semvec/pypi-packages huggingface dataset:
viur-scriptor-api 1.13.0
This is the internal Scriptor library that the viur-scriptor utilizes. The API includes components for networking as well as parts related to the Scriptor's files.
Requires Python: >=3.10

<div align="center">
    <img src="https://github.com/viur-framework/viur-artwork/raw/main/icons/icon-scriptor.svg" height="196" alt="A hexagonal logo of Scriptor" title="Scriptor logo">
    <h1>viur-scriptor-api</h1>
    <a href="https://pypi.org/project/viur-scriptor-api/">
        <img alt="Badge showing current PyPI version" title="PyPI" src="https://img.shields.io/pypi/v/viur-scriptor-api">
    </a>
    <a href="https://viur-scriptor-api.readthedocs.io/en/latest/?badge=latest">
        <img src="https://readthedocs.org/projects/viur-scriptor-api/badge/?version=latest" alt="Documentation Status">
    </a>
    <a href="LICENSE">
        <img src="https://img.shields.io/github/license/viur-framework/viur-scriptor-api" alt="Badge displaying the license" title="License badge">
    </a>
    <br>
    Universal Python API for scripting and interacting with ViUR.
</div>

## About

This is the official Scriptor API, which is used by the viur-scriptor in both the web version and the terminal version.

## Usage

Implements the `viur.scriptor`-namespace.

You can use it either in the Pyodide-based scriptor as well as in native Python using `from viur.scriptor import ...`.

## Documentation

- [viur-scriptor-api.readthedocs.io](https://viur-scriptor-api.readthedocs.io/en/latest/)

## License

Copyright © 2025 by Mausbrand Informationssysteme GmbH.<br>
Mausbrand and ViUR are registered trademarks of Mausbrand Informationssysteme GmbH.

viur-scriptor-api is free software under the MIT license.<br>
Please see the LICENSE file for details.
flet-sherpa-onnx 0.2.2
FletSherpaOnnx control for Flet
Requires Python: >=3.12

# flet-sherpa-onnx

[![zread](https://img.shields.io/badge/Ask_Zread-_.svg?style=flat&color=00b0aa&labelColor=000000&logo=data%3Aimage%2Fsvg%2Bxml%3Bbase64%2CPHN2ZyB3aWR0aD0iMTYiIGhlaWdodD0iMTYiIHZpZXdCb3g9IjAgMCAxNiAxNiIgZmlsbD0ibm9uZSIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KPHBhdGggZD0iTTQuOTYxNTYgMS42MDAxSDIuMjQxNTZDMS44ODgxIDEuNjAwMSAxLjYwMTU2IDEuODg2NjQgMS42MDE1NiAyLjI0MDFWNC45NjAxQzEuNjAxNTYgNS4zMTM1NiAxLjg4ODEgNS42MDAxIDIuMjQxNTYgNS42MDAxSDQuOTYxNTZDNS4zMTUwMiA1LjYwMDEgNS42MDE1NiA1LjMxMzU2IDUuNjAxNTYgNC45NjAxVjIuMjQwMUM1LjYwMTU2IDEuODg2NjQgNS4zMTUwMiAxLjYwMDEgNC45NjE1NiAxLjYwMDFaIiBmaWxsPSIjZmZmIi8%2BCjxwYXRoIGQ9Ik00Ljk2MTU2IDEwLjM5OTlIMi4yNDE1NkMxLjg4ODEgMTAuMzk5OSAxLjYwMTU2IDEwLjY4NjQgMS42MDE1NiAxMS4wMzk5VjEzLjc1OTlDMS42MDE1NiAxNC4xMTM0IDEuODg4MSAxNC4zOTk5IDIuMjQxNTYgMTQuMzk5OUg0Ljk2MTU2QzUuMzE1MDIgMTQuMzk5OSA1LjYwMTU2IDE0LjExMzQgNS42MDE1NiAxMy43NTk5VjExLjAzOTlDNS42MDE1NiAxMC42ODY0IDUuMzE1MDIgMTAuMzk5OSA0Ljk2MTU2IDEwLjM5OTlaIiBmaWxsPSIjZmZmIi8%2BCjxwYXRoIGQ9Ik0xMy43NTg0IDEuNjAwMUgxMS4wMzg0QzEwLjY4NSAxLjYwMDEgMTAuMzk4NCAxLjg4NjY0IDEwLjM5ODQgMi4yNDAxVjQuOTYwMUMxMC4zOTg0IDUuMzEzNTYgMTAuNjg1IDUuNjAwMSAxMS4wMzg0IDUuNjAwMUgxMy43NTg0QzE0LjExMTkgNS42MDAxIDE0LjM5ODQgNS4zMTM1NiAxNC4zOTg0IDQuOTYwMVYyLjI0MDFDMTQuMzk4NCAxLjg4NjY0IDE0LjExMTkgMS42MDAxIDEzLjc1ODQgMS42MDAxWiIgZmlsbD0iI2ZmZiIvPgo8cGF0aCBkPSJNNCAxMkwxMiA0TDQgMTJaIiBmaWxsPSIjZmZmIi8%2BCjxwYXRoIGQ9Ik00IDEyTDEyIDQiIHN0cm9rZT0iI2ZmZiIgc3Ryb2tlLXdpZHRoPSIxLjUiIHN0cm9rZS1saW5lY2FwPSJyb3VuZCIvPgo8L3N2Zz4K&logoColor=ffffff)](https://zread.ai/SamYuan1990/flet_sherpa_onnx)
[![Release Multi-Platform Build](https://github.com/SamYuan1990/flet_sherpa_onnx/actions/workflows/release.yml/badge.svg)](https://github.com/SamYuan1990/flet_sherpa_onnx/actions/workflows/release.yml)
[![Publish Python 🐍 distribution 📦 to PyPI and TestPyPI](https://github.com/SamYuan1990/flet_sherpa_onnx/actions/workflows/pip.yml/badge.svg)](https://github.com/SamYuan1990/flet_sherpa_onnx/actions/workflows/pip.yml)

