chromem-go

Easily use and train state of the art late-interaction retrieval methods (ColBERT) in any RAG pipeline. Designed for modularity and ease-of-use, backed by research.

AI Data Runtime for Agents. Provides serverless Postgres with a multimodal datalake, enabling scalable retrieval and training. Unifies vector storage, dataset management, and streaming data loading for AI agent workflows.

A lightweight, lightning-fast, in-process vector database by Alibaba with C++ core, Node.js and Python bindings, designed for RAG, agent memory, and vector search use cases.

An interactive visualization tool for large embeddings by Apple. Explore, cross-filter, and search embeddings and metadata to understand and debug embedding models, vector retrieval, and RAG system behavior.