semvec/pypi-packages huggingface dataset:
model-compare 2025.12.22083425
model-compare evaluates AI models side‑by‑side based on user tasks, rating accuracy, creativity, and efficiency to guide model choice.
License: MIT
Requires Python: >=3.9

# Model Compare Package
[![PyPI version](https://badge.fury.io/py/model-compare.svg)](https://badge.fury.io/py/model-compare)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
[![Downloads](https://static.pepy.tech/badge/model-compare)](https://pepy.tech/project/model-compare)
[![LinkedIn](https://img.shields.io/badge/LinkedIn-blue)](https://www.linkedin.com/in/eugene-evstafev-716669181/)


A new package that helps users compare and evaluate different AI language models by analyzing their performance and capabilities.

## Overview

This package takes user-provided text input describing specific tasks or scenarios and returns a structured comparison of how different models, like Gemini 3 Flash and Claude Code, would handle those tasks. It focuses on providing objective, side-by-side evaluations based on criteria such as accuracy, creativity, and efficiency, helping users make informed decisions about which model to use for their specific needs.

## Installation

```bash
pip install model_compare
```

## Usage

```python
from model_compare import model_compare

response = model_compare(
    user_input="Compare Gemini 3 Flash and Claude Code on a text-to-image generation task",
    api_key="your_llm7_api_key",
    llm=None  # optional, defaults to ChatLLM7
)
```

You can also pass your own `BaseChatModel` instance, e.g., to use a different LLM like OpenAI:

```python
from langchain_openai import ChatOpenAI
from model_compare import model_compare

llm = ChatOpenAI()
response = model_compare(
    user_input="Compare Gemini 3 Flash and Claude Code on a text-to-image generation task",
    llm=llm
)
```

Or to use Anthropic:

```python
from langchain_anthropic import ChatAnthropic
from model_compare import model_compare

llm = ChatAnthropic()
response = model_compare(
    user_input="Compare Gemini 3 Flash and Claude Code on a text-to-image generation task",
    llm=llm
)
```

Or to use Google Generative AI:

```python
from langchain_google_genai import ChatGoogleGenerativeAI
from model_compare import model_compare

llm = ChatGoogleGenerativeAI()
response = model_compare(
    user_input="Compare Gemini 3 Flash and Claude Code on a text-to-image generation task",
    llm=llm
)
```

You can also pass your own API key via the environment variable `LLM7_API_KEY` or directly as an argument.

## Rate Limits

The default rate limits for LLM7 free tier are sufficient for most use cases of this package. If you need higher rate limits, you can pass your own API key via environment variable `LLM7_API_KEY` or directly as an argument.

## Getting Started

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

## Contributing

Please submit issues and pull requests to https://github.com/chigwell/model-compare

## Author

Eugene Evstafev (eugene.evstafev-plus@email.com)

## License

This package is licensed under the MIT License.
unistruct 2025.12.22083127
unistruct extracts structured data from unstructured text, aiding project planning and management by converting free-form inputs into actionable details.
License: MIT
Requires Python: >=3.9

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


Extract structured information from unstructured text inputs with ease.

Unistruct is a Python package that takes unstructured text, such as project descriptions, meeting notes, or customer feedback, and converts it into actionable data with key details like project name, duration, milestones, and resources required.

## Installation

Get started with Unistruct by installing it with pip:
```
bash
pip install unistruct
```

## Usage

Unistruct uses the ChatLLM7 from langchain_llm7 by default, but you can pass your own LL.M instance using the `llm` parameter.

```python
from unistruct import unistruct
import os

user_input = "....... 
response = unistruct(user_input)
```

## Using your own LLM instance

To use a different LLM instance, such as the OpenAI or Anthropic models, you can pass your own instance using the `llm` parameter.

```python
from langchain_openai import ChatOpenAI
from unistruct import unistruct

llm = ChatOpenAI()
response = unistruct(user_input, llm=llm)
```

Or with Anthropic:
```python
from langchain_anthropic import ChatAnthropic
from unistruct import unistruct

llm = ChatAnthropic()
response = unistruct(user_input, llm=llm)
```

Or with Google Generative AI:
```python
from langchain_google_genai import ChatGoogleGenerativeAI
from unistruct import unistruct

llm = ChatGoogleGenerativeAI()
response = unistruct(user_input, llm=llm)
```

## Environment Variables

You can set your LLM7 API key as an environment variable `LLM7_API_KEY` to use the higher rate limits for the LLM7 free tier.

```bash
export LLM7_API_KEY="your_api_key"
```

Or, you can pass the API key directly using the `api_key` parameter.

```python
response = unistruct(user_input, api_key="your_api_key")
```

## Support and Discuss

Head over to the GitHub issues page to report any issues or discuss Unistruct: https://github.com/chigwell/unistruct

## Author

Eugene Evstafev (hi@euegne.plus)

## License

Unistruct is released under the MIT License.

## Version History

Check the CHANGELOG for the latest changes.

## Thanks

Thanks to the langchain_llm7 and langchain_openai packages for the underlying LLM functionality.