Início Rápido: recuperação por meio de agentes

Nota

Pesquisa de IA do Azure está disponível por meio do portal Azure, APIs REST e SDKs do Azure. Ele também sustenta o IQ do Foundry, a camada de conhecimento gerenciado que transforma o conteúdo da empresa em bases de conhecimento reutilizáveis e com reconhecimento de permissão para agentes no portal do Microsoft Foundry.

Importante

Recursos, funcionalidades ou propriedades marcados como (versão prévia) não são cobertos por um contrato de nível de serviço (SLA), não são recomendados para cargas de trabalho de produção e podem mudar ou ser restringidos antes da disponibilidade geral. Os termos de visualização do Pesquisa de IA do Azure se aplicam a toda funcionalidade em visualização, seja autônoma ou parte de um recurso de disponibilidade geral.

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 do Foundry.

A base de dados de conhecimento usa o planejamento de consulta baseado em LLM (versão prévia) para decompor consultas complexas em subconsultas. Em seguida, ele executa as subconsultas em uma ou mais fontes de conhecimento e retorna resultados com metadados. Por padrão, uma base de dados de conhecimento retorna conteúdo bruto de suas fontes, mas este início rápido usa síntese de resposta (versão prévia) para gerar respostas de linguagem natural.

Embora você possa usar seus próprios dados, este início rápido usa documentos JSON de amostra do e-book Earth at Night da NASA.

Dica

Quer começar imediatamente? Baixe o source code no GitHub.

Pré-requisitos

Uma conta Azure com uma assinatura ativa. Crie uma conta gratuitamente.
Um serviço Pesquisa de IA do Azure, em qualquer região que forneça recuperação por meio de agentes. Este início rápido requer a camada Básica ou superior para suporte à identidade gerenciada.
Um recurso e projeto Microsoft Foundry. Quando você cria um projeto, o recurso é criado automaticamente.
Um modelo de inserção implantado em seu projeto para conversão de texto em vetor. Você pode usar qualquer text-embedding modelo, como text-embedding-3-large.
Uma LLM implantada em seu projeto para planejamento de consultas e geração de respostas. Você pode usar qualquer LLM compatível, como gpt-5-mini.
.NET 8 ou posterior.
Git para clonar o repositório de exemplo.
O CLI do Azure para autenticação sem chave com Microsoft Entra ID.

Configurar o acesso

Antes de começar, verifique se você tem permissões para acessar o conteúdo e as operações. Este início rápido usa 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:

Entre no portal Azure.
Em seu serviço de Pesquisa de IA do Azure :
1. Habilitar o acesso baseado em função.
2. Crie uma identidade gerenciada atribuída pelo sistema.
3. Atribua as seguintes funções à sua conta de usuário: Colaborador do Serviço de Pesquisa, Colaborador de Dados de Índice de Pesquisa e Leitor de Dados de Índice de Pesquisa.
No recurso Microsoft Foundry, atribua Cognitive Services User à identidade gerenciada do serviço de pesquisa.

Importante

A recuperação agentic tem dois modelos de cobrança baseados em token:

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, consulte Disponibilidade, limites e cobrança de região.

Obter pontos de extremidade

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

Para obter os pontos finais para este início rápido:

Entre no portal Azure.
Em seu serviço de Pesquisa de IA do Azure :
1. No painel esquerdo, selecione Visão geral.
2. Copie a URL, que deve se parecer com https://my-service.search.windows.net.
No recurso Microsoft Foundry:
1. No painel esquerdo, selecione Gerenciamento de Recursos>Chaves e Ponto de Extremidade.
2. Copie a URL na guia OpenAI, que deve ser semelhante a https://my-resource.openai.azure.com/.

Configurar o ambiente

Use o Git para clonar o repositório de exemplo.

git clone https://github.com/Azure-Samples/azure-search-dotnet-samples

Vá para a pasta de início rápido.

cd azure-search-dotnet-samples/quickstart-agentic-retrieval

Em sample.env, substitua os valores de espaço reservado para SEARCH_ENDPOINT e AOAI_ENDPOINT pelas URLs obtidas em Obter pontos de extremidade.
Renomear sample.env para .env.
```
mv sample.env .env
```
Instale as dependências.
```
dotnet restore AgenticRetrievalQuickstart.csproj
```
Quando a restauração for concluída, certifique-se de que nenhum erro apareça na saída.
Para autenticação sem chave com Microsoft Entra ID, entre em sua conta Azure. Se você tiver várias assinaturas, selecione aquela que contém seus recursos Pesquisa de IA do Azure e Microsoft Foundry.
```
az login
```

Executar o código

Execute o aplicativo para criar um índice, carregar documentos, configurar uma fonte de conhecimento e uma base de dados de conhecimento e executar consultas de recuperação agente.

dotnet run --project AgenticRetrievalQuickstart.csproj

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.
Running the 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?
Response:
Suburban belts brighten more (in relative terms) in December because holiday lighting is concentrated in yards and single-family suburbs where extra decorative lights add a large percentage increase over baseline residential lighting, whereas dense urban cores already have high absolute light levels so the same added holiday lights produce a smaller relative change [ref_id:6][ref_id:2]. The documents note suburbs and outskirts show the biggest holiday increases and central urban areas show smaller percent increases (20-30% in cores, larger in suburbs) linked to available yard space and prevalence of single-family homes [ref_id:6][ref_id:2]. Phoenix's street grid appears very sharp from space because the city's regular, closely spaced north-south/east-west street lighting and bright nodes at intersections and commercial strips produce a strong, high-contrast patterned signal (grid and major corridors like Grand Avenue), while long Midwestern interstates are comparatively dim because roadway lighting is more sparse and continuous between cities and navigable rivers/long highways lack the dense, closely spaced light sources that produce visible nodes and grid patterns at night [ref_id:3][ref_id:7]. In addition, the Black Marble processing accounts for atmospheric, lunar, snow/seasonal and stray-light effects and isolates artificial emissions, so concentrated urban lighting (as in Phoenix) stands out more in the corrected radiance product than dispersed or spaced sources like isolated stretches of interstate or sparsely lit rivers and plains [ref_id:1][ref_id:4][ref_id:7].
Activity:
Activity Type: KnowledgeBaseModelQueryPlanningActivityRecord
{
  "InputTokens": 1489,
  "OutputTokens": 326,
  "Id": 0,
  "ElapsedMs": 4558,
  "Error": null
}
Activity Type: KnowledgeBaseSearchIndexActivityRecord
{
  "SearchIndexArguments": {
    "Search": "December brightening suburban belts vs urban cores light pollution causes seasonal variation \"December brightening\" satellite night lights",
    "Filter": null,
    "SourceDataFields": [
      {
        "Name": "page_chunk"
      },
      {
        "Name": "id"
      },
      {
        "Name": "page_number"
      }
    ],
    "SearchFields": [],
    "SemanticConfigurationName": "semantic_config"
  },
  "KnowledgeSourceName": "earth-knowledge-source",
  "QueryTime": "2026-02-24T14:59:41.536+00:00",
  "Count": 21,
  "Id": 1,
  "ElapsedMs": 623,
  "Error": null
}
... // Trimmed for brevity
References:
Reference Type: KnowledgeBaseSearchIndexReference
{
  "DocKey": "earth_at_night_508_page_105_verbalized",
  "Id": "0",
  "ActivitySource": 2,
  "SourceData": {},
  "RerankerScore": 2.7294974
}
... // Trimmed for brevity
Continue the conversation with this query: How do I find lava at night?
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

