Szybki start: agentowe pobieranie danych

Uwaga

Wyszukiwanie AI platformy Azure jest dostępna za pośrednictwem portalu Azure, interfejsów API REST i Azure SDKs. Jest także podstawą Foundry IQ — zarządzanej warstwy wiedzy, która przekształca treści przedsiębiorstwa w bazy wiedzy wielokrotnego użytku z uwzględnieniem uprawnień dla agentów w portalu Microsoft Foundry.

Ważne

Funkcje, możliwości lub właściwości oznaczone (wersja zapoznawcza) nie są objęte umową dotyczącą poziomu usług, nie są zalecane w przypadku obciążeń produkcyjnych i mogą ulec zmianie lub ograniczeniu, zanim staną się one ogólnie dostępne. Warunki Wyszukiwanie AI platformy Azure wersji zapoznawczej mają zastosowanie do wszystkich funkcji w wersji zapoznawczej, niezależnie od tego, czy jest ona autonomiczna, czy częścią ogólnie dostępnej funkcji.

W tym szybkim starcie użyjesz agentowego pobierania, aby utworzyć środowisko wyszukiwania konwersacyjnego oparte na dokumentach indeksowanych w usłudze Wyszukiwanie AI platformy Azure oraz dużym modelu językowym (LLM) z Azure OpenAI w Foundry Models.

Baza wiedzy wykorzystuje planowanie zapytań oparte na modelach LLM (wersja zapoznawcza) do rozkładania złożonych zapytań na podzapytania. Następnie uruchamia podzapytania względem co najmniej jednego źródła wiedzy i zwraca wyniki z metadanymi. Domyślnie baza wiedzy zwraca nieprzetworzoną zawartość ze swoich źródeł, ale w tym przewodniku Szybki start do generowania odpowiedzi w języku naturalnym używana jest synteza odpowiedzi (wersja zapoznawcza).

Mimo że możesz używać własnych danych, ten przewodnik szybkiego startu używa przykładowych dokumentów JSON z e-booka NASA Ziemia nocą.

Wskazówka

Chcesz zacząć od razu? Pobierz kod źródłowy z GitHub.

Wymagania wstępne

Konto Azure z aktywną subskrypcją. Utwórz bezpłatne konto.
Usługa Wyszukiwanie AI platformy Azure w dowolnym regionie zapewniającym agentowe pobieranie. Ten przewodnik szybkiego startu wymaga warstwy Podstawowej lub wyższej dla wsparcia tożsamości zarządzanych.
Projekt Microsoft Foundry i zasób. Podczas tworzenia projektu zasób jest tworzony automatycznie.
Model osadzania wdrożony w projekcie na potrzeby konwersji tekstu na wektor. Możesz użyć dowolnego text-embedding modelu, takiego jak text-embedding-3-large.
Usługa LLM wdrożona w projekcie na potrzeby planowania zapytań i generowania odpowiedzi. Możesz użyć dowolnej obsługiwanej usługi LLM, takiej jak gpt-5-mini.
.NET 8 lub nowszy.
Narzędzie Git do sklonowania przykładowego repozytorium.
Azure CLI na potrzeby uwierzytelniania bez klucza za pomocą Microsoft Entra ID.

Konfigurowanie dostępu

Przed rozpoczęciem upewnij się, że masz uprawnienia dostępu do zawartości i operacji. W tym przewodniku szybkiego startu użyto Microsoft Entra ID do uwierzytelniania oraz dostępu opartego na rolach w celu autoryzacji. Aby przypisać role, musisz być właścicielem lub administratorem dostępu użytkowników . Jeśli role nie są możliwe, zamiast tego użyj uwierzytelniania opartego na kluczach .

Aby skonfigurować dostęp do tego szybkiego startu:

Zaloguj się do portalu Azure.
W usłudze Wyszukiwanie AI platformy Azure:
1. Włącz dostęp oparty na rolach.
2. Utwórz tożsamość zarządzaną przypisaną przez system.
3. Przypisz następujące role do konta użytkownika: Współautor usługi wyszukiwania, Współautor danych indeksu wyszukiwania i Czytelnik danych indeksu wyszukiwania.
W zasobie Microsoft Foundry przypisz Użytkownik usług poznawczych do tożsamości zarządzanej usługi wyszukiwania.

Ważne

Agentowe pobieranie danych ma dwa modele rozliczeń oparte na tokenach:

Rozliczenia z Wyszukiwanie AI platformy Azure za wyszukiwanie oparte na agencji.
Rozliczenia za planowanie zapytań i syntezę odpowiedzi w Azure OpenAI.

Aby uzyskać więcej informacji, zobacz Dostępność regionów, limity i rozliczenia.

Pobierz punkty końcowe

Każdy zasób usługi Wyszukiwanie AI platformy Azure i Microsoft Foundry ma endpoint, który jest unikatowym adresem URL, który identyfikuje i zapewnia dostęp sieciowy do zasobu. W późniejszej sekcji określisz te punkty końcowe, aby łączyć się z zasobami programistycznie.

Aby uzyskać punkty końcowe dla tego przewodnika Szybki start:

Zaloguj się do portalu Azure.
W usłudze Wyszukiwanie AI platformy Azure:
1. W okienku po lewej stronie wybierz pozycję Przegląd.
2. Skopiuj adres URL, który powinien wyglądać następująco: https://my-service.search.windows.net.
W zasobie Microsoft Foundry:
1. W okienku po lewej stronie wybierz Zarządzanie zasobami>Klucze i punkt końcowy.
2. Skopiuj adres URL na karcie OpenAI , która powinna wyglądać następująco: https://my-resource.openai.azure.com/.

Konfigurowanie środowiska

Użyj narzędzia Git, aby sklonować przykładowe repozytorium.

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

Przejdź do folderu Szybki start.

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

W pliku sample.env zastąp wartości symboli zastępczych dla SEARCH_ENDPOINT i AOAI_ENDPOINT adresami URL, które uzyskano w sekcji Pobieranie punktów końcowych.
Zmień nazwę sample.env na .env.
```
mv sample.env .env
```
Zainstaluj zależności.
```
dotnet restore AgenticRetrievalQuickstart.csproj
```
Po zakończeniu przywracania upewnij się, że w danych wyjściowych nie są wyświetlane żadne błędy.
W przypadku uwierzytelniania bez klucza przy użyciu Microsoft Entra ID zaloguj się do konta Azure. Jeśli masz wiele subskrypcji, wybierz tę, która zawiera zasoby Wyszukiwanie AI platformy Azure i Microsoft Foundry.
```
az login
```

Uruchamianie kodu

Uruchom aplikację, aby utworzyć indeks, przesłać dokumenty, skonfigurować źródło wiedzy i bazę wiedzy oraz uruchomić zapytania sterowane przez agenta.

dotnet run --project AgenticRetrievalQuickstart.csproj

Wyjście

Dane wyjściowe aplikacji powinny być podobne do następujących:

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.

Omówienie kodu

Uwaga

Fragmenty kodu w tej sekcji mogły zostać zmodyfikowane pod kątem czytelności. Pełny przykład roboczy można znaleźć w kodzie źródłowym.

