Inicio rápido: Uso de la recuperación de agentes en Búsqueda de Azure AI

En este inicio rápido, usará la recuperación de agentes para crear una experiencia de búsqueda conversacional con tecnología de documentos indexados en Búsqueda de Azure AI y modelos de lenguaje grandes (LLM) de Azure OpenAI en Foundry Models.

Una base de conocimiento organiza la recuperación agente mediante la descomposición de consultas complejas en subconsultas, la ejecución de las subconsultas en uno o varios orígenes de conocimiento y la devolución de resultados con metadatos. De manera predeterminada, la base de conocimiento genera contenido sin procesar de los orígenes, pero en este inicio rápido se usa el modo de salida de síntesis de respuestas para la generación de respuestas en lenguaje natural.

Aunque puede proporcionar sus propios datos, en esta guía de inicio rápido se usan documentos JSON de ejemplo de la Tierra de la NASA en el libro electrónico nocturno. Los documentos describen temas generales de ciencia e imágenes de la Tierra a la noche como se observa desde el espacio.

Prerrequisitos

• Una cuenta de Azure con una suscripción activa. Cree una cuenta gratuita.

• Un Servicio de Búsqueda de Azure AI en cualquier región que proporcione recuperación con agentes.

• Un proyecto y un recurso de Microsoft Foundry . Al crear un proyecto, el recurso se crea automáticamente.

• La CLI de Azure para la autenticación sin clave con Microsoft Entra ID.

• Código de Visual Studio.

Configurar el acceso

Antes de empezar, asegúrese de que tiene permisos para acceder al contenido y las operaciones. Se recomienda Microsoft Entra ID para la autenticación y el acceso basado en roles para la autorización. Debe ser Propietario o Administrador de acceso de usuario para asignar roles. Si los roles no son factibles, use la autenticación basada en claves en su lugar.

Para configurar el acceso para este inicio rápido, seleccione las dos pestañas siguientes.

Obtención de puntos de conexión

Cada servicio Azure AI Search y el recurso de Microsoft Foundry tienen un punto de conexión, que es una dirección URL única que identifica y proporciona acceso de red al recurso. En una sección posterior, especifique estos puntos de conexión para conectarse a los recursos mediante programación.

Para obtener los puntos de conexión de este inicio rápido, seleccione las dos pestañas siguientes.

Implementación de modelos

Para este inicio rápido, debe implementar dos modelos de Azure OpenAI en el proyecto de Microsoft Foundry:

• Modelo de inserción para la conversión de texto a vector. En este inicio rápido se usa text-embedding-3-large, pero puede usar cualquier text-embedding modelo.

• Un LLM para la planeación de consultas y la generación de respuestas. En este inicio rápido, se usa gpt-5-mini, pero puede usar cualquier LLM compatible para la recuperación de agentes.

Para obtener instrucciones de implementación, consulte Implementación de modelos de Azure OpenAI con Microsoft Foundry.

Configuración del entorno

Para configurar la aplicación de consola para este inicio rápido:

1. Cree una carpeta denominada quickstart-agentic-retrieval para contener la aplicación.

2. Abra la carpeta en Visual Studio Code.

3. Seleccione Terminal>Nuevo terminal y, a continuación, ejecute el siguiente comando para crear una aplicación de consola.

   dotnet new console
   

4. Instale la biblioteca cliente de Azure AI Search para .NET.

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

5. Instale el dotenv.net paquete para cargar variables de entorno desde un .env archivo.

   dotnet add package dotenv.net
   

6. Para la autenticación sin claves con el identificador de Microsoft Entra, instale el paquete Azure.Identity .

   dotnet add package Azure.Identity
   

7. Para la autenticación sin claves con el identificador de Microsoft Entra, inicie sesión en su cuenta de Azure. Si tiene varias suscripciones, seleccione la que contiene el servicio Azure AI Search y el proyecto de Microsoft Foundry.

   az login
   

Ejecución del código

Para crear y ejecutar la canalización de recuperación de agentes:

1. Cree un archivo denominado .env en la quickstart-agentic-retrieval carpeta .

2. Pegue las siguientes variables de entorno en el .env archivo.

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

3. Establezca SEARCH_ENDPOINT y AOAI_ENDPOINT en los valores obtenidos en Obtener puntos de conexión.

4. Pegue el código siguiente en el Program.cs archivo.

   using dotenv.net;
   using System.Text.Json;
   using Azure.Identity;
   using Azure.Search.Documents;
   using Azure.Search.Documents.Indexes;
   using Azure.Search.Documents.Indexes.Models;
   using Azure.Search.Documents.KnowledgeBases;
   using Azure.Search.Documents.KnowledgeBases.Models;
   
   namespace AzureSearch.Quickstart
   {
       class Program
       {
           static async Task Main(string[] args)
           {
               // Load environment variables from the .env file
               // Ensure your .env file is in the same directory with the required variables
               DotEnv.Load();
   
               string searchEndpoint = Environment.GetEnvironmentVariable("SEARCH_ENDPOINT")
                   ?? throw new InvalidOperationException("SEARCH_ENDPOINT isn't set.");
               string aoaiEndpoint = Environment.GetEnvironmentVariable("AOAI_ENDPOINT")
                   ?? throw new InvalidOperationException("AOAI_ENDPOINT isn't set.");
   
               string aoaiEmbeddingModel = "text-embedding-3-large";
               string aoaiEmbeddingDeployment = "text-embedding-3-large";
               string aoaiGptModel = "gpt-5-mini";
               string aoaiGptDeployment = "gpt-5-mini";
   
               string indexName = "earth-at-night";
               string knowledgeSourceName = "earth-knowledge-source";
               string knowledgeBaseName = "earth-knowledge-base";
   
               var credential = new DefaultAzureCredential();
   
               // Define fields for the index
               var fields = new List<SearchField>
               {
                   new SimpleField("id", SearchFieldDataType.String) { IsKey = true, IsFilterable = true, IsSortable = true, IsFacetable = true },
                   new SearchField("page_chunk", SearchFieldDataType.String) { IsFilterable = false, IsSortable = false, IsFacetable = false },
                   new SearchField("page_embedding_text_3_large", SearchFieldDataType.Collection(SearchFieldDataType.Single)) { VectorSearchDimensions = 3072, VectorSearchProfileName = "hnsw_text_3_large" },
                   new SimpleField("page_number", SearchFieldDataType.Int32) { IsFilterable = true, IsSortable = true, IsFacetable = true }
               };
   
               // Define a vectorizer
               var vectorizer = new AzureOpenAIVectorizer(vectorizerName: "azure_openai_text_3_large")
               {
                   Parameters = new AzureOpenAIVectorizerParameters
                   {
                       ResourceUri = new Uri(aoaiEndpoint),
                       DeploymentName = aoaiEmbeddingDeployment,
                       ModelName = aoaiEmbeddingModel
                   }
               };
   
               // Define a vector search profile and algorithm
               var vectorSearch = new VectorSearch()
               {
                   Profiles =
                   {
                       new VectorSearchProfile(
                           name: "hnsw_text_3_large",
                           algorithmConfigurationName: "alg"
                       )
                       {
                           VectorizerName = "azure_openai_text_3_large"
                       }
                   },
                   Algorithms =
                   {
                       new HnswAlgorithmConfiguration(name: "alg")
                   },
                   Vectorizers =
                   {
                       vectorizer
                   }
               };
   
               // Define a semantic configuration
               var semanticConfig = new SemanticConfiguration(
                   name: "semantic_config",
                   prioritizedFields: new SemanticPrioritizedFields
                   {
                       ContentFields = { new SemanticField("page_chunk") }
                   }
               );
   
               var semanticSearch = new SemanticSearch()
               {
                   DefaultConfigurationName = "semantic_config",
                   Configurations = { semanticConfig }
               };
   
               // Create the index
               var index = new SearchIndex(indexName)
               {
                   Fields = fields,
                   VectorSearch = vectorSearch,
                   SemanticSearch = semanticSearch
               };
   
               // Create the index client, deleting and recreating the index if it exists
               var indexClient = new SearchIndexClient(new Uri(searchEndpoint), credential);
               await indexClient.CreateOrUpdateIndexAsync(index);
               Console.WriteLine($"Index '{indexName}' created or updated successfully.");
   
               // Upload sample documents from the GitHub URL
               string url = "https://raw.githubusercontent.com/Azure-Samples/azure-search-sample-data/refs/heads/main/nasa-e-book/earth-at-night-json/documents.json";
               var httpClient = new HttpClient();
               var response = await httpClient.GetAsync(url);
               response.EnsureSuccessStatusCode();
               var json = await response.Content.ReadAsStringAsync();
               var documents = JsonSerializer.Deserialize<List<Dictionary<string, object>>>(json);
               var searchClient = new SearchClient(new Uri(searchEndpoint), indexName, credential);
               var searchIndexingBufferedSender = new SearchIndexingBufferedSender<Dictionary<string, object>>(
                   searchClient,
                   new SearchIndexingBufferedSenderOptions<Dictionary<string, object>>
                   {
                       KeyFieldAccessor = doc => doc["id"].ToString(),
                   }
               );
   
               await searchIndexingBufferedSender.UploadDocumentsAsync(documents);
               await searchIndexingBufferedSender.FlushAsync();
               Console.WriteLine($"Documents uploaded to index '{indexName}' successfully.");
   
               // Create a knowledge source
               var indexKnowledgeSource = new SearchIndexKnowledgeSource(
                   name: knowledgeSourceName,
                   searchIndexParameters: new SearchIndexKnowledgeSourceParameters(searchIndexName: indexName)
                   {
                       SourceDataFields = { new SearchIndexFieldReference(name: "id"), new SearchIndexFieldReference(name: "page_chunk"), new SearchIndexFieldReference(name: "page_number") }
                   }
               );
   
               await indexClient.CreateOrUpdateKnowledgeSourceAsync(indexKnowledgeSource);
               Console.WriteLine($"Knowledge source '{knowledgeSourceName}' created or updated successfully.");
   
               // Create a knowledge base
               var openAiParameters = new AzureOpenAIVectorizerParameters
               {
                   ResourceUri = new Uri(aoaiEndpoint),
                   DeploymentName = aoaiGptDeployment,
                   ModelName = aoaiGptModel
               };
   
               var model = new KnowledgeBaseAzureOpenAIModel(azureOpenAIParameters: openAiParameters);
   
               var knowledgeBase = new KnowledgeBase(
                   name: knowledgeBaseName,
                   knowledgeSources: new KnowledgeSourceReference[] { new KnowledgeSourceReference(knowledgeSourceName) }
               )
               {
                   RetrievalReasoningEffort = new KnowledgeRetrievalLowReasoningEffort(),
                   AnswerInstructions = "Provide a two sentence concise and informative answer based on the retrieved documents.",
                   Models = { model }
               };
   
               await indexClient.CreateOrUpdateKnowledgeBaseAsync(knowledgeBase);
               Console.WriteLine($"Knowledge base '{knowledgeBaseName}' created or updated successfully.");
   
               // Set up messages
               string instructions = @"A Q&A agent that can answer questions about the Earth at night.
               If you don't have the answer, respond with ""I don't know"".";
   
               var messages = new List<Dictionary<string, string>>
               {
                   new Dictionary<string, string>
                   {
                       { "role", "system" },
                       { "content", instructions }
                   }
               };
   
               // Run agentic retrieval
               var baseClient = new KnowledgeBaseRetrievalClient(
                   endpoint: new Uri(searchEndpoint),
                   knowledgeBaseName: knowledgeBaseName,
                   tokenCredential: new DefaultAzureCredential()
               );
   
               string query = @"Why do suburban belts display larger December brightening than urban cores even though absolute light levels are higher downtown? Why is the Phoenix nighttime street grid is so sharply visible from space, whereas large stretches of the interstate between midwestern cities remain comparatively dim?";
   
               messages.Add(new Dictionary<string, string>
               {
                   { "role", "user" },
                   { "content", query }
               });
   
               Console.WriteLine($"Running the query...{query}");
               var retrievalRequest = new KnowledgeBaseRetrievalRequest();
               foreach (Dictionary<string, string> message in messages) {
                   if (message["role"] != "system") {
                       retrievalRequest.Messages.Add(new KnowledgeBaseMessage(content: new[] { new KnowledgeBaseMessageTextContent(message["content"]) }) { Role = message["role"] });
                   }
               }
               retrievalRequest.RetrievalReasoningEffort = new KnowledgeRetrievalLowReasoningEffort();
               var retrievalResult = await baseClient.RetrieveAsync(retrievalRequest);
   
               messages.Add(new Dictionary<string, string>
               {
                   { "role", "assistant" },
                   { "content", (retrievalResult.Value.Response[0].Content[0] as KnowledgeBaseMessageTextContent)!.Text }
               });
   
               // Print the response, activity, and references
               Console.WriteLine("Response:");
               Console.WriteLine((retrievalResult.Value.Response[0].Content[0] as KnowledgeBaseMessageTextContent)!.Text);
   
               Console.WriteLine("Activity:");
               foreach (var activity in retrievalResult.Value.Activity)
               {
                   Console.WriteLine($"Activity Type: {activity.GetType().Name}");
                   string activityJson = JsonSerializer.Serialize(
                       activity,
                       activity.GetType(),
                       new JsonSerializerOptions { WriteIndented = true }
                   );
                   Console.WriteLine(activityJson);
               }
   
               Console.WriteLine("References:");
               foreach (var reference in retrievalResult.Value.References)
               {
                   Console.WriteLine($"Reference Type: {reference.GetType().Name}");
                   string referenceJson = JsonSerializer.Serialize(
                       reference,
                       reference.GetType(),
                       new JsonSerializerOptions { WriteIndented = true }
                   );
                   Console.WriteLine(referenceJson);
               }
   
               // Continue the conversation
               string nextQuery = "How do I find lava at night?";
               Console.WriteLine($"Continue the conversation with this query: {nextQuery}");
               messages.Add(new Dictionary<string, string>
               {
                   { "role", "user" },
                   { "content", nextQuery }
               });
   
               retrievalRequest = new KnowledgeBaseRetrievalRequest();
               foreach (Dictionary<string, string> message in messages) {
                   if (message["role"] != "system") {
                       retrievalRequest.Messages.Add(new KnowledgeBaseMessage(content: new[] { new KnowledgeBaseMessageTextContent(message["content"]) }) { Role = message["role"] });
                   }
               }
               retrievalRequest.RetrievalReasoningEffort = new KnowledgeRetrievalLowReasoningEffort();
               retrievalResult = await baseClient.RetrieveAsync(retrievalRequest);
   
               messages.Add(new Dictionary<string, string>
               {
                   { "role", "assistant" },
                   { "content", (retrievalResult.Value.Response[0].Content[0] as KnowledgeBaseMessageTextContent)!.Text }
               });
   
               // Print the new response, activity, and references
               Console.WriteLine("Response:");
               Console.WriteLine((retrievalResult.Value.Response[0].Content[0] as KnowledgeBaseMessageTextContent)!.Text);
   
               Console.WriteLine("Activity:");
               foreach (var activity in retrievalResult.Value.Activity)
               {
                   Console.WriteLine($"Activity Type: {activity.GetType().Name}");
                   string activityJson = JsonSerializer.Serialize(
                       activity,
                       activity.GetType(),
                       new JsonSerializerOptions { WriteIndented = true }
                   );
                   Console.WriteLine(activityJson);
               }
   
               Console.WriteLine("References:");
               foreach (var reference in retrievalResult.Value.References)
               {
                   Console.WriteLine($"Reference Type: {reference.GetType().Name}");
                   string referenceJson = JsonSerializer.Serialize(
                       reference,
                       reference.GetType(),
                       new JsonSerializerOptions { WriteIndented = true }
                   );
                   Console.WriteLine(referenceJson);
               }
   
               // Clean up resources
               await indexClient.DeleteKnowledgeBaseAsync(knowledgeBaseName);
               Console.WriteLine($"Knowledge base '{knowledgeBaseName}' deleted successfully.");
   
               await indexClient.DeleteKnowledgeSourceAsync(knowledgeSourceName);
               Console.WriteLine($"Knowledge source '{knowledgeSourceName}' deleted successfully.");
   
               await indexClient.DeleteIndexAsync(indexName);
               Console.WriteLine($"Index '{indexName}' deleted successfully.");
           }
       }
   }
   

5. Compile y ejecute la aplicación.

   dotnet run
   

Salida

La salida de la aplicación debe ser similar a la siguiente:

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

Descripción del código

Ahora que ha ejecutado el código, vamos a desglosar los pasos clave:

1. Creación de un índice de búsqueda
2. Carga de documentos en el índice
3. Creación de una fuente de conocimiento
4. Creación de una base de conocimientos
5. Configurar mensajes
6. Ejecuta la canalización de recuperación
7. Continuar la conversación

Creación de un índice de búsqueda

En Azure AI Search, un índice es una colección estructurada de datos. El código siguiente define un índice denominado earth-at-night, que especificó anteriormente mediante la indexName variable .

El esquema de índice contiene campos para la identificación del documento y el contenido de la página, las incrustaciones y los números. El esquema también incluye configuraciones para la clasificación semántica y el vector de búsqueda, que usa la implementación de text-embedding-3-large para vectorizar el texto y buscar documentos en función de la similitud semántica o conceptual.

// 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.");

Cargar documentos en el índice

Actualmente, el earth-at-night índice está vacío. El siguiente código puebla el índice con documentos JSON del libro electrónico 'Earth at Night' de la NASA. Según lo requiera Azure AI Search, cada documento se ajusta a los campos y tipos de datos definidos en el 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.");

Creación de una fuente de conocimiento

Un origen de conocimiento es una referencia reutilizable a los datos de origen. El código siguiente define un origen de conocimiento denominado earth-knowledge-source que tiene como destino el earth-at-night índice.

SourceDataFields especifica los campos de índice que se incluyen en las referencias de cita. Nuestro ejemplo incluye solo campos legibles para personas para evitar incrustaciones largas e ininterpretables en las respuestas.

// 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.");

Creación de una base de conocimientos

Para dirigirse a la implementación de earth-knowledge-source y gpt-5-mini en el momento de la consulta, necesita una base de conocimiento. El código siguiente define una base de conocimiento denominada earth-knowledge-base, que especificó anteriormente mediante la knowledgeBaseName variable .

OutputMode se establece en AnswerSynthesis, que habilita respuestas en lenguaje natural que citan los documentos recuperados y siguen las directrices proporcionadas por AnswerInstructions.

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

var model = new KnowledgeBaseAzureOpenAIModel(azureOpenAIParameters: openAiParameters);

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

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

Configurar mensajes

Los mensajes son la entrada de la ruta de recuperación y contienen el historial de conversaciones. Cada mensaje incluye un rol que indica su origen, como system o user, y contenido en lenguaje natural. El LLM que usa determina qué roles son válidos.

El código siguiente crea un mensaje del sistema, que indica earth-knowledge-base a responder preguntas sobre la Tierra por la noche y responder con "No sé" cuando las respuestas no están disponibles.

// 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 }
    }
};

Ejecución de la canalización de recuperación

Está listo para ejecutar la recuperación de agentes. El código siguiente envía una consulta de usuario de dos partes a earth-knowledge-base, que:

1. Analiza toda la conversación para deducir la necesidad de información del usuario.
2. Descompone la consulta compuesta en subconsultas centradas.
3. Ejecuta las subconsultas simultáneamente en la fuente de conocimiento.
4. Usa el clasificador semántico para volver a generar y filtrar los resultados.
5. Sintetiza los resultados relevantes en una respuesta en lenguaje 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 }
});

Revisar la respuesta, la actividad y las referencias

El código siguiente muestra la respuesta, la actividad y las referencias de la canalización de recuperación, donde:

• Response proporciona una respuesta sintetizada generada por LLM para la consulta que cita los documentos recuperados. Cuando la síntesis de respuestas no está habilitada, esta sección contiene contenido extraído directamente de los documentos.

• Activity realiza un seguimiento de los pasos que se realizaron durante el proceso de recuperación, incluidas las subconsultas generadas por la implementación de gpt-5-mini y los tokens usados para la clasificación semántica, el planeamiento de consultas y la síntesis de respuestas.

• References enumera los documentos que han contribuido a la respuesta, cada uno identificado por su 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 la conversación

El código siguiente continúa la conversación con earth-knowledge-base. Después de enviar esta consulta de usuario, la base de conocimiento captura el contenido pertinente de earth-knowledge-source y anexa la respuesta a la lista de mensajes.

// 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 }
});

Revisar la nueva respuesta, actividad y referencias

El código siguiente muestra la nueva respuesta, actividad y referencias de la canalización de recuperación.

// 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);
}

Limpieza de recursos

Cuando trabaja en su propia suscripción, es una buena idea finalizar un proyecto determinando si todavía necesita los recursos que creó. Los recursos que quedan en ejecución pueden costar dinero.

En Azure Portal, puede administrar los recursos de Azure AI Search y Microsoft Foundry seleccionando Todos los recursos o grupos de recursos en el panel izquierdo.

De lo contrario, el código siguiente de Program.cs eliminó los objetos que creó en este inicio rápido.

Eliminación de la base de conocimiento

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

Eliminación de la fuente de conocimiento

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

Eliminación del índice de búsqueda

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

En este inicio rápido, usará la recuperación agente para crear una experiencia de búsqueda conversacional con tecnología de modelos de lenguaje grandes (LLM) y sus datos propietarios. La recuperación agente desglosa las consultas de usuario complejas en subconsultas, ejecuta las subconsultas en paralelo y extrae datos de base de documentos indexados en Azure AI Search. La salida está diseñada para integrarse con soluciones de chat agenticas y personalizadas.

