【Langchain大语言模型开发教程】模型、提示和解析

🔗 LangChain for LLM Application Development - DeepLearning.AI

学习目标

1、使用Langchain实例化一个LLM的接口

2、 使用Langchain的模板功能,将需要改动的部分抽象成变量,在具体的情况下替换成需要的内容,来达到模板复用效果。

3、使用Langchain提供的解析功能,将LLM的输出解析成你需要的格式,如字典。

模型实例化

python 复制代码
import os
from dotenv import load_dotenv ,find_dotenv
from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
python 复制代码
_ = load_dotenv((find_dotenv())) //使用dotenv来管理你的环境变量

我们选用智谱的API【智谱AI开放平台】来作为我们的基座大模型,通过langchain的chatOpenAI接口来实例化我们的模型。

python 复制代码
chat = ChatOpenAI(api_key=os.environ.get('ZHIPUAI_API_KEY'),
                         base_url=os.environ.get('ZHIPUAI_API_URL'),
                         model="glm-4",
                         temperature=0.98)

这里我们选用的一个例子:通过prompt来转换表达的风格

提示模板化

我们定义一个prompt

python 复制代码
template_string = """Translate the text \
that is delimited by triple backticks \
into a style that is {style}.\
text:```{text}```
"""

使用langchain的模板功能函数实例化一个模板(从输出可以看到这里是需要两个参数style和text)

python 复制代码
prompt_template = ChatPromptTemplate.from_template(template_string)

'''
ChatPromptTemplate(input_variables=['style', 'text'], 
messages=[HumanMessagePromptTemplate(prompt=PromptTemplate(
input_variables=['style', 'text'], 
template='Translate the text that is delimited 
by triple backticks into a style that is {style}.text:```{text}```\n'))])
'''

设置我们想要转化的风格和想要转化的内容

python 复制代码
#style
customer_style = """American English in a clam and respectful tone"""
#text
customer_email = """
Arrr,I be fuming that me blender lid \
flew off and splattered me kitchen walls \
with smoothie! And to make matters worse, \
the warranty don't cover the cost of \
cleaning up me kitchen. I need yer help \
right now,matey!
"""

这里我们实例化出我们的prompt

python 复制代码
customer_messages = prompt_template.format_messages(style = customer_style,text= customer_email)

