llm-data-lens 0.1.4

A Python package for calculating key metrics to assess LLM performance in various tasks, including extracting structured dataa.

LLMDataLens

LLMDataLens is a powerful and flexible framework for evaluating LLM-based applications with structured output. It provides a comprehensive suite of tools for assessing the performance of language models across various metrics, with a focus on experiment tracking and reproducibility.

🌟 Features

• Structured Output Evaluation: Assess LLM outputs against ground truth data with precision.
• Customizable Metrics: Easily define and use custom metrics for comprehensive performance assessment.
• Experiment Tracking: Built-in experiment management for reproducibility and comparison.
• Prompt Versioning: Keep track of prompt evolution and its impact on model performance.
• Model Version Tracking: Monitor performance across different model versions.
• Flexible Integration: Seamlessly integrate with existing LLM pipelines and workflows.
• Extensible Architecture: Add custom metrics, evaluators, and experiment trackers with ease.

🚀 Quick Start

Installation

Install LLMDataLens directly from PyPI:

pip install llm-data-lens

For development or to get the latest version from the repository:

1. Clone the repository:

   git clone https://github.com/codingmindset/LLMDataLens.git
   cd llmdatalens
   

2. Install the package using Poetry:

   poetry install
   

Basic Usage

Here's a simple example to get you started:

from llmdatalens.evaluators import StructuredOutputEvaluator
from llmdatalens.core import LLMOutputData, GroundTruthData
from llmdatalens.core.metrics_registry import MetricNames

# Create an evaluator with specific metrics
evaluator = StructuredOutputEvaluator(
    metrics=[MetricNames.OverallAccuracy, MetricNames.AverageLatency],
    experiment_name="Invoice Processing Experiment"
)

# Add LLM output and ground truth data
llm_output = LLMOutputData(
    raw_output="Processed invoice: $100",
    structured_output={"invoice_amount": 100},
    metadata={
        "model_info": {"name": "GPT-3.5", "version": "1.0"},
        "prompt_info": {"text": "Extract invoice amount:"}
    }
)
ground_truth = GroundTruthData(
    data={"invoice_amount": 100}
)

evaluator.add_llm_output(llm_output, latency=0.5, confidence=0.9)
evaluator.add_ground_truth(ground_truth)

# Evaluate
result = evaluator.evaluate()

# Print results
print(result.metrics)

# Access experiment data
experiment = evaluator.experiment_manager.get_experiment(evaluator.experiment_id)
print(f"Experiment: {experiment.name}")
print(f"Number of runs: {len(experiment.runs)}")
print(f"Prompts used: {len(experiment.prompts)}")
print(f"Models used: {list(experiment.models.keys())}")

📊 Advanced Features

Custom Metrics

Create and register custom metrics easily:

from llmdatalens.core.metrics_registry import register_metric
from llmdatalens.core.enums import MetricField

@register_metric("CustomF1Score", field=MetricField.Accuracy, input_keys=["y_true", "y_pred"])
def calculate_custom_f1_score(y_true, y_pred):
    """ This description will be shown in the metrics registry """
    # (Your custom F1 score calculation here
    pass

Experiment Tracking

Track experiments, prompts, and model versions:

# Get prompt history
prompt_history = evaluator.experiment_manager.get_prompt_history(evaluator.experiment_id)

# Get model history
model_history = evaluator.experiment_manager.get_model_history(evaluator.experiment_id)

# Compare runs
for run in experiment.runs:
    print(f"Run {run.id}: {run.metrics}")

For more detailed examples, check the examples/ directory in the repository. (More examples will be added soon!)

📘 Documentation

(Comming soon!)

🛠️ Project Structure

llmdatalens/
├── src/
│   └── llmdatalens/
│       ├── core/
│       │   ├── base_model.py
│       │   ├── enums.py
│       │   └── metrics_registry.py
│       ├── evaluators/
│       │   └── structured_output_evaluator.py
│       └── experiment/
│           ├── experiment_manager.py
│           └── models.py
├── tests/
│   ├── test_core/
│   ├── test_evaluators/
│   └── test_experiment/
├── examples/
├── docs/
├── pyproject.toml
└── README.md

🤝 Contributing

We welcome contributions to LLMDataLens! Here's how you can help:

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

Please read our Contributing Guidelines for more details.

📄 License

LLMDataLens is released under the MIT License. See the LICENSE file for details.

📬 Contact

If you have any questions, suggestions, or just want to say hi, feel free to reach out:

• Email: [e-mail]
• X: @codingmindset
• GitHub Issues: For bug reports and feature requests

🙏 Acknowledgements

• Thanks to all our contributors and users for their valuable feedback and support.
• Special thanks to the open-source community for the amazing tools and libraries that made this project possible.

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Built with ❤️ by Coding Mindset

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Citing LLMDataLens

If you use LLMDataLens in your research, please cite it as follows:

@software{llmdatalens,
  title = {LLMDataLens: A Framework for Evaluating LLM-based Applications},
  author = {Elvin Gomez},
  year = {2024},
  url = {https://github.com/codingmindset/LLMDataLens.git},
}