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EASY: Build Generative AI Applications

17 min • Published Oct 04, 2023
JavaScript
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✅ Sign-up for a free cluster at → https://mdb.link/free-wOdZ1hEWvjU ✅ Get help on our Community Forums → https://mdb.link/community-wOdZ1hEWvjU ** Support for the '$vectorSearch' aggregation pipeline stage is available with MongoDB Atlas 6.0.11 and 7.0.2. ** 🔥 Unlock the Power of AI with MongoDB and Atlas Vector Search 🔥 Are you ready to step into the future with the 4th Industrial Revolution? Dive deep into the world of Artificial Intelligence and Large Language Models (LLMs) with this comprehensive guide. Learn how to leverage the power of LLMs and MongoDB's Atlas Vector Search to build transformative AI-powered applications. 🚀 👉 What You'll Learn: → The evolution of AI and its role in the 4th Industrial Revolution. → How generative AI is changing the game with ChatGPT. → The magic behind Large Language Models (LLMs). → How to use MongoDB and Atlas Vector Search for semantic search applications. → Hands-on example: Building a movie recommendation system using semantic search. 🎬 🛠 Technologies Used: → MongoDB → Atlas Vector Search → HuggingFace → Node.js 🔗 Resources 🔗 ✅ Written article with Python code snippets → https://mdb.link/article-wOdZ1hEWvjU ✅ Atlas Vector Search Docs → https://mdb.link/vector-search-docs-wOdZ1hEWvjU ⏱️ Timestamps ⏱️ 00:00 - Intro 01:42 - What are vector embeddings? 02:18 - What is vector search? 03:20 - What is Atlas Vector Search? 04:14 - Semantic search for movie recommendations 05:10 - Step 1: Setup & Connect to your MongoDB instance 07:39 - Step 2: Set up the embedding creation function 10:21 - Step 3: Create and store embeddings 13:09 - Step 4: Create a vector search index 14:20 - Step 5: Query your data ------ ✅ Subscribe to our channel → https://mdb.link/subscribe
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