停止原因與備援

{
  "id": "msg_01234",
  "type": "message",
  "role": "assistant",
  "content": [
    {
      "type": "text",
      "text": "Here's the answer to your question..."
    }
  ],
  "stop_reason": "end_turn",
  "stop_sequence": null,
  "usage": {
    "input_tokens": 100,
    "output_tokens": 50
  }
}

from anthropic import Anthropic

client = Anthropic()
response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello!"}],
)
if response.stop_reason == "end_turn":
    # 處理完整的回應
    print(response.content[0].text)

# 錯誤做法：在 tool_result 之後立即加入文字
messages = [
    {"role": "user", "content": "Calculate the sum of 1234 and 5678"},
    {
        "role": "assistant",
        "content": [
            {
                "type": "tool_use",
                "id": "toolu_123",
                "name": "calculator",
                "input": {"operation": "add", "a": 1234, "b": 5678},
            }
        ],
    },
    {
        "role": "user",
        "content": [
            {"type": "tool_result", "tool_use_id": "toolu_123", "content": "6912"},
            {
                "type": "text",
                "text": "Here's the result",  # Don't add text after tool_result
            },
        ],
    },
]

# 正確做法：直接傳送工具結果，不附加額外文字
messages = [
    {"role": "user", "content": "Calculate the sum of 1234 and 5678"},
    {
        "role": "assistant",
        "content": [
            {
                "type": "tool_use",
                "id": "toolu_123",
                "name": "calculator",
                "input": {"operation": "add", "a": 1234, "b": 5678},
            }
        ],
    },
    {
        "role": "user",
        "content": [
            {"type": "tool_result", "tool_use_id": "toolu_123", "content": "6912"}
        ],
    },  # Just the tool_result, no additional text
]


# 如果修正訊息結構後仍收到空回應：
def handle_empty_response(client, messages):
    response = client.messages.create(
        model="claude-opus-4-8", max_tokens=1024, messages=messages
    )

    # 檢查回應是否為空
    if response.stop_reason == "end_turn" and not response.content:
        # 錯誤做法：不要直接用空回應重試
        # 這樣無效，因為 Claude 已判定任務完成

        # 正確做法：在新的使用者訊息中加入接續提示
        messages.append({"role": "user", "content": "Please continue"})

        response = client.messages.create(
            model="claude-opus-4-8", max_tokens=1024, messages=messages
        )

    return response

# 使用有限 token 數的請求
response = client.messages.create(
    model="claude-opus-4-8",
    max_tokens=10,
    messages=[{"role": "user", "content": "Explain quantum physics"}],
)

if response.stop_reason == "max_tokens":
    # 回應已被截斷
    print("Response was cut off at token limit")
    # 請考慮發出另一個請求以繼續

response = client.messages.create(
    model="claude-opus-4-8",
    max_tokens=1024,
    stop_sequences=["END", "STOP"],
    messages=[{"role": "user", "content": "Generate text until you say END"}],
)

if response.stop_reason == "stop_sequence":
    print(f"Stopped at sequence: {response.stop_sequence}")

from anthropic import Anthropic

client = Anthropic()
weather_tool = {
    "name": "get_weather",
    "description": "Get the current weather in a given location",
    "input_schema": {
        "type": "object",
        "properties": {
            "location": {"type": "string", "description": "City and state"},
        },
        "required": ["location"],
    },
}


def execute_tool(name, tool_input):
    """Execute a tool and return the result."""
    return f"Weather in {tool_input.get('location', 'unknown')}: 72°F"


response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    tools=[weather_tool],
    messages=[{"role": "user", "content": "What's the weather?"}],
)

if response.stop_reason == "tool_use":
    # 提取並執行工具
    for content in response.content:
        if content.type == "tool_use":
            result = execute_tool(content.name, content.input)
            # 將結果回傳給 Claude 以取得最終回應

response = client.messages.create(
    model="claude-opus-4-8",
    max_tokens=1024,
    tools=[{"type": "web_search_20250305", "name": "web_search"}],
    messages=[{"role": "user", "content": "Search for latest AI news"}],
)

if response.stop_reason == "pause_turn":
    # 透過將回應傳回以繼續對話
    messages = [
        {"role": "user", "content": original_query},
        {"role": "assistant", "content": response.content},
    ]
    continuation = client.messages.create(
        model="claude-opus-4-8",
        max_tokens=1024,
        messages=messages,
        tools=[{"type": "web_search_20250305", "name": "web_search"}],
    )

response = client.messages.create(
    model="claude-opus-4-8",
    max_tokens=1024,
    messages=[{"role": "user", "content": "[Unsafe request]"}],
)

if response.stop_reason == "refusal":
    # Claude 拒絕回應
    print("Claude was unable to process this request")
    # 請考慮重新措辭或修改請求

