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 ChatOpenAI

from 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'])