Rövid útmutató: Agentikus visszakeresés

Megjegyzés

Azure AI Keresés a Azure portálon, REST API-kon és Azure SDK-k keresztül érhető el. Emellett a Foundry IQ-t is támogatja, amely a vállalati tartalmakat újrafelhasználható, engedélytudatos tudásbázisokká alakítja át az Microsoft Foundry portálon található ügynökök számára.

Fontos

Az (előzetes verzióban) megjelölt funkciókra, képességekre vagy tulajdonságokra nem vonatkozik szolgáltatási szintű szerződés, nem ajánlott éles számítási feladatokhoz, és az általános elérhetővé válásuk előtt változhatnak vagy korlátozhatók. Az Azure AI Keresés előzetes verziójú kifejezések az összes előzetes verziós funkcióra vonatkoznak, függetlenül attól, hogy önálló vagy egy általánosan elérhető funkció része.

Ebben a gyors kezdő útmutatóban a agentic retrieval használatával hozhat létre beszélgetési keresési élményt, amelyet az Azure AI Keresés által indexelt dokumentumok és az Azure OpenAI Foundry Modellek nagy nyelvi modellje (LLM) működtet.

A tudásbázis az LLM-alapú lekérdezéstervezést (előzetes verzió) használja arra, hogy az összetett lekérdezéseket allekérdezésekre bontsa. Ezután futtatja az al lekérdezéseket egy vagy több tudásbázison , és metaadatokkal adja vissza az eredményeket. A tudásbázis alapértelmezés szerint nyers tartalmat ad vissza a forrásaiból, de ez a rövid útmutató válaszszintézist (előzetes verzió) használ a természetes nyelvű válaszok létrehozásához.

Bár használhatja a saját adatait, ez a rövid útmutató Sample JSON-dokumentumokat használ a NASA Earth at Night e-bookjából.

Tipp

Azonnal hozzá szeretne kezdeni? Töltse le a forráskódot GitHub.

Előfeltételek

Aktív előfizetéssel rendelkező Azure fiók. Hozzon létre egy fiókot ingyenesen.
Egy Azure AI Keresés szolgáltatás bármely olyan régióban, amely ügynöki lekérést biztosít. Ez a gyorsindító az alapszintű vagy annál magasabb szintet igényli a felügyelt identitások szolgáltatásához.
Egy Microsoft Foundry-projekt és erőforrás. Projekt létrehozásakor az erőforrás automatikusan létrejön.
A projektben üzembe helyezett beágyazási modell a szöveg-vektor konverzióhoz. Bármilyen text-embedding modellt használhat, például text-embedding-3-large.
A projekt számára üzembe helyezett LLM a lekérdezések megtervezéséhez és válaszok generálásához. Bármilyen támogatott LLM-et használhat, például gpt-5-mini.
.NET 8 vagy újabb.
Git a mintaadattár klónozásához.
A Microsoft Entra ID -vel történő kulcs nélküli hitelesítéshez használt Azure CLI.

Hozzáférés konfigurálása

Mielőtt hozzákezdene, győződjön meg arról, hogy rendelkezik a tartalomhoz és a műveletekhez való hozzáféréshez szükséges engedélyekkel. Ez a rövid útmutató a Microsoft Entra ID-t használja hitelesítéshez és szerepköralapú jogosultságkezeléshez. Szerepkörök hozzárendeléséhez tulajdonosnak vagy felhasználói hozzáférés-rendszergazdának kell lennie. Ha a szerepkörök nem megvalósíthatók, használja inkább a kulcsalapú hitelesítést .

A hozzáférés konfigurálása a gyors kezdéshez:

Jelentkezzen be a Azure portálra.
A Azure AI Keresés szolgáltatásban:
1. Szerepköralapú hozzáférés engedélyezése.
2. Rendszer által hozzárendelt felügyelt identitás létrehozása.
3. Rendelje hozzá a következő szerepköröket a felhasználói fiókjához: Keresési szolgáltatás közreműködője, Keresési index adatszolgáltatója és Keresési index adatolvasója.
Az Microsoft Foundry-erőforráson rendelje hozzá a Cognitive Services User szerepkört a keresési szolgáltatás felügyelt identitásához.

Fontos

Az ügynöki lekérés két tokenalapú számlázási modellel rendelkezik.

Az Azure AI Keresés ügynöki keresésének számlázása.
Az Azure OpenAI számlázása lekérdezéstervezéshez és válaszszintézishez.

További információ: Régió rendelkezésre állása, korlátai és számlázása.

Végpontok lekérése

Minden Azure AI Keresés szolgáltatás és Microsoft Foundry-erőforrás rendelkezik egy endpoint, amely egy egyedi URL-cím, amely azonosítja és hálózati hozzáférést biztosít az erőforráshoz. Egy későbbi szakaszban ezeket a végpontokat kell megadnia, hogy programozott módon csatlakozzanak az erőforrásokhoz.

A gyorsútmutató végpontjainak lekérése:

Jelentkezzen be a Azure portálra.
A Azure AI Keresés szolgáltatásban:
1. A bal oldali panelen válassza az Áttekintés lehetőséget.
2. Másolja ki az URL-címet, amelynek így https://my-service.search.windows.netkell kinéznie.
A Microsoft Foundry-erőforráson:
1. A bal oldali panelen válassza az Erőforrás-kezelési>kulcsok és a Végpont lehetőséget.
2. Másolja ki az URL-címet az OpenAI lapon, amelynek így kell kinéznie https://my-resource.openai.azure.com/.

A környezet beállítása

A Git használatával klónozza a mintaadattárat.

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

Lépjen a quickstart mappába.

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

A(z) sample.env fájlban cserélje le a SEARCH_ENDPOINT és AOAI_ENDPOINT helyőrző értékeit azokra az URL-ekre, amelyeket a Get endpoints lépésben kapott.
Átnevezés sample.env : .env.
```
mv sample.env .env
```
Telepítse a függőségeket.
```
dotnet restore AgenticRetrievalQuickstart.csproj
```
Amikor a visszaállítás befejeződött, győződjön meg arról, hogy a kimenetben nem jelennek meg hibák.
Ha kulcs nélküli hitelesítést szeretne a Microsoft Entra ID-vel, jelentkezzen be az Azure-fiókjába. Ha több előfizetéssel rendelkezik, válassza ki azt, amely tartalmazza a Azure AI Keresés és Microsoft Foundry-erőforrásokat.
```
az login
```

A kód futtatása

Futtassa az alkalmazást index létrehozásához, dokumentumok feltöltéséhez, tudásforrás és tudásbázis konfigurálásához, valamint lekérdezések futtatásához ügynöki módban.

dotnet run --project AgenticRetrievalQuickstart.csproj

Kimenet

Az alkalmazás kimenetének a következőhöz hasonlónak kell lennie:

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.

A kód ismertetése

Megjegyzés

Előfordulhat, hogy az ebben a szakaszban szereplő kódrészletek az olvashatóság érdekében módosultak. A teljes munka példához tekintse meg a forráskódot.

Most, hogy futtatta a kódot, bontsuk le a legfontosabb lépéseket:

Keresési index létrehozása
Dokumentumok feltöltése az indexbe
Tudásbázis létrehozása
Tudásbázis létrehozása
Üzenetek beállítása
A lekérési folyamat futtatása
A beszélgetés folytatása

Keresési index létrehozása

Az Azure AI Keresés-ben az index egy strukturált adatgyűjtemény. Az alábbi kód egy névvel ellátott earth-at-nightindexet határoz meg.

