.. -*- mode: rst -*-

|Azure|_ |CirrusCI|_ |Codecov|_ |CircleCI|_ |Nightly wheels|_ |Black|_ |PythonVersion|_ |PyPi|_ |DOI|_ |Benchmark|_

.. |Azure| image:: https://dev.azure.com/scikit-learn/scikit-learn/_apis/build/status/scikit-learn.scikit-learn?branchName=main
.. _Azure: https://dev.azure.com/scikit-learn/scikit-learn/_build/latest?definitionId=1&branchName=main

.. |CircleCI| image:: https://circleci.com/gh/scikit-learn/scikit-learn/tree/main.svg?style=shield&circle-token=:circle-token
.. _CircleCI: https://circleci.com/gh/scikit-learn/scikit-learn

.. |CirrusCI| image:: https://img.shields.io/cirrus/github/scikit-learn/scikit-learn/main?label=Cirrus%20CI
.. _CirrusCI: https://cirrus-ci.com/github/scikit-learn/scikit-learn/main

.. |Codecov| image:: https://codecov.io/gh/scikit-learn/scikit-learn/branch/main/graph/badge.svg?token=Pk8G9gg3y9
.. _Codecov: https://codecov.io/gh/scikit-learn/scikit-learn

.. |Nightly wheels| image:: https://github.com/scikit-learn/scikit-learn/workflows/Wheel%20builder/badge.svg?event=schedule
.. _`Nightly wheels`: https://github.com/scikit-learn/scikit-learn/actions?query=workflow%3A%22Wheel+builder%22+event%3Aschedule

.. |PythonVersion| image:: https://img.shields.io/badge/python-3.8%20%7C%203.9%20%7C%203.10-blue
.. _PythonVersion: https://pypi.org/project/scikit-learn/

.. |PyPi| image:: https://img.shields.io/pypi/v/scikit-learn
.. _PyPi: https://pypi.org/project/scikit-learn

.. |Black| image:: https://img.shields.io/badge/code%20style-black-000000.svg
.. _Black: https://github.com/psf/black

.. |DOI| image:: https://zenodo.org/badge/21369/scikit-learn/scikit-learn.svg
.. _DOI: https://zenodo.org/badge/latestdoi/21369/scikit-learn/scikit-learn

.. |Benchmark| image:: https://img.shields.io/badge/Benchmarked%20by-asv-blue
.. _`Benchmark`: https://scikit-learn.org/scikit-learn-benchmarks/

.. |PythonMinVersion| replace:: 3.8
.. |NumPyMinVersion| replace:: 1.17.3
.. |SciPyMinVersion| replace:: 1.3.2
.. |JoblibMinVersion| replace:: 1.1.1
.. |ThreadpoolctlMinVersion| replace:: 2.0.0
.. |MatplotlibMinVersion| replace:: 3.1.3
.. |Scikit-ImageMinVersion| replace:: 0.16.2
.. |PandasMinVersion| replace:: 1.0.5
.. |SeabornMinVersion| replace:: 0.9.0
.. |PytestMinVersion| replace:: 5.3.1
.. |PlotlyMinVersion| replace:: 5.10.0

.. image:: https://raw.githubusercontent.com/scikit-learn/scikit-learn/main/doc/logos/scikit-learn-logo.png
  :target: https://scikit-learn.org/

**scikit-learn** is a Python module for machine learning built on top of
SciPy and is distributed under the 3-Clause BSD license.

The project was started in 2007 by David Cournapeau as a Google Summer
of Code project, and since then many volunteers have contributed. See
the `About us <https://scikit-learn.org/dev/about.html#authors>`__ page
for a list of core contributors.

It is currently maintained by a team of volunteers.

