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To realize the long-term memory capability of LLM, the Memory Augmentation Generation (MAG) function of the MemOS system can be used, and its core operation process is divided into three steps:
- Initialization Configuration: After installing the Linux environment, use the
git clone https://github.com/MemTensor/MemOS.gitGet the latest code bymake installCompletion of the installation - memory storage: Called via Python API
add_memory()Methods, examples:mag.add_memory(user_id="user1", content="用户偏好编程语言是Python") - Personalized Calls: Automatically associate memories when generating responses:
response = mag.generate(query="推荐学习资源", user_id="user1")
The system will return Python related resources based on storage preferences
Enhancement Program: For complex scenarios this can be combined with the MemCube module through theconfig/scheduler.yamlConfigure memory weights, such as elevating the priority of recent memories.
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 Achieve Long-Term Memory Capabilities of Large Language Models to Enhance Personalized Interaction?
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