RAG chatbot architecture with Python and LangChain: PDFs are split into chunks, turned into embeddings and stored in a Chroma vector database, then retrieved context is sent to an LLM to generate an answer with sources

How to Build a RAG Chatbot Using Python and LangChain

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A step-by-step tutorial to build a RAG chatbot with Python and LangChain: load PDFs, split them into chunks, create embeddings, store them in Chroma, retrieve context, answer with an LLM and add a Streamlit chat interface.

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