Aunque puede proporcionar sus propios datos, en esta guía de inicio rápido se usan documentos JSON de ejemplo de la Tierra de la NASA en el libro electrónico nocturno. Los documentos describen temas generales de ciencia e imágenes de la Tierra a la noche como se observa desde el espacio.

Prerrequisitos

• Una cuenta de Azure con una suscripción activa. Cree una cuenta gratuita.

• Un Servicio de Búsqueda de Azure AI en cualquier región que proporcione recuperación con agentes.

• Un proyecto y un recurso de Microsoft Foundry . Al crear un proyecto, el recurso se crea automáticamente.

• La CLI de Azure para la autenticación sin clave con Microsoft Entra ID.

Configurar el acceso

Antes de empezar, asegúrese de que tiene permisos para acceder al contenido y las operaciones. Se recomienda Microsoft Entra ID para la autenticación y el acceso basado en roles para la autorización. Debe ser Propietario o Administrador de acceso de usuario para asignar roles. Si los roles no son factibles, use la autenticación basada en claves en su lugar.

Para configurar el acceso para este inicio rápido, seleccione las dos pestañas siguientes.

Obtención de puntos de conexión

Cada servicio Azure AI Search y el recurso de Microsoft Foundry tienen un punto de conexión, que es una dirección URL única que identifica y proporciona acceso de red al recurso. En una sección posterior, especifique estos puntos de conexión para conectarse a los recursos mediante programación.

Para obtener los puntos de conexión de este inicio rápido, seleccione las dos pestañas siguientes.

Implementación de modelos

Para este inicio rápido, debe implementar dos modelos de Azure OpenAI en el proyecto de Microsoft Foundry:

• Modelo de inserción para la conversión de texto a vector. En este inicio rápido se usa text-embedding-3-large, pero puede usar cualquier text-embedding modelo.

• Un LLM para la planeación de consultas y la generación de respuestas. En este inicio rápido, se usa gpt-5-mini, pero puede usar cualquier LLM compatible para la recuperación de agentes.

Para obtener instrucciones de implementación, consulte Implementación de modelos de Azure OpenAI con Microsoft Foundry.

Configuración del entorno

El ejemplo de esta guía de inicio rápido funciona con Java Runtime. Instale un kit de desarrollo de Java como Azul Zulu OpenJDK. La compilación de Microsoft de OpenJDK o su JDK preferido también deberían funcionar.

1. Instalación de Apache Maven. A continuación, ejecute mvn -v para confirmar que la instalación se ha realizado correctamente.

2. Cree una nueva carpeta quickstart-agentic-retrieval para que contenga la aplicación y abra Visual Studio Code en esa carpeta con el siguiente comando:

   mkdir quickstart-agentic-retrieval && cd quickstart-agentic-retrieval
   

3. Cree un nuevo archivo pom.xml en la raíz del proyecto y copie el siguiente código en él:

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

4. Instale las dependencias, incluida la biblioteca cliente de Búsqueda de Azure AI (Azure.Search.Documents) para Java y la biblioteca cliente de Azure Identity para Java con:

   mvn clean dependency:copy-dependencies
   

Ejecución del código

1. Cree un nuevo archivo denominado .env en la quickstart-agentic-retrieval carpeta y agregue las siguientes variables de entorno:

   AZURE_OPENAI_ENDPOINT=https://<your-ai-foundry-resource-name>.openai.azure.com/
   AZURE_OPENAI_GPT_DEPLOYMENT=gpt-5-mini
   AZURE_OPENAI_EMBEDDING_DEPLOYMENT=text-embedding-3-large
   AZURE_SEARCH_ENDPOINT=https://<your-search-service-name>.search.windows.net
   AZURE_SEARCH_INDEX_NAME=agentic-retrieval-sample
   

   Reemplace <your-search-service-name> y <your-ai-foundry-resource-name> por el nombre real del servicio Azure AI Search y el nombre del recurso Foundry.

2. Pegue el código siguiente en un nuevo archivo denominado AgenticRetrievalQuickstart.java en la quickstart-agentic-retrieval carpeta :

   import com.azure.ai.openai.OpenAIAsyncClient;
   import com.azure.ai.openai.OpenAIClientBuilder;
   import com.azure.ai.openai.models.*;
   import com.azure.core.credential.TokenCredential;
   import com.azure.core.http.HttpClient;
   import com.azure.core.http.HttpHeaders;
   import com.azure.core.http.HttpMethod;
   import com.azure.core.http.HttpRequest;
   import com.azure.core.http.HttpResponse;
   import com.azure.core.util.BinaryData;
   import com.azure.identity.DefaultAzureCredential;
   import com.azure.identity.DefaultAzureCredentialBuilder;
   import com.azure.search.documents.SearchClient;
   import com.azure.search.documents.SearchClientBuilder;
   import com.azure.search.documents.SearchDocument;
   import com.azure.search.documents.indexes.SearchIndexClient;
   import com.azure.search.documents.indexes.SearchIndexClientBuilder;
   import com.azure.search.documents.indexes.models.*;
   import com.azure.search.documents.agents.SearchKnowledgeAgentClient;
   import com.azure.search.documents.agents.SearchKnowledgeAgentClientBuilder;
   import com.azure.search.documents.agents.models.*;
   import com.fasterxml.jackson.databind.JsonNode;
   import com.fasterxml.jackson.databind.ObjectMapper;
   import com.fasterxml.jackson.databind.node.ObjectNode;
   import io.github.cdimascio.dotenv.Dotenv;
   
   import java.io.IOException;
   import java.net.URI;
   import java.net.http.HttpRequest.Builder;
   import java.time.Duration;
   import java.util.*;
   import java.util.concurrent.TimeUnit;
   
   public class AgenticRetrievalQuickstart {
   
       // Configuration - Update these values for your environment
       private static final String SEARCH_ENDPOINT;
       private static final String AZURE_OPENAI_ENDPOINT;
       private static final String AZURE_OPENAI_GPT_DEPLOYMENT;
       private static final String AZURE_OPENAI_GPT_MODEL = "gpt-5-mini";
       private static final String AZURE_OPENAI_EMBEDDING_DEPLOYMENT;
       private static final String AZURE_OPENAI_EMBEDDING_MODEL = "text-embedding-3-large";
       private static final String INDEX_NAME = "earth_at_night";
       private static final String AGENT_NAME = "earth-search-agent";
       private static final String SEARCH_API_VERSION = "2025-05-01-Preview";
   
       static {
           // Load environment variables from .env file
           Dotenv dotenv = Dotenv.configure().ignoreIfMissing().load();
   
           SEARCH_ENDPOINT = getEnvVar(dotenv, "AZURE_SEARCH_ENDPOINT", 
               "https://contoso-agentic-search-service.search.windows.net");
           AZURE_OPENAI_ENDPOINT = getEnvVar(dotenv, "AZURE_OPENAI_ENDPOINT",
               "https://contoso-proj-agentic-foundry-res.openai.azure.com/");
           AZURE_OPENAI_GPT_DEPLOYMENT = getEnvVar(dotenv, "AZURE_OPENAI_GPT_DEPLOYMENT", "gpt-5-mini");
           AZURE_OPENAI_EMBEDDING_DEPLOYMENT = getEnvVar(dotenv, "AZURE_OPENAI_EMBEDDING_DEPLOYMENT", "text-embedding-3-large");
       }
   
       private static String getEnvVar(Dotenv dotenv, String key, String defaultValue) {
           String value = dotenv.get(key);
           return (value != null && !value.isEmpty()) ? value : defaultValue;
       }
   
       public static void main(String[] args) {
           try {
               System.out.println("Starting Azure AI Search agentic retrieval quickstart...\n");
   
               // Initialize Azure credentials using managed identity (recommended)
               TokenCredential credential = new DefaultAzureCredentialBuilder().build();
   
               // Create search clients
               SearchIndexClient searchIndexClient = new SearchIndexClientBuilder()
                   .endpoint(SEARCH_ENDPOINT)
                   .credential(credential)
                   .buildClient();
   
               SearchClient searchClient = new SearchClientBuilder()
                   .endpoint(SEARCH_ENDPOINT)
                   .indexName(INDEX_NAME)
                   .credential(credential)
                   .buildClient();
   
               // Create Azure OpenAI client
               OpenAIAsyncClient openAIClient = new OpenAIClientBuilder()
                   .endpoint(AZURE_OPENAI_ENDPOINT)
                   .credential(credential)
                   .buildAsyncClient();
   
               // Step 1: Create search index with vector and semantic capabilities
               createSearchIndex(searchIndexClient);
   
               // Step 2: Upload documents
               uploadDocuments(searchClient);
   
               // Step 3: Create knowledge agent
               createKnowledgeAgent(credential);
   
               // Step 4: Run agentic retrieval with conversation
               runAgenticRetrieval(credential, openAIClient);
   
               // Step 5: Clean up - Delete knowledge agent and search index
               deleteKnowledgeAgent(credential);
               deleteSearchIndex(searchIndexClient);
   
               System.out.println("[DONE] Quickstart completed successfully!");
   
           } catch (Exception e) {
               System.err.println("[ERROR] Error in main execution: " + e.getMessage());
               e.printStackTrace();
           }
       }
   
       private static void createSearchIndex(SearchIndexClient indexClient) {
           System.out.println("[WAIT] Creating search index...");
   
           try {
               // Delete index if it exists
               try {
                   indexClient.deleteIndex(INDEX_NAME);
                   System.out.println("[DELETE] Deleted existing index '" + INDEX_NAME + "'");
               } catch (Exception e) {
                   // Index doesn't exist, which is fine
               }
   
               // Define fields
               List<SearchField> fields = Arrays.asList(
                   new SearchField("id", SearchFieldDataType.STRING)
                       .setKey(true)
                       .setFilterable(true)
                       .setSortable(true)
                       .setFacetable(true),
                   new SearchField("page_chunk", SearchFieldDataType.STRING)
                       .setSearchable(true)
                       .setFilterable(false)
                       .setSortable(false)
                       .setFacetable(false),
                   new SearchField("page_embedding_text_3_large", SearchFieldDataType.collection(SearchFieldDataType.SINGLE))
                       .setSearchable(true)
                       .setFilterable(false)
                       .setSortable(false)
                       .setFacetable(false)
                       .setVectorSearchDimensions(3072)
                       .setVectorSearchProfileName("hnsw_text_3_large"),
                   new SearchField("page_number", SearchFieldDataType.INT32)
                       .setFilterable(true)
                       .setSortable(true)
                       .setFacetable(true)
               );
   
               // Create vectorizer
               AzureOpenAIVectorizer vectorizer = new AzureOpenAIVectorizer("azure_openai_text_3_large")
                   .setParameters(new AzureOpenAIVectorizerParameters()
                       .setResourceUrl(AZURE_OPENAI_ENDPOINT)
                       .setDeploymentName(AZURE_OPENAI_EMBEDDING_DEPLOYMENT)
                       .setModelName(AzureOpenAIModelName.TEXT_EMBEDDING_3_LARGE));
   
               // Create vector search configuration
               VectorSearch vectorSearch = new VectorSearch()
                   .setProfiles(Arrays.asList(
                       new VectorSearchProfile("hnsw_text_3_large", "alg")
                           .setVectorizerName("azure_openai_text_3_large")
                   ))
                   .setAlgorithms(Arrays.asList(
                       new HnswAlgorithmConfiguration("alg")
                   ))
                   .setVectorizers(Arrays.asList(vectorizer));
   
               // Create semantic search configuration
               SemanticSearch semanticSearch = new SemanticSearch()
                   .setDefaultConfigurationName("semantic_config")
                   .setConfigurations(Arrays.asList(
                       new SemanticConfiguration("semantic_config",
                           new SemanticPrioritizedFields()
                               .setContentFields(Arrays.asList(
                                   new SemanticField("page_chunk")
                               ))
                       )
                   ));
   
               // Create the index
               SearchIndex index = new SearchIndex(INDEX_NAME)
                   .setFields(fields)
                   .setVectorSearch(vectorSearch)
                   .setSemanticSearch(semanticSearch);
   
               indexClient.createOrUpdateIndex(index);
               System.out.println("[DONE] Index '" + INDEX_NAME + "' created successfully.");
   
           } catch (Exception e) {
               System.err.println("[ERROR] Error creating index: " + e.getMessage());
               throw new RuntimeException(e);
           }
       }
   
       private static void uploadDocuments(SearchClient searchClient) {
           System.out.println("[WAIT] Uploading documents...");
   
           try {
               // Fetch documents from GitHub
               List<SearchDocument> documents = fetchEarthAtNightDocuments();
   
               searchClient.uploadDocuments(documents);
               System.out.println("[DONE] Uploaded " + documents.size() + " documents successfully.");
   
               // Wait for indexing to complete
               System.out.println("[WAIT] Waiting for document indexing to complete...");
               Thread.sleep(5000);
               System.out.println("[DONE] Document indexing completed.");
   
           } catch (Exception e) {
               System.err.println("[ERROR] Error uploading documents: " + e.getMessage());
               throw new RuntimeException(e);
           }
       }
   
       private static List<SearchDocument> fetchEarthAtNightDocuments() {
           System.out.println("[WAIT] Fetching Earth at Night documents from GitHub...");
   
           String documentsUrl = "https://raw.githubusercontent.com/Azure-Samples/azure-search-sample-data/refs/heads/main/nasa-e-book/earth-at-night-json/documents.json";
   
           try {
               java.net.http.HttpClient httpClient = java.net.http.HttpClient.newHttpClient();
               java.net.http.HttpRequest request = java.net.http.HttpRequest.newBuilder()
                   .uri(URI.create(documentsUrl))
                   .build();
   
               java.net.http.HttpResponse<String> response = httpClient.send(request, 
                   java.net.http.HttpResponse.BodyHandlers.ofString());
   
               if (response.statusCode() != 200) {
                   throw new IOException("Failed to fetch documents: " + response.statusCode());
               }
   
               ObjectMapper mapper = new ObjectMapper();
               JsonNode jsonArray = mapper.readTree(response.body());
   
               List<SearchDocument> documents = new ArrayList<>();
               for (int i = 0; i < jsonArray.size(); i++) {
                   JsonNode doc = jsonArray.get(i);
                   SearchDocument searchDoc = new SearchDocument();
   
                   searchDoc.put("id", doc.has("id") ? doc.get("id").asText() : String.valueOf(i + 1));
                   searchDoc.put("page_chunk", doc.has("page_chunk") ? doc.get("page_chunk").asText() : "");
   
                   // Handle embeddings
                   if (doc.has("page_embedding_text_3_large") && doc.get("page_embedding_text_3_large").isArray()) {
                       List<Double> embeddings = new ArrayList<>();
                       for (JsonNode embedding : doc.get("page_embedding_text_3_large")) {
                           embeddings.add(embedding.asDouble());
                       }
                       searchDoc.put("page_embedding_text_3_large", embeddings);
                   } else {
                       // Fallback embeddings
                       List<Double> fallbackEmbeddings = new ArrayList<>();
                       for (int j = 0; j < 3072; j++) {
                           fallbackEmbeddings.add(0.1);
                       }
                       searchDoc.put("page_embedding_text_3_large", fallbackEmbeddings);
                   }
   
                   searchDoc.put("page_number", doc.has("page_number") ? doc.get("page_number").asInt() : i + 1);
   
                   documents.add(searchDoc);
               }
   
               System.out.println("[DONE] Fetched " + documents.size() + " documents from GitHub");
               return documents;
   
           } catch (Exception e) {
               System.err.println("[ERROR] Error fetching documents from GitHub: " + e.getMessage());
               System.out.println("🔄 Falling back to sample documents...");
   
               // Fallback to sample documents
               List<SearchDocument> fallbackDocs = new ArrayList<>();
   
               SearchDocument doc1 = new SearchDocument();
               doc1.put("id", "1");
               doc1.put("page_chunk", "The Earth at night reveals the patterns of human settlement and economic activity. City lights trace the contours of civilization, creating a luminous map of where people live and work.");
               List<Double> embeddings1 = new ArrayList<>();
               for (int i = 0; i < 3072; i++) {
                   embeddings1.add(0.1);
               }
               doc1.put("page_embedding_text_3_large", embeddings1);
               doc1.put("page_number", 1);
   
               SearchDocument doc2 = new SearchDocument();
               doc2.put("id", "2");
               doc2.put("page_chunk", "From space, the aurora borealis appears as shimmering curtains of green and blue light dancing across the polar regions.");
               List<Double> embeddings2 = new ArrayList<>();
               for (int i = 0; i < 3072; i++) {
                   embeddings2.add(0.2);
               }
               doc2.put("page_embedding_text_3_large", embeddings2);
               doc2.put("page_number", 2);
   
               fallbackDocs.add(doc1);
               fallbackDocs.add(doc2);
   
               return fallbackDocs;
           }
       }
   
       private static void createKnowledgeAgent(TokenCredential credential) {
           System.out.println("[WAIT] Creating knowledge agent...");
   
           // Delete agent if it exists
           deleteKnowledgeAgent(credential);
   
           try {
               ObjectMapper mapper = new ObjectMapper();
               ObjectNode agentDefinition = mapper.createObjectNode();
               agentDefinition.put("name", AGENT_NAME);
               agentDefinition.put("description", "Knowledge agent for Earth at Night e-book content");
   
               ObjectNode model = mapper.createObjectNode();
               model.put("kind", "azureOpenAI");
               ObjectNode azureOpenAIParams = mapper.createObjectNode();
               azureOpenAIParams.put("resourceUri", AZURE_OPENAI_ENDPOINT);
               azureOpenAIParams.put("deploymentId", AZURE_OPENAI_GPT_DEPLOYMENT);
               azureOpenAIParams.put("modelName", AZURE_OPENAI_GPT_MODEL);
               model.set("azureOpenAIParameters", azureOpenAIParams);
               agentDefinition.set("models", mapper.createArrayNode().add(model));
   
               ObjectNode targetIndex = mapper.createObjectNode();
               targetIndex.put("indexName", INDEX_NAME);
               targetIndex.put("defaultRerankerThreshold", 2.5);
               agentDefinition.set("targetIndexes", mapper.createArrayNode().add(targetIndex));
   
               String token = getAccessToken(credential, "https://search.azure.com/.default");
   
               java.net.http.HttpClient httpClient = java.net.http.HttpClient.newHttpClient();
               java.net.http.HttpRequest request = java.net.http.HttpRequest.newBuilder()
                   .uri(URI.create(SEARCH_ENDPOINT + "/agents/" + AGENT_NAME + "?api-version=" + SEARCH_API_VERSION))
                   .header("Content-Type", "application/json")
                   .header("Authorization", "Bearer " + token)
                   .PUT(java.net.http.HttpRequest.BodyPublishers.ofString(mapper.writeValueAsString(agentDefinition)))
                   .build();
   
               java.net.http.HttpResponse<String> response = httpClient.send(request,
                   java.net.http.HttpResponse.BodyHandlers.ofString());
   
               if (response.statusCode() >= 400) {
                   throw new RuntimeException("Failed to create knowledge agent: " + response.statusCode() + " " + response.body());
               }
   
               System.out.println("[DONE] Knowledge agent '" + AGENT_NAME + "' created successfully.");
   
           } catch (Exception e) {
               System.err.println("[ERROR] Error creating knowledge agent: " + e.getMessage());
               throw new RuntimeException(e);
           }
       }
   
       private static void runAgenticRetrieval(TokenCredential credential, OpenAIAsyncClient openAIClient) {
           System.out.println("[SEARCH] Running agentic retrieval...");
   
           // Initialize messages with system instructions
           List<Map<String, String>> messages = new ArrayList<>();
   
           Map<String, String> systemMessage = new HashMap<>();
           systemMessage.put("role", "system");
           systemMessage.put("content", "A Q&A agent that can answer questions about the Earth at night.\n" +
               "Sources have a JSON format with a ref_id that must be cited in the answer.\n" +
               "If you do not have the answer, respond with \"I don't know\".");
           messages.add(systemMessage);
   
           Map<String, String> userMessage = new HashMap<>();
           userMessage.put("role", "user");
           userMessage.put("content", "Why do suburban belts display larger December brightening than urban cores even though absolute light levels are higher downtown? Why is the Phoenix nighttime street grid is so sharply visible from space, whereas large stretches of the interstate between midwestern cities remain comparatively dim?");
           messages.add(userMessage);
   
           try {
               // Call agentic retrieval API (excluding system message)
               List<Map<String, String>> userMessages = messages.stream()
                   .filter(m -> !"system".equals(m.get("role")))
                   .collect(java.util.stream.Collectors.toList());
   
               String retrievalResponse = callAgenticRetrieval(credential, userMessages);
   
               // Add assistant response to conversation history
               Map<String, String> assistantMessage = new HashMap<>();
               assistantMessage.put("role", "assistant");
               assistantMessage.put("content", retrievalResponse);
               messages.add(assistantMessage);
   
               System.out.println(retrievalResponse);
   
               // Now do chat completion with full conversation history
               generateFinalAnswer(openAIClient, messages);
   
               // Continue conversation with second question
               continueConversation(credential, openAIClient, messages);
   
           } catch (Exception e) {
               System.err.println("[ERROR] Error in agentic retrieval: " + e.getMessage());
               throw new RuntimeException(e);
           }
       }
   
       private static String callAgenticRetrieval(TokenCredential credential, List<Map<String, String>> messages) {
           try {
               ObjectMapper mapper = new ObjectMapper();
               ObjectNode retrievalRequest = mapper.createObjectNode();
   