Teraz, po uruchomieniu kodu, podzielmy kluczowe kroki:

Tworzenie indeksu wyszukiwania
Przekazywanie dokumentów do indeksu
Tworzenie źródła wiedzy
Tworzenie bazy wiedzy
Konfigurowanie komunikatów
Uruchom potok pobierania
Kontynuuj konwersację

Tworzenie indeksu wyszukiwania

W Wyszukiwanie AI platformy Azure indeks jest ustrukturyzowaną kolekcją danych. Poniższy kod definiuje indeks o nazwie earth-at-night.

Schemat indeksu zawiera pola identyfikacji dokumentu i zawartości strony, osadzania i liczb. Schemat zawiera również konfiguracje rankingu semantycznego i wyszukiwania wektorowego, które używają text-embedding-3-large wdrożenia do wektoryzacji tekstu i dopasowywania dokumentów na podstawie semantycznego lub koncepcyjnego podobieństwa.

// 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.");

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

Przesyłanie dokumentów do indeksu

earth-at-night Obecnie indeks jest pusty. Poniższy kod wypełnia indeks dokumentami JSON z NASA's Earth at Night e-book. Zgodnie z wymaganiami Wyszukiwanie AI platformy Azure każdy dokument jest zgodny z polami i typami danych zdefiniowanymi w schemacie indeksu.

// 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

Tworzenie źródła wiedzy

Źródło wiedzy to odwołanie wielokrotnego użytku do danych źródłowych. Poniższy kod definiuje źródło wiedzy o nazwie earth-knowledge-source , które jest przeznaczone dla indeksu earth-at-night .

SourceDataFields określa, które pola indeksu są uwzględniane w odwołaniach do cytatów. W tym przykładzie uwzględniono tylko pola czytelne dla człowieka, aby uniknąć długich, niezinterpretowanych osadzeń w odpowiedziach.

// 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

Tworzenie bazy wiedzy

Do określania celu earth-knowledge-source i gpt-5-mini wdrożenia w czasie wykonywania zapytań potrzebna jest baza wiedzy. Poniższy kod definiuje bazę wiedzy o nazwie earth-knowledge-base.

OutputMode (wersja zapoznawcza) jest ustawione na AnswerSynthesis, co umożliwia udzielanie odpowiedzi w języku naturalnym, które cytują pobrane dokumenty i są zgodne z podanymi w AnswerInstructions. RetrievalReasoningEffort (wersja zapoznawcza) jest ustawiony na low, aby kontrolować zakres rozumowania wykorzystywanego podczas planowania zapytań.

// 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.");

Dokumentacja:KnowledgeBaseAzureOpenAIModel, KnowledgeBase

Konfigurowanie komunikatów

Komunikaty są danymi wejściowymi trasy pobierania i zawierają historię konwersacji. Każdy komunikat zawiera rolę, która wskazuje jego pochodzenie, takie jak system lub , useri zawartość w języku naturalnym. Używany moduł LLM określa, które role są prawidłowe.

Poniższy kod tworzy komunikat systemowy, który nakazuje earth-knowledge-base odpowiedzieć na pytania dotyczące Ziemi w nocy i odpowiedzieć na "Nie wiem", gdy odpowiedzi są niedostępne.

// 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 }
    }
};

Uruchom potok przetwarzania danych

Jesteś gotowy do uruchomienia procesu agentowego wyszukiwania. Poniższy kod wysyła dwuczęściowe zapytanie użytkownika do earth-knowledge-base, które:

Analizuje całą konwersację, aby wywnioskować potrzebne informacje użytkownika.
Rozkłada złożone zapytanie na ukierunkowane podzapytania.
Uruchamia podzapytania równocześnie względem twojego źródła wiedzy.
Używa semantycznego rankera do ponownego uszeregowania i filtrowania wyników.
Syntetyzuje najlepsze wyniki w odpowiedź w języku naturalnym.

// 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 }
});

Dokumentacja:KnowledgeBaseRetrievalClient, KnowledgeBaseRetrievalRequest

Przejrzyj odpowiedzi, działania i odwołania

Poniższy kod wyświetla odpowiedź, działanie i odwołania z potoku przetwarzania danych, gdzie:

Response Udostępnia syntetyzowaną, wygenerowaną przez LLM odpowiedź na pytanie, cytującą pobrane dokumenty. Gdy synteza odpowiedzi nie jest włączona, ta sekcja zawiera zawartość wyodrębnianą bezpośrednio z dokumentów.
Activity Śledzi kroki, które zostały wykonane podczas procesu pobierania, w tym podzapytania wygenerowane przez gpt-5-mini wdrożenie i tokeny używane do semantycznego klasyfikowania, planowania zapytań i syntezy odpowiedzi.
References wyświetla listę dokumentów, które przyczyniły się do odpowiedzi, każdy z nich zidentyfikowany przez 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);
}

Kontynuuj konwersację

Poniższy kod kontynuuje konwersację z earth-knowledge-base. Po wysłaniu tego zapytania użytkownika baza wiedzy pobiera odpowiednią zawartość z earth-knowledge-source i dołącza odpowiedź do listy komunikatów.

// 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 }
});

Przejrzyj nową odpowiedź, działanie i odwołania

Poniższy kod wyświetla nową odpowiedź, działanie i odwołania ze ścieżki pobierania.

// 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);
}

Czyszczenie zasobów

Jeśli pracujesz we własnej subskrypcji, dobrym pomysłem jest zakończenie projektu przez usunięcie zasobów, których już nie potrzebujesz. Zasoby, które pozostają uruchomione, mogą generować koszty.

W portalu Azure wybierz pozycję Wszystkie zasoby lub Grupy zasobów w okienku po lewej stronie, aby znaleźć zasoby i zarządzać nimi. Zasoby można usunąć pojedynczo lub usunąć grupę zasobów, aby jednocześnie usunąć wszystkie zasoby.

W przeciwnym razie poniższy kod w elemencie program.cs usuwa utworzone obiekty w ramach tego przewodnika Szybki start.

Usuwanie bazy wiedzy

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

Usuwanie źródła wiedzy

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

Usuwanie indeksu wyszukiwania

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

Baza wiedzy korzysta z planowania zapytań opartego na modelach LLM (wersja zapoznawcza), aby rozkładać złożone zapytania na podzapytania. Następnie uruchamia podzapytania względem co najmniej jednego źródła wiedzy i zwraca wyniki z metadanymi. Domyślnie baza wiedzy zwraca nieprzetworzoną zawartość ze swoich źródeł, ale w tym przewodniku Szybki start do generowania odpowiedzi w języku naturalnym używana jest synteza odpowiedzi (wersja zapoznawcza).

Mimo że możesz używać własnych danych, ten przewodnik szybkiego startu używa przykładowych dokumentów JSON z e-booka NASA Ziemia nocą.

Wskazówka

Chcesz zacząć od razu? Pobierz kod źródłowy z GitHub.

