semvec/pypi-packages huggingface dataset:
pimpmyrice-server 0.1.0
Server for PimpMyRice
Requires Python: >=3.12

# 🍙 PimpMyRice server

_Work in progress._

Server for [PimpMyRice](https://github.com/daddodev/pimpmyrice)

## Quick start

### Install

#### [Pipx](https://pipx.pypa.io/stable/installation/)

```bash
pipx install pimpmyrice_server
```

### Start server

```bash
pimp-server start
```

## [Documentation](https://pimpmyrice.vercel.app/docs)
injex 1.0.0
DI container for Python applications
Requires Python: >=3.10
Keywords: dependency, injection, di, container, ddd

# Injex - DI Container for Python

![Build Status](https://github.com/vshulcz/di/actions/workflows/ci.yml/badge.svg)
[![pypi](https://img.shields.io/pypi/v/injex.svg)](https://pypi.python.org/pypi/injex)
[![Python Versions](https://img.shields.io/badge/python-3.10%20|%203.11%20|%203.12%20|%203.13-blue)](https://github.com/vshulcz/injex)
[![License](https://img.shields.io/github/license/vshulcz/di.svg)](https://github.com/vshulcz/injex/LICENSE)

Injex is a lightweight and easy-to-use Dependency Injection (DI) container for Python applications. It simplifies dependency management, making your code more modular, testable, and maintainable. Injex is inspired by popular DI frameworks in other programming languages and brings similar capabilities to Python.

## Features

🌟 Simple API: Easy to understand and use.
🔄 Multiple Lifestyles: Support for singleton, transient, and scoped services.
🧩 Flexible Registrations: Register services, factories, and instances.
🏷️ Named Registrations: Register multiple implementations of the same interface using names.
🔍 Property Injection: Inject dependencies into properties after object creation.
🛠 Optional Dependencies: Handle optional dependencies gracefully.
🚀 No External Dependencies: Pure Python implementation without third-party packages.

## Installation

```bash
pip install injex
```

## Why Use Dependency Injection?

**Dependency Injection is a design pattern that helps in:**

* Modularity: Breaking down your application into interchangeable components.
* Testability: Facilitating unit testing by allowing dependencies to be mocked or stubbed.
* Maintainability: Making it easier to update, replace, or refactor components without affecting other parts of the application.
* Flexibility: Configuring different implementations of the same interface for various scenarios (e.g., testing, production).

## Quick Start

Here's a simple example of usage Injex:
```python
from abc import ABC, abstractmethod
from injex import Container

class IService(ABC):
    @abstractmethod
    def perform_action(self):
        pass

class ServiceImplementation(IService):
    def perform_action(self):
        print("Service is performing an action.")

container = Container()

container.add_transient(IService, ServiceImplementation)

service = container.resolve(IService)
service.perform_action() # output: Service is performing an action.
```
Another examples in [examples folder](./examples).

## Documentation

For detailed documentation on all functionalities, usage examples, and best practices, please refer to the [Documentation](./docs/tutorial.md).

## Contributing

Contributions are welcome! Please open an issue or submit a pull request on GitHub.
llm-forwarder 0.1.0
A configurable forwarder for OpenAI-compatible LLM requests
Requires Python: >=3.7

# LLMForwarder

`LLMForwarder` is a Python package for forwarding chat requests to a locally hosted model API (such as OpenAI-compatible models). It allows users to configure the server address, port, and OpenAI API details, as well as customize request handling through an external function. This package is ideal for injecting custom context or pre-processing user messages before forwarding to the model API.

## Features

- Configurable server address and port
- Flexible OpenAI API integration
- Customizable prompt-handling function, allowing easy injection of context or prompt modifications
- Easy setup and usage for OpenAI-compatible models

## Installation

To install `LLMForwarder`, use pip:

```bash
pip install llm-forwarder