semvec/pypi-packages huggingface dataset:
ai-bridge-cli 2.0.1
AI Bridge: A unified tool for Chat & Batch processing with Kimi, Qwen, and Gemini.
Requires Python: >=3.9

# AI Batch Processor (AIB) 🚀

**AIB (AI Bridge)** 是一个专为高并发和大规模数据处理设计的统一 AI 调用桥梁。它封装了 **Kimi (Moonshot)**, **Qwen (Aliyun)**, **Gemini (Google)** 以及 **OpenAI 兼容接口**，提供了一套简单、统一且强大的命令行工具。

无论是需要毫秒级响应的**实时对话**，还是追求极致性价比的**离线批量处理**，AIB 都能轻松搞定。

## ✨ 核心特性

*   **🔌 多厂商统一支持**: 一套代码，无缝切换 Kimi, Qwen, Gemini, OpenAI。
*   **⚡ 双模式引擎**:
    *   **Realtime Mode**: 基于 `asyncio` 的本地高并发请求，适合实时性要求高的场景。
    *   **Offline Batch Mode**: 利用厂商 (Qwen/Gemini) 原生 Batch API，**成本降低 50%**，适合大规模离线任务。
*   **🛡️ 工业级稳定性**:
    *   自动重试 (Exponential Backoff) 与错误处理。
    *   智能速率限制 (Rate Limit) 保护。
    *   **断点续传**: 自动跳过已处理文件，防止重复扣费。
    *   **任务持久化**: Batch 任务状态自动保存到本地，重启不丢失。
*   **📂 智能文件处理**:
    *   支持 PDF, 图片, 视频等多模态输入。
    *   自动进行 Base64 编码或文件上传。
    *   大文件内存熔断保护。

## 📦 安装

## 📦 安装

### 从 PyPI 安装 (推荐)

```bash
# 安装
pip install ai-bridge-cli

# 升级到最新版
pip install --upgrade ai-bridge-cli
```

### 源码安装 (开发)

```bash
uv sync
```

## ⚙️ 配置

AIB 支持 **YAML 配置文件** 和 **环境变量** 两种配置方式。

### 1. 配置文件 (推荐)
在用户目录创建 `~/.aibatch/config.yaml` 或在当前目录创建 `config.yaml`：

```yaml
# 默认使用的厂商
default_vendor: "kimi"

# 默认并发数 (Realtime 模式)
concurrency: 5

# 系统配置
system:
  max_retries: 3

# 厂商凭证
vendors:
  kimi:
    api_key: "sk-xxxxxxxx"
    model: "kimi-latest"
  qwen:
    api_key: "sk-xxxxxxxx"
    model: "qwen-plus"
  gemini:
    api_key: "AIzaSy..."
    model: "gemini-1.5-flash"
  openai:
    api_key: "sk-xxxxxxxx"
    base_url: "https://api.openai.com/v1"
    model: "gpt-4o"
```

### 2. 环境变量
你也可以通过环境变量直接设置 API Key，优先级高于配置文件：
*   `AIBATCH_KIMI_API_KEY`
*   `AIBATCH_QWEN_API_KEY`
*   `AIBATCH_GEMINI_API_KEY`
*   `AIBATCH_OPENAI_API_KEY`

## 🚀 使用指南

### 1. 命令行基础

```bash
# 查看帮助
ab --help
```

### 2. 实时模式 (Realtime)
适合日常对话或少量文件处理。

```bash
# 简单对话
ab -v kimi -p "你好，介绍一下你自己"

# 处理单个文件
ab -v qwen -i ./document.pdf -p "总结这篇文章"

# 批量处理目录下的所有文件 (并发)
ab -v gemini -i "./data/*.txt" -p "提取其中的关键信息" --json
```

### 3. 离线批量模式 (Offline Batch) 💰
适合处理成千上万个文件，价格通常减半，但需要等待数小时。
**目前支持: Qwen, Gemini**

```bash
# 提交批量任务
ab -v qwen --mode offline -i "./large_dataset/*.json" -p "进行情感分析"

# 检查任务状态 (支持断点查询)
ab --check-batch
```

### 4. 常用参数

| 参数 | 简写 | 说明 |
| :--- | :--- | :--- |
| `--vendor` | `-v` | 指定厂商 (kimi, qwen, gemini, openai) |
| `--model` | `-m` | 指定模型名称 (覆盖配置) |
| `--input` | `-i` | 输入文件路径 (支持通配符 `*`) |
| `--prompt` | `-p` | 提示词 |
| `--json` | `-j` | 强制输出 JSON 格式 |
| `--mode` | | `realtime` (默认) 或 `offline` |
| `--check-batch` | | 检查所有后台 Batch 任务的状态 |

## 📝 许可证

MIT License
ethicalsuite 1.0.0
Ethical Hacking Toolkit with Port Scanner, Fingerprinting, CVE Finder
Requires Python: >=3.8

