinator.EmbedWrittenDocumentsAsync( @@ -86,7 +86,7 @@ await MemoryEmbedOnWriteCoordinator.EmbedWrittenDocumentsAsync( [Fact] public async Task Unavailable_embedder_skips_embedding_without_throwing() { - var holder = new MemoryEmbedderHolder(new UnavailableMemoryEmbedder("model-a", "not provisioned")); + var holder = new MemoryEmbedderHolder(new UnavailableMemoryEmbedder("model-a", "not provisioned"), initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: null); var written = new[] { new MemoryDocumentWriteResult("doc-1", "Title", "Body") }; await MemoryEmbedOnWriteCoordinator.EmbedWrittenDocumentsAsync( @@ -98,7 +98,7 @@ await MemoryEmbedOnWriteCoordinator.EmbedWrittenDocumentsAsync( [Fact] public async Task Embed_failure_on_one_item_is_isolated_and_does_not_throw_or_block_others() { - var holder = new MemoryEmbedderHolder(new FakeMemoryEmbedder("model-a", dimensions: 2, failOnText: "Bad\nBody")); + var holder = new MemoryEmbedderHolder(new FakeMemoryEmbedder("model-a", dimensions: 2, failOnText: "Bad\nBody"), initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: null); var written = new[] { new MemoryDocumentWriteResult("doc-bad", "Bad", "Body"), @@ -118,7 +118,7 @@ await MemoryEmbedOnWriteCoordinator.EmbedWrittenDocumentsAsync( [Fact] public async Task Empty_written_list_is_a_no_op() { - var holder = new MemoryEmbedderHolder(new FakeMemoryEmbedder("model-a", dimensions: 2)); + var holder = new MemoryEmbedderHolder(new FakeMemoryEmbedder("model-a", dimensions: 2), initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: null); await MemoryEmbedOnWriteCoordinator.EmbedWrittenDocumentsAsync( holder, _store, [], NullLogger.Instance, TestContext.Current.CancellationToken); @@ -134,7 +134,7 @@ private sealed class FakeMemoryEmbedder(string modelId, int dimensions, string? public bool IsAvailable => true; - public ValueTask> EmbedAsync(string text, CancellationToken ct) + public ValueTask> EmbedAsync(string text, EmbeddingPurpose purpose, CancellationToken ct) { if (failOnText is not null && string.Equals(text, failOnText, StringComparison.Ordinal)) throw new InvalidOperationException("simulated embed failure"); @@ -142,11 +142,11 @@ public ValueTask> EmbedAsync(string text, CancellationToke return ValueTask.FromResult>(new float[dimensions]); } - public async ValueTask>> EmbedBatchAsync(IReadOnlyList texts, CancellationToken ct) + public async ValueTask>> EmbedBatchAsync(IReadOnlyList texts, EmbeddingPurpose purpose, CancellationToken ct) { var results = new List>(texts.Count); foreach (var text in texts) - results.Add(await EmbedAsync(text, ct)); + results.Add(await EmbedAsync(text, purpose, ct)); return results; } } diff --git a/src/Netclaw.Actors.Tests/Memory/UnavailableMemoryEmbedderTests.cs b/src/Netclaw.Actors.Tests/Memory/UnavailableMemoryEmbedderTests.cs index d456bb3d8..5a30879af 100644 --- a/src/Netclaw.Actors.Tests/Memory/UnavailableMemoryEmbedderTests.cs +++ b/src/Netclaw.Actors.Tests/Memory/UnavailableMemoryEmbedderTests.cs @@ -26,7 +26,7 @@ public async Task EmbedAsync_throws_with_remediation_text_instead_of_returning_a IMemoryEmbedder embedder = new UnavailableMemoryEmbedder("snowflake-arctic-embed-m", "hash verification failed"); var ex = await Assert.ThrowsAsync( - async () => await embedder.EmbedAsync("some text", CancellationToken.None)); + async () => await embedder.EmbedAsync("some text", EmbeddingPurpose.Passage, CancellationToken.None)); Assert.Contains("hash verification failed", ex.Message, StringComparison.Ordinal); Assert.Contains("snowflake-arctic-embed-m", ex.Message, StringComparison.Ordinal); @@ -39,6 +39,6 @@ public async Task EmbedBatchAsync_throws_instead_of_returning_garbage_vectors() IMemoryEmbedder embedder = new UnavailableMemoryEmbedder("snowflake-arctic-embed-m", "runtime load error"); await Assert.ThrowsAsync( - async () => await embedder.EmbedBatchAsync(["a", "b"], CancellationToken.None)); + async () => await embedder.EmbedBatchAsync(["a", "b"], EmbeddingPurpose.Passage, CancellationToken.None)); } } diff --git a/src/Netclaw.Actors.Tests/Sessions/MemoryRecallScenarioTests.cs b/src/Netclaw.Actors.Tests/Sessions/MemoryRecallScenarioTests.cs index c00d1b00f..116bab53b 100644 --- a/src/Netclaw.Actors.Tests/Sessions/MemoryRecallScenarioTests.cs +++ b/src/Netclaw.Actors.Tests/Sessions/MemoryRecallScenarioTests.cs @@ -477,7 +477,12 @@ private SQLiteMemoryRecallCoordinator BuildHybridCoordinator(string modelId, flo new MemoryConfig(), TimeProvider.System, sessionTuning: new SessionTuning(), - embedderHolder: new MemoryEmbedderHolder(new ScriptedEmbedder(modelId, dimensions, queryVector)), + // memory-query-prefix design D3: Memory.Recall.MinCosineSimilarity now defaults to + // null (manifest-follows), so this fixture's own P09 floor (0.68 — see the class + // summary's cosine geometry comment) is supplied directly as the holder's + // manifest-carried calibration rather than a config value. + embedderHolder: new MemoryEmbedderHolder( + new ScriptedEmbedder(modelId, dimensions, queryVector), initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: 0.68), vectorIndexHolder: new MemoryVectorIndexHolder(_store)); private static object[] Row(string id, string prompt, string[] expected, string[] forbidden, bool useHybridRecall = false) @@ -646,10 +651,10 @@ private sealed class ScriptedEmbedder(string modelId, int dimensions, float[] qu public bool IsAvailable => true; - public ValueTask> EmbedAsync(string text, CancellationToken ct) + public ValueTask> EmbedAsync(string text, EmbeddingPurpose purpose, CancellationToken ct) => ValueTask.FromResult>(queryVector); - public ValueTask>> EmbedBatchAsync(IReadOnlyList texts, CancellationToken ct) + public ValueTask>> EmbedBatchAsync(IReadOnlyList texts, EmbeddingPurpose purpose, CancellationToken ct) => ValueTask.FromResult>>( texts.Select(_ => (ReadOnlyMemory)queryVector).ToList()); } diff --git a/src/Netclaw.Actors.Tests/Sessions/SQLiteMemoryRecallGateTests.cs b/src/Netclaw.Actors.Tests/Sessions/SQLiteMemoryRecallGateTests.cs index cd6d844ba..dbf2b4867 100644 --- a/src/Netclaw.Actors.Tests/Sessions/SQLiteMemoryRecallGateTests.cs +++ b/src/Netclaw.Actors.Tests/Sessions/SQLiteMemoryRecallGateTests.cs @@ -356,7 +356,12 @@ private SQLiteMemoryRecallCoordinator BuildCoordinator( }, TimeProvider.System, sessionTuning: new SessionTuning { DeterministicRetrievalEnabled = true }, - embedderHolder: new MemoryEmbedderHolder(new ScriptedEmbedder(EmbedderModelId, Dimensions, QueryVector)), + // memory-query-prefix design D3: Memory.Recall.MinCosineSimilarity now defaults to + // null (manifest-follows). Every candidate here embeds at cosine 1.0 against itself + // (SeedFloorSurvivingDocumentAsync), so any floor below 1.0 clears it identically to + // this file's pre-existing fixture geometry. + embedderHolder: new MemoryEmbedderHolder( + new ScriptedEmbedder(EmbedderModelId, Dimensions, QueryVector), initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: 0.5), vectorIndexHolder: new MemoryVectorIndexHolder(_store), relevanceScorerHolder: relevanceScorerHolder); @@ -410,10 +415,10 @@ private sealed class ScriptedEmbedder(string modelId, int dimensions, float[] qu public bool IsAvailable => true; - public ValueTask> EmbedAsync(string text, CancellationToken ct) + public ValueTask> EmbedAsync(string text, EmbeddingPurpose purpose, CancellationToken ct) => ValueTask.FromResult>(queryVector); - public ValueTask>> EmbedBatchAsync(IReadOnlyList texts, CancellationToken ct) + public ValueTask>> EmbedBatchAsync(IReadOnlyList texts, EmbeddingPurpose purpose, CancellationToken ct) => ValueTask.FromResult>>( texts.Select(_ => (ReadOnlyMemory)queryVector).ToList()); } diff --git a/src/Netclaw.Actors.Tests/Sessions/SQLiteMemoryRecallHybridTests.cs b/src/Netclaw.Actors.Tests/Sessions/SQLiteMemoryRecallHybridTests.cs index ad83d0769..f73861f51 100644 --- a/src/Netclaw.Actors.Tests/Sessions/SQLiteMemoryRecallHybridTests.cs +++ b/src/Netclaw.Actors.Tests/Sessions/SQLiteMemoryRecallHybridTests.cs @@ -36,6 +36,12 @@ public sealed class SQLiteMemoryRecallHybridTests : IAsyncDisposable private const string ModelId = "hybrid-recall-test-model"; private const int Dimensions = 2; + // memory-query-prefix design D3: the coordinator now resolves its floor from the embedder + // holder's manifest-carried calibration (config override falling back to it). This fixture's + // hand-crafted vectors only ever produce cosine 0.0 (OrthogonalVector) or 1.0 (QueryVector), + // so any value strictly between them preserves every existing admit/reject assertion below. + private const double TestFloor = 0.5; + // A unit vector and its exact opposite: cosine(QueryVector, QueryVector) == 1.0, // cosine(QueryVector, OrthogonalVector) == 0.0. Sufficient geometry for every scenario here // (either "matches the query" or "shares no direction with it at all"). @@ -153,7 +159,7 @@ await SeedDocumentAsync("doc-coverage-gap-log", "Grafana dashboard provisioning new MemoryConfig { Embeddings = new MemoryEmbeddingsConfig { Enabled = true } }, TimeProvider.System, sessionTuning: new SessionTuning { DeterministicRetrievalEnabled = true }, - embedderHolder: new MemoryEmbedderHolder(new ScriptedEmbedder(ModelId, Dimensions, QueryVector)), + embedderHolder: new MemoryEmbedderHolder(new ScriptedEmbedder(ModelId, Dimensions, QueryVector), initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: TestFloor), vectorIndexHolder: new MemoryVectorIndexHolder(_store)); var result = await coordinator.RecallAsync(new AutomaticRecallRequest( @@ -192,7 +198,7 @@ await _store.UpsertEmbeddingAsync( new MemoryConfig { Embeddings = new MemoryEmbeddingsConfig { Enabled = true } }, TimeProvider.System, sessionTuning: new SessionTuning { DeterministicRetrievalEnabled = true }, - embedderHolder: new MemoryEmbedderHolder(new ScriptedEmbedder(ModelId, Dimensions, QueryVector)), + embedderHolder: new MemoryEmbedderHolder(new ScriptedEmbedder(ModelId, Dimensions, QueryVector), initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: TestFloor), vectorIndexHolder: new MemoryVectorIndexHolder(_store)); var result = await coordinator.RecallAsync(new AutomaticRecallRequest( @@ -311,7 +317,7 @@ await SeedDocumentAsync("doc-degraded-parity", "TextForge Pricing Model", new MemoryConfig(), TimeProvider.System, sessionTuning: new SessionTuning { DeterministicRetrievalEnabled = true }, - embedderHolder: new MemoryEmbedderHolder(new UnavailableMemoryEmbedder(ModelId, "test: never provisioned")), + embedderHolder: new MemoryEmbedderHolder(new UnavailableMemoryEmbedder(ModelId, "test: never provisioned"), initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: null), vectorIndexHolder: new MemoryVectorIndexHolder(_store)); var baselineResult = await withoutHolders.RecallAsync(request, ct); @@ -339,7 +345,7 @@ public async Task Vector_degraded_log_is_debug_when_embeddings_are_disabled_by_c new MemoryConfig { Embeddings = new MemoryEmbeddingsConfig { Enabled = false } }, TimeProvider.System, sessionTuning: new SessionTuning { DeterministicRetrievalEnabled = true }, - embedderHolder: new MemoryEmbedderHolder(new UnavailableMemoryEmbedder(ModelId, "warmup has not completed yet")), + embedderHolder: new MemoryEmbedderHolder(new UnavailableMemoryEmbedder(ModelId, "warmup has not completed yet"), initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: null), vectorIndexHolder: new MemoryVectorIndexHolder(_store)); await coordinator.RecallAsync(new AutomaticRecallRequest( @@ -365,7 +371,7 @@ public async Task Vector_degraded_log_is_warning_when_embeddings_are_enabled_but new MemoryConfig { Embeddings = new MemoryEmbeddingsConfig { Enabled = true } }, TimeProvider.System, sessionTuning: new SessionTuning { DeterministicRetrievalEnabled = true }, - embedderHolder: new MemoryEmbedderHolder(new UnavailableMemoryEmbedder(ModelId, "model load failed")), + embedderHolder: new MemoryEmbedderHolder(new UnavailableMemoryEmbedder(ModelId, "model load failed"), initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: null), vectorIndexHolder: new MemoryVectorIndexHolder(_store)); await coordinator.RecallAsync(new AutomaticRecallRequest( @@ -377,6 +383,132 @@ await coordinator.RecallAsync(new AutomaticRecallRequest( Assert.Contains(recordingLogger.Entries, e => e.Level == LogLevel.Warning && e.Message.Contains("memory_recall_vector_degraded")); } + // ── Floor resolution (memory-query-prefix design D3, task 2.4) ────── + + [Fact] + public async Task Floor_resolves_from_the_active_models_manifest_calibration_when_no_override_is_configured() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + + // Below TestFloor (0.5) -- must be rejected when the manifest calibration is the + // effective floor (no config override set below). + await SeedDocumentAsync("doc-below-manifest-floor", "Grafana dashboard provisioning convention", + "Grafana dashboard provisioning convention details for the ops team.", ct); + await _store.UpsertEmbeddingAsync( + "doc-below-manifest-floor", MemoryEmbedOnWriteCoordinator.DocumentItemKind, ModelId, "hash-below", OrthogonalVector, ct); + + var recordingLogger = new RecordingLogger(); + var coordinator = new SQLiteMemoryRecallCoordinator( + _store, + recordingLogger, + new MemoryConfig { Embeddings = new MemoryEmbeddingsConfig { Enabled = true } }, // Recall.MinCosineSimilarity left null (default) + TimeProvider.System, + sessionTuning: new SessionTuning { DeterministicRetrievalEnabled = true }, + embedderHolder: new MemoryEmbedderHolder( + new ScriptedEmbedder(ModelId, Dimensions, QueryVector), initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: TestFloor), + vectorIndexHolder: new MemoryVectorIndexHolder(_store)); + + var result = await coordinator.RecallAsync(new AutomaticRecallRequest( + SessionId: (SessionId)"hybrid/floor-manifest", + Query: "what is our grafana dashboard provisioning convention?", + RecentUserMessages: ["what is our grafana dashboard provisioning convention?"], + MaxItems: 3), ct); + + Assert.False(result.Degraded); + Assert.DoesNotContain(result.Items, i => i.Id.Value == "doc-below-manifest-floor"); + Assert.Contains(recordingLogger.Entries, e => + e.Message.Contains("memory_retrieval_final") && + e.Message.Contains($"appliedFloor={TestFloor:F3}") && + e.Message.Contains("floorSource=manifest")); + } + + [Fact] + public async Task Explicit_config_override_takes_precedence_over_the_manifest_calibration() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + + // OrthogonalVector's cosine against QueryVector is 0.0 -- below TestFloor (0.5, the + // manifest calibration this holder carries) but the config override below (-0.5) is low + // enough that it must admit the candidate instead, proving the override wins. + await SeedDocumentAsync("doc-override-admits", "Grafana dashboard provisioning convention", + "Grafana dashboard provisioning convention details for the ops team.", ct); + await _store.UpsertEmbeddingAsync( + "doc-override-admits", MemoryEmbedOnWriteCoordinator.DocumentItemKind, ModelId, "hash-override", OrthogonalVector, ct); + + const double overrideFloor = -0.5; + var recordingLogger = new RecordingLogger(); + var coordinator = new SQLiteMemoryRecallCoordinator( + _store, + recordingLogger, + new MemoryConfig + { + Embeddings = new MemoryEmbeddingsConfig { Enabled = true }, + Recall = new MemoryRecallConfig { MinCosineSimilarity = overrideFloor }, + }, + TimeProvider.System, + sessionTuning: new SessionTuning { DeterministicRetrievalEnabled = true }, + embedderHolder: new MemoryEmbedderHolder( + new ScriptedEmbedder(ModelId, Dimensions, QueryVector), initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: TestFloor), + vectorIndexHolder: new MemoryVectorIndexHolder(_store)); + + var result = await coordinator.RecallAsync(new AutomaticRecallRequest( + SessionId: (SessionId)"hybrid/floor-override", + Query: "what is our grafana dashboard provisioning convention?", + RecentUserMessages: ["what is our grafana dashboard provisioning convention?"], + MaxItems: 3), ct); + + Assert.False(result.Degraded); + Assert.Contains(result.Items, i => i.Id.Value == "doc-override-admits"); + Assert.Contains(recordingLogger.Entries, e => + e.Message.Contains("memory_retrieval_final") && + e.Message.Contains($"appliedFloor={overrideFloor:F3}") && + e.Message.Contains("floorSource=override")); + } + + [Fact] + public async Task Missing_calibration_and_no_override_degrades_to_lexical_only_with_a_distinct_reason() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + + // A strong lexical match so the lexical-only composite floor still admits it -- proves + // this degraded to lexical-only rather than injecting nothing for an unrelated reason. + await SeedDocumentAsync("doc-missing-calibration", "Grafana dashboard provisioning convention", + "Grafana dashboard provisioning convention details for the ops team.", ct, + aliasesJson: "[\"grafana\",\"dashboard\",\"provisioning\",\"convention\"]"); + + var recordingLogger = new RecordingLogger(); + var coordinator = new SQLiteMemoryRecallCoordinator( + _store, + recordingLogger, + new MemoryConfig { Embeddings = new MemoryEmbeddingsConfig { Enabled = true } }, // Recall.MinCosineSimilarity left null (default) + TimeProvider.System, + sessionTuning: new SessionTuning { DeterministicRetrievalEnabled = true }, + // Available embedder, but the holder carries NO calibration (mirrors the mxbai + // fallback entry before its own floor sweep lands) -- design D3's "prefix-without- + // recalibration is unrepresentable by default." + embedderHolder: new MemoryEmbedderHolder( + new ScriptedEmbedder(ModelId, Dimensions, QueryVector), initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: null), + vectorIndexHolder: new MemoryVectorIndexHolder(_store)); + + var result = await coordinator.RecallAsync(new AutomaticRecallRequest( + SessionId: (SessionId)"hybrid/missing-calibration", + Query: "what is our grafana dashboard provisioning convention?", + RecentUserMessages: ["what is our grafana dashboard provisioning convention?"], + MaxItems: 3), ct); + + Assert.False(result.Degraded); + Assert.Contains(result.Items, i => i.Id.Value == "doc-missing-calibration"); + Assert.Contains(recordingLogger.Entries, e => + e.Level == LogLevel.Warning && + e.Message.Contains("memory_recall_vector_degraded") && + e.Message.Contains("reason=missing_calibration")); + Assert.Contains(recordingLogger.Entries, e => + e.Message.Contains("memory_retrieval_final") && e.Message.Contains("mode=lexical")); + } + // ── Fixtures ───────────────────────────────────────────────────────── private SQLiteMemoryRecallCoordinator BuildHybridCoordinator(TimeProvider timeProvider, ILogger logger) @@ -386,7 +518,7 @@ private SQLiteMemoryRecallCoordinator BuildHybridCoordinator(TimeProvider timePr new MemoryConfig(), timeProvider, sessionTuning: new SessionTuning { DeterministicRetrievalEnabled = true }, - embedderHolder: new MemoryEmbedderHolder(new ScriptedEmbedder(ModelId, Dimensions, QueryVector)), + embedderHolder: new MemoryEmbedderHolder(new ScriptedEmbedder(ModelId, Dimensions, QueryVector), initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: TestFloor), vectorIndexHolder: new MemoryVectorIndexHolder(_store)); private async Task SeedDocumentAsync( @@ -429,10 +561,10 @@ private sealed class ScriptedEmbedder(string modelId, int dimensions, float[] qu public bool IsAvailable => true; - public ValueTask> EmbedAsync(string text, CancellationToken ct) + public ValueTask> EmbedAsync(string text, EmbeddingPurpose purpose, CancellationToken ct) => ValueTask.FromResult>(queryVector); - public ValueTask>> EmbedBatchAsync(IReadOnlyList texts, CancellationToken ct) + public ValueTask>> EmbedBatchAsync(IReadOnlyList texts, EmbeddingPurpose purpose, CancellationToken ct) => ValueTask.FromResult>>( texts.Select(_ => (ReadOnlyMemory)queryVector).ToList()); } diff --git a/src/Netclaw.Actors/Memory/IMemoryEmbedder.cs b/src/Netclaw.Actors/Memory/IMemoryEmbedder.cs index 197eb3569..e3bd5ed2b 100644 --- a/src/Netclaw.Actors/Memory/IMemoryEmbedder.cs +++ b/src/Netclaw.Actors/Memory/IMemoryEmbedder.cs @@ -5,6 +5,32 @@ // ----------------------------------------------------------------------- namespace Netclaw.Actors.Memory; +/// +/// Distinguishes retrieval-query embedding from passage (document) embedding at the +/// seam (memory-query-prefix design D1). Asymmetric retrieval +/// models (e.g. snowflake-arctic-embed-m) document a query-side prefix that must NOT be +/// applied to documents — the same text embedded for each purpose can legitimately produce a +/// different vector. A batch call carries exactly one purpose: batching exists for +/// same-purpose work (embed-on-write, backfill, gap-repair), never a mix of queries and +/// documents in one call. +/// +public enum EmbeddingPurpose +{ + /// + /// Document-side embedding: embed-on-write, backfill, gap repair, and the dedup nominator's + /// proposal↔document comparison. Never carries a query prefix, regardless of the active + /// model — this is what keeps stored vectors byte-identical across a prefix-adoption change + /// like memory-query-prefix (no re-embed required). + /// + Passage, + + /// + /// A recall turn's query text. The active model's documented retrieval-query prefix (if + /// any) is applied by the embedder before tokenization. + /// + RetrievalQuery, +} + /// /// Consumer-defined seam for computing memory embeddings (memory-core-redesign D1). Owned by /// the memory subsystem, not the embedding runtime, so actor code never references OnnxRuntime @@ -44,17 +70,21 @@ public interface IMemoryEmbedder bool IsAvailable { get; } /// - /// Embed a single piece of text. Callers MUST check first; - /// calling this while unavailable throws rather than degrading silently. + /// Embed a single piece of text for the given . Callers MUST + /// check first; calling this while unavailable throws rather than + /// degrading silently. is required (not defaulted) so every call + /// site makes an explicit, reviewable choice (memory-query-prefix design D1) — there is no + /// safe default between "prefix this as a query" and "leave this as a document." /// - ValueTask> EmbedAsync(string text, CancellationToken ct); + ValueTask> EmbedAsync(string text, EmbeddingPurpose purpose, CancellationToken ct); /// - /// Embed a batch of texts, preserving input order in the output list. Batching lets - /// callers (backfill, gap-repair) amortize per-call overhead that the single-item path - /// pays every time. + /// Embed a batch of texts for the given , preserving input order + /// in the output list. Batching lets callers (backfill, gap-repair) amortize per-call + /// overhead that the single-item path pays every time. A batch carries one purpose for all + /// its texts — see . /// - ValueTask>> EmbedBatchAsync(IReadOnlyList texts, CancellationToken ct); + ValueTask>> EmbedBatchAsync(IReadOnlyList texts, EmbeddingPurpose purpose, CancellationToken ct); } /// @@ -87,10 +117,10 @@ public sealed class UnavailableMemoryEmbedder(string modelId, string reason) : I public bool IsAvailable => false; - public ValueTask> EmbedAsync(string text, CancellationToken ct) + public ValueTask> EmbedAsync(string text, EmbeddingPurpose purpose, CancellationToken ct) => throw new InvalidOperationException(BuildMessage(nameof(EmbedAsync))); - public ValueTask>> EmbedBatchAsync(IReadOnlyList texts, CancellationToken ct) + public ValueTask>> EmbedBatchAsync(IReadOnlyList texts, EmbeddingPurpose purpose, CancellationToken ct) => throw new InvalidOperationException(BuildMessage(nameof(EmbedBatchAsync))); private string BuildMessage(string calledMethod) diff --git a/src/Netclaw.Actors/Memory/MemoryCurationEvaluator.cs b/src/Netclaw.Actors/Memory/MemoryCurationEvaluator.cs index ee31e2402..4a40c2dbe 100644 --- a/src/Netclaw.Actors/Memory/MemoryCurationEvaluator.cs +++ b/src/Netclaw.Actors/Memory/MemoryCurationEvaluator.cs @@ -535,7 +535,7 @@ private async Task EvaluateCandidatesAsync( if (vectorIndex is null) return ([], 0); - var queryVector = await embedder.EmbedAsync($"{operation.Title}\n{operation.Content}", ct); + var queryVector = await embedder.EmbedAsync($"{operation.Title}\n{operation.Content}", EmbeddingPurpose.Passage, ct); var matches = vectorIndex.TopK( queryVector.Span, _curationConfig.NominatorK, _curationConfig.NominatorSimilarityThreshold); if (matches.Count == 0) diff --git a/src/Netclaw.Actors/Memory/MemoryEmbedOnWriteCoordinator.cs b/src/Netclaw.Actors/Memory/MemoryEmbedOnWriteCoordinator.cs index 4b2cab419..e863bc5fc 100644 --- a/src/Netclaw.Actors/Memory/MemoryEmbedOnWriteCoordinator.cs +++ b/src/Netclaw.Actors/Memory/MemoryEmbedOnWriteCoordinator.cs @@ -91,7 +91,7 @@ private static async Task EmbedWrittenDocumentsCoreAsync( try { var hash = MemoryContentHasher.ComputeHash(doc.Title, doc.Body); - var vector = await embedder.EmbedAsync($"{doc.Title}\n{doc.Body}", ct).ConfigureAwait(false); + var vector = await embedder.EmbedAsync($"{doc.Title}\n{doc.Body}", EmbeddingPurpose.Passage, ct).ConfigureAwait(false); await store.UpsertEmbeddingAsync( doc.DocumentId, DocumentItemKind, embedder.ModelId, hash, vector, ct).ConfigureAwait(false); } diff --git a/src/Netclaw.Actors/Memory/MemoryEmbedderHolder.cs b/src/Netclaw.Actors/Memory/MemoryEmbedderHolder.cs index 0a0c71b23..106f70d29 100644 --- a/src/Netclaw.Actors/Memory/MemoryEmbedderHolder.cs +++ b/src/Netclaw.Actors/Memory/MemoryEmbedderHolder.cs @@ -28,28 +28,70 @@ namespace Netclaw.Actors.Memory; /// still running) — the holder itself is never null-valued, only whatever it currently holds /// may report as false. /// +/// +/// +/// Why the holder also carries /, +/// not just the embedder (memory-query-prefix design D2/D3): mirrors +/// 's exact reasoning. The active model's +/// retrieval-query prefix and calibrated floor live on +/// Netclaw.Embeddings.EmbeddingModelManifestEntry, which Netclaw.Actors never +/// references — so those values must be carried alongside , set atomically +/// in the same call, rather than requiring +/// (or the doctor check) to re-resolve the manifest entry themselves. A reader can therefore +/// never observe an embedder paired with a stale (different model's) prefix or floor. +/// /// public sealed class MemoryEmbedderHolder { private volatile IMemoryEmbedder _current; + private volatile string _queryPrefix; + private object? _calibratedMinCosineSimilarityBox; - public MemoryEmbedderHolder(IMemoryEmbedder initial) + public MemoryEmbedderHolder(IMemoryEmbedder initial, string initialQueryPrefix, double? initialCalibratedMinCosineSimilarity) { ArgumentNullException.ThrowIfNull(initial); + ArgumentNullException.ThrowIfNull(initialQueryPrefix); _current = initial; + _queryPrefix = initialQueryPrefix; + _calibratedMinCosineSimilarityBox = initialCalibratedMinCosineSimilarity; } /// The embedder to use right now. Always non-null. public IMemoryEmbedder Current => _current; /// - /// Replaces the current embedder. Called only by EmbeddingWarmupHostedService once - /// provisioning completes — successfully (an OnnxMemoryEmbedder) or not (a fresh - /// carrying the failure reason). + /// The active embedder's documented retrieval-query prefix (empty when the model documents + /// none, or before warmup has populated a real value). Diagnostic use only (e.g. the doctor + /// check reporting prefix presence) — the prefix is actually applied inside + /// OnnxMemoryEmbedder itself when a caller passes , + /// not by any consumer of this holder. + /// + public string QueryPrefix => _queryPrefix; + + /// + /// The calibrated absolute cosine floor for whichever model id is + /// currently embedding with, in its documented retrieval-query encoding — set atomically + /// alongside the embedder by . null means this model id's retrieval + /// mode has not been calibrated: treats null here + /// combined with no explicit Memory.Recall.MinCosineSimilarity override as + /// hybrid-recall-unavailable (design D3) rather than guessing a floor. + /// + public double? CalibratedMinCosineSimilarity => (double?)Volatile.Read(ref _calibratedMinCosineSimilarityBox); + + /// + /// Replaces the current embedder and its manifest-carried prefix/calibration together. + /// Called only by EmbeddingWarmupHostedService once provisioning completes — + /// successfully (an OnnxMemoryEmbedder paired with its manifest entry's + /// QueryPrefix/CalibratedMinCosineSimilarity) or not (a fresh + /// carrying the failure reason, paired with the same + /// manifest values since they describe the model id, not whether it loaded). /// - public void Set(IMemoryEmbedder embedder) + public void Set(IMemoryEmbedder embedder, string queryPrefix, double? calibratedMinCosineSimilarity) { ArgumentNullException.ThrowIfNull(embedder); + ArgumentNullException.ThrowIfNull(queryPrefix); _current = embedder; + _queryPrefix = queryPrefix; + Volatile.Write(ref _calibratedMinCosineSimilarityBox, calibratedMinCosineSimilarity); } } diff --git a/src/Netclaw.Actors/Sessions/SQLiteMemoryRecallCoordinator.cs b/src/Netclaw.Actors/Sessions/SQLiteMemoryRecallCoordinator.cs index 2ca40a8dd..3c1f89f9d 100644 --- a/src/Netclaw.Actors/Sessions/SQLiteMemoryRecallCoordinator.cs +++ b/src/Netclaw.Actors/Sessions/SQLiteMemoryRecallCoordinator.cs @@ -58,6 +58,25 @@ namespace Netclaw.Actors.Sessions; /// /// /// +/// Floor resolution (memory-query-prefix, design D3): the query is embedded with +/// — the active model's documented query prefix, +/// if any, is applied inside OnnxMemoryEmbedder, not here. The absolute cosine floor +/// itself resolves per turn: an explicit +/// override always wins; otherwise the active embedder's manifest-carried +/// applies. When BOTH are +/// absent — a model whose retrieval mode has not been calibrated, with no operator override — +/// hybrid recall is treated as unavailable for the turn: the query is never embedded, and the +/// turn degrades to lexical-only with reason missing_calibration via the same rate-limited +/// memory_recall_vector_degraded log and cooldown as every other vector-degradation +/// reason. This is what makes "a prefixed encoding measured against a floor calibrated for a +/// different encoding" unrepresentable by default (design D3's motivating failure: F0.5 = 0.0 was +/// measured for the prefixed arctic encoding against the old no-prefix 0.68 floor). +/// memory_retrieval_final logs the resolved appliedFloor and its floorSource +/// (manifest or override; n/a in lexical mode, since the composite floor +/// there has no per-model calibration concept). +/// +/// +/// /// Post-floor relevance gate (memory-relevance-gate, design D5/D6/D8): in hybrid mode /// only, once produces its floor survivors, a tiny cross-encoder /// (relevanceScorerHolder) scores each of the top AutoRecallMaxItems survivors @@ -89,6 +108,12 @@ public sealed class SQLiteMemoryRecallCoordinator( private readonly SessionTuning _sessionTuning = sessionTuning ?? new SessionTuning(); private readonly MemoryRecallConfig _recallConfig = memoryConfig.Recall; + // memory-query-prefix design D3: null (default) follows the active embedder's + // manifest-carried calibration (embedderHolder.CalibratedMinCosineSimilarity, resolved per + // turn in TryEmbedQueryAsync since it depends on which model is loaded); an explicit value + // is an operator override independent of the active model. + private readonly double? _minCosineSimilarityOverride = memoryConfig.Recall.MinCosineSimilarity; + // Read once at construction (DI-resolved MemoryConfig is effectively immutable for the // process's lifetime — an operator flip requires a restart, same as every other Memory.* // setting). Drives the Debug-vs-Warning split on the degraded log: see this class's summary. @@ -269,22 +294,31 @@ public async Task RecallAsync(AutomaticRecallRequest requ string mode; RankedCandidate[] aboveFloor; int totalConsidered; + double appliedFloor; + string floorSource; // ── Vector query embedding (memory-core-redesign Slice 4, task 4.1) ── // Attempted once per turn, sub-budgeted inside the caller's overall ct. ANY - // failure here (unavailable, missing index, sub-budget timeout, embed error) - // degrades to the lexical-only path below, logged but never throws. + // failure here (unavailable, missing index, sub-budget timeout, embed error, + // or — memory-query-prefix design D3 — missing retrieval calibration) degrades + // to the lexical-only path below, logged but never throws. var embedded = await TryEmbedQueryAsync(request, ct); if (embedded is { } hybridInput) { mode = "hybrid"; + appliedFloor = hybridInput.EffectiveFloor; + floorSource = hybridInput.FloorSource; (aboveFloor, totalConsidered) = await ScoreHybrid( request, deterministicPlan, effectiveBoundary, scoredCandidates, hybridInput, ct); } else { mode = "lexical"; + // The composite floor isn't a per-model calibration — it has no + // manifest/override distinction the way the hybrid cosine floor does. + appliedFloor = minimumCompositeScore; + floorSource = "n/a"; var rankedCandidates = scoredCandidates .Select(x => new RankedCandidate( x.Item, @@ -353,12 +387,13 @@ public async Task RecallAsync(AutomaticRecallRequest requ var deterministicItems = budgeted.ToArray(); logger.LogInformation( - "memory_retrieval_final session={SessionId} mode={Mode} injectedCount={InjectedCount} filteredByFloor={FilteredByFloor} appliedFloor={AppliedFloor:F3} injectedChars={InjectedChars} droppedByBudget={DroppedByBudget} droppedByGate={DroppedByGate} gateScores={GateScores} items={Items}", + "memory_retrieval_final session={SessionId} mode={Mode} injectedCount={InjectedCount} filteredByFloor={FilteredByFloor} appliedFloor={AppliedFloor:F3} floorSource={FloorSource} injectedChars={InjectedChars} droppedByBudget={DroppedByBudget} droppedByGate={DroppedByGate} gateScores={GateScores} items={Items}", request.SessionId, mode, deterministicItems.Length, totalConsidered - aboveFloor.Length, - mode == "hybrid" ? _recallConfig.MinCosineSimilarity : minimumCompositeScore, + appliedFloor, + floorSource, injectedChars, droppedByBudget, droppedByGate, @@ -390,11 +425,12 @@ public async Task RecallAsync(AutomaticRecallRequest requ /// Attempts to embed 's query for hybrid recall /// (memory-core-redesign Slice 4, task 4.1). Returns null — logging the specific /// degradation reason via — for every failure mode: - /// no embedder wired, embedder unavailable, no vector index wired, index reload failure, - /// sub-budget timeout, or an embedding call exception. Never throws; callers treat null as - /// "run the lexical-only path," identically regardless of which reason produced it. + /// no embedder wired, embedder unavailable, missing retrieval calibration (memory-query- + /// prefix design D3), no vector index wired, index reload failure, sub-budget timeout, or an + /// embedding call exception. Never throws; callers treat null as "run the lexical-only path," + /// identically regardless of which reason produced it. /// - private async Task<(ReadOnlyMemory QueryVector, MemoryVectorIndex Index)?> TryEmbedQueryAsync( + private async Task<(ReadOnlyMemory QueryVector, MemoryVectorIndex Index, double EffectiveFloor, string FloorSource)?> TryEmbedQueryAsync( AutomaticRecallRequest request, CancellationToken ct) { var embedder = embedderHolder?.Current; @@ -410,6 +446,31 @@ public async Task RecallAsync(AutomaticRecallRequest requ return null; } + // Floor resolution (memory-query-prefix design D3): an explicit config override always + // wins; otherwise follow the active model's manifest-carried calibration. Resolved BEFORE + // touching the vector index/embedding call below — a model with no calibration and no + // override has no way to gate admission, so there is nothing to embed a query for. + double effectiveFloor; + string floorSource; + if (_minCosineSimilarityOverride is { } overrideFloor) + { + effectiveFloor = overrideFloor; + floorSource = "override"; + } + else if (embedderHolder!.CalibratedMinCosineSimilarity is { } manifestFloor) + { + effectiveFloor = manifestFloor; + floorSource = "manifest"; + } + else + { + // A prefix-without-recalibration combination (e.g. the mxbai fallback entry before + // its own floor sweep lands) is unrepresentable by default — spec scenario "Missing + // calibration degrades to lexical-only." + LogVectorDegraded(request.SessionId.Value, "missing_calibration"); + return null; + } + if (vectorIndexHolder is null) { LogVectorDegraded(request.SessionId.Value, "no_vector_index_configured"); @@ -437,8 +498,8 @@ public async Task RecallAsync(AutomaticRecallRequest requ { using var vectorCts = CancellationTokenSource.CreateLinkedTokenSource(ct); vectorCts.CancelAfter(VectorEmbedSubBudgetMs); - var vector = await embedder.EmbedAsync(request.Query, vectorCts.Token); - return (vector, index); + var vector = await embedder.EmbedAsync(request.Query, EmbeddingPurpose.RetrievalQuery, vectorCts.Token); + return (vector, index, effectiveFloor, floorSource); } catch (OperationCanceledException) when (!ct.IsCancellationRequested) { @@ -550,10 +611,10 @@ public async Task RecallAsync(AutomaticRecallRequest requ DeterministicRetrievalRequestPlan deterministicPlan, string effectiveBoundary, IReadOnlyList scoredCandidates, - (ReadOnlyMemory QueryVector, MemoryVectorIndex Index) hybridInput, + (ReadOnlyMemory QueryVector, MemoryVectorIndex Index, double EffectiveFloor, string FloorSource) hybridInput, CancellationToken ct) { - var (queryVector, vectorIndex) = hybridInput; + var (queryVector, vectorIndex, effectiveFloor, _) = hybridInput; // embeddedItemIds is read from the IDENTICAL snapshot vectorMatches was scored against // (MemoryVectorIndex.TopK's out-parameter overload) so the case-2-vs-case-3 distinction @@ -563,7 +624,7 @@ public async Task RecallAsync(AutomaticRecallRequest requ // candidate embedded-but-below-floor (case 2, excluded) be told apart from a candidate // never embedded at all (case 3, a coverage gap that bypasses the floor). var vectorMatches = vectorIndex.TopK( - queryVector.Span, VectorTopK, minCosine: _recallConfig.MinCosineSimilarity, out var embeddedItemIds) + queryVector.Span, VectorTopK, minCosine: effectiveFloor, out var embeddedItemIds) .Where(m => string.Equals(m.ItemKind, MemoryEmbedOnWriteCoordinator.DocumentItemKind, StringComparison.Ordinal)) .ToArray(); var cosineByItemId = vectorMatches.ToDictionary(m => m.ItemId, m => m.Cosine, StringComparer.Ordinal); @@ -633,7 +694,7 @@ public async Task RecallAsync(AutomaticRecallRequest requ // survivors overall is still intended, not an error: the "nothing relevant" spec // scenario, returned as a healthy empty result by the caller. var aboveFloor = fused - .Where(x => x.Cosine is not { } cosine || cosine >= _recallConfig.MinCosineSimilarity) + .Where(x => x.Cosine is not { } cosine || cosine >= effectiveFloor) .ToArray(); return (aboveFloor, fused.Length); diff --git a/src/Netclaw.Cli.Tests/Doctor/ConfigSchemaDoctorCheckTests.cs b/src/Netclaw.Cli.Tests/Doctor/ConfigSchemaDoctorCheckTests.cs index bc2592689..f5f946760 100644 --- a/src/Netclaw.Cli.Tests/Doctor/ConfigSchemaDoctorCheckTests.cs +++ b/src/Netclaw.Cli.Tests/Doctor/ConfigSchemaDoctorCheckTests.cs @@ -235,6 +235,35 @@ await File.WriteAllTextAsync(paths.NetclawConfigPath, Assert.Equal(DoctorSeverity.Pass, result.Severity); } + // memory-query-prefix design D3: MinCosineSimilarity is now nullable + // ("type": ["number", "null"]) — an explicit null (the default, meaning "follow the active + // model's manifest calibration") must remain schema-valid, not just an omitted property. + [Fact] + public async Task ReturnsPass_WhenMemoryRecallMinCosineSimilarityIsExplicitlyNull() + { + var basePath = CreateTempBasePath(); + var paths = new NetclawPaths(basePath); + paths.EnsureDirectoriesExist(); + + await File.WriteAllTextAsync(paths.NetclawConfigPath, + """ + { + "configVersion": 1, + "Memory": { + "Enabled": true, + "Recall": { + "MinCosineSimilarity": null + } + } + } + """, TestContext.Current.CancellationToken); + + var check = new ConfigSchemaDoctorCheck(paths); + var result = await check.RunAsync(TestContext.Current.CancellationToken); + + Assert.Equal(DoctorSeverity.Pass, result.Severity); + } + [Fact] public async Task ReturnsError_WhenMemoryRecallHasAnUnknownProperty() { diff --git a/src/Netclaw.Cli.Tests/Doctor/MemoryEmbeddingDoctorCheckTests.cs b/src/Netclaw.Cli.Tests/Doctor/MemoryEmbeddingDoctorCheckTests.cs index 0cc7f2ff7..ef873db03 100644 --- a/src/Netclaw.Cli.Tests/Doctor/MemoryEmbeddingDoctorCheckTests.cs +++ b/src/Netclaw.Cli.Tests/Doctor/MemoryEmbeddingDoctorCheckTests.cs @@ -173,6 +173,9 @@ await store.UpsertDocumentAsync(new SQLiteMemoryDocument( } internal static IReadOnlyDictionary FixtureAllowlist() + => FixtureAllowlist(calibratedMinCosineSimilarity: 0.42); + + private static IReadOnlyDictionary FixtureAllowlist(double? calibratedMinCosineSimilarity) { var modelBytes = File.ReadAllBytes(Path.Combine(FixturesDir, "tiny-embedder.onnx")); var vocabBytes = File.ReadAllBytes(Path.Combine(FixturesDir, "tiny-vocab.txt")); @@ -186,7 +189,53 @@ internal static IReadOnlyDictionary Fixture ModelSha256: Convert.ToHexStringLower(SHA256.HashData(modelBytes)), TokenizerSha256: Convert.ToHexStringLower(SHA256.HashData(vocabBytes)), Dimensions: 8, - ModelByteSize: modelBytes.Length), + ModelByteSize: modelBytes.Length, + QueryPrefix: "search_query: ", + CalibratedMinCosineSimilarity: calibratedMinCosineSimilarity), }; } + + // ── Effective floor + prefix reporting (memory-query-prefix design D3, task 2.3) ── + + [Fact] + public async Task Passes_and_reports_manifest_floor_source_when_healthy_and_no_override_configured() + { + var paths = CreateTempPaths(); + var config = WriteConfig(paths, enabled: true); + PrePlaceValidModelFiles(paths); + + var store = new SQLiteMemoryStore(paths.MemorySqliteDbPath, TimeProvider.System); + await store.InitializeAsync(TestContext.Current.CancellationToken); + await SeedDocumentAsync(store, "doc-1", "Doc", "body"); + var hash = MemoryContentHasher.ComputeHash("Doc", "body"); + await store.UpsertEmbeddingAsync("doc-1", "document", ModelId, hash, new float[] { 1f }, TestContext.Current.CancellationToken); + + var check = new MemoryEmbeddingDoctorCheck(paths, config, FixtureAllowlist()); + var result = await check.RunAsync(TestContext.Current.CancellationToken); + + Assert.Equal(DoctorSeverity.Pass, result.Severity); + Assert.Contains("queryPrefix=True", result.Message, StringComparison.Ordinal); + Assert.Contains("source=manifest", result.Message, StringComparison.Ordinal); + } + + [Fact] + public async Task Warns_when_the_active_model_carries_no_retrieval_calibration_and_no_override_is_configured() + { + var paths = CreateTempPaths(); + var config = WriteConfig(paths, enabled: true); + PrePlaceValidModelFiles(paths); + + var store = new SQLiteMemoryStore(paths.MemorySqliteDbPath, TimeProvider.System); + await store.InitializeAsync(TestContext.Current.CancellationToken); + await SeedDocumentAsync(store, "doc-1", "Doc", "body"); + var hash = MemoryContentHasher.ComputeHash("Doc", "body"); + await store.UpsertEmbeddingAsync("doc-1", "document", ModelId, hash, new float[] { 1f }, TestContext.Current.CancellationToken); + + // Uncalibrated entry — mirrors the mxbai fallback entry before its own floor sweep lands. + var check = new MemoryEmbeddingDoctorCheck(paths, config, FixtureAllowlist(calibratedMinCosineSimilarity: null)); + var result = await check.RunAsync(TestContext.Current.CancellationToken); + + Assert.Equal(DoctorSeverity.Warning, result.Severity); + Assert.Contains("hybrid recall degrades to lexical-only", result.Message, StringComparison.OrdinalIgnoreCase); + } } diff --git a/src/Netclaw.Cli.Tests/Memory/MemoryCommandTests.cs b/src/Netclaw.Cli.Tests/Memory/MemoryCommandTests.cs index 14c7c9882..bfc440dd6 100644 --- a/src/Netclaw.Cli.Tests/Memory/MemoryCommandTests.cs +++ b/src/Netclaw.Cli.Tests/Memory/MemoryCommandTests.cs @@ -191,7 +191,9 @@ private static IReadOnlyDictionary FixtureA ModelSha256: Convert.ToHexStringLower(SHA256.HashData(modelBytes)), TokenizerSha256: Convert.ToHexStringLower(SHA256.HashData(vocabBytes)), Dimensions: 8, - ModelByteSize: modelBytes.Length), + ModelByteSize: modelBytes.Length, + QueryPrefix: "search_query: ", + CalibratedMinCosineSimilarity: 0.42), }; } } diff --git a/src/Netclaw.Cli/Doctor/MemoryEmbeddingDoctorCheck.cs b/src/Netclaw.Cli/Doctor/MemoryEmbeddingDoctorCheck.cs index 1b8b45c97..5fac545e0 100644 --- a/src/Netclaw.Cli/Doctor/MemoryEmbeddingDoctorCheck.cs +++ b/src/Netclaw.Cli/Doctor/MemoryEmbeddingDoctorCheck.cs @@ -66,12 +66,29 @@ public async Task RunAsync(CancellationToken cancellationToke await store.InitializeAsync(cancellationToken); var coverage = await store.GetEmbeddingCoverageAsync(modelId, cancellationToken); + // Effective retrieval floor (memory-query-prefix design D3): the same config-or- + // manifest resolution SQLiteMemoryRecallCoordinator applies per turn, surfaced here so + // an operator can see the source (override vs. manifest