Quickstart: Use agentic retrieval in Azure AI Search

Note

This feature is currently in public preview. This preview is provided without a service-level agreement and isn't recommended for production workloads. Certain features might not be supported or might have constrained capabilities. For more information, see Supplemental Terms of Use for Microsoft Azure Previews.

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.

Tip

Want to get started right away? See the azure-search-dotnet-samples GitHub repository.

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.

Azure AI Search provides the agentic retrieval pipeline. Configure access for yourself and your search service to read and write data, interact with Foundry, and run the pipeline.

On your Azure AI Search service:

Enable role-based access.
Create a system-assigned managed identity.
Assign the following roles to yourself.
- Search Service Contributor
- Search Index Data Contributor
- Search Index Data Reader

Important

Agentic retrieval has two token-based billing models:

Billing from Azure AI Search for agentic retrieval.
Billing from Azure OpenAI for query planning and answer synthesis.

For more information, see Availability and pricing of agentic retrieval.

Get endpoints

Each Azure AI Search service and 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.

Sign in to the Azure portal and select your search service.
From the left pane, select Overview.
Make a note of the endpoint, which should look like https://my-service.search.windows.net.

Deploy models

To use agentic retrieval, you must deploy two Azure OpenAI models to your 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 Azure OpenAI models with Foundry.

Set up the environment

To set up the console application for this quickstart:

Create a folder named quickstart-agentic-retrieval to contain the application.
Open the folder in Visual Studio Code.
Select Terminal > New Terminal, and then run the following command to create a console application.
```
dotnet new console
```

Install the Azure AI Search client library for .NET.

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

Install the dotenv.net package to load environment variables from a .env file.
```
dotnet add package dotenv.net
```
For keyless authentication with Microsoft Entra ID, install the Azure.Identity package.
```
dotnet add package Azure.Identity
```
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 Foundry project.
```
az login
```

Run the code

To create and run the agentic retrieval pipeline:

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

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

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

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.");
        }
    }
}

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
}
Reference Type: KnowledgeBaseSearchIndexReference
{
  "DocKey": "earth_at_night_508_page_194_verbalized",
  "Id": "1",
  "ActivitySource": 3,
  "SourceData": {},
  "RerankerScore": 2.630955
}
Reference Type: KnowledgeBaseSearchIndexReference
{
  "DocKey": "earth_at_night_508_page_105_verbalized",
  "Id": "3",
  "ActivitySource": 2,
  "SourceData": {},
  "RerankerScore": 2.5884187
}
Reference Type: KnowledgeBaseSearchIndexReference
{
  "DocKey": "earth_at_night_508_page_189_verbalized",
  "Id": "4",
  "ActivitySource": 3,
  "SourceData": {},
  "RerankerScore": 2.465418
}
Reference Type: KnowledgeBaseSearchIndexReference
{
  "DocKey": "earth_at_night_508_page_193_verbalized",
  "Id": "6",
  "ActivitySource": 3,
  "SourceData": {},
  "RerankerScore": 2.4560246
}
Reference Type: KnowledgeBaseSearchIndexReference
{
  "DocKey": "earth_at_night_508_page_174_verbalized",
  "Id": "2",
  "ActivitySource": 1,
  "SourceData": {},
  "RerankerScore": 2.3254027
}
Reference Type: KnowledgeBaseSearchIndexReference
{
  "DocKey": "earth_at_night_508_page_176_verbalized",
  "Id": "5",
  "ActivitySource": 1,
  "SourceData": {},
  "RerankerScore": 2.257256
}
Reference Type: KnowledgeBaseSearchIndexReference
{
  "DocKey": "earth_at_night_508_page_177_verbalized",
  "Id": "7",
  "ActivitySource": 1,
  "SourceData": {},
  "RerankerScore": 2.1968744
}
Reference Type: KnowledgeBaseSearchIndexReference
{
  "DocKey": "earth_at_night_508_page_125_verbalized",
  "Id": "8",
  "ActivitySource": 2,
  "SourceData": {},
  "RerankerScore": 2.086579
}
Response:
... // Trimmed for brevity
Activity:
... // Trimmed for brevity
References:
... // Trimmed for brevity
Knowledge base 'earth-knowledge-base' deleted successfully.
Knowledge source 'earth-knowledge-source' deleted successfully.
Index 'earth-at-night' deleted successfully.

