Краткое руководство. Использование агентического извлечения в поиске ИИ Azure

Замечание

Эта функция сейчас доступна в общедоступной предварительной версии. Этот предварительный просмотр предоставляется без соглашения об уровне обслуживания и не предназначается для производственных рабочих нагрузок. Некоторые функции могут не поддерживаться или их возможности могут быть ограничены. Для получения дополнительной информации см. Дополнительные условия использования для предварительных версий Microsoft Azure.

В этом кратком руководстве вы используете агентическое извлечение для создания разговорного опыта поиска, основанного на документах, индексированных в Azure AI Search, и крупной языковой модели (LLM) из Azure OpenAI в модели Foundry.

База знаний оркеструет агентное извлечение путем разбиения сложных запросов на подзапросы, выполнения подзапросов для одного или нескольких источников знаний и возвращает результаты с метаданными. По умолчанию база знаний выводит сырое содержимое из источников, но в этом кратком руководстве используется режим формирования синтезированных ответов для генерации ответов на естественном языке.

Хотя вы можете предоставить собственные данные, в этом кратком руководстве используются образцы документов JSON из электронной книги НАСА «Земля ночью». В документах описываются общие научные темы и изображения Земли ночью, как наблюдалось из космоса.

Подсказка

Хотите начать сразу? См. репозиторий GitHub azure-search-dotnet-samples .

Предпосылки

Учетная запись Azure с активной подпиской. Создайте учетную запись бесплатно .
Служба поиска ИИ Azure в любой регионе, который предоставляет агентивное извлечение.
Проект и ресурс Microsoft Foundry . При создании проекта ресурс создается автоматически.
Azure CLI для проверки подлинности без ключа с помощью идентификатора Microsoft Entra.
Visual Studio Code.

Настройка доступа

Перед началом работы убедитесь, что у вас есть разрешения на доступ к содержимому и операциям. Мы рекомендуем идентификатор Microsoft Entra для проверки подлинности и доступа на основе ролей для авторизации. Для назначения ролей необходимо быть владельцем или администратором доступа пользователей . Если роли не являются возможными, используйте проверку подлинности на основе ключей .

Чтобы настроить доступ для этого краткого руководства, выберите оба следующих вкладки.

Поиск по искусственному интеллекту Azure
Microsoft Foundry

Поиск ИИ Azure предоставляет конвейер извлечения агентов. Настройте доступ для себя и службы поиска для чтения и записи данных, взаимодействия с Foundry и запуска конвейера.

В службе поиска по искусственному интеллекту Azure:

Включите доступ на основе ролей.
Создайте управляемое удостоверение, назначаемое системой.
Назначьте следующие роли себе.
- Участник службы поиска
- Участник данных индекса поиска
- Средство чтения индексов поиска

Это важно

Агентное извлечение имеет две модели выставления счетов на основе токенов.

Выставление счетов за агентный поиск в Azure AI.
Выставление счетов из Azure OpenAI для планирования запросов и синтеза ответов.

Для получения дополнительной информации см. Доступность и цены агентских запросов.

Получение конечных точек

Каждая служба поиска ИИ Azure и ресурс Foundry имеют конечную точку, которая является уникальным URL-адресом, который идентифицирует и предоставляет сетевой доступ к ресурсу. В следующем разделе описано, как указать эти конечные точки для программного подключения к ресурсам.

Чтобы получить конечные точки для этого краткого руководства, выберите оба следующих вкладки.

Поиск по искусственному интеллекту Azure
Microsoft Foundry

Войдите на портал Azure и выберите службу поиска.
В левой области выберите "Обзор".
Запишите конечную точку, которая должна выглядеть следующим https://my-service.search.windows.netобразом.

Войдите на портал Azure и выберите ресурс Foundry.
В левой области выберите ключи управления> ресурсамии конечную точку.
Выберите вкладку OpenAI .
Запишите конечную точку, которая должна выглядеть следующим https://my-resource.openai.azure.comобразом.

Развертывание моделей

Чтобы использовать агентное извлечение, необходимо развернуть две модели Azure OpenAI в проекте Foundry.

Модель внедрения для преобразования текста в вектор. В этом кратком руководстве используется text-embedding-3-large, но вы можете использовать какую-либо модель text-embedding.
LLM для планирования запросов и создания ответов. В этом кратком введении используется gpt-5-mini, но вы можете использовать любой поддерживаемый LLM для агентного извлечения.

Инструкции по развертыванию см. в статье "Развертывание моделей Azure OpenAI с помощью Foundry".

Настройка среды

Чтобы настроить консольное приложение для этого быстрого старта:

Создайте папку с именем quickstart-agentic-retrieval , чтобы содержать приложение.
Откройте папку в Visual Studio Code.
Выберите терминал>"Новый терминал" и выполните следующую команду, чтобы создать консольное приложение.
```
dotnet new console
```
Установите клиентская библиотека поиска ИИ Azure для .NET.
```
dotnet add package Azure.Search.Documents --version 11.8.0-beta.1
```
dotenv.net Установите пакет для загрузки переменных среды из .env файла.
```
dotnet add package dotenv.net
```
Для проверки подлинности без ключа с помощью идентификатора Microsoft Entra установите пакет Azure.Identity .
```
dotnet add package Azure.Identity
```
Для проверки подлинности без ключа с помощью идентификатора Microsoft Entra войдите в учетную запись Azure. Если у вас несколько подписок, выберите тот, который содержит службу поиска ИИ Azure и проект Foundry.
```
az login
```

Запустите код

Чтобы создать и запустить агентный конвейер извлечения, выполните следующие действия.

Создайте файл с именем .env в папке quickstart-agentic-retrieval .

Вставьте в файл следующие переменные .env среды.

SEARCH_ENDPOINT = PUT-YOUR-SEARCH-SERVICE-URL-HERE
AOAI_ENDPOINT = PUT-YOUR-AOAI-FOUNDRY-URL-HERE

Задайте SEARCH_ENDPOINT и AOAI_ENDPOINT укажите значения, полученные в конечных точках Get.

Вставьте следующий код в Program.cs файл.

using dotenv.net;
using System.Text.Json;
using Azure.Identity;
using Azure.Search.Documents;
using Azure.Search.Documents.Indexes;
using Azure.Search.Documents.Indexes.Models;
using Azure.Search.Documents.KnowledgeBases;
using Azure.Search.Documents.KnowledgeBases.Models;

namespace AzureSearch.Quickstart
{
    class Program
    {
        static async Task Main(string[] args)
        {
            // Load environment variables from the .env file
            // Ensure your .env file is in the same directory with the required variables
            DotEnv.Load();

            string searchEndpoint = Environment.GetEnvironmentVariable("SEARCH_ENDPOINT")
                ?? throw new InvalidOperationException("SEARCH_ENDPOINT isn't set.");
            string aoaiEndpoint = Environment.GetEnvironmentVariable("AOAI_ENDPOINT")
                ?? throw new InvalidOperationException("AOAI_ENDPOINT isn't set.");

            string aoaiEmbeddingModel = "text-embedding-3-large";
            string aoaiEmbeddingDeployment = "text-embedding-3-large";
            string aoaiGptModel = "gpt-5-mini";
            string aoaiGptDeployment = "gpt-5-mini";

            string indexName = "earth-at-night";
            string knowledgeSourceName = "earth-knowledge-source";
            string knowledgeBaseName = "earth-knowledge-base";

            var credential = new DefaultAzureCredential();

            // Define fields for the index
            var fields = new List<SearchField>
            {
                new SimpleField("id", SearchFieldDataType.String) { IsKey = true, IsFilterable = true, IsSortable = true, IsFacetable = true },
                new SearchField("page_chunk", SearchFieldDataType.String) { IsFilterable = false, IsSortable = false, IsFacetable = false },
                new SearchField("page_embedding_text_3_large", SearchFieldDataType.Collection(SearchFieldDataType.Single)) { VectorSearchDimensions = 3072, VectorSearchProfileName = "hnsw_text_3_large" },
                new SimpleField("page_number", SearchFieldDataType.Int32) { IsFilterable = true, IsSortable = true, IsFacetable = true }
            };

            // Define a vectorizer
            var vectorizer = new AzureOpenAIVectorizer(vectorizerName: "azure_openai_text_3_large")
            {
                Parameters = new AzureOpenAIVectorizerParameters
                {
                    ResourceUri = new Uri(aoaiEndpoint),
                    DeploymentName = aoaiEmbeddingDeployment,
                    ModelName = aoaiEmbeddingModel
                }
            };

            // Define a vector search profile and algorithm
            var vectorSearch = new VectorSearch()
            {
                Profiles =
                {
                    new VectorSearchProfile(
                        name: "hnsw_text_3_large",
                        algorithmConfigurationName: "alg"
                    )
                    {
                        VectorizerName = "azure_openai_text_3_large"
                    }
                },
                Algorithms =
                {
                    new HnswAlgorithmConfiguration(name: "alg")
                },
                Vectorizers =
                {
                    vectorizer
                }
            };

            // Define a semantic configuration
            var semanticConfig = new SemanticConfiguration(
                name: "semantic_config",
                prioritizedFields: new SemanticPrioritizedFields
                {
                    ContentFields = { new SemanticField("page_chunk") }
                }
            );

            var semanticSearch = new SemanticSearch()
            {
                DefaultConfigurationName = "semantic_config",
                Configurations = { semanticConfig }
            };

            // Create the index
            var index = new SearchIndex(indexName)
            {
                Fields = fields,
                VectorSearch = vectorSearch,
                SemanticSearch = semanticSearch
            };

            // Create the index client, deleting and recreating the index if it exists
            var indexClient = new SearchIndexClient(new Uri(searchEndpoint), credential);
            await indexClient.CreateOrUpdateIndexAsync(index);
            Console.WriteLine($"Index '{indexName}' created or updated successfully.");

            // Upload sample documents from the GitHub URL
            string url = "https://raw.githubusercontent.com/Azure-Samples/azure-search-sample-data/refs/heads/main/nasa-e-book/earth-at-night-json/documents.json";
            var httpClient = new HttpClient();
            var response = await httpClient.GetAsync(url);
            response.EnsureSuccessStatusCode();
            var json = await response.Content.ReadAsStringAsync();
            var documents = JsonSerializer.Deserialize<List<Dictionary<string, object>>>(json);
            var searchClient = new SearchClient(new Uri(searchEndpoint), indexName, credential);
            var searchIndexingBufferedSender = new SearchIndexingBufferedSender<Dictionary<string, object>>(
                searchClient,
                new SearchIndexingBufferedSenderOptions<Dictionary<string, object>>
                {
                    KeyFieldAccessor = doc => doc["id"].ToString(),
                }
            );

            await searchIndexingBufferedSender.UploadDocumentsAsync(documents);
            await searchIndexingBufferedSender.FlushAsync();
            Console.WriteLine($"Documents uploaded to index '{indexName}' successfully.");

            // Create a knowledge source
            var indexKnowledgeSource = new SearchIndexKnowledgeSource(
                name: knowledgeSourceName,
                searchIndexParameters: new SearchIndexKnowledgeSourceParameters(searchIndexName: indexName)
                {
                    SourceDataFields = { new SearchIndexFieldReference(name: "id"), new SearchIndexFieldReference(name: "page_chunk"), new SearchIndexFieldReference(name: "page_number") }
                }
            );

            await indexClient.CreateOrUpdateKnowledgeSourceAsync(indexKnowledgeSource);
            Console.WriteLine($"Knowledge source '{knowledgeSourceName}' created or updated successfully.");

            // Create a knowledge base
            var openAiParameters = new AzureOpenAIVectorizerParameters
            {
                ResourceUri = new Uri(aoaiEndpoint),
                DeploymentName = aoaiGptDeployment,
                ModelName = aoaiGptModel
            };

            var model = new KnowledgeBaseAzureOpenAIModel(azureOpenAIParameters: openAiParameters);

            var knowledgeBase = new KnowledgeBase(
                name: knowledgeBaseName,
                knowledgeSources: new KnowledgeSourceReference[] { new KnowledgeSourceReference(knowledgeSourceName) }
            )
            {
                RetrievalReasoningEffort = new KnowledgeRetrievalLowReasoningEffort(),
                AnswerInstructions = "Provide a two sentence concise and informative answer based on the retrieved documents.",
                Models = { model }
            };

            await indexClient.CreateOrUpdateKnowledgeBaseAsync(knowledgeBase);
            Console.WriteLine($"Knowledge base '{knowledgeBaseName}' created or updated successfully.");

            // Set up messages
            string instructions = @"A Q&A agent that can answer questions about the Earth at night.
            If you don't have the answer, respond with ""I don't know"".";

            var messages = new List<Dictionary<string, string>>
            {
                new Dictionary<string, string>
                {
                    { "role", "system" },
                    { "content", instructions }
                }
            };

            // Run agentic retrieval
            var baseClient = new KnowledgeBaseRetrievalClient(
                endpoint: new Uri(searchEndpoint),
                knowledgeBaseName: knowledgeBaseName,
                tokenCredential: new DefaultAzureCredential()
            );

            string query = @"Why do suburban belts display larger December brightening than urban cores even though absolute light levels are higher downtown? 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?";

            messages.Add(new Dictionary<string, string>
            {
                { "role", "user" },
                { "content", query }
            });

            Console.WriteLine($"Running the query...{query}");
            var retrievalRequest = new KnowledgeBaseRetrievalRequest();
            foreach (Dictionary<string, string> message in messages) {
                if (message["role"] != "system") {
                    retrievalRequest.Messages.Add(new KnowledgeBaseMessage(content: new[] { new KnowledgeBaseMessageTextContent(message["content"]) }) { Role = message["role"] });
                }
            }
            retrievalRequest.RetrievalReasoningEffort = new KnowledgeRetrievalLowReasoningEffort();
            var retrievalResult = await baseClient.RetrieveAsync(retrievalRequest);

            messages.Add(new Dictionary<string, string>
            {
                { "role", "assistant" },
                { "content", (retrievalResult.Value.Response[0].Content[0] as KnowledgeBaseMessageTextContent)!.Text }
            });

            // Print the response, activity, and references
            Console.WriteLine("Response:");
            Console.WriteLine((retrievalResult.Value.Response[0].Content[0] as KnowledgeBaseMessageTextContent)!.Text);

            Console.WriteLine("Activity:");
            foreach (var activity in retrievalResult.Value.Activity)
            {
                Console.WriteLine($"Activity Type: {activity.GetType().Name}");
                string activityJson = JsonSerializer.Serialize(
                    activity,
                    activity.GetType(),
                    new JsonSerializerOptions { WriteIndented = true }
                );
                Console.WriteLine(activityJson);
            }

            Console.WriteLine("References:");
            foreach (var reference in retrievalResult.Value.References)
            {
                Console.WriteLine($"Reference Type: {reference.GetType().Name}");
                string referenceJson = JsonSerializer.Serialize(
                    reference,
                    reference.GetType(),
                    new JsonSerializerOptions { WriteIndented = true }
                );
                Console.WriteLine(referenceJson);
            }

            // Continue the conversation
            string nextQuery = "How do I find lava at night?";
            Console.WriteLine($"Continue the conversation with this query: {nextQuery}");
            messages.Add(new Dictionary<string, string>
            {
                { "role", "user" },
                { "content", nextQuery }
            });

            retrievalRequest = new KnowledgeBaseRetrievalRequest();
            foreach (Dictionary<string, string> message in messages) {
                if (message["role"] != "system") {
                    retrievalRequest.Messages.Add(new KnowledgeBaseMessage(content: new[] { new KnowledgeBaseMessageTextContent(message["content"]) }) { Role = message["role"] });
                }
            }
            retrievalRequest.RetrievalReasoningEffort = new KnowledgeRetrievalLowReasoningEffort();
            retrievalResult = await baseClient.RetrieveAsync(retrievalRequest);

            messages.Add(new Dictionary<string, string>
            {
                { "role", "assistant" },
                { "content", (retrievalResult.Value.Response[0].Content[0] as KnowledgeBaseMessageTextContent)!.Text }
            });

            // Print the new response, activity, and references
            Console.WriteLine("Response:");
            Console.WriteLine((retrievalResult.Value.Response[0].Content[0] as KnowledgeBaseMessageTextContent)!.Text);

            Console.WriteLine("Activity:");
            foreach (var activity in retrievalResult.Value.Activity)
            {
                Console.WriteLine($"Activity Type: {activity.GetType().Name}");
                string activityJson = JsonSerializer.Serialize(
                    activity,
                    activity.GetType(),
                    new JsonSerializerOptions { WriteIndented = true }
                );
                Console.WriteLine(activityJson);
            }

            Console.WriteLine("References:");
            foreach (var reference in retrievalResult.Value.References)
            {
                Console.WriteLine($"Reference Type: {reference.GetType().Name}");
                string referenceJson = JsonSerializer.Serialize(
                    reference,
                    reference.GetType(),
                    new JsonSerializerOptions { WriteIndented = true }
                );
                Console.WriteLine(referenceJson);
            }

            // Clean up resources
            await indexClient.DeleteKnowledgeBaseAsync(knowledgeBaseName);
            Console.WriteLine($"Knowledge base '{knowledgeBaseName}' deleted successfully.");

            await indexClient.DeleteKnowledgeSourceAsync(knowledgeSourceName);
            Console.WriteLine($"Knowledge source '{knowledgeSourceName}' deleted successfully.");

            await indexClient.DeleteIndexAsync(indexName);
            Console.WriteLine($"Index '{indexName}' deleted successfully.");
        }
    }
}

Создайте и запустите приложение.
```
dotnet run
```

Выходные данные

Выходные данные приложения должны быть похожи на следующие:

Index 'earth-at-night' created or updated successfully.
Documents uploaded to index 'earth-at-night' successfully.
Knowledge source 'earth-knowledge-source' created or updated successfully.
Knowledge base 'earth-knowledge-base' created or updated successfully.
Response:
Suburban belts show larger December brightening because holiday displays concentrate in suburbs and outskirts where there is more yard space and many single‑family homes [ref_id:5], while urban cores—already having higher absolute light levels—tend to show smaller relative increases (central areas typically brighten ~20–30%) [ref_id:8][ref_id:5]. Phoenix’s nighttime street grid is sharply visible because the metropolitan area is laid out on a regular, continuously lit grid with bright commercial and industrial nodes along major corridors like Grand Avenue [ref_id:0][ref_id:3], whereas long interstate stretches between Midwestern cities cross sparsely populated or rural regions with far fewer continuous roadside lights and so appear comparatively dim [ref_id:8].
Activity:
Activity Type: KnowledgeBaseModelQueryPlanningActivityRecord
{
  "InputTokens": 1350,
  "OutputTokens": 1314,
  "Id": 0,
  "ElapsedMs": 14162,
  "Error": null
}
Activity Type: KnowledgeBaseSearchIndexActivityRecord
{
  "SearchIndexArguments": {
    "Search": "Causes of December brightening in satellite nightlights: why suburban belts show larger relative December brightening than urban cores (roles of holiday residential lighting, snow albedo, urban heat island, commercial lighting patterns)",
    "Filter": null,
    "SourceDataFields": [],
    "SearchFields": [],
    "SemanticConfigurationName": null
  },
  "KnowledgeSourceName": "earth-knowledge-source",
  "QueryTime": "2025-11-05T21:56:26.747+00:00",
  "Count": 19,
  "Id": 1,
  "ElapsedMs": 537,
  "Error": null
}
Activity Type: KnowledgeBaseSearchIndexActivityRecord
{
  "SearchIndexArguments": {
    "Search": "Why is Phoenix\u0019s nighttime street grid so sharply visible from space? (effects of streetlight density, luminaire type/aiming, spacing, urban grid layout, traffic vs roadway lighting)",
    "Filter": null,
    "SourceDataFields": [],
    "SearchFields": [],
    "SemanticConfigurationName": null
  },
  "KnowledgeSourceName": "earth-knowledge-source",
  "QueryTime": "2025-11-05T21:56:27.182+00:00",
  "Count": 7,
  "Id": 2,
  "ElapsedMs": 434,
  "Error": null
}
Activity Type: KnowledgeBaseSearchIndexActivityRecord
{
  "SearchIndexArguments": {
    "Search": "How do satellite nightlight sensor characteristics (VIIRS DNB, DMSP-OLS) \u2014 spatial resolution, dynamic range, saturation, blooming \u2014 affect observed brightness and structure of urban cores, suburbs, and long interstate stretches?",
    "Filter": null,
    "SourceDataFields": [],
    "SearchFields": [],
    "SemanticConfigurationName": null
  },
  "KnowledgeSourceName": "earth-knowledge-source",
  "QueryTime": "2025-11-05T21:56:27.786+00:00",
  "Count": 23,
  "Id": 3,
  "ElapsedMs": 604,
  "Error": null
}
Activity Type: KnowledgeBaseAgenticReasoningActivityRecord
{
  "ReasoningTokens": 70232,
  "RetrievalReasoningEffort": {},
  "Id": 4,
  "ElapsedMs": null,
  "Error": null
}
Activity Type: KnowledgeBaseModelAnswerSynthesisActivityRecord
{
  "InputTokens": 7467,
  "OutputTokens": 1710,
  "Id": 5,
  "ElapsedMs": 26663,
  "Error": null
}
Results:
Reference Type: KnowledgeBaseSearchIndexReference
{
  "DocKey": "earth_at_night_508_page_104_verbalized",
  "Id": "0",
  "ActivitySource": 2,
  "SourceData": {},
  "RerankerScore": 2.6344998
}
Reference Type: KnowledgeBaseSearchIndexReference
{
  "DocKey": "earth_at_night_508_page_194_verbalized",
  "Id": "1",
  "ActivitySource": 3,
  "SourceData": {},
  "RerankerScore": 2.630955
}
Reference Type: KnowledgeBaseSearchIndexReference
{
  "DocKey": "earth_at_night_508_page_105_verbalized",
  "Id": "3",
  "ActivitySource": 2,
  "SourceData": {},
  "RerankerScore": 2.5884187
}
Reference Type: KnowledgeBaseSearchIndexReference
{
  "DocKey": "earth_at_night_508_page_189_verbalized",
  "Id": "4",
  "ActivitySource": 3,
  "SourceData": {},
  "RerankerScore": 2.465418
}
Reference Type: KnowledgeBaseSearchIndexReference
{
  "DocKey": "earth_at_night_508_page_193_verbalized",
  "Id": "6",
  "ActivitySource": 3,
  "SourceData": {},
  "RerankerScore": 2.4560246
}
Reference Type: KnowledgeBaseSearchIndexReference
{
  "DocKey": "earth_at_night_508_page_174_verbalized",
  "Id": "2",
  "ActivitySource": 1,
  "SourceData": {},
  "RerankerScore": 2.3254027
}
Reference Type: KnowledgeBaseSearchIndexReference
{
  "DocKey": "earth_at_night_508_page_176_verbalized",
  "Id": "5",
  "ActivitySource": 1,
  "SourceData": {},
  "RerankerScore": 2.257256
}
Reference Type: KnowledgeBaseSearchIndexReference
{
  "DocKey": "earth_at_night_508_page_177_verbalized",
  "Id": "7",
  "ActivitySource": 1,
  "SourceData": {},
  "RerankerScore": 2.1968744
}
Reference Type: KnowledgeBaseSearchIndexReference
{
  "DocKey": "earth_at_night_508_page_125_verbalized",
  "Id": "8",
  "ActivitySource": 2,
  "SourceData": {},
  "RerankerScore": 2.086579
}
Response:
... // Trimmed for brevity
Activity:
... // Trimmed for brevity
References:
... // Trimmed for brevity
Knowledge base 'earth-knowledge-base' deleted successfully.
Knowledge source 'earth-knowledge-source' deleted successfully.
Index 'earth-at-night' deleted successfully.

Общие сведения о коде

Теперь, когда вы выполнили код, давайте разберем ключевые шаги:

Создание индекса поиска
Отправка документов в индекс
Создание источника знаний
Создание базы знаний
Настройка сообщений
Запуск конвейера извлечения
Продолжить беседу

Создание индекса поиска

В службе "Поиск ИИ Azure" индекс представляет собой структурированную коллекцию данных. Следующий код определяет индекс с именем earth-at-night, который вы ранее указали с помощью переменной indexName .

Схема индекса содержит поля для идентификации документов и содержимого страницы, встраиваний и числовых данных. Схема также включает конфигурации для семантического ранжирования и векторного поиска, который использует text-embedding-3-large развертывание для векторизации текста и сопоставления документов на основе семантической или концептуальной сходства.

// Define fields for the index
var fields = new List<SearchField>
{
    new SimpleField("id", SearchFieldDataType.String) { IsKey = true, IsFilterable = true, IsSortable = true, IsFacetable = true },
    new SearchField("page_chunk", SearchFieldDataType.String) { IsFilterable = false, IsSortable = false, IsFacetable = false },
    new SearchField("page_embedding_text_3_large", SearchFieldDataType.Collection(SearchFieldDataType.Single)) { VectorSearchDimensions = 3072, VectorSearchProfileName = "hnsw_text_3_large" },
    new SimpleField("page_number", SearchFieldDataType.Int32) { IsFilterable = true, IsSortable = true, IsFacetable = true }
};

// Define a vectorizer
var vectorizer = new AzureOpenAIVectorizer(vectorizerName: "azure_openai_text_3_large")
{
    Parameters = new AzureOpenAIVectorizerParameters
    {
        ResourceUri = new Uri(aoaiEndpoint),
        DeploymentName = aoaiEmbeddingDeployment,
        ModelName = aoaiEmbeddingModel
    }
};

// Define a vector search profile and algorithm
var vectorSearch = new VectorSearch()
{
    Profiles =
    {
        new VectorSearchProfile(
            name: "hnsw_text_3_large",
            algorithmConfigurationName: "alg"
        )
        {
            VectorizerName = "azure_openai_text_3_large"
        }
    },
    Algorithms =
    {
        new HnswAlgorithmConfiguration(name: "alg")
    },
    Vectorizers =
    {
        vectorizer
    }
};

// Define a semantic configuration
var semanticConfig = new SemanticConfiguration(
    name: "semantic_config",
    prioritizedFields: new SemanticPrioritizedFields
    {
        ContentFields = { new SemanticField("page_chunk") }
    }
);

var semanticSearch = new SemanticSearch()
{
    DefaultConfigurationName = "semantic_config",
    Configurations = { semanticConfig }
};

// Create the index
var index = new SearchIndex(indexName)
{
    Fields = fields,
    VectorSearch = vectorSearch,
    SemanticSearch = semanticSearch
};

// Create the index client, deleting and recreating the index if it exists
var indexClient = new SearchIndexClient(new Uri(searchEndpoint), credential);
await indexClient.CreateOrUpdateIndexAsync(index);
Console.WriteLine($"Index '{indexName}' created or updated successfully.");

Отправка документов в индекс

earth-at-night В настоящее время индекс пуст. Следующий код заполняет индекс документами JSON из электронной книги «Земля ночью» от NASA. Как требуется поисковой системой Azure AI, каждый документ соответствует полям и типам данных, определенным в схеме индекса.

// Upload sample documents from the GitHub URL
string url = "https://raw.githubusercontent.com/Azure-Samples/azure-search-sample-data/refs/heads/main/nasa-e-book/earth-at-night-json/documents.json";
var httpClient = new HttpClient();
var response = await httpClient.GetAsync(url);
response.EnsureSuccessStatusCode();
var json = await response.Content.ReadAsStringAsync();
var documents = JsonSerializer.Deserialize<List<Dictionary<string, object>>>(json);
var searchClient = new SearchClient(new Uri(searchEndpoint), indexName, credential);
var searchIndexingBufferedSender = new SearchIndexingBufferedSender<Dictionary<string, object>>(
    searchClient,
    new SearchIndexingBufferedSenderOptions<Dictionary<string, object>>
    {
        KeyFieldAccessor = doc => doc["id"].ToString(),
    }
);

await searchIndexingBufferedSender.UploadDocumentsAsync(documents);
await searchIndexingBufferedSender.FlushAsync();
Console.WriteLine($"Documents uploaded to index '{indexName}' successfully.");

Создание источника знаний

Источник знаний — это повторно используемые ссылки на исходные данные. Следующий код определяет источник знаний с именем earth-knowledge-source, который предназначен для индекса earth-at-night.

SourceDataFields указывает, какие поля индекса доступны для получения и ссылок. Наш пример включает только поля, доступные для чтения человеком, чтобы избежать длительных и непреднамеренных внедрения в ответы.

// Create a knowledge source
var indexKnowledgeSource = new SearchIndexKnowledgeSource(
    name: knowledgeSourceName,
    searchIndexParameters: new SearchIndexKnowledgeSourceParameters(searchIndexName: indexName)
    {
        SourceDataFields = { new SearchIndexFieldReference(name: "id"), new SearchIndexFieldReference(name: "page_chunk"), new SearchIndexFieldReference(name: "page_number") }
    }
);

await indexClient.CreateOrUpdateKnowledgeSourceAsync(indexKnowledgeSource);
Console.WriteLine($"Knowledge source '{knowledgeSourceName}' created or updated successfully.");

Создание базы знаний

Для нацеливания earth-knowledge-source и развертывания gpt-5-mini в процессе выполнения запроса требуется база знаний. Следующий код определяет базу знаний с именем earth-knowledge-base, которую вы ранее указали с помощью переменной knowledgeBaseName .

OutputMode задано значение AnswerSynthesis, включающее ответы на естественный язык, которые ссылаются на извлеченные документы и следуют предоставленным AnswerInstructions.

// Create a knowledge base
var openAiParameters = new AzureOpenAIVectorizerParameters
{
    ResourceUri = new Uri(aoaiEndpoint),
    DeploymentName = aoaiGptDeployment,
    ModelName = aoaiGptModel
};

var model = new KnowledgeBaseAzureOpenAIModel(azureOpenAIParameters: openAiParameters);

var knowledgeBase = new KnowledgeBase(
    name: knowledgeBaseName,
    knowledgeSources: new KnowledgeSourceReference[] { new KnowledgeSourceReference(knowledgeSourceName) }
)
{
    RetrievalReasoningEffort = new KnowledgeRetrievalLowReasoningEffort(),
    OutputMode = KnowledgeRetrievalOutputMode.AnswerSynthesis,
    AnswerInstructions = "Provide a two sentence concise and informative answer based on the retrieved documents.",
    Models = { model }
};

await indexClient.CreateOrUpdateKnowledgeBaseAsync(knowledgeBase);
Console.WriteLine($"Knowledge base '{knowledgeBaseName}' created or updated successfully.");

Настройка сообщений

Сообщения — это входные данные для маршрута извлечения и содержат журнал бесед. Каждое сообщение включает роль, которая указывает его происхождение, например system или userсодержимое на естественном языке. Используемый LLM определяет допустимые роли.

Следующий код создает системное сообщение, которое указывает earth-knowledge-base ответить на вопросы о Земле ночью и ответить на сообщение "Я не знаю", когда ответы недоступны.

// Set up messages
string instructions = @"A Q&A agent that can answer questions about the Earth at night.
If you don't have the answer, respond with ""I don't know"".";

var messages = new List<Dictionary<string, string>>
{
    new Dictionary<string, string>
    {
        { "role", "system" },
        { "content", instructions }
    }
};

Запустите поток извлечения

Вы готовы выполнить извлечение агента. Следующий код отправляет двухчастный пользовательский запрос earth-knowledge-base, в который:

Анализирует всю беседу, чтобы определить потребность пользователя в информации.
Раскомпозирует составной запрос в вложенные запросы.
Выполняет вложенные запросы параллельно с источником знаний.
Использует семантический рангировщик для повторного использования и фильтрации результатов.
Синтезирует лучшие результаты в ответ на естественный язык.

// Run agentic retrieval
var baseClient = new KnowledgeBaseRetrievalClient(
    endpoint: new Uri(searchEndpoint),
    knowledgeBaseName: knowledgeBaseName,
    tokenCredential: new DefaultAzureCredential()
);

messages.Add(new Dictionary<string, string>
{
    { "role", "user" },
    { "content", @"Why do suburban belts display larger December brightening than urban cores even though absolute light levels are higher downtown? 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?" }
});

var retrievalRequest = new KnowledgeBaseRetrievalRequest();
foreach (Dictionary<string, string> message in messages) {
    if (message["role"] != "system") {
        retrievalRequest.Messages.Add(new KnowledgeBaseMessage(content: new[] { new KnowledgeBaseMessageTextContent(message["content"]) }) { Role = message["role"] });
    }
}
retrievalRequest.RetrievalReasoningEffort = new KnowledgeRetrievalLowReasoningEffort();
var retrievalResult = await baseClient.RetrieveAsync(retrievalRequest);

messages.Add(new Dictionary<string, string>
{
    { "role", "assistant" },
    { "content", (retrievalResult.Value.Response[0].Content[0] as KnowledgeBaseMessageTextContent).Text }
});

Проверьте ответ, активность и ссылки

В следующем коде отображаются ответы, действия и ссылки из конвейера извлечения, где:

Response предоставляет синтезированный, созданный LLM-ответ на запрос, который ссылается на извлеченные документы. Если синтез ответа не включен, этот раздел содержит содержимое, извлеченное непосредственно из документов.
Activity отслеживает шаги, выполненные во время процесса извлечения, включая вложенные запросы, созданные gpt-5-mini развертыванием, и маркеры, используемые для семантического ранжирования, планирования запросов и синтеза ответов.
References перечисляет документы, использованные для создания ответа, каждый из которых определяется своим DocKey.

// Print the response, activity, and references
Console.WriteLine("Response:");
Console.WriteLine((retrievalResult.Value.Response[0].Content[0] as KnowledgeBaseMessageTextContent).Text);

Console.WriteLine("Activity:");
foreach (var activity in retrievalResult.Value.Activity)
{
    Console.WriteLine($"Activity Type: {activity.GetType().Name}");
    string activityJson = JsonSerializer.Serialize(
        activity,
        activity.GetType(),
        new JsonSerializerOptions { WriteIndented = true }
    );
    Console.WriteLine(activityJson);
}

Console.WriteLine("References:");
foreach (var reference in retrievalResult.Value.References)
{
    Console.WriteLine($"Reference Type: {reference.GetType().Name}");
    string referenceJson = JsonSerializer.Serialize(
        reference,
        reference.GetType(),
        new JsonSerializerOptions { WriteIndented = true }
    );
    Console.WriteLine(referenceJson);
}

Продолжить беседу

Следующий код продолжает беседу с earth-knowledge-base. После того как вы отправите запрос пользователя, база знаний извлекает соответствующее содержимое из earth-knowledge-source и добавляет ответ в список сообщений.

// Continue the conversation
messages.Add(new Dictionary<string, string>
{
    { "role", "user" },
    { "content", "How do I find lava at night?" }
});

retrievalRequest = new KnowledgeBaseRetrievalRequest();
foreach (Dictionary<string, string> message in messages) {
    if (message["role"] != "system") {
        retrievalRequest.Messages.Add(new KnowledgeBaseMessage(content: new[] { new KnowledgeBaseMessageTextContent(message["content"]) }) { Role = message["role"] });
    }
}
retrievalRequest.RetrievalReasoningEffort = new KnowledgeRetrievalLowReasoningEffort();
retrievalResult = await baseClient.RetrieveAsync(retrievalRequest);

messages.Add(new Dictionary<string, string>
{
    { "role", "assistant" },
    { "content", (retrievalResult.Value.Response[0].Content[0] as KnowledgeBaseMessageTextContent).Text }
});

Ознакомьтесь с новым ответом, действиями и ссылками

В следующем коде отображаются новые ответы, действия и ссылки из конвейера извлечения.

// Print the new response, activity, and references
Console.WriteLine("Response:");
Console.WriteLine((retrievalResult.Value.Response[0].Content[0] as KnowledgeBaseMessageTextContent).Text);

Console.WriteLine("Activity:");
foreach (var activity in retrievalResult.Value.Activity)
{
    Console.WriteLine($"Activity Type: {activity.GetType().Name}");
    string activityJson = JsonSerializer.Serialize(
        activity,
        activity.GetType(),
        new JsonSerializerOptions { WriteIndented = true }
    );
    Console.WriteLine(activityJson);
}

Console.WriteLine("References:");
foreach (var reference in retrievalResult.Value.References)
{
    Console.WriteLine($"Reference Type: {reference.GetType().Name}");
    string referenceJson = JsonSerializer.Serialize(
        reference,
        reference.GetType(),
        new JsonSerializerOptions { WriteIndented = true }
    );
    Console.WriteLine(referenceJson);
}

Очистите ресурсы

При работе с собственной подпиской рекомендуется завершить проект, определив, нужны ли все еще созданные ресурсы. Ресурсы, оставленные работающими, могут стоить вам денег.

На портале Azure вы можете управлять ресурсами Azure AI Search и Foundry, выбрав все ресурсы или группы ресурсов в левом меню.

В противном случае следующий код из Program.cs удалил объекты, которые вы создали в этом кратком руководстве.

Удаление базы знаний

await indexClient.DeleteKnowledgeBaseAsync(knowledgeBaseName);
Console.WriteLine($"Knowledge base '{knowledgeBaseName}' deleted successfully.");

Удаление источника знаний

await indexClient.DeleteKnowledgeSourceAsync(knowledgeSourceName);
Console.WriteLine($"Knowledge source '{knowledgeSourceName}' deleted successfully.");

Удаление индекса поиска

await indexClient.DeleteIndexAsync(indexName);
Console.WriteLine($"Index '{indexName}' deleted successfully.");

Замечание

В этом кратком руководстве вы используете агентическое извлечение для создания диалогового поиска на основе генеративных языковых моделей и ваших собственных данных. Агентическое извлечение разбивает сложные запросы пользователей на подзапросы, выполняет подзапросы параллельно и извлекает основные данные из документов, индексированных в службе "Поиск ИИ Azure". Выходные данные предназначены для интеграции с агентными и пользовательскими чат-решениями.

Подсказка

В этом кратком запуске на Java используется версия REST API 2025-05-01-preview, которая использует предыдущую терминологию "агент знаний" и не поддерживает новейшие функции, доступные в предварительной версии 2025-11-01-preview. Сведения об использовании этих функций см. в версии C#, Python или REST.

Предпосылки

Учетная запись Azure с активной подпиской. Создайте учетную запись бесплатно .
Служба поиска ИИ Azure в любой регионе, который предоставляет агентивное извлечение.
Проект Microsoft Foundry. При создании проекта Foundry вы получите ресурс Foundry (который требуется для развертываний моделей).
Azure CLI для проверки подлинности без ключа с помощью идентификатора Microsoft Entra.

Настройка доступа

Чтобы настроить доступ для этого краткого руководства, выберите оба следующих вкладки.

Поиск по искусственному интеллекту Azure
Microsoft Foundry

В службе поиска по искусственному интеллекту Azure:

Включите доступ на основе ролей.
Создайте управляемое удостоверение, назначаемое системой.
Назначьте следующие роли себе.
- Участник службы поиска
- Участник данных индекса поиска
- Средство чтения индексов поиска

Это важно

Агентное извлечение имеет две модели выставления счетов на основе токенов.

