How to Do the Retrieval in Retrieval-Augmented Generation (RAG) 0 like January 29, 2025Share this postAuthor(s): Dimitris Effrosynidis Originally published on Towards AI. Efficient Retrieval for RAG Leveraging Dense BM25 and Transformer ModelsThis member-only story is on us. Upgrade to access all of Medium.Image by author.Efficient and accurate text retrieval is a cornerstone of modern information systems, powering applications like search engines, chatbots, and knowledge bases.It is the first step in RAG (Retrieval-Augmented Generation) systems.RAG systems, first use text retrieval to find the answer to our query and then use an LLM to answer. RAG allows us to chat with our data.In this article, we explore the integration of dense retrieval, BM25 lexical search, and transformer-based reranking to create a robust and scalable text retrieval system.The project leverages the strengths of each technique:Dense Retrieval: Captures semantic meaning by embedding text into high-dimensional vector spaces, enabling similarity-based search.BM25 Lexical Search: Performs efficient keyword matching to quickly narrow down relevant results.Transformer-Based Reranking: Uses Hugging Face cross-encoders to evaluate and rank query-document pairs based on semantic relevance, ensuring precision in the final output.This hybrid approach optimizes both computational efficiency and retrieval accuracy, making it well-suited for use cases where context, relevance, and speed are critical.Chunking and Embedding:Text is segmented into chunks (e.g., sentences or paragraphs) to ensure embeddings represent actionable parts of the content.Multiple chunking strategies are explored, including fixed-length chunks and overlapping chunks, to Read the full blog for free on Medium.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post