Mulai cepat: Pengambilan agenik

Catatan

Pencarian Azure AI tersedia melalui portal Azure, REST API, dan Azure SDK. Ini juga mendukung Foundry IQ, lapisan pengetahuan terkelola yang mengubah konten perusahaan menjadi pangkalan pengetahuan yang dapat digunakan kembali dan sadar izin untuk agen di portal Microsoft Foundry.

Penting

Fitur, kemampuan, atau properti yang ditandai (pratinjau) tidak tercakup dalam perjanjian tingkat layanan, tidak disarankan untuk beban kerja produksi, dan mungkin berubah atau dibatasi sebelum tersedia secara umum. Ketentuan pratinjau Pencarian Azure AI berlaku untuk semua fungsionalitas pratinjau, baik itu mandiri atau bagian dari fitur yang tersedia secara umum.

Dalam panduan cepat ini, Anda menggunakan "agentic retrieval" untuk membuat pengalaman pencarian percakapan yang didukung oleh dokumen yang diindeks dalam Pencarian Azure AI dan model bahasa besar (LLM) dari Azure OpenAI di Model Foundry.

Basis pengetahuan menggunakan perencanaan kueri berbasis LLM (pratinjau) untuk memecah kueri kompleks menjadi subkueri. Kemudian menjalankan subkueri terhadap satu atau beberapa sumber pengetahuan dan mengembalikan hasil dengan metadata. Secara bawaan, basis pengetahuan mengembalikan konten mentah dari sumbernya, tetapi panduan memulai cepat ini menggunakan sintesis jawaban (pratinjau) untuk menghasilkan jawaban dalam bahasa alami.

Meskipun Anda dapat menggunakan data Anda sendiri, mulai cepat ini menggunakan sampel dokumen JSON dari e-book "Bumi di Malam Hari" NASA.

Tips

Ingin segera memulai? Unduh kode source di GitHub.

Prasyarat

Akun Azure dengan langganan aktif. Buat akun secara gratis.
Layanan Pencarian Azure AI dalam region apa pun yang menyediakan pengambilan berbasis agen. Mulai cepat ini memerlukan tingkat Dasar atau lebih tinggi untuk dukungan identitas terkelola.
Proyek Microsoft Foundry dan sumber daya. Saat Anda membuat proyek, sumber daya dibuat secara otomatis.
Model embedding yang diterapkan ke proyek Anda untuk konversi teks ke vektor. Anda dapat menggunakan model apa pun text-embedding , seperti text-embedding-3-large.
LLM yang diimplementasikan ke proyek Anda untuk perencanaan permintaan dan pembuatan jawaban. Anda dapat menggunakan LLM yang didukung, seperti gpt-5-mini.
.NET 8 atau yang lebih baru.
Git untuk mengkloning repositori sampel.
Azure CLI untuk autentikasi tanpa kunci dengan Microsoft Entra ID.

Mengonfigurasi akses

Sebelum memulai, pastikan Anda memiliki izin untuk mengakses konten dan operasi. Mulai cepat ini menggunakan Microsoft Entra ID untuk autentikasi dan akses berbasis peran untuk otorisasi. Anda harus menjadi Pemilik atau Administrator Akses Pengguna untuk menetapkan peran. Jika peran tidak dapat dilakukan, gunakan autentikasi berbasis kunci sebagai gantinya.

Untuk mengonfigurasi akses untuk panduan cepat ini:

Masuk ke portal Azure.
Pada layanan Pencarian Azure AI Anda:
1. Aktifkan akses berbasis peran.
2. Membuat identitas terkelola yang ditetapkan sistem.
3. Tetapkan peran berikut ke akun pengguna Anda: Kontributor Layanan Pencarian, Kontributor Data Indeks Pencarian, dan Pembaca Data Indeks Pencarian.
Pada sumber daya Microsoft Foundry Anda, tetapkan pengguna Cognitive Services ke identitas terkelola layanan pencarian Anda.

Penting

Pengambilan data oleh agen memiliki dua model tagihan berbasis token.

Penagihan dari Pencarian Azure AI untuk pemrosesan pengambilan berorientasi agen.
Penagihan dari Azure OpenAI untuk perencanaan kueri dan sintesis jawaban.

Untuk informasi selengkapnya, lihat Ketersediaan, batas, dan penagihan wilayah.

Mendapatkan titik akhir

Setiap layanan Pencarian Azure AI dan sumber daya Microsoft Foundry memiliki endpoint, yang merupakan URL unik yang mengidentifikasi dan menyediakan akses jaringan ke sumber daya. Di bagian selanjutnya, Anda menentukan titik akhir ini untuk menyambungkan ke sumber daya Anda secara terprogram.

Untuk mendapatkan titik akhir untuk panduan pemula cepat ini:

Masuk ke portal Azure.
Pada layanan Pencarian Azure AI Anda:
1. Dari panel kiri, pilih Gambaran Umum.
2. Salin URL, yang akan terlihat seperti https://my-service.search.windows.net.
Pada sumber daya Microsoft Foundry Anda:
1. Dari panel kiri, pilih Kunci Manajemen> Sumber Dayadan Titik Akhir.
2. Salin URL pada tab OpenAI , yang akan terlihat seperti https://my-resource.openai.azure.com/.

Siapkan lingkungan

Gunakan Git untuk mengkloning repositori sampel.

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

Buka folder quickstart.

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

Di sample.env, ganti nilai tempat penampung untuk SEARCH_ENDPOINT dan AOAI_ENDPOINT dengan URL yang Anda dapatkan di Dapatkan titik akhir.
Ganti nama sample.env menjadi .env.
```
mv sample.env .env
```
Pasang dependensi.
```
dotnet restore AgenticRetrievalQuickstart.csproj
```
Setelah pemulihan selesai, pastikan tidak ada kesalahan yang muncul dalam output.
Untuk autentikasi tanpa kunci dengan Microsoft Entra ID, masuk ke akun Azure Anda. Jika Anda memiliki beberapa langganan, pilih langganan yang berisi sumber daya Pencarian Azure AI dan Microsoft Foundry Anda.
```
az login
```

Jalankan kode

Jalankan aplikasi untuk membuat indeks, mengunggah dokumen, mengonfigurasi sumber pengetahuan dan pangkalan pengetahuan, dan menjalankan kueri pengambilan agenik.

dotnet run --project AgenticRetrievalQuickstart.csproj

Keluaran

Output aplikasi harus mirip dengan yang berikut:

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.

Memahami kode

Catatan

Cuplikan kode di bagian ini mungkin telah dimodifikasi untuk keterbacaan. Untuk contoh kerja lengkap, lihat kode sumber.