vs. missing) without reading + // logs. allowlist is looked up directly (not through verified/provisioned artifacts) + // since prefix/calibration describe the model id regardless of on-disk state. + allowlist.TryGetValue(modelId, out var manifestEntry); + var hasQueryPrefix = !string.IsNullOrEmpty(manifestEntry?.QueryPrefix); + var configuredFloor = memoryConfig.Recall.MinCosineSimilarity; + var (effectiveFloor, floorSource) = configuredFloor is { } overrideFloor + ? (overrideFloor, "override") + : manifestEntry?.CalibratedMinCosineSimilarity is { } manifestFloor + ? (manifestFloor, "manifest") + : ((double?)null, "missing"); + var floorDescription = effectiveFloor is { } floor + ? $"floor={floor:F3} (source={floorSource})" + : "floor=none (model carries no retrieval calibration and no override is configured — hybrid recall degrades to lexical-only)"; + if (coverage.OtherModelCount > 0) { return DoctorCheckResult.Warning( CheckName, $"Embeddings exist under another model id in addition to '{modelId}' ({coverage.OtherModelCount} items) — " + - "similarity thresholds are calibrated per model.", + $"similarity thresholds are calibrated per model. queryPrefix={hasQueryPrefix} {floorDescription}.", "Run `netclaw memory backfill-embeddings --force` to re-embed the full corpus under the active model."); } @@ -80,13 +97,24 @@ public async Task RunAsync(CancellationToken cancellationToke { return DoctorCheckResult.Warning( CheckName, - $"{missing} of {coverage.TotalRecallableDocuments} recallable documents lack a current-model embedding.", + $"{missing} of {coverage.TotalRecallableDocuments} recallable documents lack a current-model embedding. " + + $"queryPrefix={hasQueryPrefix} {floorDescription}.", "The daemon's gap-repair sweep heals this at next startup, or run `netclaw memory backfill-embeddings` now."); } + if (effectiveFloor is null) + { + return DoctorCheckResult.Warning( + CheckName, + $"Embeddings healthy: {coverage.EmbeddedCurrentHashCount}/{coverage.TotalRecallableDocuments} documents embedded under '{modelId}'. " + + $"queryPrefix={hasQueryPrefix} {floorDescription}.", + "Set Memory.Recall.MinCosineSimilarity explicitly, or wait for this model's retrieval calibration to be added to the allowlist — until then hybrid recall runs lexical-only."); + } + return DoctorCheckResult.Pass( CheckName, - $"Embeddings healthy: {coverage.EmbeddedCurrentHashCount}/{coverage.TotalRecallableDocuments} documents embedded under '{modelId}'."); + $"Embeddings healthy: {coverage.EmbeddedCurrentHashCount}/{coverage.TotalRecallableDocuments} documents embedded under '{modelId}'. " + + $"queryPrefix={hasQueryPrefix} {floorDescription}."); } catch (Exception ex) { diff --git a/src/Netclaw.Cli/Memory/MemoryCommand.cs b/src/Netclaw.Cli/Memory/MemoryCommand.cs index 8a5858e9b..7c54fa647 100644 --- a/src/Netclaw.Cli/Memory/MemoryCommand.cs +++ b/src/Netclaw.Cli/Memory/MemoryCommand.cs @@ -98,7 +98,7 @@ private static async Task RunBackfillEmbeddingsAsync( Console.WriteLine($"Loading embedder '{provisioned.ModelId}' ({provisioned.Dimensions} dims)..."); using var embedder = await OnnxMemoryEmbedder.LoadAsync( - provisioned.ModelPath, provisioned.VocabPath, provisioned.ModelId, provisioned.Dimensions); + provisioned.ModelPath, provisioned.VocabPath, provisioned.ModelId, provisioned.Dimensions, provisioned.QueryPrefix); // Direct SQLite access, same as the doctor checks: WAL mode (set by InitializeAsync's // idempotent DDL) plus Microsoft.Data.Sqlite's default busy-timeout keep each small @@ -129,7 +129,7 @@ private static async Task RunBackfillEmbeddingsAsync( IReadOnlyList> vectors; try { - vectors = await embedder.EmbedBatchAsync(texts, CancellationToken.None); + vectors = await embedder.EmbedBatchAsync(texts, EmbeddingPurpose.Passage, CancellationToken.None); } catch (Exception ex) { diff --git a/src/Netclaw.Configuration.Tests/MemoryConfigDefaultsTests.cs b/src/Netclaw.Configuration.Tests/MemoryConfigDefaultsTests.cs index 7a5db33a0..97b620a9b 100644 --- a/src/Netclaw.Configuration.Tests/MemoryConfigDefaultsTests.cs +++ b/src/Netclaw.Configuration.Tests/MemoryConfigDefaultsTests.cs @@ -90,11 +90,14 @@ public void Recall_lexical_weight_defaults_to_0_3() Assert.Equal(0.3, config.Recall.LexicalWeight); } + // memory-query-prefix design D3: the 0.68 non-null default is superseded — the manifest now + // carries 0.24 for the prefixed arctic encoding, and MinCosineSimilarity defaults to null so + // the coordinator follows whichever model's manifest calibration is active. [Fact] - public void Recall_min_cosine_similarity_defaults_to_0_68() + public void Recall_min_cosine_similarity_defaults_to_null_and_follows_the_active_models_manifest_calibration() { var config = new MemoryConfig(); - Assert.Equal(0.68, config.Recall.MinCosineSimilarity); + Assert.Null(config.Recall.MinCosineSimilarity); } [Fact] diff --git a/src/Netclaw.Configuration/MemoryConfig.cs b/src/Netclaw.Configuration/MemoryConfig.cs index 7c5d750c8..83c66a4d5 100644 --- a/src/Netclaw.Configuration/MemoryConfig.cs +++ b/src/Netclaw.Configuration/MemoryConfig.cs @@ -150,15 +150,34 @@ public sealed class MemoryRecallConfig public double LexicalWeight { get; set; } = 0.3; /// - /// Absolute relevance floor (design D6): when a query vector is available, any candidate — - /// vector- or lexical-sourced — whose cosine similarity to the query falls below this value - /// is dropped before ranking, regardless of fused score. Nothing surviving means nothing is - /// injected and the [memory-recall] block is omitted entirely — a healthy empty - /// result, not a degraded one. Calibrated (not a placeholder) against the real-traffic gold - /// set (gold-prod-2026-07, 2026-07-05): maximizes F0.5 for the shipped fp32 - /// snowflake-arctic-embed-m embedder; see design D6 for the full sweep. - /// - public double MinCosineSimilarity { get; set; } = 0.68; + /// Absolute relevance floor (design D6, recalibrated by memory-query-prefix design D3/D4): + /// when a query vector is available, any candidate — vector- or lexical-sourced — whose + /// cosine similarity to the query falls below this value is dropped before ranking, + /// regardless of fused score. Nothing surviving means nothing is injected and the + /// [memory-recall] block is omitted entirely — a healthy empty result, not a degraded + /// one. + /// + /// + /// null (default) — the effective floor follows the active embedding model's + /// manifest-carried CalibratedMinCosineSimilarity + /// (Netclaw.Embeddings.EmbeddingModelManifestEntry; 0.24 for the shipped prefixed + /// snowflake-arctic-embed-m encoding — see the memory-query-prefix design doc for the + /// full gold-set sweep). A concrete value is an explicit operator override, independent of + /// which model is active. + /// + /// + /// + /// The numeric meaning of this value is model- and encoding-specific. It is NOT a + /// portable "relevance percentage" — cosine distributions differ across models and shift + /// materially when a model's documented query prefix is adopted or removed (measured: 0.68 + /// with no prefix vs. 0.24 with the prefix, for the SAME model). A value pinned for one + /// model/encoding and silently carried into another combination can measure catastrophically + /// wrong (F0.5 = 0.0 was measured for the prefixed encoding at the old no-prefix floor). Only + /// set this explicitly after re-running the calibration-verification procedure + /// (memory-relevance-gate design doc) against the model and encoding actually active. + /// + /// + public double? MinCosineSimilarity { get; set; } /// /// Half-life, in days, for the recency-decay multiplier applied to a candidate's fused score diff --git a/src/Netclaw.Configuration/Schemas/netclaw-config.v1.schema.json b/src/Netclaw.Configuration/Schemas/netclaw-config.v1.schema.json index a91534ec8..d7aabf7a4 100644 --- a/src/Netclaw.Configuration/Schemas/netclaw-config.v1.schema.json +++ b/src/Netclaw.Configuration/Schemas/netclaw-config.v1.schema.json @@ -440,11 +440,10 @@ "description": "Weight applied to a candidate's squashed lexical selector score in the hybrid fusion score." }, "MinCosineSimilarity": { - "type": "number", + "type": ["number", "null"], "minimum": 0, "maximum": 1, - "default": 0.68, - "description": "Absolute relevance floor: when a query vector is available, any candidate below this cosine similarity is dropped before ranking, regardless of source. Calibrated against the gold-prod-2026-07 gold set for the shipped fp32 snowflake-arctic-embed-m embedder." + "description": "Absolute relevance floor: when a query vector is available, any candidate below this cosine similarity is dropped before ranking, regardless of source. When null (default), the effective floor follows the active embedding model's manifest-carried calibration (0.24 for the shipped prefixed snowflake-arctic-embed-m encoding). The value is model- and encoding-specific — cosine distributions shift materially when a model's documented query prefix is adopted or removed, so a value pinned for one model/encoding must never be carried into another without re-running the calibration procedure." }, "RecencyHalfLifeDays": { "type": "number", diff --git a/src/Netclaw.Daemon.Tests/Gateway/DaemonRuntimeStatusServiceTests.cs b/src/Netclaw.Daemon.Tests/Gateway/DaemonRuntimeStatusServiceTests.cs index 3ffa898bf..72026bd59 100644 --- a/src/Netclaw.Daemon.Tests/Gateway/DaemonRuntimeStatusServiceTests.cs +++ b/src/Netclaw.Daemon.Tests/Gateway/DaemonRuntimeStatusServiceTests.cs @@ -398,7 +398,7 @@ public async Task StatusReportsEmbeddingsOk_WhenHolderIsAvailable() var sqliteStore = new SQLiteMemoryStore(paths.MemorySqliteDbPath, TimeProvider.System); await sqliteStore.InitializeAsync(TestContext.Current.CancellationToken); - var holder = new MemoryEmbedderHolder(new FakeAvailableEmbedder("tiny-fixture")); + var holder = new MemoryEmbedderHolder(new FakeAvailableEmbedder("tiny-fixture"), initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: null); var service = CreateService( paths: paths, sqliteMemoryStore: sqliteStore, @@ -419,7 +419,7 @@ public async Task StatusReportsEmbeddingsDegraded_WhenEnabledButHolderIsUnavaila var sqliteStore = new SQLiteMemoryStore(paths.MemorySqliteDbPath, TimeProvider.System); await sqliteStore.InitializeAsync(TestContext.Current.CancellationToken); - var holder = new MemoryEmbedderHolder(new UnavailableMemoryEmbedder("tiny-fixture", "model missing")); + var holder = new MemoryEmbedderHolder(new UnavailableMemoryEmbedder("tiny-fixture", "model missing"), initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: null); var service = CreateService( paths: paths, sqliteMemoryStore: sqliteStore, @@ -502,10 +502,10 @@ private sealed class FakeAvailableEmbedder(string modelId) : IMemoryEmbedder public bool IsAvailable => true; - public ValueTask> EmbedAsync(string text, CancellationToken ct) + public ValueTask> EmbedAsync(string text, EmbeddingPurpose purpose, CancellationToken ct) => ValueTask.FromResult>(new float[Dimensions]); - public ValueTask>> EmbedBatchAsync(IReadOnlyList texts, CancellationToken ct) + public ValueTask>> EmbedBatchAsync(IReadOnlyList texts, EmbeddingPurpose purpose, CancellationToken ct) => ValueTask.FromResult>>(texts.Select(_ => (ReadOnlyMemory)new float[Dimensions]).ToList()); } } diff --git a/src/Netclaw.Daemon.Tests/Services/EmbeddingWarmupHostedServiceTests.cs b/src/Netclaw.Daemon.Tests/Services/EmbeddingWarmupHostedServiceTests.cs index 0896bdfe7..1d8e26c83 100644 --- a/src/Netclaw.Daemon.Tests/Services/EmbeddingWarmupHostedServiceTests.cs +++ b/src/Netclaw.Daemon.Tests/Services/EmbeddingWarmupHostedServiceTests.cs @@ -27,6 +27,12 @@ public sealed class EmbeddingWarmupHostedServiceTests : IAsyncLifetime private const string ModelId = "tiny-fixture"; private const int Dimensions = 8; + // memory-query-prefix design D2/D3 fixture calibration -- not a real model card figure, just + // an exercisable prefix/floor pair so tests can assert the warmup service threads both + // through to the holder. + private const string QueryPrefix = "search_query: "; + private const double CalibratedMinCosineSimilarity = 0.42; + // WarmUpRelevanceGateAsync hardcodes this constant as the relevance model id to provision // (memory-relevance-gate: there is no config knob selecting which relevance model is // active), so any fixture allowlist a test supplies must be keyed under the SAME id. @@ -37,6 +43,7 @@ public sealed class EmbeddingWarmupHostedServiceTests : IAsyncLifetime private NetclawPaths _paths = null!; private SQLiteMemoryStore _store = null!; private EmbeddingModelProvisioner _provisioner = null!; + private IReadOnlyDictionary _allowlist = null!; private static string FixturesDir => Path.Combine(AppContext.BaseDirectory, "Fixtures"); @@ -49,7 +56,7 @@ public async ValueTask InitializeAsync() var modelBytes = await File.ReadAllBytesAsync(Path.Combine(FixturesDir, "tiny-embedder.onnx")); var vocabBytes = await File.ReadAllBytesAsync(Path.Combine(FixturesDir, "tiny-vocab.txt")); - var allowlist = new Dictionary + _allowlist = new Dictionary { [ModelId] = new( ModelId, @@ -61,9 +68,11 @@ public async ValueTask InitializeAsync() ModelSha256: Sha256Hex(modelBytes), TokenizerSha256: Sha256Hex(vocabBytes), Dimensions: Dimensions, - ModelByteSize: modelBytes.Length), + ModelByteSize: modelBytes.Length, + QueryPrefix: QueryPrefix, + CalibratedMinCosineSimilarity: CalibratedMinCosineSimilarity), }; - _provisioner = new EmbeddingModelProvisioner(new HttpClient(), allowlist); + _provisioner = new EmbeddingModelProvisioner(new HttpClient(), _allowlist); } public async ValueTask DisposeAsync() => await TryDeleteDirectoryAsync(_baseDir); @@ -72,7 +81,7 @@ public async ValueTask InitializeAsync() public async Task Success_path_loads_the_fixture_model_with_no_network_and_populates_the_holder() { PrePlaceValidModelFiles(); - var holder = new MemoryEmbedderHolder(new UnavailableMemoryEmbedder(ModelId, "warmup not yet run")); + var holder = new MemoryEmbedderHolder(new UnavailableMemoryEmbedder(ModelId, "warmup not yet run"), initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: null); var memoryConfig = new MemoryConfig { Embeddings = { Enabled = true, ModelId = ModelId, AutoDownload = true } }; var service = CreateService(holder, memoryConfig); @@ -81,13 +90,18 @@ public async Task Success_path_loads_the_fixture_model_with_no_network_and_popul Assert.True(holder.Current.IsAvailable); Assert.Equal(ModelId, holder.Current.ModelId); Assert.Equal(Dimensions, holder.Current.Dimensions); + + // memory-query-prefix design D2/D3, task 1.4: the allowlist entry's QueryPrefix and + // CalibratedMinCosineSimilarity travel onto the holder alongside the embedder itself. + Assert.Equal(QueryPrefix, holder.QueryPrefix); + Assert.Equal(CalibratedMinCosineSimilarity, holder.CalibratedMinCosineSimilarity); } [Fact] public async Task Degraded_path_sets_an_unavailable_embedder_when_the_model_is_missing_and_autodownload_is_false() { // No PrePlaceValidModelFiles() call — the model directory is empty. - var holder = new MemoryEmbedderHolder(new UnavailableMemoryEmbedder(ModelId, "warmup not yet run")); + var holder = new MemoryEmbedderHolder(new UnavailableMemoryEmbedder(ModelId, "warmup not yet run"), initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: null); var memoryConfig = new MemoryConfig { Embeddings = { Enabled = true, ModelId = ModelId, AutoDownload = false } }; var service = CreateService(holder, memoryConfig); @@ -95,13 +109,18 @@ public async Task Degraded_path_sets_an_unavailable_embedder_when_the_model_is_m Assert.False(holder.Current.IsAvailable); Assert.IsType(holder.Current); + // The manifest's prefix/floor are still known even though the model failed to load -- + // they describe the model id, not whether provisioning succeeded (mirrors the relevance + // gate's own degraded-path assertion). + Assert.Equal(QueryPrefix, holder.QueryPrefix); + Assert.Equal(CalibratedMinCosineSimilarity, holder.CalibratedMinCosineSimilarity); } [Fact] public async Task Disabled_config_leaves_the_holder_at_its_initial_value() { var initial = new UnavailableMemoryEmbedder(ModelId, "embeddings disabled"); - var holder = new MemoryEmbedderHolder(initial); + var holder = new MemoryEmbedderHolder(initial, initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: null); var memoryConfig = new MemoryConfig { Embeddings = { Enabled = false, ModelId = ModelId } }; var service = CreateService(holder, memoryConfig); @@ -135,7 +154,7 @@ await _store.UpsertDocumentAsync(new SQLiteMemoryDocument( CreatedAtMs: now, UpdatedAtMs: now), TestContext.Current.CancellationToken); - var holder = new MemoryEmbedderHolder(new UnavailableMemoryEmbedder(ModelId, "warmup not yet run")); + var holder = new MemoryEmbedderHolder(new UnavailableMemoryEmbedder(ModelId, "warmup not yet run"), initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: null); var memoryConfig = new MemoryConfig { Embeddings = { Enabled = true, ModelId = ModelId, AutoDownload = true } }; var service = CreateService(holder, memoryConfig); @@ -154,7 +173,7 @@ public async Task Relevance_gate_success_path_loads_the_fixture_scorer_and_pairs PrePlaceValidModelFiles(); PrePlaceValidRelevanceModelFiles(); - var holder = new MemoryEmbedderHolder(new UnavailableMemoryEmbedder(ModelId, "warmup not yet run")); + var holder = new MemoryEmbedderHolder(new UnavailableMemoryEmbedder(ModelId, "warmup not yet run"), initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: null); var relevanceHolder = CreateRelevanceScorerHolder(); var memoryConfig = new MemoryConfig { Embeddings = { Enabled = true, ModelId = ModelId, AutoDownload = true } }; var service = CreateService(holder, memoryConfig, relevanceHolder, RelevanceFixtureAllowlist()); @@ -172,7 +191,7 @@ public async Task Relevance_gate_degraded_path_sets_an_unavailable_scorer_when_t PrePlaceValidModelFiles(); // No PrePlaceValidRelevanceModelFiles() call -- the relevance model directory is empty. - var holder = new MemoryEmbedderHolder(new UnavailableMemoryEmbedder(ModelId, "warmup not yet run")); + var holder = new MemoryEmbedderHolder(new UnavailableMemoryEmbedder(ModelId, "warmup not yet run"), initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: null); var relevanceHolder = CreateRelevanceScorerHolder(); var memoryConfig = new MemoryConfig { Embeddings = { Enabled = true, ModelId = ModelId, AutoDownload = false } }; var service = CreateService(holder, memoryConfig, relevanceHolder, RelevanceFixtureAllowlist()); @@ -191,7 +210,7 @@ public async Task Relevance_gate_degraded_path_sets_an_unavailable_scorer_when_t [Fact] public async Task Relevance_gate_disabled_config_leaves_the_relevance_holder_at_its_initial_value() { - var holder = new MemoryEmbedderHolder(new UnavailableMemoryEmbedder(ModelId, "embeddings disabled")); + var holder = new MemoryEmbedderHolder(new UnavailableMemoryEmbedder(ModelId, "embeddings disabled"), initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: null); var initialRelevance = new UnavailableRelevanceScorer(RelevanceModelId, "embeddings disabled"); var relevanceHolder = new RelevanceScorerHolder(initialRelevance, initialCalibratedThreshold: 0.0); var memoryConfig = new MemoryConfig { Embeddings = { Enabled = false, ModelId = ModelId } }; @@ -212,7 +231,7 @@ private EmbeddingWarmupHostedService CreateService( MemoryConfig memoryConfig, RelevanceScorerHolder relevanceScorerHolder, IReadOnlyDictionary relevanceAllowlist) - => new(_provisioner, _store, holder, relevanceScorerHolder, relevanceAllowlist, memoryConfig, _paths, + => new(_provisioner, _store, holder, relevanceScorerHolder, _allowlist, relevanceAllowlist, memoryConfig, _paths, NullLogger.Instance); private static RelevanceScorerHolder CreateRelevanceScorerHolder() diff --git a/src/Netclaw.Daemon/Program.cs b/src/Netclaw.Daemon/Program.cs index a31ecb39a..b33610daf 100644 --- a/src/Netclaw.Daemon/Program.cs +++ b/src/Netclaw.Daemon/Program.cs @@ -747,11 +747,22 @@ static void ConfigureDaemonServices( // EmbeddingWarmupHostedService populates it at startup (see that type's remarks for why // a mutable holder is required instead of constructor injection). services.AddHttpClient("EmbeddingModelProvisioner").AddNetclawHeaders("embedding-provisioner"); + services.AddSingleton>( + EmbeddingModelProvisioner.Allowlist); services.AddSingleton(sp => new EmbeddingModelProvisioner( sp.GetRequiredService().CreateClient("EmbeddingModelProvisioner"), EmbeddingModelProvisioner.Allowlist)); + + // Initial prefix/floor are resolved from the allowlist entry (memory-query-prefix design + // D2/D3) rather than hardcoded empty/null placeholders: an unknown ModelId degrades to + // "no prefix, no calibration" here (TryGetValue returns null) exactly like any other + // missing-manifest-entry condition elsewhere — the daemon still starts, and + // EmbeddingWarmupHostedService's own load attempt is what surfaces the loud failure. + EmbeddingModelProvisioner.Allowlist.TryGetValue(memoryConfig.Embeddings.ModelId, out var initialEmbeddingEntry); services.AddSingleton(new MemoryEmbedderHolder( - new UnavailableMemoryEmbedder(memoryConfig.Embeddings.ModelId, "embedding warmup has not completed yet"))); + new UnavailableMemoryEmbedder(memoryConfig.Embeddings.ModelId, "embedding warmup has not completed yet"), + initialQueryPrefix: initialEmbeddingEntry?.QueryPrefix ?? string.Empty, + initialCalibratedMinCosineSimilarity: initialEmbeddingEntry?.CalibratedMinCosineSimilarity)); // Vector index for the curation evaluator's embedding kNN nominator (memory-core- // redesign Slice 3 Stage B, task 3.1). Registered alongside MemoryEmbedderHolder above: diff --git a/src/Netclaw.Daemon/Services/EmbeddingWarmupHostedService.cs b/src/Netclaw.Daemon/Services/EmbeddingWarmupHostedService.cs index 4531fc699..908ec35fe 100644 --- a/src/Netclaw.Daemon/Services/EmbeddingWarmupHostedService.cs +++ b/src/Netclaw.Daemon/Services/EmbeddingWarmupHostedService.cs @@ -39,6 +39,7 @@ internal sealed class EmbeddingWarmupHostedService( SQLiteMemoryStore store, MemoryEmbedderHolder holder, RelevanceScorerHolder relevanceScorerHolder, + IReadOnlyDictionary allowlist, IReadOnlyDictionary relevanceAllowlist, MemoryConfig memoryConfig, NetclawPaths paths, @@ -71,23 +72,33 @@ internal async Task WarmUpAsync(CancellationToken ct) } var modelId = memoryConfig.Embeddings.ModelId; + + // Prefix/floor are looked up unconditionally (success or failure below) since they + // describe the model id, not whether it actually loaded (memory-query-prefix design + // D2/D3) -- mirrors WarmUpRelevanceGateAsync's calibratedThreshold lookup exactly. + allowlist.TryGetValue(modelId, out var manifestEntry); + var queryPrefix = manifestEntry?.QueryPrefix ?? string.Empty; + var calibratedMinCosineSimilarity = manifestEntry?.CalibratedMinCosineSimilarity; + IMemoryEmbedder embedder; try { - embedder = await LoadEmbedderAsync(modelId, ct).ConfigureAwait(false); + embedder = await LoadEmbedderAsync(modelId, queryPrefix, ct).ConfigureAwait(false); } catch (Exception ex) { logger.LogError(ex, "memory_embedding_unavailable model={ModelId} reason={Reason}", modelId, ex.Message); - holder.Set(new UnavailableMemoryEmbedder(modelId, ex.Message)); + holder.Set(new UnavailableMemoryEmbedder(modelId, ex.Message), queryPrefix, calibratedMinCosineSimilarity); return; } - holder.Set(embedder); + holder.Set(embedder, queryPrefix, calibratedMinCosineSimilarity); logger.LogInformation( - "memory_embedding_ready model={ModelId} dims={Dimensions}", + "memory_embedding_ready model={ModelId} dims={Dimensions} hasQueryPrefix={HasQueryPrefix} calibratedMinCosineSimilarity={CalibratedMinCosineSimilarity}", embedder.ModelId, - embedder.Dimensions); + embedder.Dimensions, + queryPrefix.Length > 0, + calibratedMinCosineSimilarity); try { @@ -174,7 +185,7 @@ await scorer.ScoreAsync("netclaw relevance gate warmup query", ["netclaw relevan return scorer; } - private async Task LoadEmbedderAsync(string modelId, CancellationToken ct) + private async Task LoadEmbedderAsync(string modelId, string queryPrefix, CancellationToken ct) { var modelDirectory = paths.EmbeddingModelDirectory(modelId); @@ -200,11 +211,15 @@ private async Task LoadEmbedderAsync(string modelId, Cancellati provisioned.VocabPath, provisioned.ModelId, provisioned.Dimensions, + queryPrefix, ct: ct).ConfigureAwait(false); // Warm-up inference (design D1/D2): pays first-call ONNX session / JIT cost here rather - // than on the first real memory write or recall query. - await embedder.EmbedAsync("netclaw embedding warmup", ct).ConfigureAwait(false); + // than on the first real memory write or recall query. Passage purpose: this is a + // generic session/JIT warm-up, not a real query, so there is nothing gained from also + // exercising the query-prefix path here (the first real recall turn pays that cost, well + // inside its own sub-budget per design D2's negligible token-count claim). + await embedder.EmbedAsync("netclaw embedding warmup", EmbeddingPurpose.Passage, ct).ConfigureAwait(false); return embedder; } @@ -234,7 +249,7 @@ private async Task GapRepairAsync(IMemoryEmbedder embedder, CancellationToken ct try { - var vectors = await embedder.EmbedBatchAsync(texts, ct).ConfigureAwait(false); + var vectors = await embedder.EmbedBatchAsync(texts, EmbeddingPurpose.Passage, ct).ConfigureAwait(false); for (var i = 0; i < batch.Length; i++) { var hash = MemoryContentHasher.ComputeHash(batch[i].Title, batch[i].Body); diff --git a/src/Netclaw.Embeddings.Tests/EmbedQueryLatencyBudgetTests.cs b/src/Netclaw.Embeddings.Tests/EmbedQueryLatencyBudgetTests.cs index 489900112..165a2b640 100644 --- a/src/Netclaw.Embeddings.Tests/EmbedQueryLatencyBudgetTests.cs +++ b/src/Netclaw.Embeddings.Tests/EmbedQueryLatencyBudgetTests.cs @@ -4,6 +4,7 @@ // // ----------------------------------------------------------------------- using System.Diagnostics; +using Netclaw.Actors.Memory; using Xunit; namespace Netclaw.Embeddings.Tests; @@ -46,6 +47,7 @@ public async ValueTask InitializeAsync() vocabPath: Path.Combine(fixturesDir, "tiny-vocab.txt"), modelId: ModelId, dimensions: Dimensions, + queryPrefix: "", maxConcurrency: 2); } @@ -62,13 +64,13 @@ public async Task Median_short_query_embed_latency_is_within_the_150ms_sub_budge // Warm-up call: absorbs first-call session/JIT costs the real // EmbeddingWarmupHostedService pays once at startup, outside the per-turn budget. - await _embedder.EmbedAsync("warm up the inference session", ct); + await _embedder.EmbedAsync("warm up the inference session", EmbeddingPurpose.RetrievalQuery, ct); var samples = new double[SampleCount]; for (var i = 0; i < SampleCount; i++) { var sw = Stopwatch.StartNew(); - await _embedder.EmbedAsync("What's our Sev2 response time for commercial support?", ct); + await _embedder.EmbedAsync("What's our Sev2 response time for commercial support?", EmbeddingPurpose.RetrievalQuery, ct); sw.Stop(); samples[i] = sw.Elapsed.TotalMilliseconds; } diff --git a/src/Netclaw.Embeddings.Tests/EmbeddingModelProvisionerTests.cs b/src/Netclaw.Embeddings.Tests/EmbeddingModelProvisionerTests.cs index 5deb1384d..6eae5bc52 100644 --- a/src/Netclaw.Embeddings.Tests/EmbeddingModelProvisionerTests.cs +++ b/src/Netclaw.Embeddings.Tests/EmbeddingModelProvisionerTests.cs @@ -55,7 +55,8 @@ public async Task ProvisionAsync_downloads_and_verifies_matching_artifacts() ["test-model"] = new EmbeddingModelManifestEntry( "test-model", modelUrl, vocabUrl, Sha256Hex(modelBytes), Sha256Hex(vocabBytes), - Dimensions: 8, ModelByteSize: modelBytes.Length), + Dimensions: 8, ModelByteSize: modelBytes.Length, + QueryPrefix: "", CalibratedMinCosineSimilarity: null), }; var provisioner = new EmbeddingModelProvisioner(_httpClient, allowlist); @@ -85,7 +86,8 @@ public async Task ProvisionAsync_skips_the_network_entirely_when_a_valid_local_c ["test-model"] = new EmbeddingModelManifestEntry( "test-model", modelUrl, vocabUrl, Sha256Hex(modelBytes), Sha256Hex(vocabBytes), - Dimensions: 8, ModelByteSize: modelBytes.Length), + Dimensions: 8, ModelByteSize: modelBytes.Length, + QueryPrefix: "", CalibratedMinCosineSimilarity: null), }; var provisioner = new EmbeddingModelProvisioner(_httpClient, allowlist); await provisioner.ProvisionAsync("test-model", _destinationDirectory, TestContext.Current.CancellationToken); @@ -138,7 +140,8 @@ public async Task TryLoadVerifiedAsync_returns_the_provisioned_model_without_net ["test-model"] = new EmbeddingModelManifestEntry( "test-model", modelUrl, vocabUrl, Sha256Hex(modelBytes), Sha256Hex(vocabBytes), - Dimensions: 8, ModelByteSize: modelBytes.Length), + Dimensions: 8, ModelByteSize: modelBytes.Length, + QueryPrefix: "", CalibratedMinCosineSimilarity: null), }; var provisioner = new EmbeddingModelProvisioner(_httpClient, allowlist); await provisioner.ProvisionAsync("test-model", _destinationDirectory, TestContext.Current.CancellationToken); @@ -182,7 +185,8 @@ public async Task ProvisionAsync_rejects_sha256_mismatch_and_leaves_nothing_behi "tampered", modelUrl, vocabUrl, ModelSha256: Sha256Hex(Encoding.UTF8.GetBytes("this-does-not-match-the-served-bytes")), TokenizerSha256: Sha256Hex(vocabBytes), - Dimensions: 8, ModelByteSize: modelBytes.Length), + Dimensions: 8, ModelByteSize: modelBytes.Length, + QueryPrefix: "", CalibratedMinCosineSimilarity: null), }; var provisioner = new EmbeddingModelProvisioner(_httpClient, allowlist); @@ -210,7 +214,8 @@ public async Task ProvisionAsync_rejects_byte_size_mismatch_before_hashing() ["wrong-size"] = new EmbeddingModelManifestEntry( "wrong-size", modelUrl, vocabUrl, Sha256Hex(modelBytes), Sha256Hex(vocabBytes), - Dimensions: 8, ModelByteSize: modelBytes.Length + 1), + Dimensions: 8, ModelByteSize: modelBytes.Length + 1, + QueryPrefix: "", CalibratedMinCosineSimilarity: null), }; var provisioner = new EmbeddingModelProvisioner(_httpClient, allowlist); @@ -234,6 +239,32 @@ public void ProductionAllowlist_has_the_two_ratified_models_with_distinct_ids() Assert.All(EmbeddingModelProvisioner.Allowlist.Values, e => Assert.Equal(64, e.TokenizerSha256.Length)); } + // ── Retrieval-mode metadata (memory-query-prefix design D2/D4) ────── + + [Fact] + public void ArcticEntry_carries_the_model_card_query_prefix_verbatim_and_its_calibrated_floor() + { + // Pins the exact model-card string (design.md D2: verified 2026-07-08 against the + // pinned HF commit) — a future model bump forces the author past this assertion too, + // so a stale prefix silently paired with new weights fails loudly here instead of only + // degrading retrieval quality at runtime. + var entry = EmbeddingModelProvisioner.Allowlist["snowflake-arctic-embed-m"]; + Assert.Equal("Represent this sentence for searching relevant passages: ", entry.QueryPrefix); + Assert.Equal(0.24, entry.CalibratedMinCosineSimilarity); + } + + [Fact] + public void MxbaiFallbackEntry_carries_a_query_prefix_but_no_retrieval_calibration() + { + // The fallback entry has not been through its own gold-set floor sweep (design D2): its + // CalibratedMinCosineSimilarity MUST stay null until that calibration lands, so + // SQLiteMemoryRecallCoordinator degrades to lexical-only rather than silently reusing a + // floor measured for a different model. + var entry = EmbeddingModelProvisioner.Allowlist["mxbai-embed-large-v1"]; + Assert.False(string.IsNullOrEmpty(entry.QueryPrefix)); + Assert.Null(entry.CalibratedMinCosineSimilarity); + } + private static EmbeddingModelManifestEntry DummyEntry(string id) - => new(id, new Uri("http://127.0.0.1:1/model.onnx"), new Uri("http://127.0.0.1:1/vocab.txt"), new string('0', 64), new string('0', 64), 8, 1); + => new(id, new Uri("http://127.0.0.1:1/model.onnx"), new Uri("http://127.0.0.1:1/vocab.txt"), new string('0', 64), new string('0', 64), 8, 1, QueryPrefix: "", CalibratedMinCosineSimilarity: null); } diff --git a/src/Netclaw.Embeddings.Tests/OnnxMemoryEmbedderTests.cs b/src/Netclaw.Embeddings.Tests/OnnxMemoryEmbedderTests.cs index 2dffa5613..3b084eb14 100644 --- a/src/Netclaw.Embeddings.Tests/OnnxMemoryEmbedderTests.cs +++ b/src/Netclaw.Embeddings.Tests/OnnxMemoryEmbedderTests.cs @@ -3,6 +3,7 @@ // Copyright (C) 2026 - 2026 Petabridge, LLC // // ----------------------------------------------------------------------- +using Netclaw.Actors.Memory; using Xunit; namespace Netclaw.Embeddings.Tests; @@ -19,7 +20,14 @@ public sealed class OnnxMemoryEmbedderTests : IAsyncLifetime private const string ModelId = "tiny-fixture"; private const int Dimensions = 8; + // memory-query-prefix design D2/D3 fixture prefix — not a real model card string, just an + // exercisable prefix for OnnxMemoryEmbedderTests.QueryPrefix-specific facts below. _embedder + // (no prefix) covers every pre-existing fact in this file unchanged; _prefixedEmbedder is + // used only by the purpose-application facts. + private const string FixtureQueryPrefix = "search_query: "; + private OnnxMemoryEmbedder _embedder = null!; + private OnnxMemoryEmbedder _prefixedEmbedder = null!; public async ValueTask InitializeAsync() { @@ -29,12 +37,21 @@ public async ValueTask InitializeAsync() vocabPath: Path.Combine(fixturesDir, "tiny-vocab.txt"), modelId: ModelId, dimensions: Dimensions, + queryPrefix: "", + maxConcurrency: 2); + _prefixedEmbedder = await OnnxMemoryEmbedder.LoadAsync( + modelPath: Path.Combine(fixturesDir, "tiny-embedder.onnx"), + vocabPath: Path.Combine(fixturesDir, "tiny-vocab.txt"), + modelId: ModelId, + dimensions: Dimensions, + queryPrefix: FixtureQueryPrefix, maxConcurrency: 2); } public ValueTask DisposeAsync() { _embedder.Dispose(); + _prefixedEmbedder.Dispose(); return ValueTask.CompletedTask; } @@ -49,8 +66,8 @@ public void Loaded_embedder_reports_its_identity() [Fact] public async Task EmbedAsync_is_deterministic_for_the_same_text() { - var v1 = await _embedder.EmbedAsync("cat sat on the mat", TestContext.Current.CancellationToken); - var v2 = await _embedder.EmbedAsync("cat sat on the mat", TestContext.Current.CancellationToken); + var v1 = await _embedder.EmbedAsync("cat sat on the mat", EmbeddingPurpose.Passage, TestContext.Current.CancellationToken); + var v2 = await _embedder.EmbedAsync("cat sat on the mat", EmbeddingPurpose.Passage, TestContext.Current.CancellationToken); Assert.Equal(v1.ToArray(), v2.ToArray()); } @@ -58,7 +75,7 @@ public async Task EmbedAsync_is_deterministic_for_the_same_text() [Fact] public async Task EmbedAsync_produces_L2_normalized_vectors_of_the_declared_dimension() { - var vector = await _embedder.EmbedAsync("hello world", TestContext.Current.CancellationToken); + var vector = await _embedder.EmbedAsync("hello world", EmbeddingPurpose.Passage, TestContext.Current.CancellationToken); Assert.Equal(Dimensions, vector.Length); var normSquared = vector.ToArray().Sum(x => (double)x * x); @@ -72,8 +89,8 @@ public async Task EmbedAsync_reflects_the_input_text_not_just_the_CLS_token() // every real token, not just position 0 — so different inputs must not collapse to // the same vector the way a naive CLS-only passthrough over an un-contextualized // Gather would. - var v1 = await _embedder.EmbedAsync("cat sat on the mat", TestContext.Current.CancellationToken); - var v2 = await _embedder.EmbedAsync("quarterly revenue grew", TestContext.Current.CancellationToken); + var v1 = await _embedder.EmbedAsync("cat sat on the mat", EmbeddingPurpose.Passage, TestContext.Current.CancellationToken); + var v2 = await _embedder.EmbedAsync("quarterly revenue grew", EmbeddingPurpose.Passage, TestContext.Current.CancellationToken); Assert.NotEqual(v1.ToArray(), v2.ToArray()); } @@ -83,12 +100,12 @@ public async Task EmbedBatchAsync_preserves_input_order() { string[] texts = ["hello world", "cat sat", "dog running", "quarterly revenue grew by percent"]; - var batch = await _embedder.EmbedBatchAsync(texts, TestContext.Current.CancellationToken); + var batch = await _embedder.EmbedBatchAsync(texts, EmbeddingPurpose.Passage, TestContext.Current.CancellationToken); Assert.Equal(texts.Length, batch.Count); for (var i = 0; i < texts.Length; i++) { - var single = await _embedder.EmbedAsync(texts[i], TestContext.Current.CancellationToken); + var single = await _embedder.EmbedAsync(texts[i], EmbeddingPurpose.Passage, TestContext.Current.CancellationToken); Assert.Equal(single.ToArray(), batch[i].ToArray()); } } @@ -96,7 +113,7 @@ public async Task EmbedBatchAsync_preserves_input_order() [Fact] public async Task EmbedBatchAsync_of_empty_input_returns_empty() { - var batch = await _embedder.EmbedBatchAsync([], TestContext.Current.CancellationToken); + var batch = await _embedder.EmbedBatchAsync([], EmbeddingPurpose.Passage, TestContext.Current.CancellationToken); Assert.Empty(batch); } @@ -119,8 +136,8 @@ public async Task EmbedBatchAsync_of_empty_input_returns_empty() [InlineData("the quarterly revenue report shows strong growth across every regional market segment this year")] public async Task EmbedAsync_with_dynamic_length_padding_is_deterministic_and_normalized(string text) { - var v1 = await _embedder.EmbedAsync(text, TestContext.Current.CancellationToken); - var v2 = await _embedder.EmbedAsync(text, TestContext.Current.CancellationToken); + var v1 = await _embedder.EmbedAsync(text, EmbeddingPurpose.Passage, TestContext.Current.CancellationToken); + var v2 = await _embedder.EmbedAsync(text, EmbeddingPurpose.Passage, TestContext.Current.CancellationToken); Assert.Equal(v1.ToArray(), v2.ToArray()); Assert.Equal(Dimensions, v1.Length); @@ -142,4 +159,68 @@ public void ComputeBucketedLength_rounds_up_to_the_nearest_bucket_of_8(int actua { Assert.Equal(expectedBucketLength, OnnxMemoryEmbedder.ComputeBucketedLength(actualTokenCount)); } + + // ── Query prefix (memory-query-prefix design D1/D2, tasks 1.3/2.4) ── + + /// + /// Byte-compat regression guard: this is the EXACT vector OnnxMemoryEmbedder.EmbedOne + /// produced for this text against this fixture model on the commit immediately before the + /// purpose-aware seam landed (captured by temporarily instrumenting the pre-change code — + /// see the memory-query-prefix change's task notes). Passage-purpose embedding must remain + /// byte-identical after adding the query-prefix seam: adopting a prefix for + /// must never re-derive a single stored + /// document vector. + /// + [Fact] + public async Task Passage_purpose_embedding_is_byte_identical_to_the_pre_prefix_seam() + { + float[] expected = + [ + 0.27457318f, 0.29614678f, 0.31772035f, 0.339294f, + 0.36086756f, 0.3824412f, 0.4040148f, 0.42558837f, + ]; + + var vector = await _embedder.EmbedAsync( + "Netclaw memory-query-prefix regression fixture text", EmbeddingPurpose.Passage, TestContext.Current.CancellationToken); + + Assert.Equal(expected, vector.ToArray()); + } + + [Fact] + public async Task RetrievalQuery_purpose_applies_the_embedders_configured_prefix() + { + const string text = "Netclaw memory-query-prefix regression fixture text"; + + var passageVector = await _prefixedEmbedder.EmbedAsync(text, EmbeddingPurpose.Passage, TestContext.Current.CancellationToken); + var queryVector = await _prefixedEmbedder.EmbedAsync(text, EmbeddingPurpose.RetrievalQuery, TestContext.Current.CancellationToken); + + // Same text, same embedder instance, different purpose -- the prefix is applied for + // RetrievalQuery only, so the two vectors must differ. + Assert.NotEqual(passageVector.ToArray(), queryVector.ToArray()); + } + + [Fact] + public async Task Passage_purpose_ignores_the_embedders_configured_prefix() + { + // _prefixedEmbedder has a real, non-empty QueryPrefix, but Passage purpose must produce + // the exact same vector as an embedder with NO prefix at all -- this is what keeps + // document-side vectors byte-identical regardless of which model's prefix is active. + const string text = "cat sat on the mat"; + + var fromUnprefixedEmbedder = await _embedder.EmbedAsync(text, EmbeddingPurpose.Passage, TestContext.Current.CancellationToken); + var fromPrefixedEmbedder = await _prefixedEmbedder.EmbedAsync(text, EmbeddingPurpose.Passage, TestContext.Current.CancellationToken); + + Assert.Equal(fromUnprefixedEmbedder.ToArray(), fromPrefixedEmbedder.ToArray()); + } + + [Fact] + public async Task RetrievalQuery_purpose_is_a_no_op_when_the_embedder_has_no_configured_prefix() + { + const string text = "cat sat on the mat"; + + var passageVector = await _embedder.EmbedAsync(text, EmbeddingPurpose.Passage, TestContext.Current.CancellationToken); + var queryVector = await _embedder.EmbedAsync(text, EmbeddingPurpose.RetrievalQuery, TestContext.Current.CancellationToken); + + Assert.Equal(passageVector.ToArray(), queryVector.ToArray()); + } } diff --git a/src/Netclaw.Embeddings/EmbeddingModelProvisioner.cs b/src/Netclaw.Embeddings/EmbeddingModelProvisioner.cs index c5a41dcf2..bf2074de3 100644 --- a/src/Netclaw.Embeddings/EmbeddingModelProvisioner.cs +++ b/src/Netclaw.Embeddings/EmbeddingModelProvisioner.cs @@ -20,6 +20,24 @@ namespace Netclaw.Embeddings; /// Expected SHA-256 (lowercase hex) of the vocab artifact. /// Embedding vector width this model produces. /// Expected byte size of the model artifact — a cheap first check before hashing. +/// +/// The model card's documented retrieval-query prefix (memory-query-prefix design D2), applied +/// verbatim by when embedding for +/// . Empty for a model that +/// documents no query-side prefix. Pinned next to the model hash in the same entry so a model +/// bump forces the author past this field too — a stale prefix silently paired with a new +/// model's weights would degrade retrieval quality without any loud failure. +/// +/// +/// The absolute cosine floor calibrated for this model id in its documented retrieval-query +/// encoding (with applied) — memory-query-prefix design D3/D4: "the +/// same manifest-carries-calibration pattern the relevance gate established with +/// ." null means this entry +/// has not been calibrated for retrieval: +/// treats an active model with no calibration and no explicit +/// Memory.Recall.MinCosineSimilarity override as hybrid-recall-unavailable (lexical-only, +/// degraded log) rather than guessing a floor calibrated for a different model or encoding mode. +/// public sealed record EmbeddingModelManifestEntry( string ModelId, Uri ModelUrl, @@ -27,10 +45,25 @@ public sealed record EmbeddingModelManifestEntry( string ModelSha256, string TokenizerSha256, int Dimensions, - long ModelByteSize); + long ModelByteSize, + string QueryPrefix, + double? CalibratedMinCosineSimilarity); -/// Files placed on disk by , ready for . -public sealed record ProvisionedEmbeddingModel(string ModelId, string ModelPath, string VocabPath, int Dimensions); +/// +/// Files placed on disk by , ready for +/// . Carries the manifest entry's +/// and +/// alongside the +/// provisioned files (memory-query-prefix design D2) — the same "download result also carries +/// the model's calibration" shape as . +/// +public sealed record ProvisionedEmbeddingModel( + string ModelId, + string ModelPath, + string VocabPath, + int Dimensions, + string QueryPrefix, + double? CalibratedMinCosineSimilarity); /// /// One entry in (memory-relevance-gate @@ -93,6 +126,13 @@ public sealed class EmbeddingModelProvisioner public static IReadOnlyDictionary Allowlist { get; } = new Dictionary(StringComparer.Ordinal) { + // Query prefix verified 2026-07-08 against the model card at the pinned HF commit + // (memory-query-prefix design D2): "Represent this sentence for searching relevant + // passages: " (trailing space is part of the documented string — the prefix and the + // query text are meant to read as one sentence, not two concatenated with no + // separator). CalibratedMinCosineSimilarity=0.24 is the gold-prod-2026-07 sweep + // optimum for this prefixed encoding (design.md D4; supersedes the no-prefix 0.68 + // figure recorded in memory-core-redesign design.md D6). ["snowflake-arctic-embed-m"] = new EmbeddingModelManifestEntry( ModelId: "snowflake-arctic-embed-m", ModelUrl: new Uri("https://huggingface.co/Snowflake/snowflake-arctic-embed-m/resolve/fc74610d18462d218e312aa986ec5c8a75a98152/onnx/model.onnx"), @@ -100,8 +140,20 @@ public sealed class EmbeddingModelProvisioner ModelSha256: "564e6c65ee0c739a486702e9e3e9b33c3f697c19c34dbe886bce9eec497ce971", TokenizerSha256: "07eced375cec144d27c900241f3e339478dec958f92fddbc551f295c992038a3", Dimensions: 768, - ModelByteSize: 435_811_541), - + ModelByteSize: 435_811_541, + QueryPrefix: "Represent this sentence for searching relevant passages: ", + CalibratedMinCosineSimilarity: 0.24), + + // Query prefix verified 2026-07-08 against the model card (mixedbread-ai's usage + // examples document the identical instruction string arctic-embed-m uses — both + // cards converge on the same widely-used E5-style retrieval instruction; this is + // NOT copy-paste drift, it is independently confirmed for this model's own card). + // CalibratedMinCosineSimilarity is null: this fallback entry has not been through + // the gold-set floor sweep, so it is deliberately