Nota

Os snippets de código nesta seção podem ter sido modificados para legibilidade. Para obter um exemplo de trabalho completo, consulte o código-fonte.

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

Em Pesquisa de IA do Azure , um índice é uma coleção estruturada de dados. O código a seguir define um índice chamado earth-at-night.

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 pesquisa vetorial, que usa seu text-embedding-3-large deployment para vetorizar texto e comparar documentos com base na semelhança 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.");

Reference:SearchField, SimpleField, VectorSearch, SemanticSearch, SearchIndex, SearchIndexClient

Carregar documentos no índice

Atualmente, o earth-at-night índice está vazio. O código a seguir preenche o índice com documentos JSON de naSA's Earth at Night e-book. Conforme exigido por Pesquisa de IA do Azure , 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.");

Reference:SearchClient, SearchIndexingBufferedSender

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. Este exemplo inclui apenas campos legíveis por humanos para evitar inserções longas e ininterpretáveis 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.");

Reference:SearchIndexKnowledgeSource

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.

OutputMode (versão prévia) está definido como AnswerSynthesis, permitindo respostas em linguagem natural que citam os documentos recuperados e seguem AnswerInstructions fornecido. RetrievalReasoningEffort (versão prévia) é definido como low para controlar o nível de raciocínio usado no planejamento de consulta.

// 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.");

Reference:KnowledgeBaseAzureOpenAIModel, KnowledgeBase

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 }
    }
};

Executar 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 contra a sua fonte de conhecimento.
Usa o classificador semântico para reclassificar e filtrar os resultados.
Sintetiza os principais resultados 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 }
});

Reference:KnowledgeBaseRetrievalClient, KnowledgeBaseRetrievalRequest

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 realizadas durante o processo de recuperação, incluindo as subconsultas geradas pela sua gpt-5-mini implantação e os tokens usados para classificação semântica, planejamento de consulta 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 conhecimento de earth-knowledge-source busca conteúdo relevante 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 recursos

Quando você trabalha em sua própria assinatura, é uma boa ideia concluir um projeto removendo os recursos de que não precisa mais. Recursos que permanecem em funcionamento podem custar dinheiro.

No portal Azure, selecione Todos recursos ou grupos Resource no painel esquerdo para localizar e gerenciar recursos. Você pode excluir recursos individualmente ou excluir o grupo de recursos para remover todos os recursos de uma só vez.

Caso contrário, o código a seguir program.cs excluirá 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.");

Embora você possa usar seus próprios dados, este início rápido usa documentos JSON de amostra do e-book Earth at Night da NASA.

Dica

Quer começar imediatamente? Baixe o source code no GitHub.

Pré-requisitos

Uma conta Azure com uma assinatura ativa. Crie uma conta gratuitamente.
Um serviço Pesquisa de IA do Azure, em qualquer região que forneça recuperação por meio de agentes. Este início rápido requer a camada Básica ou superior para suporte à identidade gerenciada.
Um recurso e projeto Microsoft Foundry. Quando você cria um projeto, o recurso é criado automaticamente.
Um modelo de inserção implantado em seu projeto para conversão de texto em vetor. Você pode usar qualquer text-embedding modelo, como text-embedding-3-large.
Uma LLM implantada em seu projeto para planejamento de consultas e geração de respostas. Você pode usar qualquer LLM compatível, como gpt-5-mini.
Java 11 ou posterior e Maven.
Git para clonar o repositório de exemplo.
O CLI do Azure para autenticação sem chave com Microsoft Entra ID.

Configurar o acesso

Para configurar o acesso para este início rápido:

Entre no portal Azure.
Em seu serviço de Pesquisa de IA do Azure :
1. Habilitar o acesso baseado em função.
2. Crie uma identidade gerenciada atribuída pelo sistema.
3. Atribua as seguintes funções à sua conta de usuário: Colaborador do Serviço de Pesquisa, Colaborador de Dados de Índice de Pesquisa e Leitor de Dados de Índice de Pesquisa.
No recurso Microsoft Foundry, atribua Cognitive Services User à identidade gerenciada do serviço de pesquisa.

Importante

A recuperação agentic tem dois modelos de cobrança baseados em token:

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, consulte Disponibilidade, limites e cobrança de região.

Obter pontos de extremidade

Para obter os pontos finais para este início rápido:

Entre no portal Azure.
Em seu serviço de Pesquisa de IA do Azure :
1. No painel esquerdo, selecione Visão geral.
2. Copie a URL, que deve se parecer com https://my-service.search.windows.net.
No recurso Microsoft Foundry:
1. No painel esquerdo, selecione Gerenciamento de Recursos>Chaves e Ponto de Extremidade.
2. Copie a URL na guia OpenAI, que deve ser semelhante a https://my-resource.openai.azure.com/.

