--data command line argument you can specify where Rasa should look
for training data on your disk. Rasa then loads any potential training files and uses
them to train your assistant.
If needed, you can also customize how Rasa imports training data. Potential use cases for this might be:
using a custom parser to load training data in other formats
using different approaches to collect training data (e.g. loading them from different resources)
You can write a custom importer and instruct Rasa to use it by adding the section
importers to your configuration file and specifying the importer with its
full class path:
name key is used to determine which importer should be loaded. Any extra
parameters are passed as constructor arguments to the loaded importer.
You can specify multiple importers. Rasa will automatically merge their results.
By default Rasa uses the importer
RasaFileImporter. If you want to use it on its
own, you don't have to specify anything in your configuration file.
If you want to use it together with other importers, add it to your
New in 1.3
This feature is currently experimental and might change or be removed in the future. Share your feedback on it in the forum to help us making this feature ready for production.
With this importer you can train a model by combining multiple reusable Rasa projects. You might, for example, handle chitchat with one project and greet your users with another. These projects can be developed in isolation, and then combined when you train your assistant.
For example, consider the following directory structure:
Here the contextual AI assistant imports the
ChitchatBot project which in turn
GreetBot project. Project imports are defined in the configuration files of
To instruct Rasa to use the
MultiProjectImporter module, you need add it to the
importers list in your root
Then, in the same file, specify which projects you want to import by adding them to the
The configuration file of the
ChitchatBot needs to reference
GreetBot project does not specify further project to import, it doesn't need a
Rasa uses paths relative from the configuration file to import projects. These can be anywhere on your filesystem where file access is permitted.
During the training process Rasa will import all required training files, combine them, and train a unified AI assistant. The training data is merged at runtime, so no additional training data files are created.
Policies and NLU Pipelines
Rasa will use the policy and NLU pipeline configuration of the root project directory during training. Policy and NLU configurations of imported projects will be ignored.
watch out for merging
Equal intents, entities, slots, responses, actions and forms will be merged,
e.g. if two projects have training data for an intent
their training data will be combined.
Writing a Custom Importer
If you are writing a custom importer, this importer has to implement the interface of