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
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
Versions
viewing: 1.10.13
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. For example, create a restaurant form to collect a city, a cuisine, and a price range.
Happy / Unhappy Paths
Happy path is when the user provides information, unhappy paths are edge cases like the user refusing to give input or changing topics.
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 interacting with 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) parses and understands 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.
Policy
Policies decide conversation flow and actions based on confidence levels.
Rasa Core
The dialogue engine that decides on what to do next in a conversation.
Rasa NLU Component
An element in the Rasa NLU pipeline performing tasks like entity extraction or intent classification.
Slot
A key-value store that Rasa uses to track information over a conversation.
Story
A conversation between a user and a bot, annotated with intents and actions.
Template / Response / Utterance
A message template used to respond to a user, which may include text and buttons.
User Goal
A goal that a user wants to achieve, expressed through messages.
Word embedding / Word vector
A vector representing word meanings; similar words have similar vectors.
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