在Langchain框架下面实现聊天提示模板的格式化输出 LangChain Chat Prompt Template OutputParser

导入模型

# 导入模型 ChatOllama
from langchain_ollama import ChatOllama

# 模型设定为qwen2.5:14b，温度为0.1，可以有一定的创造性，0为最保守，对象为llm_model
llm_model = ChatOllama(model="qwen2.5:14b",temperature = 0.1)

Prompt template 提示词模板

# 设定提示的模板  string_template
string_template = """
    你是一个经验非常有丰富的翻译家，你可以翻译用户的任何语言。
    请根据：{style} ，把用户的话：{language} 翻译出来。 
"""

# 导入 ChatPromptTemplate,HumanMesssagePromptTemplate,SystemMessagePromptTemplate3个类
from langchain.prompts import (
    ChatPromptTemplate,
    HumanMessagePromptTemplate,
    SystemMessagePromptTemplate,
)

# 通过 from_template 方法，创建一个聊天提示模板对象  prompt_template
prompt_template = ChatPromptTemplate.from_template(string_template)

# 创建与 string_template  里面占位符  {style} ,{language} 相对应的输入变量
customer_style = """ 请用一种非常专业的风格来翻译，把英语翻译成中文。"""
customer_language = """ Hi，I'm  coder, I want to study the AI。"""

# 通过 format_messages 方法，引入 style，language 的变量，格式化模板中的消息
customer_messages = prompt_template.format_messages(
    style = customer_style,
    language = customer_language,
)

# 消息与聊天模型进行交互
customer_response = llm_model(customer_messages)

# 输出大模型处理完以后的内容
print(customer_response.content)

输出解析 把大模型的输出解析成字典的形式

from langchain.output_parsers import ResponseSchema,StructuredOutputParser

#  建立 customer_review 的上下文， 
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.
"""

# review_template 的提示词模板，提示词说明了，要创建JSON格式的结果  其中变量占位符为  {text}
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}
"""

# 建立 schema 模板结构 Response定义了期望的输出结构，包括  name  description
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.")

response_schemas = [gift_schema, 
                    delivery_days_schema,
                    price_value_schema]

# 建立 review_template_2 的提示词模板  占位符变量为   {text}   {format_instructions}
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}
"""
# 通过StructuredOutputParser 的 from_response_parsers 方法 导入  response_schemas 列表格式和内容
output_parser = StructuredOutputParser.from_response_schemas(response_schemas)

# 通过 get_format_instructions 方法，获取格式化指令，指导语言模型按照指定的结构输出结果
format_instructions = output_parser.get_format_instructions()

# 通过 from_template 方法 导入  review_template_2 的提示词模板，创建实例 prompt
prompt = ChatPromptTemplate.from_template(template=review_template_2)

# 通过 format_messages 方法，创建格式化的实例  messages
messages = prompt.format_messages(
                    text = customer_review,
                    format_instructions = format_instructions
)

# 把messages内容给到大模型进行处理
response = llm_model(messages)

# parse 方法输出指定的字典格式
output_dict = output_parser.parse(response.content)

output_dict

# 获取delivery_days 的内容
output_dict.get("delivery_days")

总结

• 导入模型
  from langchain_ollama import ChatOllama
  llm_model = ChatOllama(model=”qwen2.5:14b”,tempterture=0.1)

• 导入提示词模板
  ChatPromptTemplate的方法：

  • .from_template()
  • .from_messages()
  • .format_messages()
  • 模板中占位符变量

• 格式化输出

  • ResponseSchema方法：

    • ResponseSchema(name=”str”,description=”str”) 建立结构

  • StructuredOutputParser的方法：

    • .from_response_schemas()
    • .get_format_instructions
    • .pase()