Az indexséma mezőket tartalmaz a dokumentumazonosításhoz és az oldaltartalomhoz, a beágyazásokhoz és a számokhoz. A séma olyan konfigurációkat is tartalmaz a szemantikai rangsoroláshoz és a vektorkereséshez, amelyek az text-embedding-3-large üzembe helyezését használják, hogy a szöveget vektorizálják, és a dokumentumokat szemantikai vagy fogalmi hasonlóság alapján egyeztessék.

// 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.");

Referencia:SearchField, SimpleField, VectorSearch, SzemanticSearch, SearchIndex, SearchIndexClient

Dokumentumok feltöltése az indexbe

earth-at-night Az index jelenleg üres. Az alábbi kód JSON-dokumentumokkal tölti fel az indexet NASA Earth at Night e-book. A Azure AI Keresés előírásainak megfelelően minden dokumentum megfelel az indexsémában definiált mezőknek és adattípusoknak.

// 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.");

Referencia:SearchClient, SearchIndexingBufferedSender

Tudásbázis létrehozása

A tudásbázis a forrásadatok újrafelhasználható hivatkozása. Az alábbi kód egy olyan tudásbázist earth-knowledge-source határoz meg, amely az earth-at-night indexet célozza.

SourceDataFields megadja, hogy mely indexmezők szerepelnek az idézethivatkozásokban. Ez a példa csak az ember által olvasható mezőket tartalmazza, hogy elkerülje a hosszadalmas, értelmezhetetlen beágyazásokat a válaszokba.

// 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.");

Referencia:SearchIndexKnowledgeSource

Tudásbázis létrehozása

Ahhoz, hogy a earth-knowledge-source és a gpt-5-mini üzembe helyezés célzott legyen lekérdezési időpontban, szükség van egy tudásbázisra. Az alábbi kód egy tudásbázist earth-knowledge-basehatároz meg.

OutputMode (előzetes verzió) értéke AnswerSynthesis, ami lehetővé teszi a lekért dokumentumokra hivatkozó természetes nyelvű válaszokat és a megadott AnswerInstructions követését. RetrievalReasoningEffort (előzetes verzió) a lekérdezéstervezéshez használt érvelés mennyiségének szabályozására van beállítva low .

// 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.");

Referencia:KnowledgeBaseAzureOpenAIModel, KnowledgeBase

Üzenetek beállítása

Az üzenetek a beolvasási útvonal bemenetei, és tartalmazzák a beszélgetési előzményeket. Minden üzenet tartalmaz egy szerepkört, amely jelzi annak eredetét, például system vagy user, és a tartalmat természetes nyelven. A használt LLM határozza meg, hogy mely szerepkörök érvényesek.

A következő kód létrehoz egy rendszerüzenetet, amely arra utasítja earth-knowledge-base , hogy éjszaka válaszoljon a Földdel kapcsolatos kérdésekre, és válaszoljon a "Nem tudom" üzenettel, ha a válaszok nem érhetők el.

// 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 }
    }
};

A lekérési folyamat futtatása

Készen áll az "agentic retrieval" futtatására. A következő kód egy kétrészes felhasználói lekérdezést küld a következőnek earth-knowledge-base:

Elemzi a teljes beszélgetést, hogy a felhasználó információszükségletét következtethesse.
Az összetett lekérdezést célzott alkérdezésekre bontja.
Futtatja az al-lekérdezéseket egyidejűleg a tudásforráson.
Szemantikai rangsorolót használ az eredmények újrarajzolásához és szűréséhez.
A legjobb eredményeket természetes nyelvű válaszsá szintetizálja.

// 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 }
});

Referencia:KnowledgeBaseRetrievalClient, KnowledgeBaseRetrievalRequest

A válasz, a tevékenység és a hivatkozások áttekintése

A következő kód a lekérési folyamat válaszát, tevékenységét és hivatkozásait jeleníti meg, ahol:

Response szintetizált, LLM által generált választ ad a lekért dokumentumokat idéző lekérdezésre. Ha a válaszszintézis nincs engedélyezve, ez a szakasz közvetlenül a dokumentumokból kinyert tartalmat tartalmaz.
Activity Nyomon követi a lekérési folyamat során végrehajtott lépéseket, beleértve az allekérdezéseket, amelyeket az Ön gpt-5-mini üzembe helyezése hozott létre, valamint a szemantikai rangsoroláshoz, lekérdezéstervezéshez és válaszszintézishez használt tokeneket.
References felsorolja a válaszhoz hozzájáruló dokumentumokat, amelyek mindegyikét az azonosítóik azonosítják 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);
}

A beszélgetés folytatása

Az alábbi kód folytatja a beszélgetést a következővel earth-knowledge-base: A felhasználói lekérdezés elküldése után a tudásbázis lekéri a releváns tartalmat earth-knowledge-source , és hozzáfűzi a választ az üzenetlistához.

// 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 }
});

Az új válasz, tevékenység és hivatkozások áttekintése

Az alábbi kód megjeleníti az új választ, tevékenységet és hivatkozásokat a lekérési folyamatból.

// 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);
}

Erőforrások törlése

Ha a saját előfizetésében dolgozik, érdemes befejezni egy projektet a már nem szükséges erőforrások eltávolításával. Az otthagyott futó erőforrások pénzbe kerülhetnek.

Az Azure portálon válassza a Minden erőforrás vagy Erőforráscsoportok lehetőséget a bal oldali panelen az erőforrások megkereséséhez és kezeléséhez. Az erőforrásokat egyenként törölheti, vagy törölheti az erőforráscsoportot az összes erőforrás egyidejű eltávolításához.

Ellenkező esetben a(z) program.cs alábbi kódja törli az ebben a gyorsútmutatóban létrehozott objektumokat.

A tudásbázis törlése

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

A tudásbázis törlése

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

A keresési index törlése

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

A tudásbázis az LLM-alapú lekérdezéstervezést (előzetes verzió) használja az összetett lekérdezések allekérdezésekre bontásához. Ezután futtatja az al lekérdezéseket egy vagy több tudásbázison , és metaadatokkal adja vissza az eredményeket. A tudásbázis alapértelmezés szerint nyers tartalmat ad vissza a forrásaiból, de ez a rövid útmutató válaszszintézist (előzetes verzió) használ a természetes nyelvű válaszok létrehozásához.

Bár használhatja a saját adatait, ez a rövid útmutató Sample JSON-dokumentumokat használ a NASA Earth at Night e-bookjából.

Tipp

Azonnal hozzá szeretne kezdeni? Töltse le a forráskódot GitHub.

Előfeltételek

Aktív előfizetéssel rendelkező Azure fiók. Hozzon létre egy fiókot ingyenesen.
Egy Azure AI Keresés szolgáltatás bármely olyan régióban, amely ügynöki lekérést biztosít. Ez a gyorsindító az alapszintű vagy annál magasabb szintet igényli a felügyelt identitások szolgáltatásához.
Egy Microsoft Foundry-projekt és erőforrás. Projekt létrehozásakor az erőforrás automatikusan létrejön.
A projektben üzembe helyezett beágyazási modell a szöveg-vektor konverzióhoz. Bármilyen text-embedding modellt használhat, például text-embedding-3-large.
A projekt számára üzembe helyezett LLM a lekérdezések megtervezéséhez és válaszok generálásához. Bármilyen támogatott LLM-et használhat, például gpt-5-mini.
Java 11 vagy újabb és Maven.
Git a mintaadattár klónozásához.
A Microsoft Entra ID -vel történő kulcs nélküli hitelesítéshez használt Azure CLI.

