Multimodal Embeddings in Azure Vision | Build Text-to-Image Search from Scratch

Description

✨ Learn how to build real Text-to-Image search using Azure Vision’s multimodal embeddings and unlock true multimodal retrieval in your AI applications. 🔍 In this deep-dive, I walk you through how Azure Vision vectorizes both text and images into a shared embedding space, how this differs from image verbalization, and how to call the vectorizeText and vectorizeImage APIs directly from your C# code. You’ll also learn how to secure everything with RBAC, understand API limits, and run practical multimodal vector search. What you’ll discover: 🔥 📌 How vector spaces, similarity, and embeddings actually work 📌 The difference between image verbalization and true multimodal models 📌 How to call vectorizeText and vectorizeImage APIs 📌 How to authenticate securely using RBAC + DefaultAzureCredential 📌 How to run multimodal vector search in C# 📌 Why similarity ordering matters more than similarity scores 📌 API limits and region constraints you need to know 📝 Blog post: https://deployedinazure.com/multimodal-embeddings-with-azure-vision/ ----- 0:00 Intro 1:05 Basics: Vectors, Similarity, and Vector Spaces Explained 7:36 Image Verbalization vs. Multimodal Embeddings (Azure Vision) 11:48 C# Multimodal Embeddings Example Walkthrough 17:16 Azure Vision APIs: VectorizeImage & VectorizeText 20:01 Secure Azure Vision Access with RBAC + DefaultAzureCredential 26:27 How to Perform Multimodal Vector Search 31:32 Azure Vision API Limits & Region Availability 33:00 Outro ----- #azuredeveloper #azurevision #azureai Follow me 👉 🌐 Blog: https://deployedinazure.com 💻 GitHub: https://github.com/deployed-in-azure 🔗 LinkedIn: https://www.linkedin.com/company/deployed-in-azure