Выставление счетов за агентный поиск в Azure AI.
Выставление счетов из Azure OpenAI для планирования запросов и синтеза ответов.

Для получения дополнительной информации см. Доступность и цены агентских запросов.

Получение конечных точек

Чтобы получить конечные точки для этого краткого руководства, выберите оба следующих вкладки.

Поиск по искусственному интеллекту Azure
Microsoft Foundry

Войдите на портал Azure и выберите службу поиска.
В левой области выберите "Обзор".
Запишите конечную точку, которая должна выглядеть следующим https://my-service.search.windows.netобразом.

Войдите на портал Azure и выберите ресурс Foundry.
В левой области выберите ключи управления> ресурсамии конечную точку.
Выберите вкладку OpenAI .
Запишите конечную точку, которая должна выглядеть следующим https://my-resource.openai.azure.comобразом.

Развертывание моделей

Чтобы использовать агентное извлечение, необходимо развернуть две модели Azure OpenAI в проекте Foundry.

Модель внедрения для преобразования текста в вектор. В этом кратком руководстве используется text-embedding-3-large, но вы можете использовать какую-либо модель text-embedding.
LLM для планирования запросов и создания ответов. В этом кратком введении используется gpt-5-mini, но вы можете использовать любой поддерживаемый LLM для агентного извлечения.

Инструкции по развертыванию см. в статье "Развертывание моделей Azure OpenAI с помощью Foundry".

Настройка среды

Пример, приведенный в этом кратком руководстве, работает со средой выполнения Java. Установите пакет средств разработки Java, например Azul Zulu OpenJDK. Также может работать Microsoft Build of OpenJDK или предпочтительный JDK.

Установите Apache Maven. Затем выполните команду mvn -v , чтобы подтвердить успешную установку.
Создайте новую папку quickstart-agentic-retrieval для хранения приложения и откройте Visual Studio Code в этой папке с помощью следующей команды:
```
mkdir quickstart-agentic-retrieval && cd quickstart-agentic-retrieval
```

Создайте файл pom.xml в корне проекта и скопируйте в него следующий код:

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>
    <groupId>azure.search.sample</groupId>
    <artifactId>azuresearchquickstart</artifactId>
    <version>1.0.0-SNAPSHOT</version>
    <build>
        <sourceDirectory>src</sourceDirectory>
        <plugins>
        <plugin>
            <artifactId>maven-compiler-plugin</artifactId>
            <version>3.7.0</version>
            <configuration>
            <source>1.8</source>
            <target>1.8</target>
            </configuration>
        </plugin>
        </plugins>
    </build>
    <dependencies>
        <dependency>
            <groupId>junit</groupId>
            <artifactId>junit</artifactId>
            <version>4.11</version>
            <scope>test</scope>
        </dependency>
        <dependency>
            <groupId>com.azure</groupId>
            <artifactId>azure-search-documents</artifactId>
            <version>11.8.0-beta.7</version>
        </dependency>
        <dependency>
            <groupId>com.azure</groupId>
            <artifactId>azure-core</artifactId>
            <version>1.53.0</version>
        </dependency>
        <dependency>
            <groupId>com.azure</groupId>
            <artifactId>azure-identity</artifactId>
            <version>1.15.1</version>
        </dependency>
        <dependency>
            <groupId>com.azure</groupId>
            <artifactId>azure-ai-openai</artifactId>
            <version>1.0.0-beta.16</version>
        </dependency>
        <dependency>
            <groupId>com.fasterxml.jackson.core</groupId>
            <artifactId>jackson-databind</artifactId>
            <version>2.16.1</version>
        </dependency>
        <dependency>
            <groupId>io.github.cdimascio</groupId>
            <artifactId>dotenv-java</artifactId>
            <version>3.0.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.httpcomponents.client5</groupId>
            <artifactId>httpclient5</artifactId>
            <version>5.3.1</version>
        </dependency>
    </dependencies>
</project>

Установите зависимости, в том числе клиентская библиотека поиска ИИ Azure (Azure.Search.Documents) для клиентской библиотеки Java и удостоверений Azure для Java :
```
mvn clean dependency:copy-dependencies
```

Запустите код

Создайте файл с именем .env в папке quickstart-agentic-retrieval и добавьте следующие переменные среды:
```
AZURE_OPENAI_ENDPOINT=https://<your-ai-foundry-resource-name>.openai.azure.com/
AZURE_OPENAI_GPT_DEPLOYMENT=gpt-5-mini
AZURE_OPENAI_EMBEDDING_DEPLOYMENT=text-embedding-3-large
AZURE_SEARCH_ENDPOINT=https://<your-search-service-name>.search.windows.net
AZURE_SEARCH_INDEX_NAME=agentic-retrieval-sample
```
Замените <your-search-service-name> на фактическое имя службы Azure AI Search и <your-ai-foundry-resource-name> на имя ресурса Foundry.

Вставьте следующий код в новый файл с именем AgenticRetrievalQuickstart.java в папке quickstart-agentic-retrieval :

import com.azure.ai.openai.OpenAIAsyncClient;
import com.azure.ai.openai.OpenAIClientBuilder;
import com.azure.ai.openai.models.*;
import com.azure.core.credential.TokenCredential;
import com.azure.core.http.HttpClient;
import com.azure.core.http.HttpHeaders;
import com.azure.core.http.HttpMethod;
import com.azure.core.http.HttpRequest;
import com.azure.core.http.HttpResponse;
import com.azure.core.util.BinaryData;
import com.azure.identity.DefaultAzureCredential;
import com.azure.identity.DefaultAzureCredentialBuilder;
import com.azure.search.documents.SearchClient;
import com.azure.search.documents.SearchClientBuilder;
import com.azure.search.documents.SearchDocument;
import com.azure.search.documents.indexes.SearchIndexClient;
import com.azure.search.documents.indexes.SearchIndexClientBuilder;
import com.azure.search.documents.indexes.models.*;
import com.azure.search.documents.agents.SearchKnowledgeAgentClient;
import com.azure.search.documents.agents.SearchKnowledgeAgentClientBuilder;
import com.azure.search.documents.agents.models.*;
import com.fasterxml.jackson.databind.JsonNode;
import com.fasterxml.jackson.databind.ObjectMapper;
import com.fasterxml.jackson.databind.node.ObjectNode;
import io.github.cdimascio.dotenv.Dotenv;

import java.io.IOException;
import java.net.URI;
import java.net.http.HttpRequest.Builder;
import java.time.Duration;
import java.util.*;
import java.util.concurrent.TimeUnit;

public class AgenticRetrievalQuickstart {

    // Configuration - Update these values for your environment
    private static final String SEARCH_ENDPOINT;
    private static final String AZURE_OPENAI_ENDPOINT;
    private static final String AZURE_OPENAI_GPT_DEPLOYMENT;
    private static final String AZURE_OPENAI_GPT_MODEL = "gpt-5-mini";
    private static final String AZURE_OPENAI_EMBEDDING_DEPLOYMENT;
    private static final String AZURE_OPENAI_EMBEDDING_MODEL = "text-embedding-3-large";
    private static final String INDEX_NAME = "earth_at_night";
    private static final String AGENT_NAME = "earth-search-agent";
    private static final String SEARCH_API_VERSION = "2025-05-01-Preview";

    static {
        // Load environment variables from .env file
        Dotenv dotenv = Dotenv.configure().ignoreIfMissing().load();

        SEARCH_ENDPOINT = getEnvVar(dotenv, "AZURE_SEARCH_ENDPOINT", 
            "https://contoso-agentic-search-service.search.windows.net");
        AZURE_OPENAI_ENDPOINT = getEnvVar(dotenv, "AZURE_OPENAI_ENDPOINT",
            "https://contoso-proj-agentic-foundry-res.openai.azure.com/");
        AZURE_OPENAI_GPT_DEPLOYMENT = getEnvVar(dotenv, "AZURE_OPENAI_GPT_DEPLOYMENT", "gpt-5-mini");
        AZURE_OPENAI_EMBEDDING_DEPLOYMENT = getEnvVar(dotenv, "AZURE_OPENAI_EMBEDDING_DEPLOYMENT", "text-embedding-3-large");
    }

    private static String getEnvVar(Dotenv dotenv, String key, String defaultValue) {
        String value = dotenv.get(key);
        return (value != null && !value.isEmpty()) ? value : defaultValue;
    }

    public static void main(String[] args) {
        try {
            System.out.println("Starting Azure AI Search agentic retrieval quickstart...\n");

            // Initialize Azure credentials using managed identity (recommended)
            TokenCredential credential = new DefaultAzureCredentialBuilder().build();

            // Create search clients
            SearchIndexClient searchIndexClient = new SearchIndexClientBuilder()
                .endpoint(SEARCH_ENDPOINT)
                .credential(credential)
                .buildClient();

            SearchClient searchClient = new SearchClientBuilder()
                .endpoint(SEARCH_ENDPOINT)
                .indexName(INDEX_NAME)
                .credential(credential)
                .buildClient();

            // Create Azure OpenAI client
            OpenAIAsyncClient openAIClient = new OpenAIClientBuilder()
                .endpoint(AZURE_OPENAI_ENDPOINT)
                .credential(credential)
                .buildAsyncClient();

            // Step 1: Create search index with vector and semantic capabilities
            createSearchIndex(searchIndexClient);

            // Step 2: Upload documents
            uploadDocuments(searchClient);

            // Step 3: Create knowledge agent
            createKnowledgeAgent(credential);

            // Step 4: Run agentic retrieval with conversation
            runAgenticRetrieval(credential, openAIClient);

            // Step 5: Clean up - Delete knowledge agent and search index
            deleteKnowledgeAgent(credential);
            deleteSearchIndex(searchIndexClient);

            System.out.println("[DONE] Quickstart completed successfully!");

        } catch (Exception e) {
            System.err.println("[ERROR] Error in main execution: " + e.getMessage());
            e.printStackTrace();
        }
    }

    private static void createSearchIndex(SearchIndexClient indexClient) {
        System.out.println("[WAIT] Creating search index...");

        try {
            // Delete index if it exists
            try {
                indexClient.deleteIndex(INDEX_NAME);
                System.out.println("[DELETE] Deleted existing index '" + INDEX_NAME + "'");
            } catch (Exception e) {
                // Index doesn't exist, which is fine
            }

            // Define fields
            List<SearchField> fields = Arrays.asList(
                new SearchField("id", SearchFieldDataType.STRING)
                    .setKey(true)
                    .setFilterable(true)
                    .setSortable(true)
                    .setFacetable(true),
                new SearchField("page_chunk", SearchFieldDataType.STRING)
                    .setSearchable(true)
                    .setFilterable(false)
                    .setSortable(false)
                    .setFacetable(false),
                new SearchField("page_embedding_text_3_large", SearchFieldDataType.collection(SearchFieldDataType.SINGLE))
                    .setSearchable(true)
                    .setFilterable(false)
                    .setSortable(false)
                    .setFacetable(false)
                    .setVectorSearchDimensions(3072)
                    .setVectorSearchProfileName("hnsw_text_3_large"),
                new SearchField("page_number", SearchFieldDataType.INT32)
                    .setFilterable(true)
                    .setSortable(true)
                    .setFacetable(true)
            );

            // Create vectorizer
            AzureOpenAIVectorizer vectorizer = new AzureOpenAIVectorizer("azure_openai_text_3_large")
                .setParameters(new AzureOpenAIVectorizerParameters()
                    .setResourceUrl(AZURE_OPENAI_ENDPOINT)
                    .setDeploymentName(AZURE_OPENAI_EMBEDDING_DEPLOYMENT)
                    .setModelName(AzureOpenAIModelName.TEXT_EMBEDDING_3_LARGE));

            // Create vector search configuration
            VectorSearch vectorSearch = new VectorSearch()
                .setProfiles(Arrays.asList(
                    new VectorSearchProfile("hnsw_text_3_large", "alg")
                        .setVectorizerName("azure_openai_text_3_large")
                ))
                .setAlgorithms(Arrays.asList(
                    new HnswAlgorithmConfiguration("alg")
                ))
                .setVectorizers(Arrays.asList(vectorizer));

            // Create semantic search configuration
            SemanticSearch semanticSearch = new SemanticSearch()
                .setDefaultConfigurationName("semantic_config")
                .setConfigurations(Arrays.asList(
                    new SemanticConfiguration("semantic_config",
                        new SemanticPrioritizedFields()
                            .setContentFields(Arrays.asList(
                                new SemanticField("page_chunk")
                            ))
                    )
                ));

            // Create the index
            SearchIndex index = new SearchIndex(INDEX_NAME)
                .setFields(fields)
                .setVectorSearch(vectorSearch)
                .setSemanticSearch(semanticSearch);

            indexClient.createOrUpdateIndex(index);
            System.out.println("[DONE] Index '" + INDEX_NAME + "' created successfully.");

        } catch (Exception e) {
            System.err.println("[ERROR] Error creating index: " + e.getMessage());
            throw new RuntimeException(e);
        }
    }

    private static void uploadDocuments(SearchClient searchClient) {
        System.out.println("[WAIT] Uploading documents...");

        try {
            // Fetch documents from GitHub
            List<SearchDocument> documents = fetchEarthAtNightDocuments();

            searchClient.uploadDocuments(documents);
            System.out.println("[DONE] Uploaded " + documents.size() + " documents successfully.");

            // Wait for indexing to complete
            System.out.println("[WAIT] Waiting for document indexing to complete...");
            Thread.sleep(5000);
            System.out.println("[DONE] Document indexing completed.");

        } catch (Exception e) {
            System.err.println("[ERROR] Error uploading documents: " + e.getMessage());
            throw new RuntimeException(e);
        }
    }

    private static List<SearchDocument> fetchEarthAtNightDocuments() {
        System.out.println("[WAIT] Fetching Earth at Night documents from GitHub...");

        String documentsUrl = "https://raw.githubusercontent.com/Azure-Samples/azure-search-sample-data/refs/heads/main/nasa-e-book/earth-at-night-json/documents.json";

        try {
            java.net.http.HttpClient httpClient = java.net.http.HttpClient.newHttpClient();
            java.net.http.HttpRequest request = java.net.http.HttpRequest.newBuilder()
                .uri(URI.create(documentsUrl))
                .build();

            java.net.http.HttpResponse<String> response = httpClient.send(request, 
                java.net.http.HttpResponse.BodyHandlers.ofString());

            if (response.statusCode() != 200) {
                throw new IOException("Failed to fetch documents: " + response.statusCode());
            }

            ObjectMapper mapper = new ObjectMapper();
            JsonNode jsonArray = mapper.readTree(response.body());

            List<SearchDocument> documents = new ArrayList<>();
            for (int i = 0; i < jsonArray.size(); i++) {
                JsonNode doc = jsonArray.get(i);
                SearchDocument searchDoc = new SearchDocument();

                searchDoc.put("id", doc.has("id") ? doc.get("id").asText() : String.valueOf(i + 1));
                searchDoc.put("page_chunk", doc.has("page_chunk") ? doc.get("page_chunk").asText() : "");

                // Handle embeddings
                if (doc.has("page_embedding_text_3_large") && doc.get("page_embedding_text_3_large").isArray()) {
                    List<Double> embeddings = new ArrayList<>();
                    for (JsonNode embedding : doc.get("page_embedding_text_3_large")) {
                        embeddings.add(embedding.asDouble());
                    }
                    searchDoc.put("page_embedding_text_3_large", embeddings);
                } else {
                    // Fallback embeddings
                    List<Double> fallbackEmbeddings = new ArrayList<>();
                    for (int j = 0; j < 3072; j++) {
                        fallbackEmbeddings.add(0.1);
                    }
                    searchDoc.put("page_embedding_text_3_large", fallbackEmbeddings);
                }

                searchDoc.put("page_number", doc.has("page_number") ? doc.get("page_number").asInt() : i + 1);

                documents.add(searchDoc);
            }

            System.out.println("[DONE] Fetched " + documents.size() + " documents from GitHub");
            return documents;

        } catch (Exception e) {
            System.err.println("[ERROR] Error fetching documents from GitHub: " + e.getMessage());
            System.out.println("🔄 Falling back to sample documents...");

            // Fallback to sample documents
            List<SearchDocument> fallbackDocs = new ArrayList<>();

            SearchDocument doc1 = new SearchDocument();
            doc1.put("id", "1");
            doc1.put("page_chunk", "The Earth at night reveals the patterns of human settlement and economic activity. City lights trace the contours of civilization, creating a luminous map of where people live and work.");
            List<Double> embeddings1 = new ArrayList<>();
            for (int i = 0; i < 3072; i++) {
                embeddings1.add(0.1);
            }
            doc1.put("page_embedding_text_3_large", embeddings1);
            doc1.put("page_number", 1);

            SearchDocument doc2 = new SearchDocument();
            doc2.put("id", "2");
            doc2.put("page_chunk", "From space, the aurora borealis appears as shimmering curtains of green and blue light dancing across the polar regions.");
            List<Double> embeddings2 = new ArrayList<>();
            for (int i = 0; i < 3072; i++) {
                embeddings2.add(0.2);
            }
            doc2.put("page_embedding_text_3_large", embeddings2);
            doc2.put("page_number", 2);

            fallbackDocs.add(doc1);
            fallbackDocs.add(doc2);

            return fallbackDocs;
        }
    }

    private static void createKnowledgeAgent(TokenCredential credential) {
        System.out.println("[WAIT] Creating knowledge agent...");

        // Delete agent if it exists
        deleteKnowledgeAgent(credential);

        try {
            ObjectMapper mapper = new ObjectMapper();
            ObjectNode agentDefinition = mapper.createObjectNode();
            agentDefinition.put("name", AGENT_NAME);
            agentDefinition.put("description", "Knowledge agent for Earth at Night e-book content");

            ObjectNode model = mapper.createObjectNode();
            model.put("kind", "azureOpenAI");
            ObjectNode azureOpenAIParams = mapper.createObjectNode();
            azureOpenAIParams.put("resourceUri", AZURE_OPENAI_ENDPOINT);
            azureOpenAIParams.put("deploymentId", AZURE_OPENAI_GPT_DEPLOYMENT);
            azureOpenAIParams.put("modelName", AZURE_OPENAI_GPT_MODEL);
            model.set("azureOpenAIParameters", azureOpenAIParams);
            agentDefinition.set("models", mapper.createArrayNode().add(model));

            ObjectNode targetIndex = mapper.createObjectNode();
            targetIndex.put("indexName", INDEX_NAME);
            targetIndex.put("defaultRerankerThreshold", 2.5);
            agentDefinition.set("targetIndexes", mapper.createArrayNode().add(targetIndex));

            String token = getAccessToken(credential, "https://search.azure.com/.default");

            java.net.http.HttpClient httpClient = java.net.http.HttpClient.newHttpClient();
            java.net.http.HttpRequest request = java.net.http.HttpRequest.newBuilder()
                .uri(URI.create(SEARCH_ENDPOINT + "/agents/" + AGENT_NAME + "?api-version=" + SEARCH_API_VERSION))
                .header("Content-Type", "application/json")
                .header("Authorization", "Bearer " + token)
                .PUT(java.net.http.HttpRequest.BodyPublishers.ofString(mapper.writeValueAsString(agentDefinition)))
                .build();

            java.net.http.HttpResponse<String> response = httpClient.send(request,
                java.net.http.HttpResponse.BodyHandlers.ofString());

            if (response.statusCode() >= 400) {
                throw new RuntimeException("Failed to create knowledge agent: " + response.statusCode() + " " + response.body());
            }

            System.out.println("[DONE] Knowledge agent '" + AGENT_NAME + "' created successfully.");

        } catch (Exception e) {
            System.err.println("[ERROR] Error creating knowledge agent: " + e.getMessage());
            throw new RuntimeException(e);
        }
    }

    private static void runAgenticRetrieval(TokenCredential credential, OpenAIAsyncClient openAIClient) {
        System.out.println("[SEARCH] Running agentic retrieval...");

        // Initialize messages with system instructions
        List<Map<String, String>> messages = new ArrayList<>();

        Map<String, String> systemMessage = new HashMap<>();
        systemMessage.put("role", "system");
        systemMessage.put("content", "A Q&A agent that can answer questions about the Earth at night.\n" +
            "Sources have a JSON format with a ref_id that must be cited in the answer.\n" +
            "If you do not have the answer, respond with \"I don't know\".");
        messages.add(systemMessage);

        Map<String, String> userMessage = new HashMap<>();
        userMessage.put("role", "user");
        userMessage.put("content", "Why do suburban belts display larger December brightening than urban cores even though absolute light levels are higher downtown? 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?");
        messages.add(userMessage);

        try {
            // Call agentic retrieval API (excluding system message)
            List<Map<String, String>> userMessages = messages.stream()
                .filter(m -> !"system".equals(m.get("role")))
                .collect(java.util.stream.Collectors.toList());

            String retrievalResponse = callAgenticRetrieval(credential, userMessages);

            // Add assistant response to conversation history
            Map<String, String> assistantMessage = new HashMap<>();
            assistantMessage.put("role", "assistant");
            assistantMessage.put("content", retrievalResponse);
            messages.add(assistantMessage);

            System.out.println(retrievalResponse);

            // Now do chat completion with full conversation history
            generateFinalAnswer(openAIClient, messages);

            // Continue conversation with second question
            continueConversation(credential, openAIClient, messages);

        } catch (Exception e) {
            System.err.println("[ERROR] Error in agentic retrieval: " + e.getMessage());
            throw new RuntimeException(e);
        }
    }

    private static String callAgenticRetrieval(TokenCredential credential, List<Map<String, String>> messages) {
        try {
            ObjectMapper mapper = new ObjectMapper();
            ObjectNode retrievalRequest = mapper.createObjectNode();

            // Convert messages to the correct format expected by the Knowledge agent
            com.fasterxml.jackson.databind.node.ArrayNode agentMessages = mapper.createArrayNode();
            for (Map<String, String> msg : messages) {
                ObjectNode agentMessage = mapper.createObjectNode();
                agentMessage.put("role", msg.get("role"));

                com.fasterxml.jackson.databind.node.ArrayNode content = mapper.createArrayNode();
                ObjectNode textContent = mapper.createObjectNode();
                textContent.put("type", "text");
                textContent.put("text", msg.get("content"));
                content.add(textContent);
                agentMessage.set("content", content);

                agentMessages.add(agentMessage);
            }
            retrievalRequest.set("messages", agentMessages);

            com.fasterxml.jackson.databind.node.ArrayNode targetIndexParams = mapper.createArrayNode();
            ObjectNode indexParam = mapper.createObjectNode();
            indexParam.put("indexName", INDEX_NAME);
            indexParam.put("rerankerThreshold", 2.5);
            indexParam.put("maxDocsForReranker", 100);
            indexParam.put("includeReferenceSourceData", true);
            targetIndexParams.add(indexParam);
            retrievalRequest.set("targetIndexParams", targetIndexParams);

            String token = getAccessToken(credential, "https://search.azure.com/.default");

            java.net.http.HttpClient httpClient = java.net.http.HttpClient.newHttpClient();
            java.net.http.HttpRequest request = java.net.http.HttpRequest.newBuilder()
                .uri(URI.create(SEARCH_ENDPOINT + "/agents/" + AGENT_NAME + "/retrieve?api-version=" + SEARCH_API_VERSION))
                .header("Content-Type", "application/json")
                .header("Authorization", "Bearer " + token)
                .POST(java.net.http.HttpRequest.BodyPublishers.ofString(mapper.writeValueAsString(retrievalRequest)))
                .build();

            java.net.http.HttpResponse<String> response = httpClient.send(request,
                java.net.http.HttpResponse.BodyHandlers.ofString());

            if (response.statusCode() >= 400) {
                throw new RuntimeException("Agentic retrieval failed: " + response.statusCode() + " " + response.body());
            }

            JsonNode responseJson = mapper.readTree(response.body());

            // Log activities and results
            logActivitiesAndResults(responseJson);

            // Extract response content
            if (responseJson.has("response") && responseJson.get("response").isArray()) {
                com.fasterxml.jackson.databind.node.ArrayNode responseArray = (com.fasterxml.jackson.databind.node.ArrayNode) responseJson.get("response");
                if (responseArray.size() > 0) {
                    JsonNode firstResponse = responseArray.get(0);
                    if (firstResponse.has("content") && firstResponse.get("content").isArray()) {
                        com.fasterxml.jackson.databind.node.ArrayNode contentArray = (com.fasterxml.jackson.databind.node.ArrayNode) firstResponse.get("content");
                        if (contentArray.size() > 0) {
                            JsonNode textContent = contentArray.get(0);
                            if (textContent.has("text")) {
                                return textContent.get("text").asText();
                            }
                        }
                    }
                }
            }

            return "No response content available";

        } catch (Exception e) {
            System.err.println("[ERROR] Error in agentic retrieval call: " + e.getMessage());
            throw new RuntimeException(e);
        }
    }

    private static void logActivitiesAndResults(JsonNode responseJson) {
        ObjectMapper mapper = new ObjectMapper();

        // Log activities
        System.out.println("\nActivities:");
        if (responseJson.has("activity") && responseJson.get("activity").isArray()) {
            for (JsonNode activity : responseJson.get("activity")) {
                String activityType = "UnknownActivityRecord";
                if (activity.has("InputTokens")) {
                    activityType = "KnowledgeAgentModelQueryPlanningActivityRecord";
                } else if (activity.has("TargetIndex")) {
                    activityType = "KnowledgeAgentSearchActivityRecord";
                } else if (activity.has("QueryTime")) {
                    activityType = "KnowledgeAgentSemanticRankerActivityRecord";
                }

                System.out.println("Activity Type: " + activityType);
                try {
                    System.out.println(mapper.writerWithDefaultPrettyPrinter().writeValueAsString(activity));
                } catch (Exception e) {
                    System.out.println(activity.toString());
                }
            }
        }

        // Log results
        System.out.println("Results");
        if (responseJson.has("references") && responseJson.get("references").isArray()) {
            for (JsonNode reference : responseJson.get("references")) {
                String referenceType = "KnowledgeAgentAzureSearchDocReference";

                System.out.println("Reference Type: " + referenceType);
                try {
                    System.out.println(mapper.writerWithDefaultPrettyPrinter().writeValueAsString(reference));
                } catch (Exception e) {
                    System.out.println(reference.toString());
                }
            }
        }
    }

    private static void generateFinalAnswer(OpenAIAsyncClient openAIClient, List<Map<String, String>> messages) {
        System.out.println("\n[ASSISTANT]: ");

        try {
            List<ChatRequestMessage> chatMessages = new ArrayList<>();
            for (Map<String, String> msg : messages) {
                String role = msg.get("role");
                String content = msg.get("content");

                switch (role) {
                    case "system":
                        chatMessages.add(new ChatRequestSystemMessage(content));
                        break;
                    case "user":
                        chatMessages.add(new ChatRequestUserMessage(content));
                        break;
                    case "assistant":
                        chatMessages.add(new ChatRequestAssistantMessage(content));
                        break;
                }
            }

            ChatCompletionsOptions chatOptions = new ChatCompletionsOptions(chatMessages)
                .setMaxTokens(1000)
                .setTemperature(0.7);

            ChatCompletions completion = openAIClient.getChatCompletions(AZURE_OPENAI_GPT_DEPLOYMENT, chatOptions).block();

            if (completion != null && completion.getChoices() != null && !completion.getChoices().isEmpty()) {
                String answer = completion.getChoices().get(0).getMessage().getContent();
                System.out.println(answer.replace(".", "\n"));

                // Add this response to conversation history
                Map<String, String> assistantResponse = new HashMap<>();
                assistantResponse.put("role", "assistant");
                assistantResponse.put("content", answer);
                messages.add(assistantResponse);
            }

        } catch (Exception e) {
            System.err.println("[ERROR] Error generating final answer: " + e.getMessage());
            throw new RuntimeException(e);
        }
    }

    private static void continueConversation(TokenCredential credential, OpenAIAsyncClient openAIClient, List<Map<String, String>> messages) {
        System.out.println("\n === Continuing Conversation ===");

        // Add follow-up question
        String followUpQuestion = "How do I find lava at night?";
        System.out.println("[QUESTION] Follow-up question: " + followUpQuestion);

        Map<String, String> userMessage = new HashMap<>();
        userMessage.put("role", "user");
        userMessage.put("content", followUpQuestion);
        messages.add(userMessage);

        try {
            // FILTER OUT SYSTEM MESSAGE - only send user/assistant messages to agentic retrieval
            List<Map<String, String>> userAssistantMessages = messages.stream()
                .filter(m -> !"system".equals(m.get("role")))
                .collect(java.util.stream.Collectors.toList());

            String newRetrievalResponse = callAgenticRetrieval(credential, userAssistantMessages);

            // Add assistant response to conversation history
            Map<String, String> assistantMessage = new HashMap<>();
            assistantMessage.put("role", "assistant");
            assistantMessage.put("content", newRetrievalResponse);
            messages.add(assistantMessage);

            System.out.println(newRetrievalResponse);

            // Generate final answer for follow-up
            generateFinalAnswer(openAIClient, messages);

            System.out.println("\n === Conversation Complete ===");

        } catch (Exception e) {
            System.err.println("[ERROR] Error in conversation continuation: " + e.getMessage());
            throw new RuntimeException(e);
        }
    }

    private static void deleteKnowledgeAgent(TokenCredential credential) {
        System.out.println("[DELETE] Deleting knowledge agent...");

        try {
            String token = getAccessToken(credential, "https://search.azure.com/.default");

            java.net.http.HttpClient httpClient = java.net.http.HttpClient.newHttpClient();
            java.net.http.HttpRequest request = java.net.http.HttpRequest.newBuilder()
                .uri(URI.create(SEARCH_ENDPOINT + "/agents/" + AGENT_NAME + "?api-version=" + SEARCH_API_VERSION))
                .header("Authorization", "Bearer " + token)
                .DELETE()
                .build();

            java.net.http.HttpResponse<String> response = httpClient.send(request,
                java.net.http.HttpResponse.BodyHandlers.ofString());

            if (response.statusCode() == 404) {
                System.out.println("[INFO] Knowledge agent '" + AGENT_NAME + "' does not exist or was already deleted.");
                return;
            }

            if (response.statusCode() >= 400) {
                throw new RuntimeException("Failed to delete knowledge agent: " + response.statusCode() + " " + response.body());
            }

            System.out.println("[DONE] Knowledge agent '" + AGENT_NAME + "' deleted successfully.");

        } catch (Exception e) {
            System.err.println("[ERROR] Error deleting knowledge agent: " + e.getMessage());
            // Don't throw - this is cleanup
        }
    }

    private static void deleteSearchIndex(SearchIndexClient indexClient) {
        System.out.println("[DELETE] Deleting search index...");

        try {
            indexClient.deleteIndex(INDEX_NAME);
            System.out.println("[DONE] Search index '" + INDEX_NAME + "' deleted successfully.");

        } catch (Exception e) {
            if (e.getMessage() != null && (e.getMessage().contains("404") || e.getMessage().contains("IndexNotFound"))) {
                System.out.println("[INFO] Search index '" + INDEX_NAME + "' does not exist or was already deleted.");
                return;
            }
            System.err.println("[ERROR] Error deleting search index: " + e.getMessage());
            // Don't throw - this is cleanup
        }
    }

    private static String getAccessToken(TokenCredential credential, String scope) {
        try {
            return credential.getToken(new com.azure.core.credential.TokenRequestContext().addScopes(scope)).block().getToken();
        } catch (Exception e) {
            throw new RuntimeException("Failed to get access token", e);
        }
    }
}

Войдите в Azure с помощью следующей команды:
```
az login
```

Запустите новое консольное приложение:

javac Address.java App.java Hotel.java -cp ".;target\dependency\*"
java -cp ".;target\dependency\*" App

Выходные данные

Выходные данные приложения должны выглядеть следующим образом:

Starting Azure AI Search agentic retrieval quickstart...

[WAIT] Creating search index...
[DELETE] Deleted existing index 'earth_at_night'
[DONE] Index 'earth_at_night' created successfully.
[WAIT] Uploading documents...
[WAIT] Fetching Earth at Night documents from GitHub...
[DONE] Fetched 194 documents from GitHub
[DONE] Uploaded 194 documents successfully.
[WAIT] Waiting for document indexing to complete...
[DONE] Document indexing completed.
[WAIT] Creating knowledge agent...
[DELETE] Deleting knowledge agent...
[INFO] Knowledge agent 'earth-search-agent' does not exist or was already deleted.
[DONE] Knowledge agent 'earth-search-agent' created successfully.
[SEARCH] Running agentic retrieval...

Activities:
Activity Type: UnknownActivityRecord
{
  "type" : "ModelQueryPlanning",
  "id" : 0,
  "inputTokens" : 1379,
  "outputTokens" : 545
}
Activity Type: UnknownActivityRecord
{
  "type" : "AzureSearchQuery",
  "id" : 1,
  "targetIndex" : "earth_at_night",
  "query" : {
    "search" : "Why do suburban areas show greater December brightening compared to urban cores despite higher absolute light levels downtown?",
    "filter" : null
  },
  "queryTime" : "2025-07-21T15:07:04.024Z",
  "count" : 0,
  "elapsedMs" : 2609
}
Activity Type: UnknownActivityRecord
{
  "type" : "AzureSearchQuery",
  "id" : 2,
  "targetIndex" : "earth_at_night",
  "query" : {
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[ASSISTANT]: 
The suburban belts display larger December brightening than urban cores despite higher absolute light levels downtown likely because suburban areas have more seasonal variation in lighting usage, such as increased outdoor and holiday lighting in December
 Urban cores, being brightly lit throughout the year, show less relative change


Regarding Phoenix's nighttime street grid visibility, it is sharply visible from space due to the structured and continuous lighting of the city's streets
 In contrast, large stretches of interstate highways between Midwestern cities are comparatively dim because highways typically have less intense and less frequent lighting compared to urban street grids


[Note: This explanation is based on general knowledge; no specific source with ref_id was provided
]

 === Continuing Conversation ===
[QUESTION] Follow-up question: How do I find lava at night?

