weaviate-integration

name	weaviate-integration
description	Weaviate vector database setup with GraphQL queries and hybrid search
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"]}

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