               // Convert messages to the correct format expected by the Knowledge agent
               com.fasterxml.jackson.databind.node.ArrayNode agentMessages = mapper.createArrayNode();
               for (Map<String, String> msg : messages) {
                   ObjectNode agentMessage = mapper.createObjectNode();
                   agentMessage.put("role", msg.get("role"));
   
                   com.fasterxml.jackson.databind.node.ArrayNode content = mapper.createArrayNode();
                   ObjectNode textContent = mapper.createObjectNode();
                   textContent.put("type", "text");
                   textContent.put("text", msg.get("content"));
                   content.add(textContent);
                   agentMessage.set("content", content);
   
                   agentMessages.add(agentMessage);
               }
               retrievalRequest.set("messages", agentMessages);
   
               com.fasterxml.jackson.databind.node.ArrayNode targetIndexParams = mapper.createArrayNode();
               ObjectNode indexParam = mapper.createObjectNode();
               indexParam.put("indexName", INDEX_NAME);
               indexParam.put("rerankerThreshold", 2.5);
               indexParam.put("maxDocsForReranker", 100);
               indexParam.put("includeReferenceSourceData", true);
               targetIndexParams.add(indexParam);
               retrievalRequest.set("targetIndexParams", targetIndexParams);
   
               String token = getAccessToken(credential, "https://search.azure.com/.default");
   
               java.net.http.HttpClient httpClient = java.net.http.HttpClient.newHttpClient();
               java.net.http.HttpRequest request = java.net.http.HttpRequest.newBuilder()
                   .uri(URI.create(SEARCH_ENDPOINT + "/agents/" + AGENT_NAME + "/retrieve?api-version=" + SEARCH_API_VERSION))
                   .header("Content-Type", "application/json")
                   .header("Authorization", "Bearer " + token)
                   .POST(java.net.http.HttpRequest.BodyPublishers.ofString(mapper.writeValueAsString(retrievalRequest)))
                   .build();
   
               java.net.http.HttpResponse<String> response = httpClient.send(request,
                   java.net.http.HttpResponse.BodyHandlers.ofString());
   
               if (response.statusCode() >= 400) {
                   throw new RuntimeException("Agentic retrieval failed: " + response.statusCode() + " " + response.body());
               }
   
               JsonNode responseJson = mapper.readTree(response.body());
   
               // Log activities and results
               logActivitiesAndResults(responseJson);
   
               // Extract response content
               if (responseJson.has("response") && responseJson.get("response").isArray()) {
                   com.fasterxml.jackson.databind.node.ArrayNode responseArray = (com.fasterxml.jackson.databind.node.ArrayNode) responseJson.get("response");
                   if (responseArray.size() > 0) {
                       JsonNode firstResponse = responseArray.get(0);
                       if (firstResponse.has("content") && firstResponse.get("content").isArray()) {
                           com.fasterxml.jackson.databind.node.ArrayNode contentArray = (com.fasterxml.jackson.databind.node.ArrayNode) firstResponse.get("content");
                           if (contentArray.size() > 0) {
                               JsonNode textContent = contentArray.get(0);
                               if (textContent.has("text")) {
                                   return textContent.get("text").asText();
                               }
                           }
                       }
                   }
               }
   
               return "No response content available";
   
           } catch (Exception e) {
               System.err.println("[ERROR] Error in agentic retrieval call: " + e.getMessage());
               throw new RuntimeException(e);
           }
       }
   
       private static void logActivitiesAndResults(JsonNode responseJson) {
           ObjectMapper mapper = new ObjectMapper();
   
           // Log activities
           System.out.println("\nActivities:");
           if (responseJson.has("activity") && responseJson.get("activity").isArray()) {
               for (JsonNode activity : responseJson.get("activity")) {
                   String activityType = "UnknownActivityRecord";
                   if (activity.has("InputTokens")) {
                       activityType = "KnowledgeAgentModelQueryPlanningActivityRecord";
                   } else if (activity.has("TargetIndex")) {
                       activityType = "KnowledgeAgentSearchActivityRecord";
                   } else if (activity.has("QueryTime")) {
                       activityType = "KnowledgeAgentSemanticRankerActivityRecord";
                   }
   
                   System.out.println("Activity Type: " + activityType);
                   try {
                       System.out.println(mapper.writerWithDefaultPrettyPrinter().writeValueAsString(activity));
                   } catch (Exception e) {
                       System.out.println(activity.toString());
                   }
               }
           }
   
           // Log results
           System.out.println("Results");
           if (responseJson.has("references") && responseJson.get("references").isArray()) {
               for (JsonNode reference : responseJson.get("references")) {
                   String referenceType = "KnowledgeAgentAzureSearchDocReference";
   
                   System.out.println("Reference Type: " + referenceType);
                   try {
                       System.out.println(mapper.writerWithDefaultPrettyPrinter().writeValueAsString(reference));
                   } catch (Exception e) {
                       System.out.println(reference.toString());
                   }
               }
           }
       }
   
       private static void generateFinalAnswer(OpenAIAsyncClient openAIClient, List<Map<String, String>> messages) {
           System.out.println("\n[ASSISTANT]: ");
   
           try {
               List<ChatRequestMessage> chatMessages = new ArrayList<>();
               for (Map<String, String> msg : messages) {
                   String role = msg.get("role");
                   String content = msg.get("content");
   
                   switch (role) {
                       case "system":
                           chatMessages.add(new ChatRequestSystemMessage(content));
                           break;
                       case "user":
                           chatMessages.add(new ChatRequestUserMessage(content));
                           break;
                       case "assistant":
                           chatMessages.add(new ChatRequestAssistantMessage(content));
                           break;
                   }
               }
   
               ChatCompletionsOptions chatOptions = new ChatCompletionsOptions(chatMessages)
                   .setMaxTokens(1000)
                   .setTemperature(0.7);
   
               ChatCompletions completion = openAIClient.getChatCompletions(AZURE_OPENAI_GPT_DEPLOYMENT, chatOptions).block();
   
               if (completion != null && completion.getChoices() != null && !completion.getChoices().isEmpty()) {
                   String answer = completion.getChoices().get(0).getMessage().getContent();
                   System.out.println(answer.replace(".", "\n"));
   
                   // Add this response to conversation history
                   Map<String, String> assistantResponse = new HashMap<>();
                   assistantResponse.put("role", "assistant");
                   assistantResponse.put("content", answer);
                   messages.add(assistantResponse);
               }
   
           } catch (Exception e) {
               System.err.println("[ERROR] Error generating final answer: " + e.getMessage());
               throw new RuntimeException(e);
           }
       }
   
       private static void continueConversation(TokenCredential credential, OpenAIAsyncClient openAIClient, List<Map<String, String>> messages) {
           System.out.println("\n === Continuing Conversation ===");
   
           // Add follow-up question
           String followUpQuestion = "How do I find lava at night?";
           System.out.println("[QUESTION] Follow-up question: " + followUpQuestion);
   
           Map<String, String> userMessage = new HashMap<>();
           userMessage.put("role", "user");
           userMessage.put("content", followUpQuestion);
           messages.add(userMessage);
   
           try {
               // FILTER OUT SYSTEM MESSAGE - only send user/assistant messages to agentic retrieval
               List<Map<String, String>> userAssistantMessages = messages.stream()
                   .filter(m -> !"system".equals(m.get("role")))
                   .collect(java.util.stream.Collectors.toList());
   
               String newRetrievalResponse = callAgenticRetrieval(credential, userAssistantMessages);
   
               // Add assistant response to conversation history
               Map<String, String> assistantMessage = new HashMap<>();
               assistantMessage.put("role", "assistant");
               assistantMessage.put("content", newRetrievalResponse);
               messages.add(assistantMessage);
   
               System.out.println(newRetrievalResponse);
   
               // Generate final answer for follow-up
               generateFinalAnswer(openAIClient, messages);
   
               System.out.println("\n === Conversation Complete ===");
   
           } catch (Exception e) {
               System.err.println("[ERROR] Error in conversation continuation: " + e.getMessage());
               throw new RuntimeException(e);
           }
       }
   
       private static void deleteKnowledgeAgent(TokenCredential credential) {
           System.out.println("[DELETE] Deleting knowledge agent...");
   
           try {
               String token = getAccessToken(credential, "https://search.azure.com/.default");
   
               java.net.http.HttpClient httpClient = java.net.http.HttpClient.newHttpClient();
               java.net.http.HttpRequest request = java.net.http.HttpRequest.newBuilder()
                   .uri(URI.create(SEARCH_ENDPOINT + "/agents/" + AGENT_NAME + "?api-version=" + SEARCH_API_VERSION))
                   .header("Authorization", "Bearer " + token)
                   .DELETE()
                   .build();
   
               java.net.http.HttpResponse<String> response = httpClient.send(request,
                   java.net.http.HttpResponse.BodyHandlers.ofString());
   
               if (response.statusCode() == 404) {
                   System.out.println("[INFO] Knowledge agent '" + AGENT_NAME + "' does not exist or was already deleted.");
                   return;
               }
   
               if (response.statusCode() >= 400) {
                   throw new RuntimeException("Failed to delete knowledge agent: " + response.statusCode() + " " + response.body());
               }
   
               System.out.println("[DONE] Knowledge agent '" + AGENT_NAME + "' deleted successfully.");
   
           } catch (Exception e) {
               System.err.println("[ERROR] Error deleting knowledge agent: " + e.getMessage());
               // Don't throw - this is cleanup
           }
       }
   
       private static void deleteSearchIndex(SearchIndexClient indexClient) {
           System.out.println("[DELETE] Deleting search index...");
   
           try {
               indexClient.deleteIndex(INDEX_NAME);
               System.out.println("[DONE] Search index '" + INDEX_NAME + "' deleted successfully.");
   
           } catch (Exception e) {
               if (e.getMessage() != null && (e.getMessage().contains("404") || e.getMessage().contains("IndexNotFound"))) {
                   System.out.println("[INFO] Search index '" + INDEX_NAME + "' does not exist or was already deleted.");
                   return;
               }
               System.err.println("[ERROR] Error deleting search index: " + e.getMessage());
               // Don't throw - this is cleanup
           }
       }
   
       private static String getAccessToken(TokenCredential credential, String scope) {
           try {
               return credential.getToken(new com.azure.core.credential.TokenRequestContext().addScopes(scope)).block().getToken();
           } catch (Exception e) {
               throw new RuntimeException("Failed to get access token", e);
           }
       }
   }
   

3. Inicie sesión en Azure con el siguiente comando:

   az login
   

4. Ejecute la nueva aplicación de consola:

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

Salida

La salida de la aplicación debe ser similar a la siguiente:

Starting Azure AI Search agentic retrieval quickstart...

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

Activities:
Activity Type: UnknownActivityRecord
{
  "type" : "ModelQueryPlanning",
  "id" : 0,
  "inputTokens" : 1379,
  "outputTokens" : 545
}
Activity Type: UnknownActivityRecord
{
  "type" : "AzureSearchQuery",
  "id" : 1,
  "targetIndex" : "earth_at_night",
  "query" : {
    "search" : "Why do suburban areas show greater December brightening compared to urban cores despite higher absolute light levels downtown?",
    "filter" : null
  },
  "queryTime" : "2025-07-21T15:07:04.024Z",
  "count" : 0,
  "elapsedMs" : 2609
}
Activity Type: UnknownActivityRecord
{
  "type" : "AzureSearchQuery",
  "id" : 2,
  "targetIndex" : "earth_at_night",
  "query" : {
    "search" : "Why is the Phoenix nighttime street grid sharply visible from space, while large stretches of interstate highways between Midwestern cities appear comparatively dim?",
    "filter" : null
  },
  "queryTime" : "2025-07-21T15:07:04.267Z",
  "count" : 0,
  "elapsedMs" : 243
}
Activity Type: UnknownActivityRecord
{
  "type" : "AzureSearchSemanticRanker",
  "id" : 3,
  "inputTokens" : 48602
}
Results
[]

[ASSISTANT]: 
The suburban belts display larger December brightening than urban cores despite higher absolute light levels downtown likely because suburban areas have more seasonal variation in lighting usage, such as increased outdoor and holiday lighting in December
 Urban cores, being brightly lit throughout the year, show less relative change


