semvec/pypi-packages huggingface dataset:
EthosNet 2025.12.19194014
This new package aims to analyze and summarize text snippets related to the ethical and social implications of large language models (LLMs) in the open-source ecosystem. It processes user-inputted tex
License: MIT
Requires Python: >=3.9

# EthosNet

## Overview

EthosNet is a Python package designed to analyze and summarize text snippets related to the ethical and social implications of large language models (LLMs) in the open-source ecosystem. It processes user-inputted textual discussions or excerpts to identify key themes, potential issues, and generate structured insights about the impact of AI scraping practices on community trust and the social contract within open source. This tool helps researchers, developers, and policymakers quickly grasp the core concerns and possible consequences without analyzing raw documents, focusing instead on pre-extracted content.

## Features

- **Text Analysis**: Processes user-inputted textual discussions or excerpts.
- **Theme Identification**: Identifies key themes and potential issues.
- **Structured Insights**: Generates structured insights about the impact of AI scraping practices.
- **Community Trust**: Analyzes the impact on community trust and the social contract within open source.
- **Ease of Use**: Provides a quick overview of core concerns and possible consequences.

## Installation

To install EthosNet, use pip:

```bash
pip install EthosNet
```

## Usage

Here is an example of how to use EthosNet in your Python code:

```python
# Import and invoke the main function to process user input
from EthosNet import EthosNet

# Example user input
user_input = "Your text snippet or discussion here."

# Call the EthosNet function
results = EthosNet(user_input)

# Print the results
print(results)
```

### Parameters

- `user_input` (str): The text snippet or discussion to be analyzed.
- `api_key` (Optional[str]): The API key for the language model. If not provided, it will attempt to use the environment variable `LLM7_API_KEY`.
- `llm` (Optional[BaseChatModel]): An optional language model instance to use for the analysis.

### Returns

- `List[str]`: A list of structured insights and extracted data from the input text.

## Example

```python
from EthosNet import EthosNet

# Example user input
user_input = """
Large language models (LLMs) are transforming the open-source ecosystem by enabling
advanced text generation and understanding capabilities. However, these models
raise concerns about data privacy, ethical use, and the potential for misuse.
Community trust is crucial for the sustainable growth of open-source projects.
"""

# Call the EthosNet function
results = EthosNet(user_input)

# Print the results
print(results)
```

## Dependencies

- `llmatch_messages`
- `langchain_core`
- `langchain_llm7`

## Contributing

Contributions are welcome! Please open an issue or submit a pull request on GitHub.

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.

## Contact

For any questions or feedback, please contact [your-email@example.com](mailto:your-email@example.com).