Weaviate

AI & Machine Learning

Definitions

  1. An open-source vector database that combines vector search with structured filtering and keyword search in a single engine. Weaviate can automatically vectorize data using built-in integrations with embedding models from OpenAI, Cohere, Hugging Face, and others, or accept pre-computed vectors. Its hybrid search combines dense vector similarity with BM25 keyword matching for better retrieval accuracy. Weaviate supports multi-tenancy, horizontal scaling, and multiple consistency levels. The database includes a GraphQL API for complex queries and a module system for extending functionality. Weaviate positioned itself as an enterprise-grade vector database, offering both self-hosted and managed cloud options. Its automatic vectorization feature simplifies the development workflow by eliminating the separate embedding generation step.

    In plain English: An open-source database that searches by meaning (using AI vectors) and keywords at the same time. It can even generate the AI embeddings for you automatically.

    Example: Weaviate's hybrid search finds relevant support tickets even when customers use completely different words than our documentation.