I Built a Local RAG Assistant with Ollama, ChromaDB and LangChain. Here's What I Learned

A fully local RAG assistant was built to let technicians search thousands of pages of internal and accounting manuals.

The system ingests PDFs, chunks the text, embeds it in ChromaDB, retrieves relevant passages, and uses a locally hosted Llama 3 model via Ollama to answer queries.

It was deployed with Docker Compose across the model, vector store, API, and web UI, keeping sensitive data on-premises.

The setup shows how organizations can support document search with LLMs while reducing cloud exposure and hallucinated answers.