Quickstart: Agentic retrieval

In this quickstart, you use agentic retrieval to create a conversational search experience powered by documents indexed in Azure AI Search and a large language model (LLM) from Azure OpenAI in Foundry Models.

A knowledge base orchestrates agentic retrieval by decomposing complex queries into subqueries, running the subqueries against one or more knowledge sources, and returning results with metadata. By default, the knowledge base outputs raw content from your sources, but this quickstart uses the answer synthesis output mode for natural-language answer generation.

Although you can provide your own data, this quickstart uses sample JSON documents from NASA's Earth at Night e-book. The documents describe general science topics and images of Earth at night as observed from space.

Prerequisites

• An Azure account with an active subscription. Create an account for free.

• An Azure AI Search service in any region that provides agentic retrieval.

• A Microsoft Foundry project and resource. When you create a project, the resource is automatically created.

• The Azure CLI for keyless authentication with Microsoft Entra ID.

• Visual Studio Code.

Configure access

Before you begin, make sure you have permissions to access content and operations. We recommend Microsoft Entra ID for authentication and role-based access for authorization. You must be an Owner or User Access Administrator to assign roles. If roles aren't feasible, use key-based authentication instead.

To configure access for this quickstart, select both of the following tabs.

Get endpoints

Each Azure AI Search service and Microsoft Foundry resource has an endpoint, which is a unique URL that identifies and provides network access to the resource. In a later section, you specify these endpoints to connect to your resources programmatically.

To get the endpoints for this quickstart, select both of the following tabs.

Deploy models

For this quickstart, you must deploy two Azure OpenAI models to your Microsoft Foundry project:

• An embedding model for text-to-vector conversion. This quickstart uses text-embedding-3-large, but you can use any text-embedding model.

• An LLM for query planning and answer generation. This quickstart uses gpt-5-mini, but you can use any supported LLM for agentic retrieval.

For deployment instructions, see Deploy Microsoft Foundry Models in the Foundry portal.

Set up the environment

To set up the console application for this quickstart:

1. Create a folder named quickstart-agentic-retrieval to contain the application.

2. Open the folder in Visual Studio Code.

3. Select Terminal > New Terminal, and then run the following command to create a console application.

   dotnet new console
   

4. Install the Azure AI Search client library for .NET.

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

5. Install the dotenv.net package to load environment variables from a .env file.

   dotnet add package dotenv.net
   

6. For keyless authentication with Microsoft Entra ID, install the Azure.Identity package.

   dotnet add package Azure.Identity
   

7. For keyless authentication with Microsoft Entra ID, sign in to your Azure account. If you have multiple subscriptions, select the one that contains your Azure AI Search service and Microsoft Foundry project.

   az login
   

Run the code

To create and run the agentic retrieval pipeline:

1. Create a file named .env in the quickstart-agentic-retrieval folder.

2. Paste the following environment variables into the .env file.

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

3. Set SEARCH_ENDPOINT and AOAI_ENDPOINT to the values you obtained in Get endpoints.

4. Paste the following code into the Program.cs file.

   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. Build and run the application.

   dotnet run
   

Output

The output of the application should be similar to the following:

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
}
... // Trimmed for brevity
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.

Understand the code

Now that you've run the code, let's break down the key steps:

1. Create a search index
2. Upload documents to the index
3. Create a knowledge source
4. Create a knowledge base
5. Set up messages
6. Run the retrieval pipeline
7. Continue the conversation

Create a search index

In Azure AI Search, an index is a structured collection of data. The following code defines an index named earth-at-night.

The index schema contains fields for document identification and page content, embeddings, and numbers. The schema also includes configurations for semantic ranking and vector search, which uses your text-embedding-3-large deployment to vectorize text and match documents based on semantic or conceptual similarity.

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

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

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

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

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

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

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

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

Upload documents to the index

Currently, the earth-at-night index is empty. The following code populates the index with JSON documents from NASA's Earth at Night e-book. As required by Azure AI Search, each document conforms to the fields and data types defined in the index schema.

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

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

Reference: SearchClient, SearchIndexingBufferedSender

Create a knowledge source

A knowledge source is a reusable reference to source data. The following code defines a knowledge source named earth-knowledge-source that targets the earth-at-night index.

SourceDataFields specifies which index fields are included in citation references. This example includes only human-readable fields to avoid lengthy, uninterpretable embeddings in responses.

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

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

Reference: SearchIndexKnowledgeSource

Create a knowledge base

To target earth-knowledge-source and your gpt-5-mini deployment at query time, you need a knowledge base. The following code defines a knowledge base named earth-knowledge-base.

OutputMode is set to AnswerSynthesis, enabling natural-language answers that cite the retrieved documents and follow the provided 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.");

Reference: KnowledgeBaseAzureOpenAIModel, KnowledgeBase

Set up messages

Messages are the input for the retrieval route and contain the conversation history. Each message includes a role that indicates its origin, such as system or user, and content in natural language. The LLM you use determines which roles are valid.

The following code creates a system message, which instructs earth-knowledge-base to answer questions about the Earth at night and respond with "I don't know" when answers are unavailable.

// 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 the retrieval pipeline

You're ready to run agentic retrieval. The following code sends a two-part user query to earth-knowledge-base, which:

1. Analyzes the entire conversation to infer the user's information need.
2. Decomposes the compound query into focused subqueries.
3. Runs the subqueries concurrently against your knowledge source.
4. Uses semantic ranker to rerank and filter the results.
5. Synthesizes the top results into a natural-language answer.

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

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

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

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

Reference: KnowledgeBaseRetrievalClient, KnowledgeBaseRetrievalRequest

Review the response, activity, and references

The following code displays the response, activity, and references from the retrieval pipeline, where:

• Response provides a synthesized, LLM-generated answer to the query that cites the retrieved documents. When answer synthesis isn't enabled, this section contains content extracted directly from the documents.

• Activity tracks the steps that were taken during the retrieval process, including the subqueries generated by your gpt-5-mini deployment and the tokens used for semantic ranking, query planning, and answer synthesis.

• References lists the documents that contributed to the response, each one identified by their 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);
}

Continue the conversation

The following code continues the conversation with earth-knowledge-base. After you send this user query, the knowledge base fetches relevant content from earth-knowledge-source and appends the response to the messages list.

// 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 }
});

Review the new response, activity, and references

The following code displays the new response, activity, and references from the retrieval pipeline.

// 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

When you work in your own subscription, it's a good idea to finish a project by determining whether you still need the resources you created. Resources that are left running can cost you money.

In the Azure portal, you can manage your Azure AI Search and Microsoft Foundry resources by selecting All resources or Resource groups from the left pane.

Otherwise, the following code from Program.cs deleted the objects you created in this quickstart.

Delete the knowledge base

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

Delete the knowledge source

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

Delete the search index

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

In this quickstart, you use agentic retrieval to create a conversational search experience powered by large language models (LLMs) and your proprietary data. Agentic retrieval breaks down complex user queries into subqueries, runs the subqueries in parallel, and extracts grounding data from documents indexed in Azure AI Search. The output is intended for integration with agentic and custom chat solutions.

