vidchain 1.0.1

VidChain: High-fidelity multimodal RAG framework featuring the IRIS Intelligence Agent

VidChain

High-Fidelity Multimodal RAG Framework for Forensic Video Intelligence

VidChain is a local-first multimodal RAG framework powered by the IRIS Engine (Intelligent Retrieval & Insight System). It parses video through a modular sensory matrix — fusing visual, auditory, OCR, and temporal signals into a queryable intelligence layer — designed for forensic analysis, security auditing, and automated video summarization with strict on-device privacy.

---

Features

• 4-Route Agentic Router — Classifies queries into Narrative Summarization, Local Forensic Search, Global Master Intelligence, and Conversational Dialogue.
• Global Master Intelligence — Cross-video entity tracking via a macro-graph, enabling pattern recognition across isolated sessions.
• Temporal Persistence — Chronological reasoning that bridges frame gaps and maintains state continuity between sensor logs.
• Recursive Map-Reduce Summarizer — Collapses hours of video into coherent reports without hitting LLM context limits.
• Neural Concurrency Locking — Prevents state corruption during simultaneous ingestion and query operations.
• 100% Local Execution — All inference runs on host hardware; no data leaves the machine.

---

Installation

Prerequisites

Requirement	Version
Python	3.11+
CUDA	12.1+
Ollama	Latest (running)
Node.js	v18+ (for web portal)

Steps

1. Install PyTorch with CUDA support

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

2. Clone and install VidChain

git clone https://github.com/rahulsiiitm/videochain-python
cd videochain-python
pip install -e .

3. Pull model weights

ollama pull moondream   # Vision Language Model
ollama pull llama3      # Language Model for reasoning & routing

CPU Fallback: If no CUDA device is detected, VidChain automatically degrades to CPU mode — no code changes required.

---

Quick Start

from vidchain import VidChain

vc = VidChain(db_path="./forensic_vault")

# Ingest video (runs full default pipeline)
video_id = vc.ingest(video_source="interview_01.mp4")

# Query
response = vc.ask("What is the main topic of discussion?", video_id=video_id)
print(response["text"])

# Summarize
summary = vc.summarize_video(video_id=video_id, mode="concise")
print(summary)

---

CLI Reference

vidchain-serve

Launches the FastAPI backend and Next.js dashboard.

vidchain-serve

• API available at http://localhost:8000
• Dashboard opens at http://localhost:3000
• Includes a 7-second neural warmup before accepting requests

vidchain-analyze

Headless video ingestion from the terminal.

vidchain-analyze path/to/video.mp4 --vlm moondream

Flag	Description
--vlm <model>	Vision model to use (default: moondream)
--llm <model>	Reasoning model to use (default: gemini/gemini-2.5-flash)
--fast	Replaces VLM with YOLO for high-speed detection (ideal for long CCTV footage)
--emotion	Injects DeepFace emotion analysis node
--action	Injects MobileNetV3 action classification node

Swapping models — VidChain uses LiteLLM, so any compatible model can be hot-swapped:

# Local
vidchain-analyze video.mp4 --llm "ollama/llama3"

# Cloud (requires API key export)
export GEMINI_API_KEY="your_api_key"
vidchain-analyze video.mp4 --llm "gemini/gemini-2.5-flash"

# Custom VLM
vidchain-analyze video.mp4 --vlm "llava:7b"

---

SDK: Modular Sensor Matrix

VidChain uses a LangChain-inspired composable pipeline. Each Node handles one sensing modality; chains are assembled per use case.

Available Nodes

Node	Modality	Description
AdaptiveKeyframeNode	Logic	Gaussian-differential sampling — drops redundant frames to reduce compute load
LlavaNode	Visual	Scene semantics, descriptive captions, and situational context
YoloNode	Visual	High-speed discrete object detection (lightweight fallback for LlavaNode)
WhisperNode	Audio	Speech transcription and acoustic anomaly detection (e.g., shouts)
OcrNode	Text	Digital trace extraction — license plates, screens, documents
TrackerNode	Motion	Persistent object tracking (IoU) and camera motion estimation (Optical Flow)
EmotionNode	Behavioral	Facial sentiment analysis
ActionNode	Behavioral	Human activity classification via MobileNetV3

Custom Pipeline Example

from vidchain import VidChain
from vidchain.pipeline import VideoChain
from vidchain.nodes import AdaptiveKeyframeNode, LlavaNode, OcrNode, TrackerNode

vc = VidChain(db_path="./forensic_vault")

surveillance_chain = VideoChain(nodes=[
    AdaptiveKeyframeNode(change_threshold=1.5),  # High sensitivity
    LlavaNode(model="moondream"),
    OcrNode(),
    TrackerNode()
])

video_id = vc.ingest(
    video_source="gate_camera_04.mp4",
    chain=surveillance_chain
)

response = vc.ask(
    "Were there any vehicles with visible license plates after 14:00?",
    video_id=video_id
)
print(response)

---

REST API

Exposed when running vidchain-serve.

Method	Endpoint	Description
GET	/api/health	System status and list of ingested video IDs
POST	/api/sessions	Create a new isolated neural session
POST	/api/ingest	Submit a video file path for background processing
POST	/api/query	Run a natural language query through the Agentic Router
GET	/api/media-stream	Serve local video securely for frontend playback

---

Architecture

Isolated GraphRAG

Each ingested video generates a dedicated Temporal Knowledge Graph (.pkl). The RAG engine retrieves semantically relevant chunks from ChromaDB and fuses them with structured graph data (co-occurrences, tracking IDs, timestamps). Memory boundaries are strictly enforced — no cross-video context bleed.

The Neural Lens

Every query response is paired with a Base64-encoded visual snapshot extracted directly from the referenced timestamp, providing visual proof for AI-generated claims.

---

License

MIT — See LICENSE for details.

Author: Rahul Sharma — IIIT Manipur