MongoDB Atlas Vector Search in .NET (full tutorial)

Learn more about MongoDB Atlas Vector Search: https://fnf.dev/4iLtBck Get the source code for this video for FREE → https://the-dotnet-weekly.kit.com/mongodb-atlas Want to master Clean Architecture? Go here: https://bit.ly/3PupkOJ Want to unlock Modular Monoliths? Go here: https://bit.ly/3SXlzSt In this comprehensive tutorial, I demonstrate how to implement semantic search using MongoDB Atlas Vector Search in a .NET application. Unlike traditional keyword-based search which looks for specific terms, semantic search understands the meaning behind your queries, providing more intuitive and powerful search capabilities. I'll walk you through: - Understanding vector embeddings and how they work - Setting up MongoDB Atlas Vector Search - Generating embeddings from your content using local LLMs - Implementing vector search queries in .NET - Pre-filtering search results for better performance Learn how MongoDB can function as both your primary document database and vector database, eliminating the need for separate specialized systems. This tutorial includes complete code examples for generating embeddings, creating vector search indexes, and implementing semantic search in a movie database application. Whether you're building AI-powered search, recommendation systems, or just want to improve your application's search experience, this tutorial will show you how to leverage MongoDB Atlas Vector Search for powerful semantic understanding. More docs: Adding MongoDB Atlas Vector Search to a .NET Blazor C# Application https://www.mongodb.com/developer/languages/csharp/vector-search-with-csharp-driver/ Check out my courses: https://www.milanjovanovic.tech/courses Read my Blog here: https://www.milanjovanovic.tech/blog Join my weekly .NET newsletter: https://www.milanjovanovic.tech Chapters 0:00 Why MongoDB Atlas 1:16 Vector Search and MongoDB 5:29 Exploring the sample application 8:59 Creating a MongoDB Atlas DB 11:27 Creating a Vector Search Index 16:17 Generating embeddings with MEAI 24:39 Implementing Vector Search with MongoDB