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


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

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

Activities:
Activity Type: UnknownActivityRecord
{
  "type" : "ModelQueryPlanning",
  "id" : 0,
  "inputTokens" : 1545,
  "outputTokens" : 127
}
Activity Type: UnknownActivityRecord
{
  "type" : "AzureSearchQuery",
  "id" : 1,
  "targetIndex" : "earth_at_night",
  "query" : {
    "search" : "How can I find lava at night?",
    "filter" : null
  },
  "queryTime" : "2025-07-21T15:07:15.445Z",
  "count" : 6,
  "elapsedMs" : 370
}
Activity Type: UnknownActivityRecord
{
  "type" : "AzureSearchSemanticRanker",
  "id" : 2,
  "inputTokens" : 22994
}
Results
Reference Type: KnowledgeAgentAzureSearchDocReference
{
  "type" : "AzureSearchDoc",
  "id" : "0",
  "activitySource" : 1,
  "docKey" : "earth_at_night_508_page_44_verbalized",
  "sourceData" : {
    "id" : "earth_at_night_508_page_44_verbalized",
    "page_chunk" : "## Nature's Light Shows\n\nAt night, with the light of the Sun removed, nature's brilliant glow from Earth's surface becomes visible to the naked eye from space. Some of Earth's most spectacular light shows are natural, like the aurora borealis, or Northern Lights, in the Northern Hemisphere (aurora australis, or Southern Lights, in the Southern Hemisphere). The auroras are natural electrical phenomena caused by charged particles that race from the Sun toward Earth, inducing chemical reactions in the upper atmosphere and creating the appearance of streamers of reddish or greenish light in the sky, usually near the northern or southern magnetic pole. Other natural lights can indicate danger, like a raging forest fire encroaching on a city, town, or community, or lava spewing from an erupting volcano.\n\nWhatever the source, the ability of humans to monitor nature's light shows at night has practical applications for society. For example, tracking fires during nighttime hours allows for continuous monitoring and enhances our ability to protect humans and other animals, plants, and infrastructure. Combined with other data sources, our ability to observe the light of fires at night allows emergency managers to more efficiently and accurately issue warnings and evacuation orders and allows firefighting efforts to continue through the night. With enough moonlight (e.g., full-Moon phase), it's even possible to track the movement of smoke plumes at night, which can impact air quality, regardless of time of day.\n\nAnother natural source of light at night is emitted from glowing lava flows at the site of active volcanoes. Again, with enough moonlight, these dramatic scenes can be tracked and monitored for both scientific research and public safety.\n\n\n### Figure: The Northern Lights Viewed from Space\n\n**September 17, 2011**\n\nThis photo, taken from the International Space Station on September 17, 2011, shows a spectacular display of the aurora borealis (Northern Lights) as green and reddish light in the night sky above Earth. In the foreground, part of a Soyuz spacecraft is visible, silhouetted against the bright auroral light. The green glow is generated by energetic charged particles from the Sun interacting with Earth's upper atmosphere, exciting oxygen and nitrogen atoms, and producing characteristic colors. The image demonstrates the vividness and grandeur of natural night-time light phenomena as seen from orbit."
  }
}
Reference Type: KnowledgeAgentAzureSearchDocReference
{
  "type" : "AzureSearchDoc",
  "id" : "1",
  "activitySource" : 1,
  "docKey" : "earth_at_night_508_page_65_verbalized",
  "sourceData" : {
    "id" : "earth_at_night_508_page_65_verbalized",
    "page_chunk" : "# Volcanoes\n\n## Figure: Satellite Image of Sicily and Mount Etna Lava, March 16, 2017\n\nThe annotated satellite image below shows the island of Sicily and the surrounding region at night, highlighting city lights and volcanic activity.\n\n**Description:**\n\n- **Date of image:** March 16, 2017\n- **Geographical locations labeled:**\n    - Major cities: Palermo (northwest Sicily), Marsala (western Sicily), Catania (eastern Sicily)\n    - Significant feature: Mount Etna, labeled with an adjacent \"hot lava\" region showing the glow from active lava flows\n    - Surrounding water body: Mediterranean Sea\n    - Island: Malta to the south of Sicily\n- **Other details:** \n    - The image is shown at night, with bright spots indicating city lights.\n    - The position of \"hot lava\" near Mount Etna is distinctly visible as a bright spot different from other city lights, indicating volcanic activity.\n    - A scale bar is included showing a reference length of 50 km.\n    - North direction is indicated with an arrow.\n    - Cloud cover is visible in the southwest part of the image, partially obscuring the view near Marsala and Malta.\n\n**Summary of Features Visualized:**\n\n| Feature          | Description                                           |\n|------------------|------------------------------------------------------|\n| Cities           | Bright clusters indicating locations: Palermo, Marsala, Catania |\n| Mount Etna       | Marked on the map, located on the eastern side of Sicily, with visible hot lava activity |\n| Malta            | Clearly visible to the south of Sicily               |\n| Water bodies     | Mediterranean Sea labeled                            |\n| Scale & Direction| 50 km scale bar and North indicator                  |\n| Date             | March 16, 2017                                       |\n| Cloud Cover      | Visible in the lower left (southern) part of the image |\n\nThis figure demonstrates the visibility of volcanic activity at Mount Etna from space at night, distinguishing the light from hot lava against the background city lights of Sicily and Malta."
  }
}
Reference Type: KnowledgeAgentAzureSearchDocReference
{
  "type" : "AzureSearchDoc",
  "id" : "2",
  "activitySource" : 1,
  "docKey" : "earth_at_night_508_page_64_verbalized",
  "sourceData" : {
    "id" : "earth_at_night_508_page_64_verbalized",
    "page_chunk" : "<!-- PageHeader=\"Volcanoes\" -->\n\n### Nighttime Glow at Mount Etna - Italy\n\nAt about 2:30 a.m. local time on March 16, 2017, the VIIRS DNB on the Suomi NPP satellite captured this nighttime image of lava flowing on Mount Etna in Sicily, Italy. Etna is one of the world's most active volcanoes.\n\n#### Figure: Location of Mount Etna\nA world globe is depicted, with a marker indicating the location of Mount Etna in Sicily, Italy, in southern Europe near the center of the Mediterranean Sea.\n\n<!-- PageFooter=\"Earth at Night\" -->\n<!-- PageNumber=\"48\" -->"
  }
}
Reference Type: KnowledgeAgentAzureSearchDocReference
{
  "type" : "AzureSearchDoc",
  "id" : "3",
  "activitySource" : 1,
  "docKey" : "earth_at_night_508_page_66_verbalized",
  "sourceData" : {
    "id" : "earth_at_night_508_page_66_verbalized",
    "page_chunk" : "# Volcanoes\n\n---\n\n### Mount Etna Erupts - Italy\n\nThe highly active Mount Etna in Italy sent red lava rolling down its flank on March 19, 2017. An astronaut onboard the ISS took the photograph below of the volcano and its environs that night. City lights surround the mostly dark volcanic area.\n\n---\n\n#### Figure 1: Location of Mount Etna, Italy\n\nA world map highlighting the location of Mount Etna in southern Italy. The marker indicates its geographic placement on the east coast of Sicily, Italy, in the Mediterranean region, south of mainland Europe and north of northern Africa.\n\n---\n\n#### Figure 2: Nighttime View of Mount Etna's Eruption and Surrounding Cities\n\nThis is a nighttime satellite image taken on March 19, 2017, showing the eruption of Mount Etna (southeastern cone) with visible bright red and orange coloring indicating flowing lava from a lateral vent. The surrounding areas are illuminated by city lights, with the following geographic references labeled:\n\n| Location        | Position in Image         | Visible Characteristics                    |\n|-----------------|--------------------------|--------------------------------------------|\n| Mt. Etna (southeastern cone) | Top center-left | Bright red/orange lava flow                |\n| Lateral vent    | Left of the volcano       | Faint red/orange flow extending outwards   |\n| Resort          | Below the volcano, to the left   | Small cluster of lights                    |\n| Giarre          | Top right                 | Bright cluster of city lights              |\n| Acireale        | Center right              | Large, bright area of city lights          |\n| Biancavilla     | Bottom left               | Smaller cluster of city lights             |\n\nAn arrow pointing north is shown on the image for orientation.\n\n---\n\n<!-- Earth at Night Page Footer -->\n<!-- Page Number: 50 -->"
  }
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Reference Type: KnowledgeAgentAzureSearchDocReference
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    "page_chunk" : "For the first time in perhaps a decade, Mount Etna experienced a \"flank eruption\"�erupting from its side instead of its summit�on December 24, 2018. The activity was accompanied by 130 earthquakes occurring over three hours that morning. Mount Etna, Europe�s most active volcano, has seen periodic activity on this part of the mountain since 2013. The Operational Land Imager (OLI) on the Landsat 8 satellite acquired the main image of Mount Etna on December 28, 2018.\n\nThe inset image highlights the active vent and thermal infrared signature from lava flows, which can be seen near the newly formed fissure on the southeastern side of the volcano. The inset was created with data from OLI and the Thermal Infrared Sensor (TIRS) on Landsat 8. Ash spewing from the fissure cloaked adjacent villages and delayed aircraft from landing at the nearby Catania airport. Earthquakes occurred in the subsequent days after the initial eruption and displaced hundreds of people from their homes.\n\nFor nighttime images of Mount Etna�s March 2017 eruption, see pages 48�51.\n\n---\n\n### Hazards of Volcanic Ash Plumes and Satellite Observation\n\nWith the help of moonlight, satellite instruments can track volcanic ash plumes, which present significant hazards to airplanes in flight. The volcanic ash�composed of tiny pieces of glass and rock�is abrasive to engine turbine blades, and can melt on the blades and other engine parts, causing damage and even engine stalls. This poses a danger to both the plane�s integrity and passenger safety. Volcanic ash also reduces visibility for pilots and can cause etching of windshields, further reducing pilots� ability to see. Nightlight images can be combined with thermal images to provide a more complete view of volcanic activity on Earth�s surface.\n\nThe VIIRS Day/Night Band (DNB) on polar-orbiting satellites uses faint light sources such as moonlight, airglow (the atmosphere�s self-illumination through chemical reactions), zodiacal light (sunlight scattered by interplanetary dust), and starlight from the Milky Way. Using these dim light sources, the DNB can detect changes in clouds, snow cover, and sea ice:\n\n#### Table: Light Sources Used by VIIRS DNB\n\n| Light Source         | Description                                                                  |\n|----------------------|------------------------------------------------------------------------------|\n| Moonlight            | Reflected sunlight from the Moon, illuminating Earth's surface at night      |\n| Airglow              | Atmospheric self-illumination from chemical reactions                        |\n| Zodiacal Light       | Sunlight scattered by interplanetary dust                                    |\n| Starlight/Milky Way  | Faint illumination provided by stars in the Milky Way                        |\n\nGeostationary Operational Environmental Satellites (GOES), managed by NOAA, orbit over Earth�s equator and offer uninterrupted observations of North America. High-latitude areas such as Alaska benefit from polar-orbiting satellites like Suomi NPP, which provide overlapping coverage at the poles, enabling more data collection in these regions. During polar darkness (winter months), VIIRS DNB data allow scientists to:\n\n- Observe sea ice formation\n- Monitor snow cover extent at the highest latitudes\n- Detect open water for ship navigation\n\n#### Table: Satellite Coverage Overview\n\n| Satellite Type          | Orbit           | Coverage Area         | Special Utility                              |\n|------------------------|-----------------|----------------------|----------------------------------------------|\n| GOES                   | Geostationary   | Equatorial/North America | Continuous regional monitoring              |\n| Polar-Orbiting (e.g., Suomi NPP) | Polar-orbiting    | Poles/high latitudes      | Overlapping passes; useful during polar night|\n\n---\n\n### Weather Forecasting and Nightlight Data\n\nThe use of nightlight data by weather forecasters is growing as the VIIRS instrument enables observation of clouds at night illuminated by sources such as moonlight and lightning. Scientists use these data to study the nighttime behavior of weather systems, including severe storms, which can develop and strike populous areas at night as well as during the day. Combined with thermal data, visible nightlight data allow the detection of clouds at various heights in the atmosphere, such as dense marine fog. This capability enables weather forecasters to issue marine advisories with higher confidence, leading to greater utility. (See \"Marine Layer Clouds�California\" on page 56.)\n\nIn this section of the book, you will see how nightlight data are used to observe nature�s spectacular light shows across a wide range of sources.\n\n---\n\n#### Notable Data from Mount Etna Flank Eruption (December 2018)\n\n| Event/Observation                  | Details                                                                    |\n|-------------------------------------|----------------------------------------------------------------------------|\n| Date of Flank Eruption              | December 24, 2018                                                          |\n| Number of Earthquakes               | 130 earthquakes within 3 hours                                              |\n| Image Acquisition                   | December 28, 2018 by Landsat 8 OLI                                         |\n| Location of Eruption                | Southeastern side of Mount Etna                                            |\n| Thermal Imaging Data                | From OLI and TIRS (Landsat 8), highlighting active vent and lava flows     |\n| Impact on Villages/Air Transport    | Ash covered villages; delayed aircraft at Catania airport                  |\n| Displacement                        | Hundreds of residents displaced                                            |\n| Ongoing Seismic Activity            | Earthquakes continued after initial eruption                               |\n\n---\n\n<!-- PageFooter=\"Earth at Night\" -->\n<!-- PageNumber=\"30\" -->"
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    "page_chunk" : "<!-- PageHeader=\"Volcanoes\" -->\n\n## Volcanoes\n\n### The Infrared Glows of Kilauea's Lava Flows�Hawaii\n\nIn early May 2018, an eruption on Hawaii's Kilauea volcano began to unfold. The eruption took a dangerous turn on May 3, 2018, when new fissures opened in the residential neighborhood of Leilani Estates. During the summer-long eruptive event, other fissures emerged along the East Rift Zone. Lava from vents along the rift zone flowed downslope, reaching the ocean in several areas, and filling in Kapoho Bay.\n\nA time series of Landsat 8 imagery shows the progression of the lava flows from May 16 to August 13. The night view combines thermal, shortwave infrared, and near-infrared wavelengths to tease out the very hot lava (bright white), cooling lava (red), and lava flows obstructed by clouds (purple).\n\n#### Figure: Location of Kilauea Volcano, Hawaii\n\nA globe is shown centered on North America, with a marker placed in the Pacific Ocean indicating the location of Hawaii, to the southwest of the mainland United States.\n\n<!-- PageFooter=\"Earth at Night\" -->\n<!-- PageNumber=\"44\" -->"
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[{"ref_id":0,"content":"## Nature's Light Shows\n\nAt night, with the light of the Sun removed, nature's brilliant glow from Earth's surface becomes visible to the naked eye from space. Some of Earth's most spectacular light shows are natural, like the aurora borealis, or Northern Lights, in the Northern Hemisphere (aurora australis, or Southern Lights, in the Southern Hemisphere). The auroras are natural electrical phenomena caused by charged particles that race from the Sun toward Earth, inducing chemical reactions in the upper atmosphere and creating the appearance of streamers of reddish or greenish light in the sky, usually near the northern or southern magnetic pole. Other natural lights can indicate danger, like a raging forest fire encroaching on a city, town, or community, or lava spewing from an erupting volcano.\n\nWhatever the source, the ability of humans to monitor nature's light shows at night has practical applications for society. For example, tracking fires during nighttime hours allows for continuous monitoring and enhances our ability to protect humans and other animals, plants, and infrastructure. Combined with other data sources, our ability to observe the light of fires at night allows emergency managers to more efficiently and accurately issue warnings and evacuation orders and allows firefighting efforts to continue through the night. With enough moonlight (e.g., full-Moon phase), it's even possible to track the movement of smoke plumes at night, which can impact air quality, regardless of time of day.\n\nAnother natural source of light at night is emitted from glowing lava flows at the site of active volcanoes. Again, with enough moonlight, these dramatic scenes can be tracked and monitored for both scientific research and public safety.\n\n\n### Figure: The Northern Lights Viewed from Space\n\n**September 17, 2011**\n\nThis photo, taken from the International Space Station on September 17, 2011, shows a spectacular display of the aurora borealis (Northern Lights) as green and reddish light in the night sky above Earth. In the foreground, part of a Soyuz spacecraft is visible, silhouetted against the bright auroral light. The green glow is generated by energetic charged particles from the Sun interacting with Earth's upper atmosphere, exciting oxygen and nitrogen atoms, and producing characteristic colors. The image demonstrates the vividness and grandeur of natural night-time light phenomena as seen from orbit."},{"ref_id":1,"content":"# Volcanoes\n\n## Figure: Satellite Image of Sicily and Mount Etna Lava, March 16, 2017\n\nThe annotated satellite image below shows the island of Sicily and the surrounding region at night, highlighting city lights and volcanic activity.\n\n**Description:**\n\n- **Date of image:** March 16, 2017\n- **Geographical locations labeled:**\n    - Major cities: Palermo (northwest Sicily), Marsala (western Sicily), Catania (eastern Sicily)\n    - Significant feature: Mount Etna, labeled with an adjacent \"hot lava\" region showing the glow from active lava flows\n    - Surrounding water body: Mediterranean Sea\n    - Island: Malta to the south of Sicily\n- **Other details:** \n    - The image is shown at night, with bright spots indicating city lights.\n    - The position of \"hot lava\" near Mount Etna is distinctly visible as a bright spot different from other city lights, indicating volcanic activity.\n    - A scale bar is included showing a reference length of 50 km.\n    - North direction is indicated with an arrow.\n    - Cloud cover is visible in the southwest part of the image, partially obscuring the view near Marsala and Malta.\n\n**Summary of Features Visualized:**\n\n| Feature          | Description                                           |\n|------------------|------------------------------------------------------|\n| Cities           | Bright clusters indicating locations: Palermo, Marsala, Catania |\n| Mount Etna       | Marked on the map, located on the eastern side of Sicily, with visible hot lava activity |\n| Malta            | Clearly visible to the south of Sicily               |\n| Water bodies     | Mediterranean Sea labeled                            |\n| Scale & Direction| 50 km scale bar and North indicator                  |\n| Date             | March 16, 2017                                       |\n| Cloud Cover      | Visible in the lower left (southern) part of the image |\n\nThis figure demonstrates the visibility of volcanic activity at Mount Etna from space at night, distinguishing the light from hot lava against the background city lights of Sicily and Malta."},{"ref_id":2,"content":"<!-- PageHeader=\"Volcanoes\" -->\n\n### Nighttime Glow at Mount Etna - Italy\n\nAt about 2:30 a.m. local time on March 16, 2017, the VIIRS DNB on the Suomi NPP satellite captured this nighttime image of lava flowing on Mount Etna in Sicily, Italy. Etna is one of the world's most active volcanoes.\n\n#### Figure: Location of Mount Etna\nA world globe is depicted, with a marker indicating the location of Mount Etna in Sicily, Italy, in southern Europe near the center of the Mediterranean Sea.\n\n<!-- PageFooter=\"Earth at Night\" -->\n<!-- PageNumber=\"48\" -->"},{"ref_id":3,"content":"# Volcanoes\n\n---\n\n### Mount Etna Erupts - Italy\n\nThe highly active Mount Etna in Italy sent red lava rolling down its flank on March 19, 2017. An astronaut onboard the ISS took the photograph below of the volcano and its environs that night. City lights surround the mostly dark volcanic area.\n\n---\n\n#### Figure 1: Location of Mount Etna, Italy\n\nA world map highlighting the location of Mount Etna in southern Italy. The marker indicates its geographic placement on the east coast of Sicily, Italy, in the Mediterranean region, south of mainland Europe and north of northern Africa.\n\n---\n\n#### Figure 2: Nighttime View of Mount Etna's Eruption and Surrounding Cities\n\nThis is a nighttime satellite image taken on March 19, 2017, showing the eruption of Mount Etna (southeastern cone) with visible bright red and orange coloring indicating flowing lava from a lateral vent. The surrounding areas are illuminated by city lights, with the following geographic references labeled:\n\n| Location        | Position in Image         | Visible Characteristics                    |\n|-----------------|--------------------------|--------------------------------------------|\n| Mt. Etna (southeastern cone) | Top center-left | Bright red/orange lava flow                |\n| Lateral vent    | Left of the volcano       | Faint red/orange flow extending outwards   |\n| Resort          | Below the volcano, to the left   | Small cluster of lights                    |\n| Giarre          | Top right                 | Bright cluster of city lights              |\n| Acireale        | Center right              | Large, bright area of city lights          |\n| Biancavilla     | Bottom left               | Smaller cluster of city lights             |\n\nAn arrow pointing north is shown on the image for orientation.\n\n---\n\n<!-- Earth at Night Page Footer -->\n<!-- Page Number: 50 -->"},{"ref_id":4,"content":"For the first time in perhaps a decade, Mount Etna experienced a \"flank eruption\"�erupting from its side instead of its summit�on December 24, 2018. The activity was accompanied by 130 earthquakes occurring over three hours that morning. Mount Etna, Europe�s most active volcano, has seen periodic activity on this part of the mountain since 2013. The Operational Land Imager (OLI) on the Landsat 8 satellite acquired the main image of Mount Etna on December 28, 2018.\n\nThe inset image highlights the active vent and thermal infrared signature from lava flows, which can be seen near the newly formed fissure on the southeastern side of the volcano. The inset was created with data from OLI and the Thermal Infrared Sensor (TIRS) on Landsat 8. Ash spewing from the fissure cloaked adjacent villages and delayed aircraft from landing at the nearby Catania airport. Earthquakes occurred in the subsequent days after the initial eruption and displaced hundreds of people from their homes.\n\nFor nighttime images of Mount Etna�s March 2017 eruption, see pages 48�51.\n\n---\n\n### Hazards of Volcanic Ash Plumes and Satellite Observation\n\nWith the help of moonlight, satellite instruments can track volcanic ash plumes, which present significant hazards to airplanes in flight. The volcanic ash�composed of tiny pieces of glass and rock�is abrasive to engine turbine blades, and can melt on the blades and other engine parts, causing damage and even engine stalls. This poses a danger to both the plane�s integrity and passenger safety. Volcanic ash also reduces visibility for pilots and can cause etching of windshields, further reducing pilots� ability to see. Nightlight images can be combined with thermal images to provide a more complete view of volcanic activity on Earth�s surface.\n\nThe VIIRS Day/Night Band (DNB) on polar-orbiting satellites uses faint light sources such as moonlight, airglow (the atmosphere�s self-illumination through chemical reactions), zodiacal light (sunlight scattered by interplanetary dust), and starlight from the Milky Way. Using these dim light sources, the DNB can detect changes in clouds, snow cover, and sea ice:\n\n#### Table: Light Sources Used by VIIRS DNB\n\n| Light Source         | Description                                                                  |\n|----------------------|------------------------------------------------------------------------------|\n| Moonlight            | Reflected sunlight from the Moon, illuminating Earth's surface at night      |\n| Airglow              | Atmospheric self-illumination from chemical reactions                        |\n| Zodiacal Light       | Sunlight scattered by interplanetary dust                                    |\n| Starlight/Milky Way  | Faint illumination provided by stars in the Milky Way                        |\n\nGeostationary Operational Environmental Satellites (GOES), managed by NOAA, orbit over Earth�s equator and offer uninterrupted observations of North America. High-latitude areas such as Alaska benefit from polar-orbiting satellites like Suomi NPP, which provide overlapping coverage at the poles, enabling more data collection in these regions. During polar darkness (winter months), VIIRS DNB data allow scientists to:\n\n- Observe sea ice formation\n- Monitor snow cover extent at the highest latitudes\n- Detect open water for ship navigation\n\n#### Table: Satellite Coverage Overview\n\n| Satellite Type          | Orbit           | Coverage Area         | Special Utility                              |\n|------------------------|-----------------|----------------------|----------------------------------------------|\n| GOES                   | Geostationary   | Equatorial/North America | Continuous regional monitoring              |\n| Polar-Orbiting (e.g., Suomi NPP) | Polar-orbiting    | Poles/high latitudes      | Overlapping passes; useful during polar night|\n\n---\n\n### Weather Forecasting and Nightlight Data\n\nThe use of nightlight data by weather forecasters is growing as the VIIRS instrument enables observation of clouds at night illuminated by sources such as moonlight and lightning. Scientists use these data to study the nighttime behavior of weather systems, including severe storms, which can develop and strike populous areas at night as well as during the day. Combined with thermal data, visible nightlight data allow the detection of clouds at various heights in the atmosphere, such as dense marine fog. This capability enables weather forecasters to issue marine advisories with higher confidence, leading to greater utility. (See \"Marine Layer Clouds�California\" on page 56.)\n\nIn this section of the book, you will see how nightlight data are used to observe nature�s spectacular light shows across a wide range of sources.\n\n---\n\n#### Notable Data from Mount Etna Flank Eruption (December 2018)\n\n| Event/Observation                  | Details                                                                    |\n|-------------------------------------|----------------------------------------------------------------------------|\n| Date of Flank Eruption              | December 24, 2018                                                          |\n| Number of Earthquakes               | 130 earthquakes within 3 hours                                              |\n| Image Acquisition                   | December 28, 2018 by Landsat 8 OLI                                         |\n| Location of Eruption                | Southeastern side of Mount Etna                                            |\n| Thermal Imaging Data                | From OLI and TIRS (Landsat 8), highlighting active vent and lava flows     |\n| Impact on Villages/Air Transport    | Ash covered villages; delayed aircraft at Catania airport                  |\n| Displacement                        | Hundreds of residents displaced                                            |\n| Ongoing Seismic Activity            | Earthquakes continued after initial eruption                               |\n\n---\n\n<!-- PageFooter=\"Earth at Night\" -->\n<!-- PageNumber=\"30\" -->"},{"ref_id":5,"content":"<!-- PageHeader=\"Volcanoes\" -->\n\n## Volcanoes\n\n### The Infrared Glows of Kilauea's Lava Flows�Hawaii\n\nIn early May 2018, an eruption on Hawaii's Kilauea volcano began to unfold. The eruption took a dangerous turn on May 3, 2018, when new fissures opened in the residential neighborhood of Leilani Estates. During the summer-long eruptive event, other fissures emerged along the East Rift Zone. Lava from vents along the rift zone flowed downslope, reaching the ocean in several areas, and filling in Kapoho Bay.\n\nA time series of Landsat 8 imagery shows the progression of the lava flows from May 16 to August 13. The night view combines thermal, shortwave infrared, and near-infrared wavelengths to tease out the very hot lava (bright white), cooling lava (red), and lava flows obstructed by clouds (purple).\n\n#### Figure: Location of Kilauea Volcano, Hawaii\n\nA globe is shown centered on North America, with a marker placed in the Pacific Ocean indicating the location of Hawaii, to the southwest of the mainland United States.\n\n<!-- PageFooter=\"Earth at Night\" -->\n<!-- PageNumber=\"44\" -->"}]

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

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


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


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


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


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

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

Descripción del código

Ahora que tiene el código, vamos a desglosar los componentes clave:

• Creación de un índice de búsqueda
• Carga de documentos en el índice
• Creación de un agente de conocimiento
• Configurar mensajes
• Ejecuta la canalización de recuperación
• Revisar la respuesta, la actividad y los resultados
• Creación del cliente de Azure OpenAI
• Uso de la API de finalizaciones de chat para generar una respuesta
• Continuar la conversación

Creación de un índice de búsqueda

En Azure AI Search, un índice es una colección estructurada de datos. El código siguiente define un índice denominado earth_at_night para contener texto sin formato y contenido vectorial. Puede usar un índice existente, pero debe cumplir los criterios para las cargas de trabajo de recuperación agente.

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

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

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

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

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

indexClient.createOrUpdateIndex(index);

El esquema de índice contiene campos para la identificación del documento y el contenido de la página, las incrustaciones y los números. También incluye configuraciones para consultas de clasificación semántica y vectores, que usan el text-embedding-3-large modelo que implementó anteriormente.

Cargar documentos en el índice

Actualmente, el earth_at_night índice está vacío. Ejecute el código siguiente para rellenar el índice con documentos JSON del libro electrónico 'La Tierra de Noche de la NASA'. Según lo requiera Azure AI Search, cada documento se ajusta a los campos y tipos de datos definidos en el esquema de índice.

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

Creación de un agente de conocimiento

Para conectar la Búsqueda de Azure AI a la gpt-5-mini implementación y tener como destino el índice en el momento de la earth_at_night consulta, necesita un agente de conocimiento. El código siguiente define un agente de conocimiento denominado earth-search-agent que usa la definición del agente para procesar consultas y recuperar documentos pertinentes del earth_at_night índice.

Para garantizar respuestas relevantes y semánticamente significativas, defaultRerankerThreshold está configurado para excluir respuestas con una puntuación de reranker inferior a 2.5.

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

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

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

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

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

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

Configurar mensajes

Los mensajes son la entrada de la ruta de recuperación y contienen el historial de conversaciones. Cada mensaje incluye un rol que indica su origen, como asistente o usuario, y contenido en lenguaje natural. El LLM que usa determina qué roles son válidos.

Un mensaje de usuario representa la consulta que se va a procesar, mientras que un mensaje del asistente guía al agente de conocimiento sobre cómo responder. Durante el proceso de recuperación, estos mensajes se envían a un LLM para extraer las respuestas pertinentes de los documentos indexados.

Este mensaje de asistente indica a earth-search-agent que responda preguntas sobre la Tierra durante la noche, cite fuentes usando su ref_id y responda con "No sé" cuando las respuestas no están disponibles.

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

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

Map<String, String> userMessage = new HashMap<>();
userMessage.put("role", "user");
userMessage.put("content", "Why do suburban belts display larger December brightening than urban cores even though absolute light levels are higher downtown? Why is the Phoenix nighttime street grid is so sharply visible from space, whereas large stretches of the interstate between midwestern cities remain comparatively dim?");
messages.add(userMessage);

Ejecución de la canalización de recuperación

Este paso ejecuta la canalización de recuperación para extraer información relevante del índice de búsqueda. En función de los mensajes y parámetros de la solicitud de recuperación, LLM:

1. Analiza todo el historial de conversaciones para determinar la necesidad de información subyacente.
2. Divide la consulta de usuario compuesta en subconsultas centradas.
3. Ejecuta cada subconsulta simultáneamente en campos de texto e incrustaciones vectoriales en el índice.
4. Usa el clasificador semántico para reclasificar los resultados de todas las consultas secundarias.
5. Combina los resultados en una sola cadena.

El código siguiente envía una consulta de usuario de dos partes a earth-search-agent, que deconstruye la consulta en subconsultas, ejecuta las subconsultas en campos de texto e incrustaciones vectoriales en el earth_at_night índice, y clasifica y combina los resultados. A continuación, la respuesta se anexa a la messages lista.

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

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

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

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

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

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

Revisar la respuesta, la actividad y los resultados

Ahora desea mostrar la respuesta, la actividad y los resultados del proceso de recuperación.

Cada respuesta de recuperación de Azure AI Search incluye:

• Cadena unificada que representa los datos fundamentales de los resultados de la búsqueda.

• Plan de consulta.

