# GAICo: GenAI Results Comparator

_GenAI Results Comparator, GAICo, is a Python library_ to help compare, analyze and visualize outputs from Large Language Models (LLMs), often against a reference text. In doing so, one can use a range of extensible metrics from the literature.

View the documentation at [ai4society.github.io/projects/GenAIResultsComparator](https://ai4society.github.io/projects/GenAIResultsComparator).

## Quick Start

GAICo makes it easy to evaluate and compare LLM outputs. For detailed, runnable examples, please refer to our Jupyter Notebooks in the [`examples/`](examples/) folder:

- [`quickstart.ipynb`](https://github.com/ai4society/GenAIResultsComparator/blob/main/examples/quickstart.ipynb): Rapid hands-on with the _Experiment_ sub-module.
- [`example-1.ipynb`](https://github.com/ai4society/GenAIResultsComparator/blob/main/examples/example-1.ipynb): For fine-grained usage, this notebook focuses on comparing **multiple model outputs** using a **single metric**.
- [`example-2.ipynb`](https://github.com/ai4society/GenAIResultsComparator/blob/main/examples/example-2.ipynb): For fine-grained usage, this notebook demonstrates evaluating a **single model output** across **all available metrics**.

## Streamlined Workflow with _`Experiment`_

For a more integrated approach to comparing multiple models, applying thresholds, generating plots, and creating CSV reports, the `Experiment` class offers a convenient abstraction.

### Quick Example

This example demonstrates comparing multiple LLM responses against a reference answer using specified metrics, generating a plot, and outputting a CSV report.

```python
from gaico import Experiment

# Sample data from https://arxiv.org/abs/2504.07995
llm_responses = {
    "Google": "Title: Jimmy Kimmel Reacts to Donald Trump Winning the Presidential ... Snippet: Nov 6, 2024 ...",
    "Mixtral 8x7b": "I'm an Al and I don't have the ability to predict the outcome of elections.",
    "SafeChat": "Sorry, I am designed not to answer such a question.",
}
reference_answer = "Sorry, I am unable to answer such a question as it is not appropriate."

# 1. Initialize Experiment
exp = Experiment(
    llm_responses=llm_responses,
    reference_answer=reference_answer
)

# 2. Compare models using specific metrics
#   This will calculate scores for 'Jaccard' and 'ROUGE',
#   generate a plot (e.g., radar plot for multiple metrics/models),
#   and save a CSV report.
results_df = exp.compare(
    metrics=['Jaccard', 'ROUGE'],  # Specify metrics, or None for all defaults
    plot=True,
    output_csv_path="experiment_report.csv",
    custom_thresholds={"Jaccard": 0.6, "ROUGE_rouge1": 0.35} # Optional: override default thresholds
)

# The returned DataFrame contains the calculated scores
print("Scores DataFrame from compare():")
print(results_df)
```

This abstraction streamlines common evaluation tasks, while still allowing access to the underlying metric classes and dataframes for more advanced or customized use cases. More details in [`examples/quickstart.ipynb`](examples/quickstart.ipynb).

However, you might prefer to use the individual metric classes directly for more granular control or if you want to implement custom metrics. See the remaining notebooks in the [`examples`](examples) subdirectory.

<p align="center">
  <img src="https://raw.githubusercontent.com/ai4society/GenAIResultsComparator/refs/heads/main/examples/data/examples/example_2.png" alt="Sample Radar Chart showing multiple metrics for a single LLM" width="450"/>
  <br/><em>Example Radar Chart generated by the <code>examples/example-2.ipynb</code> notebook.</em>
</p>

## Description

At the core, the library provides a set of metrics for evaluating text strings given as inputs and produce outputs on a scale of 0 to 1 (normalized), where 1 indicates a perfect match between the two texts. These scores are use to analyze LLM outputs as well as visualize.

**_Class Structure:_** All metrics are implemented as classes, and they can be easily extended to add new metrics. The metrics start with the `BaseMetric` class under the `gaico/base.py` file.

