Prompt
Specify a webhook-driven task for OpenClaw
Describe event inputs, validation, deduplication, and intended actions before integration work.
OpenClawDevelopment & Engineering
Dave AI
Chroma Integration Skill
Chroma local vector database setup and operations for development and production
instructions.md
--- name: chroma-integration-6ca79799861a description: Chroma local vector database setup and operations for development and production allowed-tools: - Read - Write - Edit - Bash - Glob - Grep graph: domains: [domain:software-engineering] specializations: [specialization:ai-agents-conversational] skillAreas: [skill-area:retrieval-augmented-generation, skill-area:search-indexing] roles: [role:ml-engineer, role:backend-engineer] workflows: [workflow:ml-model-lifecycle, workflow:feature-development] --- # 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--- name: chroma-integration-6ca79799861a description: Chroma local vector database setup and operations for development and production allowed-tools: - Read - Write - Edit - Bash - Glob - Grep graph: domains: [domain:software-engineering] specializations: [specialization:ai-agents-conversational] skillAreas: [skill-area:retrieval-augmented-generation, skill-area:search-indexing] roles: [role:ml-engineer, role:backend-engineer] workflows: [workflow:ml-model-lifecycle, workflow:feature-development] --- # 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
Platform guidance
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.