Long-term dialogue status maintenance program
The following solutions are provided for disconnection problems in complex research tasks:
- Database Configuration: 1) Ensure PostgreSQL service is running 2) Perform an alembic upgrade head to apply all migrations 3) Enable persistence by setting MEMORY_MODE=persistent in .env
- context code: 1) Modify the get_session_id method in agents/base.py to implement user-level session identifiers 2) Configure MAX_HISTORY=10 to control the memory length 3) Add summary_interval=5 to automatically generate stage summaries
- Recovery mechanisms: 1) Add session_id local storage in the front-end 2) Implement /resume interface to support breakpoints 3) Set TASK_TTL=86400 in task_manager.py to extend the task validity period
- Memory Optimization: 1) Use pgvector extension to store embedded vectors 2) Configure MEMORY_COMPRESSION=true to turn on vector compression 3) Execute prune_sessions.py periodically to clean up expired sessions
Debugging tips: watch the thread_id delivery link via langsmith, check the content update status of the storage/chat_history table, and use test_context_persistence.py for ad hoc validation.
This answer comes from the articleGemini Fullstack LangGraph: a full-stack application for intelligent research based on Gemini and LangGraphThe
May not be reproduced without permission:AI productivity tools " How to fix Gemini Fullstack LangGraph's context loss in multi-round conversations?