Website: https://scikit-learn.org

Installation
------------

Dependencies
~~~~~~~~~~~~

scikit-learn requires:

- Python (>= |PythonMinVersion|)
- NumPy (>= |NumPyMinVersion|)
- SciPy (>= |SciPyMinVersion|)
- joblib (>= |JoblibMinVersion|)
- threadpoolctl (>= |ThreadpoolctlMinVersion|)

=======

**Scikit-learn 0.20 was the last version to support Python 2.7 and Python 3.4.**
scikit-learn 1.0 and later require Python 3.7 or newer.
scikit-learn 1.1 and later require Python 3.8 or newer.

Scikit-learn plotting capabilities (i.e., functions start with ``plot_`` and
classes end with "Display") require Matplotlib (>= |MatplotlibMinVersion|).
For running the examples Matplotlib >= |MatplotlibMinVersion| is required.
A few examples require scikit-image >= |Scikit-ImageMinVersion|, a few examples
require pandas >= |PandasMinVersion|, some examples require seaborn >=
|SeabornMinVersion| and plotly >= |PlotlyMinVersion|.

User installation
~~~~~~~~~~~~~~~~~

If you already have a working installation of numpy and scipy,
the easiest way to install scikit-learn is using ``pip``::

    pip install -U scikit-learn

or ``conda``::

    conda install -c conda-forge scikit-learn

The documentation includes more detailed `installation instructions <https://scikit-learn.org/stable/install.html>`_.


Changelog
---------

See the `changelog <https://scikit-learn.org/dev/whats_new.html>`__
for a history of notable changes to scikit-learn.

Development
-----------

We welcome new contributors of all experience levels. The scikit-learn
community goals are to be helpful, welcoming, and effective. The
`Development Guide <https://scikit-learn.org/stable/developers/index.html>`_
has detailed information about contributing code, documentation, tests, and
more. We've included some basic information in this README.

Important links
~~~~~~~~~~~~~~~

- Official source code repo: https://github.com/scikit-learn/scikit-learn
- Download releases: https://pypi.org/project/scikit-learn/
- Issue tracker: https://github.com/scikit-learn/scikit-learn/issues

Source code
~~~~~~~~~~~

You can check the latest sources with the command::

    git clone https://github.com/scikit-learn/scikit-learn.git

Contributing
~~~~~~~~~~~~

To learn more about making a contribution to scikit-learn, please see our
`Contributing guide
<https://scikit-learn.org/dev/developers/contributing.html>`_.

Testing
~~~~~~~

After installation, you can launch the test suite from outside the source
directory (you will need to have ``pytest`` >= |PyTestMinVersion| installed)::

    pytest sklearn

See the web page https://scikit-learn.org/dev/developers/contributing.html#testing-and-improving-test-coverage
for more information.

    Random number generation can be controlled during testing by setting
    the ``SKLEARN_SEED`` environment variable.

Submitting a Pull Request
~~~~~~~~~~~~~~~~~~~~~~~~~

Before opening a Pull Request, have a look at the
full Contributing page to make sure your code complies
with our guidelines: https://scikit-learn.org/stable/developers/index.html

Project History
---------------

The project was started in 2007 by David Cournapeau as a Google Summer
of Code project, and since then many volunteers have contributed. See
the `About us <https://scikit-learn.org/dev/about.html#authors>`__ page
for a list of core contributors.

The project is currently maintained by a team of volunteers.

**Note**: `scikit-learn` was previously referred to as `scikits.learn`.

Help and Support
----------------

Documentation
~~~~~~~~~~~~~

- HTML documentation (stable release): https://scikit-learn.org
- HTML documentation (development version): https://scikit-learn.org/dev/
- FAQ: https://scikit-learn.org/stable/faq.html

