Programmeringsspråk som stöds av Azure OpenAI

Anmärkning

Det här dokumentet refererar till Microsoft Foundry-portalen (klassisk).

🔄 Växla till dokumentationen för Microsoft Foundry (ny) om du använder den nya portalen.

Källkod | Paket (NuGet)

Stöd för Azure OpenAI API-version

• v1 Allmänt tillgängligt API (GA) ger nu åtkomst till både ga- och förhandsversionsåtgärder. Mer information finns i livscykelguiden för API-versionen.

Installation

dotnet add package OpenAI

Autentisering

Microsoft Entra-ID

En säker och nyckellös autentiseringsmetod är att använda Microsoft Entra-ID (tidigare Azure Active Directory) via Azure Identity-biblioteket. Så här använder du biblioteket:

dotnet add package Azure.Identity

Använd önskad typ av autentiseringsuppgifter från biblioteket. Till exempel DefaultAzureCredential:

using Azure.Identity;
using OpenAI;
using OpenAI.Chat;
using System.ClientModel.Primitives;

#pragma warning disable OPENAI001

BearerTokenPolicy tokenPolicy = new(
    new DefaultAzureCredential(),
    "https://cognitiveservices.azure.com/.default");

ChatClient client = new(
    model: "gpt-4.1-nano",
    authenticationPolicy: tokenPolicy,
    options: new OpenAIClientOptions() { 
    
        Endpoint = new Uri("https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1")
   }
);

ChatCompletion completion = client.CompleteChat("Tell me about the bitter lesson.'");

Console.WriteLine($"[ASSISTANT]: {completion.Content[0].Text}");

Mer information om nyckellös Azure OpenAI-autentisering finns i snabbstartsartikeln "Kom igång med Azure OpenAI-säkerhetsbyggblocket".

API-nyckel

using OpenAI;
using OpenAI.Chat;
using System.ClientModel;

string keyFromEnvironment = Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY");

ChatClient client = new(
    model: "gpt-4.1-nano",
    credential: new ApiKeyCredential(keyFromEnvironment),
    options: new OpenAIClientOptions() { 
    
        Endpoint = new Uri("https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1")
   }
);

ChatCompletion completion = client.CompleteChat("Tell me about the bitter lesson.'");

Console.WriteLine($"[ASSISTANT]: {completion.Content[0].Text}");

Chatt

Exempel på begäran om chattavslut till en resonemangsmodell.

using OpenAI;
using OpenAI.Chat;
using System.ClientModel.Primitives;

#pragma warning disable OPENAI001 //currently required for token based authentication

BearerTokenPolicy tokenPolicy = new(
    new DefaultAzureCredential(),
    "https://cognitiveservices.azure.com/.default");

ChatClient client = new(
    model: "o4-mini",
    authenticationPolicy: tokenPolicy,
    options: new OpenAIClientOptions()
    {

        Endpoint = new Uri("https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1")
    }
);

ChatCompletionOptions options = new ChatCompletionOptions
{
    ReasoningEffortLevel = ChatReasoningEffortLevel.Low,
    MaxOutputTokenCount = 100000
};

ChatCompletion completion = client.CompleteChat(
         new DeveloperChatMessage("You are a helpful assistant"),
         new UserChatMessage("Tell me about the bitter lesson")
    );

Console.WriteLine($"[ASSISTANT]: {completion.Content[0].Text}");

Inbäddningar

using OpenAI;
using OpenAI.Embeddings;
using System.ClientModel;

string apiKey = Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY")
    ?? throw new InvalidOperationException("AZURE_OPENAI_API_KEY environment variable is not set");

EmbeddingClient client = new(
    "text-embedding-3-large",
    credential: new ApiKeyCredential(apiKey),
    options: new OpenAIClientOptions()
    {
        Endpoint = new Uri("https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1")
    }
);

string input = "This is a test";

OpenAIEmbedding embedding = client.GenerateEmbedding(input);
ReadOnlyMemory<float> vector = embedding.ToFloats();
Console.WriteLine($"Embeddings: [{string.Join(", ", vector.ToArray())}]");

API för svar

using OpenAI;
using OpenAI.Responses;
using System.ClientModel.Primitives;
using Azure.Identity;

#pragma warning disable OPENAI001 //currently required for token based authentication

BearerTokenPolicy tokenPolicy = new(
    new DefaultAzureCredential(),
    "https://cognitiveservices.azure.com/.default");

OpenAIResponseClient client = new(
    model: "o4-mini",
    authenticationPolicy: tokenPolicy,
    options: new OpenAIClientOptions()
    {
        Endpoint = new Uri("https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1")
    }
);

OpenAIResponse response = await client.CreateResponseAsync(
    userInputText: "What's the optimal strategy to win at poker?",
    new ResponseCreationOptions()
    {
        ReasoningOptions = new ResponseReasoningOptions()
        {
            ReasoningEffortLevel = ResponseReasoningEffortLevel.High,
        },
    });

Console.WriteLine(response.GetOutputText());

Strömmande

using OpenAI;
using OpenAI.Responses;
using System.ClientModel.Primitives;
using Azure.Identity;

#pragma warning disable OPENAI001 //currently required for token based authentication

BearerTokenPolicy tokenPolicy = new(
    new DefaultAzureCredential(),
    "https://cognitiveservices.azure.com/.default");

