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