Monitoring System Setup Guide
Build three major lines of surveillance defense based on Langfuse:
- Basic Indicator Kanban::
- Latency: set the SDK to automatically log the llm_latency field
- Cost: configure cost_calculation formula via OpenAI price list
- Error rate: percentage of Traces filtering status=ERROR
- Intelligent Alarm: Docking Prometheus+Grafana via API:
# 示例PromQL查询 sum(rate(trace_failures_total[5m])) by (service) > 0.05 - quality assessment::
- Manual scoring: batch labeling in the Scores interface
- Automatic evaluation: call the SDK's score() method to pass in metrics such as ROUGE
Key Configuration: For financial and other high-demand scenarios, it is recommended to persist data to S3 and set a rolling storage policy of more than 7 days (modify the retention parameter in helm values.yaml).
This answer comes from the articleLangfuse: an open source LLM application observation and debugging platformThe
May not be reproduced without permission:AI productivity tools " How to realize continuous monitoring and anomaly warning of LLM performance in production environment?
