May 2025
Intermediate to advanced
382 pages
10h 33m
Chinese
Content preview from 图解大模型 : 生成式AI 原理与实战
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语义搜索与
RAG
｜
211
现在我们已经有了可用于系统间横向对比的单一指标。若需深入了解信息检索评估指标，
可参阅
Christopher D. Manning
、
Prabhakar Raghavan
和
Hinrich Schütze
合著的
Introduction to
Information Retrieval
（
Cambridge University Press
出版）中“
Evaluation in Information Retrieval
”
一章
3
。
除均值平均精确率外，搜索系统还常使用归一化折损累积增益（
normalized discounted
cumulative gain
，
nDCG
）作为评估指标。该指标具有更精细的考量维度，因为在测试套件
和评分机制中，文档的相关性并非二元的（只有相关与不相关），而是允许标注不同等级
的相关程度。
8.3
RAG
随着
LLM
的大规模应用，用户开始频繁向其提问并期待事实性回应。模型虽然能正确
回答部分问题，但也会出现大量看似自信实则错误的答案。业界主流解决方案是采用
RAG
技术，该技术最早在
2020
年的论文“
Retrieval-Augmented Generation for Knowledge-
Intensive NLP Tasks
”
4
中提出，其架构如图
8-24
所示
问题 答案
1) 检索
RAG系统
数据源（
s
）
2) 基于事实
的生成
图 8-24：基础 RAG 流程包含检索与生成两个核心环节。LLM 接收用户问题时，检索模块获取的信
息将作为提示词被输入 LLM ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.