Configuration¶

You can provide options to rasa NLU through:

a json-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 backend is specified with the RASA_BACKEND env var.

Here is a list of all rasa NLU configuration options:

Name: Type	Remarks	Description
`backend: str`	`mitie` `spacy_sklearn` `mitie_sklearn`	backend used for intent and entity classification
`config: str`		configuration file (can only be set as env var or command line option)
`data: str`		file containing training data.
`emulate: str`	`wit` `luis` `api`	service to emulate
`language: str`	`en` (English) `de` (German)	language of your app
`mitie_file: str`		file containing `total_word_feature_extractor.dat` (see Installation)
`path: str`		where trained models will be saved.
`port: int`		port on which to run server.
`server_model_dirs: str or object`		dir containing the model to be used by server or an object describing multiple models. see HTTP server config
`token: str`		if set, all requests to server must have a `?token=<token>` query param. see Authorization
`response_log: str or null`		directory where logs will be saved (containing queries and responses. if set to `null` logging will be disabled
`num_threads: int`		number of threads used during training
`fine_tune_spacy_ner: bool`	only `spacy_sklearn`	fine tune existing spacy NER models vs training from scratch

If you want to persist your trained models to S3, there are additional configuration options, see Model Persistence