Open Source Vector Databases

Vector databases store and search numerical representations of text, images, and other data. By finding vectors that are close in meaning or features, they support semantic search, recommendation, and retrieval for applications that use large language models or other machine learning systems. Many also provide metadata filters and conventional database features, helping teams retrieve relevant information from large collections without relying only on exact keyword matches.

Open source options range from embedded libraries to standalone database servers and retrieval frameworks. When choosing one, consider search accuracy and speed, data scale, filtering and update support, deployment requirements, licensing, maintenance activity, and integration with your storage and model workflows. These tools are useful to developers building search and AI applications, as well as researchers and organizations that need control over data handling and infrastructure.