Sekarang setelah Anda menjalankan kode, mari kita uraikan langkah-langkah kunci:

Membuat indeks pencarian
Mengunggah dokumen ke indeks
Membuat sumber pengetahuan
Membuat pangkalan pengetahuan
Menyiapkan pesan
Jalankan alur pengambilan
Melanjutkan percakapan

Membuat indeks pencarian

Dalam Pencarian Azure AI, indeks adalah kumpulan data terstruktur. Kode berikut mendefinisikan indeks bernama earth-at-night.

Skema indeks berisi bidang untuk identifikasi dokumen dan konten halaman, penyematan, dan angka. Skema ini juga mencakup konfigurasi untuk peringkat semantik dan pencarian vektor, yang menggunakan penyebaran Anda text-embedding-3-large untuk mem-vektorisasi teks dan mencocokkan dokumen berdasarkan kesamaan semantik atau konseptual.

// 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.");

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

Mengunggah dokumen ke indeks

Saat ini, earth-at-night indeks kosong. Kode berikut mengisi indeks dengan dokumen JSON dari NASA's Earth at Night e-book. Seperti yang diperlukan oleh Pencarian Azure AI, setiap dokumen sesuai dengan bidang dan jenis data yang ditentukan dalam skema indeks.

// 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.");

Referensi:SearchClient, SearchIndexingBufferedSender

Membuat sumber pengetahuan

Sumber pengetahuan adalah referensi yang dapat digunakan kembali ke data sumber. Kode berikut mendefinisikan sumber pengetahuan bernama earth-knowledge-source yang menargetkan earth-at-night indeks.

SourceDataFields menentukan bidang indeks mana yang disertakan dalam referensi kutipan. Contoh ini hanya mencakup bidang yang mudah dibaca manusia untuk menghindari respons dengan penyematan yang panjang dan tidak dapat diinterpretasikan.

// 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.");

Referensi:SearchIndexKnowledgeSource

Membuat pangkalan pengetahuan

Untuk menargetkan earth-knowledge-source dan gpt-5-mini penyebaran Anda pada waktu kueri, Anda memerlukan basis pengetahuan. Kode berikut mendefinisikan pangkalan pengetahuan bernama earth-knowledge-base.

OutputMode (pratinjau) diatur ke AnswerSynthesis, mengaktifkan jawaban bahasa alami yang mengutip dokumen yang diambil dan mengikuti yang disediakan AnswerInstructions. RetrievalReasoningEffort (pratinjau) diatur ke low untuk mengontrol jumlah penalaran yang digunakan untuk perencanaan kueri.

// 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.");

Referensi:KnowledgeBaseAzureOpenAIModel, KnowledgeBase

Menyiapkan pesan

Pesan adalah input untuk rute pengambilan dan berisi riwayat percakapan. Setiap pesan menyertakan peran yang menunjukkan asalnya, seperti system atau user, dan konten dalam bahasa alami. LLM yang Anda gunakan menentukan peran mana yang valid.

Kode berikut membuat pesan sistem, yang menginstruksikan earth-knowledge-base untuk menjawab pertanyaan tentang Bumi di malam hari dan merespons dengan "Saya tidak tahu" ketika jawaban tidak tersedia.

// 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 }
    }
};

Jalankan alur pengambilan

Anda siap untuk menjalankan pengambilan agenik. Kode berikut mengirimkan dua bagian dari kueri pengguna ke earth-knowledge-base, yang:

Menganalisis seluruh percakapan untuk menyimpulkan kebutuhan informasi pengguna.
Menguraikan kueri gabungan menjadi subkueri yang difokuskan.
Menjalankan subkueri secara simultan terhadap sumber pengetahuan Anda.
Menggunakan ranker semantik untuk melakukan rerank dan memfilter hasilnya.
Menyintesis hasil terbaik menjadi jawaban bahasa yang alami.

// 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 }
});

Referensi:KnowledgeBaseRetrievalClient, KnowledgeBaseRetrievalRequest

Meninjau respons, aktivitas, dan referensi

Kode berikut menampilkan respons, aktivitas, dan referensi dari alur pengambilan, di mana:

Response menyediakan jawaban yang dihasilkan LLM yang disintesis untuk kueri yang mengutip dokumen yang diambil. Saat sintesis jawaban tidak diaktifkan, bagian ini berisi konten yang diekstrak langsung dari dokumen.
Activity melacak langkah-langkah yang diambil selama proses pengambilan, termasuk subkueri yang dihasilkan oleh penyebaran gpt-5-mini Anda dan token yang digunakan untuk penilaian semantik, perencanaan kueri, dan sintesis jawaban.
References mencantumkan dokumen yang berkontribusi pada respons, masing-masing diidentifikasi oleh mereka 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);
}

Melanjutkan percakapan

Kode berikut melanjutkan percakapan dengan earth-knowledge-base. Setelah Anda mengirim kueri pengguna ini, pangkalan pengetahuan mengambil konten yang relevan dari earth-knowledge-source dan menambahkan respons ke daftar pesan.

// 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 }
});

Tinjau respons, aktivitas, dan referensi baru

Kode berikut menampilkan respons, aktivitas, dan referensi baru dari alur pengambilan.

// 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);
}

Membersihkan sumber daya

Saat Anda bekerja di langganan Anda sendiri, ada baiknya untuk menyelesaikan proyek dengan menghapus sumber daya yang tidak lagi Anda butuhkan. Sumber daya yang dibiarkan berjalan dapat menyebabkan biaya tambahan.

Di portal Azure, pilih Semua sumber daya atau Kelompok sumber daya dari panel kiri untuk menemukan dan mengelola sumber daya. Anda dapat menghapus sumber daya satu per satu atau menghapus grup sumber daya untuk menghapus semua sumber daya sekaligus.

Jika tidak, kode berikut pada program.cs akan menghapus objek yang Anda buat dalam panduan singkat ini.

Menghapus pangkalan pengetahuan

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

Menghapus sumber pengetahuan

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

Menghapus indeks pencarian

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

Meskipun Anda dapat menggunakan data Anda sendiri, mulai cepat ini menggunakan sampel dokumen JSON dari e-book "Bumi di Malam Hari" NASA.

Tips

Ingin segera memulai? Unduh kode source di GitHub.

Prasyarat

Akun Azure dengan langganan aktif. Buat akun secara gratis.
Layanan Pencarian Azure AI dalam region apa pun yang menyediakan pengambilan berbasis agen. Mulai cepat ini memerlukan tingkat Dasar atau lebih tinggi untuk dukungan identitas terkelola.
Proyek Microsoft Foundry dan sumber daya. Saat Anda membuat proyek, sumber daya dibuat secara otomatis.
Model embedding yang diterapkan ke proyek Anda untuk konversi teks ke vektor. Anda dapat menggunakan model apa pun text-embedding , seperti text-embedding-3-large.
LLM yang diimplementasikan ke proyek Anda untuk perencanaan permintaan dan pembuatan jawaban. Anda dapat menggunakan LLM yang didukung, seperti gpt-5-mini.
Java 11 atau yang lebih baru dan Maven.
Git untuk mengkloning repositori sampel.
Azure CLI untuk autentikasi tanpa kunci dengan Microsoft Entra ID.

