建立響應

from openai import OpenAI
import json
import os
import random

# Initialize client
client = OpenAI(
    # If environment variable is not set, replace with: api_key="sk-xxx"
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # 請將{WorkspaceId}替換為真實的業務空間ID，各地區URL不同。
    base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1",
)
# Simulate user question
USER_QUESTION = "What's the weather like in Beijing?"
# Define tool list
tools = [
    {
        "type": "function",
        "name": "get_current_weather",
        "description": "Useful for querying the weather of a specified city.",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {
                    "type": "string",
                    "description": "City or district, e.g. Beijing, Hangzhou, etc.",
                }
            },
            "required": ["location"],
        },
    }
]

# Simulate weather query tool
def get_current_weather(arguments):
    weather_conditions = ["sunny", "cloudy", "rainy"]
    random_weather = random.choice(weather_conditions)
    location = arguments["location"]
    return f"Today in {location} it is {random_weather}."

# Wrapper for model response
def get_response(input_data):
    response = client.responses.create(
        model="qwen3.8-max",
        input=input_data,
        tools=tools,
    )
    return response

# Maintain conversation context
conversation = [{"role": "user", "content": USER_QUESTION}]

response = get_response(conversation)
function_calls = [item for item in response.output if item.type == "function_call"]
# If no tool calls needed, output content directly
if not function_calls:
    print(f"Final response: {response.output_text}")
else:
    # Enter tool call loop
    while function_calls:
        for fc in function_calls:
            func_name = fc.name
            arguments = json.loads(fc.arguments)
            print(f"Calling tool [{func_name}], arguments: {arguments}")
            # Execute tool
            tool_result = get_current_weather(arguments)
            print(f"Tool returned: {tool_result}")
            # Append tool call and result as pairs to context
            conversation.append(
                {
                    "type": "function_call",
                    "name": fc.name,
                    "arguments": fc.arguments,
                    "call_id": fc.call_id,
                }
            )
            conversation.append(
                {
                    "type": "function_call_output",
                    "call_id": fc.call_id,
                    "output": tool_result,
                }
            )
        # Call model again with full context
        response = get_response(conversation)
        function_calls = [
            item for item in response.output if item.type == "function_call"
        ]
    print(f"Final response: {response.output_text}")

import os
from openai import OpenAI

client = OpenAI(
    # 若沒有配置環境變數，請用百鍊API Key將下行替換為：api_key="sk-xxx"
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    base_url="https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1",
)

response = client.responses.create(
    model="qwen3.5-ocr",
    input=[
        {
            "role": "user",
            "content": [
                {
                    "type": "input_file",
                    "file_url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20260616/qmycjl/1506.02640v5.pdf",
                },
                {
                    "type": "input_text",
                    "text": "Read all the text in the file.",
                },
            ],
        }
    ],
    extra_body={
        "ocr_options": {"task": "document_parsing"}
    },
)

print(response.output_text)

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # 請將{WorkspaceId}替換為真實的業務空間ID，各地區URL不同。
    base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1",
    # 通過 default_headers 開啟 Session 緩衝
    default_headers={"x-dashscope-session-cache": "enable"}
)

# 構造超過 1024 Token 的長文本，確保能觸發緩衝建立（若未達到1024 Token，後續累積對話上下文超過1024 Token時將觸發緩衝建立）
long_context = "人工智慧是電腦科學的一個重要分支，致力於研究和開發能夠類比、延伸和擴充人類智能的理論、方法、技術及應用系統。" * 50

# 第一輪對話
response1 = client.responses.create(
    model="qwen3.8-max",
    input=long_context + "\n\n基於以上背景知識，請簡短介紹機器學習中的隨機森林演算法。",
)
print(f"第一輪迴複: {response1.output_text}")

# 第二輪對話：通過 previous_response_id 關聯上下文，緩衝由服務端自動處理
response2 = client.responses.create(
    model="qwen3.8-max",
    input="它和 GBDT 有什麼主要區別？",
    extra_body={"previous_response_id": response1.id},
)
print(f"第二輪迴複: {response2.output_text}")

# 查看快取命中情況
usage = response2.usage
print(f"輸入 Token: {usage.input_tokens}")
print(f"快取命中 Token: {usage.input_tokens_details.cached_tokens}")