EASY: Build Generative AI Applications
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** 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
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