.. _section_configuration: Configuration ============= You can provide options to Rasa NLU through: - a yaml-formatted config file - environment variables - command line arguments Environment variables override options in your config file, and command line args will override any options specified elsewhere. Environment variables are capitalised and prefixed with ``RASA_``, so the option ``pipeline`` is specified with the ``RASA_PIPELINE`` env var. Default ------- Here is an example model configuration: .. literalinclude:: ../sample_configs/config_crf.yml :language: yaml As you can see, there are a couple of top-level configuration keys, like ``language`` and ``pipeline`` - but most of the configuration is component specific. Explanations for the configuration keys of the different components are part of the :ref:`section_pipeline`. Options ------- A short explanation and examples for each configuration value. pipeline ~~~~~~~~ :Type: ``str`` or ``[dict]`` :Examples: using a pipeline template (predefined set of components with default parameters): .. code-block:: yaml pipeline: "spacy_sklearn" or alternatively specifying the components and paremters: .. code-block:: yaml pipeline: - name: "nlp_spacy" model: "en" # parameter of the spacy component - name: "ner_synonyms" :Description: The pipeline used for training. Can either be a template (passing a string) or a list of components (array) and there configuration values. For all available templates, see :ref:`section_pipeline`. The component specific parameters are listed there as well. language ~~~~~~~~ :Type: ``str`` :Examples: .. code-block:: yaml language: "en" :Description: Language the model is trained in. Underlying word vectors will be loaded by using this language. There is more info about available languages in :ref:`section_languages`.