Agent
These docs are for version 1.x of Rasa Open Source.
User Guide
- Installation
- Tutorial: Rasa Basics
- Tutorial: Building Assistants
- Command Line Interface
- Architecture
- Messaging and Voice Channels
- Evaluating Models
- Validate Data
- Configuring the HTTP API
- Deploying your Rasa Assistant
- Cloud Storage
NLU
- About
- Using NLU Only
- Training Data Format
- Choosing a Pipeline
- Language Support
- Entity Extraction
- Components
Core
- About
- Stories
- Domains
- Responses
- Actions
- Policies
- Slots
- Forms
- Retrieval Actions
- Interactive Learning
- Fallback Actions
- Knowledge Base Actions
Conversation Design
API Reference
- Action Server
- HTTP API
- Jupyter Notebooks
- Agent
- Custom NLU Components
- Rasa SDK
- Events
- Tracker
- Tracker Stores
- Event Brokers
- Lock Stores
- Training Data Importers
- Featurization of Conversations
- Migration Guide
- Rasa OSS Change Log
Migrate from (beta)
Reference
Versions
viewing: 1.7.3
Tags
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Agent
classrasa.core.agent. Agent( domain=None, policies=None, interpreter=None, generator=None, tracker_store=None, lock_store=None, action_endpoint=None, fingerprint=None, model_directory=None, model_server=None, remote_storage=None, path_to_model_archive=None)
The Agent class provides a convenient interface for the most important Rasa functionality.
This includes training, handling messages, loading a dialogue model, getting the next action, and handling a channel.
create_processor( preprocessor=None)
Instantiates a processor based on the set state of the agent.
Return type: MessageProcessor
asyncexecute_action( sender_id, action, output_channel, policy, confidence)
Handle a single message.
Return type: DialogueStateTracker
handle_channels( channels, http_port=5005, route='/webhooks/', cors=None)
Start a webserver attaching the input channels and handling msgs.
Return type: Sanic
asynchandle_message( message, message_preprocessor=None, **kwargs)
Handle a single message.
Return type: Optional[List[Dict[str, Any]]`
asynchandle_text( text_message, message_preprocessor=None, output_channel=None, sender_id='default)
Handle a single message.
If a message preprocessor is passed, the message will be passed to that function first and the return value is then used as the input for the dialogue engine.
Return type: Optional[List[Dict[str, Any]]`
is_core_ready()
Check if all necessary components and policies are ready to use the agent.
Return type: bool
is_ready()
Check if all necessary components are instantiated to use agent.
Return type: bool
classmethodload( model_path, interpreter=None, generator=None, tracker_store=None, lock_store=None, action_endpoint=None, model_server=None, remote_storage=None, path_to_model_archive=None)
Load a persisted model from the passed path.
Return type: Agent
asyncload_data( training_resource, remove_duplicates=True, unique_last_num_states=None, augmentation_factor=50, tracker_limit=None, use_story_concatenation=True, debug_plots=False, exclusion_percentage=None)
Load training data from a resource.
Return type: ListDialogueStateTracker
asynclog_message( message, message_preprocessor=None, **kwargs)
Append a message to a dialogue - does not predict actions.
Return type: DialogueStateTracker
asyncparse_message_using_nlu_interpreter( message_data, tracker=None)
Handles message text and intent payload input messages.
Return type: Dict[str, Any]
persist( model_path)
Persists this agent into a directory for later loading and usage.
Return type: None
asyncpredict_next( sender_id, **kwargs)
Handle a single message.
Return type: Optional[Dict[str, Any]]
toggle_memoization( activate)
Toggles the memoization on and off.
Return type: None
train( training_trackers, **kwargs)
Train the policies / policy ensemble using dialogue data from file.
Return type: None
asynctrigger_intent( intent_name, entities, output_channel, tracker)
Trigger a user intent, e.g. triggered by an external event.
Return type: None