Build RAG AI App | Summarization & Suggestion for 5 Questions from Uploaded PDF through LLMs | Part8

In this video, we take our previous RAG (Retrieval Augmented Generation) application and evolve it into a more sophisticated "Intelligent System" or "AI Agent" for interacting with PDFs. Using the powerful combination of LangChain, Pinecone, and Google Gemini, wrapped in a dynamic Gradio interface, we build an app where users can:

✅ Upload a PDF: A dedicated step to ingest your document.
✅ Process Intelligently: The system acts as an agent to automatically load, split, embed, and index the PDF content into Pinecone.
✅ Generate Insights: Immediately after processing, the system generates a concise summary of the document and suggests 5 key questions it can answer with LLMs.
✅ Ask Questions: A separate interface allows users to query the processed PDF content using the RAG pipeline.

We'll write the code from scratch (or build upon the previous version), explaining each step:
👉 Setting up the project and API keys (.env, config.py) - Already covered.
👉 Implementing the PDF loading, text splitting, and embedding logic using LangChain and Google Generative AI Embeddings (Already covered but have few changes like split functions into smaller functions).
👉 Storing the vector embeddings in Pinecone for efficient retrieval.
👉 Developing prompt strategies for RAG Q&A, document summarization, and question generation.
👉 Connecting these components using LangChain principles (LCEL i.e. LangChain Expression Language) to create distinct "agent" workflows (processing agent, querying agent).
👉 Structuring the Gradio UI with gr.Blocks and separate tabs for clear workflow for process PDF & Ask Questions.
👉 Making the Gradio interface interactive to trigger these different processes.

This tutorial will show you how to create a multi-functional AI application that goes beyond simple chat!

💡 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.

🔗 Watch previous parts here:
Part 1 (Theory & Concepts) 👉 https://youtu.be/E2YsOkIsihQ
Part 2 (Env Setup) 👉 https://youtu.be/DO_crG2LdOo
Part 3 (PDF to Vector DB) 👉 https://youtu.be/CJ9C00nj4X4
Part 4 (Console RAG Code) 👉 https://youtu.be/bXo7Few4gbU
Part 5 (Streamlit vs Gradio) 👉 https://youtu.be/KmRl1gjJq68
Part 6 (Console App to Interactive Web App) 👉 https://www.youtube.com/watch?v=w_2ojb7YnKQ
Part 7 (Upload PDF & Ask Question From Loaded PDF) 👉 https://www.youtube.com/watch?v=hrVfMRO2a_Q

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#AIAgent
#RAG
#RetrievalAugmentedGeneration
#LangChain
#LLMApplications
#GenerativeAI
#PineCone
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Видео Build RAG AI App | Summarization & Suggestion for 5 Questions from Uploaded PDF through LLMs | Part8 канала Abhishek Jain