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Chroma

Python-first embedded vector store; pip install and prototype RAG.

Chroma is an open-source embedding database (vector store) designed to be the storage and retrieval layer for LLM applications. It provides a simple Python and JavaScript API for adding documents, generating or supplying embeddings, and running similarity queries with metadata filtering, and can run in-memory, as a local persistent store, or in client-server mode. It is popular for its low-friction developer experience in building RAG prototypes.

Repository

chroma-core/chroma

Language

Rust

What you'd build with it

  • Storing and querying document embeddings as the retrieval backend of a RAG application
  • Rapidly prototyping semantic search locally before scaling to a hosted vector database
  • Backing agent memory or context retrieval with metadata-filtered similarity search

Tags

vector-dbembeddingsembedded