Weaviate

Weaviate is an open-source, 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:

• Quickstart - Weaviate Cloud
• Quickstart - local Docker instance

Installation

Weaviate offers multiple installation and deployment options:

• Docker
• Kubernetes
• Weaviate Cloud

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](https://docs.w