54 lines
No EOL
1.8 KiB
Markdown
54 lines
No EOL
1.8 KiB
Markdown
---
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tags:
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- llm
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- agent
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- dev
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---
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# Key Areas of Rasa Syntax:
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According to Gemini, 4 juli 2025
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- **YAML Configuration:**
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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.
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- **Training Data:**
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Training data, typically in Markdown format, is used to train the NLU model. It includes intents, entities, and examples of user input.
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- **Stories:**
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Stories, also in Markdown, define the dialogue flows of your chatbot, mapping user inputs to bot responses and actions.
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- **Actions:**
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Custom actions, written in Python, define the logic that your chatbot executes in response to user input or specific events.
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- **Rules:**
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Rules are a simplified way to define dialogue flows, often used for simple scenarios or to handle specific edge cases.
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- **Forms:**
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Forms are used to collect multiple pieces of information from the user in a structured way.
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- **Responses:**
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Responses define the text, buttons, or other elements that your chatbot sends back to the user.
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- **Flows:**
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Flows are used in Rasa Pro to define the overall structure of your conversation, including steps, conditions, and actions.
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- **Conditions:**
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Conditions are used to control the flow of conversation based on slot values, user input, or other factors.
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- **Command Line Interface (CLI):**
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Rasa provides a CLI for various tasks, including training models, running servers, and inspecting assistants.
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- **Session Management:**
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Rasa handles session management using session configuration, expiration times, and slot carryover. |