Lenguajes de programación compatibles con Azure OpenAI

Código fuente | Paquete (NuGet)

Compatibilidad con la versión de la API de Azure OpenAI

• La API disponible con carácter general (GA) v1 ahora permite el acceso a las operaciones de disponibilidad general y versión preliminar. Para más información, consulte la guía del ciclo de vida de la versión de la API.

Instalación

dotnet add package OpenAI

Autenticación

Microsoft Entra ID

Un enfoque de autenticación seguro y sin claves es usar Microsoft Entra ID (anteriormente Azure Active Directory) a través de la Biblioteca de identidades de Azure. Para usar la biblioteca:

dotnet add package Azure.Identity

Use el tipo de credencial deseado de la biblioteca. Por ejemplo, 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}");

Para obtener más información sobre la autenticación sin claves de Azure OpenAI, consulte el artículo de inicio rápido "Introducción al bloque de creación de seguridad de Azure OpenAI".

Clave de API

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}");

El chat

Ejemplo de solicitud de finalizaciones de chat a un modelo de razonamiento.

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}");

Incrustaciones

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 de respuestas

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());

Transmisión en línea

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);
    }
}

Servidor MCP

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());

Control de errores

Códigos de error

Código de estado	Tipo de error
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

Reintentos

Las clases de cliente reintentarán automáticamente los errores siguientes hasta tres veces más mediante retroceso exponencial:

• Tiempo de espera de solicitud 408
• 429 Demasiadas solicitudes
• Error interno del servidor 500
• Puerta de enlace incorrecta 502
• 503 Servicio no disponible
• Tiempo de espera de puerta de enlace 504

Código fuente | Paquete (pkg.go.dev) | Documentación de referencia de API de REST | Documentación de referencia de paquetes

Compatibilidad con la versión de la API de Azure OpenAI

• La API disponible con carácter general (GA) v1 ahora permite el acceso a las operaciones de disponibilidad general y versión preliminar. Para más información, consulte la guía del ciclo de vida de la versión de la API.

Instalación

Instale los módulos openai y azidentity con go get:

go get -u 'github.com/openai/openai-go@v2.1.1'

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

Autenticación

Microsoft Entra ID

El módulo azidentity se usa para la autenticación de Microsoft Entra ID con Azure OpenAI.

package main

import (
	"context"
	"fmt"

	"github.com/Azure/azure-sdk-for-go/sdk/azidentity"
	"github.com/openai/openai-go/v2"
	"github.com/openai/openai-go/v2/azure"
	"github.com/openai/openai-go/v2/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)
}

Para obtener más información sobre la autenticación sin claves de Azure OpenAI, consulte Uso de Azure OpenAI sin claves.

Clave de API

package main

import (
	"context"
	"fmt"

	"github.com/openai/openai-go/v2"
	"github.com/openai/openai-go/v2/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)
}

Incrustaciones

package main

import (
	"context"
	"fmt"
	"os"

	"github.com/openai/openai-go/v2"
	"github.com/openai/openai-go/v2/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/v2"
	"github.com/openai/openai-go/v2/azure"
	"github.com/openai/openai-go/v2/option"
	"github.com/openai/openai-go/v2/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())
}

Código fuente |Documentación | Documentación | Maven Central

Compatibilidad con la versión de la API de Azure OpenAI

• La API disponible con carácter general (GA) v1 ahora permite el acceso a las operaciones de disponibilidad general y versión preliminar. Para más información, consulte la guía del ciclo de vida de la versión de la API.

Instalación

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>

Autenticación

Microsoft Entra ID

La autenticación con Microsoft Entra ID requiere una configuración inicial:

Agregue el paquete de identidad de Azure:

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

Después de la instalación, puede elegir el tipo de credencial que se azure.identity va a usar. Por ejemplo, DefaultAzureCredential se puede usar para autenticar al cliente: establezca los valores del identificador de cliente, el identificador de inquilino y el secreto de cliente de la aplicación Microsoft Entra ID como variables de entorno: AZURE_CLIENT_ID, AZURE_TENANT_ID, AZURE_CLIENT_SECRET.

La autorización es más fácil mediante DefaultAzureCredential. Encuentra la mejor credencial para usar en su entorno en ejecución, aunque el uso de DefaultAzureCredential solo se recomienda para las pruebas, no para producción.

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();

Para obtener más información sobre la autenticación sin claves de Azure OpenAI, consulte Uso de Azure OpenAI sin claves.

Clave de API

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();
        }
    }
}

Código fuente | Paquete (npm) | Referencia |

Compatibilidad con la versión de la API de Azure OpenAI

• La API disponible con carácter general (GA) v1 ahora permite el acceso a las operaciones de disponibilidad general y versión preliminar. Para más información, consulte la guía del ciclo de vida de la versión de la API.

Instalación

npm install openai

Autenticación

Microsoft Entra ID

npm install @azure/identity

Sin embargo, para autenticar el cliente OpenAI, es necesario usar la función getBearerTokenProvider desde el paquete @azure/identity. Esta función crea un proveedor de tokens que OpenAI usa internamente para obtener tokens para cada solicitud. El proveedor de tokens se crea de la siguiente manera:

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
});

Para obtener más información sobre la autenticación sin claves de Azure OpenAI, consulte el artículo de inicio rápido "Introducción al bloque de creación de seguridad de Azure OpenAI".

