Rychlý úvod: Agentní načítání

V tomto rychlém startu použijete agentické načítání k vytvoření konverzačního vyhledávání pomocí dokumentů indexovaných v Azure AI Vyhledávač a rozsáhlého jazykového modelu (LLM) z Azure OpenAI v modelech Foundry.

Znalostní báze používá plánování dotazů založené na LLM (ve verzi Preview) k rozložení složitých dotazů na poddotazy. Potom spustí poddotazy na jednom nebo více zdrojích znalostí a vrátí výsledky s metadaty. Znalostní báze ve výchozím nastavení vrací nezpracovaný obsah ze svých zdrojů, ale tento rychlý start používá syntézu odpovědí (Preview) k vygenerování odpovědí v přirozeném jazyce.

I když můžete použít vlastní data, tento průvodce začátkem používá ukázkové dokumenty JSON z e-knihy NASA „Země v noci“.

Požadavky

• Účet Azure s aktivním předplatným. Vytvořte si bezplatný účet.

• Služba Azure AI Vyhledávač v libovolném regionu , který nabízí agentní vyhledávání. Tento rychlý start vyžaduje úroveň Basic nebo vyšší pro podporu spravované identity.

• Projekt Microsoft Foundry a zdroj. Při vytváření projektu se zdroj vytvoří automaticky.

• Vložený model nasazený do projektu pro převod textu na vektor. Můžete použít libovolný text-embedding model, například text-embedding-3-large.

• LLM nasazený do projektu pro plánování dotazů a generování odpovědí. Můžete použít libovolný podporovaný LLM, například gpt-5-mini.

• .NET 8 nebo novější.

• Git naklonuje ukázkové úložiště.

• Azure CLI pro ověřování bez klíčů pomocí Microsoft Entra ID.

Konfigurace přístupu

Než začnete, ujistěte se, že máte oprávnění pro přístup k obsahu a operacím. Tento rychlý start používá Microsoft Entra ID pro ověřování a přístup na základě role k autorizaci. Abyste mohli přiřadit role, musíte být vlastníkem nebo správcem uživatelských přístupů . Pokud role nejsou proveditelné, použijte místo toho ověřování založené na klíči .

Pro konfiguraci přístupu pro tento rychlý průvodce postupujte následovně:

1. Přihlaste se k portálu Azure.

2. Ve službě Azure AI Vyhledávač:

   1. Povolte přístup na základě role.

   2. Vytvořte spravovanou identitu přiřazenou systémem.

   3. Přiřaďte k vašemu uživatelskému účtu následující role: Přispěvatel vyhledávací služby, Přispěvatel datindexu vyhledávání a Čtenář dat indexu vyhledávání.

3. Na prostředku Microsoft Foundry přiřaďte Uživatel služby Cognitive Services spravované identitě vyhledávací služby.

Získání koncových bodů

Každý prostředek služby Azure AI Vyhledávač a Microsoft Foundry má endpoint, což je jedinečná adresa URL, která identifikuje a poskytuje síťový přístup k prostředku. V další části zadáte tyto koncové body, abyste se programaticky připojili k vašim prostředkům.

Získání koncových bodů pro tento rychlý start:

1. Přihlaste se k portálu Azure.

2. Ve službě Azure AI Vyhledávač:

   1. V levém podokně vyberte Přehled.

   2. Zkopírujte adresu URL, která by měla vypadat nějak https://my-service.search.windows.nettakto.

3. Ve zdroji Microsoft Foundry:

   1. V levém podokně vyberte Správa prostředků>Klíče a koncový bod.

   2. Zkopírujte adresu URL na kartě OpenAI, která by měla vypadat jako https://my-resource.openai.azure.com/.

Nastavení prostředí

1. Pomocí Gitu naklonujte ukázkové úložiště.

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

2. Přejděte do složky rychlého startu.

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

3. V sample.env nahraďte zástupné hodnoty pro SEARCH_ENDPOINT a AOAI_ENDPOINT adresami URL, které jste získali v části Get endpoints.

4. Přejmenujte sample.env na .env.

   mv sample.env .env
   

5. Nainstalujte závislosti.

   dotnet restore AgenticRetrievalQuickstart.csproj
   

   Po dokončení obnovení se ujistěte, že se ve výstupu nezobrazí žádné chyby.

6. Pro ověřování bez klíčů pomocí Microsoft Entra ID se přihlaste ke svému účtu Azure. Pokud máte více předplatných, vyberte předplatné, které obsahuje vaše prostředky Azure AI Vyhledávač a Microsoft Foundry.

   az login
   

Spuštění kódu

Spusťte aplikaci pro vytvoření indexu, nahrání dokumentů, konfiguraci zdroje znalostí a znalostní báze a spuštění agentních dotazů na načítání informací.

dotnet run --project AgenticRetrievalQuickstart.csproj

Výstup

Výstup aplikace by měl být podobný následujícímu:

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

Vysvětlení kódu

Teď, když kód spustíte, si pojďme rozdělit klíčové kroky:

1. Vytvoření indexu vyhledávání
2. Nahrání dokumentů do indexu
3. Vytvoření zdroje znalostí
4. Vytvoření znalostní báze
5. Nastavení zpráv
6. Spuštění kanálu načítání
7. Pokračovat v konverzaci

Vytvoření indexu vyhledávání

V Azure AI Vyhledávač je index strukturovanou kolekcí dat. Následující kód definuje index s názvem earth-at-night.

Schéma indexu obsahuje pole pro identifikaci dokumentu a obsah stránky, vkládání a čísla. Schéma také obsahuje konfigurace pro sémantické řazení a vektorové vyhledávání, které používá vaše text-embedding-3-large nasazení k vektorizaci textu a porovnávání dokumentů na základě sémantické nebo koncepční podobnosti.

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

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

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

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

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

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

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

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

Nahrání dokumentů do indexu

earth-at-night Index je v současné době prázdný. Následující kód naplní index dokumenty JSON z e-knihy NASA Země v noci. Podle Azure AI Vyhledávač odpovídá každý dokument polím a datovým typům definovaným ve schématu indexu.

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

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

Reference:SearchClient, SearchIndexingBufferedSender

Vytvoření zdroje znalostí

Zdroj znalostí je opakovaně použitelný odkaz na zdrojová data. Následující kód definuje zdroj znalostí s názvem earth-knowledge-source , který cílí na earth-at-night index.

SourceDataFields určuje, která pole indexu jsou zahrnuta do odkazů na citaci. Tento příklad obsahuje pouze pole čitelná pro člověka, aby nedocházelo k zdlouhavým, neinterpretovatelným vkládáním do odpovědí.

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

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

Reference:SearchIndexKnowledgeSource

Vytvoření znalostní báze

K cílení earth-knowledge-source a gpt-5-mini nasazení v době dotazu potřebujete znalostní bázi. Následující kód definuje znalostní bázi s názvem earth-knowledge-base.

OutputMode (Preview) je nastavena na AnswerSynthesis, což umožňuje odpovědi v přirozeném jazyce, které citují načtené dokumenty a řídí se poskytnutým AnswerInstructions. RetrievalReasoningEffort (preview) je nastaven na low, aby řídil míru uvažování používaného při plánování dotazů.

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

var model = new KnowledgeBaseAzureOpenAIModel(azureOpenAIParameters: openAiParameters);

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

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

Reference:KnowledgeBaseAzureOpenAIModel, KnowledgeBase

Nastavení zpráv

Zprávy jsou vstupem pro trasu načítání a obsahují historii konverzací. Každá zpráva obsahuje roli, která označuje jeho původ, například system nebo usera obsah v přirozeném jazyce. LLM, který použijete, určuje, které role jsou platné.

Následující kód vytvoří systémovou zprávu, která dává pokyn earth-knowledge-base odpovědět na otázky o Zemi v noci a odpovědět "Nevím", když jsou odpovědi nedostupné.

// 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 }
    }
};

Spuštění potrubí pro získávání

Jste připraveni zahájit agentické vyhledávání. Následující kód odešle dotaz uživatele se dvěma částmi na earth-knowledge-base, který:

1. Analyzuje celou konverzaci a odvodí potřebné informace uživatele.
2. Rozdělí složený dotaz do zaměřených poddotazů.
3. Spustí poddotazy paralelně ve vašem zdroji znalostí.
4. Pomocí sémantického rankeru můžete výsledky přeřadit a filtrovat.
5. Syntetizuje nejlepší výsledky do odpovědi přirozeného jazyka.

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

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

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

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

Reference:KnowledgeBaseRetrievalClient, KnowledgeBaseRetrievalRequest

Kontrola odpovědí, aktivit a odkazů

Následující kód zobrazí odpověď, aktivitu a odkazy z kanálu načítání, kde:

• Response poskytuje syntetizovanou odpověď vygenerovanou LLM na dotaz, který cituje načtené dokumenty. Pokud syntéza odpovědí není povolená, obsahuje tento oddíl obsah extrahovaný přímo z dokumentů.

• Activity sleduje kroky provedené během procesu načítání, včetně poddotazů vygenerovaných vaším gpt-5-mini nasazením a tokenů používaných pro sémantické řazení, plánování dotazů a syntézu odpovědí.

• References zobrazuje dokumenty, které přispěly k odpovědi, každý z nich je identifikován svým 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);
}

Pokračovat v konverzaci

Následující kód pokračuje v konverzaci s earth-knowledge-base. Po odeslání tohoto uživatelského dotazu znalostní báze načte relevantní obsah a earth-knowledge-source připojí odpověď do seznamu zpráv.

// 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 }
});

Kontrola nové odpovědi, aktivity a odkazů

Následující kód zobrazí novou odpověď, aktivitu a odkazy z kanálu načítání.

// 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);
}

Vyčištění prostředků

Když pracujete ve vlastním předplatném, je vhodné dokončit projekt odebráním prostředků, které už nepotřebujete. Prostředky, které zůstávají spuštěné, vás mohou stát peníze.

Na portálu Azure vyberte Všechny prostředky nebo Zdrojové skupiny v levém podokně a vyhledejte a spravujte prostředky. Prostředky můžete odstranit jednotlivě nebo odstranit skupinu prostředků a odebrat tak všechny prostředky najednou.

V opačném případě následující kód z program.cs odstraní objekty, které jste vytvořili v rámci tohoto rychlého startu.