uncalibrated — activating this + // model with no explicit Memory.Recall.MinCosineSimilarity override degrades hybrid + // recall to lexical-only (memory-query-prefix design D2/D3; see + // SQLiteMemoryRecallCoordinator's missing-calibration degraded path) rather than + // silently reusing a floor measured for a different model. ["mxbai-embed-large-v1"] = new EmbeddingModelManifestEntry( ModelId: "mxbai-embed-large-v1", ModelUrl: new Uri("https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1/resolve/b33106f585b9ce46904ad7443a3b52b7a63e231c/onnx/model.onnx"), @@ -109,7 +161,9 @@ public sealed class EmbeddingModelProvisioner ModelSha256: "adb53ed475faa339bfad3bd2bdb7e6a30b4f47280ade9811f81bef7953f9ab77", TokenizerSha256: "07eced375cec144d27c900241f3e339478dec958f92fddbc551f295c992038a3", Dimensions: 1024, - ModelByteSize: 1_336_854_282), + ModelByteSize: 1_336_854_282, + QueryPrefix: "Represent this sentence for searching relevant passages: ", + CalibratedMinCosineSimilarity: null), }; /// @@ -197,13 +251,13 @@ public async Task ProvisionAsync( if (await IsValidAsync(modelPath, entry.ModelSha256, entry.ModelByteSize, ct).ConfigureAwait(false) && await IsValidAsync(vocabPath, entry.TokenizerSha256, expectedByteSize: null, ct).ConfigureAwait(false)) { - return new ProvisionedEmbeddingModel(modelId, modelPath, vocabPath, entry.Dimensions); + return new ProvisionedEmbeddingModel(modelId, modelPath, vocabPath, entry.Dimensions, entry.QueryPrefix, entry.CalibratedMinCosineSimilarity); } await DownloadAndVerifyAsync(entry.ModelUrl, modelPath, entry.ModelSha256, entry.ModelByteSize, ct).ConfigureAwait(false); await DownloadAndVerifyAsync(entry.TokenizerUrl, vocabPath, entry.TokenizerSha256, expectedByteSize: null, ct).ConfigureAwait(false); - return new ProvisionedEmbeddingModel(modelId, modelPath, vocabPath, entry.Dimensions); + return new ProvisionedEmbeddingModel(modelId, modelPath, vocabPath, entry.Dimensions, entry.QueryPrefix, entry.CalibratedMinCosineSimilarity); } /// @@ -231,7 +285,7 @@ public async Task ProvisionAsync( if (!await IsValidAsync(vocabPath, entry.TokenizerSha256, expectedByteSize: null, ct).ConfigureAwait(false)) return null; - return new ProvisionedEmbeddingModel(modelId, modelPath, vocabPath, entry.Dimensions); + return new ProvisionedEmbeddingModel(modelId, modelPath, vocabPath, entry.Dimensions, entry.QueryPrefix, entry.CalibratedMinCosineSimilarity); } /// diff --git a/src/Netclaw.Embeddings/OnnxMemoryEmbedder.cs b/src/Netclaw.Embeddings/OnnxMemoryEmbedder.cs index 94caeeada..72c70af07 100644 --- a/src/Netclaw.Embeddings/OnnxMemoryEmbedder.cs +++ b/src/Netclaw.Embeddings/OnnxMemoryEmbedder.cs @@ -39,6 +39,18 @@ namespace Netclaw.Embeddings; /// /// /// +/// Query prefix (memory-query-prefix design D2): asymmetric retrieval models document a +/// query-side instruction prefix that must never reach document embeddings. This embedder is +/// handed its active model's QueryPrefix (empty for a model that documents none) at +/// time and prepends it — before tokenization, so it counts against the +/// token budget like any other text — only when a caller passes +/// . +/// embeddings are never prefixed, +/// which is what keeps them byte-identical to vectors already stored before prefix support +/// existed — no re-embed is required when a prefix is adopted. +/// +/// +/// /// Concurrency: a single supports concurrent /// calls, but an /// unbounded number of them would oversubscribe the CPU beyond what @@ -68,13 +80,15 @@ public sealed class OnnxMemoryEmbedder : IMemoryEmbedder, IDisposable private readonly BertTokenizer _tokenizer; private readonly BoundedConcurrencyGate _gate; private readonly string _outputName; + private readonly string _queryPrefix; private OnnxMemoryEmbedder( string modelId, int dimensions, InferenceSession session, BertTokenizer tokenizer, - int maxConcurrency) + int maxConcurrency, + string queryPrefix) { if (session.OutputMetadata.Count != 1) throw new InvalidOperationException( @@ -87,6 +101,7 @@ private OnnxMemoryEmbedder( _tokenizer = tokenizer; _gate = new BoundedConcurrencyGate(maxConcurrency); _outputName = session.OutputMetadata.Keys.Single(); + _queryPrefix = queryPrefix; } /// @@ -107,6 +122,14 @@ private OnnxMemoryEmbedder( /// Path to the WordPiece vocab.txt file. /// The allowlisted model id these files correspond to. /// Expected output vector width, from the allowlist manifest. + /// + /// The allowlist manifest's for this + /// model id (memory-query-prefix design D2) — pass for a model + /// that documents no retrieval-query prefix, or for a caller (a test fixture graph) that has + /// no manifest entry at all. Required rather than defaulted so every call site names its + /// choice explicitly; there is no safe default between "this model has a prefix" and "it + /// doesn't." + /// /// Maximum concurrent inference calls (default 2). /// Threads ONNX Runtime uses per inference call (default 4). public static async Task LoadAsync( @@ -114,11 +137,13 @@ public static async Task LoadAsync( string vocabPath, string modelId, int dimensions, + string queryPrefix, int maxConcurrency = 2, int intraOpNumThreads = 4, CancellationToken ct = default) { ct.ThrowIfCancellationRequested(); + ArgumentNullException.ThrowIfNull(queryPrefix); using var sessionOptions = new SessionOptions { IntraOpNumThreads = intraOpNumThreads }; var session = new InferenceSession(modelPath, sessionOptions); @@ -129,15 +154,15 @@ public static async Task LoadAsync( // tokenizer_config.json — a standard BERT-base-uncased vocabulary. await tokenizer.LoadVocabularyAsync(vocabPath, convertInputToLowercase: true); - return new OnnxMemoryEmbedder(modelId, dimensions, session, tokenizer, maxConcurrency); + return new OnnxMemoryEmbedder(modelId, dimensions, session, tokenizer, maxConcurrency, queryPrefix); } /// - public async ValueTask> EmbedAsync(string text, CancellationToken ct) - => await _gate.RunAsync(_ => Task.FromResult(EmbedOne(text)), ct).ConfigureAwait(false); + public async ValueTask> EmbedAsync(string text, EmbeddingPurpose purpose, CancellationToken ct) + => await _gate.RunAsync(_ => Task.FromResult(EmbedOne(text, purpose)), ct).ConfigureAwait(false); /// - public async ValueTask>> EmbedBatchAsync(IReadOnlyList texts, CancellationToken ct) + public async ValueTask>> EmbedBatchAsync(IReadOnlyList texts, EmbeddingPurpose purpose, CancellationToken ct) { if (texts.Count == 0) return []; @@ -150,14 +175,23 @@ public async ValueTask>> EmbedBatchAsync(IRe for (var i = 0; i < texts.Count; i++) { var text = texts[i]; - tasks[i] = _gate.RunAsync(_ => Task.FromResult(EmbedOne(text)), ct); + tasks[i] = _gate.RunAsync(_ => Task.FromResult(EmbedOne(text, purpose)), ct); } return await Task.WhenAll(tasks).ConfigureAwait(false); } - private ReadOnlyMemory EmbedOne(string text) + private ReadOnlyMemory EmbedOne(string text, EmbeddingPurpose purpose) { + // Prefix applied before tokenization (memory-query-prefix design D2) so it counts + // against the token budget/bucketing below like any other text, and so the resulting + // vector reflects the exact string the model card instructs embedding. Never applied to + // Passage purpose -- that is what keeps document-side vectors byte-identical to ones + // stored before prefix support existed. + var effectiveText = purpose == EmbeddingPurpose.RetrievalQuery && _queryPrefix.Length > 0 + ? _queryPrefix + text + : text; + var scratchIds = new long[MaxTokens]; var scratchMask = new long[MaxTokens]; var scratchTypes = new long[MaxTokens]; @@ -165,7 +199,7 @@ private ReadOnlyMemory EmbedOne(string text) // This overload writes into the caller-supplied spans instead of BertTokenizer's // internal reused buffers, so calling it from multiple gate-scheduled tasks // concurrently against the one shared _tokenizer instance is safe. - _tokenizer.Encode(text, scratchIds, scratchMask, scratchTypes, MaxTokens); + _tokenizer.Encode(effectiveText, scratchIds, scratchMask, scratchTypes, MaxTokens); // Dynamic-length padding: only feed the ONNX graph the actual tokenized length // (rounded up to DynamicLengthBucket), not the full fixed-512 scratch buffers -- see diff --git a/tools/embed-latency-bench/Program.cs b/tools/embed-latency-bench/Program.cs index ba3998aaf..8a753d946 100644 --- a/tools/embed-latency-bench/Program.cs +++ b/tools/embed-latency-bench/Program.cs @@ -24,6 +24,7 @@ using FastBertTokenizer; using Microsoft.ML.OnnxRuntime; using Microsoft.ML.OnnxRuntime.Tensors; +using Netclaw.Actors.Memory; using Netclaw.Embeddings; // Captured before any other work so the cold-load number can include .NET host/runtime @@ -216,8 +217,10 @@ .. sentenceBank.Take(5), // --- Cold load ------------------------------------------------------------------------------- var loadOnlySw = Stopwatch.StartNew(); -var embedder = await OnnxMemoryEmbedder.LoadAsync(verified.ModelPath, verified.VocabPath, verified.ModelId, verified.Dimensions); -_ = await embedder.EmbedAsync(shortQueries[0], CancellationToken.None); +var embedder = await OnnxMemoryEmbedder.LoadAsync(verified.ModelPath, verified.VocabPath, verified.ModelId, verified.Dimensions, verified.QueryPrefix); +// Cold-load's first embed mirrors EmbeddingWarmupHostedService's own warm-up call (Passage +// purpose) -- see that type's remarks. +_ = await embedder.EmbedAsync(shortQueries[0], EmbeddingPurpose.Passage, CancellationToken.None); loadOnlySw.Stop(); var processToFirstEmbedMs = (DateTime.UtcNow - processStartUtc).TotalMilliseconds; @@ -239,16 +242,16 @@ double Pct(double p) return new Row(label, sorted.Length, Pct(50), Pct(90), Pct(95), Pct(99), sorted[^1], sorted.Average()); } -async Task> RunCorpus(string[] corpus, int warmup, int timed) +async Task> RunCorpus(string[] corpus, int warmup, int timed, EmbeddingPurpose purpose) { for (var i = 0; i < warmup; i++) - _ = await embedder.EmbedAsync(corpus[i % corpus.Length], CancellationToken.None); + _ = await embedder.EmbedAsync(corpus[i % corpus.Length], purpose, CancellationToken.None); var samples = new List(timed); for (var i = 0; i < timed; i++) { var sw = Stopwatch.StartNew(); - _ = await embedder.EmbedAsync(corpus[i % corpus.Length], CancellationToken.None); + _ = await embedder.EmbedAsync(corpus[i % corpus.Length], purpose, CancellationToken.None); sw.Stop(); samples.Add(sw.Elapsed.TotalMilliseconds); } @@ -258,9 +261,13 @@ async Task> RunCorpus(string[] corpus, int warmup, int timed) var rows = new List { - Percentiles("short", await RunCorpus(shortQueries, WarmupIterations, TimedIterations)), - Percentiles("medium", await RunCorpus(mediumCorpus, WarmupIterations, TimedIterations)), - Percentiles("doc", await RunCorpus(docCorpus, WarmupIterations, TimedIterations)), + // "short" mirrors SQLiteMemoryRecallCoordinator's per-turn query embedding (memory-query- + // prefix design D2): RetrievalQuery purpose, so this measurement includes the active + // model's query prefix -- the real cost VectorEmbedSubBudgetMs must budget for. "medium"/ + // "doc" mirror embed-on-write/backfill document embedding: Passage purpose, never prefixed. + Percentiles("short", await RunCorpus(shortQueries, WarmupIterations, TimedIterations, EmbeddingPurpose.RetrievalQuery)), + Percentiles("medium", await RunCorpus(mediumCorpus, WarmupIterations, TimedIterations, EmbeddingPurpose.Passage)), + Percentiles("doc", await RunCorpus(docCorpus, WarmupIterations, TimedIterations, EmbeddingPurpose.Passage)), }; // --- Concurrency-2 short-query pass (two parallel loops share the SemaphoreSlim(2) gate) --- @@ -271,7 +278,7 @@ async Task> RunConcurrentLoop(int iterations) for (var i = 0; i < iterations; i++) { var sw = Stopwatch.StartNew(); - _ = await embedder.EmbedAsync(shortQueries[i % shortQueries.Length], CancellationToken.None); + _ = await embedder.EmbedAsync(shortQueries[i % shortQueries.Length], EmbeddingPurpose.RetrievalQuery, CancellationToken.None); sw.Stop(); samples.Add(sw.Elapsed.TotalMilliseconds); } @@ -295,7 +302,7 @@ async Task> RunConcurrentLoop(int iterations) // semantic content, should cosine-agree near 1.0 if the attention mask does its job). var fixedCorrectnessEmbeddings = new ReadOnlyMemory[correctnessSentences.Length]; for (var i = 0; i < correctnessSentences.Length; i++) - fixedCorrectnessEmbeddings[i] = await embedder.EmbedAsync(correctnessSentences[i], CancellationToken.None); + fixedCorrectnessEmbeddings[i] = await embedder.EmbedAsync(correctnessSentences[i], EmbeddingPurpose.Passage, CancellationToken.None); embedder.Dispose(); From e1bab2909e2367eeda04c1716bfa29c081857ad7 Mon Sep 17 00:00:00 2001 From: Aaron Stannard Date: Wed, 8 Jul 2026 23:26:47 +0000 Subject: [PATCH 23/37] feat(memory): int8 arctic embedder as default model with prefixed floor calibration Adds snowflake-arctic-embed-m-int8 (HF onnx/model_uint8.onnx, pinned at the same commit as the fp32 entry, shared tokenizer) to EmbeddingModelProvisioner.Allowlist and flips Memory.Embeddings.ModelId's default to it. Calibration (arctic-int8-prefix-eval, gold-prod-2026-07 + repooled-test, same widened tau sweep methodology as the fp32 prefix calibration): int8-with-prefix beats fp32-with-prefix on every axis -- F0.5 0.244 vs 0.239, recall@3 0.404 vs 0.318, zero-injection accuracy 28.3% vs 26.7% -- at ~1.7x inference speed and ~57% less steady-state RSS (already measured in memory-core-redesign's quant-eval). CalibratedMinCosineSimilarity=0.24. fp32 (snowflake-arctic-embed-m) and mxbai-embed-large-v1 remain allowlisted as explicit operator choices. Upgrade story: gap-repair (EmbeddingWarmupHostedService), the vector index, and the curation nominator are all scoped by the active model id already, so an existing install's fp32 vectors are left in place and the entire corpus re-embeds under the new default automatically at next startup; netclaw doctor's existing mixed-model-id warning covers the interim state with its backfill --force recommendation. Added a dedicated test proving a model-id switch retargets gap repair to the new id. netclaw-memory skill bumped 1.12.0 -> 1.13.0 with an upgrade-behavior note. Gates: build clean, Configuration/Embeddings/Daemon/Cli/Actors test suites green, slopwatch 0 issues, copyright headers verified. --- .../.system/files/netclaw-memory/SKILL.md | 31 +++++++--- .../MemoryConfigDefaultsTests.cs | 10 ++- src/Netclaw.Configuration/MemoryConfig.cs | 26 ++++++-- .../Schemas/netclaw-config.v1.schema.json | 6 +- .../EmbeddingWarmupHostedServiceTests.cs | 62 +++++++++++++++++++ .../EmbeddingModelProvisionerTests.cs | 29 ++++++++- .../EmbeddingModelProvisioner.cs | 47 +++++++++++--- 7 files changed, 185 insertions(+), 26 deletions(-) diff --git a/feeds/skills/.system/files/netclaw-memory/SKILL.md b/feeds/skills/.system/files/netclaw-memory/SKILL.md index cdbb0ff1c..ddb5ddb80 100644 --- a/feeds/skills/.system/files/netclaw-memory/SKILL.md +++ b/feeds/skills/.system/files/netclaw-memory/SKILL.md @@ -3,7 +3,7 @@ name: netclaw-memory description: "REQUIRED when the user asks what you remember, recall, or know from past conversations, previous sessions, cross-session memory, memory classes, or memory types. Also before using memory tools: find_memories, get_memories, store_memory, update_memory." metadata: author: netclaw - version: "1.12.0" + version: "1.13.0" --- # Netclaw Memory @@ -74,11 +74,12 @@ lexical-only — same candidate pool, no vector term or cosine floor. alone). - **Query prefix is automatic, per-model**: the turn query is embedded using whatever retrieval-query encoding the active embedding model documents — - for the shipped `snowflake-arctic-embed-m`, that means a fixed instruction - string is prepended before the query text. This is a property of the - model, not something you configure; document-side embeddings (stored - memories) are never prefixed, so this never requires re-embedding existing - content. + for the shipped default `snowflake-arctic-embed-m-int8` (and the + allowlisted fp32 `snowflake-arctic-embed-m` it's quantized from), that + means a fixed instruction string is prepended before the query text. This + is a property of the model, not something you configure; document-side + embeddings (stored memories) are never prefixed, so this never requires + re-embedding existing content. - **Absolute floor**: independent of the fused score, any candidate whose raw cosine similarity falls below the effective `MinCosineSimilarity` is dropped before ranking. If nothing clears the floor, nothing is injected — @@ -89,7 +90,11 @@ lexical-only — same candidate pool, no vector term or cosine floor. `Memory.Recall.MinCosineSimilarity` (nullable) is `null` unless an operator explicitly overrides it — when `null`, the effective floor is whichever calibration is pinned to the currently active embedding model (0.24 for - the shipped, prefixed `snowflake-arctic-embed-m` encoding). **The numeric + the shipped default `snowflake-arctic-embed-m-int8` prefixed encoding; + also 0.24 for the allowlisted fp32 `snowflake-arctic-embed-m` prefixed + encoding, calibrated independently — int8 measured as a strict + retrieval-quality improvement over fp32 on the same gold sets, not a + size/latency tradeoff). **The numeric meaning of this value is model- and encoding-specific**: cosine distributions shift materially between models, and even for the same model between a prefixed and unprefixed encoding — an old value copied @@ -117,6 +122,18 @@ lexical-only — same candidate pool, no vector term or cosine floor. should run `netclaw memory backfill-embeddings` right after turning embeddings on so the gap closes immediately instead of waiting for embed-on-write to catch up opportunistically. +- **Upgrading onto a new default model id (e.g. the fp32→int8 default + flip)**: an existing install with vectors stored under the previous + `Memory.Embeddings.ModelId` self-heals automatically — vector coverage, + the curation nominator, and hybrid recall are all scoped to the *current* + model id, so the daemon's startup gap-repair sweep sees every document as + missing a current-model embedding and re-embeds the whole corpus under the + new id with no operator action required. The old model's vectors are never + deleted, just no longer read. Until gap repair finishes, recall degrades + to lexical-only (same self-healing, logged degradation as any other + coverage gap above), and `netclaw doctor` surfaces the interim + mixed-model state as a warning recommending `netclaw memory + backfill-embeddings --force` to force it immediately instead of waiting. ### Relevance Gate (cross-encoder) diff --git a/src/Netclaw.Configuration.Tests/MemoryConfigDefaultsTests.cs b/src/Netclaw.Configuration.Tests/MemoryConfigDefaultsTests.cs index 97b620a9b..7b8146fca 100644 --- a/src/Netclaw.Configuration.Tests/MemoryConfigDefaultsTests.cs +++ b/src/Netclaw.Configuration.Tests/MemoryConfigDefaultsTests.cs @@ -23,11 +23,17 @@ public void Embeddings_disabled_by_default() Assert.False(config.Embeddings.Enabled); } + // int8 default: a dedicated prefixed-query gold-set sweep (arctic-int8-prefix-eval) + // measured the int8/uint8 quantized artifact as a strict retrieval-quality improvement over + // the fp32 weights it is quantized from (F0.5, recall@3, and zero-injection accuracy all + // better, not just smaller/faster) — see EmbeddingModelProvisioner.Allowlist's remarks for + // the full numbers. fp32 (snowflake-arctic-embed-m) remains allowlisted as an explicit + // operator choice. [Fact] - public void Embeddings_model_id_defaults_to_snowflake_arctic_embed_m() + public void Embeddings_model_id_defaults_to_snowflake_arctic_embed_m_int8() { var config = new MemoryConfig(); - Assert.Equal("snowflake-arctic-embed-m", config.Embeddings.ModelId); + Assert.Equal("snowflake-arctic-embed-m-int8", config.Embeddings.ModelId); } [Fact] diff --git a/src/Netclaw.Configuration/MemoryConfig.cs b/src/Netclaw.Configuration/MemoryConfig.cs index 83c66a4d5..ea010b505 100644 --- a/src/Netclaw.Configuration/MemoryConfig.cs +++ b/src/Netclaw.Configuration/MemoryConfig.cs @@ -66,8 +66,22 @@ public sealed class MemoryEmbeddingsConfig /// Netclaw.Embeddings). An id absent from the allowlist is a configuration error, /// surfaced by the doctor check and warmup service — never a silently-accepted arbitrary /// model source (supply-chain boundary, design D2). + /// + /// + /// Defaults to the int8/uint8 quantized artifact (snowflake-arctic-embed-m-int8), not + /// the fp32 snowflake-arctic-embed-m weights it is quantized from. A dedicated + /// prefixed-query gold-set sweep measured int8 as a strict improvement over fp32 on every + /// retrieval axis (F0.5, recall@3, zero-injection accuracy — see the allowlist entry's + /// remarks for the numbers), not merely an acceptable size/latency tradeoff, at ~57% less + /// steady-state RSS and ~1.7x the inference speed. fp32 and mxbai-embed-large-v1 stay + /// allowlisted as explicit operator choices. An existing install with fp32 vectors already + /// stored self-heals on upgrade: the daemon's gap-repair sweep (EmbeddingWarmupHostedService) + /// is scoped to the active model id, so it re-embeds the whole corpus under the new id + /// automatically, and netclaw doctor surfaces the interim mixed-model state as a + /// warning recommending netclaw memory backfill-embeddings --force. + /// /// - public string ModelId { get; set; } = "snowflake-arctic-embed-m"; + public string ModelId { get; set; } = "snowflake-arctic-embed-m-int8"; /// /// When true, the daemon downloads the model artifact at startup if not already @@ -160,10 +174,12 @@ public sealed class MemoryRecallConfig /// /// null (default) — the effective floor follows the active embedding model's /// manifest-carried CalibratedMinCosineSimilarity - /// (Netclaw.Embeddings.EmbeddingModelManifestEntry; 0.24 for the shipped prefixed - /// snowflake-arctic-embed-m encoding — see the memory-query-prefix design doc for the - /// full gold-set sweep). A concrete value is an explicit operator override, independent of - /// which model is active. + /// (Netclaw.Embeddings.EmbeddingModelManifestEntry; 0.24 for the shipped default + /// snowflake-arctic-embed-m-int8 prefixed encoding, and also 0.24 for the fp32 + /// snowflake-arctic-embed-m prefixed encoding it was calibrated independently + /// against — see the memory-query-prefix design doc and the int8 default-model calibration + /// for the full gold-set sweeps). A concrete value is an explicit operator override, + /// independent of which model is active. /// /// /// diff --git a/src/Netclaw.Configuration/Schemas/netclaw-config.v1.schema.json b/src/Netclaw.Configuration/Schemas/netclaw-config.v1.schema.json index d7aabf7a4..caaf25818 100644 --- a/src/Netclaw.Configuration/Schemas/netclaw-config.v1.schema.json +++ b/src/Netclaw.Configuration/Schemas/netclaw-config.v1.schema.json @@ -376,8 +376,8 @@ }, "ModelId": { "type": "string", - "default": "snowflake-arctic-embed-m", - "description": "Allowlisted embedding model id. An id absent from the in-code allowlist is a configuration error." + "default": "snowflake-arctic-embed-m-int8", + "description": "Allowlisted embedding model id. An id absent from the in-code allowlist is a configuration error. Defaults to the int8/uint8 quantized artifact, measured as a strict retrieval-quality improvement over the fp32 snowflake-arctic-embed-m weights it is quantized from (not merely a size/latency tradeoff), at ~57% less steady-state RSS. fp32 (snowflake-arctic-embed-m) and mxbai-embed-large-v1 remain allowlisted as explicit alternatives." }, "AutoDownload": { "type": "boolean", @@ -443,7 +443,7 @@ "type": ["number", "null"], "minimum": 0, "maximum": 1, - "description": "Absolute relevance floor: when a query vector is available, any candidate below this cosine similarity is dropped before ranking, regardless of source. When null (default), the effective floor follows the active embedding model's manifest-carried calibration (0.24 for the shipped prefixed snowflake-arctic-embed-m encoding). The value is model- and encoding-specific — cosine distributions shift materially when a model's documented query prefix is adopted or removed, so a value pinned for one model/encoding must never be carried into another without re-running the calibration procedure." + "description": "Absolute relevance floor: when a query vector is available, any candidate below this cosine similarity is dropped before ranking, regardless of source. When null (default), the effective floor follows the active embedding model's manifest-carried calibration (0.24 for the shipped default snowflake-arctic-embed-m-int8 prefixed encoding; also 0.24 for the fp32 snowflake-arctic-embed-m prefixed encoding). The value is model- and encoding-specific — cosine distributions shift materially when a model's documented query prefix is adopted or removed, so a value pinned for one model/encoding must never be carried into another without re-running the calibration procedure." }, "RecencyHalfLifeDays": { "type": "number", diff --git a/src/Netclaw.Daemon.Tests/Services/EmbeddingWarmupHostedServiceTests.cs b/src/Netclaw.Daemon.Tests/Services/EmbeddingWarmupHostedServiceTests.cs index 1d8e26c83..9496f9f43 100644 --- a/src/Netclaw.Daemon.Tests/Services/EmbeddingWarmupHostedServiceTests.cs +++ b/src/Netclaw.Daemon.Tests/Services/EmbeddingWarmupHostedServiceTests.cs @@ -165,6 +165,68 @@ await _store.UpsertDocumentAsync(new SQLiteMemoryDocument( Assert.Equal("doc-needs-embedding", row.ItemId); } + // memory-embeddings-int8-default: proves the upgrade story when Memory.Embeddings.ModelId + // switches (e.g. an existing install's fp32 `snowflake-arctic-embed-m` vectors on an + // install that predates the int8 default flip) -- gap repair is scoped to the NEW active + // model id (GetDocumentsNeedingEmbeddingAsync/GetEmbeddingsForModelAsync both filter by + // model_id), so a document with only an old-model vector still looks "missing" under the + // new id and gets re-embedded automatically at the next startup, with no operator action + // required. The old vector is never deleted -- it just stops being the one anything reads, + // since MemoryVectorIndex/the curation nominator only ever load the active model's rows + // (see MemoryVectorIndex.LoadAsync -> GetEmbeddingsForModelAsync(ModelId)). + [Fact] + public async Task Model_id_switch_gap_repair_targets_the_new_active_model_id_and_leaves_old_vectors_in_place() + { + PrePlaceValidModelFiles(); + + const string LegacyModelId = "tiny-fixture-legacy"; + const string Title = "Pre-upgrade document"; + const string Body = "this document was embedded under the old model before the default switched"; + + var anchor = _store.CreateDefaultAnchor("model-switch-gap-repair-test"); + var now = DateTimeOffset.UtcNow.ToUnixTimeMilliseconds(); + await _store.UpsertDocumentAsync(new SQLiteMemoryDocument( + DocumentId: "doc-under-legacy-model", + Anchor: anchor, + MemoryClass: "durable_fact", + Title: Title, + MarkdownBody: Body, + AliasesJson: null, + FacetsJson: null, + SlotsJson: null, + UpdateSemantics: "merge-document", + Sensitivity: "normal", + RecallMode: "auto", + Confidence: 0.9, + FreshnessAtMs: now, + ExpiresAtMs: null, + CreatedAtMs: now, + UpdatedAtMs: now), TestContext.Current.CancellationToken); + + // Simulate a pre-upgrade install: written directly (not through a real embedder) since + // only the model-id scoping behavior is under test here. + var legacyHash = MemoryContentHasher.ComputeHash(Title, Body); + await _store.UpsertEmbeddingAsync( + "doc-under-legacy-model", MemoryEmbedOnWriteCoordinator.DocumentItemKind, LegacyModelId, legacyHash, + new float[Dimensions], TestContext.Current.CancellationToken); + + var holder = new MemoryEmbedderHolder(new UnavailableMemoryEmbedder(ModelId, "warmup not yet run"), initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: null); + // Active config now points at the NEW model id (ModelId = "tiny-fixture") -- the same + // shape as an operator upgrading onto a new default embedding model. + var memoryConfig = new MemoryConfig { Embeddings = { Enabled = true, ModelId = ModelId, AutoDownload = true } }; + var service = CreateService(holder, memoryConfig); + + await service.WarmUpAsync(TestContext.Current.CancellationToken); + + var newRows = await _store.GetEmbeddingsForModelAsync(ModelId, TestContext.Current.CancellationToken); + var newRow = Assert.Single(newRows); + Assert.Equal("doc-under-legacy-model", newRow.ItemId); + + // The legacy vector is left in place -- a model-id switch never deletes old rows. + var legacyRows = await _store.GetEmbeddingsForModelAsync(LegacyModelId, TestContext.Current.CancellationToken); + Assert.Single(legacyRows); + } + // ── Relevance gate provisioning (memory-relevance-gate, design D4, task 1.4) ── [Fact] diff --git a/src/Netclaw.Embeddings.Tests/EmbeddingModelProvisionerTests.cs b/src/Netclaw.Embeddings.Tests/EmbeddingModelProvisionerTests.cs index 6eae5bc52..f3d964af3 100644 --- a/src/Netclaw.Embeddings.Tests/EmbeddingModelProvisionerTests.cs +++ b/src/Netclaw.Embeddings.Tests/EmbeddingModelProvisionerTests.cs @@ -229,11 +229,13 @@ public async Task ProvisionAsync_rejects_byte_size_mismatch_before_hashing() } [Fact] - public void ProductionAllowlist_has_the_two_ratified_models_with_distinct_ids() + public void ProductionAllowlist_has_the_three_ratified_models_with_distinct_ids() { Assert.True(EmbeddingModelProvisioner.Allowlist.ContainsKey("snowflake-arctic-embed-m")); + Assert.True(EmbeddingModelProvisioner.Allowlist.ContainsKey("snowflake-arctic-embed-m-int8")); Assert.True(EmbeddingModelProvisioner.Allowlist.ContainsKey("mxbai-embed-large-v1")); Assert.Equal(768, EmbeddingModelProvisioner.Allowlist["snowflake-arctic-embed-m"].Dimensions); + Assert.Equal(768, EmbeddingModelProvisioner.Allowlist["snowflake-arctic-embed-m-int8"].Dimensions); Assert.Equal(1024, EmbeddingModelProvisioner.Allowlist["mxbai-embed-large-v1"].Dimensions); Assert.All(EmbeddingModelProvisioner.Allowlist.Values, e => Assert.Equal(64, e.ModelSha256.Length)); Assert.All(EmbeddingModelProvisioner.Allowlist.Values, e => Assert.Equal(64, e.TokenizerSha256.Length)); @@ -253,6 +255,31 @@ public void ArcticEntry_carries_the_model_card_query_prefix_verbatim_and_its_cal Assert.Equal(0.24, entry.CalibratedMinCosineSimilarity); } + [Fact] + public void ArcticInt8Entry_is_the_default_model_pinned_to_the_uint8_artifact_with_its_own_calibrated_floor() + { + // Pins the exact artifact this repo's default now loads: onnx/model_uint8.onnx (NOT + // onnx/model_int8.onnx or onnx/model_quantized.onnx — both exist in the same upstream + // repo tree at the same byte size but a DIFFERENT sha256, a distinct dynamic-quantization + // export; only model_uint8.onnx's hash matches the artifact that was actually + // calibrated). A future re-pin that silently swapped in either sibling file would fail + // this hash assertion instead of only degrading retrieval quality at runtime. + var entry = EmbeddingModelProvisioner.Allowlist["snowflake-arctic-embed-m-int8"]; + Assert.Equal("snowflake-arctic-embed-m-int8", entry.ModelId); + Assert.Equal(768, entry.Dimensions); + Assert.Equal(110_084_023, entry.ModelByteSize); + Assert.Equal("4cfc22160ddd52bac43697b6b84a4b29ea25a82db23841c27436dbddcfd5f88a", entry.ModelSha256, StringComparer.OrdinalIgnoreCase); + Assert.Contains("model_uint8.onnx", entry.ModelUrl.ToString(), StringComparison.Ordinal); + Assert.Equal("Represent this sentence for searching relevant passages: ", entry.QueryPrefix); + Assert.Equal(0.24, entry.CalibratedMinCosineSimilarity); + + // Tokenizer is genuinely shared with the fp32 entry (same HF commit, same vocab.txt) — + // not merely coincidentally equal. + var fp32Entry = EmbeddingModelProvisioner.Allowlist["snowflake-arctic-embed-m"]; + Assert.Equal(fp32Entry.TokenizerSha256, entry.TokenizerSha256, StringComparer.OrdinalIgnoreCase); + Assert.Equal(fp32Entry.TokenizerUrl, entry.TokenizerUrl); + } + [Fact] public void MxbaiFallbackEntry_carries_a_query_prefix_but_no_retrieval_calibration() { diff --git a/src/Netclaw.Embeddings/EmbeddingModelProvisioner.cs b/src/Netclaw.Embeddings/EmbeddingModelProvisioner.cs index bf2074de3..58579cdf4 100644 --- a/src/Netclaw.Embeddings/EmbeddingModelProvisioner.cs +++ b/src/Netclaw.Embeddings/EmbeddingModelProvisioner.cs @@ -115,13 +115,15 @@ public sealed class EmbeddingModelProvisioner { /// /// Pinned allowlist: model id → download locations, expected hashes, and dimensions. - /// Primary is snowflake-arctic-embed-m (May-2026-ratified nominator model); - /// mxbai-embed-large-v1 is the allowlisted fallback. Both entries point at the - /// plain fp32 onnx/model.onnx artifact (not the int8/fp16/quantized variants also - /// published on HuggingFace) for correctness; a quantized variant is a future optimization, - /// not this stage's concern. URLs are pinned to a specific HuggingFace repo commit sha - /// (not main) so the pinned hash can never silently drift out of sync with what the - /// URL serves. + /// snowflake-arctic-embed-m-int8 is the DEFAULT (); + /// snowflake-arctic-embed-m (fp32) and mxbai-embed-large-v1 remain allowlisted + /// as explicit operator choices. The int8 entry is HuggingFace's static-quantized + /// onnx/model_uint8.onnx export of the same fp32 weights (NOT onnx/model_int8.onnx + /// or onnx/model_quantized.onnx — both exist in the same repo tree under the same byte + /// size but a *different* SHA-256, a distinct dynamic-quantization export; only + /// model_uint8.onnx's hash matches what was calibrated). All URLs are pinned to a + /// specific HuggingFace repo commit sha (not main) so the pinned hash can never + /// silently drift out of sync with what the URL serves. /// public static IReadOnlyDictionary Allowlist { get; } = new Dictionary(StringComparer.Ordinal) @@ -132,7 +134,9 @@ public sealed class EmbeddingModelProvisioner // query text are meant to read as one sentence, not two concatenated with no // separator). CalibratedMinCosineSimilarity=0.24 is the gold-prod-2026-07 sweep // optimum for this prefixed encoding (design.md D4; supersedes the no-prefix 0.68 - // figure recorded in memory-core-redesign design.md D6). + // figure recorded in memory-core-redesign design.md D6). No longer the default model + // (see snowflake-arctic-embed-m-int8 below) but remains allowlisted as an explicit, + // higher-RAM/higher-latency choice. ["snowflake-arctic-embed-m"] = new EmbeddingModelManifestEntry( ModelId: "snowflake-arctic-embed-m", ModelUrl: new Uri("https://huggingface.co/Snowflake/snowflake-arctic-embed-m/resolve/fc74610d18462d218e312aa986ec5c8a75a98152/onnx/model.onnx"), @@ -144,6 +148,33 @@ public sealed class EmbeddingModelProvisioner QueryPrefix: "Represent this sentence for searching relevant passages: ", CalibratedMinCosineSimilarity: 0.24), + // DEFAULT model (Memory.Embeddings.ModelId). Same tokenizer/vocab.txt as the fp32 + // entry above (shared across every variant HuggingFace publishes for this repo — hash + // verified identical, 07eced37...038a3). ModelUrl is onnx/model_uint8.onnx at the SAME + // pinned commit as the fp32 entry: verified 2026-07-08 against the HF tree API that + // this exact path+hash+byte-size exists in Snowflake/snowflake-arctic-embed-m at + // fc74610d18462d218e312aa986ec5c8a75a98152, and that it matches the locally-calibrated + // artifact byte-for-byte (never pin a hash without confirming upstream serves it). + // CalibratedMinCosineSimilarity=0.24 comes from a dedicated gold-prod-2026-07 + + // repooled-test sweep with the SAME documented query prefix applied + // (arctic-int8-prefix-eval, 2026-07-08) — measured BETTER than the fp32-with-prefix + // entry above on every retrieval axis (F0.5 0.244 vs 0.239, recall@3 0.404 vs 0.318, + // zero-injection accuracy 28.3% vs 26.7%), at ~1.7x the inference speed (~12ms vs + // ~20ms p50 short-query on the reference box) and ~57% less steady-state embedder RSS + // (261 MB vs 611 MB, memory-core-redesign design.md D6's quant-eval). This is a + // strict improvement, not a size/quality tradeoff, which is why int8 — not fp32 — is + // the default. + ["snowflake-arctic-embed-m-int8"] = new EmbeddingModelManifestEntry( + ModelId: "snowflake-arctic-embed-m-int8", + ModelUrl: new Uri("https://huggingface.co/Snowflake/snowflake-arctic-embed-m/resolve/fc74610d18462d218e312aa986ec5c8a75a98152/onnx/model_uint8.onnx"), + TokenizerUrl: new Uri("https://huggingface.co/Snowflake/snowflake-arctic-embed-m/resolve/fc74610d18462d218e312aa986ec5c8a75a98152/vocab.txt"), + ModelSha256: "4cfc22160ddd52bac43697b6b84a4b29ea25a82db23841c27436dbddcfd5f88a", + TokenizerSha256: "07eced375cec144d27c900241f3e339478dec958f92fddbc551f295c992038a3", + Dimensions: 768, + ModelByteSize: 110_084_023, + QueryPrefix: "Represent this sentence for searching relevant passages: ", + CalibratedMinCosineSimilarity: 0.24), + // Query prefix verified 2026-07-08 against the model card (mixedbread-ai's usage // examples document the identical instruction string arctic-embed-m uses — both // cards converge on the same widely-used E5-style retrieval instruction; this is From c0a5447c77b92d10c81fd34a48791c48bd2c8447 Mon Sep 17 00:00:00 2001 From: Aaron Stannard Date: Wed, 8 Jul 2026 23:57:47 +0000 Subject: [PATCH 24/37] chore(release): prepare 0.25.0-alpha.onnx.2 experimental prerelease Second experimental prerelease in the memory-embeddings series. All features gated behind Memory.Embeddings.Enabled (off by default). Headline features: - Hybrid semantic recall with an absolute relevance floor: FTS5 lexical + embedding-cosine candidate union, recency-decay score fusion, and a gold-set-calibrated minimum-similarity floor (zero-injection turns are normal and healthy) - Cross-encoder relevance gate: 22 MB int8 ms-marco-MiniLM-L-6-v2 reranker scores floor survivors and drops weak matches (86.8% zero-injection accuracy, 98.3% recall retention out-of-sample) - Model-documented query prefix + manifest-carried calibration: arctic-embed retrieval-mode query prefix with per-model pinned floor calibration (F0.5 +73%, recall@3 2.8x on the production gold set) - int8 arctic embedder as the new default model: pre-quantized model_uint8.onnx (105 MB vs 416 MB fp32, ~1.7x faster, better retrieval quality with the prefix) --- Directory.Build.props | 2 +- RELEASE_NOTES.md | 16 ++++++++++++++++ 2 files changed, 17 insertions(+), 1 deletion(-) diff --git a/Directory.Build.props b/Directory.Build.props index b759b0476..331eaae1c 100644 --- a/Directory.Build.props +++ b/Directory.Build.props @@ -9,7 +9,7 @@ enable true 0.25.0 - alpha.onnx.1 + alpha.onnx.2 Netclaw v0.25.0-beta.1 — SkillServer native sub-agent sync, memory curation unification, systemd PATH fix **Features** diff --git a/RELEASE_NOTES.md b/RELEASE_NOTES.md index d5023e303..1e16ca64d 100644 --- a/RELEASE_NOTES.md +++ b/RELEASE_NOTES.md @@ -1,5 +1,21 @@ # NetClaw Release Notes +## 0.25.0-alpha.onnx.2 (2026-07-08) + +> **Experimental feature build** (second in the memory-embeddings series). Everything here +> is gated behind `Memory.Embeddings.Enabled`, **off by default** — without opting in, +> behavior is identical to the mainline beta. Not published to the beta channel; install +> only by exact pin: `NETCLAW_VERSION=0.25.0-alpha.onnx.2`. + +### Memory (Experimental) +- **Hybrid semantic recall with an absolute relevance floor** — automatic pre-turn recall now unions FTS5 lexical and embedding-cosine candidates (identical policy gates for both), fuses scores with recency decay, and enforces a gold-set-calibrated minimum-similarity floor: when nothing relevant exists, nothing is injected. Zero-injection turns are normal and healthy. +- **Cross-encoder relevance gate** — a 22 MB int8 reranker (ms-marco-MiniLM-L-6-v2, hash-pinned) scores each floor survivor against the query and drops weak matches; measured out-of-sample at 86.8% zero-injection accuracy with 98.3% recall retention. Follows `Memory.Embeddings.Enabled`; degraded mode falls back to floor-only recall, never blocks a turn. +- **Model-documented query prefix + manifest-carried calibration** — recall queries now embed in arctic-embed's documented retrieval mode; each allowlisted model pins its prefix and calibrated floor together, and `Memory.Recall.MinCosineSimilarity` follows the active model's calibration unless explicitly overridden. Measured on the production gold set: F0.5 +73%, recall@3 2.8×, zero-injection accuracy 2.1× vs the unprefixed configuration. +- **int8 arctic embedder is the new default model** — Snowflake's pre-quantized `model_uint8.onnx` (105 MB vs 416 MB fp32, ~1.7× faster, measurably better retrieval quality with the prefix). fp32 and mxbai remain allowlisted as explicit choices. + +### Upgrading from 0.25.0-alpha.onnx.1 with embeddings enabled +- The default model id changes to `snowflake-arctic-embed-m-int8`. On first daemon start the warmup gap-repair sweep re-embeds your corpus under the new model automatically (recall degrades to lexical-only for unembedded documents until coverage completes); `netclaw memory backfill-embeddings --force` does it in one pass. Existing fp32 vectors are left in place and untouched; `netclaw doctor` will note the mixed-model rows until you re-backfill. Original memory content is never modified. + ## 0.25.0-alpha.onnx.1 (2026-07-08) > **Experimental feature build.** This is a named experimental prerelease of the semantic From 4e6747a91dc578810f8b860caf7884fbff6f8398 Mon Sep 17 00:00:00 2001 From: Aaron Stannard Date: Thu, 9 Jul 2026 00:44:30 +0000 Subject: [PATCH 25/37] fix(cli): dispatch netclaw memory command; netclawd --version no longer boots the daemon MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Both regressions were caught by the production canary of 0.25.0-alpha.onnx.2. Bug 1: "memory" had a working mode handler (Program.cs `if (mode is "memory")`) and was advertised in `netclaw --help`, but was missing from CliArgsParser.KnownCommands. The parser classified `netclaw memory ...` as Unknown before dispatch ever reached the handler, so `netclaw memory backfill-embeddings` failed with "'memory' is not a netclaw command." Fix: add "memory" to KnownCommands. Closes the defect class: the KnownCommands completeness test previously just duplicated the hardcoded set as its own "expected" value, so it could never catch drift between KnownCommands and the real mode handlers/help text. Replaced it with a test that derives the expected set directly from Program.cs source (every `if (mode is "...")` dispatch token, union every command listed in the `--help` "Commands:" section) and asserts KnownCommands equals that union — both directions. Also added an explicit regression test parsing `netclaw memory backfill-embeddings` / `netclaw memory --help` / `netclaw memory -h` through CliArgsParser and asserting Known, not Unknown. Bug 2: netclawd ignored `--version`/`-v` entirely and booted a full daemon instance (acquiring the lock file, starting the host). Added DaemonCliArgs.IsVersionRequest, checked before any directory creation, lock acquisition, or host startup, so `netclawd --version` prints and exits cleanly. Unit tested in isolation since Program.cs is top-level statements. Version-banner finding: both netclawd's new handler and the CLI's existing `netclaw --version` used BuildInfo.Version, which is the numeric AssemblyVersion prefix and silently drops any prerelease suffix — a beta build like "0.25.0-alpha.onnx.2" printed as plain "0.25.0", indistinguishable from a stable release. Both now use BuildInfo.FullVersion. --- .../Cli/CliArgsParserTests.cs | 125 ++++++++++++++++-- src/Netclaw.Cli/CliArgsParser.cs | 2 +- src/Netclaw.Cli/Program.cs | 6 +- .../DaemonCliArgsTests.cs | 48 +++++++ src/Netclaw.Daemon/DaemonCliArgs.cs | 23 ++++ src/Netclaw.Daemon/Program.cs | 15 +++ 6 files changed, 205 insertions(+), 14 deletions(-) create mode 100644 src/Netclaw.Daemon.Tests/DaemonCliArgsTests.cs create mode 100644 src/Netclaw.Daemon/DaemonCliArgs.cs diff --git a/src/Netclaw.Cli.Tests/Cli/CliArgsParserTests.cs b/src/Netclaw.Cli.Tests/Cli/CliArgsParserTests.cs index 5e9c47d79..73d4b3d0e 100644 --- a/src/Netclaw.Cli.Tests/Cli/CliArgsParserTests.cs +++ b/src/Netclaw.Cli.Tests/Cli/CliArgsParserTests.cs @@ -3,6 +3,7 @@ // Copyright (C) 2026 - 2026 Petabridge, LLC // // ----------------------------------------------------------------------- +using System.Text.RegularExpressions; using Netclaw.Cli; using Xunit; @@ -48,6 +49,7 @@ public void Parse_version_tokens_returns_Version(string arg) [InlineData("provider")] [InlineData("model")] [InlineData("reminder")] + [InlineData("memory")] [InlineData("secrets")] [InlineData("config")] [InlineData("update")] @@ -77,22 +79,121 @@ public void Parse_unknown_commands_returns_Unknown_with_mode(string command) } /// - /// Guard test: asserts that KnownCommands contains exactly the expected set. - /// If a new command is added to CliArgsParser.KnownCommands, this test fails, - /// reminding the author to also add a corresponding mode handler in Program.cs. - /// Update this set when adding a new command. + /// Regression test for the alpha.onnx.2 production canary: netclaw memory had a + /// working mode handler in Program.cs (if (mode is "memory")) and was advertised in + /// --help, but "memory" was missing from , so + /// the parser classified it as before dispatch ever reached + /// the handler. Exercise the exact failing invocation shape (subcommand + a help-style flag) + /// to prove it now resolves to the known "memory" command. + /// + [Theory] + [InlineData("backfill-embeddings")] + [InlineData("--help")] + [InlineData("-h")] + public void Parse_memory_command_resolves_to_Known_not_Unknown(string secondArg) + { + var result = CliArgsParser.Parse(["memory", secondArg]); + Assert.Equal(CliParseKind.Known, result.Kind); + Assert.Equal("memory", result.Mode); + } + + /// + /// Guard test: derives the "known command" ground truth directly from Program.cs source + /// instead of a hand-maintained mirror list. The previous version of this test hardcoded + /// its own copy of the expected set, so when "memory" gained a mode handler (Program.cs + /// if (mode is "memory")) and a `--help` listing but was never added to + /// , nothing caught the drift — the "expected" set + /// was just a second hand-typed copy of the same (incomplete) list, not an independent check. + /// + /// This version checks both directions against real source content: + /// - every command dispatched via `if (mode is "...")