From Natural Language to PromQL: A Catalog-Driven Framework with Dynamic Temporal Resolution for Cloud-Native Observability
📰 ArXiv cs.AI
Learn how to translate natural language questions into PromQL queries using a catalog-driven framework for cloud-native observability
Action Steps
- Define a catalog of metrics and their corresponding natural language descriptions
- Use natural language processing to parse user queries and identify relevant metrics
- Apply dynamic temporal resolution to generate executable PromQL queries
- Test and refine the framework using real-world observability data
- Integrate the framework with existing monitoring tools and workflows
Who Needs to Know This
This framework benefits platform engineers and site reliability teams who need to query time series metrics in Prometheus, but struggle with PromQL syntax
Key Insight
💡 A catalog-driven framework can bridge the gap between human intent and observability data by translating natural language questions into executable PromQL queries
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📊 Translate natural language to PromQL with a catalog-driven framework for cloud-native observability! 🚀
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