Configurar o ambiente

Use o Git para clonar o repositório de exemplo.

git clone https://github.com/Azure-Samples/azure-search-java-samples

Vá para a pasta de início rápido.

cd azure-search-java-samples/quickstart-agentic-retrieval

Em sample.env, substitua os valores de espaço reservado para SEARCH_ENDPOINT e AOAI_ENDPOINT pelas URLs obtidas em Obter pontos de extremidade.
Renomear sample.env para .env.
```
mv sample.env .env
```
Instale as dependências.
```
mvn clean dependency:copy-dependencies
```
Para autenticação sem chave com Microsoft Entra ID, entre em sua conta Azure. Se você tiver várias assinaturas, selecione aquela que contém seus recursos Pesquisa de IA do Azure e Microsoft Foundry.
```
az login
```

Executar o código

Crie e execute o aplicativo para criar um índice, carregar documentos, configurar uma fonte de conhecimento e uma base de dados de conhecimento e executar consultas de recuperação agente.

javac AgenticRetrievalQuickstart.java -cp ".;target\dependency\*"
java -cp ".;target\dependency\*" AgenticRetrievalQuickstart

javac AgenticRetrievalQuickstart.java -cp ".:target/dependency/*"
java -cp ".:target/dependency/*" AgenticRetrievalQuickstart

javac AgenticRetrievalQuickstart.java -cp ".:target/dependency/*"
java -cp ".:target/dependency/*" AgenticRetrievalQuickstart

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.
Running the 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?
Response:
December percent brightening is larger in suburban belts because many houses add seasonal residential/holiday lighting on yards and roofs, so a relatively dark suburban baseline can increase by 20-50% when those lights turn on, while dense urban cores already have high continuous lighting so the same added lights make a smaller percentage change [ref_id:2][ref_id:5][ref_id:8]. Phoenix's street grid appears sharply from space because the metropolitan layout is a regular, continuous north-south/east-west street and block grid with a major diagonal artery (Grand Avenue) and concentrated, continuous arterial and commercial lighting along intersections and corridors [ref_id:3][ref_id:0][ref_id:1]. ...
Activity:
Activity Type: KnowledgeBaseModelQueryPlanningActivityRecord
{
  "id" : 0,
  "elapsedMs" : 5229,
  "type" : "modelQueryPlanning",
  "inputTokens" : 1489,
  "outputTokens" : 383
}
Activity Type: KnowledgeBaseSearchIndexActivityRecord
{
  "id" : 1,
  "elapsedMs" : 2670,
  "knowledgeSourceName" : "earth-knowledge-source",
  "queryTime" : "2026-02-24T15:28:36.776Z",
  "count" : 3,
  "type" : "searchIndex",
  "searchIndexArguments" : {
    "search" : "December brightening suburban belts vs urban cores light pollution causes seasonal variation reasons \"December brightening\"",
    "sourceDataFields" : [ {
      "name" : "page_chunk"
    }, {
      "name" : "id"
    }, {
      "name" : "page_number"
    } ],
    "searchFields" : [ ],
    "semanticConfigurationName" : "semantic_config"
  }
}
... // Trimmed for brevity
References:
Reference Type: KnowledgeBaseSearchIndexReference
{
  "id" : "0",
  "activitySource" : 2,
  "rerankerScore" : 2.7486389,
  "type" : "searchIndex",
  "docKey" : "earth_at_night_508_page_105_verbalized"
}
... // Trimmed for brevity
Continue the conversation with this query: How do I find lava at night?
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

Nota

Os snippets de código nesta seção podem ter sido modificados para legibilidade. Para obter um exemplo de trabalho completo, consulte o código-fonte.

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

Em Pesquisa de IA do Azure , um índice é uma coleção estruturada de dados. O código a seguir define um índice chamado earth-at-night.

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

AzureOpenAIVectorizer vectorizer = new AzureOpenAIVectorizer(
        "azure_openai_text_3_large")
    .setParameters(new AzureOpenAIVectorizerParameters()
        .setResourceUrl(aoaiEndpoint)
        .setDeploymentName(aoaiEmbeddingDeployment)
        .setModelName(
            AzureOpenAIModelName.fromString(
                aoaiEmbeddingModel)));

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));

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

SearchIndex index = new SearchIndex(indexName)
    .setFields(fields)
    .setVectorSearch(vectorSearch)
    .setSemanticSearch(semanticSearch);

indexClient.createOrUpdateIndex(index);

Reference:SearchField, VectorSearch, SemanticSearch, SearchIndex, SearchIndexClient

Carregar documentos no índice

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

java.net.http.HttpClient httpClient =
    java.net.http.HttpClient.newHttpClient();
java.net.http.HttpRequest httpRequest =
    java.net.http.HttpRequest.newBuilder()
        .uri(URI.create(url))
        .build();

java.net.http.HttpResponse<String> response =
    httpClient.send(httpRequest,
        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() : "");

    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 {
        List<Double> fallback = new ArrayList<>();
        for (int j = 0; j < 3072; j++) {
            fallback.add(0.1);
        }
        searchDoc.put(
            "page_embedding_text_3_large", fallback);
    }

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

    documents.add(searchDoc);
}

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

searchClient.uploadDocuments(documents);

Reference:SearchClient, SearchDocument

Criar uma fonte de conhecimento

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

SearchIndexKnowledgeSource indexKnowledgeSource =
    new SearchIndexKnowledgeSource(
        knowledgeSourceName,
        new SearchIndexKnowledgeSourceParameters(indexName)
            .setSourceDataFields(Arrays.asList(
                new SearchIndexFieldReference("id"),
                new SearchIndexFieldReference("page_chunk"),
                new SearchIndexFieldReference("page_number")
            ))
    );

indexClient.createOrUpdateKnowledgeSource(indexKnowledgeSource);

Reference:SearchIndexKnowledgeSource

Criar uma base de dados de conhecimento

OutputMode (versão prévia) é configurado como ANSWER_SYNTHESIS para habilitar respostas em linguagem natural que citam os documentos recuperados e seguem os AnswerInstructions fornecidos. RetrievalReasoningEffort (versão prévia) é definido para low para controlar a quantidade de raciocínio usado no planejamento de consultas.

AzureOpenAIVectorizerParameters openAiParameters =
    new AzureOpenAIVectorizerParameters()
        .setResourceUrl(aoaiEndpoint)
        .setDeploymentName(aoaiGptDeployment)
        .setModelName(
            AzureOpenAIModelName.fromString(aoaiGptModel));

KnowledgeBaseAzureOpenAIModel model =
    new KnowledgeBaseAzureOpenAIModel(openAiParameters);

KnowledgeBase knowledgeBase = new KnowledgeBase(
        knowledgeBaseName,
        Arrays.asList(
            new KnowledgeSourceReference(knowledgeSourceName))
    )
    .setRetrievalReasoningEffort(
        new KnowledgeRetrievalLowReasoningEffort())
    .setOutputMode(
        KnowledgeRetrievalOutputMode.ANSWER_SYNTHESIS)
    .setAnswerInstructions(
        "Provide a two sentence concise and informative answer "
        + "based on the retrieved documents.")
    .setModels(Arrays.asList(model));

indexClient.createOrUpdateKnowledgeBase(knowledgeBase);

Reference:KnowledgeBaseAzureOpenAIModel, KnowledgeBase

Configurar mensagens

String instructions =
    "A Q&A agent that can answer questions about the "
    + "Earth at night.\n"
    + "If you don't have the answer, respond with "
    + "\"I don't know\".";

List<Map<String, String>> messages = new ArrayList<>();
Map<String, String> systemMessage = new HashMap<>();
systemMessage.put("role", "system");
systemMessage.put("content", instructions);
messages.add(systemMessage);

Executar 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 contra a sua fonte de conhecimento.
Usa o classificador semântico para reclassificar e filtrar os resultados.
Sintetiza os principais resultados em uma resposta de linguagem natural.