` in Program.cs must be in + /// KnownCommands (a mode handler with no parser entry is unreachable — this is exactly + /// the canary bug), and + /// - every command listed in the `--help` "Commands:" section must be in KnownCommands + /// (an advertised command the parser rejects is a user-facing regression), and + /// - KnownCommands must not contain anything beyond the union of the two (an entry with + /// no backing handler or help listing is unreachable/dead documentation-wise). /// [Fact] - public void KnownCommands_matches_expected_set_of_handled_modes() + public void KnownCommands_matches_every_mode_handler_and_help_listed_command() { - var expected = new HashSet(StringComparer.Ordinal) - { - "chat", "sessions", "init", "doctor", "status", "stats", - "daemon", "mcp", "provider", "model", "reminder", - "secrets", "config", "update", "pair", "skill", "webhooks", - "approvals", - }; + var programSource = ReadProgramCsSource(); + + var dispatchedModes = ExtractDispatchedModeTokens(programSource); + var helpListedCommands = ExtractHelpListedCommands(programSource); + + Assert.Contains("memory", dispatchedModes); + Assert.Contains("memory", helpListedCommands); + + var expected = new HashSet(dispatchedModes, StringComparer.Ordinal); + expected.UnionWith(helpListedCommands); Assert.Equal(expected, CliArgsParser.KnownCommands); } + + /// + /// Extracts every literal mode token dispatched via if (mode is "x") or + /// if (mode is "x" or "y") in Program.cs — the actual mode-handler ground truth the + /// KnownCommands doc comment refers to ("must stay in sync with the mode handlers"). + /// + private static IReadOnlySet ExtractDispatchedModeTokens(string programSource) + { + var tokens = new HashSet(StringComparer.Ordinal); + foreach (Match clauseMatch in Regex.Matches(programSource, @"if \(mode is (?.*?)\)")) + { + foreach (Match tokenMatch in Regex.Matches(clauseMatch.Groups["clause"].Value, "\"([a-zA-Z-]+)\"")) + { + tokens.Add(tokenMatch.Groups[1].Value); + } + } + + Assert.NotEmpty(tokens); + return tokens; + } + + /// + /// Extracts every command name listed in WriteGeneralHelp()'s "Commands:" section + /// (the first whitespace/comma-delimited token of each line), skipping "version" since it + /// resolves via the distinct path rather than + /// . + /// + private static IReadOnlySet ExtractHelpListedCommands(string programSource) + { + var sectionMatch = Regex.Match( + programSource, + "Console\\.WriteLine\\(\"Commands:\"\\);(?.*?)Console\\.WriteLine\\(\"Run `netclaw", + RegexOptions.Singleline); + Assert.True(sectionMatch.Success, "Could not locate the 'Commands:' help section in Program.cs."); + + var commands = new HashSet(StringComparer.Ordinal); + foreach (Match lineMatch in Regex.Matches(sectionMatch.Groups["body"].Value, "Console\\.WriteLine\\(\" (?[^\"]*)\"\\);")) + { + var firstToken = lineMatch.Groups["line"].Value + .Split([' ', ','], StringSplitOptions.RemoveEmptyEntries) + .FirstOrDefault(); + if (firstToken is null or "version") + continue; + + commands.Add(firstToken); + } + + Assert.NotEmpty(commands); + return commands; + } + + private static string ReadProgramCsSource() => File.ReadAllText(Path.Combine(FindRepoRoot(), "src", "Netclaw.Cli", "Program.cs")); + + private static string FindRepoRoot() + { + var directory = new DirectoryInfo(AppContext.BaseDirectory); + while (directory is not null) + { + if (File.Exists(Path.Combine(directory.FullName, "IMPLEMENTATION_PLAN.md"))) + return directory.FullName; + + directory = directory.Parent; + } + + throw new InvalidOperationException("Could not locate repository root from test output directory."); + } } diff --git a/src/Netclaw.Cli/CliArgsParser.cs b/src/Netclaw.Cli/CliArgsParser.cs index 6b5618840..57258b589 100644 --- a/src/Netclaw.Cli/CliArgsParser.cs +++ b/src/Netclaw.Cli/CliArgsParser.cs @@ -31,7 +31,7 @@ public static class CliArgsParser public static readonly IReadOnlySet KnownCommands = new HashSet(StringComparer.Ordinal) { "chat", "sessions", "init", "doctor", "status", "stats", - "daemon", "mcp", "provider", "model", "reminder", + "daemon", "mcp", "provider", "model", "reminder", "memory", "secrets", "config", "update", "pair", "skill", "webhooks", "approvals", }; diff --git a/src/Netclaw.Cli/Program.cs b/src/Netclaw.Cli/Program.cs index d95c21f0d..124183bb9 100644 --- a/src/Netclaw.Cli/Program.cs +++ b/src/Netclaw.Cli/Program.cs @@ -76,7 +76,11 @@ static async Task RunAsync(string[] args) WriteGeneralHelp(); return; case CliParseKind.Version: - Console.WriteLine($"netclaw {BuildInfo.Version} (commit {BuildInfo.CommitHash}, built {BuildInfo.BuildTimestamp})"); + // FullVersion (not Version) — Version is the numeric AssemblyVersion prefix and + // silently drops any prerelease suffix, so a beta build (e.g. "0.25.0-alpha.onnx.2") + // printed as plain "0.25.0" here, indistinguishable from a stable release + // (alpha.onnx.2 production canary finding). + Console.WriteLine($"netclaw {BuildInfo.FullVersion} (commit {BuildInfo.CommitHash}, built {BuildInfo.BuildTimestamp})"); return; case CliParseKind.Unknown: Console.Error.WriteLine($"netclaw: '{parseResult.Mode}' is not a netclaw command. See 'netclaw --help'."); diff --git a/src/Netclaw.Daemon.Tests/DaemonCliArgsTests.cs b/src/Netclaw.Daemon.Tests/DaemonCliArgsTests.cs new file mode 100644 index 000000000..b62bbe133 --- /dev/null +++ b/src/Netclaw.Daemon.Tests/DaemonCliArgsTests.cs @@ -0,0 +1,48 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using Netclaw.Daemon; +using Xunit; + +namespace Netclaw.Daemon.Tests; + +/// +/// Regression coverage for the alpha.onnx.2 production canary: netclawd --version used to +/// ignore the flag entirely and boot a full daemon instance (acquiring the lock file, starting +/// the host). Program.cs is top-level statements, so the arg-handling itself is extracted into +/// to make it unit-testable in isolation without +/// booting the host. +/// +public sealed class DaemonCliArgsTests +{ + [Theory] + [InlineData("--version")] + [InlineData("-v")] + public void IsVersionRequest_returns_true_for_version_flags(string flag) + { + Assert.True(DaemonCliArgs.IsVersionRequest([flag])); + } + + [Fact] + public void IsVersionRequest_returns_false_for_no_args() + { + Assert.False(DaemonCliArgs.IsVersionRequest([])); + } + + [Theory] + [InlineData("--help")] + [InlineData("-h")] + [InlineData("-V")] + public void IsVersionRequest_returns_false_for_non_version_args(string arg) + { + Assert.False(DaemonCliArgs.IsVersionRequest([arg])); + } + + [Fact] + public void IsVersionRequest_only_considers_the_first_argument() + { + Assert.False(DaemonCliArgs.IsVersionRequest(["start", "--version"])); + } +} diff --git a/src/Netclaw.Daemon/DaemonCliArgs.cs b/src/Netclaw.Daemon/DaemonCliArgs.cs new file mode 100644 index 000000000..a9758987b --- /dev/null +++ b/src/Netclaw.Daemon/DaemonCliArgs.cs @@ -0,0 +1,23 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +namespace Netclaw.Daemon; + +/// +/// Classifies top-level command-line arguments passed to netclawd. Kept as a small, +/// independently testable predicate because Program.cs is top-level statements — extracting +/// the check here lets a unit test cover the arg-handling without booting the host. +/// +internal static class DaemonCliArgs +{ + /// + /// Returns true if the first argument requests the version banner + /// (--version or -v). Checked before the daemon acquires its lock file or + /// starts the host so netclawd --version prints and exits without booting a real + /// daemon instance (alpha.onnx.2 production canary regression). + /// + public static bool IsVersionRequest(string[] args) + => args.Length > 0 && args[0] is "--version" or "-v"; +} diff --git a/src/Netclaw.Daemon/Program.cs b/src/Netclaw.Daemon/Program.cs index b33610daf..c512b5008 100644 --- a/src/Netclaw.Daemon/Program.cs +++ b/src/Netclaw.Daemon/Program.cs @@ -53,6 +53,21 @@ using Netclaw.Security; using static Microsoft.Extensions.Logging.LogLevel; +// Handled first, before any directory creation, lock-file acquisition, or host startup: +// `netclawd --version`/`-v` must print the version and exit rather than booting a real +// daemon instance (alpha.onnx.2 production canary regression). +if (DaemonCliArgs.IsVersionRequest(args)) +{ + // Fully qualified: Program.cs (top-level statements) sits in the global namespace, and both + // Netclaw.Daemon and Netclaw.Configuration are `using`-imported here, so the unqualified + // "BuildInfo" is ambiguous between the two. Netclaw.Daemon.BuildInfo is the daemon-specific + // facade that reads the daemon assembly's own metadata (see that type's remarks). + Console.WriteLine( + $"netclawd {Netclaw.Daemon.BuildInfo.FullVersion} " + + $"(commit {Netclaw.Daemon.BuildInfo.CommitHash}, built {Netclaw.Daemon.BuildInfo.BuildTimestamp})"); + return; +} + var bootstrapPaths = new NetclawPaths(); try { From 53fa8e976757a2adc132dc9ce3e7d951d1f9b4ce Mon Sep 17 00:00:00 2001 From: Aaron Stannard Date: Thu, 9 Jul 2026 09:35:55 -0500 Subject: [PATCH 26/37] =?UTF-8?q?fix(memory):=20relevance-gate=20cold-star?= =?UTF-8?q?t=20=E2=80=94=20keep-warm=20ticks,=20envelope-derived=20gate=20?= =?UTF-8?q?budget=20(canary=20finding)=20(#1608)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Production canary caught two memory_recall_gate_degraded events (score_failed:TaskCanceledException) in scheduled-reminder sessions waking from an idle period. Root cause: cold ONNX sessions (paged-out weights) plus host CPU contention at reminder-fire time pushed total turn latency (plan -> candidate selection -> query embed -> hybrid fusion -> gate) past the entire 300ms RecallTimeoutMs envelope before the gate even started scoring, so its own sub-budget timer never got a chance to fire on its own terms -- the OUTER ct was already cancelled by the time ScoreAsync threw. Both events show mode=hybrid (query embed succeeded, just slow), so this is a cold-start problem across the whole pipeline, not a per-call latency regression against design D5's reference-box measurement. Re-verified the "partial candidate-scoring coverage gaps of 3-12%" prior claim against the same canary log window: gapCandidates/totalCandidates in the two events are 3/61 (4.9%) and 7/57 (12.3%) -- these are memory_recall_coverage_gap counters (pre-existing candidate-pool coverage gaps for un-backfilled documents, memory-core-redesign Slice 4 gap-repair design D6), not gate failures. Confirmed: unrelated to this fix. Three-part fix: 1. Keep-warm: EmbeddingWarmupHostedService now runs a periodic keep-warm loop (every 5 minutes, while embeddings are enabled) that re-exercises both ONNX sessions with a tiny embed + tiny 1-pair CE score, so an idle gap never lets either session's working set page out entirely. Built on PeriodicTimer over the injected TimeProvider (same virtualizable pattern McpReconnectionService already uses), never throws out of a tick (rate-limited Debug log on failure), skips whichever side is unavailable, no-ops when embeddings are disabled, and stops cleanly via the existing IHostedService.StopAsync/CancellationTokenSource pattern. 2. Budget: SQLiteMemoryRecallCoordinator's relevance-gate sub-budget is now min(RelevanceGateSubBudgetMs, time remaining in the outer RecallTimeoutMs envelope) instead of a fixed value; the ceiling itself is raised from 60ms to 120ms. The outer linked CTS remains the hard cap, so a turn with headroom gets more slack than before, while a turn where earlier stages already consumed the envelope degrades immediately instead of assuming a fixed budget is always affordable. Same degraded-path semantics on timeout/failure. 3. Observability: memory_retrieval_final now logs gateElapsedMs (gate scoring latency regardless of outcome), and memory_recall_gate_degraded now logs elapsedMs too, so soak data can quantify margins against the new ceiling. Updated openspec/changes/memory-relevance-gate/design.md's 60ms figures to 120ms (envelope-clamped) with a canary-finding note; the Open Questions entry about combined sub-budget worst-case latency is marked resolved by this same finding. netclaw-memory skill does not mention the 60ms figure, so it is unchanged. Tests: keep-warm tick fires on schedule / calls embedder+scorer exactly once per tick / swallows scorer exceptions / skips unavailable sides / no ticks when disabled / stops cleanly on cancellation (FakeTimeProvider-driven, no sleeps). Budget: envelope-exhausted vs. envelope-with-headroom behavioral tests using a real-delay fake scorer, proving the applied sub-budget is smaller than the fixed ceiling when the outer envelope is nearly spent. Updated the existing sub-budget-timeout test's stale "~60ms" comment. --- docs/runbooks/memory-health-and-evals.md | 35 ++-- .../changes/memory-relevance-gate/design.md | 47 +++-- .../Sessions/SQLiteMemoryRecallGateTests.cs | 97 ++++++++- .../Sessions/SQLiteMemoryRecallCoordinator.cs | 150 +++++++++++--- .../EmbeddingWarmupHostedServiceTests.cs | 187 +++++++++++++++++- .../Services/EmbeddingWarmupHostedService.cs | 129 +++++++++++- 6 files changed, 578 insertions(+), 67 deletions(-) diff --git a/docs/runbooks/memory-health-and-evals.md b/docs/runbooks/memory-health-and-evals.md index dfca725a8..744d02d44 100644 --- a/docs/runbooks/memory-health-and-evals.md +++ b/docs/runbooks/memory-health-and-evals.md @@ -141,25 +141,34 @@ netclaw doctor enabled; otherwise provision manually and restart. 2. Check the degradation log line. When the gate is skipped for a turn - (model unavailable, its ~60 ms sub-budget exceeded, or recall running in - lexical mode because there's no query vector), the coordinator logs a - rate-limited marker instead of silently changing what gets injected: + (model unavailable, its sub-budget exceeded — a 120 ms ceiling clamped to + whatever remains of the outer 300 ms `Memory.RecallTimeoutMs` envelope, so + a turn where earlier stages already ran long gets less than 120 ms; raised + from a fixed 60 ms by a 2026-07 production-canary finding of cold-start + timeouts — or recall running in lexical mode because there's no query + vector), the coordinator logs a rate-limited marker instead of silently + changing what gets injected: ``` -memory_recall_gate_degraded session= reason= +memory_recall_gate_degraded session= reason= elapsedMs= ``` `reason` is one of `gate_disabled_by_config`, `no_scorer_configured`, `scorer_unavailable`, `sub_budget_exceeded`, or `score_failed:`. - Logged at `Warning` when the gate is enabled but a turn still degraded (a - genuine runtime condition worth noticing); logged at `Debug` when the gate - is off by config (the default, intentional state — not spam). Rate-limited - per-reason with the same cooldown as `memory_recall_vector_degraded`, so - expect at most one `Warning` line per reason per cooldown window even - under sustained degradation, not one per turn. - -3. Read `gateScores`/`droppedByGate` on `memory_retrieval_final` when - diagnosing over- or under-injection: + `elapsedMs` is 0 for the first three (no scoring attempt ever started) and + the measured time spent before degrading for the latter two — useful for + telling a genuine cold-start/contention timeout apart from an instant + failure. Logged at `Warning` when the gate is enabled but a turn still + degraded (a genuine runtime condition worth noticing); logged at `Debug` + when the gate is off by config (the default, intentional state — not + spam). Rate-limited per-reason with the same cooldown as + `memory_recall_vector_degraded`, so expect at most one `Warning` line per + reason per cooldown window even under sustained degradation, not one per + turn. + +3. Read `gateScores`/`droppedByGate`/`gateElapsedMs` on `memory_retrieval_final` + when diagnosing over- or under-injection or quantifying gate latency + margin against the 120 ms ceiling: ```bash grep memory_retrieval_final "$HOME/.netclaw/logs/daemon-$(date +%F).log" | tail -20 diff --git a/openspec/changes/memory-relevance-gate/design.md b/openspec/changes/memory-relevance-gate/design.md index a4fae17fd..b625fdb4d 100644 --- a/openspec/changes/memory-relevance-gate/design.md +++ b/openspec/changes/memory-relevance-gate/design.md @@ -224,10 +224,13 @@ vector was available): the floor already reduced the candidate set to candidate-generation protocol — the gate never sees a candidate the floor would not already have admitted). Each survivor is paired with the query and scored via `RelevanceScorerHolder.Current.ScoreAsync`, under a CE sub-budget -(~60 ms) nested inside the overall `RecallTimeoutMs` via a linked +(ceiling 120 ms, envelope-clamped — raised from 60 ms and clamped to +whatever remains of the outer envelope by a 2026-07 production-canary +finding: two live cold-start timeouts, see the Open Questions entry below) +nested inside the overall `RecallTimeoutMs` via a linked `CancellationTokenSource` — the same pattern the query-embedding sub-budget already uses (measured p95 35 ms for 3 pairs leaves roughly 1.7x headroom -before the sub-budget itself is hit). Candidates scoring below the +before the sub-budget itself is hit on a warm session). Candidates scoring below the manifest/config threshold are dropped; **zero survivors after the gate is a "nothing injected" outcome**, identical in kind to zero survivors at the floor — the `[memory-recall]` block continues to be omitted entirely, not @@ -327,15 +330,25 @@ selectivity without one of these signals firing. (397 MB), the operator's measured total is ≈763 MB against a 1 GB K8s pod limit — inside budget, but the margin (≈260 MB) is not so large that a future addition to the memory runtime gets it for free. Mitigated by - measuring rather than assuming, and by keeping the CE sub-budget (~60 ms) - small relative to the overall 300 ms recall timeout so a degraded gate - never risks the turn itself. -- [Nested sub-budgets: query-embedding (~150 ms) + gate (~60 ms) inside one - 300 ms `RecallTimeoutMs`] → worst case both sub-budgets fully elapse - (210 ms) before any lexical/ranking work runs, leaving less slack than - Slice 4 alone had. Not yet measured end-to-end under production - contention. Flagged as an open question (below), not silently assumed - safe. + measuring rather than assuming, and by keeping the CE sub-budget + (120 ms ceiling, envelope-clamped) small relative to the overall 300 ms + recall timeout so a degraded gate never risks the turn itself. +- [Nested sub-budgets: query-embedding (~150 ms) + gate (120 ms ceiling, + envelope-clamped) inside one 300 ms `RecallTimeoutMs`] → worst case both + sub-budgets fully elapse before any lexical/ranking work runs, leaving less + slack than Slice 4 alone had. **2026-07 production-canary update: this + materialized.** Two live `memory_recall_gate_degraded` events (reason + `score_failed:TaskCanceledException`) in scheduled-reminder sessions waking + from an idle period measured total turn latency already past the entire + 300 ms envelope by the time the gate started scoring — a cold ONNX session + (paged-out weights) plus host contention, not a per-call latency + regression. Fix landed as (1) a periodic keep-warm tick in + `EmbeddingWarmupHostedService` keeping both ONNX sessions' working sets + resident, and (2) raising the gate ceiling to 120 ms while clamping the + actually-applied sub-budget to whatever remains of the outer envelope (the + linked CTS was already the hard cap; this just derives the sub-budget from + it instead of assuming a fixed value is always affordable). The Open + Questions entry below is resolved by this same finding. - [Two-holders-become-three] → `MemoryEmbedderHolder` + `MemoryVectorIndexHolder` + the new `RelevanceScorerHolder` is more moving parts than a consolidated holder would be. Accepted for this change (D4) @@ -431,12 +444,16 @@ scorecard, the frozen threshold, the pinned model SHA-256) enter the repo. ## Open Questions -- Combined worst-case latency of the query-embedding sub-budget (~150 ms) +- ~~Combined worst-case latency of the query-embedding sub-budget (~150 ms) plus the new CE sub-budget (~60 ms) inside the single 300 ms `RecallTimeoutMs`, measured end-to-end under realistic contention rather - than each sub-budget's own isolated measurement — gates this change's - sub-budget sizing the same way Slice 4 gated its own latency assumption - before shipping. + than each sub-budget's own isolated measurement.~~ **Resolved by the + 2026-07 production-canary finding**: it materialized under real cold-start + contention (two live `score_failed:TaskCanceledException` degradations, + reminder sessions waking from idle). Fix: `EmbeddingWarmupHostedService` + keep-warm tick (keeps both ONNX sessions resident) + gate sub-budget + raised to a 120 ms ceiling, clamped to whatever remains of the outer + envelope rather than a fixed value. - Whether the deferred R2-mirroring decision for the embedding model artifact (memory-core-redesign, post-PoC) should extend to this second (relevance) model artifact once that decision is made. diff --git a/src/Netclaw.Actors.Tests/Sessions/SQLiteMemoryRecallGateTests.cs b/src/Netclaw.Actors.Tests/Sessions/SQLiteMemoryRecallGateTests.cs index dbf2b4867..3b511bf4b 100644 --- a/src/Netclaw.Actors.Tests/Sessions/SQLiteMemoryRecallGateTests.cs +++ b/src/Netclaw.Actors.Tests/Sessions/SQLiteMemoryRecallGateTests.cs @@ -160,9 +160,10 @@ public async Task Sub_budget_timeout_degrades_to_floor_only_unfiltered() await _store.InitializeAsync(ct); await SeedFloorSurvivingDocumentAsync("doc-timeout", ct); - // Never completes on its own; only the coordinator's ~60ms sub-budget CTS can cancel it. - // Task.Delay inside a fake is the sanctioned way to simulate latency deterministically — - // no Thread.Sleep/Task.Delay appears in this test's own orchestration. + // Never completes on its own; only the coordinator's envelope-clamped sub-budget CTS + // (ceiling 120ms, default 300ms RecallTimeoutMs here so the ceiling itself governs) can + // cancel it. Task.Delay inside a fake is the sanctioned way to simulate latency + // deterministically — no Thread.Sleep/Task.Delay appears in this test's own orchestration. var scorer = new HangingRelevanceScorer(RelevanceModelId); var coordinator = BuildCoordinator(relevanceScorerHolder: BuildHolder(scorer)); @@ -172,6 +173,49 @@ public async Task Sub_budget_timeout_degrades_to_floor_only_unfiltered() Assert.Contains(result.Items, i => i.Id.Value == "doc-timeout"); } + // ── Envelope-derived sub-budget (2026-07 production-canary fix, task 3) ──── + + [Fact] + public async Task Gate_sub_budget_is_capped_by_the_remaining_outer_envelope_not_just_the_ceiling() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + await SeedFloorSurvivingDocumentAsync("doc-envelope-exhausted", ct); + + // A real 50ms delay comfortably UNDER the 120ms gate-sub-budget ceiling -- if the fixed + // ceiling alone governed the gate's CTS, this scorer would complete in time and its + // (rejecting) score would apply. An almost-zero outer RecallTimeoutMs forces the + // envelope-derived clamp to hand the gate far less than 120ms instead, so the scorer gets + // cancelled and the turn degrades to the floor's unfiltered result. + var scorer = new DelayedRelevanceScorer(RelevanceModelId, TimeSpan.FromMilliseconds(50), score: 0.0); + var coordinator = BuildCoordinator(relevanceScorerHolder: BuildHolder(scorer), recallTimeoutMs: 1); + + var result = await coordinator.RecallAsync(BuildRequest("gate/envelope-exhausted"), ct); + + Assert.False(result.Degraded); + Assert.Contains(result.Items, i => i.Id.Value == "doc-envelope-exhausted"); + } + + [Fact] + public async Task Gate_runs_to_completion_when_the_outer_envelope_still_has_headroom() + { + var ct = TestContext.Current.CancellationToken; + await _store.InitializeAsync(ct); + await SeedFloorSurvivingDocumentAsync("doc-envelope-headroom", ct); + + // Same 50ms real delay and same rejecting score as the test above -- the only difference + // is a generous outer envelope. Proves the previous test's degradation was caused by the + // exhausted envelope specifically, not merely by the fake being slow: with headroom, the + // gate runs to completion and its score is honored (candidate dropped, not degraded). + var scorer = new DelayedRelevanceScorer(RelevanceModelId, TimeSpan.FromMilliseconds(50), score: 0.0); + var coordinator = BuildCoordinator(relevanceScorerHolder: BuildHolder(scorer), recallTimeoutMs: 5000); + + var result = await coordinator.RecallAsync(BuildRequest("gate/envelope-headroom"), ct); + + Assert.False(result.Degraded); + Assert.DoesNotContain(result.Items, i => i.Id.Value == "doc-envelope-headroom"); + } + [Fact] public async Task Gate_degraded_log_is_debug_when_disabled_by_config() { @@ -342,12 +386,14 @@ private SQLiteMemoryRecallCoordinator BuildCoordinator( bool embeddingsEnabled = true, bool? relevanceGateEnabled = null, double? thresholdOverride = null, - ILogger? logger = null) + ILogger? logger = null, + int recallTimeoutMs = 300) => new( _store, logger ?? NullLogger.Instance, new MemoryConfig { + RecallTimeoutMs = recallTimeoutMs, Embeddings = new MemoryEmbeddingsConfig { Enabled = embeddingsEnabled }, Recall = new MemoryRecallConfig { @@ -463,6 +509,29 @@ public async ValueTask> ScoreAsync(string query, IReadOnly } } + /// + /// Fake relevance scorer that completes after a fixed, finite real-wall-clock delay (2026-07 + /// production-canary envelope-derived-budget tests) rather than hanging forever like + /// — this file's own copy of a "slow but not infinite" + /// fake, needed to prove the gate's sub-budget is actually smaller than the fixed + /// RelevanceGateSubBudgetMs ceiling when the outer envelope is nearly exhausted. The + /// delay itself is real (Task.Delay inside the fake, not this test's own orchestration) — the + /// sanctioned way to simulate latency deterministically per this repo's testing guidelines. + /// + private sealed class DelayedRelevanceScorer(string modelId, TimeSpan delay, double score) : IRelevanceScorer + { + public string ModelId => modelId; + + public bool IsAvailable => true; + + [SlopwatchSuppress("SW004", "Intentional latency simulation inside a fake (never in test orchestration) -- proves the envelope-derived sub-budget clamp actually cancels a scorer that would otherwise complete within the fixed 120ms ceiling.")] + public async ValueTask> ScoreAsync(string query, IReadOnlyList candidates, CancellationToken ct) + { + await Task.Delay(delay, ct); + return candidates.Select(_ => score).ToArray(); + } + } + /// Records every (level, message) pair logged through the generic ILogger ctor seam. private sealed class RecordingLogger : ILogger { @@ -478,3 +547,23 @@ public void Log( => Entries.Add((logLevel, formatter(state, exception))); } } + +/// +/// Lightweight stand-in for Slopwatch's suppression attribute (mirrors +/// samples/Netclaw.Demo.AppHost.IntegrationTests/DemoEndToEndSmokeTests.cs's own copy) so +/// this project can build without taking a hard dependency on the slopwatch tooling. Slopwatch +/// reads the attribute name as text via the source file, so an internal definition with matching +/// shape is enough. +/// +[AttributeUsage(AttributeTargets.Method | AttributeTargets.Class | AttributeTargets.Property | AttributeTargets.Field | AttributeTargets.Constructor, AllowMultiple = true)] +internal sealed class SlopwatchSuppressAttribute : Attribute +{ + public SlopwatchSuppressAttribute(string ruleId, string reason) + { + RuleId = ruleId; + Reason = reason; + } + + public string RuleId { get; } + public string Reason { get; } +} diff --git a/src/Netclaw.Actors/Sessions/SQLiteMemoryRecallCoordinator.cs b/src/Netclaw.Actors/Sessions/SQLiteMemoryRecallCoordinator.cs index 3c1f89f9d..2671a9d76 100644 --- a/src/Netclaw.Actors/Sessions/SQLiteMemoryRecallCoordinator.cs +++ b/src/Netclaw.Actors/Sessions/SQLiteMemoryRecallCoordinator.cs @@ -80,7 +80,9 @@ namespace Netclaw.Actors.Sessions; /// Post-floor relevance gate (memory-relevance-gate, design D5/D6/D8): in hybrid mode /// only, once produces its floor survivors, a tiny cross-encoder /// (relevanceScorerHolder) scores each of the top AutoRecallMaxItems survivors -/// jointly against the query — under its own sub-budget, +/// jointly against the query — under a sub-budget capped at +/// but never larger than whatever remains of the caller's outer RecallTimeoutMs envelope +/// (2026-07 production-canary finding; see 's remarks), /// linked-CTS-nested exactly like the query-embedding sub-budget above — and drops anything /// below the active threshold ( if set, /// otherwise the scorer's manifest-carried ). @@ -108,6 +110,13 @@ public sealed class SQLiteMemoryRecallCoordinator( private readonly SessionTuning _sessionTuning = sessionTuning ?? new SessionTuning(); private readonly MemoryRecallConfig _recallConfig = memoryConfig.Recall; + // Outer recall envelope (memory-relevance-gate 2026-07 canary fix): read once at + // construction, same lifecycle assumption as every other Memory.* setting. Used to derive the + // relevance gate's ACTUAL sub-budget from how much of the envelope is left when the gate + // stage is reached, not just the fixed RelevanceGateSubBudgetMs ceiling — see that constant's + // remarks. + private readonly int _recallTimeoutMs = memoryConfig.RecallTimeoutMs; + // memory-query-prefix design D3: null (default) follows the active embedder's // manifest-carried calibration (embedderHolder.CalibratedMinCosineSimilarity, resolved per // turn in TryEmbedQueryAsync since it depends on which model is loaded); an explicit value @@ -182,14 +191,40 @@ public sealed class SQLiteMemoryRecallCoordinator( private const int VectorEmbedSubBudgetMs = 150; /// - /// Sub-budget, in milliseconds, for the per-turn cross-encoder relevance-gate scoring call - /// (memory-relevance-gate design D5), applied via a CTS linked to (nested inside) the + /// Sub-budget CEILING, in milliseconds, for the per-turn cross-encoder relevance-gate scoring + /// call (memory-relevance-gate design D5), applied via a CTS linked to (nested inside) the /// caller's overall recall ct — the same nesting pattern as /// . Not a config knob: design D5 measured ~11ms p50 / - /// ~35ms p95 to score 3 pairs (quantized int8) on the reference CPU, so 60ms leaves roughly - /// 1.7x headroom before the sub-budget itself is hit. + /// ~35ms p95 to score 3 pairs (quantized int8) on the reference CPU, so this leaves headroom + /// before the sub-budget itself is hit under normal (warm) conditions. + /// + /// + /// This is a CEILING, not the sub-budget actually applied. + /// clamps the real sub-budget to min(RelevanceGateSubBudgetMs, time remaining in the + /// caller's outer envelope) before calling + /// CancelAfter — the outer linked CTS is already the hard cap on the whole turn, so + /// this clamp can never let the gate blow past it: on a turn where earlier stages (query + /// embed, hybrid fusion) already consumed most of the envelope, the gate gets whatever sliver + /// is left (possibly far less than this ceiling, possibly ~0 which degrades immediately), + /// never more than the ceiling on a turn with headroom to spare. + /// + /// + /// + /// 2026-07 production-canary finding (raised from 60ms): two live + /// memory_recall_gate_degraded events (reason score_failed:TaskCanceledException) + /// both fired in scheduled-reminder sessions waking from an idle period, on a VM host. Log + /// timestamps showed total turn latency (plan → candidate selection → query embed → hybrid + /// fusion → gate) already past the entire 300ms RecallTimeoutMs envelope by the time + /// the gate reached its own scoring call — a cold ONNX session (paged-out weights after the + /// idle gap) plus host CPU contention at reminder-fire time, not a per-call latency + /// regression against design D5's reference-box measurement, which still held. The paired fix + /// is 's periodic keep-warm + /// tick (keeps both ONNX sessions' working sets resident across idle gaps) plus this raised, + /// envelope-clamped ceiling — more headroom on a turn that still has budget left, without + /// ever exceeding the hard 300ms cap. + /// /// - private const int RelevanceGateSubBudgetMs = 60; + private const int RelevanceGateSubBudgetMs = 120; /// Shared empty instance for turns where the gate never ran (disabled, degraded, or lexical mode). private static readonly IReadOnlyDictionary EmptyGateScores = new Dictionary(0, StringComparer.Ordinal); @@ -244,6 +279,12 @@ public sealed class SQLiteMemoryRecallCoordinator( public async Task RecallAsync(AutomaticRecallRequest request, CancellationToken ct = default) { + // Turn-start timestamp (memory-relevance-gate 2026-07 canary fix): approximates when the + // caller's own outer RecallTimeoutMs-bounded CTS started (SessionRecallManager creates it + // immediately before calling RecallAsync), so the relevance gate can later derive how much + // of that envelope is actually left rather than assuming a fixed sub-budget is always + // affordable. TimeProvider-based so tests can virtualize it. + var turnStartedAtTs = timeProvider.GetTimestamp(); try { if (_sessionTuning.DeterministicRetrievalEnabled) @@ -345,14 +386,20 @@ public async Task RecallAsync(AutomaticRecallRequest requ var gated = aboveFloor; var droppedByGate = 0; IReadOnlyDictionary gateScores = EmptyGateScores; + var gateElapsedMs = 0.0; if (mode == "hybrid" && aboveFloor.Length > 0) { - var gateOutcome = await TryApplyRelevanceGateAsync(request, aboveFloor, deterministicMaxItems, ct); - if (gateOutcome is { } outcome) + // Envelope-derived sub-budget (memory-relevance-gate 2026-07 canary fix): the + // gate never gets more than what's actually left of the caller's outer + // RecallTimeoutMs envelope — see RelevanceGateSubBudgetMs's remarks. + var remainingEnvelope = TimeSpan.FromMilliseconds(_recallTimeoutMs) - timeProvider.GetElapsedTime(turnStartedAtTs); + var gateOutcome = await TryApplyRelevanceGateAsync(request, aboveFloor, deterministicMaxItems, remainingEnvelope, ct); + gateElapsedMs = gateOutcome.ElapsedMs; + if (gateOutcome.Applied) { - gated = outcome.Survivors; - gateScores = outcome.Scores; - droppedByGate = outcome.Dropped; + gated = gateOutcome.Survivors; + gateScores = gateOutcome.Scores; + droppedByGate = gateOutcome.Dropped; } } @@ -387,7 +434,7 @@ public async Task RecallAsync(AutomaticRecallRequest requ var deterministicItems = budgeted.ToArray(); logger.LogInformation( - "memory_retrieval_final session={SessionId} mode={Mode} injectedCount={InjectedCount} filteredByFloor={FilteredByFloor} appliedFloor={AppliedFloor:F3} floorSource={FloorSource} injectedChars={InjectedChars} droppedByBudget={DroppedByBudget} droppedByGate={DroppedByGate} gateScores={GateScores} items={Items}", + "memory_retrieval_final session={SessionId} mode={Mode} injectedCount={InjectedCount} filteredByFloor={FilteredByFloor} appliedFloor={AppliedFloor:F3} floorSource={FloorSource} injectedChars={InjectedChars} droppedByBudget={DroppedByBudget} droppedByGate={DroppedByGate} gateElapsedMs={GateElapsedMs:F1} gateScores={GateScores} items={Items}", request.SessionId, mode, deterministicItems.Length, @@ -397,6 +444,7 @@ public async Task RecallAsync(AutomaticRecallRequest requ injectedChars, droppedByBudget, droppedByGate, + gateElapsedMs, string.Join("|", gateScores.Select(kv => $"{kv.Key}={kv.Value:F3}")), string.Join("|", deterministicItems.Select(i => $"{i.Id.Value}=score{i.Score:F3}"))); @@ -525,58 +573,71 @@ public async Task RecallAsync(AutomaticRecallRequest requ /// ). /// /// - /// Returns null for every degradation reason — gate disabled by config, no scorer configured, - /// scorer unavailable, sub-budget exceeded, or the scoring call itself throwing — mirroring - /// 's "never throws, null means skip" contract exactly. - /// Callers treat null as "inject the floor's own result unfiltered," identically regardless of - /// which reason produced it. + /// Returns a not-applied for every degradation reason — + /// gate disabled by config, no scorer configured, scorer unavailable, sub-budget exceeded, or + /// the scoring call itself throwing — mirroring 's "never + /// throws" contract exactly. Callers treat false as + /// "inject the floor's own result unfiltered," identically regardless of which reason produced + /// it, while still reports whatever time WAS + /// spent (2026-07 canary observability follow-up: memory_retrieval_final logs this + /// unconditionally so soak data can quantify margins even on a degraded turn). /// /// - private async Task<(RankedCandidate[] Survivors, IReadOnlyDictionary Scores, int Dropped)?> TryApplyRelevanceGateAsync( - AutomaticRecallRequest request, RankedCandidate[] aboveFloor, int maxItems, CancellationToken ct) + private async Task TryApplyRelevanceGateAsync( + AutomaticRecallRequest request, RankedCandidate[] aboveFloor, int maxItems, TimeSpan remainingEnvelope, CancellationToken ct) { if (!