• Datos de referencia que muestran qué fragmentos de los documentos de origen han contribuido a la cadena unificada.

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

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

La salida debe incluir:

• Response proporciona una cadena de texto de los documentos más relevantes (o fragmentos) en el índice de búsqueda en función de la consulta del usuario. Como se muestra más adelante en este inicio rápido, puede pasar esta cadena a un LLM para la generación de respuestas.

• Activity realiza un seguimiento de los pasos que se realizaron durante el proceso de recuperación, incluidas las subconsultas generadas por su gpt-5-mini implementación y los tokens utilizados para la planificación y ejecución de consultas.

• Results enumera los documentos que han contribuido a la respuesta, cada uno identificado por su DocKey.

Creación del cliente de Azure OpenAI

Para ampliar la canalización de recuperación de la extracción de respuestas a su generación, de respuestas, configure el cliente de Azure OpenAI para interactuar con la implementación gpt-5-mini.

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

Uso de la API de finalizaciones de chat para generar una respuesta

Una opción para la generación de respuestas es la API de finalizaciones de chat, que pasa el historial de conversaciones al LLM para su procesamiento.

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

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

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

Continuar la conversación

Continúe la conversación enviando otra consulta de usuario a earth-search-agent. El código siguiente vuelve a ejecutar la canalización de recuperación, capturando el contenido pertinente del earth_at_night índice y anexando la respuesta a la messages lista. Sin embargo, a diferencia de lo anterior, ahora puede usar el cliente de Azure OpenAI para generar una respuesta basada en el contenido recuperado.

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

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

Limpieza de recursos

Cuando trabajes en tu propia suscripción, es aconsejable finalizar un proyecto evaluando si aún necesitas los recursos que has creado. Los recursos que quedan en ejecución pueden costar dinero. Puede eliminar recursos individualmente o puede eliminar el grupo de recursos para eliminar todo el conjunto de recursos.

En Azure Portal, puede encontrar y administrar recursos seleccionando Todos los recursos o grupos de recursos en el panel izquierdo. También puede ejecutar el código siguiente para eliminar los objetos que creó en este inicio rápido.

Elimine al agente de conocimiento

El agente de conocimiento creado en este inicio rápido se eliminó mediante el siguiente fragmento de código:

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

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

Eliminación del índice de búsqueda

El índice de búsqueda creado en este inicio rápido se eliminó mediante el siguiente fragmento de código:

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

En este inicio rápido, usará la recuperación de agentes para crear una experiencia de búsqueda conversacional con tecnología de documentos indexados en Búsqueda de Azure AI y modelos de lenguaje grandes (LLM) de Azure OpenAI en Foundry Models.

Una base de conocimiento organiza la recuperación agente mediante la descomposición de consultas complejas en subconsultas, la ejecución de las subconsultas en uno o varios orígenes de conocimiento y la devolución de resultados con metadatos. De manera predeterminada, la base de conocimiento genera contenido sin procesar de los orígenes, pero en este inicio rápido se usa el modo de salida de síntesis de respuestas para la generación de respuestas en lenguaje natural.

Aunque puede proporcionar sus propios datos, en esta guía de inicio rápido se usan documentos JSON de ejemplo de la Tierra de la NASA en el libro electrónico nocturno. Los documentos describen temas generales de ciencia e imágenes de la Tierra a la noche como se observa desde el espacio.

Prerrequisitos

• Una cuenta de Azure con una suscripción activa. Cree una cuenta gratuita.

• Un Servicio de Búsqueda de Azure AI en cualquier región que proporcione recuperación con agentes.

• Un proyecto y un recurso de Microsoft Foundry . Al crear un proyecto, el recurso se crea automáticamente.

• La CLI de Azure para la autenticación sin clave con Microsoft Entra ID.

• Visual Studio Code y la última versión LTS de Node.js.

Configurar el acceso

Antes de empezar, asegúrese de que tiene permisos para acceder al contenido y las operaciones. Se recomienda Microsoft Entra ID para la autenticación y el acceso basado en roles para la autorización. Debe ser Propietario o Administrador de acceso de usuario para asignar roles. Si los roles no son factibles, use la autenticación basada en claves en su lugar.

Para configurar el acceso para este inicio rápido, seleccione las dos pestañas siguientes.

Obtención de puntos de conexión

Cada servicio Azure AI Search y el recurso de Microsoft Foundry tienen un punto de conexión, que es una dirección URL única que identifica y proporciona acceso de red al recurso. En una sección posterior, especifique estos puntos de conexión para conectarse a los recursos mediante programación.

Para obtener los puntos de conexión de este inicio rápido, seleccione las dos pestañas siguientes.

Implementación de modelos

Para este inicio rápido, debe implementar dos modelos de Azure OpenAI en el proyecto de Microsoft Foundry:

• Modelo de inserción para la conversión de texto a vector. En este inicio rápido se usa text-embedding-3-large, pero puede usar cualquier text-embedding modelo.

• Un LLM para la planeación de consultas y la generación de respuestas. En este inicio rápido, se usa gpt-5-mini, pero puede usar cualquier LLM compatible para la recuperación de agentes.

Para obtener instrucciones de implementación, consulte Implementación de modelos de Azure OpenAI con Microsoft Foundry.

Configuración del entorno

Para configurar la aplicación de consola para este inicio rápido:

1. Cree una carpeta denominada quickstart-agentic-retrieval para contener la aplicación.

2. Abra la carpeta en Visual Studio Code.

3. Seleccione Terminal>Nuevo terminal y, a continuación, ejecute los siguientes comandos para inicializar el package.json archivo.

   npm init -y
   npm pkg set type=module
   

4. Instale la biblioteca cliente de Azure AI Search para JavaScript.

   npm install @azure/search-documents@12.3.0-beta.1
   

5. Para la autenticación sin claves con el identificador de Entra de Microsoft, instale la biblioteca cliente de Identidad de Azure para JavaScript.

   npm install @azure/identity
   

6. Para la autenticación sin claves con el identificador de Microsoft Entra, inicie sesión en su cuenta de Azure. Si tiene varias suscripciones, seleccione la que contiene el servicio Azure AI Search y el proyecto de Microsoft Foundry.

   az login
   

Ejecución del código

Para crear y ejecutar la canalización de recuperación de agentes:

1. Cree un archivo denominado .env en la quickstart-agentic-retrieval carpeta .

2. Pegue las siguientes variables de entorno en el .env archivo.

   AZURE_SEARCH_ENDPOINT = https://<your-search-service-name>.search.windows.net
   AZURE_OPENAI_ENDPOINT = https://<your-ai-foundry-resource-name>.openai.azure.com/
   AZURE_OPENAI_GPT_DEPLOYMENT = gpt-5-mini
   AZURE_OPENAI_EMBEDDING_DEPLOYMENT = text-embedding-3-large
   

3. Establezca AZURE_SEARCH_ENDPOINT y AZURE_OPENAI_ENDPOINT en los valores obtenidos en Obtener puntos de conexión.

4. Cree un archivo denominado index.jsy pegue el código siguiente en el archivo.

   import { DefaultAzureCredential } from '@azure/identity';
   import {
       SearchIndexClient,
       SearchClient,
       KnowledgeRetrievalClient,
       SearchIndexingBufferedSender
   } from '@azure/search-documents';
   
   export const documentKeyRetriever = (document) => {
     return document.id;
   };
   
   export const WAIT_TIME = 4000;
   export function delay(timeInMs) {
     return new Promise((resolve) => setTimeout(resolve, timeInMs));
   }
   
   const index = {
       name: 'earth_at_night',
       fields: [
           {
               name: "id",
               type: "Edm.String",
               key: true,
               filterable: true,
               sortable: true,
               facetable: true
           },
           {
               name: "page_chunk",
               type: "Edm.String",
               searchable: true,
               filterable: false,
               sortable: false,
               facetable: false
           },
           {
               name: "page_embedding_text_3_large",
               type: "Collection(Edm.Single)",
               searchable: true,
               filterable: false,
               sortable: false,
               facetable: false,
               vectorSearchDimensions: 3072,
               vectorSearchProfileName: "hnsw_text_3_large"
           },
           {
               name: "page_number",
               type: "Edm.Int32",
               filterable: true,
               sortable: true,
               facetable: true
           }
       ],
       vectorSearch: {
           profiles: [
               {
                   name: "hnsw_text_3_large",
                   algorithmConfigurationName: "alg",
                   vectorizerName: "azure_openai_text_3_large"
               }
           ],
           algorithms: [
               {
                   name: "alg",
                   kind: "hnsw"
               }
           ],
           vectorizers: [
               {
                   vectorizerName: "azure_openai_text_3_large",
                   kind: "azureOpenAI",
                   parameters: {
                       resourceUrl: process.env.AZURE_OPENAI_ENDPOINT,
                       deploymentId: process.env.AZURE_OPENAI_EMBEDDING_DEPLOYMENT,
                       modelName: process.env.AZURE_OPENAI_EMBEDDING_DEPLOYMENT
                   }
               }
           ]
       },
       semanticSearch: {
           defaultConfigurationName: "semantic_config",
           configurations: [
               {
                   name: "semantic_config",
                   prioritizedFields: {
                       contentFields: [
                           { name: "page_chunk" }
                       ]
                   }
               }
           ]
       }
   };
   
   const credential = new DefaultAzureCredential();
   
   const searchIndexClient = new SearchIndexClient(process.env.AZURE_SEARCH_ENDPOINT, credential);
   const searchClient = new SearchClient(process.env.AZURE_SEARCH_ENDPOINT, 'earth_at_night', credential);
   
   await searchIndexClient.createOrUpdateIndex(index);
   
   // get Documents with vectors
   const response = await fetch("https://raw.githubusercontent.com/Azure-Samples/azure-search-sample-data/refs/heads/main/nasa-e-book/earth-at-night-json/documents.json");
   
   if (!response.ok) {
       throw new Error(`Failed to fetch documents: ${response.status} ${response.statusText}`);
   }
   const documents = await response.json();
   
   const bufferedClient = new SearchIndexingBufferedSender(
       searchClient,
       documentKeyRetriever,
       {
           autoFlush: true,
       },
   );
   
   await bufferedClient.uploadDocuments(documents);
   await bufferedClient.flush();
   await bufferedClient.dispose();
   
   console.log(`Waiting for indexing to complete...`);
   console.log(`Expected documents: ${documents.length}`);
   await delay(WAIT_TIME);
   
   let count = await searchClient.getDocumentsCount();
   console.log(`Current indexed count: ${count}`);
   
   while (count !== documents.length) {
       await delay(WAIT_TIME);
       count = await searchClient.getDocumentsCount();
       console.log(`Current indexed count: ${count}`);
   }
   
   console.log(`✓ All ${documents.length} documents indexed successfully!`);
   
   await searchIndexClient.createKnowledgeSource({
       name: 'earth-knowledge-source',
       description: "Knowledge source for Earth at Night e-book content",
       kind: "searchIndex",
       searchIndexParameters: {
           searchIndexName: 'earth_at_night',
           sourceDataFields: [
               { name: "id" },
               { name: "page_number" }
           ]
       }
   });
   
   console.log(`✅ Knowledge source 'earth-knowledge-source' created successfully.`);
   
   await searchIndexClient.createKnowledgeBase({
       name: 'earth-knowledge-base',
       knowledgeSources: [
           {
               name: 'earth-knowledge-source'
           }
       ],
       models: [
           {
               kind: "azureOpenAI",
               azureOpenAIParameters: {
                   resourceUrl: process.env.AZURE_OPENAI_ENDPOINT,
                   deploymentId: process.env.AZURE_OPENAI_GPT_DEPLOYMENT,
                   modelName: process.env.AZURE_OPENAI_GPT_DEPLOYMENT
               }
           }
       ],
       outputMode: "answerSynthesis",
       answerInstructions: "Provide a two sentence concise and informative answer based on the retrieved documents."
   });
   
   console.log(`✅ Knowledge base 'earth-knowledge-base' created successfully.`);
   
   const knowledgeRetrievalClient = new KnowledgeRetrievalClient(
       process.env.AZURE_SEARCH_ENDPOINT,
       'earth-knowledge-base',
       credential
   );
   
   const query1 = `Why do suburban belts display larger December brightening than urban cores even though absolute light levels are higher downtown? Why is the Phoenix nighttime street grid is so sharply visible from space, whereas large stretches of the interstate between midwestern cities remain comparatively dim?`;
   
   const retrievalRequest = {
       messages: [
           {
               role: "user",
               content: [
                   {
                       type: "text",
                       text: query1
                   }
               ]
           }
       ],
       knowledgeSourceParams: [
           {
               kind: "searchIndex",
               knowledgeSourceName: 'earth-knowledge-source',
               includeReferences: true,
               includeReferenceSourceData: true,
               alwaysQuerySource: true,
               rerankerThreshold: 2.5
           }
       ],
       includeActivity: true,
       retrievalReasoningEffort: { kind: "low" }
   };
   
   const result = await knowledgeRetrievalClient.retrieveKnowledge(retrievalRequest);
   
   console.log("\n📝 ANSWER:");
   console.log("─".repeat(80));
   if (result.response && result.response.length > 0) {
       result.response.forEach((msg) => {
           if (msg.content && msg.content.length > 0) {
               msg.content.forEach((content) => {
                   if (content.type === "text" && 'text' in content) {
                       console.log(content.text);
                   }
               });
           }
       });
   }
   console.log("─".repeat(80));
   
   if (result.activity) {
       console.log("\nActivities:");
       result.activity.forEach((activity) => {
           console.log(`Activity Type: ${activity.type}`);
           console.log(JSON.stringify(activity, null, 2));
       });
   }
   
   if (result.references) {
       console.log("\nReferences:");
       result.references.forEach((reference) => {
           console.log(`Reference Type: ${reference.type}`);
           console.log(JSON.stringify(reference, null, 2));
       });
   }
   
   // Follow-up query - to demonstrate conversational context
   const query2 = "How do I find lava at night?";
   console.log(`\n❓ Follow-up question: ${query2}`);
   
   const retrievalRequest2 = {
       messages: [
           {
               role: "user",
               content: [
                   {
                       type: "text",
                       text: query2
                   }
               ]
           }
       ],
       knowledgeSourceParams: [
           {
               kind: "searchIndex",
               knowledgeSourceName: 'earth-knowledge-source',
               includeReferences: true,
               includeReferenceSourceData: true,
               alwaysQuerySource: true,
               rerankerThreshold: 2.5
           }
       ],
       includeActivity: true,
       retrievalReasoningEffort: { kind: "low" }
   };
   
   const result2 = await knowledgeRetrievalClient.retrieveKnowledge(retrievalRequest2);
   
   console.log("\n📝 ANSWER:");
   console.log("─".repeat(80));
   if (result2.response && result2.response.length > 0) {
       result2.response.forEach((msg) => {
           if (msg.content && msg.content.length > 0) {
               msg.content.forEach((content) => {
                   if (content.type === "text" && 'text' in content) {
                       console.log(content.text);
                   }
               });
           }
       });
   }
   console.log("─".repeat(80));
   
   if (result2.activity) {
       console.log("\nActivities:");
       result2.activity.forEach((activity) => {
           console.log(`Activity Type: ${activity.type}`);
           console.log(JSON.stringify(activity, null, 2));
       });
   }
   
   if (result2.references) {
       console.log("\nReferences:");
       result2.references.forEach((reference) => {
           console.log(`Reference Type: ${reference.type}`);
           console.log(JSON.stringify(reference, null, 2));
       });
   }
   
   console.log("\n✅ Quickstart completed successfully!");
   
   // Clean up resources
   await searchIndexClient.deleteKnowledgeBase('earth-knowledge-base');
   await searchIndexClient.deleteKnowledgeSource('earth-knowledge-source');
   await searchIndexClient.deleteIndex('earth_at_night');
   
   console.log(`\n🗑️  Cleaned up resources.`);
   

5. Compile y ejecute la aplicación.

   node --env-file=.env index.js
   

Salida

La salida de la aplicación debe ser similar a la siguiente:

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

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

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

References:
Reference Type: searchIndex
{
  "type": "searchIndex",
  "id": "0",
  "activitySource": 3,
  "sourceData": {
    "id": "earth_at_night_508_page_174_verbalized",
    "page_chunk": "<!-- PageHeader=\"Holiday Lights\" -->\n\n## Holiday Lights\n\n### Bursting with Holiday Energy-United States\n\nNASA researchers found that nighttime lights in the United States shine 20 to 50 percent brighter in December due to holiday light displays and other activities during Christmas and New Year's when compared to light output during the rest of the year.\n\nThe next five maps (see also pages 161-163), created using data from the VIIRS DNB on the Suomi NPP satellite, show changes in lighting intensity and location around many major cities, comparing the nighttime light signals from December 2012 and beyond.\n\n---\n\n#### Figure 1. Location Overview\n\nA map of the western hemisphere with a marker indicating the mid-Atlantic region of the eastern United States, where the study of holiday lighting intensity was focused.\n\n---\n\n#### Figure 2. Holiday Lighting Intensity: Mid-Atlantic United States (2012–2014)\n\nA map showing Maryland, New Jersey, Delaware, Virginia, West Virginia, Ohio, Kentucky, Tennessee, North Carolina, South Carolina, and surrounding areas. Major cities labeled include Washington, D.C., Richmond, Norfolk, and Raleigh.\n\nThe map uses colors to indicate changes in holiday nighttime lighting intensity between 2012 and 2014:\n\n- **Green/bright areas**: More holiday lighting (areas shining 20–50% brighter in December).\n- **Yellow areas**: No change in lighting.\n- **Dim/grey areas**: Less holiday lighting.\n\nKey observations from the map:\n\n- The Washington, D.C. metropolitan area shows significant increases in lighting during the holidays, extending into Maryland and Virginia.\n- Urban centers such as Richmond (Virginia), Norfolk (Virginia), Raleigh (North Carolina), and clusters in Tennessee and South Carolina also experience notable increases in light intensity during December.\n- Rural areas and the interiors of West Virginia, Kentucky, and North Carolina show little change or less holiday lighting, corresponding to population density and urbanization.\n\n**Legend:**\n\n| Holiday Lighting Change | Color on Map   |\n|------------------------|---------------|\n| More                   | Green/bright  |\n| No Change              | Yellow        |\n| Less                   | Dim/grey      |\n\n_The scale bar indicates a distance of 100 km for reference._\n\n---\n\n<!-- PageFooter=\"158 Earth at Night\" -->",
    "page_number": 174
  },
  "rerankerScore": 2.6692379,
  "docKey": "earth_at_night_508_page_174_verbalized"
}
Reference Type: searchIndex
{
  "type": "searchIndex",
  "id": "1",
  "activitySource": 3,
  "sourceData": {
    "id": "earth_at_night_508_page_186_verbalized",
    "page_chunk": "## Appendix A\n\n### NASA's Black Marble Product Suite\n\nNASA's Black Marble product suite provides estimates of daily nighttime lights and other intrinsic surface optical properties of Earth at night. The product is based on the Day/Night Band (DNB) sensor of the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument onboard the Suomi National Polar-orbiting Partnership (NPP) satellite. The VIIRS DNB is a highly sensitive, low-light sensor capable of measuring daily global nighttime light emissions and reflections, allowing users to identify sources and intensities of these artificial lights, and monitor changes over a period of time. Like any optical sensor, the principal challenge in using VIIRS DNB data is to account for variations in light captured by the sensor. Certainly, there are variations in the sources and intensity of anthropogenic light due to changing human processes like urbanization, oil and gas production, nighttime commercial fishing, and infrastructure development. However, these processes can only be studied when other naturally-occurring factors that influence nighttime lights are removed. For example, variations in lunar lighting due to consistent changes in Moon phase cause fluctuations in the amount of light shining on Earth. Similarly, land-cover dynamics (e.g., seasonal vegetation, snow and ice cover), as well as atmospheric conditions (e.g., clouds, aerosols, airglow, and auroras), influence the intensity of the light captured by the sensor as it travels over various parts of the world.\n\nTo realize the full potential of the VIIRS DNB time series record for nighttime lights applications, NASA's Black Marble product suite was developed, building on a history of 20 years of research on how light changes when it reflects off of surfaces with different angular and spectral properties. Through complex modeling, scientists can now predict how moonlight, snow, vegetation, terrain, and clouds impact the lights we see from space, allowing for more accurate assessments of human and environmental activity at night.\n\n<!-- PageFooter=\"170 Earth at Night\" -->",
    "page_number": 186
  },
  "rerankerScore": 2.5997617,
  "docKey": "earth_at_night_508_page_186_verbalized"
}

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

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

Activities:
... // Trimmed for brevity

References:
... // Trimmed for brevity

✅ Quickstart completed successfully!

🗑️  Cleaned up resources.

Descripción del código

Ahora que tiene el código, vamos a desglosar los componentes clave:

1. Creación de un índice de búsqueda
2. Carga de documentos en el índice
3. Creación de una fuente de conocimiento
4. Creación de una base de conocimientos
5. Ejecuta la canalización de recuperación
6. Revisar la respuesta, la actividad y las referencias
7. Continuar la conversación

Creación de un índice de búsqueda

En Azure AI Search, un índice es una colección estructurada de datos. El código siguiente define un índice denominado earth_at_night.

El esquema de índice contiene campos para la identificación del documento y el contenido de la página, las incrustaciones y los números. El esquema también incluye configuraciones para la clasificación semántica y el vector de búsqueda, que usa la implementación de text-embedding-3-large para vectorizar texto y buscar documentos en función de la similitud semántica.