Although you can use your own data, this quickstart uses sample JSON documents from NASA's Earth at Night e-book. The documents describe general science topics and images of Earth at night as observed from space.

Prerequisites

• An Azure account with an active subscription. Create an account for free.

• An Azure AI Search service in any region that provides agentic retrieval.

• A Microsoft Foundry project and resource. When you create a project, the resource is automatically created.

• The Azure CLI for keyless authentication with Microsoft Entra ID.

Configure access

Before you begin, make sure you have permissions to access content and operations. We recommend Microsoft Entra ID for authentication and role-based access for authorization. You must be an Owner or User Access Administrator to assign roles. If roles aren't feasible, use key-based authentication instead.

To configure access for this quickstart, select both of the following tabs.

Get endpoints

Each Azure AI Search service and Microsoft Foundry resource has an endpoint, which is a unique URL that identifies and provides network access to the resource. In a later section, you specify these endpoints to connect to your resources programmatically.

To get the endpoints for this quickstart, select both of the following tabs.

Deploy models

For this quickstart, you must deploy two Azure OpenAI models to your Microsoft Foundry project:

• An embedding model for text-to-vector conversion. This quickstart uses text-embedding-3-large, but you can use any text-embedding model.

• An LLM for query planning and answer generation. This quickstart uses gpt-5-mini, but you can use any supported LLM for agentic retrieval.

For deployment instructions, see Deploy Microsoft Foundry Models in the Foundry portal.

Set up the environment

The sample in this quickstart works with the Java Runtime. Install a Java Development Kit such as Azul Zulu OpenJDK. The Microsoft Build of OpenJDK or your preferred JDK should also work.

1. Install Apache Maven. Then run mvn -v to confirm successful installation.

2. Create a new folder quickstart-agentic-retrieval to contain the application and open Visual Studio Code in that folder with the following command:

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

3. Create a new pom.xml file in the root of your project, and copy the following code into it:

   <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. Install the dependencies including the Azure AI Search client library (Azure.Search.Documents) for Java and Azure Identity client library for Java with:

   mvn clean dependency:copy-dependencies
   

Run the code

1. Create a new file named .env in the quickstart-agentic-retrieval folder and add the following environment variables:

   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
   

   Replace <your-search-service-name> and <your-ai-foundry-resource-name> with your actual Azure AI Search service name and Foundry resource name.

2. Paste the following code into a new file named AgenticRetrievalQuickstart.java in the quickstart-agentic-retrieval folder:

   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. Sign in to Azure with the following command:

   az login
   

4. Run your new console application:

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

Output

The output of the application should be similar to the following:

Starting Azure AI Search agentic retrieval quickstart...

