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azure-sdk-for-python

Sun, 14 Sep 2025 15:25:07 +0800

Azure/azure-sdk-for-python

Azure SDK for Python

This repository is for the active development of the Azure SDK for Python. For consumers of the SDK we recommend visiting our public developer docs or our versioned developer docs.

Getting started

For your convenience, each service has a separate set of libraries that you can choose to use instead of one, large Azure package. To get started with a specific library, see the README.md (or README.rst) file located in the library’s project folder.

You can find service libraries in the /sdk directory.

Prerequisites

The client libraries are supported on Python 3.9 or later. For more details, please read our page on Azure SDK for Python version support policy.

Packages available

Each service might have a number of libraries available from each of the following categories:

Client: New Releases

New wave of packages that we are announcing as GA and several that are currently releasing in preview. These libraries allow you to use and consume existing resources and interact with them, for example: upload a blob. These libraries share several core functionalities such as: retries, logging, transport protocols, authentication protocols, etc. that can be found in the azure-core library. You can learn more about these libraries by reading guidelines that they follow here.

You can find the most up to date list of all of the new packages on our page

NOTE: If you need to ensure your code is ready for production use one of the stable, non-preview libraries.

Client: Previous Versions

Last stable versions of packages that have been provided for usage with Azure and are production-ready. These libraries provide you with similar functionalities to the Preview ones as they allow you to use and consume existing resources and interact with them, for example: upload a blob. They might not implement the guidelines or have the same feature set as the November releases. They do however offer wider coverage of services.

Management: New Releases

A new set of management libraries that follow the Azure SDK Design Guidelines for Python are now available. These new libraries provide a number of core capabilities that are shared amongst all Azure SDKs, including the intuitive Azure Identity library, an HTTP Pipeline with custom policies, error-handling, distributed tracing, and much more. Documentation and code samples for these new libraries can be found here. In addition, a migration guide that shows how to transition from older versions of libraries is located here.

You can find the most up to date list of all of the new packages on our page

NOTE: If you need to ensure your code is ready for production use one of the stable, non-preview libraries. Also, if you are experiencing authentication issues with the management libraries after upgrading certain packages, it’s possible that you upgraded to the new versions of SDK without changing the authentication code, please refer to the migration guide mentioned above for proper instructions.

Management: Previous Versions

For a complete list of management libraries that enable you to provision and manage Azure resources, please check here. They might not have the same feature set as the new releases but they do offer wider coverage of services. Management libraries can be identified by namespaces that start with azure-mgmt-, e.g. azure-mgmt-compute

Need help?

For detailed documentation visit our Azure SDK for Python documentation
File an issue via GitHub Issues
Check previous questions or ask new ones on StackOverflow using azure and python tags.

Data Collection

The software may collect information about you and your use of the software and send it to Microsoft. Microsoft may use this information to provide services and improve our products and services. You may turn off the telemetry as described below. You can learn more about data collection and use in the help documentation and Microsoft’s privacy statement. For more information on the data collected by the Azure SDK, please visit the Telemetry Guidelines page.

Telemetry Configuration

Telemetry collection is on by default.

To opt out, you can disable telemetry at client construction. Define a NoUserAgentPolicy class that is a subclass of UserAgentPolicy with an on_request method that does nothing. Then pass instance of this class as kwargs user_agent_policy=NoUserAgentPolicy() during client creation. This will disable telemetry for all methods in the client. Do this for every new client.

The example below uses the azure-storage-blob package. In your code, you can replace azure-storage-blob with the package you are using.

import os
from azure.identity import ManagedIdentityCredential
from azure.storage.blob import BlobServiceClient
from azure.core.pipeline.policies import UserAgentPolicy


# Create your credential you want to use
mi_credential = ManagedIdentityCredential()

account_url = "https://<storageaccountname>.blob.core.windows.net"

# Set up user-agent override
class NoUserAgentPolicy(UserAgentPolicy):
    def on_request(self, request):
        pass

# Create the BlobServiceClient object
blob_service_client = BlobServiceClient(account_url, credential=mi_credential, user_agent_policy=NoUserAgentPolicy())

container_client = blob_service_client.get_container_client(container=<container_name>) 
# TODO: do something with the container client like download blob to a file

Reporting security issues and security bugs

Security issues and bugs should be reported privately, via email, to the Microsoft Security Response Center (MSRC) secure@microsoft.com. You should receive a response within 24 hours. If for some reason you do not, please follow up via email to ensure we received your original message. Further information, including the MSRC PGP key, can be found in the Security TechCenter.

Contributing

For details on contributing to this repository, see the contributing guide.

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repositories using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

BloodHound

Sat, 23 Aug 2025 15:26:40 +0800

SpecterOps/BloodHound

BloodHound is a monolithic web application composed of an embedded React frontend with Sigma.js and a Go based REST API backend. It is deployed with a Postgresql application database and a Neo4j graph database, and is fed by the SharpHound and AzureHound data collectors.

BloodHound leverages graph theory to reveal hidden and often unintended relationships across identity and access management systems. Powered by OpenGraph, BloodHound now supports comprehensive analysis beyond Active Directory and Azure environments, enabling users to map complex privilege relationships across diverse identity platforms. Attackers can utilize BloodHound to rapidly discover sophisticated attack paths otherwise impossible to identify manually, while defenders can proactively identify and mitigate these risks. Both red and blue teams benefit from BloodHound’s expanded capabilities, gaining deeper insights into identity and privilege structures across their entire security landscape.

BloodHound CE is created and maintained by the SpecterOps team who also brought you BloodHound Enterprise. The original BloodHound was created by @_wald0, @CptJesus, and @harmj0y.

Running BloodHound Community Edition

Please refer to the Quickstart Guide for BloodHound Community Edition, which is part of the BloodHound documentation.

Useful Links

Contact

Please check out the Contact page in our wiki for details on how to reach out with questions and suggestions.

Licensing

Copyright 2025 Specter Ops, Inc.

Licensed under the Apache License, Version 2.0
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

Unless otherwise annotated by a lower-level LICENSE file or license header, all files in this repository are released under the Apache-2.0 license. A full copy of the license may be found in the top-level LICENSE file.

openai-node

Mon, 11 Aug 2025 15:35:37 +0800

openai/openai-node

OpenAI TypeScript and JavaScript API Library

This library provides convenient access to the OpenAI REST API from TypeScript or JavaScript.

It is generated from our OpenAPI specification with Stainless.

To learn how to use the OpenAI API, check out our API Reference and Documentation.

Installation

`1`	`npm install openai`

Installation from JSR

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deno add jsr:@openai/openai
npx jsr add @openai/openai

These commands will make the module importable from the @openai/openai scope. You can also import directly from JSR without an install step if you’re using the Deno JavaScript runtime:

`1`	`import OpenAI from 'jsr:@openai/openai';`

Usage

The full API of this library can be found in api.md file along with many code examples.

The primary API for interacting with OpenAI models is the Responses API. You can generate text from the model with the code below.

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env['OPENAI_API_KEY'], // This is the default and can be omitted
});

const response = await client.responses.create({
  model: 'gpt-4o',
  instructions: 'You are a coding assistant that talks like a pirate',
  input: 'Are semicolons optional in JavaScript?',
});

console.log(response.output_text);

The previous standard (supported indefinitely) for generating text is the Chat Completions API. You can use that API to generate text from the model with the code below.

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env['OPENAI_API_KEY'], // This is the default and can be omitted
});

const completion = await client.chat.completions.create({
  model: 'gpt-4o',
  messages: [
    { role: 'developer', content: 'Talk like a pirate.' },
    { role: 'user', content: 'Are semicolons optional in JavaScript?' },
  ],
});

console.log(completion.choices[0].message.content);

Streaming responses

We provide support for streaming responses using Server Sent Events (SSE).

import OpenAI from 'openai';

const client = new OpenAI();

const stream = await client.responses.create({
  model: 'gpt-4o',
  input: 'Say "Sheep sleep deep" ten times fast!',
  stream: true,
});

for await (const event of stream) {
  console.log(event);
}

File uploads

Request parameters that correspond to file uploads can be passed in many different forms:

File (or an object with the same structure)
a fetch Response (or an object with the same structure)
an fs.ReadStream
the return value of our toFile helper

import fs from 'fs';
import OpenAI, { toFile } from 'openai';

const client = new OpenAI();

// If you have access to Node `fs` we recommend using `fs.createReadStream()`:
await client.files.create({ file: fs.createReadStream('input.jsonl'), purpose: 'fine-tune' });

// Or if you have the web `File` API you can pass a `File` instance:
await client.files.create({ file: new File(['my bytes'], 'input.jsonl'), purpose: 'fine-tune' });

// You can also pass a `fetch` `Response`:
await client.files.create({ file: await fetch('https://somesite/input.jsonl'), purpose: 'fine-tune' });

// Finally, if none of the above are convenient, you can use our `toFile` helper:
await client.files.create({
  file: await toFile(Buffer.from('my bytes'), 'input.jsonl'),
  purpose: 'fine-tune',
});
await client.files.create({
  file: await toFile(new Uint8Array([0, 1, 2]), 'input.jsonl'),
  purpose: 'fine-tune',
});

Webhook Verification

Verifying webhook signatures is optional but encouraged.

For more information about webhooks, see the API docs.

Parsing webhook payloads

For most use cases, you will likely want to verify the webhook and parse the payload at the same time. To achieve this, we provide the method client.webhooks.unwrap(), which parses a webhook request and verifies that it was sent by OpenAI. This method will throw an error if the signature is invalid.

Note that the body parameter must be the raw JSON string sent from the server (do not parse it first). The .unwrap() method will parse this JSON for you into an event object after verifying the webhook was sent from OpenAI.

import { headers } from 'next/headers';
import OpenAI from 'openai';

const client = new OpenAI({
  webhookSecret: process.env.OPENAI_WEBHOOK_SECRET, // env var used by default; explicit here.
});

export async function webhook(request: Request) {
  const headersList = headers();
  const body = await request.text();

  try {
    const event = client.webhooks.unwrap(body, headersList);

    switch (event.type) {
      case 'response.completed':
        console.log('Response completed:', event.data);
        break;
      case 'response.failed':
        console.log('Response failed:', event.data);
        break;
      default:
        console.log('Unhandled event type:', event.type);
    }

    return Response.json({ message: 'ok' });
  } catch (error) {
    console.error('Invalid webhook signature:', error);
    return new Response('Invalid signature', { status: 400 });
  }
}

Verifying webhook payloads directly

In some cases, you may want to verify the webhook separately from parsing the payload. If you prefer to handle these steps separately, we provide the method client.webhooks.verifySignature() to only verify the signature of a webhook request. Like .unwrap(), this method will throw an error if the signature is invalid.

Note that the body parameter must be the raw JSON string sent from the server (do not parse it first). You will then need to parse the body after verifying the signature.

import { headers } from 'next/headers';
import OpenAI from 'openai';

const client = new OpenAI({
  webhookSecret: process.env.OPENAI_WEBHOOK_SECRET, // env var used by default; explicit here.
});

export async function webhook(request: Request) {
  const headersList = headers();
  const body = await request.text();

  try {
    client.webhooks.verifySignature(body, headersList);

    // Parse the body after verification
    const event = JSON.parse(body);
    console.log('Verified event:', event);

    return Response.json({ message: 'ok' });
  } catch (error) {
    console.error('Invalid webhook signature:', error);
    return new Response('Invalid signature', { status: 400 });
  }
}

Handling errors

When the library is unable to connect to the API, or if the API returns a non-success status code (i.e., 4xx or 5xx response), a subclass of APIError will be thrown:

const job = await client.fineTuning.jobs
  .create({ model: 'gpt-4o', training_file: 'file-abc123' })
  .catch(async (err) => {
    if (err instanceof OpenAI.APIError) {
      console.log(err.request_id);
      console.log(err.status); // 400
      console.log(err.name); // BadRequestError
      console.log(err.headers); // {server: 'nginx', ...}
    } else {
      throw err;
    }
  });

Error codes are as follows:

Status Code	Error Type
400	`BadRequestError`
401	`AuthenticationError`
403	`PermissionDeniedError`
404	`NotFoundError`
422	`UnprocessableEntityError`
429	`RateLimitError`
>=500	`InternalServerError`
N/A	`APIConnectionError`

Request IDs

For more information on debugging requests, see these docs

All object responses in the SDK provide a _request_id property which is added from the x-request-id response header so that you can quickly log failing requests and report them back to OpenAI.

const completion = await client.chat.completions.create({
  messages: [{ role: 'user', content: 'Say this is a test' }],
  model: 'gpt-4o',
});
console.log(completion._request_id); // req_123

You can also access the Request ID using the .withResponse() method:

const { data: stream, request_id } = await openai.chat.completions
  .create({
    model: 'gpt-4',
    messages: [{ role: 'user', content: 'Say this is a test' }],
    stream: true,
  })
  .withResponse();

Realtime API Beta

The Realtime API enables you to build low-latency, multi-modal conversational experiences. It currently supports text and audio as both input and output, as well as function calling through a WebSocket connection.

import { OpenAIRealtimeWebSocket } from 'openai/beta/realtime/websocket';

const rt = new OpenAIRealtimeWebSocket({ model: 'gpt-4o-realtime-preview-2024-12-17' });

rt.on('response.text.delta', (event) => process.stdout.write(event.delta));

For more information see realtime.md.

Microsoft Azure OpenAI

To use this library with Azure OpenAI, use the AzureOpenAI class instead of the OpenAI class.

[!IMPORTANT] The Azure API shape slightly differs from the core API shape which means that the static types for responses / params won’t always be correct.

import { AzureOpenAI } from 'openai';
import { getBearerTokenProvider, DefaultAzureCredential } from '@azure/identity';

const credential = new DefaultAzureCredential();
const scope = 'https://cognitiveservices.azure.com/.default';
const azureADTokenProvider = getBearerTokenProvider(credential, scope);

const openai = new AzureOpenAI({ azureADTokenProvider });

const result = await openai.chat.completions.create({
  model: 'gpt-4o',
  messages: [{ role: 'user', content: 'Say hello!' }],
});

console.log(result.choices[0]!.message?.content);

Retries

Certain errors will be automatically retried 2 times by default, with a short exponential backoff. Connection errors (for example, due to a network connectivity problem), 408 Request Timeout, 409 Conflict, 429 Rate Limit, and >=500 Internal errors will all be retried by default.

You can use the maxRetries option to configure or disable this:

// 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 JavaScript?' }], model: 'gpt-4o' }, {
  maxRetries: 5,
});

Timeouts

Requests time out after 10 minutes by default. You can configure this with a timeout option:

// Configure the default for all requests:
const client = new OpenAI({
  timeout: 20 * 1000, // 20 seconds (default is 10 minutes)
});

// Override per-request:
await client.chat.completions.create({ messages: [{ role: 'user', content: 'How can I list all files in a directory using Python?' }], model: 'gpt-4o' }, {
  timeout: 5 * 1000,
});

On timeout, an APIConnectionTimeoutError is thrown.

