Observability Patterns for Production AI Systems: Monitoring RAG Pipelines, Vector Databases, and LLM Inference at Scale
中文标题: 生产环境中AI系统的可观测性模式：大规模监控RAG流水线、向量数据库和LLM推理
英文摘要
The paper identifies five failure modes specific to production AI systems that traditional observability misses. It proposes an observability architecture integrating Prometheus, Grafana, and OpenObserve. Metrics are defined across retrieval quality, vector database health, LLM inference performance, and end-to-end pipeline latency. The framework was validated in a production environment handling 2 million daily queries. It reduced mean time to detection by up to 97% for previously undetectable incidents.
中文摘要
本文识别了传统可观测性无法察觉的五种生产AI系统特有的故障模式。提出了一种集成Prometheus、Grafana和OpenObserve的可观测性架构。定义了检索质量、向量数据库健康度、LLM推理性能和端到端流水线延迟四个层面的指标。该框架已在每日处理200万次查询的生产环境中得到验证，将此前无法检测的事件的平均检测时间最高降低了97%。
关键要点
Identifies five unique failure modes in production AI applications not captured by traditional monitoring.
识别了生产AI应用中传统监控无法捕获的五种独特故障模式。
Proposes an observability stack using Prometheus, Grafana, and OpenObserve.
提出了使用Prometheus、Grafana和OpenObserve的可观测性技术栈。
Defines metrics across four layers: retrieval quality, vector database health, LLM inference performance, and end-to-end pipeline latency.
在四个层面定义了指标：检索质量、向量数据库健康度、LLM推理性能和端到端流水线延迟。
Validated on a production system serving 2 million daily queries, achieving up to 97% reduction in mean time to detection for previously undetectable incidents.
在一个每日处理200万查询的生产系统上验证，对于之前无法检测的事件，平均检测时间最高降低了97%。