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

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

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


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


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

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

Activities:
Activity Type: UnknownActivityRecord
{
  "type" : "ModelQueryPlanning",
  "id" : 0,
  "inputTokens" : 1545,
  "outputTokens" : 127
}
Activity Type: UnknownActivityRecord
{
  "type" : "AzureSearchQuery",
  "id" : 1,
  "targetIndex" : "earth_at_night",
  "query" : {
    "search" : "How can I find lava at night?",
    "filter" : null
  },
  "queryTime" : "2025-07-21T15:07:15.445Z",
  "count" : 6,
  "elapsedMs" : 370
}
Activity Type: UnknownActivityRecord
{
  "type" : "AzureSearchSemanticRanker",
  "id" : 2,
  "inputTokens" : 22994
}
Results
Reference Type: KnowledgeAgentAzureSearchDocReference
{
  "type" : "AzureSearchDoc",
  "id" : "0",
  "activitySource" : 1,
  "docKey" : "earth_at_night_508_page_44_verbalized",
  "sourceData" : {
    "id" : "earth_at_night_508_page_44_verbalized",
    "page_chunk" : "## Nature's Light Shows\n\nAt night, with the light of the Sun removed, nature's brilliant glow from Earth's surface becomes visible to the naked eye from space. Some of Earth's most spectacular light shows are natural, like the aurora borealis, or Northern Lights, in the Northern Hemisphere (aurora australis, or Southern Lights, in the Southern Hemisphere). The auroras are natural electrical phenomena caused by charged particles that race from the Sun toward Earth, inducing chemical reactions in the upper atmosphere and creating the appearance of streamers of reddish or greenish light in the sky, usually near the northern or southern magnetic pole. Other natural lights can indicate danger, like a raging forest fire encroaching on a city, town, or community, or lava spewing from an erupting volcano.\n\nWhatever the source, the ability of humans to monitor nature's light shows at night has practical applications for society. For example, tracking fires during nighttime hours allows for continuous monitoring and enhances our ability to protect humans and other animals, plants, and infrastructure. Combined with other data sources, our ability to observe the light of fires at night allows emergency managers to more efficiently and accurately issue warnings and evacuation orders and allows firefighting efforts to continue through the night. With enough moonlight (e.g., full-Moon phase), it's even possible to track the movement of smoke plumes at night, which can impact air quality, regardless of time of day.\n\nAnother natural source of light at night is emitted from glowing lava flows at the site of active volcanoes. Again, with enough moonlight, these dramatic scenes can be tracked and monitored for both scientific research and public safety.\n\n\n### Figure: The Northern Lights Viewed from Space\n\n**September 17, 2011**\n\nThis photo, taken from the International Space Station on September 17, 2011, shows a spectacular display of the aurora borealis (Northern Lights) as green and reddish light in the night sky above Earth. In the foreground, part of a Soyuz spacecraft is visible, silhouetted against the bright auroral light. The green glow is generated by energetic charged particles from the Sun interacting with Earth's upper atmosphere, exciting oxygen and nitrogen atoms, and producing characteristic colors. The image demonstrates the vividness and grandeur of natural night-time light phenomena as seen from orbit."
  }
}
Reference Type: KnowledgeAgentAzureSearchDocReference
{
  "type" : "AzureSearchDoc",
  "id" : "1",
  "activitySource" : 1,
  "docKey" : "earth_at_night_508_page_65_verbalized",
  "sourceData" : {
    "id" : "earth_at_night_508_page_65_verbalized",
    "page_chunk" : "# Volcanoes\n\n## Figure: Satellite Image of Sicily and Mount Etna Lava, March 16, 2017\n\nThe annotated satellite image below shows the island of Sicily and the surrounding region at night, highlighting city lights and volcanic activity.\n\n**Description:**\n\n- **Date of image:** March 16, 2017\n- **Geographical locations labeled:**\n    - Major cities: Palermo (northwest Sicily), Marsala (western Sicily), Catania (eastern Sicily)\n    - Significant feature: Mount Etna, labeled with an adjacent \"hot lava\" region showing the glow from active lava flows\n    - Surrounding water body: Mediterranean Sea\n    - Island: Malta to the south of Sicily\n- **Other details:** \n    - The image is shown at night, with bright spots indicating city lights.\n    - The position of \"hot lava\" near Mount Etna is distinctly visible as a bright spot different from other city lights, indicating volcanic activity.\n    - A scale bar is included showing a reference length of 50 km.\n    - North direction is indicated with an arrow.\n    - Cloud cover is visible in the southwest part of the image, partially obscuring the view near Marsala and Malta.\n\n**Summary of Features Visualized:**\n\n| Feature          | Description                                           |\n|------------------|------------------------------------------------------|\n| Cities           | Bright clusters indicating locations: Palermo, Marsala, Catania |\n| Mount Etna       | Marked on the map, located on the eastern side of Sicily, with visible hot lava activity |\n| Malta            | Clearly visible to the south of Sicily               |\n| Water bodies     | Mediterranean Sea labeled                            |\n| Scale & Direction| 50 km scale bar and North indicator                  |\n| Date             | March 16, 2017                                       |\n| Cloud Cover      | Visible in the lower left (southern) part of the image |\n\nThis figure demonstrates the visibility of volcanic activity at Mount Etna from space at night, distinguishing the light from hot lava against the background city lights of Sicily and Malta."
  }
}
Reference Type: KnowledgeAgentAzureSearchDocReference
{
  "type" : "AzureSearchDoc",
  "id" : "2",
  "activitySource" : 1,
  "docKey" : "earth_at_night_508_page_64_verbalized",
  "sourceData" : {
    "id" : "earth_at_night_508_page_64_verbalized",
    "page_chunk" : "<!-- PageHeader=\"Volcanoes\" -->\n\n### Nighttime Glow at Mount Etna - Italy\n\nAt about 2:30 a.m. local time on March 16, 2017, the VIIRS DNB on the Suomi NPP satellite captured this nighttime image of lava flowing on Mount Etna in Sicily, Italy. Etna is one of the world's most active volcanoes.\n\n#### Figure: Location of Mount Etna\nA world globe is depicted, with a marker indicating the location of Mount Etna in Sicily, Italy, in southern Europe near the center of the Mediterranean Sea.\n\n<!-- PageFooter=\"Earth at Night\" -->\n<!-- PageNumber=\"48\" -->"
  }
}
Reference Type: KnowledgeAgentAzureSearchDocReference
{
  "type" : "AzureSearchDoc",
  "id" : "3",
  "activitySource" : 1,
  "docKey" : "earth_at_night_508_page_66_verbalized",
  "sourceData" : {
    "id" : "earth_at_night_508_page_66_verbalized",
    "page_chunk" : "# Volcanoes\n\n---\n\n### Mount Etna Erupts - Italy\n\nThe highly active Mount Etna in Italy sent red lava rolling down its flank on March 19, 2017. An astronaut onboard the ISS took the photograph below of the volcano and its environs that night. City lights surround the mostly dark volcanic area.\n\n---\n\n#### Figure 1: Location of Mount Etna, Italy\n\nA world map highlighting the location of Mount Etna in southern Italy. The marker indicates its geographic placement on the east coast of Sicily, Italy, in the Mediterranean region, south of mainland Europe and north of northern Africa.\n\n---\n\n#### Figure 2: Nighttime View of Mount Etna's Eruption and Surrounding Cities\n\nThis is a nighttime satellite image taken on March 19, 2017, showing the eruption of Mount Etna (southeastern cone) with visible bright red and orange coloring indicating flowing lava from a lateral vent. The surrounding areas are illuminated by city lights, with the following geographic references labeled:\n\n| Location        | Position in Image         | Visible Characteristics                    |\n|-----------------|--------------------------|--------------------------------------------|\n| Mt. Etna (southeastern cone) | Top center-left | Bright red/orange lava flow                |\n| Lateral vent    | Left of the volcano       | Faint red/orange flow extending outwards   |\n| Resort          | Below the volcano, to the left   | Small cluster of lights                    |\n| Giarre          | Top right                 | Bright cluster of city lights              |\n| Acireale        | Center right              | Large, bright area of city lights          |\n| Biancavilla     | Bottom left               | Smaller cluster of city lights             |\n\nAn arrow pointing north is shown on the image for orientation.\n\n---\n\n<!-- Earth at Night Page Footer -->\n<!-- Page Number: 50 -->"
  }
}
Reference Type: KnowledgeAgentAzureSearchDocReference
{
  "type" : "AzureSearchDoc",
  "id" : "4",
  "activitySource" : 1,
  "docKey" : "earth_at_night_508_page_46_verbalized",
  "sourceData" : {
    "id" : "earth_at_night_508_page_46_verbalized",
    "page_chunk" : "For the first time in perhaps a decade, Mount Etna experienced a \"flank eruption\"�erupting from its side instead of its summit�on December 24, 2018. The activity was accompanied by 130 earthquakes occurring over three hours that morning. Mount Etna, Europe�s most active volcano, has seen periodic activity on this part of the mountain since 2013. The Operational Land Imager (OLI) on the Landsat 8 satellite acquired the main image of Mount Etna on December 28, 2018.\n\nThe inset image highlights the active vent and thermal infrared signature from lava flows, which can be seen near the newly formed fissure on the southeastern side of the volcano. The inset was created with data from OLI and the Thermal Infrared Sensor (TIRS) on Landsat 8. Ash spewing from the fissure cloaked adjacent villages and delayed aircraft from landing at the nearby Catania airport. Earthquakes occurred in the subsequent days after the initial eruption and displaced hundreds of people from their homes.\n\nFor nighttime images of Mount Etna�s March 2017 eruption, see pages 48�51.\n\n---\n\n### Hazards of Volcanic Ash Plumes and Satellite Observation\n\nWith the help of moonlight, satellite instruments can track volcanic ash plumes, which present significant hazards to airplanes in flight. The volcanic ash�composed of tiny pieces of glass and rock�is abrasive to engine turbine blades, and can melt on the blades and other engine parts, causing damage and even engine stalls. This poses a danger to both the plane�s integrity and passenger safety. Volcanic ash also reduces visibility for pilots and can cause etching of windshields, further reducing pilots� ability to see. Nightlight images can be combined with thermal images to provide a more complete view of volcanic activity on Earth�s surface.\n\nThe VIIRS Day/Night Band (DNB) on polar-orbiting satellites uses faint light sources such as moonlight, airglow (the atmosphere�s self-illumination through chemical reactions), zodiacal light (sunlight scattered by interplanetary dust), and starlight from the Milky Way. Using these dim light sources, the DNB can detect changes in clouds, snow cover, and sea ice:\n\n#### Table: Light Sources Used by VIIRS DNB\n\n| Light Source         | Description                                                                  |\n|----------------------|------------------------------------------------------------------------------|\n| Moonlight            | Reflected sunlight from the Moon, illuminating Earth's surface at night      |\n| Airglow              | Atmospheric self-illumination from chemical reactions                        |\n| Zodiacal Light       | Sunlight scattered by interplanetary dust                                    |\n| Starlight/Milky Way  | Faint illumination provided by stars in the Milky Way                        |\n\nGeostationary Operational Environmental Satellites (GOES), managed by NOAA, orbit over Earth�s equator and offer uninterrupted observations of North America. High-latitude areas such as Alaska benefit from polar-orbiting satellites like Suomi NPP, which provide overlapping coverage at the poles, enabling more data collection in these regions. During polar darkness (winter months), VIIRS DNB data allow scientists to:\n\n- Observe sea ice formation\n- Monitor snow cover extent at the highest latitudes\n- Detect open water for ship navigation\n\n#### Table: Satellite Coverage Overview\n\n| Satellite Type          | Orbit           | Coverage Area         | Special Utility                              |\n|------------------------|-----------------|----------------------|----------------------------------------------|\n| GOES                   | Geostationary   | Equatorial/North America | Continuous regional monitoring              |\n| Polar-Orbiting (e.g., Suomi NPP) | Polar-orbiting    | Poles/high latitudes      | Overlapping passes; useful during polar night|\n\n---\n\n### Weather Forecasting and Nightlight Data\n\nThe use of nightlight data by weather forecasters is growing as the VIIRS instrument enables observation of clouds at night illuminated by sources such as moonlight and lightning. Scientists use these data to study the nighttime behavior of weather systems, including severe storms, which can develop and strike populous areas at night as well as during the day. Combined with thermal data, visible nightlight data allow the detection of clouds at various heights in the atmosphere, such as dense marine fog. This capability enables weather forecasters to issue marine advisories with higher confidence, leading to greater utility. (See \"Marine Layer Clouds�California\" on page 56.)\n\nIn this section of the book, you will see how nightlight data are used to observe nature�s spectacular light shows across a wide range of sources.\n\n---\n\n#### Notable Data from Mount Etna Flank Eruption (December 2018)\n\n| Event/Observation                  | Details                                                                    |\n|-------------------------------------|----------------------------------------------------------------------------|\n| Date of Flank Eruption              | December 24, 2018                                                          |\n| Number of Earthquakes               | 130 earthquakes within 3 hours                                              |\n| Image Acquisition                   | December 28, 2018 by Landsat 8 OLI                                         |\n| Location of Eruption                | Southeastern side of Mount Etna                                            |\n| Thermal Imaging Data                | From OLI and TIRS (Landsat 8), highlighting active vent and lava flows     |\n| Impact on Villages/Air Transport    | Ash covered villages; delayed aircraft at Catania airport                  |\n| Displacement                        | Hundreds of residents displaced                                            |\n| Ongoing Seismic Activity            | Earthquakes continued after initial eruption                               |\n\n---\n\n<!-- PageFooter=\"Earth at Night\" -->\n<!-- PageNumber=\"30\" -->"
  }
}
Reference Type: KnowledgeAgentAzureSearchDocReference
{
  "type" : "AzureSearchDoc",
  "id" : "5",
  "activitySource" : 1,
  "docKey" : "earth_at_night_508_page_60_verbalized",
  "sourceData" : {
    "id" : "earth_at_night_508_page_60_verbalized",
    "page_chunk" : "<!-- PageHeader=\"Volcanoes\" -->\n\n## Volcanoes\n\n### The Infrared Glows of Kilauea's Lava Flows�Hawaii\n\nIn early May 2018, an eruption on Hawaii's Kilauea volcano began to unfold. The eruption took a dangerous turn on May 3, 2018, when new fissures opened in the residential neighborhood of Leilani Estates. During the summer-long eruptive event, other fissures emerged along the East Rift Zone. Lava from vents along the rift zone flowed downslope, reaching the ocean in several areas, and filling in Kapoho Bay.\n\nA time series of Landsat 8 imagery shows the progression of the lava flows from May 16 to August 13. The night view combines thermal, shortwave infrared, and near-infrared wavelengths to tease out the very hot lava (bright white), cooling lava (red), and lava flows obstructed by clouds (purple).\n\n#### Figure: Location of Kilauea Volcano, Hawaii\n\nA globe is shown centered on North America, with a marker placed in the Pacific Ocean indicating the location of Hawaii, to the southwest of the mainland United States.\n\n<!-- PageFooter=\"Earth at Night\" -->\n<!-- PageNumber=\"44\" -->"
  }
}
[{"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!

