rag-mcp
MCP bridge to a multimodal RAG service, enabling hybrid search and Q&A over documents with tools for knowledge base queries and health checks.
README
Multimodal RAG Service
Production-minded retrieval for text, tables, images, and PDFs. Sentence-transformer text vectors and CLIP image vectors are stored in separate FAISS indexes; BM25 sparse results are combined using weighted reciprocal-rank fusion.
Run locally
python -m venv .venv
.venv\\Scripts\\Activate.ps1
pip install -e ".[dev]"
Copy-Item .env.example .env
uvicorn app.main:app --reload
The API documentation is available at http://localhost:8000/docs. Models download automatically at their first embedding request.
API
curl -F "files=@report.pdf" http://localhost:8000/v1/documents
curl -X POST http://localhost:8000/v1/search -H "Content-Type: application/json" -d "{\"query\":\"revenue trend\",\"top_k\":5}"
curl -N -X POST http://localhost:8000/v1/chat/stream -H "Content-Type: application/json" -d "{\"query\":\"Summarize the report\"}"
MCP integration
Run the REST API, then start the stdio MCP bridge:
rag-mcp
Set RAG_API_URL to point at a non-default API address. The bridge provides search_knowledge_base, ask_knowledge_base, list_documents, and service_health tools to MCP clients.
Evaluation
Supply JSONL rows containing query, relevant_document_ids, and optionally reference_answer:
python -m app.evaluation --dataset eval/golden.jsonl
Reported metrics include Recall@K, Precision@K, MRR, p50/p95 retrieval latency, ROUGE-L, and BLEU-1.
Container deployment
docker compose up --build
The compose configuration mounts a persistent named volume at /service/data.
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