semvec/pypi-packages huggingface dataset:
polars-distance 0.5.3
Polars plugin for pairwise distance functions
Requires Python: >=3.8

Hellooo :)

This plugin is a work-in progress, main goal is to provide distance metrics on list, arrays and string datatypes.

The docs can be found here: https://ion-elgreco.github.io/polars-distance/

## Examples

```python
import polars
import polars_distance as pld

df = pl.DataFrame({
    "foo":"hello",
    "bar":"hella world"
})

df.select(
    pld.col("foo").dist_str.hamming('bar').alias('dist')
)
┌──────┐
│ dist │
│ ---  │
│ u32  │
╞══════╡
│ 7    │
└──────┘


df.select(
    pld.col('foo').dist_str.levenshtein('bar').alias('dist')
)
┌──────┐
│ dist │
│ ---  │
│ u32  │
╞══════╡
│ 6    │
└──────┘



df = pl.DataFrame(
    {
        "arr": [[1, 2, 10]],
        "arr2": [[2, 5, 9]],
    },
    schema={
        "arr": pl.Array(inner=pl.Float64, width=3),
        "arr2": pl.Array(inner=pl.Float64, width=3),
    },
)
df.select(pld.col('arr').dist_arr.euclidean('arr2').alias('dist'))
shape: (1, 1)
┌──────────┐
│ dist     │
│ ---      │
│ f64      │
╞══════════╡
│ 3.316625 │
└──────────┘
```
mcp-metal-price 0.0.4
An MCP server for metal price information
Requires Python: >=3.13

# Metal Price MCP Server

An MCP server that provides current and historical gold/precious metal prices via the [GoldAPI.io](https://www.goldapi.io/) service.

## Features

- Get current prices for gold (XAU), silver (XAG), platinum (XPT), and palladium (XPD)
- Support for multiple currencies (USD, EUR, etc.)
- Optional historical price lookup by date

## Requirements

- Python 3.7+
- Packages:
  - `mcp>=1.0.0`
  - `requests>=2.31.0`

## Setup

1. Clone this repository
2. Install dependencies:
   ```bash
   pip install -r requirements.txt
   ```
3. Set your GoldAPI.io API key as an environment variable:
   ```bash
   export GOLDAPI_API_KEY="your_api_key_here"
   ```
   (Windows users: use `set` instead of `export`)

## Usage

The server provides one MCP tool:

## Installation

This server is designed to be installed manually by adding its configuration to the `cline_mcp_settings.json` file.

1.  Add the following entry to the `mcpServers` object in your `cline_mcp_settings.json` file:

```json
"mcp_metal_price": {
  "args": [
    "/c",
    "python",
    "-m",
    "mcp_metal_price"
  ],
  "env": {
    "GOLDAPI_API_KEY": "Your GOLDAPI_API_KEY"
  }
}
```

### get_gold_price
Get current or historical metal prices.

**Parameters:**
- `currency` (string, default: "USD"): Currency code (ISO 4217 format)
- `metal` (string, default: "XAU"): Metal symbol (XAU, XAG, XPT, XPD)
- `date` (string, optional): Historical date in YYYYMMDD format

**Example Usage:**
```json
{
  "currency": "EUR",
  "metal": "XAU"
}
```

## Running the Server

Start the server with:
```bash
python src/server.py
```

## Using with MCP Clients

Once the server is running, you can connect to it from MCP clients like Cline or Claude.

### Connecting to the Server
The server runs on stdio by default. In your MCP client, you can connect using:
```bash
cmd /c python src/server.py
```

### Using the get_gold_price Tool
Example tool usage in Cline/Claude:
```xml
<use_mcp_tool>
<server_name>gold-price</server_name>
<tool_name>get_gold_price</tool_name>
<arguments>
{
  "currency": "USD",
  "metal": "XAU"
}
</arguments>
</use_mcp_tool>
```


### Response Format
The server returns price data in JSON format:
```json
{
  "timestamp": 1713600000,
  "metal": "XAU",
  "currency": "USD",
  "price": 2345.67,
  "unit": "per troy ounce"
}
```

## License

This project is licensed under the terms of the MIT license. See [LICENSE](LICENSE) file for details.
python-quantumflow 2.0.3
Python QuantumFlow: Advanced type conversion for Python
Requires Python: >=3.7


# Python QuantumFlow

A next-generation type-and-data-flow framework for Python with a lightweight footprint.

## Overview

Python QuantumFlow is a powerful yet lightweight framework that enhances Python's data flow capabilities with:

- Automatic type conversion with `flow()` and `TypeFlowContext`
- Function decorators for creating intelligent flows with `@qflow`
- Asynchronous operations with `@async_flow`
- Robust error handling with retry logic
- Beautiful terminal output with color and styling

## Installation

```bash
pip install python-quantumflow
```

## Features Comparison

Version 1.x vs Version 2.x:

| Feature           | python-typeflow V1                  | Python QuantumFlow V2              |
|-------------------|-------------------------------------|-------------------------------------|
| Type Conversion   | Basic types only                    | Complex nested structures           |
| Error Handling    | Manual try/except                   | Automatic with @retry               |
| Async Support     | Limited                             | Full async/await with backpressure  |
| Flow Composition  | Manual chaining                     | Operator-based (>>, +, etc.)        |
| Memory Usage      | Moderate                            | Optimized with streaming support    |
| Visualization     | None                                | Interactive flow diagrams           |
| CLI Tools         | None                                | Complete development toolkit        |
| Terminal Output   | Plain text                          | Rich colors and animations          |
| Testing Support   | Minimal                             | Comprehensive mocking framework     |
| Performance       | Standard                            | Up to 3x faster                     |

## Quick Start

```python
from python_quantumflow.core import flow, qflow

# Simple type conversion
numbers = [1, 2, 3, 4, 5]
str_numbers = flow(str)(numbers)
print(str_numbers)  # "[1, 2, 3, 4, 5]"

# Function decorator for automatic flow
@qflow
def process_data(items):
    return [item * 2 for item in items]

result = process_data(numbers)
print(result)  # [2, 4, 6, 8, 10]
```

## Documentation

For detailed documentation and examples, visit:
- [GitHub Repository](https://github.com/magi8101/python-quantumflow)
- [API Reference](https://python-quantumflow.readthedocs.io/)

## Made by

Created with ❤️ by Magi Sharma