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

Teams paste any PDF and get trustworthy answers instead of guessing. Every response cites its source, with no vendor lock-in.

• ◆Teams get trustworthy answers from any PDF in seconds
• ◆Every response cites its source, no guessing, no fabrication
• ◆No vendor lock-in with open-source LLM implementation
• ◆Works as a demo or scales to production deployment