REAMDE.md

Weaviate

Overview

Weaviate is a cloud-native, open source vector database that is robust, fast, and scalable.

To get started quickly, have a look at one of these pages:

Quickstart tutorial To see Weaviate in action
Contributor guide To contribute to this project

For more details, read through the summary on this page or see the system documentation.

Why Weaviate?

Weaviate uses state-of-the-art machine learning (ML) models to turn your data - text, images, and more - into a searchable vector database.

Here are some highlights.

Speed

Weaviate is fast. The core engine can run a 10-NN nearest neighbor search on millions of objects in milliseconds. See benchmarks.

Flexibility

Weaviate can vectorize your data at import time. Or, if you have already vectorized your data, you can upload your own vectors instead.

Modules give you the flexibility to tune Weaviate for your needs. More than two dozen modules connect you to popular services and model hubs such as OpenAI, Cohere, VoyageAI and HuggingFace. Use custom modules to work with your own models or third party services.

Production-readiness

Weaviate is built with scaling, replication, and security in mind so you can go smoothly from rapid prototyping to production at scale.

Beyond search

Weaviate doesn't just power lightning-fast vector searches. Other superpowers include recommendation, summarization, and integration with neural search frameworks.

Who uses Weaviate?

Software Engineers
- Weaviate is an ML-first database engine
- Out-of-the-box modules for AI-powered searches, automatic classification, and LLM integration
- Full CRUD support
- Cloud-native, distributed system that runs well on Kubernetes
- Scales with your workloads
Data Engineers
- Weaviate is a fast, flexible vector database
- Use your own ML model or third party models
- Run locally or with an inference service
Data Scientists
- Seamless handover of Machine Learning models to engineers and MLOps
- Deploy and maintain your ML models in production reliably and efficiently
- Easily package custom trained models

What can you build with Weaviate?

A Weaviate vector database can search text, images, or a combination of both. Fast vector search provides a foundation for chatbots, recommendation systems, summarizers, and classification systems.

Here are some examples that show how Weaviate integrates with other AI and ML tools:

Use Weaviate with third party embeddings

Use Weaviate as a document store

DocArray
Haystack (blogpost)

Use Weaviate as a memory backend

Demos

These demos are working applications that highlight some of Weaviate's capabilities. Their source code is available on GitHub.

How can you connect to Weaviate?

Weaviate exposes a GraphQL API and a REST API. Starting in v1.23, a new gRPC API provides even faster access to your data.

Weaviate provides client libraries for several popular languages:

There are also community supported libraries for additional languages.

Where can You learn more?

Free, self-paced courses in Weaviate Academy teach you how to use Weaviate. The Tutorials repo has code for example projects. The Recipes repo has even more project code to get you started.

The Weaviate blog and podcast regularly post stories on Weaviate and AI.

Here are some popular posts:

Blogs

Podcasts

Join our community!

At Weaviate, we love to connect with our community. We love helping amazing people build cool things. And, we love to talk with you about you passion for vector databases and AI.

Please reach out, and join our community:

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