.. _interpreters:

Interpreters
============

The job of interpreting text is mostly outside the scope of Rasa Core.
To turn text into structured data you can use Rasa NLU, or a cloud service like wit.ai.
If your bot uses button clicks or other input which isn't natural language, you don't need
an interepreter at all. You can define your own ``Interpreter`` subclass which does any custom
logic you may need. You can look at the ``RegexInterpreter`` class as an example.


To use something other than Rasa NLU, you just need to implement a
subclass of ``Interpreter``
which has a method ``parse(message)`` which takes a single string argument
and returns a dict in the following format:


.. code-block:: javascript

    {
      "text": "show me chinese restaurants",
      "intent": {
        "name": "restaurant_search",
        "confidence": 1.0
      }
      "entities": [
        {
          "start": 8,
          "end": 15,
          "value": "chinese",
          "entity": "cuisine"
        }
      ]
    }

.. note:

    The ``"start"`` and ``"end"`` values in the entities are optional

.. _fixed_intent_format:

Fixed intent & entity input
---------------------------

Sometimes, you want to make sure a message is treated as being of a fixed
intent containing defined entities. To achieve that, you can specify the
message in a markup format instead of using the text of the message.

Instead of sending a message like ``Hello I am Rasa`` and hoping that gets
classified correctly, you can circumvent the NLU and directly send the
bot a message like ``/greet{"name": "Rasa"}``. Rasa Core will treat this
incoming message like a normal message with the intent ``greet`` and the entity
``name`` with value ``Rasa``.

If you want to specify an input string, that contains multiple entity values of
the same type you can use

.. code-block:: bash

    /add_to_shopping_list{"item": ["milk", "salt"]}

Which corresponds to a message ``"I want to add milk and salt to my list"``.