---

转载改编自：
https://python.langchain.com.cn/docs/modules/model_io/output_parsers/

---

一、输出解析器 (Output Parsers)

语言模型输出文本，但很多时候希望获得 比仅文本更结构化的信息。这就是输出解析器的作用。

输出解析器是帮助 结构化语言模型响应的类。一个输出解析器必须实现两个主要方法:

• “获取格式化指令”: 一个返回包含语言模型输出 应如何格式化的字符串的方法。
• “解析”: 一个接受字符串（假设为语言模型的响应）并将其解析为某种结构的方法。

然后再加一个可选的方法:

• “带提示解析”: 一个接受字符串（假设为语言模型的响应）和提示（假设为生成此响应的提示）并将其解析为某种结构的方法。
  在需要从提示中获取信息以重试或修复输出的情况下，通常提供提示。

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快速入门

下面我们来介绍主要类型的输出解析器，PydanticOutputParser。

from langchain.prompts import PromptTemplate, ChatPromptTemplate, HumanMessagePromptTemplate
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAIfrom langchain.output_parsers import PydanticOutputParser
from pydantic import BaseModel, Field, validator
from typing import List

---

model_name = 'text-davinci-003'
temperature = 0.0
model = OpenAI(model_name=model_name, temperature=temperature)

---

# Define your desired data structure.
class Joke(BaseModel):setup: str = Field(description="question to set up a joke")punchline: str = Field(description="answer to resolve the joke")# You can add custom validation logic easily with Pydantic.@validator('setup')def question_ends_with_question_mark(cls, field):if field[-1] != '?':raise ValueError("Badly formed question!")return field

---

# Set up a parser + inject instructions into the prompt template.
parser = PydanticOutputParser(pydantic_object=Joke)

---

prompt = PromptTemplate(template="Answer the user query.\n{format_instructions}\n{query}\n",input_variables=["query"],partial_variables={"format_instructions": parser.get_format_instructions()}
)

---

# And a query intented to prompt a language model to populate the data structure.
joke_query = "Tell me a joke."
_input = prompt.format_prompt(query=joke_query)

---

output = model(_input.to_string())
parser.parse(output)
# ->    Joke(setup='Why did the chicken cross the road?', punchline='To get to the other side!')

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二、列表解析器

当您想要返回逗号分隔的项目列表时，可以使用此输出解析器。

from langchain.output_parsers import CommaSeparatedListOutputParser
from langchain.prompts import PromptTemplate, ChatPromptTemplate, HumanMessagePromptTemplate
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAIoutput_parser = CommaSeparatedListOutputParser()

---

format_instructions = output_parser.get_format_instructions()
prompt = PromptTemplate(template="List five {subject}.\n{format_instructions}",input_variables=["subject"],partial_variables={"format_instructions": format_instructions}
)

---

model = OpenAI(temperature=0)
_input = prompt.format(subject="ice cream flavors")
output = model(_input)
output_parser.parse(output)

---

    ['Vanilla','Chocolate','Strawberry','Mint Chocolate Chip','Cookies and Cream']

---

三、datetime 日期时间解析器

该输出解析器演示如何将LLM输出解析为日期时间格式。

from langchain.prompts import PromptTemplate
from langchain.output_parsers import DatetimeOutputParser
from langchain.chains import LLMChain
from langchain.llms import OpenAI

---

output_parser = DatetimeOutputParser()
template = """Answer the users question:{question}{format_instructions}"""
prompt = PromptTemplate.from_template(template,partial_variables={"format_instructions": output_parser.get_format_instructions()},
)

---

chain = LLMChain(prompt=prompt, llm=OpenAI())
output = chain.run("around when was bitcoin founded?")# output -> '\n\n2008-01-03T18:15:05.000000Z'

---

output_parser.parse(output)
# -> datetime.datetime(2008, 1, 3, 18, 15, 5)

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四、枚举解析器

本笔记本演示如何使用枚举输出解析器。

from langchain.output_parsers.enum import EnumOutputParser

---

from enum import Enumclass Colors(Enum):RED = "red"GREEN = "green"BLUE = "blue"

---

parser = EnumOutputParser(enum=Colors)
parser.parse("red")
# -> <Colors.RED: 'red'>

---

# Can handle spaces
parser.parse(" green")
# -> <Colors.GREEN: 'green'>

---

# And new lines
parser.parse("blue\n")
# -> <Colors.BLUE: 'blue'>

---

# And raises errors when appropriate
parser.parse("yellow") 

---

五、自动修复解析器

该输出解析器包装了另一个输出解析器，并在第一个解析器失败时 调用另一个LLM来修复任何错误。

但是我们除了抛出错误之外，还可以做其他事情。

具体来说，我们可以将格式错误的输出以及格式化的指令一起传递给模型，并要求它进行修复。

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六、Pydantic（JSON）解析器

该输出解析器允许用户指定任意的JSON模式，并查询符合该模式的JSON输出。

请记住，大型语言模型是有漏洞的抽象！您必须使用具有足够容量的LLM来生成格式正确的JSON。在OpenAI家族中，DaVinci的能力可靠，但Curie的能力已经大幅下降。