Understand the code

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

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, which you previously specified using the indexName variable.

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.");

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.");

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 accessible for retrieval and citations. Our 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.");

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, which you previously specified using the knowledgeBaseName variable.

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.");

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:

Analyzes the entire conversation to infer the user's information need.
Decomposes the compound query into focused subqueries.
Runs the subqueries concurrently against your knowledge source.
Uses semantic ranker to rerank and filter the results.
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 }
});

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 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.");

Note

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.

Tip

The Java version of this quickstart uses the 2025-05-01-preview REST API version, which uses the previous "knowledge agent" terminology and doesn't support the latest features available in the 2025-11-01-preview. To use these features, see the C#, Python, or REST version.

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. You get a Foundry resource (that you need for model deployments) when you create a Foundry project.
The Azure CLI for keyless authentication with Microsoft Entra ID.

Configure access

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

Azure AI Search provides the agentic retrieval pipeline. Configure access for yourself and your search service to read and write data, interact with Foundry, and run the pipeline.

On your Azure AI Search service:

Enable role-based access.
Create a system-assigned managed identity.
Assign the following roles to yourself.
- Search Service Contributor
- Search Index Data Contributor
- Search Index Data Reader

Important

Agentic retrieval has two token-based billing models:

Billing from Azure AI Search for agentic retrieval.
Billing from Azure OpenAI for query planning and answer synthesis.

For more information, see Availability and pricing of agentic retrieval.

Get endpoints

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

Sign in to the Azure portal and select your search service.
From the left pane, select Overview.
Make a note of the endpoint, which should look like https://my-service.search.windows.net.

Deploy models

To use agentic retrieval, you must deploy two Azure OpenAI models to your 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 Azure OpenAI models with Foundry.

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.

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

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>

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

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.

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);
        }
    }
}

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 look 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

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:

Analyzes the entire conversation history to determine the underlying information need.
Breaks down the compound user query into focused subqueries.
Runs each subquery simultaneously against text fields and vector embeddings in your index.
Uses semantic ranker to rerank the results of all subqueries.
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.");

Note

Tip

The JavaScript version of this quickstart uses the 2025-05-01-preview REST API version, which uses the previous "knowledge agent" terminology and doesn't support the latest features available in the 2025-11-01-preview. To use these features, see the C#, Python, or REST version.

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. You get a Foundry resource (that you need for model deployments) when you create a Foundry project.
The Azure CLI for keyless authentication with Microsoft Entra ID.

Configure access

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

Azure AI Search provides the agentic retrieval pipeline. Configure access for yourself and your search service to read and write data, interact with Foundry, and run the pipeline.

On your Azure AI Search service:

Enable role-based access.
Create a system-assigned managed identity.
Assign the following roles to yourself.
- Search Service Contributor
- Search Index Data Contributor
- Search Index Data Reader

Important

Agentic retrieval has two token-based billing models:

Billing from Azure AI Search for agentic retrieval.
Billing from Azure OpenAI for query planning and answer synthesis.

For more information, see Availability and pricing of agentic retrieval.

Get endpoints

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

Sign in to the Azure portal and select your search service.
From the left pane, select Overview.
Make a note of the endpoint, which should look like https://my-service.search.windows.net.

Deploy models

To use agentic retrieval, you must deploy two Azure OpenAI models to your 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 Azure OpenAI models with Foundry.