Odstranění znalostní báze

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

Odstranění zdroje znalostí

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

Odstranění indexu vyhledávání

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

V tomto rychlém startu použijete agentické načítání k vytvoření konverzačního vyhledávání pomocí dokumentů indexovaných v Azure AI Vyhledávač a rozsáhlého jazykového modelu (LLM) z Azure OpenAI v modelech Foundry.

Znalostní báze používá plánování dotazů založené na LLM (preview) k rozložení složitých dotazů na poddotazy. Potom spustí poddotazy na jednom nebo více zdrojích znalostí a vrátí výsledky s metadaty. Znalostní báze ve výchozím nastavení vrací nezpracovaný obsah ze svých zdrojů, ale tento rychlý start používá syntézu odpovědí (Preview) k vygenerování odpovědí v přirozeném jazyce.

I když můžete použít vlastní data, tento průvodce začátkem používá ukázkové dokumenty JSON z e-knihy NASA „Země v noci“.

Požadavky

• Účet Azure s aktivním předplatným. Vytvořte si bezplatný účet.

• Služba Azure AI Vyhledávač v libovolném regionu , který nabízí agentní vyhledávání. Tento rychlý start vyžaduje úroveň Basic nebo vyšší pro podporu spravované identity.

• Projekt Microsoft Foundry a zdroj. Při vytváření projektu se zdroj vytvoří automaticky.

• Vložený model nasazený do projektu pro převod textu na vektor. Můžete použít libovolný text-embedding model, například text-embedding-3-large.

• LLM nasazený do projektu pro plánování dotazů a generování odpovědí. Můžete použít libovolný podporovaný LLM, například gpt-5-mini.

• Java 11 nebo novější a Maven.

• Git naklonuje ukázkové úložiště.

• Azure CLI pro ověřování bez klíčů pomocí Microsoft Entra ID.

Konfigurace přístupu

Než začnete, ujistěte se, že máte oprávnění pro přístup k obsahu a operacím. Tento rychlý start používá Microsoft Entra ID pro ověřování a přístup na základě role k autorizaci. Abyste mohli přiřadit role, musíte být vlastníkem nebo správcem uživatelských přístupů . Pokud role nejsou proveditelné, použijte místo toho ověřování založené na klíči .

Pro konfiguraci přístupu pro tento rychlý průvodce postupujte následovně:

1. Přihlaste se k portálu Azure.

2. Ve službě Azure AI Vyhledávač:

   1. Povolte přístup na základě role.

   2. Vytvořte spravovanou identitu přiřazenou systémem.

   3. Přiřaďte k vašemu uživatelskému účtu následující role: Přispěvatel vyhledávací služby, Přispěvatel datindexu vyhledávání a Čtenář dat indexu vyhledávání.

3. Na prostředku Microsoft Foundry přiřaďte Uživatel služby Cognitive Services spravované identitě vyhledávací služby.

Získání koncových bodů

Každý prostředek služby Azure AI Vyhledávač a Microsoft Foundry má endpoint, což je jedinečná adresa URL, která identifikuje a poskytuje síťový přístup k prostředku. V další části zadáte tyto koncové body, abyste se programaticky připojili k vašim prostředkům.

Získání koncových bodů pro tento rychlý start:

1. Přihlaste se k portálu Azure.

2. Ve službě Azure AI Vyhledávač:

   1. V levém podokně vyberte Přehled.

   2. Zkopírujte adresu URL, která by měla vypadat nějak https://my-service.search.windows.nettakto.

3. Ve zdroji Microsoft Foundry:

   1. V levém podokně vyberte Správa prostředků>Klíče a koncový bod.

   2. Zkopírujte adresu URL na kartě OpenAI, která by měla vypadat jako https://my-resource.openai.azure.com/.

Nastavení prostředí

1. Pomocí Gitu naklonujte ukázkové úložiště.

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

2. Přejděte do složky rychlého startu.

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

3. V sample.env nahraďte zástupné hodnoty pro SEARCH_ENDPOINT a AOAI_ENDPOINT adresami URL, které jste získali v části Get endpoints.

4. Přejmenujte sample.env na .env.

   mv sample.env .env
   

5. Nainstalujte závislosti.

   mvn clean dependency:copy-dependencies
   

6. Pro ověřování bez klíčů pomocí Microsoft Entra ID se přihlaste ke svému účtu Azure. Pokud máte více předplatných, vyberte předplatné, které obsahuje vaše prostředky Azure AI Vyhledávač a Microsoft Foundry.

   az login
   

Spuštění kódu

Sestavte a spusťte aplikaci, abyste vytvořili index, nahráli dokumenty, nakonfigurovali zdroj znalostí a znalostní bázi a zahájili dotazy pro načítání dat prostřednictvím agentů.

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

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

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

Výstup

Výstup aplikace by měl být podobný následujícímu:

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

Vysvětlení kódu

Teď, když kód spustíte, si pojďme rozdělit klíčové kroky:

1. Vytvoření indexu vyhledávání
2. Nahrání dokumentů do indexu
3. Vytvoření zdroje znalostí
4. Vytvoření znalostní báze
5. Nastavení zpráv
6. Spuštění kanálu načítání
7. Pokračovat v konverzaci

Vytvoření indexu vyhledávání

V Azure AI Vyhledávač je index strukturovanou kolekcí dat. Následující kód definuje index s názvem earth-at-night.

Schéma indexu obsahuje pole pro identifikaci dokumentu a obsah stránky, vkládání a čísla. Schéma také obsahuje konfigurace pro sémantické řazení a vektorové vyhledávání, které používá vaše text-embedding-3-large nasazení k vektorizaci textu a porovnávání dokumentů na základě sémantické nebo koncepční podobnosti.

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

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

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

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

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

indexClient.createOrUpdateIndex(index);

Reference:SearchField, VectorSearch, SemanticSearch, SearchIndex, SearchIndexClient

Nahrání dokumentů do indexu

earth-at-night Index je v současné době prázdný. Následující kód naplní index dokumenty JSON z e-knihy NASA Země v noci. Podle Azure AI Vyhledávač odpovídá každý dokument polím a datovým typům definovaným ve schématu indexu.

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

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

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

if (response.statusCode() != 200) {
    throw new IOException(
        "Failed to fetch documents: " + response.statusCode());
}

ObjectMapper mapper = new ObjectMapper();
JsonNode jsonArray = mapper.readTree(response.body());

List<SearchDocument> documents = new ArrayList<>();
for (int i = 0; i < jsonArray.size(); i++) {
    JsonNode doc = jsonArray.get(i);
    SearchDocument searchDoc = new SearchDocument();

    searchDoc.put("id", doc.has("id")
        ? doc.get("id").asText() : String.valueOf(i + 1));
    searchDoc.put("page_chunk", doc.has("page_chunk")
        ? doc.get("page_chunk").asText() : "");

    if (doc.has("page_embedding_text_3_large")
            && doc.get("page_embedding_text_3_large")
                .isArray()) {
        List<Double> embeddings = new ArrayList<>();
        for (JsonNode embedding
                : doc.get("page_embedding_text_3_large")) {
            embeddings.add(embedding.asDouble());
        }
        searchDoc.put(
            "page_embedding_text_3_large", embeddings);
    } else {
        List<Double> fallback = new ArrayList<>();
        for (int j = 0; j < 3072; j++) {
            fallback.add(0.1);
        }
        searchDoc.put(
            "page_embedding_text_3_large", fallback);
    }

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

    documents.add(searchDoc);
}

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

searchClient.uploadDocuments(documents);

Reference:SearchClient, SearchDocument

Vytvoření zdroje znalostí

Zdroj znalostí je opakovaně použitelný odkaz na zdrojová data. Následující kód definuje zdroj znalostí s názvem earth-knowledge-source , který cílí na earth-at-night index.

sourceDataFields určuje, která pole indexu jsou zahrnuta do odkazů na citaci. Tento příklad obsahuje pouze pole čitelná pro člověka, aby nedocházelo k zdlouhavým, neinterpretovatelným vkládáním do odpovědí.

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

indexClient.createOrUpdateKnowledgeSource(indexKnowledgeSource);

Reference:SearchIndexKnowledgeSource

Vytvoření znalostní báze

K cílení earth-knowledge-source a gpt-5-mini nasazení v době dotazu potřebujete znalostní bázi. Následující kód definuje znalostní bázi s názvem earth-knowledge-base.

OutputMode (verze Preview) je nastavena na ANSWER_SYNTHESIS, aby umožňovala odpovědi v přirozeném jazyce, které citují získané dokumenty a řídí se pokyny zadanými v AnswerInstructions. RetrievalReasoningEffort (Preview) je nastavena na low, aby určovala míru uvažování používaného při plánování dotazů.

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

KnowledgeBaseAzureOpenAIModel model =
    new KnowledgeBaseAzureOpenAIModel(openAiParameters);

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

indexClient.createOrUpdateKnowledgeBase(knowledgeBase);

Reference:KnowledgeBaseAzureOpenAIModel, KnowledgeBase

Nastavení zpráv

Zprávy jsou vstupem pro trasu načítání a obsahují historii konverzací. Každá zpráva obsahuje roli, která označuje jeho původ, například system nebo usera obsah v přirozeném jazyce. LLM, který použijete, určuje, které role jsou platné.

Následující kód vytvoří systémovou zprávu, která dává pokyn earth-knowledge-base odpovědět na otázky o Zemi v noci a odpovědět "Nevím", když jsou odpovědi nedostupné.

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

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

Spuštění potrubí pro získávání

Jste připraveni zahájit agentické vyhledávání. Následující kód odešle dotaz uživatele se dvěma částmi na earth-knowledge-base, který:

1. Analyzuje celou konverzaci a odvodí potřebné informace uživatele.
2. Rozdělí složený dotaz do zaměřených poddotazů.
3. Spustí poddotazy paralelně ve vašem zdroji znalostí.
4. Pomocí sémantického rankeru můžete výsledky přeřadit a filtrovat.
5. Syntetizuje nejlepší výsledky do odpovědi přirozeného jazyka.

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

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

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

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

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

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

Pomocná rutina retrieve sestaví KnowledgeBaseRetrievalOptions z historie konverzace, nastaví úroveň uvažování při vyhledávání, přidá parametry zdroje znalostí a vrátí KnowledgeBaseRetrievalResult:

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

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

    return client.retrieve(request);
}

Reference:KnowledgeBaseRetrievalClient, KnowledgeBaseRetrievalOptions

Kontrola odpovědí, aktivit a odkazů

Následující kód zobrazí odpověď, aktivitu a odkazy z kanálu načítání, kde:

• Response poskytuje syntetizovanou odpověď vygenerovanou LLM na dotaz, který cituje načtené dokumenty. Pokud syntéza odpovědí není povolená, obsahuje tento oddíl obsah extrahovaný přímo z dokumentů.