Understand the code

Now that you have the code, let's break down the key components:

• Create a search index
• Upload documents to the index
• Create a knowledge agent
• Set up messages
• Run the retrieval pipeline
• Review the response, activity, and results
• Create the Azure OpenAI client
• Use the Chat Completions API to generate an answer
• Continue the conversation

Create a search index

In Azure AI Search, an index is a structured collection of data. The following code defines an index named earth_at_night to contain plain text and vector content. You can use an existing index, but it must meet the criteria for agentic retrieval workloads.

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);

The index schema contains fields for document identification and page content, embeddings, and numbers. It also includes configurations for semantic ranking and vector queries, which use the text-embedding-3-large model you previously deployed.

Upload documents to the index

Currently, the earth_at_night index is empty. Run the following code to populate the index with JSON documents from NASA's Earth at Night e-book. As required by Azure AI Search, each document conforms to the fields and data types defined in the index schema.

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;
    
}

Create a knowledge agent

To connect Azure AI Search to your gpt-5-mini deployment and target the earth_at_night index at query time, you need a knowledge agent. The following code defines a knowledge agent named earth-search-agent that uses the agent definition to process queries and retrieve relevant documents from the earth_at_night index.

To ensure relevant and semantically meaningful responses, defaultRerankerThreshold is set to exclude responses with a reranker score of 2.5 or lower.

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());

Set up messages

Messages are the input for the retrieval route and contain the conversation history. Each message includes a role that indicates its origin, such as assistant or user, and content in natural language. The LLM you use determines which roles are valid.

A user message represents the query to be processed, while an assistant message guides the knowledge agent on how to respond. During the retrieval process, these messages are sent to an LLM to extract relevant responses from indexed documents.

This assistant message instructs earth-search-agent to answer questions about the Earth at night, cite sources using their ref_id, and respond with "I don't know" when answers are unavailable.

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);

Run the retrieval pipeline

This step runs the retrieval pipeline to extract relevant information from your search index. Based on the messages and parameters on the retrieval request, the LLM:

1. Analyzes the entire conversation history to determine the underlying information need.
2. Breaks down the compound user query into focused subqueries.
3. Runs each subquery simultaneously against text fields and vector embeddings in your index.
4. Uses semantic ranker to rerank the results of all subqueries.
5. Merges the results into a single string.

The following code sends a two-part user query to earth-search-agent, which deconstructs the query into subqueries, runs the subqueries against both text fields and vector embeddings in the earth_at_night index, and ranks and merges the results. The response is then appended to the messages list.

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());

Review the response, activity, and results

Now you want to display the response, activity, and results of the retrieval pipeline.

