Início Rápido: usar a recuperação por meio de agentes na Pesquisa de IA do Azure

Observação

Esse recurso está atualmente em versão prévia pública. Essa visualização é fornecida sem um contrato de nível de serviço e não é recomendada para utilização em produção. Alguns recursos podem não ter suporte ou podem ter restrição de recursos. Para obter mais informações, consulte Termos de Uso Complementares para Versões Prévias do Microsoft Azure.

Neste início rápido, você usará a recuperação por meio de agentes para criar uma experiência de pesquisa de conversação alimentada por documentos indexados na Pesquisa de IA do Azure e um grande modelo de linguagem (LLM) do OpenAI do Azure em Modelos da Fábrica.

Uma base de dados de conhecimento orquestra a recuperação agente decompondo consultas complexas em subconsultas, executando as subconsultas em uma ou mais fontes de conhecimento e retornando resultados com metadados. Por padrão, a base de conhecimento gera conteúdo em bruto de suas fontes, mas este tutorial rápido utiliza o modo de síntese de respostas para a geração de respostas em linguagem natural.

Embora você possa fornecer seus próprios dados, este início rápido usa documentos JSON de exemplo do e-book Terra à Noite da NASA. Os documentos descrevem tópicos científicos gerais e imagens da Terra à noite, conforme observado do espaço.

Dica

Quer começar imediatamente? Consulte o repositório GitHub azure-search-dotnet-samples .

Pré-requisitos

Uma conta do Azure com uma assinatura ativa. Crie uma conta gratuitamente.
Um serviço do Azure AI Search em qualquer região que forneça recuperação por agente.
Um projeto e um recurso do Microsoft Foundry . Quando você cria um projeto, o recurso é criado automaticamente.
A CLI do Azure para autenticação sem uso de chave com a Microsoft Entra ID.
Visual Studio Code.

Configurar o acesso

Antes de começar, verifique se você tem permissões para acessar o conteúdo e as operações. Recomendamos o Microsoft Entra ID para autenticação e acesso baseado em função para autorização. Você deve ser um Proprietário ou Administrador de Acesso do Usuário para atribuir funções. Se as funções não forem viáveis, use a autenticação baseada em chave.

Para configurar o acesso para este início rápido, selecione as duas guias a seguir.

Pesquisa de IA do Azure
Microsoft Foundry

A Pesquisa de IA do Azure fornece o pipeline de recuperação com agentes autônomos. Configure o acesso para que você e seu serviço de pesquisa possam ler e gravar dados, interagir com o Foundry e executar o pipeline.

Em seu serviço do Azure AI Search:

Habilitar o acesso baseado em função.
Criar uma identidade gerenciada atribuída pelo sistema.
Atribua as funções a seguir a si mesmo.
- Colaborador do Serviço de Pesquisa
- Contribuinte de dados do índice de pesquisa
- Leitor de dados de índice de pesquisa

Importante

A recuperação do Agentic tem dois modelos de cobrança baseados em tokens:

Faturamento da Pesquisa de IA do Azure para a recuperação por meio de agentes.
Cobrança do OpenAI do Azure para planejamento de consultas e síntese de respostas.

Para obter mais informações, veja Disponibilidade e preços da recuperação de agente.

Obter pontos de extremidade

Cada serviço do Azure AI Search e cada recurso do Microsoft Foundry tem um endpoint, que é uma URL exclusiva que identifica e fornece acesso à rede ao recurso. Em uma seção posterior, especifique esses pontos de extremidade para se conectar aos seus recursos programaticamente.

Para obter os pontos de extremidade para este início rápido, selecione as duas guias a seguir.

Pesquisa de IA do Azure
Microsoft Foundry

Entre no portal do Azure e selecione seu serviço de pesquisa.
No painel esquerdo, selecione Visão geral.
Anote o ponto de extremidade, que deve ser semelhante a https://my-service.search.windows.net.

Implantar modelos

Para este início rápido, você deve implantar dois modelos do Azure OpenAI em seu projeto do Microsoft Foundry:

Um modelo de inserção para conversão de texto para vetor. Este início rápido usa text-embedding-3-large, mas você pode usar qualquer text-embedding modelo.
Uma LLM para planejamento de consulta e geração de respostas. Este início rápido usa gpt-5-mini, mas você pode usar qualquer LLM com suporte para recuperação por agente.

Para obter instruções de implantação, consulte Implantar modelos do Azure OpenAI com o Microsoft Foundry.

Configurar o ambiente

Para configurar o aplicativo de console para este início rápido:

Crie uma pasta nomeada quickstart-agentic-retrieval para conter o aplicativo.
Abra a pasta no Visual Studio Code.
Selecione Terminal>Novo Terminal e execute o comando a seguir para criar um aplicativo de console.
```
dotnet new console
```

Instale a biblioteca de clientes do Azure AI Search para .NET.

dotnet add package Azure.Search.Documents --version 11.8.0-beta.1

Instale o dotenv.net pacote para carregar variáveis de ambiente de um .env arquivo.
```
dotnet add package dotenv.net
```
Para autenticação sem chave com a ID do Microsoft Entra, instale o pacote Azure.Identity .
```
dotnet add package Azure.Identity
```
Para autenticação sem chave com a ID do Microsoft Entra, entre em sua conta do Azure. Se você tiver várias assinaturas, selecione aquela que contém o serviço Azure AI Search e o projeto Microsoft Foundry.
```
az login
```

Executar o código

Para criar e executar o agentic retrieval pipeline:

Crie um arquivo nomeado .env na quickstart-agentic-retrieval pasta.

Cole as seguintes variáveis de ambiente no arquivo .env.

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

Defina SEARCH_ENDPOINT e AOAI_ENDPOINT como os valores obtidos em Obter pontos de extremidade.

Cole o código a seguir no Program.cs arquivo.

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.");
        }
    }
}

Crie e execute o aplicativo.
```
dotnet run
```

Saída

A saída do aplicativo deve ser semelhante à seguinte:

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.

Entender o código

Agora que você executou o código, vamos dividir as etapas principais:

Criar um índice de pesquisa
Carregar documentos no índice
Criar uma fonte de conhecimento
Criar uma base de dados de conhecimento
Configurar mensagens
Execute o pipeline de recuperação
Continuar a conversa

Criar um índice de pesquisa

No Azure AI Search, um índice é uma coleção estruturada de dados. O código a seguir define um índice nomeado earth-at-night, que você especificou anteriormente usando a indexName variável.

O esquema de índice contém campos para identificação de documentos e conteúdo de página, inserções e números. O esquema também inclui configurações para classificação semântica e busca em vetores, que usa sua implantação text-embedding-3-large para vetorizar texto e corresponder documentos com base na similaridade semântica ou conceitual.

// 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.");

Carregar documentos no índice

Atualmente, o earth-at-night índice está vazio. O código a seguir popula o índice com documentos JSON do livro eletrônico Terra à Noite da NASA. Conforme exigido pelo Azure AI Search, cada documento está em conformidade com os campos e tipos de dados definidos no esquema de índice.

// 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.");

Criar uma fonte de conhecimento

Uma fonte de dados de conhecimento é uma referência reutilizável aos dados de origem. O código a seguir define uma fonte de conhecimento chamada earth-knowledge-source que tem como destino o earth-at-night índice.

SourceDataFields especifica quais campos de índice estão incluídos nas referências de citação. Nosso exemplo inclui apenas campos legíveis por humanos para evitar inserções longas e não interpretáveis em respostas.

// 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.");

Como criar uma base de dados de conhecimento

Para direcionar earth-knowledge-source e sua implantação de gpt-5-mini no momento da consulta, você precisa de uma base de dados de conhecimento. O código a seguir define uma base de dados de conhecimento chamada earth-knowledge-base, que você especificou anteriormente usando a knowledgeBaseName variável.

OutputMode é definido como AnswerSynthesis, habilitando respostas de linguagem natural que citam os documentos recuperados e seguem o AnswerInstructions fornecido.

// 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.");

Configurar mensagens

As mensagens são a entrada para a rota de recuperação e contêm o histórico da conversa. Cada mensagem inclui uma função que indica sua origem, como system ou user, e conteúdo em linguagem natural. A LLM que você usa determina quais funções são válidas.

O código a seguir cria uma mensagem do sistema, que instrui earth-knowledge-base a responder perguntas sobre a Terra à noite e responder com "Não sei" quando as respostas não estão disponíveis.

// 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 }
    }
};

Execute o pipeline de recuperação

Você está pronto para executar a recuperação por meio de agentes. O código a seguir envia uma consulta de usuário de duas partes para earth-knowledge-basea qual:

Analisa toda a conversa para inferir a necessidade de informações do usuário.
Decompõe a consulta composta em subconsultas focadas.
Executa as subconsultas simultaneamente em relação à fonte de conhecimento.
Usa o classificador semântico para reclassificador e filtrar os resultados.
Sintetiza os resultados principais em uma resposta de linguagem natural.

// 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 }
});

Examinar a resposta, a atividade e as referências

O código a seguir exibe a resposta, a atividade e as referências do pipeline de recuperação, em que:

Response fornece uma resposta sintetizada gerada por LLM para a consulta que cita os documentos recuperados. Quando a síntese de resposta não está habilitada, esta seção contém o conteúdo extraído diretamente dos documentos.
Activity rastreia as etapas que foram executadas durante o processo de recuperação, incluindo as subconsultas geradas pela sua implantação gpt-5-mini e os tokens usados para classificação semântica, planejamento de consultas e síntese de resposta.
References lista os documentos que contribuíram para a resposta, cada um identificado por sua 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);
}

Continuar a conversa

O código a seguir continua a conversa com earth-knowledge-base. Depois de enviar essa consulta de usuário, a base de dados de conhecimento busca conteúdo relevante de earth-knowledge-source e acrescenta a resposta à lista de mensagens.

// 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 }
});

Examinar a nova resposta, a atividade e as referências

O código a seguir exibe a nova resposta, a atividade e as referências do pipeline de recuperação.

// 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);
}

Limpar os recursos

Ao trabalhar em sua assinatura, é uma boa ideia finalizar um projeto determinando se você ainda precisa dos recursos que criou. Recursos que ficam em execução podem custar dinheiro.

No portal do Azure, você pode gerenciar os recursos do Azure AI Search e do Microsoft Foundry selecionando Todos os recursos ou grupos de recursos no painel esquerdo.

Caso contrário, o código a seguir de Program.cs excluiu os objetos que você criou neste início rápido.

Excluir a base de dados de conhecimento

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

Excluir a fonte de conhecimento

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

Excluir o índice de pesquisa

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

Observação

Nesse início rápido, você usa a recuperação agêntica para criar uma experiência de pesquisa conversacional alimentada por grandes modelos de linguagem (LLMs) e seus dados proprietários. A recuperação de agente divide consultas complexas de usuários em subconsultas, executa as subconsultas em paralelo e extrai dados básicos de documentos indexados na IA do Azure Search. A saída se destina à integração com soluções de chat personalizadas e agente.

Dica

A versão Java deste início rápido usa a versão da API REST 2025-05-01-preview, que usa a terminologia anterior do "Agente de Conhecimento" e não dá suporte aos recursos mais recentes disponíveis na versão 2025-11-01-preview. Para usar esses recursos, consulte a versão C#, Python ou REST.

Pré-requisitos

Uma conta do Azure com uma assinatura ativa. Crie uma conta gratuitamente.
Um serviço do Azure AI Search em qualquer região que forneça recuperação por agente.
Um projeto e um recurso do Microsoft Foundry . Quando você cria um projeto, o recurso é criado automaticamente.
A CLI do Azure para autenticação sem uso de chave com a Microsoft Entra ID.

