🧠 RAG AI App Upgrade : Integrate Knowledge from Relational DB along with...

🧠 RAG AI App Upgrade : Integrate Knowledge from Relational DB along with PDF & URL data | Part12

In this step-by-step tutorial, we take your RAG (Retrieval-Augmented Generation) app to the next level by integrating SQLite database support—adding to existing PDF and URL data ingestion. You’ll learn how to ingest structured text data from a database, generate embeddings using Google’s Gemini AI, store it in Pinecone, and build an interactive UI with Gradio.

🔍 What's Covered:

👉 Setting up a sample Relational database SQLite3 (Will replace that with PostgresDB in next video)

👉 Creating CHUNKS, EMBEDDING and LOADING Relational DB tables content into VECTOR database (PineCone DB)

👉 Generate SUMMARY & Suggesting 5 BEST QUESTIONS based Relational database contect stored in tables with LLM

👉 Integrate Backend on UI with a new Gradio tab for DB ingestion

👉 Working Demo with Querying relational database suggested questions by using Gemini LLMs for intelligent Q&A

🔧/📦 Key Tools/Tech Stack: LangChain | Gemini LLM Model with API | PineCone | Gradio | SQLite3 Python:

    - Sqlite3 to extract data from database 

- LangChain for database loading and processing

- Pinecone vector database for semantic search

- Gemini Pro model for summarization and Q&A via Gemini API (via Google Generative AI)

- Gradio Blocks UI for an interactive frontend

Whether you're a developer, ML enthusiast, or researcher building with LLMs powered with RAG AI application, or you're building a chatbot, search assistant, document intelligence, enterprise Q&A systems or custom AI workflow, this tutorial shows you how to enhance your app with live web knowledge!

https://www.youtube.com/watch?v=uQkEWrSEV8Y