## An ASR/STT library for flet basing on sherpa-onnx

### Release log

0.2.2 Bug Fix for vad stopping.
0.2.1 UX change in python code, clean code in dart. Better version system.
0.0.2 Support for vad + whisper and senseVoice, for real time ASR.

todo in 0.3.x
- [ ] onlinestreaming model(extend scope of model, worth a release)

todo in 0.4.x
- [ ] event support(need testing with flet, UX change worth a release)
- [ ] declarative example(waiting for flet, UX change worth a release)

## Design pattern

- To reduce cognitive load, all parameter set to default as possible.
- Platform support 1st, parakeet-tdt with 0.6 large.... for a little model, just limit language support. Hence, Small Model Multi-lanuage Support, unless new issue been created.

## Installation

[pypi](https://pypi.org/project/flet-sherpa-onnx/)

Add dependency to `pyproject.toml` of your Flet app:

```
dependencies = [
  "flet-sherpa-onnx==0.2.1",
  "flet>=0.28.3",
]
```

Build your app, please ref
[CI](.github/workflows/release.yml).

## Usage [Show me the code](examples/flet_sherpa_onnx_example/src/main.py)
llama-index-tools-serpex 0.1.0
LlamaIndex tools integration for SERPEX - Multi-engine web search API
Requires Python: <4.0,>=3.9
Keywords: ai, bing, brave, duckduckgo, google, llm, search, serpex, web search

# LlamaIndex Tools Integration: SERPEX

This tool allows you to use SERPEX API to search the web and get real-time results from multiple search engines within your LlamaIndex application.

## Installation

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

## Usage

```python
from llama_index.tools.serpex import SerpexToolSpec
from llama_index.agent.openai import OpenAIAgent

# Initialize the tool
serpex_tool = SerpexToolSpec(api_key="your_serpex_api_key")

# Create agent with the tool
agent = OpenAIAgent.from_tools(serpex_tool.to_tool_list(), verbose=True)

# Use the agent
response = agent.chat("What are the latest AI developments?")
print(response)
```

### Advanced Usage

```python
# Use specific search engine
serpex_tool = SerpexToolSpec(
    api_key="your_api_key",
    engine="google",  # or 'bing', 'duckduckgo', 'brave', etc.
)

# Search with time filter
results = serpex_tool.search(
    "recent AI news",
    num_results=10,
    time_range="day",  # 'day', 'week', 'month', 'year'
)

# Use different engines for different queries
results = serpex_tool.search(
    "privacy tools", engine="duckduckgo", num_results=5
)
```

## API Key

Get your API key from [SERPEX Dashboard](https://serpex.dev/dashboard).