Set up the environment

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
```
Create the package.json with the following command:
```
npm init -y
```
Install the Azure AI Search client library (Azure.Search.Documents) for JavaScript with:
```
npm install @azure/search-documents --version 12.2.0-alpha.20250606.1
```
Install the Azure OpenAI client library with:
```
npm install @azure/openai --version 5.10.1
```
Install the dotenv package to load environment variables from a .env file with:
```
npm install dotenv
```
For the recommended keyless authentication with Microsoft Entra ID, install the Azure Identity client library with:
```
npm install @azure/identity
```

Run the code

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.

Paste the following code into a new file named index.js:

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

Sign in to Azure with the following command:
```
az login
```
Run the JavaScript code with the following command:
```
node index.js
```

Output

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

[dotenv@17.2.0] injecting env (0) from .env (tip: ⚙️  override existing env vars with { override: true })
🚀 Starting Azure AI Search agentic retrieval quickstart...

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

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

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

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

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


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

Understand the code

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

Create a search index

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

Upload documents to the index

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

Create a knowledge agent

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

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

Set up messages

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.

const messages = [
    {
        role: "system",
        content: `A Q&A agent that can answer questions about the Earth at night.
Sources have a JSON format with a ref_id that must be cited in the answer.
If you do not have the answer, respond with "I don't know".`
    },
    {
        role: "user",
        content: "Why do suburban belts display larger December brightening than urban cores even though absolute light levels are higher downtown? Why is the Phoenix nighttime street grid is so sharply visible from space, whereas large stretches of the interstate between midwestern cities remain comparatively dim?"
    }
];

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:

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

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

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.

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

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.

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

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.

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

Continue the conversation

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

Clean up resources

Delete the knowledge agent

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

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

Delete the search index

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

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

Note

Tip

Want to get started right away? See the azure-search-python-samples GitHub repository.

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 latest version of Python.

Configure access

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

Azure AI Search provides the agentic retrieval pipeline. Configure access for yourself and your search service to read and write data, interact with Foundry, and run the pipeline.

On your Azure AI Search service:

Enable role-based access.
Create a system-assigned managed identity.
Assign the following roles to yourself.
- Search Service Contributor
- Search Index Data Contributor
- Search Index Data Reader

Important

Agentic retrieval has two token-based billing models:

Billing from Azure AI Search for agentic retrieval.
Billing from Azure OpenAI for query planning and answer synthesis.

For more information, see Availability and pricing of agentic retrieval.

Get endpoints

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

Sign in to the Azure portal and select your search service.
From the left pane, select Overview.
Make a note of the endpoint, which should look like https://my-service.search.windows.net.

Deploy models

To use agentic retrieval, you must deploy two Azure OpenAI models to your 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 Azure OpenAI models with Foundry.

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:

Create a folder named quickstart-agentic-retrieval.
Open the folder in Visual Studio Code.
Select Terminal > New Terminal.
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:

Run the following command to install the required packages.

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

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

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.")

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

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

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

Understand the code

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

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, which you previously specified using the index_name variable.

# 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 to the index

# 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

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 accessible for retrieval and citations. Our 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.")

Create a 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.")

Set up messages

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:

Analyzes the entire conversation to infer the user's information need.
Decomposes the compound query into focused subqueries.
Runs the subqueries concurrently against your knowledge source.
Uses semantic ranker to rerank and filter the results.
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.")

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

# 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 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.")

Note

Tip

The TypeScript version of this quickstart uses the 2025-05-01-preview REST API version, which uses the previous "knowledge agent" terminology and doesn't support the latest features available in the 2025-11-01-preview. To use these features, see the C#, Python, or REST version.

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. You get a Foundry resource (that you need for model deployments) when you create a Foundry project.
The Azure CLI for keyless authentication with Microsoft Entra ID.

Configure access

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

Azure AI Search provides the agentic retrieval pipeline. Configure access for yourself and your search service to read and write data, interact with Foundry, and run the pipeline.

On your Azure AI Search service:

Enable role-based access.
Create a system-assigned managed identity.
Assign the following roles to yourself.
- Search Service Contributor
- Search Index Data Contributor
- Search Index Data Reader

Important

Agentic retrieval has two token-based billing models:

Billing from Azure AI Search for agentic retrieval.
Billing from Azure OpenAI for query planning and answer synthesis.

For more information, see Availability and pricing of agentic retrieval.

Get endpoints

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

Sign in to the Azure portal and select your search service.
From the left pane, select Overview.
Make a note of the endpoint, which should look like https://my-service.search.windows.net.

Deploy models

To use agentic retrieval, you must deploy two Azure OpenAI models to your 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 Azure OpenAI models with Foundry.

Set up the environment

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
```
Create the package.json with the following command:
```
npm init -y
```
Update the package.json to ECMAScript with the following command:
```
npm pkg set type=module
```
Install the Azure AI Search client library (Azure.Search.Documents) for JavaScript with:
```
npm install @azure/search-documents --version 12.2.0-alpha.20250606.1
```
Install the Azure OpenAI client library with:
```
npm install @azure/openai --version 5.10.1
```
Install the dotenv package to load environment variables from a .env file with:
```
npm install dotenv
```
For the recommended keyless authentication with Microsoft Entra ID, install the Azure Identity client library with:
```
npm install @azure/identity
```