Mengonfigurasi akses

Untuk mengonfigurasi akses untuk panduan cepat ini:

Masuk ke portal Azure.
Pada layanan Pencarian Azure AI Anda:
1. Aktifkan akses berbasis peran.
2. Membuat identitas terkelola yang ditetapkan sistem.
3. Tetapkan peran berikut ke akun pengguna Anda: Kontributor Layanan Pencarian, Kontributor Data Indeks Pencarian, dan Pembaca Data Indeks Pencarian.
Pada sumber daya Microsoft Foundry Anda, tetapkan pengguna Cognitive Services ke identitas terkelola layanan pencarian Anda.

Penting

Pengambilan data oleh agen memiliki dua model tagihan berbasis token.

Penagihan dari Pencarian Azure AI untuk pemrosesan pengambilan berorientasi agen.
Penagihan dari Azure OpenAI untuk perencanaan kueri dan sintesis jawaban.

Untuk informasi selengkapnya, lihat Ketersediaan, batas, dan penagihan wilayah.

Mendapatkan titik akhir

Untuk mendapatkan titik akhir untuk panduan pemula cepat ini:

Masuk ke portal Azure.
Pada layanan Pencarian Azure AI Anda:
1. Dari panel kiri, pilih Gambaran Umum.
2. Salin URL, yang akan terlihat seperti https://my-service.search.windows.net.
Pada sumber daya Microsoft Foundry Anda:
1. Dari panel kiri, pilih Kunci Manajemen> Sumber Dayadan Titik Akhir.
2. Salin URL pada tab OpenAI , yang akan terlihat seperti https://my-resource.openai.azure.com/.

Siapkan lingkungan

Gunakan Git untuk mengkloning repositori sampel.

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

Buka folder quickstart.

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

Di sample.env, ganti nilai tempat penampung untuk SEARCH_ENDPOINT dan AOAI_ENDPOINT dengan URL yang Anda dapatkan di Dapatkan titik akhir.
Ganti nama sample.env menjadi .env.
```
mv sample.env .env
```
Pasang dependensi.
```
mvn clean dependency:copy-dependencies
```
Untuk autentikasi tanpa kunci dengan Microsoft Entra ID, masuk ke akun Azure Anda. Jika Anda memiliki beberapa langganan, pilih langganan yang berisi sumber daya Pencarian Azure AI dan Microsoft Foundry Anda.
```
az login
```

Jalankan kode

Buat dan jalankan aplikasi untuk membuat indeks, mengunggah dokumen, mengonfigurasi sumber pengetahuan dan pangkalan pengetahuan, dan menjalankan kueri pengambilan agenik.

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

Keluaran

Output aplikasi harus mirip dengan yang berikut:

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.

Memahami kode

Catatan

Cuplikan kode di bagian ini mungkin telah dimodifikasi untuk keterbacaan. Untuk contoh kerja lengkap, lihat kode sumber.

Sekarang setelah Anda menjalankan kode, mari kita uraikan langkah-langkah kunci:

Membuat indeks pencarian
Mengunggah dokumen ke indeks
Membuat sumber pengetahuan
Membuat pangkalan pengetahuan
Menyiapkan pesan
Jalankan alur pengambilan
Melanjutkan percakapan

Membuat indeks pencarian

Dalam Pencarian Azure AI, indeks adalah kumpulan data terstruktur. Kode berikut mendefinisikan indeks bernama 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);

Referensi:SearchField, VectorSearch, SemanticSearch, SearchIndex, SearchIndexClient

Mengunggah dokumen ke indeks

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);

Referensi:SearchClient, SearchDocument

Membuat sumber pengetahuan

sourceDataFields menentukan bidang indeks mana yang disertakan dalam referensi kutipan. Contoh ini hanya mencakup bidang yang mudah dibaca manusia untuk menghindari respons dengan penyematan yang panjang dan tidak dapat diinterpretasikan.

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);

Referensi:SearchIndexKnowledgeSource

Membuat pangkalan pengetahuan

OutputMode (pratinjau) diatur ke ANSWER_SYNTHESIS untuk mengaktifkan jawaban dalam bahasa alami yang mengutip dokumen yang diambil kembali dan mengikuti AnswerInstructions yang disediakan. RetrievalReasoningEffort (pratinjau) diatur ke low untuk mengontrol jumlah penalaran yang digunakan untuk perencanaan kueri.

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);

Referensi:KnowledgeBaseAzureOpenAIModel, KnowledgeBase

Menyiapkan pesan

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);

Jalankan alur pengambilan

Anda siap untuk menjalankan pengambilan agenik. Kode berikut mengirimkan dua bagian dari kueri pengguna ke earth-knowledge-base, yang:

Menganalisis seluruh percakapan untuk menyimpulkan kebutuhan informasi pengguna.
Menguraikan kueri gabungan menjadi subkueri yang difokuskan.
Menjalankan subkueri secara simultan terhadap sumber pengetahuan Anda.
Menggunakan ranker semantik untuk melakukan rerank dan memfilter hasilnya.
Menyintesis hasil terbaik menjadi jawaban bahasa yang alami.

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));

Pembantu retrieve membangun KnowledgeBaseRetrievalOptions dari riwayat percakapan, menetapkan upaya penalaran pengambilan, melampirkan parameter sumber pengetahuan, dan mengembalikan 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);
}

Referensi:KnowledgeBaseRetrievalClient, KnowledgeBaseRetrievalOptions

Meninjau respons, aktivitas, dan referensi

Kode berikut menampilkan respons, aktivitas, dan referensi dari alur pengambilan, di mana:

Response menyediakan jawaban yang dihasilkan LLM yang disintesis untuk kueri yang mengutip dokumen yang diambil. Saat sintesis jawaban tidak diaktifkan, bagian ini berisi konten yang diekstrak langsung dari dokumen.
Activity melacak langkah-langkah yang diambil selama proses pengambilan, termasuk subkueri yang dihasilkan oleh penyebaran gpt-5-mini Anda dan token yang digunakan untuk penilaian semantik, perencanaan kueri, dan sintesis jawaban.
References mencantumkan dokumen yang berkontribusi pada respons, masing-masing diidentifikasi oleh mereka 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));
}

Melanjutkan percakapan

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

retrievalResult = retrieve(baseClient, messages, knowledgeSourceName);

Tinjau respons, aktivitas, dan referensi baru

Kode berikut mengekstrak teks respons dan panggilan printResult untuk menampilkan respons, aktivitas, dan referensi baru.

