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Snips Natural Language Understanding library

Project description

Snips NLU
=========

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`Snips NLU <https://snips-nlu.readthedocs.io>`_ (Natural Language Understanding) is a Python library that allows to parse sentences written in natural language and extracts structured information.


Installing
----------

.. code-block:: python

pip install snips-nlu

We currently have pre-built binaries (wheels) for ``snips-nlu`` and its
dependencies for MacOS and Linux x86_64. If you use a different
architecture/os you will need to build these dependencies from sources
which means you will need to install
`setuptools_rust <https://github.com/PyO3/setuptools-rust>`_ and
`Rust <https://www.rust-lang.org/en-US/install.html>`_ before running the
``pip install snips-nlu`` command.

A simple example
----------------

Let’s take an example to illustrate the main purpose of this lib, and consider the following sentence:

.. code-block:: text

"What will be the weather in paris at 9pm?"

Properly trained, the Snips NLU engine will be able to extract structured data such as:

.. code-block:: json

{
"intent": {
"intentName": "searchWeatherForecast",
"probability": 0.95
},
"slots": [
{
"value": "paris",
"entity": "locality",
"slotName": "forecast_locality"
},
{
"value": {
"kind": "InstantTime",
"value": "2018-02-08 20:00:00 +00:00"
},
"entity": "snips/datetime",
"slotName": "forecast_start_datetime"
}
]
}


Documentation
-------------

To find out how to use Snips NLU please refer to our `documentation <https://snips-nlu.readthedocs.io>`_, it will provide you with a step-by-step guide on how to use and setup our library.


Links
-----
* `Snips NLU <https://github.com/snipsco/snips-nlu>`_
* `Snips NLU Rust <https://github.com/snipsco/snips-nlu-rs>`_: Rust inference pipeline implementation and bindings (C, Swift, Kotlin, Python)
* `Rustling <https://github.com/snipsco/rustling-ontology>`_: Snips NLU builtin entities parser
* `Snips <https://snips.ai/>`_
* `Bug tracker <https://github.com/snipsco/snips-nlu/issues>`_


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

Please see the `Contribution Guidelines <CONTRIBUTING.rst>`_.

Copyright
---------

This library is provided by `Snips <https://www.snips.ai>`_ as Open Source software. See `LICENSE <LICENSE>`_ for more information.


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