Weaviate Vector Database
Design and query Weaviate collections for semantic search. Generates class schemas, vectorizer configs, hybrid BM25+vector queries, multi-tenancy patterns, and async batch import for millions of objects.
Build production search with Weaviate. This skill scaffolds collection schemas, configures vectorizer modules (OpenAI, Cohere, transformers), tunes HNSW parameters, writes hybrid queries with alpha tuning, and sets up multi-tenancy for SaaS isolation.
weaviate vector-database semantic-search hybrid-search embeddings
When to use
Use for semantic search over large corpora, multi-tenant RAG, image+text retrieval, or when you need filterable vector search with hybrid scoring out of the box.
Examples
Hybrid search with alpha tuning
Blend BM25 and vector search with adjustable weighting
Set up a Weaviate collection for product search with hybrid queries and tune alpha for our review feedback data
Multi-tenant collections
Isolate customer data with Weaviate multi-tenancy
Configure multi-tenant Weaviate collections for our B2B SaaS so each customer's documents are isolated