Glossary

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

Glossary

• Action: A single step that a bot takes in a conversation (e.g. calling an API or sending a response back to the user).

• Annotation: Adding labels to messages and conversations so that they can be used to train a model.

• CMS: A Content Management System (CMS) can be used to store bot responses externally instead of directly including it as part of the domain. This provides more flexibility in changing them as they are not tightly-coupled with the training data.

• Custom Action: An action written by a Rasa developer that can run arbitrary code mainly to interact with the outside world.

• Default Action: A built-in action that comes with predefined functionality.

• Domain: Defines the inputs and outputs of an assistant. It includes a list of all the intents, entities, slots, actions, and forms that the assistant knows about.

• Entity: Structured information that can be extracted from a user message.

• Event: All conversations in Rasa are represented as a sequence of events.

• Form: A type of custom action that asks the user for multiple pieces of information.

• Happy / Unhappy Paths: If your assistant asks a user for some information and the user provides it, we call that a happy path.

• Intent: Something that a user is trying to convey or accomplish (e.g., greeting, specifying a location).

• Interactive Learning: A mode of training the bot where the user provides feedback to the bot while talking to it.

• Minimum viable assistant: A basic assistant that can handle the most important happy path stories.

• NLG: Natural Language Generation (NLG) is the process of generating natural language messages to send to a user.

• Rasa NLU: Natural Language Understanding (NLU) deals with parsing and understanding human language into a structured format.

• Pipeline: A Rasa bot’s NLU system is defined by a pipeline.

• Policy: Policies make decisions on how conversation flow should proceed.

• Rasa Core: The dialogue engine that decides on what to do next in a conversation based on the context.

• Rasa NLU Component: An element in the Rasa NLU pipeline.

• Slot: A key-value store that Rasa uses to track information over the course of a conversation.

• Story: A conversation between a user and a bot annotated with the intent / entities of the users’ messages.

• Template / Response / Utterance: A message template that is used to respond to a user.

• User Goal: A goal that a user wants to achieve.

• Word embedding / Word vector: A vector of floating point numbers which represent the meaning of a word.