LogiMind
Multi-agent RAG over public logistics operational documentation
Multi-agent RAG system for querying public logistics operational documentation in natural language.
A fixed-orchestration agent pipeline — planner, retriever, responder — runs hybrid BM25 and dense retrieval against a Qdrant vector store, served through a FastAPI backend with a Streamlit UI. The pipeline is instrumented end to end with LangSmith tracing and evaluated continuously with RAGAS.
Measured: 0.98 faithfulness, ~9.4s median latency, $0.028 per query. 108 tests, Dockerized, CI on every push, deployed live.
Architectural decisions are documented in the repo — including why the retriever agent is deliberately not LLM-backed, and why I chose fixed orchestration over an AutoGen group chat.
Stack: AutoGen, LangChain, Qdrant, RAGAS, LangSmith, FastAPI, Streamlit, hybrid BM25 + dense retrieval, Docker, GitHub Actions, AWS
Status: Deployed, CI on every push