Video Description

Learn how to build a robust and FREE medical RAG (Retrieval Augmented Generation) knowledge base using Pinecone, Next.js, and LangChain! This comprehensive tutorial guides you through every step of the process, from setting up your development environment to deploying your application. In this video, you will: Understand the fundamentals of RAG and its applications in medical AI. Set up a Next.js project and design a user-friendly interface with Tailwind CSS and Shadcn UI. Implement a backend API for seamless data updates using Pinecone. Effortlessly load and process medical documents with LangChain's powerful document loaders. Utilize Transformers.js to generate embeddings locally for FREE, saving you time and money. Optimize performance by chunking and embedding documents in batches. Create a dynamic user experience with a progress bar to track upload progress. Master the integration of Pinecone, Next.js, and LangChain to build a cutting-edge medical RAG application. This tutorial is perfect for: Developers interested in building AI-powered medical applications. Healthcare professionals looking to leverage the power of RAG for improved diagnostics and research. Anyone curious about the latest advancements in natural language processing and AI. Chapters: 00:00 Overview of the Medical Report Analysis App and RAG 01:33 Live Demo: What We're Building 04:53 Setting Up Your Development Environment: Creating a Next.js App 08:08 UI Design: Styling with Tailwind CSS and Shadcn UI 15:30 Adding File Upload Functionality 17:43 Building the Backend API: Updating Your Pinecone Database 19:31 Load PDF and Text documents using LangChain's Document Loader 22:19 Integrating Pinecone with Next.js: Initializing the Client 25:56 Batch Jobs for Upserting to Your Vector Database 28:57 Running Hugging Face Models Locally with Transformers.js 34:19 Preparing Your Data for Embedding: Chunking Documents with LangChain Text Splitters 38:03 Optimizing for Performance: Batch Processing for Embedding and Upserting 46:19 UX Improvement: Implementing a Progress Bar 54:22 UX Improvement: Fetching File List for Uploads Key Technologies Covered: RAG (Retrieval Augmented Generation) Pinecone (Vector Database) Next.js (React Framework) LangChain (LLM Application Framework) Transformers.js (Hugging Face Model Library) Tailwind CSS (Utility-First CSS Framework) Shadcn UI (React UI Component Library) By the end of this video, you'll have a solid foundation for building your own medical RAG knowledge base and be well-equipped to explore the vast potential of this exciting technology! Don't forget to like, subscribe, and hit the notification bell for more insightful AI tutorials! Links: Complete code of this tutorial here: (I have changed the name of the repository, but it's the same one) https://github.com/KoushikJit/almost-md-knowledge-base.git Medical report analyzing app demo here : https://youtu.be/ADpVf8kDJN0 Complete code of the medical report analyzing app in Next.js: https://github.com/KoushikJit/almost-md.git