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如何解析 JSON 輸出

先決條件

本指南假設您熟悉以下概念

雖然某些模型供應商支援內建方式來返回結構化輸出，但並非所有都支援。 我們可以使用輸出解析器來幫助使用者透過提示指定任意 JSON 模式、查詢模型以獲得符合該模式的輸出，並最終將該模式解析為 JSON。

注意

請記住，大型語言模型是洩漏的抽象概念！ 您必須使用具有足夠容量的 LLM 來產生格式良好的 JSON。

JsonOutputParser 是內建選項，用於提示然後解析 JSON 輸出。 雖然它在功能上與 PydanticOutputParser 類似，但它也支援串流傳回部分 JSON 物件。

以下範例說明如何將其與 Pydantic 一起使用，以方便地宣告預期的模式

%pip install -qU langchain langchain-openai

import os
from getpass import getpass

if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = getpass()
from langchain_core.output_parsers import JsonOutputParser
from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI
from pydantic import BaseModel, Field

model = ChatOpenAI(temperature=0)


# 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")


# 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 = JsonOutputParser(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()},
)

chain = prompt | model | parser

chain.invoke({"query": joke_query})
{'setup': "Why couldn't the bicycle stand up by itself?",
 'punchline': 'Because it was two tired!'}

請注意，我們正在將解析器的 format_instructions 直接傳遞到提示中。 您可以也應該嘗試在提示的其他部分新增您自己的格式提示，以增強或取代預設指示

parser.get_format_instructions()
'The output should be formatted as a JSON instance that conforms to the JSON schema below.\n\nAs an example, for the schema {"properties": {"foo": {"title": "Foo", "description": "a list of strings", "type": "array", "items": {"type": "string"}}}, "required": ["foo"]}\nthe object {"foo": ["bar", "baz"]} is a well-formatted instance of the schema. The object {"properties": {"foo": ["bar", "baz"]}} is not well-formatted.\n\nHere is the output schema:\n\`\`\`\n{"properties": {"setup": {"title": "Setup", "description": "question to set up a joke", "type": "string"}, "punchline": {"title": "Punchline", "description": "answer to resolve the joke", "type": "string"}}, "required": ["setup", "punchline"]}\n\`\`\`'

串流​

如上所述，JsonOutputParser 和 PydanticOutputParser 之間的主要區別在於 JsonOutputParser 輸出解析器支援串流部分區塊。 以下是其外觀

for s in chain.stream({"query": joke_query}):
    print(s)
{}
{'setup': ''}
{'setup': 'Why'}
{'setup': 'Why couldn'}
{'setup': "Why couldn't"}
{'setup': "Why couldn't the"}
{'setup': "Why couldn't the bicycle"}
{'setup': "Why couldn't the bicycle stand"}
{'setup': "Why couldn't the bicycle stand up"}
{'setup': "Why couldn't the bicycle stand up by"}
{'setup': "Why couldn't the bicycle stand up by itself"}
{'setup': "Why couldn't the bicycle stand up by itself?"}
{'setup': "Why couldn't the bicycle stand up by itself?", 'punchline': ''}
{'setup': "Why couldn't the bicycle stand up by itself?", 'punchline': 'Because'}
{'setup': "Why couldn't the bicycle stand up by itself?", 'punchline': 'Because it'}
{'setup': "Why couldn't the bicycle stand up by itself?", 'punchline': 'Because it was'}
{'setup': "Why couldn't the bicycle stand up by itself?", 'punchline': 'Because it was two'}
{'setup': "Why couldn't the bicycle stand up by itself?", 'punchline': 'Because it was two tired'}
{'setup': "Why couldn't the bicycle stand up by itself?", 'punchline': 'Because it was two tired!'}

不使用 Pydantic​

您也可以在不使用 Pydantic 的情況下使用 JsonOutputParser。 這將提示模型返回 JSON，但不提供有關模式應為何的具體資訊。

joke_query = "Tell me a joke."

parser = JsonOutputParser()

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

chain = prompt | model | parser

chain.invoke({"query": joke_query})
{'response': "Sure! Here's a joke for you: Why couldn't the bicycle stand up by itself? Because it was two tired!"}

後續步驟​

您現在已學習一種提示模型返回結構化 JSON 的方法。 接下來，查看關於取得結構化輸出的更廣泛指南，以了解其他技術。

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