rag-chroma

Topics in the source

• Chroma - Open-Source Embedding Database
• When to use Chroma
• Quick start
• Installation
• Basic usage (Python)
• Core operations
• 1. Create collection
• 2. Add documents

The AI-native database for building LLM applications with memory. 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 1. Use persistent client - Don't lose data on restart 2. Add metadata - Enables filtering and tracking 3. Batch operations - Add multiple docs at once 4. Choose right embedding model - Balance speed/quality 5. Use filters - Narrow search space 6. Unique IDs - Avoid collisions 7. Regular backups - Copy chromadb directory 8. Monitor collection size - Scale up if needed 9. Test embedding functions - Ensure quality 10. Use server mode for production - Better for multi-user | Operation | Latency | Notes | |-----------|---------|-------| | Add 100 docs | 1-3s | With embedding | | Query (top 10) | 50-200ms | Dep

This is an excerpt. Refer to the complete source and its current requirements.