#pragma warning disable OPENAI001

OpenAIResponseClient client = new(
    model: "o4-mini",
    authenticationPolicy: tokenPolicy,
    options: new OpenAIClientOptions()
    {
        Endpoint = new Uri("https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1")
    }
);


await foreach (StreamingResponseUpdate update
    in client.CreateResponseStreamingAsync(
        userInputText: "What's the optimal strategy to win at poker?",
        new ResponseCreationOptions()
        {
            ReasoningOptions = new ResponseReasoningOptions()
            {
                ReasoningEffortLevel = ResponseReasoningEffortLevel.High,
            },
        }))
{
    if (update is StreamingResponseOutputItemAddedUpdate itemUpdate
        && itemUpdate.Item is ReasoningResponseItem reasoningItem)
    {
        Console.WriteLine($"[Reasoning] ({reasoningItem.Status})");
    }
    else if (update is StreamingResponseOutputTextDeltaUpdate delta)
    {
        Console.Write(delta.Delta);
    }
}

MCP-server

using OpenAI;
using OpenAI.Responses;
using System.ClientModel.Primitives;
using Azure.Identity;

#pragma warning disable OPENAI001 //currently required for token based authentication

BearerTokenPolicy tokenPolicy = new(
    new DefaultAzureCredential(),
    "https://cognitiveservices.azure.com/.default");

OpenAIResponseClient client = new(
    model: "o4-mini",
    authenticationPolicy: tokenPolicy,
    options: new OpenAIClientOptions()
    {
        Endpoint = new Uri("https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1")
    }
);

ResponseCreationOptions options = new();
options.Tools.Add(ResponseTool.CreateMcpTool(
    serverLabel: "microsoft_learn",
    serverUri: new Uri("https://learn.microsoft.com/api/mcp"),
    toolCallApprovalPolicy: new McpToolCallApprovalPolicy(GlobalMcpToolCallApprovalPolicy.NeverRequireApproval)
));

OpenAIResponse response = (OpenAIResponse)client.CreateResponse([
    ResponseItem.CreateUserMessageItem([
        ResponseContentPart.CreateInputTextPart("Search for information about Azure Functions")
    ])
], options);

Console.WriteLine(response.GetOutputText());

Felhantering

Felkoder

Statuskod	Feltyp
400	Bad Request Error
401	Authentication Error
403	Permission Denied Error
404	Not Found Error
422	Unprocessable Entity Error
429	Rate Limit Error
500	Internal Server Error
503	Service Unavailable
504	Gateway Timeout

Omförsök

Klientklasserna kommer automatiskt att försöka igen upp till tre gånger på följande fel, med användning av exponentiell backoff:

• 408 Timeout för begäran
• 429 För många förfrågningar
• 500 Internt fel på servern
• 502 Felaktig gateway
• 503 Tjänsten är inte tillgänglig
• Tidsgräns för 504 Gateway

Källkod | Paket (pkg.go.dev) | Referensdokumentation | för REST APIDokumentation om paketreferens

Stöd för Azure OpenAI API-version

• v1 Allmänt tillgängligt API (GA) ger nu åtkomst till både ga- och förhandsversionsåtgärder. Mer information finns i livscykelguiden för API-versionen.

Installation

Installera modulerna openai och azidentity med go get:

go get -u 'github.com/openai/openai-go'

# optional
go get github.com/Azure/azure-sdk-for-go/sdk/azidentity

Autentisering

Microsoft Entra-ID

Azidentity-modulen används för Microsoft Entra ID-autentisering med Azure OpenAI.

package main

import (
	"context"
	"fmt"

	"github.com/Azure/azure-sdk-for-go/sdk/azidentity"
	"github.com/openai/openai-go/v3"
	"github.com/openai/openai-go/v3/azure"
	"github.com/openai/openai-go/v3/option"
)

func main() {
	// Create an Azure credential
	tokenCredential, err := azidentity.NewDefaultAzureCredential(nil)
	if err != nil {
		panic(fmt.Sprintf("Failed to create credential: %v", err))
	}

	// Create a client with Azure OpenAI endpoint and token credential
	client := openai.NewClient(
		option.WithBaseURL("https://YOUR-RESOURCE_NAME.openai.azure.com/openai/v1/"),
		azure.WithTokenCredential(tokenCredential),
	)

	// Make a completion request
	chatCompletion, err := client.Chat.Completions.New(context.TODO(), openai.ChatCompletionNewParams{
		Messages: []openai.ChatCompletionMessageParamUnion{
			openai.UserMessage("Explain what the bitter lesson is?"),
		},
		Model: "o4-mini", // Use your deployed model name on Azure
	})
	if err != nil {
		panic(err.Error())
	}

	fmt.Println(chatCompletion.Choices[0].Message.Content)
}

Mer information om nyckellös Azure OpenAI-autentisering finns i Använda Azure OpenAI utan nycklar.