Configurar o acesso

Para configurar o acesso para este início rápido, selecione as duas guias a seguir.

Pesquisa de IA do Azure
Microsoft Foundry

Em seu serviço do Azure AI Search:

Habilitar o acesso baseado em função.
Criar uma identidade gerenciada atribuída pelo sistema.
Atribua as funções a seguir a si mesmo.
- Colaborador do Serviço de Pesquisa
- Contribuinte de dados do índice de pesquisa
- Leitor de dados de índice de pesquisa

Importante

A recuperação do Agentic tem dois modelos de cobrança baseados em tokens:

Faturamento da Pesquisa de IA do Azure para a recuperação por meio de agentes.
Cobrança do OpenAI do Azure para planejamento de consultas e síntese de respostas.

Para obter mais informações, veja Disponibilidade e preços da recuperação de agente.

Obter pontos de extremidade

Para obter os pontos de extremidade para este início rápido, selecione as duas guias a seguir.

Pesquisa de IA do Azure
Microsoft Foundry

Entre no portal do Azure e selecione seu serviço de pesquisa.
No painel esquerdo, selecione Visão geral.
Anote o ponto de extremidade, que deve ser semelhante a https://my-service.search.windows.net.

Implantar modelos

Para este início rápido, você deve implantar dois modelos do Azure OpenAI em seu projeto do Microsoft Foundry:

Um modelo de inserção para conversão de texto para vetor. Este início rápido usa text-embedding-3-large, mas você pode usar qualquer text-embedding modelo.
Uma LLM para planejamento de consulta e geração de respostas. Este início rápido usa gpt-5-mini, mas você pode usar qualquer LLM com suporte para recuperação por agente.

Para obter instruções de implantação, consulte Implantar modelos do Azure OpenAI com o Microsoft Foundry.

Configurar o ambiente

O exemplo deste guia de início rápido funciona com o Runtime Java. Instalar um Kit de Desenvolvimento do Java, como o Azul Zulu OpenJDK. O Microsoft Build do OpenJDK ou seu JDK preferido também deve funcionar.

Instale o Apache Maven. Em seguida, execute mvn -v para confirmar a instalação bem-sucedida.
Crie uma nova pasta quickstart-agentic-retrieval para conter o aplicativo e abra o Visual Studio Code nessa pasta com o seguinte comando:
```
mkdir quickstart-agentic-retrieval && cd quickstart-agentic-retrieval
```

Crie um novo arquivo pom.xml na raiz do projeto e copie nele o seguinte código:

<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>

Instale as dependências, incluindo a biblioteca de clientes da Pesquisa de IA do Azure (Azure.Search.Documents) para Java e a biblioteca de clientes do Azure Identity para Java com:
```
mvn clean dependency:copy-dependencies
```

Executar o código

Crie um novo arquivo nomeado .env na quickstart-agentic-retrieval pasta e adicione as seguintes variáveis de ambiente:

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

Substitua <your-search-service-name> e <your-ai-foundry-resource-name> pelo nome real do serviço do Azure AI Search e pelo nome do recurso Foundry.

Cole o seguinte código em um novo arquivo nomeado AgenticRetrievalQuickstart.java na quickstart-agentic-retrieval pasta:

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);
        }
    }
}

Entre no Azure com o seguinte comando:
```
az login
```

Execute seu novo aplicativo de console:

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

Saída

A saída do aplicativo deve ser semelhante à seguinte:

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" : {
    "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" : "2025-07-21T15:07:04.267Z",
  "count" : 0,
  "elapsedMs" : 243
}
Activity Type: UnknownActivityRecord
{
  "type" : "AzureSearchSemanticRanker",
  "id" : 3,
  "inputTokens" : 48602
}
Results
[]

[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?

Activities:
Activity Type: UnknownActivityRecord
{
  "type" : "ModelQueryPlanning",
  "id" : 0,
  "inputTokens" : 1545,
  "outputTokens" : 127
}
Activity Type: UnknownActivityRecord
{
  "type" : "AzureSearchQuery",
  "id" : 1,
  "targetIndex" : "earth_at_night",
  "query" : {
    "search" : "How can I find lava at night?",
    "filter" : null
  },
  "queryTime" : "2025-07-21T15:07:15.445Z",
  "count" : 6,
  "elapsedMs" : 370
}
Activity Type: UnknownActivityRecord
{
  "type" : "AzureSearchSemanticRanker",
  "id" : 2,
  "inputTokens" : 22994
}
Results
Reference Type: KnowledgeAgentAzureSearchDocReference
{
  "type" : "AzureSearchDoc",
  "id" : "0",
  "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."
  }
}
Reference Type: KnowledgeAgentAzureSearchDocReference
{
  "type" : "AzureSearchDoc",
  "id" : "1",
  "activitySource" : 1,
  "docKey" : "earth_at_night_508_page_65_verbalized",
  "sourceData" : {
    "id" : "earth_at_night_508_page_65_verbalized",
    "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."
  }
}
Reference Type: KnowledgeAgentAzureSearchDocReference
{
  "type" : "AzureSearchDoc",
  "id" : "2",
  "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: KnowledgeAgentAzureSearchDocReference
{
  "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: KnowledgeAgentAzureSearchDocReference
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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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Reference Type: KnowledgeAgentAzureSearchDocReference
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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]

 === Conversation Complete ===
[DELETE] Deleting knowledge agent...
[DONE] Knowledge agent 'earth-search-agent' deleted successfully.
[DELETE] Deleting search index...
[DONE] Search index 'earth_at_night' deleted successfully.
[DONE] Quickstart completed successfully!

Entender o código

Agora que você tem o código, vamos dividir os principais componentes:

Criar um índice de pesquisa
Carregar documentos no índice
Criar um agente de conhecimento
Configurar mensagens
Execute o pipeline de recuperação
Examinar a resposta, a atividade e os resultados
Criar o cliente do Azure OpenAI
Usar a API de Conclusões de Chat para gerar uma resposta
Continuar a conversa

Criar um índice de pesquisa

No Azure AI Search, um índice é uma coleção estruturada de dados. O código a seguir define um índice nomeado earth_at_night para conter conteúdo de vetor e texto sem formatação. Você pode usar um índice existente, mas ele deve atender aos critérios para cargas de trabalho de recuperação agente.

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);

O esquema de índice contém campos para identificação de documentos e conteúdo de página, inserções e números. Ele também inclui configurações para classificação semântica e consultas de vetor, que usam o text-embedding-3-large modelo implantado anteriormente.

Carregar documentos no índice

Atualmente, o earth_at_night índice está vazio. Execute o código a seguir para preencher o índice com documentos JSON do livro eletrônico Terra à Noite da NASA. Conforme exigido pelo Azure AI Search, cada documento está em conformidade com os campos e tipos de dados definidos no esquema de índice.

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;
    
}

Criar um agente de conhecimento

Para conectar o IA do Azure Search à sua gpt-5-mini implantação e direcionar o earth_at_night índice no momento da consulta, você precisa de um agente de conhecimento. O código a seguir define um agente de conhecimento chamado earth-search-agent que usa a definição do agente para processar consultas e recuperar documentos relevantes do earth_at_night índice.

Para garantir respostas relevantes e semanticamente significativas, defaultRerankerThreshold é configurado para excluir respostas com uma pontuação de rerank igual a 2.5 ou menor.

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());

Configurar mensagens

As mensagens são a entrada para a rota de recuperação e contêm o histórico da conversa. Cada mensagem inclui uma função que indica sua origem, como assistente ou usuário, e conteúdo em linguagem natural. A LLM que você usa determina quais funções são válidas.

Uma mensagem de usuário representa a consulta a ser processada, enquanto uma mensagem de assistente orienta o agente de conhecimento sobre como responder. Durante o processo de recuperação, essas mensagens são enviadas a um LLM para extrair respostas relevantes dos documentos indexados.

Esta mensagem de assistente instrui earth-search-agent a responder perguntas sobre a Terra à noite, citar fontes usando suas ref_ide responder com "Não sei" quando as respostas não estão disponíveis.

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);

Execute o pipeline de recuperação

Esta etapa executa o pipeline de recuperação para extrair informações relevantes do índice de pesquisa. Com base nas mensagens e parâmetros na solicitação de recuperação, o LLM:

Analisa todo o histórico de conversas para determinar a necessidade de informações subjacentes.
Divide a consulta de usuário composta em subconsultas focadas.
Executa cada subconsulta simultaneamente em campos de texto e inserções de vetor em seu índice.
Usa o classificador semântico para reclassificador os resultados de todas as subconsultas.
Mescla os resultados em uma única cadeia de caracteres.

O código a seguir envia uma consulta de usuário de duas partes para earth-search-agent, que desconstrui a consulta em subconsultas, executa as subconsultas em campos de texto e inserções de vetor no earth_at_night índice, e classifica e mescla os resultados. Em seguida, a resposta é acrescentada à messages lista.

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());

Examinar a resposta, a atividade e os resultados

Agora, você deseja exibir a resposta, a atividade e os resultados do pipeline de recuperação.

Cada resposta de recuperação do Azure AI Search inclui:

Uma string unificada que representa os dados de fundamento dos resultados da pesquisa.
O plano de consulta.
Dados de referência que mostram quais partes dos documentos de origem contribuíram para a cadeia de caracteres unificada.

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());
        }
    }
}

A saída deve incluir:

Response fornece uma cadeia de caracteres de texto dos documentos mais relevantes (ou partes) no índice de pesquisa com base na consulta do usuário. Conforme mostrado mais adiante nesse início rápido, você pode passar essa cadeia de cadeia para um LLM para geração de resposta.
Activity rastreia as etapas que foram executadas durante o processo de recuperação, incluindo as subconsultas geradas pela sua implantação gpt-5-mini e os tokens usados para planejamento e execução de consultas.
Results lista os documentos que contribuíram para a resposta, cada um identificado por sua DocKey.

Criar o cliente do Azure OpenAI

Para estender o pipeline de recuperação da extração de respostas para a geração de respostas, configure o cliente do OpenAI do Azure para interagir com sua implantação do gpt-5-mini.

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

Usar a API de Conclusões de Chat para gerar uma resposta

Uma opção para a geração de respostas é a API de Conclusões de Chat, que passa o histórico de conversas para a LLM para processamento.

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();

Continuar a conversa

Continue a conversa enviando outra consulta de usuário para earth-search-agent. O código a seguir executa novamente o pipeline de recuperação, buscando conteúdo relevante do earth_at_night índice e acrescentando a resposta à messages lista. No entanto, ao contrário de antes, agora você pode usar o cliente do Azure OpenAI para gerar uma resposta com base no conteúdo recuperado.

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);

Limpar os recursos

Ao trabalhar em sua própria assinatura, é uma boa ideia finalizar um projeto determinando se você ainda precisa dos recursos que criou. Recursos que ficam em execução podem custar dinheiro. Você pode excluir recursos individualmente ou excluir o grupo de recursos para excluir todo o conjunto de recursos.

No portal do Azure, você pode encontrar e gerenciar recursos selecionando Todos os recursos ou grupos de recursos no painel esquerdo. Você também pode executar o código a seguir para excluir os objetos criados neste início rápido.

Excluir o agente de conhecimento

O agente de conhecimento criado neste início rápido foi excluído usando o seguinte snippet de código:

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());

Excluir o índice de pesquisa

O índice de pesquisa criado neste início rápido foi excluído usando o seguinte snippet de código:

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

Observação

Pré-requisitos

Uma conta do Azure com uma assinatura ativa. Crie uma conta gratuitamente.
Um serviço do Azure AI Search em qualquer região que forneça recuperação por agente.
Um projeto e um recurso do Microsoft Foundry . Quando você cria um projeto, o recurso é criado automaticamente.
A CLI do Azure para autenticação sem uso de chave com a Microsoft Entra ID.
Visual Studio Code e a versão mais recente do LTS do Node.js.

