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Welcome to Weaviate Docs

Weaviate (we-vee-eight) is an open source, AI-native vector database. Use this documentation to get started with Weaviate and to learn how to get the most out of Weaviate's features.

New to Weaviate?

Start with the Quickstart tutorial - an end-to-end demo that takes 15-30 minutes.

Questions

Please visit our forum. The Weaviate team and our awesome community can help.


Getting Started

Step 1 - Choose your deployment

Multiple deployment options are available to cater for different users and use cases. All options offer vectorizer and RAG module integration.

Evaluation
Deployment
Production
Weaviate Cloud Services
  • From evaluation (sandbox) to production
  • Serverless (infrastructure managed by Weaviate)
  • (Optional) Data replication (high-availability)
  • (Optional) Zero-downtime updates
Evaluation
Deployment
Production
Docker
  • For local evaluation & development
  • Local inference containers
  • Multi-modal models
  • Customizable configurations

Evaluation
Deployment
Production
Kubernetes
  • For development to production
  • Local inference containers
  • Multi-modal models
  • Customizable configurations
  • Self-deploy or Marketplace deployment
  • (Optional) Zero-downtime updates
Evaluation
Deployment
Production
Embedded Weaviate
  • For basic, quick evaluation
  • Conveniently launch Weaviate directly from Python or TS/JS

Step 2 - Choose your scenario

Choose your next step. Weaviate is flexible and can be used in many contexts and scenarios.

Schema configuration to searches

You can customize collections' data structures and its vectorization, RAG, multi-tenancy, or replication behavior.

Learn how to configure collections, and how to search them, using different search types and filters.

Bring your own vectors

It’s easy to import data with pre-existing vectors into Weaviate.

Learn how to work with your existing data and your own vectors.

You can perform vector searches, and even work with a vectorizer if a compatible one is available.

RAG for AI-powered apps

Retrieval augmented generation (RAG) is a powerful tool for building AI-powered applications.

Read this starter guide for retrieval augmented generation in Weaviate, which will help you get started on this journey.

What Next

We recommend starting with these sections:

What is Weaviate?

Weaviate is an open source vector search engine that stores both objects and vectors.

What can you do with Weaviate?

Features, examples, demo applications, recipes, use cases, etc..

Installation

Learn about the available options for running Weaviate, along with instructions on installation and configuration.

How-to: Configure

Discover how to configure Weaviate to suit your specific needs.

Concepts

Get the most out of Weaviate and learn about its architecture and various features.

Client Libraries