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
• Testing Your Assistant
• Setting up CI/CD
• Validate Data
• Configuring the HTTP API
• Deploying Your Rasa Assistant
• Cloud Storage

NLU

• About
• Using NLU Only
• Training Data Format
• Language Support
• Choosing a Pipeline
• Components
• Entity Extraction

Core

• About
• Stories
• Domains
• Responses
• Actions
• Reminders and External Events
• Policies
• Slots
• Forms
• Retrieval Actions
• Interactive Learning
• Fallback Actions
• Knowledge Base Actions

Conversation Design

• Dialogue Elements
• Small Talk
• Completing Tasks
• Guiding Users

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
• TensorFlow Configuration
• Migration Guide
• Rasa Open Source Change Log

Migrate from (beta)

• Dialogflow
• Wit.ai
• LUIS
• IBM Watson

Reference

• Glossary

Versions

viewing: 1.10.20

Warning: This document is for an old version of Rasa. The latest version is 1.10.26.

Agent

Class Definition

class rasa.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.

Methods

• create_processor(preprocessor=None)
  Instantiates a processor based on the set state of the agent.
  Return type: MessageProcessor

• async execute_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

• async handle_message(message, message_preprocessor=None, **kwargs)
  Handle a single message.
  Return type: Optional[List[Dict[str, Any]]]

• async handle_text(text_message, message_preprocessor=None, output_channel=None, sender_id='default')
  Handle a single message.

• 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

• async load_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: List[DialogueStateTracker]

• async log_message(message, message_preprocessor=None, **kwargs)
  Append a message to a dialogue - does not predict actions.
  Return type: DialogueStateTracker

• async parse_message_using_nlu_interpreter(message_data, tracker=None)
  Handles message text and intent payload input messages.
  Return type: Dict[str, Any]

• async train(training_trackers, **kwargs)
  Train the policies / policy ensemble using dialogue data from file.

• async trigger_intent(intent_name, entities, output_channel, tracker)
  Trigger a user intent, e.g. triggered by an external event.

Example

from rasa.core.agent import Agent
from rasa.core.interpreter import RasaNLUInterpreter
agent = Agent.load("examples/restaurantbot/models/current")
await agent.handle_text("hello")

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