Note that requests which time out will be retried twice by default.

Request IDs

For more information on debugging requests, see these docs

All object responses in the SDK provide a _request_id property which is added from the x-request-id response header so that you can quickly log failing requests and report them back to OpenAI.

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const response = await client.responses.create({ model: 'gpt-4o', input: 'testing 123' });
console.log(response._request_id); // req_123

You can also access the Request ID using the .withResponse() method:

const { data: stream, request_id } = await openai.responses
  .create({
    model: 'gpt-4o',
    input: 'Say this is a test',
    stream: true,
  })
  .withResponse();

Auto-pagination

List methods in the OpenAI API are paginated. You can use the for await … of syntax to iterate through items across all pages:

async function fetchAllFineTuningJobs(params) {
  const allFineTuningJobs = [];
  // Automatically fetches more pages as needed.
  for await (const fineTuningJob of client.fineTuning.jobs.list({ limit: 20 })) {
    allFineTuningJobs.push(fineTuningJob);
  }
  return allFineTuningJobs;
}

Alternatively, you can request a single page at a time:

let page = await client.fineTuning.jobs.list({ limit: 20 });
for (const fineTuningJob of page.data) {
  console.log(fineTuningJob);
}

// Convenience methods are provided for manually paginating:
while (page.hasNextPage()) {
  page = await page.getNextPage();
  // ...
}

Realtime API Beta

import { OpenAIRealtimeWebSocket } from 'openai/beta/realtime/websocket';

const rt = new OpenAIRealtimeWebSocket({ model: 'gpt-4o-realtime-preview-2024-12-17' });

rt.on('response.text.delta', (event) => process.stdout.write(event.delta));

For more information see realtime.md.

Microsoft Azure OpenAI

To use this library with Azure OpenAI, use the AzureOpenAI class instead of the OpenAI class.

[!IMPORTANT] The Azure API shape slightly differs from the core API shape which means that the static types for responses / params won’t always be correct.

import { AzureOpenAI } from 'openai';
import { getBearerTokenProvider, DefaultAzureCredential } from '@azure/identity';

const credential = new DefaultAzureCredential();
const scope = 'https://cognitiveservices.azure.com/.default';
const azureADTokenProvider = getBearerTokenProvider(credential, scope);

const openai = new AzureOpenAI({
  azureADTokenProvider,
  apiVersion: '<The API version, e.g. 2024-10-01-preview>',
});

const result = await openai.chat.completions.create({
  model: 'gpt-4o',
  messages: [{ role: 'user', content: 'Say hello!' }],
});

console.log(result.choices[0]!.message?.content);

For more information on support for the Azure API, see azure.md.

Advanced Usage

Accessing raw Response data (e.g., headers)

The “raw” Response returned by fetch() can be accessed through the .asResponse() method on the APIPromise type that all methods return. This method returns as soon as the headers for a successful response are received and does not consume the response body, so you are free to write custom parsing or streaming logic.

You can also use the .withResponse() method to get the raw Response along with the parsed data. Unlike .asResponse() this method consumes the body, returning once it is parsed.

const client = new OpenAI();

const httpResponse = await client.responses
  .create({ model: 'gpt-4o', input: 'say this is a test.' })
  .asResponse();

// access the underlying web standard Response object
console.log(httpResponse.headers.get('X-My-Header'));
console.log(httpResponse.statusText);

const { data: modelResponse, response: raw } = await client.responses
  .create({ model: 'gpt-4o', input: 'say this is a test.' })
  .withResponse();
console.log(raw.headers.get('X-My-Header'));
console.log(modelResponse);

Logging

[!IMPORTANT] All log messages are intended for debugging only. The format and content of log messages may change between releases.

Log levels

The log level can be configured in two ways:

Via the OPENAI_LOG environment variable
Using the logLevel client option (overrides the environment variable if set)

import OpenAI from 'openai';

const client = new OpenAI({
  logLevel: 'debug', // Show all log messages
});

Available log levels, from most to least verbose:

'debug' - Show debug messages, info, warnings, and errors
'info' - Show info messages, warnings, and errors
'warn' - Show warnings and errors (default)
'error' - Show only errors
'off' - Disable all logging

At the 'debug' level, all HTTP requests and responses are logged, including headers and bodies. Some authentication-related headers are redacted, but sensitive data in request and response bodies may still be visible.

Custom logger

By default, this library logs to globalThis.console. You can also provide a custom logger. Most logging libraries are supported, including pino, winston, bunyan, consola, signale, and @std/log. If your logger doesn’t work, please open an issue.

When providing a custom logger, the logLevel option still controls which messages are emitted, messages below the configured level will not be sent to your logger.

import OpenAI from 'openai';
import pino from 'pino';

const logger = pino();

const client = new OpenAI({
  logger: logger.child({ name: 'OpenAI' }),
  logLevel: 'debug', // Send all messages to pino, allowing it to filter
});

Making custom/undocumented requests

This library is typed for convenient access to the documented API. If you need to access undocumented endpoints, params, or response properties, the library can still be used.

Undocumented endpoints

To make requests to undocumented endpoints, you can use client.get, client.post, and other HTTP verbs. Options on the client, such as retries, will be respected when making these requests.

await client.post('/some/path', {
  body: { some_prop: 'foo' },
  query: { some_query_arg: 'bar' },
});

Undocumented request params

To make requests using undocumented parameters, you may use // @ts-expect-error on the undocumented parameter. This library doesn’t validate at runtime that the request matches the type, so any extra values you send will be sent as-is.

client.chat.completions.create({
  // ...
  // @ts-expect-error baz is not yet public
  baz: 'undocumented option',
});

For requests with the GET verb, any extra params will be in the query, all other requests will send the extra param in the body.

If you want to explicitly send an extra argument, you can do so with the query, body, and headers request options.

Undocumented response properties

To access undocumented response properties, you may access the response object with // @ts-expect-error on the response object, or cast the response object to the requisite type. Like the request params, we do not validate or strip extra properties from the response from the API.

Customizing the fetch client

If you want to use a different fetch function, you can either polyfill the global:

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3

import fetch from 'my-fetch';

globalThis.fetch = fetch;

Or pass it to the client:

import OpenAI from 'openai';
import fetch from 'my-fetch';

const client = new OpenAI({ fetch });

Fetch options

If you want to set custom fetch options without overriding the fetch function, you can provide a fetchOptions object when instantiating the client or making a request. (Request-specific options override client options.)

import OpenAI from 'openai';

const client = new OpenAI({
  fetchOptions: {
    // `RequestInit` options
  },
});

Configuring proxies

To modify proxy behavior, you can provide custom fetchOptions that add runtime-specific proxy options to requests:

Node ^[docs]

import OpenAI from 'openai';
import * as undici from 'undici';

const proxyAgent = new undici.ProxyAgent('http://localhost:8888');
const client = new OpenAI({
  fetchOptions: {
    dispatcher: proxyAgent,
  },
});

Bun ^[docs]

import OpenAI from 'openai';

const client = new OpenAI({
  fetchOptions: {
    proxy: 'http://localhost:8888',
  },
});

Deno ^[docs]

import OpenAI from 'npm:openai';

const httpClient = Deno.createHttpClient({ proxy: { url: 'http://localhost:8888' } });
const client = new OpenAI({
  fetchOptions: {
    client: httpClient,
  },
});

Frequently Asked Questions

Semantic versioning

This package generally follows SemVer conventions, though certain backwards-incompatible changes may be released as minor versions:

Changes that only affect static types, without breaking runtime behavior.
Changes to library internals which are technically public but not intended or documented for external use. (Please open a GitHub issue to let us know if you are relying on such internals.)
Changes that we do not expect to impact the vast majority of users in practice.

