Large Language Models (LLMs) can sometimes provide incorrect information due to outdated knowledge, a phenomenon known as 'hallucination.' Retrieval-Augmented Generation (RAG) addresses this by dynamically fetching relevant data from external databases, ensuring responses are accurate and up-to-date. This guide explains how RAG works, from cleaning and indexing data to retrieving and generating responses, and provides implementation steps using LangChain and LlamaIndex. Advanced techniques like Parent Document Retriever are also discussed for enhanced specificity and context.
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Retrieval-Augmented Generation (RAG) u sing LangChain, LlamaIndex, and OpenAIHow does RAG work?Basic RAG Implementation using LangChain and LlamaIndexAdvanced RAG Implementation using LangChain and LlamaIndexSummaryReferenceSort: