Azure OpenAI supported programming languages

A secure, keyless authentication approach is to use Microsoft Entra ID (formerly Azure Active Directory) via the Azure Identity library. To use the library:

dotnet add package Azure.Identity

Use the desired credential type from the library. For example, 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}");

For more information about Azure OpenAI keyless authentication, see the "Get started with the Azure OpenAI security building block" QuickStart article.

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}");

The azidentity module is used for Microsoft Entra ID authentication with 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)
}

For more information about Azure OpenAI keyless authentication, see Use Azure OpenAI without keys.

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)
}

Authentication with Microsoft Entra ID requires some initial setup:

Add the Azure Identity package:

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

After setup, you can choose which type of credential from azure.identity to use. As an example, DefaultAzureCredential can be used to authenticate the client: Set the values of the client ID, tenant ID, and client secret of the Microsoft Entra ID application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID, AZURE_CLIENT_SECRET.

Authorization is easiest using DefaultAzureCredential. It finds the best credential to use in its running environment though use of DefaultAzureCredential is only recommended for testing, not for production.

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();

For more information about Azure OpenAI keyless authentication, see Use Azure OpenAI without keys.

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

npm install @azure/identity

In order to authenticate the OpenAI client, however, we need to use the getBearerTokenProvider function from the @azure/identity package. This function creates a token provider that OpenAI uses internally to obtain tokens for each request. The token provider is created as follows:

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
});

For more information about Azure OpenAI keyless authentication, see the "Get started with the Azure OpenAI security building block" QuickStart article.

API keys aren't recommended for production use because they're less secure than other authentication methods.

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/"
    )

If you use the default environment variables of OPENAI_BASE_URL and OPENAI_API_KEY they are automatically used by the client with no further configuration required.

from openai import OpenAI

client = OpenAI()   

There is no output for client instantiation.

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)) 

For more examples, see the Responses API documentation.

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)) 

For more examples, see the Responses API documentation.

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)) 

For more examples, see the Responses API documentation.

{
  "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)

For more examples, see the Responses API documentation.

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)

For more examples, see the Responses API documentation.

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)

For more examples, see the Responses API documentation.

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"
        }
      }
    }
  ]
}

Embeddings currently do not support Microsoft Entra ID with Azure OpenAI and the 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)

The response has been truncated for brevity.

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))