Configurar o acesso

Para configurar o acesso para este início rápido, selecione as duas guias a seguir.

Pesquisa de IA do Azure
Microsoft Foundry

Em seu serviço do Azure AI Search:

Habilitar o acesso baseado em função.
Criar uma identidade gerenciada atribuída pelo sistema.
Atribua as funções a seguir a si mesmo.
- Colaborador do Serviço de Pesquisa
- Contribuinte de dados do índice de pesquisa
- Leitor de dados de índice de pesquisa

Importante

A recuperação do Agentic tem dois modelos de cobrança baseados em tokens:

Faturamento da Pesquisa de IA do Azure para a recuperação por meio de agentes.
Cobrança do OpenAI do Azure para planejamento de consultas e síntese de respostas.

Para obter mais informações, veja Disponibilidade e preços da recuperação de agente.

Obter pontos de extremidade

Para obter os pontos de extremidade para este início rápido, selecione as duas guias a seguir.

Pesquisa de IA do Azure
Microsoft Foundry

Entre no portal do Azure e selecione seu serviço de pesquisa.
No painel esquerdo, selecione Visão geral.
Anote o ponto de extremidade, que deve ser semelhante a https://my-service.search.windows.net.

Implantar modelos

Para este início rápido, você deve implantar dois modelos do Azure OpenAI em seu projeto do Microsoft Foundry:

Um modelo de inserção para conversão de texto para vetor. Este início rápido usa text-embedding-3-large, mas você pode usar qualquer text-embedding modelo.
Uma LLM para planejamento de consulta e geração de respostas. Este início rápido usa gpt-5-mini, mas você pode usar qualquer LLM com suporte para recuperação por agente.

Para obter instruções de implantação, consulte Implantar modelos do Azure OpenAI com o Microsoft Foundry.

Configurar o ambiente

Para configurar o aplicativo de console para este início rápido:

Crie uma pasta nomeada quickstart-agentic-retrieval para conter o aplicativo.
Abra a pasta no Visual Studio Code.
Selecione Terminal>Novo Terminal e execute os comandos a seguir para inicializar o package.json arquivo.
```
npm init -y
npm pkg set type=module
```
Instale a biblioteca de clientes do Azure AI Search para JavaScript.
```
npm install @azure/search-documents@12.3.0-beta.1
```
Para autenticação sem chave com o Microsoft Entra ID, instale a biblioteca de identidade do Azure para JavaScript.
```
npm install @azure/identity
```
Para autenticação sem chave com a ID do Microsoft Entra, entre em sua conta do Azure. Se você tiver várias assinaturas, selecione aquela que contém o serviço Azure AI Search e o projeto Microsoft Foundry.
```
az login
```

Executar o código

Para criar e executar o agentic retrieval pipeline:

Crie um arquivo nomeado .env na quickstart-agentic-retrieval pasta.

Cole as seguintes variáveis de ambiente no arquivo .env.

AZURE_SEARCH_ENDPOINT = https://<your-search-service-name>.search.windows.net
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

Defina AZURE_SEARCH_ENDPOINT e AZURE_OPENAI_ENDPOINT como os valores obtidos em Obter pontos de extremidade.

Crie um arquivo chamado index.jse cole o código a seguir no arquivo.

import { DefaultAzureCredential } from '@azure/identity';
import {
    SearchIndexClient,
    SearchClient,
    KnowledgeRetrievalClient,
    SearchIndexingBufferedSender
} from '@azure/search-documents';

export const documentKeyRetriever = (document) => {
  return document.id;
};

export const WAIT_TIME = 4000;
export function delay(timeInMs) {
  return new Promise((resolve) => setTimeout(resolve, timeInMs));
}

const index = {
    name: 'earth_at_night',
    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: process.env.AZURE_OPENAI_ENDPOINT,
                    deploymentId: process.env.AZURE_OPENAI_EMBEDDING_DEPLOYMENT,
                    modelName: process.env.AZURE_OPENAI_EMBEDDING_DEPLOYMENT
                }
            }
        ]
    },
    semanticSearch: {
        defaultConfigurationName: "semantic_config",
        configurations: [
            {
                name: "semantic_config",
                prioritizedFields: {
                    contentFields: [
                        { name: "page_chunk" }
                    ]
                }
            }
        ]
    }
};

const credential = new DefaultAzureCredential();

const searchIndexClient = new SearchIndexClient(process.env.AZURE_SEARCH_ENDPOINT, credential);
const searchClient = new SearchClient(process.env.AZURE_SEARCH_ENDPOINT, 'earth_at_night', credential);

await searchIndexClient.createOrUpdateIndex(index);

// get Documents with vectors
const response = await fetch("https://raw.githubusercontent.com/Azure-Samples/azure-search-sample-data/refs/heads/main/nasa-e-book/earth-at-night-json/documents.json");

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

const bufferedClient = new SearchIndexingBufferedSender(
    searchClient,
    documentKeyRetriever,
    {
        autoFlush: true,
    },
);

await bufferedClient.uploadDocuments(documents);
await bufferedClient.flush();
await bufferedClient.dispose();

console.log(`Waiting for indexing to complete...`);
console.log(`Expected documents: ${documents.length}`);
await delay(WAIT_TIME);

let count = await searchClient.getDocumentsCount();
console.log(`Current indexed count: ${count}`);

while (count !== documents.length) {
    await delay(WAIT_TIME);
    count = await searchClient.getDocumentsCount();
    console.log(`Current indexed count: ${count}`);
}

console.log(`✓ All ${documents.length} documents indexed successfully!`);

await searchIndexClient.createKnowledgeSource({
    name: 'earth-knowledge-source',
    description: "Knowledge source for Earth at Night e-book content",
    kind: "searchIndex",
    searchIndexParameters: {
        searchIndexName: 'earth_at_night',
        sourceDataFields: [
            { name: "id" },
            { name: "page_number" }
        ]
    }
});

console.log(`✅ Knowledge source 'earth-knowledge-source' created successfully.`);

await searchIndexClient.createKnowledgeBase({
    name: 'earth-knowledge-base',
    knowledgeSources: [
        {
            name: 'earth-knowledge-source'
        }
    ],
    models: [
        {
            kind: "azureOpenAI",
            azureOpenAIParameters: {
                resourceUrl: process.env.AZURE_OPENAI_ENDPOINT,
                deploymentId: process.env.AZURE_OPENAI_GPT_DEPLOYMENT,
                modelName: process.env.AZURE_OPENAI_GPT_DEPLOYMENT
            }
        }
    ],
    outputMode: "answerSynthesis",
    answerInstructions: "Provide a two sentence concise and informative answer based on the retrieved documents."
});

console.log(`✅ Knowledge base 'earth-knowledge-base' created successfully.`);

const knowledgeRetrievalClient = new KnowledgeRetrievalClient(
    process.env.AZURE_SEARCH_ENDPOINT,
    'earth-knowledge-base',
    credential
);

const query1 = `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?`;

const retrievalRequest = {
    messages: [
        {
            role: "user",
            content: [
                {
                    type: "text",
                    text: query1
                }
            ]
        }
    ],
    knowledgeSourceParams: [
        {
            kind: "searchIndex",
            knowledgeSourceName: 'earth-knowledge-source',
            includeReferences: true,
            includeReferenceSourceData: true,
            alwaysQuerySource: true,
            rerankerThreshold: 2.5
        }
    ],
    includeActivity: true,
    retrievalReasoningEffort: { kind: "low" }
};

const result = await knowledgeRetrievalClient.retrieveKnowledge(retrievalRequest);

console.log("\n📝 ANSWER:");
console.log("─".repeat(80));
if (result.response && result.response.length > 0) {
    result.response.forEach((msg) => {
        if (msg.content && msg.content.length > 0) {
            msg.content.forEach((content) => {
                if (content.type === "text" && 'text' in content) {
                    console.log(content.text);
                }
            });
        }
    });
}
console.log("─".repeat(80));

if (result.activity) {
    console.log("\nActivities:");
    result.activity.forEach((activity) => {
        console.log(`Activity Type: ${activity.type}`);
        console.log(JSON.stringify(activity, null, 2));
    });
}

if (result.references) {
    console.log("\nReferences:");
    result.references.forEach((reference) => {
        console.log(`Reference Type: ${reference.type}`);
        console.log(JSON.stringify(reference, null, 2));
    });
}

// Follow-up query - to demonstrate conversational context
const query2 = "How do I find lava at night?";
console.log(`\n❓ Follow-up question: ${query2}`);

const retrievalRequest2 = {
    messages: [
        {
            role: "user",
            content: [
                {
                    type: "text",
                    text: query2
                }
            ]
        }
    ],
    knowledgeSourceParams: [
        {
            kind: "searchIndex",
            knowledgeSourceName: 'earth-knowledge-source',
            includeReferences: true,
            includeReferenceSourceData: true,
            alwaysQuerySource: true,
            rerankerThreshold: 2.5
        }
    ],
    includeActivity: true,
    retrievalReasoningEffort: { kind: "low" }
};

const result2 = await knowledgeRetrievalClient.retrieveKnowledge(retrievalRequest2);

console.log("\n📝 ANSWER:");
console.log("─".repeat(80));
if (result2.response && result2.response.length > 0) {
    result2.response.forEach((msg) => {
        if (msg.content && msg.content.length > 0) {
            msg.content.forEach((content) => {
                if (content.type === "text" && 'text' in content) {
                    console.log(content.text);
                }
            });
        }
    });
}
console.log("─".repeat(80));

if (result2.activity) {
    console.log("\nActivities:");
    result2.activity.forEach((activity) => {
        console.log(`Activity Type: ${activity.type}`);
        console.log(JSON.stringify(activity, null, 2));
    });
}

if (result2.references) {
    console.log("\nReferences:");
    result2.references.forEach((reference) => {
        console.log(`Reference Type: ${reference.type}`);
        console.log(JSON.stringify(reference, null, 2));
    });
}

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

// Clean up resources
await searchIndexClient.deleteKnowledgeBase('earth-knowledge-base');
await searchIndexClient.deleteKnowledgeSource('earth-knowledge-source');
await searchIndexClient.deleteIndex('earth_at_night');

console.log(`\n🗑️  Cleaned up resources.`);

Crie e execute o aplicativo.
```
node --env-file=.env index.js
```

Saída

A saída do aplicativo deve ser semelhante à seguinte:

Waiting for indexing to complete...
Expected documents: 194
Current indexed count: 194
✓ All 194 documents indexed successfully!
✅ Knowledge source 'earth-knowledge-source' created successfully.
✅ Knowledge base 'earth-knowledge-base' created successfully.

