GoodQ4All: Local-First Multimodal AI Memory & Video Intelligence Stack

Offline Video Search, Scene Segmentation, Speech Transcription (Whisper), Object Detection, Visual Embeddings (DINOv2/CLIP), and SQLite + Qdrant Semantic Search on Windows 11

_{(Note: The optional Ask GoodQ voice agent is a hosted extension using ElevenLabs APIs. The core GoodQ4All memory system itself is 100% local and offline.)}

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⚡ The Local-First Multimodal Memory Stack

GoodQ4All is a 100% private, offline alternative to cloud-based media intelligence services. It ingests video, audio, and text files into queryable, structured scene-level memories, persisting the knowledge graph and vector representations locally on your computer.

Following a strict "proof-backed" system doctrine, GoodQ4All documents every perception step, tracks evidence manifests, and logs a comprehensive audit trail so that every memory claim can be verified.

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🎬 From Media to Memory

• Get This Level of Local Control (Unified Operator UI):

  
  Click the preview above to watch the high-fidelity onboarding video.

• From Video Quality as Low as This: (Raw Media Inputs):

  Apollo 11 Moon Walk (nasa_descent.gif)

  Saturn V Launch (nasa_launch.gif)

• Using This All-in-One Installer (Unified Windows Offline Installer):

  🚀 Download GoodQ4All v3.0.0 Offline Release

  [!IMPORTANT] System Requirement: Windows 11 (64-bit). GoodQ4All is a standalone, local-first system that runs 100% offline without external cloud dependencies.

  • Available Profiles:
    • CPU Baseline (PUBLIC_CPU_BASELINE): Lightweight CPU execution requiring ~35 GB free disk space.
    • GPU Enhanced (PUBLIC_GPU_ENHANCED): High-performance multimodal pipeline accelerated by NVIDIA CUDA 12.1 (requires 8GB+ VRAM and ~160 GB free disk space during installation).
  • Bundle Contents: Each release includes GoodQ4All_Setup_2.5.8.exe, LAUNCH_GOODQ.exe, signed payload packs (payloads/*.zip), and the release manifest (GoodQ4All_Setup_2.5.8.release_manifest.json).
  • SmartScreen & Elevation: Windows SmartScreen may present an "Unknown Publisher" prompt for self-signed releases. Click More info → Run anyway. Administrator rights are required for the default install location (Program Files\GoodQ4All).
  • Checksum Verification: Verify asset integrity prior to installation using PowerShell:

    Get-FileHash GoodQ4All_Setup_2.5.8.exe -Algorithm SHA256

  

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🛡️ Core Capabilities & Architecture

1. Proof-Backed Ingestion & Hardened API Facade

Machine memory should earn every claim it makes. GoodQ4All generates step-by-step logs (step_runs.jsonl), scene manifests, and intermediate features for every ingested file.

• Single-Use Confirmation Tokens: Ingestion submission routes are protected by a server-generated token handshake with single-use nonce validation, preventing unauthenticated/out-of-bounds execution.
• Epistemic Verification: Ingestion is tracked using verifiable manifests and SQLite-backed relational schemas.
• No Silent Failures: The Control Agent and Watchdog processes bubble errors directly to the operator consoles, providing absolute visibility into the execution stack.

2. Local Model Governance & VRAM Budgeting

To run large-parameter local models safely on consumer hardware (e.g. RTX 4060 / 4070 Ti SUPER) without Out-of-Memory (OOM) crashes, GoodQ4All implements strict VRAM and execution controls:

• Model Lifecycle Manager: A specialized context manager (lib/model_lifecycle.py) that audits free VRAM using PyTorch and nvidia-smi before loading models, dynamically evicting idle networks from GPU memory and providing automatic CPU fallback for memory-constrained visual embeddings.
• Local Agent Stack (MiniAgentClient): Gated LLM reasoning and local tool execution through zero-dependency policy enforcement middleware, loading schemas, policies, and contracts dynamically from the version-controlled agents/stack/ directory.
• Endpoint Fallback Orchestration: Automatically falls back from the primary local vLLM server (prefer_speed, running Qwen2.5) to a local Ollama service (prefer_quality, running Phi-4) or a CPU-safe model variant when VRAM thresholds are breached.

3. TurboQuant Hybrid Vector Caching

High-precision 32-bit floating point embeddings are persisted in Qdrant and FAISS. For rapid candidate filtering, GoodQ4All uses TurboQuant—an SQLite sidecar caching technology employing Lloyd-Max Polar Quantization and Johnson-Lindenstrauss residual projections.

• Performance: Achieves sub-millisecond candidate pre-filtering.
• Accuracy: 100% search accuracy is maintained by performing the final rank scoring on the uncompressed raw float32 vectors.

