之前的文章提到通義千問API無法通過with_structured_output/json schema的方式支持結構化輸出，如果就是想使用通義千問大模型做結構化輸出，應該怎么辦呢？有兩種辦法

使用Ollama來運行通義千問大模型

從Ollama博客文章 Structured output 中了解到，Ollama已經支持結構化輸出了，這個功能是在Ollama 0.5.0版本 引入的。通過Ollama把qwen3大模型在本地運行起來，使用下面的代碼就能看到效果。

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)

程序能夠正常運行并打印出Classification這個結構化的類對象內容（如下）

sentiment='positive' aggressiveness=0 language='Spanish'

如果把代碼中llm對象構造時的參數換回通義千問API的值，也即是改為

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"]
)

會發現之前提到的錯誤再次出現了，這證明不是通義千問大模型不支持結構化輸出，而是通義千問API服務不支持。

如果不想使用Ollama本地運行大模型，而是想使用通義千問API服務支持結構化輸出，該怎么辦呢？

使用 PydanticOutputParser 自動生成提示詞

要使用結構化輸出，通義千問文檔里提到

您可以在提示詞中明確描述所需 JSON 的鍵值結構和數據類型，并提供標準數據樣例，這會幫助大模型達到類似效果。

借助 LangChain 的 PydanticOutputParser 可以幫我們自動生成數據結構的提示詞。參考下面的代碼

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行代碼會打印出格式化后的提示詞，第一行后面的內容都是API根據提供的類結構生成的提示詞。

    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，這下可以高效的從文檔里提取結構化數據啦。

思考

從 Ollama 0.5.0的代碼修改記錄 來看，Ollama只用了很少的代碼修改就支持了結構化輸出。那么通義千問API服務什么時候能支持起來呢？