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‎docs/ai/how-to/snippets/content-filtering/AIContentFiltering.csproj

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<ItemGroup>
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<PackageReference Include="Azure.AI.OpenAI" />
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<PackageReference Include="Azure.Identity" />
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<PackageReference Include="Microsoft.Extensions.AI" Version="9.3.0-preview.1.25114.11" />
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<PackageReference Include="Microsoft.Extensions.AI.OpenAI" Version="9.3.0-preview.1.25114.11" />
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<PackageReference Include="Azure.Identity" Version="1.13.2" />
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<PackageReference Include="Microsoft.Extensions.AI" Version="9.3.0-preview.1.25161.3" />
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<PackageReference Include="Microsoft.Extensions.AI.OpenAI" Version="9.3.0-preview.1.25161.3" />
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</ItemGroup>
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</Project>

‎docs/ai/how-to/snippets/content-filtering/Program.cs

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{
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ChatResponse completion = await client.GetResponseAsync("YOUR_PROMPT");
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Console.WriteLine(completion.Message);
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Console.WriteLine(completion.Messages.Single());
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}
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catch (Exception e)
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{

‎docs/ai/quickstarts/evaluate-ai-response.md

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@@ -90,7 +90,7 @@ Complete the following steps to create an MSTest project that connects to your l
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- Sets up the <xref:Microsoft.Extensions.AI.Evaluation.ChatConfiguration>.
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- Sets the <xref:Microsoft.Extensions.AI.ChatOptions>, including the <xref:Microsoft.Extensions.AI.ChatOptions.Temperature> and the <xref:Microsoft.Extensions.AI.ChatOptions.ResponseFormat>.
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- Fetches the response to be evaluated by calling <xref:Microsoft.Extensions.AI.IChatClient.GetResponseAsync(System.Collections.Generic.IList{Microsoft.Extensions.AI.ChatMessage},Microsoft.Extensions.AI.ChatOptions,System.Threading.CancellationToken)>, and stores it in a static variable.
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- Fetches the response to be evaluated by calling <xref:Microsoft.Extensions.AI.IChatClient.GetResponseAsync(System.Collections.Generic.IEnumerable{Microsoft.Extensions.AI.ChatMessage},Microsoft.Extensions.AI.ChatOptions,System.Threading.CancellationToken)>, and stores it in a static variable.
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1. Add the `GetOllamaChatConfiguration` method, which creates the <xref:Microsoft.Extensions.AI.IChatClient> that the evaluator uses to communicate with the model.
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@@ -102,7 +102,7 @@ Complete the following steps to create an MSTest project that connects to your l
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This method does the following:
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- Invokes the <xref:Microsoft.Extensions.AI.Evaluation.Quality.CoherenceEvaluator> to evaluate the *coherence* of the response. The <xref:Microsoft.Extensions.AI.Evaluation.IEvaluator.EvaluateAsync(System.Collections.Generic.IEnumerable{Microsoft.Extensions.AI.ChatMessage},Microsoft.Extensions.AI.ChatMessage,Microsoft.Extensions.AI.Evaluation.ChatConfiguration,System.Collections.Generic.IEnumerable{Microsoft.Extensions.AI.Evaluation.EvaluationContext},System.Threading.CancellationToken)> method returns an <xref:Microsoft.Extensions.AI.Evaluation.EvaluationResult> that contains a <xref:Microsoft.Extensions.AI.Evaluation.NumericMetric>. A `NumericMetric` contains a numeric value that's typically used to represent numeric scores that fall within a well-defined range.
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- Invokes the <xref:Microsoft.Extensions.AI.Evaluation.Quality.CoherenceEvaluator> to evaluate the *coherence* of the response. The <xref:Microsoft.Extensions.AI.Evaluation.IEvaluator.EvaluateAsync(System.Collections.Generic.IEnumerable{Microsoft.Extensions.AI.ChatMessage},Microsoft.Extensions.AI.ChatResponse,Microsoft.Extensions.AI.Evaluation.ChatConfiguration,System.Collections.Generic.IEnumerable{Microsoft.Extensions.AI.Evaluation.EvaluationContext},System.Threading.CancellationToken)> method returns an <xref:Microsoft.Extensions.AI.Evaluation.EvaluationResult> that contains a <xref:Microsoft.Extensions.AI.Evaluation.NumericMetric>. A `NumericMetric` contains a numeric value that's typically used to represent numeric scores that fall within a well-defined range.
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- Retrieves the coherence score from the <xref:Microsoft.Extensions.AI.Evaluation.EvaluationResult>.
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- Validates the *default interpretation* for the returned coherence metric. Evaluators can include a default interpretation for the metrics they return. You can also change the default interpretation to suit your specific requirements, if needed.
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- Validates that no diagnostics are present on the returned coherence metric. Evaluators can include diagnostics on the metrics they return to indicate errors, warnings, or other exceptional conditions encountered during evaluation.

