Hong T., Jing W. and Jing Z
DSPy is a library designed to simplify the development and evaluation of Retrieval-Augmented Generation (RAG) models. It provides tools for efficient data retrieval, model training, and performance assessment, streamlining the process of building sophisticated RAG systems.
The modulated framework, metric definition, and model evaluation are clearly beneficial for evaluating the current RAG pipeline. Previously, we designed a model evaluation pipeline using RAGAS, linking it to any RAG pipeline performance (previous article).
In this article, through reverse engineering, we will demonstrate the model evaluation process with model evaluation dataframe derived from RAGAS evaluation pipeline, which includes ground truth generation and prediction using the RAG pipeline.
The main contribution of this article is use the DSPy evaluation module to customize metrics for RAG evaluation and evaluate model performance within DSPy framework.
This minimum viable product allow us to improve and build end to end RAG pipeline for future
Dataset preparation
Biophysics text context are generated using GPT4. Then the ground truth generator are used to generate questions and ground truth. A simple ChromaDB based RAG…
