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 leyendoCategoría: Retrieval augmented generation
Full project: RAG (Retrieval-Augmented Generation) II
Optimizing Document Retrieval for RAG Systems: Enhancing the Search Process for SMEs Using Metadata and PGVector For small and medium-sized businesses (SMEs) in Spain, accessing up-to-date, trustworthy information on government grants and financial support is essential. One effective way to help SMEs navigate this complex landscape is through a Retrieval-Augmented Generation (RAG) model. In this project, I used a specialized resource, the Plataforma Pyme guide to government grants, as the source for a RAG system
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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