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

printResult(responseText, retrievalResult);

Membersihkan sumber daya

Jika tidak, kode berikut pada AgenticRetrievalQuickstart.java akan menghapus objek yang Anda buat dalam panduan singkat ini.

Menghapus pangkalan pengetahuan

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

Menghapus sumber pengetahuan

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

Menghapus indeks pencarian

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

Basis pengetahuan menggunakan perencanaan kueri berbasis LLM (pratinjau) untuk menguraikan kueri kompleks menjadi subkueri. Kemudian menjalankan subkueri terhadap satu atau beberapa sumber pengetahuan dan mengembalikan hasil dengan metadata. Secara bawaan, basis pengetahuan mengembalikan konten mentah dari sumbernya, tetapi panduan memulai cepat ini menggunakan sintesis jawaban (pratinjau) untuk menghasilkan jawaban dalam bahasa alami.

Meskipun Anda dapat menggunakan data Anda sendiri, mulai cepat ini menggunakan sampel dokumen JSON dari e-book "Bumi di Malam Hari" NASA.

Tips

Ingin segera memulai? Unduh kode source di GitHub.

Prasyarat

Akun Azure dengan langganan aktif. Buat akun secara gratis.
Layanan Pencarian Azure AI dalam region apa pun yang menyediakan pengambilan berbasis agen. Mulai cepat ini memerlukan tingkat Dasar atau lebih tinggi untuk dukungan identitas terkelola.
Proyek Microsoft Foundry dan sumber daya. Saat Anda membuat proyek, sumber daya dibuat secara otomatis.
Model embedding yang diterapkan ke proyek Anda untuk konversi teks ke vektor. Anda dapat menggunakan model apa pun text-embedding , seperti text-embedding-3-large.
LLM yang diimplementasikan ke proyek Anda untuk perencanaan permintaan dan pembuatan jawaban. Anda dapat menggunakan LLM yang didukung, seperti gpt-5-mini.
Node.js 20 LTS atau yang lebih baru.
Git untuk mengkloning repositori sampel.
Azure CLI untuk autentikasi tanpa kunci dengan Microsoft Entra ID.

Mengonfigurasi akses

Untuk mengonfigurasi akses untuk panduan cepat ini:

Masuk ke portal Azure.
Pada layanan Pencarian Azure AI Anda:
1. Aktifkan akses berbasis peran.
2. Membuat identitas terkelola yang ditetapkan sistem.
3. Tetapkan peran berikut ke akun pengguna Anda: Kontributor Layanan Pencarian, Kontributor Data Indeks Pencarian, dan Pembaca Data Indeks Pencarian.
Pada sumber daya Microsoft Foundry Anda, tetapkan pengguna Cognitive Services ke identitas terkelola layanan pencarian Anda.

Penting

Pengambilan data oleh agen memiliki dua model tagihan berbasis token.

Penagihan dari Pencarian Azure AI untuk pemrosesan pengambilan berorientasi agen.
Penagihan dari Azure OpenAI untuk perencanaan kueri dan sintesis jawaban.

Untuk informasi selengkapnya, lihat Ketersediaan, batas, dan penagihan wilayah.

Mendapatkan titik akhir

Untuk mendapatkan titik akhir untuk panduan pemula cepat ini:

Masuk ke portal Azure.
Pada layanan Pencarian Azure AI Anda:
1. Dari panel kiri, pilih Gambaran Umum.
2. Salin URL, yang akan terlihat seperti https://my-service.search.windows.net.
Pada sumber daya Microsoft Foundry Anda:
1. Dari panel kiri, pilih Kunci Manajemen> Sumber Dayadan Titik Akhir.
2. Salin URL pada tab OpenAI , yang akan terlihat seperti https://my-resource.openai.azure.com/.

Siapkan lingkungan

Gunakan Git untuk mengkloning repositori sampel.

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

Buka folder quickstart.

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

Di sample.env, ganti nilai tempat penampung untuk AZURE_SEARCH_ENDPOINT dan AZURE_OPENAI_ENDPOINT dengan URL yang Anda dapatkan di Dapatkan titik akhir.
Ganti nama sample.env menjadi .env.
```
mv sample.env .env
```
Pasang dependensi.
```
npm install
```
Setelah penginstalan selesai, Anda akan melihat node_modules folder di direktori proyek.
Untuk autentikasi tanpa kunci dengan Microsoft Entra ID, masuk ke akun Azure Anda. Jika Anda memiliki beberapa langganan, pilih langganan yang berisi sumber daya Pencarian Azure AI dan Microsoft Foundry Anda.
```
az login
```

Jalankan kode

Jalankan aplikasi untuk membuat indeks, mengunggah dokumen, mengonfigurasi sumber pengetahuan dan pangkalan pengetahuan, dan menjalankan kueri pengambilan agenik.

npm start

Keluaran

Output aplikasi harus mirip dengan yang berikut:

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.

Memahami kode

Sekarang setelah Anda memiliki kode, mari kita uraikan komponen utama:

Membuat indeks pencarian
Mengunggah dokumen ke indeks
Membuat sumber pengetahuan
Membuat pangkalan pengetahuan
Jalankan alur pengambilan
Meninjau respons, aktivitas, dan referensi
Melanjutkan percakapan

Membuat indeks pencarian

Dalam Pencarian Azure AI, indeks adalah kumpulan data terstruktur. Kode berikut mendefinisikan indeks bernama earth_at_night.

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);

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

Mengunggah dokumen ke indeks

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!`);

Referensi:SearchIndexingBufferedSender

Membuat sumber pengetahuan

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.`);

Referensi:SearchIndexKnowledgeSource

Membuat pangkalan pengetahuan

outputMode (pratinjau) diatur ke answerSynthesis, memungkinkan jawaban dalam bahasa alami yang mengutip dokumen yang diambil kembali dan mengikuti answerInstructions yang disediakan.

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.`);

Referensi:KnowledgeBase

Jalankan alur pengambilan

Anda siap untuk menjalankan pengambilan agenik. Kode berikut mengirimkan dua bagian dari kueri pengguna ke earth-knowledge-base, yang:

Menganalisis seluruh percakapan untuk menyimpulkan kebutuhan informasi pengguna.
Menguraikan kueri gabungan menjadi subkueri yang difokuskan.
Menjalankan subkueri secara simultan terhadap sumber pengetahuan Anda.
Menggunakan ranker semantik untuk melakukan rerank dan memfilter hasilnya.
Menyintesis hasil terbaik menjadi jawaban bahasa yang alami.

retrievalReasoningEffort (pratinjau) diatur ke low untuk mengontrol jumlah penalaran yang digunakan untuk perencanaan kueri.