Each retrieval response from Azure AI Search includes:

• A unified string that represents grounding data from the search results.

• The query plan.

• Reference data that shows which chunks of the source documents contributed to the unified string.

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());
        }
    }
}

The output should include:

• Response provides a text string of the most relevant documents (or chunks) in the search index based on the user query. As shown later in this quickstart, you can pass this string to an LLM for answer generation.

• Activity tracks the steps that were taken during the retrieval process, including the subqueries generated by your gpt-5-mini deployment and the tokens used for query planning and execution.

• Results lists the documents that contributed to the response, each one identified by their DocKey.

Create the Azure OpenAI client

To extend the retrieval pipeline from answer extraction to answer generation, set up the Azure OpenAI client to interact with your gpt-5-mini deployment.

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

Use the Chat Completions API to generate an answer

One option for answer generation is the Chat Completions API, which passes the conversation history to the LLM for processing.

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();

Continue the conversation

Continue the conversation by sending another user query to earth-search-agent. The following code reruns the retrieval pipeline, fetching relevant content from the earth_at_night index and appending the response to the messages list. However, unlike before, you can now use the Azure OpenAI client to generate an answer based on the retrieved content.

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);

Clean up resources

When working in your own subscription, it's a good idea to finish a project by determining whether you still need the resources you created. Resources that are left running can cost you money. You can delete resources individually, or you can delete the resource group to delete the entire set of resources.

In the Azure portal, you can find and manage resources by selecting All resources or Resource groups from the left pane. You can also run the following code to delete the objects you created in this quickstart.

Delete the knowledge agent

The knowledge agent created in this quickstart was deleted using the following code snippet:

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());

Delete the search index

The search index created in this quickstart was deleted using the following code snippet:

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

In this quickstart, you use agentic retrieval to create a conversational search experience powered by documents indexed in Azure AI Search and a large language model (LLM) from Azure OpenAI in Foundry Models.

A knowledge base orchestrates agentic retrieval by decomposing complex queries into subqueries, running the subqueries against one or more knowledge sources, and returning results with metadata. By default, the knowledge base outputs raw content from your sources, but this quickstart uses the answer synthesis output mode for natural-language answer generation.

Although you can provide your own data, this quickstart uses sample JSON documents from NASA's Earth at Night e-book. The documents describe general science topics and images of Earth at night as observed from space.

Prerequisites

• An Azure account with an active subscription. Create an account for free.

• An Azure AI Search service in any region that provides agentic retrieval.

• A Microsoft Foundry project and resource. When you create a project, the resource is automatically created.

• The Azure CLI for keyless authentication with Microsoft Entra ID.

• Visual Studio Code and the latest LTS version of Node.js.

Configure access

Before you begin, make sure you have permissions to access content and operations. We recommend Microsoft Entra ID for authentication and role-based access for authorization. You must be an Owner or User Access Administrator to assign roles. If roles aren't feasible, use key-based authentication instead.

To configure access for this quickstart, select both of the following tabs.

Get endpoints

Each Azure AI Search service and Microsoft Foundry resource has an endpoint, which is a unique URL that identifies and provides network access to the resource. In a later section, you specify these endpoints to connect to your resources programmatically.

To get the endpoints for this quickstart, select both of the following tabs.

Deploy models

For this quickstart, you must deploy two Azure OpenAI models to your Microsoft Foundry project:

• An embedding model for text-to-vector conversion. This quickstart uses text-embedding-3-large, but you can use any text-embedding model.

• An LLM for query planning and answer generation. This quickstart uses gpt-5-mini, but you can use any supported LLM for agentic retrieval.

For deployment instructions, see Deploy Microsoft Foundry Models in the Foundry portal.

Set up the environment

To set up the console application for this quickstart:

1. Create a folder named quickstart-agentic-retrieval to contain the application.

2. Open the folder in Visual Studio Code.

3. Select Terminal > New Terminal, and then run the following commands to initialize the package.json file.

   npm init -y
   npm pkg set type=module
   

4. Install the Azure AI Search client library for JavaScript.

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

5. For keyless authentication with Microsoft Entra ID, install the Azure Identity client library for JavaScript.

   npm install @azure/identity
   

6. For keyless authentication with Microsoft Entra ID, sign in to your Azure account. If you have multiple subscriptions, select the one that contains your Azure AI Search service and Microsoft Foundry project.

   az login
   

Run the code

To create and run the agentic retrieval pipeline:

1. Create a file named .env in the quickstart-agentic-retrieval folder.

2. Paste the following environment variables into the .env file.

   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. Set AZURE_SEARCH_ENDPOINT and AZURE_OPENAI_ENDPOINT to the values you obtained in Get endpoints.

4. Create a file named index.js, and then paste the following code into the file.

   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. Build and run the application.

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

Output

The output of the application should be similar to the following:

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"
}
... // Trimmed for brevity

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

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

Activities:
... // Trimmed for brevity

References:
... // Trimmed for brevity

✅ Quickstart completed successfully!

🗑️  Cleaned up resources.

Understand the code

Now that you have the code, let's break down the key components:

1. Create a search index
2. Upload documents to the index
3. Create a knowledge source
4. Create a knowledge base
5. Run the retrieval pipeline
6. Review the response, activity, and references
7. Continue the conversation

Create a search index

In Azure AI Search, an index is a structured collection of data. The following code defines an index named earth_at_night.

The index schema contains fields for document identification and page content, embeddings, and numbers. The schema also includes configurations for semantic ranking and vector search, which uses your text-embedding-3-large deployment to vectorize text and match documents based on semantic similarity.

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);

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

Upload documents to the index

Currently, the earth-at-night index is empty. The following code populates the index with JSON documents from NASA's Earth at Night e-book. As required by Azure AI Search, each document conforms to the fields and data types defined in the index schema.

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!`);

Reference: SearchIndexingBufferedSender

Create a knowledge source

A knowledge source is a reusable reference to source data. The following code defines a knowledge source named earth-knowledge-source that targets the earth-at-night index.

source_data_fields specifies which index fields are included in citation references. This example includes only human-readable fields to avoid lengthy, uninterpretable embeddings in responses.

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

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

Reference: SearchIndexKnowledgeSource

Create a knowledge base

To target earth-knowledge-source and your gpt-5-mini deployment at query time, you need a knowledge base. The following code defines a knowledge base named earth-knowledge-base.

outputMode is set to answerSynthesis, enabling natural-language answers that cite the retrieved documents and follow the provided 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.`);

Reference: KnowledgeBase

Run the retrieval pipeline

You're ready to run agentic retrieval. The following code sends a two-part user query to earth-knowledge-base, which:

1. Analyzes the entire conversation to infer the user's information need.
2. Decomposes the compound query into focused subqueries.
3. Runs the subqueries concurrently against your knowledge source.
4. Uses semantic ranker to rerank and filter the results.
5. Synthesizes the top results into a natural-language answer.