📝 ANSWER:
────────────────────────────────────────────────────────────────────────────────
Suburban belts show larger December brightening (20–50% increases) because residential holiday lighting and seasonal decorations are concentrated there, so relative (fractional) increases over the baseline are bigger even though absolute downtown radiances remain higher; urban cores already emit strong baseline light while many suburbs add a large seasonal increment visible in VIIRS DNB observations [ref_id:0][ref_id:1]. The Phoenix street grid appears sharply from space because continuous, street‑oriented lighting with regular residential lot spacing and little vegetative masking produces strong, linear emissions, whereas long interstate stretches between Midwestern cities have sparser, access‑limited lighting, fewer adjacent developments and more shielded fixtures so they register comparatively dim on night‑light sensors like VIIRS/DNB [ref_id:0][ref_id:1].
────────────────────────────────────────────────────────────────────────────────

Activities:
Activity Type: modelQueryPlanning
{
  "id": 0,
  "type": "modelQueryPlanning",
  "elapsedMs": 5883,
  "inputTokens": 1489,
  "outputTokens": 326
}
Activity Type: searchIndex
{
  "id": 1,
  "type": "searchIndex",
  "elapsedMs": 527,
  "knowledgeSourceName": "earth-knowledge-source",
  "queryTime": "2025-12-19T15:38:23.462Z",
  "count": 1,
  "searchIndexArguments": {
    "search": "December brightening suburban belts vs urban cores light pollution causes December increase in night lights suburban vs urban",
    "filter": null,
    "sourceDataFields": [
      {
        "name": "page_chunk"
      },
      {
        "name": "id"
      },
      {
        "name": "page_number"
      }
    ],
    "searchFields": [],
    "semanticConfigurationName": "semantic_config"
  }
}
Activity Type: searchIndex
{
  "id": 2,
  "type": "searchIndex",
  "elapsedMs": 538,
  "knowledgeSourceName": "earth-knowledge-source",
  "queryTime": "2025-12-19T15:38:24.001Z",
  "count": 0,
  "searchIndexArguments": {
    "search": "factors that make Phoenix nighttime street grid highly visible from space reasons highway/interstate lighting visibility differences Midwestern interstates dim",
    "filter": null,
    "sourceDataFields": [
      {
        "name": "page_chunk"
      },
      {
        "name": "id"
      },
      {
        "name": "page_number"
      }
    ],
    "searchFields": [],
    "semanticConfigurationName": "semantic_config"
  }
}
Activity Type: searchIndex
{
  "id": 3,
  "type": "searchIndex",
  "elapsedMs": 465,
  "knowledgeSourceName": "earth-knowledge-source",
  "queryTime": "2025-12-19T15:38:24.467Z",
  "count": 2,
  "searchIndexArguments": {
    "search": "satellite nighttime lights seasonal variations suburban brightening studies December holiday lighting residential vs commercial lighting patterns",
    "filter": null,
    "sourceDataFields": [
      {
        "name": "page_chunk"
      },
      {
        "name": "id"
      },
      {
        "name": "page_number"
      }
    ],
    "searchFields": [],
    "semanticConfigurationName": "semantic_config"
  }
}
Activity Type: agenticReasoning
{
  "id": 4,
  "type": "agenticReasoning",
  "reasoningTokens": 70397,
  "retrievalReasoningEffort": {
    "kind": "low"
  }
}
Activity Type: modelAnswerSynthesis
{
  "id": 5,
  "type": "modelAnswerSynthesis",
  "elapsedMs": 4908,
  "inputTokens": 4013,
  "outputTokens": 187
}

References:
Reference Type: searchIndex
{
  "type": "searchIndex",
  "id": "0",
  "activitySource": 3,
  "sourceData": {
    "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 (2012–2014)\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 20–50% 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
  },
  "rerankerScore": 2.6692379,
  "docKey": "earth_at_night_508_page_174_verbalized"
}
Reference Type: searchIndex
{
  "type": "searchIndex",
  "id": "1",
  "activitySource": 3,
  "sourceData": {
    "id": "earth_at_night_508_page_186_verbalized",
    "page_chunk": "## Appendix A\n\n### NASA's Black Marble Product Suite\n\nNASA's Black Marble product suite provides estimates of daily nighttime lights and other intrinsic surface optical properties of Earth at night. The product is based on the Day/Night Band (DNB) sensor of the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument onboard the Suomi National Polar-orbiting Partnership (NPP) satellite. The VIIRS DNB is a highly sensitive, low-light sensor capable of measuring daily global nighttime light emissions and reflections, allowing users to identify sources and intensities of these artificial lights, and monitor changes over a period of time. Like any optical sensor, the principal challenge in using VIIRS DNB data is to account for variations in light captured by the sensor. Certainly, there are variations in the sources and intensity of anthropogenic light due to changing human processes like urbanization, oil and gas production, nighttime commercial fishing, and infrastructure development. However, these processes can only be studied when other naturally-occurring factors that influence nighttime lights are removed. For example, variations in lunar lighting due to consistent changes in Moon phase cause fluctuations in the amount of light shining on Earth. Similarly, land-cover dynamics (e.g., seasonal vegetation, snow and ice cover), as well as atmospheric conditions (e.g., clouds, aerosols, airglow, and auroras), influence the intensity of the light captured by the sensor as it travels over various parts of the world.\n\nTo realize the full potential of the VIIRS DNB time series record for nighttime lights applications, NASA's Black Marble product suite was developed, building on a history of 20 years of research on how light changes when it reflects off of surfaces with different angular and spectral properties. Through complex modeling, scientists can now predict how moonlight, snow, vegetation, terrain, and clouds impact the lights we see from space, allowing for more accurate assessments of human and environmental activity at night.\n\n<!-- PageFooter=\"170 Earth at Night\" -->",
    "page_number": 186
  },
  "rerankerScore": 2.5997617,
  "docKey": "earth_at_night_508_page_186_verbalized"
}

❓ Follow-up question: How do I find lava at night?

📝 ANSWER:
────────────────────────────────────────────────────────────────────────────────
... // Trimmed for brevity
────────────────────────────────────────────────────────────────────────────────

Activities:
... // Trimmed for brevity

References:
... // Trimmed for brevity

✅ Quickstart completed successfully!

🗑️  Cleaned up resources.

Entender o código

Agora que você tem o código, vamos dividir os principais componentes:

Criar um índice de pesquisa
Carregar documentos no índice
Criar uma fonte de conhecimento
Criar uma base de dados de conhecimento
Execute o pipeline de recuperação
Examinar a resposta, a atividade e as referências
Continuar a conversa

Criar um índice de pesquisa

No Azure AI Search, um índice é uma coleção estruturada de dados. O código a seguir define um índice chamado earth_at_night.

const index: SearchIndex = {
    name: 'earth_at_night',
    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: process.env.AZURE_OPENAI_ENDPOINT!,
                    deploymentId: process.env.AZURE_OPENAI_EMBEDDING_DEPLOYMENT!,
                    modelName: process.env.AZURE_OPENAI_EMBEDDING_DEPLOYMENT!
                } 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
};

const credential = new DefaultAzureCredential();

const searchIndexClient = new SearchIndexClient(process.env.AZURE_SEARCH_ENDPOINT, credential);
const searchClient = new SearchClient(process.env.AZURE_SEARCH_ENDPOINT, 'earth_at_night', credential);

await searchIndexClient.createOrUpdateIndex(index);

Carregar documentos no índice

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

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

const bufferedClient = new SearchIndexingBufferedSender(
    searchClient,
    documentKeyRetriever,
    {
        autoFlush: true,
    },
);

await bufferedClient.uploadDocuments(documents);
await bufferedClient.flush();
await bufferedClient.dispose();

console.log(`Waiting for indexing to complete...`);
console.log(`Expected documents: ${documents.length}`);
await delay(WAIT_TIME);

let count = await searchClient.getDocumentsCount();
console.log(`Current indexed count: ${count}`);

while (count !== documents.length) {
    await delay(WAIT_TIME);
    count = await searchClient.getDocumentsCount();
    console.log(`Current indexed count: ${count}`);
}

console.log(`✓ All ${documents.length} documents indexed successfully!`);

Criar uma fonte de conhecimento

source_data_fields especifica quais campos de índice estão incluídos nas referências de citação. Nosso exemplo inclui apenas campos legíveis por humanos para evitar inserções longas e não interpretáveis em respostas.

await searchIndexClient.createKnowledgeSource({
    name: 'earth-knowledge-source',
    description: "Knowledge source for Earth at Night e-book content",
    kind: "searchIndex",
    searchIndexParameters: {
        searchIndexName: 'earth_at_night',
        sourceDataFields: [
            { name: "id" },
            { name: "page_number" }
        ]
    }
});

console.log(`✅ Knowledge source 'earth-knowledge-source' created successfully.`);

Como criar uma base de dados de conhecimento

outputMode é definido como answerSynthesis, habilitando respostas de linguagem natural que citam os documentos recuperados e seguem o answerInstructions fornecido.

await searchIndexClient.createKnowledgeBase({
    name: 'earth-knowledge-base',
    knowledgeSources: [
        {
            name: 'earth-knowledge-source'
        }
    ],
    models: [
        {
            kind: "azureOpenAI",
            azureOpenAIParameters: {
                resourceUrl: process.env.AZURE_OPENAI_ENDPOINT,
                deploymentId: process.env.AZURE_OPENAI_GPT_DEPLOYMENT,
                modelName: process.env.AZURE_OPENAI_GPT_DEPLOYMENT
            }
        }
    ],
    outputMode: "answerSynthesis",
    answerInstructions: "Provide a two sentence concise and informative answer based on the retrieved documents."
});

console.log(`✅ Knowledge base 'earth-knowledge-base' created successfully.`);

Execute o pipeline de recuperação

Você está pronto para executar a recuperação por meio de agentes. O código a seguir envia uma consulta de usuário de duas partes para earth-knowledge-basea qual:

Analisa toda a conversa para inferir a necessidade de informações do usuário.
Decompõe a consulta composta em subconsultas focadas.
Executa as subconsultas simultaneamente em relação à fonte de conhecimento.
Usa o classificador semântico para reclassificador e filtrar os resultados.
Sintetiza os resultados principais em uma resposta de linguagem natural.

const knowledgeRetrievalClient = new KnowledgeRetrievalClient(
    process.env.AZURE_SEARCH_ENDPOINT,
    'earth-knowledge-base',
    credential
)

const query1 = `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?`;

const retrievalRequest = {
    messages: [
        {
            role: "user",
            content: [
                {
                    type: "text",
                    text: query1
                }
            ]
        }
    ],
    knowledgeSourceParams: [
        {
            kind: "searchIndex",
            knowledgeSourceName: 'earth-knowledge-source',
            includeReferences: true,
            includeReferenceSourceData: true,
            alwaysQuerySource: true,
            rerankerThreshold: 2.5
        }
    ],
    includeActivity: true,
    retrievalReasoningEffort: { kind: "low" }
};

const result = await knowledgeRetrievalClient.retrieveKnowledge(retrievalRequest);

Examinar a resposta, a atividade e as referências

O código a seguir exibe a resposta, a atividade e as referências do pipeline de recuperação, em que:

Answer fornece uma resposta sintetizada gerada por LLM para a consulta que cita os documentos recuperados. Quando a síntese de resposta não está habilitada, esta seção contém o conteúdo extraído diretamente dos documentos.
Activities rastreia as etapas que foram executadas durante o processo de recuperação, incluindo as subconsultas geradas pela sua implantação gpt-5-mini e os tokens usados para classificação semântica, planejamento de consultas e síntese de resposta.
References lista os documentos que contribuíram para a resposta, cada um identificado por sua docKey.

console.log("\n📝 ANSWER:");
console.log("─".repeat(80));
if (result.response && result.response.length > 0) {
    result.response.forEach((msg) => {
        if (msg.content && msg.content.length > 0) {
            msg.content.forEach((content) => {
                if (content.type === "text" && 'text' in content) {
                    console.log(content.text);
                }
            });
        }
    });
}
console.log("─".repeat(80));

if (result.activity) {
    console.log("\nActivities:");
    result.activity.forEach((activity) => {
        console.log(`Activity Type: ${activity.type}`);
        console.log(JSON.stringify(activity, null, 2));
    });
}

if (result.references) {
    console.log("\nReferences:");
    result.references.forEach((reference) => {
        console.log(`Reference Type: ${reference.type}`);
        console.log(JSON.stringify(reference, null, 2));
    });
}

Continuar a conversa

const query2 = "How do I find lava at night?";
console.log(`\n❓ Follow-up question: ${query2}`);

const retrievalRequest2 = {
    messages: [
        {
            role: "user",
            content: [
                {
                    type: "text",
                    text: query2
                }
            ]
        }
    ],
    knowledgeSourceParams: [
        {
            kind: "searchIndex",
            knowledgeSourceName: 'earth-knowledge-source',
            includeReferences: true,
            includeReferenceSourceData: true,
            alwaysQuerySource: true,
            rerankerThreshold: 2.5
        }
    ],
    includeActivity: true,
    retrievalReasoningEffort: { kind: "low" }
};

const result2 = await knowledgeRetrievalClient.retrieveKnowledge(retrievalRequest2);

Examinar a nova resposta, a atividade e as referências

O código a seguir exibe a nova resposta, a atividade e as referências do pipeline de recuperação.

console.log("\n📝 ANSWER:");
console.log("─".repeat(80));
if (result2.response && result2.response.length > 0) {
    result2.response.forEach((msg) => {
        if (msg.content && msg.content.length > 0) {
            msg.content.forEach((content) => {
                if (content.type === "text" && 'text' in content) {
                    console.log(content.text);
                }
            });
        }
    });
}
console.log("─".repeat(80));

if (result2.activity) {
    console.log("\nActivities:");
    result2.activity.forEach((activity) => {
        console.log(`Activity Type: ${activity.type}`);
        console.log(JSON.stringify(activity, null, 2));
    });
}

if (result2.references) {
    console.log("\nReferences:");
    result2.references.forEach((reference) => {
        console.log(`Reference Type: ${reference.type}`);
        console.log(JSON.stringify(reference, null, 2));
    });
}

Limpar os recursos

Ao trabalhar em sua assinatura, é uma boa ideia finalizar um projeto determinando se você ainda precisa dos recursos que criou. Recursos que ficam em execução podem custar dinheiro.

