Agent skill · data analytics · nousresearch
chroma
Embedding database for RAG and semantic search.
Why this skill is useful
Provides specific commands and usage patterns for integrating Chroma as a vector database in LLM applications, which the AI may not generate reliably on its own.
What it needs
Requires chromadb, sentence-transformers installed locally. About 4k tokens when loaded. Last updated 2026-08-07. 226,679 stars on the source repository.
What this skill does
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 Basic usage (Python) Core operations 1. Create collection 2. Add documents 3. Query (similarity search) 4. Get documents 5. Update documents 6. Delete documents Persistent storage Embedding functions Default (Sentence Transformers) OpenAI HuggingFace Custom embedding function Metadata filtering LangChain integration LlamaIndex integration Server mode Best practices 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 Performance Operation Latency Notes ----------- --------- ------- Add 100 docs ~1-3s With embedding Query (top 10) ~50-200ms Depends on collection size Metadata filter ~10-50ms Fast with proper indexing Resources GitHub: https://github.com/chroma-core/chroma ⭐ 24,300+ Docs: https://docs.trychroma.com Discord: https://discord.gg/MMeYNTmh3x Version: 1.3.3+ License: Apache 2.0
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nousresearch/chroma