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);

Reference: KnowledgeRetrievalClient, KnowledgeBaseRetrievalRequest

Review the response, activity, and references

The following code displays the response, activity, and references from the retrieval pipeline, where:

• Answer provides a synthesized, LLM-generated answer to the query that cites the retrieved documents. When answer synthesis isn't enabled, this section contains content extracted directly from the documents.

• Activities tracks the steps that were taken during the retrieval process, including the subqueries generated by your gpt-5-mini deployment and the tokens used for semantic ranking, query planning, and answer synthesis.

• References lists the documents that contributed to the response, each one identified by their 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));
    });
}

Continue the conversation

The following code continues the conversation with earth-knowledge-base. After you send this user query, the knowledge base fetches relevant content from earth-knowledge-source and appends the response to the messages list.

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);

Review the new response, activity, and references

The following code displays the new response, activity, and references from the retrieval pipeline.

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));
    });
}

Clean up resources

When you work in your own subscription, it's a good idea to finish a project by determining whether you still need the resources you created. Resources that are left running can cost you money.

In the Azure portal, you can manage your Azure AI Search and Microsoft Foundry resources by selecting All resources or Resource groups from the left pane.

Otherwise, the following code from index.js deleted the objects you created in this quickstart.

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

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

In this quickstart, you use agentic retrieval to create a conversational search experience powered by documents indexed in Azure AI Search and a large language model (LLM) from Azure OpenAI in Foundry Models.

A knowledge base orchestrates agentic retrieval by decomposing complex queries into subqueries, running the subqueries against one or more knowledge sources, and returning results with metadata. By default, the knowledge base outputs raw content from your sources, but this quickstart uses the answer synthesis output mode for natural-language answer generation.

Although you can provide your own data, this quickstart uses sample JSON documents from NASA's Earth at Night e-book. The documents describe general science topics and images of Earth at night as observed from space.

Prerequisites

• An Azure account with an active subscription. Create an account for free.

• An Azure AI Search service in any region that provides agentic retrieval.

• A Microsoft Foundry project and resource. When you create a project, the resource is automatically created.

• The Azure CLI for keyless authentication with Microsoft Entra ID.

• Visual Studio Code and the latest version of Python.

Configure access

Before you begin, make sure you have permissions to access content and operations. We recommend Microsoft Entra ID for authentication and role-based access for authorization. You must be an Owner or User Access Administrator to assign roles. If roles aren't feasible, use key-based authentication instead.

To configure access for this quickstart, select both of the following tabs.

Get endpoints

Each Azure AI Search service and Microsoft Foundry resource has an endpoint, which is a unique URL that identifies and provides network access to the resource. In a later section, you specify these endpoints to connect to your resources programmatically.

To get the endpoints for this quickstart, select both of the following tabs.

Deploy models

For this quickstart, you must deploy two Azure OpenAI models to your Microsoft Foundry project:

• An embedding model for text-to-vector conversion. This quickstart uses text-embedding-3-large, but you can use any text-embedding model.

• An LLM for query planning and answer generation. This quickstart uses gpt-5-mini, but you can use any supported LLM for agentic retrieval.

For deployment instructions, see Deploy Microsoft Foundry Models in the Foundry portal.

Connect from your local system

You configured role-based access to interact with Azure AI Search and Azure OpenAI in Foundry Models. Use the Azure CLI to sign in to the same subscription and tenant for both resources. For more information, see Quickstart: Connect without keys.

To connect from your local system:

1. Create a folder named quickstart-agentic-retrieval.

2. Open the folder in Visual Studio Code.

3. Select Terminal > New Terminal.

4. Run the following command to sign in to your Azure account. If you have multiple subscriptions, select the one that contains your Azure AI Search service and Foundry project.

   az login
   

Run the code

To create and run the agentic retrieval pipeline:

1. Run the following command to install the required packages.

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

2. Create a file named agentic-retrieval.py in the quickstart-agentic-retrieval folder.

3. Paste the following code into the file.

   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. Set search_endpoint and aoai_endpoint to the values you obtained in Get endpoints.

5. Run the following command to execute the code.

   python agentic-retrieval.py
   

Output

The output of the code should look similar to the following:

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"
  },
  ... // Trimmed for brevity
]
Retrieved content from 'earth-knowledge-base' successfully.
response_content:
  ... // Trimmed for brevity

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

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

Understand the code

Now that you've run the code, let's break down the key steps:

1. Create a search index
2. Upload documents to the index
3. Create a knowledge source
4. Create a knowledge base
5. Set up messages
6. Run the retrieval pipeline
7. Continue the conversation

Create a search index

In Azure AI Search, an index is a structured collection of data. The following code defines an index named earth-at-night.

The index schema contains fields for document identification and page content, embeddings, and numbers. The schema also includes configurations for semantic ranking and vector search, which uses your text-embedding-3-large deployment to vectorize text and match documents based on semantic similarity.

# 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.")

Reference: SearchField, VectorSearch, SemanticSearch, SearchIndex, SearchIndexClient

Upload documents to the index

Currently, the earth-at-night index is empty. The following code populates the index with JSON documents from NASA's Earth at Night e-book. As required by Azure AI Search, each document conforms to the fields and data types defined in the index schema.

# 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.")

Reference: SearchIndexingBufferedSender

Create a knowledge source

A knowledge source is a reusable reference to source data. The following code defines a knowledge source named earth-knowledge-source that targets the earth-at-night index.

source_data_fields specifies which index fields are included in citation references. This example includes only human-readable fields to avoid lengthy, uninterpretable embeddings in responses.

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

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

Reference: SearchIndexKnowledgeSource

Create a knowledge base

To target earth-knowledge-source and your gpt-5-mini deployment at query time, you need a knowledge base. The following code defines a knowledge base named earth-knowledge-base.

output_mode is set to ANSWER_SYNTHESIS, enabling natural-language answers that cite the retrieved documents and follow the provided 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.")

Reference: KnowledgeBase

Set up messages

Messages are the input for the retrieval route and contain the conversation history. Each message includes a role that indicates its origin, such as system or user, and content in natural language. The LLM you use determines which roles are valid.

The following code creates a system message that instructs earth-knowledge-base to answer questions about the Earth at night and respond with "I don't know" when answers are unavailable.

# 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 the retrieval pipeline

You're ready to run agentic retrieval. The following code sends a two-part user query to earth-knowledge-base, which:

1. Analyzes the entire conversation to infer the user's information need.
2. Decomposes the compound query into focused subqueries.
3. Runs the subqueries concurrently against your knowledge source.
4. Uses semantic ranker to rerank and filter the results.
5. Synthesizes the top results into a natural-language answer.

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

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

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

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

Reference: KnowledgeBaseRetrievalClient, KnowledgeBaseRetrievalRequest

Review the response, activity, and references

The following code displays the response, activity, and references from the retrieval pipeline, where:

• response_contents provides a synthesized, LLM-generated answer to the query that cites the retrieved documents. When answer synthesis isn't enabled, this section contains content extracted directly from the documents.

