本地大模型编程实战(06)从文本中提取重要信息(2)

文章目录

- 准备
- 简单推理
- 定义数据格式
- 准备消息
- 提取Person
- 总结
- 代码

本文将演示使用大语言模型从文本中提炼结构化信息。这次我们不直接使用提示词，而是使用大模型的 few-shot prompting 特性，即使用很少的例子来引导大模型做推理。
我们将用 llama3.1 和 deepseek 做一个简单的对比。

由于 langchain 可能对不同大模型支持程度不同，不同大模型的特点也不同，所以这个对比并不能说明哪个模型更好。

准备

在正式开始撸代码之前，需要准备一下编程环境。

计算机
本文涉及的所有代码可以在没有显存的环境中执行。我使用的机器配置为：
- CPU: Intel i5-8400 2.80GHz
- 内存: 16GB
Visual Studio Code 和 venv
这是很受欢迎的开发工具，相关文章的代码可以在 Visual Studio Code 中开发和调试。我们用 python 的 venv 创建虚拟环境, 详见：
在Visual Studio Code中配置venv。
Ollama
在 Ollama 平台上部署本地大模型非常方便，基于此平台，我们可以让 langchain 使用 llama3.1、qwen2.5 等各种本地大模型。详见：
在langchian中使用本地部署的llama3.1大模型。

简单推理

下面我们给大模型举几个简单的例子，看看到模型能否根据这些例子，推理出结果。

def reference(model_name):messages = [{"role": "user", "content": "2 🦜 2"},{"role": "assistant", "content": "4"},{"role": "user", "content": "2 🦜 3"},{"role": "assistant", "content": "5"},{"role": "user", "content": "3 🦜 4"},]response = ChatOllama(model=model_name,temperature=0.5,verbose=True).invoke(messages)return response.content

我们使用 llama3.1 和deepseek 做一下测试：

llama3.1	deepseek-r1-tool-calling
7	… \boxed{7}

我运行了3次，llama3.1 每次都给出了简洁的正确结果，deepseek 输出的内容比较多，推理时间也略长，只有一次推理成功，不知道是否是 langchain 的原因。

下面我们再让大模型执行复杂一点的任务。

定义数据格式

我们先用 Pydantic 来定义要提取的数据格式。

from typing import Optional
from pydantic import BaseModel, Fieldclass Person(BaseModel):"""Information about a person."""# ^ Doc-string for the entity Person.# This doc-string is sent to the LLM as the description of the schema Person,# and it can help to improve extraction results.# Note that:# 1. Each field is an `optional` -- this allows the model to decline to extract it!# 2. Each field has a `description` -- this description is used by the LLM.# Having a good description can help improve extraction results.name: Optional[str] = Field(default=None, description="The name of the person")hair_color: Optional[str] = Field(default=None, description="The color of the person's hair if known")height_in_meters: Optional[float] = Field(default=None, description="Height measured in meters")

准备消息

我们使用 LangChain 的实用函数 tool_example_to_messages 将样例转化为消息序列，用于后面的推理任务。

from langchain_core.utils.function_calling import tool_example_to_messagesexamples = [("The ocean is vast and blue. It's more than 20,000 feet deep.",Person(),),("Fiona traveled far from France to Spain.",Person(name="Fiona", height_in_meters=None, hair_color=None),),("Alan Smith is 1.83 meters tall and has blond hair.",Person(name="Alan Smith", height_in_meters=1.83, hair_color="blond"),),
]messages = []for txt, tool_call in examples:if tool_call.name is None:# This final message is optional for some providersai_response = "Detected people."else:ai_response = "Detected no people."messages.extend(tool_example_to_messages(txt, [tool_call], ai_response=ai_response))

经过处理后，这些例子将转化为三种消息：

User message
AI message with tool call
Tool message with result

我们打印出转化后的消息看一下：

for message in messages:message.pretty_print()

================================ Human Message =================================The ocean is vast and blue. It's more than 20,000 feet deep.
================================== Ai Message ==================================
Tool Calls:Person (16513a89-ca85-46b4-8fac-db02a47b03fe)Call ID: 16513a89-ca85-46b4-8fac-db02a47b03feArgs:name: Nonehair_color: Noneheight_in_meters: None
================================= Tool Message =================================You have correctly called this tool.
================================== Ai Message ==================================Detected people.
================================ Human Message =================================Fiona traveled far from France to Spain.
================================== Ai Message ==================================
Tool Calls:Person (84e608e4-f444-49e1-b983-c28c38a1c870)Call ID: 84e608e4-f444-49e1-b983-c28c38a1c870Args:name: Fionahair_color: Noneheight_in_meters: None
================================= Tool Message =================================You have correctly called this tool.
================================== Ai Message ==================================Detected no people.
================================ Human Message =================================Alan Smith is 1.83 meters tall and has blond hair.
================================== Ai Message ==================================
Tool Calls:Person (e464cd1f-fca8-48fb-85a0-5fc722328ec0)Call ID: e464cd1f-fca8-48fb-85a0-5fc722328ec0Args:name: Alan Smithhair_color: blondheight_in_meters: 1.83
================================= Tool Message =================================You have correctly called this tool.
================================== Ai Message ==================================Detected no people.

提取Person

我们定义两个方法：extract方法不使用前面生成的messages；而extract_with_messages则使用前面的messages。

def extract(model_name,text):structured_llm = ChatOllama(model=model_name,temperature=0,verbose=True).with_structured_output(schema=Person)  user_message = {"role": "user", "content":text}response = structured_llm.invoke([user_message])return responsedef extract_with_messages(model_name,text):structured_llm = ChatOllama(model=model_name,temperature=0,verbose=True).with_structured_output(schema=Person)  user_message = {"role": "user", "content":text}structured_llm.invoke(messages + [user_message])return response

现在使用两个字符串做一下测试：

Roy is 1.73 meters tall and has black hair.

	llama3.1	deepseek-r1-tool-calling
extract	name=‘Roy’ hair_color=‘black’ height_in_meters=1.73	name=‘’ hair_color=‘black’ height_in_meters=1.73
extract_with_messages	name=‘Roy’ hair_color=‘black’ height_in_meters=1.73	name=‘’ hair_color=‘black’ height_in_meters=1.73

John Doe is 1.72 meters tall and has brown hair.

	llama3.1	deepseek-r1-tool-calling
extract	name=‘John Doe’ hair_color=‘brown’ height_in_meters=1.72	name=‘’ hair_color=‘brown’ height_in_meters=1.72
extract_with_messages	name=‘John Doe’ hair_color=‘brown’ height_in_meters=1.72	name=‘’ hair_color=‘brown’ height_in_meters=1.72

通过以上简单的推理任务，我们发现 deepseek 的表现没有 llama3.1 准确；few-shot prompting 对结果影响不大。