semvec/pypi-packages huggingface dataset:
fumus 1.1.2
Functional musings
Requires Python: <4.0,>=3.12
Keywords: query, functional, processing, collections, fluent API, queries

<p align="center">
  <img src="https://github.com/kaliv0/fumus/blob/main/assets/fumus.gif?raw=true" width="400" alt="Fumus">
</p>

# FUMUS
## Functional Musings in Python


![Python 3.x](https://img.shields.io/badge/python-3.12-blue?style=flat-square&logo=Python&logoColor=white)
[![tests](https://img.shields.io/github/actions/workflow/status/kaliv0/fumus/ci.yml)](https://github.com/kaliv0/fumus/actions/workflows/ci.yml)
[![PyPI](https://img.shields.io/pypi/v/fumus.svg)](https://pypi.org/project/fumus/)
[![docs](https://readthedocs.org/projects/fumus/badge/?version=latest)](https://fumus.readthedocs.io/en/latest)
[![Downloads](https://static.pepy.tech/badge/fumus)](https://pepy.tech/projects/fumus)

<br><b>Yet another <i>functional-style</i> Queries library for processing collections and iterables using <i>fluent APIs</i></b><br>
universal-log 0.1.1
Universal logger that adds console.log, System.out.println, and more globally to Python.
Requires Python: >=3.6

# ðŸ”Š Universal Logger for Python

Write logs in Python using styles from other programming languages like JavaScript, Java, C#, Bash, and more â€” **without needing imports**.

---

## âœ¨ Supported Global Functions

- `console.log()` â€” JavaScript
- `System.out.println()` â€” Java
- `Console.WriteLine()` â€” C#
- `echo()`, `sh_echo()` â€” Bash / Shell / PHP
- `puts()` â€” Ruby
- `fmt.Println()` â€” Go
- `println_rust()` â€” Rust-style

---

## ðŸ§ª Example

```python
console.log("Hello JS style")
System.out.println("Java style")
Console.WriteLine("C# style")
echo("Echo")
puts("Puts")
fmt.Println("Go style")
python-interpreter-mcp 0.1.0
A Model Context Server providing simple features to automate python script execution via LLMs
Requires Python: >=3.13
Keywords: automation, llm, mcp, python

## python-interpreter-mcp: A MCP server to run scripts

## Overview
A lightweight, experimental MCP server designed to execute arbitrary Python scripts in a structured and reproducible environment. It leverages **[uv](https://github.com/astral-sh/uv)** to run isolated code snippets through subprocesses.


### Tools
- `run_script`
   - Runs the given script with `uv run script.py`.
   - Input: 
     - `code`(str): Script to be run
   - Return: The stdout of the given script.

## Configuration
### Usage with OpenAI Agents SDK

```python
async with MCPServerStdio(
            params={
                "command": "uvx",
                "args": ["python-interpreter-mcp"],
            }
    ) as server
...
```

### Usage with Claude Desktop
Add this to your `claude_desktop_config.json`:
```json
"mcpServers": {
  "interpreter": {
    "command": "uvx",
    "args": ["python-interpreter-mcp"]
  }
}
```

## How it works

- A script string is received by the MCP tool `run_script`.
- A hidden folder is created in cwd, and the script is saved as a `.py` file inside it.
- The script is then executed using `uv run`, which ensures dependency isolation.
- The stdout of the script is captured and returned as the response.

## Usage Warnings
**This project is in a very early stage of development.**

### ⚠️ Important notes
- It executes arbitrary Python code, which means it can run anything — including malicious or destructive commands.
- Use only in trusted, sandboxed environments.
- You should always validate, guardrail, or restrict inputs when wiring this into an LLM.
intergen 0.1.0
INTERGEN: Interactive Neural Generalization Framework

# INTERGEN: Interactive Neural Generalization

INTERGEN is a novel ensemble learning framework that allows multiple neural networks to interact and synthesize their weights based on performance metrics like RÂ².

## Features
- Neural-to-neural interaction
- RÂ²-based weight fusion
- Visualization support (heatmap, violin plot, bar plot)
- Modular architecture

## Installation

## Usage
Check out `demo.py` for an example.
matrix-transcriber 0.0.1
A Matrix bot that transcribes audio messages
Requires Python: <3.11,>=3.9

# matrix-transcriber

This Matrix bot listens to audio messages and answers with a transcript of their content.

# Installation

The bot uses [Whisper](https://github.com/openai/whisper#setup) to transcribe audio files, which requires `ffmpeg` to be installed.

# Configuration

The following environment variables are used for configuration:

