Rasa Open Source 1.x Documentation

These docs are for version 1.x of Rasa Open Source. Docs for the new version 2.0 can be found here.

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

• 1.10.26
• 1.10.25
• 1.10.24
• 1.10.23
• 1.10.22
• 1.10.21
• 1.10.20
• 1.10.19
• 1.10.18
• 1.10.17
• 1.10.16
• 1.10.15
• 1.10.14
• 1.10.13
• 1.10.12
• 1.10.11
• 1.10.10
• 1.10.9
• 1.10.8
• 1.10.7
• 1.10.6
• 1.10.5
• 1.10.4
• 1.10.3
• 1.10.2
• 1.10.1
• 1.10.0
• 1.9.7
• 1.9.6
• 1.9.5
• 1.9.4
• 1.9.3
• 1.9.2
• 1.9.1
• 1.9.0
• 1.8.3
• 1.8.2
• 1.8.1
• 1.8.0
• 1.7.4
• 1.7.3
• 1.7.2
• 1.7.1
• 1.7.0
• 1.6.2
• 1.5.3
• 1.4.6
• 1.3.10
• 1.2.9

Agent

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.

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")
[u'how can I help you?']

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

classmethod load( 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

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]

persist( model_path)

Persists this agent into a directory for later loading and usage.
Return type: None

async predict_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

async trigger_intent( intent_name, entities, output_channel, tracker)

Trigger a user intent, e.g. triggered by an external event.
Return type: None

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