API-nyckel

package main

import (
	"context"
	"fmt"

	"github.com/openai/openai-go/v3"
	"github.com/openai/openai-go/v3/option"
)

func main() {
	// Create a client with Azure OpenAI endpoint and API key
	client := openai.NewClient(
		option.WithBaseURL("https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/"),
		option.WithAPIKey("API-KEY-HERE"),
	)

	// Make a completion request
	chatCompletion, err := client.Chat.Completions.New(context.TODO(), openai.ChatCompletionNewParams{
		Messages: []openai.ChatCompletionMessageParamUnion{
			openai.UserMessage("Tell me about the bitter lesson"),
		},
		Model: "o4-mini", // Use your deployed model name on Azure
	})
	if err != nil {
		panic(err.Error())
	}

	fmt.Println(chatCompletion.Choices[0].Message.Content)
}

Inbäddningar

package main

import (
	"context"
	"fmt"
	"os"

	"github.com/openai/openai-go/v3"
	"github.com/openai/openai-go/v3/option"
)

func main() {
	// Get API key from environment variable
	apiKey := os.Getenv("AZURE_OPENAI_API_KEY")
	if apiKey == "" {
		panic("AZURE_OPENAI_API_KEY environment variable is not set")
	}

	// Create a client with Azure OpenAI endpoint and API key
	client := openai.NewClient(
		option.WithBaseURL("https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/"),
		option.WithAPIKey(apiKey),
	)

	ctx := context.Background()
	text := "The attention mechanism revolutionized natural language processing"

	// Make an embedding request
	embedding, err := client.Embeddings.New(ctx, openai.EmbeddingNewParams{
		Input: openai.EmbeddingNewParamsInputUnion{OfString: openai.String(text)},
		Model: "text-embedding-3-small", // Use your deployed model name on Azure
	})
	if err != nil {
		panic(err.Error())
	}

	// Print embedding information
	fmt.Printf("Model: %s\n", embedding.Model)
	fmt.Printf("Number of embeddings: %d\n", len(embedding.Data))
	fmt.Printf("Embedding dimensions: %d\n", len(embedding.Data[0].Embedding))
	fmt.Printf("Usage - Prompt tokens: %d, Total tokens: %d\n", embedding.Usage.PromptTokens, embedding.Usage.TotalTokens)
	
	// Print first few values of the embedding vector
	fmt.Printf("First 10 embedding values: %v\n", embedding.Data[0].Embedding[:10])
}

Responses

package main

import (
	"context"

	"github.com/Azure/azure-sdk-for-go/sdk/azidentity"
	"github.com/openai/openai-go/v3"
	"github.com/openai/openai-go/v3/azure"
	"github.com/openai/openai-go/v3/option"
	"github.com/openai/openai-go/v3/responses"
)

func main() {
	// Create Azure token credential
	tokenCredential, err := azidentity.NewDefaultAzureCredential(nil)
	if err != nil {
		panic(err)
	}

	// Create client with Azure endpoint and token credential
	client := openai.NewClient(
		option.WithBaseURL("https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/"),
		azure.WithTokenCredential(tokenCredential),
	)

	ctx := context.Background()
	question := "Tell me about the attention is all you need paper"

	resp, err := client.Responses.New(ctx, responses.ResponseNewParams{
		Input: responses.ResponseNewParamsInputUnion{OfString: openai.String(question)},
		Model: "o4-mini",
	})

	if err != nil {
		panic(err)
	}

	println(resp.OutputText())
}

Källkod |Referensdokumentation | för REST APIDokumentation om paketreferens | Maven Central

Stöd för Azure OpenAI API-version

• v1 Allmänt tillgängligt API (GA) ger nu åtkomst till både ga- och förhandsversionsåtgärder. Mer information finns i livscykelguiden för API-versionen.

Installation

Gradle

implementation("com.openai:openai-java:4.0.1")

Maven

<dependency>
  <groupId>com.openai</groupId>
  <artifactId>openai-java</artifactId>
  <version>4.0.1</version>
</dependency>

Autentisering

Microsoft Entra-ID

Autentisering med Microsoft Entra-ID kräver viss inledande konfiguration:

Lägg till Azure Identity-paketet:

<dependency>
    <groupId>com.azure</groupId>
    <artifactId>azure-identity</artifactId>
    <version>1.18.0</version>
</dependency>

Efter installationen kan du välja vilken typ av autentiseringsuppgifter från azure.identity du vill använda. Du kan till exempel DefaultAzureCredential använda för att autentisera klienten: Ange värdena för klient-ID, klient-ID och klienthemlighet för Microsoft Entra-ID-programmet som miljövariabler: AZURE_CLIENT_ID, AZURE_TENANT_ID AZURE_CLIENT_SECRET.

Auktorisering är enklast med .DefaultAzureCredential Den hittar de bästa autentiseringsuppgifterna som ska användas i den miljö som körs, men användning av DefaultAzureCredential rekommenderas endast för testning, inte för produktion.

Credential tokenCredential = BearerTokenCredential.create(
        AuthenticationUtil.getBearerTokenSupplier(
                new DefaultAzureCredentialBuilder().build(),
                "https://cognitiveservices.azure.com/.default"));
OpenAIClient client = OpenAIOkHttpClient.builder()
        .baseUrl("https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/")
        .credential(tokenCredential)
        .build();

Mer information om nyckellös Azure OpenAI-autentisering finns i Använda Azure OpenAI utan nycklar.

API-nyckel

OpenAIClient client = OpenAIOkHttpClient.builder()
                .baseUrl("https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/")
                .apiKey(apiKey)
                .build();

Responses

package com.example;

import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.ChatModel;
import com.openai.models.responses.Response;
import com.openai.models.responses.ResponseCreateParams;
import com.azure.core.credential.AzureKeyCredential;

public class OpenAITest {
    public static void main(String[] args) {
        // Get API key from environment variable for security
        String apiKey = System.getenv("OPENAI_API_KEY");
        String resourceName = "https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1";
        String modelDeploymentName = "gpt-4.1"; //replace with you model deployment name

        try {
            OpenAIClient client = OpenAIOkHttpClient.builder()
                    .baseUrl(resourceName)
                    .apiKey(apiKey)
                    .build();

            ResponseCreateParams params = ResponseCreateParams.builder()
                    .input("Tell me about the bitter lesson?")
                    .model(modelDeploymentName)
                    .build();

            Response response = client.responses().create(params);
            
            System.out.println("Response: " + response);
        } catch (Exception e) {
            System.err.println("Error: " + e.getMessage());
            e.printStackTrace();
        }
    }
}

Källkod | Paket (npm) | Referens |

Stöd för Azure OpenAI API-version

• v1 Allmänt tillgängligt API (GA) ger nu åtkomst till både ga- och förhandsversionsåtgärder. Mer information finns i livscykelguiden för API-versionen.