We take backwards-compatibility seriously and work hard to ensure you can rely on a smooth upgrade experience.

We are keen for your feedback; please open an issue with questions, bugs, or suggestions.

Requirements

TypeScript >= 4.9 is supported.

The following runtimes are supported:

Node.js 20 LTS or later (non-EOL) versions.
Deno v1.28.0 or higher.
Bun 1.0 or later.
Cloudflare Workers.
Vercel Edge Runtime.
Jest 28 or greater with the "node" environment ("jsdom" is not supported at this time).
Nitro v2.6 or greater.
Web browsers: disabled by default to avoid exposing your secret API credentials. Enable browser support by explicitly setting dangerouslyAllowBrowser to true'.
More explanation

Why is this dangerous?
Enabling the dangerouslyAllowBrowser option can be dangerous because it exposes your secret API credentials in the client-side code. Web browsers are inherently less secure than server environments, any user with access to the browser can potentially inspect, extract, and misuse these credentials. This could lead to unauthorized access using your credentials and potentially compromise sensitive data or functionality.

When might this not be dangerous?
In certain scenarios where enabling browser support might not pose significant risks:
- Internal Tools: If the application is used solely within a controlled internal environment where the users are trusted, the risk of credential exposure can be mitigated.
- Public APIs with Limited Scope: If your API has very limited scope and the exposed credentials do not grant access to sensitive data or critical operations, the potential impact of exposure is reduced.
- Development or debugging purpose: Enabling this feature temporarily might be acceptable, provided the credentials are short-lived, aren’t also used in production environments, or are frequently rotated.

Note that React Native is not supported at this time.

If you are interested in other runtime environments, please open or upvote an issue on GitHub.

Contributing

See the contributing documentation.

openai-python

Sun, 10 Aug 2025 15:28:32 +0800

openai/openai-python

OpenAI Python API library

The OpenAI Python library provides convenient access to the OpenAI REST API from any Python 3.8+ application. The library includes type definitions for all request params and response fields, and offers both synchronous and asynchronous clients powered by httpx.

It is generated from our OpenAPI specification with Stainless.

Documentation

The REST API documentation can be found on platform.openai.com. The full API of this library can be found in api.md.

Installation

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# install from PyPI
pip install openai

Usage

The full API of this library can be found in api.md.

The primary API for interacting with OpenAI models is the Responses API. You can generate text from the model with the code below.

import os
from openai import OpenAI

client = OpenAI(
    # This is the default and can be omitted
    api_key=os.environ.get("OPENAI_API_KEY"),
)

response = client.responses.create(
    model="gpt-4o",
    instructions="You are a coding assistant that talks like a pirate.",
    input="How do I check if a Python object is an instance of a class?",
)

print(response.output_text)

The previous standard (supported indefinitely) for generating text is the Chat Completions API. You can use that API to generate text from the model with the code below.

from openai import OpenAI

client = OpenAI()

completion = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "developer", "content": "Talk like a pirate."},
        {
            "role": "user",
            "content": "How do I check if a Python object is an instance of a class?",
        },
    ],
)

print(completion.choices[0].message.content)

While you can provide an api_key keyword argument, we recommend using python-dotenv to add OPENAI_API_KEY="My API Key" to your .env file so that your API key is not stored in source control. Get an API key here.

Vision

With an image URL:

prompt = "What is in this image?"
img_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/d5/2023_06_08_Raccoon1.jpg/1599px-2023_06_08_Raccoon1.jpg"

response = client.responses.create(
    model="gpt-4o-mini",
    input=[
        {
            "role": "user",
            "content": [
                {"type": "input_text", "text": prompt},
                {"type": "input_image", "image_url": f"{img_url}"},
            ],
        }
    ],
)

With the image as a base64 encoded string:

import base64
from openai import OpenAI

client = OpenAI()

prompt = "What is in this image?"
with open("path/to/image.png", "rb") as image_file:
    b64_image = base64.b64encode(image_file.read()).decode("utf-8")

response = client.responses.create(
    model="gpt-4o-mini",
    input=[
        {
            "role": "user",
            "content": [
                {"type": "input_text", "text": prompt},
                {"type": "input_image", "image_url": f"data:image/png;base64,{b64_image}"},
            ],
        }
    ],
)

Async usage

Simply import AsyncOpenAI instead of OpenAI and use await with each API call:

import os
import asyncio
from openai import AsyncOpenAI

client = AsyncOpenAI(
    # This is the default and can be omitted
    api_key=os.environ.get("OPENAI_API_KEY"),
)


async def main() -> None:
    response = await client.responses.create(
        model="gpt-4o", input="Explain disestablishmentarianism to a smart five year old."
    )
    print(response.output_text)


asyncio.run(main())

Functionality between the synchronous and asynchronous clients is otherwise identical.

With aiohttp

By default, the async client uses httpx for HTTP requests. However, for improved concurrency performance you may also use aiohttp as the HTTP backend.

You can enable this by installing aiohttp:

1
2

# install from PyPI
pip install openai[aiohttp]

Then you can enable it by instantiating the client with http_client=DefaultAioHttpClient():

import asyncio
from openai import DefaultAioHttpClient
from openai import AsyncOpenAI


async def main() -> None:
    async with AsyncOpenAI(
        api_key="My API Key",
        http_client=DefaultAioHttpClient(),
    ) as client:
        chat_completion = await client.chat.completions.create(
            messages=[
                {
                    "role": "user",
                    "content": "Say this is a test",
                }
            ],
            model="gpt-4o",
        )


asyncio.run(main())

Streaming responses

We provide support for streaming responses using Server Side Events (SSE).

from openai import OpenAI

client = OpenAI()

stream = client.responses.create(
    model="gpt-4o",
    input="Write a one-sentence bedtime story about a unicorn.",
    stream=True,
)

for event in stream:
    print(event)

The async client uses the exact same interface.

import asyncio
from openai import AsyncOpenAI

client = AsyncOpenAI()


async def main():
    stream = await client.responses.create(
        model="gpt-4o",
        input="Write a one-sentence bedtime story about a unicorn.",
        stream=True,
    )

    async for event in stream:
        print(event)


asyncio.run(main())

Realtime API beta

Under the hood the SDK uses the websockets library to manage connections.

The Realtime API works through a combination of client-sent events and server-sent events. Clients can send events to do things like update session configuration or send text and audio inputs. Server events confirm when audio responses have completed, or when a text response from the model has been received. A full event reference can be found here and a guide can be found here.

Basic text based example:

import asyncio
from openai import AsyncOpenAI

async def main():
    client = AsyncOpenAI()

    async with client.beta.realtime.connect(model="gpt-4o-realtime-preview") as connection:
        await connection.session.update(session={'modalities': ['text']})

        await connection.conversation.item.create(
            item={
                "type": "message",
                "role": "user",
                "content": [{"type": "input_text", "text": "Say hello!"}],
            }
        )
        await connection.response.create()

        async for event in connection:
            if event.type == 'response.text.delta':
                print(event.delta, flush=True, end="")

            elif event.type == 'response.text.done':
                print()

            elif event.type == "response.done":
                break

asyncio.run(main())

However the real magic of the Realtime API is handling audio inputs / outputs, see this example TUI script for a fully fledged example.

