The MemOS system contains the following main functional modules:
- Memory Augmentation Generation (MAG): Provide a unified API interface to support models combined with contextual memories for chatting and reasoning
- Modular Memory Architecture (MemCube): Flexibility to manage multiple memory types for developer customization and expansion
- Text Memory Management: Support for storing and retrieving structured or unstructured textual knowledge
- Memory scheduling mechanism: Dynamically allocating memory resources to optimize model performance in long context tasks
- Version Control and Governance: Provide access control, traceability and interpretation of memories
In addition, MemOS supports a wide range of LLM integrations and provides a rich set of community collaboration tools. Together, these functional modules form a complete memory enhancement system that can significantly improve the performance of large language models in complex tasks.
This answer comes from the articleMemOS: An Open Source System for Enhancing the Memory Capacity of Large Language ModelsThe
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