LangRAGEval 0.1.0

LangRAGEval is a library for evaluating responses based on faithfulness, context recall, answer relevancy, and context relevancy.

LangGPTEval

LangGPTEval, Evaluation library designed for Retrieval-Augmented Generation (RAG) responses.Evaluates the faithfulness, context recall, answer relevancy, and context relevancy of responses generated by various models, including OpenAI, Azure, and custom models. With a complex architecture and advanced Pydantic validation, LangGPTEval ensures reliable and accurate evaluation metrics.

📜 Table of Contents

1. Introduction
2. Features
3. Installation
4. Quick Start
5. Usage
6. Examples
7. Contributing
8. License

🌟 Introduction

LangGPTEval is designed to evaluate the quality of responses generated by RAG models. It supports multiple metrics for evaluation:

• Faithfulness: How true the response is to the given context.
• Context Recall: How well the response recalls the given context.
• Answer Relevancy: How relevant the response is to the question.
• Context Relevancy: How relevant the response is to the context.

LangGPTEval is highly customizable, allowing users to plug in their models and tailor the evaluation to their specific needs.

🚀 Features

• Modular Design: Easily integrate different models.
• Pydantic Validation: Ensures robust input validation.
• Flexible Evaluation: Evaluate multiple metrics with customizable prompts.
• Exception Handling: Graceful error handling to manage evaluation failures.

🛠️ Installation

You can install LangGPTEval using pip:

pip install LangGPTEval

⚡ Quick Start

Here’s a quick start guide to get you up and running with LangGPTEval.

1. Install the library.
2. Prepare your data.
3. Evaluate your model.

📚 Usage

Importing the Library

First, import the necessary components from the LangGPTEval library.

from LangGPTEval.models import EvaluationInput, ContextData
from LangGPTEval.evaluation import evaluate_faithfulness, evaluate_context_recall, evaluate_answer_relevancy, evaluate_context_relevancy
from langchain.llms import OpenAI

Setting Up Your Model

Create an instance of your model. Here, we demonstrate using LangChain’s OpenAI model.

class LangChainOpenAIModel:
    def __init__(self, api_key: str):
        self.llm = OpenAI(api_key=api_key)

    def invoke(self, prompt: Any) -> str:
        response = self.llm(prompt)
        score = response.strip()
        return score

Example Data

Prepare the input data for evaluation.

context = [ContextData(page_content="Test context")]
response = "Test response"
input_data = EvaluationInput(context=context, response=response)

Evaluating the Model

Use the evaluation functions to evaluate the model’s performance.

# Replace 'your-openai-api-key' with your actual OpenAI API key
api_key = 'your-openai-api-key'
openai_model = LangChainOpenAIModel(api_key)

try:
    # Evaluate with the LangChain OpenAI model
    faithfulness_result = evaluate_faithfulness(input_data, openai_model)
    context_recall_result = evaluate_context_recall(input_data, openai_model)
    answer_relevancy_result = evaluate_answer_relevancy(input_data, openai_model)
    context_relevancy_result = evaluate_context_relevancy(input_data, openai_model)

    print(faithfulness_result.score)
    print(context_recall_result.score)
    print(answer_relevancy_result.score)
    print(context_relevancy_result.score)
except ValueError as e:
    print(f"An error occurred during evaluation: {str(e)}")

🔍 Examples

Example with Custom Model

class CustomModel:
    def invoke(self, prompt):
        # Custom model implementation
        return "0.9"  # Example score

# Create a custom model instance
custom_model = CustomModel()

try:
    # Evaluate with the custom model
    faithfulness_result = evaluate_faithfulness(input_data, custom_model)
    context_recall_result = evaluate_context_recall(input_data, custom_model)
    answer_relevancy_result = evaluate_answer_relevancy(input_data, custom_model)
    context_relevancy_result = evaluate_context_relevancy(input_data, custom_model)

    print(faithfulness_result.score)
    print(context_recall_result.score)
    print(answer_relevancy_result.score)
    print(context_relevancy_result.score)
except ValueError as e:
    print(f"An error occurred during evaluation: {str(e)}")

Example with Azure Model

class AzureModel:
    def invoke(self, prompt):
        # Azure model implementation
        return "0.8"  # Example score

# Create an Azure model instance
azure_model = AzureModel()

try:
    # Evaluate with the Azure model
    faithfulness_result = evaluate_faithfulness(input_data, azure_model)
    context_recall_result = evaluate_context_recall(input_data, azure_model)
    answer_relevancy_result = evaluate_answer_relevancy(input_data, azure_model)
    context_relevancy_result = evaluate_context_relevancy(input_data, azure_model)

    print(faithfulness_result.score)
    print(context_recall_result.score)
    print(answer_relevancy_result.score)
    print(context_relevancy_result.score)
except ValueError as e:
    print(f"An error occurred during evaluation: {str(e)}")

🤝 Contributing

Contributions are welcome! Please read the contributing guidelines before making a pull request.

Steps to Contribute

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

📜 License

LangGPTEval is licensed under the MIT License. See the LICENSE file for more details.

Happy Evaluating! 🎉

LangGPTEval is here to make your RAG model evaluations precise and easy. If you have any questions or need further assistance, feel free to reach out to our support team.

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