novaeval 0.3.3

A comprehensive, open-source LLM evaluation framework for testing and benchmarking AI models

NovaEval by Noveum.ai

A comprehensive, extensible AI model evaluation framework designed for production use. NovaEval provides a unified interface for evaluating language models across various datasets, metrics, and deployment scenarios.

🚧 Development Status

⚠️ ACTIVE DEVELOPMENT - NOT PRODUCTION READY

NovaEval is currently in active development and not recommended for production use. We are actively working on improving stability, adding features, and expanding test coverage. APIs may change without notice.

We're looking for contributors! See the Contributing section below for ways to help.

🤝 We Need Your Help!

NovaEval is an open-source project that thrives on community contributions. Whether you're a seasoned developer or just getting started, there are many ways to contribute:

🎯 High-Priority Contribution Areas

We're actively looking for contributors in these key areas:

• 🧪 Unit Tests: Help us improve our test coverage (currently 23% overall, 90%+ for core modules)
• 📚 Examples: Create real-world evaluation examples and use cases
• 📝 Guides & Notebooks: Write evaluation guides and interactive Jupyter notebooks
• 📖 Documentation: Improve API documentation and user guides
• 🔍 RAG Metrics: Add more metrics specifically for Retrieval-Augmented Generation evaluation
• 🤖 Agent Evaluation: Build frameworks for evaluating AI agents and multi-turn conversations

🚀 Getting Started as a Contributor

1. Start Small: Pick up issues labeled good first issue or help wanted
2. Join Discussions: Share your ideas in GitHub Discussions
3. Review Code: Help review pull requests and provide feedback
4. Report Issues: Found a bug? Report it in GitHub Issues
5. Spread the Word: Star the repository and share with your network

🚀 Features

• Multi-Model Support: Evaluate models from OpenAI, Anthropic, AWS Bedrock, and custom providers
• Extensible Scoring: Built-in scorers for accuracy, semantic similarity, code evaluation, and custom metrics
• Dataset Integration: Support for MMLU, HuggingFace datasets, custom datasets, and more
• Production Ready: Docker support, Kubernetes deployment, and cloud integrations
• Comprehensive Reporting: Detailed evaluation reports, artifacts, and visualizations
• Secure: Built-in credential management and secret store integration
• Scalable: Designed for both local testing and large-scale production evaluations
• Cross-Platform: Tested on macOS, Linux, and Windows with comprehensive CI/CD

📦 Installation

From PyPI (Recommended)

pip install novaeval

From Source

git clone https://github.com/Noveum/NovaEval.git
cd NovaEval
pip install -e .

Docker

docker pull noveum/novaeval:latest

🏃‍♂️ Quick Start

Basic Evaluation

from novaeval import Evaluator
from novaeval.datasets import MMLUDataset
from novaeval.models import OpenAIModel
from novaeval.scorers import AccuracyScorer

# Configure for cost-conscious evaluation
MAX_TOKENS = 100  # Adjust based on budget: 5-10 for answers, 100+ for reasoning

# Initialize components
dataset = MMLUDataset(
    subset="elementary_mathematics",  # Easier subset for demo
    num_samples=10,
    split="test"
)

model = OpenAIModel(
    model_name="gpt-4o-mini",  # Cost-effective model
    temperature=0.0,
    max_tokens=MAX_TOKENS
)

scorer = AccuracyScorer(extract_answer=True)

# Create and run evaluation
evaluator = Evaluator(
    dataset=dataset,
    models=[model],
    scorers=[scorer],
    output_dir="./results"
)

results = evaluator.run()

# Display detailed results
for model_name, model_results in results["model_results"].items():
    for scorer_name, score_info in model_results["scores"].items():
        if isinstance(score_info, dict):
            mean_score = score_info.get("mean", 0)
            count = score_info.get("count", 0)
            print(f"{scorer_name}: {mean_score:.4f} ({count} samples)")

Configuration-Based Evaluation

from novaeval import Evaluator

# Load configuration from YAML/JSON
evaluator = Evaluator.from_config("evaluation_config.yaml")
results = evaluator.run()

Example Configuration

# evaluation_config.yaml
dataset:
  type: "mmlu"
  subset: "abstract_algebra"
  num_samples: 500

models:
  - type: "openai"
    model_name: "gpt-4"
    temperature: 0.0
  - type: "anthropic"
    model_name: "claude-3-opus"
    temperature: 0.0

scorers:
  - type: "accuracy"
  - type: "semantic_similarity"
    threshold: 0.8

output:
  directory: "./results"
  formats: ["json", "csv", "html"]
  upload_to_s3: true
  s3_bucket: "my-eval-results"

🏗️ Architecture

NovaEval is built with extensibility and modularity in mind:

src/novaeval/
├── datasets/          # Dataset loaders and processors
├── evaluators/        # Core evaluation logic
├── integrations/      # External service integrations
├── models/           # Model interfaces and adapters
├── reporting/        # Report generation and visualization
├── scorers/          # Scoring mechanisms and metrics
└── utils/            # Utility functions and helpers

Core Components

• Datasets: Standardized interface for loading evaluation datasets
• Models: Unified API for different AI model providers
• Scorers: Pluggable scoring mechanisms for various evaluation metrics
• Evaluators: Orchestrates the evaluation process
• Reporting: Generates comprehensive reports and artifacts
• Integrations: Handles external services (S3, credential stores, etc.)