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

const credential = new DefaultAzureCredential();

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

await searchIndexClient.createOrUpdateIndex(index);

Cargar documentos en el índice

Actualmente, el earth-at-night índice está vacío. El siguiente código puebla el índice con documentos JSON del libro electrónico 'Earth at Night' de la NASA. Según lo requiera Azure AI Search, cada documento se ajusta a los campos y tipos de datos definidos en el esquema de 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!`);

Creación de una fuente de conocimiento

Un origen de conocimiento es una referencia reutilizable a los datos de origen. El código siguiente define un origen de conocimiento denominado earth-knowledge-source que tiene como destino el earth-at-night índice.

source_data_fields especifica los campos de índice que se incluyen en las referencias de cita. Nuestro ejemplo incluye solo campos legibles para personas para evitar incrustaciones largas e ininterpretables en las respuestas.

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.`);

Creación de una base de conocimientos

Para dirigirse a la implementación de earth-knowledge-source y gpt-5-mini en el momento de la consulta, necesita una base de conocimiento. El código siguiente define una base de conocimiento denominada earth-knowledge-base.

outputMode se establece en answerSynthesis, que habilita respuestas en lenguaje natural que citan los documentos recuperados y siguen las directrices proporcionadas por answerInstructions.

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.`);

Ejecución de la canalización de recuperación

Está listo para ejecutar la recuperación de agentes. El código siguiente envía una consulta de usuario de dos partes a earth-knowledge-base, que:

1. Analiza toda la conversación para deducir la necesidad de información del usuario.
2. Descompone la consulta compuesta en subconsultas centradas.
3. Ejecuta las subconsultas simultáneamente en la fuente de conocimiento.
4. Usa el clasificador semántico para volver a generar y filtrar los resultados.
5. Sintetiza los resultados relevantes en una respuesta en lenguaje natural.

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

const query1 = `Why do suburban belts display larger December brightening than urban cores even though absolute light levels are higher downtown? Why is the Phoenix nighttime street grid is so sharply visible from space, whereas large stretches of the interstate between midwestern cities remain comparatively dim?`;

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

const result = await knowledgeRetrievalClient.retrieveKnowledge(retrievalRequest);

Revisar la respuesta, la actividad y las referencias

El código siguiente muestra la respuesta, la actividad y las referencias de la canalización de recuperación, donde:

• Answer proporciona una respuesta sintetizada generada por LLM para la consulta que cita los documentos recuperados. Cuando la síntesis de respuestas no está habilitada, esta sección contiene contenido extraído directamente de los documentos.

• Activities realiza un seguimiento de los pasos que se realizaron durante el proceso de recuperación, incluidas las subconsultas generadas por la implementación de gpt-5-mini y los tokens usados para la clasificación semántica, el planeamiento de consultas y la síntesis de respuestas.

• References enumera los documentos que han contribuido a la respuesta, cada uno identificado por su 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 la conversación

El código siguiente continúa la conversación con earth-knowledge-base. Después de enviar esta consulta de usuario, la base de conocimiento captura el contenido pertinente de earth-knowledge-source y anexa la respuesta a la lista de mensajes.

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

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

const result2 = await knowledgeRetrievalClient.retrieveKnowledge(retrievalRequest2);

Revisar la nueva respuesta, actividad y referencias

El código siguiente muestra la nueva respuesta, actividad y referencias de la canalización de recuperación.

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));
    });
}

Limpieza de recursos

Cuando trabaja en su propia suscripción, es una buena idea finalizar un proyecto determinando si todavía necesita los recursos que creó. Los recursos que quedan en ejecución pueden costar dinero.

En Azure Portal, puede administrar los recursos de Azure AI Search y Microsoft Foundry seleccionando Todos los recursos o grupos de recursos en el panel izquierdo.

De lo contrario, el código siguiente de index.js eliminó los objetos que creó en este inicio 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.`);

En este inicio rápido, usará la recuperación de agentes para crear una experiencia de búsqueda conversacional con tecnología de documentos indexados en Búsqueda de Azure AI y modelos de lenguaje grandes (LLM) de Azure OpenAI en Foundry Models.

Una base de conocimiento organiza la recuperación agente mediante la descomposición de consultas complejas en subconsultas, la ejecución de las subconsultas en uno o varios orígenes de conocimiento y la devolución de resultados con metadatos. De manera predeterminada, la base de conocimiento genera contenido sin procesar de los orígenes, pero en este inicio rápido se usa el modo de salida de síntesis de respuestas para la generación de respuestas en lenguaje natural.

Aunque puede proporcionar sus propios datos, en esta guía de inicio rápido se usan documentos JSON de ejemplo de la Tierra de la NASA en el libro electrónico nocturno. Los documentos describen temas generales de ciencia e imágenes de la Tierra a la noche como se observa desde el espacio.

Prerrequisitos

• Una cuenta de Azure con una suscripción activa. Cree una cuenta gratuita.

• Un Servicio de Búsqueda de Azure AI en cualquier región que proporcione recuperación con agentes.

• Un proyecto y un recurso de Microsoft Foundry . Al crear un proyecto, el recurso se crea automáticamente.

• La CLI de Azure para la autenticación sin clave con Microsoft Entra ID.

• Visual Studio Code y la versión más reciente de Python.

Configurar el acceso

Antes de empezar, asegúrese de que tiene permisos para acceder al contenido y las operaciones. Se recomienda Microsoft Entra ID para la autenticación y el acceso basado en roles para la autorización. Debe ser Propietario o Administrador de acceso de usuario para asignar roles. Si los roles no son factibles, use la autenticación basada en claves en su lugar.

Para configurar el acceso para este inicio rápido, seleccione las dos pestañas siguientes.

Obtención de puntos de conexión

Cada servicio Azure AI Search y el recurso de Microsoft Foundry tienen un punto de conexión, que es una dirección URL única que identifica y proporciona acceso de red al recurso. En una sección posterior, especifique estos puntos de conexión para conectarse a los recursos mediante programación.

Para obtener los puntos de conexión de este inicio rápido, seleccione las dos pestañas siguientes.

Implementación de modelos

Para este inicio rápido, debe implementar dos modelos de Azure OpenAI en el proyecto de Microsoft Foundry:

• Modelo de inserción para la conversión de texto a vector. En este inicio rápido se usa text-embedding-3-large, pero puede usar cualquier text-embedding modelo.

• Un LLM para la planeación de consultas y la generación de respuestas. En este inicio rápido, se usa gpt-5-mini, pero puede usar cualquier LLM compatible para la recuperación de agentes.

Para obtener instrucciones de implementación, consulte Implementación de modelos de Azure OpenAI con Microsoft Foundry.

Conexión desde el sistema local

Ha configurado el acceso basado en roles para interactuar con Azure AI Search y Azure OpenAI en Foundry Models. Use la CLI de Azure para iniciar sesión en la misma suscripción e inquilino para ambos recursos. Para obtener más información, consulte Inicio rápido: Conexión sin claves.

Para conectarse desde el sistema local:

1. Cree una carpeta llamada quickstart-agentic-retrieval.

2. Abra la carpeta en Visual Studio Code.

3. Seleccione Terminal>Nuevo terminal.

4. Ejecute el comando siguiente para iniciar sesión en su cuenta de Azure. Si tiene varias suscripciones, seleccione la que contiene el servicio Azure AI Search y el proyecto Foundry.

   az login
   

Ejecución del código

Para crear y ejecutar la canalización de recuperación de agentes:

1. Ejecute el siguiente comando para instalar los paquetes necesarios.

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

2. Cree un archivo denominado agentic-retrieval.py en la quickstart-agentic-retrieval carpeta .

3. Pegue el código siguiente en el archivo.

   from azure.identity import DefaultAzureCredential, get_bearer_token_provider
   from azure.search.documents.indexes.models import SearchIndex, SearchField, VectorSearch, VectorSearchProfile, HnswAlgorithmConfiguration, AzureOpenAIVectorizer, AzureOpenAIVectorizerParameters, SemanticSearch, SemanticConfiguration, SemanticPrioritizedFields, SemanticField, SearchIndexKnowledgeSource, SearchIndexKnowledgeSourceParameters, SearchIndexFieldReference, KnowledgeBase, KnowledgeBaseAzureOpenAIModel, KnowledgeSourceReference, KnowledgeRetrievalOutputMode, KnowledgeRetrievalLowReasoningEffort
   from azure.search.documents.indexes import SearchIndexClient
   from azure.search.documents import SearchIndexingBufferedSender
   from azure.search.documents.knowledgebases import KnowledgeBaseRetrievalClient
   from azure.search.documents.knowledgebases.models import KnowledgeBaseRetrievalRequest, KnowledgeBaseMessage, KnowledgeBaseMessageTextContent, SearchIndexKnowledgeSourceParams
   import requests
   import json
   
   # Define variables
   search_endpoint = "PUT-YOUR-SEARCH-SERVICE-URL-HERE"
   aoai_endpoint = "PUT-YOUR-AOAI-FOUNDRY-URL-HERE"
   aoai_embedding_model = "text-embedding-3-large"
   aoai_embedding_deployment = "text-embedding-3-large"
   aoai_gpt_model = "gpt-5-mini"
   aoai_gpt_deployment = "gpt-5-mini"
   index_name = "earth-at-night"
   knowledge_source_name = "earth-knowledge-source"
   knowledge_base_name = "earth-knowledge-base"
   search_api_version = "2025-11-01-preview"
   
   credential = DefaultAzureCredential()
   token_provider = get_bearer_token_provider(credential, "https://search.azure.com/.default")
   
   # Create an index
   azure_openai_token_provider = get_bearer_token_provider(credential, "https://cognitiveservices.azure.com/.default")
   
   index = SearchIndex(
       name=index_name,
       fields=[
           SearchField(name="id", type="Edm.String", key=True, filterable=True, sortable=True, facetable=True),
           SearchField(name="page_chunk", type="Edm.String", filterable=False, sortable=False, facetable=False),
           SearchField(name="page_embedding_text_3_large", type="Collection(Edm.Single)", stored=False, vector_search_dimensions=3072, vector_search_profile_name="hnsw_text_3_large"),
           SearchField(name="page_number", type="Edm.Int32", filterable=True, sortable=True, facetable=True)
       ],
       vector_search=VectorSearch(
           profiles=[VectorSearchProfile(name="hnsw_text_3_large", algorithm_configuration_name="alg", vectorizer_name="azure_openai_text_3_large")],
           algorithms=[HnswAlgorithmConfiguration(name="alg")],
           vectorizers=[
               AzureOpenAIVectorizer(
                   vectorizer_name="azure_openai_text_3_large",
                   parameters=AzureOpenAIVectorizerParameters(
                       resource_url=aoai_endpoint,
                       deployment_name=aoai_embedding_deployment,
                       model_name=aoai_embedding_model
                   )
               )
           ]
       ),
       semantic_search=SemanticSearch(
           default_configuration_name="semantic_config",
           configurations=[
               SemanticConfiguration(
                   name="semantic_config",
                   prioritized_fields=SemanticPrioritizedFields(
                       content_fields=[
                           SemanticField(field_name="page_chunk")
                       ]
                   )
               )
           ]
       )
   )
   
   index_client = SearchIndexClient(endpoint=search_endpoint, credential=credential)
   index_client.create_or_update_index(index)
   print(f"Index '{index_name}' created or updated successfully.")
   
   # Upload documents
   url = "https://raw.githubusercontent.com/Azure-Samples/azure-search-sample-data/refs/heads/main/nasa-e-book/earth-at-night-json/documents.json"
   documents = requests.get(url).json()
   
   with SearchIndexingBufferedSender(endpoint=search_endpoint, index_name=index_name, credential=credential) as client:
       client.upload_documents(documents=documents)
   
   print(f"Documents uploaded to index '{index_name}' successfully.")
   
   # Create a knowledge source
   ks = SearchIndexKnowledgeSource(
       name=knowledge_source_name,
       description="Knowledge source for Earth at night data",
       search_index_parameters=SearchIndexKnowledgeSourceParameters(
           search_index_name=index_name,
           source_data_fields=[SearchIndexFieldReference(name="id"), SearchIndexFieldReference(name="page_number")]
       ),
   )
   
   index_client = SearchIndexClient(endpoint=search_endpoint, credential=credential)
   index_client.create_or_update_knowledge_source(knowledge_source=ks)
   print(f"Knowledge source '{knowledge_source_name}' created or updated successfully.")
   
   # Create a knowledge base
   aoai_params = AzureOpenAIVectorizerParameters(
       resource_url=aoai_endpoint,
       deployment_name=aoai_gpt_deployment,
       model_name=aoai_gpt_model,
   )
   
   knowledge_base = KnowledgeBase(
       name=knowledge_base_name,
       models=[KnowledgeBaseAzureOpenAIModel(azure_open_ai_parameters=aoai_params)],
       knowledge_sources=[
           KnowledgeSourceReference(
               name=knowledge_source_name
           )
       ],
       output_mode=KnowledgeRetrievalOutputMode.ANSWER_SYNTHESIS,
       answer_instructions="Provide a two sentence concise and informative answer based on the retrieved documents."
   )
   
   index_client = SearchIndexClient(endpoint=search_endpoint, credential=credential)
   index_client.create_or_update_knowledge_base(knowledge_base)
   print(f"Knowledge base '{knowledge_base_name}' created or updated successfully.")
   
   # Set up messages
   instructions = """
   A Q&A agent that can answer questions about the Earth at night.
   If you don't have the answer, respond with "I don't know".
   """
   
   messages = [
       {
           "role": "system",
           "content": instructions
       }
   ]
   
   # Run agentic retrieval
   agent_client = KnowledgeBaseRetrievalClient(endpoint=search_endpoint, knowledge_base_name=knowledge_base_name, credential=credential)
   query_1 = """
       Why do suburban belts display larger December brightening than urban cores even though absolute light levels are higher downtown?
       Why is the Phoenix nighttime street grid is so sharply visible from space, whereas large stretches of the interstate between midwestern cities remain comparatively dim?
       """
   
   messages.append({
       "role": "user",
       "content": query_1
   })
   
   req = KnowledgeBaseRetrievalRequest(
       messages=[
           KnowledgeBaseMessage(
               role=m["role"],
               content=[KnowledgeBaseMessageTextContent(text=m["content"])]
           ) for m in messages if m["role"] != "system"
       ],
       knowledge_source_params=[
           SearchIndexKnowledgeSourceParams(
               knowledge_source_name=knowledge_source_name,
               include_references=True,
               include_reference_source_data=True,
               always_query_source=True
           )
       ],
       include_activity=True,
       retrieval_reasoning_effort=KnowledgeRetrievalLowReasoningEffort
   )
   
   result = agent_client.retrieve(retrieval_request=req)
   print(f"Retrieved content from '{knowledge_base_name}' successfully.")
   
   # Display the response, activity, and references
   response_contents = []
   activity_contents = []
   references_contents = []
   
   response_parts = []
   for resp in result.response:
       for content in resp.content:
           response_parts.append(content.text)
   response_content = "\n\n".join(response_parts) if response_parts else "No response found on 'result'"
   
   response_contents.append(response_content)
   
   # Print the three string values
   print("response_content:\n", response_content, "\n")
   
   messages.append({
       "role": "assistant",
       "content": response_content
   })
   
   if result.activity:
       activity_content = json.dumps([a.as_dict() for a in result.activity], indent=2)
   else:
       activity_content = "No activity found on 'result'"
   
   activity_contents.append(activity_content)
   print("activity_content:\n", activity_content, "\n")
   
   if result.references:
       references_content = json.dumps([r.as_dict() for r in result.references], indent=2)
   else:
       references_content = "No references found on 'result'"
   
   references_contents.append(references_content)
   print("references_content:\n", references_content)
   
   # Continue the conversation
   query_2 = "How do I find lava at night?"
   messages.append({
       "role": "user",
       "content": query_2
   })
   
   req = KnowledgeBaseRetrievalRequest(
       messages=[
           KnowledgeBaseMessage(
               role=m["role"],
               content=[KnowledgeBaseMessageTextContent(text=m["content"])]
           ) for m in messages if m["role"] != "system"
       ],
       knowledge_source_params=[
           SearchIndexKnowledgeSourceParams(
               knowledge_source_name=knowledge_source_name,
               include_references=True,
               include_reference_source_data=True,
               always_query_source=True
           )
       ],
       include_activity=True,
       retrieval_reasoning_effort=KnowledgeRetrievalLowReasoningEffort
   )
   
   result = agent_client.retrieve(retrieval_request=req)
   print(f"Retrieved content from '{knowledge_base_name}' successfully.")
   
   # Display the new retrieval response, activity, and references
   response_parts = []
   for resp in result.response:
       for content in resp.content:
           response_parts.append(content.text)
   response_content = "\n\n".join(response_parts) if response_parts else "No response found on 'result'"
   
   response_contents.append(response_content)
   
   # Print the three string values
   print("response_content:\n", response_content, "\n")
   
   if result.activity:
       activity_content = json.dumps([a.as_dict() for a in result.activity], indent=2)
   else:
       activity_content = "No activity found on 'result'"
   
   activity_contents.append(activity_content)
   print("activity_content:\n", activity_content, "\n")
   
   if result.references:
       references_content = json.dumps([r.as_dict() for r in result.references], indent=2)
   else:
       references_content = "No references found on 'result'"
   
   references_contents.append(references_content)
   print("references_content:\n", references_content)
   
   # Clean up resources
   index_client = SearchIndexClient(endpoint=search_endpoint, credential=credential)
   index_client.delete_knowledge_base(knowledge_base_name)
   print(f"Knowledge base '{knowledge_base_name}' deleted successfully.")
   
   index_client = SearchIndexClient(endpoint=search_endpoint, credential=credential)
   index_client.delete_knowledge_source(knowledge_source=knowledge_source_name)
   print(f"Knowledge source '{knowledge_source_name}' deleted successfully.")
   
   index_client = SearchIndexClient(endpoint=search_endpoint, credential=credential)
   index_client.delete_index(index_name)
   print(f"Index '{index_name}' deleted successfully.")
   

4. Establezca search_endpoint y aoai_endpoint en los valores obtenidos en Obtener puntos de conexión.

5. Ejecute el siguiente comando para ejecutar el código.

   python agentic-retrieval.py
   

Salida

La salida del código debe ser similar a la siguiente:

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

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

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

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

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

Descripción del código

Ahora que ha ejecutado el código, vamos a desglosar los pasos clave:

1. Creación de un índice de búsqueda
2. Carga de documentos en el índice
3. Creación de una fuente de conocimiento
4. Creación de una base de conocimientos
5. Configurar mensajes
6. Ejecuta la canalización de recuperación
7. Continuar la conversación

Creación de un índice de búsqueda

En Azure AI Search, un índice es una colección estructurada de datos. El código siguiente define un índice denominado earth-at-night, que especificó anteriormente mediante la index_name variable .

El esquema de índice contiene campos para la identificación del documento y el contenido de la página, las incrustaciones y los números. El esquema también incluye configuraciones para la clasificación semántica y el vector de búsqueda, que usa la implementación de text-embedding-3-large para vectorizar texto y buscar documentos en función de la similitud semántica.

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

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

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

Cargar documentos en el índice

Actualmente, el earth-at-night índice está vacío. El siguiente código puebla el índice con documentos JSON del libro electrónico 'Earth at Night' de la NASA. Según lo requiera Azure AI Search, cada documento se ajusta a los campos y tipos de datos definidos en el esquema de 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.")

Creación de una fuente de conocimiento

Un origen de conocimiento es una referencia reutilizable a los datos de origen. El código siguiente define un origen de conocimiento denominado earth-knowledge-source que tiene como destino el earth-at-night índice.

source_data_fields especifica los campos de índice que se incluyen en las referencias de cita. Nuestro ejemplo incluye solo campos legibles para personas para evitar incrustaciones largas e ininterpretables en las respuestas.

# 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.")

Creación de una base de conocimientos

Para dirigirse a la implementación de earth-knowledge-source y gpt-5-mini en el momento de la consulta, necesita una base de conocimiento. El código siguiente define una base de conocimiento denominada earth-knowledge-base, que especificó anteriormente mediante la knowledge_base_name variable .

output_mode se establece en ANSWER_SYNTHESIS, que habilita respuestas en lenguaje natural que citan los documentos recuperados y siguen las directrices proporcionadas por answer_instructions.

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

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

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

Configurar mensajes

Los mensajes son la entrada de la ruta de recuperación y contienen el historial de conversaciones. Cada mensaje incluye un rol que indica su origen, como system o user, y contenido en lenguaje natural. El LLM que usa determina qué roles son válidos.

El código siguiente crea un mensaje del sistema que indica earth-knowledge-base a responder preguntas sobre la Tierra por la noche y responder con "No sé" cuando las respuestas no están disponibles.

# 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
    }
]

Ejecución de la canalización de recuperación

Está listo para ejecutar la recuperación de agentes. El código siguiente envía una consulta de usuario de dos partes a earth-knowledge-base, que:

1. Analiza toda la conversación para deducir la necesidad de información del usuario.
2. Descompone la consulta compuesta en subconsultas centradas.
3. Ejecuta las subconsultas simultáneamente en la fuente de conocimiento.
4. Usa el clasificador semántico para volver a generar y filtrar los resultados.
5. Sintetiza los resultados relevantes en una respuesta en lenguaje natural.