_relevanceGateEnabledByConfig) { LogGateDegraded(request.SessionId.Value, "gate_disabled_by_config"); - return null; + return RelevanceGateOutcome.NotApplied; } var scorer = relevanceScorerHolder?.Current; if (scorer is null) { LogGateDegraded(request.SessionId.Value, "no_scorer_configured"); - return null; + return RelevanceGateOutcome.NotApplied; } if (!scorer.IsAvailable) { LogGateDegraded(request.SessionId.Value, "scorer_unavailable"); - return null; + return RelevanceGateOutcome.NotApplied; } var candidatesToScore = aboveFloor.Length > maxItems ? aboveFloor[..maxItems] : aboveFloor; var texts = candidatesToScore.Select(x => x.Item.Content ?? string.Empty).ToArray(); + // Envelope-derived sub-budget (2026-07 production-canary finding; see + // RelevanceGateSubBudgetMs's remarks): never grants more than what's actually left of the + // caller's outer RecallTimeoutMs envelope, so the outer linked CTS stays the hard cap + // regardless of how much of it earlier stages already spent. + var subBudgetMs = (int)Math.Max(0.0, Math.Min(RelevanceGateSubBudgetMs, remainingEnvelope.TotalMilliseconds)); + + var gateStartTs = timeProvider.GetTimestamp(); IReadOnlyList scores; try { using var gateCts = CancellationTokenSource.CreateLinkedTokenSource(ct); - gateCts.CancelAfter(RelevanceGateSubBudgetMs); + gateCts.CancelAfter(subBudgetMs); scores = await scorer.ScoreAsync(request.Query, texts, gateCts.Token); } catch (OperationCanceledException) when (!ct.IsCancellationRequested) { // The sub-budget's own timer fired, not the caller's outer recall ct — degrade to // floor-only rather than propagating a cancellation that would fail the whole turn. - LogGateDegraded(request.SessionId.Value, "sub_budget_exceeded"); - return null; + var elapsedMs = timeProvider.GetElapsedTime(gateStartTs).TotalMilliseconds; + LogGateDegraded(request.SessionId.Value, "sub_budget_exceeded", elapsedMs); + return RelevanceGateOutcome.NotApplied with { ElapsedMs = elapsedMs }; } catch (Exception ex) { - LogGateDegraded(request.SessionId.Value, $"score_failed:{ex.GetType().Name}"); - return null; + var elapsedMs = timeProvider.GetElapsedTime(gateStartTs).TotalMilliseconds; + LogGateDegraded(request.SessionId.Value, $"score_failed:{ex.GetType().Name}", elapsedMs); + return RelevanceGateOutcome.NotApplied with { ElapsedMs = elapsedMs }; } + var gateElapsedMs = timeProvider.GetElapsedTime(gateStartTs).TotalMilliseconds; var threshold = _relevanceGateThresholdOverride ?? relevanceScorerHolder!.CalibratedThreshold; var scoreByItemId = new Dictionary(candidatesToScore.Length, StringComparer.Ordinal); var survivors = new List(candidatesToScore.Length); @@ -592,7 +653,7 @@ public async Task RecallAsync(AutomaticRecallRequest requ dropped++; } - return (survivors.ToArray(), scoreByItemId, dropped); + return new RelevanceGateOutcome(true, survivors.ToArray(), scoreByItemId, dropped, gateElapsedMs); } /// @@ -796,7 +857,13 @@ private void LogCoverageGap(string sessionId, int gapCandidateCount, int totalCa /// unavailable, sub-budget exceeded, scoring threw) — a genuine runtime condition an operator /// should notice. /// - private void LogGateDegraded(string sessionId, string reason) + /// + /// Milliseconds actually spent before this degradation was detected (2026-07 canary + /// observability follow-up) — 0 for reasons where no scoring attempt ever started + /// (gate_disabled_by_config, no_scorer_configured, scorer_unavailable), + /// the measured elapsed time for sub_budget_exceeded/score_failed:*. + /// + private void LogGateDegraded(string sessionId, string reason, double elapsedMs = 0) { var nowMs = timeProvider.GetUtcNow().ToUnixTimeMilliseconds(); if (_lastGateDegradedLogMs.TryGetValue(reason, out var lastMs) @@ -806,9 +873,9 @@ private void LogGateDegraded(string sessionId, string reason) _lastGateDegradedLogMs[reason] = nowMs; if (_relevanceGateEnabledByConfig) - logger.LogWarning("memory_recall_gate_degraded session={SessionId} reason={Reason}", sessionId, reason); + logger.LogWarning("memory_recall_gate_degraded session={SessionId} reason={Reason} elapsedMs={ElapsedMs:F1}", sessionId, reason, elapsedMs); else - logger.LogDebug("memory_recall_gate_degraded session={SessionId} reason={Reason}", sessionId, reason); + logger.LogDebug("memory_recall_gate_degraded session={SessionId} reason={Reason} elapsedMs={ElapsedMs:F1}", sessionId, reason, elapsedMs); } private static int RecallRank(SQLiteMemoryHydratedItem document) @@ -855,4 +922,25 @@ private static int RecallRank(SQLiteMemoryHydratedItem document) /// never recorded, but any value below a positive floor rejects identically). /// private readonly record struct RankedCandidate(SQLiteMemoryHydratedItem Item, double Composite, double? Cosine); + + /// + /// Outcome of one attempt (memory-relevance-gate + /// 2026-07 canary observability follow-up). false covers every + /// degradation reason (gate disabled, no scorer, unavailable, sub-budget exceeded, scoring + /// threw) — callers treat it identically to the pre-canary-fix "returns null" contract, + /// falling back to the floor's own unfiltered result. is populated + /// whenever a scoring attempt actually started (success or failure) so + /// memory_retrieval_final can log gate latency regardless of outcome; it stays 0 only + /// when the gate was never engaged at all (disabled/no scorer/unavailable), since no time was + /// spent gating in those cases. + /// + private readonly record struct RelevanceGateOutcome( + bool Applied, + RankedCandidate[] Survivors, + IReadOnlyDictionary Scores, + int Dropped, + double ElapsedMs) + { + public static readonly RelevanceGateOutcome NotApplied = new(false, [], EmptyGateScores, 0, 0.0); + } } diff --git a/src/Netclaw.Daemon.Tests/Services/EmbeddingWarmupHostedServiceTests.cs b/src/Netclaw.Daemon.Tests/Services/EmbeddingWarmupHostedServiceTests.cs index 9496f9f43..f180a0711 100644 --- a/src/Netclaw.Daemon.Tests/Services/EmbeddingWarmupHostedServiceTests.cs +++ b/src/Netclaw.Daemon.Tests/Services/EmbeddingWarmupHostedServiceTests.cs @@ -6,6 +6,7 @@ using System.Security.Cryptography; using Microsoft.Data.Sqlite; using Microsoft.Extensions.Logging.Abstractions; +using Microsoft.Extensions.Time.Testing; using Netclaw.Actors.Memory; using Netclaw.Configuration; using Netclaw.Daemon.Services; @@ -285,6 +286,127 @@ public async Task Relevance_gate_disabled_config_leaves_the_relevance_holder_at_ Assert.Same(initialRelevance, relevanceHolder.Current); } + // ── Keep-warm ticks (memory-relevance-gate 2026-07 canary fix) ── + // + // These tests exercise KeepWarmTickAsync/KeepWarmLoopAsync directly against simple signaling + // fakes rather than going through StartAsync -- StartAsync also fires the real, fixture-backed + // WarmUpAsync in the background (task 2.7's own coverage above), which would race to overwrite + // whatever embedder/scorer these tests plant in the holders. Testing the keep-warm loop's own + // scheduling/cancellation contract in isolation is both faster and immune to that race. + + [Fact] + public async Task Keep_warm_tick_calls_both_the_embedder_and_the_scorer_exactly_once() + { + var embedder = new SignalingEmbedder(ModelId, Dimensions); + var scorer = new SignalingRelevanceScorer(RelevanceModelId); + var holder = new MemoryEmbedderHolder(embedder, initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: null); + var relevanceHolder = new RelevanceScorerHolder(scorer, initialCalibratedThreshold: 0.0); + var memoryConfig = new MemoryConfig { Embeddings = { Enabled = true } }; + var service = CreateService(holder, memoryConfig, relevanceHolder, EmptyRelevanceAllowlist); + + await service.KeepWarmTickAsync(TestContext.Current.CancellationToken); + + Assert.Equal(1, embedder.CallCount); + Assert.Equal(1, scorer.CallCount); + } + + [Fact] + public async Task Keep_warm_tick_swallows_a_scorer_exception_without_throwing() + { + var embedder = new SignalingEmbedder(ModelId, Dimensions); + var scorer = new SignalingRelevanceScorer(RelevanceModelId, throwOnScore: new InvalidOperationException("simulated ONNX scoring failure")); + var holder = new MemoryEmbedderHolder(embedder, initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: null); + var relevanceHolder = new RelevanceScorerHolder(scorer, initialCalibratedThreshold: 0.0); + var memoryConfig = new MemoryConfig { Embeddings = { Enabled = true } }; + var service = CreateService(holder, memoryConfig, relevanceHolder, EmptyRelevanceAllowlist); + + // Must not throw -- a keep-warm tick failure is background maintenance, never a caller- + // visible fault. + await service.KeepWarmTickAsync(TestContext.Current.CancellationToken); + + Assert.Equal(1, embedder.CallCount); + Assert.Equal(1, scorer.CallCount); + } + + [Fact] + public async Task Keep_warm_tick_skips_whichever_side_is_unavailable() + { + var embedder = new SignalingEmbedder(ModelId, Dimensions, isAvailable: false); + var scorer = new SignalingRelevanceScorer(RelevanceModelId, isAvailable: false); + var holder = new MemoryEmbedderHolder(embedder, initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: null); + var relevanceHolder = new RelevanceScorerHolder(scorer, initialCalibratedThreshold: 0.0); + var memoryConfig = new MemoryConfig { Embeddings = { Enabled = true } }; + var service = CreateService(holder, memoryConfig, relevanceHolder, EmptyRelevanceAllowlist); + + await service.KeepWarmTickAsync(TestContext.Current.CancellationToken); + + Assert.Equal(0, embedder.CallCount); + Assert.Equal(0, scorer.CallCount); + } + + [Fact] + public async Task Keep_warm_loop_never_ticks_when_embeddings_are_disabled() + { + var embedder = new SignalingEmbedder(ModelId, Dimensions); + var scorer = new SignalingRelevanceScorer(RelevanceModelId); + var holder = new MemoryEmbedderHolder(embedder, initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: null); + var relevanceHolder = new RelevanceScorerHolder(scorer, initialCalibratedThreshold: 0.0); + var memoryConfig = new MemoryConfig { Embeddings = { Enabled = false } }; + var time = new FakeTimeProvider(DateTimeOffset.UtcNow); + var service = CreateService(holder, memoryConfig, relevanceHolder, EmptyRelevanceAllowlist, time); + + // Returns immediately (config checked up front, before the timer is even armed). + await service.KeepWarmLoopAsync(TestContext.Current.CancellationToken); + + // Advancing time after the fact proves no timer was ever armed either. + time.Advance(EmbeddingWarmupHostedService.KeepWarmInterval * 3); + Assert.Equal(0, embedder.CallCount); + Assert.Equal(0, scorer.CallCount); + } + + [Fact] + public async Task Keep_warm_loop_ticks_on_schedule_and_stops_cleanly_on_cancellation() + { + var embedder = new SignalingEmbedder(ModelId, Dimensions); + var scorer = new SignalingRelevanceScorer(RelevanceModelId); + var holder = new MemoryEmbedderHolder(embedder, initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: null); + var relevanceHolder = new RelevanceScorerHolder(scorer, initialCalibratedThreshold: 0.0); + var memoryConfig = new MemoryConfig { Embeddings = { Enabled = true } }; + var time = new FakeTimeProvider(DateTimeOffset.UtcNow); + var service = CreateService(holder, memoryConfig, relevanceHolder, EmptyRelevanceAllowlist, time); + + using var cts = new CancellationTokenSource(); + // This is the exact cancellation contract StopAsync relies on internally (cancel the + // token passed to KeepWarmLoopAsync, then await the loop task) -- driving it directly here + // avoids also triggering StartAsync's real, fixture-backed WarmUpAsync (see this section's + // header comment). + var loopTask = service.KeepWarmLoopAsync(cts.Token); + + time.Advance(EmbeddingWarmupHostedService.KeepWarmInterval); + await embedder.WaitForCallAsync(TestContext.Current.CancellationToken); + await scorer.WaitForCallAsync(TestContext.Current.CancellationToken); + Assert.Equal(1, embedder.CallCount); + Assert.Equal(1, scorer.CallCount); + + // A second tick proves this is a recurring schedule, not a one-shot. + time.Advance(EmbeddingWarmupHostedService.KeepWarmInterval); + await embedder.WaitForCallAsync(TestContext.Current.CancellationToken); + await scorer.WaitForCallAsync(TestContext.Current.CancellationToken); + Assert.Equal(2, embedder.CallCount); + Assert.Equal(2, scorer.CallCount); + + await cts.CancelAsync(); + // PeriodicTimer.WaitForNextTickAsync throws OperationCanceledException when its token is + // cancelled (mirrors PidFileWatchdogService.StopAsync's own SuppressThrowing usage) -- + // that is how the loop unwinds, not a normal return. + await loopTask.ConfigureAwait(ConfigureAwaitOptions.SuppressThrowing | ConfigureAwaitOptions.ContinueOnCapturedContext); + + // Further time advances after cancellation must not produce more ticks. + time.Advance(EmbeddingWarmupHostedService.KeepWarmInterval * 3); + Assert.Equal(2, embedder.CallCount); + Assert.Equal(2, scorer.CallCount); + } + private EmbeddingWarmupHostedService CreateService(MemoryEmbedderHolder holder, MemoryConfig memoryConfig) => CreateService(holder, memoryConfig, CreateRelevanceScorerHolder(), EmptyRelevanceAllowlist); @@ -292,9 +414,10 @@ private EmbeddingWarmupHostedService CreateService( MemoryEmbedderHolder holder, MemoryConfig memoryConfig, RelevanceScorerHolder relevanceScorerHolder, - IReadOnlyDictionary relevanceAllowlist) + IReadOnlyDictionary relevanceAllowlist, + TimeProvider? timeProvider = null) => new(_provisioner, _store, holder, relevanceScorerHolder, _allowlist, relevanceAllowlist, memoryConfig, _paths, - NullLogger.Instance); + timeProvider ?? TimeProvider.System, NullLogger.Instance); private static RelevanceScorerHolder CreateRelevanceScorerHolder() => new(new UnavailableRelevanceScorer(RelevanceModelId, "warmup not yet run"), initialCalibratedThreshold: 0.0); @@ -367,4 +490,64 @@ private static async Task TryDeleteDirectoryAsync(string path) } } } + + /// + /// Fake embedder for the keep-warm tests above: counts calls and signals a waiter each time + /// runs, so a test driving a FakeTimeProvider-scheduled + /// can await the tick's actual completion deterministically instead + /// of racing a real-time delay against the background loop task. + /// + private sealed class SignalingEmbedder(string modelId, int dimensions, bool isAvailable = true) : IMemoryEmbedder + { + private readonly SemaphoreSlim _signal = new(0); + private int _callCount; + + public string ModelId => modelId; + + public int Dimensions => dimensions; + + public bool IsAvailable => isAvailable; + + public int CallCount => Volatile.Read(ref _callCount); + + public Task WaitForCallAsync(CancellationToken ct) => _signal.WaitAsync(ct); + + public ValueTask> EmbedAsync(string text, EmbeddingPurpose purpose, CancellationToken ct) + { + Interlocked.Increment(ref _callCount); + _signal.Release(); + return ValueTask.FromResult>(new float[dimensions]); + } + + public ValueTask>> EmbedBatchAsync(IReadOnlyList texts, EmbeddingPurpose purpose, CancellationToken ct) + => throw new NotSupportedException("Keep-warm ticks only ever call EmbedAsync, never the batch path."); + } + + /// + /// Fake relevance scorer for the keep-warm tests above — mirrors 's + /// call-counting/signaling shape, plus an optional to exercise + /// the tick's own exception-swallowing contract. + /// + private sealed class SignalingRelevanceScorer(string modelId, bool isAvailable = true, Exception? throwOnScore = null) : IRelevanceScorer + { + private readonly SemaphoreSlim _signal = new(0); + private int _callCount; + + public string ModelId => modelId; + + public bool IsAvailable => isAvailable; + + public int CallCount => Volatile.Read(ref _callCount); + + public Task WaitForCallAsync(CancellationToken ct) => _signal.WaitAsync(ct); + + public ValueTask> ScoreAsync(string query, IReadOnlyList candidates, CancellationToken ct) + { + Interlocked.Increment(ref _callCount); + _signal.Release(); + if (throwOnScore is not null) + throw throwOnScore; + return ValueTask.FromResult>(candidates.Select(_ => 1.0).ToArray()); + } + } } diff --git a/src/Netclaw.Daemon/Services/EmbeddingWarmupHostedService.cs b/src/Netclaw.Daemon/Services/EmbeddingWarmupHostedService.cs index 908ec35fe..b2f983f29 100644 --- a/src/Netclaw.Daemon/Services/EmbeddingWarmupHostedService.cs +++ b/src/Netclaw.Daemon/Services/EmbeddingWarmupHostedService.cs @@ -33,6 +33,21 @@ namespace Netclaw.Daemon.Services; /// rather than blocking so a slow/hanging download can never delay the /// rest of the host's startup sequence either. /// +/// +/// +/// Keep-warm (memory-relevance-gate 2026-07 canary fix): the one-shot warm-up call above +/// only pays first-call ONNX session / JIT cost once, at startup. On a long-lived daemon a +/// subsequent idle gap (no memory-touching turns for a while — the exact shape of a scheduled +/// reminder session waking up) lets the OS page out an ONNX session's working set entirely; the +/// next real turn then pays a full cold-start cost that a fixed per-turn sub-budget was never +/// sized for. A live production canary caught exactly this: two memory_recall_gate_degraded +/// events with TaskCanceledException, both in reminder sessions firing after an idle +/// period. re-exercises both ONNX sessions on a periodic tick +/// while embeddings are enabled, so neither ever goes cold enough to blow its sub-budget on the +/// next real turn — see 's +/// relevance-gate sub-budget remarks for the other half of this fix (the envelope-derived +/// sub-budget clamp). +/// /// internal sealed class EmbeddingWarmupHostedService( EmbeddingModelProvisioner provisioner, @@ -43,7 +58,8 @@ internal sealed class EmbeddingWarmupHostedService( IReadOnlyDictionary relevanceAllowlist, MemoryConfig memoryConfig, NetclawPaths paths, - ILogger logger) : IHostedService + TimeProvider timeProvider, + ILogger logger) : IHostedService, IDisposable { /// /// Gap-repair batch size. Kept small and yielding between batches (task 2.7) so a large @@ -52,13 +68,122 @@ internal sealed class EmbeddingWarmupHostedService( /// internal const int GapRepairBatchSize = 16; + /// + /// Keep-warm tick period (memory-relevance-gate 2026-07 canary fix). Frequent enough that + /// neither ONNX session's working set gets fully paged out between ticks on the idle-reminder + /// shape the canary caught, cheap enough (one tiny embed + one tiny 1-pair score, a handful of + /// milliseconds warm) that it is negligible background CPU for a daemon otherwise doing + /// nothing. + /// + internal static readonly TimeSpan KeepWarmInterval = TimeSpan.FromMinutes(5); + + /// Fixed, tiny keep-warm query/candidate text — content is irrelevant, only inference-path exercise matters. + private const string KeepWarmQueryText = "netclaw keep-warm probe"; + + private const string KeepWarmCandidateText = "netclaw keep-warm reference candidate"; + + /// Minimum interval between two keep-warm-failure debug log lines, mirroring the recall coordinator's degradation-log cooldowns. + private static readonly TimeSpan KeepWarmFailureLogCooldown = TimeSpan.FromMinutes(5); + + private readonly CancellationTokenSource _keepWarmCts = new(); + private Task? _keepWarmLoop; + // 0 (not long.MinValue) is the safe "never logged" sentinel: any real Unix-ms timestamp minus + // 0 is astronomically larger than KeepWarmFailureLogCooldown, so the very first failure always + // logs, and there is no risk of the subtraction below overflowing. + private long _lastKeepWarmFailureLogMs; + public Task StartAsync(CancellationToken cancellationToken) { _ = Task.Run(() => WarmUpAsync(CancellationToken.None), CancellationToken.None); + _keepWarmLoop = Task.Run(() => KeepWarmLoopAsync(_keepWarmCts.Token), CancellationToken.None); return Task.CompletedTask; } - public Task StopAsync(CancellationToken cancellationToken) => Task.CompletedTask; + public async Task StopAsync(CancellationToken cancellationToken) + { + await _keepWarmCts.CancelAsync(); + if (_keepWarmLoop is not null) + await _keepWarmLoop.ConfigureAwait(ConfigureAwaitOptions.SuppressThrowing); + } + + public void Dispose() => _keepWarmCts.Dispose(); + + /// + /// Periodic keep-warm loop (memory-relevance-gate 2026-07 canary fix): ticks every + /// for as long as embeddings are enabled, re-exercising both + /// ONNX sessions via so an idle gap between real turns never + /// lets either session's working set page out entirely. Built on + /// over the injected — the same virtualizable-timer pattern + /// McpReconnectionService already uses for its own periodic tick — so tests can drive + /// ticks deterministically with a FakeTimeProvider instead of real wall-clock delays. + /// A disabled config is checked once up front rather than per tick: an operator flip requires + /// a restart, same as every other Memory.* setting this service already assumes. + /// + internal async Task KeepWarmLoopAsync(CancellationToken ct) + { + if (!memoryConfig.Embeddings.Enabled) + return; + + using var timer = new PeriodicTimer(KeepWarmInterval, timeProvider); + while (await timer.WaitForNextTickAsync(ct).ConfigureAwait(false)) + { + await KeepWarmTickAsync(ct).ConfigureAwait(false); + } + } + + /// + /// One keep-warm tick: a single tiny embed (, + /// mirroring the shape of a real recall turn's query embed) and a single tiny 1-pair + /// cross-encoder score, each only attempted while its holder currently reports + /// IsAvailable (a holder still pointed at an Unavailable* stub — warmup not yet + /// complete, or a load that failed — has nothing to keep warm). Never throws: any failure + /// (a transient ONNX error, a holder swapped mid-tick) is caught and rate-limited-logged at + /// Debug, since a missed keep-warm tick is not itself a user-visible degradation — the next + /// tick or the next real turn's own degradation path is what would actually surface a + /// persistently broken model. + /// + internal async Task KeepWarmTickAsync(CancellationToken ct) + { + try + { + var embedder = holder.Current; + if (embedder.IsAvailable) + await embedder.EmbedAsync(KeepWarmQueryText, EmbeddingPurpose.RetrievalQuery, ct).ConfigureAwait(false); + + var scorer = relevanceScorerHolder.Current; + if (scorer.IsAvailable) + await scorer.ScoreAsync(KeepWarmQueryText, [KeepWarmCandidateText], ct).ConfigureAwait(false); + } + catch (OperationCanceledException) when (ct.IsCancellationRequested) + { + // Host shutdown mid-tick -- let this propagate so KeepWarmLoopAsync's own + // WaitForNextTickAsync(ct) loop unwinds normally instead of being masked as a tick + // failure. + throw; + } + catch (Exception ex) + { + LogKeepWarmFailed(ex); + } + } + + /// + /// Rate-limited keep-warm-failure log: at most one Debug line per + /// , the same cooldown-throttle shape + /// SQLiteMemoryRecallCoordinator's degradation logs use, so a persistently failing + /// keep-warm tick (e.g. a model that failed to load) does not spam the log every 5 minutes + /// forever. + /// + private void LogKeepWarmFailed(Exception ex) + { + var nowMs = timeProvider.GetUtcNow().ToUnixTimeMilliseconds(); + var lastMs = Interlocked.Read(ref _lastKeepWarmFailureLogMs); + if (nowMs - lastMs < KeepWarmFailureLogCooldown.TotalMilliseconds) + return; + + Interlocked.Exchange(ref _lastKeepWarmFailureLogMs, nowMs); + logger.LogDebug(ex, "memory_embedding_keep_warm_failed"); + } /// Internal entry point so tests can await warmup to completion deterministically. internal async Task WarmUpAsync(CancellationToken ct) From 944aadbf06eb30cf6b58654d01c4ee3292b229aa Mon Sep 17 00:00:00 2001 From: Aaron Stannard Date: Thu, 9 Jul 2026 14:58:03 +0000 Subject: [PATCH 27/37] chore(release): prepare 0.25.0-alpha.onnx.3 experimental prerelease --- Directory.Build.props | 2 +- RELEASE_NOTES.md | 14 ++++++++++++++ 2 files changed, 15 insertions(+), 1 deletion(-) diff --git a/Directory.Build.props b/Directory.Build.props index 331eaae1c..0d80369c6 100644 --- a/Directory.Build.props +++ b/Directory.Build.props @@ -9,7 +9,7 @@ enable true 0.25.0 - alpha.onnx.2 + alpha.onnx.3 Netclaw v0.25.0-beta.1 — SkillServer native sub-agent sync, memory curation unification, systemd PATH fix **Features** diff --git a/RELEASE_NOTES.md b/RELEASE_NOTES.md index 1e16ca64d..4f8c5364b 100644 --- a/RELEASE_NOTES.md +++ b/RELEASE_NOTES.md @@ -1,5 +1,19 @@ # NetClaw Release Notes +## 0.25.0-alpha.onnx.3 (2026-07-09) + +> **Experimental feature build** (third in the memory-embeddings series) — the canary-feedback +> batch: fixes found by running 0.25.0-alpha.onnx.2 in production. Same gating as before: +> everything rides `Memory.Embeddings.Enabled`, off by default; install only by exact pin +> (`NETCLAW_VERSION=0.25.0-alpha.onnx.3`). Upgrading from alpha.onnx.2 is a binary swap — +> no config or data changes; models re-verify from disk without re-downloading. + +### Bug Fixes +- **Relevance-gate cold starts** — after idle periods the whole recall pipeline could exceed its 300ms envelope before the cross-encoder gate ever ran (paged-out ONNX sessions + host contention), silently skipping the gate. Fixed with periodic keep-warm inference on both models, an envelope-derived gate sub-budget (120ms ceiling, clamped to remaining turn budget), and per-turn `gateElapsedMs` observability ([#1608](https://github.com/netclaw-dev/netclaw/pull/1608)) +- **`netclaw memory` command not dispatchable** — the command was advertised in help and fully implemented but missing from the CLI parser's known-command set; `backfill-embeddings` was unusable. Fixed, with a bidirectional sync test deriving ground truth from the dispatch source so the parser/handler/help trio cannot drift again +- **`netclawd --version` booted the daemon** — the daemon binary ignored the flag and started a real instance; now prints the version and exits without touching directories or the daemon lock +- **Version banners show the full version** — `--version` in both binaries previously printed the truncated numeric version (`0.25.0`), hiding the prerelease suffix; both now print the full semver + ## 0.25.0-alpha.onnx.2 (2026-07-08) > **Experimental feature build** (second in the memory-embeddings series). Everything here From fb76fa54cbf6142467c50b18605ae37fdd8af1fe Mon Sep 17 00:00:00 2001 From: Aaron Stannard Date: Thu, 9 Jul 2026 16:28:11 -0500 Subject: [PATCH 28/37] feat(memory): operator alert when embedding/reranker model provisioning fails (#1611) When Memory.Embeddings.Enabled=true and either ONNX model (the embedder or the ms-marco-minilm-l-6-v2 relevance/reranker model) fails to provision or load, the daemon previously only logged memory_embedding_unavailable / memory_relevance_gate_unavailable and left the failure to be discovered via netclaw doctor or the health endpoint -- both pull-based. Operators had no push notification that memory was running degraded. Reuses the existing IOperationalNotificationSink/OperationalAlert seam (Netclaw.Configuration) -- the same push-to-operator mechanism McpReconnectionService, ReminderManagerActor, and RoutingChatClient already use for MCP/reminder/provider degradation, wired to Slack/webhook targets by WebhookNotificationService. Two new AlertType values (MemoryEmbeddingModelUnavailable, MemoryRelevanceModelUnavailable). Each alert carries the model id, the failure reason, the concrete consequence (lexical-only recall/dedup, or an unfiltered relevance gate), and a remediation hint (check network/disk, netclaw doctor, netclaw memory backfill-embeddings where applicable) -- content mirrors the existing MemoryEmbeddingDoctorCheck/MemoryRelevanceGateDoctorCheck wording. Latched per model (Interlocked-guarded) so each model alerts at most once per daemon run, not per retry. No alert when Embeddings.Enabled=false (an intentional, not degraded, state). Deliberately did NOT wire the keep-warm loop's mid-run failure (memory_embedding_keep_warm_failed) into the same alert path -- a single keep-warm miss is a transient probe result the method's own doc comment already calls out as not user-visible degradation, and alerting on the first miss would false-positive on exactly that. Doing it properly needs a consecutive-failure threshold (mirroring ReminderManagerActor's auto-disable pattern), which is a design decision, not just plumbing -- left as a follow-up. Also fixes a pre-existing bug surfaced by testing the two-model-failure case: WarmUpAsync returned early from the embedder's catch block, so WarmUpRelevanceGateAsync was unreachable whenever the embedder itself failed -- contradicting the method's own 'runs regardless' contract for the relevance gate and silently suppressing the relevance-model alert in the worst-case (both models down) scenario. Tests: embedder-only failure, relevance-only failure, both-fail (two distinct alerts), success path (no alerts), disabled config (no alerts), and a latch test proving repeated WarmUpAsync calls don't refire. 25 tests in EmbeddingWarmupHostedServiceTests, all green. Full Netclaw.Daemon.Tests (854), Netclaw.Actors.Tests (2671), Netclaw.Embeddings.Tests (48), and Netclaw.Configuration.Tests (467) green. Full solution build clean. Updates netclaw-operations (2.25.0 -> 2.26.0, diagnostics reference) and netclaw-memory (1.13.0 -> 1.14.0, Embeddings section) skills per the constitution's skill-sync rule. --- .../.system/files/netclaw-memory/SKILL.md | 11 +- .../.system/files/netclaw-operations/SKILL.md | 2 +- .../references/diagnostics.md | 7 +- src/Netclaw.Configuration/OperationalAlert.cs | 2 + .../EmbeddingWarmupHostedServiceTests.cs | 150 +++++++++++++++++- .../Services/EmbeddingWarmupHostedService.cs | 148 ++++++++++++++--- 6 files changed, 293 insertions(+), 27 deletions(-) diff --git a/feeds/skills/.system/files/netclaw-memory/SKILL.md b/feeds/skills/.system/files/netclaw-memory/SKILL.md index ddb5ddb80..123ccc33f 100644 --- a/feeds/skills/.system/files/netclaw-memory/SKILL.md +++ b/feeds/skills/.system/files/netclaw-memory/SKILL.md @@ -3,7 +3,7 @@ name: netclaw-memory description: "REQUIRED when the user asks what you remember, recall, or know from past conversations, previous sessions, cross-session memory, memory classes, or memory types. Also before using memory tools: find_memories, get_memories, store_memory, update_memory." metadata: author: netclaw - version: "1.13.0" + version: "1.14.0" --- # Netclaw Memory @@ -288,9 +288,16 @@ Useful log events: Embeddings are provisioned at daemon start when `Memory.Embeddings.Enabled` is `true` (default `false` for now). When unavailable: -- Log: `memory_embedding_unavailable` +- Log: `memory_embedding_unavailable` (embedder) or `memory_relevance_gate_unavailable` + (relevance/cross-encoder model) - Daemon status shows: `embeddings: degraded` - Lexical recall continues to work normally +- An operator alert (`memory.embedding_model.unavailable` / + `memory.relevance_model.unavailable`, pushed via the same notification sink as + `provider.unreachable`/`reminder.execution.failed`) fires once per model per + daemon run, naming the model, the failure reason, and the consequence (lexical-only + recall/dedup, or an unfiltered relevance gate) — this is the push-based signal; + `netclaw doctor`/`netclaw status` remain the pull-based ones `netclaw doctor`'s Memory Embeddings check reports whether the active model has a query prefix (`queryPrefix=True/False`) and the effective retrieval diff --git a/feeds/skills/.system/files/netclaw-operations/SKILL.md b/feeds/skills/.system/files/netclaw-operations/SKILL.md index 863c76be2..55d305e7d 100644 --- a/feeds/skills/.system/files/netclaw-operations/SKILL.md +++ b/feeds/skills/.system/files/netclaw-operations/SKILL.md @@ -3,7 +3,7 @@ name: netclaw-operations description: "REQUIRED when the user asks about scheduling, reminders, cron jobs, timers, background jobs, diagnostics, troubleshooting, MCP tools, daemon health, identity updates, or Netclaw capabilities and self-maintenance." metadata: author: netclaw - version: "2.25.0" + version: "2.26.0" --- # Netclaw Operations diff --git a/feeds/skills/.system/files/netclaw-operations/references/diagnostics.md b/feeds/skills/.system/files/netclaw-operations/references/diagnostics.md index ec9ffd12e..440856bb3 100644 --- a/feeds/skills/.system/files/netclaw-operations/references/diagnostics.md +++ b/feeds/skills/.system/files/netclaw-operations/references/diagnostics.md @@ -28,8 +28,10 @@ Log split — one stream, partitioned locally by session: id — nothing is duplicated locally. - `daemon.log` holds only sessionless, daemon-wide lines: startup/config, session start/stop, and operational **alerts** (e.g. the `provider.unreachable` / - `provider.failover` alert raised when an inference provider goes down — surfaced - here, and to webhooks, by the notification sink). Note the *per-call* failover/retry + `provider.failover` alert raised when an inference provider goes down, or + `memory.embedding_model.unavailable` / `memory.relevance_model.unavailable` when a + memory ONNX model fails to provision or load — surfaced here, and to webhooks, by + the notification sink). Note the *per-call* failover/retry log lines emitted while serving a specific session carry that session's id, so they partition into its `session.log`; the daemon-wide outage signal is the alert in `daemon.log`. Rolled daily, capped at 10 MB per file. @@ -75,6 +77,7 @@ debugging a daemon-wide problem → read `daemon.log`. | No LLM responses | `netclaw doctor`; verify provider credentials | | Missing tools | `netclaw mcp list`; check MCP connection state | | Memory recall degraded | `netclaw status` memory section | +| Memory embedding/relevance model unavailable | Fires a `memory.embedding_model.unavailable` / `memory.relevance_model.unavailable` operational alert (once per model per daemon run) naming the model, failure reason, and consequence when `Memory.Embeddings.Enabled=true` and either ONNX model fails to provision or load; see `netclaw-memory`'s Embeddings section and `netclaw doctor`'s Memory Embeddings / Memory Relevance Gate checks | | Daemon won't start | crash logs at `~/.netclaw/logs/crash-*.log` | | Docker daemon cannot create `/home/netclaw/.netclaw/*` | Official image entrypoint repairs writable bind mounts to UID/GID `1654:1654`; if bypassed or read-only, run `sudo chown -R 1654:1654 ` or use a Docker named volume | | Discord/Slack channel offline | `netclaw status` shows the channel `disconnected` with a reason. Discord may also report `degraded` when Discord.Net says the socket is connected but the gateway is not ready, such as after a resumed session that Netclaw is replacing with a clean reconnect. A misconfigured channel (bad token, missing Discord Message Content intent) degrades only that channel — the daemon keeps running and other channels are unaffected. A transient network failure retries automatically; a config/permission failure stays offline until the operator fixes the config and restarts the daemon. | diff --git a/src/Netclaw.Configuration/OperationalAlert.cs b/src/Netclaw.Configuration/OperationalAlert.cs index 7495c3f25..d2bb7762a 100644 --- a/src/Netclaw.Configuration/OperationalAlert.cs +++ b/src/Netclaw.Configuration/OperationalAlert.cs @@ -37,6 +37,8 @@ public enum AlertType DaemonStopping, DaemonCrashed, UpdateAvailable, + MemoryEmbeddingModelUnavailable, + MemoryRelevanceModelUnavailable, } /// diff --git a/src/Netclaw.Daemon.Tests/Services/EmbeddingWarmupHostedServiceTests.cs b/src/Netclaw.Daemon.Tests/Services/EmbeddingWarmupHostedServiceTests.cs index f180a0711..4a95b526b 100644 --- a/src/Netclaw.Daemon.Tests/Services/EmbeddingWarmupHostedServiceTests.cs +++ b/src/Netclaw.Daemon.Tests/Services/EmbeddingWarmupHostedServiceTests.cs @@ -286,6 +286,137 @@ public async Task Relevance_gate_disabled_config_leaves_the_relevance_holder_at_ Assert.Same(initialRelevance, relevanceHolder.Current); } + // ── Operator alerting (memory embedding/reranker provisioning-failure alert) ── + + [Fact] + public async Task Embedder_provisioning_failure_emits_exactly_one_operator_alert_naming_the_model_and_reason() + { + // No PrePlaceValidModelFiles() call -- the embedder fails. The relevance model succeeds so + // only the embedder's alert is under test here. + PrePlaceValidRelevanceModelFiles(); + + var holder = new MemoryEmbedderHolder(new UnavailableMemoryEmbedder(ModelId, "warmup not yet run"), initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: null); + var relevanceHolder = CreateRelevanceScorerHolder(); + var memoryConfig = new MemoryConfig { Embeddings = { Enabled = true, ModelId = ModelId, AutoDownload = false } }; + var sink = new FakeNotificationSink(); + var service = CreateService(holder, memoryConfig, relevanceHolder, RelevanceFixtureAllowlist(), notificationSink: sink); + + await service.WarmUpAsync(TestContext.Current.CancellationToken); + + Assert.True(relevanceHolder.Current.IsAvailable); + var alert = Assert.Single(sink.Alerts); + Assert.Equal(AlertType.MemoryEmbeddingModelUnavailable, alert.Category); + Assert.Equal(ModelId, alert.Source); + Assert.Contains(ModelId, alert.Summary); + Assert.Equal(ModelId, alert.Context?["modelId"]); + Assert.False(string.IsNullOrWhiteSpace(alert.Context?["reason"])); + Assert.Contains("lexical-only", alert.Context?["consequence"]); + Assert.Contains("netclaw doctor", alert.Context?["remediation"]); + } + + [Fact] + public async Task Relevance_model_provisioning_failure_emits_exactly_one_operator_alert_naming_the_model_and_reason() + { + // Embedder succeeds; the relevance model fails (no PrePlaceValidRelevanceModelFiles call). + PrePlaceValidModelFiles(); + + var holder = new MemoryEmbedderHolder(new UnavailableMemoryEmbedder(ModelId, "warmup not yet run"), initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: null); + var relevanceHolder = CreateRelevanceScorerHolder(); + var memoryConfig = new MemoryConfig { Embeddings = { Enabled = true, ModelId = ModelId, AutoDownload = false } }; + var sink = new FakeNotificationSink(); + var service = CreateService(holder, memoryConfig, relevanceHolder, RelevanceFixtureAllowlist(), notificationSink: sink); + + await service.WarmUpAsync(TestContext.Current.CancellationToken); + + Assert.True(holder.Current.IsAvailable); + var alert = Assert.Single(sink.Alerts); + Assert.Equal(AlertType.MemoryRelevanceModelUnavailable, alert.Category); + Assert.Equal(RelevanceModelId, alert.Source); + Assert.Contains(RelevanceModelId, alert.Summary); + Assert.Equal(RelevanceModelId, alert.Context?["modelId"]); + Assert.False(string.IsNullOrWhiteSpace(alert.Context?["reason"])); + Assert.Contains("relevance gate is disabled", alert.Context?["consequence"]); + // The relevance model has no backfill-embeddings analogue -- its remediation must not + // suggest that command (mirrors MemoryRelevanceGateDoctorCheck's own wording). + Assert.DoesNotContain("backfill-embeddings", alert.Context?["remediation"]); + } + + [Fact] + public async Task Both_models_failing_emits_two_distinct_operator_alerts() + { + // Neither PrePlaceValidModelFiles() nor PrePlaceValidRelevanceModelFiles() is called -- + // both models fail to provision independently. + var holder = new MemoryEmbedderHolder(new UnavailableMemoryEmbedder(ModelId, "warmup not yet run"), initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: null); + var relevanceHolder = CreateRelevanceScorerHolder(); + var memoryConfig = new MemoryConfig { Embeddings = { Enabled = true, ModelId = ModelId, AutoDownload = false } }; + var sink = new FakeNotificationSink(); + var service = CreateService(holder, memoryConfig, relevanceHolder, RelevanceFixtureAllowlist(), notificationSink: sink); + + await service.WarmUpAsync(TestContext.Current.CancellationToken); + + Assert.False(holder.Current.IsAvailable); + Assert.False(relevanceHolder.Current.IsAvailable); + Assert.Equal(2, sink.Alerts.Count); + Assert.Contains(sink.Alerts, a => a.Category == AlertType.MemoryEmbeddingModelUnavailable); + Assert.Contains(sink.Alerts, a => a.Category == AlertType.MemoryRelevanceModelUnavailable); + // Distinct alert ids -- these are two independent events, not one duplicated. + Assert.NotEqual(sink.Alerts[0].AlertId, sink.Alerts[1].AlertId); + } + + [Fact] + public async Task Success_path_emits_no_operator_alerts() + { + PrePlaceValidModelFiles(); + PrePlaceValidRelevanceModelFiles(); + + var holder = new MemoryEmbedderHolder(new UnavailableMemoryEmbedder(ModelId, "warmup not yet run"), initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: null); + var relevanceHolder = CreateRelevanceScorerHolder(); + var memoryConfig = new MemoryConfig { Embeddings = { Enabled = true, ModelId = ModelId, AutoDownload = true } }; + var sink = new FakeNotificationSink(); + var service = CreateService(holder, memoryConfig, relevanceHolder, RelevanceFixtureAllowlist(), notificationSink: sink); + + await service.WarmUpAsync(TestContext.Current.CancellationToken); + + Assert.True(holder.Current.IsAvailable); + Assert.True(relevanceHolder.Current.IsAvailable); + Assert.Empty(sink.Alerts); + } + + [Fact] + public async Task Disabled_config_emits_no_operator_alerts() + { + var holder = new MemoryEmbedderHolder(new UnavailableMemoryEmbedder(ModelId, "embeddings disabled"), initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: null); + var relevanceHolder = CreateRelevanceScorerHolder(); + var memoryConfig = new MemoryConfig { Embeddings = { Enabled = false, ModelId = ModelId } }; + var sink = new FakeNotificationSink(); + var service = CreateService(holder, memoryConfig, relevanceHolder, RelevanceFixtureAllowlist(), notificationSink: sink); + + await service.WarmUpAsync(TestContext.Current.CancellationToken); + + // Embeddings disabled is an intentional, not degraded, state -- no alert should fire. + Assert.Empty(sink.Alerts); + } + + [Fact] + public async Task Provisioning_failure_alert_is_latched_and_does_not_refire_across_repeated_warmup_runs() + { + // Neither model's fixture files are placed -- both fail every time WarmUpAsync runs. + var holder = new MemoryEmbedderHolder(new UnavailableMemoryEmbedder(ModelId, "warmup not yet run"), initialQueryPrefix: "", initialCalibratedMinCosineSimilarity: null); + var relevanceHolder = CreateRelevanceScorerHolder(); + var memoryConfig = new MemoryConfig { Embeddings = { Enabled = true, ModelId = ModelId, AutoDownload = false } }; + var sink = new FakeNotificationSink(); + var service = CreateService(holder, memoryConfig, relevanceHolder, RelevanceFixtureAllowlist(), notificationSink: sink); + + await service.WarmUpAsync(TestContext.Current.CancellationToken); + await service.WarmUpAsync(TestContext.Current.CancellationToken); + + // Exactly one alert per model in total across both runs -- the latch, not the retry count, + // governs how many alerts an operator sees. + Assert.Equal(2, sink.Alerts.Count); + Assert.Single(sink.Alerts, a => a.Category == AlertType.MemoryEmbeddingModelUnavailable); + Assert.Single(sink.Alerts, a => a.Category == AlertType.MemoryRelevanceModelUnavailable); + } + // ── Keep-warm ticks (memory-relevance-gate 2026-07 canary fix) ── // // These tests exercise KeepWarmTickAsync/KeepWarmLoopAsync directly against simple signaling @@ -415,9 +546,11 @@ private EmbeddingWarmupHostedService CreateService( MemoryConfig memoryConfig, RelevanceScorerHolder relevanceScorerHolder, IReadOnlyDictionary relevanceAllowlist, - TimeProvider? timeProvider = null) + TimeProvider? timeProvider = null, + IOperationalNotificationSink? notificationSink = null) => new(_provisioner, _store, holder, relevanceScorerHolder, _allowlist, relevanceAllowlist, memoryConfig, _paths, - timeProvider ?? TimeProvider.System, NullLogger.Instance); + timeProvider ?? TimeProvider.System, notificationSink ?? NullNotificationSink.Instance, + NullLogger.Instance); private static RelevanceScorerHolder CreateRelevanceScorerHolder() => new(new UnavailableRelevanceScorer(RelevanceModelId, "warmup not yet run"), initialCalibratedThreshold: 0.0); @@ -550,4 +683,17 @@ public ValueTask> ScoreAsync(string query, IReadOnlyList>(candidates.Select(_ => 1.0).ToArray()); } } + + /// + /// Captures every emitted during a test — mirrors + /// McpReconnectionServiceTests.FakeNotificationSink's shape. Tests below only ever + /// await WarmUpAsync to completion before inspecting , so no + /// additional synchronization is needed. + /// + private sealed class FakeNotificationSink : IOperationalNotificationSink + { + public List Alerts { get; } = []; + + public void Emit(OperationalAlert alert) => Alerts.Add(alert); + } } diff --git a/src/Netclaw.Daemon/Services/EmbeddingWarmupHostedService.cs b/src/Netclaw.Daemon/Services/EmbeddingWarmupHostedService.cs index b2f983f29..2ba5128b5 100644 --- a/src/Netclaw.Daemon/Services/EmbeddingWarmupHostedService.cs +++ b/src/Netclaw.Daemon/Services/EmbeddingWarmupHostedService.cs @@ -48,6 +48,23 @@ namespace Netclaw.Daemon.Services; /// relevance-gate sub-budget remarks for the other half of this fix (the envelope-derived /// sub-budget clamp). /// +/// +/// +/// Operator alerting: the log line alone is not operator-facing — nobody watches daemon +/// logs in steady state, and the health endpoint/doctor check are pull-based (someone has to go +/// look). Each provision-or-degrade failure above additionally fires an +/// through the injected +/// (the same push-to-operator seam McpReconnectionService, ReminderManagerActor, and +/// RoutingChatClient already use for MCP/reminder/provider degradation) carrying the model +/// id, the failure reason, the concrete consequence (lexical-only recall/dedup, or an unfiltered +/// relevance gate), and a remediation hint. Latched per model (, +/// ) so a given model fires at most once per daemon run — this +/// method only ever runs once per host lifetime in production (see ), but +/// the latch is cheap insurance against a future caller awaiting it more than once, and is the +/// seam a mid-run keep-warm failure would also latch through if that path is ever wired up (see +/// 's remarks for why it currently is not). No alert fires when +/// Memory.Embeddings.Enabled is false — that is an intentional, not degraded, state. +/// /// internal sealed class EmbeddingWarmupHostedService( EmbeddingModelProvisioner provisioner, @@ -59,6 +76,7 @@ internal sealed class EmbeddingWarmupHostedService( MemoryConfig memoryConfig, NetclawPaths paths, TimeProvider timeProvider, + IOperationalNotificationSink notificationSink, ILogger logger) : IHostedService, IDisposable { /// @@ -92,6 +110,12 @@ internal sealed class EmbeddingWarmupHostedService( // logs, and there is no risk of the subtraction below overflowing. private long _lastKeepWarmFailureLogMs; + // Operator-alert latches (0/1 via Interlocked.CompareExchange): guarantee each model fires at + // most one OperationalAlert per daemon run even though this is currently only ever reachable + // from one call site each (see the class remarks' "Operator alerting" paragraph). + private int _embedderAlertFired; + private int _relevanceAlertFired; + public Task StartAsync(CancellationToken cancellationToken) { _ = Task.Run(() => WarmUpAsync(CancellationToken.None), CancellationToken.None); @@ -173,6 +197,18 @@ internal async Task KeepWarmTickAsync(CancellationToken ct) /// SQLiteMemoryRecallCoordinator's degradation logs use, so a persistently failing /// keep-warm tick (e.g. a model that failed to load) does not spam the log every 5 minutes /// forever. + /// + /// + /// Deliberately not wired to the operator-alert latches: a single keep-warm tick + /// failure is a transient probe result (a slow/hung ONNX call under load, a momentary holder + /// swap mid-tick), not proof a model "went bad" — the very next tick, 5 minutes later, may + /// well succeed. Promoting the first miss to an operator page would be a false-positive + /// alert on exactly the condition this method's own doc comment already calls out as not + /// user-visible degradation. Doing this properly needs a consecutive-failure threshold + /// (mirroring ReminderManagerActor's auto-disable threshold pattern) before treating a + /// keep-warm miss as equivalent-severity to a provisioning failure — a real design decision, + /// not just plumbing, so it is left as a follow-up rather than bolted on here. + /// /// private void LogKeepWarmFailed(Exception ex) { @@ -205,37 +241,45 @@ internal async Task WarmUpAsync(CancellationToken ct) var queryPrefix = manifestEntry?.QueryPrefix ?? string.Empty; var calibratedMinCosineSimilarity = manifestEntry?.CalibratedMinCosineSimilarity; - IMemoryEmbedder embedder; + // Nullable and only ever assigned on the success path below -- deliberately NOT an early + // return out of the catch block (a pre-existing bug this PR fixes: the relevance gate's + // provisioning attempt below was unreachable whenever the embedder itself failed, + // contradicting this method's own "runs regardless" contract for the relevance gate, and + // silently suppressing the relevance-model alert in exactly the both-models-degraded case + // an operator most needs to hear about). + IMemoryEmbedder? embedder = null; try { embedder = await LoadEmbedderAsync(modelId, queryPrefix, ct).ConfigureAwait(false); + holder.Set(embedder, queryPrefix, calibratedMinCosineSimilarity); + logger.LogInformation( + "memory_embedding_ready model={ModelId} dims={Dimensions} hasQueryPrefix={HasQueryPrefix} calibratedMinCosineSimilarity={CalibratedMinCosineSimilarity}", + embedder.ModelId, + embedder.Dimensions, + queryPrefix.Length > 0, + calibratedMinCosineSimilarity); } catch (Exception ex) { logger.LogError(ex, "memory_embedding_unavailable model={ModelId} reason={Reason}", modelId, ex.Message); holder.Set(new UnavailableMemoryEmbedder(modelId, ex.Message), queryPrefix, calibratedMinCosineSimilarity); - return; + EmitEmbedderUnavailableAlert(modelId, ex.Message); } - holder.Set(embedder, queryPrefix, calibratedMinCosineSimilarity); - logger.LogInformation( - "memory_embedding_ready model={ModelId} dims={Dimensions} hasQueryPrefix={HasQueryPrefix} calibratedMinCosineSimilarity={CalibratedMinCosineSimilarity}", - embedder.ModelId, - embedder.Dimensions, - queryPrefix.Length > 0, - calibratedMinCosineSimilarity); - - try + if (embedder is not null) { - await GapRepairAsync(embedder, ct).ConfigureAwait(false); - } - catch (Exception ex) - { - // The embedder itself is already loaded and the holder is already populated — a - // gap-repair failure (e.g. a transient store error) must not undo that or leave an - // unobserved exception on this fire-and-forget warmup task. The doctor check and - // the next daemon restart's sweep both retry whatever remains unembedded. - logger.LogWarning(ex, "memory_embedding_gap_repair_failed model={ModelId}", embedder.ModelId); + try + { + await GapRepairAsync(embedder, ct).ConfigureAwait(false); + } + catch (Exception ex) + { + // The embedder itself is already loaded and the holder is already populated — a + // gap-repair failure (e.g. a transient store error) must not undo that or leave an + // unobserved exception on this fire-and-forget warmup task. The doctor check and + // the next daemon restart's sweep both retry whatever remains unembedded. + logger.LogWarning(ex, "memory_embedding_gap_repair_failed model={ModelId}", embedder.ModelId); + } } // Relevance gate (memory-relevance-gate, design D4, task 1.4): a second, independent @@ -272,9 +316,73 @@ private async Task WarmUpRelevanceGateAsync(CancellationToken ct) { logger.LogError(ex, "memory_relevance_gate_unavailable model={ModelId} reason={Reason}", modelId, ex.Message); relevanceScorerHolder.Set(new UnavailableRelevanceScorer(modelId, ex.Message), calibratedThreshold); + EmitRelevanceModelUnavailableAlert(modelId, ex.Message); } } + /// + /// Fires at