RAG Powered AI With TimeSeries Database : Intro to TimeSeriesDB InfluxDB along with Integration For AI based System Monitoring | Part21
Welcome to Part 21 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 and TimescaleDB.
🧠 In this introductory video, we explore the concept of combining TSDBs with RAG to build AI agents capable of understanding temporal data for critical use cases like AIOps (Algorithmic IT Operations), Financial Monitoring, Healthcare, and more.
🔧/📦 We'll cover:
✅ What Time-Series Databases are and why they are crucial for dynamic data.
✅ How python continuously loading all docker containers stats into InfluxDB in real time
✅ The high-level flow of a Time-Series RAG system.
✅ Real-world use cases in different industries.
✅ A deep dive into the AIOps scenario: querying system metrics & logs for debugging performance issues ("What caused the API latency spike?").
✅ The key technical steps required to implement this integration.
✅ Our plan for the upcoming videos in the series (InfluxDB setup, data loading, backend code, frontend integration, demo).
This is the start of building a truly intelligent AI application that can reason over time-sensitive data!
👍 If you're excited about Time-Series RAG, give this video a thumbs up, share it, and subscribe for the next parts!
Next Part: setting up InfluxDB and getting some sample resource utilization data loaded, so we have something to query."
Followed by: Building Backend code to interact with TimeSeries Database through python code
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