# 以最大 token 數發出請求，盡可能取得更多內容
response = client.messages.create(
    model="claude-opus-4-8",
    max_tokens=20000,  # Python SDK requires streaming for max_tokens above ~21k (Opus 4.8 supports 128k with streaming)
    messages=[
        {"role": "user", "content": "Large input that uses most of context window..."}
    ],
)

if response.stop_reason == "model_context_window_exceeded":
    # 回應在達到 max_tokens 之前已觸及上下文視窗限制
    print("Response reached model's context window limit")
    # 回應仍然有效，但受到上下文視窗的限制

def handle_response(response):
    if response.stop_reason == "tool_use":
        return handle_tool_use(response)
    elif response.stop_reason == "max_tokens":
        return handle_truncation(response)
    elif response.stop_reason == "model_context_window_exceeded":
        return handle_context_limit(response)
    elif response.stop_reason == "pause_turn":
        return handle_pause(response)
    elif response.stop_reason == "refusal":
        return handle_refusal(response)
    else:
        # 處理 end_turn 及其他情況
        return response.content[0].text

def handle_truncated_response(response):
    if response.stop_reason in ["max_tokens", "model_context_window_exceeded"]:
        # 選項 1：警告使用者已達到特定限制
        if response.stop_reason == "max_tokens":
            message = "[Response truncated due to max_tokens limit]"
        else:
            message = "[Response truncated due to context window limit]"
        return f"{response.content[0].text}\n\n{message}"

        # 選項 2：繼續生成
        messages = [
            {"role": "user", "content": original_prompt},
            {"role": "assistant", "content": response.content[0].text},
        ]
        continuation = client.messages.create(
            model="claude-opus-4-8",
            max_tokens=1024,
            messages=messages + [{"role": "user", "content": "Please continue"}],
        )
        return response.content[0].text + continuation.content[0].text

def handle_server_tool_conversation(client, user_query, tools, max_continuations=5):
    """
    Handle server tool conversations that may require multiple continuations.

    The server runs a sampling loop when executing server tools. If the loop
    reaches its iteration limit, the API returns pause_turn. Continue the
    conversation by sending the response back to let Claude finish.
    """
    messages = [{"role": "user", "content": user_query}]

    for _ in range(max_continuations):
        response = client.messages.create(
            model="claude-opus-4-8", max_tokens=1024, messages=messages, tools=tools
        )

        if response.stop_reason != "pause_turn":
            # Claude 已完成處理 - 回傳最終回應
            return response

        # pause_turn：替換整個訊息列表以維持角色交替
        messages = [
            {"role": "user", "content": user_query},
            {"role": "assistant", "content": response.content},
        ]

    # 已達到最大續接次數 - 回傳最後一個回應
    return response

import anthropic
from anthropic import Anthropic

client = Anthropic()

try:
    response = client.messages.create(
        model="claude-sonnet-4-20250514",
        max_tokens=1024,
        messages=[{"role": "user", "content": "Hello!"}],
    )

    # 處理帶有 stop_reason 的成功回應
    if response.stop_reason == "max_tokens":
        print("Response was truncated")

except anthropic.APIStatusError as e:
    # 處理實際錯誤
    if e.status_code == 429:
        print("Rate limit exceeded")
    elif e.status_code == 500:
        print("Server error")

from anthropic import Anthropic

client = Anthropic()

with client.messages.stream(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello!"}],
) as stream:
    for event in stream:
        if event.type == "message_delta":
            stop_reason = event.delta.stop_reason
            if stop_reason:
                print(f"Stream ended with: {stop_reason}")

def complete_tool_workflow(client, user_query, tools):
    messages = [{"role": "user", "content": user_query}]

    while True:
        response = client.messages.create(
            model="claude-opus-4-8", max_tokens=1024, messages=messages, tools=tools
        )

        if response.stop_reason == "tool_use":
            # 執行工具並繼續
            tool_results = execute_tools(response.content)
            messages.append({"role": "assistant", "content": response.content})
            messages.append({"role": "user", "content": tool_results})
        else:
            # 最終回應
            return response

def get_complete_response(client, prompt, max_attempts=3):
    messages = [{"role": "user", "content": prompt}]
    full_response = ""

    for _ in range(max_attempts):
        response = client.messages.create(
            model="claude-opus-4-8", messages=messages, max_tokens=4096
        )

        full_response += response.content[0].text

        if response.stop_reason != "max_tokens":
            break

        # 從中斷處繼續
        messages = [
            {"role": "user", "content": prompt},
            {"role": "assistant", "content": full_response},
            {"role": "user", "content": "Please continue from where you left off."},
        ]

    return full_response

def get_max_possible_tokens(client, prompt):
    """
    Get as many tokens as possible within the model's context window
    without needing to calculate input token count
    """
    response = client.messages.create(
        model="claude-opus-4-8",
        messages=[{"role": "user", "content": prompt}],
        max_tokens=20000,  # Python SDK requires streaming for max_tokens above ~21k
    )

    if response.stop_reason == "model_context_window_exceeded":
        # 在給定輸入大小下取得最大可能的 token 數
        print(
            f"Generated {response.usage.output_tokens} tokens (context limit reached)"
        )
    elif response.stop_reason == "max_tokens":
        # 取得的 token 數與請求完全相符
        print(f"Generated {response.usage.output_tokens} tokens (max_tokens reached)")
    else:
        # 自然完成
        print(f"Generated {response.usage.output_tokens} tokens (natural completion)")

    return response.content[0].text