most once per daemon run + /// (see ). Content mirrors the doctor check's own remediation + /// wording (MemoryEmbeddingDoctorCheck) so an operator sees the same guidance whether + /// they are pulling netclaw doctor or reacting to a pushed alert. + /// + private void EmitEmbedderUnavailableAlert(string modelId, string reason) + { + if (Interlocked.CompareExchange(ref _embedderAlertFired, 1, 0) != 0) + return; + + const string consequence = "Memory recall/dedup is running lexical-only — semantic features are degraded."; + const string remediation = "Check network access and disk space, run `netclaw doctor`, or run " + + "`netclaw memory backfill-embeddings` — the daemon re-provisions the model on its next start."; + + notificationSink.Emit(OperationalAlert.Create( + timeProvider, + "memory.embedding_model.unavailable", + AlertType.MemoryEmbeddingModelUnavailable, + $"Memory embedding model '{modelId}' could not be provisioned or loaded: {reason} {consequence}", + AlertSeverity.Warning, + source: modelId, + context: new Dictionary + { + ["modelId"] = modelId, + ["reason"] = reason, + ["consequence"] = consequence, + ["remediation"] = remediation, + })); + } + + /// + /// Fires at most once per daemon run + /// (see ). Unlike , + /// the remediation does not mention netclaw memory backfill-embeddings — that command + /// only re-embeds the document corpus, it has no relevance-model analogue (mirrors + /// MemoryRelevanceGateDoctorCheck's own remediation wording). + /// + private void EmitRelevanceModelUnavailableAlert(string modelId, string reason) + { + if (Interlocked.CompareExchange(ref _relevanceAlertFired, 1, 0) != 0) + return; + + const string consequence = "The relevance gate is disabled — recall is unfiltered by the cross-encoder."; + const string remediation = "Check network access and disk space, then run `netclaw doctor` or restart the " + + "daemon to re-provision the model."; + + notificationSink.Emit(OperationalAlert.Create( + timeProvider, + "memory.relevance_model.unavailable", + AlertType.MemoryRelevanceModelUnavailable, + $"Memory relevance (cross-encoder) model '{modelId}' could not be provisioned or loaded: {reason} {consequence}", + AlertSeverity.Warning, + source: modelId, + context: new Dictionary + { + ["modelId"] = modelId, + ["reason"] = reason, + ["consequence"] = consequence, + ["remediation"] = remediation, + })); + } + private async Task LoadRelevanceScorerAsync(string modelId, CancellationToken ct) { // Keyed under the same ModelsDirectory root as embedding models (NetclawPaths. From f822b7d8ccd51458c329adf2c057cb038e016649 Mon Sep 17 00:00:00 2001 From: Aaron Stannard Date: Thu, 9 Jul 2026 18:00:12 -0500 Subject: [PATCH 29/37] fix(daemon,cli): graceful systemd stop; memory subcommand --help handling (canary findings) (#1612) --- .../Cli/CliArgsParserTests.cs | 34 ++++++ .../Cli/DaemonCommandDispatchTests.cs | 53 +++++++++ .../Cli/DaemonManagerGracefulShutdownTests.cs | 109 ++++++++++++++++++ .../Memory/MemoryCommandTests.cs | 41 +++++++ .../Reminder/ReminderCommandTests.cs | 91 +++++++++++++++ .../Webhooks/WebhooksCommandTests.cs | 30 +++++ src/Netclaw.Cli/CliArgsParser.cs | 23 ++++ .../Daemon/DaemonCommandDispatch.cs | 34 ++++++ src/Netclaw.Cli/Daemon/DaemonManager.cs | 48 +++++++- src/Netclaw.Cli/Memory/MemoryCommand.cs | 8 ++ src/Netclaw.Cli/Program.cs | 9 ++ src/Netclaw.Cli/Reminder/ReminderCommand.cs | 11 ++ src/Netclaw.Cli/Webhooks/WebhooksCommand.cs | 7 ++ .../DaemonConfigTests.cs | 22 ++++ src/Netclaw.Configuration/DaemonConfig.cs | 28 +++++ .../DaemonShutdownConfigurationTests.cs | 44 +++++++ .../DaemonShutdownConfiguration.cs | 32 +++++ src/Netclaw.Daemon/Program.cs | 15 +-- 18 files changed, 627 insertions(+), 12 deletions(-) create mode 100644 src/Netclaw.Cli.Tests/Cli/DaemonCommandDispatchTests.cs create mode 100644 src/Netclaw.Cli.Tests/Cli/DaemonManagerGracefulShutdownTests.cs create mode 100644 src/Netclaw.Cli.Tests/Reminder/ReminderCommandTests.cs create mode 100644 src/Netclaw.Cli/Daemon/DaemonCommandDispatch.cs create mode 100644 src/Netclaw.Daemon.Tests/DaemonShutdownConfigurationTests.cs create mode 100644 src/Netclaw.Daemon/DaemonShutdownConfiguration.cs diff --git a/src/Netclaw.Cli.Tests/Cli/CliArgsParserTests.cs b/src/Netclaw.Cli.Tests/Cli/CliArgsParserTests.cs index 73d4b3d0e..5afd7522c 100644 --- a/src/Netclaw.Cli.Tests/Cli/CliArgsParserTests.cs +++ b/src/Netclaw.Cli.Tests/Cli/CliArgsParserTests.cs @@ -181,6 +181,40 @@ private static IReadOnlySet ExtractHelpListedCommands(string programSour return commands; } + /// + /// Regression coverage for the canary "help executes instead of printing help" family of + /// bugs (netclaw memory backfill-embeddings --help ran a real embed pass; + /// netclaw daemon stop --help would have actually stopped the daemon). Every fix + /// site (MemoryCommand, the Program.cs daemon dispatch, WebhooksCommand, ReminderCommand) + /// routes through this one helper, so its own scan logic only needs proving once. + /// + [Theory] + [InlineData(new[] { "memory", "backfill-embeddings" }, false)] + [InlineData(new[] { "memory", "backfill-embeddings", "--force" }, false)] + [InlineData(new[] { "memory", "backfill-embeddings", "--help" }, true)] + [InlineData(new[] { "memory", "backfill-embeddings", "-h" }, true)] + [InlineData(new[] { "memory", "backfill-embeddings", "help" }, true)] + [InlineData(new[] { "daemon", "stop" }, false)] + [InlineData(new[] { "daemon", "stop", "--help" }, true)] + public void HasTrailingHelpToken_scans_from_startIndex(string[] args, bool expected) + { + Assert.Equal(expected, CliArgsParser.HasTrailingHelpToken(args, startIndex: 2)); + } + + [Fact] + public void HasTrailingHelpToken_ignores_tokens_before_startIndex() + { + // The subcommand itself ("help") sits at index 1, before startIndex — this helper is + // only meant to scan trailing args, so it must not double-count the subcommand slot. + Assert.False(CliArgsParser.HasTrailingHelpToken(["memory", "help"], startIndex: 2)); + } + + [Fact] + public void HasTrailingHelpToken_returns_false_for_empty_tail() + { + Assert.False(CliArgsParser.HasTrailingHelpToken(["memory", "backfill-embeddings"], startIndex: 2)); + } + private static string ReadProgramCsSource() => File.ReadAllText(Path.Combine(FindRepoRoot(), "src", "Netclaw.Cli", "Program.cs")); private static string FindRepoRoot() diff --git a/src/Netclaw.Cli.Tests/Cli/DaemonCommandDispatchTests.cs b/src/Netclaw.Cli.Tests/Cli/DaemonCommandDispatchTests.cs new file mode 100644 index 000000000..aae779ca7 --- /dev/null +++ b/src/Netclaw.Cli.Tests/Cli/DaemonCommandDispatchTests.cs @@ -0,0 +1,53 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using Netclaw.Cli.Daemon; +using Xunit; + +namespace Netclaw.Cli.Tests.Cli; + +/// +/// Regression coverage for the canary finding that netclaw daemon stop --help (and +/// start/status/install/uninstall) executed the real lifecycle action instead of printing +/// help, because Program.cs's daemon dispatch only checked the subcommand slot (args[1]) for +/// a help token. Program.cs is top-level statements, so the decision is extracted into +/// to make it independently unit-testable — mirroring +/// DaemonCliArgs's netclawd --version extraction for the same reason. +/// +public sealed class DaemonCommandDispatchTests +{ + [Theory] + [InlineData("start")] + [InlineData("stop")] + [InlineData("status")] + [InlineData("install")] + [InlineData("uninstall")] + public void ShouldShowHelpInsteadOfExecuting_true_for_lifecycle_verb_with_trailing_help(string verb) + { + Assert.True(DaemonCommandDispatch.ShouldShowHelpInsteadOfExecuting(verb, ["daemon", verb, "--help"])); + } + + [Theory] + [InlineData("start")] + [InlineData("stop")] + [InlineData("status")] + [InlineData("install")] + [InlineData("uninstall")] + public void ShouldShowHelpInsteadOfExecuting_false_for_lifecycle_verb_without_help(string verb) + { + Assert.False(DaemonCommandDispatch.ShouldShowHelpInsteadOfExecuting(verb, ["daemon", verb])); + } + + [Theory] + [InlineData("pair")] + [InlineData("devices")] + [InlineData("help")] + public void ShouldShowHelpInsteadOfExecuting_false_for_verbs_with_their_own_help_handling(string verb) + { + // `pair`/`devices` guard their own trailing --help inline in Program.cs, and "help" + // itself is normalized away before this check runs — none should be double-guarded here. + Assert.False(DaemonCommandDispatch.ShouldShowHelpInsteadOfExecuting(verb, ["daemon", verb, "--help"])); + } +} diff --git a/src/Netclaw.Cli.Tests/Cli/DaemonManagerGracefulShutdownTests.cs b/src/Netclaw.Cli.Tests/Cli/DaemonManagerGracefulShutdownTests.cs new file mode 100644 index 000000000..b06d2594f --- /dev/null +++ b/src/Netclaw.Cli.Tests/Cli/DaemonManagerGracefulShutdownTests.cs @@ -0,0 +1,109 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using System.Diagnostics; +using Microsoft.Extensions.Time.Testing; +using Netclaw.Cli.Daemon; +using Netclaw.Configuration; +using Netclaw.Tests.Utilities; +using Xunit; + +namespace Netclaw.Cli.Tests.Cli; + +/// +/// Covers the canary daemon-stop finding: systemctl --user stop netclaw.service landed +/// in failed (Result: signal) because 's SIGTERM +/// grace period (previously a hardcoded 10s) was far shorter than the ~200s the daemon's own +/// Akka CoordinatedShutdown session-drain phase is deliberately allotted — so the CLI itself +/// gave up and force-killed the daemon long before a legitimately slow (in-flight LLM call) +/// graceful shutdown could finish. It still died, so `netclaw daemon stop` (ExecStop) reported +/// success, but via SIGKILL rather than a clean exit — exactly what systemd's +/// failed (Result: signal) was observing. +/// +/// These tests exercise the two testable halves of the fix: (1) the internal +/// poll now honors an injected +/// end-to-end (not just for its deadline math), so the up-to-200s +/// wait can be driven with a instead of a real sleep; and +/// (2) the generated systemd unit's TimeoutStopSec= stays in lockstep with +/// so systemd itself never SIGKILLs the whole +/// cgroup out from under a still-legitimately-waiting ExecStop=. +/// +public sealed class DaemonManagerGracefulShutdownTests : IDisposable +{ + private readonly DisposableTempDir _dir = new(); + private readonly NetclawPaths _paths; + + public DaemonManagerGracefulShutdownTests() + { + _paths = new NetclawPaths(_dir.Path); + _paths.EnsureDirectoriesExist(); + } + + public void Dispose() => _dir.Dispose(); + + [Fact] + public async Task WaitForExitAsync_ReturnsTrue_Immediately_WhenProcessAlreadyExited() + { + var manager = new DaemonManager(_paths, TimeProvider.System); + using var exited = StartAndWaitForRealExit(); + + var result = await manager.WaitForExitAsync(exited, TimeSpan.FromSeconds(200), CancellationToken.None); + + Assert.True(result); + } + + [Fact] + public async Task WaitForExitAsync_ReturnsFalse_OnceVirtualClockPassesTimeout_WithoutRealTimeDelay() + { + var fakeTime = new FakeTimeProvider(); + var manager = new DaemonManager(_paths, fakeTime); + // The current test process never exits mid-test — stands in for a daemon still + // draining a stuck/slow session. + var neverExits = Process.GetCurrentProcess(); + + var waitTask = manager.WaitForExitAsync(neverExits, DaemonConfig.GracefulShutdownBudget, CancellationToken.None); + + // A single jump past the full budget — if the poll delay inside WaitForExitAsync were + // still a bare real-time `Task.Delay(200)` (the pre-fix shape), this test would need to + // actually wait out that real time instead of resolving from one Advance() call. + fakeTime.Advance(DaemonConfig.GracefulShutdownBudget + TimeSpan.FromSeconds(1)); + + var result = await waitTask; + + Assert.False(result); + } + + [Fact] + public void BuildDaemonUnitContent_SetsTimeoutStopSec_ConsistentWithGracefulShutdownBudget() + { + var unit = DaemonManager.BuildDaemonUnitContent( + "/opt/netclaw/netclawd", "/opt/netclaw/netclaw", "/opt/netclaw/daemon.env"); + + var expectedTimeoutStopSec = (int)(DaemonConfig.GracefulShutdownBudget + TimeSpan.FromSeconds(30)).TotalSeconds; + + Assert.Contains($"TimeoutStopSec={expectedTimeoutStopSec}", unit, StringComparison.Ordinal); + + // TimeoutStopSec bounds the ENTIRE stop job (ExecStop's own runtime included), so it + // must leave systemd comfortably behind netclaw daemon stop's own SIGTERM-wait ceiling + // — otherwise systemd would SIGKILL the cgroup mid-ExecStop before the CLI's own, + // more-informative timeout/escalation logic ever gets to run. + Assert.True( + expectedTimeoutStopSec > DaemonConfig.GracefulShutdownBudget.TotalSeconds, + "Unit TimeoutStopSec must exceed DaemonManager.StopAsync's own SIGTERM wait."); + } + + private static Process StartAndWaitForRealExit() + { + var psi = OperatingSystem.IsWindows() + ? new ProcessStartInfo("cmd.exe", "/c exit 0") + : new ProcessStartInfo("/bin/sh", "-c \"exit 0\""); + psi.UseShellExecute = false; + psi.CreateNoWindow = true; + + var process = Process.Start(psi)!; + process.WaitForExit(); + return process; + } +} diff --git a/src/Netclaw.Cli.Tests/Memory/MemoryCommandTests.cs b/src/Netclaw.Cli.Tests/Memory/MemoryCommandTests.cs index bfc440dd6..401cdca90 100644 --- a/src/Netclaw.Cli.Tests/Memory/MemoryCommandTests.cs +++ b/src/Netclaw.Cli.Tests/Memory/MemoryCommandTests.cs @@ -76,6 +76,47 @@ public async Task BackfillEmbeddings_fails_clearly_when_autodownload_is_false_an Assert.Contains("AutoDownload", stderr); } + [Theory] + [InlineData("--help")] + [InlineData("-h")] + [InlineData("help")] + public async Task BackfillEmbeddings_help_flag_prints_help_and_does_not_execute(string helpToken) + { + // Canary regression: `netclaw memory backfill-embeddings --help` was executing the real + // provision-and-embed run (downloading models, writing embeddings) instead of printing + // help, because only args[1] (the subcommand slot) was checked for a help token. Prove + // the fix by seeding a document that WOULD be embedded if the command ran for real (as + // in BackfillEmbeddings_embeds_missing_documents_and_reports_a_summary above) and + // asserting nothing was written. + var paths = CreateTempPaths(prePlaceValidModel: true); + var config = BuildConfig(autoDownload: true); + + var store = new SQLiteMemoryStore(paths.MemorySqliteDbPath, TimeProvider.System); + await store.InitializeAsync(TestContext.Current.CancellationToken); + await SeedDocumentAsync(store, "doc-1", "Doc One", "first body"); + + var (exitCode, stdout) = await RunCapturedAsync(["memory", "backfill-embeddings", helpToken], paths, config); + + Assert.Equal(0, exitCode); + Assert.Contains("Usage: netclaw memory ", stdout); + Assert.DoesNotContain("Embedding", stdout); + + var rows = await store.GetEmbeddingsForModelAsync(ModelId, TestContext.Current.CancellationToken); + Assert.Empty(rows); + } + + [Fact] + public async Task TopLevelHelp_still_prints_help() + { + var paths = CreateTempPaths(prePlaceValidModel: false); + var config = BuildConfig(autoDownload: false); + + var (exitCode, stdout) = await RunCapturedAsync(["memory", "--help"], paths, config); + + Assert.Equal(0, exitCode); + Assert.Contains("Usage: netclaw memory ", stdout); + } + [Fact] public async Task BackfillEmbeddings_with_force_re_embeds_every_recallable_document() { diff --git a/src/Netclaw.Cli.Tests/Reminder/ReminderCommandTests.cs b/src/Netclaw.Cli.Tests/Reminder/ReminderCommandTests.cs new file mode 100644 index 000000000..83e81a022 --- /dev/null +++ b/src/Netclaw.Cli.Tests/Reminder/ReminderCommandTests.cs @@ -0,0 +1,91 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using Netclaw.Cli.Reminder; +using Xunit; + +namespace Netclaw.Cli.Tests.Reminder; + +/// +/// Covers the missed-help pattern audited alongside the canary-reported +/// netclaw memory backfill-embeddings --help bug: none of +/// 's subcommand handlers had their own --help +/// check, so a trailing help token was silently ignored and the subcommand ran for +/// real. list is the sharpest example — it takes no positional arguments at +/// all, so `reminder list --help` used to reach the live daemon instead of printing +/// help. All tests pass daemonApi: null to prove the help check short-circuits +/// before the "requires a running daemon" branch is ever reached. +/// +public sealed class ReminderCommandTests +{ + [Theory] + [InlineData("--help")] + [InlineData("-h")] + [InlineData("help")] + public async Task List_TrailingHelpFlag_PrintsHelp_WithoutRequiringDaemon(string helpToken) + { + var (exitCode, stdout) = await RunCapturedAsync(["reminder", "list", helpToken]); + + Assert.Equal(0, exitCode); + Assert.Contains("Usage: netclaw reminder ", stdout); + Assert.DoesNotContain("requires a running daemon", stdout); + } + + [Fact] + public async Task List_WithoutHelpFlag_StillRequiresDaemon() + { + // Regression guard: the new trailing-help scan must not swallow ordinary + // subcommand invocations that legitimately need the daemon. + var (exitCode, _, stderr) = await RunCapturedWithStderrAsync(["reminder", "list"]); + + Assert.Equal(1, exitCode); + Assert.Contains("requires a running daemon", stderr); + } + + [Fact] + public async Task Create_TrailingHelpFlag_AfterFullArgs_PrintsHelp_WithoutRequiringDaemon() + { + var (exitCode, stdout) = await RunCapturedAsync( + ["reminder", "create", "id", "once", "30m", "do it", "--help"]); + + Assert.Equal(0, exitCode); + Assert.Contains("Usage: netclaw reminder ", stdout); + } + + [Fact] + public async Task TopLevelHelp_StillPrintsHelp() + { + var (exitCode, stdout) = await RunCapturedAsync(["reminder", "--help"]); + + Assert.Equal(0, exitCode); + Assert.Contains("Usage: netclaw reminder ", stdout); + } + + private static async Task<(int ExitCode, string Stdout)> RunCapturedAsync(string[] args) + { + var (exitCode, stdout, _) = await RunCapturedWithStderrAsync(args); + return (exitCode, stdout); + } + + private static async Task<(int ExitCode, string Stdout, string Stderr)> RunCapturedWithStderrAsync(string[] args) + { + var originalOut = Console.Out; + var originalError = Console.Error; + using var stdout = new StringWriter(); + using var stderr = new StringWriter(); + Console.SetOut(stdout); + Console.SetError(stderr); + try + { + var exitCode = await ReminderCommand.RunAsync(args, daemonApi: null); + return (exitCode, stdout.ToString(), stderr.ToString()); + } + finally + { + Console.SetOut(originalOut); + Console.SetError(originalError); + } + } +} diff --git a/src/Netclaw.Cli.Tests/Webhooks/WebhooksCommandTests.cs b/src/Netclaw.Cli.Tests/Webhooks/WebhooksCommandTests.cs index 8fba901bf..ee91488cf 100644 --- a/src/Netclaw.Cli.Tests/Webhooks/WebhooksCommandTests.cs +++ b/src/Netclaw.Cli.Tests/Webhooks/WebhooksCommandTests.cs @@ -523,6 +523,36 @@ public async Task HelpFlag_ReturnsZero() Assert.Equal(0, result); } + [Theory] + [InlineData("--help")] + [InlineData("-h")] + public async Task List_TrailingHelpFlag_PrintsHelp_AndDoesNotList(string helpToken) + { + // A configured route WOULD show up in `webhooks list`'s output if the command ran for + // real, so its absence from stdout proves the help check pre-empted execution rather + // than just happening to print a route table that also mentions "Usage". + CreateValidRoute("test-route"); + + using var stdout = new StringWriter(); + var result = await WebhooksCommand.RunAsync(["webhooks", "list", helpToken], _paths, stdout); + + Assert.Equal(0, result); + Assert.Contains("Usage: netclaw webhooks ", stdout.ToString()); + Assert.DoesNotContain("test-route", stdout.ToString()); + } + + [Fact] + public async Task Set_TrailingHelpFlag_PrintsMoreSpecificSetHelp_NotGenericHelp() + { + // `set` has its own more specific WriteSetHelp() and must not be shadowed by the + // generic trailing-help check added for list/show/delete/validate. + using var stdout = new StringWriter(); + var result = await WebhooksCommand.RunAsync(["webhooks", "set", "test-route", "--help"], _paths, stdout); + + Assert.Equal(0, result); + Assert.Contains("Usage: netclaw webhooks set [options]", stdout.ToString()); + } + private void CreateValidRoute(string routeName, string secret = "test-secret", string prompt = "Test prompt") { var route = new WebhookRouteConfig diff --git a/src/Netclaw.Cli/CliArgsParser.cs b/src/Netclaw.Cli/CliArgsParser.cs index 57258b589..248dd2dab 100644 --- a/src/Netclaw.Cli/CliArgsParser.cs +++ b/src/Netclaw.Cli/CliArgsParser.cs @@ -40,6 +40,29 @@ public static class CliArgsParser public static bool IsHelpToken(string token) => token is "help" or "-h" or "--help"; + /// + /// Returns true if any argument at or after is a help + /// token. Subcommand dispatchers whose action verbs take no further positional arguments + /// (e.g. daemon stop, memory backfill-embeddings, webhooks list) must + /// not just check the subcommand slot itself for "help"/"-h"/"--help" — a trailing help + /// token elsewhere in the args was otherwise silently ignored and the verb executed for + /// real instead of printing help (production canary: netclaw memory backfill-embeddings + /// --help ran a real provision-and-embed pass; netclaw daemon stop --help would + /// have actually stopped the daemon). Callers that DO have their own more specific + /// --help handling for a subcommand (e.g. webhooks set) should exclude that + /// subcommand from this check so the more specific help text is not shadowed. + /// + public static bool HasTrailingHelpToken(string[] args, int startIndex) + { + for (var i = startIndex; i < args.Length; i++) + { + if (IsHelpToken(args[i])) + return true; + } + + return false; + } + public static CliParseResult Parse(string[] args) { if (args.Length == 0) diff --git a/src/Netclaw.Cli/Daemon/DaemonCommandDispatch.cs b/src/Netclaw.Cli/Daemon/DaemonCommandDispatch.cs new file mode 100644 index 000000000..bb6d31558 --- /dev/null +++ b/src/Netclaw.Cli/Daemon/DaemonCommandDispatch.cs @@ -0,0 +1,34 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +namespace Netclaw.Cli.Daemon; + +/// +/// Help-token gating for netclaw daemon <subcommand>, extracted out of Program.cs's +/// top-level-statement dispatch so it is independently unit-testable. +/// +/// +/// pair and devices take a nested action word and already guard their own +/// trailing --help inline in Program.cs. The remaining lifecycle verbs +/// (start/stop/status/install/uninstall) take no further +/// positional arguments, so previously a trailing help token anywhere after the verb was +/// silently ignored and the verb executed for real — e.g. netclaw daemon stop --help +/// actually stopped the daemon instead of printing help (canary finding, same missed-help +/// pattern audited for the memory subcommand). +/// +internal static class DaemonCommandDispatch +{ + private static readonly HashSet LifecycleVerbsRequiringTrailingHelpGuard = + new(StringComparer.Ordinal) { "start", "stop", "status", "install", "uninstall" }; + + /// + /// Returns true if is one of the guarded lifecycle + /// verbs and carries a trailing help token (anywhere at or after + /// index 2 — i.e. after netclaw daemon <subcommand>). + /// + public static bool ShouldShowHelpInsteadOfExecuting(string subcommand, string[] args) + => LifecycleVerbsRequiringTrailingHelpGuard.Contains(subcommand) + && CliArgsParser.HasTrailingHelpToken(args, startIndex: 2); +} diff --git a/src/Netclaw.Cli/Daemon/DaemonManager.cs b/src/Netclaw.Cli/Daemon/DaemonManager.cs index 57ddaabba..981f6fbb1 100644 --- a/src/Netclaw.Cli/Daemon/DaemonManager.cs +++ b/src/Netclaw.Cli/Daemon/DaemonManager.cs @@ -177,8 +177,16 @@ await http.PostAsync( process.Kill(); } - // Wait up to 10 seconds for graceful exit. - if (!await WaitForExitAsync(process, TimeSpan.FromSeconds(10), cancellationToken)) + // Wait for graceful exit. Matches DaemonConfig.GracefulShutdownBudget: the daemon's + // own Akka CoordinatedShutdown "before-service-unbind" phase is allotted this long to + // drain any in-flight LLM turn (TurnLlmTimeout defaults to 3 minutes) before sessions + // passivate. A shorter wait here (previously a hardcoded 10s) gave up and force-killed + // the daemon long before its own graceful drain could finish — the daemon still died, + // so this method reported success, but via SIGKILL mid-shutdown rather than a clean + // exit. That SIGKILL is exactly what systemd's `failed (Result: signal)` was observing + // even though `netclaw daemon stop` (ExecStop) itself exited 0 (canary finding). + var exitedGracefully = await WaitForExitAsync(process, DaemonConfig.GracefulShutdownBudget, cancellationToken); + if (!exitedGracefully) { // Timed out — hard cutoff. string? killError = null; @@ -203,7 +211,18 @@ await http.PostAsync( } CleanupPidFile(); - return new DaemonResult(true, $"Daemon stopped (was PID {pid})."); + + // Surface whether this was a clean exit or a forced kill: a force-kill means + // something (typically a session mid-LLM-call) did not finish draining within + // DaemonConfig.GracefulShutdownBudget, which is worth an operator's attention if + // it recurs even though the daemon did eventually stop. + return exitedGracefully + ? new DaemonResult(true, $"Daemon stopped (was PID {pid}).") + : new DaemonResult(true, + $"Daemon stopped (was PID {pid}), but did not exit gracefully within " + + $"{DaemonConfig.GracefulShutdownBudget.TotalSeconds:F0}s and had to be force-killed. " + + "This usually means a session was still mid-LLM-call at shutdown; if it recurs, check " + + "for stuck sessions before stopping the daemon."); } /// @@ -349,6 +368,17 @@ internal void RemoveDaemonEnvironmentFile() File.Delete(envFilePath); } + /// + /// systemd's TimeoutStopSec= for the generated unit: comfortably longer than + /// 's own graceful-shutdown wait + /// () so systemd never SIGKILLs the whole + /// cgroup out from under ExecStop= (netclaw daemon stop) while that command is + /// still legitimately waiting on the daemon's own CoordinatedShutdown drain. Existing + /// installs only pick this up after re-running netclaw daemon install. + /// + private static readonly int TimeoutStopSecValue = + (int)(DaemonConfig.GracefulShutdownBudget + TimeSpan.FromSeconds(30)).TotalSeconds; + /// /// Builds the systemd --user unit content. The daemon's shell-tool PATH is /// supplied out-of-band via EnvironmentFile= (see @@ -368,6 +398,7 @@ internal static string BuildDaemonUnitContent(string binaryPath, string cliBinar Type=simple ExecStart={binaryPath} ExecStop={cliBinaryPath} daemon stop + TimeoutStopSec={TimeoutStopSecValue} Restart=always RestartSec=5 Environment=DOTNET_ENVIRONMENT=Production @@ -637,7 +668,14 @@ private static bool SendSignal(int pid, Signal signal) return kill(pid, (int)signal) == 0; } - private async Task WaitForExitAsync(Process process, TimeSpan timeout, CancellationToken cancellationToken) + /// + /// Polls until it exits or elapses. + /// Internal (not private) so tests can drive the up-to-200-second graceful-shutdown wait + /// via an injected without a real wall-clock sleep: the poll + /// delay is scheduled against (matching this repo's virtualized- + /// timer convention, e.g. ConfigWatcherService), not a bare Task.Delay(ms). + /// + internal async Task WaitForExitAsync(Process process, TimeSpan timeout, CancellationToken cancellationToken) { var deadline = _timeProvider.GetUtcNow() + timeout; while (_timeProvider.GetUtcNow() < deadline) @@ -647,7 +685,7 @@ private async Task WaitForExitAsync(Process process, TimeSpan timeout, Can if (process.HasExited) return true; - await Task.Delay(200, cancellationToken); + await Task.Delay(TimeSpan.FromMilliseconds(200), _timeProvider, cancellationToken); } return process.HasExited; diff --git a/src/Netclaw.Cli/Memory/MemoryCommand.cs b/src/Netclaw.Cli/Memory/MemoryCommand.cs index 7c54fa647..879e25b1c 100644 --- a/src/Netclaw.Cli/Memory/MemoryCommand.cs +++ b/src/Netclaw.Cli/Memory/MemoryCommand.cs @@ -39,6 +39,14 @@ internal static Task RunAsync( if (subcommand is "help" or "-h" or "--help") return Task.FromResult(WriteHelp()); + // `backfill-embeddings` takes no required positional arguments (only the optional + // `--force` flag), so a trailing `--help`/`-h` would otherwise be silently ignored + // and the real provision-and-embed run would execute instead of printing help + // (canary finding: `netclaw memory backfill-embeddings --help` downloaded/embedded + // for real). Scan the full argument list, not just the subcommand slot. + if (CliArgsParser.HasTrailingHelpToken(args, startIndex: 2)) + return Task.FromResult(WriteHelp()); + return subcommand switch { "backfill-embeddings" => RunBackfillEmbeddingsAsync(args, paths, configuration, allowlist), diff --git a/src/Netclaw.Cli/Program.cs b/src/Netclaw.Cli/Program.cs index 124183bb9..941dfa68a 100644 --- a/src/Netclaw.Cli/Program.cs +++ b/src/Netclaw.Cli/Program.cs @@ -483,6 +483,15 @@ static async Task RunAsync(string[] args) if (IsHelpToken(subcommand)) subcommand = "help"; + // See DaemonCommandDispatch remarks: `pair`/`devices` guard their own trailing --help + // below; the remaining lifecycle verbs previously executed for real on a trailing help + // token (canary finding). Fail toward help, not execution. + if (DaemonCommandDispatch.ShouldShowHelpInsteadOfExecuting(subcommand, args)) + { + WriteDaemonHelp(); + return; + } + var paths = new NetclawPaths(); paths.EnsureDirectoriesExist(); var manager = new DaemonManager(paths, TimeProvider.System); diff --git a/src/Netclaw.Cli/Reminder/ReminderCommand.cs b/src/Netclaw.Cli/Reminder/ReminderCommand.cs index 3acc3301c..4725a3b7e 100644 --- a/src/Netclaw.Cli/Reminder/ReminderCommand.cs +++ b/src/Netclaw.Cli/Reminder/ReminderCommand.cs @@ -37,6 +37,17 @@ public static async Task RunAsync(string[] args, DaemonApi? daemonApi) return 0; } + // None of the subcommands below have their own --help handling, so a trailing + // --help/-h was previously ignored and the subcommand ran for real — e.g. + // `reminder list --help` still hit the live daemon and printed reminders instead + // of help (same missed-help pattern reported for `netclaw memory backfill-embeddings + // --help`). Scan the full argument list, not just the subcommand slot. + if (CliArgsParser.HasTrailingHelpToken(args, startIndex: 2)) + { + WriteHelp(); + return 0; + } + // validate is offline — no daemon needed if (subcommand is "validate") return RunValidate(args); diff --git a/src/Netclaw.Cli/Webhooks/WebhooksCommand.cs b/src/Netclaw.Cli/Webhooks/WebhooksCommand.cs index 6098d2d5d..657bbd6f0 100644 --- a/src/Netclaw.Cli/Webhooks/WebhooksCommand.cs +++ b/src/Netclaw.Cli/Webhooks/WebhooksCommand.cs @@ -23,6 +23,13 @@ public static Task RunAsync(string[] args, NetclawPaths paths, TextWriter? if (subcommand is "help" or "-h" or "--help") return Task.FromResult(WriteHelp(output)); + // list/show/delete/validate take no --help of their own, so a trailing --help/-h + // was previously ignored and the subcommand ran for real (e.g. `webhooks list --help` + // still listed routes). `set` is excluded — it already has its own more specific + // WriteSetHelp() gated on HasFlag(args, "--help"/"-h"). + if (subcommand is not "set" && CliArgsParser.HasTrailingHelpToken(args, startIndex: 2)) + return Task.FromResult(WriteHelp(output)); + var store = new WebhookRouteStore(paths); return Task.FromResult(subcommand switch diff --git a/src/Netclaw.Configuration.Tests/DaemonConfigTests.cs b/src/Netclaw.Configuration.Tests/DaemonConfigTests.cs index 7adb3a7d2..a31011aab 100644 --- a/src/Netclaw.Configuration.Tests/DaemonConfigTests.cs +++ b/src/Netclaw.Configuration.Tests/DaemonConfigTests.cs @@ -287,4 +287,26 @@ public void Validator_rejects_invalid_trusted_proxy_entry() Assert.Contains(issues, issue => issue.Message.Contains("not-an-ip", StringComparison.OrdinalIgnoreCase)); } + + /// + /// Regression guard for the canary daemon-stop finding: + /// is the single source of truth shared by + /// the daemon's CoordinatedShutdown session-drain phase, its generic-host ShutdownTimeout, + /// the CLI's SIGTERM grace period, and the generated systemd unit's TimeoutStopSec. It only + /// does its job if it comfortably exceeds how long a session can legitimately still be + /// mid-LLM-call at shutdown ('s default) — shrink + /// it below that and the CLI (or systemd) will force-kill the daemon mid-graceful-drain + /// again, exactly the bug this constant exists to prevent. + /// + [Fact] + public void GracefulShutdownBudget_exceeds_default_TurnLlmTimeout() + { + var defaultTurnLlmTimeout = new SessionConfig().TurnLlmTimeout; + + Assert.True( + DaemonConfig.GracefulShutdownBudget > defaultTurnLlmTimeout, + $"GracefulShutdownBudget ({DaemonConfig.GracefulShutdownBudget}) must exceed the default " + + $"TurnLlmTimeout ({defaultTurnLlmTimeout}) so an in-flight LLM turn can finish draining " + + "before the daemon's graceful-shutdown budget is exhausted."); + } } diff --git a/src/Netclaw.Configuration/DaemonConfig.cs b/src/Netclaw.Configuration/DaemonConfig.cs index 6b881865d..5625b1b6e 100644 --- a/src/Netclaw.Configuration/DaemonConfig.cs +++ b/src/Netclaw.Configuration/DaemonConfig.cs @@ -20,6 +20,34 @@ public sealed record DaemonConfig /// public const int DefaultPort = 5199; + /// + /// Worst-case time the daemon's graceful shutdown drain is allotted before something + /// gives up and forces termination. Sized to comfortably exceed + /// 's default (3 minutes) so a session mid-LLM-call + /// during shutdown can finish draining instead of being interrupted. + /// + /// Single source of truth shared by four surfaces that must stay in lockstep: + /// - Netclaw.Daemon's Akka coordinated-shutdown.phases.before-service-unbind.timeout + /// HOCON override (where the actual session drain runs) + /// - Netclaw.Daemon's generic-host HostOptions.ShutdownTimeout + /// - 's SIGTERM grace period in + /// netclaw daemon stop (the systemd unit's ExecStop=) + /// - the generated systemd unit's TimeoutStopSec= + /// (see DaemonManager.BuildDaemonUnitContent) + /// + /// A production canary regression traced to these four being inconsistent: the CLI's + /// SIGTERM wait was hardcoded to 10 seconds — far short of the 200s the daemon's own + /// CoordinatedShutdown phase is deliberately allotted — so a session still mid-LLM-call + /// caused the CLI to give up and force-kill the daemon itself long before the daemon's + /// own graceful drain could finish. The daemon still died, so netclaw daemon stop + /// (ExecStop) reported success, but via SIGKILL mid-shutdown rather than a clean exit — + /// exactly what systemd's failed (Result: signal) was observing. + /// + /// Changing this value requires re-running netclaw daemon install on existing + /// hosts to regenerate the unit file with the new TimeoutStopSec=. + /// + public static readonly TimeSpan GracefulShutdownBudget = TimeSpan.FromSeconds(200); + /// /// IP address the daemon binds to. Defaults to loopback (127.0.0.1). /// diff --git a/src/Netclaw.Daemon.Tests/DaemonShutdownConfigurationTests.cs b/src/Netclaw.Daemon.Tests/DaemonShutdownConfigurationTests.cs new file mode 100644 index 000000000..ac6c3683c --- /dev/null +++ b/src/Netclaw.Daemon.Tests/DaemonShutdownConfigurationTests.cs @@ -0,0 +1,44 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using Netclaw.Configuration; +using Netclaw.Daemon; +using Xunit; + +namespace Netclaw.Daemon.Tests; + +/// +/// Regression coverage for the canary daemon-stop finding (see +/// remarks): the Akka CoordinatedShutdown +/// "before-service-unbind" phase timeout — where session draining actually happens — must +/// track , not a hardcoded literal that can +/// silently drift out of sync with the CLI's SIGTERM wait or the generated systemd unit's +/// TimeoutStopSec. +/// +public sealed class DaemonShutdownConfigurationTests +{ + [Fact] + public void BuildCoordinatedShutdownHocon_UsesGracefulShutdownBudget() + { + var hocon = DaemonShutdownConfiguration.BuildCoordinatedShutdownHocon(DaemonConfig.GracefulShutdownBudget); + + Assert.Contains( + $"phases.before-service-unbind.timeout = {(int)DaemonConfig.GracefulShutdownBudget.TotalSeconds}s", + hocon, + StringComparison.Ordinal); + Assert.Contains("exit-clr = off", hocon, StringComparison.Ordinal); + } + + [Theory] + [InlineData(30)] + [InlineData(200)] + [InlineData(600)] + public void BuildCoordinatedShutdownHocon_InterpolatesArbitraryTimeouts(int seconds) + { + var hocon = DaemonShutdownConfiguration.BuildCoordinatedShutdownHocon(TimeSpan.FromSeconds(seconds)); + + Assert.Contains($"phases.before-service-unbind.timeout = {seconds}s", hocon, StringComparison.Ordinal); + } +} diff --git a/src/Netclaw.Daemon/DaemonShutdownConfiguration.cs b/src/Netclaw.Daemon/DaemonShutdownConfiguration.cs new file mode 100644 index 000000000..eafcc465e --- /dev/null +++ b/src/Netclaw.Daemon/DaemonShutdownConfiguration.cs @@ -0,0 +1,32 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using Netclaw.Configuration; + +namespace Netclaw.Daemon; + +/// +/// Builds the Akka CoordinatedShutdown HOCON override that bounds the daemon's session-drain +/// phase. Extracted into its own testable method (rather than an inline string literal in +/// Program.cs's top-level statements) so a unit test can assert the interpolated timeout +/// tracks instead of drifting back to a +/// hardcoded literal — the exact class of bug behind the canary daemon-stop finding (see +/// remarks for the full story). +/// +internal static class DaemonShutdownConfiguration +{ + /// + /// Coordinated-shutdown HOCON: disables the CLR-exit side effect (the daemon's own + /// restart loop owns process lifetime, not CoordinatedShutdown) and sizes the + /// before-service-unbind phase — where session draining + /// (SessionDrainHelper.DrainAsync) actually runs — to . + /// + public static string BuildCoordinatedShutdownHocon(TimeSpan gracefulShutdownBudget) => $$""" + akka.coordinated-shutdown { + exit-clr = off + phases.before-service-unbind.timeout = {{(int)gracefulShutdownBudget.TotalSeconds}}s + } + """; +} diff --git a/src/Netclaw.Daemon/Program.cs b/src/Netclaw.Daemon/Program.cs index c512b5008..333d6cbeb 100644 --- a/src/Netclaw.Daemon/Program.cs +++ b/src/Netclaw.Daemon/Program.cs @@ -430,7 +430,13 @@ static void ConfigureDaemonServices( services.Configure(options => { - options.ShutdownTimeout = TimeSpan.FromSeconds(30); + // Must comfortably exceed DaemonConfig.GracefulShutdownBudget below (the Akka + // `before-service-unbind` CoordinatedShutdown phase timeout, where session draining + // actually happens) plus headroom for the handful of default-timeout phases that run + // after it. Previously a bare 30s — shorter than the 200s session-drain budget, which + // let this host-level timeout diverge from the Akka phase it is supposed to bound + // (canary finding: see DaemonConfig.GracefulShutdownBudget remarks for the full story). + options.ShutdownTimeout = DaemonConfig.GracefulShutdownBudget + TimeSpan.FromSeconds(30); }); // Resolve models for session config @@ -1063,12 +1069,7 @@ static void ConfigureDaemonServices( // mid-LLM-call (TurnLlmTimeout defaults to 3 minutes) must finish before // passivation can begin. akkaBuilder.AddHocon( - """ - akka.coordinated-shutdown { - exit-clr = off - phases.before-service-unbind.timeout = 200s - } - """, + DaemonShutdownConfiguration.BuildCoordinatedShutdownHocon(DaemonConfig.GracefulShutdownBudget), HoconAddMode.Prepend); akkaBuilder = akkaBuilder.ConfigureLoggers(setup => From 71431707a66998f86266cf27899776355353e189 Mon Sep 17 00:00:00 2001 From: Aaron Stannard Date: Fri, 10 Jul 2026 02:46:42 +0000 Subject: [PATCH 30/37] chore(release): prepare 0.25.0-alpha.onnx.4 experimental prerelease Operational alert on model provisioning failure (#1611), embedder-failure no longer blocks relevance model (#1611), graceful daemon shutdown budget (#1612), --help no longer executes commands (#1612). Memory eval 6/6 (run 6cc18657); code validated by full PR CI on #1611/#1612. --- Directory.Build.props | 2 +- RELEASE_NOTES.md | 16 ++++++++++++++++ 2 files changed, 17 insertions(+), 1 deletion(-) diff --git a/Directory.Build.props b/Directory.Build.props index 0d80369c6..3d967ef63 100644 --- a/Directory.Build.props +++ b/Directory.Build.props @@ -9,7 +9,7 @@ enable true 0.25.0 - alpha.onnx.3 + alpha.onnx.4 Netclaw v0.25.0-beta.1 — SkillServer native sub-agent sync, memory curation unification, systemd PATH fix **Features** diff --git a/RELEASE_NOTES.md b/RELEASE_NOTES.md index 4f8c5364b..442124825 100644 --- a/RELEASE_NOTES.md +++ b/RELEASE_NOTES.md @@ -1,5 +1,21 @@ # NetClaw Release Notes +## 0.25.0-alpha.onnx.4 (2026-07-09) + +> **Experimental feature build** (fourth in the memory-embeddings series). Same gating: +> everything rides `Memory.Embeddings.Enabled`, off by default; install only by exact pin +> (`NETCLAW_VERSION=0.25.0-alpha.onnx.4`). **Upgrade note:** the systemd unit template +> changed — after installing, re-run `netclaw daemon install` to regenerate the unit and +> pick up the new graceful-shutdown settings. + +### Features +- **Operational alert on model provisioning failure** — if either ONNX model (embedder or relevance reranker) cannot be downloaded, verified, or loaded while embeddings are enabled, the daemon now pushes an operational alert to configured notification targets (same channel as reminder-failure alerts) with the failure reason and remediation, once per model per daemon run — semantic-memory degradation is no longer discoverable only via doctor/logs ([#1611](https://github.com/netclaw-dev/netclaw/pull/1611)) + +### Bug Fixes +- **Embedder failure no longer blocks the relevance model** — a provisioning failure in the embedding model made the reranker's provisioning unreachable, silently disabling the relevance gate alongside it ([#1611](https://github.com/netclaw-dev/netclaw/pull/1611)) +- **Graceful daemon shutdown** — `netclaw daemon stop` self-escalated to SIGKILL after 10s while the daemon's own shutdown budget allows 200s to drain in-flight turns; one `GracefulShutdownBudget` now governs the Akka shutdown phase, host shutdown timeout, CLI wait, and the generated unit's `TimeoutStopSec` ([#1612](https://github.com/netclaw-dev/netclaw/pull/1612)) +- **`--help` no longer executes commands** — `netclaw memory backfill-embeddings --help` ran a real backfill; worse, `netclaw daemon stop --help` actually stopped the daemon. Trailing help tokens are now handled uniformly across memory, daemon, webhooks, and reminder subcommands ([#1612](https://github.com/netclaw-dev/netclaw/pull/1612)) + ## 0.25.0-alpha.onnx.3 (2026-07-09) > **Experimental feature build** (third in the memory-embeddings series) — the canary-feedback From 3ee94a919a3d5d9430253b7b8ddadd932f592e94 Mon Sep 17 00:00:00 2001 From: Aaron Stannard Date: Sun, 12 Jul 2026 12:00:04 -0500 Subject: [PATCH 31/37] onnx.5: sync dev (fail-closed sub-agent approvals #1616 + fixes), prepare 0.25.0-alpha.onnx.5 (#1619) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * fix: support Discord DM reminders (#1609) * Refactor ModelContextProtocol versioning in props file (#1614) Updated ModelContextProtocol package versions to use a variable for versioning. Signed-off-by: Aaron Stannard * fix: serialize Slack processing status updates (#1556) Co-authored-by: Aaron Stannard * ci: run required checks for merge queue groups (#1617) * Bump MessagePack from 3.1.7 to 3.1.8 (#1605) --- updated-dependencies: - dependency-name: MessagePack dependency-version: 3.1.8 dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * fix(cli): model set/picker preserve hand-set modalities on re-set (#1127) (#1610) * fix(cli): model set/picker preserve hand-set modalities on re-set (#1127) Re-selecting a model that is already configured wiped operator-set attributes on it. The write path rebuilt the Models[role] entry from scratch via ModelEntryWriter, which only writes modalities it was handed (a probe result). Modalities have no CLI input and can only be hand-edited, so a manual 'model set' (or a context-window tweak, or the TUI picker re-selecting the same model) passed null and silently deleted a hand-set InputModalities/OutputModalities — the concrete #1127 loss. Add ModelEntryWriter.WriteRole, a non-destructive persist: when the role already points at the same (provider, modelId), preserve the existing modalities and context window the caller did not supply; switching to a different model still starts clean (old attributes belonged to the old model). Routed 'model set' and the TUI model manager through it. Verified against the shipped binary: on stock beta 0.25.0, 'model set main --context-window N' wipes InputModalities; with this fix the same command preserves it. No config-shape or schema change, so this is fully backwards compatible. Tests: WriteRole_SameModelWithoutModalities_PreservesHandSetModalities, WriteRole_DifferentModel_DropsPreviousModelModalities. * fix(cli): make model-set metadata operator-owned; discovery never clobbers it Hardens the non-destructive `model set`/picker rewrite (#1127) against every issue surfaced reviewing #1610, and closes the loop on modality overrides. ContextWindow and modalities are documented to "take precedence over provider-reported capability detection", so they are now treated as operator-owned overrides with a single precedence rule: explicit operator input > existing stored value > probe. A fresh probe seeds a first-time set or a model switch but never overwrites a value already on disk. Changes: 1. ContextWindow clamp preserved on same-model re-set. WriteRole takes the explicit --context-window and the probe default separately; the old callers collapsed them (`contextWindow ?? discovered`), so probe/picker paths always passed a non-null value and the operator's clamp was overwritten on every re-selection. 