KernelMemory.Evaluation 0.0.1

KM Evaluation

This repository contains the code for the evaluation of the Knowledge Management (KM) system. The evaluation is based on the following metrics:

• Faithfulness: Ensuring the generated text accurately represents the source information.
• Answer Relevancy: Assessing the pertinence of the answer in relation to the query.
• Context Recall: Measuring the proportion of relevant context retrieved.
• Context Precision: Evaluating the accuracy of the retrieved context.
• Context Relevancy: Determining the relevance of the provided context to the query.
• Context Entity Recall: Checking the retrieval of key entities within the context.
• Answer Semantic Similarity: Comparing the semantic similarity between the generated answer and the expected answer.
• Answer Correctness: Verifying the factual correctness of the generated answers.

Usage

Test set generation

To evaluate the KM, you must first create a test set containing the queries and the expected answers. Since this is a manual process, this might be fastidious for large datasets. To help you with this task, we provide a generator that creates a test set from a given KM memory and index.

using Microsoft.KernelMemory.Evaluation;

var testSetGenerator = new TestSetGeneratorBuilder(memoryBuilder.Services)
                            .AddEvaluatorKernel(kernel)
                            .Build();

var distribution = new Distribution
{
    Simple = .5f,
    Reasoning = .16f,
    MultiContext = .17f,
    Conditioning = .17f
};

var testSet = testSetGenerator.GenerateTestSetsAsync(index: "default", count: 10, retryCount: 3, distribution: distribution);

await foreach (var test in testSet)
{
    Console.WriteLine(test.Question);
}

Evaluation

To evaluate the KM, you can use the following code:

var evaluation = new TestSetEvaluatorBuilder()
                            .AddEvaluatorKernel(kernel)
                            .WithMemory(memoryBuilder.Build())
                            .Build();

var results = evaluation.EvaluateTestSetAsync(index: "default", await testSet.ToArrayAsync());

await foreach (var result in results)
{
    Console.WriteLine($"Faithfulness: {result.Metrics.Faithfulness}, ContextRecall: {result.Metrics.ContextRecall}");
}

Credits

This project is an implementation of RAGAS: Evaluation framework for your Retrieval Augmented Generation (RAG) pipelines.

License

This project is licensed under the MIT License.