Metadata-Version: 2.1
Name: pychatl
Version: 2.0.3
Summary: Tiny DSL to generate training dataset for NLU engines
Home-page: https://github.com/atlassistant/chatl
Author: Julien LEICHER
License: GPL-3.0
Description: chatl: python flavor
        ====================
        
        Installation
        ------------
        
        pip
        ~~~
        
        .. code-block:: bash
        
          $ pip install pychatl
        
        source
        ~~~~~~
        
        .. code-block:: bash
        
          $ git clone https://github.com/atlassistant/chatl.git
          $ cd chatl/python
          $ python setup.py install
        
        or
        
        .. code-block:: bash
        
          $ pip install -e .
        
        Usage
        -----
        
        From the terminal
        ~~~~~~~~~~~~~~~~~
        
        .. code-block:: bash
        
          usage: pychatl [-h] [--version] [-a ADAPTER] [-m MERGE] [--pretty]
                       files [files ...]
        
          Generates training dataset from a simple DSL.
        
          positional arguments:
            files                 One or more DSL files to process
        
          optional arguments:
            -h, --help            show this help message and exit
            --version             show program's version number and exit
            -a ADAPTER, --adapter ADAPTER
                                  Name of the adapter to use
            -m MERGE, --merge MERGE
                                  Options file to merge with the final result
            --pretty              Pretty output
        
        From the code
        ~~~~~~~~~~~~~
        
        .. code-block:: python
        
          from pychatl import parse
        
          result = parse("""
          %[get_forecast]
            will it rain in @[city] @[dateStart]
        
          ~[new york]
            ny
            nyc
        
          @[dateStart](type=snips/datetime)
            at the end of the day
            tomorrow
            today
        
          @[city]
            ~[new york]
            paris
          """)
        
          # Now you got a parsed dataset so you may want to process it for a specific NLU engines
        
          from pychatl.adapters import snips
        
          snips_dataset = snips(result) # Or give options with `snips(result, language='en')`
        
          # And now you got your dataset ready to be fitted within snips-nlu!
        
        Testing
        -------
        
        .. code-block:: bash
        
          $ pip install -e .[test]
          $ python -m nose --with-doctest --with-coverage --cover-package=pychatl
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Provides-Extra: test