2. Modalities are no longer silently overwritten by discovery. Previously a probe that reported modalities replaced a stored override (the #1127 loss's twin); now the stored value wins, matching the field's "manual override bypasses detection" contract. 3. Operators can change/remove those overrides. Since discovery no longer edits them, add `--input-modalities`, `--output-modalities`, and `--clear-modalities` to `model set`. Explicit set replaces the stored value; clear removes it (runtime detection resolves). Supplying any of them (like --context-window) skips the probe as manual configuration. 4. Corrupt/legacy existing entry no longer aborts the command. ReadSameModelEntry guards the deserialize (catch JsonException): an unreadable entry (e.g. an unrecognized modality enum string) degrades to "nothing to preserve" and the command overwrites/repairs it. 5. No false-match on ModelReference defaults. Provider/ModelId default to the stock local-ollama model, so an entry omitting either key deserialized to that default and would false-match a re-set of the stock model; preservation now requires both keys. 6. Provenance not downgraded. A same-model re-set that did not re-resolve the ID (no probe → Manual) keeps a previously discovered origin (Live/Defaults); only a fresh discovery updates it. Tests: ModelEntryWriter unit coverage for each precedence path (clamp-over-probe, probe- does-not-override-existing-modalities, explicit set, clear-over-probe, first-time seeding, default-model false-match, corrupt-entry overwrite, provenance preserve/update) plus CLI end-to-end coverage for the new flags. Full CLI suite green; slopwatch clean; model-manager smoke tape passes. * fix(cli): harden model-set overrides + add --clear-context-window (#1610) Addresses code-review findings on the non-destructive model-set change: - probe gate: only --context-window short-circuits the probe; a modality flag no longer skips model-existence validation and context-window discovery - preservation read: a corrupt modality enum string no longer discards a valid operator-owned ContextWindow (field-tolerant recovery) - arg parsing: missing flag values and unknown args fail loudly instead of being silently dropped - cleared modality is now sticky: discovery is hands-off once a same-model entry exists, so a later probe cannot resurrect a --clear-modalities removal - TryParseModalities rejects raw numeric strings (named flags only) - provenance: preserve a prior discovered origin on any non-Live re-set (was only guarding Manual) - new --clear-context-window flag to force window re-detection (symmetry with --clear-modalities) Also hardens LoadModelSelection: a corrupt/legacy config no longer crashes `model set` (repairs it) or `model list` (reports it cleanly) or the TUI. Generalizes ModalityOverride into a shared ValueOverride tri-state. Updates netclaw-operations skill (providers.md). Docs website tracked in netclaw-dev/netclaw-website#83. * feat(config): preserve model definitions across role switches * fix(config): validate named model role references * fix(cli): preserve models when editing providers * chore(deps): bump dotnet-sdk from 10.0.300 to 10.0.301 (#1381) Bumps [dotnet-sdk](https://github.com/dotnet/sdk) from 10.0.300 to 10.0.301. - [Release notes](https://github.com/dotnet/sdk/releases) - [Commits](https://github.com/dotnet/sdk/compare/v10.0.300...v10.0.301) --- updated-dependencies: - dependency-name: dotnet-sdk dependency-version: 10.0.301 dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * fix(subagents): fail closed for unattended approvals (#1616) * chore(release): prepare 0.25.0-alpha.onnx.5 experimental prerelease --------- Signed-off-by: Aaron Stannard Signed-off-by: dependabot[bot] Co-authored-by: petabridge-netclaw[bot] <289234546+petabridge-netclaw[bot]@users.noreply.github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/pr_validation.yml | 5 + .github/workflows/smoke.yml | 5 + Directory.Build.props | 2 +- Directory.Packages.props | 7 +- RELEASE_NOTES.md | 23 + docs/integrations/discord-channel.md | 15 +- docs/spec/configuration.md | 49 +- .../references/providers.md | 31 +- .../references/scheduling.md | 4 + global.json | 2 +- .../.openspec.yaml | 2 + .../design.md | 59 +++ .../proposal.md | 34 ++ .../specs/named-model-definitions/spec.md | 50 ++ .../specs/netclaw-cli/spec.md | 15 + .../specs/netclaw-model-providers/spec.md | 21 + .../specs/netclaw-testing/spec.md | 16 + .../tasks.md | 20 + scripts/docker/test-model-config-upgrade.sh | 92 ++++ .../SlackSessionBindingContractTests.cs | 96 ++++ .../DiscordReminderTargetResolverTests.cs | 79 ++- .../Reminders/SetReminderToolTests.cs | 95 +++- .../SessionToolExecutionPipelineTests.cs | 68 +++ .../SubAgents/SubAgentSpawnerTests.cs | 100 ++++ .../Reminders/SetReminderTool.cs | 9 +- .../Pipelines/SessionToolExecutionPipeline.cs | 4 +- .../SubAgents/SubAgentSpawner.cs | 7 +- .../DiscordReminderTargetResolver.cs | 89 +++- .../SlackThreadBindingActor.cs | 39 +- .../Config/ModelEntryWriterTests.cs | 327 ++++++++++++- .../Doctor/DoctorFixServiceTests.cs | 35 ++ .../Model/ModelCommandTests.cs | 266 ++++++++++- .../Tui/ModelManagerViewModelTests.cs | 43 +- .../Tui/Wizard/SectionEditorLeafTests.cs | 2 + .../Tui/Wizard/WizardConfigBuilderTests.cs | 4 +- src/Netclaw.Cli/Config/ConfigFileHelper.cs | 32 +- src/Netclaw.Cli/Config/ModelEntryWriter.cs | 452 ++++++++++++++++++ .../Doctor/ChatClientDoctorCheck.cs | 2 +- .../Doctor/ContextWindowDoctorCheck.cs | 18 +- src/Netclaw.Cli/Doctor/DoctorFixService.cs | 35 +- src/Netclaw.Cli/Model/ModelCommand.cs | 260 ++++++++-- src/Netclaw.Cli/Program.cs | 3 +- src/Netclaw.Cli/Provider/ProviderRenamer.cs | 21 + src/Netclaw.Cli/Tui/ModelManagerViewModel.cs | 27 +- .../Tui/Wizard/Steps/ProviderStepViewModel.cs | 8 +- .../Tui/Wizard/WizardConfigBuilder.cs | 17 +- .../NamedModelConfigurationTests.cs | 86 ++++ .../ProviderRuntimeValidationTests.cs | 34 ++ .../NamedModelConfiguration.cs | 126 +++++ .../ProviderRuntimeValidation.cs | 55 +++ .../Schemas/netclaw-config.v1.schema.json | 42 +- ...hannelIntegrationRegistrationExtensions.cs | 2 +- .../Configuration/RemoteChatChannelBuilder.cs | 11 + src/Netclaw.Daemon/Program.cs | 14 +- tests/smoke/assertions/init-wizard.sh | 5 +- 55 files changed, 2791 insertions(+), 174 deletions(-) create mode 100644 openspec/changes/preserve-model-definitions-across-role-switches/.openspec.yaml create mode 100644 openspec/changes/preserve-model-definitions-across-role-switches/design.md create mode 100644 openspec/changes/preserve-model-definitions-across-role-switches/proposal.md create mode 100644 openspec/changes/preserve-model-definitions-across-role-switches/specs/named-model-definitions/spec.md create mode 100644 openspec/changes/preserve-model-definitions-across-role-switches/specs/netclaw-cli/spec.md create mode 100644 openspec/changes/preserve-model-definitions-across-role-switches/specs/netclaw-model-providers/spec.md create mode 100644 openspec/changes/preserve-model-definitions-across-role-switches/specs/netclaw-testing/spec.md create mode 100644 openspec/changes/preserve-model-definitions-across-role-switches/tasks.md create mode 100755 scripts/docker/test-model-config-upgrade.sh create mode 100644 src/Netclaw.Configuration.Tests/NamedModelConfigurationTests.cs create mode 100644 src/Netclaw.Configuration/NamedModelConfiguration.cs diff --git a/.github/workflows/pr_validation.yml b/.github/workflows/pr_validation.yml index f3e7ca2be..926626864 100644 --- a/.github/workflows/pr_validation.yml +++ b/.github/workflows/pr_validation.yml @@ -1,6 +1,11 @@ name: pr_validation on: + # Merge queues test a synthetic merge group rather than the pull request ref. + # Required checks must subscribe to this event or queued PRs will never merge. + merge_group: + types: + - checks_requested push: branches: - master diff --git a/.github/workflows/smoke.yml b/.github/workflows/smoke.yml index e7a61b259..5e9062704 100644 --- a/.github/workflows/smoke.yml +++ b/.github/workflows/smoke.yml @@ -11,6 +11,11 @@ name: smoke # surface via GitHub's built-in Actions notifications. on: + # Merge queues test a synthetic merge group rather than the pull request ref. + # Required checks must subscribe to this event or queued PRs will never merge. + merge_group: + types: + - checks_requested workflow_dispatch: schedule: # 07:00 UTC daily — catches external-dependency drift during quiet diff --git a/Directory.Build.props b/Directory.Build.props index 3d967ef63..a9f59e71b 100644 --- a/Directory.Build.props +++ b/Directory.Build.props @@ -9,7 +9,7 @@ enable true 0.25.0 - alpha.onnx.4 + alpha.onnx.5 Netclaw v0.25.0-beta.1 — SkillServer native sub-agent sync, memory curation unification, systemd PATH fix **Features** diff --git a/Directory.Packages.props b/Directory.Packages.props index 88d7af217..1cd139e82 100644 --- a/Directory.Packages.props +++ b/Directory.Packages.props @@ -19,6 +19,7 @@ that trips NU1902 under our TreatWarningsAsErrors policy. --> 13.4.6 13.4.0 + 1.4.1 @@ -43,8 +44,8 @@ - - + + @@ -96,7 +97,7 @@ trips NU1903 (GHSA-hv8m-jj95-wg3x, LZ4 decompression AccessViolation). Keep this on the patched 3.x line already used by dev until Aspire ships a non-vulnerable transitive dependency on its own. --> - + -- Never state runtime facts (versions, status, availability) without checking with a tool. -- Never claim you performed an action unless your tool call history shows you did. -- Never claim a tool doesn't exist without calling search_tools first. -- Never silently substitute a different answer. If you can't complete the actual task, - say so explicitly. Don't present results from a different source as if they answer - the original question. Tell the user what failed and ask how to proceed. -- "I don't know" beats a confident wrong answer. +## Recurring Workflows -## Search Decision Rules + -Use web_search IMMEDIATELY (do not ask first) when the user's question involves: -- Prices, availability, stock, deals, or comparisons -- Current events, news, or anything that changes over time -- Specific products, services, businesses, or competitors -- Travel: flights, hotels, bookings, availability -- Local info: restaurants, stores, services near a location -- Any verifiable factual claim you are not certain of +## Skill Selection -Do NOT search for: stable concepts, definitions, how-things-work, math, coding, opinions. + -When in doubt, search. A redundant search costs seconds; a hallucinated fact costs trust. +## Delegation -After searching: every specific claim MUST include an inline hyperlink to its source. -Format: [descriptive text](url) — no footnotes, no [1]-style references. -No URL means do not state the fact. + -**Full citation & search guidance:** `file_read("{{SYSTEM_SKILLS_DIR}}/search-citation/SKILL.md")` +## Review and Quality Gates -## Media Attachments + -When a user sends an image or file, it is saved to the session media directory. -The exact path is provided in the [session] context block each turn as media_dir. -Use shell_execute to list files there, then process with available tools. -Do not claim you cannot access user-attached media. +## Organizational Conventions -## Scheduling - -When the user says "remind me", "every day at", "check this weekly", "schedule", -or any time-based instruction: use set_reminder immediately. Do not explain how -reminders work — create the reminder. - -**Full scheduling parameters, CLI commands, and Netclaw operations:** -`file_read("{{SYSTEM_SKILLS_DIR}}/netclaw-operations/SKILL.md")` - -## Proactive Check-Back - -When you kick off work that will complete asynchronously — builds, CI pipelines, -deployments, long-running shell commands, or external jobs — schedule a check-back -reminder before reporting that the job started. Do not wait for the user to ask -"is it done yet?" - -Use `current_session` delivery so the follow-up lands in the same thread: -1. Start the job -2. Estimate completion time from context (build size, typical CI duration, history) -3. Call `set_reminder` with `schedule: once`, `delivery_kind: current_session`, - and `delivery_instructions` describing what to check -4. Tell the user the job is running and when you'll report back - -If the check-back finds the job still running, schedule another — do not leave the -user hanging. If the user re-engages before the timer fires, cancel the reminder. - -Do not schedule check-backs for synchronous operations, commands under ~30 seconds, -or one-off lookups where the user is actively waiting. - -## Background Shell Execution - -Shell commands expected to run longer than the session timeout can be submitted -as background jobs using `_background: true` in the shell_execute tool call -metadata. Background jobs run independently of the session — results are -delivered asynchronously when the job completes. - -**Rules:** -- Only `shell_execute` supports background mode. Other tools ignore `_background`. -- `_timeout_seconds` alone does NOT trigger background execution. You must - explicitly set `_background: true`. -- Approval gates are evaluated before job submission — the user must approve - the command before it starts running in the background. -- Use `check_background_job` to query status or cancel a running job. -- Schedule a check-back reminder for background jobs so you report results - proactively. - -## Subagent Delegation - -Use spawn_agent to delegate bounded, self-contained tasks to specialist subagents. -Available subagents are listed in the [available-subagents] context block. -Delegation protects this session's context window from token-heavy work — a -subagent returns a synthesized summary, not a transcript. - -**When to delegate:** -- Research requiring 2+ sources or multiple searches -- Parallelizable tasks (multiple independent queries can run concurrently) -- Any work that would otherwise pull large files or web pages into this - session's context — the subagent reads them, you get the synthesis -- Background prep work that doesn't block immediate response -- Code analysis on large files or multiple files -- Summarization of long documents or web pages -- Preliminary passes on topics before diving deep - -**When NOT to delegate:** -- Simple single searches (use web_search directly) -- Tasks requiring tools outside the current audience/profile policy -- Interactive browser tasks when the current audience/profile does not expose browser tools -- Tasks where coordination overhead outweighs parallelization benefits - -**Per-call specialization:** spawn_agent accepts an optional `context` -argument — pass workspace details, the user's broader goal, or facts the -subagent would otherwise have to rediscover. Use it to specialize a -general-purpose subagent for the current invocation instead of authoring -a whole new agent file. Do not duplicate the agent's built-in instructions. - -**Parallelization tip:** When researching multiple independent topics, spawn -separate subagents for each — they run concurrently and reduce total wait time. - -spawn_agent is NOT the same as search_tools. Subagents are named specialists -(e.g., "research-assistant", "code-analyst", "summarizer"). MCP tools are -discovered via search_tools. - -**Creating custom subagents:** Prefer specializing existing agents via `context` first. -When you need a new agent, see `file_read("{{SYSTEM_SKILLS_DIR}}/subagent-authoring/SKILL.md")` - -## Skill Reference - -BEFORE answering questions about scheduling, memory, search, or operations topics, -load the relevant skill via file_read to get accurate instructions: - -| BEFORE you... | Load skill first | -|---------------|------------------| -| Schedule reminders, set timers, create cron jobs | `{{SYSTEM_SKILLS_DIR}}/netclaw-operations/SKILL.md` | -| Web search, verify facts, cite sources, compare prices | `{{SYSTEM_SKILLS_DIR}}/search-citation/SKILL.md` | -| Answer what you remember, save knowledge, recall past sessions | `{{SYSTEM_SKILLS_DIR}}/netclaw-memory/SKILL.md` | -| Discover MCP tools, check daemon health, diagnose issues | `{{SYSTEM_SKILLS_DIR}}/netclaw-operations/SKILL.md` | -| Update user preferences, profile, tone, workflow rules | `{{SYSTEM_SKILLS_DIR}}/netclaw-identity/SKILL.md` | -| Create a repeatable workflow as a skill file | `{{SYSTEM_SKILLS_DIR}}/skill-authoring/SKILL.md` | -| Reference a project, organize work, set up a workspace | `{{SYSTEM_SKILLS_DIR}}/netclaw-projects/SKILL.md` | -| Create, edit, or debug a subagent definition | `{{SYSTEM_SKILLS_DIR}}/subagent-authoring/SKILL.md` | - -## Identity Files - -Identity configuration lives in `{{IDENTITY_DIR}}/`: - -| File | Purpose | -|------|---------| -| `{{SOUL_PATH}}` | Personality, tone, user profile | -| `{{AGENTS_PATH}}` | Operating rules, meta-guidance (this file) | -| `{{TOOLING_PATH}}` | Host environment capabilities | - -To update these files, use `file_read` to check current content first, then `file_write` to update. -Keep top-level files concise. For depth, create detail files in matching subdirectories: -`{{SOUL_DETAIL_DIR}}/`, `{{AGENTS_DETAIL_DIR}}/`, `{{TOOLING_DETAIL_DIR}}/` - -## Memory Triage - -| Information Type | Destination | -|-----------------|-------------| -| Personal facts (name, family, preferences) | `SOUL.md` | -| Operating rules, workflow preferences | `AGENTS.md` | -| Environment capabilities, tool configs | `TOOLING.md` | -| World knowledge, project details, solutions | Memory tools (`store_memory`, `find_memories`) | -| Procedures, reusable workflows | Skill files in `{{SKILLS_DIR}}/` | - -## Cross-Session Memory - -Use `find_memories` to recall information from prior sessions, saved knowledge, -or project context. Save important findings proactively with `store_memory`. + diff --git a/src/Netclaw.Cli/Tui/Wizard/Steps/IdentityStepViewModel.cs b/src/Netclaw.Cli/Tui/Wizard/Steps/IdentityStepViewModel.cs index 95e50bf4a..ace46c2fd 100644 --- a/src/Netclaw.Cli/Tui/Wizard/Steps/IdentityStepViewModel.cs +++ b/src/Netclaw.Cli/Tui/Wizard/Steps/IdentityStepViewModel.cs @@ -135,10 +135,9 @@ public SectionContribution BuildContribution(IWizardStepViewModel editor) } /// - /// Write SOUL.md and TOOLING.md identity files. Called during config finalization. + /// Write SOUL.md and TOOLING.md identity files and seed the deployment playbook. /// Reads templates from embedded resources and substitutes placeholders. - /// AGENTS.md is no longer written to disk — it is loaded from embedded resources - /// in per audience at runtime. + /// Existing AGENTS.md content is operator-owned and is never overwritten. /// public void WriteIdentityFiles(NetclawPaths paths) { @@ -180,6 +179,12 @@ public void WriteIdentityFiles(NetclawPaths paths) File.WriteAllText(paths.ToolingPath, SubstitutePlaceholders( ReadEmbeddedTemplate("TOOLING.template.md"), substitutions)); + + if (!File.Exists(paths.AgentsPath)) + { + File.WriteAllText(paths.AgentsPath, SubstitutePlaceholders( + ReadEmbeddedTemplate("AGENTS.template.md"), substitutions)); + } } private static string ReadEmbeddedTemplate(string fileName) @@ -210,16 +215,20 @@ public string BuildOnboardingTrigger(NetclawPaths paths) var userName = string.IsNullOrWhiteSpace(UserName) ? "User" : UserName; var commStyle = CommunicationStyle ?? "Concise & casual"; var soulPath = paths.SoulPath; + var agentsPath = paths.AgentsPath; return $""" I just finished setting up. My name is {userName} and I chose "{commStyle}" as my communication style. - This is our first conversation. I'd like you to get to know me so you can be more helpful. Please: + This is our first conversation. I'd like you to learn both who I am and what mission this deployment should perform. Please: 1. Introduce yourself briefly 2. Ask me what I'd primarily like to use you for - 3. Ask if there's anything else you should know about me — my background, how I work, tools I use, preferences, etc. - 4. After our conversation, update my profile in SOUL.md ({soulPath}) with what you've learned. Use file_read to check current content first, then file_write to update it. Keep the existing structure but enrich it with the details from our conversation. + 3. Ask what successful work looks like, which workflows recur, which skills you should use, when you should delegate, and what mistakes or quality problems you must catch before delivering work + 4. Ask what else you should know about me — my background, how I work, tools I use, and communication preferences + 5. Keep operator and personality context in SOUL.md ({soulPath}). Keep the deployment mission, workflows, skill-selection rules, delegation practices, and review gates in AGENTS.md ({agentsPath}). Never put secrets or audience-private data in AGENTS.md. + 6. When you understand the mission, summarize the playbook you propose and ask me to confirm it before writing either file. + 7. After I confirm, use file_read on both files, preserve their existing structure and useful content, then use file_write to update them. Tell me the new playbook will apply on my next message. Keep it natural and conversational — don't ask everything at once. """; diff --git a/src/Netclaw.Configuration.Tests/FileSystemPromptProviderAudienceTests.cs b/src/Netclaw.Configuration.Tests/FileSystemPromptProviderAudienceTests.cs index 00100660a..f1229c2ea 100644 --- a/src/Netclaw.Configuration.Tests/FileSystemPromptProviderAudienceTests.cs +++ b/src/Netclaw.Configuration.Tests/FileSystemPromptProviderAudienceTests.cs @@ -117,20 +117,75 @@ public void Personal_audience_includes_project_instructions() } [Fact] - public void Placeholder_substitution_replaces_path_tokens_for_team() + public void Placeholder_substitution_replaces_identity_tokens_without_exposing_skill_root() { var prompt = _provider.GetSystemPrompt(TrustAudience.Team); - // Full AGENTS.md contains placeholders like {{SYSTEM_SKILLS_DIR}} that - // should be resolved to actual paths from NetclawPaths + // Identity paths remain explicit because file tools edit them directly. + // Skill access is logical, so the physical system-skill root stays absent. Assert.DoesNotContain("{{SYSTEM_SKILLS_DIR}}", prompt); Assert.DoesNotContain("{{IDENTITY_DIR}}", prompt); Assert.DoesNotContain("{{SOUL_PATH}}", prompt); Assert.DoesNotContain("{{AGENTS_PATH}}", prompt); Assert.DoesNotContain("{{TOOLING_PATH}}", prompt); - // Verify the actual paths appear in the substituted output - Assert.Contains(_paths.SystemSkillsDirectory, prompt); Assert.Contains(_paths.IdentityDirectory, prompt); + Assert.DoesNotContain(_paths.SystemSkillsDirectory, prompt); + } + + [Theory] + [InlineData(TrustAudience.Public)] + [InlineData(TrustAudience.Team)] + [InlineData(TrustAudience.Personal)] + public void Every_audience_gets_deployment_playbook_after_embedded_core(TrustAudience audience) + { + File.WriteAllText(_paths.AgentsPath, + "Always review customer email before delivery. Identity: {{IDENTITY_DIR}}"); + + var prompt = _provider.GetSystemPrompt(audience); + + var embeddedIndex = prompt.IndexOf("Operating Rules", StringComparison.Ordinal); + var headingIndex = prompt.IndexOf("Deployment Mission and Operating Playbook", StringComparison.Ordinal); + var playbookIndex = prompt.IndexOf("Always review customer email", StringComparison.Ordinal); + Assert.True(embeddedIndex >= 0); + Assert.True(headingIndex > embeddedIndex); + Assert.True(playbookIndex > headingIndex); + Assert.Contains(_paths.IdentityDirectory, prompt); + Assert.DoesNotContain("{{IDENTITY_DIR}}", prompt); + } + + [Theory] + [InlineData(TrustAudience.Public)] + [InlineData(TrustAudience.Team)] + [InlineData(TrustAudience.Personal)] + public void Operating_rules_include_deployment_playbook_for_every_audience(TrustAudience audience) + { + File.WriteAllText(_paths.AgentsPath, "Use the deployment review checklist."); + + var rules = _provider.GetOperatingRules(audience); + + Assert.NotNull(rules); + Assert.Contains("Operating Rules", rules); + Assert.Contains("Use the deployment review checklist.", rules); + } + + [Fact] + public void Missing_deployment_playbook_uses_embedded_rules_only() + { + var rules = _provider.GetOperatingRules(TrustAudience.Team); + + Assert.NotNull(rules); + Assert.Contains("Operating Rules", rules); + Assert.DoesNotContain("Deployment Mission and Operating Playbook", rules); + } + + [Fact] + public void Unreadable_deployment_playbook_is_not_silently_skipped() + { + File.WriteAllText(_paths.AgentsPath, "Mission"); + using var locked = new FileStream( + _paths.AgentsPath, FileMode.Open, FileAccess.ReadWrite, FileShare.None); + + Assert.Throws(() => _provider.GetSystemPrompt(TrustAudience.Team)); } } diff --git a/src/Netclaw.Configuration/ISystemPromptProvider.cs b/src/Netclaw.Configuration/ISystemPromptProvider.cs index c26702882..ba4475648 100644 --- a/src/Netclaw.Configuration/ISystemPromptProvider.cs +++ b/src/Netclaw.Configuration/ISystemPromptProvider.cs @@ -26,9 +26,9 @@ public interface ISystemPromptProvider string? GetProjectInstructions(TrustAudience audience, string? projectDirectory); /// - /// Get the embedded AGENTS.md operating rules for the given audience. - /// Returns null for Public audience; substituted content for Team/Personal. - /// Used by sub-agents to inherit the core safety/operating policy layer. + /// Get the audience-specific embedded operating core followed by the optional + /// deployment AGENTS.md playbook. Used by sub-agents to inherit platform rules + /// and the operator's mission without receiving SOUL.md or TOOLING.md. /// string? GetOperatingRules(TrustAudience audience); } @@ -161,8 +161,9 @@ public string GetContextLayer(TrustAudience audience) /// /// Loads system prompt layers from the filesystem under . -/// AGENTS.md is loaded from embedded resources per audience — Public gets a stripped-down version, -/// Team/Personal get the full version with placeholder substitution. +/// Operating rules combine audience-specific embedded resources with an optional deployment +/// playbook from disk. Public gets a stripped-down embedded core; all audiences receive the same +/// operator-authored deployment mission when present. /// Missing files are silently skipped. Falls back to legacy soul/ paths for SOUL.md if identity /// files don't exist yet. /// @@ -189,10 +190,9 @@ public string GetSystemPrompt(TrustAudience audience, string? projectDirectory = // SOUL.md: always from disk, all audiences var soul = TryReadFile(_paths.SoulPath) ?? TryReadFile(_paths.PersonalityPath); - // AGENTS.md: from embedded resources, audience-dependent - var agents = audience == TrustAudience.Public - ? CachedAgentsPublic.Value - : SubstitutePlaceholders(CachedAgents.Value); + // Operating rules: audience-specific embedded machinery followed by the + // deployment's mission and workflow playbook. + var agents = ComposeOperatingRules(audience); // TOOLING.md and project instructions: suppressed for Public string? tooling = null; @@ -220,10 +220,35 @@ public string GetSystemPrompt(TrustAudience audience, string? projectDirectory = public string? GetOperatingRules(TrustAudience audience) { - if (audience == TrustAudience.Public) + return ComposeOperatingRules(audience); + } + + private string ComposeOperatingRules(TrustAudience audience) + { + var embedded = audience == TrustAudience.Public + ? CachedAgentsPublic.Value + : CachedAgents.Value; + var embeddedRules = SubstitutePlaceholders(embedded); + var deploymentPlaybook = ReadDeploymentPlaybook(); + + if (string.IsNullOrWhiteSpace(deploymentPlaybook)) + return embeddedRules; + + return string.Concat( + embeddedRules.TrimEnd(), + "\n\n# Deployment Mission and Operating Playbook\n\n", + SubstitutePlaceholders(deploymentPlaybook).Trim()); + } + + private string? ReadDeploymentPlaybook() + { + if (!File.Exists(_paths.AgentsPath)) return null; - return SubstitutePlaceholders(CachedAgents.Value); + // Unlike an absent optional file, an unreadable configured playbook is an + // operational failure. Propagate the exception instead of silently dropping + // the deployment's mission and quality controls. + return File.ReadAllText(_paths.AgentsPath); } /// diff --git a/src/Netclaw.Configuration/Resources/AGENTS.md b/src/Netclaw.Configuration/Resources/AGENTS.md index a0666f6de..e322bb40d 100644 --- a/src/Netclaw.Configuration/Resources/AGENTS.md +++ b/src/Netclaw.Configuration/Resources/AGENTS.md @@ -85,7 +85,7 @@ After searching: every specific claim MUST include an inline hyperlink to its so Format: [descriptive text](url) — no footnotes, no [1]-style references. No URL means do not state the fact. -**Full citation & search guidance:** `file_read("{{SYSTEM_SKILLS_DIR}}/search-citation/SKILL.md")` +**Full citation & search guidance:** `skill_load(name="search-citation")` ## Media Attachments @@ -106,7 +106,7 @@ commands in the current session first to trigger and persist approval. If unsure what commands the reminder will need, execute a dry-run now. **Full scheduling parameters, CLI commands, and Netclaw operations:** -`file_read("{{SYSTEM_SKILLS_DIR}}/netclaw-operations/SKILL.md")` +`skill_load(name="netclaw-operations")` ## Proactive Check-Back @@ -207,7 +207,7 @@ spawn_agent is NOT the same as search_tools. Subagents are named specialists discovered via search_tools. **Creating custom subagents:** Prefer specializing existing agents via `context` first. -When you need a new agent, see `file_read("{{SYSTEM_SKILLS_DIR}}/subagent-authoring/SKILL.md")` +When you need a new agent, call `skill_load(name="subagent-authoring")`. ## Skill Loading (MANDATORY) @@ -219,7 +219,6 @@ generating any answer text. - Web search, facts, citations, sources, prices → skill_load(name="search-citation") - Memory, what you remember, recall, past sessions → skill_load(name="netclaw-memory") - Daemon health, diagnostics, MCP tools, troubleshooting → skill_load(name="netclaw-operations") -- Identity, preferences, profile, tone → skill_load(name="netclaw-identity") - Skill creation, workflows, automation → skill_load(name="skill-authoring") - Projects, workspaces, project setup → skill_load(name="netclaw-projects") - JS-heavy sites, browser, social media fetching → skill_load(name="web-content-retrieval") @@ -232,13 +231,21 @@ If unsure whether a skill applies, load it — a redundant load costs nothing. Identity configuration lives in `{{IDENTITY_DIR}}/`: -| File | Purpose | -|------|---------| -| `{{SOUL_PATH}}` | Agent personality & tone; foundational user grounding (name, timezone) | -| `{{AGENTS_PATH}}` | Operating rules, meta-guidance (this file) | -| `{{TOOLING_PATH}}` | Host environment capabilities | - -To update these files, use `file_read` to check current content first, then `file_write` to update. +- `{{SOUL_PATH}}` defines who the agent is and who it serves: personality, + tone, operator identity, and communication style. +- `{{AGENTS_PATH}}` defines how the deployment performs its mission: recurring + workflows, skill selection, delegation, and review or quality gates. +- `{{TOOLING_PATH}}` defines what the agent can use: host capabilities, + available tools, and environment configuration. + +The embedded operating core you are reading defines Netclaw's machinery and has +priority over conflicting deployment guidance. `{{AGENTS_PATH}}` augments that +core with the operator's mission; it cannot relax runtime ACL, approval, or tool +policy. Because the deployment playbook can reach every configured audience and +sub-agent, never store secrets or audience-private data in it. + +To update identity files, use `file_read` to check current content first, propose +mission changes for operator confirmation, then use `file_write` to update. Keep top-level files concise. For depth, create detail files in matching subdirectories: `{{SOUL_DETAIL_DIR}}/`, `{{AGENTS_DETAIL_DIR}}/`, `{{TOOLING_DETAIL_DIR}}/` @@ -247,7 +254,7 @@ Keep top-level files concise. For depth, create detail files in matching subdire | Information Type | Destination | |-----------------|-------------| | Agent personality & tone; user's name/timezone (set at init) | `SOUL.md` | -| Agent operating rules & conventions | `AGENTS.md` | +| Deployment mission, workflows, skill selection, delegation, quality gates | `AGENTS.md` | | Environment capabilities, tool configs | `TOOLING.md` | | Durable facts & preferences about the user (favorites, family, history, working preferences) | Memory tools (`store_memory`, `find_memories`) | | World knowledge, project details, solutions | Memory tools (`store_memory`, `find_memories`) | diff --git a/src/Netclaw.Daemon.Tests/Mcp/McpProcessBoundStdioTests.cs b/src/Netclaw.Daemon.Tests/Mcp/McpProcessBoundStdioTests.cs new file mode 100644 index 000000000..fbcf5baa3 --- /dev/null +++ b/src/Netclaw.Daemon.Tests/Mcp/McpProcessBoundStdioTests.cs @@ -0,0 +1,90 @@ +// ----------------------------------------------------------------------- +// +// Copyright (C) 2026 - 2026 Petabridge, LLC +// +// ----------------------------------------------------------------------- +using System.Diagnostics; +using System.Text.Json; +using Netclaw.Actors.Tools; +using Netclaw.Configuration; +using Netclaw.Tools; +using Xunit; + +namespace Netclaw.Daemon.Tests.Mcp; + +public sealed class McpProcessBoundStdioTests +{ + [Fact] + public async Task DifferentSessions_UseOneConfiguredProcess_WithoutArgumentRewriting() + { + using var cts = new CancellationTokenSource(TimeSpan.FromMinutes(2)); + var entry = CreateEntry("--netclaw-pass-through-probe"); + var registry = new ToolRegistry(); + await using var harness = McpSmokeHarness.Create( + new Dictionary { ["browser_playwright"] = entry }, registry); + + await harness.Manager.StartAsync(cts.Token); + + var first = await GetProcessInfoAsync(harness, "slack/channel/thread-a", cts.Token); + var second = await GetProcessInfoAsync(harness, "slack/channel/thread-b", cts.Token); + + Assert.Equal(first.ProcessId, second.ProcessId); + Assert.Contains("--netclaw-pass-through-probe", first.Arguments); + Assert.DoesNotContain("--isolated", first.Arguments); + + using var process = Process.GetProcessById(first.ProcessId); + await harness.Manager.StopAsync(cts.Token); + await process.WaitForExitAsync(cts.Token); + Assert.True(process.HasExited); + } + + [Fact] + public async Task ExplicitIsolatedArgument_IsPreservedExactlyOnce() + { + using var cts = new CancellationTokenSource(TimeSpan.FromMinutes(2)); + var registry = new ToolRegistry(); + await using var harness = McpSmokeHarness.Create( + new Dictionary + { + ["browser_playwright"] = CreateEntry("--isolated"), + }, + registry); + + await harness.Manager.StartAsync(cts.Token); + + var info = await GetProcessInfoAsync(harness, "slack/channel/thread", cts.Token); + + Assert.Single(info.Arguments, argument => argument == "--isolated"); + } + + private static McpServerEntry CreateEntry(params string[] extraArguments) + => new() + { + Transport = "stdio", + Command = "dotnet", + Arguments = [SmokeMcpServerLocator.LocateDll(), .. extraArguments], + Enabled = true, + }; + + private static async Task GetProcessInfoAsync( + McpSmokeHarness harness, + string sessionId, + CancellationToken ct) + { + var result = await harness.Manager.InvokeAsync( + "browser_playwright", + "process-info", + null, + new ToolExecutionContext(sessionId, null) { Audience = TrustAudience.Personal }, + ct); + + return JsonSerializer.Deserialize(result, JsonOptions)!; + } + + private static readonly JsonSerializerOptions JsonOptions = new() + { + PropertyNameCaseInsensitive = true, + }; + + private sealed record ProcessInfo(int ProcessId, string[] Arguments); +} diff --git a/src/Netclaw.Daemon.Tests/Services/SystemSkillSyncServiceTests.cs b/src/Netclaw.Daemon.Tests/Services/SystemSkillSyncServiceTests.cs index 84b0d44fd..90ea64b5d 100644 --- a/src/Netclaw.Daemon.Tests/Services/SystemSkillSyncServiceTests.cs +++ b/src/Netclaw.Daemon.Tests/Services/SystemSkillSyncServiceTests.cs @@ -110,8 +110,7 @@ public async Task StartAsync_SkipFeedSync_WhenDisableSystemSkillSyncTrue() new HttpClient(handler), _paths, new SkillSyncConfig { DisableSystemSkillSync = true }, - _skillRegistry, - _skillIndexLayer, + CreateInventoryRefresher(), TimeProvider.System, _scanner, _logger, @@ -564,14 +563,20 @@ private SystemSkillSyncService CreateService(FakeHttpMessageHandler handler, str httpClient, _paths, new SkillSyncConfig(), - _skillRegistry, - _skillIndexLayer, + CreateInventoryRefresher(), TimeProvider.System, scanner ?? _scanner, _logger, daemonVersion); } + private SkillInventoryRefresher CreateInventoryRefresher() => new( + _paths, + new SkillFeedsConfig(), + [], + _skillRegistry, + _skillIndexLayer); + private SkillSyncState ReadSyncState() { var json = File.ReadAllText(_paths.SkillSyncStatePath); diff --git a/src/Netclaw.Daemon/Configuration/SkillToolRegistration.cs b/src/Netclaw.Daemon/Configuration/SkillToolRegistration.cs index d5d3141b7..45289dd33 100644 --- a/src/Netclaw.Daemon/Configuration/SkillToolRegistration.cs +++ b/src/Netclaw.Daemon/Configuration/SkillToolRegistration.cs @@ -27,12 +27,11 @@ public static void RegisterSkillTools(IServiceProvider services) { var registry = services.GetRequiredService(); var skillRegistry = services.GetRequiredService(); - var skillIndexLayer = services.GetRequiredService(); var paths = services.GetRequiredService(); var toolConfig = services.GetRequiredService(); var pathPolicy = services.GetRequiredService(); var scanner = services.GetRequiredService(); - var externalSources = services.GetRequiredService>(); + var inventoryRefresher = services.GetRequiredService(); var metrics = services.GetService(); var subAgentRegistry = services.GetService(); var subAgentSpawner = services.GetService(); @@ -45,10 +44,9 @@ public static void RegisterSkillTools(IServiceProvider services) registry.WithSkillTools( skillRegistry, - skillIndexLayer, paths, scanner, - externalSources, + inventoryRefresher, metrics, subAgentRegistry, subAgentSpawner, diff --git a/src/Netclaw.Daemon/Mcp/McpClientManager.cs b/src/Netclaw.Daemon/Mcp/McpClientManager.cs index 17dfdd08d..f63f214b8 100644 --- a/src/Netclaw.Daemon/Mcp/McpClientManager.cs +++ b/src/Netclaw.Daemon/Mcp/McpClientManager.cs @@ -17,8 +17,6 @@ namespace Netclaw.Daemon.Mcp; internal sealed class McpClientManager : IHostedService, IDisposable, IMcpToolInvoker, IMcpReconnectable { - private const string PlaywrightServerName = "browser_playwright"; - private readonly Dictionary _serverEntries; private readonly ToolRegistry _toolRegistry; private readonly ToolConfig _toolConfig; @@ -35,16 +33,6 @@ internal sealed class McpClientManager : IHostedService, IDisposable, IMcpToolIn private readonly ConcurrentDictionary _statuses = new(); - private readonly ConcurrentDictionary _sessionScopedServers = new(); - - private readonly ConcurrentDictionary>> _scopedClients = - new(StringComparer.OrdinalIgnoreCase); - - private readonly SemaphoreSlim _scopedCleanupGate = new(1, 1); - private readonly TimeSpan _scopedClientIdleTimeout = TimeSpan.FromMinutes(10); - private readonly TimeSpan _scopedCleanupInterval = TimeSpan.FromMinutes(1); - private long _nextScopedCleanupAtMs; - public McpClientManager( Dictionary serverEntries, ToolRegistry toolRegistry, @@ -74,7 +62,6 @@ public async Task StartAsync(CancellationToken cancellationToken) if (!entry.Enabled) { _statuses[serverName] = new McpServerStatus(serverName, McpConnectionState.Disabled, 0, null); - _sessionScopedServers.TryRemove(serverName, out _); _logger.LogInformation("MCP server '{Name}' is disabled, skipping", name); continue; } @@ -100,9 +87,6 @@ public async Task StopAsync(CancellationToken cancellationToken) _clients.Clear(); _sharedToolFunctions.Clear(); - _sessionScopedServers.Clear(); - - await DisposeAllScopedClientsAsync(); } public McpClient? GetClient(McpServerName serverName) @@ -135,7 +119,6 @@ public async Task TryReconnectAsync(McpServerName serverName, Cancellation } _sharedToolFunctions.TryRemove(serverName, out _); - await DisposeScopedClientsForServerAsync(serverName); return await ConnectAsync(serverName, entry, ct); } @@ -150,9 +133,6 @@ public async Task InvokeAsync( var server = new McpServerName(serverName); var tool = new ToolName(toolName); - if (UsesSessionScopedClient(server)) - return await InvokeScopedAsync(server, tool, arguments, context, ct); - return await InvokeSharedAsync(server, tool, arguments, ct); } @@ -194,38 +174,6 @@ private async Task InvokeSharedAsync( } } - private async Task InvokeScopedAsync( - McpServerName serverName, - ToolName toolName, - IDictionary? arguments, - ToolExecutionContext? context, - CancellationToken ct) - { - var scopeId = ResolveScopeId(context); - var handle = await GetOrCreateScopedClientHandleAsync(serverName, scopeId, ct); - - await CleanupIdleScopedClientsIfDueAsync(ct); - await handle.ExecutionGate.WaitAsync(ct); - - try - { - handle.Touch(_timeProvider.GetUtcNow()); - - if (!handle.Tools.TryGetValue(toolName.Value, out var function)) - { - throw new InvalidOperationException( - $"MCP tool '{toolName.Value}' is not available on server '{serverName.Value}'."); - } - - return await InvokeFunctionAsync(function, $"{serverName.Value}/{toolName.Value}", arguments, ct); - } - finally - { - handle.Touch(_timeProvider.GetUtcNow()); - handle.ExecutionGate.Release(); - } - } - // qualifiedToolName is the server-qualified "server/tool" name (not the bare // function.Name, which omits the server) so MCP error attribution matches the // bound-tool path (McpToolAdapter.Name) — otherwise the same error renders @@ -256,160 +204,6 @@ private bool TryGetSharedFunction(McpServerName serverName, string toolName, out return serverTools.TryGetValue(toolName, out function); } - private bool UsesSessionScopedClient(McpServerName serverName) - { - return _sessionScopedServers.TryGetValue(serverName, out var enabled) && enabled; - } - - private async Task GetOrCreateScopedClientHandleAsync( - McpServerName serverName, - string scopeId, - CancellationToken ct) - { - var key = BuildScopedClientKey(serverName.Value, scopeId); - - while (true) - { - var lazy = _scopedClients.GetOrAdd(key, _ => - new Lazy>( - () => CreateScopedClientHandleAsync(serverName), - LazyThreadSafetyMode.ExecutionAndPublication)); - - try - { - var handle = await lazy.Value.WaitAsync(ct); - handle.Touch(_timeProvider.GetUtcNow()); - return handle; - } - catch - { - _scopedClients.TryRemove(new KeyValuePair>>(key, lazy)); - await DisposeLazyScopedHandleAsync(lazy); - throw; - } - } - } - - private async Task CreateScopedClientHandleAsync(McpServerName serverName) - { - if (!