langchain（1）：模型、提示和输出解析器

1. 使用f””” “””来指定和说明提示词
2. 确定风格和需要修改的文本

如果可以建立一个prompt让llm使用如上图所示的特定关键词（思维链），那这个prompt可以同解析器结合，提取出特定关键词标记的文本，目的是抽象的指定llm的输入，然后让解析器正确的解释llm的输出

流程：

• 1. 设置llm输出要求提示词（你希望怎么处理，得到什么类型的结果）

  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}
  """
  
  from langchain.prompts import ChatPromptTemplate
  prompt_template = ChatPromptTemplate.from_template(review_template)
  

  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)
  

• 2.设置实际内容提示词，可在内容中设置参数用来动态修改（实际的提问内容）

  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.
  """
  
  messages = prompt_template.format_messages(text=customer_review)
  

  template_string = """Translate the text \
  that is delimited by triple backticks \
  into a style that is {style}. \
  text: ```{text}```
  """
  prompt_template = ChatPromptTemplate.from_template(template_string)
  
  # 设置提问内容中的参数
  customer_style = """American English \
  in a anger and unhappy tone
  """
  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!
  """
  # 整合
  customer_messages = prompt_template.format_messages(
                      style=customer_style,
                      text=customer_email)
  

  # 修改得到你希望的输出格式（json）
  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]
  # 输出解释器，设定输出格式          
  output_parser = StructuredOutputParser.from_response_schemas(response_schemas)
  format_instructions = output_parser.get_format_instructions()
  # 这是步骤一的内容
  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)
  # 将输出格式设置到提示词中
  messages = prompt.format_messages(text=customer_review, 
                                  format_instructions=format_instructions)
  

• 3.设置对话的大语言模型，将之前步骤总和的提示词输入，得到最终输出

  chat = ChatOpenAI(temperature=0.0, model=llm_model)
  response = chat(messages)