Stop Guessing! Implement RAG in Your MERN App using MongoDB Atlas & OpenAI in Quiz APP (Part 1) скачать в хорошем качестве

Stop Guessing! Implement RAG in Your MERN App using MongoDB Atlas & OpenAI in Quiz APP (Part 1)

Take your MERN stack applications to the next level by integrating Retrieval-Augmented Generation (RAG)! In Part 1 of this series, we move beyond basic CRUD and dive into the world of AI-powered semantic search. What you’ll learn in this video: In this session, we explore how to turn raw text into "intelligence" using Vector Embeddings. We’ll integrate OpenAI’s embedding models and MongoDB Atlas Vector Search to allow our Quiz App to find relevant information based on meaning rather than just keywords. Key Topics Covered: What is RAG? Understanding why external context makes LLMs smarter and reduces hallucinations. Embeddings 101: How to use text-embedding-3-small to convert quiz data into numeric vectors. MongoDB Atlas Vector Search: Setting up a vector index and storing embeddings alongside your MERN data. Semantic Search vs. Keyword Search: Why "Ram is a good boy" and "Ram is a nice guy" are neighbors in vector space. Code Implementation: Connecting the OpenAI SDK to your Node/Express backend. Code Snippet Teaser: JavaScript const embedding = await openai.embeddings.create({ model: "text-embedding-3-small", input: "How does RAG work in a MERN app?", encoding_format: "float", }); Resources: MongoDB Atlas Vector Search Docs: https://www.mongodb.com/docs/atlas/at... OpenAI Embeddings Guide: https://platform.openai.com/docs/guid... Don't forget to Like and Subscribe to follow the full AI-MERN Series! #MERNStack #OpenAI #MongoDB #VectorSearch #RAG #WebDevelopment #AI #Javascript #Part3