No portal do Azure, você pode gerenciar os recursos do Azure AI Search e do Microsoft Foundry selecionando Todos os recursos ou grupos de recursos no painel esquerdo.

Caso contrário, o código a seguir de index.js excluiu os objetos que você criou neste início rápido.

await searchIndexClient.deleteKnowledgeBase('earth-knowledge-base');
await searchIndexClient.deleteKnowledgeSource('earth-knowledge-source');
await searchIndexClient.deleteIndex('earth_at_night');

console.log(`\n🗑️  Cleaned up resources.`);

Observação

Dica

Quer começar imediatamente? Consulte o repositório GitHub azure-search-python-samples .

Pré-requisitos

Uma conta do Azure com uma assinatura ativa. Crie uma conta gratuitamente.
Um serviço do Azure AI Search em qualquer região que forneça recuperação por agente.
Um projeto e um recurso do Microsoft Foundry . Quando você cria um projeto, o recurso é criado automaticamente.
A CLI do Azure para autenticação sem uso de chave com a Microsoft Entra ID.
Visual Studio Code e a versão mais recente do Python.

Configurar o acesso

Para configurar o acesso para este início rápido, selecione as duas guias a seguir.

Pesquisa de IA do Azure
Microsoft Foundry

Em seu serviço do Azure AI Search:

Habilitar o acesso baseado em função.
Criar uma identidade gerenciada atribuída pelo sistema.
Atribua as funções a seguir a si mesmo.
- Colaborador do Serviço de Pesquisa
- Contribuinte de dados do índice de pesquisa
- Leitor de dados de índice de pesquisa

Importante

A recuperação do Agentic tem dois modelos de cobrança baseados em tokens:

Faturamento da Pesquisa de IA do Azure para a recuperação por meio de agentes.
Cobrança do OpenAI do Azure para planejamento de consultas e síntese de respostas.

Para obter mais informações, veja Disponibilidade e preços da recuperação de agente.

Obter pontos de extremidade

Para obter os pontos de extremidade para este início rápido, selecione as duas guias a seguir.

Pesquisa de IA do Azure
Microsoft Foundry

Entre no portal do Azure e selecione seu serviço de pesquisa.
No painel esquerdo, selecione Visão geral.
Anote o ponto de extremidade, que deve ser semelhante a https://my-service.search.windows.net.

Implantar modelos

Para este início rápido, você deve implantar dois modelos do Azure OpenAI em seu projeto do Microsoft Foundry:

Um modelo de inserção para conversão de texto para vetor. Este início rápido usa text-embedding-3-large, mas você pode usar qualquer text-embedding modelo.
Uma LLM para planejamento de consulta e geração de respostas. Este início rápido usa gpt-5-mini, mas você pode usar qualquer LLM com suporte para recuperação por agente.

Para obter instruções de implantação, consulte Implantar modelos do Azure OpenAI com o Microsoft Foundry.

Conectar-se a partir de seu sistema local

Você configurou o acesso baseado em função para interagir com o Azure AI Search e o Azure OpenAI em Foundry Models. Use a CLI do Azure para entrar na mesma assinatura e locatário para ambos os recursos. Para obter mais informações, consulte Início Rápido: Conectar sem chaves.

Para se conectar a partir do sistema local:

Crie uma pasta chamada quickstart-agentic-retrieval.
Abra a pasta no Visual Studio Code.
Selecione Terminal>Novo Terminal.
Execute o seguinte comando para entrar na sua conta do Azure. Se você tiver várias assinaturas, selecione aquela que contém o serviço do Azure AI Search e o projeto Foundry.
```
az login
```

Executar o código

Para criar e executar o agentic retrieval pipeline:

Execute o comando a seguir para instalar os pacotes necessários.

pip install azure-identity requests azure-search-documents --pre

Crie um arquivo nomeado agentic-retrieval.py na quickstart-agentic-retrieval pasta.

Cole o código a seguir no arquivo.

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.")

Defina search_endpoint e aoai_endpoint como os valores obtidos em Obter pontos de extremidade.
Execute o comando a seguir para executar o código.
```
python agentic-retrieval.py
```

Saída

A saída do código deve ser semelhante à seguinte:

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.

Entender o código

Agora que você executou o código, vamos dividir as etapas principais:

Criar um índice de pesquisa
Carregar documentos no índice
Criar uma fonte de conhecimento
Criar uma base de dados de conhecimento
Configurar mensagens
Execute o pipeline de recuperação
Continuar a conversa

Criar um índice de pesquisa

# 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.")

Carregar documentos no índice

# 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.")

Criar uma fonte de conhecimento

# 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.")

Como criar uma base de dados de conhecimento

output_mode é definido como ANSWER_SYNTHESIS, habilitando respostas de linguagem natural que citam os documentos recuperados e seguem o answer_instructions fornecido.

# 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.")

Configurar mensagens

O código a seguir cria uma mensagem do sistema que instrui earth-knowledge-base a responder perguntas sobre a Terra à noite e responder com "Não sei" quando as respostas não estão disponíveis.

# 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
    }
]

Execute o pipeline de recuperação

Você está pronto para executar a recuperação por meio de agentes. O código a seguir envia uma consulta de usuário de duas partes para earth-knowledge-basea qual:

Analisa toda a conversa para inferir a necessidade de informações do usuário.
Decompõe a consulta composta em subconsultas focadas.
Executa as subconsultas simultaneamente em relação à fonte de conhecimento.
Usa o classificador semântico para reclassificador e filtrar os resultados.
Sintetiza os resultados principais em uma resposta de linguagem natural.

# 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.")

Examinar a resposta, a atividade e as referências

O código a seguir exibe a resposta, a atividade e as referências do pipeline de recuperação, em que:

response_contents fornece uma resposta sintetizada gerada por LLM para a consulta que cita os documentos recuperados. Quando a síntese de resposta não está habilitada, esta seção contém o conteúdo extraído diretamente dos documentos.
activity_contents rastreia as etapas que foram executadas durante o processo de recuperação, incluindo as subconsultas geradas pela sua implantação gpt-5-mini e os tokens usados para classificação semântica, planejamento de consultas e síntese de resposta.
references_contents lista os documentos que contribuíram para a resposta, cada um identificado por sua 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)

Continuar a conversa

# 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.")

Examinar a nova resposta, a atividade e as referências

O código a seguir exibe a nova resposta, a atividade e as referências do pipeline de recuperação.

# 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)

Limpar os recursos

Ao trabalhar em sua assinatura, é uma boa ideia finalizar um projeto determinando se você ainda precisa dos recursos que criou. Recursos que ficam em execução podem custar dinheiro.

No portal do Azure, você pode gerenciar os recursos do Azure AI Search e do Microsoft Foundry selecionando Todos os recursos ou grupos de recursos no painel esquerdo.

Caso contrário, o código a seguir de agentic-retrieval.py excluiu os objetos que você criou neste início rápido.

Excluir a base de dados de conhecimento

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.")

Excluir a fonte de conhecimento

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.")

Excluir o índice de pesquisa

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

Observação

Pré-requisitos

Uma conta do Azure com uma assinatura ativa. Crie uma conta gratuitamente.
Um serviço do Azure AI Search em qualquer região que forneça recuperação por agente.
Um projeto e um recurso do Microsoft Foundry . Quando você cria um projeto, o recurso é criado automaticamente.
A CLI do Azure para autenticação sem uso de chave com a Microsoft Entra ID.
Visual Studio Code e a versão mais recente do LTS do Node.js.

Configurar o acesso

Para configurar o acesso para este início rápido, selecione as duas guias a seguir.

Pesquisa de IA do Azure
Microsoft Foundry

Em seu serviço do Azure AI Search:

Habilitar o acesso baseado em função.
Criar uma identidade gerenciada atribuída pelo sistema.
Atribua as funções a seguir a si mesmo.
- Colaborador do Serviço de Pesquisa
- Contribuinte de dados do índice de pesquisa
- Leitor de dados de índice de pesquisa

Importante

A recuperação do Agentic tem dois modelos de cobrança baseados em tokens:

Faturamento da Pesquisa de IA do Azure para a recuperação por meio de agentes.
Cobrança do OpenAI do Azure para planejamento de consultas e síntese de respostas.

Para obter mais informações, veja Disponibilidade e preços da recuperação de agente.

Obter pontos de extremidade

Cada serviço do Azure AI Search e o recurso Microsoft Foundry têm um endpoint, que é uma URL exclusiva que identifica e fornece acesso de rede ao recurso. Em uma seção posterior, especifique esses pontos de extremidade para se conectar aos seus recursos programaticamente.

Para obter os pontos de extremidade para este início rápido, selecione as duas guias a seguir.

Pesquisa de IA do Azure
Microsoft Foundry

Entre no portal do Azure e selecione seu serviço de pesquisa.
No painel esquerdo, selecione Visão geral.
Anote o ponto de extremidade, que deve ser semelhante a https://my-service.search.windows.net.

Implantar modelos

Para este início rápido, você deve implantar dois modelos do Azure OpenAI em seu projeto do Microsoft Foundry:

Um modelo de inserção para conversão de texto para vetor. Este início rápido usa text-embedding-3-large, mas você pode usar qualquer text-embedding modelo.
Uma LLM para planejamento de consulta e geração de respostas. Este início rápido usa gpt-5-mini, mas você pode usar qualquer LLM com suporte para recuperação por agente.

Para obter instruções de implantação, consulte Implantar modelos do Azure OpenAI com o Microsoft Foundry.