Installation

npm install openai

Autentisering

Microsoft Entra-ID

npm install @azure/identity

För att kunna autentisera OpenAI klienten måste vi dock använda getBearerTokenProvider funktionen från @azure/identity paketet. Den här funktionen skapar en tokenprovider som OpenAI använder internt för att hämta token för varje begäran. Tokenprovidern skapas på följande sätt:

import { DefaultAzureCredential, getBearerTokenProvider } from "@azure/identity";
import { OpenAI } from "openai";

const tokenProvider = getBearerTokenProvider(
    new DefaultAzureCredential(),
    'https://cognitiveservices.azure.com/.default');
const client = new OpenAI({
    baseURL: "https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/",
    apiKey: tokenProvider
});

Mer information om nyckellös Azure OpenAI-autentisering finns i snabbstartsartikeln "Kom igång med Azure OpenAI-säkerhetsbyggblocket".

API-nyckel

API-nycklar rekommenderas inte för produktionsanvändning eftersom de är mindre säkra än andra autentiseringsmetoder.

import { OpenAI } from "openai";

const client = new OpenAI({
    baseURL: "https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/",
    apiKey: process.env['OPENAI_API_KEY'] //Your Azure OpenAI API key
});

Viktigt!

Använd API-nycklar med försiktighet. Inkludera inte API-nyckeln direkt i koden och publicera den aldrig offentligt. Om du använder en API-nyckel lagrar du den på ett säkert sätt i Azure Key Vault. Mer information om hur du använder API-nycklar på ett säkert sätt i dina appar finns i API-nycklar med Azure Key Vault.

Mer information om säkerhet för AI-tjänster finns i Autentisera begäranden till Azure AI-tjänster.

Responses

responses.create

import { DefaultAzureCredential, getBearerTokenProvider } from "@azure/identity";
import { OpenAI } from "openai";

const tokenProvider = getBearerTokenProvider(
    new DefaultAzureCredential(),
    'https://cognitiveservices.azure.com/.default');
const client = new OpenAI({
    baseURL: "https://YOUR-RESORCE-NAME.openai.azure.com/openai/v1/",
    apiKey: tokenProvider
});

const response = await client.responses.create({
  model: 'gpt-4.1-nano', //model deployment name
  instructions: 'You are a helpful AI agent',
  input: 'Tell me about the bitter lesson?',
});

console.log(response.output_text);

Strömmande

import { DefaultAzureCredential, getBearerTokenProvider } from "@azure/identity";
import { OpenAI } from "openai";

const tokenProvider = getBearerTokenProvider(
    new DefaultAzureCredential(),
    'https://cognitiveservices.azure.com/.default');
const client = new OpenAI({
    baseURL: "https://YOUR-RESORCE-NAME.openai.azure.com/openai/v1/",
    apiKey: tokenProvider
});

const stream = await client.responses.create({
  model: 'gpt-4.1-nano', // model deployment name
  input: 'Provide a brief history of the attention is all you need paper.',
  stream: true,
});

for await (const event of stream) {
  if (event.type === 'response.output_text.delta' && event.delta) {
    process.stdout.write(event.delta);
  }
}

MCP-server

import { DefaultAzureCredential, getBearerTokenProvider } from "@azure/identity";
import { OpenAI } from "openai";

const tokenProvider = getBearerTokenProvider(
    new DefaultAzureCredential(),
    'https://cognitiveservices.azure.com/.default');
const client = new OpenAI({
    baseURL: "https://YOUR-RESORCE-NAME.openai.azure.com/openai/v1/",
    apiKey: tokenProvider
});

const resp = await client.responses.create({
  model: "gpt-5",
  tools: [
    {
      type: "mcp",
      server_label: "microsoft_learn",
      server_description: "Microsoft Learn MCP server for searching and fetching Microsoft documentation.",
      server_url: "https://learn.microsoft.com/api/mcp",
      require_approval: "never",
    },
  ],
  input: "Search for information about Azure Functions",
});

console.log(resp.output_text);

Chatt

chat.completions.create

import { DefaultAzureCredential, getBearerTokenProvider } from "@azure/identity";
import { OpenAI } from "openai";

const tokenProvider = getBearerTokenProvider(
    new DefaultAzureCredential(),
    'https://cognitiveservices.azure.com/.default');
const client = new OpenAI({
    baseURL: "https://france-central-test-001.openai.azure.com/openai/v1/",
    apiKey: tokenProvider
});

const messages = [
    { role: 'system', content: 'You are a helpful assistant.' },
    { role: 'user', content: 'Tell me about the attention is all you need paper' }
];

// Make the API request with top-level await
const result = await client.chat.completions.create({ 
    messages, 
    model: 'gpt-4.1-nano', // model deployment name
    max_tokens: 100 
});

// Print the full response
console.log('Full response:', result);

