Implementation of a multi-dimensional approach to improving model responsiveness
Performance optimization recommendations for 10 models of concurrency:
- infrastructure layer::
- PostgreSQL Configuration Optimization: Tuning
shared_buffersFor memory 25%, increase thework_mem - Enable Redis caching for frequently accessed session data (self-extension required)
- Setting CPU/Memory Limits to Avoid Resource Contention During Docker Deployment
- PostgreSQL Configuration Optimization: Tuning
- Application Layer Configuration::
- Enable in admin panel
智能路由Function to automatically select models based on historical response times - Set timeout thresholds for different models (30s for Claude and 15s for Gemini are recommended)
- Limit the number of concurrent requests for a single user (default 3, can be set in the
.env(Adjustments)
- Enable in admin panel
- usage policy::
- Prefer locally deployed Ollama models for tasks with high real-time requirements
- Batch processing tasks use asynchronous mode (via the
await(Parameter enabled) - Periodic cleanup of historical session data (administrator panel provides batch operation)
Monitoring recommendation: monitor P99 latency for each model via Vercel Analytics or Prometheus.
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