'''
[HumanMessage(content="Translate the text that is delimited 
by triple backticks into a style 
that is American English in a clam and respectful tone.
text:
```\n
Arrr,I be fuming that me blender lid flew off and 
splattered me kitchen walls with smoothie! 
And to make matters worse, 
the warranty don't cover the cost of cleaning up me kitchen. 
I need yer help right now,matey!
\n```\n")]
'''

这里我们给出一个回复的内容和转化的格式

python 复制代码
service_reply= 
"""
Hey there customer,the warranty does 
not cover cleaning expenses for your kitchen 
because it's your fault that you misused your blender 
by forgetting to put the lid on before starting the blender.
Tough luck! see ya!
"""

service_style = """
a polite tone that speaks in English pirate
"""

实例化

python 复制代码
service_messages = prompt_template.format_messages(style = service_style , text = service_reply)

调用LLM查看结果

python 复制代码
service_response = chat(service_messages)
print(service_response.content)

'''
Avast there, dear customer! Ye be knowin' that the warranty 
be not stretchin' to cover the cleanin' costs of yer kitchen, 
for 'tis a matter of misadventure on yer part. 
Ye did forget to secure the lid upon the blender before engagement, 
leading to a spot o' trouble. Aar, 
such be the ways of the sea! 
No hard feelings, and may the wind be at yer back on the next journey. 
Fare thee well!
'''

回复结构化

我们现在获得了某个商品的用户评价,我们想要提取其中的关键信息(下面这种形式)

python 复制代码
customer_review = """\
This leaf blower is pretty amazing.  It has four settings:\
candle blower, gentle breeze, windy city, and tornado. \
It arrived in two days, just in time for my wife's \
anniversary present. \
I think my wife liked it so much she was speechless. \
So far I've been the only one using it, and I've been \
using it every other morning to clear the leaves on our lawn. \
It's slightly more expensive than the other leaf blowers \
out there, but I think it's worth it for the extra features.
"""

{
  "gift": False,
  "delivery_days": 5,
  "price_value": "pretty affordable!"
}

构建一个prompt 模板

python 复制代码
review_template = """\
For the following text, extract the following information:

gift: Was the item purchased as a gift for someone else? \
Answer True if yes, False if not or unknown.

delivery_days: How many days did it take for the product \
to arrive? If this information is not found, output -1.

price_value: Extract any sentences about the value or price,\
and output them as a comma separated Python list.

Format the output as JSON with the following keys:
gift
delivery_days
price_value

text: {text}
"""
python 复制代码
prompt_template = ChatPromptTemplate.from_template(review_template)
message = prompt_template.format_messages(text = customer_review)
reponse = chat(message)

下面是模型的回复看起来好像一样

python 复制代码
{
  "gift": true,
  "delivery_days": 2,
  "price_value": ["It's slightly more expensive than the other leaf blowers out there, but I think it's worth it for the extra features."]
}

我们打印他的类型的时候,发现这其实是一个字符串类型,这是不能根据key来获取value值的。

引入Langchain的ResponseSchema

python 复制代码
from langchain.output_parsers import ResponseSchema
from langchain.output_parsers import StructuredOutputParser

gift_schema = ResponseSchema(name="gift",description="Was the item purchased as a gift for someone else? Answer True if yes,False if not or unknown.")
delivery_days_schema = ResponseSchema(name="delivery_days", description="How many days did it take for the product to arrive? If this information is not found,output -1.")
price_value_schema = ResponseSchema(name="price_value", description="Extract any sentences about the value or price, and output them as a comma separated Python list.")
python 复制代码
response_schemas = [gift_schema,delivery_days_schema,price_value_schema]
output_parser = StructuredOutputParser.from_response_schemas(response_schemas)
format_instructions = output_parser.get_format_instructions()

查看一下我们构建的这个结构

重新构建prompt模板,并进行实例

python 复制代码
review_template_2 = """\
For the following text, extract the following information:

gift: Was the item purchased as a gift for someone else? \
Answer True if yes, False if not or unknown.

delivery_days: How many days did it take for the product\
to arrive? If this information is not found, output -1.

price_value: Extract any sentences about the value or price,\
and output them as a comma separated Python list.

text: {text}

{format_instructions}
"""

prompt = ChatPromptTemplate.from_template(template=review_template_2)

messages = prompt.format_messages(text=customer_review,format_instructions=format_instructions)

我们将结果进行解析

python 复制代码
output_dict = output_parser.parse(reponse.content)

{
 'gift': 'True',
 'delivery_days': '2',
 'price_value': "It's slightly more expensive than the other leaf blowers out there, but I think it's worth it for the extra features."
}

我们再次查看其类型,发现已经变成了字典类型,并可以通过key去获取value值。

相关推荐
阿坡RPA14 小时前
手搓MCP客户端&服务端:从零到实战极速了解MCP是什么?
人工智能·aigc
用户277844910499314 小时前
借助DeepSeek智能生成测试用例:从提示词到Excel表格的全流程实践
人工智能·python
机器之心14 小时前
刚刚,DeepSeek公布推理时Scaling新论文,R2要来了?
人工智能
算AI16 小时前
人工智能+牙科:临床应用中的几个问题
人工智能·算法
凯子坚持 c17 小时前
基于飞桨框架3.0本地DeepSeek-R1蒸馏版部署实战
人工智能·paddlepaddle
你觉得20517 小时前
哈尔滨工业大学DeepSeek公开课:探索大模型原理、技术与应用从GPT到DeepSeek|附视频与讲义下载方法
大数据·人工智能·python·gpt·学习·机器学习·aigc
8K超高清17 小时前
中国8K摄像机:科技赋能文化传承新图景
大数据·人工智能·科技·物联网·智能硬件
hyshhhh18 小时前
【算法岗面试题】深度学习中如何防止过拟合?
网络·人工智能·深度学习·神经网络·算法·计算机视觉
薛定谔的猫-菜鸟程序员18 小时前
零基础玩转深度神经网络大模型:从Hello World到AI炼金术-详解版(含:Conda 全面使用指南)
人工智能·神经网络·dnn
币之互联万物18 小时前
2025 AI智能数字农业研讨会在苏州启幕,科技助农与数据兴业成焦点
人工智能·科技