// Print just the message content from the response
console.log('Response content:', result.choices[0].message.content);

Felhantering

Felkoder

Statuskod	Feltyp
400	Bad Request Error
401	Authentication Error
403	Permission Denied Error
404	Not Found Error
422	Unprocessable Entity Error
429	Rate Limit Error
500	Internal Server Error
503	Service Unavailable
504	Gateway Timeout

Omförsök

Följande fel dras automatiskt tillbaka två gånger som standard med en kort exponentiell backoff:

• Anslutningsfel
• 408 Timeout för begäran
• 429 hastighetsgräns
• >=500 interna fel

Använd maxRetries för att ange/inaktivera återförsöksbeteendet:

// Configure the default for all requests:
const client = new OpenAI({
  maxRetries: 0, // default is 2
});

// Or, configure per-request:
await client.chat.completions.create({ messages: [{ role: 'user', content: 'How can I get the name of the current day in Node.js?' }], model: '' }, {
  maxRetries: 5,
});

Bibliotekskällkod | Paket (PyPi) | Referens |

Anmärkning

Det här biblioteket underhålls av OpenAI. Se versionshistoriken för att spåra de senaste uppdateringarna i biblioteket.

Stöd för Azure OpenAI API-version

• v1 Allmänt tillgängligt API (GA) ger nu åtkomst till både ga- och förhandsversionsåtgärder. Mer information finns i livscykelguiden för API-versionen.

Installation

pip install openai

För den senaste versionen:

pip install openai --upgrade

Autentisering

Slutpunkter och API-nycklar för dina resurser kan hämtas från Azure-portalen eller Foundry-portalen:

• Logga in på Azure-portalen> och välj resurs >Resurshantering>Nycklar och Slutpunkt.
• Logga in på Foundry-portalen> och välj din resurs.

Microsoft Entra-ID

from openai import OpenAI
from azure.identity import DefaultAzureCredential, get_bearer_token_provider

token_provider = get_bearer_token_provider(
    DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"
)

client = OpenAI(  
  base_url = "https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/",  
  api_key = token_provider  
)

API-nyckel

Viktigt!

Använd API-nycklar med försiktighet. Inkludera inte API-nyckeln direkt i koden och publicera den aldrig offentligt. Om du använder en API-nyckel lagrar du den på ett säkert sätt i Azure Key Vault. Mer information om hur du använder API-nycklar på ett säkert sätt i dina appar finns i API-nycklar med Azure Key Vault.

Mer information om säkerhet för AI-tjänster finns i Autentisera begäranden till Azure AI-tjänster.

 import os
from openai import OpenAI
    
client = OpenAI(
    api_key=os.getenv("AZURE_OPENAI_API_KEY"),  
    base_url="https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/"
    )

Miljövariabler

Om du använder standardmiljövariablerna OPENAI_BASE_URL för och OPENAI_API_KEY de används automatiskt av klienten utan ytterligare konfiguration krävs.

Miljövariabel	Värde
OPENAI_BASE_URL	https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/
OPENAI_API_KEY	Azure OpenAI- eller Foundry API-nyckel.

from openai import OpenAI

client = OpenAI()   

Svar

Det finns inga utdata för klient-instansiering.

API för svar

responses.create()

Microsoft Entra-ID

from openai import OpenAI
from azure.identity import DefaultAzureCredential, get_bearer_token_provider

token_provider = get_bearer_token_provider(
    DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"
)

client = OpenAI(  
  base_url = "https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/",  
  api_key=token_provider,
)

response = client.responses.create(
    model="gpt-4.1-nano",
    input= "This is a test" 
)

print(response.model_dump_json(indent=2)) 

Fler exempel finns i dokumentationen för API för svar .

API-nyckel

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("AZURE_OPENAI_API_KEY"),
    base_url="https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/",
)

response = client.responses.create(   
  model="gpt-4.1-nano", # Replace with your model deployment name 
  input="This is a test.",
)

print(response.model_dump_json(indent=2)) 

Fler exempel finns i dokumentationen för API för svar .

Miljövariabler

from openai import OpenAI

client = OpenAI()

response = client.responses.create(   
  model="gpt-4.1-nano", # Replace with your model deployment name 
  input="This is a test.",
)

print(response.model_dump_json(indent=2)) 

Fler exempel finns i dokumentationen för API för svar .

Svar

{
  "id": "resp_088ffc6b5f37c64d0068e187d0ceac819499c9331c2a02e92e",
  "created_at": 1759610832.0,
  "error": null,
  "incomplete_details": null,
  "instructions": null,
  "metadata": {},
  "model": "gpt-4.1-nano",
  "object": "response",
  "output": [
    {
      "id": "msg_088ffc6b5f37c64d0068e187d15f8c819495f84e214435175d",
      "content": [
        {
          "annotations": [],
          "text": "Hello! I see you've sent a message saying \"This is a test.\" How can I assist you today?",
          "type": "output_text",
          "logprobs": []
        }
      ],
      "role": "assistant",
      "status": "completed",
      "type": "message"
    }
  ],
  "parallel_tool_calls": true,
  "temperature": 1.0,
  "tool_choice": "auto",
  "tools": [],
  "top_p": 1.0,
  "background": false,
  "conversation": null,
  "max_output_tokens": null,
  "max_tool_calls": null,
  "previous_response_id": null,
  "prompt": null,
  "prompt_cache_key": null,
  "reasoning": {
    "effort": null,
    "generate_summary": null,
    "summary": null
  },
  "safety_identifier": null,
  "service_tier": "default",
  "status": "completed",
  "text": {
    "format": {
      "type": "text"
    },
    "verbosity": "medium"
  },
  "top_logprobs": 0,
  "truncation": "disabled",
  "usage": {
    "input_tokens": 11,
    "input_tokens_details": {
      "cached_tokens": 0
    },
    "output_tokens": 23,
    "output_tokens_details": {
      "reasoning_tokens": 0
    },
    "total_tokens": 34
  },
  "user": null,
  "content_filters": null,
  "store": true
}

