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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

1
2

# 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:

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# 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

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.

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

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 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). The .unwrap() method will parse this JSON for you into an event object after verifying the webhook was sent from OpenAI.

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:

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import openai
print(openai.__version__)

Requirements

Python 3.8 or higher.

Contributing

See the contributing documentation.

telegram-search

Wed, 28 May 2025 15:29:34 +0800

groupultra/telegram-search

Telegram Search

English | 快速开始

一个功能强大的 Telegram 聊天记录搜索工具，支持向量搜索和语义匹配。基于 OpenAI 的语义向量技术，让你的 Telegram 消息检索更智能、更精准。

欢迎 PR 贡献！
由于项目处于快速迭代阶段，可能会出现数据库不兼容的情况，建议定期备份数据。

💖 赞助者

🚀 快速开始

安装步骤

克隆仓库：

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git clone https://github.com/GramSearch/telegram-search.git
cd telegram-search

安装依赖：

`1`	`pnpm install`

配置环境：

`1`	`cp config/config.example.yaml config/config.yaml`

启动数据库容器：

`1`	`docker compose up -d`

同步数据库表结构：

`1`	`pnpm run db:migrate`

启动服务：

# 启动后端服务
pnpm run dev:server

# 启动前端界面
pnpm run dev:frontend

访问 http://localhost:3333 即可打开搜索界面。

🚀 Activity

📝 License

PyWxDump

Sat, 17 May 2025 15:27:26 +0800

xaoyaoo/PyWxDump

PyWxDump

Welcome to provide more ideas or code to improve this project together.

If you are a novice, please pay attention to the Official Accounts: `逍遥之芯` (the QR code is below), and reply: `PyWxDump` to get a picture text tutorial.

If you have any questions, please check first: FAQ Whether there is an answer, or follow the Official Accounts to reply: `FAQ`.

QQ GROUP：276392799 or 276392799（PASSWORD,please read:UserGuide.md）.

I. Project Introduction

1. Brief Introduction

PyWxDump is a tool for obtaining wx account information (nicknames/accounts/phones/emails/database keys), decrypting databases, viewing wx chat, and exporting chat as html backups.

Super eager for stars, if you’ve come across this project, please give me a ! Thank you so much~

2. Feature

2.1 Core

(1) Get the base address offset of WeChat nickname, WeChat account, WeChat phone number, WeChat email, and WeChat KEY
(2) Get the WeChat nickname, WeChat account, WeChat phone number, WeChat email, WeChat KEY, WeChat original ID (wxid_******), and WeChat folder path of the currently logged-in WeChat
(3) Decrypt WeChat database based on key
(4) Combine multiple types of databases for unified viewing

2.2 Extend Function

(1) View chat history through the web
(2) Support exporting chat logs as html, csv, and backing up WeChat chat logs
(3) Remote viewing of WeChat chat history (must be network accessible, such as a local area network)

2.3 Document Class

(1) Provide descriptions of some fields in the database
(2) Provide CE to obtain the base address offset method
(3) Provide a decryption method for MAC database

2.4 Other functions

(1) Added a minimalist version of pywxdumpmini, which provides only the ability to obtain database keys and database locations
(2) Support multiple WeChat opening scenarios, obtain multiple user information, etc.

Utilize the scene

Network security…
Daily backup archiving
View chat history remotely (view chat history through the web)
Wait……………

3. Update plan

1.Analyze chat logs of each person and generate word clouds.
~~2.Analyze the number of chats per person per day and generate a line chart (day-number of chats)~~
~~3.Analyze the monthly and annual chat volume of different people and generate a line chart~~
~~4.Generate annual visualization reports~~
8.Increase support for enterprise WeChat
12.Viewing and backing up of the circle of friends
13.Clean up WeChat storage space and reduce the space occupied by WeChat (hopefully by selecting a person or group and finding out the media files involved in the chat logs of this group, such as pictures, videos, files, voice recordings, etc., and selectively (such as time periods) or batch-wise clearing them from the computer’s cache by group conversation.)
14.Automatically send messages to specified people through UI control

4. Other

PyWxDump is a refactored python language version of SharpWxDump, with many new features added.

