RAG AI APP TimeSeriesDB: Setup InfluxDB, Load API Latency Metrics Data W...

RAG AI APP with TimeSeries Database : Setup InfluxDB, Load Payment Service Metrics Data With regular interval Spikes for System Monitoring AIOps  | Part22

Welcome to Part 22 of our RAG Series! We're pushing the boundaries of our multi-source RAG AI application beyond static documents and databases to integrate dynamic, real-time knowledge from Time-Series Databases (TSDBs) like InfluxDB. 

🧠 In this introductory video, We are setting up InfluxDB with Continuosly feeding data for Payment Service to build Knowledge Base for System monitoring to help Site Reliability Engineers who are interested in knowing system performance especially for API latency Cause, probablity of occuring and other performance issues related to latency. Learn how to set up InfluxDB 2 using Docker, configure a bucket through the UI, and implement Python scripts to write and read data payment service data. This step-by-step tutorial is perfect for developers and data enthusiasts aiming to harness the power of time-series databases with RAG powered AI application to make intellignet decision way ahead of time.

🔧/📦  We'll cover:

✅ Setup InfluxDB V2 With Docker Latest Image  

✅ Quick Working Demo for basic Concepts like Bucket, Measurements, Tags, Fields, TimeStamp, Point, Line Protocol

✅ How to Visualize Data on InfluxDB UI

✅ How python continuously loads data for api_latency for payment service with endpoint process

✅ How to Have Real time data with regularly having spikes in api latency for payment service

✅ How to read the data through Python code as it would be needed in upcoming parts to create Knowledge base for RAG powered Prompt context for our LLMs

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 you're excited about Time-Series RAG, give this video a thumbs up, share it, and subscribe for the next parts!

🎯 Next Part: Define Logical flow with writing Backend Code to utilize loaded data for api latency for payment service