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: 1.10.11

Training Data Format

You can provide training data as Markdown or as JSON, as a single file or as a directory containing multiple files. Note that Markdown is usually easier to work with.

Data Formats

• Markdown Format: Markdown is the easiest Rasa NLU format for humans to read and write. Examples are listed using the unordered list syntax.
• JSON Format: The JSON format consists of a top-level object called rasa_nlu_data, with the keys common_examples, entity_synonyms and regex_features.

Improving Intent Classification and Entity Recognition

Common examples have three components: text, intent and entities. The first two are strings while the last one is an array.

Common Examples

• The text is the user message
• The intent is the intent that should be associated with the text
• The entities are specific parts of the text which need to be identified

Regular Expression Features

Regular expressions can be used to support the intent classification and entity extraction.

Lookup Tables

Lookup tables provide a convenient way to supply a list of entity examples.

Normalizing Data

Entity Synonyms

If you define entities as having the same value they will be treated as synonyms.