semvec/pypi-packages huggingface dataset:
wxauto 39.1.18
wxauto 3.9 V2 version
Requires Python: <=3.15,>=3.8

[![plus](https://plus.wxauto.org/images/wxauto_plus_logo3.png)](https://plus.wxauto.org)

# wxautoV2版本

**文档**：
[使用文档](https://docs.wxauto.org) |
[云服务器wxauto部署指南](https://docs.wxauto.org/other/deploy)

|  环境  | 版本 |
| :----: | :--: |
|   OS   | [![Windows](https://img.shields.io/badge/Windows-10\|11\|Server2016+-white?logo=windows&logoColor=white)](https://www.microsoft.com/)  |
|  微信  | [![Wechat](https://img.shields.io/badge/%E5%BE%AE%E4%BF%A1-3.9.X-07c160?logo=wechat&logoColor=white)](https://pan.baidu.com/s/1FvSw0Fk54GGvmQq8xSrNjA?pwd=vsmj) |
| Python | [![Python](https://img.shields.io/badge/Python-3.9\+-blue?logo=python&logoColor=white)](https://www.python.org/)|



[![Star History Chart](https://api.star-history.com/svg?repos=cluic/wxauto&type=Date)](https://star-history.com/#cluic/wxauto)


## 使用示例

### 1. 基本使用

```python
from wxauto import WeChat

# 初始化微信实例
wx = WeChat()

# 发送消息
wx.SendMsg("你好", who="张三")

# 获取当前聊天窗口消息
msgs = wx.GetAllMessage()
for msg in msgs:
    print(f"消息内容: {msg.content}, 消息类型: {msg.type}")
```

### 2. 监听消息

```python
from wxauto import WeChat
from wxauto.msgs import FriendMessage
import time

wx = WeChat()

# 消息处理函数
def on_message(msg, chat):
    text = f'[{msg.type} {msg.attr}]{chat} - {msg.content}'
    print(text)
    with open('msgs.txt', 'a', encoding='utf-8') as f:
        f.write(text + '\n')

    if msg.type in ('image', 'video'):
        print(msg.download())

    if isinstance(msg, FriendMessage):
        time.sleep(len(msg.content))
        msg.quote('收到')

    ...# 其他处理逻辑，配合Message类的各种方法，可以实现各种功能

# 添加监听，监听到的消息用on_message函数进行处理
wx.AddListenChat(nickname="张三", callback=on_message)

# ... 程序运行一段时间后 ...

# 移除监听
wx.RemoveListenChat(nickname="张三")
```
## 交流

[微信交流群](https://plus.wxauto.org/plus/#%E8%8E%B7%E5%8F%96plus)

## 最后
如果对您有帮助，希望可以帮忙点个Star，如果您正在使用这个项目，可以将右上角的 Unwatch 点为 Watching，以便在我更新或修复某些 Bug 后即使收到反馈，感谢您的支持，非常感谢！

## 免责声明
代码仅用于对UIAutomation技术的交流学习使用，禁止用于实际生产项目，请勿用于非法用途和商业用途！如因此产生任何法律纠纷，均与作者无关！
nonebot-adapter-afdian 0.1.8
爱发电适配器
Requires Python: <4,>=3.10

# nonebot-adapter-afdian

基于 NoneBot2 的爱发电适配器

## 使用

1. 安装

    ```shell
    pip install nonebot-adapter-afdian
    ```

2. 启用

    ```toml
    adapters = [
        { name = "Afdian", module_name = "nonebot.adapter.afdian" }
    ]
    ```

3. 配置

    - NoneBot2

        ```dotenv
        DRIVER=~fastapi+~httpx
        HOST=0.0.0.0
        AFDIAN_HOOK_SECRET='<Your Secret>'
        AFDIAN_BOTS='[
            {
                "user_id": "<Your User Id>",
                "token": "<Your Token>"
            }
        ]'
        ```

    - 爱发电开发者控制台

        ```shell
        http://<IP>:<PORT>/afdian/webhooks/<user_id>
        ```

        or

        ```shell
        https://<IP>:<PORT>/afdian/<Your Secret>/webhooks/<user_id>
        ```