Configurar o ambiente

Para configurar o aplicativo de console para este início rápido:

Crie uma pasta nomeada quickstart-agentic-retrieval para conter o aplicativo.
Abra a pasta no Visual Studio Code.
Selecione Terminal>Novo Terminal e execute os comandos a seguir para inicializar o package.json arquivo.
```
npm init -y
npm pkg set type=module
```
Instale o TypeScript como uma dependência de desenvolvimento.
```
npm install --save-dev typescript @types/node
```
Instale a biblioteca de clientes do Azure AI Search para JavaScript.
```
npm install @azure/search-documents@12.3.0-beta.1
```
Para autenticação sem chave com o Microsoft Entra ID, instale a biblioteca de identidade do Azure para JavaScript.
```
npm install @azure/identity
```
Para autenticação sem chave com a ID do Microsoft Entra, entre em sua conta do Azure. Se você tiver várias assinaturas, selecione aquela que contém o serviço Azure AI Search e o projeto Microsoft Foundry.
```
az login
```

Executar o código

Para criar e executar o agentic retrieval pipeline:

Crie um arquivo nomeado .env na quickstart-agentic-retrieval pasta.

Cole as seguintes variáveis de ambiente no arquivo .env.

AZURE_SEARCH_ENDPOINT = https://<your-search-service-name>.search.windows.net
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

Defina AZURE_SEARCH_ENDPOINT e AZURE_OPENAI_ENDPOINT como os valores obtidos em Obter pontos de extremidade.

Crie um arquivo chamado index.tse cole o código a seguir no arquivo.

import { DefaultAzureCredential } from '@azure/identity';
import {
    SearchIndexClient,
    SearchClient,
    SearchIndex,
    SearchField,
    VectorSearch,
    VectorSearchProfile,
    HnswAlgorithmConfiguration,
    AzureOpenAIVectorizer,
    AzureOpenAIParameters,
    KnowledgeRetrievalClient,
    SemanticSearch,
    SemanticConfiguration,
    SemanticPrioritizedFields,
    SemanticField,
    SearchIndexingBufferedSender,
    KnowledgeRetrievalOutputMode,
    IndexDocumentsAction
} from '@azure/search-documents';
import type { IndexDocumentsResult } from '@azure/search-documents';

interface EarthAtNightDocument {
    id: string;
    page_chunk: string;
    page_embedding_text_3_large: number[];
    page_number: number;
}

export const documentKeyRetriever: (document: EarthAtNightDocument) => string = (document: EarthAtNightDocument): string => {
  return document.id!;
};

export const WAIT_TIME = 4000;
export function delay(timeInMs: number): Promise<void> {
  return new Promise((resolve) => setTimeout(resolve, timeInMs));
}

const index: SearchIndex = {
    name: 'earth_at_night',
    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: process.env.AZURE_OPENAI_ENDPOINT!,
                    deploymentId: process.env.AZURE_OPENAI_EMBEDDING_DEPLOYMENT!,
                    modelName: process.env.AZURE_OPENAI_EMBEDDING_DEPLOYMENT!
                } 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
};

const credential = new DefaultAzureCredential();

const searchIndexClient = new SearchIndexClient(process.env.AZURE_SEARCH_ENDPOINT!, credential);
const searchClient = new SearchClient<EarthAtNightDocument>(process.env.AZURE_SEARCH_ENDPOINT!, 'earth_at_night', credential);

await searchIndexClient.createOrUpdateIndex(index);

// get Documents with vectors
const response = await fetch("https://raw.githubusercontent.com/Azure-Samples/azure-search-sample-data/refs/heads/main/nasa-e-book/earth-at-night-json/documents.json");

if (!response.ok) {
    throw new Error(`Failed to fetch documents: ${response.status} ${response.statusText}`);
}
const documents = await response.json() as any[];

const bufferedClient = new SearchIndexingBufferedSender<EarthAtNightDocument>(
    searchClient,
    documentKeyRetriever,
    {
        autoFlush: true,
    },
);

await bufferedClient.uploadDocuments(documents);
await bufferedClient.flush();
await bufferedClient.dispose();

console.log(`Waiting for indexing to complete...`);
console.log(`Expected documents: ${documents.length}`);
await delay(WAIT_TIME);

let count = await searchClient.getDocumentsCount();
console.log(`Current indexed count: ${count}`);

while (count !== documents.length) {
    await delay(WAIT_TIME);
    count = await searchClient.getDocumentsCount();
    console.log(`Current indexed count: ${count}`);
}

console.log(`✓ All ${documents.length} documents indexed successfully!`);

await searchIndexClient.createKnowledgeSource({
    name: 'earth-knowledge-source',
    description: "Knowledge source for Earth at Night e-book content",
    kind: "searchIndex",
    searchIndexParameters: {
        searchIndexName: 'earth_at_night',
        sourceDataFields: [
            { name: "id" },
            { name: "page_number" }
        ]
    }
});

console.log(`✅ Knowledge source 'earth-knowledge-source' created successfully.`);

await searchIndexClient.createKnowledgeBase({
    name: 'earth-knowledge-base',
    knowledgeSources: [
        {
            name: 'earth-knowledge-source'
        }
    ],
    models: [
        {
            kind: "azureOpenAI",
            azureOpenAIParameters: {
                resourceUrl: process.env.AZURE_OPENAI_ENDPOINT!,
                deploymentId: process.env.AZURE_OPENAI_GPT_DEPLOYMENT!,
                modelName: process.env.AZURE_OPENAI_GPT_DEPLOYMENT!
            }
        }
    ],
    outputMode: "answerSynthesis" as KnowledgeRetrievalOutputMode,
    answerInstructions: "Provide a two sentence concise and informative answer based on the retrieved documents."
});

console.log(`✅ Knowledge base 'earth-knowledge-base' created successfully.`);

const knowledgeRetrievalClient = new KnowledgeRetrievalClient(
    process.env.AZURE_SEARCH_ENDPOINT!,
    'earth-knowledge-base',
    credential
);

const query1 = `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?`;

const retrievalRequest = {
    messages: [
        {
            role: "user",
            content: [
                {
                    type: "text" as const,
                    text: query1
                }
            ]
        }
    ],
    knowledgeSourceParams: [
        {
            kind: "searchIndex" as const,
            knowledgeSourceName: 'earth-knowledge-source',
            includeReferences: true,
            includeReferenceSourceData: true,
            alwaysQuerySource: true,
            rerankerThreshold: 2.5
        }
    ],
    includeActivity: true,
    retrievalReasoningEffort: { kind: "low" as const }
};

const result = await knowledgeRetrievalClient.retrieveKnowledge(retrievalRequest);

console.log("\n📝 ANSWER:");
console.log("─".repeat(80));
if (result.response && result.response.length > 0) {
    result.response.forEach((msg) => {
        if (msg.content && msg.content.length > 0) {
            msg.content.forEach((content) => {
                if (content.type === "text" && 'text' in content) {
                    console.log(content.text);
                }
            });
        }
    });
}
console.log("─".repeat(80));

if (result.activity) {
    console.log("\nActivities:");
    result.activity.forEach((activity) => {
        console.log(`Activity Type: ${activity.type}`);
        console.log(JSON.stringify(activity, null, 2));
    });
}

if (result.references) {
    console.log("\nReferences:");
    result.references.forEach((reference) => {
        console.log(`Reference Type: ${reference.type}`);
        console.log(JSON.stringify(reference, null, 2));
    });
}

// Follow-up query - to demonstrate conversational context
const query2 = "How do I find lava at night?";
console.log(`\n❓ Follow-up question: ${query2}`);

const retrievalRequest2 = {
    messages: [
        {
            role: "user",
            content: [
                {
                    type: "text" as const,
                    text: query2
                }
            ]
        }
    ],
    knowledgeSourceParams: [
        {
            kind: "searchIndex" as const,
            knowledgeSourceName: 'earth-knowledge-source',
            includeReferences: true,
            includeReferenceSourceData: true,
            alwaysQuerySource: true,
            rerankerThreshold: 2.5
        }
    ],
    includeActivity: true,
    retrievalReasoningEffort: { kind: "low" as const }
};

const result2 = await knowledgeRetrievalClient.retrieveKnowledge(retrievalRequest2);

console.log("\n📝 ANSWER:");
console.log("─".repeat(80));
if (result2.response && result2.response.length > 0) {
    result2.response.forEach((msg) => {
        if (msg.content && msg.content.length > 0) {
            msg.content.forEach((content) => {
                if (content.type === "text" && 'text' in content) {
                    console.log(content.text);
                }
            });
        }
    });
}
console.log("─".repeat(80));

if (result2.activity) {
    console.log("\nActivities:");
    result2.activity.forEach((activity) => {
        console.log(`Activity Type: ${activity.type}`);
        console.log(JSON.stringify(activity, null, 2));
    });
}

if (result2.references) {
    console.log("\nReferences:");
    result2.references.forEach((reference) => {
        console.log(`Reference Type: ${reference.type}`);
        console.log(JSON.stringify(reference, null, 2));
    });
}

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

// Clean up resources
await searchIndexClient.deleteKnowledgeBase('earth-knowledge-base');
await searchIndexClient.deleteKnowledgeSource('earth-knowledge-source');
await searchIndexClient.deleteIndex('earth_at_night');

console.log(`\n🗑️  Cleaned up resources.`);

Crie um arquivo chamado tsconfig.json, e cole o seguinte JSON para ECMAScript no arquivo.

{
  "compilerOptions": {
    "target": "ES2022",
    "module": "ES2022",
    "lib": ["ES2022", "DOM"],
    "moduleResolution": "node",
    "types": ["node"],
    "outDir": "./dist",
    "rootDir": "./",
    "strict": true,
    "esModuleInterop": true,
    "skipLibCheck": true,
    "forceConsistentCasingInFileNames": true,
    "resolveJsonModule": true,
    "declaration": true,
    "declarationMap": true,
    "sourceMap": true
  },
  "include": ["*.ts"],
  "exclude": ["node_modules", "dist"]
}

Transpile o código de TypeScript para JavaScript.
```
npx tsc
```
Crie e execute o aplicativo.
```
node --env-file ./.env dist/index.js
```

Saída

A saída do aplicativo deve ser semelhante à seguinte:

Waiting for indexing to complete...
Expected documents: 194
Current indexed count: 194
✓ All 194 documents indexed successfully!
✅ Knowledge source 'earth-knowledge-source' created successfully.
✅ Knowledge base 'earth-knowledge-base' created successfully.

📝 ANSWER:
────────────────────────────────────────────────────────────────────────────────
Suburban belts show larger December brightening (20–50% increases) because residential holiday lighting and seasonal decorations are concentrated there, so relative (fractional) increases over the baseline are bigger even though absolute downtown radiances remain higher; urban cores already emit strong baseline light while many suburbs add a large seasonal increment visible in VIIRS DNB observations [ref_id:0][ref_id:1]. The Phoenix street grid appears sharply from space because continuous, street‑oriented lighting with regular residential lot spacing and little vegetative masking produces strong, linear emissions, whereas long interstate stretches between Midwestern cities have sparser, access‑limited lighting, fewer adjacent developments and more shielded fixtures so they register comparatively dim on night‑light sensors like VIIRS/DNB [ref_id:0][ref_id:1].
────────────────────────────────────────────────────────────────────────────────