• Activity sleduje kroky provedené během procesu načítání, včetně poddotazů vygenerovaných vaším gpt-5-mini nasazením a tokenů používaných pro sémantické řazení, plánování dotazů a syntézu odpovědí.

• References zobrazuje dokumenty, které přispěly k odpovědi, každý z nich je identifikován svým docKey.

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

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

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

Pokračovat v konverzaci

Následující kód pokračuje v konverzaci s earth-knowledge-base. Po odeslání tohoto uživatelského dotazu znalostní báze načte relevantní obsah a earth-knowledge-source připojí odpověď do seznamu zpráv.

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

retrievalResult = retrieve(baseClient, messages, knowledgeSourceName);

Kontrola nové odpovědi, aktivity a odkazů

Následující kód extrahuje text odpovědi a volání printResult pro zobrazení nové odpovědi, aktivity a odkazů.

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

printResult(responseText, retrievalResult);

Vyčištění prostředků

Když pracujete ve vlastním předplatném, je vhodné dokončit projekt odebráním prostředků, které už nepotřebujete. Prostředky, které zůstávají spuštěné, vás mohou stát peníze.

Na portálu Azure vyberte Všechny prostředky nebo Zdrojové skupiny v levém podokně a vyhledejte a spravujte prostředky. Prostředky můžete odstranit jednotlivě nebo odstranit skupinu prostředků a odebrat tak všechny prostředky najednou.

V opačném případě následující kód z AgenticRetrievalQuickstart.java odstraní objekty, které jste vytvořili v rámci tohoto rychlého startu.

Odstranění znalostní báze

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

Odstranění zdroje znalostí

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

Odstranění indexu vyhledávání

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

V tomto rychlém startu použijete agentické načítání k vytvoření konverzačního vyhledávání pomocí dokumentů indexovaných v Azure AI Vyhledávač a rozsáhlého jazykového modelu (LLM) z Azure OpenAI v modelech Foundry.

Znalostní báze používá plánování dotazů založené na LLM (ve verzi Preview) k rozložení složitých dotazů na poddotazy. Potom spustí poddotazy na jednom nebo více zdrojích znalostí a vrátí výsledky s metadaty. Znalostní báze ve výchozím nastavení vrací nezpracovaný obsah ze svých zdrojů, ale tento rychlý start používá syntézu odpovědí (Preview) k vygenerování odpovědí v přirozeném jazyce.

I když můžete použít vlastní data, tento průvodce začátkem používá ukázkové dokumenty JSON z e-knihy NASA „Země v noci“.

Požadavky

• Účet Azure s aktivním předplatným. Vytvořte si bezplatný účet.

• Služba Azure AI Vyhledávač v libovolném regionu , který nabízí agentní vyhledávání. Tento rychlý start vyžaduje úroveň Basic nebo vyšší pro podporu spravované identity.

• Projekt Microsoft Foundry a zdroj. Při vytváření projektu se zdroj vytvoří automaticky.

• Vložený model nasazený do projektu pro převod textu na vektor. Můžete použít libovolný text-embedding model, například text-embedding-3-large.

• LLM nasazený do projektu pro plánování dotazů a generování odpovědí. Můžete použít libovolný podporovaný LLM, například gpt-5-mini.

• Node.js 20 LTS nebo novější.

• Git naklonuje ukázkové úložiště.

• Azure CLI pro ověřování bez klíčů pomocí Microsoft Entra ID.

Konfigurace přístupu

Než začnete, ujistěte se, že máte oprávnění pro přístup k obsahu a operacím. Tento rychlý start používá Microsoft Entra ID pro ověřování a přístup na základě role k autorizaci. Abyste mohli přiřadit role, musíte být vlastníkem nebo správcem uživatelských přístupů . Pokud role nejsou proveditelné, použijte místo toho ověřování založené na klíči .

Pro konfiguraci přístupu pro tento rychlý průvodce postupujte následovně:

1. Přihlaste se k portálu Azure.

2. Ve službě Azure AI Vyhledávač:

   1. Povolte přístup na základě role.

   2. Vytvořte spravovanou identitu přiřazenou systémem.

   3. Přiřaďte k vašemu uživatelskému účtu následující role: Přispěvatel vyhledávací služby, Přispěvatel datindexu vyhledávání a Čtenář dat indexu vyhledávání.

3. Na prostředku Microsoft Foundry přiřaďte Uživatel služby Cognitive Services spravované identitě vyhledávací služby.

Získání koncových bodů

Každý prostředek služby Azure AI Vyhledávač a Microsoft Foundry má endpoint, což je jedinečná adresa URL, která identifikuje a poskytuje síťový přístup k prostředku. V další části zadáte tyto koncové body, abyste se programaticky připojili k vašim prostředkům.

Získání koncových bodů pro tento rychlý start:

1. Přihlaste se k portálu Azure.

2. Ve službě Azure AI Vyhledávač:

   1. V levém podokně vyberte Přehled.

   2. Zkopírujte adresu URL, která by měla vypadat nějak https://my-service.search.windows.nettakto.

3. Ve zdroji Microsoft Foundry:

   1. V levém podokně vyberte Správa prostředků>Klíče a koncový bod.

   2. Zkopírujte adresu URL na kartě OpenAI, která by měla vypadat jako https://my-resource.openai.azure.com/.

Nastavení prostředí

1. Pomocí Gitu naklonujte ukázkové úložiště.

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

2. Přejděte do složky rychlého startu.

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

3. V sample.env nahraďte zástupné hodnoty pro AZURE_SEARCH_ENDPOINT a AZURE_OPENAI_ENDPOINT adresami URL, které jste získali v části Get endpoints.

4. Přejmenujte sample.env na .env.

   mv sample.env .env
   

5. Nainstalujte závislosti.

   npm install
   

   Po dokončení instalace se v adresáři projektu zobrazí node_modules složka.

6. Pro ověřování bez klíčů pomocí Microsoft Entra ID se přihlaste ke svému účtu Azure. Pokud máte více předplatných, vyberte předplatné, které obsahuje vaše prostředky Azure AI Vyhledávač a Microsoft Foundry.

   az login
   

Spuštění kódu

Spusťte aplikaci pro vytvoření indexu, nahrání dokumentů, konfiguraci zdroje znalostí a znalostní báze a spuštění agentních dotazů na načítání informací.

npm start

Výstup

Výstup aplikace by měl být podobný následujícímu:

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

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

Activities:
Activity Type: modelQueryPlanning
{
  "id": 0,
  "type": "modelQueryPlanning",
  "elapsedMs": 5883,
  "inputTokens": 1489,
  "outputTokens": 326
}
Activity Type: searchIndex
{
  "id": 1,
  "type": "searchIndex",
  "elapsedMs": 527,
  "knowledgeSourceName": "earth-knowledge-source",
  "queryTime": "2025-12-19T15:38:23.462Z",
  "count": 1,
  "searchIndexArguments": {
    "search": "December brightening suburban belts vs urban cores light pollution causes December increase in night lights suburban vs urban",
    "filter": null,
    "sourceDataFields": [
      {
        "name": "page_chunk"
      },
      {
        "name": "id"
      },
      {
        "name": "page_number"
      }
    ],
    "searchFields": [],
    "semanticConfigurationName": "semantic_config"
  }
}
... // Trimmed for brevity
Activity Type: agenticReasoning
{
  "id": 4,
  "type": "agenticReasoning",
  "reasoningTokens": 70397,
  "retrievalReasoningEffort": {
    "kind": "low"
  }
}
Activity Type: modelAnswerSynthesis
{
  "id": 5,
  "type": "modelAnswerSynthesis",
  "elapsedMs": 4908,
  "inputTokens": 4013,
  "outputTokens": 187
}

References:
Reference Type: searchIndex
{
  "type": "searchIndex",
  "id": "0",
  "activitySource": 3,
  "sourceData": {
    "id": "earth_at_night_508_page_174_verbalized",
    "page_chunk": "<!-- PageHeader=\"Holiday Lights\" -->\n\n## Holiday Lights\n\n### Bursting with Holiday Energy-United States\n\nNASA researchers found that nighttime lights in the United States shine 20 to 50 percent brighter in December due to holiday light displays and other activities during Christmas and New Year's when compared to light output during the rest of the year.\n\nThe next five maps (see also pages 161-163), created using data from the VIIRS DNB on the Suomi NPP satellite, show changes in lighting intensity and location around many major cities, comparing the nighttime light signals from December 2012 and beyond.\n\n---\n\n#### Figure 1. Location Overview\n\nA map of the western hemisphere with a marker indicating the mid-Atlantic region of the eastern United States, where the study of holiday lighting intensity was focused.\n\n---\n\n#### Figure 2. Holiday Lighting Intensity: Mid-Atlantic United States (2012–2014)\n\nA map showing Maryland, New Jersey, Delaware, Virginia, West Virginia, Ohio, Kentucky, Tennessee, North Carolina, South Carolina, and surrounding areas. Major cities labeled include Washington, D.C., Richmond, Norfolk, and Raleigh.\n\nThe map uses colors to indicate changes in holiday nighttime lighting intensity between 2012 and 2014:\n\n- **Green/bright areas**: More holiday lighting (areas shining 20–50% brighter in December).\n- **Yellow areas**: No change in lighting.\n- **Dim/grey areas**: Less holiday lighting.\n\nKey observations from the map:\n\n- The Washington, D.C. metropolitan area shows significant increases in lighting during the holidays, extending into Maryland and Virginia.\n- Urban centers such as Richmond (Virginia), Norfolk (Virginia), Raleigh (North Carolina), and clusters in Tennessee and South Carolina also experience notable increases in light intensity during December.\n- Rural areas and the interiors of West Virginia, Kentucky, and North Carolina show little change or less holiday lighting, corresponding to population density and urbanization.\n\n**Legend:**\n\n| Holiday Lighting Change | Color on Map   |\n|------------------------|---------------|\n| More                   | Green/bright  |\n| No Change              | Yellow        |\n| Less                   | Dim/grey      |\n\n_The scale bar indicates a distance of 100 km for reference._\n\n---\n\n<!-- PageFooter=\"158 Earth at Night\" -->",
    "page_number": 174
  },
  "rerankerScore": 2.6692379,
  "docKey": "earth_at_night_508_page_174_verbalized"
}
... // Trimmed for brevity

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

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

Activities:
... // Trimmed for brevity

References:
... // Trimmed for brevity

✅ Quickstart completed successfully!