Run the code

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.

Paste the following code into a new file named index.ts:

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

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

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

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

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

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

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

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

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

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

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

        // Upload sample documents
        await uploadDocuments(searchClient);

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

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

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

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

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

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

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

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

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

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

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

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

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

    try {
        const response = await fetch(documentsUrl);

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

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

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

        return transformedDocuments;

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

    const messages: KnowledgeAgentMessage[] = [
        {
            role: "system",
            content: `A Q&A agent that can answer questions about the Earth at night.
Sources have a JSON format with a ref_id that must be cited in the answer.
If you do not have the answer, respond with "I don't know".`
        },
        {
            role: "user",
            content: "Why do suburban belts display larger December brightening than urban cores even though absolute light levels are higher downtown? Why is the Phoenix nighttime street grid is so sharply visible from space, whereas large stretches of the interstate between midwestern cities remain comparatively dim?"
        }
    ];

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

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

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

        console.log(assistantContent);

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

    return await response.json() as AgenticRetrievalResponse;
}

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

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

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

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

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

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

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

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

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

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

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

        console.log(assistantContent);

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

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

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

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

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

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

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

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

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

Create the tsconfig.json file to transpile the TypeScript code and copy the following code for ECMAScript.

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

Transpile from TypeScript to JavaScript.
```
tsc
```
Sign in to Azure with the following command:
```
az login
```
Run the JavaScript code with the following command:
```
node index.js
```

Output

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

[dotenv@17.2.0] injecting env (0) from .env (tip: ⚙️  override existing env vars with { override: true })
🚀 Starting Azure AI Search agentic retrieval quickstart...

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

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

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

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

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


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

Understand the code

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

Create a search index

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

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

Upload documents to the index

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

Create a knowledge agent

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

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

Set up messages

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.

const messages: KnowledgeAgentMessage[] = [
    {
        role: "system",
        content: `A Q&A agent that can answer questions about the Earth at night.
Sources have a JSON format with a ref_id that must be cited in the answer.
If you do not have the answer, respond with "I don't know".`
    },
    {
        role: "user",
        content: "Why do suburban belts display larger December brightening than urban cores even though absolute light levels are higher downtown? Why is the Phoenix nighttime street grid is so sharply visible from space, whereas large stretches of the interstate between midwestern cities remain comparatively dim?"
    }
];

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:

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

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

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

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

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

return await response.json() as AgenticRetrievalResponse;

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.

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

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

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.

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

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.

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

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

Continue the conversation

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

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

Clean up resources

Delete the knowledge agent

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

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

Delete the search index

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

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

Note

Tip

Want to get started right away? See the azure-search-rest-samples GitHub repository.

