az cognitiveservices account create \
  --name foundry-guide-ais-02 \
  --resource-group rg-guide-ais-02 \
  --kind AIServices --sku S0 --location westeurope \
  --custom-domain foundry-guide-ais-02 --yes

az cognitiveservices account deployment create \
  --name foundry-guide-ais-02 \
  --resource-group rg-guide-ais-02 \
  --deployment-name text-embedding-3-small \
  --model-name text-embedding-3-small \
  --model-version 1 --model-format OpenAI \
  --sku-capacity 1 --sku-name GlobalStandard

az login
ID=$(az ad signed-in-user show --query id -o tsv)
SUB=$(az account show --query id -o tsv)
BASE="/subscriptions/$SUB/resourceGroups/rg-guide-ais-02/providers"
SEARCH="$BASE/Microsoft.Search/searchServices/ais-guide-02"
MODELS="$BASE/Microsoft.CognitiveServices/accounts/foundry-guide-ais-02"

az role assignment create --assignee-object-id $ID --assignee-principal-type User \
  --role "Search Service Contributor" --scope $SEARCH
az role assignment create --assignee-object-id $ID --assignee-principal-type User \
  --role "Search Index Data Contributor" --scope $SEARCH
az role assignment create --assignee-object-id $ID --assignee-principal-type User \
  --role "Cognitive Services OpenAI User" --scope $MODELS

dotnet new console -n search-demo
cd search-demo
dotnet add package Azure.Identity --version 1.21.0
dotnet add package Azure.Search.Documents --version 12.0.0
dotnet add package OpenAI --version 2.14.0

using Azure.Identity;
using Azure.Search.Documents;
using Azure.Search.Documents.Indexes;
using Azure.Search.Documents.Indexes.Models;
using Azure.Search.Documents.Models;
using OpenAI;
using OpenAI.Embeddings;
using System.ClientModel.Primitives;

#pragma warning disable OPENAI001

const string search = "https://ais-guide-02.search.windows.net";
const string models = "https://foundry-guide-ais-02.openai.azure.com/openai/v1";
const string model = "text-embedding-3-small";
const string indexName = "documents";

DefaultAzureCredential credential = new();

BearerTokenPolicy tokenPolicy = new(
    credential,
    "https://cognitiveservices.azure.com/.default");
EmbeddingClient embedder = new(
    model,
    tokenPolicy,
    new OpenAIClientOptions { Endpoint = new Uri(models) });

string[] texts =
[
    "A resource group gathers the resources of a solution and is deleted in one go.",
    "Azure Container Apps runs containers without managing machines or Kubernetes.",
    "Azure AI Search indexes documents and finds them by words or by similarity.",
    "Azure Bastion opens the desktop of a virtual machine inside the browser.",
    "A container registry keeps the images the containers start from.",
];

// 1. The index, with a vector field of 1536 numbers.
SearchIndexClient indexClient = new(new Uri(search), credential);

SearchIndex definition = new(indexName)
{
    Fields =
    {
        new SimpleField("id", SearchFieldDataType.String) { IsKey = true },
        new SearchableField("text"),
        new SearchField("vector", SearchFieldDataType.Collection(SearchFieldDataType.Single))
        {
            IsSearchable = true,
            VectorSearchDimensions = 1536,
            VectorSearchProfileName = "profile",
        },
    },
    VectorSearch = new VectorSearch
    {
        Algorithms = { new HnswAlgorithmConfiguration("hnsw") },
        Profiles = { new VectorSearchProfile("profile", "hnsw") },
    },
};

await indexClient.CreateOrUpdateIndexAsync(definition);
Console.WriteLine($"Index '{indexName}' ready.");

// 2. One vector per text, then the upload.
SearchClient searchClient = new(new Uri(search), indexName, credential);

List<SearchDocument> documents = [];
for (int i = 0; i < texts.Length; i++)
{
    var vector = await embedder.GenerateEmbeddingAsync(texts[i]);
    documents.Add(new SearchDocument
    {
        ["id"] = (i + 1).ToString(),
        ["text"] = texts[i],
        ["vector"] = vector.Value.ToFloats().ToArray(),
    });
}

await searchClient.UploadDocumentsAsync(documents);
Console.WriteLine($"Uploaded {documents.Count} documents.");

await Task.Delay(TimeSpan.FromSeconds(3));

// 3. The question becomes a vector and the three nearest come back.
const string question = "how do I delete everything at once?";
var questionVector = await embedder.GenerateEmbeddingAsync(question);

SearchOptions options = new() { Size = 3 };
options.Select.Add("text");
options.VectorSearch = new VectorSearchOptions
{
    Queries =
    {
        new VectorizedQuery(questionVector.Value.ToFloats())
        {
            KNearestNeighborsCount = 3,
            Fields = { "vector" },
        },
    },
};

Console.WriteLine($"\nQuestion: {question}\n");

var results = await searchClient.SearchAsync<SearchDocument>(searchText: null, options);
await foreach (var r in results.Value.GetResultsAsync())
{
    Console.WriteLine($"{r.Score:F4}  {r.Document["text"]}");
}

dotnet run

Index 'documents' ready.
Uploaded 5 documents.

Question: how do I delete everything at once?

0.6154  A resource group gathers the resources of a solution and is deleted in one go.
0.5310  Azure Bastion opens the desktop of a virtual machine inside the browser.
0.5228  Azure Container Apps runs containers without managing machines or Kubernetes.

az cognitiveservices account purge \
  --name foundry-guide-ais-02 \
  --location westeurope \
  --resource-group rg-guide-ais-02