eval-ai-library 0.7.1

Comprehensive AI Model Evaluation Framework with support for multiple LLM providers

Eval AI Library

Based on firstlinesoftware/eval-ai-library. This is an independently maintained version with additional features and PyPI distribution.

Comprehensive AI model evaluation framework for RAG systems and AI agents. Supports 35+ evaluation metrics, 12 LLM providers, built-in test data generation from documents, and an interactive web dashboard for visualization and analysis. Implements advanced techniques including G-Eval probability-weighted scoring and Temperature-Controlled Verdict Aggregation via Generalized Power Mean.

Installation

pip install eval-ai-library

Full version with document parsing and OCR support:

pip install eval-ai-library[full]

Lite version (core evaluation only):

pip install eval-ai-library[lite]

Quick Start

from eval_lib import EvalAI

evaluator = EvalAI(model="gpt-4o")

result = evaluator.evaluate(
    input="What is Python?",
    actual_output="Python is a programming language.",
    expected_output="Python is a high-level programming language.",
    metrics=["answer_relevancy", "faithfulness"]
)

print(result.score)

Documentation

Full documentation is available at library.eval-ai.com.

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Citation

If you use this library in your research, please cite:

@software{eval_ai_library,
  author = {Meshkov, Aleksandr},
  title = {Eval AI Library: Comprehensive AI Model Evaluation Framework},
  year = {2025},
  url = {https://github.com/meshkovQA/Eval-ai-library.git}
}

References

This library implements techniques from:

@inproceedings{liu2023geval,
  title={G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment},
  author={Liu, Yang and Iter, Dan and Xu, Yichong and Wang, Shuohang and Xu, Ruochen and Zhu, Chenguang},
  booktitle={Proceedings of EMNLP},
  year={2023}
}

Support

• Issues: GitHub Issues
• Documentation: library.eval-ai.com