{"data":{"slug":"epsilla-cloud-vectordb","name":"vectordb","tagline":"High performance Vector Database Management System","github_url":"https://github.com/epsilla-cloud/vectordb","owner":"epsilla-cloud","repo":"vectordb","owner_avatar_url":"https://avatars.githubusercontent.com/u/132872759?v=4","primary_language":"C++","stars":875,"forks":46,"topics":["ai","chatgpt","data","data-science","database","embeddings","embeddings-similarity","infrastructure","llms","machine-learning","neural-network","neural-search","rag","retrieval","search-engine","vector-database","vector-search"],"archived":false,"github_pushed_at":"2025-11-29T16:02:22+00:00","maintenance_label":"Slowing","stars_delta_30d":0,"url":"https://www.graphcanon.com/tools/epsilla-cloud-vectordb","markdown_url":"https://www.graphcanon.com/tools/epsilla-cloud-vectordb.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/epsilla-cloud-vectordb","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=epsilla-cloud-vectordb","description":"Epsilla is a high performance Vector Database Management System","homepage_url":"https://epsilla.com","license":"GPL-3.0","open_issues":16,"watchers":5,"ai_summary":"Epsilla offers a vector database management system for efficient data and embedding storage, retrieval, and neural search.","readme_excerpt":"## Quick Start using Docker\n\n**1. Run Backend in Docker**\n```shell\ndocker pull epsilla/vectordb\ndocker run --pull=always -d -p 8888:8888 -v /data:/data epsilla/vectordb\n```\n\n**2. Interact with Python Client**\n```shell\npip install pyepsilla\n```\n\n```python\nfrom pyepsilla import vectordb\n\nclient = vectordb.Client(host='localhost', port='8888')\nclient.load_db(db_name=\"MyDB\", db_path=\"/data/epsilla\")\nclient.use_db(db_name=\"MyDB\")\n\nclient.create_table(\n    table_name=\"MyTable\",\n    table_fields=[\n        {\"name\": \"ID\", \"dataType\": \"INT\", \"primaryKey\": True},\n        {\"name\": \"Doc\", \"dataType\": \"STRING\"},\n    ],\n    indices=[\n      {\"name\": \"Index\", \"field\": \"Doc\"},\n    ]\n)\n\nclient.insert(\n    table_name=\"MyTable\",\n    records=[\n        {\"ID\": 1, \"Doc\": \"Jupiter is the largest planet in our solar system.\"},\n        {\"ID\": 2, \"Doc\": \"Cheetahs are the fastest land animals, reaching speeds over 60 mph.\"},\n        {\"ID\": 3, \"Doc\": \"Vincent van Gogh painted the famous work \\\"Starry Night.\\\"\"},\n        {\"ID\": 4, \"Doc\": \"The Amazon River is the longest river in the world.\"},\n        {\"ID\": 5, \"Doc\": \"The Moon completes one orbit around Earth every 27 days.\"},\n    ],\n)\n\nclient.query(\n    table_name=\"MyTable\",\n    query_text=\"Celestial bodies and their characteristics\",\n    limit=2\n)\n\n---\n\n## (Experimental) Use Epsilla as a python library without starting a docker image\n\n**1. Build Epsilla Python Bindings lib package**\n```shell\ncd engine/scripts\n(If on Ubuntu, run this first: bash setup-dev.sh)\nbash install_oatpp_modules.sh\ncd ..\nbash build.sh\nls -lh build/*.so\n```\n\n**2. Run test with python bindings lib \"epsilla.so\" \"libvectordb_dylib.so in the folder \"build\" built in the previous step**\n```shell\ncd engine\nexport PYTHONPATH=./build/\nexport DB_PATH=/tmp/db33\npython3 test/bindings/python/test.py\n```\n\nHere are some sample code:\n```python\nimport epsilla\n\nepsilla.load_db(db_name=\"db\", db_path=\"/data/epsilla\")\nepsilla.use_db(db_name=\"db\")\nepsilla.create_table(\n    table_name=\"MyTable\",\n    table_fields=[\n        {\"name\": \"ID\", \"dataType\": \"INT\", \"primaryKey\": True},\n        {\"name\": \"Doc\", \"dataType\": \"STRING\"},\n        {\"name\": \"EmbeddingEuclidean\", \"dataType\": \"VECTOR_FLOAT\", \"dimensions\": 4, \"metricType\": \"EUCLIDEAN\"}\n    ]\n)\nepsilla.insert(\n    table_name=\"MyTable\",\n    records=[\n        {\"ID\": 1, \"Doc\": \"Berlin\", \"EmbeddingEuclidean\": [0.05, 0.61, 0.76, 0.74]},\n        {\"ID\": 2, \"Doc\": \"London\", \"EmbeddingEuclidean\": [0.19, 0.81, 0.75, 0.11]},\n        {\"ID\": 3, \"Doc\": \"Moscow\", \"EmbeddingEuclidean\": [0.36, 0.55, 0.47, 0.94]}\n    ]\n)\n(code, response) = epsilla.query(\n    table_name=\"MyTable\",\n    query_field=\"EmbeddingEuclidean\",\n    response_fields=[\"ID\", \"Doc\", \"EmbeddingEuclidean\"],\n    query_vector=[0.35, 0.55, 0.47, 0.94],\n    filter=\"ID < 6\",\n    limit=10,\n    with_distance=True\n)\nprint(code, response)\n```","github_created_at":"2023-07-09T02:28:31+00:00","created_at":"2026-07-11T11:26:58.827658+00:00","updated_at":"2026-08-21T18:02:19.712526+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":"ai","name":"ai"},{"slug":"chatgpt","name":"chatgpt"},{"slug":"data-science","name":"data-science"},{"slug":"embeddings","name":"embeddings"},{"slug":"embeddings-similarity","name":"embeddings-similarity"},{"slug":"infrastructure","name":"infrastructure"},{"slug":"llms","name":"llms"},{"slug":"machine-learning","name":"machine-learning"}],"trust":{"provenance":{"is_fork":false,"github_id":664133375,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-21T18:02:18.895Z","maintenance":{"label":"Slowing","score":36,"methodology":"github_public_v1","releases_90d":0,"days_since_push":265,"last_release_at":"2025-09-07T15:47:22Z","stars_delta_30d":0,"open_issues_delta_30d":0},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T11:26:59.896Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-21T18:02:19.348Z"},"languages":{"value":["c++"],"source":"github.language","observed_at":"2026-08-21T18:02:19.348Z"},"license_spdx":{"value":"GPL-3.0","source":"github.license","observed_at":"2026-08-21T18:02:19.348Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["If you require C++-based integration within your project, vectordb provides a native option that ensures seamless operation without the need for additional language bindings or adapters.","Ideal when focusing on community-driven developments with strong adherence to open-source principles given its GPL-3.0 license."],"when_not_to_use":["If your application demands proprietary technologies and you wish to avoid open-source software, vectordb's GPL-3.0 licensing terms may pose a limitation.","Avoid using vectordb in environments where alternative languages to C++ are preferred or required for consistency with the existing codebase."],"source":"enrich:decision_facts","observed_at":"2026-07-15T10:01:48.488Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"vectordb is an open-source vector database management system ideal for high-performance neural search and embedding storage."}]}}