novaeval 0.1.0

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

NovaEval

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.

🚀 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

📦 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

# Initialize components
dataset = MMLUDataset(subset="abstract_algebra", num_samples=100)
model = OpenAIModel(model_name="gpt-4")
scorer = AccuracyScorer()

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

results = evaluator.run()
print(f"Accuracy: {results['accuracy']:.2%}")

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"

📈 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! Please see our Contributing Guide for details.

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

📄 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