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            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: 3 :: Only",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Scientific/Engineering :: Bio-Informatics",
            "Topic :: Scientific/Engineering :: Chemistry",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Scientific/Engineering :: Mathematics"
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        "description": "Embedding Database\n==================\nThis package provides a database schema and Python wrapper\nfor storing the embeddings generated through various representation\nlearning packages.\n\nCurrently, this package focuses on using a SQL database with SQLAlchemy,\nbut might be extended to use a NoSQL database as an alternative.\n\nInstallation\n------------\nInstall ``embeddingdb`` directly from GitHub with:\n\n.. code-block:: sh\n\n   $ pip install git+https://github.com/cthoyt/embeddingdb\n\nSet the environment variable ``EMBEDDINGDB_CONNECTION`` to a valid\nSQLAlchemy connection string for a PostgreSQL instance, as this package uses\nthe PostgreSQL-specific ``ARRAY`` type.\n\nCommand Line Interface\n----------------------\nThis package installs an entrypoint ``embeddingdb`` that can be used directly from\nthe shell.\n\nUploading Entity Embeddings\n~~~~~~~~~~~~~~~~~~~~~~~~~~~\nEntities can be embedded and stored from various types of representation learning,\nincluding network representation learning, knowledge graph embedding, and textual\nlearning.\n\nUpload embeddings generated by ``word2vec`` by specifying the file path with:\n\n.. code-block:: sh\n\n   $ embeddingdb upload --fmt word2vec --path ~/path/to/file.txt\n\nUpload embeddings generated by ``pykeen`` by specifying the output directory\nwith:\n\n.. code-block:: sh\n\n   $ embeddingdb upload --fmt keen --path ~/path/to/directory/\n\nListing Entity Embeddings\n~~~~~~~~~~~~~~~~~~~~~~~~~\nAfter uploading, the collections can be listed with:\n\n.. code-block:: sh\n\n   $ embeddingdb ls\n\nAnalyzing Entity Embeddings' Correlations\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOne of the motivations for building this repository was to make a convenient way to\ncompare the embeddings for entities generated through orthogonal embedding tecnhiques.\nFor example, we wanted to know to what extent the embeddings for proteins generated from\ntheir sequences with ``ratvec`` contained the same information as the embeddings generated\nfrom protein-protein interaction networks with ``pykeen`` or ``nrl``.\n\nThe two positional arguments correspond to the collection identifiers in the database.\n\n.. code-block:: sh\n\n   $ embeddingdb analyze 1 2\n\nRunning with Docker\n-------------------\nAfter installing Docker, the entire web application can be instantiated with:\n\n.. code-block:: sh\n\n   $ docker-compose up\n\nGet the endpoint ``/test`` to instantiate the database and add a test collection.\n\n\n",
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        "keywords": "Knowledge Graph Embeddings,Machine Learning,Data Mining,Linked Data",
        "license": "MIT",
        "maintainer": "",
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        "name": "embeddingdb",
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            "scikit-learn",
            "click",
            "easy-config",
            "pandas",
            "joblib",
            "sphinx ; extra == 'docs'",
            "sphinx-rtd-theme ; extra == 'docs'",
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            "sphinx-autodoc-typehints ; extra == 'docs'",
            "flask ; extra == 'web'",
            "flask-bootstrap ; extra == 'web'",
            "flask-sqlalchemy ; extra == 'web'",
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        "summary": "A package for storing and querying knowledge graph embeddings",
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