The system implements a triple message management mechanism:
- Tiered Storage Architecture::
- Short-term memory: retains the original messages of the current session
- Long-term memory: dockable vector database to store historical knowledge
- Context Caching: Optimizing Duplicate Query Responses
- Intelligent Filtration System::
- Automatic removal of redundant messages
- Sensitive Information Screening
- Key message highlighting
- Human intervention interface::
- Support for real-time message modification
- Historical Dialogue Labeling
- Knowledge base updated instantly
Experimental data shows that the design can improve the context accuracy of multi-round conversations by 471 TP3T while reducing the Token consumption by 301 TP3T. Memory management strategies are included:
- LRU cache elimination
- Message Compression Algorithm
- Automatic summary generation
This answer comes from the articleLangGraph Supervisor: a tool for managing multi-intelligence collaboration using supervising intelligencesThe
May not be reproduced without permission:AI productivity tools " What are some of LangGraph Supervisor's innovative designs for message history management?
