之前的文章提到通义千问API无法通过with_structured_output/json schema的方式支持结构化输出，如果就是想使用通义千问大模型做结构化输出，应该怎么办呢？有两种办法
使用Ollama来运行通义千问大模型
从Ollama博客文章 Structured output 中了解到，Ollama已经支持结构化输出了，这个功能是在Ollama 0.5.0版本 引入的。通过Ollama把qwen3大模型在本地运行起来，使用下面的代码就能看到效果。
from langchain.chat_models import init_chat_model
from langchain_core.prompts import ChatPromptTemplate
from pydantic import BaseModel, Field llm = init_chat_model( model_provider="openai", model="qwen3:8b", base_url="http://localhost:11434/v1", api_key="123456"
) tagging_prompt = ChatPromptTemplate.from_template( """ Extract the desired information from the following passage. Only extract the properties mentioned in the 'Classification' function. Passage: {input} """) class Classification(BaseModel): sentiment: str = Field(description="The sentiment of the text") aggressiveness: int = Field(description="How aggressive the text is on a scale from 1 to 10") language: str = Field(description="The language the text is written in") structured_llm = llm.with_structured_output(Classification) inp = "Estoy increiblemente contento de haberte conocido! Creo que seremos muy buenos amigos!"
prompt = tagging_prompt.invoke({"input": inp})
response = structured_llm.invoke(prompt)
print(response)
程序能够正常运行并打印出Classification这个结构化的类对象内容（如下）
sentiment='positive' aggressiveness=0 language='Spanish'
如果把代码中llm对象构造时的参数换回通义千问API的值，也即是改为
llm = init_chat_model( model_provider="openai", model="qwen-plus-latest", base_url="https://dashscope.aliyuncs.com/compatible-mode/v1", api_key=os.environ["QWEN_API_KEY"]
)
会发现之前提到的错误再次出现了，这证明不是通义千问大模型不支持结构化输出，而是通义千问API服务不支持。
如果不想使用Ollama本地运行大模型，而是想使用通义千问API服务支持结构化输出，该怎么办呢？
使用 PydanticOutputParser 自动生成提示词
要使用结构化输出，通义千问文档里提到
您可以在提示词中明确描述所需 JSON 的键值结构和数据类型，并提供标准数据样例，这会帮助大模型达到类似效果。
借助 LangChain 的 PydanticOutputParser 可以帮我们自动生成数据结构的提示词。参考下面的代码
import os
from langchain_core.output_parsers import PydanticOutputParser
from langchain_core.prompts import ChatPromptTemplate
from pydantic import BaseModel, Field
from langchain.chat_models import init_chat_model llm = init_chat_model( model_provider="openai", model="qwen-plus-latest", base_url="https://dashscope.aliyuncs.com/compatible-mode/v1", api_key=os.environ["QWEN_API_KEY"]
) class Classification(BaseModel): sentiment: str = Field(description="The sentiment of the text") aggressiveness: int = Field(description="How aggressive the text is on a scale from 1 to 10") language: str = Field(description="The language the text is written in") parser = PydanticOutputParser(pydantic_object=Classification) tagging_prompt = ChatPromptTemplate.from_messages([ ("system", "Extract the desired information into JSON format from the following passage." "{format_instructions}" ), ("human", "{input}")
]).partial(format_instructions=parser.get_format_instructions()) inp = "Estoy increiblemente contento de haberte conocido! Creo que seremos muy buenos amigos!"
print(tagging_prompt.invoke({"input": inp})) chain = tagging_prompt | llm | parser
response = chain.invoke({"input": inp})
print(response)
第30行代码会打印出格式化后的提示词，第一行后面的内容都是API根据提供的类结构生成的提示词。
Extract the desired information into JSON format from the following passage.The output should be formatted as a JSON instance that conforms to the JSON schema below.As an example, for the schema {"properties": {"foo": {"title": "Foo", "description": "a list of strings", "type": "array", "items": {"type": "string"}}}, "required": ["foo"]}the object {"foo": ["bar", "baz"]} is a well-formatted instance of the schema. The object {"properties": {"foo": ["bar", "baz"]}} is not well-formatted.Here is the output schema:{"properties": {"sentiment": {"description": "The sentiment of the text","title": "Sentiment","type": "string"},"aggressiveness": {"description": "How aggressive the text is on a scale from 1 to 10","title": "Aggressiveness","type": "integer"},"language": {"description": "The language the text is written in","title": "Language","type": "string"}},"required": ["sentiment","aggressiveness","language"]}
真是太方便了，这下可以高效的从文档里提取结构化数据啦。
思考
从 Ollama 0.5.0的代码修改记录 来看，Ollama只用了很少的代码修改就支持了结构化输出。那么通义千问API服务什么时候能支持起来呢？
