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Build a FREE Medical RAG Knowledge Base - Next.js & LangChain Tutorial
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In this video, you will:
Understand the fundamentals of RAG and its applications in medical AI.
Implement a backend API for seamless data updates using Pinecone.
Effortlessly load and process medical documents with LangChain's powerful document loaders.
Optimize performance by chunking and embedding documents in batches.
Create a dynamic user experience with a progress bar to track upload progress.
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
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
25:56 Batch Jobs for Upserting to Your Vector Database
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)
LangChain (LLM Application Framework)
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)
Medical report analyzing app demo here :
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