The Reranker and the Power of the Prompt Welcome to the fourth installment of our full RAG (Retrieval-Augmented Generation) project. If you’ve followed the series so far, you already know how to prepare documents, build the index, and retrieve relevant results using multiple strategies. Now it’s time to go one level deeper: filtering, refining, and shaping the final answer. This is where one of the most important pieces of the RAG puzzle comes into play:
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Full project: RAG (Retrieval-Augmented Generation) III
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
Seguir leyendoFull project: RAG (Retrieval-Augmented Generation) I
Retrieval-Augmented Generation, known as RAG, harnesses the capabilities of LLMs (Large Language Models) to offer an effective method for accessing external information. LLMs comprehend the inquiry and leverage the contextual details in the given external materials to formulate a response on the subject. This process essentially bridges the gap between the vast general knowledge of an LLM and the specific, often niche, information contained within a user’s own documents. The aim is to create a
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