Unterstützte Programmiersprachen für Azure OpenAI

Ein sicherer, schlüsselloser Authentifizierungsansatz besteht darin, Microsoft Entra ID (früher Azure Active Directory) über die Azure Identity-Bibliothek zu verwenden. So verwenden Sie die Bibliothek:

dotnet add package Azure.Identity

Verwenden Sie den gewünschten Anmeldeinformationstyp aus der Bibliothek. Beispiel: 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}");

Weitere Informationen zur schlüssellosen Azure OpenAI-Authentifizierung finden Sie im Schnellstartartikel „Erste Schritte mit dem Azure OpenAI-Sicherheitsbaustein“.

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}");

Das Azidentity-Modul wird für die Microsoft Entra ID-Authentifizierung mit Azure OpenAI verwendet.

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)
}

Weitere Informationen zur schlüssellosen Azure OpenAI-Authentifizierung finden Sie unter Verwenden von Azure OpenAI ohne Schlüssel.

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)
}

Für die Authentifizierung mit Microsoft Entra ID ist eine anfängliche Einrichtung erforderlich:

Hinzufügen des Azure Identity-Pakets:

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

Nach der Einrichtung können Sie auswählen, welche Art von Anmeldeinformation aus azure.identity verwendet werden sollen. Beispielsweise kann DefaultAzureCredential zum Authentifizieren des Clients verwendet werden: Legen Sie die Werte der Client-ID, der Mandanten-ID und des geheimen Clientschlüssels der Microsoft Entra ID-Anwendung als Umgebungsvariablen fest: AZURE_CLIENT_ID, AZURE_TENANT_ID, AZURE_CLIENT_SECRET.

Autorisieren ist am einfachsten mit DefaultAzureCredential. ES ermittelt die besten Anmeldeinformationen, die in seiner Ausführungsumgebung verwendet werden können, obwohl die Verwendung von DefaultAzureCredential nur zu Testzwecken empfohlen wird und nicht für Produktionsumgebungen.

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();

Weitere Informationen zur schlüssellosen Azure OpenAI-Authentifizierung finden Sie unter Verwenden von Azure OpenAI ohne Schlüssel.

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

npm install @azure/identity

Um den OpenAI-Client zu authentifizieren, müssen Sie jedoch die getBearerTokenProvider-Funktion aus dem @azure/identity-Paket verwenden. Diese Funktion erstellt einen Tokenanbieter, der OpenAI intern verwendet, um Token für jede Anforderung abzurufen. Der Tokenanbieter wird wie folgt erstellt:

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
});

Weitere Informationen zur schlüssellosen Azure OpenAI-Authentifizierung finden Sie im Schnellstartartikel „Erste Schritte mit dem Azure OpenAI-Sicherheitsbaustein“.

API-Schlüssel werden für die Verwendung in der Produktion nicht empfohlen, da sie nicht so sicher sind wie andere Authentifizierungsmethoden.

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
});

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  
)

 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/"
    )

Wenn Sie die Standardumgebungsvariablen OPENAI_BASE_URL verwenden und OPENAI_API_KEY diese automatisch vom Client verwendet werden, ohne dass eine weitere Konfiguration erforderlich ist.

from openai import OpenAI

client = OpenAI()   

Es gibt keine Ausgabe für die Clientinstanziierung.

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)) 

Weitere Beispiele finden Sie in der Dokumentation zur Antwort-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)) 

Weitere Beispiele finden Sie in der Dokumentation zur Antwort-API .

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)) 

Weitere Beispiele finden Sie in der Dokumentation zur Antwort-API .

{
  "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
}

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)

Weitere Beispiele finden Sie in der Dokumentation zur Antwort-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)

Weitere Beispiele finden Sie in der Dokumentation zur Antwort-API .

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)

Weitere Beispiele finden Sie in der Dokumentation zur Antwort-API .

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!

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))

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))

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))

{
  "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"
        }
      }
    }
  ]
}

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='',)

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='',)

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='',)

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

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))

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))

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))

{
  "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"
        }
      }
    }
  ]
}

Einbettungen unterstützen derzeit keine Microsoft Entra-ID mit Azure OpenAI und der v1-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)

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)

Die Antwort wurde aus Platzgründen abgeschnitten.

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))