```sh
# Which Whisper model should be used?
WHISPER_MODEL=base

# Which Matrix server should be used to log in to?
MATRIX_SERVER=https://matrix.example.org

# What Matrix account shall the bot use?
MATRIX_USER=@transcription:example.org

# What it its password?
MATRIX_PASSWORD=<Password>

# Which directory should the bot use to store its state in?
STATE_DIR=/var/lib/matrix-transcriber

# Who is allowed to invite the bot into new rooms?
INVITERS="@me:example.org
@friend1:example.org
@friend2:example.com"
# Every invitation of someone else will be declined.
```

# Usage

Execute `main.py` and invite the bot into a room. If someone sends an audio message into the room, it will be transcribed. (If not, check what the script wrote on its output and open an issue)
mcp-server-rag-MiniLM 0.0.1
MCP SAP Commerce RAG server
Requires Python: >=3.10

# MCP Server RAG

RAG (Retrieval-Augmented Generation) server for MCP (Model Control Protocol). This server provides vector-based document retrieval functionality to enhance LLM interactions with contextual information.

## Overview

The MCP Server RAG implementation provides a bridge between your LLM applications and document collections, offering:

- Vector-based document search using ChromaDB and Sentence Transformers
- Multiple specialized collections (liv, ken, ufa, sap commerce)
- Strongly typed API responses
- Integration with the MCP server infrastructure

## Installation

```bash
pip install mcp-server-rag-MiniLM
```

## Usage

After installation, you can run the server with:

```bash
mcp-server-rag-MiniLM
```

Or using Python module syntax:

```bash
python -m mcp-server-rag-MiniLM
```

## Features

- Vector-based search for contextual document retrieval
- Persistent storage of document embeddings via ChromaDB
- Multiple collection support for domain-specific searches
- Configurable via environment variables
- Fully typed response objects with detailed metadata
- Seamless integration with the MCP protocol

## Available Collections

The server provides access to four specialized collections:

- `liv-rag`: LIV document collection (`retrieve_liv_context` tool)
- `ken-rag`: Kennametal document collection (`retrieve_ken_context` tool)
- `ufa-rag`: UFA document collection (`retrieve_ufa_context` tool)
- `sap-comm-rag`: SAP Commerce document collection (`retrieve_sap_comm_context` tool)

## Configuration

The following environment variables can be used to configure the server:

| Variable              | Description                                       | Default                                                                     |
| --------------------- | ------------------------------------------------- | --------------------------------------------------------------------------- |
| `RAG_PERSIST_DIR`     | Directory where ChromaDB will store its data      | `~/Documents/chroma_db`                                                     |
| `RAG_EMBEDDING_MODEL` | Path or name of the Sentence Transformer model    | `~/LLM/huggingface/hub/models--sentence-transformers--all-MiniLM-L6-v2/...` |
| `RAG_N_RESULTS`       | Default number of results to return from searches | `5`                                                                         |

## Dependencies

- click
- mcp (version 1.2.0 or higher)
- chromadb
- sentence-transformers

## Development

### Building and Publishing

The repo includes a `publish.sh` script that helps with building and publishing the package.

```bash
./publish.sh
```

This script will clean previous build artifacts, install build dependencies, build the package, and check it before providing instructions for publishing to PyPI.

## License

MIT