🗑️  Cleaned up resources.

Vysvětlení kódu

Teď, když máte kód, pojďme rozdělit klíčové komponenty:

1. Vytvoření indexu vyhledávání
2. Nahrání dokumentů do indexu
3. Vytvoření zdroje znalostí
4. Vytvoření znalostní báze
5. Spuštění kanálu načítání
6. Kontrola odpovědí, aktivit a odkazů
7. Pokračovat v konverzaci

Vytvoření indexu vyhledávání

V Azure AI Vyhledávač je index strukturovanou kolekcí dat. Následující kód definuje index s názvem earth_at_night.

Schéma indexu obsahuje pole pro identifikaci dokumentu a obsah stránky, vkládání a čísla. Schéma také obsahuje konfigurace pro sémantické řazení a vektorové vyhledávání, které používá vaše text-embedding-3-large nasazení k vektorizaci textu a porovnávání dokumentů na základě sémantické podobnosti.

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

const credential = new DefaultAzureCredential();

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

await searchIndexClient.createOrUpdateIndex(index);

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

Nahrání dokumentů do indexu

earth-at-night Index je v současné době prázdný. Následující kód naplní index dokumenty JSON z e-knihy NASA Země v noci. Podle Azure AI Vyhledávač odpovídá každý dokument polím a datovým typům definovaným ve schématu indexu.

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

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

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

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

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

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

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

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

Reference:SearchIndexingBufferedSender

Vytvoření zdroje znalostí

Zdroj znalostí je opakovaně použitelný odkaz na zdrojová data. Následující kód definuje zdroj znalostí s názvem earth-knowledge-source , který cílí na earth-at-night index.

sourceDataFields určuje, která pole indexu jsou zahrnuta do odkazů na citaci. Tento příklad obsahuje pouze pole čitelná pro člověka, aby nedocházelo k zdlouhavým, neinterpretovatelným vkládáním do odpovědí.

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

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

Reference:SearchIndexKnowledgeSource

Vytvoření znalostní báze

K cílení earth-knowledge-source a gpt-5-mini nasazení v době dotazu potřebujete znalostní bázi. Následující kód definuje znalostní bázi s názvem earth-knowledge-base.

outputMode (preview) je nastavena na answerSynthesis, což umožňuje odpovědi v přirozeném jazyce, které citují načtené dokumenty a řídí se poskytnutým answerInstructions.

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

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

Referenční informace:KnowledgeBase

Spuštění potrubí pro získávání

Jste připraveni zahájit agentické vyhledávání. Následující kód odešle dotaz uživatele se dvěma částmi na earth-knowledge-base, který:

1. Analyzuje celou konverzaci a odvodí potřebné informace uživatele.
2. Rozdělí složený dotaz do zaměřených poddotazů.
3. Spustí poddotazy paralelně ve vašem zdroji znalostí.
4. Pomocí sémantického rankeru můžete výsledky přeřadit a filtrovat.
5. Syntetizuje nejlepší výsledky do odpovědi přirozeného jazyka.

retrievalReasoningEffort (Preview) je nastaveno na low, aby řídilo míru uvažování používaného při plánování dotazů.

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

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

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

const result = await knowledgeRetrievalClient.retrieve(retrievalRequest);

Reference:KnowledgeRetrievalClient, KnowledgeBaseRetrievalRequest

Kontrola odpovědí, aktivit a odkazů

Následující kód zobrazí odpověď, aktivitu a odkazy z kanálu načítání, kde:

• Answer poskytuje syntetizovanou odpověď vygenerovanou LLM na dotaz, který cituje načtené dokumenty. Pokud syntéza odpovědí není povolená, obsahuje tento oddíl obsah extrahovaný přímo z dokumentů.

• Activities sleduje kroky provedené během procesu načítání, včetně poddotazů vygenerovaných vaším gpt-5-mini nasazením a tokenů používaných pro sémantické řazení, plánování dotazů a syntézu odpovědí.

• References zobrazuje dokumenty, které přispěly k odpovědi, každý z nich je identifikován svým docKey.

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

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

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

Pokračovat v konverzaci

Následující kód pokračuje v konverzaci s earth-knowledge-base. Po odeslání tohoto uživatelského dotazu znalostní báze načte relevantní obsah a earth-knowledge-source připojí odpověď do seznamu zpráv.

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

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

const result2 = await knowledgeRetrievalClient.retrieve(retrievalRequest2);

Kontrola nové odpovědi, aktivity a odkazů

Následující kód zobrazí novou odpověď, aktivitu a odkazy z kanálu načítání.

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

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

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

Vyčištění prostředků

Když pracujete ve vlastním předplatném, je vhodné dokončit projekt odebráním prostředků, které už nepotřebujete. Prostředky, které zůstávají spuštěné, vás mohou stát peníze.

Na portálu Azure vyberte Všechny prostředky nebo Zdrojové skupiny v levém podokně a vyhledejte a spravujte prostředky. Prostředky můžete odstranit jednotlivě nebo odstranit skupinu prostředků a odebrat tak všechny prostředky najednou.

V opačném případě následující kód z index.js odstraní objekty, které jste vytvořili v rámci tohoto rychlého startu.

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

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

V tomto rychlém startu použijete agentické načítání k vytvoření konverzačního vyhledávání pomocí dokumentů indexovaných v Azure AI Vyhledávač a rozsáhlého jazykového modelu (LLM) z Azure OpenAI v modelech Foundry.

Znalostní báze využívá plánování dotazů založené na LLM (preview) k rozložení složitých dotazů na poddotazy. Potom spustí poddotazy na jednom nebo více zdrojích znalostí a vrátí výsledky s metadaty. Znalostní báze ve výchozím nastavení vrací nezpracovaný obsah ze svých zdrojů, ale tento rychlý start používá syntézu odpovědí (Preview) k vygenerování odpovědí v přirozeném jazyce.

I když můžete použít vlastní data, tento průvodce začátkem používá ukázkové dokumenty JSON z e-knihy NASA „Země v noci“.

Požadavky

• Účet Azure s aktivním předplatným. Vytvořte si bezplatný účet.

• Služba Azure AI Vyhledávač v libovolném regionu , který nabízí agentní vyhledávání. Tento rychlý start vyžaduje úroveň Basic nebo vyšší pro podporu spravované identity.

• Projekt Microsoft Foundry a zdroj. Při vytváření projektu se zdroj vytvoří automaticky.

• Vložený model nasazený do projektu pro převod textu na vektor. Můžete použít libovolný text-embedding model, například text-embedding-3-large.

• LLM nasazený do projektu pro plánování dotazů a generování odpovědí. Můžete použít libovolný podporovaný LLM, například gpt-5-mini.

• Python 3.8 nebo novější.

• Visual Studio Code s rozšířeními Python a Jupyter.

• Git naklonuje ukázkové úložiště.

• Azure CLI pro ověřování bez klíčů pomocí Microsoft Entra ID.

Konfigurace přístupu

Než začnete, ujistěte se, že máte oprávnění pro přístup k obsahu a operacím. Tento rychlý start používá Microsoft Entra ID pro ověřování a přístup na základě role k autorizaci. Abyste mohli přiřadit role, musíte být vlastníkem nebo správcem uživatelských přístupů . Pokud role nejsou proveditelné, použijte místo toho ověřování založené na klíči .

Pro konfiguraci přístupu pro tento rychlý průvodce postupujte následovně:

1. Přihlaste se k portálu Azure.

2. Ve službě Azure AI Vyhledávač:

   1. Povolte přístup na základě role.

   2. Vytvořte spravovanou identitu přiřazenou systémem.

   3. Přiřaďte k vašemu uživatelskému účtu následující role: Přispěvatel vyhledávací služby, Přispěvatel datindexu vyhledávání a Čtenář dat indexu vyhledávání.

3. Na prostředku Microsoft Foundry přiřaďte Uživatel služby Cognitive Services spravované identitě vyhledávací služby.

Získání koncových bodů

Každý prostředek služby Azure AI Vyhledávač a Microsoft Foundry má endpoint, což je jedinečná adresa URL, která identifikuje a poskytuje síťový přístup k prostředku. V další části zadáte tyto koncové body, abyste se programaticky připojili k vašim prostředkům.

Získání koncových bodů pro tento rychlý start:

1. Přihlaste se k portálu Azure.

2. Ve službě Azure AI Vyhledávač:

   1. V levém podokně vyberte Přehled.

   2. Zkopírujte adresu URL, která by měla vypadat nějak https://my-service.search.windows.nettakto.

3. Ve zdroji Microsoft Foundry:

   1. V levém podokně vyberte Správa prostředků>Klíče a koncový bod.

   2. Zkopírujte adresu URL na kartě OpenAI, která by měla vypadat jako https://my-resource.openai.azure.com/.

Nastavení prostředí

1. Pomocí Gitu naklonujte ukázkové úložiště.

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

2. Přejděte do složky rychlý start a otevřete ji v Visual Studio Code.

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

3. V sample.env nahraďte zástupné hodnoty pro SEARCH_ENDPOINT a AOAI_ENDPOINT adresami URL, které jste získali v části Get endpoints.

4. Přejmenujte sample.env na .env.

   mv sample.env .env
   

5. Otevřít quickstart-agentic-retrieval.ipynb.

6. Stiskněte Kombinaci kláves Ctrl+Shift+P, vyberte Poznámkový blok: Vyberte jádro poznámkového bloku a podle pokynů vytvořte virtuální prostředí. Vyberte requirements.txt pro závislosti.

   Po dokončení by se měla v adresáři projektu zobrazit .venv složka.

7. Pro ověřování bez klíčů pomocí Microsoft Entra ID se přihlaste ke svému účtu Azure. Pokud máte více předplatných, vyberte předplatné, které obsahuje vaše prostředky Azure AI Vyhledávač a Microsoft Foundry.

   az login
   

Spuštění kódu

1. Spusťte buňku Load connections, abyste nainstalovali požadované balíčky a načetli proměnné prostředí.

2. Spuštěním zbývajících buněk postupně vytvořte index, nahrajte dokumenty, nakonfigurujte zdroj znalostí a znalostní bázi a spusťte dotazy pro agentické vyhledávání.

