Asynchronous Performance Optimization Solution
TaskingAI's FastAPI-based asynchronous architecture improves concurrency in the following ways:
- Batch request processing: Initiate multiple API calls at once using asyncio.gather()
- Connection Pool Configuration: Adjust Docker's taskingai-service container environment variable:
MAX_CONNECTIONS=100
TIMEOUT=300 - caching strategy: Enable LRU caching for frequently queried RAG collections
- Load Monitoring: Analyze response time hotspots through the console's logging dashboards
Additional tip: For hyperscale deployments, consider Kubernetes to scale service nodes horizontally.
This answer comes from the articleTaskingAI: An Open Source Platform for Developing AI Native ApplicationsThe
May not be reproduced without permission:AI productivity tools " How to optimize AI task processing performance in highly concurrent scenarios?
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