semvec/pypi-packages huggingface dataset:
django-objectid 1.0.10
django objectid
Requires Python: >=3.6

# Django-objectid

Django-objectid用于Django框架在分布式环境中高效生成不重复ID值。

### 依赖
+ django >= 2.2.0

### 安装
+ pip安装
```shell
pip install django-objectid
```
+ 源码安装
```shell
python setup.py install
```
### 发布包
```shell
# 打包
python setup.py bdist_wheel
# 上传
twine upload -u __token__ ./dist/*
```

### 使用
Django-objectid包括两个类，`ObjectID`，`ObjectidModel`。
##### class objectid.ObjectID(id=None)
生成、解析objectid。
参数id是一个长度为24的字符，如果是`None`则生成新的ObjectID实例，如果提供一个有效ID则解析这个ID为ObjectID实例。ObjectID实例有如下属性：
+ `timestamp`，ID中的时间戳，也就是ID创建的时间。 
+ `host`，ID中的主机名，是实际主机名经过哈希处理后的值。
+ `pid`，进程ID，可能与实际值不一致。
+ `count`，同一进程一秒内生成的ID序号。


```python
from objectid import ObjectID, create_objectid
# 创建ID对象
id = ObjectID()
# 获得id字符串
idstr = str(id)
# 直接创建id字符串
idstr = create_objectid()
# 解析id字符串
id = ObjectID('5fb1ee9af1d79721f0000015')
```
#### class objectid.ObjectidModel()
`ObjectidModel`是一个抽象类，不能单独使用，作为定义django模型类的基类。实例对象有如下属性和方法：
+ get_id_datetime()，返回id中的时间戳。
+ 多数据中心布署时，settings中增加配置 `DCID`，数据中心ID，默认为0。
amistock 0.6.1
Python module to interact with stock data via Django API
Requires Python: >=3.6

Để đảm bảo bạn có thể dễ dàng copy toàn bộ nội dung Markdown vào file của bạn, tôi sẽ loại bỏ các khối mã thụt đầu dòng và cung cấp nội dung dạng text gọn gàng hơn:

---

# Amistock

Amistock is a Python module that allows you to interact with stock market data through a Django-based API. It provides various functions to retrieve stock information, historical data, and more by making API calls.

## Features

- **Get stock information**: Retrieve detailed information about a stock, including its name, symbol, and other metadata.  
- **Get historical stock data**: Retrieve the stock data (OHLCV) for a given stock over a specified date range.  
- **Get stocks by market**: Fetch stocks associated with a specific market (e.g., HOSE, HNX).  
- **Get API key info**: Retrieve information about the API key, including expiration date and status.  
- **API key authentication**: Use API keys to authenticate requests for accessing stock data.  

## Installation

You can install the `amistock` module directly from PyPI using pip:

```bash
pip install amistock
```

## Usage

### Getting Stock Information

You can retrieve information about a stock by symbol using the `get_stock_info()` function:

```python
import amistock

symbol = 'AAPL'  # Example stock symbol
api_key = 'your_api_key'

stock_info = amistock.get_stock_info(symbol, api_key)
print(stock_info)
```

### Getting Historical Stock Data

To retrieve historical stock data (OHLCV) for a given stock symbol within a date range:

```python
import amistock
from datetime import datetime

symbol = 'AAPL'
api_key = 'your_api_key'
from_date = datetime(2020, 1, 1)
to_date = datetime(2021, 1, 1)

stock_data = amistock.get_stock_data(symbol, from_date, to_date, api_key)
print(stock_data)
```

### Getting Stocks by Market

You can fetch all stocks associated with a particular market:

```python
import amistock

market = 'HOSE'  # Example market
api_key = 'your_api_key'

stocks = amistock.get_stocks_by_market(market, api_key)
print(stocks)
```

### Getting API Key Info

To retrieve information about your API key, such as expiration date, status, and other details, you can use the `get_key_info()` function:

```python
import amistock

api_key = 'your_api_key'

key_info = amistock.get_key_info(api_key)
print(key_info)
```

## Authentication

Each request made to the API requires an API key for authentication. Make sure to pass a valid API key when calling any of the functions above.

## Contributing

We welcome contributions to this project! If you'd like to help, please fork the repository, make your changes, and submit a pull request. Be sure to follow the code of conduct and review the guidelines before contributing.

## License

This project is licensed under the MIT License - see the LICENSE file for details.

## Contact

If you have any questions or suggestions, feel free to reach out to us at [amistockpro@gmail.com](mailto:amistockpro@gmail.com).
linkedin-automation-mcp 0.0.2
A LinkedIn scraping and automation tool
Requires Python: >=3.10

# LinkedIn Automation MCP

A Model Context Protocol (MCP) server that provides LinkedIn automation and scraping functionality. This package allows you to automate various LinkedIn tasks including profile scraping, post creation, and connection management.

