Integrating Azure AI Search with .NET using Embeddings from Microsoft Foundry

[TallesValiatti]

Introduction

Hello everyone,

This second part of the series focuses on integrating Azure AI Search with .NET, using models hosted in Microsoft Foundry to generate embeddings and enable vector-based search scenarios.

Building on the fundamentals introduced in Part 1, this article demonstrates how Microsoft Foundry fits naturally into modern search architectures by providing managed, scalable embedding models that can be consumed directly from .NET applications. The goal is to move beyond traditional keyword search and introduce semantic understanding through vectors, while keeping the architecture clean and production-ready.

The article is available here:

https://tallesvaliatti.com/integrando-o-azure-ai-search-service-com-net-parte-2-e18f60a1384f

This content is designed to show how developers can combine Microsoft Foundry, Azure AI Search, and .NET to build intelligent search experiences using enterprise-grade services.

Features and Screenshots

Technical Details

• End-to-end embedding generation using Microsoft Foundry models
• Vector-enabled Azure AI Search index definition
• Semantic similarity search using vector queries
• Clean separation between embedding generation, indexing, and querying
• Fully managed AI and search infrastructure

Challenges and Solutions

Language:

• C# (.NET)

AI Platform:

• Microsoft Foundry

Models:

• Text embedding models hosted in Foundry

Search Service:

• Azure AI Search

• SDKs:

• Azure AI / Foundry SDK

• Azure.Search.Documents

Search Capabilities:

• Full-text search
• Vector search (embeddings)
• Vector Configuration: HNSW profiles
• Development Tools: Visual Studio / Rider