Introduction#
Retrieval-Augmented Generation (RAG) is a powerful pattern that combines the strengths of large language models with external knowledge retrieval. Instead of relying solely on a model’s training data, RAG pipelines fetch relevant documents from a vector store and inject them into the LLM context at query time. This approach dramatically reduces hallucinations and enables your application to reason over proprietary or up-to-date data.
In this tutorial, you will build a complete RAG application using LangChain, ChromaDB as the vector store, and OpenAI as the LLM provider. By the end, you will have a working command-line QA system that answers questions from your own documents.