# Run agentic retrieval
agent_client = KnowledgeBaseRetrievalClient(endpoint=search_endpoint, knowledge_base_name=knowledge_base_name, credential=credential)
query_1 = """
    Why do suburban belts display larger December brightening than urban cores even though absolute light levels are higher downtown?
    Why is the Phoenix nighttime street grid is so sharply visible from space, whereas large stretches of the interstate between midwestern cities remain comparatively dim?
    """

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

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

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

Revisar la respuesta, la actividad y las referencias

El código siguiente muestra la respuesta, la actividad y las referencias de la canalización de recuperación, donde:

• response_contents proporciona una respuesta sintetizada generada por LLM para la consulta que cita los documentos recuperados. Cuando la síntesis de respuestas no está habilitada, esta sección contiene contenido extraído directamente de los documentos.

• activity_contents realiza un seguimiento de los pasos que se realizaron durante el proceso de recuperación, incluidas las subconsultas generadas por la implementación de gpt-5-mini y los tokens usados para la clasificación semántica, el planeamiento de consultas y la síntesis de respuestas.

• references_contents enumera los documentos que han contribuido a la respuesta, cada uno identificado por su 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 la conversación

El código siguiente continúa la conversación con earth-knowledge-base. Después de enviar esta consulta de usuario, la base de conocimiento captura el contenido pertinente de earth-knowledge-source y anexa la respuesta a la lista de mensajes.

# 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.")

Revisar la nueva respuesta, actividad y referencias

El código siguiente muestra la nueva respuesta, actividad y referencias de la canalización de recuperación.

# 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)

Limpieza de recursos

Cuando trabaja en su propia suscripción, es una buena idea finalizar un proyecto determinando si todavía necesita los recursos que creó. Los recursos que quedan en ejecución pueden costar dinero.

En Azure Portal, puede administrar los recursos de Azure AI Search y Microsoft Foundry seleccionando Todos los recursos o grupos de recursos en el panel izquierdo.

De lo contrario, el código siguiente de agentic-retrieval.py eliminó los objetos que creó en este inicio rápido.

Eliminación de la base de conocimiento

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.")

Eliminación de la fuente de conocimiento

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.")

Eliminación del índice de búsqueda

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

En este inicio rápido, usará la recuperación de agentes para crear una experiencia de búsqueda conversacional con tecnología de documentos indexados en Búsqueda de Azure AI y modelos de lenguaje grandes (LLM) de Azure OpenAI en Foundry Models.

Una base de conocimiento organiza la recuperación agente mediante la descomposición de consultas complejas en subconsultas, la ejecución de las subconsultas en uno o varios orígenes de conocimiento y la devolución de resultados con metadatos. De manera predeterminada, la base de conocimiento genera contenido sin procesar de los orígenes, pero en este inicio rápido se usa el modo de salida de síntesis de respuestas para la generación de respuestas en lenguaje natural.

Aunque puede proporcionar sus propios datos, en esta guía de inicio rápido se usan documentos JSON de ejemplo de la Tierra de la NASA en el libro electrónico nocturno. Los documentos describen temas generales de ciencia e imágenes de la Tierra a la noche como se observa desde el espacio.

Prerrequisitos

• Una cuenta de Azure con una suscripción activa. Cree una cuenta gratuita.

• Un Servicio de Búsqueda de Azure AI en cualquier región que proporcione recuperación con agentes.

• Un proyecto y un recurso de Microsoft Foundry . Al crear un proyecto, el recurso se crea automáticamente.

• La CLI de Azure para la autenticación sin clave con Microsoft Entra ID.

• Visual Studio Code y la última versión LTS de Node.js.

Configurar el acceso

Antes de empezar, asegúrese de que tiene permisos para acceder al contenido y las operaciones. Se recomienda Microsoft Entra ID para la autenticación y el acceso basado en roles para la autorización. Debe ser Propietario o Administrador de acceso de usuario para asignar roles. Si los roles no son factibles, use la autenticación basada en claves en su lugar.

Para configurar el acceso para este inicio rápido, seleccione las dos pestañas siguientes.

Obtención de puntos de conexión

Cada servicio Azure AI Search y el recurso de Microsoft Foundry tienen un punto de conexión, que es una dirección URL única que identifica y proporciona acceso de red al recurso. En una sección posterior, especifique estos puntos de conexión para conectarse a los recursos mediante programación.

Para obtener los puntos de conexión de este inicio rápido, seleccione las dos pestañas siguientes.

Implementación de modelos

Para este inicio rápido, debe implementar dos modelos de Azure OpenAI en el proyecto de Microsoft Foundry:

• Modelo de inserción para la conversión de texto a vector. En este inicio rápido se usa text-embedding-3-large, pero puede usar cualquier text-embedding modelo.

• Un LLM para la planeación de consultas y la generación de respuestas. En este inicio rápido, se usa gpt-5-mini, pero puede usar cualquier LLM compatible para la recuperación de agentes.

Para obtener instrucciones de implementación, consulte Implementación de modelos de Azure OpenAI con Microsoft Foundry.

Configuración del entorno

Para configurar la aplicación de consola para este inicio rápido:

1. Cree una carpeta denominada quickstart-agentic-retrieval para contener la aplicación.

2. Abra la carpeta en Visual Studio Code.

3. Seleccione Terminal>Nuevo terminal y, a continuación, ejecute los siguientes comandos para inicializar el package.json archivo.

   npm init -y
   npm pkg set type=module
   

4. Instale TypeScript como dependencia de desarrollo.

   npm install --save-dev typescript @types/node
   

5. Instale la biblioteca cliente de Azure AI Search para JavaScript.

   npm install @azure/search-documents@12.3.0-beta.1
   

6. Para la autenticación sin claves con el identificador de Entra de Microsoft, instale la biblioteca cliente de Identidad de Azure para JavaScript.

   npm install @azure/identity
   

7. Para la autenticación sin claves con el identificador de Microsoft Entra, inicie sesión en su cuenta de Azure. Si tiene varias suscripciones, seleccione la que contiene el servicio Azure AI Search y el proyecto de Microsoft Foundry.

   az login
   

Ejecución del código

Para crear y ejecutar la canalización de recuperación de agentes:

1. Cree un archivo denominado .env en la quickstart-agentic-retrieval carpeta .

2. Pegue las siguientes variables de entorno en el .env archivo.

   AZURE_SEARCH_ENDPOINT = https://<your-search-service-name>.search.windows.net
   AZURE_OPENAI_ENDPOINT = https://<your-ai-foundry-resource-name>.openai.azure.com/
   AZURE_OPENAI_GPT_DEPLOYMENT = gpt-5-mini
   AZURE_OPENAI_EMBEDDING_DEPLOYMENT = text-embedding-3-large
   

3. Establezca AZURE_SEARCH_ENDPOINT y AZURE_OPENAI_ENDPOINT en los valores obtenidos en Obtener puntos de conexión.

4. Cree un archivo denominado index.tsy pegue el código siguiente en el archivo.

   import { DefaultAzureCredential } from '@azure/identity';
   import {
       SearchIndexClient,
       SearchClient,
       SearchIndex,
       SearchField,
       VectorSearch,
       VectorSearchProfile,
       HnswAlgorithmConfiguration,
       AzureOpenAIVectorizer,
       AzureOpenAIParameters,
       KnowledgeRetrievalClient,
       SemanticSearch,
       SemanticConfiguration,
       SemanticPrioritizedFields,
       SemanticField,
       SearchIndexingBufferedSender,
       KnowledgeRetrievalOutputMode,
       IndexDocumentsAction
   } from '@azure/search-documents';
   import type { IndexDocumentsResult } from '@azure/search-documents';
   
   interface EarthAtNightDocument {
       id: string;
       page_chunk: string;
       page_embedding_text_3_large: number[];
       page_number: number;
   }
   
   export const documentKeyRetriever: (document: EarthAtNightDocument) => string = (document: EarthAtNightDocument): string => {
     return document.id!;
   };
   
   export const WAIT_TIME = 4000;
   export function delay(timeInMs: number): Promise<void> {
     return new Promise((resolve) => setTimeout(resolve, timeInMs));
   }
   
   const index: SearchIndex = {
       name: 'earth_at_night',
       fields: [
           {
               name: "id",
               type: "Edm.String",
               key: true,
               filterable: true,
               sortable: true,
               facetable: true
           } as SearchField,
           {
               name: "page_chunk",
               type: "Edm.String",
               searchable: true,
               filterable: false,
               sortable: false,
               facetable: false
           } as SearchField,
           {
               name: "page_embedding_text_3_large",
               type: "Collection(Edm.Single)",
               searchable: true,
               filterable: false,
               sortable: false,
               facetable: false,
               vectorSearchDimensions: 3072,
               vectorSearchProfileName: "hnsw_text_3_large"
           } as SearchField,
           {
               name: "page_number",
               type: "Edm.Int32",
               filterable: true,
               sortable: true,
               facetable: true
           } as SearchField
       ],
       vectorSearch: {
           profiles: [
               {
                   name: "hnsw_text_3_large",
                   algorithmConfigurationName: "alg",
                   vectorizerName: "azure_openai_text_3_large"
               } as VectorSearchProfile
           ],
           algorithms: [
               {
                   name: "alg",
                   kind: "hnsw"
               } as HnswAlgorithmConfiguration
           ],
           vectorizers: [
               {
                   vectorizerName: "azure_openai_text_3_large",
                   kind: "azureOpenAI",
                   parameters: {
                       resourceUrl: process.env.AZURE_OPENAI_ENDPOINT!,
                       deploymentId: process.env.AZURE_OPENAI_EMBEDDING_DEPLOYMENT!,
                       modelName: process.env.AZURE_OPENAI_EMBEDDING_DEPLOYMENT!
                   } as AzureOpenAIParameters
               } as AzureOpenAIVectorizer
           ]
       } as VectorSearch,
       semanticSearch: {
           defaultConfigurationName: "semantic_config",
           configurations: [
               {
                   name: "semantic_config",
                   prioritizedFields: {
                       contentFields: [
                           { name: "page_chunk" } as SemanticField
                       ]
                   } as SemanticPrioritizedFields
               } as SemanticConfiguration
           ]
       } as SemanticSearch
   };
   
   const credential = new DefaultAzureCredential();
   
   const searchIndexClient = new SearchIndexClient(process.env.AZURE_SEARCH_ENDPOINT!, credential);
   const searchClient = new SearchClient<EarthAtNightDocument>(process.env.AZURE_SEARCH_ENDPOINT!, 'earth_at_night', credential);
   
   await searchIndexClient.createOrUpdateIndex(index);
   
   // get Documents with vectors
   const response = await fetch("https://raw.githubusercontent.com/Azure-Samples/azure-search-sample-data/refs/heads/main/nasa-e-book/earth-at-night-json/documents.json");
   
   if (!response.ok) {
       throw new Error(`Failed to fetch documents: ${response.status} ${response.statusText}`);
   }
   const documents = await response.json() as any[];
   
   const bufferedClient = new SearchIndexingBufferedSender<EarthAtNightDocument>(
       searchClient,
       documentKeyRetriever,
       {
           autoFlush: true,
       },
   );
   
   await bufferedClient.uploadDocuments(documents);
   await bufferedClient.flush();
   await bufferedClient.dispose();
   
   console.log(`Waiting for indexing to complete...`);
   console.log(`Expected documents: ${documents.length}`);
   await delay(WAIT_TIME);
   
   let count = await searchClient.getDocumentsCount();
   console.log(`Current indexed count: ${count}`);
   
   while (count !== documents.length) {
       await delay(WAIT_TIME);
       count = await searchClient.getDocumentsCount();
       console.log(`Current indexed count: ${count}`);
   }
   
   console.log(`✓ All ${documents.length} documents indexed successfully!`);
   
   await searchIndexClient.createKnowledgeSource({
       name: 'earth-knowledge-source',
       description: "Knowledge source for Earth at Night e-book content",
       kind: "searchIndex",
       searchIndexParameters: {
           searchIndexName: 'earth_at_night',
           sourceDataFields: [
               { name: "id" },
               { name: "page_number" }
           ]
       }
   });
   
   console.log(`✅ Knowledge source 'earth-knowledge-source' created successfully.`);
   
   await searchIndexClient.createKnowledgeBase({
       name: 'earth-knowledge-base',
       knowledgeSources: [
           {
               name: 'earth-knowledge-source'
           }
       ],
       models: [
           {
               kind: "azureOpenAI",
               azureOpenAIParameters: {
                   resourceUrl: process.env.AZURE_OPENAI_ENDPOINT!,
                   deploymentId: process.env.AZURE_OPENAI_GPT_DEPLOYMENT!,
                   modelName: process.env.AZURE_OPENAI_GPT_DEPLOYMENT!
               }
           }
       ],
       outputMode: "answerSynthesis" as KnowledgeRetrievalOutputMode,
       answerInstructions: "Provide a two sentence concise and informative answer based on the retrieved documents."
   });
   
   console.log(`✅ Knowledge base 'earth-knowledge-base' created successfully.`);
   
   const knowledgeRetrievalClient = new KnowledgeRetrievalClient(
       process.env.AZURE_SEARCH_ENDPOINT!,
       'earth-knowledge-base',
       credential
   );
   
   const query1 = `Why do suburban belts display larger December brightening than urban cores even though absolute light levels are higher downtown? Why is the Phoenix nighttime street grid is so sharply visible from space, whereas large stretches of the interstate between midwestern cities remain comparatively dim?`;
   
   const retrievalRequest = {
       messages: [
           {
               role: "user",
               content: [
                   {
                       type: "text" as const,
                       text: query1
                   }
               ]
           }
       ],
       knowledgeSourceParams: [
           {
               kind: "searchIndex" as const,
               knowledgeSourceName: 'earth-knowledge-source',
               includeReferences: true,
               includeReferenceSourceData: true,
               alwaysQuerySource: true,
               rerankerThreshold: 2.5
           }
       ],
       includeActivity: true,
       retrievalReasoningEffort: { kind: "low" as const }
   };
   
   const result = await knowledgeRetrievalClient.retrieveKnowledge(retrievalRequest);
   
   console.log("\n📝 ANSWER:");
   console.log("─".repeat(80));
   if (result.response && result.response.length > 0) {
       result.response.forEach((msg) => {
           if (msg.content && msg.content.length > 0) {
               msg.content.forEach((content) => {
                   if (content.type === "text" && 'text' in content) {
                       console.log(content.text);
                   }
               });
           }
       });
   }
   console.log("─".repeat(80));
   
   if (result.activity) {
       console.log("\nActivities:");
       result.activity.forEach((activity) => {
           console.log(`Activity Type: ${activity.type}`);
           console.log(JSON.stringify(activity, null, 2));
       });
   }
   
   if (result.references) {
       console.log("\nReferences:");
       result.references.forEach((reference) => {
           console.log(`Reference Type: ${reference.type}`);
           console.log(JSON.stringify(reference, null, 2));
       });
   }
   
   // Follow-up query - to demonstrate conversational context
   const query2 = "How do I find lava at night?";
   console.log(`\n❓ Follow-up question: ${query2}`);
   
   const retrievalRequest2 = {
       messages: [
           {
               role: "user",
               content: [
                   {
                       type: "text" as const,
                       text: query2
                   }
               ]
           }
       ],
       knowledgeSourceParams: [
           {
               kind: "searchIndex" as const,
               knowledgeSourceName: 'earth-knowledge-source',
               includeReferences: true,
               includeReferenceSourceData: true,
               alwaysQuerySource: true,
               rerankerThreshold: 2.5
           }
       ],
       includeActivity: true,
       retrievalReasoningEffort: { kind: "low" as const }
   };
   
   const result2 = await knowledgeRetrievalClient.retrieveKnowledge(retrievalRequest2);
   
   console.log("\n📝 ANSWER:");
   console.log("─".repeat(80));
   if (result2.response && result2.response.length > 0) {
       result2.response.forEach((msg) => {
           if (msg.content && msg.content.length > 0) {
               msg.content.forEach((content) => {
                   if (content.type === "text" && 'text' in content) {
                       console.log(content.text);
                   }
               });
           }
       });
   }
   console.log("─".repeat(80));
   
   if (result2.activity) {
       console.log("\nActivities:");
       result2.activity.forEach((activity) => {
           console.log(`Activity Type: ${activity.type}`);
           console.log(JSON.stringify(activity, null, 2));
       });
   }
   
   if (result2.references) {
       console.log("\nReferences:");
       result2.references.forEach((reference) => {
           console.log(`Reference Type: ${reference.type}`);
           console.log(JSON.stringify(reference, null, 2));
       });
   }
   
   console.log("\n✅ Quickstart completed successfully!");
   
   // Clean up resources
   await searchIndexClient.deleteKnowledgeBase('earth-knowledge-base');
   await searchIndexClient.deleteKnowledgeSource('earth-knowledge-source');
   await searchIndexClient.deleteIndex('earth_at_night');
   
   console.log(`\n🗑️  Cleaned up resources.`);
   

5. Cree un archivo denominado tsconfig.jsony pegue el siguiente JSON para ECMAScript en el archivo.

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

6. Transpile el código de TypeScript a JavaScript.

   npx tsc
   

7. Compile y ejecute la aplicación.

   node --env-file ./.env dist/index.js
   

Salida

La salida de la aplicación debe ser similar a la siguiente:

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

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

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

References:
Reference Type: searchIndex
{
  "type": "searchIndex",
  "id": "0",
  "activitySource": 3,
  "sourceData": {
    "id": "earth_at_night_508_page_174_verbalized",
    "page_chunk": "<!-- PageHeader=\"Holiday Lights\" -->\n\n## Holiday Lights\n\n### Bursting with Holiday Energy-United States\n\nNASA researchers found that nighttime lights in the United States shine 20 to 50 percent brighter in December due to holiday light displays and other activities during Christmas and New Year's when compared to light output during the rest of the year.\n\nThe next five maps (see also pages 161-163), created using data from the VIIRS DNB on the Suomi NPP satellite, show changes in lighting intensity and location around many major cities, comparing the nighttime light signals from December 2012 and beyond.\n\n---\n\n#### Figure 1. Location Overview\n\nA map of the western hemisphere with a marker indicating the mid-Atlantic region of the eastern United States, where the study of holiday lighting intensity was focused.\n\n---\n\n#### Figure 2. Holiday Lighting Intensity: Mid-Atlantic United States (2012–2014)\n\nA map showing Maryland, New Jersey, Delaware, Virginia, West Virginia, Ohio, Kentucky, Tennessee, North Carolina, South Carolina, and surrounding areas. Major cities labeled include Washington, D.C., Richmond, Norfolk, and Raleigh.\n\nThe map uses colors to indicate changes in holiday nighttime lighting intensity between 2012 and 2014:\n\n- **Green/bright areas**: More holiday lighting (areas shining 20–50% brighter in December).\n- **Yellow areas**: No change in lighting.\n- **Dim/grey areas**: Less holiday lighting.\n\nKey observations from the map:\n\n- The Washington, D.C. metropolitan area shows significant increases in lighting during the holidays, extending into Maryland and Virginia.\n- Urban centers such as Richmond (Virginia), Norfolk (Virginia), Raleigh (North Carolina), and clusters in Tennessee and South Carolina also experience notable increases in light intensity during December.\n- Rural areas and the interiors of West Virginia, Kentucky, and North Carolina show little change or less holiday lighting, corresponding to population density and urbanization.\n\n**Legend:**\n\n| Holiday Lighting Change | Color on Map   |\n|------------------------|---------------|\n| More                   | Green/bright  |\n| No Change              | Yellow        |\n| Less                   | Dim/grey      |\n\n_The scale bar indicates a distance of 100 km for reference._\n\n---\n\n<!-- PageFooter=\"158 Earth at Night\" -->",
    "page_number": 174
  },
  "rerankerScore": 2.6692379,
  "docKey": "earth_at_night_508_page_174_verbalized"
}
Reference Type: searchIndex
{
  "type": "searchIndex",
  "id": "1",
  "activitySource": 3,
  "sourceData": {
    "id": "earth_at_night_508_page_186_verbalized",
    "page_chunk": "## Appendix A\n\n### NASA's Black Marble Product Suite\n\nNASA's Black Marble product suite provides estimates of daily nighttime lights and other intrinsic surface optical properties of Earth at night. The product is based on the Day/Night Band (DNB) sensor of the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument onboard the Suomi National Polar-orbiting Partnership (NPP) satellite. The VIIRS DNB is a highly sensitive, low-light sensor capable of measuring daily global nighttime light emissions and reflections, allowing users to identify sources and intensities of these artificial lights, and monitor changes over a period of time. Like any optical sensor, the principal challenge in using VIIRS DNB data is to account for variations in light captured by the sensor. Certainly, there are variations in the sources and intensity of anthropogenic light due to changing human processes like urbanization, oil and gas production, nighttime commercial fishing, and infrastructure development. However, these processes can only be studied when other naturally-occurring factors that influence nighttime lights are removed. For example, variations in lunar lighting due to consistent changes in Moon phase cause fluctuations in the amount of light shining on Earth. Similarly, land-cover dynamics (e.g., seasonal vegetation, snow and ice cover), as well as atmospheric conditions (e.g., clouds, aerosols, airglow, and auroras), influence the intensity of the light captured by the sensor as it travels over various parts of the world.\n\nTo realize the full potential of the VIIRS DNB time series record for nighttime lights applications, NASA's Black Marble product suite was developed, building on a history of 20 years of research on how light changes when it reflects off of surfaces with different angular and spectral properties. Through complex modeling, scientists can now predict how moonlight, snow, vegetation, terrain, and clouds impact the lights we see from space, allowing for more accurate assessments of human and environmental activity at night.\n\n<!-- PageFooter=\"170 Earth at Night\" -->",
    "page_number": 186
  },
  "rerankerScore": 2.5997617,
  "docKey": "earth_at_night_508_page_186_verbalized"
}

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

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

Activities:
... // Trimmed for brevity

References:
... // Trimmed for brevity

✅ Quickstart completed successfully!

