Chroma
Embeddings-native local DB
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Data Infrastructure Embeddings-native local DB
Running an embedded vector database locally for development, prototyping, and small-scale RAG applications without needing a separate server. It stores embeddings alongside documents and metadata in-process, making it the fastest way to get vector search working in a Python project.
Install with pip install chromadb and create a client in two lines of Python: import chromadb; client = chromadb.Client(). Create a collection, add documents (Chroma generates embeddings automatically using a default model), and query with natural language. No server, Docker, or API key needed.
$ pip install chromadb and create a client in two lines of Python: import chromadb Be the first to share a Chroma case study and get discovered by clients.
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