Multi-model Co-optimization Solution
DeepResearch enhances the quality of research through model routing mechanisms with specific implementation steps:
- Model Characteristic Matching::
- Google Gemini: suitable for technical documentation parsing (API settings)
model_type=gemini-pro) - GPT-4: for viewpoint summarization (configured in .env)
OPENAI_PREFER_MODEL=gpt-4) - Local Ollama: handling sensitive data (loaded at startup)
ollama serve)
- Google Gemini: suitable for technical documentation parsing (API settings)
- Hybrid Validation Process::
- Initial screening stage: 200+ web pages crawled with Claude-2
- Analysis phase: GPT-4 generates prototype reports
- Validation phase: Gemini cross-checks for data consistency
- Quality Control Indicators::
- Diversity of sources cited (at least 3 independent sources)
- Opposing viewpoints detection (automatic flagging of contradictory statements)
- Timeliness filter (default to retain content within 2 years)
Advanced users can do this by modifying model_router.py:
confidence_threshold=0.7(Confidence thresholds for results)
fallback_chain=["gpt4","gemini","claude"](Failover sequence)
This answer comes from the articleDeepResearch: a fully open source AI assistant for automated deep researchThe
May not be reproduced without permission:AI productivity tools " How to optimize the quality of research in multi-model collaborations?
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