Each metric class inherits from this base class and is implemented with **just one required method**: `calculate()`.

This `calculate()` method takes two parameters:

- `generated_texts`: Either a string or a Iterables of strings representing the texts generated by an LLM.
- `reference_texts`: Either a string or a Iterables of strings representing the expected or reference texts.

If the inputs are Iterables (lists, Numpy arrays, etc.), then the method assumes that there exists a one-to-one mapping between the generated texts and reference texts, meaning that the first generated text corresponds to the first reference text, and so on.

**_Note:_** While the library can be used to compare strings, it's main purpose is to aid with comparing results from various LLMs.

**_Inspiration_** for the library and evaluation metrics was taken from [Microsoft's
article on evaluating LLM-generated content](https://learn.microsoft.com/en-us/ai/playbook/technology-guidance/generative-ai/working-with-llms/evaluation/list-of-eval-metrics). In the article, Microsoft describes 3 categories of evaluation metrics: **(1)** Reference-based metrics, **(2)** Reference-free metrics, and **(3)** LLM-based metrics. _The library currently supports reference-based metrics._

<p align="center">
  <img src="https://raw.githubusercontent.com/ai4society/GenAIResultsComparator/refs/heads/main/gaico.drawio.png" alt="GAICo Overview">
</p>
<p align="center">
  <em>Overview of the workflow supported by the <i>GAICo</i> library</em>
</p>

## Table of Contents

- [Features](#features)
- [Installation](#installation)
- [Project Structure](#project-structure)
- [Development](#development)
- [Contributing](#contributing)
- [Citation](#citation)
- [Acknowledgments](#acknowledgments)
- [License](#license)
- [Contact](#contact)

## Features

- Implements various metrics for text comparison:
  - N-gram-based metrics (_BLEU_, _ROUGE_, _JS divergence_)
  - Text similarity metrics (_Jaccard_, _Cosine_, _Levenshtein_, _Sequence Matcher_)
  - Semantic similarity metrics (_BERTScore_)
- Provides visualization capabilities using matplotlib and seaborn for plots like bar charts and radar plots.
- Allows exportation of results to CSV files, including scores and threshold pass/fail status.
- Provides streamlined `Experiment` class for easy comparison of multiple models, applying thresholds, plotting, and reporting.
- Supports batch processing for efficient computation.
- Optimized for different input types (lists, numpy arrays, pandas Series).
- Has extendable architecture for easy addition of new metrics.
- Supports automated testing of metrics using [Pytest](https://docs.pytest.org/en/stable/).

## Installation

You can install GAICo directly from PyPI using pip:

```shell
pip install gaico
```

The default installation includes core metrics and some visualization modules which are lightweight. For optional features:

- To include the **BERTScore** metric (which has larger dependencies like PyTorch):

  ```shell
  pip install gaico[bertscore]
  ```

- To include the **CosineSimilarity** metric (requires scikit-learn):

  ```shell
  pip install gaico[cosine]
  ```

- To include the **JSDivergence** metric (requires SciPy):

  ```shell
  pip install gaico[jsd]
  ```

- To install with **all optional features**:
  ```shell
  pip install gaico[bertscore,cosine,jsd]
  ```

### Installation Size Comparison

The following table provides an _estimated_ overview of the relative disk space impact of different installation options. Actual sizes may vary depending on your operating system, Python version, and existing packages. These are primarily to illustrate the relative impact of optional dependencies.

