ragscope 0.1.0

A comprehensive suite for evaluating RAG pipelines.

🏆 RAG Evaluation Suite

A comprehensive, asynchronous, and framework-agnostic library for evaluating Retrieval-Augmented Generation (RAG) pipelines.

This tool provides a complete, end-to-end workflow for RAG evaluation—from automatically synthesizing a high-quality test set from your own documents to running a suite of sophisticated, AI-powered diagnostic metrics.

---

✨ Key Features

• Comprehensive "RAG Triad" Metrics: Evaluate Context Relevance, Faithfulness, and Answer Relevance.
• Advanced Diagnostics: Includes Answer Completeness to pinpoint why an answer is strong or weak.
• Automated Test Case Generation: Use the built-in Data Synthesizer to create test cases from any document.
• High-Performance Async Pipeline: Powered by asyncio for fast, parallel execution.
• Framework-Agnostic: Works with any RAG system—LangChain, LlamaIndex, or plain Python.
• Flexible Judge Model: Supports GPT-4o, Claude 3 Opus, or local models (e.g., via Ollama).

---

🚀 Quick Start

1. Installation

pip install rag-eval-suite

2. Run Your First Evaluation

Create a Python script using the RAGEvaluator:

import asyncio
from rag_eval_suite import RAGEvaluator
from rag_eval_suite.data_models import TestCase, RAGResult

# 1. Instantiate the evaluator (uses local Ollama model by default)
evaluator = RAGEvaluator(judge_model="ollama/llama3")

# 2. Define test case and RAG system output
test_case = TestCase(
    question="What are the notable features of the stadium's pitch and roof?",
    ground_truth_context=["The stadium features a fully retractable roof... and a hybrid pitch..."],
    ground_truth_answer="The stadium has a fully retractable roof and a hybrid pitch."
)

rag_result = RAGResult(
    retrieved_context=["The stadium features a fully retractable roof... and a hybrid pitch..."],
    final_answer="The stadium has a fully retractable roof."  # Intentionally incomplete
)

# 3. Run the evaluation
async def main():
    evaluation_result = await evaluator.aevaluate(test_case, rag_result)
    print(evaluation_result.scores)

if __name__ == "__main__":
    asyncio.run(main())

---

🔧 Configuration

To use other models (e.g., OpenAI’s GPT-4o), configure via environment variables:

import os
os.environ["OPENAI_API_KEY"] = "sk-..."

evaluator = RAGEvaluator(judge_model="gpt-4o")

---

🤝 Contributing

Contributions are welcome! Feel free to open an issue or submit a pull request.

---

📄 License

This project is licensed under the MIT License.