Hozzáférés konfigurálása

A hozzáférés konfigurálása a gyors kezdéshez:

Jelentkezzen be a Azure portálra.
A Azure AI Keresés szolgáltatásban:
1. Szerepköralapú hozzáférés engedélyezése.
2. Rendszer által hozzárendelt felügyelt identitás létrehozása.
3. Rendelje hozzá a következő szerepköröket a felhasználói fiókjához: Keresési szolgáltatás közreműködője, Keresési index adatszolgáltatója és Keresési index adatolvasója.
Az Microsoft Foundry-erőforráson rendelje hozzá a Cognitive Services User szerepkört a keresési szolgáltatás felügyelt identitásához.

Fontos

Az ügynöki lekérés két tokenalapú számlázási modellel rendelkezik.

Az Azure AI Keresés ügynöki keresésének számlázása.
Az Azure OpenAI számlázása lekérdezéstervezéshez és válaszszintézishez.

További információ: Régió rendelkezésre állása, korlátai és számlázása.

Végpontok lekérése

A gyorsútmutató végpontjainak lekérése:

Jelentkezzen be a Azure portálra.
A Azure AI Keresés szolgáltatásban:
1. A bal oldali panelen válassza az Áttekintés lehetőséget.
2. Másolja ki az URL-címet, amelynek így https://my-service.search.windows.netkell kinéznie.
A Microsoft Foundry-erőforráson:
1. A bal oldali panelen válassza az Erőforrás-kezelési>kulcsok és a Végpont lehetőséget.
2. Másolja ki az URL-címet az OpenAI lapon, amelynek így kell kinéznie https://my-resource.openai.azure.com/.

A környezet beállítása

A Git használatával klónozza a mintaadattárat.

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

Lépjen a quickstart mappába.

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

A(z) sample.env fájlban cserélje le a SEARCH_ENDPOINT és AOAI_ENDPOINT helyőrző értékeit azokra az URL-ekre, amelyeket a Get endpoints lépésben kapott.
Átnevezés sample.env : .env.
```
mv sample.env .env
```
Telepítse a függőségeket.
```
mvn clean dependency:copy-dependencies
```
Ha kulcs nélküli hitelesítést szeretne a Microsoft Entra ID-vel, jelentkezzen be az Azure-fiókjába. Ha több előfizetéssel rendelkezik, válassza ki azt, amely tartalmazza a Azure AI Keresés és Microsoft Foundry-erőforrásokat.
```
az login
```

A kód futtatása

Az alkalmazás felépítése és futtatása az index létrehozásához, a dokumentumok feltöltéséhez, egy tudásforrás és egy tudásbázis konfigurálásához, valamint ügynöki lekérési lekérdezések futtatásához.

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

Kimenet

Az alkalmazás kimenetének a következőhöz hasonlónak kell lennie:

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.

A kód ismertetése

Megjegyzés

Előfordulhat, hogy az ebben a szakaszban szereplő kódrészletek az olvashatóság érdekében módosultak. A teljes munka példához tekintse meg a forráskódot.

Most, hogy futtatta a kódot, bontsuk le a legfontosabb lépéseket:

Keresési index létrehozása
Dokumentumok feltöltése az indexbe
Tudásbázis létrehozása
Tudásbázis létrehozása
Üzenetek beállítása
A lekérési folyamat futtatása
A beszélgetés folytatása

Keresési index létrehozása

Az Azure AI Keresés-ben az index egy strukturált adatgyűjtemény. Az alábbi kód egy névvel ellátott earth-at-nightindexet határoz meg.

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);

Referencia:SearchField, VectorSearch, SzemanticSearch, SearchIndex, SearchIndexClient

Dokumentumok feltöltése az indexbe

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);

Referencia:SearchClient, SearchDocument

Tudásbázis létrehozása

A tudásbázis a forrásadatok újrafelhasználható hivatkozása. Az alábbi kód egy olyan tudásbázist earth-knowledge-source határoz meg, amely az earth-at-night indexet célozza.

sourceDataFields megadja, hogy mely indexmezők szerepelnek az idézethivatkozásokban. Ez a példa csak az ember által olvasható mezőket tartalmazza, hogy elkerülje a hosszadalmas, értelmezhetetlen beágyazásokat a válaszokba.

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);

Referencia:SearchIndexKnowledgeSource

Tudásbázis létrehozása

OutputMode (előzetes verzió) ANSWER_SYNTHESIS értékre van állítva, hogy lehetővé tegye a természetes nyelvű, a lekért dokumentumokat idéző és a megadott AnswerInstructions követő válaszokat. RetrievalReasoningEffort (előzetes verzió) a lekérdezéstervezéshez használt érvelés mennyiségének szabályozására van beállítva low .

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);

Referencia:KnowledgeBaseAzureOpenAIModel, KnowledgeBase

Üzenetek beállítása

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);

A lekérési folyamat futtatása

Készen áll az "agentic retrieval" futtatására. A következő kód egy kétrészes felhasználói lekérdezést küld a következőnek earth-knowledge-base:

Elemzi a teljes beszélgetést, hogy a felhasználó információszükségletét következtethesse.
Az összetett lekérdezést célzott alkérdezésekre bontja.
Futtatja az al-lekérdezéseket egyidejűleg a tudásforráson.
Szemantikai rangsorolót használ az eredmények újrarajzolásához és szűréséhez.
A legjobb eredményeket természetes nyelvű válaszsá szintetizálja.

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));

A retrieve segédfüggvény a beszélgetési előzményekből létrehoz egy KnowledgeBaseRetrievalOptions elemet, beállítja a visszakeresési következtetés erőfeszítési szintjét, csatolja a tudásforrás paramétereit, és egy KnowledgeBaseRetrievalResult értéket ad vissza:

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);
}

Referencia:KnowledgeBaseRetrievalClient, KnowledgeBaseRetrievalOptions

A válasz, a tevékenység és a hivatkozások áttekintése

A következő kód a lekérési folyamat válaszát, tevékenységét és hivatkozásait jeleníti meg, ahol:

Response szintetizált, LLM által generált választ ad a lekért dokumentumokat idéző lekérdezésre. Ha a válaszszintézis nincs engedélyezve, ez a szakasz közvetlenül a dokumentumokból kinyert tartalmat tartalmaz.
Activity Nyomon követi a lekérési folyamat során végrehajtott lépéseket, beleértve az allekérdezéseket, amelyeket az Ön gpt-5-mini üzembe helyezése hozott létre, valamint a szemantikai rangsoroláshoz, lekérdezéstervezéshez és válaszszintézishez használt tokeneket.
References felsorolja a válaszhoz hozzájáruló dokumentumokat, amelyek mindegyikét az azonosítóik azonosítják 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));
}

A beszélgetés folytatása

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

retrievalResult = retrieve(baseClient, messages, knowledgeSourceName);

Az új válasz, tevékenység és hivatkozások áttekintése

Az alábbi kód kinyeri a válasz szövegét, és a printResult meghívásával megjeleníti az új választ, a tevékenységet és a hivatkozásokat.

