RAG AIOps:Translate Natural Language Question to TSDB Query to extract &...

RAG AIOps : Translate Natural Language Question to InfluxDB Query to extract TimeSeries Data & format data for LLM | Part24

🚀 Welcome to Part 24 of our RAG Series! We're building an AI application capable of understanding time-series data for AIOps. After setting up InfluxDB (Part 22) and building the Executor to run queries (Part 23), the challenge is: How do we turn a natural language question like "What caused the API latency spike?" into a database query?

🧠 Answer is time_series_handler.py! This core logic service acts as an intelligent translator, understanding your question and figuring out what specific data to retrieve from InfluxDB.

In this video, we walk through the handler's code and logic:

🧠 Query Parsing: Identifying metrics (latency, CPU), entities (payment service, container), and time ranges from your question.

🛠️ Flux Query Building: Translating the parsed elements into the correct Flux query string for InfluxDB.

🔄 Orchestration: Using the Executor to run the query and getting the formatted time-series data results.

🚪 Query Routing: The 'bouncer' logic (is_aiops_time_series_query) that decides if your question is even relevant to time-series data.

🔧/📦 This handler is key to providing automated, intelligent insights by enabling the AI to query live/recent system data. Your insightful questions have been invaluable throughout this challenging series!

🎯 This is the start of building a truly intelligent AI application that can reason over time-sensitive data! We have copy of real time data for payment service with regular spikes so now we need to do most challenging & interesting part to integrate with RAG powered AI Application to do not only System monitoring but also help SREs or concerned group aware with intelligent patterns, trends about stored timeseries data for services.

👍 If this video helps you understand how to translate natural language into database queries for Time-Series RAG, please give it a thumbs up, share it, and subscribe for the next part where we combine everything!