Výstup

Každá buňka kódu vytiskne svůj výstup do poznámkového bloku. Následující příklad ukazuje výstup po spuštění všech buněk:

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

activity_content:
 [
  {
    "id": 0,
    "type": "modelQueryPlanning",
    "elapsed_ms": 16946,
    "input_tokens": 1354,
    "output_tokens": 906
  },
  {
    "id": 1,
    "type": "searchIndex",
    "elapsed_ms": 887,
    "knowledge_source_name": "earth-knowledge-source",
    "query_time": "2025-11-05T16:17:48.345Z",
    "count": 22,
    "search_index_arguments": {
      "search": "December brightening in satellite nighttime lights: why do suburban belts show larger relative increases in December than urban cores despite higher absolute downtown light levels?"
    }
  },
  ... // Trimmed for brevity
  {
    "id": 4,
    "type": "agenticReasoning",
    "reasoning_tokens": 72191,
    "retrieval_reasoning_effort": {
      "kind": "low"
    }
  },
  {
    "id": 5,
    "type": "modelAnswerSynthesis",
    "elapsed_ms": 22353,
    "input_tokens": 7564,
    "output_tokens": 1645
  }
]

references_content:
 [
  {
    "type": "searchIndex",
    "id": "0",
    "activity_source": 2,
    "source_data": {
      "id": "earth_at_night_508_page_105_verbalized",
      "page_chunk": "# Urban Structure\n\n## March 16, 2013\n\n### Phoenix Metropolitan Area at Night\n\nThis figure presents a nighttime satellite view of the Phoenix metropolitan area, highlighting urban structure and transport corridors. City lights illuminate the layout of several cities and major thoroughfares.\n\n**Labeled Urban Features:**\n\n- **Phoenix:** Central and brightest area in the right-center of the image.\n- **Glendale:** Located to the west of Phoenix, this city is also brightly lit.\n- **Peoria:** Further northwest, this area is labeled and its illuminated grid is seen.\n- **Grand Avenue:** Clearly visible as a diagonal, brightly lit thoroughfare running from Phoenix through Glendale and Peoria.\n- **Salt River Channel:** Identified in the southeast portion, running through illuminated sections.\n- **Phoenix Mountains:** Dark, undeveloped region to the northeast of Phoenix.\n- **Agricultural Fields:** Southwestern corner of the image, grid patterns are visible but with much less illumination, indicating agricultural land use.\n\n**Additional Notes:**\n\n- The overall pattern shows a grid-like urban development typical of western U.S. cities, with scattered bright nodes at major intersections or city centers.\n- There is a clear transition from dense urban development to sparsely populated or agricultural land, particularly evident towards the bottom and left of the image.\n- The illuminated areas follow the existing road and street grids, showcasing the extensive spread of the metropolitan area.\n\n**Figure Description:**  \nA satellite nighttime image captured on March 16, 2013, showing Phoenix and surrounding areas (including Glendale and Peoria). Major landscape and infrastructural features, such as the Phoenix Mountains, Grand Avenue, the Salt River Channel, and agricultural fields, are labeled. The image reveals the extent of urbanization and the characteristic street grid illuminated by city lights.\n\n---\n\nPage 89",
      "page_number": 105
    },
    "reranker_score": 2.722408,
    "doc_key": "earth_at_night_508_page_105_verbalized"
  },
  ... // Trimmed for brevity
]
Retrieved content from 'earth-knowledge-base' successfully.
response_content:
  ... // Trimmed for brevity

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

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

Vysvětlení kódu

Teď, když kód spustíte, si pojďme rozdělit klíčové kroky:

1. Vytvoření indexu vyhledávání
2. Nahrání dokumentů do indexu
3. Vytvoření zdroje znalostí
4. Vytvoření znalostní báze
5. Nastavení zpráv
6. Spuštění kanálu načítání
7. Pokračovat v konverzaci

Vytvoření indexu vyhledávání

V Azure AI Vyhledávač je index strukturovanou kolekcí dat. Následující kód definuje index s názvem earth-at-night.

Schéma indexu obsahuje pole pro identifikaci dokumentu a obsah stránky, vkládání a čísla. Schéma také obsahuje konfigurace pro sémantické řazení a vektorové vyhledávání, které používá vaše text-embedding-3-large nasazení k vektorizaci textu a porovnávání dokumentů na základě sémantické podobnosti.

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

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

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

Reference:SearchField, VectorSearch, SemanticSearch, SearchIndex, SearchIndexClient

Nahrání dokumentů do indexu

earth-at-night Index je v současné době prázdný. Následující kód naplní index dokumenty JSON z e-knihy NASA Země v noci. Podle Azure AI Vyhledávač odpovídá každý dokument polím a datovým typům definovaným ve schématu indexu.

# Upload documents
url = "https://raw.githubusercontent.com/Azure-Samples/azure-search-sample-data/refs/heads/main/nasa-e-book/earth-at-night-json/documents.json"
documents = requests.get(url).json()

with SearchIndexingBufferedSender(endpoint=search_endpoint, index_name=index_name, credential=credential) as client:
    client.upload_documents(documents=documents)

print(f"Documents uploaded to index '{index_name}' successfully.")

Reference:SearchIndexingBufferedSender

Vytvoření zdroje znalostí

Zdroj znalostí je opakovaně použitelný odkaz na zdrojová data. Následující kód definuje zdroj znalostí s názvem earth-knowledge-source , který cílí na earth-at-night index.

source_data_fields určuje, která pole indexu jsou zahrnuta do odkazů na citaci. Tento příklad obsahuje pouze pole čitelná pro člověka, aby nedocházelo k zdlouhavým, neinterpretovatelným vkládáním do odpovědí.

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

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

Reference:SearchIndexKnowledgeSource

Vytvoření znalostní báze

K cílení earth-knowledge-source a gpt-5-mini nasazení v době dotazu potřebujete znalostní bázi. Následující kód definuje znalostní bázi s názvem earth-knowledge-base.

output_mode (preview) je nastavena na answerSynthesis, což umožňuje odpovědi v přirozeném jazyce, které citují vyhledané dokumenty a řídí se poskytnutým 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="answerSynthesis",
    answer_instructions="Provide a 2 sentence concise and informative answer based on the retrieved documents."
)

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

Referenční informace:KnowledgeBase

Nastavení zpráv

Zprávy jsou vstupem pro trasu načítání a obsahují historii konverzací. Každá zpráva obsahuje roli, která označuje jeho původ, například system nebo usera obsah v přirozeném jazyce. LLM, který použijete, určuje, které role jsou platné.

Následující kód vytvoří systémovou zprávu, která dává pokyn earth-knowledge-base odpovědět na otázky ohledně Země v noci a odpovědět "Nevím", když jsou odpovědi nedostupné.

# 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
    }
]

Spuštění potrubí pro získávání

Jste připraveni zahájit agentické vyhledávání. Následující kód odešle dotaz uživatele se dvěma částmi na earth-knowledge-base, který:

1. Analyzuje celou konverzaci a odvodí potřebné informace uživatele.
2. Rozdělí složený dotaz do zaměřených poddotazů.
3. Spustí poddotazy paralelně ve vašem zdroji znalostí.
4. Pomocí sémantického rankeru můžete výsledky přeřadit a filtrovat.
5. Syntetizuje nejlepší výsledky do odpovědi přirozeného jazyka.

retrieval_reasoning_effort (Preview) je nastavena na low, aby určovala míru uvažování používanou při plánování dotazů.

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

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

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

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

Reference:KnowledgeBaseRetrievalClient, KnowledgeBaseRetrievalRequest

Kontrola odpovědí, aktivit a odkazů

Následující kód zobrazí odpověď, aktivitu a odkazy z kanálu načítání, kde:

• response_contents poskytuje syntetizovanou odpověď vygenerovanou LLM na dotaz, který cituje načtené dokumenty. Pokud syntéza odpovědí není povolená, obsahuje tento oddíl obsah extrahovaný přímo z dokumentů.

• activity_contents sleduje kroky provedené během procesu načítání, včetně poddotazů vygenerovaných vaším gpt-5-mini nasazením a tokenů používaných pro sémantické řazení, plánování dotazů a syntézu odpovědí.

• references_contents zobrazuje dokumenty, které přispěly k odpovědi, každý z nich je identifikován svým 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)

Pokračovat v konverzaci

Následující kód pokračuje v konverzaci s earth-knowledge-base. Po odeslání tohoto uživatelského dotazu znalostní báze načte relevantní obsah a earth-knowledge-source připojí odpověď do seznamu zpráv.

# 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.")

Kontrola nové odpovědi, aktivity a odkazů

Následující kód zobrazí novou odpověď, aktivitu a odkazy z kanálu načítání.

# 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)

Vyčištění prostředků

Když pracujete ve vlastním předplatném, je vhodné dokončit projekt odebráním prostředků, které už nepotřebujete. Prostředky, které zůstávají spuštěné, vás mohou stát peníze.

Na portálu Azure vyberte Všechny prostředky nebo Zdrojové skupiny v levém podokně a vyhledejte a spravujte prostředky. Prostředky můžete odstranit jednotlivě nebo odstranit skupinu prostředků a odebrat tak všechny prostředky najednou.

V opačném případě následující kód z quickstart-agentic-retrieval.ipynb odstraní objekty, které jste vytvořili v rámci tohoto rychlého startu.

Odstranění znalostní báze

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.")

Odstranění zdroje znalostí

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.")

Odstranění indexu vyhledávání

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

V tomto rychlém startu použijete agentické načítání k vytvoření konverzačního vyhledávání pomocí dokumentů indexovaných v Azure AI Vyhledávač a rozsáhlého jazykového modelu (LLM) z Azure OpenAI v modelech Foundry.

Znalostní báze využívá plánování dotazů založené na LLM (verze Preview) k rozložení složitých dotazů na poddotazy. Potom spustí poddotazy na jednom nebo více zdrojích znalostí a vrátí výsledky s metadaty. Znalostní báze ve výchozím nastavení vrací nezpracovaný obsah ze svých zdrojů, ale tento rychlý start používá syntézu odpovědí (Preview) k vygenerování odpovědí v přirozeném jazyce.

I když můžete použít vlastní data, tento průvodce začátkem používá ukázkové dokumenty JSON z e-knihy NASA „Země v noci“.