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

printResult(responseText, retrievalResult);

Erőforrások törlése

Ha a saját előfizetésében dolgozik, érdemes befejezni egy projektet a már nem szükséges erőforrások eltávolításával. Az otthagyott futó erőforrások pénzbe kerülhetnek.

Ellenkező esetben a(z) AgenticRetrievalQuickstart.java alábbi kódja törli az ebben a gyorsútmutatóban létrehozott objektumokat.

A tudásbázis törlése

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

A tudásbázis törlése

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

A keresési index törlése

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

A tudásbázis LLM-alapú lekérdezéstervezést (előzetes verzió) használ az összetett lekérdezések allekérdezésekre bontásához. Ezután futtatja az al lekérdezéseket egy vagy több tudásbázison , és metaadatokkal adja vissza az eredményeket. A tudásbázis alapértelmezés szerint nyers tartalmat ad vissza a forrásaiból, de ez a rövid útmutató válaszszintézist (előzetes verzió) használ a természetes nyelvű válaszok létrehozásához.

Bár használhatja a saját adatait, ez a rövid útmutató Sample JSON-dokumentumokat használ a NASA Earth at Night e-bookjából.

Tipp

Azonnal hozzá szeretne kezdeni? Töltse le a forráskódot GitHub.

Előfeltételek

Aktív előfizetéssel rendelkező Azure fiók. Hozzon létre egy fiókot ingyenesen.
Egy Azure AI Keresés szolgáltatás bármely olyan régióban, amely ügynöki lekérést biztosít. Ez a gyorsindító az alapszintű vagy annál magasabb szintet igényli a felügyelt identitások szolgáltatásához.
Egy Microsoft Foundry-projekt és erőforrás. Projekt létrehozásakor az erőforrás automatikusan létrejön.
A projektben üzembe helyezett beágyazási modell a szöveg-vektor konverzióhoz. Bármilyen text-embedding modellt használhat, például text-embedding-3-large.
A projekt számára üzembe helyezett LLM a lekérdezések megtervezéséhez és válaszok generálásához. Bármilyen támogatott LLM-et használhat, például gpt-5-mini.
Node.js 20 LTS vagy újabb.
Git a mintaadattár klónozásához.
A Microsoft Entra ID -vel történő kulcs nélküli hitelesítéshez használt Azure CLI.

Hozzáférés konfigurálása

A hozzáférés konfigurálása a gyors kezdéshez:

Jelentkezzen be a Azure portálra.
A Azure AI Keresés szolgáltatásban:
1. Szerepköralapú hozzáférés engedélyezése.
2. Rendszer által hozzárendelt felügyelt identitás létrehozása.
3. Rendelje hozzá a következő szerepköröket a felhasználói fiókjához: Keresési szolgáltatás közreműködője, Keresési index adatszolgáltatója és Keresési index adatolvasója.
Az Microsoft Foundry-erőforráson rendelje hozzá a Cognitive Services User szerepkört a keresési szolgáltatás felügyelt identitásához.

Fontos

Az ügynöki lekérés két tokenalapú számlázási modellel rendelkezik.

Az Azure AI Keresés ügynöki keresésének számlázása.
Az Azure OpenAI számlázása lekérdezéstervezéshez és válaszszintézishez.

További információ: Régió rendelkezésre állása, korlátai és számlázása.

Végpontok lekérése

A gyorsútmutató végpontjainak lekérése:

Jelentkezzen be a Azure portálra.
A Azure AI Keresés szolgáltatásban:
1. A bal oldali panelen válassza az Áttekintés lehetőséget.
2. Másolja ki az URL-címet, amelynek így https://my-service.search.windows.netkell kinéznie.
A Microsoft Foundry-erőforráson:
1. A bal oldali panelen válassza az Erőforrás-kezelési>kulcsok és a Végpont lehetőséget.
2. Másolja ki az URL-címet az OpenAI lapon, amelynek így kell kinéznie https://my-resource.openai.azure.com/.

A környezet beállítása

A Git használatával klónozza a mintaadattárat.

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

Lépjen a quickstart mappába.

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

A(z) sample.env fájlban cserélje le a AZURE_SEARCH_ENDPOINT és AZURE_OPENAI_ENDPOINT helyőrző értékeit azokra az URL-ekre, amelyeket a Get endpoints lépésben kapott.
Átnevezés sample.env : .env.
```
mv sample.env .env
```
Telepítse a függőségeket.
```
npm install
```
Amikor a telepítés befejeződik, megjelenik egy node_modules mappa a projektkönyvtárban.
Ha kulcs nélküli hitelesítést szeretne a Microsoft Entra ID-vel, jelentkezzen be az Azure-fiókjába. Ha több előfizetéssel rendelkezik, válassza ki azt, amely tartalmazza a Azure AI Keresés és Microsoft Foundry-erőforrásokat.
```
az login
```

A kód futtatása

Futtassa az alkalmazást index létrehozásához, dokumentumok feltöltéséhez, tudásforrás és tudásbázis konfigurálásához, valamint lekérdezések futtatásához ügynöki módban.

npm start

Kimenet

Az alkalmazás kimenetének a következőhöz hasonlónak kell lennie:

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.

A kód ismertetése

Most, hogy már rendelkezik a kóddal, bontsuk le a legfontosabb összetevőket:

Keresési index létrehozása
Dokumentumok feltöltése az indexbe
Tudásbázis létrehozása
Tudásbázis létrehozása
A lekérési folyamat futtatása
A válasz, a tevékenység és a hivatkozások áttekintése
A beszélgetés folytatása

Keresési index létrehozása

Az Azure AI Keresés-ben az index egy strukturált adatgyűjtemény. Az alábbi kód egy névvel ellátott earth_at_nightindexet határoz meg.

Az indexséma mezőket tartalmaz a dokumentumazonosításhoz és az oldaltartalomhoz, a beágyazásokhoz és a számokhoz. A séma magában foglalja a szemantikai rangsorolás és a vektorkeresés konfigurációit is, amelyek a text-embedding-3-large üzembe helyezését használják arra, hogy vektorizálják a szöveget, és szemantikai hasonlóság alapján párosítsák a dokumentumokat.

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);

Referencia:SearchField, VectorSearch, SzemanticSearch, SearchIndex, SearchIndexClient, SearchClient, DefaultAzureCredential

Dokumentumok feltöltése az indexbe

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!`);

Referencia:SearchIndexingBufferedSender

Tudásbázis létrehozása

A tudásbázis a forrásadatok újrafelhasználható hivatkozása. Az alábbi kód egy olyan tudásbázist earth-knowledge-source határoz meg, amely az earth-at-night indexet célozza.

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.`);

Referencia:SearchIndexKnowledgeSource

Tudásbázis létrehozása

outputMode (előzetes verzió) értéke answerSynthesis, így lehetővé teszi a lekért dokumentumokra hivatkozó, természetes nyelvű válaszok használatát, amelyek követik a megadott answerInstructions-t.