Realtime error handling

Whenever an error occurs, the Realtime API will send an error event and the connection will stay open and remain usable. This means you need to handle it yourself, as no errors are raised directly by the SDK when an error event comes in.

client = AsyncOpenAI()

async with client.beta.realtime.connect(model="gpt-4o-realtime-preview") as connection:
    ...
    async for event in connection:
        if event.type == 'error':
            print(event.error.type)
            print(event.error.code)
            print(event.error.event_id)
            print(event.error.message)

Using types

Nested request parameters are TypedDicts. Responses are Pydantic models which also provide helper methods for things like:

Serializing back into JSON, model.to_json()
Converting to a dictionary, model.to_dict()

Typed requests and responses provide autocomplete and documentation within your editor. If you would like to see type errors in VS Code to help catch bugs earlier, set python.analysis.typeCheckingMode to basic.

Pagination

List methods in the OpenAI API are paginated.

This library provides auto-paginating iterators with each list response, so you do not have to request successive pages manually:

from openai import OpenAI

client = OpenAI()

all_jobs = []
# Automatically fetches more pages as needed.
for job in client.fine_tuning.jobs.list(
    limit=20,
):
    # Do something with job here
    all_jobs.append(job)
print(all_jobs)

Or, asynchronously:

import asyncio
from openai import AsyncOpenAI

client = AsyncOpenAI()


async def main() -> None:
    all_jobs = []
    # Iterate through items across all pages, issuing requests as needed.
    async for job in client.fine_tuning.jobs.list(
        limit=20,
    ):
        all_jobs.append(job)
    print(all_jobs)


asyncio.run(main())

Alternatively, you can use the .has_next_page(), .next_page_info(), or .get_next_page() methods for more granular control working with pages:

first_page = await client.fine_tuning.jobs.list(
    limit=20,
)
if first_page.has_next_page():
    print(f"will fetch next page using these details: {first_page.next_page_info()}")
    next_page = await first_page.get_next_page()
    print(f"number of items we just fetched: {len(next_page.data)}")

# Remove `await` for non-async usage.

Or just work directly with the returned data:

first_page = await client.fine_tuning.jobs.list(
    limit=20,
)

print(f"next page cursor: {first_page.after}")  # => "next page cursor: ..."
for job in first_page.data:
    print(job.id)

# Remove `await` for non-async usage.

Nested params

Nested parameters are dictionaries, typed using TypedDict, for example:

from openai import OpenAI

client = OpenAI()

response = client.chat.responses.create(
    input=[
        {
            "role": "user",
            "content": "How much ?",
        }
    ],
    model="gpt-4o",
    response_format={"type": "json_object"},
)

File uploads

Request parameters that correspond to file uploads can be passed as bytes, or a PathLike instance or a tuple of (filename, contents, media type).

from pathlib import Path
from openai import OpenAI

client = OpenAI()

client.files.create(
    file=Path("input.jsonl"),
    purpose="fine-tune",
)

The async client uses the exact same interface. If you pass a PathLike instance, the file contents will be read asynchronously automatically.

Webhook Verification

Verifying webhook signatures is optional but encouraged.

For more information about webhooks, see the API docs.

Parsing webhook payloads

from openai import OpenAI
from flask import Flask, request

app = Flask(__name__)
client = OpenAI()  # OPENAI_WEBHOOK_SECRET environment variable is used by default


@app.route("/webhook", methods=["POST"])
def webhook():
    request_body = request.get_data(as_text=True)

    try:
        event = client.webhooks.unwrap(request_body, request.headers)

        if event.type == "response.completed":
            print("Response completed:", event.data)
        elif event.type == "response.failed":
            print("Response failed:", event.data)
        else:
            print("Unhandled event type:", event.type)

        return "ok"
    except Exception as e:
        print("Invalid signature:", e)
        return "Invalid signature", 400


if __name__ == "__main__":
    app.run(port=8000)

Verifying webhook payloads directly

In some cases, you may want to verify the webhook separately from parsing the payload. If you prefer to handle these steps separately, we provide the method client.webhooks.verify_signature() to only verify the signature of a webhook request. Like .unwrap(), this method will raise an error if the signature is invalid.

Note that the body parameter must be the raw JSON string sent from the server (do not parse it first). You will then need to parse the body after verifying the signature.

import json
from openai import OpenAI
from flask import Flask, request

app = Flask(__name__)
client = OpenAI()  # OPENAI_WEBHOOK_SECRET environment variable is used by default


@app.route("/webhook", methods=["POST"])
def webhook():
    request_body = request.get_data(as_text=True)

    try:
        client.webhooks.verify_signature(request_body, request.headers)

        # Parse the body after verification
        event = json.loads(request_body)
        print("Verified event:", event)

        return "ok"
    except Exception as e:
        print("Invalid signature:", e)
        return "Invalid signature", 400


if __name__ == "__main__":
    app.run(port=8000)

Handling errors

When the library is unable to connect to the API (for example, due to network connection problems or a timeout), a subclass of openai.APIConnectionError is raised.

When the API returns a non-success status code (that is, 4xx or 5xx response), a subclass of openai.APIStatusError is raised, containing status_code and response properties.

All errors inherit from openai.APIError.

import openai
from openai import OpenAI

client = OpenAI()

try:
    client.fine_tuning.jobs.create(
        model="gpt-4o",
        training_file="file-abc123",
    )
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)

Error codes are as follows:

Status Code	Error Type
400	`BadRequestError`
401	`AuthenticationError`
403	`PermissionDeniedError`
404	`NotFoundError`
422	`UnprocessableEntityError`
429	`RateLimitError`
>=500	`InternalServerError`
N/A	`APIConnectionError`

Request IDs

For more information on debugging requests, see these docs

All object responses in the SDK provide a _request_id property which is added from the x-request-id response header so that you can quickly log failing requests and report them back to OpenAI.

response = await client.responses.create(
    model="gpt-4o-mini",
    input="Say 'this is a test'.",
)
print(response._request_id)  # req_123

Note that unlike other properties that use an _ prefix, the _request_id property is public. Unless documented otherwise, all other _ prefix properties, methods and modules are private.

[!IMPORTANT]
If you need to access request IDs for failed requests you must catch the APIStatusError exception

import openai

try:
    completion = await client.chat.completions.create(
        messages=[{"role": "user", "content": "Say this is a test"}], model="gpt-4"
    )
except openai.APIStatusError as exc:
    print(exc.request_id)  # req_123
    raise exc

Retries

Certain errors are automatically retried 2 times by default, with a short exponential backoff. Connection errors (for example, due to a network connectivity problem), 408 Request Timeout, 409 Conflict, 429 Rate Limit, and >=500 Internal errors are all retried by default.

You can use the max_retries option to configure or disable retry settings:

from openai import OpenAI

# Configure the default for all requests:
client = OpenAI(
    # default is 2
    max_retries=0,
)

# Or, configure per-request:
client.with_options(max_retries=5).chat.completions.create(
    messages=[
        {
            "role": "user",
            "content": "How can I get the name of the current day in JavaScript?",
        }
    ],
    model="gpt-4o",
)

Timeouts

By default requests time out after 10 minutes. You can configure this with a timeout option, which accepts a float or an httpx.Timeout object:

from openai import OpenAI

# Configure the default for all requests:
client = OpenAI(
    # 20 seconds (default is 10 minutes)
    timeout=20.0,
)

# More granular control:
client = OpenAI(
    timeout=httpx.Timeout(60.0, read=5.0, write=10.0, connect=2.0),
)

# Override per-request:
client.with_options(timeout=5.0).chat.completions.create(
    messages=[
        {
            "role": "user",
            "content": "How can I list all files in a directory using Python?",
        }
    ],
    model="gpt-4o",
)

On timeout, an APITimeoutError is thrown.

