Multi-model performance optimization solution
Auto-Deep-Research supports flexible switching of the LLM model with specific optimization strategies including:
- Model Characterization Matching:
- OpenAI GPT-4 is recommended for high-precision analysis.
- Prioritize Deepseek for processing Chinese content
- Requires free program configurable Grok (requires XAI API key)
- parameter specifies the method:The startup is done via the
--COMPLETION_MODELparameter specifies the model, e.g.--COMPLETION_MODEL deepseek - Performance Monitoring Tips:
- Observe the processing time and token usage in the terminal output
- Different combinations of models to test the same subject compare the quality of results
- Complex tasks are recommended to be split into subtasks to be executed separately
- API Cost Control:
- Use of small sample sizes in the testing phase
- Sensitive information is handled with local models
- Setting budget reminders to prevent overages
Attention:There is a trade-off between model effectiveness and API responsiveness, and it is recommended that geographically similar API nodes be selected based on the network environment.
This answer comes from the articleAuto-Deep-Research: Multi-Agent Collaboration to Execute Literature Queries and Generate Research ReportsThe
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