‎docs/ai/quickstarts/snippets/chat-with-data/azure-openai/CloudService.cs

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using Microsoft.Extensions.VectorData;
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using System;
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using System.Collections.Generic;
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using System.Linq;
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using System.Text;
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using System.Threading.Tasks;
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namespace VectorDataAI
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{

‎docs/ai/quickstarts/snippets/chat-with-data/azure-openai/Program.cs

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using Azure.AI.OpenAI;
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using Azure.Identity;
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using Microsoft.Extensions.AI;
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using Microsoft.Extensions.VectorData;
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using Microsoft.Extensions.Configuration;
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using Microsoft.Extensions.VectorData;
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using Microsoft.SemanticKernel.Connectors.InMemory;
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using VectorDataAI;
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// Create and populate the vector store
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var vectorStore = new InMemoryVectorStore();
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var cloudServicesStore = vectorStore.GetCollection<int, CloudService>("cloudServices");
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Microsoft.Extensions.VectorData.IVectorStoreRecordCollection<int, CloudService> cloudServicesStore = vectorStore.GetCollection<int, CloudService>("cloudServices");
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await cloudServicesStore.CreateCollectionIfNotExistsAsync();
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foreach (var service in cloudServices)
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foreach (CloudService service in cloudServices)
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{
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service.Vector = await generator.GenerateEmbeddingVectorAsync(service.Description);
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await cloudServicesStore.UpsertAsync(service);
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}
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// Convert a search query to a vector and search the vector store
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var query = "Which Azure service should I use to store my Word documents?";
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var queryEmbedding = await generator.GenerateEmbeddingVectorAsync(query);
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string query = "Which Azure service should I use to store my Word documents?";
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ReadOnlyMemory<float> queryEmbedding = await generator.GenerateEmbeddingVectorAsync(query);
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var results = await cloudServicesStore.VectorizedSearchAsync(queryEmbedding, new VectorSearchOptions()
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VectorSearchResults<CloudService> results =
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await cloudServicesStore.VectorizedSearchAsync(queryEmbedding, new VectorSearchOptions<CloudService>()
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{
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Top = 1,
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VectorPropertyName = "Vector"
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Top = 1
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});
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await foreach (var result in results.Results)
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await foreach (VectorSearchResult<CloudService> result in results.Results)
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{
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Console.WriteLine($"Name: {result.Record.Name}");
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Console.WriteLine($"Description: {result.Record.Description}");

‎docs/ai/quickstarts/snippets/chat-with-data/azure-openai/VectorDataAI.csproj

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<ItemGroup>
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<PackageReference Include="Azure.Identity" Version="1.13.2" />
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<PackageReference Include="Azure.AI.OpenAI" Version="2.1.0" />
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<PackageReference Include="Microsoft.Extensions.AI.OpenAI" Version="9.0.1-preview.1.24570.5" />
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<PackageReference Include="Microsoft.Extensions.VectorData.Abstractions" Version="9.0.0-preview.1.25078.1" />
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<PackageReference Include="Microsoft.SemanticKernel.Connectors.InMemory" Version="1.31.0-preview" />
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<PackageReference Include="Microsoft.Extensions.Configuration" Version="9.0.2" />
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<PackageReference Include="Microsoft.Extensions.Configuration.UserSecrets" Version="9.0.2" />
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<PackageReference Include="Microsoft.Extensions.AI.OpenAI" Version="9.3.0-preview.1.25161.3" />
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<PackageReference Include="Microsoft.Extensions.VectorData.Abstractions" Version="9.0.0-preview.1.25161.1" />
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<PackageReference Include="Microsoft.SemanticKernel.Connectors.InMemory" Version="1.41.0-preview" />
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<PackageReference Include="Microsoft.Extensions.Configuration" Version="10.0.0-preview.1.25080.5" />
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<PackageReference Include="Microsoft.Extensions.Configuration.UserSecrets" Version="10.0.0-preview.1.25080.5" />
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</ItemGroup>
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</Project>

‎docs/ai/quickstarts/snippets/chat-with-data/openai/CloudService.cs

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using Microsoft.Extensions.VectorData;
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using System;
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using System.Collections.Generic;
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using System.Linq;
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using System.Text;
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using System.Threading.Tasks;
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namespace VectorDataAI
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‎docs/ai/quickstarts/snippets/chat-with-data/openai/Program.cs