🗑️  Cleaned up resources.

Descripción del código

Ahora que tiene el código, vamos a desglosar los componentes clave:

1. Creación de un índice de búsqueda
2. Carga de documentos en el índice
3. Creación de una fuente de conocimiento
4. Creación de una base de conocimientos
5. Ejecuta la canalización de recuperación
6. Revisar la respuesta, la actividad y las referencias
7. Continuar la conversación

Creación de un índice de búsqueda

En Azure AI Search, un índice es una colección estructurada de datos. El código siguiente define un índice denominado earth_at_night.

El esquema de índice contiene campos para la identificación del documento y el contenido de la página, las incrustaciones y los números. El esquema también incluye configuraciones para la clasificación semántica y el vector de búsqueda, que usa la implementación de text-embedding-3-large para vectorizar texto y buscar documentos en función de la similitud semántica.

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);

Cargar documentos en el índice

Actualmente, el earth-at-night índice está vacío. El siguiente código puebla el índice con documentos JSON del libro electrónico 'Earth at Night' de la NASA. Según lo requiera Azure AI Search, cada documento se ajusta a los campos y tipos de datos definidos en el esquema de 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!`);

Creación de una fuente de conocimiento

Un origen de conocimiento es una referencia reutilizable a los datos de origen. El código siguiente define un origen de conocimiento denominado earth-knowledge-source que tiene como destino el earth-at-night índice.

source_data_fields especifica los campos de índice que se incluyen en las referencias de cita. Nuestro ejemplo incluye solo campos legibles para personas para evitar incrustaciones largas e ininterpretables en las respuestas.

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.`);

Creación de una base de conocimientos

Para dirigirse a la implementación de earth-knowledge-source y gpt-5-mini en el momento de la consulta, necesita una base de conocimiento. El código siguiente define una base de conocimiento denominada earth-knowledge-base.

outputMode se establece en answerSynthesis, que habilita respuestas en lenguaje natural que citan los documentos recuperados y siguen las directrices proporcionadas por answerInstructions.

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.`); 

Ejecución de la canalización de recuperación

Está listo para ejecutar la recuperación de agentes. El código siguiente envía una consulta de usuario de dos partes a earth-knowledge-base, que:

1. Analiza toda la conversación para deducir la necesidad de información del usuario.
2. Descompone la consulta compuesta en subconsultas centradas.
3. Ejecuta las subconsultas simultáneamente en la fuente de conocimiento.
4. Usa el clasificador semántico para volver a generar y filtrar los resultados.
5. Sintetiza los resultados relevantes en una respuesta en lenguaje natural.

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

const query1 = `Why do suburban belts display larger December brightening than urban cores even though absolute light levels are higher downtown? Why is the Phoenix nighttime street grid is so sharply visible from space, whereas large stretches of the interstate between midwestern cities remain comparatively dim?`;

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

const result = await knowledgeRetrievalClient.retrieveKnowledge(retrievalRequest);

Revisar la respuesta, la actividad y las referencias

El código siguiente muestra la respuesta, la actividad y las referencias de la canalización de recuperación, donde:

• Answer proporciona una respuesta sintetizada generada por LLM para la consulta que cita los documentos recuperados. Cuando la síntesis de respuestas no está habilitada, esta sección contiene contenido extraído directamente de los documentos.

• Activities realiza un seguimiento de los pasos que se realizaron durante el proceso de recuperación, incluidas las subconsultas generadas por la implementación de gpt-5-mini y los tokens usados para la clasificación semántica, el planeamiento de consultas y la síntesis de respuestas.

• References enumera los documentos que han contribuido a la respuesta, cada uno identificado por su 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 la conversación

El código siguiente continúa la conversación con earth-knowledge-base. Después de enviar esta consulta de usuario, la base de conocimiento captura el contenido pertinente de earth-knowledge-source y anexa la respuesta a la lista de mensajes.

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

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

const result2 = await knowledgeRetrievalClient.retrieveKnowledge(retrievalRequest2);

Revisar la nueva respuesta, actividad y referencias

El código siguiente muestra la nueva respuesta, actividad y referencias de la canalización de recuperación.

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!");

Limpieza de recursos

Cuando trabaja en su propia suscripción, es una buena idea finalizar un proyecto determinando si todavía necesita los recursos que creó. Los recursos que quedan en ejecución pueden costar dinero.

En Azure Portal, puede administrar los recursos de Azure AI Search y Microsoft Foundry seleccionando Todos los recursos o grupos de recursos en el panel izquierdo.

De lo contrario, el código siguiente de index.ts eliminó los objetos que creó en este inicio 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.`);

En este inicio rápido, usará la recuperación de agentes para crear una experiencia de búsqueda conversacional con tecnología de documentos indexados en Búsqueda de Azure AI y modelos de lenguaje grandes (LLM) de Azure OpenAI en Foundry Models.

Una base de conocimiento organiza la recuperación agente mediante la descomposición de consultas complejas en subconsultas, la ejecución de las subconsultas en uno o varios orígenes de conocimiento y la devolución de resultados con metadatos. De manera predeterminada, la base de conocimiento genera contenido sin procesar de los orígenes, pero en este inicio rápido se usa el modo de salida de síntesis de respuestas para la generación de respuestas en lenguaje natural.

Aunque puede proporcionar sus propios datos, en esta guía de inicio rápido se usan documentos JSON de ejemplo de la Tierra de la NASA en el libro electrónico nocturno. Los documentos describen temas generales de ciencia e imágenes de la Tierra a la noche como se observa desde el espacio.

Prerrequisitos

• Una cuenta de Azure con una suscripción activa. Cree una cuenta gratuita.

• Un Servicio de Búsqueda de Azure AI en cualquier región que proporcione recuperación con agentes.

• Un proyecto y un recurso de Microsoft Foundry . Al crear un proyecto, el recurso se crea automáticamente.

• La CLI de Azure para la autenticación sin clave con Microsoft Entra ID.

• Visual Studio Code con la extensión de cliente REST.

Configurar el acceso

Antes de empezar, asegúrese de que tiene permisos para acceder al contenido y las operaciones. Se recomienda Microsoft Entra ID para la autenticación y el acceso basado en roles para la autorización. Debe ser Propietario o Administrador de acceso de usuario para asignar roles. Si los roles no son factibles, use la autenticación basada en claves en su lugar.

Para configurar el acceso para este inicio rápido, seleccione las dos pestañas siguientes.

Obtención de puntos de conexión

Cada servicio Azure AI Search y el recurso de Microsoft Foundry tienen un punto de conexión, que es una dirección URL única que identifica y proporciona acceso de red al recurso. En una sección posterior, especifique estos puntos de conexión para conectarse a los recursos mediante programación.

Para obtener los puntos de conexión de este inicio rápido, seleccione las dos pestañas siguientes.

Implementación de modelos

Para este inicio rápido, debe implementar dos modelos de Azure OpenAI en el proyecto de Microsoft Foundry:

• Modelo de inserción para la conversión de texto a vector. En este inicio rápido se usa text-embedding-3-large, pero puede usar cualquier text-embedding modelo.

• Un LLM para la planeación de consultas y la generación de respuestas. En este inicio rápido, se usa gpt-5-mini, pero puede usar cualquier LLM compatible para la recuperación de agentes.

Para obtener instrucciones de implementación, consulte Implementación de modelos de Azure OpenAI con Microsoft Foundry.

Conexión desde el sistema local

Ha configurado el acceso basado en roles para interactuar con Azure AI Search y Azure OpenAI en Foundry Models. Use la CLI de Azure para iniciar sesión en la misma suscripción e inquilino para ambos recursos. Para obtener más información, consulte Inicio rápido: Conexión sin claves.

Para conectarse desde el sistema local:

1. Cree una carpeta llamada quickstart-agentic-retrieval.

2. Abra la carpeta en Visual Studio Code.

3. Seleccione Terminal>Nuevo terminal.

4. Ejecute el comando siguiente para iniciar sesión en su cuenta de Azure. Si tiene varias suscripciones, seleccione la que contiene el servicio Azure AI Search y el proyecto Foundry.

   az login
   

5. Ejecute el siguiente comando para generar un token de id. de Microsoft Entra.

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

6. Anote el token para su uso en la sección siguiente.

Ejecución del código

Para crear y ejecutar la canalización de recuperación de agentes:

1. Cree un archivo denominado agentic-retrieval.rest en la quickstart-agentic-retrieval carpeta .

2. Pegue las siguientes variables y solicitudes en el archivo.

   ### Set variables
   @search-url = PUT-YOUR-SEARCH-SERVICE-URL-HERE
   @token = PUT-YOUR-MICROSOFT-ENTRA-TOKEN-HERE
   @aoai-url = PUT-YOUR-AOAI-FOUNDRY-URL-HERE
   @aoai-embedding-model = text-embedding-3-large
   @aoai-embedding-deployment = text-embedding-3-large
   @aoai-gpt-model = gpt-5-mini
   @aoai-gpt-deployment = gpt-5-mini
   @index-name = earth-at-night
   @knowledge-source-name = earth-knowledge-source
   @knowledge-base-name = earth-knowledge-base
   @api-version = 2025-11-01-preview
   
   ### Create an index
   PUT {{search-url}}/indexes/{{index-name}}?api-version={{api-version}}  HTTP/1.1
   Content-Type: application/json
   Authorization: Bearer {{token}}
   
   {
       "name": "{{index-name}}",
       "fields": [
           {
               "name": "id",
               "type": "Edm.String",
               "key": true
           },
           {
               "name": "page_chunk",
               "type": "Edm.String",
               "searchable": true
           },
           {
               "name": "page_embedding_text_3_large",
               "type": "Collection(Edm.Single)",
               "stored": false,
               "dimensions": 3072,
               "vectorSearchProfile": "hnsw_text_3_large"
           },
           {
               "name": "page_number",
               "type": "Edm.Int32",
               "filterable": true
           }
       ],
       "semantic": {
           "defaultConfiguration": "semantic_config",
           "configurations": [
               {
                   "name": "semantic_config",
                   "prioritizedFields": {
                   "prioritizedContentFields": [
                       {
                           "fieldName": "page_chunk"
                       }
                   ]
                   }
               }
           ]
       },
       "vectorSearch": {
           "profiles": [
               {
                   "name": "hnsw_text_3_large",
                   "algorithm": "alg",
                   "vectorizer": "azure_openai_text_3_large"
               }
           ],
           "algorithms": [
               {
                   "name": "alg",
                   "kind": "hnsw"
               }
           ],
           "vectorizers": [
               {
                   "name": "azure_openai_text_3_large",
                   "kind": "azureOpenAI",
                   "azureOpenAIParameters": {
                   "resourceUri": "{{aoai-url}}",
                   "deploymentId": "{{aoai-embedding-deployment}}",
                   "modelName": "{{aoai-embedding-model}}"
                   }
               }
           ]
       }
   }
   
   ### Upload documents
   POST {{search-url}}/indexes/{{index-name}}/docs/index?api-version={{api-version}}  HTTP/1.1
   Content-Type: application/json
   Authorization: Bearer {{token}}
   
   {
       "value": [
           {
               "@search.action": "upload",
               "id": "earth_at_night_508_page_104_verbalized",
               "page_chunk": "<!-- PageHeader=\"Urban Structure\" -->\n\n### Location of Phoenix, Arizona\n\nThe image depicts a globe highlighting the location of Phoenix, Arizona, in the southwestern United States, marked with a blue pinpoint on the map of North America. Phoenix is situated in the central part of Arizona, which is in the southwestern region of the United States.\n\n---\n\n### Grid of City Blocks-Phoenix, Arizona\n\nLike many large urban areas of the central and western United States, the Phoenix metropolitan area is laid out along a regular grid of city blocks and streets. While visible during the day, this grid is most evident at night, when the pattern of street lighting is clearly visible from the low-Earth-orbit vantage point of the ISS.\n\nThis astronaut photograph, taken on March 16, 2013, includes parts of several cities in the metropolitan area, including Phoenix (image right), Glendale (center), and Peoria (left). While the major street grid is oriented north-south, the northwest-southeast oriented Grand Avenue cuts across the three cities at image center. Grand Avenue is a major transportation corridor through the western metropolitan area; the lighting patterns of large industrial and commercial properties are visible along its length. Other brightly lit properties include large shopping centers, strip malls, and gas stations, which tend to be located at the intersections of north-south and east-west trending streets.\n\nThe urban grid encourages growth outwards along a city's borders by providing optimal access to new real estate. Fueled by the adoption of widespread personal automobile use during the twentieth century, the Phoenix metropolitan area today includes 25 other municipalities (many of them largely suburban and residential) linked by a network of surface streets and freeways.\n\nWhile much of the land area highlighted in this image is urbanized, there are several noticeably dark areas. The Phoenix Mountains are largely public parks and recreational land. To the west, agricultural fields provide a sharp contrast to the lit streets of residential developments. The Salt River channel appears as a dark ribbon within the urban grid.\n\n\n<!-- PageFooter=\"Earth at Night\" -->\n<!-- PageNumber=\"88\" -->",
               "page_embedding_text_3_large": [
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               "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",
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               ],
               "page_number": 105
           }
       ]
   }
   
   ### Create a knowledge source
   POST {{search-url}}/knowledgesources?api-version={{api-version}}  HTTP/1.1
   Content-Type: application/json
   Authorization: Bearer {{token}}
   
   {
       "name": "{{knowledge-source-name}}",
       "description": "This knowledge source pulls from a search index that contains pages from the Earth at Night e-book.",
       "kind": "searchIndex",
       "searchIndexParameters": {
           "searchIndexName": "{{index-name}}",
           "sourceDataFields": [
               { "name": "id" },
               { "name": "page_chunk" },
               { "name": "page_number" }
           ]
       }
   }
   
   ### Create a knowledge base
   PUT {{search-url}}/knowledgebases/{{knowledge-base-name}}?api-version={{api-version}}  HTTP/1.1
   Content-Type: application/json
   Authorization: Bearer {{token}}
   
   {
       "name": "{{knowledge-base-name}}",
       "knowledgeSources": [
           {
               "name": "{{knowledge-source-name}}"
           }
       ],
       "models": [
           {
               "kind": "azureOpenAI",
               "azureOpenAIParameters": {
                   "resourceUri": "{{aoai-url}}",
                   "deploymentId": "{{aoai-gpt-deployment}}",
                   "modelName": "{{aoai-gpt-model}}"
               }
           }
       ],
       "outputMode": "answerSynthesis",
       "answerInstructions": "Provide a two sentence concise and informative answer based on the retrieved documents."
   }
   
   ### Run agentic retrieval
   POST {{search-url}}/knowledgebases/{{knowledge-base-name}}/retrieve?api-version={{api-version}}  HTTP/1.1
   Content-Type: application/json
   Authorization: Bearer {{token}}
   
   {
       "messages": [
           {
               "role": "user",
               "content": [
                   {
                       "type": "text",
                       "text": "Why do suburban belts display larger December brightening than urban cores even though absolute light levels are higher downtown? Why is the Phoenix nighttime street grid is so sharply visible from space, whereas large stretches of the interstate between midwestern cities remain comparatively dim?"
                   }
               ]
           }
       ],
       "knowledgeSourceParams": [
           {
               "knowledgeSourceName": "{{knowledge-source-name}}",
               "kind": "searchIndex",
               "includeReferences": true,
               "includeReferenceSourceData": true,
               "alwaysQuerySource": true,
               "rerankerThreshold": 2.5
           }
       ],
       "includeActivity": true,
       "retrievalReasoningEffort": { "kind": "low" }
   }
   
   ### Delete the knowledge base
   DELETE {{search-url}}/knowledgebases/{{knowledge-base-name}}?api-version={{api-version}}  HTTP/1.1
   Content-Type: application/json
   Authorization: Bearer {{token}}
   
   ### Delete the knowledge source
   DELETE {{search-url}}/knowledgesources/{{knowledge-source-name}}?api-version={{api-version}}  HTTP/1.1
   Content-Type: application/json
   Authorization: Bearer {{token}}
   
   ### Delete the index
   DELETE {{search-url}}/indexes/{{index-name}}?api-version={{api-version}}  HTTP/1.1
   Content-Type: application/json
   Authorization: Bearer {{token}}
   

3. Establezca @search-url y aoai-url en los valores obtenidos en Obtener puntos de conexión.

4. Establezca @token en el valor que obtuvo en Conectar desde el sistema local.

5. Envíe cada solicitud sucesivamente, empezando por ### Create an index.

   Cada solicitud debe devolver un 200 OKcódigo de estado , 201 Createdo 204 No Content . Si recibe un error, verifique si hay errores tipográficos en la solicitud y asegúrese de que el token sea válido.

Salida

Cada solicitud devuelve un JSON diferente en función de la operación. La salida clave es de ### Run agentic retrieval, que debe tener un aspecto similar al siguiente:

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

Descripción del código

Ahora que ha ejecutado el código, vamos a desglosar los pasos clave:

1. Creación de un índice de búsqueda
2. Carga de documentos en el índice
3. Creación de una fuente de conocimiento
4. Creación de una base de conocimientos
5. Ejecuta la canalización de recuperación

Creación de un índice de búsqueda

En Azure AI Search, un índice es una colección estructurada de datos. El código siguiente usa Indexes - Create (API REST) para definir un índice denominado earth-at-night, que especificó anteriormente mediante la @index-name variable .

El esquema de índice contiene campos para la identificación del documento y el contenido de la página, las incrustaciones y los números. El esquema también incluye configuraciones para la clasificación semántica y el vector de búsqueda, que usa la implementación de text-embedding-3-large para vectorizar texto y buscar documentos en función de la similitud semántica.

### 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}}"
                }
            }
        ]
    }
}

Cargar documentos en el índice

Actualmente, el earth-at-night índice está vacío. El siguiente código utiliza Documents - Index (API REST) para rellenar el índice con documentos JSON del libro electrónico "Earth at Night" de la NASA. Según lo requiera Azure AI Search, cada documento se ajusta a los campos y tipos de datos definidos en el 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\" -->",
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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
        }
    ]
}

Creación de una fuente de conocimiento

Un origen de conocimiento es una referencia reutilizable a los datos de origen. El código siguiente usa Orígenes de conocimiento: creación (API REST) para definir un origen de conocimiento denominado earth-knowledge-source que tiene como destino el earth-at-night índice.

sourceDataFields especifica los campos de índice que se incluyen en las referencias de cita. Nuestro ejemplo incluye solo campos legibles para personas para evitar incrustaciones largas e ininterpretables en las respuestas.

### 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" }
        ]
    }
}

Creación de una base de conocimientos

Para dirigir la implementación de earth-knowledge-source y gpt-5-mini en el momento de la consulta, necesita una base de conocimiento. El código siguiente usa Knowledge Bases- Create (API REST) para definir una base denominada earth-knowledge-base, que especificó anteriormente mediante la @knowledge-base-name variable .

outputMode se establece en answerSynthesis, que habilita respuestas en lenguaje natural que citan los documentos recuperados y siguen las directrices proporcionadas por answerInstructions.

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

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

Ejecución de la canalización de recuperación

Está listo para ejecutar la recuperación de agentes. El siguiente código utiliza Knowledge Retrieval - Retrieve (API REST) para enviar una consulta de usuario dividida en dos partes a earth-knowledge-base, que:

1. Analiza toda la conversación para deducir la necesidad de información del usuario.
2. Descompone la consulta compuesta en subconsultas centradas.
3. Ejecuta las subconsultas simultáneamente en la fuente de conocimiento.
4. Usa el clasificador semántico para volver a generar y filtrar los resultados. En nuestro ejemplo se excluyen las respuestas con una puntuación de reranker de 2.5 o inferior.
5. Sintetiza los resultados relevantes en una respuesta en lenguaje natural.

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

{
    "messages": [
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "Why do suburban belts display larger December brightening than urban cores even though absolute light levels are higher downtown? Why is the Phoenix nighttime street grid is so sharply visible from space, whereas large stretches of the interstate between midwestern cities remain comparatively dim?"
                }
            ]
        }
    ],
    "knowledgeSourceParams": [
        {
            "knowledgeSourceName": "{{knowledge-source-name}}",
            "kind": "searchIndex",
            "includeReferences": true,
            "includeReferenceSourceData": true,
            "alwaysQuerySource": true,
            "rerankerThreshold": 2.5
        }
    ],
    "includeActivity": true,
    "retrievalReasoningEffort": { "kind": "low" }
}

La salida debe contener los siguientes componentes:

• response proporciona una respuesta sintetizada generada por LLM para la consulta que cita los documentos recuperados. Cuando la síntesis de respuestas no está habilitada, esta sección contiene contenido extraído directamente de los documentos.

• activity realiza un seguimiento de los pasos que se realizaron durante el proceso de recuperación, incluidas las subconsultas generadas por la implementación de gpt-5-mini y los tokens usados para la clasificación semántica, el planeamiento de consultas y la síntesis de respuestas.

• references enumera los documentos que han contribuido a la respuesta, cada uno identificado por su docKey.

Limpieza de recursos

Cuando trabaja en su propia suscripción, es una buena idea finalizar un proyecto determinando si todavía necesita los recursos que creó. Los recursos que quedan en ejecución pueden costar dinero.

En Azure Portal, puede administrar los recursos de Azure AI Search y Microsoft Foundry seleccionando Todos los recursos o grupos de recursos en el panel izquierdo.

De lo contrario, las siguientes solicitudes de agentic-retrieval.rest eliminaron los objetos que creó en este inicio rápido.

Eliminación de la base de conocimiento

### 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}}

Eliminación de la fuente de conocimiento

### 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}}

Eliminación del índice de búsqueda

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