KnowledgeBaseRetrievalClient baseClient =
    new KnowledgeBaseRetrievalClientBuilder()
        .endpoint(searchEndpoint)
        .knowledgeBaseName(knowledgeBaseName)
        .credential(
            new DefaultAzureCredentialBuilder().build())
        .buildClient();

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(Map.of("role", "user", "content", query));

KnowledgeBaseRetrievalResult retrievalResult =
    retrieve(baseClient, messages, knowledgeSourceName);

String responseText =
    ((KnowledgeBaseMessageTextContent) retrievalResult
        .getResponse().get(0).getContent().get(0))
        .getText();

messages.add(
    Map.of("role", "assistant", "content", responseText));

O auxiliar retrieve cria um KnowledgeBaseRetrievalOptions a partir do histórico da conversa, define o nível de esforço de raciocínio para recuperação, anexa os parâmetros da fonte de conhecimento e retorna um KnowledgeBaseRetrievalResult:

private static KnowledgeBaseRetrievalResult retrieve(
        KnowledgeBaseRetrievalClient client,
        List<Map<String, String>> messages,
        String knowledgeSourceName) {
    List<KnowledgeBaseMessage> retrievalMessages = new ArrayList<>();
    for (Map<String, String> message : messages) {
        String role = message.get("role");
        if ("system".equals(role)) {
            continue;
        }
        retrievalMessages.add(
            new KnowledgeBaseMessage(
                new KnowledgeBaseMessageTextContent(
                    message.get("content")))
                .setRole(role));
    }

    KnowledgeBaseRetrievalOptions request =
        new KnowledgeBaseRetrievalOptions()
            .setMessages(retrievalMessages)
            .setRetrievalReasoningEffort(
                new KnowledgeRetrievalLowReasoningEffort())
            .setIncludeActivity(true)
            .setKnowledgeSourceParams(Arrays.asList(
                new SearchIndexKnowledgeSourceParams(knowledgeSourceName)
                    .setIncludeReferences(true)
                    .setIncludeReferenceSourceData(true)
            ));

    return client.retrieve(request);
}

Reference:KnowledgeBaseRetrievalClient, KnowledgeBaseRetrievalOptions

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 realizadas durante o processo de recuperação, incluindo as subconsultas geradas pela sua gpt-5-mini implantação e os tokens usados para classificação semântica, planejamento de consulta e síntese de resposta.
References lista os documentos que contribuíram para a resposta, cada um identificado por sua docKey.

System.out.println("Response:");
System.out.println(responseText);

System.out.println("Activity:");
for (KnowledgeBaseActivityRecord activity
        : retrievalResult.getActivity()) {
    System.out.println("Activity Type: "
        + activity.getClass().getSimpleName());
    System.out.println(toJsonString(activity));
}

System.out.println("References:");
for (KnowledgeBaseReference reference
        : retrievalResult.getReferences()) {
    System.out.println("Reference Type: "
        + reference.getClass().getSimpleName());
    System.out.println(toJsonString(reference));
}

Continuar a conversa

String nextQuery = "How do I find lava at night?";
messages.add(
    Map.of("role", "user", "content", nextQuery));

retrievalResult = retrieve(baseClient, messages, knowledgeSourceName);

Examinar a nova resposta, a atividade e as referências

O código a seguir extrai o texto de resposta e as chamadas printResult para exibir a nova resposta, a atividade e as referências.

responseText =
    ((KnowledgeBaseMessageTextContent) retrievalResult
        .getResponse().get(0).getContent().get(0))
        .getText();
messages.add(
    Map.of("role", "assistant", "content", responseText));

printResult(responseText, retrievalResult);

Limpar recursos

Quando você trabalha em sua própria assinatura, é uma boa ideia concluir um projeto removendo os recursos de que não precisa mais. Recursos que permanecem em funcionamento podem custar dinheiro.

Caso contrário, o código a seguir AgenticRetrievalQuickstart.java excluirá os objetos que você criou neste início rápido.

Excluir a base de dados de conhecimento

indexClient.deleteKnowledgeBase(knowledgeBaseName);
System.out.println("Knowledge base '" + knowledgeBaseName
    + "' deleted successfully.");

Excluir a fonte de conhecimento

indexClient.deleteKnowledgeSource(knowledgeSourceName);
System.out.println("Knowledge source '" + knowledgeSourceName
    + "' deleted successfully.");

Excluir o índice de pesquisa

indexClient.deleteIndex(indexName);
System.out.println("Index '" + indexName
    + "' deleted successfully.");

Embora você possa usar seus próprios dados, este início rápido usa documentos JSON de amostra do e-book Earth at Night da NASA.

Dica

Quer começar imediatamente? Baixe o source code no GitHub.

Pré-requisitos

Uma conta Azure com uma assinatura ativa. Crie uma conta gratuitamente.
Um serviço Pesquisa de IA do Azure, em qualquer região que forneça recuperação por meio de agentes. Este início rápido requer a camada Básica ou superior para suporte à identidade gerenciada.
Um recurso e projeto Microsoft Foundry. Quando você cria um projeto, o recurso é criado automaticamente.
Um modelo de inserção implantado em seu projeto para conversão de texto em vetor. Você pode usar qualquer text-embedding modelo, como text-embedding-3-large.
Uma LLM implantada em seu projeto para planejamento de consultas e geração de respostas. Você pode usar qualquer LLM compatível, como gpt-5-mini.
Node.js 20 LTS ou posterior.
Git para clonar o repositório de exemplo.
O CLI do Azure para autenticação sem chave com Microsoft Entra ID.

