chroma-integration

Solid

Chroma local vector database setup and operations for development and production

AI & Automation 1,160 stars 71 forks Updated today MIT

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Skill Content

# Chroma Integration Skill ## Capabilities - Set up Chroma (ephemeral, persistent, client-server) - Create and manage collections - Implement document ingestion with embeddings - Configure metadata filtering - Set up multi-tenant collections - Implement where and where_document filters ## Target Processes - vector-database-setup - rag-pipeline-implementation ## Implementation Details ### Deployment Modes 1. **Ephemeral**: In-memory for testing 2. **Persistent**: Local file-based storage 3. **Client-Server**: Chroma server deployment ### Core Operations - Collection creation with embedding functions - Add/update/delete documents - Query with filters - Metadata management ### Configuration Options - Embedding function selection - Persistence directory - Distance metric (l2, ip, cosine) - Collection metadata - Server configuration ### Best Practices - Use persistent mode for development - Deploy server mode for production - Design metadata schema upfront - Implement proper ID strategies ### Dependencies - chromadb - langchain-chroma

Details

Author
a5c-ai
Repository
a5c-ai/babysitter
Created
4 months ago
Last Updated
today
Language
JavaScript
License
MIT

Integrates with

LangChain · AI ChromaDB · Database

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