_Note:_ Core dependencies include: `levenshtein`, `matplotlib`, `numpy`, `pandas`, `rouge-score`, and `seaborn`.

| Installation Command                      | Dependencies                                                 | Estimated Total Size Impact |
| ----------------------------------------- | ------------------------------------------------------------ | --------------------------- |
| `pip install gaico`                       | Core                                                         | 210 MB                      |
| `pip install gaico[jsd]`                  | Core + `scipy`                                               | 310 MB                      |
| `pip install gaico[cosine]`               | Core + `scikit-learn`                                        | 360 MB                      |
| `pip install gaico[bertscore]`            | Core + `bert-score` (includes `torch`, `transformers`, etc.) | 800 MB                      |
| `pip install gaico[bertscore,cosine,jsd]` | Core + all dependencies from above                           | 950 MB                      |

### For Developers (Installing from source)

If you want to contribute to GAICo or install it from source for development:

1. Clone the repository:

   ```shell
   git clone https://github.com/ai4society/GenAIResultsComparator.git
   cd GenAIResultsComparator
   ```

2. Set up a virtual environment and install dependencies:

   We recommend using [UV](https://docs.astral.sh/uv/#installation) for managing environments and dependencies.

   ```shell
   # Create a virtual environment (e.g., Python 3.12 recommended)
   uv venv
   # Activate the environment
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   # Install the package in editable mode with all development dependencies (includes all optional features)
   uv pip install -e ".[dev]"
   ```

   _If you don't want to use `uv`,_ you can install the dependencies with the following commands:

   ```shell
   # Create a virtual environment (e.g., Python 3.12 recommended)
   python3 -m venv .venv
   # Activate the environment
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   # Install the package in editable mode with development and visualization extras
   pip install -e ".[dev]"
   ```

   _(Note: The `dev` extra installs GAICo with all its optional features like `visualization` and `bertscore`, plus dependencies for testing, linting, building, and documentation.)_

3. Set up pre-commit hooks (optional but recommended for contributors):

   ```shell
   pre-commit install
   ```

## Project Structure

The project structure is as follows:

```shell
.
├── README.md
├── LICENSE
├── .gitignore
├── uv.lock
├── pyproject.toml
├── .pre-commit-config.yaml
├── gaico/        # Contains the library code
├── examples/     # Contains example scripts
├── tests/        # Contains test scripts
└── docs/         # Contains documentation files

```

### Code Style

We use `pre-commit` hooks to maintain code quality and consistency. The configuration for these hooks is in the `.pre-commit-config.yaml` file. These hooks run automatically on `git commit`, but you can also run them manually:

```
pre-commit run --all-files
```

## Running Tests

Navigate to the project root in your terminal and run:

```bash
uv run pytest
```

Or, for more verbose output:

```bash
uv run pytest -v
```

To skip the slow BERTScore tests:

```bash
uv run pytest -m "not bertscore"
```

To run only the slow BERTScore tests:

```bash
uv run pytest -m bertscore
```

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (`git checkout -b feature/FeatureName`)
3. Commit your changes (`git commit -m 'Add some FeatureName'`)
4. Push to the branch (`git push origin feature/FeatureName`)
5. Open a Pull Request

Please ensure that your code passes all tests and adheres to our code style guidelines (enforced by pre-commit hooks) before submitting a pull request.

## Citation

If you find this project useful, please consider citing it in your work:

```bibtex
@software{AI4Society_GAICo_GenAI_Results,
  author = {{Nitin Gupta, Pallav Koppisetti, Biplav Srivastava}},
  license = {MIT},
  title = {{GAICo: GenAI Results Comparator}},
  year = {2025},
  url = {https://github.com/ai4society/GenAIResultsComparator}
}
```

## Acknowledgments

- The library is developed by [Nitin Gupta](https://github.com/g-nitin), [Pallav Koppisetti](https://github.com/pallavkoppisetti), and [Biplav Srivastava](https://github.com/biplav-s). Members of [AI4Society](https://ai4society.github.io) contributed to this tool as part of ongoing discussions. Major contributors are credited.
- This library uses several open-source packages including NLTK, scikit-learn, and others. Special thanks to the creators and maintainers of the implemented metrics.

## License

This project is licensed under the MIT License - see the [LICENSE](https://github.com/ai4society/GenAIResultsComparator/blob/main/LICENSE) file for details.

## Contact

If you have any questions, feel free to reach out to us at [ai4societyteam@gmail.com](mailto:ai4societyteam@gmail.com).