Wymagania wstępne

Konto Azure z aktywną subskrypcją. Utwórz bezpłatne konto.
Usługa Wyszukiwanie AI platformy Azure w dowolnym regionie zapewniającym agentowe pobieranie. Ten przewodnik szybkiego startu wymaga warstwy Podstawowej lub wyższej dla wsparcia tożsamości zarządzanych.
Projekt Microsoft Foundry i zasób. Podczas tworzenia projektu zasób jest tworzony automatycznie.
Model osadzania wdrożony w projekcie na potrzeby konwersji tekstu na wektor. Możesz użyć dowolnego text-embedding modelu, takiego jak text-embedding-3-large.
Usługa LLM wdrożona w projekcie na potrzeby planowania zapytań i generowania odpowiedzi. Możesz użyć dowolnej obsługiwanej usługi LLM, takiej jak gpt-5-mini.
Java 11 lub nowszy i Maven.
Narzędzie Git do sklonowania przykładowego repozytorium.
Azure CLI na potrzeby uwierzytelniania bez klucza za pomocą Microsoft Entra ID.

Konfigurowanie dostępu

Aby skonfigurować dostęp do tego szybkiego startu:

Zaloguj się do portalu Azure.
W usłudze Wyszukiwanie AI platformy Azure:
1. Włącz dostęp oparty na rolach.
2. Utwórz tożsamość zarządzaną przypisaną przez system.
3. Przypisz następujące role do konta użytkownika: Współautor usługi wyszukiwania, Współautor danych indeksu wyszukiwania i Czytelnik danych indeksu wyszukiwania.
W zasobie Microsoft Foundry przypisz Użytkownik usług poznawczych do tożsamości zarządzanej usługi wyszukiwania.

Ważne

Agentowe pobieranie danych ma dwa modele rozliczeń oparte na tokenach:

Rozliczenia z Wyszukiwanie AI platformy Azure za wyszukiwanie oparte na agencji.
Rozliczenia za planowanie zapytań i syntezę odpowiedzi w Azure OpenAI.

Aby uzyskać więcej informacji, zobacz Dostępność regionów, limity i rozliczenia.

Pobierz punkty końcowe

Aby uzyskać punkty końcowe dla tego przewodnika Szybki start:

Zaloguj się do portalu Azure.
W usłudze Wyszukiwanie AI platformy Azure:
1. W okienku po lewej stronie wybierz pozycję Przegląd.
2. Skopiuj adres URL, który powinien wyglądać następująco: https://my-service.search.windows.net.
W zasobie Microsoft Foundry:
1. W okienku po lewej stronie wybierz Zarządzanie zasobami>Klucze i punkt końcowy.
2. Skopiuj adres URL na karcie OpenAI , która powinna wyglądać następująco: https://my-resource.openai.azure.com/.

Konfigurowanie środowiska

Użyj narzędzia Git, aby sklonować przykładowe repozytorium.

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

Przejdź do folderu Szybki start.

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

W pliku sample.env zastąp wartości symboli zastępczych dla SEARCH_ENDPOINT i AOAI_ENDPOINT adresami URL, które uzyskano w sekcji Pobieranie punktów końcowych.
Zmień nazwę sample.env na .env.
```
mv sample.env .env
```
Zainstaluj zależności.
```
mvn clean dependency:copy-dependencies
```
W przypadku uwierzytelniania bez klucza przy użyciu Microsoft Entra ID zaloguj się do konta Azure. Jeśli masz wiele subskrypcji, wybierz tę, która zawiera zasoby Wyszukiwanie AI platformy Azure i Microsoft Foundry.
```
az login
```

Uruchamianie kodu

Skompiluj i uruchom aplikację, aby utworzyć indeks, przesłać dokumenty, skonfigurować źródło wiedzy i bazę wiedzy oraz uruchomić zapytania do agentowego pobierania danych.

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

Wyjście

Dane wyjściowe aplikacji powinny być podobne do następujących:

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.

Omówienie kodu

Uwaga

Fragmenty kodu w tej sekcji mogły zostać zmodyfikowane pod kątem czytelności. Pełny przykład roboczy można znaleźć w kodzie źródłowym.

Teraz, po uruchomieniu kodu, podzielmy kluczowe kroki:

Tworzenie indeksu wyszukiwania
Przekazywanie dokumentów do indeksu
Tworzenie źródła wiedzy
Tworzenie bazy wiedzy
Konfigurowanie komunikatów
Uruchom potok pobierania
Kontynuuj konwersację

Tworzenie indeksu wyszukiwania

W Wyszukiwanie AI platformy Azure indeks jest ustrukturyzowaną kolekcją danych. Poniższy kod definiuje indeks o nazwie earth-at-night.

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

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

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

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

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

indexClient.createOrUpdateIndex(index);

Dokumentacja:SearchField, VectorSearch, SemanticSearch, SearchIndex, SearchIndexClient

Przesyłanie dokumentów do indeksu

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

Tworzenie źródła wiedzy

sourceDataFields określa, które pola indeksu są uwzględniane w odwołaniach do cytatów. W tym przykładzie uwzględniono tylko pola czytelne dla człowieka, aby uniknąć długich, niezinterpretowanych osadzeń w odpowiedziach.

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

Tworzenie bazy wiedzy

Do określania celu earth-knowledge-source i gpt-5-mini wdrożenia w czasie wykonywania zapytań potrzebna jest baza wiedzy. Poniższy kod definiuje bazę wiedzy o nazwie earth-knowledge-base.

OutputMode (wersja zapoznawcza) jest ustawiona na ANSWER_SYNTHESIS, aby włączyć odpowiedzi w języku naturalnym, które cytują pobrane dokumenty i są zgodne z podanym AnswerInstructions. RetrievalReasoningEffort (wersja zapoznawcza) jest ustawione na low, aby kontrolować poziom rozumowania wykorzystywanego do planowania zapytań.

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);

Dokumentacja:KnowledgeBaseAzureOpenAIModel, KnowledgeBase

Konfigurowanie komunikatów

Poniższy kod tworzy komunikat systemowy, który nakazuje earth-knowledge-base odpowiedzieć na pytania dotyczące Ziemi w nocy i odpowiedzieć na "Nie wiem", gdy odpowiedzi są niedostępne.

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);

Uruchom potok przetwarzania danych

Jesteś gotowy do uruchomienia procesu agentowego wyszukiwania. Poniższy kod wysyła dwuczęściowe zapytanie użytkownika do earth-knowledge-base, które:

Analizuje całą konwersację, aby wywnioskować potrzebne informacje użytkownika.
Rozkłada złożone zapytanie na ukierunkowane podzapytania.
Uruchamia podzapytania równocześnie względem twojego źródła wiedzy.
Używa semantycznego rankera do ponownego uszeregowania i filtrowania wyników.
Syntetyzuje najlepsze wyniki w odpowiedź w języku naturalnym.

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));

Funkcja pomocnicza retrieve tworzy obiekt KnowledgeBaseRetrievalOptions na podstawie historii rozmowy, ustawia poziom wysiłku wnioskowania na potrzeby pobierania, dołącza parametry źródła wiedzy i zwraca obiekt 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);
}

Dokumentacja:KnowledgeBaseRetrievalClient, KnowledgeBaseRetrievalOptions

Przejrzyj odpowiedzi, działania i odwołania

Poniższy kod wyświetla odpowiedź, działanie i odwołania z potoku przetwarzania danych, gdzie:

Response Udostępnia syntetyzowaną, wygenerowaną przez LLM odpowiedź na pytanie, cytującą pobrane dokumenty. Gdy synteza odpowiedzi nie jest włączona, ta sekcja zawiera zawartość wyodrębnianą bezpośrednio z dokumentów.
Activity Śledzi kroki, które zostały wykonane podczas procesu pobierania, w tym podzapytania wygenerowane przez gpt-5-mini wdrożenie i tokeny używane do semantycznego klasyfikowania, planowania zapytań i syntezy odpowiedzi.
References wyświetla listę dokumentów, które przyczyniły się do odpowiedzi, każdy z nich zidentyfikowany przez 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));
}

Kontynuuj konwersację

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

retrievalResult = retrieve(baseClient, messages, knowledgeSourceName);

Przejrzyj nową odpowiedź, działanie i odwołania

Poniższy kod wyodrębnia tekst odpowiedzi i wywołuje polecenie printResult , aby wyświetlić nową odpowiedź, działanie i odwołania.

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

printResult(responseText, retrievalResult);

Czyszczenie zasobów

W przeciwnym razie poniższy kod w elemencie AgenticRetrievalQuickstart.java usuwa utworzone obiekty w ramach tego przewodnika Szybki start.

Usuwanie bazy wiedzy

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

Usuwanie źródła wiedzy

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

Usuwanie indeksu wyszukiwania

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

Baza wiedzy wykorzystuje planowanie zapytań oparte na modelach LLM (wersja zapoznawcza) do rozkładania złożonych zapytań na podzapytania. Następnie uruchamia podzapytania względem co najmniej jednego źródła wiedzy i zwraca wyniki z metadanymi. Domyślnie baza wiedzy zwraca nieprzetworzona zawartość ze swoich źródeł, ale w tym przewodniku Szybki start do generowania odpowiedzi w języku naturalnym jest używana synteza odpowiedzi (wersja zapoznawcza).

Mimo że możesz używać własnych danych, ten przewodnik szybkiego startu używa przykładowych dokumentów JSON z e-booka NASA Ziemia nocą.

Wskazówka

Chcesz zacząć od razu? Pobierz kod źródłowy z GitHub.

Wymagania wstępne

Konto Azure z aktywną subskrypcją. Utwórz bezpłatne konto.
Usługa Wyszukiwanie AI platformy Azure w dowolnym regionie zapewniającym agentowe pobieranie. Ten przewodnik szybkiego startu wymaga warstwy Podstawowej lub wyższej dla wsparcia tożsamości zarządzanych.
Projekt Microsoft Foundry i zasób. Podczas tworzenia projektu zasób jest tworzony automatycznie.
Model osadzania wdrożony w projekcie na potrzeby konwersji tekstu na wektor. Możesz użyć dowolnego text-embedding modelu, takiego jak text-embedding-3-large.
Usługa LLM wdrożona w projekcie na potrzeby planowania zapytań i generowania odpowiedzi. Możesz użyć dowolnej obsługiwanej usługi LLM, takiej jak gpt-5-mini.
Node.js 20 LTS lub nowszym.
Narzędzie Git do sklonowania przykładowego repozytorium.
Azure CLI na potrzeby uwierzytelniania bez klucza za pomocą Microsoft Entra ID.

Konfigurowanie dostępu

Aby skonfigurować dostęp do tego szybkiego startu:

Zaloguj się do portalu Azure.
W usłudze Wyszukiwanie AI platformy Azure:
1. Włącz dostęp oparty na rolach.
2. Utwórz tożsamość zarządzaną przypisaną przez system.
3. Przypisz następujące role do konta użytkownika: Współautor usługi wyszukiwania, Współautor danych indeksu wyszukiwania i Czytelnik danych indeksu wyszukiwania.
W zasobie Microsoft Foundry przypisz Użytkownik usług poznawczych do tożsamości zarządzanej usługi wyszukiwania.

Ważne

Agentowe pobieranie danych ma dwa modele rozliczeń oparte na tokenach:

Rozliczenia z Wyszukiwanie AI platformy Azure za wyszukiwanie oparte na agencji.
Rozliczenia za planowanie zapytań i syntezę odpowiedzi w Azure OpenAI.

Aby uzyskać więcej informacji, zobacz Dostępność regionów, limity i rozliczenia.

Pobierz punkty końcowe

Aby uzyskać punkty końcowe dla tego przewodnika Szybki start:

Zaloguj się do portalu Azure.
W usłudze Wyszukiwanie AI platformy Azure:
1. W okienku po lewej stronie wybierz pozycję Przegląd.
2. Skopiuj adres URL, który powinien wyglądać następująco: https://my-service.search.windows.net.
W zasobie Microsoft Foundry:
1. W okienku po lewej stronie wybierz Zarządzanie zasobami>Klucze i punkt końcowy.
2. Skopiuj adres URL na karcie OpenAI , która powinna wyglądać następująco: https://my-resource.openai.azure.com/.

Konfigurowanie środowiska

Użyj narzędzia Git, aby sklonować przykładowe repozytorium.

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

Przejdź do folderu Szybki start.

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

W pliku sample.env zastąp wartości symboli zastępczych dla AZURE_SEARCH_ENDPOINT i AZURE_OPENAI_ENDPOINT adresami URL, które uzyskano w sekcji Pobieranie punktów końcowych.
Zmień nazwę sample.env na .env.
```
mv sample.env .env
```
Zainstaluj zależności.
```
npm install
```
Po zakończeniu instalacji zostanie wyświetlony node_modules folder w katalogu projektu.
W przypadku uwierzytelniania bez klucza przy użyciu Microsoft Entra ID zaloguj się do konta Azure. Jeśli masz wiele subskrypcji, wybierz tę, która zawiera zasoby Wyszukiwanie AI platformy Azure i Microsoft Foundry.
```
az login
```

Uruchamianie kodu

Uruchom aplikację, aby utworzyć indeks, przesłać dokumenty, skonfigurować źródło wiedzy i bazę wiedzy oraz uruchomić zapytania sterowane przez agenta.

npm start

Wyjście

Dane wyjściowe aplikacji powinny być podobne do następujących:

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.

Omówienie kodu

Teraz, gdy masz kod, podzielmy kluczowe składniki:

Tworzenie indeksu wyszukiwania
Przekazywanie dokumentów do indeksu
Tworzenie źródła wiedzy
Tworzenie bazy wiedzy
Uruchom potok pobierania
Przejrzyj reakcje, działania i referencje
Kontynuuj konwersację

Tworzenie indeksu wyszukiwania

W Wyszukiwanie AI platformy Azure indeks jest ustrukturyzowaną kolekcją danych. Poniższy kod definiuje indeks o nazwie earth_at_night.

Schemat indeksu zawiera pola identyfikacji dokumentu i zawartości strony, osadzania i liczb. Schemat zawiera również konfiguracje semantycznego klasyfikowania i wyszukiwania wektorów, które używają text-embedding-3-large wdrożenia do wektoryzacji tekstu i dopasowywania dokumentów na podstawie podobieństwa semantycznego.

