DocumentDB Vector Search for .NET

02/12/2026

This .NET 8.0 sample application demonstrates vector similarity searches using Azure DocumentDB with different vector search algorithms (IVF, HNSW, and DiskANN). The application uses hotel data and text embeddings generated by Azure OpenAI.

Features

Generate vector embeddings for hotel descriptions using Azure OpenAI
Compare performance across IVF, HNSW, and DiskANN vector search algorithms
Seamless integration with DocumentDB and Azure OpenAI services
Interactive menu-driven application

Prerequisites

Azure Services

Azure DocumentDB account
Azure OpenAI Service with text-embedding-3-small model deployed

Development Environment

.NET 8.0 SDK
Visual Studio Code or Visual Studio 2022
Azure CLI
Azure DocumentDB for VS Code

Setup

Clone the repository:

git clone https://github.com/documentdb-samples
cd ai/vector-search-dotnet

az login

Update appsettings.json with your Azure service details:

{
  "AzureOpenAI": {
    "Endpoint": "https://your-openai-service-name.openai.azure.com/"
  },
  "MongoDB": {
    "TenantId": "your-azure-tenant-id",
    "ClusterName": "your-documentdb-mongodb-cluster-name"
  }
}

Run the application:

dotnet restore
dotnet build
dotnet run

The application provides an interactive menu with the following options:

Create embeddings for data (required first step)
Show all database indexes
Run IVF vector search (fast approximate search)
Run HNSW vector search (high accuracy, memory-intensive)
Run DiskANN search (balanced performance)

Vector Search Algorithms

IVF (Inverted File): Fast approximate search with low memory usage
HNSW (Hierarchical Navigable Small World): High accuracy but memory-intensive
DiskANN: Balanced performance and memory efficiency

Project Structure

Models/ - Configuration and data models
Services/ - Core business logic services
Utilities/ - Azure authentication helpers
data/ - Sample hotel data files
Program.cs - Application entry point
appsettings.json - Configuration file

Development Setup

Option 1: Dev Container

This project includes a dev container with all required tools. Open in VS Code and click "Reopen in Container".

Option 2: Manual Setup

Install .NET 8.0 SDK and Azure CLI, then run:

dotnet restore
dotnet build

Configuration

Update the following in appsettings.json:

AzureOpenAI.Endpoint: Your Azure OpenAI service endpoint
MongoDB.TenantId: Your Azure tenant ID
MongoDB.ClusterName: Your DocumentDB cluster name

Troubleshooting

Authentication errors: Run az login to authenticate with Azure
Connection issues: Verify DocumentDB cluster is running and accessible
Embedding errors: Ensure Azure OpenAI service is deployed with text-embedding-3-small model
Index failures: Check vector dimensions match (1536) and sufficient storage is available

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