RAG AIOps with TSDB(InfluxDB)- Natural Language Queries for Time-Series Data in Your LLM App| Part25

Описание: 🚀 Welcome to Part 25 of our comprehensive RAG (Retrieval Augmented Generation) series! In this exciting episode, we're taking our RAG application to the next level by integrating AIOps capabilities. Learn how to empower your LLM-powered chatbot to understand and answer questions about real-time system metrics and time-series data stored in InfluxDB.

🔥 What you'll learn in this video:
1. Integrating the InfluxDB querying logic (Flux queries) into the main RAG query engine.
2. Routing user questions: How the system decides whether to query Pinecone (for documents) or InfluxDB (for time-series data).
3. Passing time-series context (raw data, "no data" messages, or errors) to the LLM (Google Gemini).
4. How the LLM synthesizes natural language answers based on complex time-series data.
5. Demonstration of asking AIOps-style questions like "What was the API latency for the payment service in the last 5 minutes?" or "Show me CPU usage for the mongodb container."
6. Code walkthrough of the key integration points in `query_engine.py` and updates to `config.py`.
7. See the fully functional application in Gradio, now handling both document Q&A and AIOps insights!

💡 Key Technologies Covered:
Python
LangChain
Pinecone (Vector Database)
InfluxDB (Time-Series Database)
Flux Query Language
Google Gemini (LLM)
Gradio (Web UI)
AIOps Concepts
Natural Language Processing (NLP)

👨‍💻 Who is this for?
Developers building advanced RAG applications.
Engineers interested in AIOps and observability.
Anyone looking to make time-series data accessible via natural language.
Followers of our RAG series wanting to see the full integration.

📈 By the end of this video, you'll understand how to build a powerful, hybrid RAG system that can tap into both unstructured documents and structured time-series data to provide truly comprehensive answers.

👍 If you find this video helpful, please like, share, and subscribe for more content on AI, LLMs, and application development! Don't forget to hit the bell icon to get notified about new videos.

💬 Questions or suggestions? Drop them in the comments below!

🔄 RAG Series Playlist: [Link to your Full RAG Playlist Here]
Part1 (Theory & Concepts) 👉    • How to Build an AI App with RAG in Python ...  
Part2 (Env Setup) 👉    • RAG Explained Simply: Fixing LLM Problems ...  
Part3 (PDF to Vector DB) 👉    • Load PDFs into Vector Database (PineCone D...  
Part4 (Console RAG Code) 👉    • Build RAG AI Agent | Question→ SearchDocum...  
Part5 (Streamlit vs Gradio) 👉    • Build RAG Chain | Streamlit vs Gradio for ...  
Part6 (Console App to Interactive Web App) 👉    • Build RAG AI App with Gradio UI | Refactor...  
Part7 (Upload PDF & Ask Question From Loaded PDF) 👉    • Build RAG AI App|Upload PDF, Ask Question ...  
Part8 (Summarization & Suggestion for 5 Questions) 👉    • Build RAG AI App | Summarization & Suggest...  
Part9 (Fixing Vector Similarity Limitations with RERANK Models) 👉    • Build RAG AI App| Why RERANK is Critical i...  
Part10 (RERANK Model Integration with Pinecone To Fine-Tuning RAG Retrieval Quality) 👉    • Build RAG AI App| RERANK Model Integration...  
Part11 (Extract data from Web Search (URL) for RAG powered AI app ) 👉    • 🧠 RAG AI App Upgrade : From PDF to Web Sea...  
Part12 (Integrate Knowledge from Relational DB along with PDF & URL data ) 👉    • 🧠 RAG AI App Upgrade : Integrate Knowledge...  
Part13 (POSTGRESQL - Setup & Load 733 Questions/Answers for 50 Topics of System Design) 👉    • RAG AI App | POSTGRESQL - Setup & Load 733...  
Part14 (Integrate PostgreSQL Database as LLM Knowledge Base for system design concepts) 👉    • RAG Powered AI App : Integrate PostgreSQL ...  
Part15 (MongoDB - Setup & Load data) 👉    • RAG Powered AI App | Adding MongoDB along ...  
Part16 (Refactored Code) 👉    • Refactoring RAG AI App Code Base for Futur...  
Part17 (Integrate MongoDB Database as LLM Knowledge Base for system design concepts) 👉    • RAG Powered AI App : Integrate MongoDB as ...  
Part18 (Integrate CSV Data as LLM Knowledge Base for system design concepts) 👉    • RAG Powered AI App : Integrate CSV Data as...  
Part19 (Integrate Restful API as LLM Knowledge Base for real time dynamic data source) 👉    • RAG Powered AI App: Integrate REST API As ...  
Part20 (Integrate Restful API Line by Line Code Explanation) 👉    • RAG Powered AI App:How To Integrate REST A...  
Part21 (Integrate TimeSeries Database InfluxDB) 👉    • RAG Powered AI: Intro to TimeSeriesDB Infl...  
Part22 (Load TimeSeries Data TO InfluxDB) 👉    • RAG AI APP TimeSeriesDB: Setup InfluxDB, L...  
PartXX (Approach Diference for TIMESERIES & Other Data sources) 👉    • Build AI Monitoring RAG App | Why Time-Ser...  
Part23 (Querying & Format Influx DB Data for Prompt Creation for LLMs) 👉    • RAG TimeSeriesDB AIOps App: Querying & For...  
Part24 (Translate Natural Language Question to InfluxDB Query to extract TimeSeries Data & format data for LLM) 👉    • RAG AIOps:Translate Natural Language Quest...  

#RAG #AIOps #InfluxDB #TimeSeries #LLM #GoogleGemini #LangChain #Python #Gradio #AI #MachineLearning #NaturalLanguageProcessing #Observability #python #Flux #Monitoring