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    "page_chunk" : "## Nature's Light Shows\n\nAt night, with the light of the Sun removed, nature's brilliant glow from Earth's surface becomes visible to the naked eye from space. Some of Earth's most spectacular light shows are natural, like the aurora borealis, or Northern Lights, in the Northern Hemisphere (aurora australis, or Southern Lights, in the Southern Hemisphere). The auroras are natural electrical phenomena caused by charged particles that race from the Sun toward Earth, inducing chemical reactions in the upper atmosphere and creating the appearance of streamers of reddish or greenish light in the sky, usually near the northern or southern magnetic pole. Other natural lights can indicate danger, like a raging forest fire encroaching on a city, town, or community, or lava spewing from an erupting volcano.\n\nWhatever the source, the ability of humans to monitor nature's light shows at night has practical applications for society. For example, tracking fires during nighttime hours allows for continuous monitoring and enhances our ability to protect humans and other animals, plants, and infrastructure. Combined with other data sources, our ability to observe the light of fires at night allows emergency managers to more efficiently and accurately issue warnings and evacuation orders and allows firefighting efforts to continue through the night. With enough moonlight (e.g., full-Moon phase), it's even possible to track the movement of smoke plumes at night, which can impact air quality, regardless of time of day.\n\nAnother natural source of light at night is emitted from glowing lava flows at the site of active volcanoes. Again, with enough moonlight, these dramatic scenes can be tracked and monitored for both scientific research and public safety.\n\n\n### Figure: The Northern Lights Viewed from Space\n\n**September 17, 2011**\n\nThis photo, taken from the International Space Station on September 17, 2011, shows a spectacular display of the aurora borealis (Northern Lights) as green and reddish light in the night sky above Earth. In the foreground, part of a Soyuz spacecraft is visible, silhouetted against the bright auroral light. The green glow is generated by energetic charged particles from the Sun interacting with Earth's upper atmosphere, exciting oxygen and nitrogen atoms, and producing characteristic colors. The image demonstrates the vividness and grandeur of natural night-time light phenomena as seen from orbit."
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    "page_chunk" : "# Volcanoes\n\n## Figure: Satellite Image of Sicily and Mount Etna Lava, March 16, 2017\n\nThe annotated satellite image below shows the island of Sicily and the surrounding region at night, highlighting city lights and volcanic activity.\n\n**Description:**\n\n- **Date of image:** March 16, 2017\n- **Geographical locations labeled:**\n    - Major cities: Palermo (northwest Sicily), Marsala (western Sicily), Catania (eastern Sicily)\n    - Significant feature: Mount Etna, labeled with an adjacent \"hot lava\" region showing the glow from active lava flows\n    - Surrounding water body: Mediterranean Sea\n    - Island: Malta to the south of Sicily\n- **Other details:** \n    - The image is shown at night, with bright spots indicating city lights.\n    - The position of \"hot lava\" near Mount Etna is distinctly visible as a bright spot different from other city lights, indicating volcanic activity.\n    - A scale bar is included showing a reference length of 50 km.\n    - North direction is indicated with an arrow.\n    - Cloud cover is visible in the southwest part of the image, partially obscuring the view near Marsala and Malta.\n\n**Summary of Features Visualized:**\n\n| Feature          | Description                                           |\n|------------------|------------------------------------------------------|\n| Cities           | Bright clusters indicating locations: Palermo, Marsala, Catania |\n| Mount Etna       | Marked on the map, located on the eastern side of Sicily, with visible hot lava activity |\n| Malta            | Clearly visible to the south of Sicily               |\n| Water bodies     | Mediterranean Sea labeled                            |\n| Scale & Direction| 50 km scale bar and North indicator                  |\n| Date             | March 16, 2017                                       |\n| Cloud Cover      | Visible in the lower left (southern) part of the image |\n\nThis figure demonstrates the visibility of volcanic activity at Mount Etna from space at night, distinguishing the light from hot lava against the background city lights of Sicily and Malta."
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    "page_chunk" : "<!-- PageHeader=\"Volcanoes\" -->\n\n### Nighttime Glow at Mount Etna - Italy\n\nAt about 2:30 a.m. local time on March 16, 2017, the VIIRS DNB on the Suomi NPP satellite captured this nighttime image of lava flowing on Mount Etna in Sicily, Italy. Etna is one of the world's most active volcanoes.\n\n#### Figure: Location of Mount Etna\nA world globe is depicted, with a marker indicating the location of Mount Etna in Sicily, Italy, in southern Europe near the center of the Mediterranean Sea.\n\n<!-- PageFooter=\"Earth at Night\" -->\n<!-- PageNumber=\"48\" -->"
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    "page_chunk" : "# Volcanoes\n\n---\n\n### Mount Etna Erupts - Italy\n\nThe highly active Mount Etna in Italy sent red lava rolling down its flank on March 19, 2017. An astronaut onboard the ISS took the photograph below of the volcano and its environs that night. City lights surround the mostly dark volcanic area.\n\n---\n\n#### Figure 1: Location of Mount Etna, Italy\n\nA world map highlighting the location of Mount Etna in southern Italy. The marker indicates its geographic placement on the east coast of Sicily, Italy, in the Mediterranean region, south of mainland Europe and north of northern Africa.\n\n---\n\n#### Figure 2: Nighttime View of Mount Etna's Eruption and Surrounding Cities\n\nThis is a nighttime satellite image taken on March 19, 2017, showing the eruption of Mount Etna (southeastern cone) with visible bright red and orange coloring indicating flowing lava from a lateral vent. The surrounding areas are illuminated by city lights, with the following geographic references labeled:\n\n| Location        | Position in Image         | Visible Characteristics                    |\n|-----------------|--------------------------|--------------------------------------------|\n| Mt. Etna (southeastern cone) | Top center-left | Bright red/orange lava flow                |\n| Lateral vent    | Left of the volcano       | Faint red/orange flow extending outwards   |\n| Resort          | Below the volcano, to the left   | Small cluster of lights                    |\n| Giarre          | Top right                 | Bright cluster of city lights              |\n| Acireale        | Center right              | Large, bright area of city lights          |\n| Biancavilla     | Bottom left               | Smaller cluster of city lights             |\n\nAn arrow pointing north is shown on the image for orientation.\n\n---\n\n<!-- Earth at Night Page Footer -->\n<!-- Page Number: 50 -->"
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    "page_chunk" : "For the first time in perhaps a decade, Mount Etna experienced a \"flank eruption\"�erupting from its side instead of its summit�on December 24, 2018. The activity was accompanied by 130 earthquakes occurring over three hours that morning. Mount Etna, Europe�s most active volcano, has seen periodic activity on this part of the mountain since 2013. The Operational Land Imager (OLI) on the Landsat 8 satellite acquired the main image of Mount Etna on December 28, 2018.\n\nThe inset image highlights the active vent and thermal infrared signature from lava flows, which can be seen near the newly formed fissure on the southeastern side of the volcano. The inset was created with data from OLI and the Thermal Infrared Sensor (TIRS) on Landsat 8. Ash spewing from the fissure cloaked adjacent villages and delayed aircraft from landing at the nearby Catania airport. Earthquakes occurred in the subsequent days after the initial eruption and displaced hundreds of people from their homes.\n\nFor nighttime images of Mount Etna�s March 2017 eruption, see pages 48�51.\n\n---\n\n### Hazards of Volcanic Ash Plumes and Satellite Observation\n\nWith the help of moonlight, satellite instruments can track volcanic ash plumes, which present significant hazards to airplanes in flight. The volcanic ash�composed of tiny pieces of glass and rock�is abrasive to engine turbine blades, and can melt on the blades and other engine parts, causing damage and even engine stalls. This poses a danger to both the plane�s integrity and passenger safety. Volcanic ash also reduces visibility for pilots and can cause etching of windshields, further reducing pilots� ability to see. Nightlight images can be combined with thermal images to provide a more complete view of volcanic activity on Earth�s surface.\n\nThe VIIRS Day/Night Band (DNB) on polar-orbiting satellites uses faint light sources such as moonlight, airglow (the atmosphere�s self-illumination through chemical reactions), zodiacal light (sunlight scattered by interplanetary dust), and starlight from the Milky Way. Using these dim light sources, the DNB can detect changes in clouds, snow cover, and sea ice:\n\n#### Table: Light Sources Used by VIIRS DNB\n\n| Light Source         | Description                                                                  |\n|----------------------|------------------------------------------------------------------------------|\n| Moonlight            | Reflected sunlight from the Moon, illuminating Earth's surface at night      |\n| Airglow              | Atmospheric self-illumination from chemical reactions                        |\n| Zodiacal Light       | Sunlight scattered by interplanetary dust                                    |\n| Starlight/Milky Way  | Faint illumination provided by stars in the Milky Way                        |\n\nGeostationary Operational Environmental Satellites (GOES), managed by NOAA, orbit over Earth�s equator and offer uninterrupted observations of North America. High-latitude areas such as Alaska benefit from polar-orbiting satellites like Suomi NPP, which provide overlapping coverage at the poles, enabling more data collection in these regions. During polar darkness (winter months), VIIRS DNB data allow scientists to:\n\n- Observe sea ice formation\n- Monitor snow cover extent at the highest latitudes\n- Detect open water for ship navigation\n\n#### Table: Satellite Coverage Overview\n\n| Satellite Type          | Orbit           | Coverage Area         | Special Utility                              |\n|------------------------|-----------------|----------------------|----------------------------------------------|\n| GOES                   | Geostationary   | Equatorial/North America | Continuous regional monitoring              |\n| Polar-Orbiting (e.g., Suomi NPP) | Polar-orbiting    | Poles/high latitudes      | Overlapping passes; useful during polar night|\n\n---\n\n### Weather Forecasting and Nightlight Data\n\nThe use of nightlight data by weather forecasters is growing as the VIIRS instrument enables observation of clouds at night illuminated by sources such as moonlight and lightning. Scientists use these data to study the nighttime behavior of weather systems, including severe storms, which can develop and strike populous areas at night as well as during the day. Combined with thermal data, visible nightlight data allow the detection of clouds at various heights in the atmosphere, such as dense marine fog. This capability enables weather forecasters to issue marine advisories with higher confidence, leading to greater utility. (See \"Marine Layer Clouds�California\" on page 56.)\n\nIn this section of the book, you will see how nightlight data are used to observe nature�s spectacular light shows across a wide range of sources.\n\n---\n\n#### Notable Data from Mount Etna Flank Eruption (December 2018)\n\n| Event/Observation                  | Details                                                                    |\n|-------------------------------------|----------------------------------------------------------------------------|\n| Date of Flank Eruption              | December 24, 2018                                                          |\n| Number of Earthquakes               | 130 earthquakes within 3 hours                                              |\n| Image Acquisition                   | December 28, 2018 by Landsat 8 OLI                                         |\n| Location of Eruption                | Southeastern side of Mount Etna                                            |\n| Thermal Imaging Data                | From OLI and TIRS (Landsat 8), highlighting active vent and lava flows     |\n| Impact on Villages/Air Transport    | Ash covered villages; delayed aircraft at Catania airport                  |\n| Displacement                        | Hundreds of residents displaced                                            |\n| Ongoing Seismic Activity            | Earthquakes continued after initial eruption                               |\n\n---\n\n<!-- PageFooter=\"Earth at Night\" -->\n<!-- PageNumber=\"30\" -->"
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    "page_chunk" : "<!-- PageHeader=\"Volcanoes\" -->\n\n## Volcanoes\n\n### The Infrared Glows of Kilauea's Lava Flows�Hawaii\n\nIn early May 2018, an eruption on Hawaii's Kilauea volcano began to unfold. The eruption took a dangerous turn on May 3, 2018, when new fissures opened in the residential neighborhood of Leilani Estates. During the summer-long eruptive event, other fissures emerged along the East Rift Zone. Lava from vents along the rift zone flowed downslope, reaching the ocean in several areas, and filling in Kapoho Bay.\n\nA time series of Landsat 8 imagery shows the progression of the lava flows from May 16 to August 13. The night view combines thermal, shortwave infrared, and near-infrared wavelengths to tease out the very hot lava (bright white), cooling lava (red), and lava flows obstructed by clouds (purple).\n\n#### Figure: Location of Kilauea Volcano, Hawaii\n\nA globe is shown centered on North America, with a marker placed in the Pacific Ocean indicating the location of Hawaii, to the southwest of the mainland United States.\n\n<!-- PageFooter=\"Earth at Night\" -->\n<!-- PageNumber=\"44\" -->"
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[{"ref_id":0,"content":"## Nature's Light Shows\n\nAt night, with the light of the Sun removed, nature's brilliant glow from Earth's surface becomes visible to the naked eye from space. Some of Earth's most spectacular light shows are natural, like the aurora borealis, or Northern Lights, in the Northern Hemisphere (aurora australis, or Southern Lights, in the Southern Hemisphere). The auroras are natural electrical phenomena caused by charged particles that race from the Sun toward Earth, inducing chemical reactions in the upper atmosphere and creating the appearance of streamers of reddish or greenish light in the sky, usually near the northern or southern magnetic pole. Other natural lights can indicate danger, like a raging forest fire encroaching on a city, town, or community, or lava spewing from an erupting volcano.\n\nWhatever the source, the ability of humans to monitor nature's light shows at night has practical applications for society. For example, tracking fires during nighttime hours allows for continuous monitoring and enhances our ability to protect humans and other animals, plants, and infrastructure. Combined with other data sources, our ability to observe the light of fires at night allows emergency managers to more efficiently and accurately issue warnings and evacuation orders and allows firefighting efforts to continue through the night. With enough moonlight (e.g., full-Moon phase), it's even possible to track the movement of smoke plumes at night, which can impact air quality, regardless of time of day.\n\nAnother natural source of light at night is emitted from glowing lava flows at the site of active volcanoes. Again, with enough moonlight, these dramatic scenes can be tracked and monitored for both scientific research and public safety.\n\n\n### Figure: The Northern Lights Viewed from Space\n\n**September 17, 2011**\n\nThis photo, taken from the International Space Station on September 17, 2011, shows a spectacular display of the aurora borealis (Northern Lights) as green and reddish light in the night sky above Earth. In the foreground, part of a Soyuz spacecraft is visible, silhouetted against the bright auroral light. The green glow is generated by energetic charged particles from the Sun interacting with Earth's upper atmosphere, exciting oxygen and nitrogen atoms, and producing characteristic colors. The image demonstrates the vividness and grandeur of natural night-time light phenomena as seen from orbit."},{"ref_id":1,"content":"# Volcanoes\n\n## Figure: Satellite Image of Sicily and Mount Etna Lava, March 16, 2017\n\nThe annotated satellite image below shows the island of Sicily and the surrounding region at night, highlighting city lights and volcanic activity.\n\n**Description:**\n\n- **Date of image:** March 16, 2017\n- **Geographical locations labeled:**\n    - Major cities: Palermo (northwest Sicily), Marsala (western Sicily), Catania (eastern Sicily)\n    - Significant feature: Mount Etna, labeled with an adjacent \"hot lava\" region showing the glow from active lava flows\n    - Surrounding water body: Mediterranean Sea\n    - Island: Malta to the south of Sicily\n- **Other details:** \n    - The image is shown at night, with bright spots indicating city lights.\n    - The position of \"hot lava\" near Mount Etna is distinctly visible as a bright spot different from other city lights, indicating volcanic activity.\n    - A scale bar is included showing a reference length of 50 km.\n    - North direction is indicated with an arrow.\n    - Cloud cover is visible in the southwest part of the image, partially obscuring the view near Marsala and Malta.\n\n**Summary of Features Visualized:**\n\n| Feature          | Description                                           |\n|------------------|------------------------------------------------------|\n| Cities           | Bright clusters indicating locations: Palermo, Marsala, Catania |\n| Mount Etna       | Marked on the map, located on the eastern side of Sicily, with visible hot lava activity |\n| Malta            | Clearly visible to the south of Sicily               |\n| Water bodies     | Mediterranean Sea labeled                            |\n| Scale & Direction| 50 km scale bar and North indicator                  |\n| Date             | March 16, 2017                                       |\n| Cloud Cover      | Visible in the lower left (southern) part of the image |\n\nThis figure demonstrates the visibility of volcanic activity at Mount Etna from space at night, distinguishing the light from hot lava against the background city lights of Sicily and Malta."},{"ref_id":2,"content":"<!-- PageHeader=\"Volcanoes\" -->\n\n### Nighttime Glow at Mount Etna - Italy\n\nAt about 2:30 a.m. local time on March 16, 2017, the VIIRS DNB on the Suomi NPP satellite captured this nighttime image of lava flowing on Mount Etna in Sicily, Italy. Etna is one of the world's most active volcanoes.\n\n#### Figure: Location of Mount Etna\nA world globe is depicted, with a marker indicating the location of Mount Etna in Sicily, Italy, in southern Europe near the center of the Mediterranean Sea.\n\n<!-- PageFooter=\"Earth at Night\" -->\n<!-- PageNumber=\"48\" -->"},{"ref_id":3,"content":"# Volcanoes\n\n---\n\n### Mount Etna Erupts - Italy\n\nThe highly active Mount Etna in Italy sent red lava rolling down its flank on March 19, 2017. An astronaut onboard the ISS took the photograph below of the volcano and its environs that night. City lights surround the mostly dark volcanic area.\n\n---\n\n#### Figure 1: Location of Mount Etna, Italy\n\nA world map highlighting the location of Mount Etna in southern Italy. The marker indicates its geographic placement on the east coast of Sicily, Italy, in the Mediterranean region, south of mainland Europe and north of northern Africa.\n\n---\n\n#### Figure 2: Nighttime View of Mount Etna's Eruption and Surrounding Cities\n\nThis is a nighttime satellite image taken on March 19, 2017, showing the eruption of Mount Etna (southeastern cone) with visible bright red and orange coloring indicating flowing lava from a lateral vent. The surrounding areas are illuminated by city lights, with the following geographic references labeled:\n\n| Location        | Position in Image         | Visible Characteristics                    |\n|-----------------|--------------------------|--------------------------------------------|\n| Mt. Etna (southeastern cone) | Top center-left | Bright red/orange lava flow                |\n| Lateral vent    | Left of the volcano       | Faint red/orange flow extending outwards   |\n| Resort          | Below the volcano, to the left   | Small cluster of lights                    |\n| Giarre          | Top right                 | Bright cluster of city lights              |\n| Acireale        | Center right              | Large, bright area of city lights          |\n| Biancavilla     | Bottom left               | Smaller cluster of city lights             |\n\nAn arrow pointing north is shown on the image for orientation.\n\n---\n\n<!-- Earth at Night Page Footer -->\n<!-- Page Number: 50 -->"},{"ref_id":4,"content":"For the first time in perhaps a decade, Mount Etna experienced a \"flank eruption\"�erupting from its side instead of its summit�on December 24, 2018. The activity was accompanied by 130 earthquakes occurring over three hours that morning. Mount Etna, Europe�s most active volcano, has seen periodic activity on this part of the mountain since 2013. The Operational Land Imager (OLI) on the Landsat 8 satellite acquired the main image of Mount Etna on December 28, 2018.\n\nThe inset image highlights the active vent and thermal infrared signature from lava flows, which can be seen near the newly formed fissure on the southeastern side of the volcano. The inset was created with data from OLI and the Thermal Infrared Sensor (TIRS) on Landsat 8. Ash spewing from the fissure cloaked adjacent villages and delayed aircraft from landing at the nearby Catania airport. Earthquakes occurred in the subsequent days after the initial eruption and displaced hundreds of people from their homes.\n\nFor nighttime images of Mount Etna�s March 2017 eruption, see pages 48�51.\n\n---\n\n### Hazards of Volcanic Ash Plumes and Satellite Observation\n\nWith the help of moonlight, satellite instruments can track volcanic ash plumes, which present significant hazards to airplanes in flight. The volcanic ash�composed of tiny pieces of glass and rock�is abrasive to engine turbine blades, and can melt on the blades and other engine parts, causing damage and even engine stalls. This poses a danger to both the plane�s integrity and passenger safety. Volcanic ash also reduces visibility for pilots and can cause etching of windshields, further reducing pilots� ability to see. Nightlight images can be combined with thermal images to provide a more complete view of volcanic activity on Earth�s surface.\n\nThe VIIRS Day/Night Band (DNB) on polar-orbiting satellites uses faint light sources such as moonlight, airglow (the atmosphere�s self-illumination through chemical reactions), zodiacal light (sunlight scattered by interplanetary dust), and starlight from the Milky Way. Using these dim light sources, the DNB can detect changes in clouds, snow cover, and sea ice:\n\n#### Table: Light Sources Used by VIIRS DNB\n\n| Light Source         | Description                                                                  |\n|----------------------|------------------------------------------------------------------------------|\n| Moonlight            | Reflected sunlight from the Moon, illuminating Earth's surface at night      |\n| Airglow              | Atmospheric self-illumination from chemical reactions                        |\n| Zodiacal Light       | Sunlight scattered by interplanetary dust                                    |\n| Starlight/Milky Way  | Faint illumination provided by stars in the Milky Way                        |\n\nGeostationary Operational Environmental Satellites (GOES), managed by NOAA, orbit over Earth�s equator and offer uninterrupted observations of North America. High-latitude areas such as Alaska benefit from polar-orbiting satellites like Suomi NPP, which provide overlapping coverage at the poles, enabling more data collection in these regions. During polar darkness (winter months), VIIRS DNB data allow scientists to:\n\n- Observe sea ice formation\n- Monitor snow cover extent at the highest latitudes\n- Detect open water for ship navigation\n\n#### Table: Satellite Coverage Overview\n\n| Satellite Type          | Orbit           | Coverage Area         | Special Utility                              |\n|------------------------|-----------------|----------------------|----------------------------------------------|\n| GOES                   | Geostationary   | Equatorial/North America | Continuous regional monitoring              |\n| Polar-Orbiting (e.g., Suomi NPP) | Polar-orbiting    | Poles/high latitudes      | Overlapping passes; useful during polar night|\n\n---\n\n### Weather Forecasting and Nightlight Data\n\nThe use of nightlight data by weather forecasters is growing as the VIIRS instrument enables observation of clouds at night illuminated by sources such as moonlight and lightning. Scientists use these data to study the nighttime behavior of weather systems, including severe storms, which can develop and strike populous areas at night as well as during the day. Combined with thermal data, visible nightlight data allow the detection of clouds at various heights in the atmosphere, such as dense marine fog. This capability enables weather forecasters to issue marine advisories with higher confidence, leading to greater utility. (See \"Marine Layer Clouds�California\" on page 56.)\n\nIn this section of the book, you will see how nightlight data are used to observe nature�s spectacular light shows across a wide range of sources.\n\n---\n\n#### Notable Data from Mount Etna Flank Eruption (December 2018)\n\n| Event/Observation                  | Details                                                                    |\n|-------------------------------------|----------------------------------------------------------------------------|\n| Date of Flank Eruption              | December 24, 2018                                                          |\n| Number of Earthquakes               | 130 earthquakes within 3 hours                                              |\n| Image Acquisition                   | December 28, 2018 by Landsat 8 OLI                                         |\n| Location of Eruption                | Southeastern side of Mount Etna                                            |\n| Thermal Imaging Data                | From OLI and TIRS (Landsat 8), highlighting active vent and lava flows     |\n| Impact on Villages/Air Transport    | Ash covered villages; delayed aircraft at Catania airport                  |\n| Displacement                        | Hundreds of residents displaced                                            |\n| Ongoing Seismic Activity            | Earthquakes continued after initial eruption                               |\n\n---\n\n<!-- PageFooter=\"Earth at Night\" -->\n<!-- PageNumber=\"30\" -->"},{"ref_id":5,"content":"<!-- PageHeader=\"Volcanoes\" -->\n\n## Volcanoes\n\n### The Infrared Glows of Kilauea's Lava Flows�Hawaii\n\nIn early May 2018, an eruption on Hawaii's Kilauea volcano began to unfold. The eruption took a dangerous turn on May 3, 2018, when new fissures opened in the residential neighborhood of Leilani Estates. During the summer-long eruptive event, other fissures emerged along the East Rift Zone. Lava from vents along the rift zone flowed downslope, reaching the ocean in several areas, and filling in Kapoho Bay.\n\nA time series of Landsat 8 imagery shows the progression of the lava flows from May 16 to August 13. The night view combines thermal, shortwave infrared, and near-infrared wavelengths to tease out the very hot lava (bright white), cooling lava (red), and lava flows obstructed by clouds (purple).\n\n#### Figure: Location of Kilauea Volcano, Hawaii\n\nA globe is shown centered on North America, with a marker placed in the Pacific Ocean indicating the location of Hawaii, to the southwest of the mainland United States.\n\n<!-- PageFooter=\"Earth at Night\" -->\n<!-- PageNumber=\"44\" -->"}]

[ASSISTANT]: 
To find lava at night, you can look for the visible glow of active lava flows from erupting volcanoes, which emit light detectable from space during nighttime
 For example:

- The active lava flows of Mount Etna in Sicily, Italy, have been clearly observed at night by satellites and astronauts aboard the International Space Station
 The bright red and orange glow of lava distinguishes it from surrounding city lights (refs 1, 3)


- Similarly, the Kilauea volcano in Hawaii emits an infrared glow from its lava flows, which can be captured in nighttime satellite imagery combining thermal and near-infrared wavelengths (ref 5)


- Nighttime satellite instruments like the VIIRS Day/Night Band (DNB) on the Suomi NPP satellite use faint light sources such as moonlight to detect the glow of lava and volcanic activity even when direct sunlight is absent (refs 2, 4)


Therefore, to find lava at night, monitoring nighttime satellite imagery over active volcanic regions is effective, as the glowing lava stands out distinctly against the dark landscape and city lights


References: [1], [2], [3], [4], [5]

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Создание индекса поиска

В службе "Поиск ИИ Azure" индекс представляет собой структурированную коллекцию данных. Следующий код определяет индекс с именем earth_at_night , содержащий обычный текст и векторное содержимое. Существующий индекс можно использовать, но он должен соответствовать критериям для агентно-ориентированных рабочих нагрузок извлечения.

List<SearchField> fields = Arrays.asList(
    new SearchField("id", SearchFieldDataType.STRING)
        .setKey(true)
        .setFilterable(true)
        .setSortable(true)
        .setFacetable(true),
    new SearchField("page_chunk", SearchFieldDataType.STRING)
        .setSearchable(true)
        .setFilterable(false)
        .setSortable(false)
        .setFacetable(false),
    new SearchField("page_embedding_text_3_large", SearchFieldDataType.collection(SearchFieldDataType.SINGLE))
        .setSearchable(true)
        .setFilterable(false)
        .setSortable(false)
        .setFacetable(false)
        .setVectorSearchDimensions(3072)
        .setVectorSearchProfileName("hnsw_text_3_large"),
    new SearchField("page_number", SearchFieldDataType.INT32)
        .setFilterable(true)
        .setSortable(true)
        .setFacetable(true)
);

// Create vectorizer
AzureOpenAIVectorizer vectorizer = new AzureOpenAIVectorizer("azure_openai_text_3_large")
    .setParameters(new AzureOpenAIVectorizerParameters()
        .setResourceUrl(AZURE_OPENAI_ENDPOINT)
        .setDeploymentName(AZURE_OPENAI_EMBEDDING_DEPLOYMENT)
        .setModelName(AzureOpenAIModelName.TEXT_EMBEDDING_3_LARGE));

// Create vector search configuration
VectorSearch vectorSearch = new VectorSearch()
    .setProfiles(Arrays.asList(
        new VectorSearchProfile("hnsw_text_3_large", "alg")
            .setVectorizerName("azure_openai_text_3_large")
    ))
    .setAlgorithms(Arrays.asList(
        new HnswAlgorithmConfiguration("alg")
    ))
    .setVectorizers(Arrays.asList(vectorizer));

// Create semantic search configuration
SemanticSearch semanticSearch = new SemanticSearch()
    .setDefaultConfigurationName("semantic_config")
    .setConfigurations(Arrays.asList(
        new SemanticConfiguration("semantic_config",
            new SemanticPrioritizedFields()
                .setContentFields(Arrays.asList(
                    new SemanticField("page_chunk")
                ))
        )
    ));

// Create the index
SearchIndex index = new SearchIndex(INDEX_NAME)
    .setFields(fields)
    .setVectorSearch(vectorSearch)
    .setSemanticSearch(semanticSearch);

indexClient.createOrUpdateIndex(index);

Схема индекса содержит поля для идентификации документов и содержимого страницы, встраиваний и числовых данных. Она также включает конфигурации для семантического ранжирования и векторных запросов, которые используют text-embedding-3-large модель, которую вы ранее развернули.

Отправка документов в индекс

earth_at_night В настоящее время индекс пуст. Выполните следующий код, чтобы заполнить индекс JSON-документами из электронной книги "Земля ночью" от НАСА. Как требуется поисковой системой Azure AI, каждый документ соответствует полям и типам данных, определенным в схеме индекса.

String documentsUrl = "https://raw.githubusercontent.com/Azure-Samples/azure-search-sample-data/refs/heads/main/nasa-e-book/earth-at-night-json/documents.json";
        
try {
    java.net.http.HttpClient httpClient = java.net.http.HttpClient.newHttpClient();
    java.net.http.HttpRequest request = java.net.http.HttpRequest.newBuilder()
        .uri(URI.create(documentsUrl))
        .build();
    
    java.net.http.HttpResponse<String> response = httpClient.send(request, 
        java.net.http.HttpResponse.BodyHandlers.ofString());
    
    if (response.statusCode() != 200) {
        throw new IOException("Failed to fetch documents: " + response.statusCode());
    }
    
    ObjectMapper mapper = new ObjectMapper();
    JsonNode jsonArray = mapper.readTree(response.body());
    
    List<SearchDocument> documents = new ArrayList<>();
    for (int i = 0; i < jsonArray.size(); i++) {
        JsonNode doc = jsonArray.get(i);
        SearchDocument searchDoc = new SearchDocument();
        
        searchDoc.put("id", doc.has("id") ? doc.get("id").asText() : String.valueOf(i + 1));
        searchDoc.put("page_chunk", doc.has("page_chunk") ? doc.get("page_chunk").asText() : "");
        
        // Handle embeddings
        if (doc.has("page_embedding_text_3_large") && doc.get("page_embedding_text_3_large").isArray()) {
            List<Double> embeddings = new ArrayList<>();
            for (JsonNode embedding : doc.get("page_embedding_text_3_large")) {
                embeddings.add(embedding.asDouble());
            }
            searchDoc.put("page_embedding_text_3_large", embeddings);
        } else {
            // Fallback embeddings
            List<Double> fallbackEmbeddings = new ArrayList<>();
            for (int j = 0; j < 3072; j++) {
                fallbackEmbeddings.add(0.1);
            }
            searchDoc.put("page_embedding_text_3_large", fallbackEmbeddings);
        }
        
        searchDoc.put("page_number", doc.has("page_number") ? doc.get("page_number").asInt() : i + 1);
        
        documents.add(searchDoc);
    }
    
    System.out.println("[DONE] Fetched " + documents.size() + " documents from GitHub");
    return documents;
    
}

Создание агента знаний

Чтобы подключить поиск Azure AI к gpt-5-mini развертыванию и сосредоточиться на индексе earth_at_night во время выполнения запроса, вам потребуется интеллектуальный агент. Следующий код определяет агент знаний с именем earth-search-agent , который использует определение агента для обработки запросов и получения соответствующих документов из earth_at_night индекса.

Чтобы обеспечить релевантные и семантически значимые ответы, defaultRerankerThreshold устанавливается так, чтобы исключать ответы с оценкой ререйтинга 2.5 или ниже.

ObjectMapper mapper = new ObjectMapper();
ObjectNode agentDefinition = mapper.createObjectNode();
agentDefinition.put("name", AGENT_NAME);
agentDefinition.put("description", "Knowledge agent for Earth at Night e-book content");

ObjectNode model = mapper.createObjectNode();
model.put("kind", "azureOpenAI");
ObjectNode azureOpenAIParams = mapper.createObjectNode();
azureOpenAIParams.put("resourceUri", AZURE_OPENAI_ENDPOINT);
azureOpenAIParams.put("deploymentId", AZURE_OPENAI_GPT_DEPLOYMENT);
azureOpenAIParams.put("modelName", AZURE_OPENAI_GPT_MODEL);
model.set("azureOpenAIParameters", azureOpenAIParams);
agentDefinition.set("models", mapper.createArrayNode().add(model));

ObjectNode targetIndex = mapper.createObjectNode();
targetIndex.put("indexName", INDEX_NAME);
targetIndex.put("defaultRerankerThreshold", 2.5);
agentDefinition.set("targetIndexes", mapper.createArrayNode().add(targetIndex));

String token = getAccessToken(credential, "https://search.azure.com/.default");

java.net.http.HttpClient httpClient = java.net.http.HttpClient.newHttpClient();
java.net.http.HttpRequest request = java.net.http.HttpRequest.newBuilder()
    .uri(URI.create(SEARCH_ENDPOINT + "/agents/" + AGENT_NAME + "?api-version=" + SEARCH_API_VERSION))
    .header("Content-Type", "application/json")
    .header("Authorization", "Bearer " + token)
    .PUT(java.net.http.HttpRequest.BodyPublishers.ofString(mapper.writeValueAsString(agentDefinition)))
    .build();

java.net.http.HttpResponse<String> response = httpClient.send(request,
    java.net.http.HttpResponse.BodyHandlers.ofString());

Настройка сообщений

Сообщения — это входные данные для маршрута извлечения и содержат журнал бесед. Каждое сообщение включает роль, которая указывает его происхождение, например помощник или пользователь, и содержимое на естественном языке. Используемый LLM определяет допустимые роли.

Сообщение пользователя представляет обрабатываемый запрос, а сообщение помощника направляет агента знаний относительно того, как реагировать. Во время процесса извлечения эти сообщения отправляются в LLM для извлечения соответствующих ответов из индексированных документов.

Это сообщение помощника предписывает earth-search-agent ответить на вопросы о Земле ночью, ссылаться на источники с их помощью ref_idи отвечать на "Я не знаю", когда ответы недоступны.

List<Map<String, String>> messages = new ArrayList<>();

Map<String, String> systemMessage = new HashMap<>();
systemMessage.put("role", "system");
systemMessage.put("content", "A Q&A agent that can answer questions about the Earth at night.\n" +
    "Sources have a JSON format with a ref_id that must be cited in the answer.\n" +
    "If you do not have the answer, respond with \"I don't know\".");
messages.add(systemMessage);

Map<String, String> userMessage = new HashMap<>();
userMessage.put("role", "user");
userMessage.put("content", "Why do suburban belts display larger December brightening than urban cores even though absolute light levels are higher downtown? 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?");
messages.add(userMessage);

Запустите поток извлечения

На этом шаге выполняется поток извлечения для получения релевантной информации из вашего индекса поиска. В зависимости от сообщений и параметров запроса на получение, LLM:

Анализирует всю историю бесед, чтобы определить необходимые сведения.
Разбивает составной запрос пользователя на целенаправленные подзапросы.
Выполняет каждый подзапрос одновременно с текстовыми полями и векторными представлениями в вашем индексе.
Использует семантический рангировщик для повторной сортировки результатов всех подзапросов.
Объединяет результаты в одну строку.

Следующий код отправляет двухчастный пользовательский запрос earth-search-agent, который декомпозирует запрос на вложенные запросы, выполняет вложенные запросы как по текстовым полям, так и по векторным внедрениям в earth_at_night индекс, и затем ранжирует и объединяет результаты. Затем ответ добавляется в список messages.

ObjectMapper mapper = new ObjectMapper();
ObjectNode retrievalRequest = mapper.createObjectNode();

// Convert messages to the correct format expected by the Knowledge agent
com.fasterxml.jackson.databind.node.ArrayNode agentMessages = mapper.createArrayNode();
for (Map<String, String> msg : messages) {
    ObjectNode agentMessage = mapper.createObjectNode();
    agentMessage.put("role", msg.get("role"));
    
    com.fasterxml.jackson.databind.node.ArrayNode content = mapper.createArrayNode();
    ObjectNode textContent = mapper.createObjectNode();
    textContent.put("type", "text");
    textContent.put("text", msg.get("content"));
    content.add(textContent);
    agentMessage.set("content", content);
    
    agentMessages.add(agentMessage);
}
retrievalRequest.set("messages", agentMessages);

com.fasterxml.jackson.databind.node.ArrayNode targetIndexParams = mapper.createArrayNode();
ObjectNode indexParam = mapper.createObjectNode();
indexParam.put("indexName", INDEX_NAME);
indexParam.put("rerankerThreshold", 2.5);
indexParam.put("maxDocsForReranker", 100);
indexParam.put("includeReferenceSourceData", true);
targetIndexParams.add(indexParam);
retrievalRequest.set("targetIndexParams", targetIndexParams);

String token = getAccessToken(credential, "https://search.azure.com/.default");

java.net.http.HttpClient httpClient = java.net.http.HttpClient.newHttpClient();
java.net.http.HttpRequest request = java.net.http.HttpRequest.newBuilder()
    .uri(URI.create(SEARCH_ENDPOINT + "/agents/" + AGENT_NAME + "/retrieve?api-version=" + SEARCH_API_VERSION))
    .header("Content-Type", "application/json")
    .header("Authorization", "Bearer " + token)
    .POST(java.net.http.HttpRequest.BodyPublishers.ofString(mapper.writeValueAsString(retrievalRequest)))
    .build();

java.net.http.HttpResponse<String> response = httpClient.send(request,
    java.net.http.HttpResponse.BodyHandlers.ofString());

Просмотр ответа, действия и результатов

Теперь вы хотите отобразить ответ, активность и результаты конвейера извлечения.

Каждый ответ на запрос из поиска ИИ Azure включает:

Единая строка, представляющая данные об основе результатов поиска.
План запроса.
Справочные данные, показывающие, какие блоки исходных документов способствовали единой строке.

ObjectMapper mapper = new ObjectMapper();
        
// Log activities
System.out.println("\nActivities:");
if (responseJson.has("activity") && responseJson.get("activity").isArray()) {
    for (JsonNode activity : responseJson.get("activity")) {
        String activityType = "UnknownActivityRecord";
        if (activity.has("InputTokens")) {
            activityType = "KnowledgeAgentModelQueryPlanningActivityRecord";
        } else if (activity.has("TargetIndex")) {
            activityType = "KnowledgeAgentSearchActivityRecord";
        } else if (activity.has("QueryTime")) {
            activityType = "KnowledgeAgentSemanticRankerActivityRecord";
        }
        
        System.out.println("Activity Type: " + activityType);
        try {
            System.out.println(mapper.writerWithDefaultPrettyPrinter().writeValueAsString(activity));
        } catch (Exception e) {
            System.out.println(activity.toString());
        }
    }
}

// Log results
System.out.println("Results");
if (responseJson.has("references") && responseJson.get("references").isArray()) {
    for (JsonNode reference : responseJson.get("references")) {
        String referenceType = "KnowledgeAgentAzureSearchDocReference";
        
        System.out.println("Reference Type: " + referenceType);
        try {
            System.out.println(mapper.writerWithDefaultPrettyPrinter().writeValueAsString(reference));
        } catch (Exception e) {
            System.out.println(reference.toString());
        }
    }
}

Выходные данные должны включать:

Response предоставляет текстовую строку наиболее релевантных документов (или фрагментов) в индексе поиска на основе запроса пользователя. Как показано далее в этом кратком руководстве, вы можете передать эту строку в LLM для создания ответов.
Activity отслеживает шаги, выполненные во время процесса извлечения, включая подзапросы, созданные развертыванием gpt-5-mini, и токены, используемые для планирования запросов и выполнения.
Results перечисляет документы, использованные для создания ответа, каждый из которых определяется своим DocKey.

Создание клиента Azure OpenAI

Чтобы расширить конвейер от извлечения ответов до генерации ответов, настройте клиент Azure OpenAI для взаимодействия с вашим gpt-5-mini развертыванием.

OpenAIAsyncClient openAIClient = new OpenAIClientBuilder()
    .endpoint(AZURE_OPENAI_ENDPOINT)
    .credential(credential)
    .buildAsyncClient();

Создание ответа с помощью API завершения чата

Одним из вариантов создания ответов является API завершения чата, который передает журнал бесед в LLM для обработки.

List<ChatRequestMessage> chatMessages = new ArrayList<>();
for (Map<String, String> msg : messages) {
    String role = msg.get("role");
    String content = msg.get("content");
    
    switch (role) {
        case "system":
            chatMessages.add(new ChatRequestSystemMessage(content));
            break;
        case "user":
            chatMessages.add(new ChatRequestUserMessage(content));
            break;
        case "assistant":
            chatMessages.add(new ChatRequestAssistantMessage(content));
            break;
    }
}

ChatCompletionsOptions chatOptions = new ChatCompletionsOptions(chatMessages)
    .setMaxTokens(1000)
    .setTemperature(0.7);

ChatCompletions completion = openAIClient.getChatCompletions(AZURE_OPENAI_GPT_DEPLOYMENT, chatOptions).block();

Продолжить беседу

Продолжите беседу, отправив ещё один пользовательский запрос на earth-search-agent. Следующий код повторно запускает конвейер извлечения, извлекает соответствующее содержимое из earth_at_night индекса и добавляет ответ в messages список. Однако в отличие от этого, теперь можно использовать клиент Azure OpenAI для создания ответа на основе полученного содержимого.

String followUpQuestion = "How do I find lava at night?";
System.out.println("[QUESTION] Follow-up question: " + followUpQuestion);

Map<String, String> userMessage = new HashMap<>();
userMessage.put("role", "user");
userMessage.put("content", followUpQuestion);
messages.add(userMessage);

Очистите ресурсы

При работе с собственной подпиской рекомендуется завершить проект, определив, необходимы ли вам по-прежнему созданные ресурсы. Ресурсы, оставленные работающими, могут стоить вам денег. Вы можете удалить ресурсы по отдельности или удалить группу ресурсов, чтобы удалить весь набор ресурсов.

На портале Azure можно найти ресурсы и управлять ими, выбрав все ресурсы или группы ресурсов на левой панели. Вы также можете запустить следующий код, чтобы удалить объекты, созданные в этом кратком руководстве.

Удалите агента знаний

Агент знаний, созданный в этом кратком руководстве, был удален с помощью следующего примера кода:

String token = getAccessToken(credential, "https://search.azure.com/.default");
            
java.net.http.HttpClient httpClient = java.net.http.HttpClient.newHttpClient();
java.net.http.HttpRequest request = java.net.http.HttpRequest.newBuilder()
    .uri(URI.create(SEARCH_ENDPOINT + "/agents/" + AGENT_NAME + "?api-version=" + SEARCH_API_VERSION))
    .header("Authorization", "Bearer " + token)
    .DELETE()
    .build();

java.net.http.HttpResponse<String> response = httpClient.send(request,
    java.net.http.HttpResponse.BodyHandlers.ofString());

Удаление индекса поиска

Индекс поиска, созданный в этом кратком руководстве, был удален с помощью следующего фрагмента кода:

indexClient.deleteIndex(INDEX_NAME);
System.out.println("[DONE] Search index '" + INDEX_NAME + "' deleted successfully.");

Замечание

Подсказка

В этом быстром старте JavaScript используется версия REST API 2025-05-01-preview, которая использует предыдущую терминологию «агент знаний» и не поддерживает последние функции, доступные в версии 2025-11-01-preview. Сведения об использовании этих функций см. в версии C#, Python или REST.

Предпосылки

Учетная запись Azure с активной подпиской. Создайте учетную запись бесплатно .
Служба поиска ИИ Azure в любой регионе, который предоставляет агентивное извлечение.
Проект Microsoft Foundry. При создании проекта Foundry вы получите ресурс Foundry (который требуется для развертываний моделей).
Azure CLI для проверки подлинности без ключа с помощью идентификатора Microsoft Entra.

Настройка доступа

Чтобы настроить доступ для этого краткого руководства, выберите оба следующих вкладки.

Поиск по искусственному интеллекту Azure
Microsoft Foundry

В службе поиска по искусственному интеллекту Azure:

Включите доступ на основе ролей.
Создайте управляемое удостоверение, назначаемое системой.
Назначьте следующие роли себе.
- Участник службы поиска
- Участник данных индекса поиска
- Средство чтения индексов поиска

Это важно

Агентное извлечение имеет две модели выставления счетов на основе токенов.