Clave de API

Las claves de API no se recomiendan para su uso en producción porque son menos seguras que otros métodos de autenticación.

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
});

Importante

Use las claves de API con precaución. No incluya la clave de API directamente en el código ni la exponga nunca públicamente. Si está usando una clave de API, almacénela de forma segura en Azure Key Vault. Para más información sobre cómo utilizar las claves de API de forma segura en sus aplicaciones, consulte Claves de API con Azure Key Vault.

Para más información sobre la seguridad de los servicios de AI, consulte Autenticación de solicitudes a los servicios de Azure AI.

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);

Transmisión en línea

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);
  }
}

Servidor MCP

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);

El chat

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);

Control de errores

Códigos de error

Código de estado	Tipo de error
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

Reintentos

Los errores siguientes se retiran automáticamente dos veces de manera predeterminada con un breve retroceso exponencial:

• Errores de conexión
• Tiempo de espera de solicitud 408
• 429 Límite de velocidad
• errores internos de>=500

Use maxRetries para establecer o deshabilitar el comportamiento de reintento:

// 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,
});

Código fuente de la biblioteca | Package (PyPi) | Referencia |

Nota:

OpenAI mantiene esta biblioteca. Consulte el historial de versiones para realizar el seguimiento de las actualizaciones más recientes de la biblioteca.

Compatibilidad con la versión de la API de Azure OpenAI

• La API disponible con carácter general (GA) v1 ahora permite el acceso a las operaciones de disponibilidad general y versión preliminar. Para más información, consulte la guía del ciclo de vida de la versión de la API.

Instalación

pip install openai

Para la versión más reciente:

pip install openai --upgrade

Autenticación

Los puntos de conexión y las claves de API de los recursos se pueden recuperar desde Azure Portal o AI Foundry:

• Inicie sesión en Azure portal> seleccione su recurso >Administración de recursos>Claves y punto de conexión
• Inicie sesión en el portal >. Seleccione el recurso.

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  
)

Clave de API

Importante

Use las claves de API con precaución. No incluya la clave de API directamente en el código ni la exponga nunca públicamente. Si está usando una clave de API, almacénela de forma segura en Azure Key Vault. Para más información sobre cómo utilizar las claves de API de forma segura en sus aplicaciones, consulte Claves de API con Azure Key Vault.

Para más información sobre la seguridad de los servicios de AI, consulte Autenticación de solicitudes a los servicios de Azure AI.

 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/"
    )

Variables de entorno

Si usa las variables de entorno predeterminadas de OPENAI_BASE_URL y OPENAI_API_KEY el cliente las usa automáticamente sin ninguna configuración adicional necesaria.

Variable de entorno	Importancia
OPENAI_BASE_URL	https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/
OPENAI_API_KEY	Clave de API de Azure OpenAI o AI Foundry.

from openai import OpenAI

client = OpenAI()   

Respuesta

No hay ninguna salida para la instanciación del cliente.

API de respuestas

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)) 

Para obtener más ejemplos, consulte la documentación de La API de respuestas .

Clave de API

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)) 

Para obtener más ejemplos, consulte la documentación de La API de respuestas .

Variables de entorno

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)) 

Para obtener más ejemplos, consulte la documentación de La API de respuestas .

Respuesta

{
  "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() con la herramienta de servidor MCP

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)

Para obtener más ejemplos, consulte la documentación de La API de respuestas .

Clave de API

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)

Para obtener más ejemplos, consulte la documentación de La API de respuestas .

Variables de entorno

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)

Para obtener más ejemplos, consulte la documentación de La API de respuestas .

Respuesta

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!

El chat

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))

Clave de API

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))

Variables de entorno

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))

Respuesta

{
  "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(): streaming

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='',)

Clave de API

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='',)

Variables de entorno

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='',)

Respuesta

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

chat.completions.create(): entrada de imagen

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))

Clave de API

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))

Variables de entorno

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))

Respuesta

{
  "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"
        }
      }
    }
  ]
}

Incrustaciones

embeddings.create()

Microsoft Entra ID

Actualmente, las incrustaciones no admiten el identificador de Entra de Microsoft con Azure OpenAI y la API v1.

Clave de API

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)

Variables de entorno

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)

Respuesta

La respuesta se ha truncado por brevedad.

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))

Ajuste preciso

Ajuste preciso del artículo de procedimientos de Python

Control de errores

# 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)

Códigos de error

Código de estado	Tipo de error
400	BadRequestError
401	AuthenticationError
403	PermissionDeniedError
404	NotFoundError
422	UnprocessableEntityError
429	RateLimitError
>=500	InternalServerError
No disponible	APIConnectionError

Identificadores de solicitud

Para recuperar el identificador de la solicitud, puede usar la propiedad _request_id que corresponde al encabezado de respuesta x-request-id.

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

Reintentos

Los errores siguientes se retiran automáticamente dos veces de manera predeterminada con un breve retroceso exponencial:

• Errores de conexión
• Tiempo de espera de solicitud 408
• 429 Límite de velocidad
• errores internos de>=500

Use max_retries para establecer o deshabilitar el comportamiento de reintento:

# 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",
)

Pasos siguientes

• Para ver qué modelos se admiten actualmente, consulte la página Modelos de Azure OpenAI