Configurar o acesso

Para configurar o acesso para este início rápido:

Entre no portal Azure.
Em seu serviço de Pesquisa de IA do Azure :
1. Habilitar o acesso baseado em função.
2. Crie uma identidade gerenciada atribuída pelo sistema.
3. Atribua as seguintes funções à sua conta de usuário: Colaborador do Serviço de Pesquisa, Colaborador de Dados de Índice de Pesquisa e Leitor de Dados de Índice de Pesquisa.
No recurso Microsoft Foundry, atribua Cognitive Services User à identidade gerenciada do serviço de pesquisa.

Importante

A recuperação agentic tem dois modelos de cobrança baseados em token:

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, consulte Disponibilidade, limites e cobrança de região.

Obter pontos de extremidade

Para obter os pontos finais para este início rápido:

Entre no portal Azure.
Em seu serviço de Pesquisa de IA do Azure :
1. No painel esquerdo, selecione Visão geral.
2. Copie a URL, que deve se parecer com https://my-service.search.windows.net.
No recurso Microsoft Foundry:
1. No painel esquerdo, selecione Gerenciamento de Recursos>Chaves e Ponto de Extremidade.
2. Copie a URL na guia OpenAI, que deve ser semelhante a https://my-resource.openai.azure.com/.

Configurar o ambiente

Use o Git para clonar o repositório de exemplo.

git clone https://github.com/Azure-Samples/azure-search-javascript-samples

Vá para a pasta de início rápido.

cd azure-search-javascript-samples/quickstart-agentic-retrieval-js

Em sample.env, substitua os valores de espaço reservado para AZURE_SEARCH_ENDPOINT e AZURE_OPENAI_ENDPOINT pelas URLs obtidas em Obter pontos de extremidade.
Renomear sample.env para .env.
```
mv sample.env .env
```
Instale as dependências.
```
npm install
```
Quando a instalação for concluída, você verá uma node_modules pasta no diretório do projeto.
Para autenticação sem chave com Microsoft Entra ID, entre em sua conta Azure. Se você tiver várias assinaturas, selecione aquela que contém seus recursos Pesquisa de IA do Azure e Microsoft Foundry.
```
az login
```

Executar o código

Execute o aplicativo para criar um índice, carregar documentos, configurar uma fonte de conhecimento e uma base de dados de conhecimento e executar consultas de recuperação agente.

npm start

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"
  }
}
... // Trimmed for brevity
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"
}
... // Trimmed for brevity

❓ 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

Em Pesquisa de IA do Azure , um índice é uma coleção estruturada de dados. O código a seguir define um índice chamado earth_at_night.

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 vetorial, que utiliza sua text-embedding-3-large implantação para vetorização de texto e comparar documentos com base na similaridade semântica.

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);

Reference:SearchField, VectorSearch, SemanticSearch, SearchIndex, SearchIndexClient, SearchClient, DefaultAzureCredential

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!`);

Referência:SearchIndexingBufferedSender

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.`);

Reference:SearchIndexKnowledgeSource

Criar uma base de dados de conhecimento

outputMode (versão prévia) está definida como answerSynthesis, permitindo respostas em 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.`);

Reference:KnowledgeBase

Executar 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 contra a sua fonte de conhecimento.
Usa o classificador semântico para reclassificar e filtrar os resultados.
Sintetiza os principais resultados em uma resposta de linguagem natural.

retrievalReasoningEffort (versão prévia) é definido como low para controlar a quantidade de raciocínio usada no planejamento de consultas.

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.retrieve(retrievalRequest);

Reference:KnowledgeRetrievalClient, KnowledgeBaseRetrievalRequest

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 realizadas durante o processo de recuperação, incluindo as subconsultas geradas pela sua gpt-5-mini implantação e os tokens usados para classificação semântica, planejamento de consulta 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.retrieve(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 recursos

Quando você trabalha em sua própria assinatura, é uma boa ideia concluir um projeto removendo os recursos de que não precisa mais. Recursos que permanecem em funcionamento podem custar dinheiro.

Caso contrário, o código a seguir index.js excluirá 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.`);

Embora você possa usar seus próprios dados, este início rápido usa documentos JSON de amostra do e-book Earth at Night da NASA.

Dica

Quer começar imediatamente? Baixe o source code no GitHub.

Pré-requisitos

Uma conta Azure com uma assinatura ativa. Crie uma conta gratuitamente.
Um serviço Pesquisa de IA do Azure, em qualquer região que forneça recuperação por meio de agentes. Este início rápido requer a camada Básica ou superior para suporte à identidade gerenciada.
Um recurso e projeto Microsoft Foundry. Quando você cria um projeto, o recurso é criado automaticamente.
Um modelo de inserção implantado em seu projeto para conversão de texto em vetor. Você pode usar qualquer text-embedding modelo, como text-embedding-3-large.
Uma LLM implantada em seu projeto para planejamento de consultas e geração de respostas. Você pode usar qualquer LLM compatível, como gpt-5-mini.
Python 3.8 ou posterior.
Visual Studio Code com as extensões Python e Jupyter.
Git para clonar o repositório de exemplo.
O CLI do Azure para autenticação sem chave com Microsoft Entra ID.

Configurar o acesso

Para configurar o acesso para este início rápido:

Entre no portal Azure.
Em seu serviço de Pesquisa de IA do Azure :
1. Habilitar o acesso baseado em função.
2. Crie uma identidade gerenciada atribuída pelo sistema.
3. Atribua as seguintes funções à sua conta de usuário: Colaborador do Serviço de Pesquisa, Colaborador de Dados de Índice de Pesquisa e Leitor de Dados de Índice de Pesquisa.
No recurso Microsoft Foundry, atribua Cognitive Services User à identidade gerenciada do serviço de pesquisa.

Importante

A recuperação agentic tem dois modelos de cobrança baseados em token:

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, consulte Disponibilidade, limites e cobrança de região.

Obter pontos de extremidade

Para obter os pontos finais para este início rápido:

Entre no portal Azure.
Em seu serviço de Pesquisa de IA do Azure :
1. No painel esquerdo, selecione Visão geral.
2. Copie a URL, que deve se parecer com https://my-service.search.windows.net.
No recurso Microsoft Foundry:
1. No painel esquerdo, selecione Gerenciamento de Recursos>Chaves e Ponto de Extremidade.
2. Copie a URL na guia OpenAI, que deve ser semelhante a https://my-resource.openai.azure.com/.