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.`);

Referencia:KnowledgeBase

A lekérési folyamat futtatása

Készen áll az "agentic retrieval" futtatására. A következő kód egy kétrészes felhasználói lekérdezést küld a következőnek earth-knowledge-base:

Elemzi a teljes beszélgetést, hogy a felhasználó információszükségletét következtethesse.
Az összetett lekérdezést célzott alkérdezésekre bontja.
Futtatja az al-lekérdezéseket egyidejűleg a tudásforráson.
Szemantikai rangsorolót használ az eredmények újrarajzolásához és szűréséhez.
A legjobb eredményeket természetes nyelvű válaszsá szintetizálja.

retrievalReasoningEffort (előzetes verzió) a lekérdezéstervezéshez használt érvelés mennyiségének szabályozására van beállítva low .

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);

Referencia:KnowledgeRetrievalClient, KnowledgeBaseRetrievalRequest

A válasz, a tevékenység és a hivatkozások áttekintése

A következő kód a lekérési folyamat válaszát, tevékenységét és hivatkozásait jeleníti meg, ahol:

Answer szintetizált, LLM által generált választ ad a lekért dokumentumokat idéző lekérdezésre. Ha a válaszszintézis nincs engedélyezve, ez a szakasz közvetlenül a dokumentumokból kinyert tartalmat tartalmaz.
Activities Nyomon követi a lekérési folyamat során végrehajtott lépéseket, beleértve az allekérdezéseket, amelyeket az Ön gpt-5-mini üzembe helyezése hozott létre, valamint a szemantikai rangsoroláshoz, lekérdezéstervezéshez és válaszszintézishez használt tokeneket.
References felsorolja a válaszhoz hozzájáruló dokumentumokat, amelyek mindegyikét az azonosítóik azonosítják 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));
    });
}

A beszélgetés folytatása

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);

Az új válasz, tevékenység és hivatkozások áttekintése

Az alábbi kód megjeleníti az új választ, tevékenységet és hivatkozásokat a lekérési folyamatból.

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));
    });
}

Erőforrások törlése

Ha a saját előfizetésében dolgozik, érdemes befejezni egy projektet a már nem szükséges erőforrások eltávolításával. Az otthagyott futó erőforrások pénzbe kerülhetnek.

Ellenkező esetben a(z) index.js alábbi kódja törli az ebben a gyorsútmutatóban létrehozott objektumokat.

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

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

Bár használhatja a saját adatait, ez a rövid útmutató Sample JSON-dokumentumokat használ a NASA Earth at Night e-bookjából.

Tipp

Azonnal hozzá szeretne kezdeni? Töltse le a forráskódot GitHub.

Előfeltételek

Aktív előfizetéssel rendelkező Azure fiók. Hozzon létre egy fiókot ingyenesen.
Egy Azure AI Keresés szolgáltatás bármely olyan régióban, amely ügynöki lekérést biztosít. Ez a gyorsindító az alapszintű vagy annál magasabb szintet igényli a felügyelt identitások szolgáltatásához.
Egy Microsoft Foundry-projekt és erőforrás. Projekt létrehozásakor az erőforrás automatikusan létrejön.
A projektben üzembe helyezett beágyazási modell a szöveg-vektor konverzióhoz. Bármilyen text-embedding modellt használhat, például text-embedding-3-large.
A projekt számára üzembe helyezett LLM a lekérdezések megtervezéséhez és válaszok generálásához. Bármilyen támogatott LLM-et használhat, például gpt-5-mini.
Python 3.8 vagy újabb.
Visual Studio Code Python és Jupyter kiterjesztéssel.
Git a mintaadattár klónozásához.
A Microsoft Entra ID -vel történő kulcs nélküli hitelesítéshez használt Azure CLI.

Hozzáférés konfigurálása

A hozzáférés konfigurálása a gyors kezdéshez:

Jelentkezzen be a Azure portálra.
A Azure AI Keresés szolgáltatásban:
1. Szerepköralapú hozzáférés engedélyezése.
2. Rendszer által hozzárendelt felügyelt identitás létrehozása.
3. Rendelje hozzá a következő szerepköröket a felhasználói fiókjához: Keresési szolgáltatás közreműködője, Keresési index adatszolgáltatója és Keresési index adatolvasója.
Az Microsoft Foundry-erőforráson rendelje hozzá a Cognitive Services User szerepkört a keresési szolgáltatás felügyelt identitásához.

Fontos

Az ügynöki lekérés két tokenalapú számlázási modellel rendelkezik.

Az Azure AI Keresés ügynöki keresésének számlázása.
Az Azure OpenAI számlázása lekérdezéstervezéshez és válaszszintézishez.

További információ: Régió rendelkezésre állása, korlátai és számlázása.

Végpontok lekérése

A gyorsútmutató végpontjainak lekérése:

Jelentkezzen be a Azure portálra.
A Azure AI Keresés szolgáltatásban:
1. A bal oldali panelen válassza az Áttekintés lehetőséget.
2. Másolja ki az URL-címet, amelynek így https://my-service.search.windows.netkell kinéznie.
A Microsoft Foundry-erőforráson:
1. A bal oldali panelen válassza az Erőforrás-kezelési>kulcsok és a Végpont lehetőséget.
2. Másolja ki az URL-címet az OpenAI lapon, amelynek így kell kinéznie https://my-resource.openai.azure.com/.

A környezet beállítása

A Git használatával klónozza a mintaadattárat.

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

Lépjen a quickstart mappába, és nyissa meg azt a Visual Studio Code-ban.
```
cd azure-search-python-samples/Quickstart-Agentic-Retrieval
code .
```
A(z) sample.env fájlban cserélje le a SEARCH_ENDPOINT és AOAI_ENDPOINT helyőrző értékeit azokra az URL-ekre, amelyeket a Get endpoints lépésben kapott.
Átnevezés sample.env : .env.
```
mv sample.env .env
```
Nyisd meg quickstart-agentic-retrieval.ipynb.
Nyomja le a Ctrl+Shift+P billentyűkombinációt, válassza a Jegyzetfüzet: Jegyzetfüzet kernel kiválasztása lehetőséget, és kövesse az utasításokat egy virtuális környezet létrehozásához. Válassza a requirements.txt a függőségekhez.

Ha elkészült, meg kell jelennie egy .venv mappának a projektkönyvtárban.
Ha kulcs nélküli hitelesítést szeretne a Microsoft Entra ID-vel, jelentkezzen be az Azure-fiókjába. Ha több előfizetéssel rendelkezik, válassza ki azt, amely tartalmazza a Azure AI Keresés és Microsoft Foundry-erőforrásokat.
```
az login
```

A kód futtatása

Futtassa a Load connections cellát a szükséges csomagok telepítéséhez és a környezeti változók betöltéséhez.
A fennmaradó cellákat egymás után futtatva hozzon létre egy indexet, töltsön fel dokumentumokat, konfiguráljon egy tudásforrást és egy tudásbázist, és futtassa az ügynök-alapú visszakeresési lekérdezéseket.

Kimenet

Minden kódcella kinyomtatja a kimenetét a jegyzetfüzetbe. Az alábbi példa az összes cella futtatása után mutatja be a kimenetet:

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.

A kód ismertetése

Megjegyzés

Előfordulhat, hogy az ebben a szakaszban szereplő kódrészletek az olvashatóság érdekében módosultak. A teljes munka példához tekintse meg a forráskódot.

