如何解析 YAML 输出

前置条件

本指南假设您熟悉以下概念：

来自不同提供商的大型语言模型（LLMs）往往因其训练数据的具体差异而各具优势。这也意味着，在生成非 JSON 格式的输出时，某些模型可能表现更佳且更为可靠。

此输出解析器允许用户指定任意模式，并使用 YAML 格式化响应来查询大语言模型（LLM），以生成符合该模式的输出。

注意

请记住，大型语言模型是渗漏的抽象！您必须使用具有足够容量以生成格式良好 YAML 的 LLM。

%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()

我们使用 Pydantic 配合 YamlOutputParser 来声明我们的数据模型，并为模型提供更多上下文信息，以明确其应生成何种类型的 YAML：

from langchain.output_parsers import YamlOutputParser
from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI
from pydantic import BaseModel, Field


# 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")


model = ChatOpenAI(temperature=0)

# 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 = YamlOutputParser(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})

API 参考:YamlOutputParser |提示模板 |ChatOpenAI

Joke(setup="Why couldn't the bicycle find its way home?", punchline='Because it lost its bearings!')

解析器将自动解析输出的 YAML 并创建一个包含该数据的 Pydantic 模型。我们可以看到添加到提示中的解析器的format_instructions：

parser.get_format_instructions()

'The output should be formatted as a YAML instance that conforms to the given JSON schema below.\n\n# Examples\n## Schema\n\`\`\`\n{"title": "Players", "description": "A list of players", "type": "array", "items": {"$ref": "#/definitions/Player"}, "definitions": {"Player": {"title": "Player", "type": "object", "properties": {"name": {"title": "Name", "description": "Player name", "type": "string"}, "avg": {"title": "Avg", "description": "Batting average", "type": "number"}}, "required": ["name", "avg"]}}}\n\`\`\`\n## Well formatted instance\n\`\`\`\n- name: John Doe\n  avg: 0.3\n- name: Jane Maxfield\n  avg: 1.4\n\`\`\`\n\n## Schema\n\`\`\`\n{"properties": {"habit": { "description": "A common daily habit", "type": "string" }, "sustainable_alternative": { "description": "An environmentally friendly alternative to the habit", "type": "string"}}, "required": ["habit", "sustainable_alternative"]}\n\`\`\`\n## Well formatted instance\n\`\`\`\nhabit: Using disposable water bottles for daily hydration.\nsustainable_alternative: Switch to a reusable water bottle to reduce plastic waste and decrease your environmental footprint.\n\`\`\` \n\nPlease follow the standard YAML formatting conventions with an indent of 2 spaces and make sure that the data types adhere strictly to the following JSON 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\`\`\`\n\nMake sure to always enclose the YAML output in triple backticks (\`\`\`). Please do not add anything other than valid YAML output!'

您可以并且应该在提示的其他部分添加您自己的格式提示，以增强或替换默认指令。

下一步

您现在已学会如何提示模型返回 YAML。接下来，请查看关于获取结构化输出的更广泛指南，了解其他相关技术。

下一步​

下一步