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Merge pull request #5703 from eric-urban/eur/agentic-retrieval-sdk
agentic search C# SDK
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‎articles/search/includes/quickstarts/agentic-retrieval-csharp.md‎

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@@ -48,7 +48,7 @@ Although you can provide your own data, this quickstart uses [sample JSON docume
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dotnet add package Azure.Search.Documents --version 11.7.0-beta.4
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```
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1. Install the Azure OpenAI client library ([Azure.AI.OpenAI](/dotnet/api/overview/azure.ai.openai-readme)) for .NET with:
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1. Install the Azure OpenAI client library ([Azure.AI.OpenAI](/dotnet/api/azure.ai.openai)) for .NET with:
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```console
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dotnet add package Azure.AI.OpenAI --version 2.1.0
@@ -103,18 +103,21 @@ Although you can provide your own data, this quickstart uses [sample JSON docume
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using OpenAI.Chat;
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namespace AzureSearch.Quickstart
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{ class Program
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{
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class Program
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{
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static async Task Main(string[] args)
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{
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{
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// Load environment variables from .env file
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// Ensure you have a .env file in the same directory with the required variables.
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DotEnv.Load();
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string endpoint = Environment.GetEnvironmentVariable("AZURE_SEARCH_ENDPOINT")
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?? throw new InvalidOperationException("AZURE_SEARCH_ENDPOINT is not set.");
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string azureOpenAIEndpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
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?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set."); string azureOpenAIGptDeployment = "gpt-4.1-mini";
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?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
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string azureOpenAIGptDeployment = "gpt-4.1-mini";
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string azureOpenAIGptModel = "gpt-4.1-mini";
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string azureOpenAIEmbeddingDeployment = "text-embedding-3-large";
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string azureOpenAIEmbeddingModel = "text-embedding-3-large";
@@ -123,15 +126,35 @@ Although you can provide your own data, this quickstart uses [sample JSON docume
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string agentName = "earth-search-agent";
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var credential = new DefaultAzureCredential();
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// Define the fields for the index
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var fields = new List<SearchField>
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{
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new SimpleField("id", SearchFieldDataType.String) { IsKey = true, IsFilterable = true, IsSortable = true, IsFacetable = true },
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new SearchField("page_chunk", SearchFieldDataType.String) { IsFilterable = false, IsSortable = false, IsFacetable = false },
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new SearchField("page_embedding_text_3_large", SearchFieldDataType.Collection(SearchFieldDataType.Single)) { VectorSearchDimensions = 3072, VectorSearchProfileName = "hnsw_text_3_large" },
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new SimpleField("page_number", SearchFieldDataType.Int32) { IsFilterable = true, IsSortable = true, IsFacetable = true }
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};
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// Define the vectorizer
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new SimpleField("id", SearchFieldDataType.String)
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{
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IsKey = true,
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IsFilterable = true,
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IsSortable = true,
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IsFacetable = true
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},
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new SearchField("page_chunk", SearchFieldDataType.String)
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{
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IsFilterable = false,
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IsSortable = false,
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IsFacetable = false
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},
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new SearchField("page_embedding_text_3_large", SearchFieldDataType.Collection(SearchFieldDataType.Single))
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{
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VectorSearchDimensions = 3072,
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VectorSearchProfileName = "hnsw_text_3_large"
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},
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new SimpleField("page_number", SearchFieldDataType.Int32)
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{
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IsFilterable = true,
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IsSortable = true,
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IsFacetable = true
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}
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};// Define the vectorizer
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var vectorizer = new AzureOpenAIVectorizer(vectorizerName: "azure_openai_text_3_large")
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{
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Parameters = new AzureOpenAIVectorizerParameters
@@ -191,7 +214,7 @@ Although you can provide your own data, this quickstart uses [sample JSON docume
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SemanticSearch = semanticSearch
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};
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// Create the index client and delete the index if it exists, then create it
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// Create the index client. Delete the index if it exists and then recreate it.