## API

```python
from nonebot import on_notice
from nonebot.adapters.afdian import OrderNotifyEvent, Bot

test_afd = on_notice()


@test_afd.handle()
async def handle_afd(bot: Bot, event: OrderNotifyEvent):
    print("订单：" + event.data.order)

    result1 = await bot.query_order_by_page(page=1)
    print(result1)

    result2 = await bot.query_order_by_out_trade_no(
        out_trade_no="202308200000000000000000001"
        )
    print(result2)

    result3 = await bot.query_order_by_order_list(
        order_list=["202308200000000000000000000", "202308200000000000000000001"]
        )
    print(result3)

    result4 = await bot.query_sponsor(page=1)
    print(result4)

    result5 = await bot.query_sponsor(page=1, per_page=20) # 查询第一页，每页20个
    print(result5)
```

## 特别感谢

- [NoneBot2](https://github.com/nonebot/nonebot2)：开发框架。

## 贡献与支持

觉得好用可以给这个项目点个 `Star` 或者去 [爱发电](https://afdian.com/a/17TheWord) 投喂我。

有意见或者建议也欢迎提交 [Issues](https://github.com/MineGraphCN/nonebot-adapter-afdian/issues)
和 [Pull requests](https://github.com/MineGraphCN/nonebot-adapter-afdian/pulls) 。

## 开源许可

本项目使用 [MIT](./LICENSE) 作为开源许可证。
eratos-sdk 0.20.0
Python SDK for interacting with Eratos platform and services
Requires Python: >=3.7

# Python SDK to interact with the Eratos Platform.
Copyright (c) Eratos Group Pty Ltd 2019-2025.

## Installation

Run the following to bring in the package dependencies and the SDK package.

```shell
pip install .
```

If you wish to develop locally, please install with the following command instead.

> Note: Use the `[-e/--editable]` flag to symlink local changes to the installed package.

```shell
pip install .[dev]
```

If you are using zsh...

```shell
pip install '.[dev]'
```

If you wish to make wheel or sdist build you can do so with the following with the hatchling backend:
```shell
hatch build
```

## Tests

To run tests make sure you have the `dev` dependencies installed and run the following:

```shell
pytest tests/
```

`pytest` will run both the legacy `python.unittest` tests as well as the new `pytest` tests but not the other way so using `pytest` is preferred onwards from now.
The choice to adopt `pytest` has been made to make use of test fixtures to speed up development of future tests.
See [latest pytest api reference](https://docs.pytest.org/en/latest/reference/reference.html) for more info or the [getting started docs](https://docs.pytest.org/en/latest/getting-started.html).

### Integration tests

Integration tests in `tests/test_add_objects.py` will be skipped if the environment variables `ERATOS_ID` and `ERATOS_SECRET` are unset.
Add these API key values in a `.env` file in the root of the project or export as an environment variable to run the tests.
llama-index-tools-scrapegraphai 0.2.2
llama-index tools integrating ScrapegraphAI
Requires Python: <4.0,>=3.10
Keywords: scraping

# LlamaIndex Tool - Scrapegraph

This tool integrates [Scrapegraph](https://scrapegraphai.com) with LlamaIndex, providing intelligent web scraping capabilities with structured data extraction.

## Installation

```bash
pip install llama-index-tools-scrapegraph
```

## Usage

First, import and initialize the ScrapegraphToolSpec:

```python
from llama_index.tools.scrapegraph import ScrapegraphToolSpec

scrapegraph_tool = ScrapegraphToolSpec()
```

### Available Functions

The tool provides the following capabilities:

1. **Smart Scraper**

```python
from pydantic import BaseModel


# Define your schema (optional)
class ProductSchema(BaseModel):
    name: str
    price: float
    description: str


schema = [ProductSchema]

# Perform the scraping
result = scrapegraph_tool.scrapegraph_smartscraper(
    prompt="Extract product information",
    url="https://example.com/product",
    api_key="your-api-key",
    schema=schema,  # Optional
)
```

2. **Markdownify**

Convert webpage content to markdown format:

```python
markdown_content = scrapegraph_tool.scrapegraph_markdownify(
    url="https://example.com", api_key="your-api-key"
)
```

3. **Local Scrape**

Extract structured data from raw text:

```python
text = """
Your raw text content here...
"""

structured_data = scrapegraph_tool.scrapegraph_local_scrape(
    text=text, api_key="your-api-key"
)
```

## Requirements

- Python 3.8+
- `scrapegraph-py` package
- Valid Scrapegraph API key