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

Przesyłanie dokumentów do indeksu

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

Tworzenie źródła wiedzy

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

Tworzenie bazy wiedzy

Do określania celu earth-knowledge-source i gpt-5-mini wdrożenia w czasie wykonywania zapytań potrzebna jest baza wiedzy. Poniższy kod definiuje bazę wiedzy o nazwie earth-knowledge-base.

outputMode (wersja zapoznawcza) jest ustawione na answerSynthesis, co umożliwia generowanie odpowiedzi w języku naturalnym, które odwołują się do pobranych dokumentów i są zgodne z podanym 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.`);

Dokumentacja:Baza wiedzy

Uruchom potok przetwarzania danych

Jesteś gotowy do uruchomienia procesu agentowego wyszukiwania. Poniższy kod wysyła dwuczęściowe zapytanie użytkownika do earth-knowledge-base, które:

Analizuje całą konwersację, aby wywnioskować potrzebne informacje użytkownika.
Rozkłada złożone zapytanie na ukierunkowane podzapytania.
Uruchamia podzapytania równocześnie względem twojego źródła wiedzy.
Używa semantycznego rankera do ponownego uszeregowania i filtrowania wyników.
Syntetyzuje najlepsze wyniki w odpowiedź w języku naturalnym.

retrievalReasoningEffort (wersja zapoznawcza) jest ustawione na low, aby kontrolować poziom rozumowania wykorzystywanego podczas planowania zapytań.

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);

Dokumentacja:KnowledgeRetrievalClient, KnowledgeBaseRetrievalRequest

Przejrzyj odpowiedzi, działania i odwołania

Poniższy kod wyświetla odpowiedź, działanie i odwołania z potoku przetwarzania danych, gdzie:

Answer Udostępnia syntetyzowaną, wygenerowaną przez LLM odpowiedź na pytanie, cytującą pobrane dokumenty. Gdy synteza odpowiedzi nie jest włączona, ta sekcja zawiera zawartość wyodrębnianą bezpośrednio z dokumentów.
Activities Śledzi kroki, które zostały wykonane podczas procesu pobierania, w tym podzapytania wygenerowane przez gpt-5-mini wdrożenie i tokeny używane do semantycznego klasyfikowania, planowania zapytań i syntezy odpowiedzi.
References wyświetla listę dokumentów, które przyczyniły się do odpowiedzi, każdy z nich zidentyfikowany przez 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));
    });
}

Kontynuuj konwersację

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);

Przejrzyj nową odpowiedź, działanie i odwołania

Poniższy kod wyświetla nową odpowiedź, działanie i odwołania ze ścieżki pobierania.

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));
    });
}

Czyszczenie zasobów

W przeciwnym razie poniższy kod w elemencie index.js usuwa utworzone obiekty w ramach tego przewodnika Szybki start.

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

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

Baza wiedzy wykorzystuje planowanie zapytań oparte na modelach LLM (wersja zapoznawcza) do dzielenia złożonych zapytań na podzapytania. Następnie uruchamia podzapytania względem co najmniej jednego źródła wiedzy i zwraca wyniki z metadanymi. Domyślnie baza wiedzy zwraca nieprzetworzoną zawartość ze swoich źródeł, ale w tym przewodniku Szybki start używana jest synteza odpowiedzi (wersja zapoznawcza) do generowania odpowiedzi w języku naturalnym.

Mimo że możesz używać własnych danych, ten przewodnik szybkiego startu używa przykładowych dokumentów JSON z e-booka NASA Ziemia nocą.

Wskazówka

Chcesz zacząć od razu? Pobierz kod źródłowy z GitHub.

Wymagania wstępne

Konto Azure z aktywną subskrypcją. Utwórz bezpłatne konto.
Usługa Wyszukiwanie AI platformy Azure w dowolnym regionie zapewniającym agentowe pobieranie. Ten przewodnik szybkiego startu wymaga warstwy Podstawowej lub wyższej dla wsparcia tożsamości zarządzanych.
Projekt Microsoft Foundry i zasób. Podczas tworzenia projektu zasób jest tworzony automatycznie.
Model osadzania wdrożony w projekcie na potrzeby konwersji tekstu na wektor. Możesz użyć dowolnego text-embedding modelu, takiego jak text-embedding-3-large.
Usługa LLM wdrożona w projekcie na potrzeby planowania zapytań i generowania odpowiedzi. Możesz użyć dowolnej obsługiwanej usługi LLM, takiej jak gpt-5-mini.
Python 3.8 lub nowszy.
Visual Studio Code z rozszerzeniami Python i Jupyter.
Narzędzie Git do sklonowania przykładowego repozytorium.
Azure CLI na potrzeby uwierzytelniania bez klucza za pomocą Microsoft Entra ID.

Konfigurowanie dostępu

Aby skonfigurować dostęp do tego szybkiego startu:

Zaloguj się do portalu Azure.
W usłudze Wyszukiwanie AI platformy Azure:
1. Włącz dostęp oparty na rolach.
2. Utwórz tożsamość zarządzaną przypisaną przez system.
3. Przypisz następujące role do konta użytkownika: Współautor usługi wyszukiwania, Współautor danych indeksu wyszukiwania i Czytelnik danych indeksu wyszukiwania.
W zasobie Microsoft Foundry przypisz Użytkownik usług poznawczych do tożsamości zarządzanej usługi wyszukiwania.

Ważne

Agentowe pobieranie danych ma dwa modele rozliczeń oparte na tokenach:

Rozliczenia z Wyszukiwanie AI platformy Azure za wyszukiwanie oparte na agencji.
Rozliczenia za planowanie zapytań i syntezę odpowiedzi w Azure OpenAI.

Aby uzyskać więcej informacji, zobacz Dostępność regionów, limity i rozliczenia.

Pobierz punkty końcowe

Aby uzyskać punkty końcowe dla tego przewodnika Szybki start:

Zaloguj się do portalu Azure.
W usłudze Wyszukiwanie AI platformy Azure:
1. W okienku po lewej stronie wybierz pozycję Przegląd.
2. Skopiuj adres URL, który powinien wyglądać następująco: https://my-service.search.windows.net.
W zasobie Microsoft Foundry:
1. W okienku po lewej stronie wybierz Zarządzanie zasobami>Klucze i punkt końcowy.
2. Skopiuj adres URL na karcie OpenAI , która powinna wyglądać następująco: https://my-resource.openai.azure.com/.

Konfigurowanie środowiska

Użyj narzędzia Git, aby sklonować przykładowe repozytorium.

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

Przejdź do folderu Szybki start i otwórz go w Visual Studio Code.

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

W pliku sample.env zastąp wartości symboli zastępczych dla SEARCH_ENDPOINT i AOAI_ENDPOINT adresami URL, które uzyskano w sekcji Pobieranie punktów końcowych.
Zmień nazwę sample.env na .env.
```
mv sample.env .env
```
Otwórz plik quickstart-agentic-retrieval.ipynb.
Naciśnij klawisze Ctrl+Shift+P, wybierz pozycję Notes: Wybierz pozycję Jądro notesu i postępuj zgodnie z monitami, aby utworzyć środowisko wirtualne. Wybierz requirements.txt dla zależności.

Po zakończeniu w katalogu projektu powinien pojawić się folder .venv.
W przypadku uwierzytelniania bez klucza przy użyciu Microsoft Entra ID zaloguj się do konta Azure. Jeśli masz wiele subskrypcji, wybierz tę, która zawiera zasoby Wyszukiwanie AI platformy Azure i Microsoft Foundry.
```
az login
```

Uruchamianie kodu

Uruchom komórkę, Load connections aby zainstalować wymagane pakiety i załadować zmienne środowiskowe.
Uruchom pozostałe komórki sekwencyjnie, aby utworzyć indeks, przesłać dokumenty, skonfigurować źródło wiedzy i bazę wiedzy oraz uruchomić zapytania alternatywnego pozyskiwania.

