--- title: "weaviate" type: "tool" slug: "weaviate-weaviate" canonical_url: "https://www.graphcanon.com/tools/weaviate-weaviate" github_url: "https://github.com/weaviate/weaviate" homepage_url: "https://weaviate.io/developers/weaviate/" stars: 16534 forks: 1339 primary_language: "Go" license: "BSD-3-Clause" categories: ["data-retrieval", "vector-databases"] tags: ["grpc", "information-retrieval", "mlops", "approximate-nearest-neighbor-search", "hybrid-search", "hnsw", "generative-search", "image-search"] updated_at: "2026-07-07T19:48:43.251848+00:00" --- # weaviate > Open-source vector database for semantic search Weaviate is an open-source, cloud-native vector database designed to store both objects and vectors. It supports approximate nearest neighbor search, hybrid search techniques, recommender systems, and more. ## Facts - Repository: https://github.com/weaviate/weaviate - Homepage: https://weaviate.io/developers/weaviate/ - Stars: 16,534 ยท Forks: 1,339 ยท Open issues: 578 ยท Watchers: 138 - Primary language: Go - License: BSD-3-Clause - Last pushed: 2026-07-07T18:50:50+00:00 ## Categories - [Data & Retrieval](/categories/data-retrieval.md) - [Vector Databases](/categories/vector-databases.md) ## Tags grpc, information-retrieval, mlops, approximate nearest neighbor search, hybrid search, hnsw, generative-search, image-search ## Related tools - [transformers](/tools/huggingface-transformers.md) - ๐Ÿค— Transformers: the model-definition framework for state-of-the-art machine learning models (โ˜… 162,350) - [langflow](/tools/langflow-ai-langflow.md) - Langflow is a powerful platform for building and deploying AI-powered agents and workflows. 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(โ˜… 58,449) ## README (excerpt) ```text # Weaviate Weaviate logo **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](https://docs.weaviate.io/weaviate/model-providers) (OpenAI, Cohere, HuggingFace, and others) or direct import of [pre-computed vector embeddings](https://docs.weaviate.io/weaviate/starter-guides/custom-vectors). Production deployments benefit from built-in multi-tenancy, replication, RBAC authorization, and [many other features](#weaviate-features). To get started quickly, have a look at one of these tutorials: - [Quickstart - Weaviate Cloud](https://docs.weaviate.io/weaviate/quickstart) - [Quickstart - local Docker instance](https://docs.weaviate.io/weaviate/quickstart/local) ## Installation Weaviate offers multiple installation and deployment options: - [Docker](https://docs.weaviate.io/deploy/installation-guides/docker-installation) - [Kubernetes](https://docs.weaviate.io/deploy/installation-guides/k8s-installation) - [Weaviate Cloud](https://console.weaviate.cloud) See the [installation docs](https://docs.weaviate.io/deploy) for more deployment options, such as [AWS](https://docs.weaviate.io/deploy/installation-guides/aws-marketplace) and [GCP](https://docs.weaviate.io/deploy/installation-guides/gcp-marketplace). ## Getting started You can easily start Weaviate and a local vector embedding model with [Docker](https://docs.docker.com/desktop/). Create a `docker-compose.yml` file: ```yml 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: ```bash docker compose up -d ``` Install the Python client (or use another [client library](#client-libraries-and-apis)): ```bash 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: ```python 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](https://docs.weaviate.io/weaviate/model-providers) or [bring your own pre-generated vectors](https://docs.w ``` --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/tools/weaviate-weaviate`](/api/graphcanon/tools/weaviate-weaviate) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_