Enterprise RAG Knowledge Assistant with FAISS

Problem

Users cannot safely paste long PDFs into generic LLMs. Enterprise knowledge bases require grounded answers with source citations, and knowledge must update when documents change without retraining entire models.

Approach

Built a production RAG (Retrieval-Augmented Generation) system using FAISS vector store for similarity search with sentence embeddings (MiniLM-L6-v2). Integrated open-weights LLM (Gemma-2B) to prevent vendor lock-in. Deployed on Hugging Face Spaces with Gradio UI and documented in reproducible Jupyter notebooks.

Result

Production RAG system delivering document-grounded answers that prevent hallucination through retrieval-based context. Supports optional API keys for model experiments while maintaining open-source LLM path for data sovereignty.

• ◆Production RAG system with FAISS retrieval
• ◆Document-grounded answers preventing hallucination
• ◆Open-source LLM implementation (no vendor lock-in)
• ◆Hugging Face Space demo deployment