Wyjście

Każda komórka kodu wyświetla swoje dane wyjściowe w notebooku. Poniższy przykład przedstawia dane wyjściowe po uruchomieniu wszystkich komórek:

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.

Omówienie kodu

Uwaga

Fragmenty kodu w tej sekcji mogły zostać zmodyfikowane pod kątem czytelności. Pełny przykład roboczy można znaleźć w kodzie źródłowym.

Teraz, po uruchomieniu kodu, podzielmy kluczowe kroki:

Tworzenie indeksu wyszukiwania
Przekazywanie dokumentów do indeksu
Tworzenie źródła wiedzy
Tworzenie bazy wiedzy
Konfigurowanie komunikatów
Uruchom potok pobierania
Kontynuuj konwersację

Tworzenie indeksu wyszukiwania

W Wyszukiwanie AI platformy Azure indeks jest ustrukturyzowaną kolekcją danych. Poniższy kod definiuje indeks o nazwie earth-at-night.

# 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.")

Dokumentacja:SearchField, VectorSearch, SemanticSearch, SearchIndex, SearchIndexClient

Przesyłanie dokumentów do indeksu

# 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

Tworzenie źródła wiedzy

source_data_fields określa, które pola indeksu są uwzględniane w odwołaniach do cytatów. W tym przykładzie uwzględniono tylko pola czytelne dla człowieka, aby uniknąć długich, niezinterpretowanych osadzeń w odpowiedziach.

# 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

Tworzenie bazy wiedzy

Do określania celu earth-knowledge-source i gpt-5-mini wdrożenia w czasie wykonywania zapytań potrzebna jest baza wiedzy. Poniższy kod definiuje bazę wiedzy o nazwie earth-knowledge-base.

output_mode (wersja zapoznawcza) jest ustawione na answerSynthesis, co umożliwia udzielanie odpowiedzi w języku naturalnym, które odwołują się do pobranych dokumentów i są zgodne z podanym 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.")

Dokumentacja:Baza wiedzy

Konfigurowanie komunikatów

Poniższy kod tworzy komunikat systemowy, który nakazuje earth-knowledge-base odpowiedzieć na pytania dotyczące Ziemi w nocy i odpowiedzieć na "Nie wiem", gdy odpowiedzi są niedostępne.

# 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
    }
]

Uruchom potok przetwarzania danych

Jesteś gotowy do uruchomienia procesu agentowego wyszukiwania. Poniższy kod wysyła dwuczęściowe zapytanie użytkownika do earth-knowledge-base, które:

Analizuje całą konwersację, aby wywnioskować potrzebne informacje użytkownika.
Rozkłada złożone zapytanie na ukierunkowane podzapytania.
Uruchamia podzapytania równocześnie względem twojego źródła wiedzy.
Używa semantycznego rankera do ponownego uszeregowania i filtrowania wyników.
Syntetyzuje najlepsze wyniki w odpowiedź w języku naturalnym.

retrieval_reasoning_effort (wersja zapoznawcza) jest ustawione na low, aby kontrolować poziom wnioskowania używanego do planowania zapytań.

# 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.")

Dokumentacja:KnowledgeBaseRetrievalClient, KnowledgeBaseRetrievalRequest

Przejrzyj odpowiedzi, działania i odwołania

Poniższy kod wyświetla odpowiedź, działanie i odwołania z potoku przetwarzania danych, gdzie:

response_contents Udostępnia syntetyzowaną, wygenerowaną przez LLM odpowiedź na pytanie, cytującą pobrane dokumenty. Gdy synteza odpowiedzi nie jest włączona, ta sekcja zawiera zawartość wyodrębnianą bezpośrednio z dokumentów.
activity_contents Śledzi kroki, które zostały wykonane podczas procesu pobierania, w tym podzapytania wygenerowane przez gpt-5-mini wdrożenie i tokeny używane do semantycznego klasyfikowania, planowania zapytań i syntezy odpowiedzi.
references_contents wyświetla listę dokumentów, które przyczyniły się do odpowiedzi, każdy z nich zidentyfikowany przez 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)

Kontynuuj konwersację

# 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.")

Przejrzyj nową odpowiedź, działanie i odwołania

Poniższy kod wyświetla nową odpowiedź, działanie i odwołania ze ścieżki pobierania.

# 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)

Czyszczenie zasobów

W przeciwnym razie poniższy kod w elemencie quickstart-agentic-retrieval.ipynb usuwa utworzone obiekty w ramach tego przewodnika Szybki start.

Usuwanie bazy wiedzy

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.")

Usuwanie źródła wiedzy

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.")

Usuwanie indeksu wyszukiwania

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

Baza wiedzy wykorzystuje planowanie zapytań oparte na modelach LLM (wersja zapoznawcza) do rozkładania złożonych zapytań na podzapytania. Następnie uruchamia podzapytania względem co najmniej jednego źródła wiedzy i zwraca wyniki z metadanymi. Domyślnie baza wiedzy zwraca surową zawartość ze swoich źródeł, ale w tym przewodniku Szybki start do generowania odpowiedzi w języku naturalnym wykorzystywana jest synteza odpowiedzi (wersja zapoznawcza).

Mimo że możesz używać własnych danych, ten przewodnik szybkiego startu używa przykładowych dokumentów JSON z e-booka NASA Ziemia nocą.

Wskazówka

Chcesz zacząć od razu? Pobierz kod źródłowy z GitHub.

Wymagania wstępne

Konto Azure z aktywną subskrypcją. Utwórz bezpłatne konto.
Usługa Wyszukiwanie AI platformy Azure w dowolnym regionie zapewniającym agentowe pobieranie. Ten przewodnik szybkiego startu wymaga warstwy Podstawowej lub wyższej dla wsparcia tożsamości zarządzanych.
Projekt Microsoft Foundry i zasób. Podczas tworzenia projektu zasób jest tworzony automatycznie.
Model osadzania wdrożony w projekcie na potrzeby konwersji tekstu na wektor. Możesz użyć dowolnego text-embedding modelu, takiego jak text-embedding-3-large.
Usługa LLM wdrożona w projekcie na potrzeby planowania zapytań i generowania odpowiedzi. Możesz użyć dowolnej obsługiwanej usługi LLM, takiej jak gpt-5-mini.
Node.js 20 LTS lub nowszym.
TypeScript do kompilowania języka TypeScript do języka JavaScript.
Narzędzie Git do sklonowania przykładowego repozytorium.
Azure CLI na potrzeby uwierzytelniania bez klucza za pomocą Microsoft Entra ID.

Konfigurowanie dostępu

Aby skonfigurować dostęp do tego szybkiego startu:

Zaloguj się do portalu Azure.
W usłudze Wyszukiwanie AI platformy Azure:
1. Włącz dostęp oparty na rolach.
2. Utwórz tożsamość zarządzaną przypisaną przez system.
3. Przypisz następujące role do konta użytkownika: Współautor usługi wyszukiwania, Współautor danych indeksu wyszukiwania i Czytelnik danych indeksu wyszukiwania.
W zasobie Microsoft Foundry przypisz Użytkownik usług poznawczych do tożsamości zarządzanej usługi wyszukiwania.