responses.create() med MCP-serververktyget

Microsoft Entra-ID

from openai import OpenAI
from azure.identity import DefaultAzureCredential, get_bearer_token_provider

token_provider = get_bearer_token_provider(
    DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"
)

client = OpenAI(  
  base_url = "https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/",  
  api_key=token_provider,
)

resp = client.responses.create(
    model="gpt-5",
    tools=[
        {
            "type": "mcp",
            "server_label": "microsoft_learn",
            "server_description": "Microsoft Learn MCP server for searching and fetching Microsoft documentation.",
            "server_url": "https://learn.microsoft.com/api/mcp",
            "require_approval": "never",
        },
    ],
    input="Search for information about Azure Functions",
)

print(resp.output_text)

Fler exempel finns i dokumentationen för API för svar .

API-nyckel

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("AZURE_OPENAI_API_KEY"),
    base_url="https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/",
)

resp = client.responses.create(
    model="gpt-5",
    tools=[
        {
            "type": "mcp",
            "server_label": "microsoft_learn",
            "server_description": "Microsoft Learn MCP server for searching and fetching Microsoft documentation.",
            "server_url": "https://learn.microsoft.com/api/mcp",
            "require_approval": "never",
        },
    ],
    input="Search for information about Azure Functions",
)

print(resp.output_text)

Fler exempel finns i dokumentationen för API för svar .

Miljövariabler

from openai import OpenAI

client = OpenAI()

resp = client.responses.create(
    model="gpt-5",
    tools=[
        {
            "type": "mcp",
            "server_label": "microsoft_learn",
            "server_description": "Microsoft Learn MCP server for searching and fetching Microsoft documentation.",
            "server_url": "https://learn.microsoft.com/api/mcp",
            "require_approval": "never",
        },
    ],
    input="Search for information about Azure Functions",
)

print(resp.output_text)

Fler exempel finns i dokumentationen för API för svar .

Svar

Here is a summary of Azure Functions based on official Microsoft documentation:

## What is Azure Functions?
Azure Functions is a serverless compute service that enables you to run event-driven code without managing infrastructure. You write code in your preferred language—such as C#, Java, JavaScript, PowerShell, or Python—and Azure Functions handles the hosting, scaling, and maintenance for you. You pay only for the compute time you consume, making it cost-effective for many use cases.

- **Official overview:** [What is Azure Functions?](https://learn.microsoft.com/en-us/azure/azure-functions/functions-overview)

## Key Scenarios for Azure Functions
Azure Functions supports a wide range of cloud application scenarios, including:

- Processing file uploads from blob storage.
- Real-time stream and event processing (IoT, analytics).
- Executing AI and machine learning inference.
- Running scheduled tasks or background jobs.
- Building scalable web APIs with HTTP endpoints.
- Orchestrating serverless workflows (using Durable Functions).
- Responding to data or database changes.
- Creating reliable message processing systems (with queues, Service Bus, Event Hubs).

Read more about scenarios here: [Azure Functions scenarios](https://learn.microsoft.com/en-us/azure/azure-functions/functions-scenarios).

## How It Works
- **Triggers and Bindings:** Functions are executed by triggers (events like HTTP requests, file uploads, timers, etc.) and can use bindings to automatically interact with other Azure services.
- **Coding:** Write only the code you need for business logic; Azure Functions integrates with IDEs like Visual Studio and VS Code for development and debugging.
- **Monitoring:** Built-in integration with Azure Monitor and Application Insights.
- **Hosting plans:** Choose serverless (Consumption plan), Premium, Dedicated, or deploy in custom containers.

## Getting Started
You can create your first Azure Function quickly using:

- [Azure CLI or Developer CLI](https://learn.microsoft.com/en-us/azure/azure-functions/create-first-function-azure-developer-cli)
- [Visual Studio Code](https://learn.microsoft.com/en-us/azure/azure-functions/how-to-create-function-vs-code)
- [Visual Studio](https://learn.microsoft.com/en-us/azure/azure-functions/functions-create-your-first-function-visual-studio)

See quickstarts for all languages: [Getting started with Azure Functions](https://learn.microsoft.com/en-us/azure/azure-functions/functions-get-started)

## Supported Languages
- Native: C#, Java, JavaScript, PowerShell, Python
- With custom handlers: Go, Rust, and more

## Learn More
- [Overview of Azure Functions](https://learn.microsoft.com/en-us/azure/azure-functions/functions-overview)
- [Supported languages](https://learn.microsoft.com/en-us/azure/azure-functions/supported-languages)
- [Triggers and bindings](https://learn.microsoft.com/en-us/azure/azure-functions/functions-triggers-bindings)

Let me know if you're looking for step-by-step tutorials, specific language samples, or deeper technical details!

