The contrarian take on building better retrieval augmented generation systems.

MeiliSearch is an open-source search engine that provides fast and relevant search capabilities for developers. Readers can learn about search algorithms, indexing techniques, and building search functionality into their applications. With documentation, tutorials, and community support, MeiliSearch empowers developers to enhance the search experience for their users.

Meilisearch

This post argues against the use of vector databases for retrieval augmented generation (RAG) systems, highlighting their limitations in query refinement and precision. It suggests a more effective approach using hybrid search that combines full-text and semantic capabilities, mirroring human search behaviors to improve relevance and simplicity.

Why you shouldn't use vector databases for RAG

The engineer's shortcut: Vector databases

Problem 1: Unrefined queries lead to irrelevant results

Problem 2: Vector search sacrifices precision for recall

The solution: Simpler (and better) RAG with search engines

The power of simplicity in RAG: Back to search basics

<p>Just a thought, hybrid search is supported by opensearch, milvus and chromadb as well. But the core insight from this article is about creating a good enough query from LLM before searching in RAG</p>