Communication
~~~~~~~~~~~~~

- Mailing list: https://mail.python.org/mailman/listinfo/scikit-learn
- Gitter: https://gitter.im/scikit-learn/scikit-learn
- Logos & Branding: https://github.com/scikit-learn/scikit-learn/tree/main/doc/logos
- Blog: https://blog.scikit-learn.org
- Calendar: https://blog.scikit-learn.org/calendar/
- Twitter: https://twitter.com/scikit_learn
- Twitter (commits): https://twitter.com/sklearn_commits
- Stack Overflow: https://stackoverflow.com/questions/tagged/scikit-learn
- Github Discussions: https://github.com/scikit-learn/scikit-learn/discussions
- Website: https://scikit-learn.org
- LinkedIn: https://www.linkedin.com/company/scikit-learn
- YouTube: https://www.youtube.com/channel/UCJosFjYm0ZYVUARxuOZqnnw/playlists
- Facebook: https://www.facebook.com/scikitlearnofficial/
- Instagram: https://www.instagram.com/scikitlearnofficial/
- TikTok: https://www.tiktok.com/@scikit.learn

Citation
~~~~~~~~

If you use scikit-learn in a scientific publication, we would appreciate citations: https://scikit-learn.org/stable/about.html#citing-scikit-learn


scikit-learn

A library for efficient similarity search and clustering of dense vectors.


## Unofficial prebuilt binary for Linux and MacOS

The repo that builds this project can be found here: 
[https://github.com/onfido/faiss_prebuilt](https://github.com/onfido/faiss_prebuilt)

## Original readme:

Faiss is a library for efficient similarity search and clustering of dense 
vectors. It contains algorithms that search in sets of vectors of any size,
 up to ones that possibly do not fit in RAM. It also contains supporting 
code for evaluation and parameter tuning. Faiss is written in C++ with 
complete wrappers for Python/numpy. Some of the most useful algorithms 
are implemented on the GPU. It is developed by Facebook AI Research.




faiss

Fine-tune BERT to generate sentence embedding for cosine similarity

💫 Industrial-strength Natural Language Processing (NLP) in Python

spacy

The Python micro framework for building web applications.

Flask
=====

Flask is a lightweight `WSGI`_ web application framework. It is designed
to make getting started quick and easy, with the ability to scale up to
complex applications. It began as a simple wrapper around `Werkzeug`_
and `Jinja`_ and has become one of the most popular Python web
application frameworks.

Flask offers suggestions, but doesn't enforce any dependencies or
project layout. It is up to the developer to choose the tools and
libraries they want to use. There are many extensions provided by the
community that make adding new functionality easy.

.. _WSGI: https://wsgi.readthedocs.io/
.. _Werkzeug: https://werkzeug.palletsprojects.com/
.. _Jinja: https://jinja.palletsprojects.com/


Installing
----------

Install and update using `pip`_:

.. code-block:: text

    $ pip install -U Flask

.. _pip: https://pip.pypa.io/en/stable/getting-started/


A Simple Example
----------------

.. code-block:: python

    # save this as app.py
    from flask import Flask

    app = Flask(__name__)

    @app.route("/")
    def hello():
        return "Hello, World!"

.. code-block:: text

    $ flask run
      * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)


Contributing
------------

For guidance on setting up a development environment and how to make a
contribution to Flask, see the `contributing guidelines`_.

.. _contributing guidelines: https://github.com/pallets/flask/blob/main/CONTRIBUTING.rst


Donate
------

The Pallets organization develops and supports Flask and the libraries
it uses. In order to grow the community of contributors and users, and
allow the maintainers to devote more time to the projects, `please
donate today`_.

.. _please donate today: https://palletsprojects.com/donate


Links
-----

-   Documentation: https://flask.palletsprojects.com/
-   Changes: https://flask.palletsprojects.com/changes/
-   PyPI Releases: https://pypi.org/project/Flask/
-   Source Code: https://github.com/pallets/flask/
-   Issue Tracker: https://github.com/pallets/flask/issues/
-   Website: https://palletsprojects.com/p/flask/
-   Twitter: https://twitter.com/PalletsTeam
-   Chat: https://discord.gg/pallets


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Open Weaver – Develop Applications Faster with Open Source

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