Raisons d’arrêt et repli

client = anthropic.Anthropic()

response = client.messages.create(
    model="claude-opus-5-5",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello!"}],
)
if response.stop_reason == "end_turn":
    # Traiter la réponse complète
    for block in response.content:
        if block.type == "text":
            print(block.text)

client = anthropic.Anthropic()
# Requête avec un nombre de tokens limité.
response = client.messages.create(
    model="claude-opus-5-5",
    max_tokens=10,
    messages=[{"role": "user", "content": "Explain quantum physics"}],
)

if response.stop_reason == "max_tokens":
    # La réponse a été tronquée.
    print("Response was cut off at token limit")
    # Envisagez d'effectuer une autre requête pour continuer.

client = anthropic.Anthropic()
response = client.messages.create(
    model="claude-opus-5-5",
    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}")

client = anthropic.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):
    """Run a tool and return the result."""
    return f"Weather in {tool_input.get('location', 'unknown')}: 72°F"


response = client.messages.create(
    model="claude-opus-5-5",
    max_tokens=1024,
    tools=[weather_tool],
    messages=[{"role": "user", "content": "What is the weather in San Francisco?"}],
)

if response.stop_reason == "tool_use":
    # Extraire et exécuter l'outil.
    for block in response.content:
        if block.type == "tool_use":
            result = execute_tool(block.name, block.input)
            # Renvoyer le résultat à Claude pour la réponse finale.

response = client.messages.create(
    model="claude-opus-5-5",
    max_tokens=4096,
    tools=[{"type": "web_search_20250305", "name": "web_search"}],
    messages=[{"role": "user", "content": "Search for latest AI news"}],
)

if response.stop_reason == "pause_turn":
    # Poursuivez la conversation en renvoyant la réponse.
    messages = [
        {"role": "user", "content": "Search for latest AI news"},
        {"role": "assistant", "content": response.content},
    ]
    continuation = client.messages.create(
        model="claude-opus-5-5",
        max_tokens=4096,
        messages=messages,
        tools=[{"type": "web_search_20250305", "name": "web_search"}],
    )

client = anthropic.Anthropic()
response = client.messages.create(
    model="claude-opus-5-5",
    max_tokens=1024,
    messages=[{"role": "user", "content": "[Unsafe request]"}],
)

if response.stop_reason == "refusal":
    # Claude a refusé de répondre.
    print("Claude was unable to process this request")
    # Envisagez de reformuler ou de modifier la requête.

client = anthropic.Anthropic()

# Requête avec le nombre maximal de tokens pour obtenir le plus de contenu possible.
response = client.beta.messages.create(
    model="claude-opus-5-5",
    max_tokens=20000,  # Python SDK requires streaming for max_tokens above ~21k
    messages=[
        {"role": "user", "content": "Large input that uses most of context window..."}
    ],
)

if response.stop_reason == "model_context_window_exceeded":
    # La réponse a atteint la limite de la fenêtre de contexte avant max_tokens.
    print("Response reached model's context window limit")
    # La réponse reste valide, mais a été limitée par la fenêtre de contexte.

def handle_response(response):
    match response.stop_reason:
        case "tool_use":
            return handle_tool_use(response)
        case "max_tokens":
            return handle_truncation(response)
        case "model_context_window_exceeded":
            return handle_context_limit(response)
        case "pause_turn":
            return handle_pause(response)
        case "refusal":
            return handle_refusal(response)
        case _:
            # Gérer end_turn et les autres cas.
            return next(
                (block.text for block in response.content if block.type == "text"),
                "",
            )

def handle_truncated_response(response):
    text = next((block.text for block in response.content if block.type == "text"), "")
    if response.stop_reason in ["max_tokens", "model_context_window_exceeded"]:
        if response.stop_reason == "max_tokens":
            note = "[Response truncated due to max_tokens limit]"
        else:
            note = "[Response truncated due to context window limit]"
        return f"{text}\n\n{note}"
    return text

client = anthropic.Anthropic()


def handle_server_tool_conversation(client, user_query, tools, max_requests=5):
    messages = [{"role": "user", "content": user_query}]
    container_id = None

    for _ in range(max_requests):
        response = client.messages.create(
            model="claude-opus-5-5",
            max_tokens=4096,
            messages=messages,
            tools=tools,
            container=container_id,
        )

        if response.stop_reason != "pause_turn":
            # Pas en pause : renvoyer la réponse.
            return response

        # pause_turn : ajouter la réponse en pause comme message assistant, puis continuer.
        messages.append({"role": "assistant", "content": response.content})

        # Si les outils ont tourné dans un conteneur, renvoyer son ID pour conserver ses fichiers.
        if response.container:
            container_id = response.container.id

    # Toujours en pause après max_requests requêtes : renvoyer la dernière réponse.
    return response


response = handle_server_tool_conversation(
    client,
    "Use the code execution tool to find the 1,000th prime number.",
    [{"type": "code_execution_20250825", "name": "code_execution"}],
)
print(response.stop_reason)

client = anthropic.Anthropic()

try:
    response = client.messages.create(
        model="claude-opus-5-5",
        max_tokens=1024,
        messages=[{"role": "user", "content": "Hello!"}],
    )

    # Gérer la réponse réussie avec stop_reason
    if response.stop_reason == "max_tokens":
        print("Response was truncated")

except anthropic.APIStatusError as e:
    # Gérer les erreurs réelles
    match e.status_code:
        case 429:
            print("Rate limit exceeded")
        case 500:
            print("Server error")

client = anthropic.Anthropic()

with client.messages.stream(
    model="claude-opus-5-5",
    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-5-5",
            max_tokens=1024,
            messages=messages,
            tools=tools,
        )

        if response.stop_reason == "tool_use":
            # Exécuter les outils et continuer
            tool_results = execute_tools(response.content)
            messages.append({"role": "assistant", "content": response.content})
            messages.append({"role": "user", "content": tool_results})
        else:
            # Réponse finale
            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-5-5", messages=messages, max_tokens=4096
        )

        full_response += next(
            (block.text for block in response.content if block.type == "text"), ""
        )

        if response.stop_reason != "max_tokens":
            break

        # Reprendre là où il s'est arrêté
        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.beta.messages.create(
        model="claude-opus-5-5",
        messages=[{"role": "user", "content": prompt}],
        max_tokens=20000,  # Python SDK requires streaming for max_tokens above ~21k
    )

    match response.stop_reason:
        case "model_context_window_exceeded":
            # Nombre maximal de tokens possible obtenu compte tenu de la taille de l'entrée.
            print(
                f"Generated {response.usage.output_tokens} tokens (context limit reached)"
            )
        case "max_tokens":
            # Nombre exact de tokens demandés obtenu.
            print(
                f"Generated {response.usage.output_tokens} tokens (max_tokens reached)"
            )
        case _:
            # Fin naturelle de la génération.
            print(
                f"Generated {response.usage.output_tokens} tokens (natural completion)"
            )

    return next((block.text for block in response.content if block.type == "text"), "")