Weaviate is an open source vector database that is robust, scalable, cloud-native, and fast.
If you just want to get started, great! Try:
And you can find our documentation here.
If you have a bit more time, stick around and check out our summary below 😉
With Weaviate, you can turn your text, images and more into a searchable vector database using state-of-the-art ML models.
Some of its highlights are:
Weaviate typically performs a 10-NN neighbor search out of millions of objects in single-digit milliseconds. See benchmarks.
You can use Weaviate to conveniently vectorize your data at import time, or alternatively you can upload your own vectors.
These vectorization options are enabled by Weaviate modules. Modules enable use of popular services and model hubs such as OpenAI, Cohere or HuggingFace and much more, including use of local and custom models.
Weaviate is designed to take you from rapid prototyping all the way to production at scale.
To this end, Weaviate is built with scaling, replication, and security in mind, among others.
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.
For starters, you can build vector databases with text, images, or a combination of both.
You can also build question and answer extraction, summarization and classification systems.
You can see code examples here, and you might find these blog posts useful:
Examples and/or documentation of Weaviate integrations (a-z).
Speaking of content - we love connecting with our community through these. We love helping amazing people build cool things with Weaviate, and we love getting to know them as well as talking to them about their passions.
To this end, our team does an amazing job with our blog and podcast.
Some of our past favorites include:
Subscribe to our 🗞️ newsletter to keep up to date including new releases, meetup news and of course all of the content,.
We invite you to:
You can also say hi to us below:
Software Engineers - Who use Weaviate as an ML-first database for your applications.
Data Engineers - Who use Weaviate as fast, flexible vector database
Data Scientists - Who use Weaviate for a seamless handover of their Machine Learning models to MLOps.
Read more in our documentation
You can use Weaviate with any of these clients:
You can also use its GraphQL API to retrieve objects and properties.