Project address: https://github.com/xaoyaoo/PyWxDump
Currently tested only under Windows, there may be issues under mac and Linux.
If you find any missing or incorrect information, bugs, or suggestions for improvement in the WX_OFFS.json, please submit an issue on GitHub.
For common issues, please refer to FAQ, and for the update log, please refer to CHANGELOG
Web UI repository location wxdump_web
If you are interested in the implementation principle of wxdump, please pay attention to the Official Accounts: 逍遥之芯, reply: 原理 to get the principle analysis.
:sparkling_heart: Support Me

5. Star History

click to expand

Ⅱ. Instructions For Use

Detailed instructions, see: UserGuide.md
the minimalist version, see: pywxdumpmini
If you want to modify the UI, clone the wx_dump_web and modify it as needed (the UI is developed using VUE+ElementUI)

【note】:

For obtaining the base address using cheat engine, refer to CE obtaining base address.md (This method can be replaced by the wxdump bias command, and is only used for learning principles.)
For database parsing, refer to wx database brief.md

Ⅲ. Disclaimer (VERY VERY VERY IMPORTANT ! ! ! ! ! !)

1. Purpose of use

This project is only for learning and communication purposes, please do not use it for illegal purposes, please do not use it for illegal purposes, please do not use it for illegal purposes, otherwise the consequences will be borne by yourself.
Users understand and agree that any violation of laws and regulations, infringement of the legitimate rights and interests of others, is unrelated to this project and its developers, and the consequences are borne by the user themselves.

2. Usage Period

You should delete the source code and (compiled) program of this project within 24 hours of downloading, saving, compiling, and using it; any use beyond this period is not related to this project or its developer.

3. Operation specifications

This project only allows backup and viewing of the database under authorization. It is strictly prohibited for illegal purposes, otherwise all related responsibilities will be borne by the user. Any legal liability incurred by the user due to violation of this regulation will be borne by the user, and is unrelated to this project and its developer.
It is strictly prohibited to use it to steal others’ privacy. Otherwise, all relevant responsibilities shall be borne by yourself.
It is strictly prohibited to conduct secondary development, otherwise all related responsibilities shall be borne by yourself.

4. Acceptance of Disclaimer

Downloading, saving, further browsing the source code, or downloading, installing, compiling, and using this program indicates that you agree with this warning and promise to abide by it;

5. Forbidden for illegal testing or penetration

It is prohibited to use the relevant technologies of this project to engage in illegal testing or penetration, and it is prohibited to use the relevant codes or related technologies of this project to engage in any illegal work. Any adverse consequences arising therefrom are not related to this project and its developers.
Any resulting adverse consequences, including but not limited to data leakage, system failure, and privacy infringement, are not related to this project or its developers and are the responsibility of the user.

6. Modification of disclaimer

This disclaimer may be modified and adjusted based on the project’s operating conditions and changes in laws and regulations. Users should regularly check this page for the latest version of the disclaimer, and should comply with the latest version of the disclaimer when using this project.

7. Others

In addition to the provisions of this disclaimer, users should comply with relevant laws, regulations, and ethical norms during the use of this project. The project and its developers will not be held responsible for any disputes or losses caused by users’ violation of relevant regulations.
Users are requested to carefully read and understand all contents of this disclaimer, and ensure that they strictly comply with relevant regulations when using this project.

Ⅳ. Acknowledgments

UI CONTRIBUTORS:

otherContributors:

643104191 (add ctypes_utils, Accelerated the acquisition of wxinfo; 9e3e4cb)

Chat on Producthunt daily

openai-python

openai/openai-python

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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

Handling errors

Request IDs

Retries

Timeouts

Advanced

Logging

How to tell whether None means null or missing

Accessing raw response data (e.g. headers)

.with_streaming_response

Making custom/undocumented requests

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💖 赞助者

🚀 快速开始

安装步骤

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📝 License

PyWxDump

xaoyaoo/PyWxDump

PyWxDump

If you are a novice, please pay attention to the Official Accounts: 逍遥之芯 (the QR code is below), and reply: PyWxDump to get a picture text tutorial.

If you have any questions, please check first: FAQ Whether there is an answer, or follow the Official Accounts to reply: FAQ.

I. Project Introduction

1. Brief Introduction

2. Feature

2.1 Core

2.2 Extend Function

2.3 Document Class

2.4 Other functions

3. Update plan

4. Other

5. Star History

Ⅱ. Instructions For Use

Ⅲ. Disclaimer (VERY VERY VERY IMPORTANT ! ! ! ! ! !)

1. Purpose of use

2. Usage Period

3. Operation specifications

4. Acceptance of Disclaimer

5. Forbidden for illegal testing or penetration

6. Modification of disclaimer

7. Others

Ⅳ. Acknowledgments

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

`.with_streaming_response`

If you are a novice, please pay attention to the Official Accounts: `逍遥之芯` (the QR code is below), and reply: `PyWxDump` to get a picture text tutorial.

If you have any questions, please check first: FAQ Whether there is an answer, or follow the Official Accounts to reply: `FAQ`.