Intelligent Body Latency Optimization Program
Solving function call latency requires a system-level optimization approach:
- Infrastructure optimization::
- Use the Continuous Batch feature of vLLM:
vllm serve --enforce-eager --max-num-seqs=128 - Enable Triton Inference Server Acceleration at Deployment Time
- Register local cache for HF tools (e.g. SQLite storage API response)
- Use the Continuous Batch feature of vLLM:
- Call Policy Optimization::
- Preloaded descriptions of commonly used tools:
model.register_tool('weather_api', schema=weather_schema, cache=True) - Setting up a timeout fallback mechanism: when the tool response times out for 2 seconds, it automatically switches to model estimation.
- Batch processing of parallel requests: use
asyncio.gatherMerging multiple tool calls
- Preloaded descriptions of commonly used tools:
- Architecture Design Optimization::
- Simple Tools
non-thinkingMode Rapid Response - Complex processes use
thinking+cotmodel step-by-step execution - Enable streaming output for time-sensitive tasks:
for chunk in model.stream_chat(tokenizer, '实时股票分析'): print(chunk)
- Simple Tools
After testing, the above method can reduce the average response time of e-commerce customer service robots from 3.2 seconds to 0.8 seconds, in which the tool call latency is reduced by 76%. It is recommended to cooperate with Prometheus to monitor the time consumed in each session.
This answer comes from the articleGLM-4.5: Open Source Multimodal Large Model Supporting Intelligent Reasoning and Code GenerationThe
May not be reproduced without permission:AI productivity tools " How to overcome the latency problem of GLM-4-5 function calls in smart body development?
