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: 1.10.26

Architecture

Message Handling

This diagram shows the basic steps of how an assistant built with Rasa responds to a message:

The steps are:

1. The message is received and passed to an Interpreter, which converts it into a dictionary including the original text, the intent, and any entities that were found. This part is handled by NLU.

2. The Tracker is the object which keeps track of conversation state. It receives the info that a new message has come in.

3. The policy receives the current state of the tracker.

4. The policy chooses which action to take next.

5. The chosen action is logged by the tracker.

6. A response is sent to the user.

Note Messages can be text typed by a human, or structured input like a button press.

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