Atlas Vector Search
This course will provide you with an introduction to artificial intelligence and vector search. Then, you'll learn how to generate embeddings for your data, store your embeddings in MongoDB Atlas, and index and search your embeddings to perform a semantic search. You'll also learn how to build and use hybrid search that leverages text search capabilities of Atlas Search and semantic search capacilities of Atlas Vector Search. After you become familiar with how vector search works, you'll use it to build a retrieval-augmented generation (RAG) application to build a custom chatbot. Finally, you'll learn key commands for managing your vector search indexes in the Atlas CLI and MongoDB Shell.
Milestone
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Introduction to AI and Vector Search
Required
Unit
| Parker Faucher, Katie Redmiles, Emily Pope, Vick Mena30 Minutes
View DetailsLearn about the foundations of AI and how Atlas Vector Search fits in.Using Vector Search for Semantic Search
Required
Unit
| Parker Faucher, Sarah Evans, Vick Mena, John McCambridge, Emily Pope, Harshad Dhavale1.75 Hours
View DetailsLearn all about Atlas Vector Search as you build a semantic search feature. Leverage both Atlas Search and Atlas Vector Search to identify the most relevant search results.Using Atlas Vector Search for RAG Applications
Required
Unit
| Henry Weller, Parker Faucher, Aaron Becker, Emily Pope, Daniel Curran, Manuel Fontan Garcia5 Hours
View DetailsLearn how to implement retrieval-augmented generation (RAG) with MongoDB in your application. Learn what retrieval augmented generation is and set it up using the MongoDB Python driver.Managing Atlas Vector Search Indexes
Required
Unit
| Parker Faucher1.5 Hours
View DetailsLearn how to manage your Atlas Vector Search indexes using the Atlas CLI and MongoDB Shell.