Most, hogy futtatta a kódot, bontsuk le a legfontosabb lépéseket:

Keresési index létrehozása
Dokumentumok feltöltése az indexbe
Tudásbázis létrehozása
Tudásbázis létrehozása
Üzenetek beállítása
A lekérési folyamat futtatása
A beszélgetés folytatása

Keresési index létrehozása

Az Azure AI Keresés-ben az index egy strukturált adatgyűjtemény. Az alábbi kód egy névvel ellátott earth-at-nightindexet határoz meg.

# 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.")

Referencia:SearchField, VectorSearch, SzemanticSearch, SearchIndex, SearchIndexClient

Dokumentumok feltöltése az indexbe

# 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.")

Referencia:SearchIndexingBufferedSender

Tudásbázis létrehozása

A tudásbázis a forrásadatok újrafelhasználható hivatkozása. Az alábbi kód egy olyan tudásbázist earth-knowledge-source határoz meg, amely az earth-at-night indexet célozza.

source_data_fields megadja, hogy mely indexmezők szerepelnek az idézethivatkozásokban. Ez a példa csak az ember által olvasható mezőket tartalmazza, hogy elkerülje a hosszadalmas, értelmezhetetlen beágyazásokat a válaszokba.

# 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.")

Referencia:SearchIndexKnowledgeSource

Tudásbázis létrehozása

output_mode (előzetes verzió) értéke answerSynthesis, így lehetővé teszi a lekért dokumentumokra hivatkozó, természetes nyelvű válaszokat, amelyek követik a megadott answer_instructions elemet.

# 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.")

Referencia:KnowledgeBase

Üzenetek beállítása

# 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
    }
]

A lekérési folyamat futtatása

Készen áll az "agentic retrieval" futtatására. A következő kód egy kétrészes felhasználói lekérdezést küld a következőnek earth-knowledge-base:

Elemzi a teljes beszélgetést, hogy a felhasználó információszükségletét következtethesse.
Az összetett lekérdezést célzott alkérdezésekre bontja.
Futtatja az al-lekérdezéseket egyidejűleg a tudásforráson.
Szemantikai rangsorolót használ az eredmények újrarajzolásához és szűréséhez.
A legjobb eredményeket természetes nyelvű válaszsá szintetizálja.

retrieval_reasoning_effort (előzetes verzió) a lekérdezéstervezéshez használt érvelés mennyiségének szabályozására van beállítva low .

# 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.")

Referencia:KnowledgeBaseRetrievalClient, KnowledgeBaseRetrievalRequest

A válasz, a tevékenység és a hivatkozások áttekintése

A következő kód a lekérési folyamat válaszát, tevékenységét és hivatkozásait jeleníti meg, ahol:

response_contents szintetizált, LLM által generált választ ad a lekért dokumentumokat idéző lekérdezésre. Ha a válaszszintézis nincs engedélyezve, ez a szakasz közvetlenül a dokumentumokból kinyert tartalmat tartalmaz.
activity_contents Nyomon követi a lekérési folyamat során végrehajtott lépéseket, beleértve az allekérdezéseket, amelyeket az Ön gpt-5-mini üzembe helyezése hozott létre, valamint a szemantikai rangsoroláshoz, lekérdezéstervezéshez és válaszszintézishez használt tokeneket.
references_contents felsorolja a válaszhoz hozzájáruló dokumentumokat, amelyek mindegyikét az azonosítóik azonosítják 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)

A beszélgetés folytatása

# 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.")

Az új válasz, tevékenység és hivatkozások áttekintése

Az alábbi kód megjeleníti az új választ, tevékenységet és hivatkozásokat a lekérési folyamatból.

# 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)

Erőforrások törlése

Ha a saját előfizetésében dolgozik, érdemes befejezni egy projektet a már nem szükséges erőforrások eltávolításával. Az otthagyott futó erőforrások pénzbe kerülhetnek.

Ellenkező esetben a(z) quickstart-agentic-retrieval.ipynb alábbi kódja törli az ebben a gyorsútmutatóban létrehozott objektumokat.

A tudásbázis törlése

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.")

A tudásbázis törlése

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.")

A keresési index törlése

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

A tudásbázis az LLM-alapú lekérdezéstervezés (előzetes verzió) segítségével bontja az összetett lekérdezéseket részlekérdezésekre. Ezután futtatja az al lekérdezéseket egy vagy több tudásbázison , és metaadatokkal adja vissza az eredményeket. A tudásbázis alapértelmezés szerint nyers tartalmat ad vissza a forrásaiból, de ez a rövid útmutató válaszszintézist (előzetes verzió) használ a természetes nyelvű válaszok létrehozásához.

Bár használhatja a saját adatait, ez a rövid útmutató Sample JSON-dokumentumokat használ a NASA Earth at Night e-bookjából.

Tipp

Azonnal hozzá szeretne kezdeni? Töltse le a forráskódot GitHub.

Előfeltételek

Aktív előfizetéssel rendelkező Azure fiók. Hozzon létre egy fiókot ingyenesen.
Egy Azure AI Keresés szolgáltatás bármely olyan régióban, amely ügynöki lekérést biztosít. Ez a gyorsindító az alapszintű vagy annál magasabb szintet igényli a felügyelt identitások szolgáltatásához.
Egy Microsoft Foundry-projekt és erőforrás. Projekt létrehozásakor az erőforrás automatikusan létrejön.
A projektben üzembe helyezett beágyazási modell a szöveg-vektor konverzióhoz. Bármilyen text-embedding modellt használhat, például text-embedding-3-large.
A projekt számára üzembe helyezett LLM a lekérdezések megtervezéséhez és válaszok generálásához. Bármilyen támogatott LLM-et használhat, például gpt-5-mini.
Node.js 20 LTS vagy újabb.
TypeScript a TypeScript JavaScriptre való fordításához.
Git a mintaadattár klónozásához.
A Microsoft Entra ID -vel történő kulcs nélküli hitelesítéshez használt Azure CLI.

Hozzáférés konfigurálása

A hozzáférés konfigurálása a gyors kezdéshez:

Jelentkezzen be a Azure portálra.
A Azure AI Keresés szolgáltatásban:
1. Szerepköralapú hozzáférés engedélyezése.
2. Rendszer által hozzárendelt felügyelt identitás létrehozása.
3. Rendelje hozzá a következő szerepköröket a felhasználói fiókjához: Keresési szolgáltatás közreműködője, Keresési index adatszolgáltatója és Keresési index adatolvasója.
Az Microsoft Foundry-erőforráson rendelje hozzá a Cognitive Services User szerepkört a keresési szolgáltatás felügyelt identitásához.

Fontos

Az ügynöki lekérés két tokenalapú számlázási modellel rendelkezik.

Az Azure AI Keresés ügynöki keresésének számlázása.
Az Azure OpenAI számlázása lekérdezéstervezéshez és válaszszintézishez.

További információ: Régió rendelkezésre állása, korlátai és számlázása.

Végpontok lekérése

A gyorsútmutató végpontjainak lekérése:

Jelentkezzen be a Azure portálra.
A Azure AI Keresés szolgáltatásban:
1. A bal oldali panelen válassza az Áttekintés lehetőséget.
2. Másolja ki az URL-címet, amelynek így https://my-service.search.windows.netkell kinéznie.
A Microsoft Foundry-erőforráson:
1. A bal oldali panelen válassza az Erőforrás-kezelési>kulcsok és a Végpont lehetőséget.
2. Másolja ki az URL-címet az OpenAI lapon, amelynek így kell kinéznie https://my-resource.openai.azure.com/.

