结构化输出

from openai import OpenAI
import os

client = OpenAI(
  # 若没有配置环境变量，请将下行替换为：api_key="sk-xxx"
  api_key=os.getenv("DASHSCOPE_API_KEY"),
  base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
)

completion = client.chat.completions.create(
  model="qwen3.8-max",
  messages=[
    {"role": "system", "content": "请抽取用户的姓名与年龄信息，以JSON格式返回"},
    {"role": "user", "content": "大家好，我叫刘五，今年34岁，邮箱是liuwu@example.com，平时喜欢打篮球和旅游"},
  ],
  response_format={"type": "json_object"},
)
json_string = completion.choices[0].message.content
print(json_string)

import os
from openai import OpenAI

client = OpenAI(
  api_key=os.getenv("DASHSCOPE_API_KEY"),
  base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
)

completion = client.chat.completions.create(
  model="qwen3.8-max",
  messages=[
    {
      "role": "system",
      "content": [{"type": "text", "text": "You are a helpful assistant."}],
    },
    {
      "role": "user",
      "content": [
        {
          "type": "image_url",
          "image_url": {
            "url": "http://duguang-labelling.oss-cn-shanghai.aliyuncs.com/demo_ocr/receipt_zh_demo.jpg"
          },
        },
        {"type": "text", "text": "提取图中ticket(数组类型，包括 travel_date、trains、seat_num、arrival_site、price)和 invoice 的信息（数组类型，包括 invoice_code 和 invoice_number ），请输出包含 ticket 和 invoice 数组的JSON"},
      ],
    },
  ],
  response_format={"type": "json_object"},
)
json_string = completion.choices[0].message.content
print(json_string)

from openai import OpenAI
import os

client = OpenAI(
  api_key=os.getenv("DASHSCOPE_API_KEY"),
  base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
)

messages = [
  {"role": "system", "content": "请抽取用户的姓名与年龄信息，以JSON格式返回"},
  {"role": "user", "content": "大家好，我叫刘五，今年34岁，邮箱是liuwu@example.com，平时喜欢打篮球和旅游"},
]

completion = client.chat.completions.create(
  model="qwen3.8-max",
  messages=messages,
  extra_body={"enable_thinking": True},
  stream=True,
  stream_options={"include_usage": True},
  response_format={"type": "json_object"},
)

reasoning_content = ""  # 完整思考过程
answer_content = ""     # 完整回复
is_answering = False    # 是否进入回复阶段

print("\n" + "=" * 20 + "思考过程" + "=" * 20 + "\n")
for chunk in completion:
  if not chunk.choices:
    print("\nUsage:")
    print(chunk.usage)
    continue
  delta = chunk.choices[0].delta
  # 只收集思考内容
  if hasattr(delta, "reasoning_content") and delta.reasoning_content is not None:
    if not is_answering:
      print(delta.reasoning_content, end="", flush=True)
    reasoning_content += delta.reasoning_content
  # 收到 content，开始进行回复
  if hasattr(delta, "content") and delta.content:
    if not is_answering:
      print("\n" + "=" * 20 + "完整回复" + "=" * 20 + "\n")
      is_answering = True
    print(delta.content, end="", flush=True)
    answer_content += delta.content

from openai import OpenAI
import os

client = OpenAI(
  api_key=os.getenv("DASHSCOPE_API_KEY"),
  base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
)

system_prompt = """..."""  # 替换为上文的 System Prompt

completion = client.chat.completions.create(
  model="qwen3.8-max",
  messages=[
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": "大家好，我叫刘五，今年34岁，邮箱是liuwu@example.com，平时喜欢打篮球和旅游"},
  ],
  response_format={"type": "json_object"},
)
print(completion.choices[0].message.content)

from pydantic import BaseModel, Field
from openai import OpenAI
import os

client = OpenAI(
  api_key=os.getenv("DASHSCOPE_API_KEY"),
  base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
)

class UserInfo(BaseModel):
  name: str = Field(description="用户的姓名")
  age: int = Field(description="用户的年龄，单位为岁")

completion = client.chat.completions.parse(
  model="qwen3.8-max",
  messages=[
    {"role": "system", "content": "提取姓名与年龄信息。"},
    {"role": "user", "content": "我叫刘五，今年25岁。"},
  ],
  response_format=UserInfo,          # ← 直接传入 Pydantic 类
)
result = completion.choices[0].message.parsed
print(f"姓名：{result.name}，年龄：{result.age}")