Building Intelligent RAG Systems
So far in this book, we’ve talked about LLMs and tokens and working with them in LangChain. Retrieval-Augmented Generation (RAG) extends LLMs by dynamically incorporating external knowledge during generation, addressing limitations of fixed training data, hallucinations, and context windows. A RAG system, in simple terms, takes a query, converts it directly into a semantic vector embedding, runs a search extracting relevant documents, and passes these to a model that generates a context-appropriate user-facing response.
This chapter explores RAG systems and the core components of RAG, including vector stores, document processing, retrieval strategies, implementation, and evaluation techniques. After that, we’ll put into practice a lot of what we’ve learned so far in this book by building a chatbot. We’ll build a production-ready RAG pipeline that streamlines the creation and validation of corporate project documentation...
