The value of innovation in technology architecture
DeepGemini's technological strengths are at three main levels:
- scheduling engine::
- adoptionDirected acyclic graph (DAG)Scheduling model with support for conditional branching and parallel execution
- Configurable differentiation parameters for each step (e.g. temperature=0.7 only works on specific models)
- internally installed
step_typeMarkup enables semantic process control
- Interface compatibility::
- Fully compatible with the OpenAI API specification, existing applications can be accessed by modifying the endpoint.
- Streaming response (SSE) support for real-time token returns
- Automatically handle differences in APIs across providers (e.g. Claude's message format conversion)
- collaborative learning::
- Discussion group functionality to enable debate-based interaction between models (requires configuration of at least 2 roles)
- Supports preset models such as SWOT analysis and automatic assignment of role positions
- Conversation history persistent storage facilitates retrospective analysis
Note that since multiple model calls are involved, theAPI costs can increase exponentiallyIt is recommended that the choreography function be used at key points.
This answer comes from the articleDeepGemini: Multi-model orchestration of tasks and encapsulation into an API interfaceThe
May not be reproduced without permission:AI productivity tools " What are the unique advantages of DeepGemini's technical implementation over a single model call?
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