vidchain 0.8.8

Edge-optimized multimodal RAG framework for video understanding

VidChain: The "LangChain for Videos"

v0.8.8-Stable — The Definitive Forensic Intelligence Release. Optimized for speed, integrity, and responsiveness on the seminar floor.

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High-Integrity Forensic Architecture

VidChain v0.8.8-Stable is powered by the B.A.B.U.R.A.O. Engine (Behavioral Analysis & Broadcasting Unit for Real-time Artificial Observation). This version introduces the Forensic Integrity Hub and Snappy Ingest optimizations.

graph TD
    %% --- Ingestion Stage ---
    subgraph "1. Ingestion & Optimization Layer"
        VS[Video Source] --> AK[Adaptive Gaussian Filter]
        AK -- "Delta > Threshold" --> PK[Promote to Keyframe]
        AK -- "Redundant" --> DROP{{GPU Compute Firewall}}
    end

    %% --- Inference Stage ---
    subgraph "2. Sensory Node Matrix (Late Fusion)"
        PK --> VLM[LlavaNode: Scene Semantics]
        PK --> ASR[WhisperNode: Audio Trace]
        PK --> OCR[OcrNode: Digital Trace]
        PK --> TRK[TrackerNode: Motion Flow]
        
        %% Optional Sensors
        PK -.-> ACT[ActionNode: Situational Verbs]
        PK -.-> EMT[EmotionNode: Sentiment]
    end

    %% --- Intelligence Logic ---
    subgraph "3. B.A.B.U.R.A.O. Cognitive Engine"
        VLM & ASR & OCR & TRK & ACT & EMT --> FUSE[Semantic Fusion Pipeline]
        FUSE --> RDN[Recursive Map-Reduce Summarizer]
    end

    %% --- Persistence ---
    subgraph "4. Forensic Memory Vault"
        FUSE --> KV[(ChromaDB Vector Store)]
        FUSE --> KG[[Temporal Knowledge Graph]]
    end

    %% --- Interaction Stage ---
    subgraph "5. Spider-Net Intelligence Portal"
        USER[User Query] --> IR{Intent Router}
        IR -- "Forensic Search" --> RAG[RAG Retrieval Loop]
        IR -- "Executive Overview" --> RDN
        RAG <--> KV
        RAG <--> KG
        RDN --> REPORT([Intelligence Report])
        RAG --> DISCOVERY([Discovery Hub])
    end

    %% --- Hardware Loop ---
    HM[NVML Hardware Monitor] -.-> AK
    HM -.-> VLM
    HM -.-> DISCOVERY

    style VS fill:#1e1e2e,stroke:#74c7ec,stroke-width:2px;
    style DISCOVERY fill:#11111b,stroke:#a6e3a1,stroke-width:3px;
    style REPORT fill:#11111b,stroke:#a6e3a1,stroke-width:3px;
    style DROP fill:#313244,stroke-dasharray: 5 5;
    style AK fill:#1e1e2e,stroke:#fab387;

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Key Features (v0.8.8 Evolution)

Snappy Ingest Optimization [NEW]

Ingestion is now up to 50% faster. By shifting intelligence summarization from a mandatory post-ingest task to an on-demand chat feature, the system marks evidence as READY the millisecond the sensor nodes finish processing.

Forensic Integrity Lock

Strict session-to-video binding. B.A.B.U.R.A.O. now cleans its active memory during every context switch, ensuring zero leakage or "random noises" between investigations.

Flex-Engine Responsive HUD

The Spider-Net Portal now features a collapsible Telemetry HUD and responsive Ingest Bar, ensuring a clean layout on any screen size from laptops to forensic monitors.

Precision Evidence Player

A surgical forensic review tool with frame-by-frame 33ms seeking, real-time semantic heatmap overlays, and hardware-accelerated local media resolution.

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Installation

# Core installation
pip install VidChain

# Setup local AI backends (Ollama)
ollama pull moondream   # Optimized Edge VLM (1.7GB)
ollama pull llama3      # Local Reasoning Hub (4.7GB)

# Verify Hardware Readiness (Bundled utility)
python -m vidchain.scripts.check_gpu

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📜 Changelog (The Seminar Milestone)

• v0.8.8: Snappy Ingest. Decoupled auto-summarization from the ingest pipeline for 2x speed.
• v0.8.7: Flex-Engine Layout. Collapsible HUD, responsive status bar, and UI collision fixes.
• v0.8.6: Forensic Integrity Hub. Removed dangerous global fallbacks; enforced strict session isolation.
• v0.8.5: Forensic Flow Restoration. Fixed 404 media reloads and improved rename input UX.
• v0.8.3: Relative Path Migration. Fixed broken production fetches and asset routing.
• v0.8.1: Implemented Auto-Launch browser integration for vidchain-serve.
• v0.8.0: The Modular Revolution. Deprecated monolithic processors for Node framework.

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Author

Rahul Sharma — IIIT Manipur
SEM Project Version 0.8.8-Stable