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);

Referensi:KnowledgeRetrievalClient, KnowledgeBaseRetrievalRequest

Meninjau respons, aktivitas, dan referensi

Kode berikut menampilkan respons, aktivitas, dan referensi dari alur pengambilan, di mana:

Answer menyediakan jawaban yang dihasilkan LLM yang disintesis untuk kueri yang mengutip dokumen yang diambil. Saat sintesis jawaban tidak diaktifkan, bagian ini berisi konten yang diekstrak langsung dari dokumen.
Activities melacak langkah-langkah yang diambil selama proses pengambilan, termasuk subkueri yang dihasilkan oleh penyebaran gpt-5-mini Anda dan token yang digunakan untuk penilaian semantik, perencanaan kueri, dan sintesis jawaban.
References mencantumkan dokumen yang berkontribusi pada respons, masing-masing diidentifikasi oleh mereka 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));
    });
}

Melanjutkan percakapan

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);

Tinjau respons, aktivitas, dan referensi baru

Kode berikut menampilkan respons, aktivitas, dan referensi baru dari alur pengambilan.

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));
    });
}

Membersihkan sumber daya

Jika tidak, kode berikut pada index.js akan menghapus objek yang Anda buat dalam panduan singkat ini.

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

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

Basis pengetahuan menggunakan perencanaan kueri berbasis LLM (pratinjau) untuk memecah kueri kompleks menjadi subkueri. Kemudian menjalankan subkueri terhadap satu atau beberapa sumber pengetahuan dan mengembalikan hasil dengan metadata. Secara bawaan, basis pengetahuan mengembalikan konten mentah dari sumbernya, tetapi panduan mulai cepat ini menggunakan sintesis jawaban (pratinjau) untuk menghasilkan jawaban dalam bahasa alami.

Meskipun Anda dapat menggunakan data Anda sendiri, mulai cepat ini menggunakan sampel dokumen JSON dari e-book "Bumi di Malam Hari" NASA.

Tips

Ingin segera memulai? Unduh kode source di GitHub.

Prasyarat

Akun Azure dengan langganan aktif. Buat akun secara gratis.
Layanan Pencarian Azure AI dalam region apa pun yang menyediakan pengambilan berbasis agen. Mulai cepat ini memerlukan tingkat Dasar atau lebih tinggi untuk dukungan identitas terkelola.
Proyek Microsoft Foundry dan sumber daya. Saat Anda membuat proyek, sumber daya dibuat secara otomatis.
Model embedding yang diterapkan ke proyek Anda untuk konversi teks ke vektor. Anda dapat menggunakan model apa pun text-embedding , seperti text-embedding-3-large.
LLM yang diimplementasikan ke proyek Anda untuk perencanaan permintaan dan pembuatan jawaban. Anda dapat menggunakan LLM yang didukung, seperti gpt-5-mini.
Python 3.8 atau yang lebih baru.
Visual Studio Code dengan ekstensi Python dan Jupyter.
Git untuk mengkloning repositori sampel.
Azure CLI untuk autentikasi tanpa kunci dengan Microsoft Entra ID.

Mengonfigurasi akses

Untuk mengonfigurasi akses untuk panduan cepat ini:

Masuk ke portal Azure.
Pada layanan Pencarian Azure AI Anda:
1. Aktifkan akses berbasis peran.
2. Membuat identitas terkelola yang ditetapkan sistem.
3. Tetapkan peran berikut ke akun pengguna Anda: Kontributor Layanan Pencarian, Kontributor Data Indeks Pencarian, dan Pembaca Data Indeks Pencarian.
Pada sumber daya Microsoft Foundry Anda, tetapkan pengguna Cognitive Services ke identitas terkelola layanan pencarian Anda.

Penting

Pengambilan data oleh agen memiliki dua model tagihan berbasis token.

Penagihan dari Pencarian Azure AI untuk pemrosesan pengambilan berorientasi agen.
Penagihan dari Azure OpenAI untuk perencanaan kueri dan sintesis jawaban.

Untuk informasi selengkapnya, lihat Ketersediaan, batas, dan penagihan wilayah.

Mendapatkan titik akhir

Untuk mendapatkan titik akhir untuk panduan pemula cepat ini:

Masuk ke portal Azure.
Pada layanan Pencarian Azure AI Anda:
1. Dari panel kiri, pilih Gambaran Umum.
2. Salin URL, yang akan terlihat seperti https://my-service.search.windows.net.
Pada sumber daya Microsoft Foundry Anda:
1. Dari panel kiri, pilih Kunci Manajemen> Sumber Dayadan Titik Akhir.
2. Salin URL pada tab OpenAI , yang akan terlihat seperti https://my-resource.openai.azure.com/.

Siapkan lingkungan

Gunakan Git untuk mengkloning repositori sampel.

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

Buka folder quickstart lalu buka folder tersebut di Visual Studio Code.
```
cd azure-search-python-samples/Quickstart-Agentic-Retrieval
code .
```
Di sample.env, ganti nilai tempat penampung untuk SEARCH_ENDPOINT dan AOAI_ENDPOINT dengan URL yang Anda dapatkan di Dapatkan titik akhir.
Ganti nama sample.env menjadi .env.
```
mv sample.env .env
```
Buka quickstart-agentic-retrieval.ipynb.
Tekan Ctrl+Shift+P, pilih Notebook: Pilih Notebook Kernel, dan ikuti perintah untuk membuat lingkungan virtual. Pilih requirements.txt untuk dependensi.

Setelah selesai, Anda akan melihat .venv folder di direktori proyek.
Untuk autentikasi tanpa kunci dengan Microsoft Entra ID, masuk ke akun Azure Anda. Jika Anda memiliki beberapa langganan, pilih langganan yang berisi sumber daya Pencarian Azure AI dan Microsoft Foundry Anda.
```
az login
```

Jalankan kode

Jalankan Load connections sel untuk menginstal paket yang diperlukan dan memuat variabel lingkungan.
Jalankan sel yang tersisa secara berurutan untuk membuat indeks, mengunggah dokumen, mengonfigurasi sumber pengetahuan dan pangkalan pengetahuan, dan menjalankan kueri pengambilan agenik.

Keluaran

Setiap sel kode mencetak outputnya ke buku catatan. Contoh berikut menunjukkan output setelah menjalankan semua sel:

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.

Memahami kode

Catatan

Cuplikan kode di bagian ini mungkin telah dimodifikasi untuk keterbacaan. Untuk contoh kerja lengkap, lihat kode sumber.