## Features

- **Scrape LinkedIn Posts**: Retrieve recent posts from specified LinkedIn profiles
- **Send LinkedIn Connection Requests**: Find profiles based on a search query and send connection requests, optionally including a personalized note
- **Get Profile Information**: Obtain basic information about specific LinkedIn profiles
- **Post Content to LinkedIn**: Create and post new content on LinkedIn on behalf of the user
- Combine all these to automate a lot of linkedin tasks together

## Components

### Prompts

The server provides several prompts for different LinkedIn automation tasks:

- `scrape_content_and_post_to_linkedin`: Scrape content and create LinkedIn posts
- `research_and_create_linkedin_post`: Research topics and generate posts
- `post_to_linkedin`: Publish content to LinkedIn
- `get_profile_info`: Extract LinkedIn profile information
- `scrape_posts`: Collect posts from LinkedIn
- `send_connection_requests`: Manage connection requests
- `generate_post`: Create engaging LinkedIn content

### Tools

The package implements various tools in the `tools` directory:
- LinkedIn automation utilities
- Browser interaction helpers
- Content generation tools
- Data models for structured information

## Installation

Requires UV (Fast Python package and project manager)

If UV isn't installed:

```bash
# Using Homebrew on macOS 
brew install uv

# On macOS and Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# On Windows
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
```

Install the package:

```bash
# Install from PyPI
uv pip install linkedin-automation-mcp

# Install from source
uv pip install git+https://github.com/shahshrey/linkedin-automation.git
```

## Claude Desktop
Add this tool as a mcp server by editing the Claude config file.

The config file location depends on your operating system:
- MacOS: `~/Library/Application Support/Claude/claude_desktop_config.json`
- Windows: `%APPDATA%/Claude/claude_desktop_config.json`

```json
{
  "linkedin-automation": {
    "command": "uv",
    "args": ["run", "linkedin-automation-mcp"],
    "env": {
      "LINKEDIN_EMAIL": "your_email@example.com",
      "LINKEDIN_PASSWORD": "your_password"
    }
  }
}
```

To verify the server is working. Open the Claude client and use a prompt like "search about RAG, write a post on it in my style and post it on my linkedin". You should see an alert box open to confirm tool usage. Click "Allow for this chat"

![alt text](image.png)
## Development

1. Clone the repository
2. Install dependencies:
```bash
uv pip install -e .
```
3. Run tests:
```bash
pytest
```

## Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request
xero-agent-toolkit 0.0.5
A Xero toolkit for AI agents
Requires Python: >=3.8

# Xero Python Agent Toolkit

* The Xero Agent Toolkit library enables agent frameworks to integrate with Xero APIs through function calling. 
* The current version showcases one scenario where a user would like to create a payment received by a contact.
* The current version works with langchain agents, but can be used or can be adjusted to work with other 
agent frameworks.
* The framework currently supports Xero custom connections.

## Installation


```sh
pip install xero-agent-toolkit
```

### Requirements

- Python 3.8+

## Usage

### 1. Authentication
The library needs to be configured with your Xero client id and client secret.

See more info [here](https://developer.xero.com/documentation/guides/oauth2/custom-connections/) on Xero custom connections. 

```python
from xero-agent-toolkit import XeroAgentToolkit

xero_agent_toolkit = XeroAgentToolkit(client_id, client_secret)
```

### 2. Getting the tools

Once you have create the XeroAgentToolkit, and passed the authentication info, 
you can grab the Xero tools to pass to the langchain agent

```python
model = ...
tools = xero_agent_toolkit.get_tools()
agent_executor = create_react_agent(model, tools, ...)
```

### 3. Initialization 

The last thing before using the agent is calling the "system_init"
method that allows the agent toolkit to update the agent's system prompt if needed

```python
xero_agent_toolkit.system_init(agent_executor, config)
```

### Example Scenario

> _Agent_: How can I help you?
>
> _User_: I received a 20$ payment from Ridgeway
> 
> _Agent_: I found two contacts with the nickname "Ridgeway":
> 1. Ridgeway Bank
> 2. Ridgeway University
>
> Please confirm which one you would like to associate the payment with.
> 
> _User_: The University
> 
> _Agent_: I found the following invoice for Ridgeway University:
>
> - Invoice Number: INV-0025 
> - Amount Due: $6187.50
> - Status: AUTHORIZED
>
> please confirm you would like to link the payment to this invoice 
>
> _User_: Yes
> 
> _Agent_: I need a numeric account code to create the payment. Please provide the account code to proceed.
> 
> _User_: 091
> 
> _Agent_: The payment of $20 has been successfully created and linked to invoice INV-0025 for Ridgeway University.
> If you need any further assistance, feel free to ask!
> 
> _User_: What is the new amount due?
> 
> _Agent_: The new amount due for invoice INV-0025 is now 
> 6167.50 after the payment of 20 was applied.