GBC MedAI achieves response rate optimization through the following technical solutions:
- asynchronous framework: Back-end service based on FastAPI development, supporting high concurrency asynchronous request processing
- Smart Cache: Adopt Redis as semantic caching layer to effectively reduce model calls for repeated queries
- model scheduling: Supports parallel access to multiple AI models and can intelligently allocate computing resources based on query complexity
- Search Optimization: Integrate multi-search engine automatic selection mechanism, prioritize the fastest response to call the service source
- Front-end Streaming Response: Streaming interface implemented in Vue 3, with support for segmented real-time rendering of dialog content.
Empirical tests show that the response time of the same semantic query can be reduced by 60% with Redis caching, while the asynchronous framework enables the system to handle 200+ concurrent requests at the same time without performance bottlenecks.
This answer comes from the articleGBC MedAI: An Intelligent Medical Assistant with Access to Multiple AI Models and Search EnginesThe
May not be reproduced without permission:AI productivity tools " What are effective ways to optimize the responsiveness of healthcare AI assistants?
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