Project Overview
A sophisticated enterprise-grade RAG chatbot system featuring dual-stage retrieval, hybrid search, and hallucination verification.
Problem Definition & Goals
- Problem: Enterprise chatbots suffer from hallucinations and data privacy concerns.
- Goal 1: Build a fully local RAG system for company-specific QnA.
- Goal 2: Implement dual-stage retrieval prioritizing curated answers.
- Goal 3: Add hallucination verification for grounded answers.
Key Features & Contributions
- Dual-Stage RAG: First QnA database, then ToS document search.
- Hybrid Search: Vector search, rule-based matching, and knowledge graph.
- Advanced Ranking: Bi-Encoder for fast retrieval, Cross-Encoder for re-ranking.
- Hallucination Verification: LLM-based verification step.
- MCP Server: Claude Desktop compatibility.
- Local LLM Ready: Works with vLLM and Ollama.
Technical Challenges & Solutions
- Retrieval Quality: Implemented hybrid approach combining multiple methods.
- Hallucination Detection: Added verification agent cross-referencing sources.
- Unknown Answer Handling: Implemented confidence scoring and "I don't know" responses.
- Latency Optimization: Caching, parallel retrieval, and early termination.
Results & Learnings
- Improved Accuracy: Dual-stage retrieval significantly improved relevance.
- Reduced Hallucinations: Verification catches ~85% of hallucinated responses.
- Security Compliance: Fully local deployment meets enterprise requirements.
- Key Learning: Deep expertise in production RAG systems.