semvec/pypi-packages huggingface dataset:
iflow-mcp_howe829-websearch-mcp-server 0.1.3
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Requires Python: >=3.11

# WebSearch MCP Server

A simple web search server that supports free search and converts URL content to Markdown. [中文](README-zh.md)

## Features

-   **Web Search:** Perform web searches using different providers. Supported providers: Bing, Baidu, Google(with browser_use).
-   **Markdown Conversion:** Convert the content of a URL into Markdown format.
-   **Wechat Official Account Articles:** Search and retrive wechat official account articles content.
-   **GitHub Search:** Easily search for anything on GitHub — repositories, users, issues, and more.

## Getting Started

### Prerequisites

-   Python 3.12 or higher
-   uv for dependency management (or pip)

### Installation

1.  Clone the repository:

    ```bash
    git clone https://github.com/Howe829/websearch-mcp-server.git
    cd websearch-mcp-server
    ```

2.  Install the dependencies using uv (Recomend):

    ```bash
    uv venv && uv sync
    ```

    Or, if you prefer using pip:

    ```bash
    pip install -r requirements.txt
    ```

### Configuration

1.  Create a `.env` file based on the `.env.example` file:

    ```bash
    cp .env.example .env
    ```

2.  Modify the `.env` file with your desired settings:

    ```
    BING_SEARCH_BASE_URL="https://www.bing.com"
    LANGUAGE="en"
    CC="us"
    IMPERSONATE="edge"
    HOST=127.0.0.1
    PORT=8002
    SERVER_MODE=streamable-http
    ```

### Usage

Run the server using uv:

```bash
uv run python server.py
```

Or run the server using python:

```bash
source .venv/bin/activate
python server.py
```

## Contribution
Contributions are welcome!

This project uses pytest for unit tests
```bash
uv pip install pytest
uv pip install pytest-asyncio
uv run pytest
```

And use the ruff for code sytle formatting
```bash
uv pip install ruff
ruff format .
```

use mcp inpector to debug this server
```bash
uv run fastmcp dev server.py
```
aws-annoying 0.8.4
Utils to handle some annoying AWS tasks.
Requires Python: <4.0,>=3.9

# aws-annoying

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![codecov](https://codecov.io/gh/lasuillard-s/aws-annoying/graph/badge.svg?token=gbcHMVVz2k)](https://codecov.io/gh/lasuillard-s/aws-annoying)
[![PyPI - Version](https://img.shields.io/pypi/v/aws-annoying)](https://pypi.org/project/aws-annoying/)

Utils to handle some annoying AWS tasks.

## ❓ About

This project aims to provide a set of utilities and examples to help with some annoying tasks when working with AWS.

Major directories of the project:

- **aws_annoying** Python package containing CLI and utility functions.
- **console** Utilities to help working with AWS Console.
- **examples** Examples of how to use the package.
- **devcontainer-features** Devcontainer features to help set up development environment.

## 🚀 Installation

It is recommended to use [pipx](https://pipx.pypa.io/stable/) to install `aws-annoying` CLI:

```bash
$ pipx install aws-annoying
$ aws-annoying --help

 Usage: aws-annoying [OPTIONS] COMMAND [ARGS]...

...
```

As the package also provides some utility functions, you can install `aws-annoying` via pip, if you are going to use those utils.

## 💡 Usage

Refer to the CLI help or documentation for detailed information on how to use each command.

## 💖 Contributing

Any feedback, suggestions or contributions are welcome! Feel free to open an issue or a pull request.
docling-metrics-core 0.1.0
Metric types and evaluation utilities for Docling document processing
Requires Python: <4.0,>=3.10
Keywords: docling, evaluation, metrics, types

# docling-metrics-core

A minimal interface for computing and aggregating metrics on paired data samples.

## Overview

This package provides base types for building metrics that:
- Evaluate pairs of input samples (e.g., ground-truth vs. prediction)
- Produce per-sample results traceable by sample ID
- Aggregate results across multiple samples

## Core Types

- **`BaseInputSample`** — Input data with a unique `id` shared between sample pairs
- **`BaseSampleResult`** — Output of evaluating a single sample pair
- **`BaseAggregateResult`** — Output of aggregating multiple sample results
- **`BaseMetric`** — Abstract interface defining `evaluate_sample`, `aggregate`, and `evaluate_dataset`

## Installation

```bash
pip install docling-metrics-core
```

## Requirements

- Python >=3.10,<4.0

## Usage

*Coming soon*

## Development

```bash
# Install in development mode
uv sync

# Run tests
uv run pytest
```

## License

MIT
py-geodetector 0.2.1
A simple Python package for the geodetector
Requires Python: >=3.7

# py-geodetector

A simple and efficient Python package for the **Geographical Detector** (GeoDetector).

## Features

- **Four Detectors**: Factor, Interaction, Risk, and Ecological detectors.
- **High Performance**: Vectorized calculations using Pandas and NumPy for large-scale datasets.
- **Flexible API**: Supports both batch processing and interactive exploratory analysis.
- **Auto Detection**: Automatically identifies discrete/categorical variables as factors.
- **Visualization**: Built-in heatmap for interaction results with statistical significance markers.

## Install

```bash
pip install py-geodetector
```

## Usage

### 1. Data Preparation
- **Format**: pandas DataFrame.
- **Y (Dependent Variable)**: Numerical/Continuous.
- **X (Independent Variable)**: **Categorical/Discrete**. If your $X$ is continuous, you must discretize it first (e.g., using `pd.qcut` or Jenks natural breaks).

### 2. Quick Start

```python
from geodetector import load_example_data, GeoDetector

# Load example disease dataset
df = load_example_data()

# Initialize: Automatically detects discrete columns as factors if 'factors' is not provided
gd = GeoDetector(df, y='incidence')
print(f"Detected factors: {gd.factors}")

# 1. Factor Detector
# Batch detection for all factors
factor_df = gd.factor_detector()
# Single factor detection: returns (q_value, p_value)
q, p = gd.factor_detector('type')

# 2. Interaction Detector
# Full matrix calculation
interaction_df = gd.interaction_detector()
# Pairwise detection with relationship description
q_inter, relationship = gd.interaction_detector('type', 'region', relationship=True)

# 3. Ecological Detector
# Determine if the impact of two factors are significantly different
eco_df = gd.ecological_detector()

# 4. Risk Detector
# Compare average Y between sub-groups of a factor
risk_result = gd.risk_detector('type')
print(risk_result['risk']) # Mean values for each stratum

# 5. Visualization
# Plot interaction heatmap (red markers indicate significant ecological difference)
gd.plot(factors=['type', 'region', 'level'])
```

### 3. Visualization Result
The `plot()` method generates a heatmap of the interaction $q$-statistics. Red text indicates that the ecological detector shows a significant difference ($p < 0.05$) between those factors.

![](./example_result.png)

## References

```bibtex
@article{wang2010geographical,
  title={Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun Region, China},
  author={Wang, Jin-Feng and Li, Xin-Hu and Christakos, George and Liao, Yi-Lan and Zhang, Tin and Gu, Xue and Zheng, Xiao-Ying},
  journal={International Journal of Geographical Information Science},
  volume={24},
  number={1},
  pages={107-127},
  year={2010},
  publisher={Taylor \& Francis}
}
```