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The RAG Pipeline: first we load data from documents, chunk them up, and generate embeddings and store in a database (more below). Then, we can retrieve documents deemed relevant to a user’s query and use a LLM to then generate an answer to our question that’s grounded on the sourced documents. Source: Langchain - ABC
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