Retrieval-Augmented Generation (RAG) Documentation Assistant

Important knowledge is usually locked inside PDFs - manuals, contracts, reports, research - where it is hard to search and easy to misremember. Asking a plain LLM about those documents invites confident guesses, because the model answers from general training rather than from the specific file in front of it.

The need was a tool that could turn a pile of PDFs into a genuinely searchable knowledge base, and answer questions with exact facts drawn from those files rather than plausible-sounding fabrications.

I built a documentation assistant that uses Retrieval-Augmented Generation to keep answers grounded in the source material. The pipeline handles the full journey from raw PDF to answer: document chunking, embedding, storage and semantic search.

Google Gemini serves as the generation backbone, LangChain orchestrates the pipeline, and ChromaDB stores the vector embeddings for fast semantic retrieval. When a user asks a question, the system retrieves the most relevant passages from their files and generates an answer strictly from that retrieved context.

The result is a professional-grade RAG pipeline that turns any PDF collection into a knowledge base whose answers are accurate and traceable to the source, not guessed.