Anthropic

stream = client.chat.completions.create(
    model="openai-main/gpt-4o-mini",
    messages=[{"role": "user", "content": "Count from 1 to 5."}],
    stream=True,
)
for chunk in stream:
    if (
        chunk.choices
        and len(chunk.choices) > 0
        and chunk.choices[0].delta.content is not None
    ):
        print(chunk.choices[0].delta.content, end="", flush=True)

import json

tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "Get the current weather for a city.",
        "parameters": {
            "type": "object",
            "properties": {"city": {"type": "string"}},
            "required": ["city"],
        },
    },
}]

messages = [{"role": "user", "content": "Weather in Bengaluru?"}]
first = client.chat.completions.create(
    model="anthropic-main/claude-sonnet-4-20250514",
    messages=messages,
    tools=tools,
    tool_choice={"type": "function", "function": {"name": "get_weather"}},
)

assistant_msg = first.choices[0].message
tool_calls = assistant_msg.tool_calls or []
if tool_calls:
    tool_call = tool_calls[0]
    messages.append(assistant_msg)
    messages.append({
        "role": "tool",
        "tool_call_id": tool_call.id,
        "content": json.dumps({"city": "Bengaluru", "temp_c": 28, "summary": "partly cloudy"}),
    })
    second = client.chat.completions.create(
        model="anthropic-main/claude-sonnet-4-20250514",
        messages=messages,
    )
    print(second.choices[0].message.content)

image_url = (
    "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
)

response = client.chat.completions.create(
    model="anthropic-main/claude-sonnet-4-20250514",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "Describe this image in one sentence."},
            {"type": "image_url", "image_url": {"url": image_url}},
        ],
    }],
)
print(response.choices[0].message.content)

import base64

with open("sample.pdf", "rb") as f:
    pdf_b64 = base64.b64encode(f.read()).decode("ascii")

response = client.chat.completions.create(
    model="anthropic-main/claude-sonnet-4-20250514",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "What text is in this PDF?"},
            {
                "type": "file",
                "file": {
                    "filename": "sample.pdf",
                    "file_data": f"data:application/pdf;base64,{pdf_b64}",
                },
            },
        ],
    }],
)
print(response.choices[0].message.content)

import json

schema = {
    "name": "person",
    "schema": {
        "type": "object",
        "properties": {
            "name": {"type": "string"},
            "age": {"type": "integer"},
            "hobbies": {"type": "array", "items": {"type": "string"}},
        },
        "required": ["name", "age", "hobbies"],
        "additionalProperties": False,
    },
    "strict": True,
}

response = client.chat.completions.create(
    model="anthropic-main/claude-sonnet-4-20250514",
    messages=[{"role": "user", "content": "Invent a fictional person with name, age, and three hobbies."}],
    response_format={"type": "json_schema", "json_schema": schema},
)

message = response.choices[0].message
if getattr(message, "refusal", None):
    print("model refused:", message.refusal)
elif not message.content:
    print("model returned empty content")
else:
    print(json.dumps(json.loads(message.content), indent=2))

response = client.chat.completions.create(
    model="anthropic-main/claude-sonnet-4-20250514",
    messages=[
        {
            "role": "system",
            "content": [
                {
                    "type": "text",
                    "text": "<LONG_SYSTEM_PROMPT_OVER_1024_TOKENS>",
                    "cache_control": {"type": "ephemeral", "ttl": "5m"},
                },
            ],
        },
        {"role": "user", "content": "What should I check in a Helm chart review?"},
    ],
)
usage = response.usage
extra = getattr(usage, "model_extra", {}) or {}
print("cache_creation:", extra.get("cache_creation_input_tokens", 0))
print("cache_read    :", extra.get("cache_read_input_tokens", 0))

response = client.chat.completions.create(
    model="anthropic-main/claude-sonnet-4-20250514",
    messages=[{"role": "user", "content": "A bat and ball cost $1.10.The bat costs $1.00 more than the ball. How much is the ball?"}],
    reasoning_effort="high",
    max_tokens=8000,
)

msg = response.choices[0].message
print("answer:", msg.content)
print("reasoning:", getattr(msg, "reasoning_content", None))
# thinking_blocks carry signatures for multi-turn continuity
for block in getattr(msg, "thinking_blocks", []) or []:
    print("  block:", block.get("type"), "signature:", block.get("signature", "")[:30])

with client.messages.stream(
    model="anthropic-main/claude-sonnet-4-20250514",
    max_tokens=256,
    messages=[{"role": "user", "content": "Count from 1 to 5, one per line."}],
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)