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var indexClient = new SearchIndexClient(new Uri(endpoint), credential);
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try
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{
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catch (Exception ex)
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{
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Console.WriteLine($"Index '{indexName}' could not be deleted or did not exist: {ex.Message}");
204-
}
227+
}
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await indexClient.CreateOrUpdateIndexAsync(index);
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Console.WriteLine($"Index '{indexName}' created or updated successfully");
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// Download the documents from the GitHub URL
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{
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KeyFieldAccessor = doc => doc["id"].ToString(),
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}
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);
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);
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await searchIndexingBufferedSender.UploadDocumentsAsync(documents);
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await searchIndexingBufferedSender.FlushAsync();
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Console.WriteLine($"Documents uploaded to index '{indexName}'");
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229-
Console.WriteLine($"Documents uploaded to index '{indexName}'");
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var openAiParameters = new AzureOpenAIVectorizerParameters
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{
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ResourceUri = new Uri(azureOpenAIEndpoint),
@@ -240,8 +260,7 @@ Although you can provide your own data, this quickstart uses [sample JSON docume
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var targetIndex = new KnowledgeAgentTargetIndex(indexName)
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{
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DefaultRerankerThreshold = 2.5f
243-
};
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};
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// Create the knowledge agent
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var agent = new KnowledgeAgent(
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name: agentName,
@@ -250,7 +269,6 @@ Although you can provide your own data, this quickstart uses [sample JSON docume
250269
await indexClient.CreateOrUpdateKnowledgeAgentAsync(agent);
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Console.WriteLine($"Search agent '{agentName}' created or updated successfully");
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string instructions = @"
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A Q&A agent that can answer questions about the Earth at night.
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Sources have a JSON format with a ref_id that must be cited in the answer.
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{ "role", "system" },
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{ "content", instructions }
266284
}
267-
};
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};
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var agentClient = new KnowledgeAgentRetrievalClient(
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endpoint: new Uri(endpoint),
271288
agentName: agentName,
272289
tokenCredential: new DefaultAzureCredential()
273-
);
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);
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messages.Add(new Dictionary<string, object>
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{
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{ "role", "user" },
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{ "content", @"
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Why do suburban belts display larger December brightening than urban cores even though absolute light levels are higher downtown?
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Why is the Phoenix nighttime street grid is so sharply visible from space, whereas large stretches of the interstate between midwestern cities remain comparatively dim?
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" }
282-
});
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296+
Why do suburban belts display larger December brightening than urban cores even though absolute light levels are higher downtown?
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Why is the Phoenix nighttime street grid is so sharply visible from space, whereas large stretches of the interstate between midwestern cities remain comparatively dim?
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" }
299+
});
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var retrievalResult = await agentClient.RetrieveAsync(
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retrievalRequest: new KnowledgeAgentRetrievalRequest(
286-
messages: messages
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.Where(message => message["role"].ToString() != "system")
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.Select(
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message => new KnowledgeAgentMessage(
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role: message["role"].ToString(),
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content: new[] { new KnowledgeAgentMessageTextContent(message["content"].ToString()) }))
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.ToList()
293-
)
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{
295-
TargetIndexParams = { new KnowledgeAgentIndexParams { IndexName = indexName, RerankerThreshold = 2.5f } }
296-
}
297-
);
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messages: messages
