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: 1.10.15

Agent

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.

execute_action( sender_id, action, output_channel, policy, confidence)

Handle a single message.

handle_channels( channels, http_port=5005, route='/webhooks/', cors=None)

Start a webserver attaching the input channels and handling msgs.

handle_message( message, message_preprocessor=None, **kwargs)

Handle a single message.

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.

is_ready()

Check if all necessary components are instantiated to use agent.

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.

load_data( training_resource, remove_duplicates=True, **kwargs)

Load training data from a resource.

log_message( message, message_preprocessor=None, **kwargs)

Append a message to a dialogue - does not predict actions.

parse_message_using_nlu_interpreter( message_data, tracker=None)

Handles message text and intent payload input messages.

persist( model_path)

Persists this agent into a directory for later loading and usage.

predict_next( sender_id, **kwargs)

Handle a single message.

toggle_memoization( activate)

Toggles the memoization on and off.

train( training_trackers, **kwargs)

Train the policies / policy ensemble using dialogue data from file.

trigger_intent( intent_name, entities, output_channel, tracker)

Trigger a user intent, e.g. triggered by an external event.

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