Activities:
Activity Type: modelQueryPlanning
{
  "id": 0,
  "type": "modelQueryPlanning",
  "elapsedMs": 5883,
  "inputTokens": 1489,
  "outputTokens": 326
}
Activity Type: searchIndex
{
  "id": 1,
  "type": "searchIndex",
  "elapsedMs": 527,
  "knowledgeSourceName": "earth-knowledge-source",
  "queryTime": "2025-12-19T15:38:23.462Z",
  "count": 1,
  "searchIndexArguments": {
    "search": "December brightening suburban belts vs urban cores light pollution causes December increase in night lights suburban vs urban",
    "filter": null,
    "sourceDataFields": [
      {
        "name": "page_chunk"
      },
      {
        "name": "id"
      },
      {
        "name": "page_number"
      }
    ],
    "searchFields": [],
    "semanticConfigurationName": "semantic_config"
  }
}
Activity Type: searchIndex
{
  "id": 2,
  "type": "searchIndex",
  "elapsedMs": 538,
  "knowledgeSourceName": "earth-knowledge-source",
  "queryTime": "2025-12-19T15:38:24.001Z",
  "count": 0,
  "searchIndexArguments": {
    "search": "factors that make Phoenix nighttime street grid highly visible from space reasons highway/interstate lighting visibility differences Midwestern interstates dim",
    "filter": null,
    "sourceDataFields": [
      {
        "name": "page_chunk"
      },
      {
        "name": "id"
      },
      {
        "name": "page_number"
      }
    ],
    "searchFields": [],
    "semanticConfigurationName": "semantic_config"
  }
}
Activity Type: searchIndex
{
  "id": 3,
  "type": "searchIndex",
  "elapsedMs": 465,
  "knowledgeSourceName": "earth-knowledge-source",
  "queryTime": "2025-12-19T15:38:24.467Z",
  "count": 2,
  "searchIndexArguments": {
    "search": "satellite nighttime lights seasonal variations suburban brightening studies December holiday lighting residential vs commercial lighting patterns",
    "filter": null,
    "sourceDataFields": [
      {
        "name": "page_chunk"
      },
      {
        "name": "id"
      },
      {
        "name": "page_number"
      }
    ],
    "searchFields": [],
    "semanticConfigurationName": "semantic_config"
  }
}
Activity Type: agenticReasoning
{
  "id": 4,
  "type": "agenticReasoning",
  "reasoningTokens": 70397,
  "retrievalReasoningEffort": {
    "kind": "low"
  }
}
Activity Type: modelAnswerSynthesis
{
  "id": 5,
  "type": "modelAnswerSynthesis",
  "elapsedMs": 4908,
  "inputTokens": 4013,
  "outputTokens": 187
}

References:
Reference Type: searchIndex
{
  "type": "searchIndex",
  "id": "0",
  "activitySource": 3,
  "sourceData": {
    "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 (2012–2014)\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 20–50% 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
  },
  "rerankerScore": 2.6692379,
  "docKey": "earth_at_night_508_page_174_verbalized"
}
Reference Type: searchIndex
{
  "type": "searchIndex",
  "id": "1",
  "activitySource": 3,
  "sourceData": {
    "id": "earth_at_night_508_page_186_verbalized",
    "page_chunk": "## Appendix A\n\n### NASA's Black Marble Product Suite\n\nNASA's Black Marble product suite provides estimates of daily nighttime lights and other intrinsic surface optical properties of Earth at night. The product is based on the Day/Night Band (DNB) sensor of the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument onboard the Suomi National Polar-orbiting Partnership (NPP) satellite. The VIIRS DNB is a highly sensitive, low-light sensor capable of measuring daily global nighttime light emissions and reflections, allowing users to identify sources and intensities of these artificial lights, and monitor changes over a period of time. Like any optical sensor, the principal challenge in using VIIRS DNB data is to account for variations in light captured by the sensor. Certainly, there are variations in the sources and intensity of anthropogenic light due to changing human processes like urbanization, oil and gas production, nighttime commercial fishing, and infrastructure development. However, these processes can only be studied when other naturally-occurring factors that influence nighttime lights are removed. For example, variations in lunar lighting due to consistent changes in Moon phase cause fluctuations in the amount of light shining on Earth. Similarly, land-cover dynamics (e.g., seasonal vegetation, snow and ice cover), as well as atmospheric conditions (e.g., clouds, aerosols, airglow, and auroras), influence the intensity of the light captured by the sensor as it travels over various parts of the world.\n\nTo realize the full potential of the VIIRS DNB time series record for nighttime lights applications, NASA's Black Marble product suite was developed, building on a history of 20 years of research on how light changes when it reflects off of surfaces with different angular and spectral properties. Through complex modeling, scientists can now predict how moonlight, snow, vegetation, terrain, and clouds impact the lights we see from space, allowing for more accurate assessments of human and environmental activity at night.\n\n<!-- PageFooter=\"170 Earth at Night\" -->",
    "page_number": 186
  },
  "rerankerScore": 2.5997617,
  "docKey": "earth_at_night_508_page_186_verbalized"
}

❓ Follow-up question: How do I find lava at night?

📝 ANSWER:
────────────────────────────────────────────────────────────────────────────────
... // Trimmed for brevity
────────────────────────────────────────────────────────────────────────────────

Activities:
... // Trimmed for brevity

References:
... // Trimmed for brevity

✅ Quickstart completed successfully!

🗑️  Cleaned up resources.

Entender o código

Agora que você tem o código, vamos dividir os principais componentes:

Criar um índice de pesquisa
Carregar documentos no índice
Criar uma fonte de conhecimento
Criar uma base de dados de conhecimento
Execute o pipeline de recuperação
Examinar a resposta, a atividade e as referências
Continuar a conversa

Criar um índice de pesquisa

No Azure AI Search, um índice é uma coleção estruturada de dados. O código a seguir define um índice chamado earth_at_night.

const index: SearchIndex = {
    name: 'earth_at_night',
    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: process.env.AZURE_OPENAI_ENDPOINT!,
                    deploymentId: process.env.AZURE_OPENAI_EMBEDDING_DEPLOYMENT!,
                    modelName: process.env.AZURE_OPENAI_EMBEDDING_DEPLOYMENT!
                } 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
};

const credential = new DefaultAzureCredential();

const searchIndexClient = new SearchIndexClient(process.env.AZURE_SEARCH_ENDPOINT!, credential);
const searchClient = new SearchClient<EarthAtNightDocument>(process.env.AZURE_SEARCH_ENDPOINT!, 'earth_at_night', credential);

await searchIndexClient.createOrUpdateIndex(index);

Carregar documentos no índice

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

if (!response.ok) {
    throw new Error(`Failed to fetch documents: ${response.status} ${response.statusText}`);
}
const documents = await response.json() as any[];

const bufferedClient = new SearchIndexingBufferedSender<EarthAtNightDocument>(
    searchClient,
    documentKeyRetriever,
    {
        autoFlush: true,
    },
);

await bufferedClient.uploadDocuments(documents);
await bufferedClient.flush();
await bufferedClient.dispose();

console.log(`Waiting for indexing to complete...`);
console.log(`Expected documents: ${documents.length}`);
await delay(WAIT_TIME);

let count = await searchClient.getDocumentsCount();
console.log(`Current indexed count: ${count}`);

while (count !== documents.length) {
    await delay(WAIT_TIME);
    count = await searchClient.getDocumentsCount();
    console.log(`Current indexed count: ${count}`);
}

console.log(`✓ All ${documents.length} documents indexed successfully!`);

Criar uma fonte de conhecimento

await searchIndexClient.createKnowledgeSource({
    name: 'earth-knowledge-source',
    description: "Knowledge source for Earth at Night e-book content",
    kind: "searchIndex",
    searchIndexParameters: {
        searchIndexName: 'earth_at_night',
        sourceDataFields: [
            { name: "id" },
            { name: "page_number" }
        ]
    }
});

console.log(`✅ Knowledge source 'earth-knowledge-source' created successfully.`);

Como criar uma base de dados de conhecimento

outputMode é definido como answerSynthesis, habilitando respostas de linguagem natural que citam os documentos recuperados e seguem o answerInstructions fornecido.

await searchIndexClient.createKnowledgeBase({
    name: 'earth-knowledge-base',
    knowledgeSources: [
        {
            name: 'earth-knowledge-source'
        }
    ],
    models: [
        {
            kind: "azureOpenAI",
            azureOpenAIParameters: {
                resourceUrl: process.env.AZURE_OPENAI_ENDPOINT!,
                deploymentId: process.env.AZURE_OPENAI_GPT_DEPLOYMENT!,
                modelName: process.env.AZURE_OPENAI_GPT_DEPLOYMENT!
            }
        }
    ],
    outputMode: "answerSynthesis" as KnowledgeRetrievalOutputMode,
    answerInstructions: "Provide a two sentence concise and informative answer based on the retrieved documents."
});

console.log(`✅ Knowledge base 'earth-knowledge-base' created successfully.`);

Execute o pipeline de recuperação

Você está pronto para executar a recuperação por meio de agentes. O código a seguir envia uma consulta de usuário de duas partes para earth-knowledge-basea qual:

Analisa toda a conversa para inferir a necessidade de informações do usuário.
Decompõe a consulta composta em subconsultas focadas.
Executa as subconsultas simultaneamente em relação à fonte de conhecimento.
Usa o classificador semântico para reclassificador e filtrar os resultados.
Sintetiza os resultados principais em uma resposta de linguagem natural.

const knowledgeRetrievalClient = new KnowledgeRetrievalClient(
    process.env.AZURE_SEARCH_ENDPOINT!,
    'earth-knowledge-base',
    credential
);

const query1 = `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?`;

const retrievalRequest = {
    messages: [
        {
            role: "user",
            content: [
                {
                    type: "text" as const,
                    text: query1
                }
            ]
        }
    ],
    knowledgeSourceParams: [
        {
            kind: "searchIndex" as const,
            knowledgeSourceName: 'earth-knowledge-source',
            includeReferences: true,
            includeReferenceSourceData: true,
            alwaysQuerySource: true,
            rerankerThreshold: 2.5
        }
    ],
    includeActivity: true,
    retrievalReasoningEffort: { kind: "low" as const }
};

const result = await knowledgeRetrievalClient.retrieveKnowledge(retrievalRequest);

Examinar a resposta, a atividade e as referências

O código a seguir exibe a resposta, a atividade e as referências do pipeline de recuperação, em que:

Answer fornece uma resposta sintetizada gerada por LLM para a consulta que cita os documentos recuperados. Quando a síntese de resposta não está habilitada, esta seção contém o conteúdo extraído diretamente dos documentos.
Activities rastreia as etapas que foram executadas durante o processo de recuperação, incluindo as subconsultas geradas pela sua implantação gpt-5-mini e os tokens usados para classificação semântica, planejamento de consultas e síntese de resposta.
References lista os documentos que contribuíram para a resposta, cada um identificado por sua docKey.

console.log("\n📝 ANSWER:");
console.log("─".repeat(80));
if (result.response && result.response.length > 0) {
    result.response.forEach((msg) => {
        if (msg.content && msg.content.length > 0) {
            msg.content.forEach((content) => {
                if (content.type === "text" && 'text' in content) {
                    console.log(content.text);
                }
            });
        }
    });
}
console.log("─".repeat(80));

if (result.activity) {
    console.log("\nActivities:");
    result.activity.forEach((activity) => {
        console.log(`Activity Type: ${activity.type}`);
        console.log(JSON.stringify(activity, null, 2));
    });
}

if (result.references) {
    console.log("\nReferences:");
    result.references.forEach((reference) => {
        console.log(`Reference Type: ${reference.type}`);
        console.log(JSON.stringify(reference, null, 2));
    });
}

Continuar a conversa

const query2 = "How do I find lava at night?";
console.log(`\n❓ Follow-up question: ${query2}`);

const retrievalRequest2 = {
    messages: [
        {
            role: "user",
            content: [
                {
                    type: "text" as const,
                    text: query2
                }
            ]
        }
    ],
    knowledgeSourceParams: [
        {
            kind: "searchIndex" as const,
            knowledgeSourceName: 'earth-knowledge-source',
            includeReferences: true,
            includeReferenceSourceData: true,
            alwaysQuerySource: true,
            rerankerThreshold: 2.5
        }
    ],
    includeActivity: true,
    retrievalReasoningEffort: { kind: "low" as const }
};

const result2 = await knowledgeRetrievalClient.retrieveKnowledge(retrievalRequest2);

Examinar a nova resposta, a atividade e as referências

O código a seguir exibe a nova resposta, a atividade e as referências do pipeline de recuperação.

console.log("\n📝 ANSWER:");
console.log("─".repeat(80));
if (result2.response && result2.response.length > 0) {
    result2.response.forEach((msg) => {
        if (msg.content && msg.content.length > 0) {
            msg.content.forEach((content) => {
                if (content.type === "text" && 'text' in content) {
                    console.log(content.text);
                }
            });
        }
    });
}
console.log("─".repeat(80));

if (result2.activity) {
    console.log("\nActivities:");
    result2.activity.forEach((activity) => {
        console.log(`Activity Type: ${activity.type}`);
        console.log(JSON.stringify(activity, null, 2));
    });
}

if (result2.references) {
    console.log("\nReferences:");
    result2.references.forEach((reference) => {
        console.log(`Reference Type: ${reference.type}`);
        console.log(JSON.stringify(reference, null, 2));
    });
}

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

Limpar os recursos

Ao trabalhar em sua assinatura, é uma boa ideia finalizar um projeto determinando se você ainda precisa dos recursos que criou. Recursos que ficam em execução podem custar dinheiro.

