==========
OmniVector
==========
.. image:: https://img.shields.io/pypi/v/omnivector.svg
:target: https://pypi.python.org/pypi/omnivector
.. image:: https://img.shields.io/travis/vinid/omnivector.svg
:target: https://travis-ci.com/vinid/omnivector
.. image:: https://readthedocs.org/projects/omnivector/badge/?version=latest
:target: https://omnivector.readthedocs.io/en/latest/?version=latest
:alt: Documentation Status
OmniVector provides a simple interface to vector databases. We integrate the main functionalities of different vector dbs,
generally indexing and searching, into a single interface. This allows us to easily switch between different vector dbs.
.. code-block:: python
db = WeaviateDB() # or PineconeDB() or LanceDB()
encoder = SentenceTransformerEmbedder("paraphrase-MiniLM-L6-v2", device="cpu")
docs = ["the cat is on the table", "the table is on the cat", "the dog is mining bitcoins"]
ids = list(range(4, len(docs) + 4))
embeddings = encoder.embed(docs)
db.create_index(ids, docs, embeddings)
search_vector = encoder.embed(["the dog is mining bitcoins"])[0]
print(db.vector_search(search_vector, k=1))
* Free software: MIT license
* Documentation: https://omnivector.readthedocs.io.
Features
--------
* The AbstractDB requires setting OMNIVECTOR_CONFIG env variable to a config file (an example is in config.yaml)
Credits
-------
This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.
.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
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"description": "==========\nOmniVector\n==========\n\n\n.. image:: https://img.shields.io/pypi/v/omnivector.svg\n :target: https://pypi.python.org/pypi/omnivector\n\n.. image:: https://img.shields.io/travis/vinid/omnivector.svg\n :target: https://travis-ci.com/vinid/omnivector\n\n.. image:: https://readthedocs.org/projects/omnivector/badge/?version=latest\n :target: https://omnivector.readthedocs.io/en/latest/?version=latest\n :alt: Documentation Status\n\n\n\n\nOmniVector provides a simple interface to vector databases. We integrate the main functionalities of different vector dbs,\ngenerally indexing and searching, into a single interface. This allows us to easily switch between different vector dbs.\n\n\n.. code-block:: python\n\n db = WeaviateDB() # or PineconeDB() or LanceDB()\n\n encoder = SentenceTransformerEmbedder(\"paraphrase-MiniLM-L6-v2\", device=\"cpu\")\n docs = [\"the cat is on the table\", \"the table is on the cat\", \"the dog is mining bitcoins\"]\n\n\n ids = list(range(4, len(docs) + 4))\n embeddings = encoder.embed(docs)\n\n db.create_index(ids, docs, embeddings)\n\n search_vector = encoder.embed([\"the dog is mining bitcoins\"])[0]\n print(db.vector_search(search_vector, k=1))\n\n* Free software: MIT license\n* Documentation: https://omnivector.readthedocs.io.\n\n\nFeatures\n--------\n\n* The AbstractDB requires setting OMNIVECTOR_CONFIG env variable to a config file (an example is in config.yaml)\n\nCredits\n-------\n\nThis package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.\n\n.. _Cookiecutter: https://github.com/audreyr/cookiecutter\n.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage\n",
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