Požadavky

• Účet Azure s aktivním předplatným. Vytvořte si bezplatný účet.

• Služba Azure AI Vyhledávač v libovolném regionu , který nabízí agentní vyhledávání. Tento rychlý start vyžaduje úroveň Basic nebo vyšší pro podporu spravované identity.

• Projekt Microsoft Foundry a zdroj. Při vytváření projektu se zdroj vytvoří automaticky.

• Vložený model nasazený do projektu pro převod textu na vektor. Můžete použít libovolný text-embedding model, například text-embedding-3-large.

• LLM nasazený do projektu pro plánování dotazů a generování odpovědí. Můžete použít libovolný podporovaný LLM, například gpt-5-mini.

• Node.js 20 LTS nebo novější.

• TypeScript ke kompilaci TypeScriptu do JavaScriptu

• Git naklonuje ukázkové úložiště.

• Azure CLI pro ověřování bez klíčů pomocí Microsoft Entra ID.

Konfigurace přístupu

Než začnete, ujistěte se, že máte oprávnění pro přístup k obsahu a operacím. Tento rychlý start používá Microsoft Entra ID pro ověřování a přístup na základě role k autorizaci. Abyste mohli přiřadit role, musíte být vlastníkem nebo správcem uživatelských přístupů . Pokud role nejsou proveditelné, použijte místo toho ověřování založené na klíči .

Pro konfiguraci přístupu pro tento rychlý průvodce postupujte následovně:

1. Přihlaste se k portálu Azure.

2. Ve službě Azure AI Vyhledávač:

   1. Povolte přístup na základě role.

   2. Vytvořte spravovanou identitu přiřazenou systémem.

   3. Přiřaďte k vašemu uživatelskému účtu následující role: Přispěvatel vyhledávací služby, Přispěvatel datindexu vyhledávání a Čtenář dat indexu vyhledávání.

3. Na prostředku Microsoft Foundry přiřaďte Uživatel služby Cognitive Services spravované identitě vyhledávací služby.

Získání koncových bodů

Každý prostředek služby Azure AI Vyhledávač a Microsoft Foundry má endpoint, což je jedinečná adresa URL, která identifikuje a poskytuje síťový přístup k prostředku. V další části zadáte tyto koncové body, abyste se programaticky připojili k vašim prostředkům.

Získání koncových bodů pro tento rychlý start:

1. Přihlaste se k portálu Azure.

2. Ve službě Azure AI Vyhledávač:

   1. V levém podokně vyberte Přehled.

   2. Zkopírujte adresu URL, která by měla vypadat nějak https://my-service.search.windows.nettakto.

3. Ve zdroji Microsoft Foundry:

   1. V levém podokně vyberte Správa prostředků>Klíče a koncový bod.

   2. Zkopírujte adresu URL na kartě OpenAI, která by měla vypadat jako https://my-resource.openai.azure.com/.

Nastavení prostředí

1. Pomocí Gitu naklonujte ukázkové úložiště.

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

2. Přejděte do složky rychlého startu.

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

3. V sample.env nahraďte zástupné hodnoty pro AZURE_SEARCH_ENDPOINT a AZURE_OPENAI_ENDPOINT adresami URL, které jste získali v části Get endpoints.

4. Přejmenujte sample.env na .env.

   mv sample.env .env
   

5. Nainstalujte závislosti.

   npm install
   

   Po dokončení instalace se v adresáři projektu zobrazí node_modules složka.

6. Zkompilujte soubory TypeScriptu do JavaScriptu.

   npm run build
   

7. Pro ověřování bez klíčů pomocí Microsoft Entra ID se přihlaste ke svému účtu Azure. Pokud máte více předplatných, vyberte předplatné, které obsahuje vaše prostředky Azure AI Vyhledávač a Microsoft Foundry.

   az login
   

Spuštění kódu

Spusťte aplikaci pro vytvoření indexu, nahrání dokumentů, konfiguraci zdroje znalostí a znalostní báze a spuštění agentních dotazů na načítání informací.

npm start

Výstup

Výstup aplikace by měl být podobný následujícímu:

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

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

Activities:
Activity Type: modelQueryPlanning
{
  "id": 0,
  "type": "modelQueryPlanning",
  "elapsedMs": 5883,
  "inputTokens": 1489,
  "outputTokens": 326
}
Activity Type: searchIndex
{
  "id": 1,
  "type": "searchIndex",
  "elapsedMs": 527,
  "knowledgeSourceName": "earth-knowledge-source",
  "queryTime": "2025-12-19T15:38:23.462Z",
  "count": 1,
  "searchIndexArguments": {
    "search": "December brightening suburban belts vs urban cores light pollution causes December increase in night lights suburban vs urban",
    "filter": null,
    "sourceDataFields": [
      {
        "name": "page_chunk"
      },
      {
        "name": "id"
      },
      {
        "name": "page_number"
      }
    ],
    "searchFields": [],
    "semanticConfigurationName": "semantic_config"
  }
}
... // Trimmed for brevity
Activity Type: agenticReasoning
{
  "id": 4,
  "type": "agenticReasoning",
  "reasoningTokens": 70397,
  "retrievalReasoningEffort": {
    "kind": "low"
  }
}
Activity Type: modelAnswerSynthesis
{
  "id": 5,
  "type": "modelAnswerSynthesis",
  "elapsedMs": 4908,
  "inputTokens": 4013,
  "outputTokens": 187
}

References:
Reference Type: searchIndex
{
  "type": "searchIndex",
  "id": "0",
  "activitySource": 3,
  "sourceData": {
    "id": "earth_at_night_508_page_174_verbalized",
    "page_chunk": "<!-- PageHeader=\"Holiday Lights\" -->\n\n## Holiday Lights\n\n### Bursting with Holiday Energy-United States\n\nNASA researchers found that nighttime lights in the United States shine 20 to 50 percent brighter in December due to holiday light displays and other activities during Christmas and New Year's when compared to light output during the rest of the year.\n\nThe next five maps (see also pages 161-163), created using data from the VIIRS DNB on the Suomi NPP satellite, show changes in lighting intensity and location around many major cities, comparing the nighttime light signals from December 2012 and beyond.\n\n---\n\n#### Figure 1. Location Overview\n\nA map of the western hemisphere with a marker indicating the mid-Atlantic region of the eastern United States, where the study of holiday lighting intensity was focused.\n\n---\n\n#### Figure 2. Holiday Lighting Intensity: Mid-Atlantic United States (2012–2014)\n\nA map showing Maryland, New Jersey, Delaware, Virginia, West Virginia, Ohio, Kentucky, Tennessee, North Carolina, South Carolina, and surrounding areas. Major cities labeled include Washington, D.C., Richmond, Norfolk, and Raleigh.\n\nThe map uses colors to indicate changes in holiday nighttime lighting intensity between 2012 and 2014:\n\n- **Green/bright areas**: More holiday lighting (areas shining 20–50% brighter in December).\n- **Yellow areas**: No change in lighting.\n- **Dim/grey areas**: Less holiday lighting.\n\nKey observations from the map:\n\n- The Washington, D.C. metropolitan area shows significant increases in lighting during the holidays, extending into Maryland and Virginia.\n- Urban centers such as Richmond (Virginia), Norfolk (Virginia), Raleigh (North Carolina), and clusters in Tennessee and South Carolina also experience notable increases in light intensity during December.\n- Rural areas and the interiors of West Virginia, Kentucky, and North Carolina show little change or less holiday lighting, corresponding to population density and urbanization.\n\n**Legend:**\n\n| Holiday Lighting Change | Color on Map   |\n|------------------------|---------------|\n| More                   | Green/bright  |\n| No Change              | Yellow        |\n| Less                   | Dim/grey      |\n\n_The scale bar indicates a distance of 100 km for reference._\n\n---\n\n<!-- PageFooter=\"158 Earth at Night\" -->",
    "page_number": 174
  },
  "rerankerScore": 2.6692379,
  "docKey": "earth_at_night_508_page_174_verbalized"
}
... // Trimmed for brevity

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

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

Activities:
... // Trimmed for brevity

References:
... // Trimmed for brevity

✅ Quickstart completed successfully!

🗑️  Cleaned up resources.

Vysvětlení kódu

Teď, když máte kód, pojďme rozdělit klíčové komponenty:

1. Vytvoření indexu vyhledávání
2. Nahrání dokumentů do indexu
3. Vytvoření zdroje znalostí
4. Vytvoření znalostní báze
5. Spuštění kanálu načítání
6. Kontrola odpovědí, aktivit a odkazů
7. Pokračovat v konverzaci

Vytvoření indexu vyhledávání

V Azure AI Vyhledávač je index strukturovanou kolekcí dat. Následující kód definuje index s názvem earth_at_night.

Schéma indexu obsahuje pole pro identifikaci dokumentu a obsah stránky, vkládání a čísla. Schéma také obsahuje konfigurace pro sémantické řazení a vektorové vyhledávání, které používá vaše text-embedding-3-large nasazení k vektorizaci textu a porovnávání dokumentů na základě sémantické podobnosti.

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

const credential = new DefaultAzureCredential();

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

await searchIndexClient.createOrUpdateIndex(index);

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

Nahrání dokumentů do indexu

earth-at-night Index je v současné době prázdný. Následující kód naplní index dokumenty JSON z e-knihy NASA Země v noci. Podle Azure AI Vyhledávač odpovídá každý dokument polím a datovým typům definovaným ve schématu indexu.

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

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

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

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

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

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

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

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

Reference:SearchIndexingBufferedSender

Vytvoření zdroje znalostí

Zdroj znalostí je opakovaně použitelný odkaz na zdrojová data. Následující kód definuje zdroj znalostí s názvem earth-knowledge-source , který cílí na earth-at-night index.

sourceDataFields určuje, která pole indexu jsou zahrnuta do odkazů na citaci. Tento příklad obsahuje pouze pole čitelná pro člověka, aby nedocházelo k zdlouhavým, neinterpretovatelným vkládáním do odpovědí.

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

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

Reference:SearchIndexKnowledgeSource

Vytvoření znalostní báze

K cílení earth-knowledge-source a gpt-5-mini nasazení v době dotazu potřebujete znalostní bázi. Následující kód definuje znalostní bázi s názvem earth-knowledge-base.

outputMode (preview) je nastavena na answerSynthesis, což umožňuje odpovědi v přirozeném jazyce, které citují získané dokumenty a řídí se poskytnutým answerInstructions.