A környezet beállítása

A Git használatával klónozza a mintaadattárat.

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

Lépjen a quickstart mappába.

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

A(z) sample.env fájlban cserélje le a AZURE_SEARCH_ENDPOINT és AZURE_OPENAI_ENDPOINT helyőrző értékeit azokra az URL-ekre, amelyeket a Get endpoints lépésben kapott.
Átnevezés sample.env : .env.
```
mv sample.env .env
```
Telepítse a függőségeket.
```
npm install
```
Amikor a telepítés befejeződik, megjelenik egy node_modules mappa a projektkönyvtárban.
A TypeScript-fájlok fordítása JavaScript-fájlba.
```
npm run build
```
Ha kulcs nélküli hitelesítést szeretne a Microsoft Entra ID-vel, jelentkezzen be az Azure-fiókjába. Ha több előfizetéssel rendelkezik, válassza ki azt, amely tartalmazza a Azure AI Keresés és Microsoft Foundry-erőforrásokat.
```
az login
```

A kód futtatása

Futtassa az alkalmazást index létrehozásához, dokumentumok feltöltéséhez, tudásforrás és tudásbázis konfigurálásához, valamint lekérdezések futtatásához ügynöki módban.

npm start

Megjegyzés

Ez a parancs futtatja a lefordított .js fájlokat a dist mappából. A Node.js megköveteli, hogy a TypeScript-kódot JavaScript-kóddá fordítsák, mielőtt futtatni tudná, ezért futtatta korábban a npm run build parancsot.

Kimenet

Az alkalmazás kimenetének a következőhöz hasonlónak kell lennie:

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.

A kód ismertetése

Most, hogy már rendelkezik a kóddal, bontsuk le a legfontosabb összetevőket:

Keresési index létrehozása
Dokumentumok feltöltése az indexbe
Tudásbázis létrehozása
Tudásbázis létrehozása
A lekérési folyamat futtatása
A válasz, a tevékenység és a hivatkozások áttekintése
A beszélgetés folytatása

Keresési index létrehozása

Az Azure AI Keresés-ben az index egy strukturált adatgyűjtemény. Az alábbi kód egy névvel ellátott earth_at_nightindexet határoz meg.

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);

Referencia:SearchField, VectorSearch, SzemanticSearch, SearchIndex, SearchIndexClient, SearchClient, DefaultAzureCredential

Dokumentumok feltöltése az indexbe

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!`);

Referencia:SearchIndexingBufferedSender

Tudásbázis létrehozása

A tudásbázis a forrásadatok újrafelhasználható hivatkozása. Az alábbi kód egy olyan tudásbázist earth-knowledge-source határoz meg, amely az earth-at-night indexet célozza.

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.`);

Referencia:SearchIndexKnowledgeSource

Tudásbázis létrehozása

outputMode (előzetes verzió) beállítása: answerSynthesis, ami lehetővé teszi a lekért dokumentumokra hivatkozó, természetes nyelvű válaszok használatát, amelyek követik a megadott answerInstructions elemet.

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.`);

Referencia:KnowledgeBase

A lekérési folyamat futtatása

Készen áll az "agentic retrieval" futtatására. A következő kód egy kétrészes felhasználói lekérdezést küld a következőnek earth-knowledge-base:

Elemzi a teljes beszélgetést, hogy a felhasználó információszükségletét következtethesse.
Az összetett lekérdezést célzott alkérdezésekre bontja.
Futtatja az al-lekérdezéseket egyidejűleg a tudásforráson.
Szemantikai rangsorolót használ az eredmények újrarajzolásához és szűréséhez.
A legjobb eredményeket természetes nyelvű válaszsá szintetizálja.

retrievalReasoningEffort (előzetes verzió) a lekérdezéstervezéshez használt érvelés mennyiségének szabályozására van beállítva low .

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);

Referencia:KnowledgeRetrievalClient, KnowledgeBaseRetrievalRequest

A válasz, a tevékenység és a hivatkozások áttekintése

A következő kód a lekérési folyamat válaszát, tevékenységét és hivatkozásait jeleníti meg, ahol:

Answer szintetizált, LLM által generált választ ad a lekért dokumentumokat idéző lekérdezésre. Ha a válaszszintézis nincs engedélyezve, ez a szakasz közvetlenül a dokumentumokból kinyert tartalmat tartalmaz.
Activities Nyomon követi a lekérési folyamat során végrehajtott lépéseket, beleértve az allekérdezéseket, amelyeket az Ön gpt-5-mini üzembe helyezése hozott létre, valamint a szemantikai rangsoroláshoz, lekérdezéstervezéshez és válaszszintézishez használt tokeneket.
References felsorolja a válaszhoz hozzájáruló dokumentumokat, amelyek mindegyikét az azonosítóik azonosítják 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));
    });
}

A beszélgetés folytatása

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);

Az új válasz, tevékenység és hivatkozások áttekintése

Az alábbi kód megjeleníti az új választ, tevékenységet és hivatkozásokat a lekérési folyamatból.

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!");

Erőforrások törlése

Ha a saját előfizetésében dolgozik, érdemes befejezni egy projektet a már nem szükséges erőforrások eltávolításával. Az otthagyott futó erőforrások pénzbe kerülhetnek.

Ellenkező esetben a(z) index.ts alábbi kódja törli az ebben a gyorsútmutatóban létrehozott objektumokat.

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

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

A tudásbázis LLM-alapú lekérdezéstervezést (előzetes verzió) használ az összetett lekérdezések részlekérdezésekre bontásához. Ezután futtatja az al lekérdezéseket egy vagy több tudásbázison , és metaadatokkal adja vissza az eredményeket. A tudásbázis alapértelmezés szerint nyers tartalmat ad vissza a forrásaiból, de ez a rövid útmutató válaszszintézist (előzetes verzió) használ a természetes nyelvű válaszok létrehozásához.

Bár használhatja a saját adatait, ez a rövid útmutató Sample JSON-dokumentumokat használ a NASA Earth at Night e-bookjából.

Tipp

Azonnal hozzá szeretne kezdeni? Töltse le a forráskódot GitHub.

Előfeltételek

Aktív előfizetéssel rendelkező Azure fiók. Hozzon létre egy fiókot ingyenesen.
Egy Azure AI Keresés szolgáltatás bármely olyan régióban, amely ügynöki lekérést biztosít. Ez a gyorsindító az alapszintű vagy annál magasabb szintet igényli a felügyelt identitások szolgáltatásához.
Egy Microsoft Foundry-projekt és erőforrás. Projekt létrehozásakor az erőforrás automatikusan létrejön.
A projektben üzembe helyezett beágyazási modell a szöveg-vektor konverzióhoz. Bármilyen text-embedding modellt használhat, például text-embedding-3-large.
A projekt számára üzembe helyezett LLM a lekérdezések megtervezéséhez és válaszok generálásához. Bármilyen támogatott LLM-et használhat, például gpt-5-mini.
Visual Studio Code a REST ügyfélkiterjesztéssel.
Git a mintaadattár klónozásához.
A Microsoft Entra ID -vel történő kulcs nélküli hitelesítéshez használt Azure CLI.