Ważne

Agentowe pobieranie danych ma dwa modele rozliczeń oparte na tokenach:

Rozliczenia z Wyszukiwanie AI platformy Azure za wyszukiwanie oparte na agencji.
Rozliczenia za planowanie zapytań i syntezę odpowiedzi w Azure OpenAI.

Aby uzyskać więcej informacji, zobacz Dostępność regionów, limity i rozliczenia.

Pobierz punkty końcowe

Aby uzyskać punkty końcowe dla tego przewodnika Szybki start:

Zaloguj się do portalu Azure.
W usłudze Wyszukiwanie AI platformy Azure:
1. W okienku po lewej stronie wybierz pozycję Przegląd.
2. Skopiuj adres URL, który powinien wyglądać następująco: https://my-service.search.windows.net.
W zasobie Microsoft Foundry:
1. W okienku po lewej stronie wybierz Zarządzanie zasobami>Klucze i punkt końcowy.
2. Skopiuj adres URL na karcie OpenAI , która powinna wyglądać następująco: https://my-resource.openai.azure.com/.

Konfigurowanie środowiska

Użyj narzędzia Git, aby sklonować przykładowe repozytorium.

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

Przejdź do folderu Szybki start.

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

W pliku sample.env zastąp wartości symboli zastępczych dla AZURE_SEARCH_ENDPOINT i AZURE_OPENAI_ENDPOINT adresami URL, które uzyskano w sekcji Pobieranie punktów końcowych.
Zmień nazwę sample.env na .env.
```
mv sample.env .env
```
Zainstaluj zależności.
```
npm install
```
Po zakończeniu instalacji zostanie wyświetlony node_modules folder w katalogu projektu.
Skompiluj pliki TypeScript do języka JavaScript.
```
npm run build
```
W przypadku uwierzytelniania bez klucza przy użyciu Microsoft Entra ID zaloguj się do konta Azure. Jeśli masz wiele subskrypcji, wybierz tę, która zawiera zasoby Wyszukiwanie AI platformy Azure i Microsoft Foundry.
```
az login
```

Uruchamianie kodu

Uruchom aplikację, aby utworzyć indeks, przesłać dokumenty, skonfigurować źródło wiedzy i bazę wiedzy oraz uruchomić zapytania sterowane przez agenta.

npm start

Uwaga

To polecenie uruchamia skompilowane .js pliki z dist folderu . Node.js wymaga transpilacji kodu TypeScript do JavaScriptu, zanim będzie można go wykonać, dlatego wcześniej uruchomiono polecenie npm run build.

Wyjście

Dane wyjściowe aplikacji powinny być podobne do następujących:

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.

Omówienie kodu

Teraz, gdy masz kod, podzielmy kluczowe składniki:

Tworzenie indeksu wyszukiwania
Przekazywanie dokumentów do indeksu
Tworzenie źródła wiedzy
Tworzenie bazy wiedzy
Uruchom potok pobierania
Przejrzyj reakcje, działania i referencje
Kontynuuj konwersację

Tworzenie indeksu wyszukiwania

W Wyszukiwanie AI platformy Azure indeks jest ustrukturyzowaną kolekcją danych. Poniższy kod definiuje indeks o nazwie earth_at_night.

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

Przesyłanie dokumentów do indeksu

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

Tworzenie źródła wiedzy

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

Tworzenie bazy wiedzy

Do określania celu earth-knowledge-source i gpt-5-mini wdrożenia w czasie wykonywania zapytań potrzebna jest baza wiedzy. Poniższy kod definiuje bazę wiedzy o nazwie earth-knowledge-base.

outputMode (wersja zapoznawcza) jest ustawione na answerSynthesis, co umożliwia udzielanie odpowiedzi w języku naturalnym, które odwołują się do pobranych dokumentów i są zgodne z dostarczonymi 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.`);

Dokumentacja:Baza wiedzy

Uruchom potok przetwarzania danych

Jesteś gotowy do uruchomienia procesu agentowego wyszukiwania. Poniższy kod wysyła dwuczęściowe zapytanie użytkownika do earth-knowledge-base, które:

Analizuje całą konwersację, aby wywnioskować potrzebne informacje użytkownika.
Rozkłada złożone zapytanie na ukierunkowane podzapytania.
Uruchamia podzapytania równocześnie względem twojego źródła wiedzy.
Używa semantycznego rankera do ponownego uszeregowania i filtrowania wyników.
Syntetyzuje najlepsze wyniki w odpowiedź w języku naturalnym.

retrievalReasoningEffort (wersja zapoznawcza) jest ustawiona na low, aby kontrolować ilość rozumowania używanego do planowania zapytań.

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);

Dokumentacja:KnowledgeRetrievalClient, KnowledgeBaseRetrievalRequest

Przejrzyj odpowiedzi, działania i odwołania

Poniższy kod wyświetla odpowiedź, działanie i odwołania z potoku przetwarzania danych, gdzie:

Answer Udostępnia syntetyzowaną, wygenerowaną przez LLM odpowiedź na pytanie, cytującą pobrane dokumenty. Gdy synteza odpowiedzi nie jest włączona, ta sekcja zawiera zawartość wyodrębnianą bezpośrednio z dokumentów.
Activities Śledzi kroki, które zostały wykonane podczas procesu pobierania, w tym podzapytania wygenerowane przez gpt-5-mini wdrożenie i tokeny używane do semantycznego klasyfikowania, planowania zapytań i syntezy odpowiedzi.
References wyświetla listę dokumentów, które przyczyniły się do odpowiedzi, każdy z nich zidentyfikowany przez 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));
    });
}

Kontynuuj konwersację

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);

Przejrzyj nową odpowiedź, działanie i odwołania

Poniższy kod wyświetla nową odpowiedź, działanie i odwołania ze ścieżki pobierania.

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!");

Czyszczenie zasobów

W przeciwnym razie poniższy kod w elemencie index.ts usuwa utworzone obiekty w ramach tego przewodnika Szybki start.

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

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

Mimo że możesz używać własnych danych, ten przewodnik szybkiego startu używa przykładowych dokumentów JSON z e-booka NASA Ziemia nocą.

Wskazówka

Chcesz zacząć od razu? Pobierz kod źródłowy z GitHub.

Wymagania wstępne

Konto Azure z aktywną subskrypcją. Utwórz bezpłatne konto.
Usługa Wyszukiwanie AI platformy Azure w dowolnym regionie zapewniającym agentowe pobieranie. Ten przewodnik szybkiego startu wymaga warstwy Podstawowej lub wyższej dla wsparcia tożsamości zarządzanych.
Projekt Microsoft Foundry i zasób. Podczas tworzenia projektu zasób jest tworzony automatycznie.
Model osadzania wdrożony w projekcie na potrzeby konwersji tekstu na wektor. Możesz użyć dowolnego text-embedding modelu, takiego jak text-embedding-3-large.
Usługa LLM wdrożona w projekcie na potrzeby planowania zapytań i generowania odpowiedzi. Możesz użyć dowolnej obsługiwanej usługi LLM, takiej jak gpt-5-mini.
Visual Studio Code z rozszerzeniem klienta REST.
Narzędzie Git do sklonowania przykładowego repozytorium.
Azure CLI na potrzeby uwierzytelniania bez klucza za pomocą Microsoft Entra ID.

