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
- 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
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)
Reference
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. For example a telephone number, a person’s name, a location, the name of a product.
Event
All conversations in Rasa are represented as a sequence of events. For instance, a UserUttered represents a user entering a message, and an ActionExecuted represents the assistant executing an action.
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. Unhappy paths are all the possible edge cases of a bot.
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, which is a list of NLU components in a particular order.
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 as well as the sequence of actions to be performed by the bot.
Template / Response / Utterance
A message template that is used to respond to a user.