Выставление счетов за агентный поиск в Azure AI.
Выставление счетов из Azure OpenAI для планирования запросов и синтеза ответов.

Для получения дополнительной информации см. Доступность и цены агентских запросов.

Получение конечных точек

Чтобы получить конечные точки для этого краткого руководства, выберите оба следующих вкладки.

Поиск по искусственному интеллекту Azure
Microsoft Foundry

Войдите на портал Azure и выберите службу поиска.
В левой области выберите "Обзор".
Запишите конечную точку, которая должна выглядеть следующим https://my-service.search.windows.netобразом.

Войдите на портал Azure и выберите ресурс Foundry.
В левой области выберите ключи управления> ресурсамии конечную точку.
Выберите вкладку OpenAI .
Запишите конечную точку, которая должна выглядеть следующим https://my-resource.openai.azure.comобразом.

Развертывание моделей

Чтобы использовать агентное извлечение, необходимо развернуть две модели Azure OpenAI в проекте Foundry.

Модель внедрения для преобразования текста в вектор. В этом кратком руководстве используется text-embedding-3-large, но вы можете использовать какую-либо модель text-embedding.
LLM для планирования запросов и создания ответов. В этом кратком введении используется gpt-5-mini, но вы можете использовать любой поддерживаемый LLM для агентного извлечения.

Инструкции по развертыванию см. в статье "Развертывание моделей Azure OpenAI с помощью Foundry".

Настройка среды

Создайте новую папку quickstart-agentic-retrieval для хранения приложения и откройте Visual Studio Code в этой папке с помощью следующей команды:
```
mkdir quickstart-agentic-retrieval && cd quickstart-agentic-retrieval
```
Создайте package.json с помощью следующей команды:
```
npm init -y
```
Установите клиентскую библиотеку поиска ИИ Azure (Azure.Search.Documents) для JavaScript:
```
npm install @azure/search-documents --version 12.2.0-alpha.20250606.1
```
Установите клиентская библиотека Azure OpenAI с помощью:
```
npm install @azure/openai --version 5.10.1
```
Установите пакет dotenv для загрузки переменных среды из файла .env с помощью:
```
npm install dotenv
```
Для рекомендуемой проверки подлинности без ключа с помощью идентификатора Microsoft Entra установите клиентская библиотека удостоверений Azure с помощью:
```
npm install @azure/identity
```

Запустите код

Создайте файл с именем .env в папке quickstart-agentic-retrieval и добавьте следующие переменные среды:
```
AZURE_OPENAI_ENDPOINT=https://<your-ai-foundry-resource-name>.openai.azure.com/
AZURE_OPENAI_GPT_DEPLOYMENT=gpt-5-mini
AZURE_OPENAI_EMBEDDING_DEPLOYMENT=text-embedding-3-large
AZURE_SEARCH_ENDPOINT=https://<your-search-service-name>.search.windows.net
AZURE_SEARCH_INDEX_NAME=agentic-retrieval-sample
```
Замените <your-search-service-name> на фактическое имя службы Azure AI Search и <your-ai-foundry-resource-name> на имя ресурса Foundry.

Вставьте следующий код в новый файл с именем index.js:

import { DefaultAzureCredential, getBearerTokenProvider } from '@azure/identity';
import { SearchIndexClient, SearchClient } from '@azure/search-documents';
import { AzureOpenAI } from "openai/index.mjs";
// Load the .env file if it exists
import * as dotenv from "dotenv";
dotenv.config();
// Configuration - Update these values for your environment
const config = {
    searchEndpoint: process.env.AZURE_SEARCH_ENDPOINT || "https://your-search-service.search.windows.net",
    azureOpenAIEndpoint: process.env.AZURE_OPENAI_ENDPOINT || "https://your-ai-foundry-resource.openai.azure.com/",
    azureOpenAIGptDeployment: process.env.AZURE_OPENAI_GPT_DEPLOYMENT || "gpt-5-mini",
    azureOpenAIGptModel: "gpt-5-mini",
    azureOpenAIApiVersion: process.env.OPENAI_API_VERSION || "2025-03-01-preview",
    azureOpenAIEmbeddingDeployment: process.env.AZURE_OPENAI_EMBEDDING_DEPLOYMENT || "text-embedding-3-large",
    azureOpenAIEmbeddingModel: "text-embedding-3-large",
    indexName: "earth_at_night",
    agentName: "earth-search-agent",
    searchApiVersion: "2025-05-01-Preview"
};
async function main() {
    try {
        console.log("🚀 Starting Azure AI Search agentic retrieval quickstart...\n");
        // Initialize Azure credentials using managed identity (recommended)
        const credential = new DefaultAzureCredential();
        // Create search clients
        const searchIndexClient = new SearchIndexClient(config.searchEndpoint, credential);
        const searchClient = new SearchClient(config.searchEndpoint, config.indexName, credential);
        // Create Azure OpenAI client
        const scope = "https://cognitiveservices.azure.com/.default";
        const azureADTokenProvider = getBearerTokenProvider(credential, scope);
        const openAIClient = new AzureOpenAI({
            endpoint: config.azureOpenAIEndpoint,
            apiVersion: config.azureOpenAIApiVersion,
            azureADTokenProvider,
        });
        // Create search index with vector and semantic capabilities
        await createSearchIndex(searchIndexClient);
        // Upload sample documents
        await uploadDocuments(searchClient);
        // Create knowledge agent for agentic retrieval
        await createKnowledgeAgent(credential);
        // Run agentic retrieval with conversation
        await runAgenticRetrieval(credential, openAIClient);
        // Clean up - Delete knowledge agent and search index
        await deleteKnowledgeAgent(credential);
        await deleteSearchIndex(searchIndexClient);
        console.log("✅ Quickstart completed successfully!");
    }
    catch (error) {
        console.error("❌ Error in main execution:", error);
        throw error;
    }
}
async function createSearchIndex(indexClient) {
    console.log("📊 Creating search index...");
    const index = {
        name: config.indexName,
        fields: [
            {
                name: "id",
                type: "Edm.String",
                key: true,
                filterable: true,
                sortable: true,
                facetable: true
            },
            {
                name: "page_chunk",
                type: "Edm.String",
                searchable: true,
                filterable: false,
                sortable: false,
                facetable: false
            },
            {
                name: "page_embedding_text_3_large",
                type: "Collection(Edm.Single)",
                searchable: true,
                filterable: false,
                sortable: false,
                facetable: false,
                vectorSearchDimensions: 3072,
                vectorSearchProfileName: "hnsw_text_3_large"
            },
            {
                name: "page_number",
                type: "Edm.Int32",
                filterable: true,
                sortable: true,
                facetable: true
            }
        ],
        vectorSearch: {
            profiles: [
                {
                    name: "hnsw_text_3_large",
                    algorithmConfigurationName: "alg",
                    vectorizerName: "azure_openai_text_3_large"
                }
            ],
            algorithms: [
                {
                    name: "alg",
                    kind: "hnsw"
                }
            ],
            vectorizers: [
                {
                    vectorizerName: "azure_openai_text_3_large",
                    kind: "azureOpenAI",
                    parameters: {
                        resourceUrl: config.azureOpenAIEndpoint,
                        deploymentId: config.azureOpenAIEmbeddingDeployment,
                        modelName: config.azureOpenAIEmbeddingModel
                    }
                }
            ]
        },
        semanticSearch: {
            defaultConfigurationName: "semantic_config",
            configurations: [
                {
                    name: "semantic_config",
                    prioritizedFields: {
                        contentFields: [
                            { name: "page_chunk" }
                        ]
                    }
                }
            ]
        }
    };
    try {
        await indexClient.createOrUpdateIndex(index);
        console.log(`✅ Index '${config.indexName}' created or updated successfully.`);
    }
    catch (error) {
        console.error("❌ Error creating index:", error);
        throw error;
    }
}
async function deleteSearchIndex(indexClient) {
    console.log("🗑️ Deleting search index...");
    try {
        await indexClient.deleteIndex(config.indexName);
        console.log(`✅ Search index '${config.indexName}' deleted successfully.`);
    }
    catch (error) {
        if (error?.statusCode === 404 || error?.code === 'IndexNotFound') {
            console.log(`ℹ️ Search index '${config.indexName}' does not exist or was already deleted.`);
            return;
        }
        console.error("❌ Error deleting search index:", error);
        throw error;
    }
}
// Fetch Earth at Night documents from GitHub
async function fetchEarthAtNightDocuments() {
    console.log("📡 Fetching Earth at Night documents from GitHub...");
    const documentsUrl = "https://raw.githubusercontent.com/Azure-Samples/azure-search-sample-data/refs/heads/main/nasa-e-book/earth-at-night-json/documents.json";
    try {
        const response = await fetch(documentsUrl);
        if (!response.ok) {
            throw new Error(`Failed to fetch documents: ${response.status} ${response.statusText}`);
        }
        const documents = await response.json();
        console.log(`✅ Fetched ${documents.length} documents from GitHub`);
        // Validate and transform documents to match our interface
        const transformedDocuments = documents.map((doc, index) => {
            return {
                id: doc.id || String(index + 1),
                page_chunk: doc.page_chunk || doc.content || '',
                page_embedding_text_3_large: doc.page_embedding_text_3_large || new Array(3072).fill(0.1),
                page_number: doc.page_number || index + 1
            };
        });
        return transformedDocuments;
    }
    catch (error) {
        console.error("❌ Error fetching documents from GitHub:", error);
        console.log("🔄 Falling back to sample documents...");
        // Fallback to sample documents if fetch fails
        return [
            {
                id: "1",
                page_chunk: "The Earth at night reveals the patterns of human settlement and economic activity. City lights trace the contours of civilization, creating a luminous map of where people live and work.",
                page_embedding_text_3_large: new Array(3072).fill(0.1),
                page_number: 1
            },
            {
                id: "2",
                page_chunk: "From space, the aurora borealis appears as shimmering curtains of green and blue light dancing across the polar regions.",
                page_embedding_text_3_large: new Array(3072).fill(0.2),
                page_number: 2
            }
            // Add more fallback documents as needed
        ];
    }
}
async function uploadDocuments(searchClient) {
    console.log("📄 Uploading documents...");
    try {
        // Fetch documents from GitHub
        const documents = await fetchEarthAtNightDocuments();
        const result = await searchClient.uploadDocuments(documents);
        console.log(`✅ Uploaded ${result.results.length} documents successfully.`);
        // Wait for indexing to complete
        console.log("⏳ Waiting for document indexing to complete...");
        await new Promise(resolve => setTimeout(resolve, 5000));
        console.log("✅ Document indexing completed.");
    }
    catch (error) {
        console.error("❌ Error uploading documents:", error);
        throw error;
    }
}
async function createKnowledgeAgent(credential) {
    // In case the agent already exists, delete it first
    await deleteKnowledgeAgent(credential);
    console.log("🤖 Creating knowledge agent...");
    const agentDefinition = {
        name: config.agentName,
        description: "Knowledge agent for Earth at Night e-book content",
        models: [
            {
                kind: "azureOpenAI",
                azureOpenAIParameters: {
                    resourceUri: config.azureOpenAIEndpoint,
                    deploymentId: config.azureOpenAIGptDeployment,
                    modelName: config.azureOpenAIGptModel
                }
            }
        ],
        targetIndexes: [
            {
                indexName: config.indexName,
                defaultRerankerThreshold: 2.5
            }
        ]
    };
    try {
        const token = await getAccessToken(credential, "https://search.azure.com/.default");
        const response = await fetch(`${config.searchEndpoint}/agents/${config.agentName}?api-version=${config.searchApiVersion}`, {
            method: 'PUT',
            headers: {
                'Content-Type': 'application/json',
                'Authorization': `Bearer ${token}`
            },
            body: JSON.stringify(agentDefinition)
        });
        if (!response.ok) {
            const errorText = await response.text();
            throw new Error(`Failed to create knowledge agent: ${response.status} ${response.statusText}\n${errorText}`);
        }
        console.log(`✅ Knowledge agent '${config.agentName}' created successfully.`);
    }
    catch (error) {
        console.error("❌ Error creating knowledge agent:", error);
        throw error;
    }
}
async function runAgenticRetrieval(credential, openAIClient) {
    console.log("🔍 Running agentic retrieval...");
    const messages = [
        {
            role: "system",
            content: `A Q&A agent that can answer questions about the Earth at night.
Sources have a JSON format with a ref_id that must be cited in the answer.
If you do not have the answer, respond with "I don't know".`
        },
        {
            role: "user",
            content: "Why do suburban belts display larger December brightening than urban cores even though absolute light levels are higher downtown? 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?"
        }
    ];
    try {
        // Call agentic retrieval API
        const userMessages = messages.filter(m => m.role !== "system");
        const retrievalResponse = await callAgenticRetrieval(credential, userMessages);
        // Extract the assistant response from agentic retrieval
        let assistantContent = '';
        if (typeof retrievalResponse.response === 'string') {
            assistantContent = retrievalResponse.response;
        }
        else if (Array.isArray(retrievalResponse.response)) {
            assistantContent = JSON.stringify(retrievalResponse.response);
        }
        // Add assistant response to conversation history
        messages.push({
            role: "assistant",
            content: assistantContent
        });
        console.log(assistantContent);
        // Log activities and results...
        console.log("\nActivities:");
        if (retrievalResponse.activity && Array.isArray(retrievalResponse.activity)) {
            retrievalResponse.activity.forEach((activity) => {
                const activityType = activity.activityType || activity.type || 'UnknownActivityRecord';
                console.log(`Activity Type: ${activityType}`);
                console.log(JSON.stringify(activity, null, 2));
            });
        }
        console.log("Results");
        if (retrievalResponse.references && Array.isArray(retrievalResponse.references)) {
            retrievalResponse.references.forEach((reference) => {
                const referenceType = reference.referenceType || reference.type || 'AzureSearchDoc';
                console.log(`Reference Type: ${referenceType}`);
                console.log(JSON.stringify(reference, null, 2));
            });
        }
        // Now do chat completion with full conversation history
        await generateFinalAnswer(openAIClient, messages);
        // Continue conversation with second question
        await continueConversation(credential, openAIClient, messages);
    }
    catch (error) {
        console.error("❌ Error in agentic retrieval:", error);
        throw error;
    }
}
async function generateFinalAnswer(openAIClient, messages) {
    console.log("\n[ASSISTANT]: ");
    try {
        const completion = await openAIClient.chat.completions.create({
            model: config.azureOpenAIGptDeployment,
            messages: messages.map(m => ({ role: m.role, content: m.content })),
            max_tokens: 1000,
            temperature: 0.7
        });
        const answer = completion.choices[0].message.content;
        console.log(answer?.replace(/\./g, "\n"));
        // Add this response to conversation history
        if (answer) {
            messages.push({
                role: "assistant",
                content: answer
            });
        }
    }
    catch (error) {
        console.error("❌ Error generating final answer:", error);
        throw error;
    }
}
async function callAgenticRetrieval(credential, messages) {
    // Convert messages to the correct format expected by the Knowledge agent
    const agentMessages = messages.map(msg => ({
        role: msg.role,
        content: [
            {
                type: "text",
                text: msg.content
            }
        ]
    }));
    const retrievalRequest = {
        messages: agentMessages,
        targetIndexParams: [
            {
                indexName: config.indexName,
                rerankerThreshold: 2.5,
                maxDocsForReranker: 100,
                includeReferenceSourceData: true
            }
        ]
    };
    const token = await getAccessToken(credential, "https://search.azure.com/.default");
    const response = await fetch(`${config.searchEndpoint}/agents/${config.agentName}/retrieve?api-version=${config.searchApiVersion}`, {
        method: 'POST',
        headers: {
            'Content-Type': 'application/json',
            'Authorization': `Bearer ${token}`
        },
        body: JSON.stringify(retrievalRequest)
    });
    if (!response.ok) {
        const errorText = await response.text();
        throw new Error(`Agentic retrieval failed: ${response.status} ${response.statusText}\n${errorText}`);
    }
    return await response.json();
}
async function deleteKnowledgeAgent(credential) {
    console.log("🗑️ Deleting knowledge agent...");
    try {
        const token = await getAccessToken(credential, "https://search.azure.com/.default");
        const response = await fetch(`${config.searchEndpoint}/agents/${config.agentName}?api-version=${config.searchApiVersion}`, {
            method: 'DELETE',
            headers: {
                'Authorization': `Bearer ${token}`
            }
        });
        if (!response.ok) {
            if (response.status === 404) {
                console.log(`ℹ️ Knowledge agent '${config.agentName}' does not exist or was already deleted.`);
                return;
            }
            const errorText = await response.text();
            throw new Error(`Failed to delete knowledge agent: ${response.status} ${response.statusText}\n${errorText}`);
        }
        console.log(`✅ Knowledge agent '${config.agentName}' deleted successfully.`);
    }
    catch (error) {
        console.error("❌ Error deleting knowledge agent:", error);
        throw error;
    }
}
async function continueConversation(credential, openAIClient, messages) {
    console.log("\n💬 === Continuing Conversation ===");
    // Add follow-up question
    const followUpQuestion = "How do I find lava at night?";
    console.log(`❓ Follow-up question: ${followUpQuestion}`);
    messages.push({
        role: "user",
        content: followUpQuestion
    });
    try {
        // Don't include system messages in this retrieval
        const userAssistantMessages = messages.filter((m) => m.role !== "system");
        const newRetrievalResponse = await callAgenticRetrieval(credential, userAssistantMessages);
        // Extract assistant response and add to conversation
        let assistantContent = '';
        if (typeof newRetrievalResponse.response === 'string') {
            assistantContent = newRetrievalResponse.response;
        }
        else if (Array.isArray(newRetrievalResponse.response)) {
            assistantContent = JSON.stringify(newRetrievalResponse.response);
        }
        // Add assistant response to conversation history
        messages.push({
            role: "assistant",
            content: assistantContent
        });
        console.log(assistantContent);
        // Log activities and results like the first retrieval
        console.log("\nActivities:");
        if (newRetrievalResponse.activity && Array.isArray(newRetrievalResponse.activity)) {
            newRetrievalResponse.activity.forEach((activity) => {
                const activityType = activity.activityType || activity.type || 'UnknownActivityRecord';
                console.log(`Activity Type: ${activityType}`);
                console.log(JSON.stringify(activity, null, 2));
            });
        }
        console.log("Results");
        if (newRetrievalResponse.references && Array.isArray(newRetrievalResponse.references)) {
            newRetrievalResponse.references.forEach((reference) => {
                const referenceType = reference.referenceType || reference.type || 'AzureSearchDoc';
                console.log(`Reference Type: ${referenceType}`);
                console.log(JSON.stringify(reference, null, 2));
            });
        }
        // Generate final answer for follow-up
        await generateFinalAnswer(openAIClient, messages);
        console.log("\n🎉 === Conversation Complete ===");
    }
    catch (error) {
        console.error("❌ Error in conversation continuation:", error);
        throw error;
    }
}
async function getAccessToken(credential, scope) {
    const tokenResponse = await credential.getToken(scope);
    return tokenResponse.token;
}
// Error handling wrapper
async function runWithErrorHandling() {
    try {
        await main();
    }
    catch (error) {
        console.error("💥 Application failed:", error);
        process.exit(1);
    }
}
// Execute the application - ES module style
runWithErrorHandling();
export { main, createSearchIndex, deleteSearchIndex, fetchEarthAtNightDocuments, uploadDocuments, createKnowledgeAgent, deleteKnowledgeAgent, runAgenticRetrieval };

Войдите в Azure с помощью следующей команды:
```
az login
```
Запустите код JavaScript со следующей командой:
```
node index.js
```

Выходные данные

Выходные данные приложения должны выглядеть следующим образом:

[[email protected]] injecting env (0) from .env (tip: ⚙️  override existing env vars with { override: true })
🚀 Starting Azure AI Search agentic retrieval quickstart...

📊 Creating search index...
✅ Index 'earth_at_night' created or updated successfully.
📄 Uploading documents...
📡 Fetching Earth at Night documents from GitHub...
✅ Fetched 194 documents from GitHub
✅ Uploaded 194 documents successfully.
⏳ Waiting for document indexing to complete...
✅ Document indexing completed.
🗑️ Deleting knowledge agent...
ℹ️ Knowledge agent 'earth-search-agent' does not exist or was already deleted.
🤖 Creating knowledge agent...
✅ Knowledge agent 'earth-search-agent' created successfully.
🔍 Running agentic retrieval...
[{"role":"assistant","content":[{"type":"text","text":"[]"}]}]

Activities:
Activity Type: ModelQueryPlanning
{
  "type": "ModelQueryPlanning",
  "id": 0,
  "inputTokens": 1379,
  "outputTokens": 551
}
Activity Type: AzureSearchQuery
{
  "type": "AzureSearchQuery",
  "id": 1,
  "targetIndex": "earth_at_night",
  "query": {
    "search": "Why do suburban areas show greater December brightening compared to urban cores despite higher absolute light levels downtown?",
    "filter": null
  },
  "queryTime": "2025-07-20T16:12:59.804Z",
  "count": 0,
  "elapsedMs": 549
}
Activity Type: AzureSearchQuery
{
  "type": "AzureSearchQuery",
  "id": 2,
  "targetIndex": "earth_at_night",
  "query": {
    "search": "Why is the Phoenix nighttime street grid sharply visible from space, while large stretches of interstate highways between Midwestern cities appear comparatively dim?",
    "filter": null
  },
  "queryTime": "2025-07-20T16:13:00.061Z",
  "count": 0,
  "elapsedMs": 256
}
Activity Type: AzureSearchSemanticRanker
{
  "type": "AzureSearchSemanticRanker",
  "id": 3,
  "inputTokens": 47630
}
Results

[ASSISTANT]: 
Suburban belts show larger December brightening than urban cores despite higher absolute light levels downtown because suburban areas often have more seasonal variation in lighting usage, such as increased decorative and outdoor lighting during the holiday season in December
 Urban cores typically have more constant and dense lighting throughout the year, so the relative increase in brightness during December is less pronounced compared to suburban areas
\n\nThe Phoenix nighttime street grid is sharply visible from space because the city has a well-planned, extensive grid of streets with consistent and bright street lighting
 In contrast, large stretches of interstate highways between Midwestern cities appear comparatively dim because these highways have less continuous lighting and lower intensity lights, making them less visible from space
\n\n(Note: These explanations are based on general knowledge about urban lighting patterns and visibility from space; specific studies or sources were not provided
)

💬 === Continuing Conversation ===
❓ Follow-up question: How do I find lava at night?
[{"role":"assistant","content":[{"type":"text","text":"[{\"ref_id\":0,\"content\":\"<!-- PageHeader=\\\"Volcanoes\\\" -->\\n\\n### Nighttime Glow at Mount Etna - Italy\\n\\nAt about 2:30 a.m. local time on March 16, 2017, the VIIRS DNB on the Suomi NPP satellite captured this nighttime image of lava flowing on Mount Etna in Sicily, Italy. Etna is one of the world's most active volcanoes.\\n\\n#### Figure: Location of Mount Etna\\nA world globe is depicted, with a marker indicating the location of Mount Etna in Sicily, Italy, in southern Europe near the center of the Mediterranean Sea.\\n\\n<!-- PageFooter=\\\"Earth at Night\\\" -->\\n<!-- PageNumber=\\\"48\\\" -->\"},{\"ref_id\":1,\"content\":\"<!-- PageHeader=\\\"Volcanoes\\\" -->\\n\\n## Volcanoes\\n\\n### The Infrared Glows of Kilauea's Lava Flows—Hawaii\\n\\nIn early May 2018, an eruption on Hawaii's Kilauea volcano began to unfold. The eruption took a dangerous turn on May 3, 2018, when new fissures opened in the residential neighborhood of Leilani Estates. During the summer-long eruptive event, other fissures emerged along the East Rift Zone. Lava from vents along the rift zone flowed downslope, reaching the ocean in several areas, and filling in Kapoho Bay.\\n\\nA time series of Landsat 8 imagery shows the progression of the lava flows from May 16 to August 13. The night view combines thermal, shortwave infrared, and near-infrared wavelengths to tease out the very hot lava (bright white), cooling lava (red), and lava flows obstructed by clouds (purple).\\n\\n#### Figure: Location of Kilauea Volcano, Hawaii\\n\\nA globe is shown centered on North America, with a marker placed in the Pacific Ocean indicating the location of Hawaii, to the southwest of the mainland United States.\\n\\n<!-- PageFooter=\\\"Earth at Night\\\" -->\\n<!-- PageNumber=\\\"44\\\" -->\"},{\"ref_id\":2,\"content\":\"For the first time in perhaps a decade, Mount Etna experienced a \\\"flank eruption\\\"—erupting from its side instead of its summit—on December 24, 2018. The activity was accompanied by 130 earthquakes occurring over three hours that morning. Mount Etna, Europe’s most active volcano, has seen periodic activity on this part of the mountain since 2013. The Operational Land Imager (OLI) on the Landsat 8 satellite acquired the main image of Mount Etna on December 28, 2018.\\n\\nThe inset image highlights the active vent and thermal infrared signature from lava flows, which can be seen near the newly formed fissure on the southeastern side of the volcano. The inset was created with data from OLI and the Thermal Infrared Sensor (TIRS) on Landsat 8. Ash spewing from the fissure cloaked adjacent villages and delayed aircraft from landing at the nearby Catania airport. Earthquakes occurred in the subsequent days after the initial eruption and displaced hundreds of people from their homes.\\n\\nFor nighttime images of Mount Etna’s March 2017 eruption, see pages 48–51.\\n\\n---\\n\\n### Hazards of Volcanic Ash Plumes and Satellite Observation\\n\\nWith the help of moonlight, satellite instruments can track volcanic ash plumes, which present significant hazards to airplanes in flight. The volcanic ash—composed of tiny pieces of glass and rock—is abrasive to engine turbine blades, and can melt on the blades and other engine parts, causing damage and even engine stalls. This poses a danger to both the plane’s integrity and passenger safety. Volcanic ash also reduces visibility for pilots and can cause etching of windshields, further reducing pilots’ ability to see. Nightlight images can be combined with thermal images to provide a more complete view of volcanic activity on Earth’s surface.\\n\\nThe VIIRS Day/Night Band (DNB) on polar-orbiting satellites uses faint light sources such as moonlight, airglow (the atmosphere’s self-illumination through chemical reactions), zodiacal light (sunlight scattered by interplanetary dust), and starlight from the Milky Way. Using these dim light sources, the DNB can detect changes in clouds, snow cover, and sea ice:\\n\\n#### Table: Light Sources Used by VIIRS DNB\\n\\n| Light Source         | Description                                                                  |\\n|----------------------|------------------------------------------------------------------------------|\\n| Moonlight            | Reflected sunlight from the Moon, illuminating Earth's surface at night      |\\n| Airglow              | Atmospheric self-illumination from chemical reactions                        |\\n| Zodiacal Light       | Sunlight scattered by interplanetary dust                                    |\\n| Starlight/Milky Way  | Faint illumination provided by stars in the Milky Way                        |\\n\\nGeostationary Operational Environmental Satellites (GOES), managed by NOAA, orbit over Earth’s equator and offer uninterrupted observations of North America. High-latitude areas such as Alaska benefit from polar-orbiting satellites like Suomi NPP, which provide overlapping coverage at the poles, enabling more data collection in these regions. During polar darkness (winter months), VIIRS DNB data allow scientists to:\\n\\n- Observe sea ice formation\\n- Monitor snow cover extent at the highest latitudes\\n- Detect open water for ship navigation\\n\\n#### Table: Satellite Coverage Overview\\n\\n| Satellite Type          | Orbit           | Coverage Area         | Special Utility                              |\\n|------------------------|-----------------|----------------------|----------------------------------------------|\\n| GOES                   | Geostationary   | Equatorial/North America | Continuous regional monitoring              |\\n| Polar-Orbiting (e.g., Suomi NPP) | Polar-orbiting    | Poles/high latitudes      | Overlapping passes; useful during polar night|\\n\\n---\\n\\n### Weather Forecasting and Nightlight Data\\n\\nThe use of nightlight data by weather forecasters is growing as the VIIRS instrument enables observation of clouds at night illuminated by sources such as moonlight and lightning. Scientists use these data to study the nighttime behavior of weather systems, including severe storms, which can develop and strike populous areas at night as well as during the day. Combined with thermal data, visible nightlight data allow the detection of clouds at various heights in the atmosphere, such as dense marine fog. This capability enables weather forecasters to issue marine advisories with higher confidence, leading to greater utility. (See \\\"Marine Layer Clouds—California\\\" on page 56.)\\n\\nIn this section of the book, you will see how nightlight data are used to observe nature’s spectacular light shows across a wide range of sources.\\n\\n---\\n\\n#### Notable Data from Mount Etna Flank Eruption (December 2018)\\n\\n| Event/Observation                  | Details                                                                    |\\n|-------------------------------------|----------------------------------------------------------------------------|\\n| Date of Flank Eruption              | December 24, 2018                                                          |\\n| Number of Earthquakes               | 130 earthquakes within 3 hours                                              |\\n| Image Acquisition                   | December 28, 2018 by Landsat 8 OLI                                         |\\n| Location of Eruption                | Southeastern side of Mount Etna                                            |\\n| Thermal Imaging Data                | From OLI and TIRS (Landsat 8), highlighting active vent and lava flows     |\\n| Impact on Villages/Air Transport    | Ash covered villages; delayed aircraft at Catania airport                  |\\n| Displacement                        | Hundreds of residents displaced                                            |\\n| Ongoing Seismic Activity            | Earthquakes continued after initial eruption                               |\\n\\n---\\n\\n<!-- PageFooter=\\\"Earth at Night\\\" -->\\n<!-- PageNumber=\\\"30\\\" -->\"},{\"ref_id\":3,\"content\":\"# Volcanoes\\n\\n---\\n\\n### Mount Etna Erupts - Italy\\n\\nThe highly active Mount Etna in Italy sent red lava rolling down its flank on March 19, 2017. An astronaut onboard the ISS took the photograph below of the volcano and its environs that night. City lights surround the mostly dark volcanic area.\\n\\n---\\n\\n#### Figure 1: Location of Mount Etna, Italy\\n\\nA world map highlighting the location of Mount Etna in southern Italy. The marker indicates its geographic placement on the east coast of Sicily, Italy, in the Mediterranean region, south of mainland Europe and north of northern Africa.\\n\\n---\\n\\n#### Figure 2: Nighttime View of Mount Etna's Eruption and Surrounding Cities\\n\\nThis is a nighttime satellite image taken on March 19, 2017, showing the eruption of Mount Etna (southeastern cone) with visible bright red and orange coloring indicating flowing lava from a lateral vent. The surrounding areas are illuminated by city lights, with the following geographic references labeled:\\n\\n| Location        | Position in Image         | Visible Characteristics                    |\\n|-----------------|--------------------------|--------------------------------------------|\\n| Mt. Etna (southeastern cone) | Top center-left | Bright red/orange lava flow                |\\n| Lateral vent    | Left of the volcano       | Faint red/orange flow extending outwards   |\\n| Resort          | Below the volcano, to the left   | Small cluster of lights                    |\\n| Giarre          | Top right                 | Bright cluster of city lights              |\\n| Acireale        | Center right              | Large, bright area of city lights          |\\n| Biancavilla     | Bottom left               | Smaller cluster of city lights             |\\n\\nAn arrow pointing north is shown on the image for orientation.\\n\\n---\\n\\n<!-- Earth at Night Page Footer -->\\n<!-- Page Number: 50 -->\"},{\"ref_id\":4,\"content\":\"## Nature's Light Shows\\n\\nAt night, with the light of the Sun removed, nature's brilliant glow from Earth's surface becomes visible to the naked eye from space. Some of Earth's most spectacular light shows are natural, like the aurora borealis, or Northern Lights, in the Northern Hemisphere (aurora australis, or Southern Lights, in the Southern Hemisphere). The auroras are natural electrical phenomena caused by charged particles that race from the Sun toward Earth, inducing chemical reactions in the upper atmosphere and creating the appearance of streamers of reddish or greenish light in the sky, usually near the northern or southern magnetic pole. Other natural lights can indicate danger, like a raging forest fire encroaching on a city, town, or community, or lava spewing from an erupting volcano.\\n\\nWhatever the source, the ability of humans to monitor nature's light shows at night has practical applications for society. For example, tracking fires during nighttime hours allows for continuous monitoring and enhances our ability to protect humans and other animals, plants, and infrastructure. Combined with other data sources, our ability to observe the light of fires at night allows emergency managers to more efficiently and accurately issue warnings and evacuation orders and allows firefighting efforts to continue through the night. With enough moonlight (e.g., full-Moon phase), it's even possible to track the movement of smoke plumes at night, which can impact air quality, regardless of time of day.\\n\\nAnother natural source of light at night is emitted from glowing lava flows at the site of active volcanoes. Again, with enough moonlight, these dramatic scenes can be tracked and monitored for both scientific research and public safety.\\n\\n\\n### Figure: The Northern Lights Viewed from Space\\n\\n**September 17, 2011**\\n\\nThis photo, taken from the International Space Station on September 17, 2011, shows a spectacular display of the aurora borealis (Northern Lights) as green and reddish light in the night sky above Earth. In the foreground, part of a Soyuz spacecraft is visible, silhouetted against the bright auroral light. The green glow is generated by energetic charged particles from the Sun interacting with Earth's upper atmosphere, exciting oxygen and nitrogen atoms, and producing characteristic colors. The image demonstrates the vividness and grandeur of natural night-time light phenomena as seen from orbit.\"}]"}]}]

