weaviate-integration

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Weaviate vector database setup with GraphQL queries and hybrid search

AI & Automation 814 stars 53 forks Updated today MIT

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Skill Content

# Weaviate Integration Skill ## Capabilities - Set up Weaviate cluster (cloud or self-hosted) - Define schemas with properties and vectorizers - Implement GraphQL queries - Configure hybrid search (vector + keyword) - Set up multi-tenancy - Implement batch import operations ## Target Processes - vector-database-setup - rag-pipeline-implementation ## Implementation Details ### Core Operations 1. **Schema Management**: Class definitions and properties 2. **Data Import**: Single and batch object creation 3. **Vector Search**: nearVector, nearText queries 4. **Hybrid Search**: Combined vector and BM25 5. **GraphQL**: Flexible querying with Get and Aggregate ### Configuration Options - Vectorizer modules (text2vec-*, multi2vec-*) - Replication factor - Sharding configuration - Multi-tenancy settings - Module configuration ### Best Practices - Design schema for query patterns - Use appropriate vectorizer - Enable hybrid search for better recall - Configure proper backups - Monitor resource usage ### Dependencies - weaviate-client - langchain-weaviate

Details

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

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