Configurar o ambiente

Use o Git para clonar o repositório de exemplo.

git clone https://github.com/Azure-Samples/azure-search-python-samples

Vá para a pasta de início rápido e abra-a no Visual Studio Code.

cd azure-search-python-samples/Quickstart-Agentic-Retrieval
code .

Em sample.env, substitua os valores de espaço reservado para SEARCH_ENDPOINT e AOAI_ENDPOINT pelas URLs obtidas em Obter pontos de extremidade.
Renomear sample.env para .env.
```
mv sample.env .env
```
Abra quickstart-agentic-retrieval.ipynb.
Pressione Ctrl+Shift+P, selecione Bloco de Anotações: Selecione Kernel do Bloco de Anotações e siga os prompts para criar um ambiente virtual. Selecione requirements.txt para as dependências.

Quando concluído, você deverá ver uma .venv pasta no diretório do projeto.
Para autenticação sem chave com Microsoft Entra ID, entre em sua conta Azure. Se você tiver várias assinaturas, selecione aquela que contém seus recursos Pesquisa de IA do Azure e Microsoft Foundry.
```
az login
```

Executar o código

Execute a Load connections célula para instalar os pacotes necessários e carregar variáveis de ambiente.
Execute as células restantes sequencialmente para criar um índice, carregar documentos, configurar uma fonte de conhecimento e uma base de dados de conhecimento e executar consultas de recuperação agente.

Saída

Cada célula de código imprime sua saída no notebook. O exemplo a seguir mostra a saída depois de executar todas as células:

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?"
    }
  },
  ... // Trimmed for brevity
  {
    "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"
  },
  ... // Trimmed for brevity
]
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

Nota

Os snippets de código nesta seção podem ter sido modificados para legibilidade. Para obter um exemplo de trabalho completo, consulte o código-fonte.

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

Em Pesquisa de IA do Azure , um índice é uma coleção estruturada de dados. O código a seguir define um índice chamado earth-at-night.

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 vetorial, que utiliza sua text-embedding-3-large implantação para vetorização de texto e comparar documentos com base na similaridade semântica.

# Create an index
azure_openai_token_provider = get_bearer_token_provider(credential, "https://ai.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.")

Reference:SearchField, VectorSearch, SemanticSearch, SearchIndex, SearchIndexClient

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.")

Referência:SearchIndexingBufferedSender

Criar uma fonte de conhecimento

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

# 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.")

Reference:SearchIndexKnowledgeSource

Criar uma base de dados de conhecimento

output_mode (versão prévia) está definido como answerSynthesis, permitindo respostas em 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="answerSynthesis",
    answer_instructions="Provide a 2 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.")

Reference:KnowledgeBase

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
    }
]

Executar 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 contra a sua fonte de conhecimento.
Usa o classificador semântico para reclassificar e filtrar os resultados.
Sintetiza os principais resultados em uma resposta de linguagem natural.

retrieval_reasoning_effort (versão prévia) é definido como low para controlar a quantidade de raciocínio usada no planejamento de consultas.

# 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.")

Reference:KnowledgeBaseRetrievalClient, KnowledgeBaseRetrievalRequest

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 realizadas durante o processo de recuperação, incluindo as subconsultas geradas pela sua gpt-5-mini implantação e os tokens usados para classificação semântica, planejamento de consulta 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 recursos

Quando você trabalha em sua própria assinatura, é uma boa ideia concluir um projeto removendo os recursos de que não precisa mais. Recursos que permanecem em funcionamento podem custar dinheiro.

Caso contrário, o código a seguir quickstart-agentic-retrieval.ipynb excluirá 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.")

Embora você possa usar seus próprios dados, este início rápido usa documentos JSON de amostra do e-book Earth at Night da NASA.

Dica

Quer começar imediatamente? Baixe o source code no GitHub.

Pré-requisitos

Uma conta Azure com uma assinatura ativa. Crie uma conta gratuitamente.
Um serviço Pesquisa de IA do Azure, em qualquer região que forneça recuperação por meio de agentes. Este início rápido requer a camada Básica ou superior para suporte à identidade gerenciada.
Um recurso e projeto Microsoft Foundry. Quando você cria um projeto, o recurso é criado automaticamente.
Um modelo de inserção implantado em seu projeto para conversão de texto em vetor. Você pode usar qualquer text-embedding modelo, como text-embedding-3-large.
Uma LLM implantada em seu projeto para planejamento de consultas e geração de respostas. Você pode usar qualquer LLM compatível, como gpt-5-mini.
Node.js 20 LTS ou posterior.
TypeScript para compilar TypeScript para JavaScript.
Git para clonar o repositório de exemplo.
O CLI do Azure para autenticação sem chave com Microsoft Entra ID.

Configurar o acesso

Para configurar o acesso para este início rápido:

Entre no portal Azure.
Em seu serviço de Pesquisa de IA do Azure :
1. Habilitar o acesso baseado em função.
2. Crie uma identidade gerenciada atribuída pelo sistema.
3. Atribua as seguintes funções à sua conta de usuário: Colaborador do Serviço de Pesquisa, Colaborador de Dados de Índice de Pesquisa e Leitor de Dados de Índice de Pesquisa.
No recurso Microsoft Foundry, atribua Cognitive Services User à identidade gerenciada do serviço de pesquisa.

Importante

A recuperação agentic tem dois modelos de cobrança baseados em token:

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, consulte Disponibilidade, limites e cobrança de região.

Obter pontos de extremidade

Para obter os pontos finais para este início rápido:

Entre no portal Azure.
Em seu serviço de Pesquisa de IA do Azure :
1. No painel esquerdo, selecione Visão geral.
2. Copie a URL, que deve se parecer com https://my-service.search.windows.net.
No recurso Microsoft Foundry:
1. No painel esquerdo, selecione Gerenciamento de Recursos>Chaves e Ponto de Extremidade.
2. Copie a URL na guia OpenAI, que deve ser semelhante a https://my-resource.openai.azure.com/.