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

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

Referenční informace:KnowledgeBase

Spuštění potrubí pro získávání

Jste připraveni zahájit agentické vyhledávání. Následující kód odešle dotaz uživatele se dvěma částmi na earth-knowledge-base, který:

1. Analyzuje celou konverzaci a odvodí potřebné informace uživatele.
2. Rozdělí složený dotaz do zaměřených poddotazů.
3. Spustí poddotazy paralelně ve vašem zdroji znalostí.
4. Pomocí sémantického rankeru můžete výsledky přeřadit a filtrovat.
5. Syntetizuje nejlepší výsledky do odpovědi přirozeného jazyka.

retrievalReasoningEffort (preview) je nastavena na low, aby určovala míru uvažování používanou při plánování dotazů.

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

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

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

const result = await knowledgeRetrievalClient.retrieve(retrievalRequest);

Reference:KnowledgeRetrievalClient, KnowledgeBaseRetrievalRequest

Kontrola odpovědí, aktivit a odkazů

Následující kód zobrazí odpověď, aktivitu a odkazy z kanálu načítání, kde:

• Answer poskytuje syntetizovanou odpověď vygenerovanou LLM na dotaz, který cituje načtené dokumenty. Pokud syntéza odpovědí není povolená, obsahuje tento oddíl obsah extrahovaný přímo z dokumentů.

• Activities sleduje kroky provedené během procesu načítání, včetně poddotazů vygenerovaných vaším gpt-5-mini nasazením a tokenů používaných pro sémantické řazení, plánování dotazů a syntézu odpovědí.

• References zobrazuje dokumenty, které přispěly k odpovědi, každý z nich je identifikován svým docKey.

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

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

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

Pokračovat v konverzaci

Následující kód pokračuje v konverzaci s earth-knowledge-base. Po odeslání tohoto uživatelského dotazu znalostní báze načte relevantní obsah a earth-knowledge-source připojí odpověď do seznamu zpráv.

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

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

const result2 = await knowledgeRetrievalClient.retrieve(retrievalRequest2);

Kontrola nové odpovědi, aktivity a odkazů

Následující kód zobrazí novou odpověď, aktivitu a odkazy z kanálu načítání.

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

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

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

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

Vyčištění prostředků

Když pracujete ve vlastním předplatném, je vhodné dokončit projekt odebráním prostředků, které už nepotřebujete. Prostředky, které zůstávají spuštěné, vás mohou stát peníze.

Na portálu Azure vyberte Všechny prostředky nebo Zdrojové skupiny v levém podokně a vyhledejte a spravujte prostředky. Prostředky můžete odstranit jednotlivě nebo odstranit skupinu prostředků a odebrat tak všechny prostředky najednou.

V opačném případě následující kód z index.ts odstraní objekty, které jste vytvořili v rámci tohoto rychlého startu.

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

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

V tomto rychlém startu použijete agentické načítání k vytvoření konverzačního vyhledávání pomocí dokumentů indexovaných v Azure AI Vyhledávač a rozsáhlého jazykového modelu (LLM) z Azure OpenAI v modelech Foundry.

Znalostní báze využívá plánování dotazů založené na LLM (verze Preview) k rozložení složitých dotazů na poddotazy. Potom spustí poddotazy na jednom nebo více zdrojích znalostí a vrátí výsledky s metadaty. Znalostní báze ve výchozím nastavení vrací nezpracovaný obsah ze svých zdrojů, ale tento rychlý start používá syntézu odpovědí (Preview) k vygenerování odpovědí v přirozeném jazyce.

I když můžete použít vlastní data, tento průvodce začátkem používá ukázkové dokumenty JSON z e-knihy NASA „Země v noci“.

Požadavky

• Účet Azure s aktivním předplatným. Vytvořte si bezplatný účet.

• Služba Azure AI Vyhledávač v libovolném regionu , který nabízí agentní vyhledávání. Tento rychlý start vyžaduje úroveň Basic nebo vyšší pro podporu spravované identity.

• Projekt Microsoft Foundry a zdroj. Při vytváření projektu se zdroj vytvoří automaticky.

• Vložený model nasazený do projektu pro převod textu na vektor. Můžete použít libovolný text-embedding model, například text-embedding-3-large.

• LLM nasazený do projektu pro plánování dotazů a generování odpovědí. Můžete použít libovolný podporovaný LLM, například gpt-5-mini.

• Visual Studio Code s rozšířením klienta REST.

• Git naklonuje ukázkové úložiště.

• Azure CLI pro ověřování bez klíčů pomocí Microsoft Entra ID.

Konfigurace přístupu

Než začnete, ujistěte se, že máte oprávnění pro přístup k obsahu a operacím. Tento rychlý start používá Microsoft Entra ID pro ověřování a přístup na základě role k autorizaci. Abyste mohli přiřadit role, musíte být vlastníkem nebo správcem uživatelských přístupů . Pokud role nejsou proveditelné, použijte místo toho ověřování založené na klíči .

Pro konfiguraci přístupu pro tento rychlý průvodce postupujte následovně:

1. Přihlaste se k portálu Azure.

2. Ve službě Azure AI Vyhledávač:

   1. Povolte přístup na základě role.

   2. Vytvořte spravovanou identitu přiřazenou systémem.

   3. Přiřaďte k vašemu uživatelskému účtu následující role: Přispěvatel vyhledávací služby, Přispěvatel datindexu vyhledávání a Čtenář dat indexu vyhledávání.

3. Na prostředku Microsoft Foundry přiřaďte Uživatel služby Cognitive Services spravované identitě vyhledávací služby.

Získání koncových bodů

Každý prostředek služby Azure AI Vyhledávač a Microsoft Foundry má endpoint, což je jedinečná adresa URL, která identifikuje a poskytuje síťový přístup k prostředku. V další části zadáte tyto koncové body, abyste se programaticky připojili k vašim prostředkům.

Získání koncových bodů pro tento rychlý start:

1. Přihlaste se k portálu Azure.

2. Ve službě Azure AI Vyhledávač:

   1. V levém podokně vyberte Přehled.

   2. Zkopírujte adresu URL, která by měla vypadat nějak https://my-service.search.windows.nettakto.

3. Ve zdroji Microsoft Foundry:

   1. V levém podokně vyberte Správa prostředků>Klíče a koncový bod.

   2. Zkopírujte adresu URL na kartě OpenAI, která by měla vypadat jako https://my-resource.openai.azure.com/.

Nastavení prostředí

1. Pomocí Gitu naklonujte ukázkové úložiště.

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

2. Přejděte do složky rychlý start a otevřete ji v Visual Studio Code.

   cd azure-search-rest-samples/Quickstart-agentic-retrieval
   code .
   

3. V agentic-retrieval.rest nahraďte zástupné hodnoty pro @search-url a @aoai-url adresami URL, které jste získali v části Get endpoints.

4. Pro ověřování bez klíčů pomocí Microsoft Entra ID se přihlaste ke svému účtu Azure. Pokud máte více předplatných, vyberte předplatné, které obsahuje vaše prostředky Azure AI Vyhledávač a Microsoft Foundry.

   az login
   

5. Pro ověřování bez klíčů pomocí Microsoft Entra ID vygenerujte přístupový token.

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

6. Nahraďte zástupnou hodnotu @token tokenem z předchozího kroku.

Spuštění kódu

Každou žádost posílejte postupně, počínaje .### Create an index

Každý požadavek by měl vrátit 200 OK stavový kód, 201 Created stavový kód, nebo 204 No Content stavový kód. Pokud dojde k chybě, zkontrolujte požadavek na překlepy a ujistěte se, že je token platný.

Výstup

Každý požadavek vrátí jiný JSON na základě operace. Výstup klíče je z ### Run agentic retrieval, který by měl vypadat nějak takto:

{
  "response": [
    {
      "content": [
        {
          "type": "text",
          "text": "Causes (mechanisms) — seasonal and lighting behavior: Residential suburban areas often show larger relative (percentage) December brightening because many homes turn on more outdoor and indoor lights in winter evenings (longer nights and holiday lighting) compared with their usual baseline, producing a bigger percent increase even if absolute downtown lighting remains higher [ref_id:1][ref_id:0]. Snow and increased surface reflectance in winter amplify light seen from orbit, increasing apparent brightness especially where lights are horizontal and near ground level (e.g., suburban streets and yards) [ref_id:0].\n\nResidential vs commercial/industrial lighting and activity patterns: Urban cores have high absolute lumen outputs from continuous commercial, industrial, and dense street lighting that change less seasonally, so percent brightening is smaller; suburbs have lower baseline light but larger seasonal/holiday additions, so their relative brightening is larger [ref_id:1][ref_id:0].\n\nWhy Phoenix’s nighttime street grid is sharply visible (causes and consequences): The Phoenix metropolitan area has a regular, dense grid of north–south/east–west streets and the diagonal Grand Avenue corridor with continuous street and commercial lighting whose spacing and lumen output produce linear, high-contrast features from low-Earth orbit; bright nodes occur at major intersections and commercial properties, making the grid and corridors stand out in satellite night images [ref_id:1][ref_id:0]. Darker pockets (Phoenix Mountains, Salt River channel, agricultural fields) increase contrast and make lit streets appear sharper [ref_id:1][ref_id:0].\n\nWhy long interstates between Midwestern cities appear comparatively dim: Rural interstate stretches often lack continuous high-intensity lighting (limited lamps, wider spacing, fewer ramps and commercial nodes), use lower-output or shielded fixtures, and have long unlit segments between cities, so linear continuity and contrast are much lower than a densely lit urban grid [ref_id:1][ref_id:0].\n\nSensor and dataset effects (consequences for observed brightness): Night-light datasets and orbital sensors (as used to capture images like the Phoenix photo) emphasize linear, continuous lighting patterns; sensor behaviors such as gain and saturation make very bright urban cores register high absolute values while limiting the apparent dynamic range, so relative changes (percentage brightening) in dimmer suburban areas can appear proportionally larger in some satellite products [ref_id:1][ref_id:0].\n\nOverall: larger December brightening in suburban belts is a percentage effect from seasonal/holiday increases, snow reflectance, and residential lighting behavior on a low baseline, whereas urban cores remain brighter in absolute terms but show smaller relative changes; Phoenix’s dense, continuous street-grid lighting and contrasting dark areas create sharp linear features from space, while long, sparsely lit interstates lack that continuity and contrast and therefore appear dimmer [ref_id:1][ref_id:0]."
        }
      ]
    }
  ],
  "activity": [
    {
      "type": "modelQueryPlanning",
      "id": 0,
      "inputTokens": 1350,
      "outputTokens": 1538,
      "elapsedMs": 20780
    },
    {
      "type": "searchIndex",
      "id": 1,
      "knowledgeSourceName": "earth-knowledge-source",
      "queryTime": "2025-11-05T19:42:09.673Z",
      "count": 0,
      "elapsedMs": 694,
      "searchIndexArguments": {
        "search": "December brightening in satellite night lights: why do suburban belts show larger December brightening than urban cores? causes: snow reflectance, holiday/residential lighting, leaf-off, VIIRS/DMSP sensor saturation",
        "filter": null
      }
    },
    {
      "type": "searchIndex",
      "id": 2,
      "knowledgeSourceName": "earth-knowledge-source",
      "queryTime": "2025-11-05T19:42:09.999Z",
      "count": 2,
      "elapsedMs": 325,
      "searchIndexArguments": {
        "search": "Why is the Phoenix nighttime street grid so sharply visible from space while long stretches of interstate between Midwestern cities remain comparatively dim? factors: streetlight spacing, lighting type/shielding, vegetation/tree cover, land use, VIIRS DNB detection",
        "filter": null
      }
    },
    {
      "type": "agenticReasoning",
      "id": 3,
      "retrievalReasoningEffort": {
        "kind": "low"
      },
      "reasoningTokens": 1566
    },
    {
      "type": "modelAnswerSynthesis",
      "id": 4,
      "inputTokens": 3656,
      "outputTokens": 1909,
      "elapsedMs": 21988
    }
  ],
  "references": [
    {
      "type": "searchIndex",
      "id": "0",
      "activitySource": 2,
      "sourceData": {
        "id": "earth_at_night_508_page_104_verbalized",
        "page_chunk": "<!-- PageHeader=\"Urban Structure\" -->\n\n### Location of Phoenix, Arizona\n\nThe image depicts a globe highlighting the location of Phoenix, Arizona, in the southwestern United States, marked with a blue pinpoint on the map of North America. Phoenix is situated in the central part of Arizona, which is in the southwestern region of the United States.\n\n---\n\n### Grid of City Blocks-Phoenix, Arizona\n\nLike many large urban areas of the central and western United States, the Phoenix metropolitan area is laid out along a regular grid of city blocks and streets. While visible during the day, this grid is most evident at night, when the pattern of street lighting is clearly visible from the low-Earth-orbit vantage point of the ISS.\n\nThis astronaut photograph, taken on March 16, 2013, includes parts of several cities in the metropolitan area, including Phoenix (image right), Glendale (center), and Peoria (left). While the major street grid is oriented north-south, the northwest-southeast oriented Grand Avenue cuts across the three cities at image center. Grand Avenue is a major transportation corridor through the western metropolitan area; the lighting patterns of large industrial and commercial properties are visible along its length. Other brightly lit properties include large shopping centers, strip malls, and gas stations, which tend to be located at the intersections of north-south and east-west trending streets.\n\nThe urban grid encourages growth outwards along a city's borders by providing optimal access to new real estate. Fueled by the adoption of widespread personal automobile use during the twentieth century, the Phoenix metropolitan area today includes 25 other municipalities (many of them largely suburban and residential) linked by a network of surface streets and freeways.\n\nWhile much of the land area highlighted in this image is urbanized, there are several noticeably dark areas. The Phoenix Mountains are largely public parks and recreational land. To the west, agricultural fields provide a sharp contrast to the lit streets of residential developments. The Salt River channel appears as a dark ribbon within the urban grid.\n\n\n<!-- PageFooter=\"Earth at Night\" -->\n<!-- PageNumber=\"88\" -->",
        "page_number": 104
      },
      "rerankerScore": 2.6394622,
      "docKey": "earth_at_night_508_page_104_verbalized"
    },
    {
      "type": "searchIndex",
      "id": "1",
      "activitySource": 2,
      "sourceData": {
        "id": "earth_at_night_508_page_105_verbalized",
        "page_chunk": "# Urban Structure\n\n## March 16, 2013\n\n### Phoenix Metropolitan Area at Night\n\nThis figure presents a nighttime satellite view of the Phoenix metropolitan area, highlighting urban structure and transport corridors. City lights illuminate the layout of several cities and major thoroughfares.\n\n**Labeled Urban Features:**\n\n- **Phoenix:** Central and brightest area in the right-center of the image.\n- **Glendale:** Located to the west of Phoenix, this city is also brightly lit.\n- **Peoria:** Further northwest, this area is labeled and its illuminated grid is seen.\n- **Grand Avenue:** Clearly visible as a diagonal, brightly lit thoroughfare running from Phoenix through Glendale and Peoria.\n- **Salt River Channel:** Identified in the southeast portion, running through illuminated sections.\n- **Phoenix Mountains:** Dark, undeveloped region to the northeast of Phoenix.\n- **Agricultural Fields:** Southwestern corner of the image, grid patterns are visible but with much less illumination, indicating agricultural land use.\n\n**Additional Notes:**\n\n- The overall pattern shows a grid-like urban development typical of western U.S. cities, with scattered bright nodes at major intersections or city centers.\n- There is a clear transition from dense urban development to sparsely populated or agricultural land, particularly evident towards the bottom and left of the image.\n- The illuminated areas follow the existing road and street grids, showcasing the extensive spread of the metropolitan area.\n\n**Figure Description:**  \nA satellite nighttime image captured on March 16, 2013, showing Phoenix and surrounding areas (including Glendale and Peoria). Major landscape and infrastructural features, such as the Phoenix Mountains, Grand Avenue, the Salt River Channel, and agricultural fields, are labeled. The image reveals the extent of urbanization and the characteristic street grid illuminated by city lights.\n\n---\n\nPage 89",
        "page_number": 105
      },
      "rerankerScore": 2.565024,
      "docKey": "earth_at_night_508_page_105_verbalized"
    }
  ]
}

Vysvětlení kódu

Teď, když kód spustíte, si pojďme rozdělit klíčové kroky:

1. Vytvoření indexu vyhledávání
2. Nahrání dokumentů do indexu
3. Vytvoření zdroje znalostí
4. Vytvoření znalostní báze
5. Spuštění kanálu načítání

Vytvoření indexu vyhledávání

V Azure AI Vyhledávač je index strukturovanou kolekcí dat. Následující kód definuje index s názvem earth-at-night.

Schéma indexu obsahuje pole pro identifikaci dokumentu a obsah stránky, vkládání a čísla. Schéma také obsahuje konfigurace pro sémantické řazení a vektorové vyhledávání, které používá vaše text-embedding-3-large nasazení k vektorizaci textu a porovnávání dokumentů na základě sémantické podobnosti.

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

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

Reference:Indexy – vytvoření

Nahrání dokumentů do indexu

earth-at-night Index je v současné době prázdný. Následující kód naplní index dokumenty JSON z knihy NASA Earth at Night. Podle Azure AI Vyhledávač odpovídá každý dokument polím a datovým typům definovaným ve schématu indexu.

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

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

Reference:Documents – index

Vytvoření zdroje znalostí

Zdroj znalostí je opakovaně použitelný odkaz na zdrojová data. Následující kód definuje zdroj znalostí s názvem earth-knowledge-source , který cílí na earth-at-night index.

sourceDataFields určuje, která pole indexu jsou zahrnuta do odkazů na citaci. Tento příklad obsahuje pouze pole čitelná pro člověka, aby nedocházelo k zdlouhavým, neinterpretovatelným vkládáním do odpovědí.

### 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" }
        ]
    }
}

Referenční informace:Zdroje znalostí – Vytvoření

Vytvoření znalostní báze

K cílení na vaše earth-knowledge-source a gpt-5-mini nasazení v době dotazu potřebujete znalostní bázi. Následující kód definuje základ s názvem earth-knowledge-base.

outputMode (Preview) je nastavena na answerSynthesis, což umožňuje odpovědi v přirozeném jazyce, které citují vyhledané dokumenty a řídí se poskytnutým 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."
}

Referenční informace:Znalostní báze – Vytvoření

Spuštění potrubí pro získávání

Jste připraveni zahájit agentické vyhledávání. Následující kód odešle dotaz uživatele se dvěma částmi na earth-knowledge-base, který:

1. Analyzuje celou konverzaci a odvodí potřebné informace uživatele.
2. Rozdělí složený dotaz do zaměřených poddotazů.
3. Spustí poddotazy paralelně ve vašem zdroji znalostí.
4. Pomocí sémantického rankeru můžete výsledky přeřadit a filtrovat. Tento příklad vylučuje odpovědi se skóre rerankeru 2.5 nebo nižší.
5. Syntetizuje nejlepší výsledky do odpovědi přirozeného jazyka.

retrievalReasoningEffort (preview) je nastavena na low, aby řídila míru uvažování používanou při plánování dotazů.

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

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

Reference:Získávání znalostí – Získávání

Výstup obsahuje následující komponenty:

• response poskytuje syntetizovanou odpověď vygenerovanou LLM na dotaz, který cituje načtené dokumenty. Pokud syntéza odpovědí není povolená, obsahuje tento oddíl obsah extrahovaný přímo z dokumentů.

• activity sleduje kroky provedené během procesu načítání, včetně poddotazů vygenerovaných vaším gpt-5-mini nasazením a tokenů používaných pro sémantické řazení, plánování dotazů a syntézu odpovědí.

• references zobrazuje dokumenty, které přispěly k odpovědi, každý z nich je identifikován svým docKey.

Vyčištění prostředků

Když pracujete ve vlastním předplatném, je vhodné dokončit projekt odebráním prostředků, které už nepotřebujete. Prostředky, které zůstávají spuštěné, vás mohou stát peníze.

Na portálu Azure vyberte Všechny prostředky nebo Zdrojové skupiny v levém podokně a vyhledejte a spravujte prostředky. Prostředky můžete odstranit jednotlivě nebo odstranit skupinu prostředků a odebrat tak všechny prostředky najednou.

V opačném případě následující požadavky z agentic-retrieval.rest odstraní objekty, které jste vytvořili v tomto rychlém startu.

Odstranění znalostní báze

### 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}}

Odstranění zdroje znalostí

### 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}}

Odstranění indexu vyhledávání

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