Part 4: Output Parsers（输出解析器）

4.1 概述

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4.2 StrOutputParser

from dotenv import load_dotenv
load_dotenv()

from langchain_openai import ChatOpenAI
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate

model = ChatOpenAI(model="gpt-4o-mini")
parser = StrOutputParser()

# 单独使用
result = model.invoke("解释 Python")
parsed = parser.invoke(result)  # 提取 content 字符串

# 在链中使用（最常见）
chain = ChatPromptTemplate.from_template("解释{concept}") | model | StrOutputParser()
result = chain.invoke({"concept": "机器学习"})  # 直接返回字符串

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4.3 PydanticOutputParser

所有方法

方法	说明	返回类型
get_format_instructions()	获取格式说明	str
invoke(text)	解析文本	BaseModel
parse(text)	解析文本	BaseModel
parse_result(result)	解析模型输出	BaseModel

基本用法

from dotenv import load_dotenv
load_dotenv()

from pydantic import BaseModel, Field
from langchain_openai import ChatOpenAI
from langchain_core.output_parsers import PydanticOutputParser
from langchain_core.prompts import ChatPromptTemplate

class MovieReview(BaseModel):
    title: str = Field(description="电影标题")
    rating: float = Field(description="评分，范围 1-10")
    summary: str = Field(description="一句话简介")
    recommendation: str = Field(description="推荐 或 不推荐")
    pros: list[str] = Field(description="优点列表")
    cons: list[str] = Field(description="缺点列表")

parser = PydanticOutputParser(pydantic_object=MovieReview)
print(parser.get_format_instructions())

prompt = ChatPromptTemplate.from_messages([
    ("system", "你是电影评论家。\n{format_instructions}"),
    ("human", "评价电影《星际穿越》"),
])

chain = prompt.partial(format_instructions=parser.get_format_instructions()) | ChatOpenAI(model="gpt-4o-mini", temperature=0.3) | parser

result = chain.invoke({})
print(f"标题: {result.title}")
print(f"评分: {result.rating}")
print(f"优点: {result.pros}")
print(f"字典: {result.model_dump()}")

嵌套模型

from pydantic import BaseModel, Field
from typing import Optional

class Address(BaseModel):
    city: str = Field(description="城市")
    street: str = Field(description="街道")

class Person(BaseModel):
    name: str = Field(description="姓名")
    age: int = Field(description="年龄")
    address: Address = Field(description="地址")

parser = PydanticOutputParser(pydantic_object=Person)
prompt = ChatPromptTemplate.from_messages([
    ("system", "提取人物信息。\n{format_instructions}"),
    ("human", "张三30岁，住在北京中关村大街1号"),
])
chain = prompt.partial(format_instructions=parser.get_format_instructions()) | ChatOpenAI(model="gpt-4o-mini", temperature=0.0) | parser
result = chain.invoke({})
print(f"城市: {result.address.city}")

可选字段

from pydantic import BaseModel, Field
from typing import Optional

class BookInfo(BaseModel):
    title: str = Field(description="书名")
    author: str = Field(description="作者")
    year: Optional[int] = Field(default=None, description="出版年份")
    genre: Optional[str] = Field(default=None, description="类型")

parser = PydanticOutputParser(pydantic_object=BookInfo)

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4.4 JsonOutputParser

from langchain_openai import ChatOpenAI
from langchain_core.output_parsers import JsonOutputParser
from langchain_core.prompts import ChatPromptTemplate

parser = JsonOutputParser()
prompt = ChatPromptTemplate.from_messages([
    ("system", "请以 JSON 格式输出，包含 name 和 age。"),
    ("human", "张三今年25岁"),
])

chain = prompt | ChatOpenAI(model="gpt-4o-mini", temperature=0.0) | parser
result = chain.invoke({})
print(f"类型: {type(result)}")  # dict
print(f"姓名: {result.get('name')}")

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4.5 CommaSeparatedListOutputParser

from langchain_openai import ChatOpenAI
from langchain_core.output_parsers import CommaSeparatedListOutputParser
from langchain_core.prompts import ChatPromptTemplate

parser = CommaSeparatedListOutputParser()
prompt = ChatPromptTemplate.from_messages([
    ("system", "{format_instructions}"),
    ("human", "列出 5 种编程语言"),
])

chain = prompt.partial(format_instructions=parser.get_format_instructions()) | ChatOpenAI(model="gpt-4o-mini", temperature=0.0) | parser
result = chain.invoke({})
print(f"类型: {type(result)}")  # list

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4.6 StructuredOutput

from dotenv import load_dotenv
load_dotenv()

from pydantic import BaseModel, Field
from typing import TypedDict
from langchain_openai import ChatOpenAI

model = ChatOpenAI(model="gpt-4o-mini", temperature=0.0)

# 方式一：Pydantic 模型
class Recipe(BaseModel):
    name: str = Field(description="菜名")
    ingredients: list[str] = Field(description="食材列表")
    steps: list[str] = Field(description="烹饪步骤")
    cooking_time: int = Field(description="烹饪时间（分钟）")

structured_model = model.with_structured_output(Recipe)
result = structured_model.invoke("教我做番茄炒蛋")
print(f"菜名: {result.name}")
print(f"食材: {result.ingredients}")

# 方式二：TypedDict（1.3 新特性）
class MovieInfo(TypedDict):
    title: str
    year: int
    director: str
    rating: float

typed_model = model.with_structured_output(MovieInfo)
result = typed_model.invoke("告诉我电影《盗梦空间》的信息")
print(result)  # dict

# 方式三：JSON Schema
json_schema = {
    "type": "object",
    "properties": {
        "sentiment": {"type": "string", "enum": ["positive", "negative", "neutral"]},
        "confidence": {"type": "number"},
    },
    "required": ["sentiment", "confidence"],
}
schema_model = model.with_structured_output(json_schema)
result = schema_model.invoke("这个产品太好用了！")
print(result)

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4.7 自定义解析器

import re
from langchain_core.output_parsers import BaseOutputParser
from langchain_core.exceptions import OutputParserException
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

class NumberExtractorParser(BaseOutputParser[int]):
    def parse(self, text: str) -> int:
        match = re.search(r'\d+', text)
        if match:
            return int(match.group())
        raise OutputParserException(f"无法提取数字: {text}")

    @property
    def _type(self) -> str:
        return "number_extractor"

parser = NumberExtractorParser()
print(parser.parse("评分是 8.5 分"))  # 8

# 在链中使用
prompt = ChatPromptTemplate.from_messages([
    ("system", "只回答数字"),
    ("human", "Python 有多少个关键字？"),
])
chain = prompt | ChatOpenAI(model="gpt-4o-mini", temperature=0.0) | NumberExtractorParser()
result = chain.invoke({})
print(f"结果: {result} (类型: {type(result).__name__})")

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4.8 输出解析最佳实践

场景	推荐解析器	原因
只需要纯文本	StrOutputParser	简单直接
结构化数据	with_structured_output	最简洁可靠
复杂嵌套结构	PydanticOutputParser	支持嵌套
灵活 JSON	JsonOutputParser	无需定义模型
列表	CommaSeparatedListOutputParser	简单列表
特殊格式	自定义解析器	完全自定义

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