Activities:
Activity Type: ModelQueryPlanning
{
  "type": "ModelQueryPlanning",
  "id": 0,
  "inputTokens": 1598,
  "outputTokens": 159
}
Activity Type: AzureSearchQuery
{
  "type": "AzureSearchQuery",
  "id": 1,
  "targetIndex": "earth_at_night",
  "query": {
    "search": "How can I locate lava flows during nighttime?",
    "filter": null
  },
  "queryTime": "2025-07-20T16:13:10.659Z",
  "count": 5,
  "elapsedMs": 260
}
Activity Type: AzureSearchSemanticRanker
{
  "type": "AzureSearchSemanticRanker",
  "id": 2,
  "inputTokens": 24146
}
Results
Reference Type: AzureSearchDoc
{
  "type": "AzureSearchDoc",
  "id": "0",
  "activitySource": 1,
  "docKey": "earth_at_night_508_page_64_verbalized",
  "sourceData": {
    "id": "earth_at_night_508_page_64_verbalized",
    "page_chunk": "<!-- PageHeader=\"Volcanoes\" -->\n\n### Nighttime Glow at Mount Etna - Italy\n\nAt about 2:30 a.m. local time on March 16, 2017, the VIIRS DNB on the Suomi NPP satellite captured this nighttime image of lava flowing on Mount Etna in Sicily, Italy. Etna is one of the world's most active volcanoes.\n\n#### Figure: Location of Mount Etna\nA world globe is depicted, with a marker indicating the location of Mount Etna in Sicily, Italy, in southern Europe near the center of the Mediterranean Sea.\n\n<!-- PageFooter=\"Earth at Night\" -->\n<!-- PageNumber=\"48\" -->"
  }
}
Reference Type: AzureSearchDoc
{
  "type": "AzureSearchDoc",
  "id": "1",
  "activitySource": 1,
  "docKey": "earth_at_night_508_page_60_verbalized",
  "sourceData": {
    "id": "earth_at_night_508_page_60_verbalized",
    "page_chunk": "<!-- PageHeader=\"Volcanoes\" -->\n\n## Volcanoes\n\n### The Infrared Glows of Kilauea's Lava Flows—Hawaii\n\nIn early May 2018, an eruption on Hawaii's Kilauea volcano began to unfold. The eruption took a dangerous turn on May 3, 2018, when new fissures opened in the residential neighborhood of Leilani Estates. During the summer-long eruptive event, other fissures emerged along the East Rift Zone. Lava from vents along the rift zone flowed downslope, reaching the ocean in several areas, and filling in Kapoho Bay.\n\nA time series of Landsat 8 imagery shows the progression of the lava flows from May 16 to August 13. The night view combines thermal, shortwave infrared, and near-infrared wavelengths to tease out the very hot lava (bright white), cooling lava (red), and lava flows obstructed by clouds (purple).\n\n#### Figure: Location of Kilauea Volcano, Hawaii\n\nA globe is shown centered on North America, with a marker placed in the Pacific Ocean indicating the location of Hawaii, to the southwest of the mainland United States.\n\n<!-- PageFooter=\"Earth at Night\" -->\n<!-- PageNumber=\"44\" -->"
  }
}
Reference Type: AzureSearchDoc
{
  "type": "AzureSearchDoc",
  "id": "2",
  "activitySource": 1,
  "docKey": "earth_at_night_508_page_46_verbalized",
  "sourceData": {
    "id": "earth_at_night_508_page_46_verbalized",
    "page_chunk": "For the first time in perhaps a decade, Mount Etna experienced a \"flank eruption\"—erupting from its side instead of its summit—on December 24, 2018. The activity was accompanied by 130 earthquakes occurring over three hours that morning. Mount Etna, Europe’s most active volcano, has seen periodic activity on this part of the mountain since 2013. The Operational Land Imager (OLI) on the Landsat 8 satellite acquired the main image of Mount Etna on December 28, 2018.\n\nThe inset image highlights the active vent and thermal infrared signature from lava flows, which can be seen near the newly formed fissure on the southeastern side of the volcano. The inset was created with data from OLI and the Thermal Infrared Sensor (TIRS) on Landsat 8. Ash spewing from the fissure cloaked adjacent villages and delayed aircraft from landing at the nearby Catania airport. Earthquakes occurred in the subsequent days after the initial eruption and displaced hundreds of people from their homes.\n\nFor nighttime images of Mount Etna’s March 2017 eruption, see pages 48–51.\n\n---\n\n### Hazards of Volcanic Ash Plumes and Satellite Observation\n\nWith the help of moonlight, satellite instruments can track volcanic ash plumes, which present significant hazards to airplanes in flight. The volcanic ash—composed of tiny pieces of glass and rock—is abrasive to engine turbine blades, and can melt on the blades and other engine parts, causing damage and even engine stalls. This poses a danger to both the plane’s integrity and passenger safety. Volcanic ash also reduces visibility for pilots and can cause etching of windshields, further reducing pilots’ ability to see. Nightlight images can be combined with thermal images to provide a more complete view of volcanic activity on Earth’s surface.\n\nThe VIIRS Day/Night Band (DNB) on polar-orbiting satellites uses faint light sources such as moonlight, airglow (the atmosphere’s self-illumination through chemical reactions), zodiacal light (sunlight scattered by interplanetary dust), and starlight from the Milky Way. Using these dim light sources, the DNB can detect changes in clouds, snow cover, and sea ice:\n\n#### Table: Light Sources Used by VIIRS DNB\n\n| Light Source         | Description                                                                  |\n|----------------------|------------------------------------------------------------------------------|\n| Moonlight            | Reflected sunlight from the Moon, illuminating Earth's surface at night      |\n| Airglow              | Atmospheric self-illumination from chemical reactions                        |\n| Zodiacal Light       | Sunlight scattered by interplanetary dust                                    |\n| Starlight/Milky Way  | Faint illumination provided by stars in the Milky Way                        |\n\nGeostationary Operational Environmental Satellites (GOES), managed by NOAA, orbit over Earth’s equator and offer uninterrupted observations of North America. High-latitude areas such as Alaska benefit from polar-orbiting satellites like Suomi NPP, which provide overlapping coverage at the poles, enabling more data collection in these regions. During polar darkness (winter months), VIIRS DNB data allow scientists to:\n\n- Observe sea ice formation\n- Monitor snow cover extent at the highest latitudes\n- Detect open water for ship navigation\n\n#### Table: Satellite Coverage Overview\n\n| Satellite Type          | Orbit           | Coverage Area         | Special Utility                              |\n|------------------------|-----------------|----------------------|----------------------------------------------|\n| GOES                   | Geostationary   | Equatorial/North America | Continuous regional monitoring              |\n| Polar-Orbiting (e.g., Suomi NPP) | Polar-orbiting    | Poles/high latitudes      | Overlapping passes; useful during polar night|\n\n---\n\n### Weather Forecasting and Nightlight Data\n\nThe use of nightlight data by weather forecasters is growing as the VIIRS instrument enables observation of clouds at night illuminated by sources such as moonlight and lightning. Scientists use these data to study the nighttime behavior of weather systems, including severe storms, which can develop and strike populous areas at night as well as during the day. Combined with thermal data, visible nightlight data allow the detection of clouds at various heights in the atmosphere, such as dense marine fog. This capability enables weather forecasters to issue marine advisories with higher confidence, leading to greater utility. (See \"Marine Layer Clouds—California\" on page 56.)\n\nIn this section of the book, you will see how nightlight data are used to observe nature’s spectacular light shows across a wide range of sources.\n\n---\n\n#### Notable Data from Mount Etna Flank Eruption (December 2018)\n\n| Event/Observation                  | Details                                                                    |\n|-------------------------------------|----------------------------------------------------------------------------|\n| Date of Flank Eruption              | December 24, 2018                                                          |\n| Number of Earthquakes               | 130 earthquakes within 3 hours                                              |\n| Image Acquisition                   | December 28, 2018 by Landsat 8 OLI                                         |\n| Location of Eruption                | Southeastern side of Mount Etna                                            |\n| Thermal Imaging Data                | From OLI and TIRS (Landsat 8), highlighting active vent and lava flows     |\n| Impact on Villages/Air Transport    | Ash covered villages; delayed aircraft at Catania airport                  |\n| Displacement                        | Hundreds of residents displaced                                            |\n| Ongoing Seismic Activity            | Earthquakes continued after initial eruption                               |\n\n---\n\n<!-- PageFooter=\"Earth at Night\" -->\n<!-- PageNumber=\"30\" -->"
  }
}
Reference Type: AzureSearchDoc
{
  "type": "AzureSearchDoc",
  "id": "3",
  "activitySource": 1,
  "docKey": "earth_at_night_508_page_66_verbalized",
  "sourceData": {
    "id": "earth_at_night_508_page_66_verbalized",
    "page_chunk": "# Volcanoes\n\n---\n\n### Mount Etna Erupts - Italy\n\nThe highly active Mount Etna in Italy sent red lava rolling down its flank on March 19, 2017. An astronaut onboard the ISS took the photograph below of the volcano and its environs that night. City lights surround the mostly dark volcanic area.\n\n---\n\n#### Figure 1: Location of Mount Etna, Italy\n\nA world map highlighting the location of Mount Etna in southern Italy. The marker indicates its geographic placement on the east coast of Sicily, Italy, in the Mediterranean region, south of mainland Europe and north of northern Africa.\n\n---\n\n#### Figure 2: Nighttime View of Mount Etna's Eruption and Surrounding Cities\n\nThis is a nighttime satellite image taken on March 19, 2017, showing the eruption of Mount Etna (southeastern cone) with visible bright red and orange coloring indicating flowing lava from a lateral vent. The surrounding areas are illuminated by city lights, with the following geographic references labeled:\n\n| Location        | Position in Image         | Visible Characteristics                    |\n|-----------------|--------------------------|--------------------------------------------|\n| Mt. Etna (southeastern cone) | Top center-left | Bright red/orange lava flow                |\n| Lateral vent    | Left of the volcano       | Faint red/orange flow extending outwards   |\n| Resort          | Below the volcano, to the left   | Small cluster of lights                    |\n| Giarre          | Top right                 | Bright cluster of city lights              |\n| Acireale        | Center right              | Large, bright area of city lights          |\n| Biancavilla     | Bottom left               | Smaller cluster of city lights             |\n\nAn arrow pointing north is shown on the image for orientation.\n\n---\n\n<!-- Earth at Night Page Footer -->\n<!-- Page Number: 50 -->"
  }
}
Reference Type: AzureSearchDoc
{
  "type": "AzureSearchDoc",
  "id": "4",
  "activitySource": 1,
  "docKey": "earth_at_night_508_page_44_verbalized",
  "sourceData": {
    "id": "earth_at_night_508_page_44_verbalized",
    "page_chunk": "## Nature's Light Shows\n\nAt night, with the light of the Sun removed, nature's brilliant glow from Earth's surface becomes visible to the naked eye from space. Some of Earth's most spectacular light shows are natural, like the aurora borealis, or Northern Lights, in the Northern Hemisphere (aurora australis, or Southern Lights, in the Southern Hemisphere). The auroras are natural electrical phenomena caused by charged particles that race from the Sun toward Earth, inducing chemical reactions in the upper atmosphere and creating the appearance of streamers of reddish or greenish light in the sky, usually near the northern or southern magnetic pole. Other natural lights can indicate danger, like a raging forest fire encroaching on a city, town, or community, or lava spewing from an erupting volcano.\n\nWhatever the source, the ability of humans to monitor nature's light shows at night has practical applications for society. For example, tracking fires during nighttime hours allows for continuous monitoring and enhances our ability to protect humans and other animals, plants, and infrastructure. Combined with other data sources, our ability to observe the light of fires at night allows emergency managers to more efficiently and accurately issue warnings and evacuation orders and allows firefighting efforts to continue through the night. With enough moonlight (e.g., full-Moon phase), it's even possible to track the movement of smoke plumes at night, which can impact air quality, regardless of time of day.\n\nAnother natural source of light at night is emitted from glowing lava flows at the site of active volcanoes. Again, with enough moonlight, these dramatic scenes can be tracked and monitored for both scientific research and public safety.\n\n\n### Figure: The Northern Lights Viewed from Space\n\n**September 17, 2011**\n\nThis photo, taken from the International Space Station on September 17, 2011, shows a spectacular display of the aurora borealis (Northern Lights) as green and reddish light in the night sky above Earth. In the foreground, part of a Soyuz spacecraft is visible, silhouetted against the bright auroral light. The green glow is generated by energetic charged particles from the Sun interacting with Earth's upper atmosphere, exciting oxygen and nitrogen atoms, and producing characteristic colors. The image demonstrates the vividness and grandeur of natural night-time light phenomena as seen from orbit."
  }
}

[ASSISTANT]: 
To find lava at night, satellite instruments like the VIIRS Day/Night Band (DNB) and thermal infrared sensors are used to detect the glow of very hot lava flows on the Earth's surface
 For example, nighttime satellite images have captured lava flowing from active volcanoes such as Mount Etna in Italy and Kilauea in Hawaii, where the hot lava emits bright light visible from space even at night
 Scientists combine thermal, shortwave infrared, and near-infrared data to distinguish very hot lava (bright white), cooling lava (red), and areas obscured by clouds
 Additionally, moonlight and other faint natural light sources help illuminate the surroundings to improve observation of volcanic activity at night
 Monitoring lava flow at night is important for scientific research and public safety, as it helps track volcanic eruptions and associated hazards such as ash plumes that can affect air travel and nearby communities [refs 0,1,2,3,4]


🎉 === Conversation Complete ===
🗑️ Deleting knowledge agent...
✅ Knowledge agent 'earth-search-agent' deleted successfully.
🗑️ Deleting search index...
✅ Search index 'earth_at_night' deleted successfully.
✅ Quickstart completed successfully!

Общие сведения о коде

Теперь, когда у вас есть код, давайте разберем ключевые компоненты:

Создание индекса поиска
Отправка документов в индекс
Создание агента знаний
Настройка сообщений
Запуск конвейера извлечения
Просмотр ответа, действия и результатов
Создание клиента Azure OpenAI
Создание ответа с помощью API завершения чата
Продолжить беседу

Создание индекса поиска

const index = {
    name: config.indexName,
    fields: [
        {
            name: "id",
            type: "Edm.String",
            key: true,
            filterable: true,
            sortable: true,
            facetable: true
        },
        {
            name: "page_chunk",
            type: "Edm.String",
            searchable: true,
            filterable: false,
            sortable: false,
            facetable: false
        },
        {
            name: "page_embedding_text_3_large",
            type: "Collection(Edm.Single)",
            searchable: true,
            filterable: false,
            sortable: false,
            facetable: false,
            vectorSearchDimensions: 3072,
            vectorSearchProfileName: "hnsw_text_3_large"
        },
        {
            name: "page_number",
            type: "Edm.Int32",
            filterable: true,
            sortable: true,
            facetable: true
        }
    ],
    vectorSearch: {
        profiles: [
            {
                name: "hnsw_text_3_large",
                algorithmConfigurationName: "alg",
                vectorizerName: "azure_openai_text_3_large"
            }
        ],
        algorithms: [
            {
                name: "alg",
                kind: "hnsw"
            }
        ],
        vectorizers: [
            {
                vectorizerName: "azure_openai_text_3_large",
                kind: "azureOpenAI",
                parameters: {
                    resourceUrl: config.azureOpenAIEndpoint,
                    deploymentId: config.azureOpenAIEmbeddingDeployment,
                    modelName: config.azureOpenAIEmbeddingModel
                }
            }
        ]
    },
    semanticSearch: {
        defaultConfigurationName: "semantic_config",
        configurations: [
            {
                name: "semantic_config",
                prioritizedFields: {
                    contentFields: [
                        { name: "page_chunk" }
                    ]
                }
            }
        ]
    }
};

Отправка документов в индекс

console.log("📡 Fetching Earth at Night documents from GitHub...");
const documentsUrl = "https://raw.githubusercontent.com/Azure-Samples/azure-search-sample-data/refs/heads/main/nasa-e-book/earth-at-night-json/documents.json";
try {
    const response = await fetch(documentsUrl);
    if (!response.ok) {
        throw new Error(`Failed to fetch documents: ${response.status} ${response.statusText}`);
    }
    const documents = await response.json();
    console.log(`✅ Fetched ${documents.length} documents from GitHub`);
    // Validate and transform documents to match our interface
    const transformedDocuments = documents.map((doc, index) => {
        return {
            id: doc.id || String(index + 1),
            page_chunk: doc.page_chunk || doc.content || '',
            page_embedding_text_3_large: doc.page_embedding_text_3_large || new Array(3072).fill(0.1),
            page_number: doc.page_number || index + 1
        };
    });
    return transformedDocuments;
}

Создание агента знаний

const agentDefinition = {
    name: config.agentName,
    description: "Knowledge agent for Earth at Night e-book content",
    models: [
        {
            kind: "azureOpenAI",
            azureOpenAIParameters: {
                resourceUri: config.azureOpenAIEndpoint,
                deploymentId: config.azureOpenAIGptDeployment,
                modelName: config.azureOpenAIGptModel
            }
        }
    ],
    targetIndexes: [
        {
            indexName: config.indexName,
            defaultRerankerThreshold: 2.5
        }
    ]
};

Настройка сообщений

const messages = [
    {
        role: "system",
        content: `A Q&A agent that can answer questions about the Earth at night.
Sources have a JSON format with a ref_id that must be cited in the answer.
If you do not have the answer, respond with "I don't know".`
    },
    {
        role: "user",
        content: "Why do suburban belts display larger December brightening than urban cores even though absolute light levels are higher downtown? 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?"
    }
];

Запустите поток извлечения

Анализирует всю историю бесед, чтобы определить необходимые сведения.
Разбивает составной запрос пользователя на целенаправленные подзапросы.
Выполняет каждый подзапрос одновременно с текстовыми полями и векторными представлениями в вашем индексе.
Использует семантический рангировщик для повторной сортировки результатов всех подзапросов.
Объединяет результаты в одну строку.

const agentMessages = messages.map(msg => ({
    role: msg.role,
    content: [
        {
            type: "text",
            text: msg.content
        }
    ]
}));
const retrievalRequest = {
    messages: agentMessages,
    targetIndexParams: [
        {
            indexName: config.indexName,
            rerankerThreshold: 2.5,
            maxDocsForReranker: 100,
            includeReferenceSourceData: true
        }
    ]
};
const token = await getAccessToken(credential, "https://search.azure.com/.default");
const response = await fetch(`${config.searchEndpoint}/agents/${config.agentName}/retrieve?api-version=${config.searchApiVersion}`, {
    method: 'POST',
    headers: {
        'Content-Type': 'application/json',
        'Authorization': `Bearer ${token}`
    },
    body: JSON.stringify(retrievalRequest)
});
if (!response.ok) {
    const errorText = await response.text();
    throw new Error(`Agentic retrieval failed: ${response.status} ${response.statusText}\n${errorText}`);
}
return await response.json();

Просмотр ответа, действия и результатов

Теперь вы хотите отобразить ответ, активность и результаты конвейера извлечения.

Каждый ответ на запрос из поиска ИИ Azure включает:

Единая строка, представляющая данные об основе результатов поиска.
План запроса.
Справочные данные, показывающие, какие блоки исходных документов способствовали единой строке.

console.log("\nActivities:");
if (retrievalResponse.activity && Array.isArray(retrievalResponse.activity)) {
    retrievalResponse.activity.forEach((activity) => {
        const activityType = activity.activityType || activity.type || 'UnknownActivityRecord';
        console.log(`Activity Type: ${activityType}`);
        console.log(JSON.stringify(activity, null, 2));
    });
}
console.log("Results");
if (retrievalResponse.references && Array.isArray(retrievalResponse.references)) {
    retrievalResponse.references.forEach((reference) => {
        const referenceType = reference.referenceType || reference.type || 'AzureSearchDoc';
        console.log(`Reference Type: ${referenceType}`);
        console.log(JSON.stringify(reference, null, 2));
    });
}

Выходные данные должны включать:

Response предоставляет текстовую строку наиболее релевантных документов (или фрагментов) в индексе поиска на основе запроса пользователя. Как показано далее в этом кратком руководстве, вы можете передать эту строку в LLM для создания ответов.
Activity отслеживает шаги, выполненные во время процесса извлечения, включая подзапросы, созданные развертыванием gpt-5-mini, и токены, используемые для планирования запросов и выполнения.
Results перечисляет документы, использованные для создания ответа, каждый из которых определяется своим DocKey.

Создание клиента Azure OpenAI

const scope = "https://cognitiveservices.azure.com/.default";
const azureADTokenProvider = getBearerTokenProvider(credential, scope);
const openAIClient = new AzureOpenAI({
    endpoint: config.azureOpenAIEndpoint,
    apiVersion: config.azureOpenAIApiVersion,
    azureADTokenProvider,
});

Создание ответа с помощью API завершения чата

const completion = await openAIClient.chat.completions.create({
    model: config.azureOpenAIGptDeployment,
    messages: messages.map(m => ({ role: m.role, content: m.content })),
    max_tokens: 1000,
    temperature: 0.7
});
const answer = completion.choices[0].message.content;
console.log(answer?.replace(/\./g, "\n"));

Продолжить беседу

const followUpQuestion = "How do I find lava at night?";
console.log(`❓ Follow-up question: ${followUpQuestion}`);
messages.push({
    role: "user",
    content: followUpQuestion
});

Очистите ресурсы

Удалите агента знаний

Агент знаний, созданный в этом кратком руководстве, был удален с помощью следующего примера кода:

const token = await getAccessToken(credential, "https://search.azure.com/.default");
const response = await fetch(`${config.searchEndpoint}/agents/${config.agentName}?api-version=${config.searchApiVersion}`, {
    method: 'DELETE',
    headers: {
        'Authorization': `Bearer ${token}`
    }
});

Удаление индекса поиска

Индекс поиска, созданный в этом кратком руководстве, был удален с помощью следующего фрагмента кода:

await indexClient.deleteIndex(config.indexName);
console.log(`✅ Search index '${config.indexName}' deleted successfully.`);

Замечание

Подсказка

Хотите начать сразу? См. репозиторий GitHub azure-search-python-samples .

Предпосылки

Учетная запись Azure с активной подпиской. Создайте учетную запись бесплатно .
Служба поиска ИИ Azure в любой регионе, который предоставляет агентивное извлечение.
Проект и ресурс Microsoft Foundry . При создании проекта ресурс создается автоматически.
Azure CLI для проверки подлинности без ключа с помощью идентификатора Microsoft Entra.
Visual Studio Code с последней версией Python.

Настройка доступа

Чтобы настроить доступ для этого краткого руководства, выберите оба следующих вкладки.

Поиск по искусственному интеллекту Azure
Microsoft Foundry

В службе поиска по искусственному интеллекту Azure:

Включите доступ на основе ролей.
Создайте управляемое удостоверение, назначаемое системой.
Назначьте следующие роли себе.
- Участник службы поиска
- Участник данных индекса поиска
- Средство чтения индексов поиска

Это важно

Агентное извлечение имеет две модели выставления счетов на основе токенов.

Выставление счетов за агентный поиск в Azure AI.
Выставление счетов из Azure OpenAI для планирования запросов и синтеза ответов.

Для получения дополнительной информации см. Доступность и цены агентских запросов.

Получение конечных точек

Чтобы получить конечные точки для этого краткого руководства, выберите оба следующих вкладки.

Поиск по искусственному интеллекту Azure
Microsoft Foundry

Войдите на портал Azure и выберите службу поиска.
В левой области выберите "Обзор".
Запишите конечную точку, которая должна выглядеть следующим https://my-service.search.windows.netобразом.

Войдите на портал Azure и выберите ресурс Foundry.
В левой области выберите ключи управления> ресурсамии конечную точку.
Выберите вкладку OpenAI .
Запишите конечную точку, которая должна выглядеть следующим https://my-resource.openai.azure.comобразом.

Развертывание моделей

Чтобы использовать агентное извлечение, необходимо развернуть две модели Azure OpenAI в проекте Foundry.

Модель внедрения для преобразования текста в вектор. В этом кратком руководстве используется text-embedding-3-large, но вы можете использовать какую-либо модель text-embedding.
LLM для планирования запросов и создания ответов. В этом кратком введении используется gpt-5-mini, но вы можете использовать любой поддерживаемый LLM для агентного извлечения.

Инструкции по развертыванию см. в статье "Развертывание моделей Azure OpenAI с помощью Foundry".

Подключение из локальной системы

Вы настроили доступ на основе ролей для взаимодействия с поиском ИИ Azure и Azure OpenAI в модели Foundry. Используйте Azure CLI для входа в одну подписку и клиент для обоих ресурсов. Дополнительные сведения см. в кратком руководстве по подключению без ключей.

Чтобы подключиться из локальной системы, выполните приведенные действия.

Создайте папку с именем quickstart-agentic-retrieval.
Откройте папку в Visual Studio Code.
Выберите терминал>"Новый терминал".
Выполните следующую команду, чтобы войти в учетную запись Azure. Если у вас несколько подписок, выберите тот, который содержит службу поиска ИИ Azure и проект Foundry.
```
az login
```

Запустите код

Чтобы создать и запустить агентный конвейер извлечения, выполните следующие действия.

Выполните следующую команду, чтобы установить необходимые пакеты.
```
pip install azure-identity requests azure-search-documents --pre
```
Создайте файл с именем agentic-retrieval.py в папке quickstart-agentic-retrieval .

Вставьте следующий код в файл.

from azure.identity import DefaultAzureCredential, get_bearer_token_provider
from azure.search.documents.indexes.models import SearchIndex, SearchField, VectorSearch, VectorSearchProfile, HnswAlgorithmConfiguration, AzureOpenAIVectorizer, AzureOpenAIVectorizerParameters, SemanticSearch, SemanticConfiguration, SemanticPrioritizedFields, SemanticField, SearchIndexKnowledgeSource, SearchIndexKnowledgeSourceParameters, SearchIndexFieldReference, KnowledgeBase, KnowledgeBaseAzureOpenAIModel, KnowledgeSourceReference, KnowledgeRetrievalOutputMode, KnowledgeRetrievalLowReasoningEffort
from azure.search.documents.indexes import SearchIndexClient
from azure.search.documents import SearchIndexingBufferedSender
from azure.search.documents.knowledgebases import KnowledgeBaseRetrievalClient
from azure.search.documents.knowledgebases.models import KnowledgeBaseRetrievalRequest, KnowledgeBaseMessage, KnowledgeBaseMessageTextContent, SearchIndexKnowledgeSourceParams
import requests
import json

# Define variables
search_endpoint = "PUT-YOUR-SEARCH-SERVICE-URL-HERE"
aoai_endpoint = "PUT-YOUR-AOAI-FOUNDRY-URL-HERE"
aoai_embedding_model = "text-embedding-3-large"
aoai_embedding_deployment = "text-embedding-3-large"
aoai_gpt_model = "gpt-5-mini"
aoai_gpt_deployment = "gpt-5-mini"
index_name = "earth-at-night"
knowledge_source_name = "earth-knowledge-source"
knowledge_base_name = "earth-knowledge-base"
search_api_version = "2025-11-01-preview"

credential = DefaultAzureCredential()
token_provider = get_bearer_token_provider(credential, "https://search.azure.com/.default")

# Create an index
azure_openai_token_provider = get_bearer_token_provider(credential, "https://cognitiveservices.azure.com/.default")

index = SearchIndex(
    name=index_name,
    fields=[
        SearchField(name="id", type="Edm.String", key=True, filterable=True, sortable=True, facetable=True),
        SearchField(name="page_chunk", type="Edm.String", filterable=False, sortable=False, facetable=False),
        SearchField(name="page_embedding_text_3_large", type="Collection(Edm.Single)", stored=False, vector_search_dimensions=3072, vector_search_profile_name="hnsw_text_3_large"),
        SearchField(name="page_number", type="Edm.Int32", filterable=True, sortable=True, facetable=True)
    ],
    vector_search=VectorSearch(
        profiles=[VectorSearchProfile(name="hnsw_text_3_large", algorithm_configuration_name="alg", vectorizer_name="azure_openai_text_3_large")],
        algorithms=[HnswAlgorithmConfiguration(name="alg")],
        vectorizers=[
            AzureOpenAIVectorizer(
                vectorizer_name="azure_openai_text_3_large",
                parameters=AzureOpenAIVectorizerParameters(
                    resource_url=aoai_endpoint,
                    deployment_name=aoai_embedding_deployment,
                    model_name=aoai_embedding_model
                )
            )
        ]
    ),
    semantic_search=SemanticSearch(
        default_configuration_name="semantic_config",
        configurations=[
            SemanticConfiguration(
                name="semantic_config",
                prioritized_fields=SemanticPrioritizedFields(
                    content_fields=[
                        SemanticField(field_name="page_chunk")
                    ]
                )
            )
        ]
    )
)

index_client = SearchIndexClient(endpoint=search_endpoint, credential=credential)
index_client.create_or_update_index(index)
print(f"Index '{index_name}' created or updated successfully.")

# Upload documents
url = "https://raw.githubusercontent.com/Azure-Samples/azure-search-sample-data/refs/heads/main/nasa-e-book/earth-at-night-json/documents.json"
documents = requests.get(url).json()

with SearchIndexingBufferedSender(endpoint=search_endpoint, index_name=index_name, credential=credential) as client:
    client.upload_documents(documents=documents)

print(f"Documents uploaded to index '{index_name}' successfully.")

# Create a knowledge source
ks = SearchIndexKnowledgeSource(
    name=knowledge_source_name,
    description="Knowledge source for Earth at night data",
    search_index_parameters=SearchIndexKnowledgeSourceParameters(
        search_index_name=index_name,
        source_data_fields=[SearchIndexFieldReference(name="id"), SearchIndexFieldReference(name="page_number")]
    ),
)

index_client = SearchIndexClient(endpoint=search_endpoint, credential=credential)
index_client.create_or_update_knowledge_source(knowledge_source=ks)
print(f"Knowledge source '{knowledge_source_name}' created or updated successfully.")

# Create a knowledge base
aoai_params = AzureOpenAIVectorizerParameters(
    resource_url=aoai_endpoint,
    deployment_name=aoai_gpt_deployment,
    model_name=aoai_gpt_model,
)

knowledge_base = KnowledgeBase(
    name=knowledge_base_name,
    models=[KnowledgeBaseAzureOpenAIModel(azure_open_ai_parameters=aoai_params)],
    knowledge_sources=[
        KnowledgeSourceReference(
            name=knowledge_source_name
        )
    ],
    output_mode=KnowledgeRetrievalOutputMode.ANSWER_SYNTHESIS,
    answer_instructions="Provide a two sentence concise and informative answer based on the retrieved documents."
)

index_client = SearchIndexClient(endpoint=search_endpoint, credential=credential)
index_client.create_or_update_knowledge_base(knowledge_base)
print(f"Knowledge base '{knowledge_base_name}' created or updated successfully.")

# Set up messages
instructions = """
A Q&A agent that can answer questions about the Earth at night.
If you don't have the answer, respond with "I don't know".
"""

messages = [
    {
        "role": "system",
        "content": instructions
    }
]

# Run agentic retrieval
agent_client = KnowledgeBaseRetrievalClient(endpoint=search_endpoint, knowledge_base_name=knowledge_base_name, credential=credential)
query_1 = """
    Why do suburban belts display larger December brightening than urban cores even though absolute light levels are higher downtown?
    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?
    """

messages.append({
    "role": "user",
    "content": query_1
})

req = KnowledgeBaseRetrievalRequest(
    messages=[
        KnowledgeBaseMessage(
            role=m["role"],
            content=[KnowledgeBaseMessageTextContent(text=m["content"])]
        ) for m in messages if m["role"] != "system"
    ],
    knowledge_source_params=[
        SearchIndexKnowledgeSourceParams(
            knowledge_source_name=knowledge_source_name,
            include_references=True,
            include_reference_source_data=True,
            always_query_source=True
        )
    ],
    include_activity=True,
    retrieval_reasoning_effort=KnowledgeRetrievalLowReasoningEffort
)

result = agent_client.retrieve(retrieval_request=req)
print(f"Retrieved content from '{knowledge_base_name}' successfully.")

# Display the response, activity, and references
response_contents = []
activity_contents = []
references_contents = []

response_parts = []
for resp in result.response:
    for content in resp.content:
        response_parts.append(content.text)
response_content = "\n\n".join(response_parts) if response_parts else "No response found on 'result'"

response_contents.append(response_content)

# Print the three string values
print("response_content:\n", response_content, "\n")

messages.append({
    "role": "assistant",
    "content": response_content
})

if result.activity:
    activity_content = json.dumps([a.as_dict() for a in result.activity], indent=2)
else:
    activity_content = "No activity found on 'result'"

activity_contents.append(activity_content)
print("activity_content:\n", activity_content, "\n")

if result.references:
    references_content = json.dumps([r.as_dict() for r in result.references], indent=2)
else:
    references_content = "No references found on 'result'"

references_contents.append(references_content)
print("references_content:\n", references_content)

# Continue the conversation
query_2 = "How do I find lava at night?"
messages.append({
    "role": "user",
    "content": query_2
})

req = KnowledgeBaseRetrievalRequest(
    messages=[
        KnowledgeBaseMessage(
            role=m["role"],
            content=[KnowledgeBaseMessageTextContent(text=m["content"])]
        ) for m in messages if m["role"] != "system"
    ],
    knowledge_source_params=[
        SearchIndexKnowledgeSourceParams(
            knowledge_source_name=knowledge_source_name,
            include_references=True,
            include_reference_source_data=True,
            always_query_source=True
        )
    ],
    include_activity=True,
    retrieval_reasoning_effort=KnowledgeRetrievalLowReasoningEffort
)

result = agent_client.retrieve(retrieval_request=req)
print(f"Retrieved content from '{knowledge_base_name}' successfully.")

# Display the new retrieval response, activity, and references
response_parts = []
for resp in result.response:
    for content in resp.content:
        response_parts.append(content.text)
response_content = "\n\n".join(response_parts) if response_parts else "No response found on 'result'"

response_contents.append(response_content)

# Print the three string values
print("response_content:\n", response_content, "\n")

if result.activity:
    activity_content = json.dumps([a.as_dict() for a in result.activity], indent=2)
else:
    activity_content = "No activity found on 'result'"

activity_contents.append(activity_content)
print("activity_content:\n", activity_content, "\n")

if result.references:
    references_content = json.dumps([r.as_dict() for r in result.references], indent=2)
else:
    references_content = "No references found on 'result'"

references_contents.append(references_content)
print("references_content:\n", references_content)

# Clean up resources
index_client = SearchIndexClient(endpoint=search_endpoint, credential=credential)
index_client.delete_knowledge_base(knowledge_base_name)
print(f"Knowledge base '{knowledge_base_name}' deleted successfully.")

index_client = SearchIndexClient(endpoint=search_endpoint, credential=credential)
index_client.delete_knowledge_source(knowledge_source=knowledge_source_name)
print(f"Knowledge source '{knowledge_source_name}' deleted successfully.")

index_client = SearchIndexClient(endpoint=search_endpoint, credential=credential)
index_client.delete_index(index_name)
print(f"Index '{index_name}' deleted successfully.")

Задайте search_endpoint и aoai_endpoint укажите значения, полученные в конечных точках Get.
Выполните следующую команду, чтобы выполнить код.
```
python agentic-retrieval.py
```

Выходные данные

Выходные данные кода должны выглядеть примерно так:

Documents uploaded to index 'earth-at-night' successfully.
Knowledge source 'earth-knowledge-source' created or updated successfully.
Knowledge base 'earth-knowledge-base' created or updated successfully.
Retrieved content from 'earth-knowledge-base' successfully.
response_content:
 Suburban belts brighten more in December because holiday lighting is concentrated in suburbs and outskirts—where yard space and single-family homes allow more displays—while central urban cores already have much higher absolute light levels so their fractional increase is smaller [ref_id:4][ref_id:7].
The Phoenix street grid is sharply visible from space because its regular block pattern plus continuous street, commercial, and corridor lighting (including the diagonal Grand Avenue) produce a bright, grid-like signature at night [ref_id:3][ref_id:0], whereas interstate corridors between Midwestern cities often appear comparatively dim because light is concentrated at urban nodes and ports while long stretches of highway and rivers lack continuous lighting [ref_id:7][ref_id:2].

activity_content:
 [
  {
    "id": 0,
    "type": "modelQueryPlanning",
    "elapsed_ms": 16946,
    "input_tokens": 1354,
    "output_tokens": 906
  },
  {
    "id": 1,
    "type": "searchIndex",
    "elapsed_ms": 887,
    "knowledge_source_name": "earth-knowledge-source",
    "query_time": "2025-11-05T16:17:48.345Z",
    "count": 22,
    "search_index_arguments": {
      "search": "December brightening in satellite nighttime lights: why do suburban belts show larger relative increases in December than urban cores despite higher absolute downtown light levels?"
    }
  },
  {
    "id": 2,
    "type": "searchIndex",
    "elapsed_ms": 632,
    "knowledge_source_name": "earth-knowledge-source",
    "query_time": "2025-11-05T16:17:48.985Z",
    "count": 10,
    "search_index_arguments": {
      "search": "Why is Phoenix's nighttime street grid so sharply visible from space? factors: street-light layout, lamp type, urban form, light scattering, and satellite sensor characteristics in Phoenix, Arizona."
    }
  },
  {
    "id": 3,
    "type": "searchIndex",
    "elapsed_ms": 420,
    "knowledge_source_name": "earth-knowledge-source",
    "query_time": "2025-11-05T16:17:49.406Z",
    "count": 11,
    "search_index_arguments": {
      "search": "Why are long stretches of interstate highways between Midwestern cities comparatively dim in satellite nighttime images? factors: highway lighting design, lamp spacing and type, vehicle headlights vs fixed lighting, and detection limits of nighttime sensors"
    }
  },
  {
    "id": 4,
    "type": "agenticReasoning",
    "reasoning_tokens": 72191,
    "retrieval_reasoning_effort": {
      "kind": "low"
    }
  },
  {
    "id": 5,
    "type": "modelAnswerSynthesis",
    "elapsed_ms": 22353,
    "input_tokens": 7564,
    "output_tokens": 1645
  }
]

