Build RAG AI App|Upload PDF, Ask Question With loaded PDF(Pinecone+ Gemi...

Build RAG AI App | Upload PDF & Ask Question From Loaded PDF (Pinecone + Gemini LLM Model + LangChain + Gradio) | Part7

In this video, Learn how to add PDF upload capability to your RAG application! We enhance our existing LangChain RAG app (using Pinecone & Google Gemini) 

by adding a Gradio interface element that allows users to upload their own PDFs. The app then automatically loads, splits, embeds, and indexes the PDF 

content into Pinecone, enabling users to ask questions about the newly uploaded document. Now, users can simply upload a PDF, and the app will process, 

index, and allow them to ask questions directly about its content! 🛠️💻

We'll walk through the code changes required to:

✅ Accept PDF files via the Gradio interface.

✅ Load and split the PDF content using LangChain's PyPDFLoader and RecursiveCharacterTextSplitter.

✅ Embed the text chunks using Google Generative AI Embeddings.

✅ Upload these embeddings to our Pinecone vector database (into a specific namespace).

✅ Modify the query function to retrieve relevant information from the newly indexed PDF data (alongside any existing data in the specified namespace).

✅ Leverage Google's Gemini LLM to generate answers based on the retrieved context.

 

💡 Whether you're a beginner or an AI enthusiast, this project-based series will guide you to build your own AI-powered document Q&A system.

Upcoming Video : Part8 : Adding feature to summarize loaded PDF with suggested questions to user to ask

https://www.youtube.com/watch?v=hrVfMRO2a_Q