iso27diy-corp/Corpus/Various/Key Areas of Rasa Syntax.md

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---
tags:
- llm
- agent
- dev
---
# Key Areas of Rasa Syntax:
According to Gemini, 4 juli 2025
- **YAML Configuration:**
Rasa uses YAML files (e.g., `config.yml`, `domain.yml`, `endpoints.yml`) to define the core components of your chatbot, including the NLU pipeline, domain, and endpoint configurations. 
- **Training Data:**
Training data, typically in Markdown format, is used to train the NLU model. It includes intents, entities, and examples of user input. 
- **Stories:**
Stories, also in Markdown, define the dialogue flows of your chatbot, mapping user inputs to bot responses and actions. 
- **Actions:**
Custom actions, written in Python, define the logic that your chatbot executes in response to user input or specific events. 
- **Rules:**
Rules are a simplified way to define dialogue flows, often used for simple scenarios or to handle specific edge cases. 
- **Forms:**
Forms are used to collect multiple pieces of information from the user in a structured way. 
- **Responses:**
Responses define the text, buttons, or other elements that your chatbot sends back to the user. 
- **Flows:**
Flows are used in Rasa Pro to define the overall structure of your conversation, including steps, conditions, and actions. 
- **Conditions:**
Conditions are used to control the flow of conversation based on slot values, user input, or other factors. 
- **Command Line Interface (CLI):**
Rasa provides a CLI for various tasks, including training models, running servers, and inspecting assistants. 
- **Session Management:**
Rasa handles session management using session configuration, expiration times, and slot carryover.