semvec/pypi-packages huggingface dataset:
ag-llm 0.1.0a1
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Requires Python: >=3.8

# ag-llm

A Python package that integrates LLM functionality directly into your Python shell experience.

## Installation

```bash
uv install ag-llm
```

Or alternatively:

```bash
pip install ag-llm
```

## Usage

Once installed, you can use `ag` directly in your Python shell with a simple syntax:

```python
import ag
ag/"how are you"
```

This sends your query to the default LLM and streams the response directly in your terminal with beautiful markdown rendering thanks to [Streamdown](https://github.com/day50-dev/Streamdown).

## Features

- **Direct Python Integration**: Use natural Python syntax with the `/` operator
- **Streaming Responses**: Get real-time output as the model generates responses
- **Markdown Rendering**: Beautifully formatted output with code highlighting
- **IPython Integration**: Automatically hooks into IPython for seamless usage
- **Simple Setup**: Just import and start querying

## Requirements

- Python >= 3.8
- [llm](https://github.com/simonw/llm) for LLM integration
- [streamdown](https://github.com/day50-dev/Streamdown) for markdown rendering

## Configuration

The package uses the default configuration from the `llm` package for model selection. You can configure your preferred model using:

```bash
llm models default your-model-name
```

See the [LLM documentation](https://github.com/simonw/llm) for more configuration options.

## Example

```python
import ag

# Simple query
ag/"What is the capital of France?"

# More complex query
ag/"Explain quantum computing in simple terms"
```

## How It Works

When you use `ag/"your query"`, the package:

1. Sends your query to the configured LLM model via the `llm` package
2. Streams the response token by token
3. Renders the output using Streamdown for beautiful formatting
4. Handles all the complexity behind the scenes

## Development

To develop on this package:

```bash
git clone [repository-url]
cd ag-llm
uv install
```

## License

MIT
llama-index-readers-webdav 0.2.1
llama-index readers webdav integration
Requires Python: <4.0,>=3.9

# LlamaIndex Readers Integration: WebDav

## Overview

Simple WebDav reader allows to read files from a WebDav server both fully or incrementally.

## Installation

```
pip install llama-index-readers-webdav
```

## Usage

```python
from llama_index.readers.webdav import WebDAVReader
from llama_index.readers.file import (
    PDFReader,
)

# Initialize the server
parser = PDFReader()
file_extractor = {".pdf": parser}
reader = WebDAVReader(
    webdav_options={
        "webdav_hostname": "<URL>",
        "webdav_login": "<USERNAME>",
        "webdav_password": "<PASSWORD>",
    },  # pass any other webdav3 options https://pypi.org/project/webdavclient3/
    file_extractor=file_extractor,
    remote_path="/",
    folder_etag_propagated_to_root=False,  # If your webdav server propagates etag updates up to the `remote_path` folder enable it to skip the scan of folders with no updates
)

# Lazy load emails from the given mailbox
documents = reader.load_data(incremental=True)
```

---

This loader is designed to be used as a way to load data into [LlamaIndex](https://github.com/run-llama/llama_index/tree/main/llama_index).
iflow-mcp_martinpsdev_curl-mcp 0.1.0
Natural Language Curl Commander - Execute curl commands using natural language in English and Spanish
Requires Python: >=3.11

# Curl MCP - Natural Language Curl Commander

Execute curl commands using natural language in English and Spanish.

## Prerequisites

- Python 3.13 or higher
- curl (usually pre-installed on Linux)
- Git

## Installation

1. Clone the repository:
```bash
git clone https://github.com/MartinPSDev/curl-mcp.git
cd curl-mcp
```

2. Create and activate a virtual environment:
```bash
python3 -m venv .env
source .env/bin/activate
```

3. Install dependencies:
```bash
pip install -r requirements.txt
```

4. Start the MCP server:
```bash
python3 main.py
```

<img src="./demo.png" width="70%" height="70%" alt="demo">

## Configuration

Add this to your MCP settings:

```json
{
    "mcpServers": {
        "curl-mcp": {
            "command": "/usr/bin/python3",
            "args": [
                "/path/to/your/curl-mcp/main.py"
            ],
            "env": {
                "PYTHONPATH": "/path/to/your/curl-mcp/.env/lib/python3.11/site-packages"
            }
        }
    }
}
```

Note: Replace `/path/to/your/curl-mcp` with the actual path where you cloned the repository.

## Usage

1. Start the MCP server:
```bash
python3 main.py
```

2. The server can now receive natural language commands in English or Spanish. Here are some examples:

### Basic Examples:
- "Get headers from https://example.com"
- "Show raw response from https://api.example.com"
- "Download https://example.com and save as page.html"

### Advanced Examples:
- Headers and Authentication:
  - "Make a request to https://api.example.com with header Authorization: Bearer mytoken"
  - "Get https://api.example.com using basic auth user:password"

- Data Handling:
  - "POST to https://api.example.com/users with data name=John and age=25"
  - "Send form data to https://upload.example.com with file image.jpg"
  - "POST urlencoded data user=test+name to https://api.example.com"

- Security and Options:
  - "Get https://example.com ignoring SSL verification"
  - "Request https://api.example.com with timeout 30 seconds"
  - "Get https://example.com through proxy localhost:8080"

- User Agents:
  - "Get https://example.com as Chrome"
  - "Request https://mobile.example.com as iPhone"