Chat with your documents tool — RAG (vector DBs + cosine sim.) & Claude API implementation

A detailed implementation of a RAG system for a law firm that processes 1TB of legal documents using vector embeddings, FAISS indexing, and Claude API. The system chunks documents, creates embeddings with a trilingual MiniLM model, performs cosine similarity search, and includes citation verification to prevent hallucinations. Key features include OCR processing, privacy-focused local deployment, sub-20ms query response times, and costs around $0.02 per query.