Mastering RAG: Chat With Your Documents Locally

What is RAG?

RAG combines a language model with a search system. It retrieves relevant documents from your database and feeds them into the LLM's context window.

Vector Databases

To build a fast RAG system, use a vector database like ChromaDB or Qdrant. They store text as embeddings (numbers) for rapid similarity search.

Putting it Together with LangChain

LangChain simplifies the pipeline. You load a PDF, split it into chunks, embed it, and create a retrieval chain. All of this can run offline using Hugging Face embeddings and Llama.cpp.