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.
     
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 two approaches to store vectors: 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 many other features.
To get started quickly, have a look at one of these tutorials:
Weaviate offers multiple installation and deployment options:
See the installation docs for more deployment options, such as AWS and GCP.
You can easily start Weaviate and a local vector embedding model with Docker.
Create a docker-compose.yml file:
```yml
services:
weaviate:
image: cr.weaviate.io/semitechnologies/weaviate:1.36.0
ports:
- "8080:8080"
- "50051:50051"
environment:
ENABLE_MODULES: text2vec-mod
... [truncated — view full README on GitHub]
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