references_content:
 [
  {
    "type": "searchIndex",
    "id": "0",
    "activity_source": 2,
    "source_data": {
      "id": "earth_at_night_508_page_105_verbalized",
      "page_chunk": "# Urban Structure\n\n## March 16, 2013\n\n### Phoenix Metropolitan Area at Night\n\nThis figure presents a nighttime satellite view of the Phoenix metropolitan area, highlighting urban structure and transport corridors. City lights illuminate the layout of several cities and major thoroughfares.\n\n**Labeled Urban Features:**\n\n- **Phoenix:** Central and brightest area in the right-center of the image.\n- **Glendale:** Located to the west of Phoenix, this city is also brightly lit.\n- **Peoria:** Further northwest, this area is labeled and its illuminated grid is seen.\n- **Grand Avenue:** Clearly visible as a diagonal, brightly lit thoroughfare running from Phoenix through Glendale and Peoria.\n- **Salt River Channel:** Identified in the southeast portion, running through illuminated sections.\n- **Phoenix Mountains:** Dark, undeveloped region to the northeast of Phoenix.\n- **Agricultural Fields:** Southwestern corner of the image, grid patterns are visible but with much less illumination, indicating agricultural land use.\n\n**Additional Notes:**\n\n- The overall pattern shows a grid-like urban development typical of western U.S. cities, with scattered bright nodes at major intersections or city centers.\n- There is a clear transition from dense urban development to sparsely populated or agricultural land, particularly evident towards the bottom and left of the image.\n- The illuminated areas follow the existing road and street grids, showcasing the extensive spread of the metropolitan area.\n\n**Figure Description:**  \nA satellite nighttime image captured on March 16, 2013, showing Phoenix and surrounding areas (including Glendale and Peoria). Major landscape and infrastructural features, such as the Phoenix Mountains, Grand Avenue, the Salt River Channel, and agricultural fields, are labeled. The image reveals the extent of urbanization and the characteristic street grid illuminated by city lights.\n\n---\n\nPage 89",
      "page_number": 105
    },
    "reranker_score": 2.722408,
    "doc_key": "earth_at_night_508_page_105_verbalized"
  },
  {
    "type": "searchIndex",
    "id": "3",
    "activity_source": 2,
    "source_data": {
      "id": "earth_at_night_508_page_104_verbalized",
      "page_chunk": "<!-- PageHeader=\"Urban Structure\" -->\n\n### Location of Phoenix, Arizona\n\nThe image depicts a globe highlighting the location of Phoenix, Arizona, in the southwestern United States, marked with a blue pinpoint on the map of North America. Phoenix is situated in the central part of Arizona, which is in the southwestern region of the United States.\n\n---\n\n### Grid of City Blocks-Phoenix, Arizona\n\nLike many large urban areas of the central and western United States, the Phoenix metropolitan area is laid out along a regular grid of city blocks and streets. While visible during the day, this grid is most evident at night, when the pattern of street lighting is clearly visible from the low-Earth-orbit vantage point of the ISS.\n\nThis astronaut photograph, taken on March 16, 2013, includes parts of several cities in the metropolitan area, including Phoenix (image right), Glendale (center), and Peoria (left). While the major street grid is oriented north-south, the northwest-southeast oriented Grand Avenue cuts across the three cities at image center. Grand Avenue is a major transportation corridor through the western metropolitan area; the lighting patterns of large industrial and commercial properties are visible along its length. Other brightly lit properties include large shopping centers, strip malls, and gas stations, which tend to be located at the intersections of north-south and east-west trending streets.\n\nThe urban grid encourages growth outwards along a city's borders by providing optimal access to new real estate. Fueled by the adoption of widespread personal automobile use during the twentieth century, the Phoenix metropolitan area today includes 25 other municipalities (many of them largely suburban and residential) linked by a network of surface streets and freeways.\n\nWhile much of the land area highlighted in this image is urbanized, there are several noticeably dark areas. The Phoenix Mountains are largely public parks and recreational land. To the west, agricultural fields provide a sharp contrast to the lit streets of residential developments. The Salt River channel appears as a dark ribbon within the urban grid.\n\n\n<!-- PageFooter=\"Earth at Night\" -->\n<!-- PageNumber=\"88\" -->",
      "page_number": 104
    },
    "reranker_score": 2.6451337,
    "doc_key": "earth_at_night_508_page_104_verbalized"
  },
  {
    "type": "searchIndex",
    "id": "1",
    "activity_source": 1,
    "source_data": {
      "id": "earth_at_night_508_page_174_verbalized",
      "page_chunk": "<!-- PageHeader=\"Holiday Lights\" -->\n\n## Holiday Lights\n\n### Bursting with Holiday Energy-United States\n\nNASA researchers found that nighttime lights in the United States shine 20 to 50 percent brighter in December due to holiday light displays and other activities during Christmas and New Year's when compared to light output during the rest of the year.\n\nThe next five maps (see also pages 161-163), created using data from the VIIRS DNB on the Suomi NPP satellite, show changes in lighting intensity and location around many major cities, comparing the nighttime light signals from December 2012 and beyond.\n\n---\n\n#### Figure 1. Location Overview\n\nA map of the western hemisphere with a marker indicating the mid-Atlantic region of the eastern United States, where the study of holiday lighting intensity was focused.\n\n---\n\n#### Figure 2. Holiday Lighting Intensity: Mid-Atlantic United States (2012\u20132014)\n\nA map showing Maryland, New Jersey, Delaware, Virginia, West Virginia, Ohio, Kentucky, Tennessee, North Carolina, South Carolina, and surrounding areas. Major cities labeled include Washington, D.C., Richmond, Norfolk, and Raleigh.\n\nThe map uses colors to indicate changes in holiday nighttime lighting intensity between 2012 and 2014:\n\n- **Green/bright areas**: More holiday lighting (areas shining 20\u201350% brighter in December).\n- **Yellow areas**: No change in lighting.\n- **Dim/grey areas**: Less holiday lighting.\n\nKey observations from the map:\n\n- The Washington, D.C. metropolitan area shows significant increases in lighting during the holidays, extending into Maryland and Virginia.\n- Urban centers such as Richmond (Virginia), Norfolk (Virginia), Raleigh (North Carolina), and clusters in Tennessee and South Carolina also experience notable increases in light intensity during December.\n- Rural areas and the interiors of West Virginia, Kentucky, and North Carolina show little change or less holiday lighting, corresponding to population density and urbanization.\n\n**Legend:**\n\n| Holiday Lighting Change | Color on Map   |\n|------------------------|---------------|\n| More                   | Green/bright  |\n| No Change              | Yellow        |\n| Less         
          | Dim/grey      |\n\n_The scale bar indicates a distance of 100 km for reference._\n\n---\n\n<!-- PageFooter=\"158 Earth at Night\" -->",
      "page_number": 174
    },
    "reranker_score": 2.476761,
    "doc_key": "earth_at_night_508_page_174_verbalized"
  },
  {
    "type": "searchIndex",
    "id": "2",
    "activity_source": 3,
    "source_data": {
      "id": "earth_at_night_508_page_124_verbalized",
      "page_chunk": "# Urban Development\n\n## Figure: Location Highlight on Globe\n\nThis figure depicts a globe focused on North America, with a marker pinpointing the central region of the United States. The highlighted location represents the geographical focus of the text discussion on US urban development and transportation networks.\n\n---\n\n## Urban Development\n\n### Lighting Paths\u2014Across the United States\n\nThe United States has more miles of roads than any other nation in the world\u20144.1 million miles (6.6 million kilometers) to be precise, which is roughly 40 percent more than second-ranked India. About 47,000 miles (75,639 kilometers) of those roads are part of the Interstate Highway System, established by President Dwight Eisenhower in the 1950s. The country/region also has 127,000 miles (204,000 kilometers) of railroad tracks and about 25,000 miles (40,000 kilometers) of navigable rivers and canals (not including the Great Lakes). The imprint of that transportation web becomes easy to see at night.\n\nThe VIIRS DNB on the Suomi NPP satellite acquired this nighttime view (top image, right) of the continental United States on October 1, 2013. The roadway map (bottom image, right) traces the path of the major interstate highways, railroads, and rivers of the United States. Comparing the two images, you quickly see how the cities and settlements align with the transportation corridors. In the early days of the republic, post roads and toll roads for horse-drawn carts and carriages were built to connect eastern cities like Boston, New York, Baltimore, and Philadelphia, though relatively few travelers made the long, unlit journeys. Railroads became the dominant transportation method for people and cargo in the middle of the nineteenth century, establishing longer links across the Nation and waypoints across the Midwest, the Great Plains, and the Rockies. Had nighttime satellite images existed in that era, they probably would show only dim pearls of light around major cities in the east and scattered across the country/region; the strands of steel tracks and cobbled roads that connected them would be invisible from space.\n\nEventually, cars and trucks became the dominant form of transportation in the United States. Drivers then needed roads and lighting to keep them safe on those roads. As the Nation grew in the twentieth century, the development of new cities and suburbs often conformed to the path of the interstate highways, adding light along the paths between the cities.\n\nOver the years, the length of navigable rivers has been a constant, as is their relative lack of light. Even today the only light seems to be the occasional port cities along riverbanks and the light of ships themselves.\n\n---\n\n**Table: Summary of U.S. Transportation Infrastructure**\n\n| Infrastructure Type     | Total Mileage (mi) | Total Mileage (km) |\n|------------------------|--------------------|--------------------|\n| Roads (All)            | 4,100,000          | 6,600,000          |\n| Interstate Highways    | 47,000             | 75,639             |\n| Railroads              | 127,000            | 204,000            |\n| Navigable Rivers/Canals| 25,000             | 40,000             |\n\n---\n\n<!-- PageFooter=\"108 Earth at Night\" -->",
      "page_number": 124
    },
    "reranker_score": 2.466304,
    "doc_key": "earth_at_night_508_page_124_verbalized"
  },
  {
    "type": "searchIndex",
    "id": "4",
    "activity_source": 1,
    "source_data": {
      "id": "earth_at_night_508_page_176_verbalized",
      "page_chunk": "# Holiday Lights\n\n## Figure 1: Location Marker on Globe\n\n**Description:**  \nA world map focused on the Western Hemisphere, with a marker placed in the eastern United States. This image serves to indicate the geographic focus of the following data and discussion about holiday lighting patterns, particularly those observed in the United States.\n\n---\n\nHoliday lights increase most dramatically in the suburbs and outskirts of major cities, where there is more yard space and a prevalence of single-family homes. Central urban areas do not see as large an increase in lighting, but they still experience a brightening of 20 to 30 percent during the holidays. This pattern holds true across the U.S., which remains ethnically and religiously diverse but participates in a nationally shared tradition of increased holiday lighting during holiday seasons.\n\nBeyond the cultural implications, this trend has significant consequences for energy consumption. The availability of a daily, global dynamic dataset of nighttime lights offers new insights into the broad societal forces influencing energy decisions. As noted by the Intergovernmental Panel on Climate Change, improvements in energy efficiency and conservation are essential to reducing greenhouse gas emissions. Examining daily nightlight data provides a valuable perspective on urban and suburban life, helping to reveal the underlying patterns and drivers of energy use.\n\n*(Images continue on pages 161-163)*\n\n---\n\n*Page 160 Earth at Night*",
      "page_number": 176
    },
    "reranker_score": 2.3416197,
    "doc_key": "earth_at_night_508_page_176_verbalized"
  },
  {
    "type": "searchIndex",
    "id": "6",
    "activity_source": 1,
    "source_data": {
      "id": "earth_at_night_508_page_175_verbalized",
      "page_chunk": "# Holiday Lights\n\nFrom 2013 to the average light output for the rest of 2012 to 2014, the change in light usage is subtle on any given night. However, when averaged over days and weeks, the pattern becomes more perceptible. Areas where light usage increased in December are marked in green, areas with little change are marked in yellow, and areas where less light was used are marked in red.\n\nThe light output from 70 U.S. cities was examined as a first step toward determining patterns in urban energy use. Researchers found that light intensity increased by 30 to 50 percent in December in many areas, which may be related to holiday lighting.\n\n---\n\n## Figure 1: Location Reference\n\nA globe highlights the southeastern region of the United States, pinpointing the area of interest for the study of holiday light usage, focusing on states like Tennessee, North Carolina, South Carolina, Georgia, and Alabama.\n\n---\n\n## Figure 2: Holiday Lighting Patterns in the Southeastern United States (2012\u20132014)\n\nThis map highlights several cities in the southeastern United States, including Nashville, Charlotte, Columbia, Birmingham, and Atlanta. The states of Tennessee, North Carolina, South Carolina, Alabama, and Georgia are outlined, along with the Atlantic Ocean to the east.\n\n### Key Observations:\n- The most significant concentrations of nighttime lighting are seen in major metropolitan areas, with Atlanta having the largest and most intense area of light.\n- Other notable clusters of increased light output are visible in Nashville, Charlotte, Birmingham, and Columbia.\n- The map reflects changes in light usage during December of 2012\u20132014, with \u201cmore\u201d lighting (green shading) concentrated around urban areas, indicating an increase due to holiday lighting displays.\n\n**Map Details:**\n- Time frame: 2012\u20132014\n- Locations marked: Nashville (Tennessee), Charlotte (North Carolina), Columbia (South Carolina), Birmingham (Alabama), Atlanta (Georgia)\n- Scale: 100 km bar provided\n- North directional arrow included\n\n---\n\n### Legend:\n- **Green Shading**: Areas where light usage increased in December (likely due to holiday lights)\n- **Yellow Shading**: Areas with little change in light usage\n- **Red Shading**: Areas where less light was used\n\n---\n\n#### Page Footer: \u201cno change\u201d\n#### Page Number: 159",
      "page_number": 175
    },
    "reranker_score": 2.3052866,
    "doc_key": "earth_at_night_508_page_175_verbalized"
  },
  {
    "type": "searchIndex",
    "id": "9",
    "activity_source": 1,
    "source_data": {
      "id": "earth_at_night_508_page_177_verbalized",
      "page_chunk": "# Holiday Lights\n\n## Holiday Lighting in Florida (2012\u20132014)\n\nThis figure presents a nighttime satellite map of Florida, highlighting changes in holiday lighting between 2012 and 2014. The map covers major urban areas including Jacksonville, Orlando, Tampa Bay, and Miami, with the Gulf of Mexico to the west.\n\nKey observations from the figure:\n- The map displays areas of increased, decreased, or unchanged outdoor lighting intensity during the holiday season.\n- Major metropolitan regions such as Miami, Tampa Bay, Orlando, and Jacksonville show noticeable concentrations of holiday lighting, with many surrounding areas experiencing changes in brightness compared to the non-holiday period.\n- Color coding (not described in the image but referenced):  \n  - **Less**: Areas where holiday lighting decreased  \n  - **No change**: Areas where lighting remained stable  \n  - **More**: Areas where holiday lighting increased\n\n**Legend:**\n- The figure includes a scale bar indicating a span of 100 km for distance estimation.\n- The map is oriented with north at the top.\n\n**Geographic Labels:**\n- Jacksonville (northeast Florida)\n- Orlando (central Florida)\n- Tampa Bay (west-central Florida)\n- Miami (southeast Florida)\n- The Gulf of Mexico (to the west of the peninsula)\n\n**Takeaway:**\nThe map visualizes spatial patterns in holiday lighting, indicating that urban and suburban areas in Florida experience substantial increases in nighttime brightness during the holiday period, particularly in and around major cities.\n\n<!-- PageNumber=\"161\" -->",
      "page_number": 177
    },
    "reranker_score": 2.2241132,
    "doc_key": "earth_at_night_508_page_177_verbalized"
  },
  {
    "type": "searchIndex",
    "id": "5",
    "activity_source": 3,
    "source_data": {
      "id": "earth_at_night_508_page_12_verbalized",
      "page_chunk": "## Preface\n\nTo keen observers, the nocturnal Earth is not pitch black, featureless, or static. The stars and the Moon provide illumination that differs from, and complements, daylight. Natural Earth processes such as volcanic eruptions, auroras, lightning, and meteors entering the atmosphere generate localized visible light on timescales ranging from subsecond (lightning), to days, weeks (forest fires), and months (volcanic eruptions).\n\nMost interesting and unique (as far as we know) to Earth, is the nighttime visible illumination emitted from our planet that is associated with human activities. Whether purposefully designed to banish darkness (such as lighting for safety, industrial activities, commerce, and transportation) or a secondary result of (such as gas flares associated with mining and hydrocarbon extraction activities, or nocturnal commercial fishing), anthropogenic sources of nighttime light are often broadly distributed in space and sustained in time\u2014over years and even decades. Because these light sources are inextricably tied to human activities and societies, extensive and long-term measurement and monitoring of Earth's anthropogenic nocturnal lights can provide valuable insights into the spatial distribution of our species and the ways in which society is changing\u2014and is changed by\u2014the environment on a wide range of time scales.\n\nOver the past four decades, sensitive imaging instruments have been operated on low-Earth-orbiting satellites to measure natural and human-caused visible nocturnal illumination, both reflected and Earth-generated. The satellite sensors provide unique imagery: global coverage yet with high spatial resolution, and frequent measurements over long periods of time.\n\nThe combined, multisatellite global nocturnal illumination dataset contains a treasure trove of unique information about our planet and our species\u2014and the",
      "page_number": 12
    },
    "reranker_score": 2.128052,
    "doc_key": "earth_at_night_508_page_12_verbalized"
  },
  {
    "type": "searchIndex",
    "id": "7",
    "activity_source": 3,
    "source_data": {
      "id": "earth_at_night_508_page_125_verbalized",
      "page_chunk": "# Urban Development\n\n**Date:** October 1, 2013\n\n---\n\n## Figure: Urban Development and Infrastructure in the United States\n\nThis figure comprises two maps of the continental United States, highlighting the patterns of urban development and infrastructure.\n\n### Top Panel: Nighttime Lights Map (October 1, 2013)\n\nThis map displays the United States as seen from space at night on October 1, 2013. Major observations include:\n\n- A dense concentration of bright spots representing urban and suburban areas with prominent lighting, especially along the east coast, the Midwest (notably around Chicago), Texas, and California.\n- The west and central parts of the country/region, such as the Rocky Mountains and deserts, appear much darker, indicating sparse population and fewer urban centers.\n- The boundaries of the United States are outlined for reference.\n- Major urban corridors are clearly visible, including the heavily lit regions running from Boston through New York City, Philadelphia, Baltimore, D.C., Atlanta, and further south, as well as the line of cities from Los Angeles through southern California.\n\n### Bottom Panel: Major Transport and River Networks\n\nThis map outlines the primary interstate highways, railroad lines, and major river systems in the continental United States, using different colors to distinguish among them:\n\n| Feature    | Color       | Description                                                                       |\n|------------|-------------|-----------------------------------------------------------------------------------|\n| Interstate | Red         | Key high-speed roadways, forming a vast national network and connecting cities     |\n| Railroad   | Green       | Major rail lines paralleling some highway routes, providing freight and passenger service |\n| River      | Blue        | Major river systems used historically for transport, industry, and urban siting    |\n\n- The locations of interstate highways closely follow the distribution of nighttime lights, as seen in the top panel.\n- Railroad networks are especially dense in the Midwest and northeast, regions with both high population density and industrial activity.\n- Major rivers, such as the Mississippi, Missouri, and Ohio, are marked in blue, reflecting their importance for historical urban development.\n\n**Scale:** Both maps include a scale bar representing 500 km and a North arrow for orientation.\n\n---\n\n**Summary:**  \nThe figure visually demonstrates the relationship between urban development (as observed through nighttime satellite imagery) and the underlying networks of interstates, railroads, and rivers that have historically influenced the growth and connectivity of American cities. Most urbanized and densely lit areas correspond to nodes and crossroads within this transportation and river network.\n\n---\n\n**Page 109**",
      "page_number": 125
    },
    "reranker_score": 2.108246,
    "doc_key": "earth_at_night_508_page_125_verbalized"
  },
  {
    "type": "searchIndex",
    "id": "8",
    "activity_source": 2,
    "source_data": {
      "id": "earth_at_night_508_page_179_verbalized",
      "page_chunk": "# Holiday Lights\n\n## Figure 1: Geographic Context of Holiday Lighting Study\n\nThis figure shows a map of the globe focused on North America, with a blue marker pointing to the region in the southwestern United States. This highlighted area includes parts of California, Nevada, and Arizona, encompassing the cities of Los Angeles, San Diego, Las Vegas, and Phoenix. This is the region of the study of holiday lighting.\n\n---\n\n## Figure 2: Changes in Holiday Lighting (2012\u20132014)\n\nThis figure is a satellite map of the southwestern United States and northwestern Mexico, annotated with state and city names:\n\n- **California** (including Los Angeles and San Diego)\n- **Nevada** (including Las Vegas)\n- **Arizona** (including Phoenix)\n- **Mexico** (including Tijuana)\n\nThe map shows holiday lighting patterns, using color to indicate change in light intensity during the holiday period (presumably Christmas season) between 2012 and 2014.\n\n### Map Legend\n\n| Color      | Meaning                     |\n|------------|-----------------------------|\n| Greenish   | More holiday lighting       |\n| Yellow     | No change in lighting       |\n| Red        | Less holiday lighting       |\n\n### Observations\n\n- Major urban areas such as Los Angeles, San Diego, Las Vegas, and Phoenix show increased lighting during the holiday period (marked primarily in green).\n- Some areas show no significant change, especially in less densely populated zones.\n- A few small areas may show a decrease in holiday lighting (if red is present).\n\n- The scale of the map includes a reference bar showing 50 km for distance.\n\n---\n\n### Holiday Lighting Change Key\n\n- **Less** (Red)\n- **No change** (Yellow)\n- **More** (Green)\n\n---\n\n<!-- PageNumber=\"163\" -->",
      "page_number": 179
    },
    "reranker_score": 2.1016884,
    "doc_key": "earth_at_night_508_page_179_verbalized"
  }
]
Retrieved content from 'earth-knowledge-base' successfully.
response_content:
  ... // Trimmed for brevity

activity_content:
 [
  ... // Trimmed for brevity
]

references_content:
 [
  ... // Trimmed for brevity
]
Knowledge base 'earth-knowledge-base' deleted successfully.
Knowledge source 'earth-knowledge-source' deleted successfully.
Index 'earth-at-night' deleted successfully.

Общие сведения о коде

Теперь, когда вы выполнили код, давайте разберем ключевые шаги:

Создание индекса поиска
Отправка документов в индекс
Создание источника знаний
Создание базы знаний
Настройка сообщений
Запуск конвейера извлечения
Продолжить беседу

Создание индекса поиска

# Create an index
azure_openai_token_provider = get_bearer_token_provider(credential, "https://cognitiveservices.azure.com/.default")

index = SearchIndex(
    name=index_name,
    fields=[
        SearchField(name="id", type="Edm.String", key=True, filterable=True, sortable=True, facetable=True),
        SearchField(name="page_chunk", type="Edm.String", filterable=False, sortable=False, facetable=False),
        SearchField(name="page_embedding_text_3_large", type="Collection(Edm.Single)", stored=False, vector_search_dimensions=3072, vector_search_profile_name="hnsw_text_3_large"),
        SearchField(name="page_number", type="Edm.Int32", filterable=True, sortable=True, facetable=True)
    ],
    vector_search=VectorSearch(
        profiles=[VectorSearchProfile(name="hnsw_text_3_large", algorithm_configuration_name="alg", vectorizer_name="azure_openai_text_3_large")],
        algorithms=[HnswAlgorithmConfiguration(name="alg")],
        vectorizers=[
            AzureOpenAIVectorizer(
                vectorizer_name="azure_openai_text_3_large",
                parameters=AzureOpenAIVectorizerParameters(
                    resource_url=aoai_endpoint,
                    deployment_name=aoai_embedding_deployment,
                    model_name=aoai_embedding_model
                )
            )
        ]
    ),
    semantic_search=SemanticSearch(
        default_configuration_name="semantic_config",
        configurations=[
            SemanticConfiguration(
                name="semantic_config",
                prioritized_fields=SemanticPrioritizedFields(
                    content_fields=[
                        SemanticField(field_name="page_chunk")
                    ]
                )
            )
        ]
    )
)

index_client = SearchIndexClient(endpoint=search_endpoint, credential=credential)
index_client.create_or_update_index(index)
print(f"Index '{index_name}' created or updated successfully.")

Отправка документов в индекс

# Upload documents
url = "https://raw.githubusercontent.com/Azure-Samples/azure-search-sample-data/refs/heads/main/nasa-e-book/earth-at-night-json/documents.json"
documents = requests.get(url).json()

with SearchIndexingBufferedSender(endpoint=search_endpoint, index_name=index_name, credential=credential) as client:
    client.upload_documents(documents=documents)

print(f"Documents uploaded to index '{index_name}' successfully.")

Создание источника знаний

source_data_fields указывает, какие поля индекса доступны для получения и ссылок. Наш пример включает только поля, доступные для чтения человеком, чтобы избежать длительных и непреднамеренных внедрения в ответы.

# Create a knowledge source
ks = SearchIndexKnowledgeSource(
    name=knowledge_source_name,
    description="Knowledge source for Earth at night data",
    search_index_parameters=SearchIndexKnowledgeSourceParameters(
        search_index_name=index_name,
        source_data_fields=[SearchIndexFieldReference(name="id"), SearchIndexFieldReference(name="page_number")]
    ),
)

index_client = SearchIndexClient(endpoint=search_endpoint, credential=credential)
index_client.create_or_update_knowledge_source(knowledge_source=ks)
print(f"Knowledge source '{knowledge_source_name}' created or updated successfully.")

Создание базы знаний

output_mode задано значение ANSWER_SYNTHESIS, включающее ответы на естественный язык, которые ссылаются на извлеченные документы и следуют предоставленным answer_instructions.

# Create a knowledge base
aoai_params = AzureOpenAIVectorizerParameters(
    resource_url=aoai_endpoint,
    deployment_name=aoai_gpt_deployment,
    model_name=aoai_gpt_model,
)

knowledge_base = KnowledgeBase(
    name=knowledge_base_name,
    models=[KnowledgeBaseAzureOpenAIModel(azure_open_ai_parameters=aoai_params)],
    knowledge_sources=[
        KnowledgeSourceReference(
            name=knowledge_source_name
        )
    ],
    output_mode=KnowledgeRetrievalOutputMode.ANSWER_SYNTHESIS,
    answer_instructions="Provide a two sentence concise and informative answer based on the retrieved documents."
)

index_client = SearchIndexClient(endpoint=search_endpoint, credential=credential)
index_client.create_or_update_knowledge_base(knowledge_base)
print(f"Knowledge base '{knowledge_base_name}' created or updated successfully.")

Настройка сообщений

# Set up messages
instructions = """
A Q&A agent that can answer questions about the Earth at night.
If you don't have the answer, respond with "I don't know".
"""

messages = [
    {
        "role": "system",
        "content": instructions
    }
]

Запустите поток извлечения

Анализирует всю беседу, чтобы определить потребность пользователя в информации.
Раскомпозирует составной запрос в вложенные запросы.
Выполняет вложенные запросы параллельно с источником знаний.
Использует семантический рангировщик для повторного использования и фильтрации результатов.
Синтезирует лучшие результаты в ответ на естественный язык.

# Run agentic retrieval
agent_client = KnowledgeBaseRetrievalClient(endpoint=search_endpoint, knowledge_base_name=knowledge_base_name, credential=credential)
query_1 = """
    Why do suburban belts display larger December brightening than urban cores even though absolute light levels are higher downtown?
    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?
    """

messages.append({
    "role": "user",
    "content": query_1
})

req = KnowledgeBaseRetrievalRequest(
    messages=[
        KnowledgeBaseMessage(
            role=m["role"],
            content=[KnowledgeBaseMessageTextContent(text=m["content"])]
        ) for m in messages if m["role"] != "system"
    ],
    knowledge_source_params=[
        SearchIndexKnowledgeSourceParams(
            knowledge_source_name=knowledge_source_name,
            include_references=True,
            include_reference_source_data=True,
            always_query_source=True
        )
    ],
    include_activity=True,
    retrieval_reasoning_effort=KnowledgeRetrievalLowReasoningEffort
)

result = agent_client.retrieve(retrieval_request=req)
print(f"Retrieved content from '{knowledge_base_name}' successfully.")

Проверьте ответ, активность и ссылки

В следующем коде отображаются ответы, действия и ссылки из конвейера извлечения, где:

response_contents предоставляет синтезированный, созданный LLM-ответ на запрос, который ссылается на извлеченные документы. Если синтез ответа не включен, этот раздел содержит содержимое, извлеченное непосредственно из документов.
activity_contents отслеживает шаги, выполненные во время процесса извлечения, включая вложенные запросы, созданные gpt-5-mini развертыванием, и маркеры, используемые для семантического ранжирования, планирования запросов и синтеза ответов.
references_contents перечисляет документы, использованные для создания ответа, каждый из которых определяется своим doc_key.

# Display the response, activity, and references
response_contents = []
activity_contents = []
references_contents = []

response_parts = []
for resp in result.response:
    for content in resp.content:
        response_parts.append(content.text)
response_content = "\n\n".join(response_parts) if response_parts else "No response found on 'result'"

response_contents.append(response_content)

# Print the three string values
print("response_content:\n", response_content, "\n")

messages.append({
    "role": "assistant",
    "content": response_content
})

if result.activity:
    activity_content = json.dumps([a.as_dict() for a in result.activity], indent=2)
else:
    activity_content = "No activity found on 'result'"
    
activity_contents.append(activity_content)
print("activity_content:\n", activity_content, "\n")

if result.references:
    references_content = json.dumps([r.as_dict() for r in result.references], indent=2)
else:
    references_content = "No references found on 'result'"
    
references_contents.append(references_content)
print("references_content:\n", references_content)

Продолжить беседу

# Continue the conversation
query_2 = "How do I find lava at night?"
messages.append({
    "role": "user",
    "content": query_2
})

req = KnowledgeBaseRetrievalRequest(
    messages=[
        KnowledgeBaseMessage(
            role=m["role"],
            content=[KnowledgeBaseMessageTextContent(text=m["content"])]
        ) for m in messages if m["role"] != "system"
    ],
    knowledge_source_params=[
        SearchIndexKnowledgeSourceParams(
            knowledge_source_name=knowledge_source_name,
            include_references=True,
            include_reference_source_data=True,
            always_query_source=True
        )
    ],
    include_activity=True,
    retrieval_reasoning_effort=KnowledgeRetrievalLowReasoningEffort
)

result = agent_client.retrieve(retrieval_request=req)
print(f"Retrieved content from '{knowledge_base_name}' successfully.")

Ознакомьтесь с новым ответом, действиями и ссылками

В следующем коде отображаются новые ответы, действия и ссылки из конвейера извлечения.

# Display the new retrieval response, activity, and references
response_parts = []
for resp in result.response:
    for content in resp.content:
        response_parts.append(content.text)
response_content = "\n\n".join(response_parts) if response_parts else "No response found on 'result'"

response_contents.append(response_content)

# Print the three string values
print("response_content:\n", response_content, "\n")

if result.activity:
    activity_content = json.dumps([a.as_dict() for a in result.activity], indent=2)
else:
    activity_content = "No activity found on 'result'"
    
activity_contents.append(activity_content)
print("activity_content:\n", activity_content, "\n")

if result.references:
    references_content = json.dumps([r.as_dict() for r in result.references], indent=2)
else:
    references_content = "No references found on 'result'"
    
references_contents.append(references_content)
print("references_content:\n", references_content)

Очистите ресурсы

На портале Azure вы можете управлять ресурсами поиска и поиска ИИ Azure, выбрав все ресурсы или группы ресурсов в левой области.

В противном случае следующий код из agentic-retrieval.py удалил объекты, которые вы создали в этом кратком руководстве.

Удаление базы знаний

index_client = SearchIndexClient(endpoint=search_endpoint, credential=credential)
index_client.delete_knowledge_base(knowledge_base_name)
print(f"Knowledge base '{knowledge_base_name}' deleted successfully.")

Удаление источника знаний

index_client = SearchIndexClient(endpoint=search_endpoint, credential=credential)
index_client.delete_knowledge_source(knowledge_source=knowledge_source_name)
print(f"Knowledge source '{knowledge_source_name}' deleted successfully.")

Удаление индекса поиска

index_client = SearchIndexClient(endpoint=search_endpoint, credential=credential)
index_client.delete_index(index_name)
print(f"Index '{index_name}' deleted successfully.")

Замечание

Подсказка

В этом быстром старте TypeScript используется версия REST API 2025-05-01-preview, которая применяет устаревшую терминологию "агента знаний" и не поддерживает последние функции, доступные в версии 2025-11-01-preview. Сведения об использовании этих функций см. в версии C#, Python или REST.

Предпосылки

Учетная запись Azure с активной подпиской. Создайте учетную запись бесплатно .
Служба поиска ИИ Azure в любой регионе, который предоставляет агентивное извлечение.
Проект Microsoft Foundry. При создании проекта Foundry вы получите ресурс Foundry (который требуется для развертываний моделей).
Azure CLI для проверки подлинности без ключа с помощью идентификатора Microsoft Entra.

Настройка доступа

Чтобы настроить доступ для этого краткого руководства, выберите оба следующих вкладки.

Поиск по искусственному интеллекту Azure
Microsoft Foundry

В службе поиска по искусственному интеллекту Azure:

Включите доступ на основе ролей.
Создайте управляемое удостоверение, назначаемое системой.
Назначьте следующие роли себе.
- Участник службы поиска
- Участник данных индекса поиска
- Средство чтения индексов поиска

Это важно

Агентное извлечение имеет две модели выставления счетов на основе токенов.

Выставление счетов за агентный поиск в Azure AI.
Выставление счетов из Azure OpenAI для планирования запросов и синтеза ответов.

Для получения дополнительной информации см. Доступность и цены агентских запросов.

Получение конечных точек

Чтобы получить конечные точки для этого краткого руководства, выберите оба следующих вкладки.

Поиск по искусственному интеллекту Azure
Microsoft Foundry

Войдите на портал Azure и выберите службу поиска.
В левой области выберите "Обзор".
Запишите конечную точку, которая должна выглядеть следующим https://my-service.search.windows.netобразом.

Войдите на портал Azure и выберите ресурс Foundry.
В левой области выберите ключи управления> ресурсамии конечную точку.
Выберите вкладку OpenAI .
Запишите конечную точку, которая должна выглядеть следующим https://my-resource.openai.azure.comобразом.

Развертывание моделей

Чтобы использовать агентное извлечение, необходимо развернуть две модели Azure OpenAI в проекте Foundry.

Модель внедрения для преобразования текста в вектор. В этом кратком руководстве используется text-embedding-3-large, но вы можете использовать какую-либо модель text-embedding.
LLM для планирования запросов и создания ответов. В этом кратком введении используется gpt-5-mini, но вы можете использовать любой поддерживаемый LLM для агентного извлечения.

Инструкции по развертыванию см. в статье "Развертывание моделей Azure OpenAI с помощью Foundry".

Настройка среды

Создайте новую папку quickstart-agentic-retrieval для хранения приложения и откройте Visual Studio Code в этой папке с помощью следующей команды:
```
mkdir quickstart-agentic-retrieval && cd quickstart-agentic-retrieval
```
Создайте package.json с помощью следующей команды:
```
npm init -y
```
Обновите package.json на ECMAScript с помощью следующей команды:
```
npm pkg set type=module
```
Установите клиентскую библиотеку поиска ИИ Azure (Azure.Search.Documents) для JavaScript:
```
npm install @azure/search-documents --version 12.2.0-alpha.20250606.1
```
Установите клиентская библиотека Azure OpenAI с помощью:
```
npm install @azure/openai --version 5.10.1
```
Установите пакет dotenv для загрузки переменных среды из файла .env с помощью:
```
npm install dotenv
```
Для рекомендуемой проверки подлинности без ключа с помощью идентификатора Microsoft Entra установите клиентская библиотека удостоверений Azure с помощью:
```
npm install @azure/identity
```

Запустите код

Создайте файл с именем .env в папке quickstart-agentic-retrieval и добавьте следующие переменные среды:
```
AZURE_OPENAI_ENDPOINT=https://<your-ai-foundry-resource-name>.openai.azure.com/
AZURE_OPENAI_GPT_DEPLOYMENT=gpt-5-mini
AZURE_OPENAI_EMBEDDING_DEPLOYMENT=text-embedding-3-large
AZURE_SEARCH_ENDPOINT=https://<your-search-service-name>.search.windows.net
AZURE_SEARCH_INDEX_NAME=agentic-retrieval-sample
```
Замените <your-search-service-name> на фактическое имя службы Azure AI Search и <your-ai-foundry-resource-name> на имя ресурса Foundry.

Вставьте следующий код в новый файл с именем index.ts:

import { DefaultAzureCredential, getBearerTokenProvider } from '@azure/identity';
import { 
    SearchIndexClient, 
    SearchClient,
    SearchIndex,
    SearchField,
    VectorSearch,
    VectorSearchProfile,
    HnswAlgorithmConfiguration,
    AzureOpenAIVectorizer,
    AzureOpenAIParameters,
    SemanticSearch,
    SemanticConfiguration,
    SemanticPrioritizedFields,
    SemanticField
} from '@azure/search-documents';
import { AzureOpenAI } from "openai/index.mjs";

// Load the .env file if it exists
import * as dotenv from "dotenv";
dotenv.config();

// Configuration - Update these values for your environment
const config = {
    searchEndpoint: process.env.AZURE_SEARCH_ENDPOINT || "https://your-search-service.search.windows.net",
    azureOpenAIEndpoint: process.env.AZURE_OPENAI_ENDPOINT || "https://your-ai-foundry-resource.openai.azure.com/",
    azureOpenAIGptDeployment: process.env.AZURE_OPENAI_GPT_DEPLOYMENT || "gpt-5-mini",
    azureOpenAIGptModel: "gpt-5-mini",
    azureOpenAIApiVersion: process.env.OPENAI_API_VERSION || "2025-03-01-preview",
    azureOpenAIEmbeddingDeployment: process.env.AZURE_OPENAI_EMBEDDING_DEPLOYMENT || "text-embedding-3-large",
    azureOpenAIEmbeddingModel: "text-embedding-3-large",
    indexName: "earth_at_night",
    agentName: "earth-search-agent",
    searchApiVersion: "2025-05-01-Preview"
};

// Earth at Night document interface
interface EarthAtNightDocument {
    id: string;
    page_chunk: string;
    page_embedding_text_3_large: number[];
    page_number: number;
}

// Knowledge agent message interface
interface KnowledgeAgentMessage {
    role: 'user' | 'assistant' | 'system';
    content: string;
}

// Agentic retrieval response interface
interface AgenticRetrievalResponse {
    response?: string | any[];
    references?: Array<{
        docKey?: string;
        content?: string;
        score?: number;
        referenceType?: string;
        type?: string;
        SourceData?: any;
        Id?: string;
        ActivitySource?: number;
        // Allow any additional properties
        [key: string]: any;
    }>;
    activity?: Array<{
        step?: string;
        description?: string;
        tokensUsed?: number;
        activityType?: string;
        type?: string;
        InputTokens?: number;
        OutputTokens?: number;
        TargetIndex?: string;
        QueryTime?: string;
        Query?: any;
        Count?: number;
        ElapsedMs?: number | null;
        Id?: number;
        // Allow any additional properties
        [key: string]: any;
    }>;
    // Add any other possible response fields
    [key: string]: any;
}

async function main(): Promise<void> {
    try {
        console.log("🚀 Starting Azure AI Search agentic retrieval quickstart...\n");

        // Initialize Azure credentials using managed identity (recommended)
        const credential = new DefaultAzureCredential();

        // Create search clients
        const searchIndexClient = new SearchIndexClient(config.searchEndpoint, credential);
        const searchClient = new SearchClient<EarthAtNightDocument>(config.searchEndpoint, config.indexName, credential);

        // Create Azure OpenAI client
        const scope = "https://cognitiveservices.azure.com/.default";
        const azureADTokenProvider = getBearerTokenProvider(credential, scope);
        const openAIClient = new AzureOpenAI({
            endpoint: config.azureOpenAIEndpoint,
            apiVersion: config.azureOpenAIApiVersion,
            azureADTokenProvider,
        });

        // Create search index with vector and semantic capabilities
        await createSearchIndex(searchIndexClient);

        // Upload sample documents
        await uploadDocuments(searchClient);

        // Create knowledge agent for agentic retrieval
        await createKnowledgeAgent(credential);

        // Run agentic retrieval with conversation
        await runAgenticRetrieval(credential, openAIClient);

        // Clean up - Delete knowledge agent and search index
        await deleteKnowledgeAgent(credential);
        await deleteSearchIndex(searchIndexClient);

        console.log("✅ Quickstart completed successfully!");

    } catch (error) {
        console.error("❌ Error in main execution:", error);
        throw error;
    }
}

async function createSearchIndex(indexClient: SearchIndexClient): Promise<void> {
    console.log("📊 Creating search index...");

    const index: SearchIndex = {
        name: config.indexName,
        fields: [
            {
                name: "id",
                type: "Edm.String",
                key: true,
                filterable: true,
                sortable: true,
                facetable: true
            } as SearchField,
            {
                name: "page_chunk",
                type: "Edm.String",
                searchable: true,
                filterable: false,
                sortable: false,
                facetable: false
            } as SearchField,
            {
                name: "page_embedding_text_3_large",
                type: "Collection(Edm.Single)",
                searchable: true,
                filterable: false,
                sortable: false,
                facetable: false,
                vectorSearchDimensions: 3072,
                vectorSearchProfileName: "hnsw_text_3_large"
            } as SearchField,
            {
                name: "page_number",
                type: "Edm.Int32",
                filterable: true,
                sortable: true,
                facetable: true
            } as SearchField
        ],
        vectorSearch: {
            profiles: [
                {
                    name: "hnsw_text_3_large",
                    algorithmConfigurationName: "alg",
                    vectorizerName: "azure_openai_text_3_large"
                } as VectorSearchProfile
            ],
            algorithms: [
                {
                    name: "alg",
                    kind: "hnsw"
                } as HnswAlgorithmConfiguration
            ],
            vectorizers: [
                {
                    vectorizerName: "azure_openai_text_3_large",
                    kind: "azureOpenAI",
                    parameters: {
                        resourceUrl: config.azureOpenAIEndpoint,
                        deploymentId: config.azureOpenAIEmbeddingDeployment,
                        modelName: config.azureOpenAIEmbeddingModel
                    } as AzureOpenAIParameters
                } as AzureOpenAIVectorizer
            ]
        } as VectorSearch,
        semanticSearch: {
            defaultConfigurationName: "semantic_config",
            configurations: [
                {
                    name: "semantic_config",
                    prioritizedFields: {
                        contentFields: [
                            { name: "page_chunk" } as SemanticField
                        ]
                    } as SemanticPrioritizedFields
                } as SemanticConfiguration
            ]
        } as SemanticSearch
    };

    try {
        await indexClient.createOrUpdateIndex(index);
        console.log(`✅ Index '${config.indexName}' created or updated successfully.`);
    } catch (error) {
        console.error("❌ Error creating index:", error);
        throw error;
    }
}

async function deleteSearchIndex(indexClient: SearchIndexClient): Promise<void> {
    console.log("🗑️ Deleting search index...");

    try {
        await indexClient.deleteIndex(config.indexName);
        console.log(`✅ Search index '${config.indexName}' deleted successfully.`);

    } catch (error: any) {
        if (error?.statusCode === 404 || error?.code === 'IndexNotFound') {
            console.log(`ℹ️ Search index '${config.indexName}' does not exist or was already deleted.`);
            return;
        }
        console.error("❌ Error deleting search index:", error);
        throw error;
    }
}

// Fetch Earth at Night documents from GitHub
async function fetchEarthAtNightDocuments(): Promise<EarthAtNightDocument[]> {
    console.log("📡 Fetching Earth at Night documents from GitHub...");

    const documentsUrl = "https://raw.githubusercontent.com/Azure-Samples/azure-search-sample-data/refs/heads/main/nasa-e-book/earth-at-night-json/documents.json";

    try {
        const response = await fetch(documentsUrl);

        if (!response.ok) {
            throw new Error(`Failed to fetch documents: ${response.status} ${response.statusText}`);
        }

        const documents = await response.json();
        console.log(`✅ Fetched ${documents.length} documents from GitHub`);

        // Validate and transform documents to match our interface
        const transformedDocuments: EarthAtNightDocument[] = documents.map((doc: any, index: number) => {
            return {
                id: doc.id || String(index + 1),
                page_chunk: doc.page_chunk || doc.content || '',
                page_embedding_text_3_large: doc.page_embedding_text_3_large || new Array(3072).fill(0.1),
                page_number: doc.page_number || index + 1
            };
        });

        return transformedDocuments;

    } catch (error) {
        console.error("❌ Error fetching documents from GitHub:", error);
        console.log("🔄 Falling back to sample documents...");

