This article explores techniques for optimizing Retrieval-Augmented Generation (RAG) systems by implementing hybrid search and re-ranking mechanisms. It details how to combine dense vector embeddings with sparse keyword matching, such as BM25, to improve retrieval accuracy, followed by the use of a cross-encoder reranker to ensure only the most relevant context is passed to a Large Language Model in production environments.
An article discussing the use of embeddings in natural language processing, focusing on comparing open source and closed source embedding models for semantic search, including techniques like clustering and re-ranking.
Re-ranking is integral to retrieval pipelines, but implementation methods vary. We introduce rerankers, a Python library offering a unified interface for common re-ranking approaches.
The Towards Data Science team highlights recent articles on the rise of open-source LLMs, ethical considerations with chatbots, potential manipulation of LLM recommendations, and techniques for temperature scaling and re-ranking in generative AI.
