mag-safe-finder 2025.12.21232119
mag-safe-finder recommends the perfect MagSafe cable based on your inputs, highlighting compatibility, pros/cons, and care tips.
License: MIT
Requires Python: >=3.9

# Mag-Safe-Finder
[![PyPI version](https://badge.fury.io/py/mag-safe-finder.svg)](https://badge.fury.io/py/mag-safe-finder)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
[![Downloads](https://static.pepy.tech/badge/mag-safe-finder)](https://pepy.tech/project/mag-safe-finder)
[![LinkedIn](https://img.shields.io/badge/LinkedIn-blue)](https://www.linkedin.com/in/eugene-evstafev-716669181/)


A Python package that helps users find the perfect replacement for their lost or damaged MagSafe cable. The package processes user input to generate a structured list of recommended MagSafe cables, highlighting key features, compatibility, and potential drawbacks. It also provides troubleshooting tips and maintenance advice.

## Installation

You can install the package via pip:

```bash
pip install mag_safe_finder
```

## Usage

Here's a basic example of how to use the package:

```python
from mag_safe_finder import mag_safe_finder

user_input = "I need a durable MagSafe cable for my MacBook Pro that won't fray easily."
response = mag_safe_finder(user_input=user_input)
print(response)
```

### Using a Custom LLM

By default, the package uses `ChatLLM7` from `langchain_llm7` (see [PyPI](https://pypi.org/project/langchain-llm7/)). However, you can pass your own LangChain-compatible LLM instance (based on [LangChain documentation](https://docs.langchain.com/docs/components/llms/)):

#### Using OpenAI
```python
from langchain_openai import ChatOpenAI
from mag_safe_finder import mag_safe_finder

llm = ChatOpenAI()
response = mag_safe_finder(user_input="Your input here", llm=llm)
```

#### Using Anthropic
```python
from langchain_anthropic import ChatAnthropic
from mag_safe_finder import mag_safe_finder

llm = ChatAnthropic()
response = mag_safe_finder(user_input="Your input here", llm=llm)
```

#### Using Google Generative AI
```python
from langchain_google_genai import ChatGoogleGenerativeAI
from mag_safe_finder import mag_safe_finder

llm = ChatGoogleGenerativeAI()
response = mag_safe_finder(user_input="Your input here", llm=llm)
```

## Parameters

- `user_input` (str): The user input text to process.
- `llm` (Optional[BaseChatModel]): The LangChain LLM instance to use. If not provided, the default `ChatLLM7` will be used.
- `api_key` (Optional[str]): The API key for LLM7. If not provided, the package will try to use the `LLM7_API_KEY` environment variable.

## API Key for LLM7

The default rate limits for LLM7 free tier are sufficient for most use cases. If you need higher rate limits, you can:

1. Set the `LLM7_API_KEY` environment variable, or
2. Pass the API key directly: `mag_safe_finder(..., api_key="your_api_key")`

You can get a free API key by registering at [https://token.llm7.io/](https://token.llm7.io/).

## Issues

If you encounter any issues or have feature requests, please open an issue on [GitHub](https://github.com/chigwell/mag-safe-finder/issues).

## Author

- **Eugene Evstafev** - [hi@euegne.plus](mailto:hi@euegne.plus)
- GitHub: [chigwell](https://github.com/chigwell)