GenAI QNA Chatbot with Blog URL and PDF Using LangChain RAG and Google Gemini Pro LLM | Streamlit UI

Do you want to know how to get answers from any URL with just one click? Or talk to PDF documents to extract insights effortlessly?

Welcome to Part 6 of the Learn RAG From Scratch series! In this exciting video, we’ll create two amazing Generative AI-enabled apps:
1. Chat with URLs: Extract data from a URL, store it in vector embeddings using FAISS, and get intelligent responses using RAG and LLMs.
2. Chat with PDFs: Interact with PDF documents, extract key information, and generate insightful answers using Google Gemini Pro 1.5 and FAISS vector databases.

What’s Covered in This Video?
1. A quick introduction to Retrieval-Augmented Generation (RAG).
2. Step-by-step coding tutorial for creating these applications.
3. Tools Used:
a. LLM: Google Gemini Pro 1.5
b. Embedding Model: Google GenerativeAI Embeddings
c. Vector Database: FAISS

Whether you're an AI enthusiast, a data scientist, or a developer curious about how to integrate AI into practical applications, this video is for you!

Upcoming Project Teaser:
Next up, we’ll build a GenAI-enabled Anime/Manga Chatbot that lets fans discuss theories, ask about characters, and get recommendations. Anime lovers, stay tuned! 🎉

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[jhs]

What will be the dependencies of that project because you didn't share the requirement.txt file ?

[amansaiyed]

Sir make a video for Ai agent 🫠 please

[jhs]

This embedding model is open source ??

[侯章祥]

Thanks for your sharing. Can you provide the url.py and pdfchat.py files for refenence? I can't find the link to acess the files.