大模型结构化输出示例

from anthropic import BaseModel
from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI
from pydantic import Field


model_name = 'qwen3-vl-plus'
base_url = "https://dashscope.aliyuncs.com/compatible-mode/v1"  # 本地模型用：http://localhost:6006/v1

api_key = 'sk-cbe6d658ada24abbbb8abc2333fcc1ba'  # 本地模型用：非空字符串
extra_body = {"enable_thinking": True, "thinking_budget": 81920}

llm = ChatOpenAI(
    model=model_name,
    api_key=api_key,
    base_url=base_url,
    temperature=0.8,
    extra_body=extra_body,
)


class Joke(BaseModel):
    title: str = Field(description="笑话的标题")
    content: str = Field(description="笑话的内容")
    parse: str = Field(description="笑点的解释说明")
    score: float = Field(description="笑话的评分，范围是0到100")


prompt_template = PromptTemplate.from_template("帮我生成一个关于{topic}的笑话。")
# runnable = llm.with_structured_output(Joke)
runnable = llm.bind_tools([Joke])

chain = prompt_template | runnable

resp = chain.invoke({"topic": "美女"})

print(resp.tool_calls[0].get('args'))