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

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

Azure AI Search provides the agentic retrieval pipeline. Configure access for yourself and your search service to read and write data, interact with Foundry, and run the pipeline.

On your Azure AI Search service:

Enable role-based access.
Create a system-assigned managed identity.
Assign the following roles to yourself.
- Search Service Contributor
- Search Index Data Contributor
- Search Index Data Reader

Important

Agentic retrieval has two token-based billing models:

Billing from Azure AI Search for agentic retrieval.
Billing from Azure OpenAI for query planning and answer synthesis.

For more information, see Availability and pricing of agentic retrieval.

Get endpoints

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

Sign in to the Azure portal and select your search service.
From the left pane, select Overview.
Make a note of the endpoint, which should look like https://my-service.search.windows.net.

Deploy models

To use agentic retrieval, you must deploy two Azure OpenAI models to your 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 Azure OpenAI models with Foundry.

Connect from your local system

To connect from your local system:

Create a folder named quickstart-agentic-retrieval.
Open the folder in Visual Studio Code.
Select Terminal > New Terminal.
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 following command to generate a Microsoft Entra ID token.

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

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

Run the code

To create and run the agentic retrieval pipeline:

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

Paste the following variables and requests into the file.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Set @search-url and aoai-url to the values you obtained in Get endpoints.
Set @token to the value you obtained in Connect from your local system.
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:

Create a search index

In Azure AI Search, an index is a structured collection of data. The following code uses Indexes - Create (REST API) to define an index named earth-at-night, which you previously specified using the @index-name variable.

### 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 to the index

Currently, the earth-at-night index is empty. The following code uses Documents - Index (REST API) 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.


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

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

Create a knowledge source

A knowledge source is a reusable reference to source data. The following code uses Knowledge Sources - Create (REST API) to define a knowledge source named earth-knowledge-source that targets the earth-at-night index.

sourceDataFields specifies which index fields are accessible for retrieval and citations. Our 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" }
            ]
        }
    }

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 uses Knowledge Bases - Create (REST API) to define a base named earth-knowledge-base, which you previously specified using the @knowledge-base-name variable.

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."
    }

Run the retrieval pipeline

You're ready to run agentic retrieval. The following code uses Knowledge Retrieval - Retrieve (REST API) to send a two-part user query to earth-knowledge-base, which:

Analyzes the entire conversation to infer the user's information need.
Decomposes the compound query into focused subqueries.
Runs the subqueries concurrently against your knowledge source.
Uses semantic ranker to rerank and filter the results. Our example excludes responses with a reranker score of 2.5 or lower.
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" }
    }

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 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}}

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Last updated on 2025-11-18

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Quickstart: Use agentic retrieval in Azure AI Search

Prerequisites

Configure access

Get endpoints

Deploy models

Set up the environment

Run the code

Output

Understand the code

Create a search index

Upload documents to the index

Create a knowledge source

Create a knowledge base

Set up messages

Run the retrieval pipeline

Review the response, activity, and references

Continue the conversation

Review the new response, activity, and references

Clean up resources

Delete the knowledge base

Delete the knowledge source

Delete the search index

Prerequisites

Configure access

Get endpoints

Deploy models

Set up the environment

Run the code

Output

Understand the code

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

Clean up resources

Delete the knowledge agent

Delete the search index

Prerequisites

Configure access

Get endpoints

Deploy models

Set up the environment

Run the code

Output

Understand the code

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

Clean up resources

Delete the knowledge agent

Delete the search index

Prerequisites

Configure access

Get endpoints

Deploy models

Connect from your local system

Run the code

Output

Understand the code

Create a search index

Upload documents to the index

Create a knowledge source

Create a knowledge base

Set up messages

Run the retrieval pipeline

Review the response, activity, and references

Continue the conversation

Review the new response, activity, and references