使用Pydantic来声明您的数据模型。Pydantic的BaseModel类似于Python的数据类，但具有真正的类型检查和强制转换功能。

from langchain.prompts import (PromptTemplate,ChatPromptTemplate,HumanMessagePromptTemplate,
)
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAIfrom langchain.output_parsers import PydanticOutputParser
from pydantic import BaseModel, Field, validator
from typing import List

---

model_name = "text-davinci-003"
temperature = 0.0
model = OpenAI(model_name=model_name, temperature=temperature)

---

# Define your desired data structure.
class Joke(BaseModel):setup: str = Field(description="question to set up a joke")punchline: str = Field(description="answer to resolve the joke")# You can add custom validation logic easily with Pydantic.@validator("setup")def question_ends_with_question_mark(cls, field):if field[-1] != "?":raise ValueError("Badly formed question!")return field# And a query intented to prompt a language model to populate the data structure.
joke_query = "Tell me a joke."# Set up a parser + inject instructions into the prompt template.
parser = PydanticOutputParser(pydantic_object=Joke)prompt = PromptTemplate(template="Answer the user query.\n{format_instructions}\n{query}\n",input_variables=["query"],partial_variables={"format_instructions": parser.get_format_instructions()},
)_input = prompt.format_prompt(query=joke_query)output = model(_input.to_string())parser.parse(output)
# -> Joke(setup='Why did the chicken cross the road?', punchline='To get to the other side!')

---

# Here's another example, but with a compound typed field.
class Actor(BaseModel):name: str = Field(description="name of an actor")film_names: List[str] = Field(description="list of names of films they starred in")actor_query = "Generate the filmography for a random actor."parser = PydanticOutputParser(pydantic_object=Actor)prompt = PromptTemplate(template="Answer the user query.\n{format_instructions}\n{query}\n",input_variables=["query"],partial_variables={"format_instructions": parser.get_format_instructions()},
)_input = prompt.format_prompt(query=actor_query)output = model(_input.to_string())parser.parse(output)
# -> Actor(name='Tom Hanks', film_names=['Forrest Gump', 'Saving Private Ryan', 'The Green Mile', 'Cast Away', 'Toy Story'])

---

七、重试解析器

在某些情况下，通过仅查看输出就可以修复任何解析错误，但在其他情况下，则不太可能。

一个例子是当输出不仅格式不正确，而且部分不完整时。请考虑下面的例子。

from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAIfrom langchain.prompts import (PromptTemplate,ChatPromptTemplate,HumanMessagePromptTemplate,
)
from langchain.output_parsers import (PydanticOutputParser,OutputFixingParser,RetryOutputParser,
)
from pydantic import BaseModel, Field, validator
from typing import List

---

template = """Based on the user question, provide an Action and Action Input for what step should be taken.
{format_instructions}
Question: {query}
Response:"""class Action(BaseModel):action: str = Field(description="action to take")action_input: str = Field(description="input to the action")parser = PydanticOutputParser(pydantic_object=Action)

---

prompt = PromptTemplate(template="Answer the user query.\n{format_instructions}\n{query}\n",input_variables=["query"],partial_variables={"format_instructions": parser.get_format_instructions()},
)prompt_value = prompt.format_prompt(query="who is leo di caprios gf?")
bad_response = '{"action": "search"}'

---

这个 response 无法 parse，或报错

parser.parse(bad_response)

---

八、结构化输出解析器 structured

当您想要返回多个字段时，可以使用此输出解析器。尽管 Pydantic/JSON 解析器更强大，但我们最初尝试的数据结构仅具有文本字段。

from langchain.output_parsers import StructuredOutputParser, ResponseSchema
from langchain.prompts import PromptTemplate, ChatPromptTemplate, HumanMessagePromptTemplate
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI

---

这里我们定义了我们想要接收的响应模式。

response_schemas = [ResponseSchema(name="answer", description="answer to the user's question"),ResponseSchema(name="source", description="source used to answer the user's question, should be a website.")
]
output_parser = StructuredOutputParser.from_response_schemas(response_schemas)

---

我们现在获得一个包含响应格式化指令的字符串，然后将其插入到我们的提示中。

format_instructions = output_parser.get_format_instructions()
prompt = PromptTemplate(template="answer the users question as best as possible.\n{format_instructions}\n{question}",input_variables=["question"],partial_variables={"format_instructions": format_instructions}
)

---

我们现在可以使用这个来格式化一个提示，发送给语言模型，然后解析返回的结果。

model = OpenAI(temperature=0)_input = prompt.format_prompt(question="what's the capital of france?")output = model(_input.to_string())output_parser.parse(output)

---

    {'answer': 'Paris','source': 'https://www.worldatlas.com/articles/what-is-the-capital-of-france.html'}

---

这里是一个在聊天模型中使用它的例子

chat_model = ChatOpenAI(temperature=0)

---

prompt = ChatPromptTemplate(messages=[HumanMessagePromptTemplate.from_template("answer the users question as best as possible.\n{format_instructions}\n{question}")  ],input_variables=["question"],partial_variables={"format_instructions": format_instructions}
)

---

_input = prompt.format_prompt(question="what's the capital of france?")output = chat_model(_input.to_messages())output_parser.parse(output.content)
# -> {'answer': 'Paris', 'source': 'https://en.wikipedia.org/wiki/Paris'}

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2024-04-08(一)