Architecture Level Solutions
The MemCube module of MemOS enables multi-hop inference optimization through a hierarchical storage design:
- three-tier memory structure::
- Working memory: active data for high-frequency calls (LRU algorithm management)
- Scene Memory: Associated Knowledge Base by Topic
- Long-term memory: compressed stored historical data
- Real-world configuration: in
config/memcube.yamlSet in:layer_weights:
working: 0.6
scenario: 0.3
longterm: 0.1 - Performance monitoring: Use the built-in analysis tool to view hop count correlations:
python -m memos.analyzer --task=multihop --log_level=debug
typical caseWhen dealing with a query such as "Compare the advantages and disadvantages of technology A and technology B", which requires multi-layer reasoning, the system automatically extracts the technical documents from the scenario memory layer, and at the same time obtains the recent discussion records from the working memory layer.
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 Optimize Memory Retrieval Efficiency of LLM in Multi-hop Reasoning Scenarios?
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