Value realization of dynamic model routing at the end-user level
Zola's first runtime model switching function solves the technical limitations of a single AI service. In terms of technical realization, the front-end manages the currently selected model through React state, and the back-end dynamically calls the corresponding API according to the selection. the system's pre-set model configuration includes engines with different parameter levels such as GPT-3.5, Mistral-7B, etc., and the user is free to choose according to the dimensions of responsiveness and creative ability.
Real-world usage data shows that the OpenAI model is 221 TP3T more accurate than Mistral in complex logical reasoning tasks, while the latter reduces latency by 401 TP3T in localized deployment scenarios.This flexibility allows legal advisors to opt for the rigorous GPT-4 to handle contractual terms, while creative practitioners choose to brainstorm with a model that is more open-ended in terms of parameters.
The underlying architecture uses a policy pattern to implement the model factory, and new access services can automatically appear in the client options by simply registering for API routing. The model performance dashboard feature under development will assist user decision-making through satisfaction scores of historical conversations, which will further strengthen the core competitiveness of personalized AI experience.
This answer comes from the articleZola: Open Source AI Chat Web App with Document Upload and Multi-Model SupportThe
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