Configurar o ambiente

Use o Git para clonar o repositório de exemplo.

git clone https://github.com/Azure-Samples/azure-search-javascript-samples

Vá para a pasta de início rápido.

cd azure-search-javascript-samples/quickstart-agentic-retrieval-ts

Em sample.env, substitua os valores de espaço reservado para AZURE_SEARCH_ENDPOINT e AZURE_OPENAI_ENDPOINT pelas URLs obtidas em Obter pontos de extremidade.
Renomear sample.env para .env.
```
mv sample.env .env
```
Instale as dependências.
```
npm install
```
Quando a instalação for concluída, você verá uma node_modules pasta no diretório do projeto.
Compile os arquivos TypeScript em JavaScript.
```
npm run build
```
Para autenticação sem chave com Microsoft Entra ID, entre em sua conta Azure. Se você tiver várias assinaturas, selecione aquela que contém seus recursos Pesquisa de IA do Azure e Microsoft Foundry.
```
az login
```

Executar o código

Execute o aplicativo para criar um índice, carregar documentos, configurar uma fonte de conhecimento e uma base de dados de conhecimento e executar consultas de recuperação agente.

npm start

Nota

Esse comando executa os arquivos compilados .js da dist pasta. Node.js requer que o código TypeScript seja transpilado para JavaScript antes que ele possa ser executado, e é por isso que você executou npm run buildanteriormente.

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"
  }
}
... // Trimmed for brevity
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"
}
... // Trimmed for brevity

❓ 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

Em Pesquisa de IA do Azure , um índice é uma coleção estruturada de dados. O código a seguir define um índice chamado earth_at_night.

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 vetorial, que utiliza sua text-embedding-3-large implantação para vetorização de texto e comparar documentos com base na similaridade semântica.

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);

Reference:SearchField, VectorSearch, SemanticSearch, SearchIndex, SearchIndexClient, SearchClient, DefaultAzureCredential

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!`);

Referência:SearchIndexingBufferedSender

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.`);

Reference:SearchIndexKnowledgeSource

Criar uma base de dados de conhecimento

outputMode (versão prévia) está configurada como answerSynthesis, o que permite respostas em linguagem natural que citam os documentos recuperados e seguem as answerInstructions fornecidas.

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.`);

Reference:KnowledgeBase

Executar 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 contra a sua fonte de conhecimento.
Usa o classificador semântico para reclassificar e filtrar os resultados.
Sintetiza os principais resultados em uma resposta de linguagem natural.

retrievalReasoningEffort (versão prévia) é definido como low para controlar a quantidade de raciocínio usada no planejamento de consultas.

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.retrieve(retrievalRequest);

Reference:KnowledgeRetrievalClient, KnowledgeBaseRetrievalRequest

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 realizadas durante o processo de recuperação, incluindo as subconsultas geradas pela sua gpt-5-mini implantação e os tokens usados para classificação semântica, planejamento de consulta 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.retrieve(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 recursos

Quando você trabalha em sua própria assinatura, é uma boa ideia concluir um projeto removendo os recursos de que não precisa mais. Recursos que permanecem em funcionamento podem custar dinheiro.

Caso contrário, o código a seguir index.ts excluirá 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.`);

Embora você possa usar seus próprios dados, este início rápido usa documentos JSON de amostra do e-book Earth at Night da NASA.

Dica

Quer começar imediatamente? Baixe o source code no GitHub.

Pré-requisitos

Uma conta Azure com uma assinatura ativa. Crie uma conta gratuitamente.
Um serviço Pesquisa de IA do Azure, em qualquer região que forneça recuperação por meio de agentes. Este início rápido requer a camada Básica ou superior para suporte à identidade gerenciada.
Um recurso e projeto Microsoft Foundry. Quando você cria um projeto, o recurso é criado automaticamente.
Um modelo de inserção implantado em seu projeto para conversão de texto em vetor. Você pode usar qualquer text-embedding modelo, como text-embedding-3-large.
Uma LLM implantada em seu projeto para planejamento de consultas e geração de respostas. Você pode usar qualquer LLM compatível, como gpt-5-mini.
Visual Studio Code com a extensão do cliente REST.
Git para clonar o repositório de exemplo.
O CLI do Azure para autenticação sem chave com Microsoft Entra ID.

Configurar o acesso

Para configurar o acesso para este início rápido:

Entre no portal Azure.
Em seu serviço de Pesquisa de IA do Azure :
1. Habilitar o acesso baseado em função.
2. Crie uma identidade gerenciada atribuída pelo sistema.
3. Atribua as seguintes funções à sua conta de usuário: Colaborador do Serviço de Pesquisa, Colaborador de Dados de Índice de Pesquisa e Leitor de Dados de Índice de Pesquisa.
No recurso Microsoft Foundry, atribua Cognitive Services User à identidade gerenciada do serviço de pesquisa.

Importante

A recuperação agentic tem dois modelos de cobrança baseados em token:

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, consulte Disponibilidade, limites e cobrança de região.

Obter pontos de extremidade

Para obter os pontos finais para este início rápido:

Entre no portal Azure.
Em seu serviço de Pesquisa de IA do Azure :
1. No painel esquerdo, selecione Visão geral.
2. Copie a URL, que deve se parecer com https://my-service.search.windows.net.
No recurso Microsoft Foundry:
1. No painel esquerdo, selecione Gerenciamento de Recursos>Chaves e Ponto de Extremidade.
2. Copie a URL na guia OpenAI, que deve ser semelhante a https://my-resource.openai.azure.com/.

Configurar o ambiente

Use o Git para clonar o repositório de exemplo.

git clone https://github.com/Azure-Samples/azure-search-rest-samples

Vá para a pasta de início rápido e abra-a no Visual Studio Code.
```
cd azure-search-rest-samples/Quickstart-agentic-retrieval
code .
```
Em agentic-retrieval.rest, substitua os valores de espaço reservado para @search-url e @aoai-url pelas URLs obtidas em Obter pontos de extremidade.
Para autenticação sem chave com Microsoft Entra ID, entre em sua conta Azure. Se você tiver várias assinaturas, selecione aquela que contém seus recursos Pesquisa de IA do Azure e Microsoft Foundry.
```
az login
```

Para autenticação sem chave com Microsoft Entra ID, gere um token de acesso.

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

Substitua o valor do espaço reservado pelo @token token da etapa anterior.