Set as environment variable:

```bash
export SERPEX_API_KEY=your_api_key
```

## Features

- **Multiple Search Engines**: Auto-routing, Google, Bing, DuckDuckGo, Brave, Yahoo, Yandex
- **Real-time Results**: Get up-to-date search results via API
- **Time Filtering**: Filter by day, week, month, or year
- **Fast & Reliable**: 99.9% uptime SLA with global proxy network
- **Structured Data**: Clean JSON responses optimized for AI applications
- **Cost Effective**: Only 1 credit per request, failed requests free

## Search Engines

- `auto` - Automatically routes to the best available engine (default)
- `google` - Google Search
- `bing` - Microsoft Bing
- `duckduckgo` - Privacy-focused DuckDuckGo
- `brave` - Brave Search
- `yahoo` - Yahoo Search
- `yandex` - Yandex Search

## Links

- [SERPEX Website](https://serpex.dev)
- [SERPEX Documentation](https://serpex.dev/docs)
- [SERPEX Dashboard](https://serpex.dev/dashboard)
- [LlamaIndex](https://llamaindex.ai)
panta-rhei 0.7.2
Miscellaneous fenics/dolfin utilities that are used in various projects.
Requires Python: >=3.10

# Panta Rhei
A collection of utilities for fluid flow modelling using FeNICS.

Note: This package was originally Pantarei, but was renamed to Panta Rhei in a hurry to avoid conflicts with a package of the same name on PyPI. For backwards compatibility,  it is still imported as `pantarei`, but this will be changed in the future.

## Dependencies
The package depends on [FEniCS](https://fenicsproject.org/download/) and [SVMTK](https://github.com/SVMTK/SVMTK), which is not installed by default, since they are not available on PyPI. They are most easily installed using conda, by running the following commands: 
```bash
conda create -n fenicsproject -c conda-forge/label/fenics-dev  -c conda-forge fenics SVMTK h5py
conda activate fenicsproject
```
`h5py` should also be installed, or else there might be conflicts between the version of `h5py` that is installed with `fenics` and the one that is installed by `meshio` (listed as a pip-installable dependency).

## Installation
The panta-rhei package itself may be installed using `pip`,
```bash
pip install panta-rhei
```
or by cloning the repository and running the following command in the root directory:
```bash
pip install -e .
```
## Running tests
Tests depends on the Pixi, a python package manager tightly integrated with conda. It can be installed by running
```bash
curl -fsSL https://pixi.sh/install.sh | bash
```
The tests can be run by running the following command in the root directory:
```bash
pixi run test
```
For more information on Pixi see [https://pixi.sh/](https://pixi.sh/), or [this guide by Eric Ma](https://ericmjl.github.io/blog/2024/8/16/its-time-to-try-out-pixi).
mcp-chenzhi 0.0.9
辰知MCP工具
Requires Python: >=3.10

# mcp-chenzhi

辰知MCP工具 (Chenzhi MCP Tool)

这是一个基于 [MCP](https://github.com/agi-merge/mcp) 框架的工具服务器，提供了查询天气和计算加法的功能。

## 功能

- **查询天气**: 通过 `query_weather` 工具，输入城市名称获取实时天气信息。
- **计算加法**: 通过 `add` 工具，计算两个整数的和。

## 安装

1.  克隆本项目到本地:
    ```bash
    git clone https://github.com/chris666-sys/mcp-chenzhi.git
    cd mcp-chenzhi
    ```

2.  安装依赖:
    建议在虚拟环境中使用 pip 安装项目依赖。
    ```bash
    pip install .
    ```
    或者
    ```bash
    pip install -r requirements.txt 
    ```
    (如果项目提供了 `requirements.txt` 文件)

## 使用

### 方式一：通过 uvx 运行（推荐）⭐

**uvx** 是最简单的使用方式，无需手动管理虚拟环境：

```bash
# 直接运行（uvx 会自动下载和管理依赖）
uvx mcp-chenzhi
```

**安装 uv 工具：**
```bash
# macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# 或使用 pip
pip install uv

# 或使用 Homebrew (macOS)
brew install uv
```

### 方式二：传统安装运行

```bash
# 安装
pip install mcp-chenzhi

# 运行
mcp-chenzhi
```

## MCP 客户端配置

### Claude Desktop 配置

编辑配置文件 `~/Library/Application Support/Claude/claude_desktop_config.json`：

```json
{
    "mcpServers": {
        "mcp-chenzhi": {
            "command": "uvx",
            "args": ["mcp-chenzhi"]
        }
    }
}
```

### Cline/Continue 配置

在项目的 `.vscode/settings.json` 或 MCP 配置文件中添加：

```json
{
    "mcpServers": {
        "weather-server": {
            "command": "uvx",
            "args": ["mcp-chenzhi"],
            "env": {}
        }
    }
}
```

配置完成后，重启客户端，MCP Agent 就可以连接到此服务器并使用注册的工具。