Combining Self-Query and MMR Retrievers in RAG Pipelines: A Practical Guide In Retrieval-Augmented Generation (RAG) pipelines, the retriever plays a central role. Before the LLM can generate answers, it needs relevant information — and retrievers are the components in charge of finding it. Whether pulling from a vector database, a search index, or a hybrid of both, retrievers define what information the model can see. In this post, we walk through a Python implementation using
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