Sekarang setelah Anda menjalankan kode, mari kita uraikan langkah-langkah kunci:

Membuat indeks pencarian
Mengunggah dokumen ke indeks
Membuat sumber pengetahuan
Membuat pangkalan pengetahuan
Menyiapkan pesan
Jalankan alur pengambilan
Melanjutkan percakapan

Membuat indeks pencarian

Dalam Pencarian Azure AI, indeks adalah kumpulan data terstruktur. Kode berikut mendefinisikan indeks bernama 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.")

Referensi:SearchField, VectorSearch, SemanticSearch, SearchIndex, SearchIndexClient

Mengunggah dokumen ke indeks

# 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.")

Referensi:SearchIndexingBufferedSender

Membuat sumber pengetahuan

source_data_fields menentukan bidang indeks mana yang disertakan dalam referensi kutipan. Contoh ini hanya mencakup bidang yang mudah dibaca manusia untuk menghindari respons dengan penyematan yang panjang dan tidak dapat diinterpretasikan.

# 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.")

Referensi:SearchIndexKnowledgeSource

Membuat pangkalan pengetahuan

output_mode (pratinjau) diatur ke answerSynthesis, sehingga memungkinkan jawaban dalam bahasa alami yang mengutip dokumen yang ditemukan dan mengikuti answer_instructions yang disediakan.

# 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.")

Referensi:KnowledgeBase

Menyiapkan pesan

Kode berikut membuat pesan sistem yang menginstruksikan earth-knowledge-base untuk menjawab pertanyaan tentang Bumi di malam hari dan merespons dengan "Saya tidak tahu" ketika jawaban tidak tersedia.

# 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
    }
]

Jalankan alur pengambilan

Anda siap untuk menjalankan pengambilan agenik. Kode berikut mengirimkan dua bagian dari kueri pengguna ke earth-knowledge-base, yang:

Menganalisis seluruh percakapan untuk menyimpulkan kebutuhan informasi pengguna.
Menguraikan kueri gabungan menjadi subkueri yang difokuskan.
Menjalankan subkueri secara simultan terhadap sumber pengetahuan Anda.
Menggunakan ranker semantik untuk melakukan rerank dan memfilter hasilnya.
Menyintesis hasil terbaik menjadi jawaban bahasa yang alami.

retrieval_reasoning_effort (pratinjau) diatur ke low untuk mengontrol jumlah penalaran yang digunakan untuk perencanaan kueri.

# 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.")

Referensi:KnowledgeBaseRetrievalClient, KnowledgeBaseRetrievalRequest

Meninjau respons, aktivitas, dan referensi

Kode berikut menampilkan respons, aktivitas, dan referensi dari alur pengambilan, di mana:

response_contents menyediakan jawaban yang dihasilkan LLM yang disintesis untuk kueri yang mengutip dokumen yang diambil. Saat sintesis jawaban tidak diaktifkan, bagian ini berisi konten yang diekstrak langsung dari dokumen.
activity_contents melacak langkah-langkah yang diambil selama proses pengambilan, termasuk subkueri yang dihasilkan oleh penyebaran gpt-5-mini Anda dan token yang digunakan untuk penilaian semantik, perencanaan kueri, dan sintesis jawaban.
references_contents mencantumkan dokumen yang berkontribusi pada respons, masing-masing diidentifikasi oleh mereka 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)

Melanjutkan percakapan

# 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.")

Tinjau respons, aktivitas, dan referensi baru

Kode berikut menampilkan respons, aktivitas, dan referensi baru dari alur pengambilan.

# 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)

Membersihkan sumber daya

Jika tidak, kode berikut pada quickstart-agentic-retrieval.ipynb akan menghapus objek yang Anda buat dalam panduan singkat ini.

Menghapus pangkalan pengetahuan

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.")

Menghapus sumber pengetahuan

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.")

Menghapus indeks pencarian

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

Basis pengetahuan menggunakan perencanaan kueri berbasis LLM (pratinjau) untuk memecah kueri kompleks menjadi subkueri. Kemudian menjalankan subkueri terhadap satu atau beberapa sumber pengetahuan dan mengembalikan hasil dengan metadata. Secara bawaan, basis pengetahuan mengembalikan konten mentah dari sumbernya, tetapi panduan mulai cepat ini menggunakan sintesis jawaban (pratinjau) untuk menghasilkan jawaban dalam bahasa alami.

Meskipun Anda dapat menggunakan data Anda sendiri, mulai cepat ini menggunakan sampel dokumen JSON dari e-book "Bumi di Malam Hari" NASA.

Tips

Ingin segera memulai? Unduh kode source di GitHub.

Prasyarat

Akun Azure dengan langganan aktif. Buat akun secara gratis.
Layanan Pencarian Azure AI dalam region apa pun yang menyediakan pengambilan berbasis agen. Mulai cepat ini memerlukan tingkat Dasar atau lebih tinggi untuk dukungan identitas terkelola.
Proyek Microsoft Foundry dan sumber daya. Saat Anda membuat proyek, sumber daya dibuat secara otomatis.
Model embedding yang diterapkan ke proyek Anda untuk konversi teks ke vektor. Anda dapat menggunakan model apa pun text-embedding , seperti text-embedding-3-large.
LLM yang diimplementasikan ke proyek Anda untuk perencanaan permintaan dan pembuatan jawaban. Anda dapat menggunakan LLM yang didukung, seperti gpt-5-mini.
Node.js 20 LTS atau yang lebih baru.
TypeScript untuk mengkompilasi TypeScript ke JavaScript.
Git untuk mengkloning repositori sampel.
Azure CLI untuk autentikasi tanpa kunci dengan Microsoft Entra ID.

Mengonfigurasi akses

Untuk mengonfigurasi akses untuk panduan cepat ini:

Masuk ke portal Azure.
Pada layanan Pencarian Azure AI Anda:
1. Aktifkan akses berbasis peran.
2. Membuat identitas terkelola yang ditetapkan sistem.
3. Tetapkan peran berikut ke akun pengguna Anda: Kontributor Layanan Pencarian, Kontributor Data Indeks Pencarian, dan Pembaca Data Indeks Pencarian.
Pada sumber daya Microsoft Foundry Anda, tetapkan pengguna Cognitive Services ke identitas terkelola layanan pencarian Anda.

Penting

Pengambilan data oleh agen memiliki dua model tagihan berbasis token.

Penagihan dari Pencarian Azure AI untuk pemrosesan pengambilan berorientasi agen.
Penagihan dari Azure OpenAI untuk perencanaan kueri dan sintesis jawaban.

Untuk informasi selengkapnya, lihat Ketersediaan, batas, dan penagihan wilayah.

