The Rasa Core Dialogue Engine
These docs are for version 1.x of Rasa Open Source. Docs for the new version 2.0 can be found here.
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: 1.10.0
Warning: This document is for an old version of Rasa. The latest version is 1.10.26.
The Rasa Core Dialogue Engine
What am I looking at?
Rasa Core is a dialogue engine for building AI assistants.
It's part of the open source Rasa framework.
What's cool about it?
Rather than a bunch of if/else statements, it uses a machine learning model trained on example conversations to decide what to do next.
That sounds harder than writing a few if statements.
In the beginning of a project, it seems easier to just hard-code some logic.
Rasa helps you when you want to go past that and create a bot that can handle more complexity. This blog post explains the philosophy behind Rasa Core.
Can I see it in action?
We thought you'd never ask!
Head over to the Rasa Tutorial for an interactive example.
👋 I can help you get started with Rasa and answer your technical questions.