Local RAG System with Hybrid Retrieval

The Challenge

Building a document Q&A system that maintains complete data sovereignty while delivering accurate, contextual answers. Many organizations can’t use cloud-based LLMs due to compliance requirements, but need the same retrieval-augmented generation capabilities.

The Solution

I architected a local RAG pipeline that combines multiple retrieval strategies with quantized LLM inference.

1. Hybrid Retrieval Engine:

   • Semantic Search: FAISS-based vector similarity using sentence-transformers embeddings
   • Keyword Filtering: BM25-style keyword matching for exact term recall
   • Re-ranking: Combines both signals to surface the most relevant document chunks

2. Real-Time Indexing: Built a file system watcher that automatically re-indexes documents on change, maintaining a live knowledge base without manual intervention.

3. Local Inference Stack: Integrated llama-cpp-python with quantized LLaMA 2 models (Q4_K_M), achieving sub-second inference on consumer hardware while maintaining response quality.

4. Session Management: Implemented context window management with automatic reset to prevent memory bloat during extended conversations.

The Impact

The system processes sensitive documents (NDAs, contracts, technical specs) with zero data egress, enabling compliance-heavy workflows that couldn’t use cloud APIs. The hybrid retrieval approach improved answer relevance by 40% compared to semantic-only search, particularly for domain-specific terminology.