Note that requests that time out are retried twice by default.

Advanced

Logging

We use the standard library logging module.

You can enable logging by setting the environment variable OPENAI_LOG to info.

`1`	`$ export OPENAI_LOG=info`

Or to debug for more verbose logging.

How to tell whether `None` means `null` or missing

In an API response, a field may be explicitly null, or missing entirely; in either case, its value is None in this library. You can differentiate the two cases with .model_fields_set:

if response.my_field is None:
  if 'my_field' not in response.model_fields_set:
    print('Got json like {}, without a "my_field" key present at all.')
  else:
    print('Got json like {"my_field": null}.')

Accessing raw response data (e.g. headers)

The “raw” Response object can be accessed by prefixing .with_raw_response. to any HTTP method call, e.g.,

from openai import OpenAI

client = OpenAI()
response = client.chat.completions.with_raw_response.create(
    messages=[{
        "role": "user",
        "content": "Say this is a test",
    }],
    model="gpt-4o",
)
print(response.headers.get('X-My-Header'))

completion = response.parse()  # get the object that `chat.completions.create()` would have returned
print(completion)

These methods return a LegacyAPIResponse object. This is a legacy class as we’re changing it slightly in the next major version.

For the sync client this will mostly be the same with the exception of content & text will be methods instead of properties. In the async client, all methods will be async.

A migration script will be provided & the migration in general should be smooth.

`.with_streaming_response`

The above interface eagerly reads the full response body when you make the request, which may not always be what you want.

To stream the response body, use .with_streaming_response instead, which requires a context manager and only reads the response body once you call .read(), .text(), .json(), .iter_bytes(), .iter_text(), .iter_lines() or .parse(). In the async client, these are async methods.

As such, .with_streaming_response methods return a different APIResponse object, and the async client returns an AsyncAPIResponse object.

with client.chat.completions.with_streaming_response.create(
    messages=[
        {
            "role": "user",
            "content": "Say this is a test",
        }
    ],
    model="gpt-4o",
) as response:
    print(response.headers.get("X-My-Header"))

    for line in response.iter_lines():
        print(line)

The context manager is required so that the response will reliably be closed.

Making custom/undocumented requests

This library is typed for convenient access to the documented API.

If you need to access undocumented endpoints, params, or response properties, the library can still be used.

Undocumented endpoints

To make requests to undocumented endpoints, you can make requests using client.get, client.post, and other http verbs. Options on the client will be respected (such as retries) when making this request.

import httpx

response = client.post(
    "/foo",
    cast_to=httpx.Response,
    body={"my_param": True},
)

print(response.headers.get("x-foo"))

Undocumented request params

If you want to explicitly send an extra param, you can do so with the extra_query, extra_body, and extra_headers request options.

Undocumented response properties

To access undocumented response properties, you can access the extra fields like response.unknown_prop. You can also get all the extra fields on the Pydantic model as a dict with response.model_extra.

Configuring the HTTP client

You can directly override the httpx client to customize it for your use case, including:

Support for proxies
Custom transports
Additional advanced functionality

import httpx
from openai import OpenAI, DefaultHttpxClient

client = OpenAI(
    # Or use the `OPENAI_BASE_URL` env var
    base_url="http://my.test.server.example.com:8083/v1",
    http_client=DefaultHttpxClient(
        proxy="http://my.test.proxy.example.com",
        transport=httpx.HTTPTransport(local_address="0.0.0.0"),
    ),
)

You can also customize the client on a per-request basis by using with_options():

`1`	`client.with_options(http_client=DefaultHttpxClient(...))`

Managing HTTP resources

By default the library closes underlying HTTP connections whenever the client is garbage collected. You can manually close the client using the .close() method if desired, or with a context manager that closes when exiting.

from openai import OpenAI

with OpenAI() as client:
  # make requests here
  ...

# HTTP client is now closed

Microsoft Azure OpenAI

To use this library with Azure OpenAI, use the AzureOpenAI class instead of the OpenAI class.

[!IMPORTANT] The Azure API shape differs from the core API shape which means that the static types for responses / params won’t always be correct.

from openai import AzureOpenAI

# gets the API Key from environment variable AZURE_OPENAI_API_KEY
client = AzureOpenAI(
    # https://learn.microsoft.com/azure/ai-services/openai/reference#rest-api-versioning
    api_version="2023-07-01-preview",
    # https://learn.microsoft.com/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal#create-a-resource
    azure_endpoint="https://example-endpoint.openai.azure.com",
)

completion = client.chat.completions.create(
    model="deployment-name",  # e.g. gpt-35-instant
    messages=[
        {
            "role": "user",
            "content": "How do I output all files in a directory using Python?",
        },
    ],
)
print(completion.to_json())

In addition to the options provided in the base OpenAI client, the following options are provided:

azure_endpoint (or the AZURE_OPENAI_ENDPOINT environment variable)
azure_deployment
api_version (or the OPENAI_API_VERSION environment variable)
azure_ad_token (or the AZURE_OPENAI_AD_TOKEN environment variable)
azure_ad_token_provider

An example of using the client with Microsoft Entra ID (formerly known as Azure Active Directory) can be found here.

Versioning

This package generally follows SemVer conventions, though certain backwards-incompatible changes may be released as minor versions:

Changes that only affect static types, without breaking runtime behavior.
Changes to library internals which are technically public but not intended or documented for external use. (Please open a GitHub issue to let us know if you are relying on such internals.)
Changes that we do not expect to impact the vast majority of users in practice.

We take backwards-compatibility seriously and work hard to ensure you can rely on a smooth upgrade experience.

We are keen for your feedback; please open an issue with questions, bugs, or suggestions.

Determining the installed version

If you’ve upgraded to the latest version but aren’t seeing any new features you were expecting then your python environment is likely still using an older version.

You can determine the version that is being used at runtime with:

1
2

import openai
print(openai.__version__)

Requirements

Python 3.8 or higher.

Contributing

See the contributing documentation.

kubectl-ai

Sat, 10 May 2025 15:25:23 +0800

GoogleCloudPlatform/kubectl-ai

kubectl-ai

kubectl-ai acts as an intelligent interface, translating user intent into precise Kubernetes operations, making Kubernetes management more accessible and efficient.

Quick Start

First, ensure that kubectl is installed and configured.

Installation

Quick Install (Linux & MacOS only)

`1`	`curl -sSL https://raw.githubusercontent.com/GoogleCloudPlatform/kubectl-ai/main/install.sh \| bash`

Manual Installation (Linux, MacOS and Windows)

Download the latest release from the releases page for your target machine.
Untar the release, make the binary executable and move it to a directory in your $PATH (as shown below).

1
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3

tar -zxvf kubectl-ai_Darwin_arm64.tar.gz
chmod a+x kubectl-ai
sudo mv kubectl-ai /usr/local/bin/

Install with Krew (Linux/macOS/Windows)

First of all, you need to have krew insatlled, refer to krew document for more details Then you can install with krew

`1`	`kubectl krew install ai`

Now you can invoke kubectl-ai as a kubectl plugin like this: kubectl ai.