Executar o código

Enviar cada solicitação sequencialmente, 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 se há erros de digitação na solicitação e certifique-se de que seu 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": "Causes (mechanisms) — seasonal and lighting behavior: Residential suburban areas often show larger relative (percentage) December brightening because many homes turn on more outdoor and indoor lights in winter evenings (longer nights and holiday lighting) compared with their usual baseline, producing a bigger percent increase even if absolute downtown lighting remains higher [ref_id:1][ref_id:0]. Snow and increased surface reflectance in winter amplify light seen from orbit, increasing apparent brightness especially where lights are horizontal and near ground level (e.g., suburban streets and yards) [ref_id:0].\n\nResidential vs commercial/industrial lighting and activity patterns: Urban cores have high absolute lumen outputs from continuous commercial, industrial, and dense street lighting that change less seasonally, so percent brightening is smaller; suburbs have lower baseline light but larger seasonal/holiday additions, so their relative brightening is larger [ref_id:1][ref_id:0].\n\nWhy Phoenix’s nighttime street grid is sharply visible (causes and consequences): The Phoenix metropolitan area has a regular, dense grid of north–south/east–west streets and the diagonal Grand Avenue corridor with continuous street and commercial lighting whose spacing and lumen output produce linear, high-contrast features from low-Earth orbit; bright nodes occur at major intersections and commercial properties, making the grid and corridors stand out in satellite night images [ref_id:1][ref_id:0]. Darker pockets (Phoenix Mountains, Salt River channel, agricultural fields) increase contrast and make lit streets appear sharper [ref_id:1][ref_id:0].\n\nWhy long interstates between Midwestern cities appear comparatively dim: Rural interstate stretches often lack continuous high-intensity lighting (limited lamps, wider spacing, fewer ramps and commercial nodes), use lower-output or shielded fixtures, and have long unlit segments between cities, so linear continuity and contrast are much lower than a densely lit urban grid [ref_id:1][ref_id:0].\n\nSensor and dataset effects (consequences for observed brightness): Night-light datasets and orbital sensors (as used to capture images like the Phoenix photo) emphasize linear, continuous lighting patterns; sensor behaviors such as gain and saturation make very bright urban cores register high absolute values while limiting the apparent dynamic range, so relative changes (percentage brightening) in dimmer suburban areas can appear proportionally larger in some satellite products [ref_id:1][ref_id:0].\n\nOverall: larger December brightening in suburban belts is a percentage effect from seasonal/holiday increases, snow reflectance, and residential lighting behavior on a low baseline, whereas urban cores remain brighter in absolute terms but show smaller relative changes; Phoenix’s dense, continuous street-grid lighting and contrasting dark areas create sharp linear features from space, while long, sparsely lit interstates lack that continuity and contrast and therefore appear dimmer [ref_id:1][ref_id:0]."
        }
      ]
    }
  ],
  "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

Nota

Os snippets de código nesta seção podem ter sido modificados para legibilidade. Para obter um exemplo de trabalho completo, consulte o código-fonte.

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

Em Pesquisa de IA do Azure , um índice é uma coleção estruturada de dados. O código a seguir define um índice chamado earth-at-night.

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 vetorial, que utiliza sua text-embedding-3-large implantação para vetorização de texto e comparar documentos com base na similaridade semântica.

### 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}}"
                }
            }
        ]
    }
}

Reference:Índices – Criação

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 por Pesquisa de IA do Azure , 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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0.020431026816368103, -0.04772809147834778, 0.03262887895107269, -0.02760087326169014, 0.03305421024560928, 0.009068641811609268, -0.003104528645053506, -0.035727713257074356, -0.04490268602967262, -0.039403777569532394, 0.026491977274417877, 0.01214468851685524, 0.037732839584350586, 0.08652425557374954, 0.005525491200387478, -0.0031994683668017387, 0.04684705287218094, 0.02872495912015438, 0.010481344535946846, 0.024076711386442184, 0.015813158825039864, 0.023180481046438217, -0.015949871391057968, 0.014749834313988686, -0.0006285008857958019, 0.0005739105399698019, 0.007192632649093866, 0.05787524953484535, 0.0043748221360147, 0.00038687934284098446, 0.04110509902238846, -0.0028273046482354403, 0.01397512573748827, -0.009106617420911789, -0.00770910456776619, -0.015304281376302242, 0.002582360291853547, -0.01092945970594883, -0.008552169427275658, 0.06665527075529099, 0.04657362401485443, -0.012197853997349739, -0.028861673548817635, -0.08925852179527283, -0.003831766778603196, 0.056538499891757965, -0.023453906178474426, -0.03083641827106476, -0.022223487496376038, -0.010253489017486572, 0.010807937011122704, 0.00313301058486104, 0.033904869109392166, -0.010116775520145893, 0.01742333546280861, 0.02594512514770031, -0.007777461316436529, -0.0002520649286452681, 0.005202696193009615, -0.02594512514770031, 0.010648438706994057, -0.01740814559161663, 0.0031254154164344072, -0.007542010862380266, 0.026142599061131477, 0.01731700450181961, 0.013504224829375744, -0.036183424293994904, -0.006357163190841675, -0.010428178124129772, -0.061338648200035095, 0.005457134917378426, 0.05316624045372009, 0.007861007936298847, 0.04311022534966469, 0.03867464140057564, -0.0021570303943008184, 0.016496725380420685, 0.05246748402714729, 0.007200228050351143, -0.003930503968149424, 0.007785056717693806, 0.017484096810221672, -0.003875439055263996, -0.0031538973562419415, 0.03180859982967377, -0.02497294172644615, 0.03627456724643707, 0.02533750981092453, 0.008385075256228447, 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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
        }
    ]
}

Reference:Documents – Index

Criar uma fonte de conhecimento

### 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" }
        ]
    }
}

Referência:Fontes de Conhecimento – Criar

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 define uma base chamada earth-knowledge-base.

outputMode (versão prévia) está definido como answerSynthesis, permitindo respostas em 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."
}

Referência:Bases de Dados de Conhecimento – Criar

Executar 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 contra a sua fonte de conhecimento.
Usa o classificador semântico para reclassificar e filtrar os resultados. Este exemplo exclui respostas com pontuação do reranqueador igual ou inferior a 2.5.
Sintetiza os principais resultados em uma resposta de linguagem natural.

retrievalReasoningEffort (versão prévia) é definido como low para controlar a quantidade de raciocínio empregada no planejamento de consultas.

### 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" }
}

Referência:Recuperação de Conhecimento – Recuperar

A saída contém 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 realizadas durante o processo de recuperação, incluindo as subconsultas geradas pela sua gpt-5-mini implantação e os tokens usados para classificação semântica, planejamento de consulta e síntese de resposta.
references lista os documentos que contribuíram para a resposta, cada um identificado por sua docKey.

Limpar recursos

Quando você trabalha em sua própria assinatura, é uma boa ideia concluir um projeto removendo os recursos de que não precisa mais. Recursos que permanecem em funcionamento podem custar dinheiro.

Caso contrário, as seguintes requisições de agentic-retrieval.rest excluirão os objetos que você criou neste guia de 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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