Solutions to AI's Long-Term Memory of User Preferences
Traditional AI dialog systems often lack persistent memory capabilities, resulting in the need for the user to repeat information with each interaction.Memobase addresses this problem by following specific steps:
- Create a user profile: Use
add_user()method initializes the user profile and stores basic information - Dynamic update mechanism: By
update_user()The interface is updated with the latest user preference data in real time - Dialog Memory Storage: Utilization
ChatBlobobject saves the full dialog context, sample code:messages = [{"role": "user", "content": "我喜欢科幻小说"},...]
uid.insert(ChatBlob(messages)) - timestamp management: Automatic labeling of data collection time to distinguish between old and new preference data
Recommendations for implementation: weekly use is recommendedget_user()Check data integrity and set up memory retention policies in conjunction with business scenarios.
This answer comes from the articleMemobase: a user profile-based long-term memory solution for AI applicationsThe
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