Note

Hybrid Precision Caching Model: GoodQ4All uses an additive sidecar vector cache architecture. High-precision 32-bit floating point (float32) embeddings remain the authoritative truth of the system, stored in Qdrant and FAISS. Performance-oriented query pre-filtering is handled via lightweight TurboQuant fields (Lloyd-Max Polar Quantization + Johnson–Lindenstrauss residual corrections) stored in SQLite. This ensures zero data loss, guarantees rollback capability, and cuts memory usage.

4. Adaptive Hardware Profiles

The pipeline dynamically adjusts its computational needs to match your system specs:

• BASELINE (CPU-safe): Fully operational, offline-ready execution on standard CPU hardware. Bypasses GPU requirements gracefully.
• GPU_ENHANCED: Activates local NVIDIA GPU (CUDA 12.1) acceleration for rapid video segmentation, object detection, DINOv2 / CLIP visual embeddings, and CLAP audio embedding.

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⚙️ Setup Paths

Route A: Standalone User Installation (Recommended)

GoodQ4All provides an isolated embedded Python runtime, native Qdrant vector database, FFmpeg/Poppler/Tesseract binaries, and pre-staged model packs in a single managed installer:

1. Download & Extract: Download the release bundle for your desired profile (PUBLIC_CPU_BASELINE or PUBLIC_GPU_ENHANCED) from GitHub Releases. Ensure the payloads/ directory sits alongside GoodQ4All_Setup_2.5.8.exe.
2. Install: Run GoodQ4All_Setup_2.5.8.exe (or execute GoodQ4All_Setup_2.5.8.exe /S for a silent install). The installer extracts dependencies, verifies payload cryptographic signatures, and registers runtime paths.
3. Launch: Double-click the GoodQ4All desktop shortcut or run LAUNCH_GOODQ.exe.
4. Explore & Ingest: Open the Retro Memory Explorer in your browser at http://127.0.0.1:30000/ui/retro_console_v1/. Drag-and-drop video/audio files onto the Upload Pad to begin local offline ingestion.

Route B: Developer Source Setup (Advanced)

If you are developing, customizing the pipeline, or running from source:

Developer Source Setup Steps (Advanced)

1. Developer Onboarding Video

2. Step-by-Step Developer Installation

Step	Type or do this	Demo frame
1	Clone the official source: git clone https://github.com/GoodQ02/goodq4all.git
2	Enter the project cabin: cd goodq4all
3	Run the bootstrap installer: python scripts/bootstrap_install.py CPU-safe first-run variant: python scripts/bootstrap_install.py --disable-gpu --disable-wsl-audio --skip-model-prefetch.
4	Customize local config: edit the bootstrap-created .env.local when using local model, cache, or provider settings.
5	Validate the bootstrap: .\scripts\bootstrap_validate.bat
6	Run the launcher/readiness check: .\LAUNCH_GOODQ.ps1
7	Start Watchdog, then copy one small media file into the import inbox zone (defaults to %USERPROFILE%\GoodQ_Data\import_inbox): conda run --no-capture-output -n goodq_core python -m cli.watchdog
8	Start the API and inspect proof: conda run --no-capture-output -n goodq_core python -m api.server

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📺 User Interfaces

GoodQ4All ships with two local operator console variants:

• Classic Operator Console (served at /ui/operator_console_v1/): Exposes the current scope strip, flight deck, proof/evidence status, recurrence reports, and video inventories.
• Retro Memory Explorer (v1.4.7) (served at /ui/retro_console_v1/): A premium cyber-CRT dashboard featuring a four-panel resizable/collapsible layout with floating restore tabs, an entity co-occurrence graph with dynamic zoom and flight transitions, an Inspector panel containing keyframe image/transcript views, and bidirectional timeline checklists.

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📖 Authoritative Documentation

Start Here

• Guided demo: docs/guides/DEMO.md
• First run: docs/guides/FIRST_RUN.md
• Install: docs/bootstrap/INSTALL_BOOTSTRAP.md
• Historical quickstart: docs/archive/guides/install/QUICKSTART.md
• Clean memory start: docs/guides/CLEAN_MEMORY_START.md
• Historical privacy guide: docs/archive/guides/general/PRIVACY.md

Technical Details

• Architecture: docs/architecture/SYSTEM_ARCHITECTURE.md
• Memory Storage: docs/architecture/MEMORY_STORAGE.md
• Hybrid Caching: docs/architecture/TURBOQUANT_HYBRID_CACHING.md
• Current Agent State: docs/agent/CURRENT_STATE.md
• RAG Context Pack: docs/GOODQ_RAG_CONTEXT_PACK.md

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📄 License

MIT. See LICENSE.