Effective Methods for Enhancing Comprehension of Multi-Round Dialogues
LangGraph CodeAct guarantees the quality of continuous dialog through the following technologies:
- Message History Saving: automatically saves the complete conversation history
- Variable persistence: Python variable retention across dialogs, with support for natural language references
- Memory checkpoints: Built-in MemorySaver to maintain session state
Operating Instructions:
- Configure the checkpointer=MemorySaver() parameter during initialization
- Use the standard message format for conversations: [{"role": "user", "content": "question "}]
- Step-by-step questions are recommended for complex tasks, such as calculating 3+5 and then asking "multiply the result by 2".
- Get the full response with context via agent.invoke()
Optimization suggestion: for specialized domain conversations, domain knowledge can be added to the system prompts
This answer comes from the articleLangGraph CodeAct: generating code to help intelligences solve complex tasksThe
May not be reproduced without permission:AI productivity tools " How to Improve Contextual Understanding of Intelligentsia in Multi-Round Conversations?
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