Konfigurowanie dostępu

Aby skonfigurować dostęp do tego szybkiego startu:

Zaloguj się do portalu Azure.
W usłudze Wyszukiwanie AI platformy Azure:
1. Włącz dostęp oparty na rolach.
2. Utwórz tożsamość zarządzaną przypisaną przez system.
3. Przypisz następujące role do konta użytkownika: Współautor usługi wyszukiwania, Współautor danych indeksu wyszukiwania i Czytelnik danych indeksu wyszukiwania.
W zasobie Microsoft Foundry przypisz Użytkownik usług poznawczych do tożsamości zarządzanej usługi wyszukiwania.

Ważne

Agentowe pobieranie danych ma dwa modele rozliczeń oparte na tokenach:

Rozliczenia z Wyszukiwanie AI platformy Azure za wyszukiwanie oparte na agencji.
Rozliczenia za planowanie zapytań i syntezę odpowiedzi w Azure OpenAI.

Aby uzyskać więcej informacji, zobacz Dostępność regionów, limity i rozliczenia.

Pobierz punkty końcowe

Aby uzyskać punkty końcowe dla tego przewodnika Szybki start:

Zaloguj się do portalu Azure.
W usłudze Wyszukiwanie AI platformy Azure:
1. W okienku po lewej stronie wybierz pozycję Przegląd.
2. Skopiuj adres URL, który powinien wyglądać następująco: https://my-service.search.windows.net.
W zasobie Microsoft Foundry:
1. W okienku po lewej stronie wybierz Zarządzanie zasobami>Klucze i punkt końcowy.
2. Skopiuj adres URL na karcie OpenAI , która powinna wyglądać następująco: https://my-resource.openai.azure.com/.

Konfigurowanie środowiska

Użyj narzędzia Git, aby sklonować przykładowe repozytorium.

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

Przejdź do folderu Szybki start i otwórz go w Visual Studio Code.
```
cd azure-search-rest-samples/Quickstart-agentic-retrieval
code .
```
W pliku agentic-retrieval.rest zastąp wartości symboli zastępczych dla @search-url i @aoai-url adresami URL, które uzyskano w sekcji Pobieranie punktów końcowych.
W przypadku uwierzytelniania bez klucza przy użyciu Microsoft Entra ID zaloguj się do konta Azure. Jeśli masz wiele subskrypcji, wybierz tę, która zawiera zasoby Wyszukiwanie AI platformy Azure i Microsoft Foundry.
```
az login
```

W przypadku uwierzytelniania bez klucza za pomocą Microsoft Entra ID wygeneruj token dostępu.

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

Zastąp wartość @token symbolu zastępczego tokenem z poprzedniego kroku.

Uruchamianie kodu

Wyślij każde żądanie sekwencyjnie, zaczynając od ### Create an index.

Każde żądanie powinno zwrócić kod stanu 200 OK, 201 Created lub 204 No Content. Jeśli wystąpi błąd, sprawdź, czy w żądaniu nie ma literówek, i upewnij się, że token jest prawidłowy.

Wyjście

Każde żądanie zwraca różne dane JSON na podstawie operacji. Kluczowe dane wyjściowe pochodzą z ### Run agentic retrieval, który powinien wyglądać podobnie jak przedstawiony poniżej:

{
  "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"
    }
  ]
}

Omówienie kodu

Uwaga

Fragmenty kodu w tej sekcji mogły zostać zmodyfikowane pod kątem czytelności. Pełny przykład roboczy można znaleźć w kodzie źródłowym.

Teraz, po uruchomieniu kodu, podzielmy kluczowe kroki:

Tworzenie indeksu wyszukiwania
Przekazywanie dokumentów do indeksu
Tworzenie źródła wiedzy
Tworzenie bazy wiedzy
Uruchom potok pobierania

Tworzenie indeksu wyszukiwania

W Wyszukiwanie AI platformy Azure indeks jest ustrukturyzowaną kolekcją danych. Poniższy kod definiuje indeks o nazwie earth-at-night.

### 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}}"
                }
            }
        ]
    }
}

Dokumentacja:Indeksy — tworzenie

Przesyłanie dokumentów do indeksu

earth-at-night Obecnie indeks jest pusty. Poniższy kod wypełnia indeks dokumentami JSON z Nasa Earth at Night e-book. Zgodnie z wymaganiami Wyszukiwanie AI platformy Azure każdy dokument jest zgodny z polami i typami danych zdefiniowanymi w schemacie indeksu.

### 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
        }
    ]
}

Dokumentacja:Dokumenty — indeks

Tworzenie źródła wiedzy

### 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" }
        ]
    }
}

Dokumentacja:Źródła wiedzy — tworzenie

Tworzenie bazy wiedzy

Aby kierować wdrożeniem earth-knowledge-source i gpt-5-mini podczas zapytań, potrzebna jest baza wiedzy. Poniższy kod definiuje bazę o nazwie earth-knowledge-base.

outputMode (wersja zapoznawcza) jest ustawiona na answerSynthesis, co umożliwia odpowiedzi w języku naturalnym, które odwołują się do pobranych dokumentów i są zgodne z podanymi 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."
}

Dokumentacja:Bazy wiedzy — tworzenie

Uruchom potok przetwarzania danych

Jesteś gotowy do uruchomienia procesu agentowego wyszukiwania. Poniższy kod wysyła dwuczęściowe zapytanie użytkownika do earth-knowledge-base, które:

Analizuje całą konwersację, aby wywnioskować potrzebne informacje użytkownika.
Rozkłada złożone zapytanie na ukierunkowane podzapytania.
Uruchamia podzapytania równocześnie względem twojego źródła wiedzy.
Używa semantycznego rankera do ponownego uszeregowania i filtrowania wyników. Ten przykład wyklucza odpowiedzi z wynikiem rerankera równym 2.5 lub niższym.
Syntetyzuje najlepsze wyniki w odpowiedź w języku naturalnym.

retrievalReasoningEffort (wersja zapoznawcza) ma wartość low, aby kontrolować poziom rozumowania używanego do planowania zapytań.

### 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" }
}

Referencja:Odzyskiwanie wiedzy - Pobieranie

Dane wyjściowe zawierają następujące składniki:

response Udostępnia syntetyzowaną, wygenerowaną przez LLM odpowiedź na pytanie, cytującą pobrane dokumenty. Gdy synteza odpowiedzi nie jest włączona, ta sekcja zawiera zawartość wyodrębnianą bezpośrednio z dokumentów.
activity Śledzi kroki, które zostały wykonane podczas procesu pobierania, w tym podzapytania wygenerowane przez gpt-5-mini wdrożenie i tokeny używane do semantycznego klasyfikowania, planowania zapytań i syntezy odpowiedzi.
references wyświetla listę dokumentów, które przyczyniły się do odpowiedzi, każdy z nich zidentyfikowany przez docKey.

Czyszczenie zasobów

W przeciwnym razie poniższe żądania z poziomu agentic-retrieval.rest usuwają obiekty utworzone w tym przewodniku Szybki start.

Usuwanie bazy wiedzy

### 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}}

Usuwanie źródła wiedzy

### 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}}

Usuwanie indeksu wyszukiwania

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

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Last updated on 2026-09-17