Hozzáférés konfigurálása

A hozzáférés konfigurálása a gyors kezdéshez:

Jelentkezzen be a Azure portálra.
A Azure AI Keresés szolgáltatásban:
1. Szerepköralapú hozzáférés engedélyezése.
2. Rendszer által hozzárendelt felügyelt identitás létrehozása.
3. Rendelje hozzá a következő szerepköröket a felhasználói fiókjához: Keresési szolgáltatás közreműködője, Keresési index adatszolgáltatója és Keresési index adatolvasója.
Az Microsoft Foundry-erőforráson rendelje hozzá a Cognitive Services User szerepkört a keresési szolgáltatás felügyelt identitásához.

Fontos

Az ügynöki lekérés két tokenalapú számlázási modellel rendelkezik.

Az Azure AI Keresés ügynöki keresésének számlázása.
Az Azure OpenAI számlázása lekérdezéstervezéshez és válaszszintézishez.

További információ: Régió rendelkezésre állása, korlátai és számlázása.

Végpontok lekérése

A gyorsútmutató végpontjainak lekérése:

Jelentkezzen be a Azure portálra.
A Azure AI Keresés szolgáltatásban:
1. A bal oldali panelen válassza az Áttekintés lehetőséget.
2. Másolja ki az URL-címet, amelynek így https://my-service.search.windows.netkell kinéznie.
A Microsoft Foundry-erőforráson:
1. A bal oldali panelen válassza az Erőforrás-kezelési>kulcsok és a Végpont lehetőséget.
2. Másolja ki az URL-címet az OpenAI lapon, amelynek így kell kinéznie https://my-resource.openai.azure.com/.

A környezet beállítása

A Git használatával klónozza a mintaadattárat.

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

Lépjen a quickstart mappába, és nyissa meg azt a Visual Studio Code-ban.
```
cd azure-search-rest-samples/Quickstart-agentic-retrieval
code .
```
A(z) agentic-retrieval.rest fájlban cserélje le a @search-url és @aoai-url helyőrző értékeit azokra az URL-ekre, amelyeket a Get endpoints lépésben kapott.
Ha kulcs nélküli hitelesítést szeretne a Microsoft Entra ID-vel, jelentkezzen be az Azure-fiókjába. Ha több előfizetéssel rendelkezik, válassza ki azt, amely tartalmazza a Azure AI Keresés és Microsoft Foundry-erőforrásokat.
```
az login
```

A Microsoft Entra ID kulcs nélküli hitelesítéséhez hozzon létre egy hozzáférési jogkivonatot.

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

Cserélje le a @token helyőrző értékét az előző lépés tokenjével.

A kód futtatása

Küldje el az egyes kéréseket egymás után, kezdve a .### Create an index

Minden kérésnek egy , 200 OKvagy 201 Created állapotkódot kell visszaadnia204 No Content. Ha hibaüzenetet kap, ellenőrizze, hogy nincs-e elírás a kérésben, és győződjön meg arról, hogy a token érvényes.

Kimenet

Minden kérés különböző JSON-t ad vissza a művelet alapján. A kulcs kimenete a következőhöz ### Run agentic retrievalhasonlóan jelenik meg:

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

A kód ismertetése

Megjegyzés

Előfordulhat, hogy az ebben a szakaszban szereplő kódrészletek az olvashatóság érdekében módosultak. A teljes munka példához tekintse meg a forráskódot.

Most, hogy futtatta a kódot, bontsuk le a legfontosabb lépéseket:

Keresési index létrehozása
Dokumentumok feltöltése az indexbe
Tudásbázis létrehozása
Tudásbázis létrehozása
A lekérési folyamat futtatása

Keresési index létrehozása

Az Azure AI Keresés-ben az index egy strukturált adatgyűjtemény. Az alábbi kód egy névvel ellátott earth-at-nightindexet határoz meg.

### 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}}"
                }
            }
        ]
    }
}

Referencia:Indexek – Létrehozása

Dokumentumok feltöltése az indexbe

earth-at-night Az index jelenleg üres. Az alábbi kód JSON-dokumentumokkal tölti fel az indexet a NASA Earth at Night e-bookjából. A Azure AI Keresés előírásainak megfelelően minden dokumentum megfelel az indexsémában definiált mezőknek és adattípusoknak.

### 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
        }
    ]
}

Referencia:Dokumentumok – Index

Tudásbázis létrehozása

A tudásbázis a forrásadatok újrafelhasználható hivatkozása. Az alábbi kód egy olyan tudásbázist earth-knowledge-source határoz meg, amely az earth-at-night indexet célozza.

### 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" }
        ]
    }
}

Referencia:Tudásforrások - Létrehozás

Tudásbázis létrehozása

Ahhoz, hogy a lekérdezés végrehajtásakor megcélozzák earth-knowledge-source és gpt-5-mini telepítését, szüksége van egy tudásbázisra. Az alábbi kód egy alapnevet earth-knowledge-basehatároz meg.

outputMode (előzetes verzió) beállítása answerSynthesis értékre történik, lehetővé téve a lekért dokumentumokra hivatkozó, természetes nyelvű válaszokat, valamint a megadott answerInstructions követését.

### 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."
}

Referencia:Tudásbázisok - Létrehozása

A lekérési folyamat futtatása

Készen áll az "agentic retrieval" futtatására. A következő kód egy kétrészes felhasználói lekérdezést küld a következőnek earth-knowledge-base:

Elemzi a teljes beszélgetést, hogy a felhasználó információszükségletét következtethesse.
Az összetett lekérdezést célzott alkérdezésekre bontja.
Futtatja az al-lekérdezéseket egyidejűleg a tudásforráson.
Szemantikai rangsorolót használ az eredmények újrarajzolásához és szűréséhez. Ez a példa kizárja a reranker-pontszámmal 2.5 rendelkező vagy annál alacsonyabb válaszokat.
A legjobb eredményeket természetes nyelvű válaszsá szintetizálja.

retrievalReasoningEffort (előzetes verzió) a lekérdezéstervezéshez használt érvelés mennyiségének szabályozására van beállítva low .

### 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" }
}

Referencia:Tudáslekérés – Lekérés

A kimenet a következő összetevőket tartalmazza:

response szintetizált, LLM által generált választ ad a lekért dokumentumokat idéző lekérdezésre. Ha a válaszszintézis nincs engedélyezve, ez a szakasz közvetlenül a dokumentumokból kinyert tartalmat tartalmaz.
activity Nyomon követi a lekérési folyamat során végrehajtott lépéseket, beleértve az allekérdezéseket, amelyeket az Ön gpt-5-mini üzembe helyezése hozott létre, valamint a szemantikai rangsoroláshoz, lekérdezéstervezéshez és válaszszintézishez használt tokeneket.
references felsorolja a válaszhoz hozzájáruló dokumentumokat, amelyek mindegyikét az azonosítóik azonosítják docKey.

Erőforrások törlése

Ha a saját előfizetésében dolgozik, érdemes befejezni egy projektet a már nem szükséges erőforrások eltávolításával. Az otthagyott futó erőforrások pénzbe kerülhetnek.

Ellenkező esetben a agentic-retrieval.rest következő kérelmek törlik az ebben a gyorsútmutatóban létrehozott objektumokat.

A tudásbázis törlése

### 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}}

A tudásbázis törlése

### 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}}

A keresési index törlése

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

Visszajelzés

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Utolsó frissítés időpontja: 2026-09-17