Comment commuter entre les points de terminaison OpenAI et Azure OpenAI

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("OPENAI_API_KEY")
)

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/"
)

using OpenAI;
using System;
using System.ClientModel;

string apiKey = Environment.GetEnvironmentVariable("OPENAI_API_KEY");
OpenAIClient client = new(new ApiKeyCredential(apiKey));

using OpenAI;
using System;
using System.ClientModel;

// code omitted for brevity

OpenAIClientOptions clientOptions = new()
{
    Endpoint = new Uri($"{resourceEndpoint}/openai/v1/")
};

string apiKey = Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY");
OpenAIClient client = new(new ApiKeyCredential(apiKey), clientOptions);

import { OpenAI } from "openai";
import "dotenv/config";

const apiKey = process.env["OPENAI_API_KEY"];

if (!endpoint) {
  throw new Error("Please set the OPENAI_API_KEY environment variable.");
}

const client = new OpenAI({ apiKey });

import { OpenAI } from "openai";
import "dotenv/config";

const endpoint = process.env["AZURE_OPENAI_ENDPOINT"];

if (!endpoint) {
  throw new Error("Please set the AZURE_OPENAI_ENDPOINT environment variable.");
}

const apiKey = process.env["AZURE_OPENAI_API_KEY"];

if (!apiKey) {
  throw new Error("Please set the AZURE_OPENAI_API_KEY environment variable.");
}

const client = new OpenAI({ baseURL: endpoint + "/openai/v1", apiKey });

import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;

public class OpenAISample {

    public static void main(String[] args) {
        OpenAIClient openAIClient = OpenAIOkHttpClient.builder()
                .apiKey(System.getenv("OPENAI_API_KEY"))
                .build();
    }
}

import com.openai.azure.credential.AzureApiKeyCredential;
import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;

public class AzureOpenAISample {

    public static void main(String[] args) {
        OpenAIClient azureOpenAIClient = OpenAIOkHttpClient.builder()
                .baseUrl(System.getenv("AZURE_OPENAI_ENDPOINT"))
                .credential(AzureApiKeyCredential.create(System.getenv("AZURE_OPENAI_API_KEY")))
                .build();
    }
}

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

client := openai.NewClient(
    option.WithAPIKey(os.Getenv("OPENAI_API_KEY")),
)

// import (
//    "github.com/openai/openai-go/v3"
//    "github.com/openai/openai-go/v3/azure"
//    "github.com/openai/openai-go/v3/option"
// )

client := openai.NewClient(
    option.WithBaseURL("https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/"),
    azure.WithAPIKey(os.Getenv("AZURE_OPENAI_API_KEY")),
)

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

chat_completion = client.chat.completions.create(
    model="gpt-4o", 
    messages="<messages>"
)

embedding = client.embeddings.create(
    model="text-embedding-3-large", 
    input="<input>"
)

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

chat_completion = client.chat.completions.create(
    model="gpt-4o", # model = "deployment_name".
    messages="<messages>"
)

embedding = client.embeddings.create(
    model="text-embedding-3-large", # model = "deployment_name".
    input="<input>"
)

string modelName = "gpt-4.1-nano";
OpenAIResponseClient response = client.GetOpenAIResponseClient(modelName);

modelName = "gpt-4o";
ChatClient chatCompletion = client.GetChatClient(modelName);

modelName = "text-embedding-3-large";
EmbeddingClient embedding = client.GetEmbeddingClient(modelName);

string deploymentName = "my-gpt-4.1-nano-deployment";
OpenAIResponseClient response = client.GetOpenAIResponseClient(deploymentName);

deploymentName = "my-gpt-4o-deployment";
ChatClient chatCompletion = client.GetChatClient(deploymentName);

deploymentName = "my-text-embedding-3-large-deployment";
EmbeddingClient embedding = client.GetEmbeddingClient(deploymentName);

const response = await client.responses.create({
  model: "gpt-4.1-nano",
  input: "This is a test",
});

const chatCompletions = await client.chat.completions.create({
  model: "gpt-4o",
  messages: ["<messages>"],
});

const embeddings = await client.embeddings.create({
  model: "text-embedding-3-large",
  input: "<input>",
});

const response = await client.responses.create({
  model: "gpt-4.1-nano", // Replace with your model deployment name 
  input: "This is a test",
});

const chatCompletions = await client.chat.completions.create({
  model: "gpt-4o", // Replace with your model deployment name 
  messages: ["<messages>"],
});

const embeddings = await client.embeddings.create({
  model: "text-embedding-3-large", // Replace with your model deployment name
  input: "<input>",
});

ResponseCreateParams responseCreateParams = ResponseCreateParams.builder()
        .input("This is a test")
        .model(ChatModel.GPT_4_1_NANO)
        .build();

Response response = client.responses().create(responseCreateParams);

ChatCompletionCreateParams chatCompletionCreateParams = ChatCompletionCreateParams.builder()
        .model(ChatModel.GPT_4O)
        .addUserMessage("<message>")
        .build();

ChatCompletion chatCompletion = client.chat().completions().create(chatCompletionCreateParams);

