Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/

Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database​.

💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows

Virtual Assistant - GPT Smart Search Engine - Bot Framework + Azure OpenAI + Azure AI Search + Azure SQL + Bing API + Azure Document Intelligence + LangChain + CosmosDB

🌌 A complete search engine and RAG pipeline in your browser, server or edge network with support for full-text, vector, and hybrid search in less than 2kb.

Website for the Weaviate vector database

Python client for Qdrant vector search engine

Collections of vector search related libraries, service and research papers

Framework for benchmarking vector search engines

Vald. A Highly Scalable Distributed Vector Search Engine

Weaviate vector database – examples

NucliaDB, The AI Search database for RAG

Rust client for Qdrant vector search engine

Real time vector search engine

Comprehensive Vector Data Tooling. The universal interface for all vector database, datasets and RAG platforms. Easily export, import, backup, re-embed (using any model) or access your vector data from any vector databases or repository.

🔍 DiscovAI-Search: An AI-powered search engine for AI tools and custom data. Built with Next.js, OpenAI, Supabase, and more. Features vector-based search, Redis caching, and LLM-powered responses.

Cottontail DB is a column store vector database aimed at multimedia retrieval. It allows for classical boolean as well as vector-space retrieval (nearest neighbour search) used in similarity search using a unified data and query model.

Ruby wrapper for the Weaviate vector search database API

Awesome Weaviate

(distributed) vector database