feat: Adds Qdrant Vector Search#683
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WalkthroughAdds Qdrant as a supported vector store: updates config model and dependency, documents Qdrant options in templates/examples/docs, implements async QdrantVectorStore (upsert/query/delete/close), and wires it into the store factory. ChangesQdrant Vector Store Integration
Sequence Diagram(s)sequenceDiagram
participant App
participant QdrantVectorStore
participant QdrantClient
App->>QdrantVectorStore: upsert_many(namespace, vectors)
QdrantVectorStore->>QdrantClient: ensure_collection / upsert Points
QdrantClient-->>QdrantVectorStore: upsert result / errors
App->>QdrantVectorStore: query(namespace, embedding, top_k, filters)
QdrantVectorStore->>QdrantClient: search nearest with filter
QdrantClient-->>QdrantVectorStore: hits with scores
QdrantVectorStore-->>App: VectorQueryResult list (score→distance, metadata)
Estimated code review effort🎯 3 (Moderate) | ⏱️ ~20 minutes Suggested reviewers
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✅ Passed checks (4 passed)
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Actionable comments posted: 1
🧹 Nitpick comments (1)
src/vector_store/qdrant.py (1)
20-21: 💤 Low valueConsider documenting the UUID namespace choice.
The function uses
uuid.NAMESPACE_DNSto generate deterministic UUIDs. While this works correctly, a brief comment explaining why NAMESPACE_DNS was chosen (or noting that any fixed namespace would work) would help future maintainers understand this is an arbitrary but consistent choice.🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the rest with a brief reason, keep changes minimal, and validate. In `@src/vector_store/qdrant.py` around lines 20 - 21, The helper _point_id uses uuid.uuid5 with uuid.NAMESPACE_DNS to produce deterministic IDs but lacks explanation; add a short comment above the _point_id function (or inline) stating that NAMESPACE_DNS is chosen only as a fixed, arbitrary namespace for deterministic UUIDv5 generation and that any constant namespace would work, so maintainers understand the choice (reference: function _point_id).
🤖 Prompt for all review comments with AI agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
Inline comments:
In `@src/vector_store/qdrant.py`:
- Around line 66-138: Add Google-style docstrings to each public method
upsert_many, query, delete_many, delete_namespace, and close in the Qdrant
vector store class: for each method document Args (types and description for
namespace, vectors, embedding, top_k, filters, max_distance, ids), Returns
(e.g., list[VectorQueryResult] or None), and Raises (e.g., VectorStoreError on
failures or any exceptions propagated), and include brief summary line and any
notes about side effects (creates collection in upsert_many, filters applied in
query). Ensure docstrings follow Google style (one-line summary, blank line,
Args/Returns/Raises sections) and reference types used in the file such as
VectorRecord and VectorQueryResult.
---
Nitpick comments:
In `@src/vector_store/qdrant.py`:
- Around line 20-21: The helper _point_id uses uuid.uuid5 with
uuid.NAMESPACE_DNS to produce deterministic IDs but lacks explanation; add a
short comment above the _point_id function (or inline) stating that
NAMESPACE_DNS is chosen only as a fixed, arbitrary namespace for deterministic
UUIDv5 generation and that any constant namespace would work, so maintainers
understand the choice (reference: function _point_id).
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Fix all unresolved CodeRabbit comments on this PR:
- Push a commit to this branch (recommended)
- Create a new PR with the fixes
ℹ️ Review info
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Configuration used: Organization UI
Review profile: CHILL
Plan: Pro
Run ID: 6f79ae20-91f2-4cab-a8c9-74563de256f5
⛔ Files ignored due to path filters (1)
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📒 Files selected for processing (10)
.env.templateCHANGELOG.mdREADME.mdconfig.toml.exampledocs/changelog/introduction.mdxdocs/v3/contributing/configuration.mdxpyproject.tomlsrc/config.pysrc/vector_store/__init__.pysrc/vector_store/qdrant.py
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Just bumping this PR. Please take a look when possible. |
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Description
This PR adds support for using Qdrant as a vector search provider in Honcho.
Qdrant is an open-source vector search engine built for high-performance and massive-scale.
Testing
I've unit tested this integration against a local Qdrant instance.
Setup
You can run Qdrant with
Then set,
The dashboard is accessible at http://localhost:6333/dashboard
Summary by CodeRabbit