• activity_contents tracks the steps that were taken during the retrieval process, including the subqueries generated by your gpt-5-mini deployment and the tokens used for semantic ranking, query planning, and answer synthesis.

• references_contents lists the documents that contributed to the response, each one identified by their 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)

Continue the conversation

The following code continues the conversation with earth-knowledge-base. After you send this user query, the knowledge base fetches relevant content from earth-knowledge-source and appends the response to the messages list.

# 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.")

Review the new response, activity, and references

The following code displays the new response, activity, and references from the retrieval pipeline.

# 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

When you work in your own subscription, it's a good idea to finish a project by determining whether you still need the resources you created. Resources that are left running can cost you money.

In the Azure portal, you can manage your Azure AI Search and Microsoft Foundry resources by selecting All resources or Resource groups from the left pane.

Otherwise, the following code from agentic-retrieval.py deleted the objects you created in this quickstart.

Delete the knowledge base

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.")

Delete the knowledge source

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.")

Delete the search index

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

In this quickstart, you use agentic retrieval to create a conversational search experience powered by documents indexed in Azure AI Search and a large language model (LLM) from Azure OpenAI in Foundry Models.

A knowledge base orchestrates agentic retrieval by decomposing complex queries into subqueries, running the subqueries against one or more knowledge sources, and returning results with metadata. By default, the knowledge base outputs raw content from your sources, but this quickstart uses the answer synthesis output mode for natural-language answer generation.

Although you can provide your own data, this quickstart uses sample JSON documents from NASA's Earth at Night e-book. The documents describe general science topics and images of Earth at night as observed from space.

Prerequisites

• An Azure account with an active subscription. Create an account for free.

• An Azure AI Search service in any region that provides agentic retrieval.

• A Microsoft Foundry project and resource. When you create a project, the resource is automatically created.

• The Azure CLI for keyless authentication with Microsoft Entra ID.

• Visual Studio Code and the latest LTS version of Node.js.

Configure access

Before you begin, make sure you have permissions to access content and operations. We recommend Microsoft Entra ID for authentication and role-based access for authorization. You must be an Owner or User Access Administrator to assign roles. If roles aren't feasible, use key-based authentication instead.

To configure access for this quickstart, select both of the following tabs.

Get endpoints

Each Azure AI Search service and Microsoft Foundry resource has an endpoint, which is a unique URL that identifies and provides network access to the resource. In a later section, you specify these endpoints to connect to your resources programmatically.

To get the endpoints for this quickstart, select both of the following tabs.

Deploy models

For this quickstart, you must deploy two Azure OpenAI models to your Microsoft Foundry project:

• An embedding model for text-to-vector conversion. This quickstart uses text-embedding-3-large, but you can use any text-embedding model.

• An LLM for query planning and answer generation. This quickstart uses gpt-5-mini, but you can use any supported LLM for agentic retrieval.

For deployment instructions, see Deploy Microsoft Foundry Models in the Foundry portal.

Set up the environment

To set up the console application for this quickstart:

1. Create a folder named quickstart-agentic-retrieval to contain the application.

2. Open the folder in Visual Studio Code.

3. Select Terminal > New Terminal, and then run the following commands to initialize the package.json file.

   npm init -y
   npm pkg set type=module
   

4. Install TypeScript as a development dependency.

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

5. Install the Azure AI Search client library for JavaScript.

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

6. For keyless authentication with Microsoft Entra ID, install the Azure Identity client library for JavaScript.

   npm install @azure/identity
   

7. For keyless authentication with Microsoft Entra ID, sign in to your Azure account. If you have multiple subscriptions, select the one that contains your Azure AI Search service and Microsoft Foundry project.

   az login
   

Run the code

To create and run the agentic retrieval pipeline:

1. Create a file named .env in the quickstart-agentic-retrieval folder.

2. Paste the following environment variables into the .env file.

   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. Set AZURE_SEARCH_ENDPOINT and AZURE_OPENAI_ENDPOINT to the values you obtained in Get endpoints.

4. Create a file named index.ts, and then paste the following code into the file.

   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. Create a file named tsconfig.json, and then paste the following JSON for ECMAScript into the file.

   {
     "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 the code from TypeScript to JavaScript.

   npx tsc
   

7. Build and run the application.

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

Output

The output of the application should be similar to the following:

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"
}
... // Trimmed for brevity

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

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

Activities:
... // Trimmed for brevity

References:
... // Trimmed for brevity

✅ Quickstart completed successfully!

🗑️  Cleaned up resources.

Understand the code

Now that you have the code, let's break down the key components:

1. Create a search index
2. Upload documents to the index
3. Create a knowledge source
4. Create a knowledge base
5. Run the retrieval pipeline
6. Review the response, activity, and references
7. Continue the conversation

Create a search index

In Azure AI Search, an index is a structured collection of data. The following code defines an index named earth_at_night.

The index schema contains fields for document identification and page content, embeddings, and numbers. The schema also includes configurations for semantic ranking and vector search, which uses your text-embedding-3-large deployment to vectorize text and match documents based on semantic similarity.

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

const credential = new DefaultAzureCredential();

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

await searchIndexClient.createOrUpdateIndex(index);

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

Upload documents to the index

Currently, the earth-at-night index is empty. The following code populates the index with JSON documents from NASA's Earth at Night e-book. As required by Azure AI Search, each document conforms to the fields and data types defined in the index schema.

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!`);

Reference: SearchIndexingBufferedSender

Create a knowledge source

A knowledge source is a reusable reference to source data. The following code defines a knowledge source named earth-knowledge-source that targets the earth-at-night index.

source_data_fields specifies which index fields are included in citation references. This example includes only human-readable fields to avoid lengthy, uninterpretable embeddings in responses.

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

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

Reference: SearchIndexKnowledgeSource

Create a knowledge base

To target earth-knowledge-source and your gpt-5-mini deployment at query time, you need a knowledge base. The following code defines a knowledge base named earth-knowledge-base.

outputMode is set to answerSynthesis, enabling natural-language answers that cite the retrieved documents and follow the provided 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.`); 

Reference: KnowledgeBase

Run the retrieval pipeline

You're ready to run agentic retrieval. The following code sends a two-part user query to earth-knowledge-base, which:

1. Analyzes the entire conversation to infer the user's information need.
2. Decomposes the compound query into focused subqueries.
3. Runs the subqueries concurrently against your knowledge source.
4. Uses semantic ranker to rerank and filter the results.
5. Synthesizes the top results into a natural-language answer.

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);

Reference: KnowledgeRetrievalClient, KnowledgeBaseRetrievalRequest

Review the response, activity, and references

The following code displays the response, activity, and references from the retrieval pipeline, where:

• Answer provides a synthesized, LLM-generated answer to the query that cites the retrieved documents. When answer synthesis isn't enabled, this section contains content extracted directly from the documents.

