Weaviate
An open-source, cloud-native vector database that combines semantic search with structured filtering for AI applications.
What is Weaviate?
Weaviate is an open-source vector database that stores objects alongside their vector embeddings, enabling semantic search at scale. It combines vector similarity search with structured filtering and retrieval-augmented generation (RAG) in a single query interface, designed for building AI-powered applications like recommendation engines, chatbots, and content classification systems.
Developers and data engineers building AI applications that require semantic search, hybrid search, or retrieval-augmented generation (RAG) capabilities, particularly those needing production-ready scalability and integration with modern embedding models.
Developers choose Weaviate for its ability to unify vector search with traditional database features like filtering and multi-tenancy, its production-ready scalability, and its extensive integrations with popular embedding providers and AI frameworks.
Overview
Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database.
Use Cases
Best For
- Building retrieval-augmented generation (RAG) systems for chatbots and Q&A applications
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.