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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高级文本生成技术与工具
｜
169
现的框架代表，
LangChain
通过精妙的抽象层简化了与
LLM
的交互流程。当前值得关注的
新兴框架还包括
DSPy
和
Haystack
。图
7-1
展示了部分抽象层的实现逻辑，其中的检索模
块将在下一章详述。
• 提示词
• LLM
• 输出解析器
• 对话记忆
• 摘要
• 嵌入向量
• 文档
• 向量数据库
• 自主
• ReAct
• 对话
模型输入/输出 记忆 检索 智能体
LangChain
模块相互链式连接
图 7-1：LangChain 作为全功能 LLM 应用框架，其模块化组件可通过链式架构构建复杂的 LLM 系统
这些技术各有独特优势，但其真正的价值体现在协同应用时产生的倍增效应。当我们将这
些技术有机整合时，便能构建出性能卓越的
LLM
应用系统。正是通过不同技术间的协同
与融合，
LLM
的潜能才得以充分释放。
7.1
模型输入
/
输出
：
基于
LangChain
加载量化模型
在利用
LangChain
扩展
LLM
能力之前，需先完成模型加载。延续前几章的惯例，我们仍
选用
Phi-3
模型，但本次将采用其
GGUF
变体版本。该版本通过量化技术对原始模型进行
压缩，有效减少了
LLM
参数存储所需的位数。
位，也称比特（
bit
），即由一系列
0
和
1
组成的二进制编码，用于表示数值。位数越多，
能够表示的数值范围就越广，但相应地，就需要更多的内存空间来存储（如图
7-2
所示）。
32位浮点数
1 0 0 0 0 0 0 00
(–1)
0
2× 1.5707964
高精度
1
位
× 3.1415927=
(–1)
0
2× 1.571 ...
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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.