• Activities tracks the steps that were taken during the retrieval process, including the subqueries generated by your gpt-5-mini deployment and the tokens used for semantic ranking, query planning, and answer synthesis.

• References lists the documents that contributed to the response, each one identified by their 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));
    });
}

Continue the conversation

The following code continues the conversation with earth-knowledge-base. After you send this user query, the knowledge base fetches relevant content from earth-knowledge-source and appends the response to the messages list.

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);

Review the new response, activity, and references

The following code displays the new response, activity, and references from the retrieval pipeline.

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

When you work in your own subscription, it's a good idea to finish a project by determining whether you still need the resources you created. Resources that are left running can cost you money.

In the Azure portal, you can manage your Azure AI Search and Microsoft Foundry resources by selecting All resources or Resource groups from the left pane.

Otherwise, the following code from index.ts deleted the objects you created in this quickstart.

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

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

In this quickstart, you use agentic retrieval to create a conversational search experience powered by documents indexed in Azure AI Search and a large language model (LLM) from Azure OpenAI in Foundry Models.

A knowledge base orchestrates agentic retrieval by decomposing complex queries into subqueries, running the subqueries against one or more knowledge sources, and returning results with metadata. By default, the knowledge base outputs raw content from your sources, but this quickstart uses the answer synthesis output mode for natural-language answer generation.

Although you can use your own data, this quickstart uses sample JSON documents from NASA's Earth at Night e-book. The documents describe general science topics and images of Earth at night as observed from space.

Prerequisites

• An Azure account with an active subscription. Create an account for free.

• An Azure AI Search service in any region that provides agentic retrieval.

• A Microsoft Foundry project and resource. When you create a project, the resource is automatically created.

• The Azure CLI for keyless authentication with Microsoft Entra ID.

• Visual Studio Code with the REST Client extension.

Configure access

Before you begin, make sure you have permissions to access content and operations. We recommend Microsoft Entra ID for authentication and role-based access for authorization. You must be an Owner or User Access Administrator to assign roles. If roles aren't feasible, use key-based authentication instead.

To configure access for this quickstart, select both of the following tabs.

Get endpoints

Each Azure AI Search service and Microsoft Foundry resource has an endpoint, which is a unique URL that identifies and provides network access to the resource. In a later section, you specify these endpoints to connect to your resources programmatically.

To get the endpoints for this quickstart, select both of the following tabs.

Deploy models

For this quickstart, you must deploy two Azure OpenAI models to your Microsoft Foundry project:

• An embedding model for text-to-vector conversion. This quickstart uses text-embedding-3-large, but you can use any text-embedding model.

• An LLM for query planning and answer generation. This quickstart uses gpt-5-mini, but you can use any supported LLM for agentic retrieval.

For deployment instructions, see Deploy Microsoft Foundry Models in the Foundry portal.

Connect from your local system

You configured role-based access to interact with Azure AI Search and Azure OpenAI in Foundry Models. Use the Azure CLI to sign in to the same subscription and tenant for both resources. For more information, see Quickstart: Connect without keys.

To connect from your local system:

1. Create a folder named quickstart-agentic-retrieval.

2. Open the folder in Visual Studio Code.

3. Select Terminal > New Terminal.

4. Run the following command to sign in to your Azure account. If you have multiple subscriptions, select the one that contains your Azure AI Search service and Foundry project.

   az login
   

5. Run the following command to generate a Microsoft Entra ID token.

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

6. Make a note of the token for use in the next section.

Run the code

To create and run the agentic retrieval pipeline:

1. Create a file named agentic-retrieval.rest in the quickstart-agentic-retrieval folder.

2. Paste the following variables and requests into the file.

   ### 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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               "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. Set @search-url and aoai-url to the values you obtained in Get endpoints.

4. Set @token to the value you obtained in Connect from your local system.

5. Send each request in succession, starting with ### Create an index.

   Each request should return a 200 OK, 201 Created, or 204 No Content status code. If you receive an error, check the request for typos and ensure that your token is valid.

Output

Each request returns different JSON based on the operation. The key output is from ### Run agentic retrieval, which should look similar to the following:

{
  "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"
    }
  ]
}

Understand the code

Now that you've run the code, let's break down the key steps:

1. Create a search index
2. Upload documents to the index
3. Create a knowledge source
4. Create a knowledge base
5. Run the retrieval pipeline

Create a search index

In Azure AI Search, an index is a structured collection of data. The following code defines an index named earth-at-night.

The index schema contains fields for document identification and page content, embeddings, and numbers. The schema also includes configurations for semantic ranking and vector search, which uses your text-embedding-3-large deployment to vectorize text and match documents based on semantic similarity.

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

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

Reference: Indexes - Create

Upload documents to the index

Currently, the earth-at-night index is empty. The following code populates the index with JSON documents from NASA's Earth at Night e-book. As required by Azure AI Search, each document conforms to the fields and data types defined in the index schema.

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

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

Reference: Documents - Index

Create a knowledge source

A knowledge source is a reusable reference to source data. The following code defines a knowledge source named earth-knowledge-source that targets the earth-at-night index.

sourceDataFields specifies which index fields are included in citation references. This example includes only human-readable fields to avoid lengthy, uninterpretable embeddings in responses.

### 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" }
        ]
    }
}

Reference: Knowledge Sources - Create

Create a knowledge base

To target your earth-knowledge-source and gpt-5-mini deployment at query time, you need a knowledge base. The following code defines a base named earth-knowledge-base.

outputMode is set to answerSynthesis, enabling natural-language answers that cite the retrieved documents and follow the provided 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."
}

Reference: Knowledge Bases - Create

Run the retrieval pipeline

You're ready to run agentic retrieval. The following code sends a two-part user query to earth-knowledge-base, which:

1. Analyzes the entire conversation to infer the user's information need.
2. Decomposes the compound query into focused subqueries.
3. Runs the subqueries concurrently against your knowledge source.
4. Uses semantic ranker to rerank and filter the results. Our example excludes responses with a reranker score of 2.5 or lower.
5. Synthesizes the top results into a natural-language answer.

### 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" }
}

Reference: Knowledge Retrieval - Retrieve

The output should contain the following components:

• response provides a synthesized, LLM-generated answer to the query that cites the retrieved documents. When answer synthesis isn't enabled, this section contains content extracted directly from the documents.

• activity tracks the steps that were taken during the retrieval process, including the subqueries generated by your gpt-5-mini deployment and the tokens used for semantic ranking, query planning, and answer synthesis.

• references lists the documents that contributed to the response, each one identified by their docKey.

Clean up resources

When you work in your own subscription, it's a good idea to finish a project by determining whether you still need the resources you created. Resources that are left running can cost you money.

In the Azure portal, you can manage your Azure AI Search and Microsoft Foundry resources by selecting All resources or Resource groups from the left pane.

Otherwise, the following requests from agentic-retrieval.rest deleted the objects you created in this quickstart.

Delete the knowledge base

### 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 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 search index

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