🧠 RAG AI App Upgrade : From PDF to Web Search (URL) with LangChain + Vec...

🧠 RAG AI App Upgrade : From PDF to Web Search (URL) with LangChain + VectorDB + Gemini LLM | Part11

📖 Description: In this video, we take our Retrieval-Augmented Generation (RAG) powered AI app to the next level! 🚀

Previously, we handled data from PDFs — now we're expanding to process and search data directly from online web pages via URLs.

🔍 What You'll Learn:

👉 How to fetch and process web data using LangChain's WebBaseLoader (or AsyncHtmlLoader)

👉 Chunking of web data content with RecursiveCharacterTextSplitter

👉 Creating embeddings with Embedding model

👉 Storing vector data in a vector database like Pinecone

👉 Generate summaries and suggested questions

👉 Querying web-loaded data using Gemini LLMs for intelligent Q&A

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

- LangChain for document loading and processing

- Pinecone vector database for semantic search

- Gemini Pro model for summarization and Q&A

- 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! 

💬 Drop your questions in the comments and don’t forget to subscribe for more GenAI/AI Tools/RAG/FineTuning/AI Agents tutorials!