_serverEntries.TryGetValue(serverName.Value, out var entry)) - { - throw new InvalidOperationException($"MCP server '{serverName.Value}' is not configured."); - } - - var client = await CreateClientAsync(serverName, entry, CancellationToken.None, updateStatusOnAuthFailure: false); - if (client is null) - throw new InvalidOperationException($"MCP server '{serverName.Value}' requires OAuth authorization."); - - var tools = await client.ListToolsAsync(cancellationToken: CancellationToken.None); - var toolMap = CreateFunctionMap(tools); - - _logger.LogInformation( - "Created scoped MCP client for server '{ServerName}' (tools={ToolCount})", - serverName.Value, - tools.Count); - - return new ScopedClientHandle(client, toolMap, _timeProvider.GetUtcNow()); - } - - private async Task DisposeScopedClientsForServerAsync(McpServerName serverName) - { - var prefix = serverName.Value + "::"; - - foreach (var (key, _) in _scopedClients.ToArray()) - { - if (!key.StartsWith(prefix, StringComparison.OrdinalIgnoreCase)) - continue; - - if (_scopedClients.TryRemove(key, out var lazy)) - await DisposeLazyScopedHandleAsync(lazy); - } - } - - private async Task DisposeAllScopedClientsAsync() - { - foreach (var (key, _) in _scopedClients.ToArray()) - { - if (_scopedClients.TryRemove(key, out var lazy)) - await DisposeLazyScopedHandleAsync(lazy); - } - } - - private async Task DisposeLazyScopedHandleAsync(Lazy> lazy) - { - if (!lazy.IsValueCreated) - return; - - try - { - var handle = await lazy.Value; - await handle.DisposeAsync(); - } - catch (Exception ex) - { - _logger.LogDebug(ex, "Error disposing scoped MCP client handle"); - } - } - - private async Task CleanupIdleScopedClientsIfDueAsync(CancellationToken ct) - { - if (_scopedClients.IsEmpty) - return; - - var nowMs = _timeProvider.GetUtcNow().ToUnixTimeMilliseconds(); - if (nowMs < Volatile.Read(ref _nextScopedCleanupAtMs)) - return; - - if (!await _scopedCleanupGate.WaitAsync(0, ct)) - return; - - try - { - nowMs = _timeProvider.GetUtcNow().ToUnixTimeMilliseconds(); - if (nowMs < Volatile.Read(ref _nextScopedCleanupAtMs)) - return; - - Volatile.Write( - ref _nextScopedCleanupAtMs, - nowMs + (long)_scopedCleanupInterval.TotalMilliseconds); - - var idleBeforeMs = nowMs - (long)_scopedClientIdleTimeout.TotalMilliseconds; - - foreach (var (key, lazy) in _scopedClients.ToArray()) - { - if (!lazy.IsValueCreated) - continue; - - Task handleTask; - try - { - handleTask = lazy.Value; - } - catch - { - continue; - } - - if (!handleTask.IsCompletedSuccessfully) - continue; - - var handle = handleTask.Result; - if (handle.LastUsedAtMs > idleBeforeMs) - continue; - - if (handle.ExecutionGate.CurrentCount == 0) - continue; - - if (_scopedClients.TryRemove(new KeyValuePair>>(key, lazy))) - await handle.DisposeAsync(); - } - } - finally - { - _scopedCleanupGate.Release(); - } - } - private async Task ConnectAsync(McpServerName name, McpServerEntry entry, CancellationToken ct) { // Holds the client until ownership passes to _clients. If the connect @@ -425,15 +219,12 @@ private async Task ConnectAsync(McpServerName name, McpServerEntry entry, var tools = await client.ListToolsAsync(cancellationToken: ct); var sharedFunctions = CreateFunctionMap(tools); - var requiresSessionScopedClient = RequiresSessionScopedClient(name, entry); - LogToolDrift(name, tools); _toolRegistry.WithMcpTools(name.Value, tools, entry.GrantCategory, this, _maxToolDescriptionChars, _maxToolSchemaWarnChars, _logger); _sharedToolFunctions[name] = sharedFunctions; - _sessionScopedServers[name] = requiresSessionScopedClient; _clients[name] = client; client = null; _statuses[name] = new McpServerStatus(name, McpConnectionState.Connected, tools.Count, null); @@ -457,7 +248,6 @@ private async Task ConnectAsync(McpServerName name, McpServerEntry entry, } _sharedToolFunctions.TryRemove(name, out _); - _sessionScopedServers.TryRemove(name, out _); var hasCachedTokens = _oauthService.GetTokenSet(name) is not null; var hasOAuthRuntimeHints = HasOAuthRuntimeHints(name, entry); @@ -557,12 +347,10 @@ private IClientTransport CreateTransport(McpServerName serverName, McpServerEntr { if (entry.Transport is "stdio") { - var args = BuildStdioArguments(serverName, entry); - return new StdioClientTransport(new StdioClientTransportOptions { Command = entry.Command!, - Arguments = args, + Arguments = entry.Arguments ?? [], EnvironmentVariables = entry.EnvironmentVariables.ToRawNullableValues(StringComparer.OrdinalIgnoreCase), Name = serverName.Value, ShutdownTimeout = TimeSpan.FromSeconds(10), @@ -742,21 +530,6 @@ private static bool IsAuthFailure(Exception ex) return null; } - private static string[] BuildStdioArguments(McpServerName serverName, McpServerEntry entry) - { - var args = entry.Arguments is { Length: > 0 } - ? entry.Arguments.ToList() - : []; - - if (IsPlaywrightServer(serverName, entry) - && !args.Contains("--isolated", StringComparer.OrdinalIgnoreCase)) - { - args.Add("--isolated"); - } - - return [.. args]; - } - private static Dictionary CreateFunctionMap(IList tools) { var map = new Dictionary(StringComparer.OrdinalIgnoreCase); @@ -767,47 +540,6 @@ private static Dictionary CreateFunctionMap(IList IsPlaywrightServer(serverName, entry); - - private static bool IsPlaywrightServer(McpServerName serverName, McpServerEntry entry) - { - if (serverName.Value.Equals(PlaywrightServerName, StringComparison.OrdinalIgnoreCase)) - return true; - - if (!string.IsNullOrWhiteSpace(entry.Command) - && entry.Command.Contains("playwright", StringComparison.OrdinalIgnoreCase)) - { - return true; - } - - if (entry.Arguments is not { Length: > 0 }) - return false; - - foreach (var arg in entry.Arguments) - { - if (arg.Contains("@playwright/mcp", StringComparison.OrdinalIgnoreCase) - || arg.Contains("playwright/mcp", StringComparison.OrdinalIgnoreCase) - || arg.Contains("playwright-mcp", StringComparison.OrdinalIgnoreCase)) - { - return true; - } - } - - return false; - } - - private string ResolveScopeId(ToolExecutionContext? context) - { - if (!string.IsNullOrWhiteSpace(context?.SessionId)) - return context.SessionId!; - - return $"sessionless/{_timeProvider.GetUtcNow().ToUnixTimeMilliseconds()}-{Guid.NewGuid():N}"; - } - - private static string BuildScopedClientKey(string serverName, string scopeId) - => $"{serverName}::{scopeId}"; - /// /// Compares discovered tools against /// across all audience profiles and logs warnings for drift. @@ -865,75 +597,8 @@ public void Dispose() catch (Exception ex) { _logger.LogDebug(ex, "Error disposing MCP client during shutdown"); } } - foreach (var lazy in _scopedClients.Values) - { - if (!lazy.IsValueCreated) - continue; - - try - { - var task = lazy.Value; - if (!task.IsCompletedSuccessfully) - continue; - - task.Result.Dispose(); - } - catch (Exception ex) - { - _logger.LogDebug(ex, "Error disposing scoped MCP client during shutdown"); - } - } - _clients.Clear(); _sharedToolFunctions.Clear(); - _scopedClients.Clear(); - _sessionScopedServers.Clear(); - } - - private sealed class ScopedClientHandle : IAsyncDisposable, IDisposable - { - private int _disposed; - - public ScopedClientHandle( - McpClient client, - Dictionary tools, - DateTimeOffset createdAt) - { - Client = client; - Tools = tools; - Touch(createdAt); - } - - public McpClient Client { get; } - public Dictionary Tools { get; } - public SemaphoreSlim ExecutionGate { get; } = new(1, 1); - - private long _lastUsedAtMs; - - public long LastUsedAtMs => Volatile.Read(ref _lastUsedAtMs); - - public void Touch(DateTimeOffset now) - { - Volatile.Write(ref _lastUsedAtMs, now.ToUnixTimeMilliseconds()); - } - - public async ValueTask DisposeAsync() - { - if (Interlocked.Exchange(ref _disposed, 1) == 1) - return; - - ExecutionGate.Dispose(); - await Client.DisposeAsync(); - } - - public void Dispose() - { - if (Interlocked.Exchange(ref _disposed, 1) == 1) - return; - - ExecutionGate.Dispose(); - (Client as IDisposable)?.Dispose(); - } } } diff --git a/src/Netclaw.Daemon/Program.cs b/src/Netclaw.Daemon/Program.cs index 197f76822..ece75e6e3 100644 --- a/src/Netclaw.Daemon/Program.cs +++ b/src/Netclaw.Daemon/Program.cs @@ -715,21 +715,6 @@ static void ConfigureDaemonServices( .Get() ?? new SkillFeedsConfig(); services.AddSingleton(skillFeedsConfig); - var resolvedServerFeedSources = new List(); - foreach (var feed in skillFeedsConfig.Feeds.Where(f => f.Enabled)) - { - var feedDir = paths.ServerFeedDirectory(feed.Name); - if (Directory.Exists(feedDir)) - resolvedServerFeedSources.Add(new ResolvedExternalSource( - $"server-feed:{feed.Name}", [feedDir], AllowSymlinks: false)); - } - IReadOnlyList serverFeeds = resolvedServerFeedSources; - services.AddKeyedSingleton("server-feeds", serverFeeds); - - // Scan native skills first (highest precedence), then server feeds, then external sources - var initialSkillScan = SkillScanner.ScanAndMerge( - paths.SkillsDirectory, serverFeeds, resolvedExternalSources); - skillRegistry.ReplaceAll(initialSkillScan.AcceptedSkills, initialSkillScan.Issues); services.AddSingleton(skillRegistry); // Subagent definition registry and file loader @@ -883,11 +868,14 @@ static void ConfigureDaemonServices( services.AddSingleton(); services.AddSingleton(sp => sp.GetRequiredService()); - // Skill index context layer — compressed format pointing at files on disk, rebuilt by sync service + // Skill index context layer — origin-free logical catalog rebuilt with the complete inventory. var skillIndexLayer = new SkillIndexContextLayer(skillSyncConfig); - skillIndexLayer.Update(skillRegistry.GenerateIndex(paths.SkillsDirectory, resolvedExternalSources)); services.AddSingleton(skillIndexLayer); services.AddSingleton(skillIndexLayer); + var skillInventoryRefresher = new SkillInventoryRefresher( + paths, skillFeedsConfig, resolvedExternalSources, skillRegistry, skillIndexLayer); + var initialSkillScan = skillInventoryRefresher.Refresh(); + services.AddSingleton(skillInventoryRefresher); // Skill tools are registered post-build so ISkillContentScanner resolves from DI. // See SkillToolRegistration call after app.Build(). @@ -928,6 +916,7 @@ static void ConfigureDaemonServices( // Current time context layer — transient per-turn grounding for date/time-sensitive prompts services.AddSingleton(); + services.AddSingleton(); // Expose all context layers as IReadOnlyList for actor DI resolution services.AddSingleton>(sp => @@ -1036,6 +1025,7 @@ static void ConfigureDaemonServices( sp.GetRequiredService(), sp.GetRequiredService(), sp.GetRequiredService>(), + sp.GetRequiredService(), sp.GetRequiredService(), sp.GetRequiredService())); diff --git a/src/Netclaw.Daemon/Services/ServerFeedSkillSyncService.cs b/src/Netclaw.Daemon/Services/ServerFeedSkillSyncService.cs index b850e93d5..fbbc88513 100644 --- a/src/Netclaw.Daemon/Services/ServerFeedSkillSyncService.cs +++ b/src/Netclaw.Daemon/Services/ServerFeedSkillSyncService.cs @@ -31,18 +31,34 @@ internal sealed class ServerFeedSkillSyncService : BackgroundService private readonly SkillFeedsConfig _feedsConfig; private readonly NetclawPaths _paths; - private readonly SkillRegistry _skillRegistry; - private readonly SkillIndexContextLayer _skillIndexLayer; + private readonly SkillInventoryRefresher _inventoryRefresher; private readonly TimeProvider _timeProvider; private readonly ISkillContentScanner _scanner; private readonly ILogger _logger; - private readonly IReadOnlyList _externalSources; private readonly Func _clientFactory; // Random jitter (0–5 min) so multiple daemon instances don't all poll at once private readonly TimeSpan _initialJitter; public ServerFeedSkillSyncService( + SkillFeedsConfig feedsConfig, + NetclawPaths paths, + SkillInventoryRefresher inventoryRefresher, + TimeProvider timeProvider, + ISkillContentScanner scanner, + ILogger logger) + : this( + feedsConfig, + paths, + inventoryRefresher, + timeProvider, + scanner, + logger, + CreateSkillServerClient) + { + } + + internal ServerFeedSkillSyncService( SkillFeedsConfig feedsConfig, NetclawPaths paths, SkillRegistry skillRegistry, @@ -74,15 +90,33 @@ internal ServerFeedSkillSyncService( ILogger logger, IReadOnlyList externalSources, Func clientFactory) + : this( + feedsConfig, + paths, + new SkillInventoryRefresher( + paths, feedsConfig, externalSources, skillRegistry, skillIndexLayer), + timeProvider, + scanner, + logger, + clientFactory) + { + } + + private ServerFeedSkillSyncService( + SkillFeedsConfig feedsConfig, + NetclawPaths paths, + SkillInventoryRefresher inventoryRefresher, + TimeProvider timeProvider, + ISkillContentScanner scanner, + ILogger logger, + Func clientFactory) { _feedsConfig = feedsConfig; _paths = paths; - _skillRegistry = skillRegistry; - _skillIndexLayer = skillIndexLayer; + _inventoryRefresher = inventoryRefresher; _timeProvider = timeProvider; _scanner = scanner; _logger = logger; - _externalSources = externalSources; _clientFactory = clientFactory; _initialJitter = TimeSpan.FromSeconds(Random.Shared.Next(0, 300)); } @@ -804,20 +838,7 @@ private static bool IsZipSymlink(ZipArchiveEntry entry) private void RescanAndUpdateIndex() { - var resolvedServerFeeds = new List(); - foreach (var feed in _feedsConfig.Feeds.Where(f => f.Enabled)) - { - var feedDir = _paths.ServerFeedDirectory(feed.Name); - if (Directory.Exists(feedDir)) - resolvedServerFeeds.Add(new ResolvedExternalSource( - $"server-feed:{feed.Name}", [feedDir], AllowSymlinks: false)); - } - - var mergedResult = SkillScanner.ScanAndMerge( - _paths.SkillsDirectory, resolvedServerFeeds, _externalSources); - SkillRegistryUpdater.ApplyMergedScanResult( - _skillRegistry, _skillIndexLayer, mergedResult, - _paths.SkillsDirectory, _externalSources); + var mergedResult = _inventoryRefresher.Refresh(); if (mergedResult.Issues.Count > 0) { diff --git a/src/Netclaw.Daemon/Services/SkillDirectoryWatcherService.cs b/src/Netclaw.Daemon/Services/SkillDirectoryWatcherService.cs index 9bff7dcf6..d91ad480d 100644 --- a/src/Netclaw.Daemon/Services/SkillDirectoryWatcherService.cs +++ b/src/Netclaw.Daemon/Services/SkillDirectoryWatcherService.cs @@ -20,10 +20,9 @@ public sealed class SkillDirectoryWatcherService : BackgroundService private static readonly TimeSpan DebounceInterval = TimeSpan.FromMilliseconds(500); private readonly NetclawPaths _paths; - private readonly IReadOnlyList _serverFeedSources; + private readonly SkillFeedsConfig _feedsConfig; private readonly IReadOnlyList _externalSources; - private readonly SkillRegistry _registry; - private readonly SkillIndexContextLayer _indexLayer; + private readonly SkillInventoryRefresher _inventoryRefresher; private readonly ILogger _logger; private readonly List _watchers = []; @@ -33,18 +32,15 @@ public sealed class SkillDirectoryWatcherService : BackgroundService public SkillDirectoryWatcherService( NetclawPaths paths, - [Microsoft.Extensions.DependencyInjection.FromKeyedServices("server-feeds")] - IReadOnlyList serverFeedSources, + SkillFeedsConfig feedsConfig, IReadOnlyList externalSources, - SkillRegistry registry, - SkillIndexContextLayer indexLayer, + SkillInventoryRefresher inventoryRefresher, ILogger logger) { _paths = paths; - _serverFeedSources = serverFeedSources; + _feedsConfig = feedsConfig; _externalSources = externalSources; - _registry = registry; - _indexLayer = indexLayer; + _inventoryRefresher = inventoryRefresher; _logger = logger; } @@ -55,6 +51,9 @@ protected override Task ExecuteAsync(CancellationToken stoppingToken) // Watch the native skills directory TryCreateWatcher(_paths.SkillsDirectory, "native"); + foreach (var feed in _feedsConfig.Feeds.Where(static feed => feed.Enabled)) + TryCreateWatcher(_paths.ServerFeedDirectory(feed.Name), $"server-feed:{feed.Name}"); + // Watch each external source directory. A single source may cover multiple // paths (e.g. claude-code = ~/.claude/skills + ~/.claude/commands + one // path per installed plugin marketplace under @@ -162,10 +161,7 @@ private void OnDebounceTimerFired() { _logger.LogDebug("Debounce timer fired, starting skill rescan"); - var result = SkillScanner.ScanAndMerge( - _paths.SkillsDirectory, _serverFeedSources, _externalSources); - SkillRegistryUpdater.ApplyMergedScanResult( - _registry, _indexLayer, result, _paths.SkillsDirectory, _externalSources); + var result = _inventoryRefresher.Refresh(); _logger.LogInformation( "Skill directory rescan complete: {SkillCount} skills loaded, {IssueCount} issues", diff --git a/src/Netclaw.Daemon/Services/SystemSkillSyncService.cs b/src/Netclaw.Daemon/Services/SystemSkillSyncService.cs index 4249386b3..06bf33f21 100644 --- a/src/Netclaw.Daemon/Services/SystemSkillSyncService.cs +++ b/src/Netclaw.Daemon/Services/SystemSkillSyncService.cs @@ -15,8 +15,8 @@ namespace Netclaw.Daemon.Services; /// /// Syncs system skills from the feed CDN at daemon startup and rebuilds the -/// description menu context layer. The LLM discovers skills by reading the -/// menu and loads them via file_read — no keyword matching needed. +/// description menu context layer. The LLM discovers skills from the logical +/// catalog and loads them with skill_load. /// Never blocks startup on network — if the feed is unreachable, the daemon /// starts with whatever skills are already on disk. /// @@ -25,30 +25,23 @@ internal sealed class SystemSkillSyncService : IHostedService private readonly HttpClient _httpClient; private readonly NetclawPaths _paths; private readonly SkillSyncConfig _skillSyncConfig; - private readonly SkillRegistry _skillRegistry; - private readonly SkillIndexContextLayer _skillIndexLayer; + private readonly SkillInventoryRefresher _inventoryRefresher; private readonly TimeProvider _timeProvider; private readonly ILogger _logger; private readonly string _daemonVersion; private readonly ISkillContentScanner _scanner; - private readonly IReadOnlyList _serverFeedSources; - private readonly IReadOnlyList _externalSources; public SystemSkillSyncService( HttpClient httpClient, NetclawPaths paths, SkillSyncConfig skillSyncConfig, - SkillRegistry skillRegistry, - SkillIndexContextLayer skillIndexLayer, + SkillInventoryRefresher inventoryRefresher, TimeProvider timeProvider, ISkillContentScanner scanner, ILogger logger, - [Microsoft.Extensions.DependencyInjection.FromKeyedServices("server-feeds")] - IReadOnlyList serverFeedSources, - IReadOnlyList externalSources, IChatClientProvider? chatClientProvider = null) - : this(httpClient, paths, skillSyncConfig, skillRegistry, skillIndexLayer, - timeProvider, scanner, logger, BuildInfo.Version, serverFeedSources, externalSources) + : this(httpClient, paths, skillSyncConfig, inventoryRefresher, + timeProvider, scanner, logger, BuildInfo.Version) { } @@ -57,26 +50,20 @@ internal SystemSkillSyncService( HttpClient httpClient, NetclawPaths paths, SkillSyncConfig skillSyncConfig, - SkillRegistry skillRegistry, - SkillIndexContextLayer skillIndexLayer, + SkillInventoryRefresher inventoryRefresher, TimeProvider timeProvider, ISkillContentScanner scanner, ILogger logger, - string daemonVersion, - IReadOnlyList? serverFeedSources = null, - IReadOnlyList? externalSources = null) + string daemonVersion) { _httpClient = httpClient; _paths = paths; _skillSyncConfig = skillSyncConfig; - _skillRegistry = skillRegistry; - _skillIndexLayer = skillIndexLayer; + _inventoryRefresher = inventoryRefresher; _timeProvider = timeProvider; _scanner = scanner; _logger = logger; _daemonVersion = daemonVersion; - _serverFeedSources = serverFeedSources ?? []; - _externalSources = externalSources ?? []; } public async Task StartAsync(CancellationToken cancellationToken) @@ -336,10 +323,7 @@ await SkillSyncHelpers.ReplaceSkillDirectoryAsync( /// private void RescanAndUpdateIndex() { - var mergedResult = SkillScanner.ScanAndMerge( - _paths.SkillsDirectory, _serverFeedSources, _externalSources); - SkillRegistryUpdater.ApplyMergedScanResult( - _skillRegistry, _skillIndexLayer, mergedResult, _paths.SkillsDirectory, _externalSources); + var mergedResult = _inventoryRefresher.Refresh(); if (mergedResult.Issues.Count > 0) { diff --git a/src/Netclaw.Search.Tests/SearXngBackendIntegrationTests.cs b/src/Netclaw.Search.Tests/SearXngBackendIntegrationTests.cs index d36baa59b..068de101f 100644 --- a/src/Netclaw.Search.Tests/SearXngBackendIntegrationTests.cs +++ b/src/Netclaw.Search.Tests/SearXngBackendIntegrationTests.cs @@ -27,6 +27,12 @@ public class SearXngBackendIntegrationTests : IAsyncLifetime public async ValueTask InitializeAsync() { + if (OperatingSystem.IsWindows()) + { + Assert.Skip("SearXNG container integration tests are not supported on Windows."); + return; + } + var settingsYml = LoadFixture("searxng-settings.yml"); IContainer? container = null; diff --git a/src/Netclaw.Tools.Abstractions/ToolExecutionContext.cs b/src/Netclaw.Tools.Abstractions/ToolExecutionContext.cs index d7d57a429..0c0fe6eba 100644 --- a/src/Netclaw.Tools.Abstractions/ToolExecutionContext.cs +++ b/src/Netclaw.Tools.Abstractions/ToolExecutionContext.cs @@ -18,6 +18,22 @@ public sealed record ModelInputFileInfo(string FilePath, string FileName, MimeTy /// public sealed record FileAttachmentInfo(string FilePath, string FileName, MimeType MimeType); +/// +/// Machine-readable working-context handoff from an ephemeral subagent run. +/// Confirmed changes have first-party tool provenance; observed changes are +/// derived from shared worktree state and do not imply authorship. +/// +public sealed record SubAgentWorkingContextInfo +{ + public string? ProjectDirectory { get; init; } + public string? Worktree { get; init; } + public string? Branch { get; init; } + public string? Head { get; init; } + public IReadOnlyList ReadFiles { get; init; } = []; + public IReadOnlyList ConfirmedChangedFiles { get; init; } = []; + public IReadOnlyList ObservedChangedFiles { get; init; } = []; +} + /// /// Lightweight subagent activity notification for the tools abstraction layer. /// Tools emit these via ; @@ -34,6 +50,7 @@ public sealed record SubAgentNotificationInfo public SubAgentOutcomeReason? OutcomeReason { get; init; } public TimeSpan Duration { get; init; } public IReadOnlyList Findings { get; init; } = []; + public SubAgentWorkingContextInfo? WorkingContext { get; init; } } /// @@ -241,6 +258,12 @@ public IReadOnlySet OneTimeApprovedPatterns /// public string? ProjectDirectory { get; set; } + /// + /// Read-only snapshot of the parent session's recently used files. This is + /// grounding for delegated work and does not grant filesystem authority. + /// + public IReadOnlyList RecentFiles { get; init; } = []; + /// /// Resolves the working directory for a shell-style invocation. Returns /// the first non-empty value of: diff --git a/tests/Netclaw.SmokeMcpServer/Program.cs b/tests/Netclaw.SmokeMcpServer/Program.cs index bfb5f60d3..840e73e11 100644 --- a/tests/Netclaw.SmokeMcpServer/Program.cs +++ b/tests/Netclaw.SmokeMcpServer/Program.cs @@ -14,11 +14,12 @@ // // Two modes: // stdio (default) -// Exposes three fully-deterministic tools whose output is a pure +// Exposes deterministic tools whose output is a pure // function of their input: // add(a, b) -> a + b // echo(text) -> text // record-tasks(tasks, ref) -> a summary of the structured arguments +// process-info() -> process ID and command-line arguments // // Determinism is the whole point: add(2, 2) is always 4, so a smoke // scenario can hard-assert on the tool RESULT even though the @@ -86,6 +87,15 @@ public static string RecordTasks( return $"reference={reference} count={tasks.Length} kinds=[{kinds}]"; } + [McpServerTool(Name = "process-info")] + [Description("Returns this server process ID and command-line arguments for lifecycle tests.")] + public static string ProcessInfo() + => JsonSerializer.Serialize(new + { + processId = Environment.ProcessId, + arguments = Environment.GetCommandLineArgs().Skip(1).ToArray(), + }); + /// /// HTTP-mode-only tool: returns the Authorization header attached to /// the most recent request the server received. Returns the literal @@ -139,6 +149,7 @@ private static async Task RunStdioAsync() McpServerTool.Create(Add, new McpServerToolCreateOptions { Name = "add" }), McpServerTool.Create(Echo, new McpServerToolCreateOptions { Name = "echo" }), McpServerTool.Create(RecordTasks, new McpServerToolCreateOptions { Name = "record-tasks" }), + McpServerTool.Create(ProcessInfo, new McpServerToolCreateOptions { Name = "process-info" }), }; var options = new McpServerOptions diff --git a/tests/smoke/assertions/init-wizard.sh b/tests/smoke/assertions/init-wizard.sh index 11c513222..2f7185680 100755 --- a/tests/smoke/assertions/init-wizard.sh +++ b/tests/smoke/assertions/init-wizard.sh @@ -9,6 +9,7 @@ # 3) Provider/model/posture fields in netclaw.json match what the # tape typed # 4) Identity/SOUL.md contains the typed user name +# 5) Identity/AGENTS.md contains the deployment playbook scaffold only set -euo pipefail @@ -23,6 +24,18 @@ if [[ ! -f "$CONFIG_PATH" ]]; then exit 1 fi +agents_path="${NETCLAW_HOME}/identity/AGENTS.md" +echo "init-wizard: checking deployment mission scaffold..." +if ! grep -q 'Deployment Mission and Operating Playbook' "$agents_path" 2>/dev/null; then + echo "FAIL: ${agents_path} does not contain the deployment mission scaffold." >&2 + assert_fail=1 +elif grep -q 'Search Decision Rules' "$agents_path" 2>/dev/null; then + echo "FAIL: ${agents_path} duplicated embedded Netclaw operating rules." >&2 + assert_fail=1 +else + echo " ok identity/AGENTS.md contains only the deployment scaffold" +fi + config_json="$(read_config_json)" if ! printf '%s' "$config_json" | jq empty >/dev/null 2>&1; then echo "FAIL: ${CONFIG_PATH} is not valid JSON." >&2 diff --git a/tests/smoke/scenarios/mcp-setup.sh b/tests/smoke/scenarios/mcp-setup.sh index f74cd1764..5a5b16bd9 100755 --- a/tests/smoke/scenarios/mcp-setup.sh +++ b/tests/smoke/scenarios/mcp-setup.sh @@ -3,7 +3,7 @@ # connects to it and indexes its tools. # # The deterministic test server (Netclaw.SmokeMcpServer) exposes -# add/echo/record-tasks over stdio. This scenario hard-verifies netclaw's +# add/echo/record-tasks/process-info over stdio. This scenario hard-verifies netclaw's # MCP integration: # `mcp add` records the server in config, and on daemon startup the daemon # spawns the stdio server, completes the MCP handshake, and registers its @@ -87,12 +87,12 @@ else die "daemon log: no 'MCP server ${MCP_SERVER_NAME} connected' line — stdio handshake failed" fi -# The test server exposes exactly three tools (add, echo, record-tasks) — +# The test server exposes exactly four tools (add, echo, record-tasks, process-info) — # confirm the daemon registered all of them. -if [[ "$connect_line" == *"(3 tools)"* ]]; then - pass "daemon log: MCP server registered 3 tools (add, echo, record-tasks)" +if [[ "$connect_line" == *"(4 tools)"* ]]; then + pass "daemon log: MCP server registered 4 tools (add, echo, record-tasks, process-info)" else - die "daemon log: expected '(3 tools)' in the connection line, got: $connect_line" + die "daemon log: expected '(4 tools)' in the connection line, got: $connect_line" fi summarize diff --git 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-q 'connected (4 tools)'; do sleep 2; done" Enter Wait+Screen@60s /TAPE\$/ Show @@ -63,26 +63,26 @@ Type "netclaw mcp permissions" Enter # ─── Frame 1: ServerList ───────────────────────────────────────────── -# smoke-math should appear as "Connected, 3 tools". Do NOT anchor on -# "smoke-math" or "3 tools" alone — both already sit in the shell +# smoke-math should appear as "Connected, 4 tools". Do NOT anchor on +# "smoke-math" or "4 tools" alone — both already sit in the shell # scrollback before the TUI ever paints: # - "smoke-math" appears in the setup output above ("Added MCP server # 'smoke-math' (stdio)" / "...adjust approvals for 'smoke-math'."). -# - "3 tools" appears in this tape's own typed readiness-loop command, -# still visible on screen: `... grep -q 'connected (3 tools)' ...`. +# - "4 tools" appears in this tape's own typed readiness-loop command, +# still visible on screen: `... grep -q 'connected (4 tools)' ...`. # Immediately after Enter, before the TUI switches to the alternate # screen buffer, Wait+Screen can match that leftover transcript text and # return instantly, capturing the raw shell instead of the rendered TUI # (README.md rule 5: anchor on the *next view*, not text that predates # it). Anchor on TUI-only chrome instead: "MCP Permissions" is the page # title from McpToolPermissionsPage.BuildHeader (proves the alt screen -# painted), and "Connected, 3 tools" is the exact rendered server-row +# painted), and "Connected, 4 tools" is the exact rendered server-row # text from McpToolPermissionsPage.BuildServerList — "{Name} ({Status}, # {ToolCount} tools)" with capital-C Status and a comma, which the -# transcript's lowercase, comma-less "connected (3 tools)" never +# transcript's lowercase, comma-less "connected (4 tools)" never # produces. Neither anchor occurs anywhere in the shell transcript. Wait+Screen@15s /MCP Permissions/ -Wait+Screen@5s /Connected, 3 tools/ +Wait+Screen@5s /Connected, 4 tools/ # Sleep 3s: let the server-list frame fully settle before capturing. The # first match of the anchors above can be a transient render; the # daemon may push a state update (re-index, status refresh) immediately @@ -98,7 +98,7 @@ Screenshot "/tmp/shot-mcp-permissions-server-list.png" # land on an empty server list (daemon cleared it mid-transition), causing # the TUI to navigate to a blank or non-existent tool grid. Wait+Screen@20s /smoke-math/ -Wait+Screen@10s /3 tools/ +Wait+Screen@10s /4 tools/ # Sleep 5s: extended settle guard (was 2s). Daemon MCP state updates can # arrive at any point; 5s provides substantially more headroom under CI # load where the re-index cycle can be slow. @@ -107,7 +107,7 @@ Enter # ─── Frame 2: ToolGrid ─────────────────────────────────────────────── # All header rows (Server, Audience, Server enabled, Server default) plus -# all tool rows (add, echo, record-tasks) must be visible simultaneously. +# all tool rows (add, echo, record-tasks, process-info) must be visible simultaneously. # If the #1424 regression reappears, tool rows will overwrite the header. # Timeout is 30s (was 15s) to give the tool grid more headroom to load # under CI load. /Server default:/ (with colon) matches the rendered From 2077e4b30c649b3bbdf84fecc3d3a636ee4936e1 Mon Sep 17 00:00:00 2001 From: Aaron Stannard Date: Thu, 16 Jul 2026 01:00:23 +0000 Subject: [PATCH 33/37] fix: bump System.Numerics.Tensors pin to 10.0.10 to match Microsoft.Extensions.AI 10.8.0 floor Dependabot bumped Microsoft.Extensions.AI 10.6.0 -> 10.8.0 (upstream #1650) without updating the transitively-required System.Numerics.Tensors floor (now >= 10.0.10), leaving upstream/dev's own build broken via NU1109 package downgrade. Re-pins to 10.0.10, matching $(MicrosoftAspNetCoreVersion) per the existing comment policy. --- Directory.Packages.props | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/Directory.Packages.props b/Directory.Packages.props index a2f455407..2fd84605d 100644 --- a/Directory.Packages.props +++ b/Directory.Packages.props @@ -120,11 +120,11 @@ - - + From 3aa561761f2b9863a55ab106da847ac83e972dc0 Mon Sep 17 00:00:00 2001 From: Aaron Stannard Date: Thu, 16 Jul 2026 11:10:55 -0500 Subject: [PATCH 34/37] fix(memory): raise curation LLM timeout default from 10s to 60s (#1679) --- src/Netclaw.Actors/Memory/MemoryCurationEvaluator.cs | 5 ++++- .../MemoryConfigDefaultsTests.cs | 4 ++-- src/Netclaw.Configuration/MemoryConfig.cs | 11 +++++++++-- .../Schemas/netclaw-config.v1.schema.json | 2 +- 4 files changed, 16 insertions(+), 6 deletions(-) diff --git a/src/Netclaw.Actors/Memory/MemoryCurationEvaluator.cs b/src/Netclaw.Actors/Memory/MemoryCurationEvaluator.cs index 4a40c2dbe..68ee1f892 100644 --- a/src/Netclaw.Actors/Memory/MemoryCurationEvaluator.cs +++ b/src/Netclaw.Actors/Memory/MemoryCurationEvaluator.cs @@ -646,7 +646,10 @@ private async Task EvaluateCandidatesAsync( } catch (OperationCanceledException) { - log.Warning("curation_llm_timeout anchor={0}", operation.AnchorCanonicalName); + log.Warning( + "curation_llm_timeout anchor={0} timeoutSeconds={1}", + operation.AnchorCanonicalName, + curationConfig.LlmTimeoutSeconds); return null; } catch (Exception ex) diff --git a/src/Netclaw.Configuration.Tests/MemoryConfigDefaultsTests.cs b/src/Netclaw.Configuration.Tests/MemoryConfigDefaultsTests.cs index 7b8146fca..11eb9599f 100644 --- a/src/Netclaw.Configuration.Tests/MemoryConfigDefaultsTests.cs +++ b/src/Netclaw.Configuration.Tests/MemoryConfigDefaultsTests.cs @@ -74,10 +74,10 @@ public void Curation_llm_max_output_tokens_defaults_to_4096() } [Fact] - public void Curation_llm_timeout_seconds_defaults_to_10() + public void Curation_llm_timeout_seconds_defaults_to_60() { var config = new MemoryConfig(); - Assert.Equal(10, config.Curation.LlmTimeoutSeconds); + Assert.Equal(60, config.Curation.LlmTimeoutSeconds); } // ── MemoryRecallConfig (memory-core-redesign Slice 4, task 4.5) ── diff --git a/src/Netclaw.Configuration/MemoryConfig.cs b/src/Netclaw.Configuration/MemoryConfig.cs index ea010b505..a3be16ed2 100644 --- a/src/Netclaw.Configuration/MemoryConfig.cs +++ b/src/Netclaw.Configuration/MemoryConfig.cs @@ -134,8 +134,15 @@ public sealed class MemoryCurationConfig /// /// Wall-clock timeout, in seconds, for the curation LLM call. Bounds latency when a model /// ignores reasoning suppression and thinks at length regardless of the token cap above. - /// - public int LlmTimeoutSeconds { get; set; } = 10; + /// Curation is background quality work — success matters far more than latency — so this is + /// sized to let the 4096-token ceiling actually be reached on + /// real providers rather than to bound perceived latency. The July 2026 canary + /// (0.25.0-alpha.onnx.7) measured a 46% curation LLM failure rate (11/24 over 14 days), 100% + /// attributable to curation_llm_timeout at the previous 10-second default — zero parse + /// errors or exceptions among the failures — because generating a full merged-body reply + /// routinely took longer than that. + /// + public int LlmTimeoutSeconds { get; set; } = 60; } /// diff --git a/src/Netclaw.Configuration/Schemas/netclaw-config.v1.schema.json b/src/Netclaw.Configuration/Schemas/netclaw-config.v1.schema.json index a9b6d1a26..4ac92dbe4 100644 --- a/src/Netclaw.Configuration/Schemas/netclaw-config.v1.schema.json +++ b/src/Netclaw.Configuration/Schemas/netclaw-config.v1.schema.json @@ -441,7 +441,7 @@ "type": "integer", "minimum": 1, "maximum": 300, - "default": 10, + "default": 60, "description": "Wall-clock timeout in seconds for the curation LLM call." } }, From 8e71acdcaff96dcb904c56b6cb1d834a0d1c4ab8 Mon Sep 17 00:00:00 2001 From: Aaron Stannard Date: Tue, 4 Aug 2026 00:05:31 +0000 Subject: [PATCH 35/37] feat(memory): default ONNX embeddings to enabled MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit All four slices (embedding foundation, kNN/L curation, hybrid recall, relevance gate) are now shipping together — the default-off stance from Slice 2 was a staging guardrail, not a permanent posture. Gap-repair runs on every daemon startup via EmbeddingWarmupHostedService, and the doctor check already surfaces model health and backfill gaps. --- src/Netclaw.Configuration/MemoryConfig.cs | 14 ++++++-------- 1 file changed, 6 insertions(+), 8 deletions(-) diff --git a/src/Netclaw.Configuration/MemoryConfig.cs b/src/Netclaw.Configuration/MemoryConfig.cs index a3be16ed2..87b782178 100644 --- a/src/Netclaw.Configuration/MemoryConfig.cs +++ b/src/Netclaw.Configuration/MemoryConfig.cs @@ -1,4 +1,4 @@ -// ----------------------------------------------------------------------- +// ----------------------------------------------------------------------- // // Copyright (C) 2026 - 2026 Petabridge, LLC // @@ -28,8 +28,8 @@ public sealed class MemoryConfig public int AutoRecallMaxItems { get; set; } = 3; /// - /// Embedding-based semantic memory settings (memory-core-redesign Slice 2: embedding - /// foundation). See for why this defaults off. + /// Embedding-based semantic memory settings (memory-core-redesign). + /// Enables ONNX-based local embeddings, hybrid recall, and the cross-encoder relevance gate. /// public MemoryEmbeddingsConfig Embeddings { get; set; } = new(); @@ -54,12 +54,10 @@ public sealed class MemoryEmbeddingsConfig /// /// When true, the daemon provisions/loads the embedding model at startup /// (EmbeddingWarmupHostedService) and computes embeddings on memory writes. - /// Defaults to false for Slice 2 ("embedding foundation"): this slice only writes - /// vectors — nothing in the write or read path consumes them yet (nominate/decide dedup is - /// Slice 3, hybrid recall is Slice 4). Flipping this default to true is a deliberate - /// decision left to whichever of those slices ships first, not an oversight here. + /// When false, the entire semantic memory pipeline is disabled: no models are loaded, + /// no embeddings are computed, and hybrid recall degrades to lexical-only. /// - public bool Enabled { get; set; } + public bool Enabled { get; set; } = true; /// /// Allowlisted embedding model id (see EmbeddingModelProvisioner.Allowlist in From 3ed49d575c9020b1a7f84a7a52ae119c4fe1a33d Mon Sep 17 00:00:00 2001 From: Aaron Stannard Date: Tue, 4 Aug 2026 00:14:02 +0000 Subject: [PATCH 36/37] test: flip Embeddings disabled-by-default bear trap to enabled-by-default Match the MemoryEmbeddingsConfig.Enabled default change from false to true. --- .../MemoryConfigDefaultsTests.cs | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/src/Netclaw.Configuration.Tests/MemoryConfigDefaultsTests.cs b/src/Netclaw.Configuration.Tests/MemoryConfigDefaultsTests.cs index 11eb9599f..29d3dde15 100644 --- a/src/Netclaw.Configuration.Tests/MemoryConfigDefaultsTests.cs +++ b/src/Netclaw.Configuration.Tests/MemoryConfigDefaultsTests.cs @@ -8,19 +8,17 @@ namespace Netclaw.Configuration.Tests; /// -/// Bear-trap tests for defaults (memory-core-redesign -/// Slice 2, task 2.11). If you change a default, you must update these assertions — forcing a -/// deliberate decision rather than an accidental drift. -/// defaults to false in particular: flipping it is a deliberate Slice 3/4 decision, not something -/// that should silently change because a refactor touched the property initializer. +/// Bear-trap tests for defaults. If you change a default, +/// you must update these assertions — forcing a deliberate decision rather than an accidental +/// drift. /// public sealed class MemoryConfigDefaultsTests { [Fact] - public void Embeddings_disabled_by_default() + public void Embeddings_enabled_by_default() { var config = new MemoryConfig(); - Assert.False(config.Embeddings.Enabled); + Assert.True(config.Embeddings.Enabled); } // int8 default: a dedicated prefixed-query gold-set sweep (arctic-int8-prefix-eval) From 5c45914aefbdfed58eb640ddfb4aed92e7af9c89 Mon Sep 17 00:00:00 2001 From: Aaron Stannard Date: Tue, 4 Aug 2026 01:09:55 +0000 Subject: [PATCH 37/37] fix(doctor): downgrade missing-model from Error to Warning when AutoDownload is true MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit When Memory.Embeddings.AutoDownload is true, the daemon provisions models on next startup — this is a self-healing condition. The doctor should report it as a Warning (daemon will fix it), not an Error (operator must act). Error is now reserved for the AutoDownload=false case where the operator must manually provision models. Same pattern applied to both MemoryEmbeddingDoctorCheck and MemoryRelevanceGateDoctorCheck. Tests split to cover both branches. --- .../Doctor/MemoryEmbeddingDoctorCheckTests.cs | 22 +++++++++++++++---- .../MemoryRelevanceGateDoctorCheckTests.cs | 22 +++++++++++++++---- .../Doctor/MemoryEmbeddingDoctorCheck.cs | 12 +++++++--- .../Doctor/MemoryRelevanceGateDoctorCheck.cs | 12 +++++++--- 4 files changed, 54 insertions(+), 14 deletions(-) diff --git a/src/Netclaw.Cli.Tests/Doctor/MemoryEmbeddingDoctorCheckTests.cs b/src/Netclaw.Cli.Tests/Doctor/MemoryEmbeddingDoctorCheckTests.cs index ef873db03..2f5eae49d 100644 --- a/src/Netclaw.Cli.Tests/Doctor/MemoryEmbeddingDoctorCheckTests.cs +++ b/src/Netclaw.Cli.Tests/Doctor/MemoryEmbeddingDoctorCheckTests.cs @@ -39,10 +39,24 @@ public async Task Passes_with_embeddings_disabled_message_when_config_off() } [Fact] - public async Task Errors_when_enabled_but_model_is_missing() + public async Task Warns_when_enabled_but_model_is_missing_and_auto_download_is_true() { var paths = CreateTempPaths(); - var config = WriteConfig(paths, enabled: true); + var config = WriteConfig(paths, enabled: true, autoDownload: true); + // No model files placed at paths.EmbeddingModelDirectory(ModelId). + var check = new MemoryEmbeddingDoctorCheck(paths, config, FixtureAllowlist()); + + var result = await check.RunAsync(TestContext.Current.CancellationToken); + + Assert.Equal(DoctorSeverity.Warning, result.Severity); + Assert.Contains(ModelId, result.Message, StringComparison.Ordinal); + } + + [Fact] + public async Task Errors_when_enabled_but_model_is_missing_and_auto_download_is_false() + { + var paths = CreateTempPaths(); + var config = WriteConfig(paths, enabled: true, autoDownload: false); // No model files placed at paths.EmbeddingModelDirectory(ModelId). var check = new MemoryEmbeddingDoctorCheck(paths, config, FixtureAllowlist()); @@ -119,7 +133,7 @@ private static NetclawPaths CreateTempPaths() return paths; } - private static IConfiguration WriteConfig(NetclawPaths paths, bool enabled) + private static IConfiguration WriteConfig(NetclawPaths paths, bool enabled, bool autoDownload = true) { var config = new Dictionary { @@ -129,7 +143,7 @@ private static IConfiguration WriteConfig(NetclawPaths paths, bool enabled) { ["Enabled"] = enabled, ["ModelId"] = ModelId, - ["AutoDownload"] = true, + ["AutoDownload"] = autoDownload, } } }; diff --git a/src/Netclaw.Cli.Tests/Doctor/MemoryRelevanceGateDoctorCheckTests.cs b/src/Netclaw.Cli.Tests/Doctor/MemoryRelevanceGateDoctorCheckTests.cs index db58a430c..d859028d4 100644 --- a/src/Netclaw.Cli.Tests/Doctor/MemoryRelevanceGateDoctorCheckTests.cs +++ b/src/Netclaw.Cli.Tests/Doctor/MemoryRelevanceGateDoctorCheckTests.cs @@ -51,10 +51,24 @@ public async Task Passes_with_disabled_message_when_explicitly_disabled_despite_ } [Fact] - public async Task Errors_when_gate_active_but_model_is_missing() + public async Task Warns_when_gate_active_but_model_is_missing_and_auto_download_is_true() { var paths = CreateTempPaths(); - var config = WriteConfig(paths, embeddingsEnabled: true, gateEnabled: null); + var config = WriteConfig(paths, embeddingsEnabled: true, gateEnabled: null, autoDownload: true); + // No model files placed at paths.EmbeddingModelDirectory(DefaultRelevanceModelId). + var check = new MemoryRelevanceGateDoctorCheck(paths, config, FixtureAllowlist()); + + var result = await check.RunAsync(TestContext.Current.CancellationToken); + + Assert.Equal(DoctorSeverity.Warning, result.Severity); + Assert.Contains(EmbeddingModelProvisioner.DefaultRelevanceModelId, result.Message, StringComparison.Ordinal); + } + + [Fact] + public async Task Errors_when_gate_active_but_model_is_missing_and_auto_download_is_false() + { + var paths = CreateTempPaths(); + var config = WriteConfig(paths, embeddingsEnabled: true, gateEnabled: null, autoDownload: false); // No model files placed at paths.EmbeddingModelDirectory(DefaultRelevanceModelId). var check = new MemoryRelevanceGateDoctorCheck(paths, config, FixtureAllowlist()); @@ -86,7 +100,7 @@ private static NetclawPaths CreateTempPaths() return paths; } - private static IConfiguration WriteConfig(NetclawPaths paths, bool embeddingsEnabled, bool? gateEnabled) + private static IConfiguration WriteConfig(NetclawPaths paths, bool embeddingsEnabled, bool? gateEnabled, bool autoDownload = true) { var recall = new Dictionary { @@ -102,7 +116,7 @@ private static IConfiguration WriteConfig(NetclawPaths paths, bool embeddingsEna ["Embeddings"] = new Dictionary { ["Enabled"] = embeddingsEnabled, - ["AutoDownload"] = true, + ["AutoDownload"] = autoDownload, }, ["Recall"] = recall, } diff --git a/src/Netclaw.Cli/Doctor/MemoryEmbeddingDoctorCheck.cs b/src/Netclaw.Cli/Doctor/MemoryEmbeddingDoctorCheck.cs index 5fac545e0..a5239cc44 100644 --- a/src/Netclaw.Cli/Doctor/MemoryEmbeddingDoctorCheck.cs +++ b/src/Netclaw.Cli/Doctor/MemoryEmbeddingDoctorCheck.cs @@ -54,12 +54,18 @@ public async Task RunAsync(CancellationToken cancellationToke var verified = await provisioner.TryLoadVerifiedAsync(modelId, modelDirectory, cancellationToken); if (verified is null) { + if (memoryConfig.Embeddings.AutoDownload) + { + return DoctorCheckResult.Warning( + CheckName, + $"Embedding model '{modelId}' is not yet provisioned. The daemon will download and verify it on next startup.", + "Restart the daemon, or run `netclaw memory backfill-embeddings` to provision now."); + } + return DoctorCheckResult.Error( CheckName, $"Embedding model '{modelId}' is missing or fails hash verification at {modelDirectory}.", - memoryConfig.Embeddings.AutoDownload - ? "Restart the daemon to re-provision, or run `netclaw memory backfill-embeddings`." - : "Memory.Embeddings.AutoDownload is false — provision the model manually, or enable AutoDownload and restart the daemon."); + "Memory.Embeddings.AutoDownload is false — provision the model manually, or enable AutoDownload and restart the daemon."); } var store = new SQLiteMemoryStore(paths.MemorySqliteDbPath, TimeProvider.System); diff --git a/src/Netclaw.Cli/Doctor/MemoryRelevanceGateDoctorCheck.cs b/src/Netclaw.Cli/Doctor/MemoryRelevanceGateDoctorCheck.cs index edc5e0bd7..708f24264 100644 --- a/src/Netclaw.Cli/Doctor/MemoryRelevanceGateDoctorCheck.cs +++ b/src/Netclaw.Cli/Doctor/MemoryRelevanceGateDoctorCheck.cs @@ -63,12 +63,18 @@ public async Task RunAsync(CancellationToken cancellationToke var verified = await provisioner.TryLoadVerifiedRelevanceModelAsync(modelId, allowlist, modelDirectory, cancellationToken); if (verified is null) { + if (memoryConfig.Embeddings.AutoDownload) + { + return DoctorCheckResult.Warning( + CheckName, + $"Relevance model '{modelId}' is not yet provisioned. The daemon will download and verify it on next startup.", + "Restart the daemon to provision the relevance model."); + } + return DoctorCheckResult.Error( CheckName, $"Relevance model '{modelId}' is missing or fails hash verification at {modelDirectory}.", - memoryConfig.Embeddings.AutoDownload - ? "Restart the daemon to re-provision the relevance model." - : "Memory.Embeddings.AutoDownload is false — provision the model manually, or enable AutoDownload and restart the daemon."); + "Memory.Embeddings.AutoDownload is false — provision the model manually, or enable AutoDownload and restart the daemon."); } return DoctorCheckResult.Pass(