MCP-POWERED RAG
VIDEO INTELLIGENCE PLATFORM
An in-depth technical breakdown of an agentic semantic video search system built with Python, Streamlit, ChromaDB, and Anthropic's Model Context Protocol (MCP) — enabling sub-200ms clip grounding and conversational querying.
1. SYSTEM ARCHITECTURE & PIPELINE DATA FLOW
The platform decomposes long-form video content into temporal transcript chunks and synchronized keyframes. These elements are embedded into a local ChromaDB vector store and exposed via an MCP Server API so autonomous AI agents can invoke video queries directly as standard function tools.
2. TRADE-OFF ANALYSIS: WHAT WAS TRIED & DISCARDED
End-to-End Cloud Multimodal Embeddings
Initially attempted sending every video frame to cloud multi-modal embedding APIs.
Why Discarded: High API request costs ($12+/hr video) and excessive network roundtrip latency (>1.8s per query), rendering interactive chat unusable.
Monolithic Synchronous UI Ingestion
Initially ran frame extraction and embedding generation synchronously within the Streamlit UI thread.
Why Discarded: Froze the dashboard UI completely during long video processing runs. Rebuilt as an isolated background task queue with status polling.
3. BENCHMARK METHODOLOGY (SUB-200MS LATENCY CLAIM)
To achieve consistent sub-200ms retrieval bounds, query execution timing was benchmarked across 500 test queries on local NVMe storage using ChromaDB's HNSW index algorithm.
4. FUTURE ARCHITECTURAL IMPROVEMENTS (WITH MORE TIME)
Add a secondary cross-encoder re-ranking pass to refine top-k vector search results before sending payload bounds to the LLM.
Implement Server-Sent Events (SSE) streaming directly in the MCP tool endpoint so tokens stream to AI agents in real time.
Upgrade local ChromaDB to Qdrant or Milvus cloud cluster for multi-node vector index distribution across millions of video hours.