📊 Supported Datasets

• MMLU: Massive Multitask Language Understanding
• HuggingFace: Any dataset from the HuggingFace Hub
• Custom: JSON, CSV, or programmatic dataset definitions
• Code Evaluation: Programming benchmarks and code generation tasks
• Agent Traces: Multi-turn conversation and agent evaluation

🤖 Supported Models

• OpenAI: GPT-3.5, GPT-4, and newer models
• Anthropic: Claude family models
• AWS Bedrock: Amazon's managed AI services
• Noveum AI Gateway: Integration with Noveum's model gateway
• Custom: Extensible interface for any API-based model

📏 Built-in Scorers

Accuracy-Based

• ExactMatch: Exact string matching
• Accuracy: Classification accuracy
• F1Score: F1 score for classification tasks

Semantic-Based

• SemanticSimilarity: Embedding-based similarity scoring
• BERTScore: BERT-based semantic evaluation
• RougeScore: ROUGE metrics for text generation

Code-Specific

• CodeExecution: Execute and validate code outputs
• SyntaxChecker: Validate code syntax
• TestCoverage: Code coverage analysis

Custom

• LLMJudge: Use another LLM as a judge
• HumanEval: Integration with human evaluation workflows

🚀 Deployment

Local Development

# Install dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run example evaluation
python examples/basic_evaluation.py

Docker

# Build image
docker build -t nova-eval .

# Run evaluation
docker run -v $(pwd)/config:/config -v $(pwd)/results:/results nova-eval --config /config/eval.yaml

Kubernetes

# Deploy to Kubernetes
kubectl apply -f kubernetes/

# Check status
kubectl get pods -l app=nova-eval

🔧 Configuration

NovaEval supports configuration through:

• YAML/JSON files: Declarative configuration
• Environment variables: Runtime configuration
• Python code: Programmatic configuration
• CLI arguments: Command-line overrides

Environment Variables

export NOVA_EVAL_OUTPUT_DIR="./results"
export NOVA_EVAL_LOG_LEVEL="INFO"
export OPENAI_API_KEY="your-api-key"
export AWS_ACCESS_KEY_ID="your-aws-key"

CI/CD Integration

NovaEval includes optimized GitHub Actions workflows:

• Unit tests run on all PRs and pushes for quick feedback
• Integration tests run on main branch only to minimize API costs
• Cross-platform testing on macOS, Linux, and Windows

📈 Reporting and Artifacts

NovaEval generates comprehensive evaluation reports:

• Summary Reports: High-level metrics and insights
• Detailed Results: Per-sample predictions and scores
• Visualizations: Charts and graphs for result analysis
• Artifacts: Model outputs, intermediate results, and debug information
• Export Formats: JSON, CSV, HTML, PDF

Example Report Structure

results/
├── summary.json              # High-level metrics
├── detailed_results.csv      # Per-sample results
├── artifacts/
│   ├── model_outputs/        # Raw model responses
│   ├── intermediate/         # Processing artifacts
│   └── debug/               # Debug information
├── visualizations/
│   ├── accuracy_by_category.png
│   ├── score_distribution.png
│   └── confusion_matrix.png
└── report.html              # Interactive HTML report

🔌 Extending NovaEval

Custom Datasets

from novaeval.datasets import BaseDataset

class MyCustomDataset(BaseDataset):
    def load_data(self):
        # Implement data loading logic
        return samples

    def get_sample(self, index):
        # Return individual sample
        return sample

Custom Scorers

from novaeval.scorers import BaseScorer

class MyCustomScorer(BaseScorer):
    def score(self, prediction, ground_truth, context=None):
        # Implement scoring logic
        return score

Custom Models

from novaeval.models import BaseModel

class MyCustomModel(BaseModel):
    def generate(self, prompt, **kwargs):
        # Implement model inference
        return response

🤝 Contributing

We welcome contributions! NovaEval is actively seeking contributors to help build a robust AI evaluation framework. Please see our Contributing Guide for detailed guidelines.

🎯 Priority Contribution Areas

As mentioned in the We Need Your Help section, we're particularly looking for help with:

1. Unit Tests - Expand test coverage beyond the current 23%
2. Examples - Real-world evaluation scenarios and use cases
3. Guides & Notebooks - Interactive evaluation tutorials
4. Documentation - API docs, user guides, and tutorials
5. RAG Metrics - Specialized metrics for retrieval-augmented generation
6. Agent Evaluation - Frameworks for multi-turn and agent-based evaluations

Development Setup

# Clone repository
git clone https://github.com/Noveum/NovaEval.git
cd NovaEval

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install development dependencies
pip install -e ".[dev]"

# Install pre-commit hooks
pre-commit install

# Run tests
pytest

# Run with coverage
pytest --cov=src/novaeval --cov-report=html

🏗️ Contribution Workflow

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes following our coding standards
4. Add tests for your changes
5. Commit your changes (git commit -m 'Add amazing feature')
6. Push to the branch (git push origin feature/amazing-feature)
7. Open a Pull Request

📋 Contribution Guidelines

• Code Quality: Follow PEP 8 and use the provided pre-commit hooks
• Testing: Add unit tests for new features and bug fixes
• Documentation: Update documentation for API changes
• Commit Messages: Use conventional commit format
• Issues: Reference relevant issues in your PR description

🎉 Recognition

Contributors will be:

• Listed in our contributors page
• Mentioned in release notes for significant contributions
• Invited to join our contributor Discord community

📄 License

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

🙏 Acknowledgments

• Inspired by evaluation frameworks like DeepEval, Confident AI, and Braintrust
• Built with modern Python best practices and industry standards
• Designed for the AI evaluation community

📞 Support

• Documentation: https://noveum.github.io/NovaEval
• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: [e-mail]

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Made with ❤️ by the Noveum.ai team