Performance Optimization Solutions
MemOS can improve the accuracy of temporal inference 159% through an innovative memory scheduling mechanism, the implementation of which contains:
- timestamp: Automatically add time dimension metadata when storing memories
Sample code:mag.add_memory(user_id="projectX", content="实验阶段A完成", timestamp="2024-06-01") - dynamic retrieval strategy: The system automatically activates the MemScheduler's temporal sorting algorithm based on the temporal keywords in the query (e.g., "after", "last month").
- Validation Methods: Effectiveness can be verified by double testing:
- Basic test: query "Experiment Update" when memory scheduling is turned off.
- Comparison test: the same query with MemOS enabled will accurately return stage A information
caveat: When dealing with ambiguous temporal expressions (e.g., "the other day"), it is recommended to use themag.set_time_anchor()Set the reference time point.
This answer comes from the articleMemOS: An Open Source System for Enhancing the Memory Capacity of Large Language ModelsThe
May not be reproduced without permission:AI productivity tools " How to solve the problem of low accuracy of large language models in temporal reasoning tasks?
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