Mendapatkan titik akhir

Untuk mendapatkan titik akhir untuk panduan pemula cepat ini:

Masuk ke portal Azure.
Pada layanan Pencarian Azure AI Anda:
1. Dari panel kiri, pilih Gambaran Umum.
2. Salin URL, yang akan terlihat seperti https://my-service.search.windows.net.
Pada sumber daya Microsoft Foundry Anda:
1. Dari panel kiri, pilih Kunci Manajemen> Sumber Dayadan Titik Akhir.
2. Salin URL pada tab OpenAI , yang akan terlihat seperti https://my-resource.openai.azure.com/.

Siapkan lingkungan

Gunakan Git untuk mengkloning repositori sampel.

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

Buka folder quickstart.

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

Di sample.env, ganti nilai tempat penampung untuk AZURE_SEARCH_ENDPOINT dan AZURE_OPENAI_ENDPOINT dengan URL yang Anda dapatkan di Dapatkan titik akhir.
Ganti nama sample.env menjadi .env.
```
mv sample.env .env
```
Pasang dependensi.
```
npm install
```
Setelah penginstalan selesai, Anda akan melihat node_modules folder di direktori proyek.
Kompilasi file TypeScript ke JavaScript.
```
npm run build
```
Untuk autentikasi tanpa kunci dengan Microsoft Entra ID, masuk ke akun Azure Anda. Jika Anda memiliki beberapa langganan, pilih langganan yang berisi sumber daya Pencarian Azure AI dan Microsoft Foundry Anda.
```
az login
```

Jalankan kode

Jalankan aplikasi untuk membuat indeks, mengunggah dokumen, mengonfigurasi sumber pengetahuan dan pangkalan pengetahuan, dan menjalankan kueri pengambilan agenik.

npm start

Catatan

Perintah ini menjalankan file yang dikompilasi .js dari dist folder. Node.js mengharuskan kode TypeScript ditranspilasi ke JavaScript sebelum dapat dijalankan, itulah sebabnya Anda sebelumnya menjalankan npm run build.

Keluaran

Output aplikasi harus mirip dengan yang berikut:

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.

Memahami kode

Sekarang setelah Anda memiliki kode, mari kita uraikan komponen utama:

Membuat indeks pencarian
Mengunggah dokumen ke indeks
Membuat sumber pengetahuan
Membuat pangkalan pengetahuan
Jalankan alur pengambilan
Meninjau respons, aktivitas, dan referensi
Melanjutkan percakapan

Membuat indeks pencarian

Dalam Pencarian Azure AI, indeks adalah kumpulan data terstruktur. Kode berikut mendefinisikan indeks bernama 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);

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

Mengunggah dokumen ke indeks

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!`);

Referensi:SearchIndexingBufferedSender

Membuat sumber pengetahuan

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.`);

Referensi:SearchIndexKnowledgeSource

Membuat pangkalan pengetahuan

outputMode (pratinjau) diatur ke answerSynthesis, memungkinkan jawaban bahasa alami yang mengutip dokumen yang diambil dan mengikuti answerInstructions yang disediakan.

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.`);

Referensi:KnowledgeBase

Jalankan alur pengambilan

Anda siap untuk menjalankan pengambilan agenik. Kode berikut mengirimkan dua bagian dari kueri pengguna ke earth-knowledge-base, yang:

Menganalisis seluruh percakapan untuk menyimpulkan kebutuhan informasi pengguna.
Menguraikan kueri gabungan menjadi subkueri yang difokuskan.
Menjalankan subkueri secara simultan terhadap sumber pengetahuan Anda.
Menggunakan ranker semantik untuk melakukan rerank dan memfilter hasilnya.
Menyintesis hasil terbaik menjadi jawaban bahasa yang alami.

retrievalReasoningEffort (pratinjau) diatur ke low untuk mengontrol tingkat penalaran yang digunakan dalam perencanaan kueri.

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);

Referensi:KnowledgeRetrievalClient, KnowledgeBaseRetrievalRequest

Meninjau respons, aktivitas, dan referensi

Kode berikut menampilkan respons, aktivitas, dan referensi dari alur pengambilan, di mana:

Answer menyediakan jawaban yang dihasilkan LLM yang disintesis untuk kueri yang mengutip dokumen yang diambil. Saat sintesis jawaban tidak diaktifkan, bagian ini berisi konten yang diekstrak langsung dari dokumen.
Activities melacak langkah-langkah yang diambil selama proses pengambilan, termasuk subkueri yang dihasilkan oleh penyebaran gpt-5-mini Anda dan token yang digunakan untuk penilaian semantik, perencanaan kueri, dan sintesis jawaban.
References mencantumkan dokumen yang berkontribusi pada respons, masing-masing diidentifikasi oleh mereka 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));
    });
}

Melanjutkan percakapan

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);

Tinjau respons, aktivitas, dan referensi baru

Kode berikut menampilkan respons, aktivitas, dan referensi baru dari alur pengambilan.

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!");

Membersihkan sumber daya

Jika tidak, kode berikut pada index.ts akan menghapus objek yang Anda buat dalam panduan singkat ini.

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

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

Basis pengetahuan menggunakan perencanaan kueri berbasis LLM (pratinjau) untuk memecah kueri kompleks menjadi subkueri. Kemudian menjalankan subkueri terhadap satu atau beberapa sumber pengetahuan dan mengembalikan hasil dengan metadata. Secara bawaan, basis pengetahuan mengembalikan konten mentah dari sumbernya, tetapi panduan mulai cepat ini menggunakan sintesis jawaban (pratinjau) untuk menghasilkan jawaban dalam bahasa alami.

Meskipun Anda dapat menggunakan data Anda sendiri, mulai cepat ini menggunakan sampel dokumen JSON dari e-book "Bumi di Malam Hari" NASA.

Tips

Ingin segera memulai? Unduh kode source di GitHub.

Prasyarat

Akun Azure dengan langganan aktif. Buat akun secara gratis.
Layanan Pencarian Azure AI dalam region apa pun yang menyediakan pengambilan berbasis agen. Mulai cepat ini memerlukan tingkat Dasar atau lebih tinggi untuk dukungan identitas terkelola.
Proyek Microsoft Foundry dan sumber daya. Saat Anda membuat proyek, sumber daya dibuat secara otomatis.
Model embedding yang diterapkan ke proyek Anda untuk konversi teks ke vektor. Anda dapat menggunakan model apa pun text-embedding , seperti text-embedding-3-large.
LLM yang diimplementasikan ke proyek Anda untuk perencanaan permintaan dan pembuatan jawaban. Anda dapat menggunakan LLM yang didukung, seperti gpt-5-mini.
Visual Studio Code dengan ekstensi Klien REST.
Git untuk mengkloning repositori sampel.
Azure CLI untuk autentikasi tanpa kunci dengan Microsoft Entra ID.

