Anthropic相容-Messages

import anthropic
import os

client = anthropic.Anthropic(
    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/apps/anthropic",
)

stream = client.messages.create(
    model="qwen3.8-max",
    max_tokens=1024,
    stream=True,
    messages=[
        {
            "role": "user",
            "content": "請簡單介紹一下人工智慧。"
        }
    ],
    thinking={"type": "disabled"},
)

for chunk in stream:
    if chunk.type == "content_block_delta":
        if hasattr(chunk.delta, 'text'):
            print(chunk.delta.text, end="", flush=True)

import anthropic
import os

client = anthropic.Anthropic(
    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/apps/anthropic",
)

stream = client.messages.create(
    model="qwen3.8-max",
    max_tokens=2048,
    stream=True,
    thinking={
        "type": "enabled",
        "budget_tokens": 1024
    },
    messages=[
        {
            "role": "user",
            "content": "分析一下量子計算的發展前景。"
        }
    ]
)

for chunk in stream:
    if chunk.type == "content_block_delta":
        if hasattr(chunk.delta, 'thinking'):
            print(chunk.delta.thinking, end="", flush=True)
        elif hasattr(chunk.delta, 'text'):
            print(chunk.delta.text, end="", flush=True)

import anthropic
import os

client = anthropic.Anthropic(
    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/apps/anthropic",
)

stream = client.messages.create(
    model="qwen3.8-max",
    max_tokens=1024,
    stream=True,
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "source": {
                        "type": "url",
                        "url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20250414/mqqmiy/animal_01.jpg",
                    },
                },
                {
                    "type": "text",
                    "text": "描述這張圖片的內容。"
                },
            ],
        }
    ],
    thinking={"type": "disabled"},
)

for chunk in stream:
    if chunk.type == "content_block_delta":
        if hasattr(chunk.delta, 'text'):
            print(chunk.delta.text, end="", flush=True)

import anthropic
import os

client = anthropic.Anthropic(
    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/apps/anthropic",
)

stream = client.messages.create(
    model="qwen3.8-max",
    max_tokens=1024,
    stream=True,
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "video",
                    "source": {
                        "type": "url",
                        "url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20251208/zpupby/3e81ef38-98f0-4d55-bbb6-259334ca18d0.mp4",
                    },
                },
                {
                    "type": "text",
                    "text": "描述這段視頻的內容。"
                },
            ],
        }
    ],
    thinking={"type": "disabled"},
)

for chunk in stream:
    if chunk.type == "content_block_delta":
        if hasattr(chunk.delta, 'text'):
            print(chunk.delta.text, end="", flush=True)

import anthropic
import os

client = anthropic.Anthropic(
    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/apps/anthropic",
)

tools = [
    {
        "name": "get_weather",
        "description": "擷取指定城市的天氣資訊",
        "input_schema": {
            "type": "object",
            "properties": {
                "city": {
                    "type": "string",
                    "description": "城市名稱"
                }
            },
            "required": ["city"]
        }
    }
]

message = client.messages.create(
    model="qwen3.8-max",
    max_tokens=1024,
    tools=tools,
    messages=[
        {
            "role": "user",
            "content": "杭州今天天氣怎麼樣？"
        }
    ]
)

print(message.content)

import anthropic
import os

client = anthropic.Anthropic(
    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/apps/anthropic",
)

# 類比代碼倉庫內容，需達到最小可緩衝長度（1024 Token）
long_text_content = "<Your Code Here>" * 400

def get_completion(user_input):
    response = client.messages.create(
        # 選擇支援顯式緩衝的模型
        model="qwen3.8-max",
        max_tokens=1024,
        system=[
            {
                "type": "text",
                "text": long_text_content,
                # 在 text 塊上添加 cache_control 即標記緩衝斷點；也可放在 messages 數組的 content 塊上
                "cache_control": {"type": "ephemeral"},
            }
        ],
        messages=[
            {"role": "user", "content": user_input},
        ],
    )
    return response

# 第一次請求：建立緩衝
first = get_completion("這段代碼的內容是什麼")
print(f"建立緩衝 Token：{first.usage.cache_creation_input_tokens}")
print(f"命中緩衝 Token：{first.usage.cache_read_input_tokens}")
print("=" * 20)
# 第二次請求：長內容相同，僅修改提問 → 命中緩衝
second = get_completion("這段代碼可以怎麼最佳化")
print(f"建立緩衝 Token：{second.usage.cache_creation_input_tokens}")
print(f"命中緩衝 Token：{second.usage.cache_read_input_tokens}")

import anthropic
import os

client = anthropic.Anthropic(
    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/apps/anthropic",
)

message = client.messages.create(
    model="deepseek-v4-pro",
    max_tokens=1024,
    messages=[
        {
            "role": "user",
            "content": "提取以下郵件的關鍵資訊：張三 (zhangsan@example.com) 對企業版方案感興趣，希望預約下周二下午 2 點的示範。"
        }
    ],
    extra_body={
        "output_config": {
            "format": {
                "type": "json_schema",
                "schema": {
                    "type": "object",
                    "properties": {
                        "name": {"type": "string"},
                        "email": {"type": "string"},
                        "plan_interest": {"type": "string"},
                        "demo_requested": {"type": "boolean"}
                    },
                    "required": ["name", "email", "plan_interest", "demo_requested"],
                    "additionalProperties": False
                }
            }
        }
    },
)

# deepseek-v4-pro 模型會返回 thinking 塊，需要找到 type='text' 的內容塊
text_block = next(block for block in message.content if block.type == "text")
print(text_block.text)