        // Fallback to sample documents if fetch fails
        return [
            {
                id: "1",
                page_chunk: "The Earth at night reveals the patterns of human settlement and economic activity. City lights trace the contours of civilization, creating a luminous map of where people live and work.",
                page_embedding_text_3_large: new Array(3072).fill(0.1),
                page_number: 1
            },
            {
                id: "2", 
                page_chunk: "From space, the aurora borealis appears as shimmering curtains of green and blue light dancing across the polar regions.",
                page_embedding_text_3_large: new Array(3072).fill(0.2),
                page_number: 2
            }
            // Add more fallback documents as needed
        ];
    }
}

async function uploadDocuments(searchClient: SearchClient<EarthAtNightDocument>): Promise<void> {
    console.log("📄 Uploading documents...");

    try {
        // Fetch documents from GitHub
        const documents = await fetchEarthAtNightDocuments();

        const result = await searchClient.uploadDocuments(documents);
        console.log(`✅ Uploaded ${result.results.length} documents successfully.`);

        // Wait for indexing to complete
        console.log("⏳ Waiting for document indexing to complete...");
        await new Promise(resolve => setTimeout(resolve, 5000));
        console.log("✅ Document indexing completed.");

    } catch (error) {
        console.error("❌ Error uploading documents:", error);
        throw error;
    }
}

async function createKnowledgeAgent(credential: DefaultAzureCredential): Promise<void> {

    // In case the agent already exists, delete it first
    await deleteKnowledgeAgent(credential);

    console.log("🤖 Creating knowledge agent...");

    const agentDefinition = {
        name: config.agentName,
        description: "Knowledge agent for Earth at Night e-book content",
        models: [
            {
                kind: "azureOpenAI",
                azureOpenAIParameters: {
                    resourceUri: config.azureOpenAIEndpoint,
                    deploymentId: config.azureOpenAIGptDeployment,
                    modelName: config.azureOpenAIGptModel
                }
            }
        ],
        targetIndexes: [
            {
                indexName: config.indexName,
                defaultRerankerThreshold: 2.5
            }
        ]
    };

    try {
        const token = await getAccessToken(credential, "https://search.azure.com/.default");
        const response = await fetch(`${config.searchEndpoint}/agents/${config.agentName}?api-version=${config.searchApiVersion}`, {
            method: 'PUT',
            headers: {
                'Content-Type': 'application/json',
                'Authorization': `Bearer ${token}`
            },
            body: JSON.stringify(agentDefinition)
        });

        if (!response.ok) {
            const errorText = await response.text();
            throw new Error(`Failed to create knowledge agent: ${response.status} ${response.statusText}\n${errorText}`);
        }

        console.log(`✅ Knowledge agent '${config.agentName}' created successfully.`);

    } catch (error) {
        console.error("❌ Error creating knowledge agent:", error);
        throw error;
    }
}

async function runAgenticRetrieval(credential: DefaultAzureCredential, openAIClient: AzureOpenAI): Promise<void> {
    console.log("🔍 Running agentic retrieval...");

    const messages: KnowledgeAgentMessage[] = [
        {
            role: "system",
            content: `A Q&A agent that can answer questions about the Earth at night.
Sources have a JSON format with a ref_id that must be cited in the answer.
If you do not have the answer, respond with "I don't know".`
        },
        {
            role: "user",
            content: "Why do suburban belts display larger December brightening than urban cores even though absolute light levels are higher downtown? 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?"
        }
    ];

    try {
        // Call agentic retrieval API
        const userMessages = messages.filter(m => m.role !== "system");
        const retrievalResponse = await callAgenticRetrieval(credential, userMessages);

        // Extract the assistant response from agentic retrieval
        let assistantContent = '';
        if (typeof retrievalResponse.response === 'string') {
            assistantContent = retrievalResponse.response;
        } else if (Array.isArray(retrievalResponse.response)) {
            assistantContent = JSON.stringify(retrievalResponse.response);
        }

        // Add assistant response to conversation history
        messages.push({
            role: "assistant",
            content: assistantContent
        });

        console.log(assistantContent);

        // Log activities and results...
        console.log("\nActivities:");
        if (retrievalResponse.activity && Array.isArray(retrievalResponse.activity)) {
            retrievalResponse.activity.forEach((activity) => {
                const activityType = activity.activityType || activity.type || 'UnknownActivityRecord';
                console.log(`Activity Type: ${activityType}`);
                console.log(JSON.stringify(activity, null, 2));
            });
        }

        console.log("Results");
        if (retrievalResponse.references && Array.isArray(retrievalResponse.references)) {
            retrievalResponse.references.forEach((reference) => {
                const referenceType = reference.referenceType || reference.type || 'AzureSearchDoc';
                console.log(`Reference Type: ${referenceType}`);
                console.log(JSON.stringify(reference, null, 2));
            });
        }

        // Now do chat completion with full conversation history
        await generateFinalAnswer(openAIClient, messages);

        // Continue conversation with second question
        await continueConversation(credential, openAIClient, messages);

    } catch (error) {
        console.error("❌ Error in agentic retrieval:", error);
        throw error;
    }
}

async function generateFinalAnswer(
    openAIClient: AzureOpenAI,
    messages: KnowledgeAgentMessage[]
): Promise<void> {

    console.log("\n[ASSISTANT]: ");

    try {
        const completion = await openAIClient.chat.completions.create({
            model: config.azureOpenAIGptDeployment,
            messages: messages.map(m => ({ role: m.role, content: m.content })) as any,
            max_tokens: 1000,
            temperature: 0.7
        });

        const answer = completion.choices[0].message.content;
        console.log(answer?.replace(/\./g, "\n"));

        // Add this response to conversation history
        if (answer) {
            messages.push({
                role: "assistant",
                content: answer
            });
        }

    } catch (error) {
        console.error("❌ Error generating final answer:", error);
        throw error;
    }
}

async function callAgenticRetrieval(
    credential: DefaultAzureCredential, 
    messages: KnowledgeAgentMessage[]
): Promise<AgenticRetrievalResponse> {

    // Convert messages to the correct format expected by the Knowledge agent
    const agentMessages = messages.map(msg => ({
        role: msg.role,
        content: [
            {
                type: "text",
                text: msg.content
            }
        ]
    }));

    const retrievalRequest = {
        messages: agentMessages,
        targetIndexParams: [
            {
                indexName: config.indexName,
                rerankerThreshold: 2.5,
                maxDocsForReranker: 100,
                includeReferenceSourceData: true
            }
        ]
    };

    const token = await getAccessToken(credential, "https://search.azure.com/.default");
    const response = await fetch(
        `${config.searchEndpoint}/agents/${config.agentName}/retrieve?api-version=${config.searchApiVersion}`,
        {
            method: 'POST',
            headers: {
                'Content-Type': 'application/json',
                'Authorization': `Bearer ${token}`
            },
            body: JSON.stringify(retrievalRequest)
        }
    );

    if (!response.ok) {
        const errorText = await response.text();
        throw new Error(`Agentic retrieval failed: ${response.status} ${response.statusText}\n${errorText}`);
    }

    return await response.json() as AgenticRetrievalResponse;
}

async function deleteKnowledgeAgent(credential: DefaultAzureCredential): Promise<void> {
    console.log("🗑️ Deleting knowledge agent...");

    try {
        const token = await getAccessToken(credential, "https://search.azure.com/.default");
        const response = await fetch(`${config.searchEndpoint}/agents/${config.agentName}?api-version=${config.searchApiVersion}`, {
            method: 'DELETE',
            headers: {
                'Authorization': `Bearer ${token}`
            }
        });

        if (!response.ok) {
            if (response.status === 404) {
                console.log(`ℹ️ Knowledge agent '${config.agentName}' does not exist or was already deleted.`);
                return;
            }
            const errorText = await response.text();
            throw new Error(`Failed to delete knowledge agent: ${response.status} ${response.statusText}\n${errorText}`);
        }

        console.log(`✅ Knowledge agent '${config.agentName}' deleted successfully.`);

    } catch (error) {
        console.error("❌ Error deleting knowledge agent:", error);
        throw error;
    }
}

async function continueConversation(
    credential: DefaultAzureCredential,
    openAIClient: AzureOpenAI,
    messages: KnowledgeAgentMessage[]
): Promise<void> {
    console.log("\n💬 === Continuing Conversation ===");

    // Add follow-up question
    const followUpQuestion = "How do I find lava at night?"; 
    console.log(`❓ Follow-up question: ${followUpQuestion}`);

    messages.push({
        role: "user",
        content: followUpQuestion
    });

    try {
        // Don't include system messages in this retrieval
        const userAssistantMessages = messages.filter((m: KnowledgeAgentMessage) => m.role !== "system");
        const newRetrievalResponse = await callAgenticRetrieval(credential, userAssistantMessages);

        // Extract assistant response and add to conversation
        let assistantContent = '';
        if (typeof newRetrievalResponse.response === 'string') {
            assistantContent = newRetrievalResponse.response;
        } else if (Array.isArray(newRetrievalResponse.response)) {
            assistantContent = JSON.stringify(newRetrievalResponse.response);
        }

        // Add assistant response to conversation history
        messages.push({
            role: "assistant",
            content: assistantContent
        });

        console.log(assistantContent);

        // Log activities and results like the first retrieval
        console.log("\nActivities:");
        if (newRetrievalResponse.activity && Array.isArray(newRetrievalResponse.activity)) {
            newRetrievalResponse.activity.forEach((activity) => {
                const activityType = activity.activityType || activity.type || 'UnknownActivityRecord';
                console.log(`Activity Type: ${activityType}`);
                console.log(JSON.stringify(activity, null, 2));
            });
        }

        console.log("Results");
        if (newRetrievalResponse.references && Array.isArray(newRetrievalResponse.references)) {
            newRetrievalResponse.references.forEach((reference) => {
                const referenceType = reference.referenceType || reference.type || 'AzureSearchDoc';
                console.log(`Reference Type: ${referenceType}`);
                console.log(JSON.stringify(reference, null, 2));
            });
        }

        // Generate final answer for follow-up
        await generateFinalAnswer(openAIClient, messages);

        console.log("\n🎉 === Conversation Complete ===");

    } catch (error) {
        console.error("❌ Error in conversation continuation:", error);
        throw error;
    }
}

async function getAccessToken(credential: DefaultAzureCredential, scope: string): Promise<string> {
    const tokenResponse = await credential.getToken(scope);
    return tokenResponse.token;
}

// Error handling wrapper
async function runWithErrorHandling(): Promise<void> {
    try {
        await main();
    } catch (error) {
        console.error("💥 Application failed:", error);
        process.exit(1);
    }
}

// Execute the application - ES module style
runWithErrorHandling();

export {
    main,
    createSearchIndex,
    deleteSearchIndex,
    fetchEarthAtNightDocuments,
    uploadDocuments,
    createKnowledgeAgent,
    deleteKnowledgeAgent,
    runAgenticRetrieval,
    EarthAtNightDocument,
    KnowledgeAgentMessage,
    AgenticRetrievalResponse
};

tsconfig.json Создайте файл для транспиля кода TypeScript и скопируйте следующий код для ECMAScript.

{
    "compilerOptions": {
      "module": "NodeNext",
      "target": "ES2022", // Supports top-level await
      "moduleResolution": "NodeNext",
      "skipLibCheck": true, // Avoid type errors from node_modules
      "strict": true // Enable strict type-checking options
    },
    "include": ["*.ts"]
}

Транспилировать код с TypeScript на JavaScript.
```
tsc
```
Войдите в Azure с помощью следующей команды:
```
az login
```
Запустите код JavaScript со следующей командой:
```
node index.js
```

Выходные данные

Выходные данные приложения должны выглядеть следующим образом:

[[email protected]] injecting env (0) from .env (tip: ⚙️  override existing env vars with { override: true })
🚀 Starting Azure AI Search agentic retrieval quickstart...

📊 Creating search index...
✅ Index 'earth_at_night' created or updated successfully.
📄 Uploading documents...
📡 Fetching Earth at Night documents from GitHub...
✅ Fetched 194 documents from GitHub
✅ Uploaded 194 documents successfully.
⏳ Waiting for document indexing to complete...
✅ Document indexing completed.
🗑️ Deleting knowledge agent...
ℹ️ Knowledge agent 'earth-search-agent' does not exist or was already deleted.
🤖 Creating knowledge agent...
✅ Knowledge agent 'earth-search-agent' created successfully.
🔍 Running agentic retrieval...
[{"role":"assistant","content":[{"type":"text","text":"[]"}]}]

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Activity Type: AzureSearchQuery
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  "targetIndex": "earth_at_night",
  "query": {
    "search": "Why is the Phoenix nighttime street grid sharply visible from space, while large stretches of interstate highways between Midwestern cities appear comparatively dim?",
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Results

[ASSISTANT]: 
Suburban belts show larger December brightening than urban cores despite higher absolute light levels downtown because suburban areas often have more seasonal variation in lighting usage, such as increased decorative and outdoor lighting during the holiday season in December
 Urban cores typically have more constant and dense lighting throughout the year, so the relative increase in brightness during December is less pronounced compared to suburban areas
\n\nThe Phoenix nighttime street grid is sharply visible from space because the city has a well-planned, extensive grid of streets with consistent and bright street lighting
 In contrast, large stretches of interstate highways between Midwestern cities appear comparatively dim because these highways have less continuous lighting and lower intensity lights, making them less visible from space
\n\n(Note: These explanations are based on general knowledge about urban lighting patterns and visibility from space; specific studies or sources were not provided
)

💬 === Continuing Conversation ===
❓ Follow-up question: How do I find lava at night?
[{"role":"assistant","content":[{"type":"text","text":"[{\"ref_id\":0,\"content\":\"<!-- PageHeader=\\\"Volcanoes\\\" -->\\n\\n### Nighttime Glow at Mount Etna - Italy\\n\\nAt about 2:30 a.m. local time on March 16, 2017, the VIIRS DNB on the Suomi NPP satellite captured this nighttime image of lava flowing on Mount Etna in Sicily, Italy. Etna is one of the world's most active volcanoes.\\n\\n#### Figure: Location of Mount Etna\\nA world globe is depicted, with a marker indicating the location of Mount Etna in Sicily, Italy, in southern Europe near the center of the Mediterranean Sea.\\n\\n<!-- PageFooter=\\\"Earth at Night\\\" -->\\n<!-- PageNumber=\\\"48\\\" -->\"},{\"ref_id\":1,\"content\":\"<!-- PageHeader=\\\"Volcanoes\\\" -->\\n\\n## Volcanoes\\n\\n### The Infrared Glows of Kilauea's Lava Flows—Hawaii\\n\\nIn early May 2018, an eruption on Hawaii's Kilauea volcano began to unfold. The eruption took a dangerous turn on May 3, 2018, when new fissures opened in the residential neighborhood of Leilani Estates. During the summer-long eruptive event, other fissures emerged along the East Rift Zone. Lava from vents along the rift zone flowed downslope, reaching the ocean in several areas, and filling in Kapoho Bay.\\n\\nA time series of Landsat 8 imagery shows the progression of the lava flows from May 16 to August 13. The night view combines thermal, shortwave infrared, and near-infrared wavelengths to tease out the very hot lava (bright white), cooling lava (red), and lava flows obstructed by clouds (purple).\\n\\n#### Figure: Location of Kilauea Volcano, Hawaii\\n\\nA globe is shown centered on North America, with a marker placed in the Pacific Ocean indicating the location of Hawaii, to the southwest of the mainland United States.\\n\\n<!-- PageFooter=\\\"Earth at Night\\\" -->\\n<!-- PageNumber=\\\"44\\\" -->\"},{\"ref_id\":2,\"content\":\"For the first time in perhaps a decade, Mount Etna experienced a \\\"flank eruption\\\"—erupting from its side instead of its summit—on December 24, 2018. The activity was accompanied by 130 earthquakes occurring over three hours that morning. Mount Etna, Europe’s most active volcano, has seen periodic activity on this part of the mountain since 2013. The Operational Land Imager (OLI) on the Landsat 8 satellite acquired the main image of Mount Etna on December 28, 2018.\\n\\nThe inset image highlights the active vent and thermal infrared signature from lava flows, which can be seen near the newly formed fissure on the southeastern side of the volcano. The inset was created with data from OLI and the Thermal Infrared Sensor (TIRS) on Landsat 8. Ash spewing from the fissure cloaked adjacent villages and delayed aircraft from landing at the nearby Catania airport. Earthquakes occurred in the subsequent days after the initial eruption and displaced hundreds of people from their homes.\\n\\nFor nighttime images of Mount Etna’s March 2017 eruption, see pages 48–51.\\n\\n---\\n\\n### Hazards of Volcanic Ash Plumes and Satellite Observation\\n\\nWith the help of moonlight, satellite instruments can track volcanic ash plumes, which present significant hazards to airplanes in flight. The volcanic ash—composed of tiny pieces of glass and rock—is abrasive to engine turbine blades, and can melt on the blades and other engine parts, causing damage and even engine stalls. This poses a danger to both the plane’s integrity and passenger safety. Volcanic ash also reduces visibility for pilots and can cause etching of windshields, further reducing pilots’ ability to see. Nightlight images can be combined with thermal images to provide a more complete view of volcanic activity on Earth’s surface.\\n\\nThe VIIRS Day/Night Band (DNB) on polar-orbiting satellites uses faint light sources such as moonlight, airglow (the atmosphere’s self-illumination through chemical reactions), zodiacal light (sunlight scattered by interplanetary dust), and starlight from the Milky Way. Using these dim light sources, the DNB can detect changes in clouds, snow cover, and sea ice:\\n\\n#### Table: Light Sources Used by VIIRS DNB\\n\\n| Light Source         | Description                                                                  |\\n|----------------------|------------------------------------------------------------------------------|\\n| Moonlight            | Reflected sunlight from the Moon, illuminating Earth's surface at night      |\\n| Airglow              | Atmospheric self-illumination from chemical reactions                        |\\n| Zodiacal Light       | Sunlight scattered by interplanetary dust                                    |\\n| Starlight/Milky Way  | Faint illumination provided by stars in the Milky Way                        |\\n\\nGeostationary Operational Environmental Satellites (GOES), managed by NOAA, orbit over Earth’s equator and offer uninterrupted observations of North America. High-latitude areas such as Alaska benefit from polar-orbiting satellites like Suomi NPP, which provide overlapping coverage at the poles, enabling more data collection in these regions. During polar darkness (winter months), VIIRS DNB data allow scientists to:\\n\\n- Observe sea ice formation\\n- Monitor snow cover extent at the highest latitudes\\n- Detect open water for ship navigation\\n\\n#### Table: Satellite Coverage Overview\\n\\n| Satellite Type          | Orbit           | Coverage Area         | Special Utility                              |\\n|------------------------|-----------------|----------------------|----------------------------------------------|\\n| GOES                   | Geostationary   | Equatorial/North America | Continuous regional monitoring              |\\n| Polar-Orbiting (e.g., Suomi NPP) | Polar-orbiting    | Poles/high latitudes      | Overlapping passes; useful during polar night|\\n\\n---\\n\\n### Weather Forecasting and Nightlight Data\\n\\nThe use of nightlight data by weather forecasters is growing as the VIIRS instrument enables observation of clouds at night illuminated by sources such as moonlight and lightning. Scientists use these data to study the nighttime behavior of weather systems, including severe storms, which can develop and strike populous areas at night as well as during the day. Combined with thermal data, visible nightlight data allow the detection of clouds at various heights in the atmosphere, such as dense marine fog. This capability enables weather forecasters to issue marine advisories with higher confidence, leading to greater utility. (See \\\"Marine Layer Clouds—California\\\" on page 56.)\\n\\nIn this section of the book, you will see how nightlight data are used to observe nature’s spectacular light shows across a wide range of sources.\\n\\n---\\n\\n#### Notable Data from Mount Etna Flank Eruption (December 2018)\\n\\n| Event/Observation                  | Details                                                                    |\\n|-------------------------------------|----------------------------------------------------------------------------|\\n| Date of Flank Eruption              | December 24, 2018                                                          |\\n| Number of Earthquakes               | 130 earthquakes within 3 hours                                              |\\n| Image Acquisition                   | December 28, 2018 by Landsat 8 OLI                                         |\\n| Location of Eruption                | Southeastern side of Mount Etna                                            |\\n| Thermal Imaging Data                | From OLI and TIRS (Landsat 8), highlighting active vent and lava flows     |\\n| Impact on Villages/Air Transport    | Ash covered villages; delayed aircraft at Catania airport                  |\\n| Displacement                        | Hundreds of residents displaced                                            |\\n| Ongoing Seismic Activity            | Earthquakes continued after initial eruption                               |\\n\\n---\\n\\n<!-- PageFooter=\\\"Earth at Night\\\" -->\\n<!-- PageNumber=\\\"30\\\" -->\"},{\"ref_id\":3,\"content\":\"# Volcanoes\\n\\n---\\n\\n### Mount Etna Erupts - Italy\\n\\nThe highly active Mount Etna in Italy sent red lava rolling down its flank on March 19, 2017. An astronaut onboard the ISS took the photograph below of the volcano and its environs that night. City lights surround the mostly dark volcanic area.\\n\\n---\\n\\n#### Figure 1: Location of Mount Etna, Italy\\n\\nA world map highlighting the location of Mount Etna in southern Italy. The marker indicates its geographic placement on the east coast of Sicily, Italy, in the Mediterranean region, south of mainland Europe and north of northern Africa.\\n\\n---\\n\\n#### Figure 2: Nighttime View of Mount Etna's Eruption and Surrounding Cities\\n\\nThis is a nighttime satellite image taken on March 19, 2017, showing the eruption of Mount Etna (southeastern cone) with visible bright red and orange coloring indicating flowing lava from a lateral vent. The surrounding areas are illuminated by city lights, with the following geographic references labeled:\\n\\n| Location        | Position in Image         | Visible Characteristics                    |\\n|-----------------|--------------------------|--------------------------------------------|\\n| Mt. Etna (southeastern cone) | Top center-left | Bright red/orange lava flow                |\\n| Lateral vent    | Left of the volcano       | Faint red/orange flow extending outwards   |\\n| Resort          | Below the volcano, to the left   | Small cluster of lights                    |\\n| Giarre          | Top right                 | Bright cluster of city lights              |\\n| Acireale        | Center right              | Large, bright area of city lights          |\\n| Biancavilla     | Bottom left               | Smaller cluster of city lights             |\\n\\nAn arrow pointing north is shown on the image for orientation.\\n\\n---\\n\\n<!-- Earth at Night Page Footer -->\\n<!-- Page Number: 50 -->\"},{\"ref_id\":4,\"content\":\"## Nature's Light Shows\\n\\nAt night, with the light of the Sun removed, nature's brilliant glow from Earth's surface becomes visible to the naked eye from space. Some of Earth's most spectacular light shows are natural, like the aurora borealis, or Northern Lights, in the Northern Hemisphere (aurora australis, or Southern Lights, in the Southern Hemisphere). The auroras are natural electrical phenomena caused by charged particles that race from the Sun toward Earth, inducing chemical reactions in the upper atmosphere and creating the appearance of streamers of reddish or greenish light in the sky, usually near the northern or southern magnetic pole. Other natural lights can indicate danger, like a raging forest fire encroaching on a city, town, or community, or lava spewing from an erupting volcano.\\n\\nWhatever the source, the ability of humans to monitor nature's light shows at night has practical applications for society. For example, tracking fires during nighttime hours allows for continuous monitoring and enhances our ability to protect humans and other animals, plants, and infrastructure. Combined with other data sources, our ability to observe the light of fires at night allows emergency managers to more efficiently and accurately issue warnings and evacuation orders and allows firefighting efforts to continue through the night. With enough moonlight (e.g., full-Moon phase), it's even possible to track the movement of smoke plumes at night, which can impact air quality, regardless of time of day.\\n\\nAnother natural source of light at night is emitted from glowing lava flows at the site of active volcanoes. Again, with enough moonlight, these dramatic scenes can be tracked and monitored for both scientific research and public safety.\\n\\n\\n### Figure: The Northern Lights Viewed from Space\\n\\n**September 17, 2011**\\n\\nThis photo, taken from the International Space Station on September 17, 2011, shows a spectacular display of the aurora borealis (Northern Lights) as green and reddish light in the night sky above Earth. In the foreground, part of a Soyuz spacecraft is visible, silhouetted against the bright auroral light. The green glow is generated by energetic charged particles from the Sun interacting with Earth's upper atmosphere, exciting oxygen and nitrogen atoms, and producing characteristic colors. The image demonstrates the vividness and grandeur of natural night-time light phenomena as seen from orbit.\"}]"}]}]

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    "page_chunk": "<!-- PageHeader=\"Volcanoes\" -->\n\n### Nighttime Glow at Mount Etna - Italy\n\nAt about 2:30 a.m. local time on March 16, 2017, the VIIRS DNB on the Suomi NPP satellite captured this nighttime image of lava flowing on Mount Etna in Sicily, Italy. Etna is one of the world's most active volcanoes.\n\n#### Figure: Location of Mount Etna\nA world globe is depicted, with a marker indicating the location of Mount Etna in Sicily, Italy, in southern Europe near the center of the Mediterranean Sea.\n\n<!-- PageFooter=\"Earth at Night\" -->\n<!-- PageNumber=\"48\" -->"
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    "page_chunk": "<!-- PageHeader=\"Volcanoes\" -->\n\n## Volcanoes\n\n### The Infrared Glows of Kilauea's Lava Flows—Hawaii\n\nIn early May 2018, an eruption on Hawaii's Kilauea volcano began to unfold. The eruption took a dangerous turn on May 3, 2018, when new fissures opened in the residential neighborhood of Leilani Estates. During the summer-long eruptive event, other fissures emerged along the East Rift Zone. Lava from vents along the rift zone flowed downslope, reaching the ocean in several areas, and filling in Kapoho Bay.\n\nA time series of Landsat 8 imagery shows the progression of the lava flows from May 16 to August 13. The night view combines thermal, shortwave infrared, and near-infrared wavelengths to tease out the very hot lava (bright white), cooling lava (red), and lava flows obstructed by clouds (purple).\n\n#### Figure: Location of Kilauea Volcano, Hawaii\n\nA globe is shown centered on North America, with a marker placed in the Pacific Ocean indicating the location of Hawaii, to the southwest of the mainland United States.\n\n<!-- PageFooter=\"Earth at Night\" -->\n<!-- PageNumber=\"44\" -->"
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    "page_chunk": "For the first time in perhaps a decade, Mount Etna experienced a \"flank eruption\"—erupting from its side instead of its summit—on December 24, 2018. The activity was accompanied by 130 earthquakes occurring over three hours that morning. Mount Etna, Europe’s most active volcano, has seen periodic activity on this part of the mountain since 2013. The Operational Land Imager (OLI) on the Landsat 8 satellite acquired the main image of Mount Etna on December 28, 2018.\n\nThe inset image highlights the active vent and thermal infrared signature from lava flows, which can be seen near the newly formed fissure on the southeastern side of the volcano. The inset was created with data from OLI and the Thermal Infrared Sensor (TIRS) on Landsat 8. Ash spewing from the fissure cloaked adjacent villages and delayed aircraft from landing at the nearby Catania airport. Earthquakes occurred in the subsequent days after the initial eruption and displaced hundreds of people from their homes.\n\nFor nighttime images of Mount Etna’s March 2017 eruption, see pages 48–51.\n\n---\n\n### Hazards of Volcanic Ash Plumes and Satellite Observation\n\nWith the help of moonlight, satellite instruments can track volcanic ash plumes, which present significant hazards to airplanes in flight. The volcanic ash—composed of tiny pieces of glass and rock—is abrasive to engine turbine blades, and can melt on the blades and other engine parts, causing damage and even engine stalls. This poses a danger to both the plane’s integrity and passenger safety. Volcanic ash also reduces visibility for pilots and can cause etching of windshields, further reducing pilots’ ability to see. Nightlight images can be combined with thermal images to provide a more complete view of volcanic activity on Earth’s surface.\n\nThe VIIRS Day/Night Band (DNB) on polar-orbiting satellites uses faint light sources such as moonlight, airglow (the atmosphere’s self-illumination through chemical reactions), zodiacal light (sunlight scattered by interplanetary dust), and starlight from the Milky Way. Using these dim light sources, the DNB can detect changes in clouds, snow cover, and sea ice:\n\n#### Table: Light Sources Used by VIIRS DNB\n\n| Light Source         | Description                                                                  |\n|----------------------|------------------------------------------------------------------------------|\n| Moonlight            | Reflected sunlight from the Moon, illuminating Earth's surface at night      |\n| Airglow              | Atmospheric self-illumination from chemical reactions                        |\n| Zodiacal Light       | Sunlight scattered by interplanetary dust                                    |\n| Starlight/Milky Way  | Faint illumination provided by stars in the Milky Way                        |\n\nGeostationary Operational Environmental Satellites (GOES), managed by NOAA, orbit over Earth’s equator and offer uninterrupted observations of North America. High-latitude areas such as Alaska benefit from polar-orbiting satellites like Suomi NPP, which provide overlapping coverage at the poles, enabling more data collection in these regions. During polar darkness (winter months), VIIRS DNB data allow scientists to:\n\n- Observe sea ice formation\n- Monitor snow cover extent at the highest latitudes\n- Detect open water for ship navigation\n\n#### Table: Satellite Coverage Overview\n\n| Satellite Type          | Orbit           | Coverage Area         | Special Utility                              |\n|------------------------|-----------------|----------------------|----------------------------------------------|\n| GOES                   | Geostationary   | Equatorial/North America | Continuous regional monitoring              |\n| Polar-Orbiting (e.g., Suomi NPP) | Polar-orbiting    | Poles/high latitudes      | Overlapping passes; useful during polar night|\n\n---\n\n### Weather Forecasting and Nightlight Data\n\nThe use of nightlight data by weather forecasters is growing as the VIIRS instrument enables observation of clouds at night illuminated by sources such as moonlight and lightning. Scientists use these data to study the nighttime behavior of weather systems, including severe storms, which can develop and strike populous areas at night as well as during the day. Combined with thermal data, visible nightlight data allow the detection of clouds at various heights in the atmosphere, such as dense marine fog. This capability enables weather forecasters to issue marine advisories with higher confidence, leading to greater utility. (See \"Marine Layer Clouds—California\" on page 56.)\n\nIn this section of the book, you will see how nightlight data are used to observe nature’s spectacular light shows across a wide range of sources.\n\n---\n\n#### Notable Data from Mount Etna Flank Eruption (December 2018)\n\n| Event/Observation                  | Details                                                                    |\n|-------------------------------------|----------------------------------------------------------------------------|\n| Date of Flank Eruption              | December 24, 2018                                                          |\n| Number of Earthquakes               | 130 earthquakes within 3 hours                                              |\n| Image Acquisition                   | December 28, 2018 by Landsat 8 OLI                                         |\n| Location of Eruption                | Southeastern side of Mount Etna                                            |\n| Thermal Imaging Data                | From OLI and TIRS (Landsat 8), highlighting active vent and lava flows     |\n| Impact on Villages/Air Transport    | Ash covered villages; delayed aircraft at Catania airport                  |\n| Displacement                        | Hundreds of residents displaced                                            |\n| Ongoing Seismic Activity            | Earthquakes continued after initial eruption                               |\n\n---\n\n<!-- PageFooter=\"Earth at Night\" -->\n<!-- PageNumber=\"30\" -->"
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    "page_chunk": "# Volcanoes\n\n---\n\n### Mount Etna Erupts - Italy\n\nThe highly active Mount Etna in Italy sent red lava rolling down its flank on March 19, 2017. An astronaut onboard the ISS took the photograph below of the volcano and its environs that night. City lights surround the mostly dark volcanic area.\n\n---\n\n#### Figure 1: Location of Mount Etna, Italy\n\nA world map highlighting the location of Mount Etna in southern Italy. The marker indicates its geographic placement on the east coast of Sicily, Italy, in the Mediterranean region, south of mainland Europe and north of northern Africa.\n\n---\n\n#### Figure 2: Nighttime View of Mount Etna's Eruption and Surrounding Cities\n\nThis is a nighttime satellite image taken on March 19, 2017, showing the eruption of Mount Etna (southeastern cone) with visible bright red and orange coloring indicating flowing lava from a lateral vent. The surrounding areas are illuminated by city lights, with the following geographic references labeled:\n\n| Location        | Position in Image         | Visible Characteristics                    |\n|-----------------|--------------------------|--------------------------------------------|\n| Mt. Etna (southeastern cone) | Top center-left | Bright red/orange lava flow                |\n| Lateral vent    | Left of the volcano       | Faint red/orange flow extending outwards   |\n| Resort          | Below the volcano, to the left   | Small cluster of lights                    |\n| Giarre          | Top right                 | Bright cluster of city lights              |\n| Acireale        | Center right              | Large, bright area of city lights          |\n| Biancavilla     | Bottom left               | Smaller cluster of city lights             |\n\nAn arrow pointing north is shown on the image for orientation.\n\n---\n\n<!-- Earth at Night Page Footer -->\n<!-- Page Number: 50 -->"
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    "page_chunk": "## Nature's Light Shows\n\nAt night, with the light of the Sun removed, nature's brilliant glow from Earth's surface becomes visible to the naked eye from space. Some of Earth's most spectacular light shows are natural, like the aurora borealis, or Northern Lights, in the Northern Hemisphere (aurora australis, or Southern Lights, in the Southern Hemisphere). The auroras are natural electrical phenomena caused by charged particles that race from the Sun toward Earth, inducing chemical reactions in the upper atmosphere and creating the appearance of streamers of reddish or greenish light in the sky, usually near the northern or southern magnetic pole. Other natural lights can indicate danger, like a raging forest fire encroaching on a city, town, or community, or lava spewing from an erupting volcano.\n\nWhatever the source, the ability of humans to monitor nature's light shows at night has practical applications for society. For example, tracking fires during nighttime hours allows for continuous monitoring and enhances our ability to protect humans and other animals, plants, and infrastructure. Combined with other data sources, our ability to observe the light of fires at night allows emergency managers to more efficiently and accurately issue warnings and evacuation orders and allows firefighting efforts to continue through the night. With enough moonlight (e.g., full-Moon phase), it's even possible to track the movement of smoke plumes at night, which can impact air quality, regardless of time of day.\n\nAnother natural source of light at night is emitted from glowing lava flows at the site of active volcanoes. Again, with enough moonlight, these dramatic scenes can be tracked and monitored for both scientific research and public safety.\n\n\n### Figure: The Northern Lights Viewed from Space\n\n**September 17, 2011**\n\nThis photo, taken from the International Space Station on September 17, 2011, shows a spectacular display of the aurora borealis (Northern Lights) as green and reddish light in the night sky above Earth. In the foreground, part of a Soyuz spacecraft is visible, silhouetted against the bright auroral light. The green glow is generated by energetic charged particles from the Sun interacting with Earth's upper atmosphere, exciting oxygen and nitrogen atoms, and producing characteristic colors. The image demonstrates the vividness and grandeur of natural night-time light phenomena as seen from orbit."
  }
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[ASSISTANT]: 
To find lava at night, satellite instruments like the VIIRS Day/Night Band (DNB) and thermal infrared sensors are used to detect the glow of very hot lava flows on the Earth's surface
 For example, nighttime satellite images have captured lava flowing from active volcanoes such as Mount Etna in Italy and Kilauea in Hawaii, where the hot lava emits bright light visible from space even at night
 Scientists combine thermal, shortwave infrared, and near-infrared data to distinguish very hot lava (bright white), cooling lava (red), and areas obscured by clouds
 Additionally, moonlight and other faint natural light sources help illuminate the surroundings to improve observation of volcanic activity at night
 Monitoring lava flow at night is important for scientific research and public safety, as it helps track volcanic eruptions and associated hazards such as ash plumes that can affect air travel and nearby communities [refs 0,1,2,3,4]


🎉 === Conversation Complete ===
🗑️ Deleting knowledge agent...
✅ Knowledge agent 'earth-search-agent' deleted successfully.
🗑️ Deleting search index...
✅ Search index 'earth_at_night' deleted successfully.
✅ Quickstart completed successfully!

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Создание индекса поиска

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        } as SearchField
    ],
    vectorSearch: {
        profiles: [
            {
                name: "hnsw_text_3_large",
                algorithmConfigurationName: "alg",
                vectorizerName: "azure_openai_text_3_large"
            } as VectorSearchProfile
        ],
        algorithms: [
            {
                name: "alg",
                kind: "hnsw"
            } as HnswAlgorithmConfiguration
        ],
        vectorizers: [
            {
                vectorizerName: "azure_openai_text_3_large",
                kind: "azureOpenAI",
                parameters: {
                    resourceUrl: config.azureOpenAIEndpoint,
                    deploymentId: config.azureOpenAIEmbeddingDeployment,
                    modelName: config.azureOpenAIEmbeddingModel
                } as AzureOpenAIParameters
            } as AzureOpenAIVectorizer
        ]
    } as VectorSearch,
    semanticSearch: {
        defaultConfigurationName: "semantic_config",
        configurations: [
            {
                name: "semantic_config",
                prioritizedFields: {
                    contentFields: [
                        { name: "page_chunk" } as SemanticField
                    ]
                } as SemanticPrioritizedFields
            } as SemanticConfiguration
        ]
    } as SemanticSearch
};

try {
    await indexClient.createOrUpdateIndex(index);
    console.log(`✅ Index '${config.indexName}' created or updated successfully.`);
} catch (error) {
    console.error("❌ Error creating index:", error);
    throw error;
}

Отправка документов в индекс

const documentsUrl = "https://raw.githubusercontent.com/Azure-Samples/azure-search-sample-data/refs/heads/main/nasa-e-book/earth-at-night-json/documents.json";
    
try {
    const response = await fetch(documentsUrl);
    
    if (!response.ok) {
        throw new Error(`Failed to fetch documents: ${response.status} ${response.statusText}`);
    }
    
    const documents = await response.json();
    console.log(`✅ Fetched ${documents.length} documents from GitHub`);
    
    // Validate and transform documents to match our interface
    const transformedDocuments: EarthAtNightDocument[] = documents.map((doc: any, index: number) => {
        return {
            id: doc.id || String(index + 1),
            page_chunk: doc.page_chunk || doc.content || '',
            page_embedding_text_3_large: doc.page_embedding_text_3_large || new Array(3072).fill(0.1),
            page_number: doc.page_number || index + 1
        };
    });
    
    return transformedDocuments;
    
}

Создание агента знаний

const agentDefinition = {
    name: config.agentName,
    description: "Knowledge agent for Earth at Night e-book content",
    models: [
        {
            kind: "azureOpenAI",
            azureOpenAIParameters: {
                resourceUri: config.azureOpenAIEndpoint,
                deploymentId: config.azureOpenAIGptDeployment,
                modelName: config.azureOpenAIGptModel
            }
        }
    ],
    targetIndexes: [
        {
            indexName: config.indexName,
            defaultRerankerThreshold: 2.5
        }
    ]
};

Настройка сообщений

const messages: KnowledgeAgentMessage[] = [
    {
        role: "system",
        content: `A Q&A agent that can answer questions about the Earth at night.
Sources have a JSON format with a ref_id that must be cited in the answer.
If you do not have the answer, respond with "I don't know".`
    },
    {
        role: "user",
        content: "Why do suburban belts display larger December brightening than urban cores even though absolute light levels are higher downtown? 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?"
    }
];

Запустите поток извлечения

Анализирует всю историю бесед, чтобы определить необходимые сведения.
Разбивает составной запрос пользователя на целенаправленные подзапросы.
Выполняет каждый подзапрос одновременно с текстовыми полями и векторными представлениями в вашем индексе.
Использует семантический рангировщик для повторной сортировки результатов всех подзапросов.
Объединяет результаты в одну строку.

const agentMessages = messages.map(msg => ({
    role: msg.role,
    content: [
        {
            type: "text",
            text: msg.content
        }
    ]
}));

const retrievalRequest = {
    messages: agentMessages,
    targetIndexParams: [
        {
            indexName: config.indexName,
            rerankerThreshold: 2.5,
            maxDocsForReranker: 100,
            includeReferenceSourceData: true
        }
    ]
};

const token = await getAccessToken(credential, "https://search.azure.com/.default");
const response = await fetch(
    `${config.searchEndpoint}/agents/${config.agentName}/retrieve?api-version=${config.searchApiVersion}`,
    {
        method: 'POST',
        headers: {
            'Content-Type': 'application/json',
            'Authorization': `Bearer ${token}`
        },
        body: JSON.stringify(retrievalRequest)
    }
);

if (!response.ok) {
    const errorText = await response.text();
    throw new Error(`Agentic retrieval failed: ${response.status} ${response.statusText}\n${errorText}`);
}

return await response.json() as AgenticRetrievalResponse;

Просмотр ответа, действия и результатов

Теперь вы хотите отобразить ответ, активность и результаты конвейера извлечения.

