Workflow execution efficiency can be improved in the following three dimensions:
- Model Selection: Preference is given to models with fewer parameters at the same accuracy (e.g., version 7B) through the
ollama listView loaded models - Workflow design: Change serial nodes to parallel execution, and utilize the "branching" module for task splitting.
- caching mechanism: Configure the TTL parameter of the "Database" node to cache HF query results.
It is recommended to use the "Real-time Monitoring" panel to observe the time consumption of each node after deployment, and upgrade the hardware configuration for bottleneck nodes (e.g., allocate more GPU memory for LLM nodes). When deploying in the cloud, choose a geographically close region to reduce network latency.
This answer comes from the articleSim: Open Source Tools for Rapidly Building and Deploying AI Agent WorkflowsThe
May not be reproduced without permission:AI productivity tools " How can I optimize the responsiveness of my AI agent workflow?
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