{"data":{"slug":"jina-ai-vectordb","name":"vectordb","tagline":"A Python vector database for embedding similarity and neural search.","github_url":"https://github.com/jina-ai/vectordb","owner":"jina-ai","repo":"vectordb","owner_avatar_url":"https://avatars.githubusercontent.com/u/60539444?v=4","primary_language":"Python","stars":653,"forks":50,"topics":["embedding-similarity","neural-search","sentence-embeddings","vector-database","vector-database-embedding","vector-search"],"archived":false,"github_pushed_at":"2024-03-04T17:10:06+00:00","maintenance_label":"Dormant","stars_delta_30d":3,"url":"https://www.graphcanon.com/tools/jina-ai-vectordb","markdown_url":"https://www.graphcanon.com/tools/jina-ai-vectordb.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/jina-ai-vectordb","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=jina-ai-vectordb","description":"A Python vector database you just need - no more, no less.","homepage_url":null,"license":"Apache-2.0","open_issues":9,"watchers":10,"ai_summary":"Jina AI's vectordb is a Python library designed for efficient vector database operations, supporting local and cloud-based deployment, and integration with various communication protocols.","readme_excerpt":"## Install\n\n```bash\npip install vectordb\n```\n\n<table>\n  <tr>\n    <td>\n      <a href=\"#getting-started-with-vectordb-locally\">\n        <img src=\"https://github.com/jina-ai/vectordb/blob/main/.github%2Fimages%2Fguide-1.png?raw=true\" alt=\"Use vectordb from Jina AI locally\" width=\"100%\">\n      </a>\n    </td>\n    <td>\n      <a href=\"#getting-started-with-vectordb-as-a-service\">\n        <img src=\"https://github.com/jina-ai/vectordb/blob/main/.github%2Fimages%2Fguide-2.png?raw=true\" alt=\"Use vectordb from Jina AI as a service\" width=\"100%\">\n      </a>\n    </td>\n    <td>\n      <a href=\"#hosting-vectordb-on-jina-ai-cloud\">\n        <img src=\"https://github.com/jina-ai/vectordb/blob/main/.github%2Fimages%2Fguide-3.png?raw=true\" alt=\"Use vectordb from Jina AI on Jina AI Cloud\" width=\"100%\">\n      </a>\n    </td>\n  </tr>\n</table>\n\n---\n\n## Getting started with `vectordb` locally\n\n1. Kick things off by defining a Document schema with the [DocArray](https://docs.docarray.org/user_guide/representing/first_step/) dataclass syntax:\n\n```python\nfrom docarray import BaseDoc\nfrom docarray.typing import NdArray\n\nclass ToyDoc(BaseDoc):\n  text: str = ''\n  embedding: NdArray[128]\n```\n\n2. Opt for a pre-built database (like `InMemoryExactNNVectorDB` or `HNSWVectorDB`), and apply the schema:\n\n```python\nfrom docarray import DocList\nimport numpy as np\nfrom vectordb import InMemoryExactNNVectorDB, HNSWVectorDB\n\n---\n\n## Getting started with `vectordb` as a service\n\n`vectordb` is designed to be easily served as a service, supporting `gRPC`, `HTTP`, and `Websocket` communication protocols.\n\n---\n\n## Hosting `vectordb` on Jina AI Cloud\n\nYou can seamlessly deploy your `vectordb` instance to Jina AI Cloud, which ensures access to your database from any location.\n\nStart by embedding your database instance or class into a Python file:\n\n```python","github_created_at":"2023-05-02T14:59:09+00:00","created_at":"2026-07-11T11:27:49.530932+00:00","updated_at":"2026-09-20T04:31:28.500222+00:00","categories":[{"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":"embedding-similarity","name":"embedding-similarity"},{"slug":"neural-search","name":"neural-search"},{"slug":"sentence-embeddings","name":"sentence-embeddings"},{"slug":"vector-database","name":"vector-database"},{"slug":"vector-database-embedding","name":"vector-database-embedding"},{"slug":"vector-search","name":"vector-search"}],"trust":{"provenance":{"is_fork":false,"github_id":635374751,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-09-18T08:31:54.421Z","maintenance":{"label":"Dormant","score":18,"methodology":"github_public_v1","releases_90d":0,"days_since_push":927,"last_release_at":"2024-03-04T17:10:17Z","stars_delta_30d":3,"open_issues_delta_30d":0},"security_summary":{"status":"ok","scanner":"osv@v1","low_count":0,"high_count":0,"last_scan_at":"2026-09-18T08:31:56.716Z","medium_count":0,"scan_profile":"deps","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-09-18T08:31:53.288Z"},"languages":{"value":["python"],"source":"github.language","observed_at":"2026-09-18T08:31:53.288Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-09-18T08:31:53.288Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["Use vectordb when you need a Python library that supports both local and cloud-based deployment for vector database operations.","Choose vectordb if you require seamless integration with communication protocols such as gRPC, HTTP, and Websocket.","Opt for vectordb when you want to leverage pre-built databases like InMemoryExactNNVectorDB or HNSWVectorDB with your custom document schema."],"when_not_to_use":["Avoid vectordb if you are looking for a solution that does not support Python or if you prefer a non-Python environment.","Do not use vectordb if you need a vector database that does not offer cloud-based deployment options or if you strictly require on-premises solutions.","If your project does not require or benefit from integration with gRPC, HTTP, or Websocket, vectordb might not be the best choice."],"source":"enrich:decision_facts","observed_at":"2026-09-18T10:21:48.083Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"vectordb is a Python library for efficient vector database operations, supporting local and cloud-based deployment, and integration with various communication protocols."}]}}