{"data":{"slug":"weaviate-weaviate","name":"weaviate","tagline":"Open-source vector database for storing objects and vectors with structured filtering","github_url":"https://github.com/weaviate/weaviate","owner":"weaviate","repo":"weaviate","owner_avatar_url":"https://avatars.githubusercontent.com/u/37794290?v=4","primary_language":"Go","stars":16681,"forks":1357,"topics":["approximate-nearest-neighbor-search","generative-search","grpc","hnsw","hybrid-search","image-search","information-retrieval","mlops","nearest-neighbor-search","neural-search","recommender-system","search-engine","semantic-search","semantic-search-engine","similarity-search","vector-database","vector-search","vector-search-engine","vectors","weaviate"],"archived":false,"github_pushed_at":"2026-08-01T13:30:33+00:00","maintenance_label":"Very active","url":"https://www.graphcanon.com/tools/weaviate-weaviate","markdown_url":"https://www.graphcanon.com/tools/weaviate-weaviate.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/weaviate-weaviate","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=weaviate-weaviate","description":"Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database​.","homepage_url":"https://weaviate.io/developers/weaviate/","license":"BSD-3-Clause","open_issues":620,"watchers":137,"ai_summary":"Weaviate is an open-source vector database designed for hybrid search operations that integrate both vector search and traditional query conditions. It supports scalable cloud-native architectures and offers deployment flexibility across Docker, Kubernetes, AWS, GCP, and a managed Cloud option.","readme_excerpt":"## Installation\n\nWeaviate offers multiple installation and deployment options:\n\n- [Docker](https://docs.weaviate.io/deploy/installation-guides/docker-installation)\n- [Kubernetes](https://docs.weaviate.io/deploy/installation-guides/k8s-installation)\n- [Weaviate Cloud](https://console.weaviate.cloud)\n\nSee 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).\n\n---\n\n## Getting started\n\nYou can easily start Weaviate and a local vector embedding model with [Docker](https://docs.docker.com/desktop/).\nCreate a `docker-compose.yml` file:\n\n```yml\nservices:\n  weaviate:\n    image: cr.weaviate.io/semitechnologies/weaviate:1.36.0\n    ports:\n      - \"8080:8080\"\n      - \"50051:50051\"\n    environment:\n      ENABLE_MODULES: text2vec-model2vec\n      MODEL2VEC_INFERENCE_API: http://text2vec-model2vec:8080\n\n  # A lightweight embedding model that will generate vectors from objects during import\n  text2vec-model2vec:\n    image: cr.weaviate.io/semitechnologies/model2vec-inference:minishlab-potion-base-32M\n```\n\nStart Weaviate and the embedding service with:\n\n```bash\ndocker compose up -d\n```\n\nInstall the Python client (or use another [client library](#client-libraries-and-apis)):\n\n```bash\npip install -U weaviate-client\n```\n\nThe following Python example shows how easy it is to populate a Weaviate database with data, create vector embeddings and perform semantic search:\n\n```python\nimport weaviate\nfrom weaviate.classes.config import Configure, DataType, Property\n\n---\n\n## License\n\nBSD 3-Clause License. See [LICENSE](./LICENSE) for details.","github_created_at":"2016-03-30T15:03:17+00:00","created_at":"2026-07-07T17:43:41.021121+00:00","updated_at":"2026-08-02T18:01:14.324723+00:00","categories":[{"slug":"data-retrieval","name":"Data & Retrieval","url":"https://www.graphcanon.com/categories/data-retrieval","markdown_url":"https://www.graphcanon.com/categories/data-retrieval.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/data-retrieval"},{"slug":"vector-databases","name":"Vector Databases","url":"https://www.graphcanon.com/categories/vector-databases","markdown_url":"https://www.graphcanon.com/categories/vector-databases.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/vector-databases"}],"tags":[{"slug":"approximate-nearest-neighbor-search","name":"approximate-nearest-neighbor-search"},{"slug":"grpc","name":"grpc"},{"slug":"hybrid-search","name":"hybrid-search"},{"slug":"information-retrieval","name":"information-retrieval"},{"slug":"mlops","name":"mlops"},{"slug":"nearest-neighbor-search","name":"nearest-neighbor-search"},{"slug":"semantic-search","name":"semantic-search"},{"slug":"similarity-search","name":"similarity-search"}],"trust":{"provenance":{"is_fork":false,"github_id":55072677,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-02T18:01:13.359Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":30,"days_since_push":1,"last_release_at":"2026-07-29T16:57:51Z"},"security_summary":{"status":"findings","scanner":"osv@v1","low_count":12,"high_count":0,"last_scan_at":"2026-07-11T23:19:57.629Z","medium_count":0,"scan_profile":"deps","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-02T18:01:13.991Z"},"deploy":{"source":"dockerfile:Dockerfile","self_host":true,"observed_at":"2026-08-02T18:01:13.991Z","managed_saas":false},"languages":{"value":["go","python"],"source":"github.language+pyproject.toml","observed_at":"2026-08-02T18:01:13.991Z"},"has_docker":{"value":true,"source":"dockerfile:Dockerfile","observed_at":"2026-08-02T18:01:13.991Z"},"license_spdx":{"value":"BSD-3-Clause","source":"github.license","observed_at":"2026-08-02T18:01:13.991Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":{"notes":["Deployment on Docker requires a Docker environment.","For cloud deployments, compatible environments such as AWS, GCP require associated accounts and configurations."],"min_ram_gb":null,"requires_docker":true},"constraints":{"min_ram_gb":null,"requires_docker":true},"when_to_use":["When you need to integrate both vector search capabilities and traditional SQL-like structured queries into your application.","If you are building a system that requires flexible deployment options, such as Docker, Kubernetes, AWS, GCP, or managed Weaviate Cloud services.","For projects that benefit from the support of the Go language stack and require an open-source solution under the BSD-3-Clause License."],"when_not_to_use":["If your project requires a proprietary license; Weaviate's open-source nature may not align with restrictive licensing needs.","When you need immediate access to specific vector embedding models that are not natively supported by Weaviate, without the flexibility of integrating additional models via Docker or Kubernetes."],"source":"enrich:decision_facts","observed_at":"2026-07-15T10:18:55.364Z"},"constraint_facets":{"min_ram_gb":null,"requires_docker":true},"decision_summary":[{"label":"Requirements","value":"Requires Docker; Deployment on Docker requires a Docker environment.; For cloud deployments, compatible environments such as AWS, GCP require associated accounts and configurations."},{"label":"Adopt for","value":"Weaviate is an open-source vector database with strong support for hybrid searches and scalable cloud-native deployments."}]}}