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Key Areas of Rasa Syntax:
According to Gemini, 4 juli 2025
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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.
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Stories:
Stories, also in Markdown, define the dialogue flows of your chatbot, mapping user inputs to bot responses and actions.
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Actions:
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:
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:
Forms are used to collect multiple pieces of information from the user in a structured way.
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Responses:
Responses define the text, buttons, or other elements that your chatbot sends back to the user.
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Flows:
Flows are used in Rasa Pro to define the overall structure of your conversation, including steps, conditions, and actions.
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Conditions:
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):
Rasa provides a CLI for various tasks, including training models, running servers, and inspecting assistants.
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Session Management:
Rasa handles session management using session configuration, expiration times, and slot carryover.