PRODUCTION RAG Retrieval-Augmented Generation & Vector Search

DocMindAI: Enterprise Hybrid RAG Engine

Enterprise document intelligence platform combining FAISS dense vector search with BM25 keyword matching via Reciprocal Rank Fusion (RRF). Delivers grounded answers with exact source citations, per-doc scoping, and SQL analytics.

LangChain FAISS Vector Store BM25 Keyword Search Reciprocal Rank Fusion (RRF) Claude Haiku Docker
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🖼️ Visual System Architecture & Interface
DocMindAI: Enterprise Hybrid RAG Engine
FAISS (MiniLM)
Dense Embeddings
rank_bm25
Sparse Keyword
Reciprocal Rank (RRF)
Ranking Algorithm
6/6 Passing
Unit Test Suite

🎯 Hallucination-Resistant Retrieval

Standard vector search frequently misses exact part numbers, model codes, and acronyms inside dense engineering manuals. DocMindAI fuses dense semantic vectors with sparse lexical keyword matching.

⚡ Hybrid Retrieval Architecture: FAISS + BM25 + RRF

  1. Dense Vector Search: On-disk FAISS index using all-MiniLM-L6-v2 (384-dimensional semantic embeddings).
  2. Sparse Keyword Matching: rank_bm25 index to catch exact part numbers and serial codes.
  3. Reciprocal Rank Fusion (RRF): Merges dense and sparse rankings with reciprocal rank math, preventing hallucinations and surfacing exact matches.
  4. Interactive CLI Demo: Includes demo_hybrid_retrieval.py for side-by-side benchmarking of vector vs. keyword vs. hybrid fusion.

🛡️ Production Capabilities

  • Exact Document Scoping: Restricts query context to user-selected PDF manuals or whole libraries.
  • SQL Analytics: Natural language questions translate into exact SQL analytics against metadata tables.
  • Containerized: Production Dockerfile and .dockerignore for instant container deployment.

💻 Tech Stack & Repositories

  • Frameworks & Search: Python, Flask, LangChain, FAISS, rank_bm25.
  • LLM Synthesis: Claude 3.5 Haiku API with strict citation grounding.
  • DevOps: Docker, pytest (6/6 passing tests).
  • GitHub Repository: docmindai-rag-chatbot
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