LlamaIndex 发布 OpenAI Cookbook：RAG 系统评测指南
OpenAI Cookbook: Evaluating RAG systems
LlamaIndex 发布 OpenAI Cookbook，指导如何使用 LlamaIndex 评测 RAG 系统。指南分三部分：RAG 系统原理与构建阶段、以 Paul Graham 文章为例用 VectorStoreIndex 构建 RAG、以及从检索系统和响应生成两方面评测，评测采用其 generate_question_context_pairs 合成数据集生成方法。
We’re excited to unveil our OpenAI Cookbook, a guide to evaluating Retrieval-Augmented Generation (RAG) systems using LlamaIndex. We hope you’ll find it useful in enhancing the effectiveness of your RAG systems, and we’re thrilled to share it with you.
Explore our free and paid plans today.
The OpenAI Cookbook has three sections:
- Understanding Retrieval-Augmented Generation (RAG): provides a detailed overview of RAG systems, including the various stages involved in building the RAG system.
- Building RAG with LlamaIndex: Here, we dive into the practical aspects, demonstrating how to construct a RAG system using LlamaIndex, specifically applied to Paul Graham’s essay, utilizing the
VectorStoreIndex. - Evaluating RAG with LlamaIndex: The final section focuses on assessing the RAG system’s performance in two critical areas: the Retrieval System and Response Generation.
We use our unique synthetic dataset generation method, generate_question_context_pairs to conduct thorough evaluations in these areas.
Our goal with this cookbook is to provide the community with an essential resource for effectively evaluating and enhancing RAG systems developed using LlamaIndex.
Join us in exploring the depths of RAG system evaluation and discover how to leverage the full potential of your RAG implementations with LlamaIndex.
Keep building with LlamaIndex!🦙
来源：LlamaIndex：产品、工程与评测 · llamaindex.ai