Chatt

chat.completions.create()

Microsoft Entra-ID

from openai import OpenAI
from azure.identity import DefaultAzureCredential, get_bearer_token_provider

token_provider = get_bearer_token_provider(
    DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"
)

client = OpenAI(  
  base_url = "https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/",  
  api_key=token_provider,
)

completion = client.chat.completions.create(
  model="gpt-4o", # Replace with your model deployment name.
  messages=[
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "When was Microsoft founded?"}
  ]
)

#print(completion.choices[0].message)
print(completion.model_dump_json(indent=2))

API-nyckel

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("AZURE_OPENAI_API_KEY"),
    base_url="https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/",
)

completion = client.chat.completions.create(
  model="gpt-4o", # Replace with your model deployment name.
  messages=[
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "When was Microsoft founded?"}
  ]
)

#print(completion.choices[0].message)
print(completion.model_dump_json(indent=2))

Miljövariabler

from openai import OpenAI
client = OpenAI()

completion = client.chat.completions.create(
  model="gpt-4o", # Replace with your model deployment name.
  messages=[
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "When was Microsoft founded?"}
  ]
)

#print(completion.choices[0].message)
print(completion.model_dump_json(indent=2))

Svar

{
  "id": "chatcmpl-AUhZ11g6aNb1Nnxjp4hFUNcszw3uf",
  "choices": [
    {
      "finish_reason": "stop",
      "index": 0,
      "logprobs": null,
      "message": {
        "content": "Microsoft was founded on April 4, 1975, by Bill Gates and Paul Allen in Albuquerque, New Mexico.",
        "refusal": null,
        "role": "assistant",
        "function_call": null,
        "tool_calls": null
      },
      "content_filter_results": {
        "hate": {
          "filtered": false,
          "severity": "safe"
        },
        "self_harm": {
          "filtered": false,
          "severity": "safe"
        },
        "sexual": {
          "filtered": false,
          "severity": "safe"
        },
        "violence": {
          "filtered": false,
          "severity": "safe"
        }
      }
    }
  ],
  "created": 1731880663,
  "model": "gpt-4o-2024-08-06",
  "object": "chat.completion",
  "service_tier": null,
  "system_fingerprint": "fp_04751d0b65",
  "usage": {
    "completion_tokens": 24,
    "prompt_tokens": 22,
    "total_tokens": 46,
    "completion_tokens_details": null,
    "prompt_tokens_details": null
  },
  "prompt_filter_results": [
    {
      "prompt_index": 0,
      "content_filter_results": {
        "hate": {
          "filtered": false,
          "severity": "safe"
        },
        "jailbreak": {
          "filtered": false,
          "detected": false
        },
        "self_harm": {
          "filtered": false,
          "severity": "safe"
        },
        "sexual": {
          "filtered": false,
          "severity": "safe"
        },
        "violence": {
          "filtered": false,
          "severity": "safe"
        }
      }
    }
  ]
}

chat.completions.create() – direktuppspelning

Microsoft Entra-ID

from openai import OpenAI
from azure.identity import DefaultAzureCredential, get_bearer_token_provider

token_provider = get_bearer_token_provider(
    DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"
)

client = OpenAI(  
  base_url = "https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/",  
  api_key=token_provider,
)

completion = client.chat.completions.create(
  model="gpt-4o", # Replace with your model deployment name.
  messages=[
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "When was Microsoft founded?"}
  ],
  stream=True
)

for chunk in completion:
    if chunk.choices and chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end='',)

API-nyckel

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("AZURE_OPENAI_API_KEY"),
    base_url="https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/",
)

completion = client.chat.completions.create(
  model="gpt-4o", # Replace with your model deployment name.
  messages=[
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "When was Microsoft founded?"}
  ],
  stream=True
)

for chunk in completion:
    if chunk.choices and chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end='',)

Miljövariabler

from openai import OpenAI

client = OpenAI()

completion = client.chat.completions.create(
  model="gpt-4o", # Replace with your model deployment name.
  messages=[
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "When was Microsoft founded?"}
  ],
  stream=True
)

for chunk in completion:
    if chunk.choices and chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end='',)

Svar

Microsoft was founded on April 4, 1975, by Bill Gates and Paul Allen.

chat.completions.create() – bildinmatning

Microsoft Entra-ID

from openai import OpenAI
from azure.identity import DefaultAzureCredential, get_bearer_token_provider

token_provider = get_bearer_token_provider(
    DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"
)

client = OpenAI(  
  base_url = "https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/",  
  api_key=token_provider,
)

completion = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What's in this image?"},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://raw.githubusercontent.com/MicrosoftDocs/azure-ai-docs/main/articles/ai-foundry/openai/media/how-to/generated-seattle.png",
                    }
                },
            ],
        }
    ],
    max_tokens=300,
)

print(completion.model_dump_json(indent=2))

API-nyckel

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("AZURE_OPENAI_API_KEY"),
    base_url="https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/",
)

completion = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What's in this image?"},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://raw.githubusercontent.com/MicrosoftDocs/azure-ai-docs/main/articles/ai-foundry/openai/media/how-to/generated-seattle.png",
                    }
                },
            ],
        }
    ],
    max_tokens=300,
)

print(completion.model_dump_json(indent=2))

Miljövariabler

from openai import OpenAI

client = OpenAI()

completion = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What's in this image?"},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://raw.githubusercontent.com/MicrosoftDocs/azure-ai-docs/main/articles/ai-foundry/openai/media/how-to/generated-seattle.png",
                    }
                },
            ],
        }
    ],
    max_tokens=300,
)

print(completion.model_dump_json(indent=2))

