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How do I implement context retention in a Rasa chatbot?

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How do I implement context retention in a Rasa chatbot?

Asked on Nov 10, 2025

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Context retention in a Rasa chatbot involves maintaining conversation state across multiple user interactions. This is achieved through Rasa's dialogue management system, which uses stories and rules to handle context.

Example Concept: In Rasa, context retention is managed using slots to store information and the tracker to keep track of conversation state. Slots can be set based on user inputs or custom actions, and they influence the dialogue flow by being referenced in stories and rules. This allows the bot to remember user preferences and details throughout the conversation.

Additional Comment:

Use slots to store and retrieve user-specific data during conversations.
Define stories and rules in your domain file to guide the bot's behavior based on slot values.
Implement custom actions if you need to manipulate slot values dynamically.

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How do I implement context retention in a Rasa chatbot?

Asked on Nov 10, 2025

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