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It is an open-source, self-hostable vector database for semantic similarity search that specializes in low query latency. It bridges the gap between information retrieval and memory retention in Large Language Models. | It is a Postgres extension that automates the transformation and orchestration of text to embeddings and provides hooks into the most popular LLMs. This allows you to do vector search and build LLM applications on existing data with as little as two function calls. |
High performance and production-scale similarity search for embedding vectors;
Full fledged database management system with familiar database, table, and field concepts. Vector is just another field type;
Native Python support and REST API interface | Workflows for both vector search and RAG;
Integrations with OpenAI's embeddings;
Automated creation of Postgres triggers to keep your embeddings up to date |
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Milvus is an open source vector database. Built with heterogeneous computing architecture for the best cost efficiency. Searches over billion-scale vectors take only milliseconds with minimum computing resources.

It is an open-source Vector Search Engine and Vector Database written in Rust. It deploys as an API service providing search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more.

It is an open-source embedding database. Chroma makes it easy to build LLM apps by making knowledge, facts, and skills pluggable for LLMs.

It is an open-source database for vector search built with persistent storage, which greatly simplifies retrieval, filtering, and management of embeddings.

It is an open-source PostgreSQL database extension to store vector data, generate embeddings, and handle vector search operations. It provides a new index type for vector columns which speeds up ORDER BY ... LIMIT queries.

It is a simple, serverless, distributed vector database that can be used as an API. It is designed to handle large amounts of vector text data, making it suitable for projects with high data volumes.