Migration Guide¶

This page contains information about changes between major versions and how you can migrate from one version to another.

0.9.x to 0.10.0¶

Warning

This is a release breaking backwards compatibility. You can no longer load old models with this version, due to the addition of the default action ActionDefaultFallback. Please make sure to retrain your model before using this version

There have been some API changes to classes and methods:

if you use dispatcher.utter_template or dispatcher.utter_button_template in your custom actions run code, they now need the tracker as a second argument, e.g. dispatcher.utter_template("utter_greet", tracker)
all input and output channels should have a name. If you are using a custom channel, make sure to implement a class method that returns the name. The name needs to be added to the input channel and the output channel. You can find examples in rasa_core.channels.direct.CollectingOutputChannel:
```
@classmethod
def name(cls):
    """Every channel needs a name"""
    return "collector"
```

the RasaNLUHttpInterpreter when created now needs to be passed an instance of EndpointConfig instead of server and token, e.g.:

from rasa_core.utils import EndpointConfig

endpoint = EndpointConfig("http://localhost:500", token="mytoken")
interpreter = RasaNLUHttpInterpreter("mymodelname", endpoint)

0.8.x to 0.9.0¶

Warning

This is a release breaking backwards compatibility. Unfortunately, it is not possible to load previously trained models (as the stored file formats have changed as well as the configuration and metadata). Please make sure to retrain a model before trying to use it with this improved version.

loading data should be done either using
```
from rasa_core import training

training_data = training.load_data(...)
```
or using an agent instance:
```
training_data = agent.load_data(...)
agent.train(training_data, ...)
```
It is deprecated to pass the training data file directly to agent.train. Instead, the data should be loaded in one of the above ways and then passed to train.
ScoringPolicy got removed and replaced by AugmentedMemoizationPolicy which is similar, but is able to match more states to states it has seen during trainer (e.g. it is able to handle slots better)

if you use custom featurizers, you need to pass them directly to the policy that should use them. This allows the policies to use different featurizers. Passing a featurizer is optional. Accordingly, the max_history parameter moved to that featurizer:

from rasa_core.featurizers import (MaxHistoryTrackerFeaturizer,
                                   BinarySingleStateFeaturizer)

featurizer = MaxHistoryTrackerFeaturizer(BinarySingleStateFeaturizer(),
                                         max_history=5)

agent = Agent(domain_file,
              policies=[MemoizationPolicy(max_history=5),
                        KerasPolicy(featurizer)])

If no featurizer is passed during policy creation, the policies default featurizer will be used. The MemoizationPolicy allows passing in the max_history parameter directly, without creating a featurizer.

the ListSlot now stores a list of entities (with the same name) present in an utterance

0.7.x to 0.8.0¶

Credentials for the facebook connector changed. Instead of providing

# OLD FORMAT
verify: "rasa-bot"
secret: "3e34709d01ea89032asdebfe5a74518"
page-tokens:
  1730621093913654: "EAAbHPa7H9rEBAAuFk4Q3gPKbDedQnx4djJJ1JmQ7CAqO4iJKrQcNT0wtD"

you should now pass the configuration parameters like this:

# NEW FORMAT
verify: "rasa-bot"
secret: "3e34709d01ea89032asdebfe5a74518"
page-access-token: "EAAbHPa7H9rEBAAuFk4Q3gPKbDedQnx4djJJ1JmQ7CAqO4iJKrQcNT0wtD"

As you can see, the new facebook connector only supports a single page. Same change happened to the in code arguments for the connector which should be changed to:

from rasa_core.channels.facebook import FacebookInput

FacebookInput(
      credentials.get("verify"),
      credentials.get("secret"),
      credentials.get("page-access-token"))

Story file format changed from * _intent_greet[name=Rasa] to * intent_greet{"name": "Rasa"} (old format is still supported but deprecated). Instead of writing

## story_07715946                     <!-- name of the story - just for debugging -->
* _greet
   - action_ask_howcanhelp
* _inform[location=rome,price=cheap]
   - action_on_it                     <!-- user utterance, in format _intent[entities] -->
   - action_ask_cuisine

The new format looks like this:

## story_07715946                     <!-- name of the story - just for debugging -->
* greet
   - action_ask_howcanhelp
* inform{"location": "rome", "price": "cheap"}
   - action_on_it                     <!-- user utterance, in format _intent[entities] -->
   - action_ask_cuisine