chromalisted

# Chroma - Open-Source Embedding Database The AI-native database for building LLM applications with memory. ## When to use Chroma **Use Chroma when:** - Building RAG (retrieval-augmented generation) applications - Need local/self-hosted vector database - Want open-source solution (Apache 2.0) - Prototyping in notebooks - Semantic search over documents - Storing embeddings with metadata **Metrics**: - **24,300+ GitHub stars** - **1,900+ forks** - **v1.3.3** (stable, weekly releases) - **Apache 2.0 license** **Use alternatives instead**: - **Pinecone**: Managed cloud, auto-scaling - **FAISS**: Pure similarity search, no metadata - **Weaviate**: Production ML-native database - **Qdrant**: High performance, Rust-based ## Quick start ### Installation ```bash # Python pip install chromadb # JavaScript/TypeScript npm install chromadb @chroma-core/default-embed ``` ### Basic usage (Python) ```python import chromadb # Create client client = chromadb.Client() # Create collection collection = client.create_collection(name="my_collection") # Add documents collection.add( documents=["This is document 1", "This is document 2"], metadatas=[{"source": "doc1"}, {"source": "doc2"}], ids=["id1", "id2"] ) # Query results = collection.query( query_texts=["document about topic"], n_results=2 ) print(results) ``` ## Core operations ### 1. Create collection ```python # Simple collection collection = client.create_collection("my_docs") # With custom embedding f