Use the app in an ethical manner and NEVER use it to hurt anyone
The app has -
[1] Port Scanner                         
[2] Service Fingerprinter                
[3] Automatic Exploit Finder (CVE + PoC) 
[0] Exit
All these features are easy to run and the app is fast
    ____________  _______________    __       _____ __  __________________
   / ____/_  __/ / / /  _/ ____/   |  / /      / ___// / / /  _/_  __/ ____/
  / __/   / / / /_/ // // /   / /| | / /       \__ \/ / / // /  / / / __/   
 / /___  / / / __  // // /___/ ___ |/ /___    ___/ / /_/ // /  / / / /___   
/_____/ /_/ /_/ /_/___/\____/_/  |_/_____/   /____/\____/___/ /_/ /_____/   
                                                                            

 Ethical Hacking Toolkit v1.0
 Made by Czax foundation
                             Thanks for Using 
                              -Czax foundation
zxcvbn-rs-py 0.3.0
Python bindings for zxcvbn-rs, the Rust implementation of zxcvbn
Requires Python: >=3.10

# zxcvbn-rs-py

<p align="center">
    <em>Python bindings for <a href="https://github.com/shssoichiro/zxcvbn-rs">zxcvbn-rs</a>, the Rust implementation of zxcvbn</em>
</p>

[![build](https://github.com/fief-dev/zxcvbn-rs-py/workflows/CI/badge.svg)](https://github.com/fief-dev/zxcvbn-rs-py/actions)
[![PyPI version](https://badge.fury.io/py/zxcvbn-rs-py.svg)](https://badge.fury.io/py/zxcvbn-rs-py)

---

**Documentation**: <a href="https://fief-dev.github.io/zxcvbn-rs-py/" target="_blank">https://fief-dev.github.io/zxcvbn-rs-py/</a>

**Source Code**: <a href="https://github.com/fief-dev/zxcvbn-rs-py" target="_blank">https://github.com/fief-dev/zxcvbn-rs-py</a>

---

## Installation

```sh
pip install zxcvbn-rs-py
```

## Quickstart

```py
from zxcvbn_rs_py import zxcvbn

r = zxcvbn("correcthorsebatterystaple")
print(r.score)
```

## Benchmark

Thanks to its Rust core, zxcvbn-rs-py is **~5 times faster** than the pure Python implementation, [zxcvbn-python](https://github.com/dwolfhub/zxcvbn-python).

![zxcvbn-rs-py benchmark](https://raw.githubusercontent.com/fief-dev/zxcvbn-rs-py/main/benchmark/benchmark.svg?sanitize=true)

## Serve the documentation

You can serve the Mkdocs documentation with:

```bash
hatch run docs-serve
```

It'll automatically watch for changes in your code.

## License

This project is licensed under the terms of the MIT license.
cmost 0.1.4
A processing tool for FITS files related to LAMOST
Requires Python: >=3.10


<img src="docs/ico.png" width="90" height="90" align="left" />

# CMOST: A Python Toolkit for LAMOST Astronomical Data
![Scrutinizer build (GitHub/Bitbucket)](https://img.shields.io/scrutinizer/build/g/YunyuG/Cmost)
![Static Badge](https://img.shields.io/pypi/pyversions/cmost)
[![PyPI Version](https://img.shields.io/pypi/v/cmost?color=blue)](https://pypi.org/project/cmost/)
![Codecov](https://img.shields.io/codecov/c/github/YunyuG/Cmost)
![GitHub License](https://img.shields.io/github/license/YunyuG/Cmost)



CMOST is a Python toolkit specifically designed for processing FITS files from LAMOST astronomical telescope observations. At its core, it's a secondary encapsulation of `astropy`, offering efficient and user-friendly data parsing and basic analysis capabilities.

### Key Features
- 🚀 Simple API interface `read_fits`, making FITS file handling as effortless as using `pandas` to read Excel files.
- 🔍 line index calculation algorithms.
- 📊 Basic spectral preprocessing methods.
- 🌐 FTP API for downloading official LAMOST FITS files with the support of multithreading.


# Install
You can use `pip` to install Cmost.
```bash
pip3 install cmost
```

# Read the fits file
The goal of `Cmost` is to simplify the process of reading FITS files. You can achieve this with minimal code, as shown below:
```python
import cmost as cst
import numpy as np

fits_data = cst.read_fits("path/to/fits")

wavelength = fits_data['Wavelength']
flux = fits_data['Flux']

# You can visit the info of header directly like this.

redshift = fits_data['z']
```

# Download
In fact,`pylamost` has already implemented this method,and we just copy it to our lib and created a functional interface of the multithreading to reduce the time spent on downloading.
```python
import cmost as cst
import numpy as np

obsids:list = ['100**',...] 

fits_downloader = cst.FitsDownloader("dr10","v2.0",TOKEN="*****")

for o in obsids:
    fits_downloader.download_fits(o)

# Using MultThread
fits_downloader.download_fits_use_MultiThreading(obsids)

```

# LickIndices
LickIndices is one of the important features to be used in machine learning.We also implement it.
```python
lick_indices = cst.lick.compute_LickLineIndices(test_fits_data)

print(lick_indices) # It is a pd.Series if pandas is installed, otherwise it is a dict.
 ```

We do not guarantee that the calculation results are 100% correct. If you find any defects or issues, please contact us. We would greatly appreciate it.