{"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":45,"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 is a vector database management system that supports high-performance vector storage and retrieval, suitable for applications involving embeddings 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":"2026-09-20T04:31:18.199763+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":"ai","name":"ai"},{"slug":"data-science","name":"data-science"},{"slug":"database","name":"database"},{"slug":"embeddings","name":"embeddings"},{"slug":"llms","name":"llms"},{"slug":"machine-learning","name":"machine-learning"},{"slug":"neural-search","name":"neural-search"},{"slug":"retrieval","name":"retrieval"}],"trust":{"provenance":{"is_fork":false,"github_id":664133375,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-09-18T08:30:30.267Z","maintenance":{"label":"Slowing","score":36,"methodology":"github_public_v1","releases_90d":0,"days_since_push":292,"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":"2026-09-18T08:30:32.526Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-09-18T08:30:29.170Z"},"languages":{"value":["c++"],"source":"github.language","observed_at":"2026-09-18T08:30:29.170Z"},"license_spdx":{"value":"GPL-3.0","source":"github.license","observed_at":"2026-09-18T08:30:29.170Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["When you need a high-performance vector database management system that supports embeddings and neural search.","If your application requires efficient storage and retrieval of vector data, vectordb can be a suitable choice.","For applications that demand a robust infrastructure for handling embeddings and similarity searches, vectordb offers a reliable solution."],"when_not_to_use":["If your project requires a proprietary license, vectordb's GPL-3.0 license may not be compatible.","If you are looking for a solution that does not require setting up a Docker container or building Python bindings, vectordb might not be the best fit.","For applications that do not involve embeddings or neural search, vectordb may offer more features than necessary, potentially complicating the setup and maintenance."],"source":"enrich:decision_facts","observed_at":"2026-09-18T09:05:51.375Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"vectordb is a high-performance vector database management system written in C++ under the GPL-3.0 license, designed for applications involving embeddings and neural search."}]}}