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Weaviate

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Hybrid Search

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Open Source

About Weaviate

Weaviate is an open-source, cloud-native vector database that stores both objects and vectors, enabling semantic search at scale. It combines vector similarity search with keyword filtering, retrieval-augmented generation (RAG), and reranking in a single query interface. Common use cases include RAG systems, semantic and image search, recommendation engines, chatbots, and content classification. Weaviate supports automatic vectorization at import using integrated models (OpenAI, Cohere, HuggingFace, and others) or direct import of pre-computed vector embeddings. Production deployments benefit from built-in multi-tenancy, replication, RBAC authorization, and support for multiple deployment options including Docker, Kubernetes, and cloud services.

Key Features

Hybrid search combining vector similarity and keyword (BM25) filtering
Native retrieval-augmented generation (RAG) support with reranking
Automatic vectorization via integrated AI models (OpenAI, Cohere, HuggingFace, etc.)
Direct import of pre-computed vector embeddings
Cloud-native scalability with fault tolerance and replication
Multi-tenancy and RBAC authorization for production deployments
Multiple deployment options: Docker, Kubernetes, Weaviate Cloud, AWS, GCP

Pros & Cons

Pros
  • Open-source with an active community (16.6k+ stars on GitHub)
  • Combines vector search and keyword filtering for hybrid search
  • Built-in RAG pipeline for generative AI applications
  • Scalable and cloud-native with replication and multi-tenancy
  • Supports popular embedding model providers (OpenAI, Cohere, HuggingFace)

Best For

RAG systems (retrieval-augmented generation)Semantic searchImage searchRecommendation enginesChatbots and conversational AIContent classification

FAQ

What is Weaviate?
Weaviate is an open-source, cloud-native vector database that stores both objects and vectors, enabling semantic search, hybrid search (vector + keyword), and retrieval-augmented generation (RAG).
How do I install Weaviate?
Weaviate can be installed via Docker, Kubernetes, or used through Weaviate Cloud. A quick start with Docker Compose is provided in the GitHub README.
Does Weaviate support automatic vectorization?
Yes, Weaviate can automatically generate vector embeddings during import using integrated AI models from OpenAI, Cohere, HuggingFace, and others.