What is Weaviate?
Weaviate is an open-source vector database that combines vector search capabilities with GraphQL API access and built-in vectorization, making it easy to build AI-powered applications with minimal infrastructure setup. It stores objects with vector embeddings and enables semantic search, while also supporting traditional filtering, aggregations, and GraphQL-style queries. Weaviate's architecture emphasizes developer experience and ease of integration with AI applications.
The database offers several distinctive features: built-in vectorization modules that automatically generate embeddings using various models (OpenAI, Cohere, Hugging Face, etc.), eliminating the need to manage embedding generation separately; GraphQL API that provides a familiar query interface; support for hybrid search combining vector and keyword retrieval; and multi-tenancy capabilities for SaaS applications. Weaviate can run locally, self-hosted, or in Weaviate Cloud Services for managed deployment.
Weaviate has gained adoption for its developer-friendly design and comprehensive feature set. The automatic vectorization reduces boilerplate code and complexity compared to manually managing embeddings. The GraphQL interface appeals to developers familiar with modern API patterns. Built-in modules for various AI services simplify integration. While it may not match the raw performance of specialized systems at extreme scale, Weaviate offers an excellent balance of features, ease of use, and flexibility for many AI applications. It integrates well with frameworks like LangChain and LlamaIndex.