Weaviate
Weaviate is a 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:
Installation
Weaviate offers multiple installation and deployment options:
See the installation docs for more deployment options, such as AWS and GCP.
Getting started
You can easily start Weaviate and a local vector embedding model with Docker.
Create a docker-compose.yml file:
services:
weaviate:
image: cr.weaviate.io/semitechnologies/weaviate:1.36.0
ports:
- "8080:8080"
- "50051:50051"
environment:
ENABLE_MODULES: text2vec-model2vec
MODEL2VEC_INFERENCE_API: http://text2vec-model2vec:8080
# A lightweight embedding model that will generate vectors from objects during import
text2vec-model2vec:
image: cr.weaviate.io/semitechnologies/model2vec-inference:minishlab-potion-base-32M
Start Weaviate and the embedding service with:
docker compose up -d
Install the Python client (or use another client library):
pip install -U weaviate-client
The following Python example shows how easy it is to populate a Weaviate database with data, create vector embeddings and perform semantic search:
import weaviate
from weaviate.classes.config import Configure, DataType, Property
# Connect to Weaviate
client = weaviate.connect_to_local()
# Create a collection
client.collections.create(
name="Article",
properties=[Property(name="content", data_type=DataType.TEXT)],
vector_config=Configure.Vectors.text2vec_model2vec(), # Use a vectorizer to generate embeddings during import
# vector_config=Configure.Vectors.self_provided() # If you want to import your own pre-generated embeddings
)
# Insert objects and generate embeddings
articles = client.collections.get("Article")
articles.data.insert_many(
[
{"content": "Vector databases enable semantic search"},
{"content": "Machine learning models generate embeddings"},
{"content": "Weaviate supports hybrid search capabilities"},
]
)
# Perform semantic search
results = articles.query.near_text(query="Search objects by meaning", limit=1)
print(results.objects[0])
client.close()
This example uses the Model2Vec vectorizer, but you can choose any other embedding model provider or bring your own pre-generated vectors.
Client libraries and APIs
Weaviate provides client libraries for several programming languages:
There are also additional community-maintained libraries.
Weaviate exposes REST API, gRPC API, and GraphQL API to communicate with the database server.
Weaviate features
These features enable you to build AI-powered applications:
-
⚡ Fast Search Performance: Perform complex semantic searches over billions of vectors in milliseconds. Weaviate's architecture is built in Go for speed and reliability, ensuring your AI applications are highly responsive even under heavy load. See our ANN benchmarks for more info.
-
🔌 Flexible Vectorization: Seamlessly vectorize data at import time with integrated vectorizers from OpenAI, Cohere, HuggingFace, Google, and more. Or you can import your own vector embeddings.
-
🔍 Advanced Hybrid & Image Search: Combine the power of semantic search with traditional keyword (BM25) search, image search and advanced filtering to get the best results with a single API call.
-
🤖 Integrated RAG & Reranking: Go beyond simple retrieval with built-in generative search (RAG) and reranking capabilities. Power sophisticated Q&A systems, chatbots, and summarizers directly from your database without additional tooling.
-
📈 Production-Ready & Scalable: Weaviate is built for mission-critical applications. Go from rapid prototyping to production at scale with native support for horizontal scaling, multi-tenancy, , and fine-grained .
For a complete list of all functionalities, visit the official Weaviate documentation.
Useful resources
AI Agent Skills
Weaviate Agent Skills is a collection of skills for AI coding agents (Claude Code, Cursor, GitHub Copilot, and others) that enable them to work with Weaviate more accurately and efficiently. Skills cover searching, querying, collection management, data import, and full application blueprints (RAG, agentic RAG, chatbots, and more).
Install with:
npx skills add weaviate/agent-skills
Demo projects & recipes
These demos are working applications that highlight some of Weaviate's capabilities. Their source code is available on GitHub.
- Elysia (GitHub): Elysia is a decision tree based agentic system which intelligently decides what tools to use, what results have been obtained, whether it should continue the process or whether its goal has been completed.
- Verba (GitHub): A community-driven open-source application designed to offer an end-to-end, streamlined, and user-friendly interface for Retrieval-Augmented Generation (RAG) out of the box.
- Healthsearch (GitHub): An open-source project aimed at showcasing the potential of leveraging user-written reviews and queries to retrieve supplement products based on specific health effects.
- Awesome-Moviate (GitHub): A movie search and recommendation engine that allows keyword-based (BM25), semantic, and hybrid searches.
We also maintain extensive repositories of Jupyter Notebooks and TypeScript code snippets that cover how to use Weaviate features and integrations:
Blog posts
Integrations
Weaviate integrates with many external services:
| Category | Description | Integrations |
|---|
| Cloud Hyperscalers | Large-scale computing and storage | AWS, Google |
| Compute Infrastructure | Run and scale containerized applications | Modal, Replicate, Replicated |
| Data Platforms | Data ingestion and web scraping | Airbyte, Aryn, Boomi, Box, Confluent, Astronomer, Context Data, , , , |
Contributing
We welcome and appreciate contributions! Please see our Contributor guide for the development setup, code style guidelines, testing requirements and the pull request process. If you wish to contribute to our Enterprise Edition, your contributions will be under the BSD license.
Join our Community forum to discuss ideas and get help.
License
This repository contains code in our Community Edition and our Enterprise Edition. Most of this repository is available under the BSD 3-Clause License. Files in our wl/ folder are only available under a commercial license with a license key issued by us. If you are interested in a commercial license, please contact us at http://weaviate.io/enterprise-edition. Our binaries, available for download here, combine these two elements. Enterprise features require a license key.
Please see our licensing FAQ for more information.
| Delivery | Community Edition | Enterprise Edition |
|---|
| Source code | Under BSD in our GitHub repo | Available for review in folder wl/ of our GitHub repo, but not licensed there |
| Docker image | Under BSD. Single Docker image combines this with Enterprise Edition | Available under our enterprise license. Requires license key to enable enterprise edition features. |