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using Microsoft.Extensions.AI;
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using OpenAI;
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using System.ClientModel;
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using Microsoft.Extensions.AI;
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using Microsoft.Extensions.Configuration;
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using Microsoft.Extensions.VectorData;
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using Microsoft.SemanticKernel.Connectors.InMemory;
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using OpenAI;
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using VectorDataAI;
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using System.ClientModel;
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using Microsoft.Extensions.Configuration;
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var cloudServices = new List<CloudService>()
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{
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new CloudService
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{
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new() {
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Key=0,
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Name="Azure App Service",
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Description="Host .NET, Java, Node.js, and Python web applications and APIs in a fully managed Azure service. You only need to deploy your code to Azure. Azure takes care of all the infrastructure management like high availability, load balancing, and autoscaling."
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},
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new CloudService
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{
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new() {
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Name="Azure Service Bus",
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},
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new CloudService
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{
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new() {
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Description="Azure Blob Storage allows your applications to store and retrieve files in the cloud. Azure Storage is highly scalable to store massive amounts of data and data is stored redundantly to ensure high availability."
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},
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new CloudService
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{
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new() {
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Description="Manage user identities and control access to your apps, data, and resources.."
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},
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new CloudService
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{
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new() {
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},
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new CloudService
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{
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new() {
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Description="Information retrieval at scale for traditional and conversational search applications, with security and options for AI enrichment and vectorization."
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}
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};
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// Load the configuration values
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var config = new ConfigurationBuilder().AddUserSecrets<Program>().Build();
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// Load the configuration values.
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IConfigurationRoot config = new ConfigurationBuilder().AddUserSecrets<Program>().Build();
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string model = config["ModelName"];
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string key = config["OpenAIKey"];
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// Create the embedding generator
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// Create the embedding generator.
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IEmbeddingGenerator<string, Embedding<float>> generator =
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new OpenAIClient(new ApiKeyCredential(key))
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.AsEmbeddingGenerator(modelId: model);
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// Create and populate the vector store
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// Create and populate the vector store.
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var vectorStore = new InMemoryVectorStore();
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var cloudServicesStore = vectorStore.GetCollection<int, CloudService>("cloudServices");
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IVectorStoreRecordCollection<int, CloudService> cloudServicesStore = vectorStore.GetCollection<int, CloudService>("cloudServices");
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await cloudServicesStore.CreateCollectionIfNotExistsAsync();
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foreach (var service in cloudServices)
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foreach (CloudService service in cloudServices)
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{
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service.Vector = await generator.GenerateEmbeddingVectorAsync(service.Description);
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await cloudServicesStore.UpsertAsync(service);
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}
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// Convert a search query to a vector and search the vector store
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var query = "Which Azure service should I use to store my Word documents?";
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var queryEmbedding = await generator.GenerateEmbeddingVectorAsync(query);
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// Convert a search query to a vector and search the vector store.
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string query = "Which Azure service should I use to store my Word documents?";
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ReadOnlyMemory<float> queryEmbedding = await generator.GenerateEmbeddingVectorAsync(query);
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var results = await cloudServicesStore.VectorizedSearchAsync(queryEmbedding, new VectorSearchOptions()
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await cloudServicesStore.VectorizedSearchAsync(queryEmbedding, new VectorSearchOptions<CloudService>()
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{
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VectorPropertyName = "Vector"
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Top = 1
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});
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await foreach (var result in results.Results)
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await foreach (VectorSearchResult<CloudService> result in results.Results)
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{
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Console.WriteLine($"Name: {result.Record.Name}");
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Console.WriteLine($"Description: {result.Record.Description}");

‎docs/ai/quickstarts/snippets/chat-with-data/openai/VectorDataAI.csproj

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</PropertyGroup>
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<ItemGroup>
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<PackageReference Include="Microsoft.Extensions.AI.OpenAI" Version="9.0.1-preview.1.24570.5" />
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<PackageReference Include="Microsoft.Extensions.VectorData.Abstractions" Version="9.0.0-preview.1.25078.1" />
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<PackageReference Include="Microsoft.SemanticKernel.Connectors.InMemory" Version="1.31.0-preview" />
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<PackageReference Include="Microsoft.Extensions.Configuration" Version="9.0.2" />
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<PackageReference Include="Microsoft.Extensions.Configuration.UserSecrets" Version="9.0.2" />
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<PackageReference Include="Microsoft.Extensions.AI.OpenAI" Version="9.3.0-preview.1.25161.3" />
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<PackageReference Include="Microsoft.Extensions.VectorData.Abstractions" Version="9.0.0-preview.1.25161.1" />
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<PackageReference Include="Microsoft.SemanticKernel.Connectors.InMemory" Version="1.41.0-preview" />
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<PackageReference Include="Microsoft.Extensions.Configuration" Version="10.0.0-preview.1.25080.5" />
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<PackageReference Include="Microsoft.Extensions.Configuration.UserSecrets" Version="10.0.0-preview.1.25080.5" />
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</ItemGroup>
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</Project>

‎docs/ai/quickstarts/snippets/function-calling/openai/FunctionCallingAI.csproj

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</PropertyGroup>
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<ItemGroup>
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<PackageReference Include="Microsoft.Extensions.AI" Version="9.3.0-preview.1.25114.11" />
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<PackageReference Include="Microsoft.Extensions.AI.OpenAI" Version="9.3.0-preview.1.25114.11" />
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<PackageReference Include="Microsoft.Extensions.Configuration" Version="9.0.2" />
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<PackageReference Include="Microsoft.Extensions.Configuration.UserSecrets" Version="9.0.2" />
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<PackageReference Include="Microsoft.Extensions.AI" Version="9.3.0-preview.1.25161.3" />
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<PackageReference Include="Microsoft.Extensions.AI.OpenAI" Version="9.3.0-preview.1.25161.3" />
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<PackageReference Include="Microsoft.Extensions.Configuration" Version="10.0.0-preview.1.25080.5" />
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<PackageReference Include="Microsoft.Extensions.Configuration.UserSecrets" Version="10.0.0-preview.1.25080.5" />
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</ItemGroup>
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</Project>

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