Case Study // vidchain

VidChain

Multimodal RAG framework for forensic video intelligence — 'LangChain for Videos'.

EngineIRIS
ReasoningGraphRAG
DeploymentEdge/Local
IntelligenceMultimodal
DEMO_RECORDING
[ VIDEO ASSET REQUIRED ]

02 // Architecture Execution

Architected a local-first, edge-optimized RAG framework using the IRIS engine. Integrated multi-sensor fusion and GraphRAG to allow for complex natural language queries across distributed video nodes, enabling automated suspect tracking and event deduction.

01 // The Problem Context

Forensic video analysis typically requires manual scrubbing through massive datasets, making it impossible to perform cross-video entity tracking or complex reasoning in real-time.

System Stack

PythonPyTorchFastAPIGraphRAGOpenCV
core_module.ts
# IRIS Engine: Cross-Modal Reasoning
def forensic_query(query, streams):
    context = engine.extract_multimodal_context(streams)
    graph = graph_rag.construct(context)
    result = graph.query(query, reasoning_mode='deductive')
    return result # Forensic evidence generated
PyPI PackageSource Code