Key recommendations for use
Special attention is required to ensure the quality of the study and the stability of the system:
1. Operational environment
- network requirement: Ensure that Docker can access the network properly
- Hardware Recommendations: 8GB or more RAM for a better experience
- Dependency management: Periodically update packages in requirements.txt
2. Study design
- clear-cut theme: Enter a specific research question rather than a broad topic
- Source verification: Recommend manual review of web-crawled data
- Model Selection: Scaling LLM to task complexity
3. Risk control
- API dosage: Monitor token consumption to avoid overruns
- data security: Local modeling is recommended for sensitive data
- Results Backup: Regular export of generated intermediate reports
performance optimization
When encountering performance issues, try 1) using cloud LLM instead of local model 2) limiting the scope of network search 3) reducing the number of concurrent tasks.
This answer comes from the articleAuto-Deep-Research: Multi-Agent Collaboration to Execute Literature Queries and Generate Research ReportsThe
May not be reproduced without permission:AI productivity tools " What are the considerations when using Auto-Deep-Research for automated research?