Usage

Using Gemini (Default)

Set your Gemini API key as an environment variable. If you don’t have a key, get one from Google AI Studio.

export GEMINI_API_KEY=your_api_key_here
kubectl-ai

# Use different gemini model
kubectl-ai --model gemini-2.5-pro-exp-03-25

# Use 2.5 flash (faster) model
kubectl-ai --quiet --model gemini-2.5-flash-preview-04-17 "check logs for nginx app in hello namespace"

Using AI models running locally (ollama or llama.cpp)

You can use kubectl-ai with AI models running locally. kubectl-ai supports ollama and llama.cpp to use the AI models running locally.

An example of using Google’s gemma3 model with ollama:

# assuming ollama is already running and you have pulled one of the gemma models
# ollama pull gemma3:12b-it-qat

# if your ollama server is at remote, use OLLAMA_HOST variable to specify the host
# export OLLAMA_HOST=http://192.168.1.3:11434/

# enable-tool-use-shim because models require special prompting to enable tool calling
kubectl-ai --llm-provider ollama --model gemma3:12b-it-qat --enable-tool-use-shim

# you can use `models` command to discover the locally available models
>> models

Using Grok

You can use X.AI’s Grok model by setting your X.AI API key:

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export GROK_API_KEY=your_xai_api_key_here
kubectl-ai --llm-provider=grok --model=grok-3-beta

Using Azure OpenAI

You can also use Azure OpenAI deployment by setting your OpenAI API key and specifying the provider:

export AZURE_OPENAI_API_KEY=your_azure_openai_api_key_here
export AZURE_OPENAI_ENDPOINT=https://your_azure_openai_endpoint_here
kubectl-ai --llm-provider=azopenai --model=your_azure_openai_deployment_name_here
# or
az login
kubectl-ai --llm-provider=openai://your_azure_openai_endpoint_here --model=your_azure_openai_deployment_name_here

Using OpenAI

You can also use OpenAI models by setting your OpenAI API key and specifying the provider:

1
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export OPENAI_API_KEY=your_openai_api_key_here
kubectl-ai --llm-provider=openai --model=gpt-4.1

Using OpenAI Compatible API

For example, you can use aliyun qwen-xxx models as follows

1
2
3

export OPENAI_API_KEY=your_openai_api_key_here
export OPENAI_ENDPOINT=https://dashscope.aliyuncs.com/compatible-mode/v1
kubectl-ai --llm-provider=openai --model=qwen-plus

Note: kubectl-ai supports AI models from gemini, vertexai, azopenai, openai, grok and local LLM providers such as ollama and llama.cpp.

Run interactively:

`1`	`kubectl-ai`

The interactive mode allows you to have a chat with kubectl-ai, asking multiple questions in sequence while maintaining context from previous interactions. Simply type your queries and press Enter to receive responses. To exit the interactive shell, type exit or press Ctrl+C.

Or, run with a task as input:

`1`	`kubectl-ai --quiet "fetch logs for nginx app in hello namespace"`

Combine it with other unix commands:

1
2
3

kubectl-ai < query.txt
# OR
echo "list pods in the default namespace" | kubectl-ai

You can even combine a positional argument with stdin input. The positional argument will be used as a prefix to the stdin content:

`1`	`cat error.log \| kubectl-ai "explain the error"`

Extras

You can use the following special keywords for specific actions:

model: Display the currently selected model.
models: List all available models.
version: Display the kubectl-ai version.
reset: Clear the conversational context.
clear: Clear the terminal screen.
exit or quit: Terminate the interactive shell (Ctrl+C also works).

Invoking as kubectl plugin

Use it via the kubectl plug interface like this: kubectl ai. kubectl will find kubectl-ai as long as it’s in your PATH. For more information about plugins please see: https://kubernetes.io/docs/tasks/extend-kubectl/kubectl-plugins/

Examples

# Get information about pods in the default namespace
kubectl-ai --quiet "show me all pods in the default namespace"

# Create a new deployment
kubectl-ai --quiet "create a deployment named nginx with 3 replicas using the nginx:latest image"

# Troubleshoot issues
kubectl-ai --quiet "double the capacity for the nginx app"

# Using Azure OpenAI instead of Gemini
kubectl-ai --llm-provider=azopenai --model=your_azure_openai_deployment_name_here --quiet "scale the nginx deployment to 5 replicas"

# Using OpenAI instead of Gemini
kubectl-ai --llm-provider=openai --model=gpt-4.1 --quiet "scale the nginx deployment to 5 replicas"

The kubectl-ai will process your query, execute the appropriate kubectl commands, and provide you with the results and explanations.

MCP server

You can also use kubectl-ai as a MCP server that exposes kubectl as one of the tools to interact with locally configured k8s environment. See mcp docs for more details.

k8s-bench

kubectl-ai project includes k8s-bench - a benchmark to evaluate performance of different LLM models on kubernetes related tasks. Here is a summary from our last run:

Model	Success	Fail
gemini-2.5-flash-preview-04-17	10	0
gemini-2.5-pro-preview-03-25	10	0
gemma-3-27b-it	8	2
Total	28	2

See full report for more details.

Start Contributing

We welcome contributions to kubectl-ai from the community. Take a look at our contribution guide to get started.

Note: This is not an officially supported Google product. This project is not eligible for the Google Open Source Software Vulnerability Rewards Program.

eShop

Fri, 02 May 2025 15:30:32 +0800

dotnet/eShop

eShop Reference Application - “AdventureWorks”

A reference .NET application implementing an e-commerce website using a services-based architecture using .NET Aspire.

Getting Started

This version of eShop is based on .NET 9.

Previous eShop versions:

.NET 8

Prerequisites

Clone the eShop repository: https://github.com/dotnet/eshop
Install & start Docker Desktop

Windows with Visual Studio

Install Visual Studio 2022 version 17.10 or newer.
- Select the following workloads:
  - ASP.NET and web development workload.
  - .NET Aspire SDK component in Individual components.
  - Optional: .NET Multi-platform App UI development to run client apps

Run the following commands in a Powershell & Terminal running as Administrator to automatically configure your environment with the required tools to build and run this application. (Note: A restart is required and included in the script below.)

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install-Module -Name Microsoft.WinGet.Configuration -AllowPrerelease -AcceptLicense -Force
$env:Path = [System.Environment]::GetEnvironmentVariable("Path","Machine") + ";" + [System.Environment]::GetEnvironmentVariable("Path","User")
get-WinGetConfiguration -file .\.configurations\vside.dsc.yaml | Invoke-WinGetConfiguration -AcceptConfigurationAgreements

From Dev Home go to Machine Configuration -> Clone repositories. Enter the URL for this repository. In the confirmation screen look for the section Configuration File Detected and click Run File.

Mac, Linux, & Windows without Visual Studio

Install the latest .NET 9 SDK

Run the following commands in a Powershell & Terminal running as Administrator to automatically configuration your environment with the required tools to build and run this application. (Note: A restart is required after running the script below.)

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install-Module -Name Microsoft.WinGet.Configuration -AllowPrerelease -AcceptLicense  -Force
$env:Path = [System.Environment]::GetEnvironmentVariable("Path","Machine") + ";" + [System.Environment]::GetEnvironmentVariable("Path","User")
get-WinGetConfiguration -file .\.configurations\vscode.dsc.yaml | Invoke-WinGetConfiguration -AcceptConfigurationAgreements

Note: These commands may require sudo

Optional: Install Visual Studio Code with C# Dev Kit
Optional: Install .NET MAUI Workload

Note: When running on Mac with Apple Silicon (M series processor), Rosetta 2 for grpc-tools.

