chroma

Chroma is an open-source AI-native embedding database designed for simplicity and developer experience, providing an easy-to-use interface for storing, querying, and managing vector embeddings in AI applications — particularly retrieval-augmented generation (RAG) pipelines and semantic search. Chroma prioritizes developer ergonomics with a minimal API that enables getting started in just a few lines of code, making it popular for prototyping, research, and small-to-medium scale production deployments. Key features include: simple Python API (collections are created with a single call, documents can be added with automatic embedding generation, and queries return semantically similar results — all in 3-5 lines of code), automatic embedding (pluggable embedding functions including OpenAI, Cohere, Hugging Face sentence-transformers, and custom models — Chroma handles vectorization transparently), metadata filtering (combining vector similarity with where-clause filters on document metadata for precise retrieval), document storage (storing original documents alongside their embeddings, eliminating the need for a separate document store), full-text search (hybrid search combining semantic similarity with keyword matching), and multi-modal support (storing and querying embeddings from text, images, and other modalities). Chroma operates in multiple modes: in-memory (ephemeral — for testing and experimentation), persistent (local disk storage for development), and client-server (HTTP-based for production deployment with distributed backends). The architecture uses a pluggable backend system — the default uses DuckDB+Parquet for persistent storage, while production deployments can use ClickHouse or other backends. Chroma integrates seamlessly with LLM frameworks: LangChain (as a vector store component), LlamaIndex (as a storage backend), and direct integration with OpenAI, Anthropic, and other LLM APIs. While Chroma may not match the scalability of Pinecone or Qdrant for billion-scale deployments, its simplicity and developer experience make it ideal for AI application prototyping, educational projects, and production applications with moderate scale requirements.