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.Where(message => message["role"].ToString() != "system")
304+
.Select(message => new KnowledgeAgentMessage(
305+
role: message["role"].ToString(),
306+
content: new[] { new KnowledgeAgentMessageTextContent(message["content"].ToString()) }))
307+
.ToList()
308+
)
309+
{
310+
TargetIndexParams = { new KnowledgeAgentIndexParams { IndexName = indexName, RerankerThreshold = 2.5f } }
311+
}
312+
);
299313
messages.Add(new Dictionary<string, object>
300314
{
301315
{ "role", "assistant" },
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{ "content", (retrievalResult.Value.Response[0].Content[0] as KnowledgeAgentMessageTextContent).Text }
303317
});
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// Print
306-
Console.WriteLine((retrievalResult.Value.Response[0].Content[0] as KnowledgeAgentMessageTextContent).Text);
319+
Console.WriteLine((retrievalResult.Value.Response[0].Content[0] as KnowledgeAgentMessageTextContent).Text);
320+
307321
Console.WriteLine("Activities:");
308322
foreach (var activity in retrievalResult.Value.Activity)
309323
{
@@ -325,50 +339,46 @@ Although you can provide your own data, this quickstart uses [sample JSON docume
325339
reference.GetType(),
326340
new JsonSerializerOptions { WriteIndented = true }
327341
);
328-
Console.WriteLine(referenceJson);
329-
}
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342+
Console.WriteLine(referenceJson); }
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AzureOpenAIClient azureClient = new(
333345
new Uri(azureOpenAIEndpoint),
334346
new DefaultAzureCredential());
335-
ChatClient chatClient = azureClient.GetChatClient(azureOpenAIGptDeployment);
347+
ChatClient chatClient = azureClient.GetChatClient(azureOpenAIGptDeployment);
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336349
List<ChatMessage> chatMessages = messages
337350
.Select<Dictionary<string, object>, ChatMessage>(m => m["role"].ToString() switch
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{
339352
"user" => new UserChatMessage(m["content"].ToString()),
340353
"assistant" => new AssistantChatMessage(m["content"].ToString()),
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"system" => new SystemChatMessage(m["content"].ToString()),
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_ => null
343-
})
356+
})
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.Where(m => m != null)
345358
.ToList();
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var result = await chatClient.CompleteChatAsync(chatMessages);
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350361
Console.WriteLine($"[ASSISTANT]: {result.Value.Content[0].Text.Replace(".", "\n")}");
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352363
messages.Add(new Dictionary<string, object>
353364
{
354365
{ "role", "user" },
355-
{ "content", "How do I find lava at night?" }
366+
{ "content", "How do I find lava at night?" }
356367
});
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358369
var retrievalResult2 = await agentClient.RetrieveAsync(
359370
retrievalRequest: new KnowledgeAgentRetrievalRequest(
360-
messages: messages
361-
.Where(message => message["role"].ToString() != "system")
362-
.Select(
363-
message => new KnowledgeAgentMessage(
364-
role: message["role"].ToString(),
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content: new[] { new KnowledgeAgentMessageTextContent(message["content"].ToString()) }))
366-
.ToList()
367-
)
368-
{
369-
TargetIndexParams = { new KnowledgeAgentIndexParams { IndexName = indexName, RerankerThreshold = 2.5f } }
370-
}
371-
);
371+
messages: messages
372+
.Where(message => message["role"].ToString() != "system")
373+
.Select(message => new KnowledgeAgentMessage(
374+
role: message["role"].ToString(),
375+
content: new[] { new KnowledgeAgentMessageTextContent(message["content"].ToString()) }))
376+
.ToList()
377+
)
378+
{
379+
TargetIndexParams = { new KnowledgeAgentIndexParams { IndexName = indexName, RerankerThreshold = 2.5f } }
380+
}
381+
);
372382
373383
messages.Add(new Dictionary<string, object>
374384
{
@@ -400,29 +410,27 @@ Although you can provide your own data, this quickstart uses [sample JSON docume
400410
new JsonSerializerOptions { WriteIndented = true }
401411
);
402412
Console.WriteLine(referenceJson2);
403-
} List<ChatMessage> chatMessages2 = messages
413+
}
414+
415+
List<ChatMessage> chatMessages2 = messages
404416
.Select<Dictionary<string, object>, ChatMessage>(m => m["role"].ToString() switch
405417
{
406418
"user" => new UserChatMessage(m["content"].ToString()),
407419
"assistant" => new AssistantChatMessage(m["content"].ToString()),
408420
"system" => new SystemChatMessage(m["content"].ToString()),
409421
_ => null
410-
})
422+
})
411423
.Where(m => m != null)
412424
.ToList();
413425
414-
415426
var result2 = await chatClient.CompleteChatAsync(chatMessages2);
416-
417427
Console.WriteLine($"[ASSISTANT]: {result2.Value.Content[0].Text.Replace(".", "\n")}");
418428
419429
await indexClient.DeleteKnowledgeAgentAsync(agentName);
420430
System.Console.WriteLine($"Search agent '{agentName}' deleted successfully");
421431
422-
await indexClient.DeleteIndexAsync(indexName);
432+
await indexClient.DeleteIndexAsync(indexName);
423433
System.Console.WriteLine($"Index '{indexName}' deleted successfully");
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426434
}
427435
}
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}

‎articles/search/search-get-started-agentic-retrieval.md‎

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::: zone pivot="programming-language-csharp"
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[!INCLUDE [Python quickstart](includes/quickstarts/agentic-retrieval-csharp.md)]
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[!INCLUDE [C# quickstart](includes/quickstarts/agentic-retrieval-csharp.md)]
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::: zone-end
2020

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