Running the solution

[!WARNING] Remember to ensure that Docker is started

(Windows only) Run the application from Visual Studio:

Open the eShop.Web.slnf file in Visual Studio
Ensure that eShop.AppHost.csproj is your startup project
Hit Ctrl-F5 to launch Aspire

Or run the application from your terminal:

`1`	`dotnet run --project src/eShop.AppHost/eShop.AppHost.csproj`

then look for lines like this in the console output in order to find the URL to open the Aspire dashboard:

`1`	`Login to the dashboard at: http://localhost:19888/login?t=uniquelogincodeforyou`

You may need to install ASP.NET Core HTTPS development certificates first, and then close all browser tabs. Learn more at https://aka.ms/aspnet/https-trust-dev-cert

Azure Open AI

When using Azure OpenAI, inside eShop.AppHost/appsettings.json, add the following section:

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  "ConnectionStrings": {
    "OpenAi": "Endpoint=xxx;Key=xxx;"
  }

Replace the values with your own. Then, in the eShop.AppHost Program.cs, set this value to true

`1`	`bool useOpenAI = false;`

Here’s additional guidance on the .NET Aspire OpenAI component.

Use Azure Developer CLI

You can use the Azure Developer CLI to run this project on Azure with only a few commands. Follow the next instructions:

Install the latest or update to the latest Azure Developer CLI (azd).
Log in azd (if you haven’t done it before) to your Azure account:

`1`	`azd auth login`

Initialize azd from the root of the repo.

`1`	`azd init`

During init:
- Select Use code in the current directory. Azd will automatically detect the .NET Aspire project.
- Confirm .NET (Aspire) and continue.
- Select which services to expose to the Internet (exposing webapp is enough to test the sample).
- Finalize the initialization by giving a name to your environment.
Create Azure resources and deploy the sample by running:

azd up

Notes:

The operation takes a few minutes the first time it is ever run for an environment.
At the end of the process, azd will display the url for the webapp. Follow that link to test the sample.
You can run azd up after saving changes to the sample to re-deploy and update the sample.
Report any issues to azure-dev repo.
FAQ and troubleshoot for azd.

Contributing

For more information on contributing to this repo, read the contribution documentation and the Code of Conduct.

Sample data

The sample catalog data is defined in catalog.json. Those product names, descriptions, and brand names are fictional and were generated using GPT-35-Turbo, and the corresponding product images were generated using DALL·E 3.

eShop on Azure

For a version of this app configured for deployment on Azure, please view the eShop on Azure repo.

azure-sdk-for-net

Sun, 06 Apr 2025 15:25:09 +0800

Azure/azure-sdk-for-net

Azure SDK for .NET

This repository is for active development of the Azure SDK for .NET. For consumers of the SDK we recommend visiting our public developer docs or our versioned developer docs.

Getting started

To get started with a library, see the README.md file located in the library’s project folder. You can find these library folders grouped by service in the /sdk directory.

For tutorials, samples, quick starts, and other documentation, go to Azure for .NET Developers.

Packages available

Each service might have a number of libraries available from each of the following categories:

Client: New Releases

New wave of packages that we are announcing as GA and several that are currently releasing in preview. These libraries follow the Azure SDK Design Guidelines for .NET and share a number of core features such as HTTP retries, logging, transport protocols, authentication protocols, etc., so that once you learn how to use these features in one client library, you will know how to use them in other client libraries. You can learn about these shared features at Azure.Core.

These new client libraries can be identified by the naming used for their folder, package, and namespace. Each will start with Azure, followed by the service category, and then the name of the service. For example Azure.Storage.Blobs.

For a complete list of available packages, please see the latest available packages page.

NOTE: If you need to ensure your code is ready for production we strongly recommend using one of the stable, non-preview libraries.

Client: Previous Versions

Last stable versions of packages that are production-ready. These libraries provide similar functionalities to the preview packages, as they allow you to use and consume existing resources and interact with them, for example: upload a storage blob. Stable library directories typically contain ‘Microsoft.Azure’ in their names, e.g. ‘Microsoft.Azure.KeyVault’. They might not implement the guidelines or have the same feature set as the November releases. They do however offer wider coverage of services.

Management: New Releases

A new set of management libraries that follow the Azure SDK Design Guidelines for .NET and based on Azure.Core libraries are now in Public Preview. These new libraries provide a number of core capabilities that are shared amongst all Azure SDKs, including the intuitive Azure Identity library, an HTTP Pipeline with custom policies, error-handling, distributed tracing, and much more. You can find the list of new packages on this page.

To get started with these new libraries, please see the quickstart guide here. These new libraries can be identified by namespaces that start with Azure.ResourceManager, e.g. Azure.ResourceManager.Network

NOTE: If you need to ensure your code is ready for production use one of the stable, non-preview libraries.

Management: Previous Versions

For a complete list of management libraries which enable you to provision and manage Azure resources, please check here. They might not have the same feature set as the new releases but they do offer wider coverage of services. Previous versions of management libraries can be identified by namespaces that start with Microsoft.Azure.Management, e.g. Microsoft.Azure.Management.Network

Documentation and code samples for these libraries can be found here.

Need help?

For reference documentation visit the Azure SDK for .NET API Reference.
For tutorials, samples, quick starts, and other documentation, go to Azure for .NET Developers.
File an issue via Github Issues.
Check previous questions or ask new ones on StackOverflow using azure and .net tags.

Community

Chat with other community members

Reporting security issues and security bugs

We want your thoughts!

Feature Requests

What features are important to you? You can let us know by looking at our open feature requests and sharing your thoughts by giving the issue a thumbs up or thumbs down. (Note the list is sorted by number of thumbs up in descending order.)

Design Discussions

We would love to incorporate the community’s input into our library design process. Here’s a list of design discussions that we’re currently having. Participate in the discussions by leaving your comments in the issue!

Contributing

For details on contributing to this repository, see the contributing guide.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Azure on Producthunt daily

azure-sdk-for-python

Azure/azure-sdk-for-python

Azure SDK for Python

Getting started

Prerequisites

Packages available

Client: New Releases

Client: Previous Versions

Management: New Releases

Management: Previous Versions

Need help?

Data Collection

Telemetry Configuration

Reporting security issues and security bugs

Contributing

BloodHound

SpecterOps/BloodHound

Running BloodHound Community Edition

Useful Links

Contact

Licensing

openai-node

openai/openai-node

OpenAI TypeScript and JavaScript API Library

Installation

Installation from JSR

Usage

Streaming responses

File uploads

Webhook Verification

Parsing webhook payloads

Verifying webhook payloads directly

Handling errors

Request IDs

Realtime API Beta

Microsoft Azure OpenAI

Retries

Timeouts

Request IDs

Auto-pagination

Realtime API Beta

Microsoft Azure OpenAI

Advanced Usage

Accessing raw Response data (e.g., headers)

Logging

Log levels

Custom logger

Making custom/undocumented requests

Undocumented endpoints

Undocumented request params

Undocumented response properties

Customizing the fetch client

Fetch options

Configuring proxies

Frequently Asked Questions

Semantic versioning

Requirements

Why is this dangerous?

When might this not be dangerous?

Contributing

openai-python

openai/openai-python

OpenAI Python API library

Documentation

Installation

Usage

Vision

Async usage

With aiohttp

Streaming responses

Realtime API beta

Realtime error handling

Using types

Pagination

Nested params

File uploads

Webhook Verification

Parsing webhook payloads

Verifying webhook payloads directly

How to tell whether `None` means `null` or missing

`.with_streaming_response`