I'm curious if anyone in the DevOps community has tried using vector search or Agentic RAG for log monitoring and incident report management. I've heard some setups utilize agents to scan logs in real-time, identifying anomalies and suggesting possible root causes based on historical data. While I haven't tested this myself, it seems like a promising route to cut down on alert fatigue. I'm particularly interested in how an agent could aid in reducing Mean Time to Recovery (MTTR) by analyzing logs, traces, and metrics to propose root causes and remediation steps, continuously improving diagnostics through past incident analysis. The idea involves storing incident metadata and logs as JSON documents, embedding them for similarity-based retrieval, and enabling high-throughput data ingestion with quick querying for real-time analysis. Some argue against using a vector database for logs, so I'd like to hear other opinions on this. Additionally, are there other use cases for vector search beyond log monitoring?
5 Answers
Another option to check out is using traditional query-based systems for real-time log monitoring. They're specifically built to handle high-throughput scenarios and often outperform more experimental AI solutions.
Have a look at VictoriaMetrics; they’ve introduced modules for anomaly detection and have recently added MCP features. It might be worth exploring for your needs!
Using vector search for log monitoring is an intriguing notion, but I’ve seen mixed results in practice. Operational logs and incident patterns often don’t translate well into a vector space that assists debugging. In my work at an AI consultancy, clients frequently found that traditional monitoring tools were more effective, as logs often hinge on specific patterns and thresholds. However, vector search shines in post-mortem analysis and knowledge management, allowing you to store past incidents and quickly find relevant solutions which can indeed reduce MTTR. For real-time log monitoring, tools like Elastic Stack or Splunk are usually better suited. I’ve had success with vector search for configuration drift detection, but that’s more about patterns in documentation than live operational insights. What specific challenges are you facing that traditional tools seem unable to tackle?
In our experience at Parseable, we found that other models like MCP outperformed RAG setups in terms of speed and accuracy for root-cause analysis. We focused on zero-shot forecasting for our time-series data, which ended up providing results that were often better than RAG pipelines with significantly less ongoing maintenance. We documented our findings if you want to dive deeper!
This is super helpful, thanks for sharing! It makes sense that in many scenarios, MCP outperforms RAG.
Definitely worth considering knowledge graphs and GraphRAG approaches for incident management. We developed a production-ready GraphRAG using PostgreSQL that significantly aids in root cause analysis. Check out our insights for a deeper understanding!
Thanks for the resources! This looks like a great read.
Thanks for such a detailed response! This really clears things up for me.