Mengonfigurasi akses

Untuk mengonfigurasi akses untuk panduan cepat ini:

Masuk ke portal Azure.
Pada layanan Pencarian Azure AI Anda:
1. Aktifkan akses berbasis peran.
2. Membuat identitas terkelola yang ditetapkan sistem.
3. Tetapkan peran berikut ke akun pengguna Anda: Kontributor Layanan Pencarian, Kontributor Data Indeks Pencarian, dan Pembaca Data Indeks Pencarian.
Pada sumber daya Microsoft Foundry Anda, tetapkan pengguna Cognitive Services ke identitas terkelola layanan pencarian Anda.

Penting

Pengambilan data oleh agen memiliki dua model tagihan berbasis token.

Penagihan dari Pencarian Azure AI untuk pemrosesan pengambilan berorientasi agen.
Penagihan dari Azure OpenAI untuk perencanaan kueri dan sintesis jawaban.

Untuk informasi selengkapnya, lihat Ketersediaan, batas, dan penagihan wilayah.

Mendapatkan titik akhir

Untuk mendapatkan titik akhir untuk panduan pemula cepat ini:

Masuk ke portal Azure.
Pada layanan Pencarian Azure AI Anda:
1. Dari panel kiri, pilih Gambaran Umum.
2. Salin URL, yang akan terlihat seperti https://my-service.search.windows.net.
Pada sumber daya Microsoft Foundry Anda:
1. Dari panel kiri, pilih Kunci Manajemen> Sumber Dayadan Titik Akhir.
2. Salin URL pada tab OpenAI , yang akan terlihat seperti https://my-resource.openai.azure.com/.

Siapkan lingkungan

Gunakan Git untuk mengkloning repositori sampel.

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

Buka folder quickstart lalu buka folder tersebut di Visual Studio Code.
```
cd azure-search-rest-samples/Quickstart-agentic-retrieval
code .
```
Di agentic-retrieval.rest, ganti nilai tempat penampung untuk @search-url dan @aoai-url dengan URL yang Anda dapatkan di Dapatkan titik akhir.
Untuk autentikasi tanpa kunci dengan Microsoft Entra ID, masuk ke akun Azure Anda. Jika Anda memiliki beberapa langganan, pilih langganan yang berisi sumber daya Pencarian Azure AI dan Microsoft Foundry Anda.
```
az login
```

Untuk autentikasi tanpa kunci dengan Microsoft Entra ID, buat token akses.

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

Ganti nilai tempat penampung untuk @token dengan token dari langkah sebelumnya.

Jalankan kode

Kirim setiap permintaan secara berurutan, dimulai dengan ### Create an index.

Setiap permintaan harus mengembalikan 200 OKkode status , 201 Created, atau 204 No Content . Jika Anda mendapatkan kesalahan, periksa apakah ada salah ketik pada permintaan dan pastikan token Anda valid.

Keluaran

Setiap permintaan mengembalikan JSON yang berbeda berdasarkan operasi. Output utama berasal dari ### Run agentic retrieval, yang akan terlihat mirip dengan yang berikut ini:

{
  "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"
    }
  ]
}

Memahami kode

Catatan

Cuplikan kode di bagian ini mungkin telah dimodifikasi untuk keterbacaan. Untuk contoh kerja lengkap, lihat kode sumber.

Sekarang setelah Anda menjalankan kode, mari kita uraikan langkah-langkah kunci:

Membuat indeks pencarian
Mengunggah dokumen ke indeks
Membuat sumber pengetahuan
Membuat pangkalan pengetahuan
Jalankan alur pengambilan

Membuat indeks pencarian

Dalam Pencarian Azure AI, indeks adalah kumpulan data terstruktur. Kode berikut mendefinisikan indeks bernama 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}}"
                }
            }
        ]
    }
}

Referensi:Indeks - Buat

Mengunggah dokumen ke indeks

Saat ini, earth-at-night indeks kosong. Kode berikut mengisi indeks dengan dokumen JSON dari e-book Bumi di Malam Hari NASA. Seperti yang diperlukan oleh Pencarian Azure AI, setiap dokumen sesuai dengan bidang dan jenis data yang ditentukan dalam skema indeks.

### 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
        }
    ]
}

Referensi:Dokumen - Indeks

Membuat sumber pengetahuan

### 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" }
        ]
    }
}

Referensi:Sumber Pengetahuan - Buat

Membuat pangkalan pengetahuan

Untuk menargetkan penyebaran earth-knowledge-source dan gpt-5-mini pada waktu kueri, Anda memerlukan basis pengetahuan. Kode berikut mendefinisikan basis bernama earth-knowledge-base.

outputMode (pratinjau) disetel ke answerSynthesis, sehingga memungkinkan jawaban dalam bahasa alami yang mengutip dokumen yang diambil dan mengikuti answerInstructions yang diberikan.

### 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."
}

Referensi:Pangkalan Pengetahuan - Buat

Jalankan alur pengambilan

Anda siap untuk menjalankan pengambilan agenik. Kode berikut mengirimkan dua bagian dari kueri pengguna ke earth-knowledge-base, yang:

Menganalisis seluruh percakapan untuk menyimpulkan kebutuhan informasi pengguna.
Menguraikan kueri gabungan menjadi subkueri yang difokuskan.
Menjalankan subkueri secara simultan terhadap sumber pengetahuan Anda.
Menggunakan ranker semantik untuk melakukan rerank dan memfilter hasilnya. Contoh ini mengecualikan respons dengan skor reranker 2.5 atau lebih rendah.
Menyintesis hasil terbaik menjadi jawaban bahasa yang alami.

retrievalReasoningEffort (pratinjau) diatur ke low untuk mengontrol jumlah penalaran yang digunakan untuk perencanaan kueri.

### 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" }
}

Referensi:Pengambilan Informasi - Ambil

Output berisi komponen berikut:

response menyediakan jawaban yang dihasilkan LLM yang disintesis untuk kueri yang mengutip dokumen yang diambil. Saat sintesis jawaban tidak diaktifkan, bagian ini berisi konten yang diekstrak langsung dari dokumen.
activity melacak langkah-langkah yang diambil selama proses pengambilan, termasuk subkueri yang dihasilkan oleh penyebaran gpt-5-mini Anda dan token yang digunakan untuk penilaian semantik, perencanaan kueri, dan sintesis jawaban.
references mencantumkan dokumen yang berkontribusi pada respons, masing-masing diidentifikasi oleh mereka docKey.

Membersihkan sumber daya

Jika tidak, permintaan berikut dari agentic-retrieval.rest akan menghapus objek yang Anda buat dalam panduan memulai cepat ini.

Menghapus pangkalan pengetahuan

### 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}}

Menghapus sumber pengetahuan

### 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}}

Menghapus indeks pencarian

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

Saran dan Komentar

Apakah halaman ini membantu?

Terakhir diperbarui pada 2026-09-17