Ask any question about Chatbots here... and get an instant response.

How can I implement context memory in a chatbot conversation using Rasa?

Asked on Dec 04, 2025

Answer

Implementing context memory in a Rasa chatbot involves using slots to store and manage conversation context across different turns. Slots in Rasa are designed to retain information that can be used to influence the conversation flow.

Example Concept: In Rasa, slots are used to maintain context memory by storing values that persist throughout the conversation. You define slots in the `domain.yml` file, and they can be filled automatically via entity extraction or manually through custom actions. Slots can influence dialogue decisions by being referenced in stories or rules, allowing the bot to remember user preferences or previous interactions.

Additional Comment:

Define slots in the `domain.yml` file under the `slots` section, specifying their type (e.g., text, categorical).
Use stories or rules to manage how slot values affect the conversation flow.
Custom actions can be used to set or modify slot values programmatically.
Consider using slot mappings in the `nlu.yml` to automatically fill slots based on extracted entities.

✅ Answered with Chatbot best practices.

Ask any question about Chatbots here... and get an instant response.

How can I implement context memory in a chatbot conversation using Rasa?

Asked on Dec 04, 2025

Answer

The Q&A Network