Weaviate is an open source vector database that stores both objects and vectors, allowing for combining vector search with structured filtering with the fault-tolerance and scalability of a cloud-native database, all accessible through GraphQL, REST, and various language clients.

With Weaviate, you can turn your text, images and more into a searchable vector database using state-of-the-art ML models. Weaviate typically performs a 10-NN neighbor search out of millions of objects in single-digit milliseconds. You can use Weaviate to conveniently vectorize your data at import time, or alternatively you can upload your own vectors (say, if you download a model from OpenAI or HuggingFace). Weaviate powers lightning-fast vector searches, but it is capable of much more. Some of its other superpowers include recommendation, summarization, and integrations with neural search frameworks.