EmbeddingCreateParams embeddingCreateParams = EmbeddingCreateParams.builder()
        .input("<input>")
        .model(EmbeddingModel.TEXT_EMBEDDING_3_LARGE)
        .build();

CreateEmbeddingResponse createEmbeddingResponse = client.embeddings().create(embeddingCreateParams);

ResponseCreateParams responseCreateParams = ResponseCreateParams.builder()
        .input("This is a test")
        .model(ChatModel.GPT_4_1_NANO) // Replace with your model deployment name 
        .build();

Response response = client.responses().create(responseCreateParams);

ChatCompletionCreateParams chatCompletionCreateParams = ChatCompletionCreateParams.builder()
        .model(ChatModel.GPT_4O) // Replace with your model deployment name
        .addUserMessage("<message>")
        .build();

ChatCompletion chatCompletion = client.chat().completions().create(chatCompletionCreateParams);

EmbeddingCreateParams embeddingCreateParams = EmbeddingCreateParams.builder()
        .input("<input>")
        .model(EmbeddingModel.TEXT_EMBEDDING_3_LARGE) // Replace with your model deployment name
        .build();

CreateEmbeddingResponse createEmbeddingResponse = client.embeddings().create(embeddingCreateParams);

resp, err := client.Responses.New(context.TODO(), responses.ResponseNewParams{
    Model: "gpt-4.1-nano", 
    Input: responses.ResponseNewParamsInputUnion{
        OfString: openai.String("This is a test."),
    },
})

resp, err := client.Chat.Completions.New(context.TODO(), openai.ChatCompletionNewParams{
    Model: "gpt-4o", 
    Messages: []openai.ChatCompletionMessageParamUnion{
        // messages
    },
})

resp, err := client.Embeddings.New(context.TODO(), openai.EmbeddingNewParams{
    Model: "text-embedding-3-large", 
    Input: openai.EmbeddingNewParamsInputUnion{
        OfString: openai.String("<input>"),
    },
})

resp, err := client.Responses.New(context.TODO(), responses.ResponseNewParams{
    Model: "gpt-4.1-nano", // Replace with your model deployment name
    Input: responses.ResponseNewParamsInputUnion{
        OfString: openai.String("This is a test."),
    },
})

resp, err := client.Chat.Completions.New(context.TODO(), openai.ChatCompletionNewParams{
    Model: "gpt-4o", // Model = "deployment_name"
    Messages: []openai.ChatCompletionMessageParamUnion{
        // messages
    },
})

resp, err := client.Embeddings.New(context.TODO(), openai.EmbeddingNewParams{
    Model: "text-embedding-3-large", // Model = "deployment_name"
    Input: openai.EmbeddingNewParamsInputUnion{
        OfString: openai.String("<input>"),
    },
})

inputs = ["A", "B", "C"] 

embedding = client.embeddings.create(
    input=inputs,
    model="text-embedding-3-large"
)

inputs = ["A", "B", "C"] #max array size=2048

embedding = client.embeddings.create(
    input=inputs,
    model="my-text-embedding-3-large-deployment" # This must match the custom deployment name you chose for your model.
    # engine="text-embedding-ada-002"
)

string[] inputs = [ "A", "B", "C" ];

EmbeddingClient embedding = client.GetEmbeddingClient(
    model: "text-embedding-3-large"
).GenerateEmbedding(
    input: inputs
);

string[] inputs = [ "A", "B", "C" ]; //max array size=2048

EmbeddingClient embedding = client.GetEmbeddingClient(
    model: "my-text-embedding-3-large-deployment" // This must match the custom deployment name you chose for your model.
    // engine:"text-embedding-ada-002"
).GenerateEmbedding(
    input: inputs
);

const embeddings = await client.embeddings.create({
    model: "text-embedding-3-large",
    inputs: ["A", "B", "C"],
})

const embeddings = await client.embeddings.create({
    model: "text-embedding-3-large",
    inputs: ["A", "B", "C"],
})

EmbeddingCreateParams embeddingCreateParams = EmbeddingCreateParams.builder()
        .inputOfArrayOfStrings(List.of("A", "B", "C"))
        .model(EmbeddingModel.TEXT_EMBEDDING_3_LARGE)
        .build();

CreateEmbeddingResponse createEmbeddingResponse = client.embeddings().create(embeddingCreateParams);

EmbeddingCreateParams embeddingCreateParams = EmbeddingCreateParams.builder()
        .inputOfArrayOfStrings(List.of("A", "B", "C"))
        .model(EmbeddingModel.TEXT_EMBEDDING_3_LARGE)
        .build();

CreateEmbeddingResponse createEmbeddingResponse = client.embeddings().create(embeddingCreateParams);

resp, err := client.Embeddings.New(context.TODO(), openai.EmbeddingNewParams{
    Model: "text-embedding-3-large",
    Input: openai.EmbeddingNewParamsInputUnion{
        OfArrayOfStrings: []string{"A", "B", "C"},
    },
})

resp, err := client.Embeddings.New(context.TODO(), openai.EmbeddingNewParams{
    Model: "text-embedding-3-large",
    Input: openai.EmbeddingNewParamsInputUnion{
        OfArrayOfStrings: []string{"A", "B", "C"},
    },
})