Svar

{
  "id": "chatcmpl-AUisNBsjzPisMbx3k5Uz5SOKN63KN",
  "choices": [
    {
      "finish_reason": "stop",
      "index": 0,
      "logprobs": null,
      "message": {
        "content": "This image is a watercolor painting of a city skyline, featuring a prominent tower that resembles the Space Needle, which is located in Seattle. The painting uses a blend of colors to depict the cityscape and sky.",
        "refusal": null,
        "role": "assistant",
        "function_call": null,
        "tool_calls": null
      },
      "content_filter_results": {
        "hate": {
          "filtered": false,
          "severity": "safe"
        },
        "self_harm": {
          "filtered": false,
          "severity": "safe"
        },
        "sexual": {
          "filtered": false,
          "severity": "safe"
        },
        "violence": {
          "filtered": false,
          "severity": "safe"
        }
      }
    }
  ],
  "created": 1731885707,
  "model": "gpt-4o-2024-08-06",
  "object": "chat.completion",
  "service_tier": null,
  "system_fingerprint": "fp_04751d0b65",
  "usage": {
    "completion_tokens": 42,
    "prompt_tokens": 639,
    "total_tokens": 681,
    "completion_tokens_details": null,
    "prompt_tokens_details": null
  },
  "prompt_filter_results": [
    {
      "prompt_index": 0,
      "content_filter_results": {
        "jailbreak": {
          "filtered": false,
          "detected": false
        }
      }
    },
    {
      "prompt_index": 1,
      "content_filter_results": {
        "sexual": {
          "filtered": false,
          "severity": "safe"
        },
        "violence": {
          "filtered": false,
          "severity": "safe"
        },
        "hate": {
          "filtered": false,
          "severity": "safe"
        },
        "self_harm": {
          "filtered": false,
          "severity": "safe"
        }
      }
    }
  ]
}

Inbäddningar

embeddings.create()

Microsoft Entra-ID

Inbäddningar stöder för närvarande inte Microsoft Entra-ID med Azure OpenAI och v1-API:et.

API-nyckel

client = OpenAI(
    api_key=os.getenv("AZURE_OPENAI_API_KEY"),
    base_url="https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/",
)

embedding = client.embeddings.create(
  model="text-embedding-3-large", # Replace with your model deployment name
  input="Attenion is all you need",
  encoding_format="float" 
)

print(embedding)

Miljövariabler

client = OpenAI()

embedding = client.embeddings.create(
  model="text-embedding-3-large", # Replace with your model deployment name
  input="Attenion is all you need",
  encoding_format="float" 
)

print(embedding)

Svar

Svaret har kortats ner för att bli kortare.

CreateEmbeddingResponse(data=[Embedding(embedding=[0.009098228, -0.010369237, -0.00048062875, -0.014328566, 0.019677775, 0.010049199, -0.005600668, 0.003858746, -0.007818076, 0.012554641, 0.005134327, 0.004514824, -0.020262988, -0.0039181816, 0.025475038, 0.016733425, 0.002136255, 0.0155172795, 0.0058978465, 0.012911255, -0.014273703, -0.016806576, 0.0265906, 0.037673064, 0.003909038, 0.0265906, -0.001935088, -0.014913779, 0.01781241, -0.017821554, 0.0016596265, -0.002987785, -0.014346854, -0.000962972, 0.0068671047, 0.004405097, -0.015764166, -0.007539185, -0.030394483, -0.01586475, 0.0074706054, -0.013761641, 0.010186358, 0.008805621, -0.009939471, 0.013944521, -0.010113207, -0.015745878, -0.021927187, 0.03231471, 0.0026951786, 0.004759425, 0.0065196347, 0.010927018, 0.017263774, 0.0055229445, 0.009381691, -0.042903405], index=0, object='embedding')], model='text-embedding-3-large', object='list', usage=Usage(prompt_tokens=7, total_tokens=7))

Finjustering

Finjustering med python-instruktioner

Felhantering

# from openai import OpenAI
# client = OpenAI()

import openai

try:
    client.fine_tuning.jobs.create(
        model="gpt-4o",
        training_file="file-test",
    )
except openai.APIConnectionError as e:
    print("The server could not be reached")
    print(e.__cause__)  # an underlying Exception, likely raised within httpx.
except openai.RateLimitError as e:
    print("A 429 status code was received; we should back off a bit.")
except openai.APIStatusError as e:
    print("Another non-200-range status code was received")
    print(e.status_code)
    print(e.response)

Felkoder

Statuskod	Feltyp
400	BadRequestError
401	AuthenticationError
403	PermissionDeniedError
404	NotFoundError
422	UnprocessableEntityError
429	RateLimitError
>=500	InternalServerError
Inte tillgänglig	APIConnectionError

Förfrågnings-ID

Om du vill hämta ID:t för din begäran kan du använda _request_id egenskapen som motsvarar svarshuvudet x-request-id .

print(completion._request_id) 
print(legacy_completion._request_id)

Omförsök

Följande fel dras automatiskt tillbaka två gånger som standard med en kort exponentiell backoff:

• Anslutningsfel
• 408 Timeout för begäran
• 429 hastighetsgräns
• >=500 interna fel

Använd max_retries för att ange/inaktivera återförsöksbeteendet:

# For all requests

from openai import OpenAI
client = OpenAI(
      max_retries=0
)

# max retires for specific requests

client.with_options(max_retries=5).chat.completions.create(
    messages=[
        {
            "role": "user",
            "content": "When was Microsoft founded?",
        }
    ],
    model="gpt-4o",
)