Каждый ответ на запрос из поиска ИИ Azure включает:

Единая строка, представляющая данные об основе результатов поиска.
План запроса.
Справочные данные, показывающие, какие блоки исходных документов способствовали единой строке.

console.log("\nActivities:");
if (retrievalResponse.activity && Array.isArray(retrievalResponse.activity)) {
    retrievalResponse.activity.forEach((activity) => {
        const activityType = activity.activityType || activity.type || 'UnknownActivityRecord';
        console.log(`Activity Type: ${activityType}`);
        console.log(JSON.stringify(activity, null, 2));
    });
}

console.log("Results");
if (retrievalResponse.references && Array.isArray(retrievalResponse.references)) {
    retrievalResponse.references.forEach((reference) => {
        const referenceType = reference.referenceType || reference.type || 'AzureSearchDoc';
        console.log(`Reference Type: ${referenceType}`);
        console.log(JSON.stringify(reference, null, 2));
    });
}

Выходные данные должны включать:

Response предоставляет текстовую строку наиболее релевантных документов (или фрагментов) в индексе поиска на основе запроса пользователя. Как показано далее в этом кратком руководстве, вы можете передать эту строку в LLM для создания ответов.
Activity отслеживает шаги, выполненные во время процесса извлечения, включая подзапросы, созданные развертыванием gpt-5-mini, и токены, используемые для планирования запросов и выполнения.
Results перечисляет документы, использованные для создания ответа, каждый из которых определяется своим DocKey.

Создание клиента Azure OpenAI

const scope = "https://cognitiveservices.azure.com/.default";
const azureADTokenProvider = getBearerTokenProvider(credential, scope);
const openAIClient = new AzureOpenAI({
    endpoint: config.azureOpenAIEndpoint,
    apiVersion: config.azureOpenAIApiVersion,
    azureADTokenProvider,
});

Создание ответа с помощью API завершения чата

const completion = await openAIClient.chat.completions.create({
    model: config.azureOpenAIGptDeployment,
    messages: messages.map(m => ({ role: m.role, content: m.content })) as any,
    max_tokens: 1000,
    temperature: 0.7
});

const answer = completion.choices[0].message.content;
console.log(answer?.replace(/\./g, "\n"));

Продолжить беседу

const followUpQuestion = "How do I find lava at night?"; 
console.log(`❓ Follow-up question: ${followUpQuestion}`);

messages.push({
    role: "user",
    content: followUpQuestion
});

Очистите ресурсы

Удалите агента знаний

Агент знаний, созданный в этом кратком руководстве, был удален с помощью следующего примера кода:

const token = await getAccessToken(credential, "https://search.azure.com/.default");
const response = await fetch(`${config.searchEndpoint}/agents/${config.agentName}?api-version=${config.searchApiVersion}`, {
    method: 'DELETE',
    headers: {
        'Authorization': `Bearer ${token}`
    }
});

Удаление индекса поиска

Индекс поиска, созданный в этом кратком руководстве, был удален с помощью следующего фрагмента кода:

await indexClient.deleteIndex(config.indexName);
console.log(`✅ Search index '${config.indexName}' deleted successfully.`);

Замечание

Подсказка

Хотите начать сразу? См. репозиторий GitHub azure-search-rest-samples .

Предпосылки

Учетная запись Azure с активной подпиской. Создайте учетную запись бесплатно .
Служба поиска ИИ Azure в любой регионе, который предоставляет агентивное извлечение.
Проект и ресурс Microsoft Foundry . При создании проекта ресурс создается автоматически.
Azure CLI для проверки подлинности без ключа с помощью идентификатора Microsoft Entra.
Visual Studio Code с расширением клиента REST.

Настройка доступа

Чтобы настроить доступ для этого краткого руководства, выберите оба следующих вкладки.

Поиск по искусственному интеллекту Azure
Microsoft Foundry

В службе поиска по искусственному интеллекту Azure:

Включите доступ на основе ролей.
Создайте управляемое удостоверение, назначаемое системой.
Назначьте следующие роли себе.
- Участник службы поиска
- Участник данных индекса поиска
- Средство чтения индексов поиска

Это важно

Агентное извлечение имеет две модели выставления счетов на основе токенов.

Выставление счетов за агентный поиск в Azure AI.
Выставление счетов из Azure OpenAI для планирования запросов и синтеза ответов.

Для получения дополнительной информации см. Доступность и цены агентских запросов.

Получение конечных точек

Чтобы получить конечные точки для этого краткого руководства, выберите оба следующих вкладки.

Поиск по искусственному интеллекту Azure
Microsoft Foundry

Войдите на портал Azure и выберите службу поиска.
В левой области выберите "Обзор".
Запишите конечную точку, которая должна выглядеть следующим https://my-service.search.windows.netобразом.

Войдите на портал Azure и выберите ресурс Foundry.
В левой области выберите ключи управления> ресурсамии конечную точку.
Выберите вкладку OpenAI .
Запишите конечную точку, которая должна выглядеть следующим https://my-resource.openai.azure.comобразом.

Развертывание моделей

Чтобы использовать агентное извлечение, необходимо развернуть две модели Azure OpenAI в проекте Foundry.

Модель внедрения для преобразования текста в вектор. В этом кратком руководстве используется text-embedding-3-large, но вы можете использовать какую-либо модель text-embedding.
LLM для планирования запросов и создания ответов. В этом кратком введении используется gpt-5-mini, но вы можете использовать любой поддерживаемый LLM для агентного извлечения.

Инструкции по развертыванию см. в статье "Развертывание моделей Azure OpenAI с помощью Foundry".

Подключение из локальной системы

Чтобы подключиться из локальной системы, выполните приведенные действия.

Создайте папку с именем quickstart-agentic-retrieval.
Откройте папку в Visual Studio Code.
Выберите терминал>"Новый терминал".
Выполните следующую команду, чтобы войти в учетную запись Azure. Если у вас несколько подписок, выберите тот, который содержит службу поиска ИИ Azure и проект Foundry.
```
az login
```
Выполните следующую команду, чтобы создать маркер идентификатора Microsoft Entra.
```
az account get-access-token --scope https://search.azure.com/.default --query accessToken --output tsv
```
Запишите маркер для использования в следующем разделе.

Запустите код

Чтобы создать и запустить агентный конвейер извлечения, выполните следующие действия.

Создайте файл с именем agentic-retrieval.rest в папке quickstart-agentic-retrieval .

Вставьте в файл следующие переменные и запросы.

@aoai-embedding-model = text-embedding-3-large
@aoai-embedding-deployment = text-embedding-3-large
@aoai-gpt-model = gpt-5-mini
@aoai-gpt-deployment = gpt-5-mini
@index-name = earth-at-night
@knowledge-source-name = earth-knowledge-source
@knowledge-base-name = earth-knowledge-base
@api-version = 2025-11-01-preview

### Create an index
PUT {{search-url}}/indexes/{{index-name}}?api-version={{api-version}}  HTTP/1.1
    Content-Type: application/json
    Authorization: Bearer {{token}}

    {
        "name": "{{index-name}}",
        "fields": [
            {
                "name": "id",
                "type": "Edm.String",
                "key": true
            },
            {
                "name": "page_chunk",
                "type": "Edm.String",
                "searchable": true
            },
            {
                "name": "page_embedding_text_3_large",
                "type": "Collection(Edm.Single)",
                "stored": false,
                "dimensions": 3072,
                "vectorSearchProfile": "hnsw_text_3_large"
            },
            {
                "name": "page_number",
                "type": "Edm.Int32",
                "filterable": true
            }
        ],
        "semantic": {
            "defaultConfiguration": "semantic_config",
            "configurations": [
                {
                    "name": "semantic_config",
                    "prioritizedFields": {
                    "prioritizedContentFields": [
                        {
                            "fieldName": "page_chunk"
                        }
                    ]
                    }
                }
            ]
        },
        "vectorSearch": {
            "profiles": [
                {
                    "name": "hnsw_text_3_large",
                    "algorithm": "alg",
                    "vectorizer": "azure_openai_text_3_large"
                }
            ],
            "algorithms": [
                {
                    "name": "alg",
                    "kind": "hnsw"
                }
            ],
            "vectorizers": [
                {
                    "name": "azure_openai_text_3_large",
                    "kind": "azureOpenAI",
                    "azureOpenAIParameters": {
                    "resourceUri": "{{aoai-url}}",
                    "deploymentId": "{{aoai-embedding-deployment}}",
                    "modelName": "{{aoai-embedding-model}}"
                    }
                }
            ]
        }
    }

### Upload documents
POST {{search-url}}/indexes/{{index-name}}/docs/index?api-version={{api-version}}  HTTP/1.1
    Content-Type: application/json
    Authorization: Bearer {{token}}

    {
        "value": [
            {
                "@search.action": "upload",
                "id": "earth_at_night_508_page_104_verbalized",
                "page_chunk": "<!-- PageHeader=\"Urban Structure\" -->\n\n### Location of Phoenix, Arizona\n\nThe image depicts a globe highlighting the location of Phoenix, Arizona, in the southwestern United States, marked with a blue pinpoint on the map of North America. Phoenix is situated in the central part of Arizona, which is in the southwestern region of the United States.\n\n---\n\n### Grid of City Blocks-Phoenix, Arizona\n\nLike many large urban areas of the central and western United States, the Phoenix metropolitan area is laid out along a regular grid of city blocks and streets. While visible during the day, this grid is most evident at night, when the pattern of street lighting is clearly visible from the low-Earth-orbit vantage point of the ISS.\n\nThis astronaut photograph, taken on March 16, 2013, includes parts of several cities in the metropolitan area, including Phoenix (image right), Glendale (center), and Peoria (left). While the major street grid is oriented north-south, the northwest-southeast oriented Grand Avenue cuts across the three cities at image center. Grand Avenue is a major transportation corridor through the western metropolitan area; the lighting patterns of large industrial and commercial properties are visible along its length. Other brightly lit properties include large shopping centers, strip malls, and gas stations, which tend to be located at the intersections of north-south and east-west trending streets.\n\nThe urban grid encourages growth outwards along a city's borders by providing optimal access to new real estate. Fueled by the adoption of widespread personal automobile use during the twentieth century, the Phoenix metropolitan area today includes 25 other municipalities (many of them largely suburban and residential) linked by a network of surface streets and freeways.\n\nWhile much of the land area highlighted in this image is urbanized, there are several noticeably dark areas. The Phoenix Mountains are largely public parks and recreational land. To the west, agricultural fields provide a sharp contrast to the lit streets of residential developments. The Salt River channel appears as a dark ribbon within the urban grid.\n\n\n<!-- PageFooter=\"Earth at Night\" -->\n<!-- PageNumber=\"88\" -->",
                "page_embedding_text_3_large": [
                -0.002984904684126377, 0.0007500237552449107, -0.004803949501365423, 0.010587676428258419, -0.008392670191824436, -0.043565936386585236, 0.05432070791721344, 0.024532422423362732, -0.03305421024560928, -0.011362385004758835, 0.0029678153805434704, 0.0520421527326107, 0.019276559352874756, -0.05398651957511902, -0.025550175458192825, 0.018592992797493935, -0.02951485849916935, 0.036365706473588943, -0.02734263800084591, 0.028664197772741318, 0.027874300256371498, 0.008255957625806332, -0.05046235769987106, 0.01759042963385582, -0.003096933476626873, 0.03682141751050949, -0.002149434993043542, 0.009190164506435394, 0.0026716035790741444, -0.0031633912585675716, -0.014354884624481201, 0.004758378490805626, 0.01637520082294941, -0.010299060493707657, 0.004705212078988552, 0.016587866470217705, 0.0440824069082737, 0.019033513963222504, 0.039130352437496185, 0.04028481990098953, 0.018760086968541145, -0.05720687285065651, 0.030608562752604485, 0.010526915080845356, 0.020431026816368103, -0.04772809147834778, 0.03262887895107269, -0.02760087326169014, 0.03305421024560928, 0.009068641811609268, -0.003104528645053506, -0.035727713257074356, -0.04490268602967262, -0.039403777569532394, 0.026491977274417877, 0.01214468851685524, 0.037732839584350586, 0.08652425557374954, 0.005525491200387478, -0.0031994683668017387, 0.04684705287218094, 0.02872495912015438, 0.010481344535946846, 0.024076711386442184, 0.015813158825039864, 0.023180481046438217, -0.015949871391057968, 0.014749834313988686, -0.0006285008857958019, 0.0005739105399698019, 0.007192632649093866, 0.05787524953484535, 0.0043748221360147, 0.00038687934284098446, 0.04110509902238846, -0.0028273046482354403, 0.01397512573748827, -0.009106617420911789, -0.00770910456776619, -0.015304281376302242, 0.002582360291853547, -0.01092945970594883, -0.008552169427275658, 0.06665527075529099, 0.04657362401485443, -0.012197853997349739, -0.028861673548817635, -0.08925852179527283, -0.003831766778603196, 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                "page_chunk": "# Urban Structure\n\n## March 16, 2013\n\n### Phoenix Metropolitan Area at Night\n\nThis figure presents a nighttime satellite view of the Phoenix metropolitan area, highlighting urban structure and transport corridors. City lights illuminate the layout of several cities and major thoroughfares.\n\n**Labeled Urban Features:**\n\n- **Phoenix:** Central and brightest area in the right-center of the image.\n- **Glendale:** Located to the west of Phoenix, this city is also brightly lit.\n- **Peoria:** Further northwest, this area is labeled and its illuminated grid is seen.\n- **Grand Avenue:** Clearly visible as a diagonal, brightly lit thoroughfare running from Phoenix through Glendale and Peoria.\n- **Salt River Channel:** Identified in the southeast portion, running through illuminated sections.\n- **Phoenix Mountains:** Dark, undeveloped region to the northeast of Phoenix.\n- **Agricultural Fields:** Southwestern corner of the image, grid patterns are visible but with much less illumination, indicating agricultural land use.\n\n**Additional Notes:**\n\n- The overall pattern shows a grid-like urban development typical of western U.S. cities, with scattered bright nodes at major intersections or city centers.\n- There is a clear transition from dense urban development to sparsely populated or agricultural land, particularly evident towards the bottom and left of the image.\n- The illuminated areas follow the existing road and street grids, showcasing the extensive spread of the metropolitan area.\n\n**Figure Description:**  \nA satellite nighttime image captured on March 16, 2013, showing Phoenix and surrounding areas (including Glendale and Peoria). Major landscape and infrastructural features, such as the Phoenix Mountains, Grand Avenue, the Salt River Channel, and agricultural fields, are labeled. The image reveals the extent of urbanization and the characteristic street grid illuminated by city lights.\n\n---\n\nPage 89",
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                ],
                "page_number": 105
            }
        ]
    }

### Create a knowledge source
POST {{search-url}}/knowledgesources?api-version={{api-version}}  HTTP/1.1
    Content-Type: application/json
    Authorization: Bearer {{token}}

    {
        "name": "{{knowledge-source-name}}",
        "description": "This knowledge source pulls from a search index that contains pages from the Earth at Night e-book.",
        "kind": "searchIndex",
        "searchIndexParameters": {
            "searchIndexName": "{{index-name}}",
            "sourceDataFields": [
                { "name": "id" },
                { "name": "page_chunk" },
                { "name": "page_number" }
            ]
        }
    }

### Create a knowledge base
PUT {{search-url}}/knowledgebases/{{knowledge-base-name}}?api-version={{api-version}}  HTTP/1.1
    Content-Type: application/json
    Authorization: Bearer {{token}}

    {
        "name": "{{knowledge-base-name}}",
        "knowledgeSources": [
            {
                "name": "{{knowledge-source-name}}"
            }
        ],
        "models": [
            {
                "kind": "azureOpenAI",
                "azureOpenAIParameters": {
                    "resourceUri": "{{aoai-url}}",
                    "deploymentId": "{{aoai-gpt-deployment}}",
                    "modelName": "{{aoai-gpt-model}}"
                }
            }
        ],
        "outputMode": "answerSynthesis",
        "answerInstructions": "Provide a two sentence concise and informative answer based on the retrieved documents."
    }

### Run agentic retrieval
POST {{search-url}}/knowledgebases/{{knowledge-base-name}}/retrieve?api-version={{api-version}}  HTTP/1.1
    Content-Type: application/json
    Authorization: Bearer {{token}}

    {
        "messages": [
            {
                "role": "user",
                "content": [
                    {
                        "type": "text",
                        "text": "Why do suburban belts display larger December brightening than urban cores even though absolute light levels are higher downtown? 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?"
                    }
                ]
            }
        ],
        "knowledgeSourceParams": [
        {
            "knowledgeSourceName": "{{knowledge-source-name}}",
            "kind": "searchIndex",
            "includeReferences": true,
            "includeReferenceSourceData": true,
            "alwaysQuerySource": true,
            "rerankerThreshold": 2.5
        }
    ],
    "includeActivity": true,
    "retrievalReasoningEffort": { "kind": "low" }
    }

### Delete the knowledge base
DELETE {{search-url}}/knowledgebases/{{knowledge-base-name}}?api-version={{api-version}}  HTTP/1.1
    Content-Type: application/json
    Authorization: Bearer {{token}}

### Delete the knowledge source
DELETE {{search-url}}/knowledgesources('{{knowledge-source-name}}')?api-version={{api-version}}  HTTP/1.1
    Content-Type: application/json
    Authorization: Bearer {{token}}

### Delete the index
DELETE {{search-url}}//indexes/{{index-name}}?api-version={{api-version}}  HTTP/1.1
    Content-Type: application/json
    Authorization: Bearer {{token}}

Задайте @search-url и aoai-url укажите значения, полученные в конечных точках Get.
Задайте @token значение, полученное в Connect из локальной системы.
Отправка каждого запроса в последовательности, начиная с ### Create an index.

Каждый запрос должен возвращать 200 OK, 201 Created или 204 No Content код состояния. Если вы получаете ошибку, проверьте запрос на опечатки и убедитесь, что маркер действителен.

Выходные данные

Каждый запрос возвращает разные json в зависимости от операции. Ключевой результат должен поступать от ### Run agentic retrieval и выглядеть следующим образом:

{
  "response": [
    {
      "content": [
        {
          "type": "text",
          "text": "The retrieved documents do not provide an explanation for why suburban belts show larger December brightening than urban cores, so no reason for that seasonal contrast is given in these sources [ref_id:0][ref_id:1]. Phoenix’s street grid is sharply visible from orbit because the metropolitan area is laid out on a regular street‑block grid [ref_id:0][ref_id:1], with brightly lit linear corridors like Grand Avenue [ref_id:0][ref_id:1] and concentrated lights from industrial/commercial properties and shopping nodes at intersections [ref_id:0][ref_id:1], while dark areas such as the Phoenix Mountains, agricultural fields, and the Salt River channel increase contrast and make the grid stand out [ref_id:0][ref_id:1]."
        }
      ]
    }
  ],
  "activity": [
    {
      "type": "modelQueryPlanning",
      "id": 0,
      "inputTokens": 1350,
      "outputTokens": 1538,
      "elapsedMs": 20780
    },
    {
      "type": "searchIndex",
      "id": 1,
      "knowledgeSourceName": "earth-knowledge-source",
      "queryTime": "2025-11-05T19:42:09.673Z",
      "count": 0,
      "elapsedMs": 694,
      "searchIndexArguments": {
        "search": "December brightening in satellite night lights: why do suburban belts show larger December brightening than urban cores? causes: snow reflectance, holiday/residential lighting, leaf-off, VIIRS/DMSP sensor saturation",
        "filter": null
      }
    },
    {
      "type": "searchIndex",
      "id": 2,
      "knowledgeSourceName": "earth-knowledge-source",
      "queryTime": "2025-11-05T19:42:09.999Z",
      "count": 2,
      "elapsedMs": 325,
      "searchIndexArguments": {
        "search": "Why is the Phoenix nighttime street grid so sharply visible from space while long stretches of interstate between Midwestern cities remain comparatively dim? factors: streetlight spacing, lighting type/shielding, vegetation/tree cover, land use, VIIRS DNB detection",
        "filter": null
      }
    },
    {
      "type": "agenticReasoning",
      "id": 3,
      "retrievalReasoningEffort": {
        "kind": "low"
      },
      "reasoningTokens": 1566
    },
    {
      "type": "modelAnswerSynthesis",
      "id": 4,
      "inputTokens": 3656,
      "outputTokens": 1909,
      "elapsedMs": 21988
    }
  ],
  "references": [
    {
      "type": "searchIndex",
      "id": "0",
      "activitySource": 2,
      "sourceData": {
        "id": "earth_at_night_508_page_104_verbalized",
        "page_chunk": "<!-- PageHeader=\"Urban Structure\" -->\n\n### Location of Phoenix, Arizona\n\nThe image depicts a globe highlighting the location of Phoenix, Arizona, in the southwestern United States, marked with a blue pinpoint on the map of North America. Phoenix is situated in the central part of Arizona, which is in the southwestern region of the United States.\n\n---\n\n### Grid of City Blocks-Phoenix, Arizona\n\nLike many large urban areas of the central and western United States, the Phoenix metropolitan area is laid out along a regular grid of city blocks and streets. While visible during the day, this grid is most evident at night, when the pattern of street lighting is clearly visible from the low-Earth-orbit vantage point of the ISS.\n\nThis astronaut photograph, taken on March 16, 2013, includes parts of several cities in the metropolitan area, including Phoenix (image right), Glendale (center), and Peoria (left). While the major street grid is oriented north-south, the northwest-southeast oriented Grand Avenue cuts across the three cities at image center. Grand Avenue is a major transportation corridor through the western metropolitan area; the lighting patterns of large industrial and commercial properties are visible along its length. Other brightly lit properties include large shopping centers, strip malls, and gas stations, which tend to be located at the intersections of north-south and east-west trending streets.\n\nThe urban grid encourages growth outwards along a city's borders by providing optimal access to new real estate. Fueled by the adoption of widespread personal automobile use during the twentieth century, the Phoenix metropolitan area today includes 25 other municipalities (many of them largely suburban and residential) linked by a network of surface streets and freeways.\n\nWhile much of the land area highlighted in this image is urbanized, there are several noticeably dark areas. The Phoenix Mountains are largely public parks and recreational land. To the west, agricultural fields provide a sharp contrast to the lit streets of residential developments. The Salt River channel appears as a dark ribbon within the urban grid.\n\n\n<!-- PageFooter=\"Earth at Night\" -->\n<!-- PageNumber=\"88\" -->",
        "page_number": 104
      },
      "rerankerScore": 2.6394622,
      "docKey": "earth_at_night_508_page_104_verbalized"
    },
    {
      "type": "searchIndex",
      "id": "1",
      "activitySource": 2,
      "sourceData": {
        "id": "earth_at_night_508_page_105_verbalized",
        "page_chunk": "# Urban Structure\n\n## March 16, 2013\n\n### Phoenix Metropolitan Area at Night\n\nThis figure presents a nighttime satellite view of the Phoenix metropolitan area, highlighting urban structure and transport corridors. City lights illuminate the layout of several cities and major thoroughfares.\n\n**Labeled Urban Features:**\n\n- **Phoenix:** Central and brightest area in the right-center of the image.\n- **Glendale:** Located to the west of Phoenix, this city is also brightly lit.\n- **Peoria:** Further northwest, this area is labeled and its illuminated grid is seen.\n- **Grand Avenue:** Clearly visible as a diagonal, brightly lit thoroughfare running from Phoenix through Glendale and Peoria.\n- **Salt River Channel:** Identified in the southeast portion, running through illuminated sections.\n- **Phoenix Mountains:** Dark, undeveloped region to the northeast of Phoenix.\n- **Agricultural Fields:** Southwestern corner of the image, grid patterns are visible but with much less illumination, indicating agricultural land use.\n\n**Additional Notes:**\n\n- The overall pattern shows a grid-like urban development typical of western U.S. cities, with scattered bright nodes at major intersections or city centers.\n- There is a clear transition from dense urban development to sparsely populated or agricultural land, particularly evident towards the bottom and left of the image.\n- The illuminated areas follow the existing road and street grids, showcasing the extensive spread of the metropolitan area.\n\n**Figure Description:**  \nA satellite nighttime image captured on March 16, 2013, showing Phoenix and surrounding areas (including Glendale and Peoria). Major landscape and infrastructural features, such as the Phoenix Mountains, Grand Avenue, the Salt River Channel, and agricultural fields, are labeled. The image reveals the extent of urbanization and the characteristic street grid illuminated by city lights.\n\n---\n\nPage 89",
        "page_number": 105
      },
      "rerankerScore": 2.565024,
      "docKey": "earth_at_night_508_page_105_verbalized"
    }
  ]
}

Общие сведения о коде

Теперь, когда вы выполнили код, давайте разберем ключевые шаги:

Создание индекса поиска
Отправка документов в индекс
Создание источника знаний
Создание базы знаний
Запуск конвейера извлечения

Создание индекса поиска

В службе "Поиск ИИ Azure" индекс представляет собой структурированную коллекцию данных. Следующий код использует индексы — создание (REST API) для определения индекса с именем earth-at-night, который вы ранее указали с помощью переменной @index-name .

### Create an index
PUT {{search-url}}/indexes/{{index-name}}?api-version={{api-version}}  HTTP/1.1
    Content-Type: application/json
    Authorization: Bearer {{token}}

    {
        "name": "{{index-name}}",
        "fields": [
            {
                "name": "id",
                "type": "Edm.String",
                "key": true
            },
            {
                "name": "page_chunk",
                "type": "Edm.String",
                "searchable": true
            },
            {
                "name": "page_embedding_text_3_large",
                "type": "Collection(Edm.Single)",
                "stored": false,
                "dimensions": 3072,
                "vectorSearchProfile": "hnsw_text_3_large"
            },
            {
                "name": "page_number",
                "type": "Edm.Int32",
                "filterable": true
            }
        ],
        "semantic": {
            "defaultConfiguration": "semantic_config",
            "configurations": [
                {
                    "name": "semantic_config",
                    "prioritizedFields": {
                    "prioritizedContentFields": [
                        {
                            "fieldName": "page_chunk"
                        }
                    ]
                    }
                }
            ]
        },
        "vectorSearch": {
            "profiles": [
                {
                    "name": "hnsw_text_3_large",
                    "algorithm": "alg",
                    "vectorizer": "azure_openai_text_3_large"
                }
            ],
            "algorithms": [
                {
                    "name": "alg",
                    "kind": "hnsw"
                }
            ],
            "vectorizers": [
                {
                    "name": "azure_openai_text_3_large",
                    "kind": "azureOpenAI",
                    "azureOpenAIParameters": {
                    "resourceUri": "{{aoai-url}}",
                    "deploymentId": "{{aoai-embedding-deployment}}",
                    "modelName": "{{aoai-embedding-model}}"
                    }
                }
            ]
        }
    }

Отправка документов в индекс

earth-at-night В настоящее время индекс пуст. Следующий код использует Документы — Индекс (REST API) для заполнения индекса документами JSON из электронной книги NASA "Земля ночью". Как требуется поисковой системой Azure AI, каждый документ соответствует полям и типам данных, определенным в схеме индекса.


### Upload documents
POST {{search-url}}/indexes/{{index-name}}/docs/index?api-version={{api-version}}  HTTP/1.1
    Content-Type: application/json
    Authorization: Bearer {{token}}

    {
        "value": [
            {
                "@search.action": "upload",
                "id": "earth_at_night_508_page_104_verbalized",
                "page_chunk": "<!-- PageHeader=\"Urban Structure\" -->\n\n### Location of Phoenix, Arizona\n\nThe image depicts a globe highlighting the location of Phoenix, Arizona, in the southwestern United States, marked with a blue pinpoint on the map of North America. Phoenix is situated in the central part of Arizona, which is in the southwestern region of the United States.\n\n---\n\n### Grid of City Blocks-Phoenix, Arizona\n\nLike many large urban areas of the central and western United States, the Phoenix metropolitan area is laid out along a regular grid of city blocks and streets. While visible during the day, this grid is most evident at night, when the pattern of street lighting is clearly visible from the low-Earth-orbit vantage point of the ISS.\n\nThis astronaut photograph, taken on March 16, 2013, includes parts of several cities in the metropolitan area, including Phoenix (image right), Glendale (center), and Peoria (left). While the major street grid is oriented north-south, the northwest-southeast oriented Grand Avenue cuts across the three cities at image center. Grand Avenue is a major transportation corridor through the western metropolitan area; the lighting patterns of large industrial and commercial properties are visible along its length. Other brightly lit properties include large shopping centers, strip malls, and gas stations, which tend to be located at the intersections of north-south and east-west trending streets.\n\nThe urban grid encourages growth outwards along a city's borders by providing optimal access to new real estate. Fueled by the adoption of widespread personal automobile use during the twentieth century, the Phoenix metropolitan area today includes 25 other municipalities (many of them largely suburban and residential) linked by a network of surface streets and freeways.\n\nWhile much of the land area highlighted in this image is urbanized, there are several noticeably dark areas. The Phoenix Mountains are largely public parks and recreational land. To the west, agricultural fields provide a sharp contrast to the lit streets of residential developments. The Salt River channel appears as a dark ribbon within the urban grid.\n\n\n<!-- PageFooter=\"Earth at Night\" -->\n<!-- PageNumber=\"88\" -->",
                "page_embedding_text_3_large": [
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                "page_chunk": "# Urban Structure\n\n## March 16, 2013\n\n### Phoenix Metropolitan Area at Night\n\nThis figure presents a nighttime satellite view of the Phoenix metropolitan area, highlighting urban structure and transport corridors. City lights illuminate the layout of several cities and major thoroughfares.\n\n**Labeled Urban Features:**\n\n- **Phoenix:** Central and brightest area in the right-center of the image.\n- **Glendale:** Located to the west of Phoenix, this city is also brightly lit.\n- **Peoria:** Further northwest, this area is labeled and its illuminated grid is seen.\n- **Grand Avenue:** Clearly visible as a diagonal, brightly lit thoroughfare running from Phoenix through Glendale and Peoria.\n- **Salt River Channel:** Identified in the southeast portion, running through illuminated sections.\n- **Phoenix Mountains:** Dark, undeveloped region to the northeast of Phoenix.\n- **Agricultural Fields:** Southwestern corner of the image, grid patterns are visible but with much less illumination, indicating agricultural land use.\n\n**Additional Notes:**\n\n- The overall pattern shows a grid-like urban development typical of western U.S. cities, with scattered bright nodes at major intersections or city centers.\n- There is a clear transition from dense urban development to sparsely populated or agricultural land, particularly evident towards the bottom and left of the image.\n- The illuminated areas follow the existing road and street grids, showcasing the extensive spread of the metropolitan area.\n\n**Figure Description:**  \nA satellite nighttime image captured on March 16, 2013, showing Phoenix and surrounding areas (including Glendale and Peoria). Major landscape and infrastructural features, such as the Phoenix Mountains, Grand Avenue, the Salt River Channel, and agricultural fields, are labeled. The image reveals the extent of urbanization and the characteristic street grid illuminated by city lights.\n\n---\n\nPage 89",
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                ],
                "page_number": 105
            }
        ]
    }

Создание источника знаний

Источник знаний — это повторно используемые ссылки на исходные данные. В следующем коде используется источников знания — создание (REST API), чтобы определить источник знаний с именем earth-knowledge-source, который нацелен на индекс earth-at-night.

sourceDataFields указывает, какие поля индекса доступны для получения и ссылок. Наш пример включает только поля, доступные для чтения человеком, чтобы избежать длительных и непреднамеренных внедрения в ответы.

### Create a knowledge source
POST {{search-url}}/knowledgesources?api-version={{api-version}}  HTTP/1.1
    Content-Type: application/json
    Authorization: Bearer {{token}}

    {
        "name": "{{knowledge-source-name}}",
        "description": "This knowledge source pulls from a search index that contains pages from the Earth at Night e-book.",
        "kind": "searchIndex",
        "searchIndexParameters": {
            "searchIndexName": "{{index-name}}",
            "sourceDataFields": [
                { "name": "id" },
                { "name": "page_chunk" },
                { "name": "page_number" }
            ]
        }
    }

Создание базы знаний

Для нацеливания вашего earth-knowledge-source и развертывания gpt-5-mini в момент запроса требуется база знаний. Следующий код использует базы знаний — создание (REST API) для определения базового имени earth-knowledge-base, который вы ранее указали с помощью переменной @knowledge-base-name .

outputMode задано значение answerSynthesis, включающее ответы на естественный язык, которые ссылаются на извлеченные документы и следуют предоставленным answerInstructions.

### Create a knowledge base
PUT {{search-url}}/knowledgebases/{{knowledge-base-name}}?api-version={{api-version}}  HTTP/1.1
    Content-Type: application/json
    Authorization: Bearer {{token}}

    {
        "name": "{{knowledge-base-name}}",
        "knowledgeSources": [
            {
                "name": "{{knowledge-source-name}}"
            }
        ],
        "models": [
            {
                "kind": "azureOpenAI",
                "azureOpenAIParameters": {
                    "resourceUri": "{{aoai-url}}",
                    "deploymentId": "{{aoai-gpt-deployment}}",
                    "modelName": "{{aoai-gpt-model}}"
                }
            }
        ],
        "outputMode": "answerSynthesis",
        "answerInstructions": "Provide a two sentence concise and informative answer based on the retrieved documents."
    }

Запустите поток извлечения

Вы готовы выполнить извлечение агента. Следующий код использует Knowledge Retrieval - Retrieve (REST API) для отправки двухчастного пользовательского запроса earth-knowledge-base на:

Анализирует всю беседу, чтобы определить потребность пользователя в информации.
Раскомпозирует составной запрос в вложенные запросы.
Выполняет вложенные запросы параллельно с источником знаний.
Использует семантический рангировщик для повторного использования и фильтрации результатов. Наш пример исключает ответы с оценкой 2.5 повторного ранжирования или ниже.
Синтезирует лучшие результаты в ответ на естественный язык.

### Run agentic retrieval
POST {{search-url}}/knowledgebases/{{knowledge-base-name}}/retrieve?api-version={{api-version}}  HTTP/1.1
    Content-Type: application/json
    Authorization: Bearer {{token}}

    {
        "messages": [
            {
                "role": "user",
                "content": [
                    {
                        "type": "text",
                        "text": "Why do suburban belts display larger December brightening than urban cores even though absolute light levels are higher downtown? 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?"
                    }
                ]
            }
        ],
        "knowledgeSourceParams": [
            {
                "knowledgeSourceName": "{{knowledge-source-name}}",
                "kind": "searchIndex",
                "includeReferences": true,
                "includeReferenceSourceData": true,
                "alwaysQuerySource": true,
                "rerankerThreshold": 2.5
            }
        ],
        "includeActivity": true,
        "retrievalReasoningEffort": { "kind": "low" }
    }

Выходные данные должны содержать следующие компоненты:

response предоставляет синтезированный, созданный LLM-ответ на запрос, который ссылается на извлеченные документы. Если синтез ответа не включен, этот раздел содержит содержимое, извлеченное непосредственно из документов.
activity отслеживает шаги, выполненные во время процесса извлечения, включая вложенные запросы, созданные gpt-5-mini развертыванием, и маркеры, используемые для семантического ранжирования, планирования запросов и синтеза ответов.
references перечисляет документы, использованные для создания ответа, каждый из которых определяется своим docKey.

Очистите ресурсы

В противном случае следующие запросы из agentic-retrieval.rest удалят объекты, созданные в этом кратком руководстве.

Удаление базы знаний

### Delete the knowledge base
DELETE {{search-url}}/knowledgebases/{{knowledge-base-name}}?api-version={{api-version}}  HTTP/1.1
    Content-Type: application/json
    Authorization: Bearer {{token}}

Удаление источника знаний

### Delete the knowledge source
DELETE {{search-url}}/knowledgesources('{{knowledge-source-name}}')?api-version={{api-version}}  HTTP/1.1
    Content-Type: application/json
    Authorization: Bearer {{token}}

Удаление индекса поиска

### Delete the index
DELETE {{search-url}}//indexes/{{index-name}}?api-version={{api-version}}  HTTP/1.1
    Content-Type: application/json
    Authorization: Bearer {{token}}

Обратная связь

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Last updated on 2025-11-18

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Краткое руководство. Использование агентического извлечения в поиске ИИ Azure

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Проверьте ответ, активность и ссылки

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Очистите ресурсы

Удаление базы знаний

Удаление источника знаний

Удаление индекса поиска

Предпосылки

Настройка доступа

Получение конечных точек

Развертывание моделей

Настройка среды

Запустите код

Выходные данные

Общие сведения о коде

Создание индекса поиска

Отправка документов в индекс

Создание агента знаний

Настройка сообщений

Запустите поток извлечения

Просмотр ответа, действия и результатов

Создание клиента Azure OpenAI

Создание ответа с помощью API завершения чата

Продолжить беседу

Очистите ресурсы

Удалите агента знаний

Удаление индекса поиска

Предпосылки

Настройка доступа

Получение конечных точек

Развертывание моделей

Настройка среды

Запустите код

Выходные данные

Общие сведения о коде

Создание индекса поиска

Отправка документов в индекс

Создание агента знаний

Настройка сообщений

Запустите поток извлечения

Просмотр ответа, действия и результатов

Создание клиента Azure OpenAI

Создание ответа с помощью API завершения чата

Продолжить беседу

Очистите ресурсы

Удалите агента знаний

Удаление индекса поиска

Предпосылки

Настройка доступа

Получение конечных точек

Развертывание моделей

Подключение из локальной системы

Запустите код

Выходные данные

Общие сведения о коде

Создание индекса поиска

Отправка документов в индекс

Создание источника знаний

Создание базы знаний

Настройка сообщений

Запустите поток извлечения

Проверьте ответ, активность и ссылки

Продолжить беседу

Ознакомьтесь с новым ответом, действиями и ссылками