No portal do Azure, você pode gerenciar os recursos do Azure AI Search e do Microsoft Foundry selecionando Todos os recursos ou grupos de recursos no painel esquerdo.

Caso contrário, o código a seguir de index.ts excluiu os objetos que você criou neste início rápido.

await searchIndexClient.deleteKnowledgeBase('earth-knowledge-base');
await searchIndexClient.deleteKnowledgeSource('earth-knowledge-source');
await searchIndexClient.deleteIndex('earth_at_night');

console.log(`\n🗑️  Cleaned up resources.`);

Observação

Dica

Quer começar imediatamente? Consulte o repositório GitHub azure-search-rest-samples .

Pré-requisitos

Uma conta do Azure com uma assinatura ativa. Crie uma conta gratuitamente.
Um serviço do Azure AI Search em qualquer região que forneça recuperação por agente.
Um projeto e um recurso do Microsoft Foundry . Quando você cria um projeto, o recurso é criado automaticamente.
A CLI do Azure para autenticação sem uso de chave com a Microsoft Entra ID.
Visual Studio Code com a extensão cliente REST.

Configurar o acesso

Para configurar o acesso para este início rápido, selecione as duas guias a seguir.

Pesquisa de IA do Azure
Microsoft Foundry

Em seu serviço do Azure AI Search:

Habilitar o acesso baseado em função.
Criar uma identidade gerenciada atribuída pelo sistema.
Atribua as funções a seguir a si mesmo.
- Colaborador do Serviço de Pesquisa
- Contribuinte de dados do índice de pesquisa
- Leitor de dados de índice de pesquisa

Importante

A recuperação do Agentic tem dois modelos de cobrança baseados em tokens:

Faturamento da Pesquisa de IA do Azure para a recuperação por meio de agentes.
Cobrança do OpenAI do Azure para planejamento de consultas e síntese de respostas.

Para obter mais informações, veja Disponibilidade e preços da recuperação de agente.

Obter pontos de extremidade

Para obter os pontos de extremidade para este início rápido, selecione as duas guias a seguir.

Pesquisa de IA do Azure
Microsoft Foundry

Entre no portal do Azure e selecione seu serviço de pesquisa.
No painel esquerdo, selecione Visão geral.
Anote o ponto de extremidade, que deve ser semelhante a https://my-service.search.windows.net.

Implantar modelos

Para este início rápido, você deve implantar dois modelos do Azure OpenAI em seu projeto do Microsoft Foundry:

Um modelo de inserção para conversão de texto para vetor. Este início rápido usa text-embedding-3-large, mas você pode usar qualquer text-embedding modelo.
Uma LLM para planejamento de consulta e geração de respostas. Este início rápido usa gpt-5-mini, mas você pode usar qualquer LLM com suporte para recuperação por agente.

Para obter instruções de implantação, consulte Implantar modelos do Azure OpenAI com o Microsoft Foundry.

Conectar-se a partir de seu sistema local

Para se conectar a partir do sistema local:

Crie uma pasta chamada quickstart-agentic-retrieval.
Abra a pasta no Visual Studio Code.
Selecione Terminal>Novo Terminal.
Execute o seguinte comando para entrar na sua conta do Azure. Se você tiver várias assinaturas, selecione aquela que contém o serviço do Azure AI Search e o projeto Foundry.
```
az login
```

Execute o comando a seguir para gerar um token de ID do Microsoft Entra.

az account get-access-token --scope https://search.azure.com/.default --query accessToken --output tsv

Anote o token para uso na próxima seção.

Executar o código

Para criar e executar o agentic retrieval pipeline:

Crie um arquivo nomeado agentic-retrieval.rest na quickstart-agentic-retrieval pasta.

Cole as variáveis e solicitações a seguir no arquivo.

### Set variables
@search-url = PUT-YOUR-SEARCH-SERVICE-URL-HERE
@token = PUT-YOUR-MICROSOFT-ENTRA-TOKEN-HERE
@aoai-url = 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
@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\" -->",
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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}}

Defina @search-url e aoai-url como os valores obtidos em Obter pontos de extremidade.
Defina @token como o valor obtido em Conectar seu sistema local.
Enviar cada solicitação sucessivamente, começando com ### Create an index.

Cada solicitação deve retornar um código de status 200 OK, 201 Created ou 204 No Content. Se você receber um erro, verifique a solicitação em busca de erros de digitação e verifique se o token é válido.

Saída

Cada solicitação retorna JSON diferente com base na operação. A saída da chave é de ### Run agentic retrieval, que deve ser semelhante à seguinte:

{
  "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"
    }
  ]
}

Entender o código

Agora que você executou o código, vamos dividir as etapas principais:

Criar um índice de pesquisa
Carregar documentos no índice
Criar uma fonte de conhecimento
Criar uma base de dados de conhecimento
Execute o pipeline de recuperação

Criar um índice de pesquisa

No Azure AI Search, um índice é uma coleção estruturada de dados. O código a seguir usa Índices – Criar (API REST) para definir um índice nomeado earth-at-night, que você especificou anteriormente usando a @index-name variável.

### 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}}"
                }
            }
        ]
    }
}

Carregar documentos no índice

Atualmente, o earth-at-night índice está vazio. O código a seguir usa Documentos - Índice (API REST) para preencher o índice com documentos JSON do e-book Terra à Noite da NASA. Conforme exigido pelo Azure AI Search, cada documento está em conformidade com os campos e tipos de dados definidos no esquema de índice.


### 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
        }
    ]
}

Criar uma fonte de conhecimento

Uma fonte de dados de conhecimento é uma referência reutilizável aos dados de origem. O código a seguir usa fontes de conhecimento – Criar (API REST) para definir uma fonte de conhecimento chamada earth-knowledge-source que tem como destino o earth-at-night índice.

sourceDataFields especifica quais campos de índice estão incluídos nas referências de citação. Nosso exemplo inclui apenas campos legíveis por humanos para evitar inserções longas e não interpretáveis em respostas.

### 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" }
        ]
    }
}

Como criar uma base de dados de conhecimento

Para direcionar a sua implantação de earth-knowledge-source e gpt-5-mini no momento da consulta, você precisa de uma base de dados de conhecimento. O código a seguir usa Bases de Dados de Conhecimento – Criar (API REST) para definir uma base nomeada earth-knowledge-base, que você especificou anteriormente usando a @knowledge-base-name variável.

outputMode é definido como answerSynthesis, habilitando respostas de linguagem natural que citam os documentos recuperados e seguem o answerInstructions fornecido.

### 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."
}

Execute o pipeline de recuperação

Você está pronto para executar a recuperação por meio de agentes. O código a seguir usa Recuperação de Conhecimento – Recuperar (API REST) para enviar uma consulta de usuário de duas partes para earth-knowledge-base, que:

Analisa toda a conversa para inferir a necessidade de informações do usuário.
Decompõe a consulta composta em subconsultas focadas.
Executa as subconsultas simultaneamente em relação à fonte de conhecimento.
Usa o classificador semântico para reclassificador e filtrar os resultados. Nosso exemplo exclui respostas com uma pontuação de reclassificador de 2.5 ou menor.
Sintetiza os resultados principais em uma resposta de linguagem natural.

### 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" }
}

A saída deve conter os seguintes componentes:

response fornece uma resposta sintetizada gerada por LLM para a consulta que cita os documentos recuperados. Quando a síntese de resposta não está habilitada, esta seção contém o conteúdo extraído diretamente dos documentos.
activity rastreia as etapas que foram executadas durante o processo de recuperação, incluindo as subconsultas geradas pela sua implantação gpt-5-mini e os tokens usados para classificação semântica, planejamento de consultas e síntese de resposta.
references lista os documentos que contribuíram para a resposta, cada um identificado por sua docKey.

Limpar os recursos

Ao trabalhar em sua assinatura, é uma boa ideia finalizar um projeto determinando se você ainda precisa dos recursos que criou. Recursos que ficam em execução podem custar dinheiro.

No portal do Azure, você pode gerenciar os recursos do Azure AI Search e do Microsoft Foundry selecionando Todos os recursos ou grupos de recursos no painel esquerdo.

Caso contrário, as solicitações a seguir de agentic-retrieval.rest excluíram os objetos que você criou neste início rápido.

Excluir a base de dados de conhecimento

### 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}}

Excluir a fonte de conhecimento

### 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}}

Excluir o índice de pesquisa

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

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Last updated on 2025-12-23

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Início Rápido: usar a recuperação por meio de agentes na Pesquisa de IA do Azure

Pré-requisitos

Configurar o acesso

Obter pontos de extremidade

Implantar modelos

Configurar o ambiente

Executar o código

Saída

Entender o código

Criar um índice de pesquisa

Carregar documentos no índice

Criar uma fonte de conhecimento

Como criar uma base de dados de conhecimento

Configurar mensagens

Execute o pipeline de recuperação

Examinar a resposta, a atividade e as referências

Continuar a conversa

Examinar a nova resposta, a atividade e as referências

Limpar os recursos

Excluir a base de dados de conhecimento

Excluir a fonte de conhecimento

Excluir o índice de pesquisa

Pré-requisitos

Configurar o acesso

Obter pontos de extremidade

Implantar modelos

Configurar o ambiente

Executar o código

Saída

Entender o código

Criar um índice de pesquisa

Carregar documentos no índice

Criar um agente de conhecimento

Configurar mensagens

Execute o pipeline de recuperação

Examinar a resposta, a atividade e os resultados

Criar o cliente do Azure OpenAI

Usar a API de Conclusões de Chat para gerar uma resposta

Continuar a conversa

Limpar os recursos

Excluir o agente de conhecimento

Excluir o índice de pesquisa

Pré-requisitos

Configurar o acesso

Obter pontos de extremidade

Implantar modelos

Configurar o ambiente

Executar o código

Saída

Entender o código

Criar um índice de pesquisa

Carregar documentos no índice

Criar uma fonte de conhecimento

Como criar uma base de dados de conhecimento

Execute o pipeline de recuperação

Examinar a resposta, a atividade e as referências

Continuar a conversa

Examinar a nova resposta, a atividade e as referências

Limpar os recursos

Pré-requisitos

Configurar o acesso

Obter pontos de extremidade

Implantar modelos

Conectar-se a partir de seu sistema local

Executar o código

Saída

Entender o código

Criar um índice de pesquisa

Carregar documentos no índice

Criar uma fonte de conhecimento

Como criar uma base de dados de conhecimento

Configurar mensagens

Execute o pipeline de recuperação

Examinar a resposta, a atividade e as referências

Continuar a conversa

Examinar a nova resposta, a atividade e as referências

Limpar os recursos

Excluir a base de dados de conhecimento

Excluir a fonte de conhecimento