Evaluating RAG Performance: A Comprehensive Guide

Retrieval Augmented Generation (RAG) systems are intricate, with various components influencing their performance. Implementing enhancements to these components can significantly boost system performance. However, making changes without proper evaluation is futile. In this article, we will delve into a comprehensive guide on evaluating RAG systems using a synthetic evaluation dataset and Language Models (LLMs) as judges.

The code and datasets have been created by Aymeric Roucher, and this is a walkthrough I created that describes each part of the code. You can find the full script here. The code runs smoother on Google Colab, and it requires a HuggingFace API token, which one can obtain from their HuggingFace account.

Technologies used here: NLP, RAG, Langchain, LLMs, Python

Understanding RAG Systems

Before we dive into evaluation, let’s briefly understand the complexity of RAG systems. The RAG diagram depicts various possibilities for system enhancement. Each improvement can have a substantial impact on performance, making benchmarking crucial for tuning the system effectively.

Summary of the RAG process. The blue text are suggested improvements to enhance performance (Credit: Aymeric Roucher)

Components of the Evaluation Pipeline

To evaluate a RAG system, we need:

1. An evaluation dataset with question & answer pairs (QA couples).
2. An evaluator to compute the accuracy of the system on the evaluation dataset.

Language Models (LLMs) can assist us throughout this evaluation process. First we need to install and import the appropriate packages.

!pip install -q torch transformers transformers langchain sentence-transformers faiss-gpu openpyxl openai

from tqdm.notebook import tqdm
import pandas as pd
from typing import Optional, List, Tuple
from langchain_core.language_models import BaseChatModel
import json
import datasets

1. Synthetic Dataset Creation

The evaluation dataset is synthetically generated using LLMs. We use Mixtral for QA couple generation. The generated questions are then filtered using critique agents, which rate questions based on groundedness, relevance, and stand-alone criteria.