Evaluate Your RAG System Performance: Giskard

Education

Introduction

Introduction

In this article, we will explore how to generate a comprehensive performance evaluation report for a Retrieval-Augmented Generation (RAG) system using the Giskard library. We will walk through the process of evaluating different components of a RAG system including the generator, retriever, rewriter, router, and the knowledge base itself. Additionally, we'll discuss the steps to set up a minimalistic RAG system, run evaluations, and interpret the results.

Components of a RAG System

A RAG system typically consists of the following components:

  • Generator: Synthesizes responses.
  • Retriever: Retrieves documents from the vector store.
  • Rewriter: Rewrites complex questions in a vector-index friendly manner to enhance relevance.
  • Router: Assesses user queries to determine whether to retrieve documents from the vector store or perform web searches, and figures out user intentions.
  • Knowledge Base: The indexed repository of documents.

Each component is scored between 0 to 100, the higher the score, the better the performance.

Evaluation Process

Setting Up the Evaluation

  1. Data Preparation: Create a dataset consisting of questions, corresponding correct answers (ground truth), and contexts.
  2. Running Evaluations:
    • Provide the test dataset to the evaluation system.
    • Query the RAG system to generate responses.
    • Compare the generated responses with the ground truth to compute correctness scores.

Comprehensive Report Analysis

We classify questions into multiple groups, analyze their correctness over different categories, and visualize embeddings in two dimensions. For illustrative purposes, we use a Climate Change Report, generating 120 questions to test the system.

Metrics

Using the Giskard library, we measure various metrics such as context recall, context precision, faithfulness, and answer relevance for the RAG system. For example:

  • Context Recall: Measures how well the context is remembered.
  • Context Precision: Measures the accuracy of the retrieved context.

Step-by-Step Code Implementation

Initial Setup

  • Loading Data: Load the evaluation dataset into a pandas DataFrame.
  • Initialize RAG System: Using the llama_index library, set up the RAG system with Llama 3.1 as the language model.
  • Question Processing: Define a function to process and retrieve context for each question.
## Introduction
import llm

def query_rag_system(question):
    # Your code to query the RAG system
    response = ('response': 'generated answer', 'context': 'retrieved context')
    return response

## Introduction
questions_df = pd.DataFrame([...])  # Sample DataFrame containing questions and corresponding ground truth

## Introduction
def query_function(question):
    response = query_rag_system(question)
    return response['response'], response['context']

## Introduction
evaluate_result = giskard.evaluate(query_function, questions_df, metrics=["precision", "recall"])

Generating the Report

## Introduction
evaluate_result.save("evaluation_report")
print("Evaluation report generated successfully.")

Detailed Report Analysis

The evaluation report provides comprehensive performance insights:

  • Score Distribution: Detailed scores for generator, retriever, rewriter, router, and knowledge base.
  • Topic-wise and Type-wise Analysis: Evaluate correctness and performance across different topics and question types.
  • RAGAS Metrics: Analyze context recall, precision, faithfulness, and answer relevance.

Insights

For example, while the system might perform well in general questions, it might struggle with specific topics like global GHG emissions or complex questions.

Conclusion

Using Giskard to evaluate a RAG system offers deep insights into its performance, helping identify areas for improvement. By employing different metrics and detailed analysis, one can enhance the effectiveness and correctness of the RAG system.

Keywords

  • RAG System
  • Giskard
  • Performance Evaluation
  • Generator
  • Retriever
  • Rewriter
  • Router
  • Knowledge Base
  • Context Recall
  • Context Precision
  • Faithfulness
  • Answer Relevance

FAQ

What is a RAG system?

A RAG (Retrieval-Augmented Generation) system combines document retrieval and text generation to generate accurate and contextually relevant responses.

What components are evaluated in a RAG system?

Key components include the generator, retriever, rewriter, router, and the knowledge base.

How does Giskard help in evaluating RAG systems?

Giskard provides a library for evaluating RAG systems by computing metrics like context recall, precision, faithfulness, and answer relevance.

Why is it beneficial to classify questions into different groups?

Classifying questions helps to analyze the system's performance across various topics and question types, identifying strengths and weaknesses.

Can open-source models be used for evaluating RAG systems with Giskard?

Yes, open-source models can be used, but they might be slower compared to commercial API services like OpenAI.