import anthropic
import os

message = anthropic.Anthropic(
    api_key=os.environ.get("ANTHROPIC_API_KEY")
).messages.create(
    model="claude-opus-4-8",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello, Claude"}],
)
print(message)

import anthropic

message = anthropic.Anthropic().messages.create(
    model="claude-opus-4-8",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Hello, Claude"},
        {"role": "assistant", "content": "Hello!"},
        {"role": "user", "content": "Can you describe LLMs to me?"},
    ],
)
print(message)

message = anthropic.Anthropic().messages.create(
    model="claude-sonnet-4-5",
    max_tokens=1,
    messages=[
        {
            "role": "user",
            "content": "What is latin for Ant? (A) Apoidea, (B) Rhopalocera, (C) Formicidae",
        },
        {"role": "assistant", "content": "The answer is ("},
    ],
)
print(message.content[0].text)

import anthropic
import base64
import httpx

# 选项 1：Base64 编码的图片
image_url = "https://upload.wikimedia.org/wikipedia/commons/a/a7/Camponotus_flavomarginatus_ant.jpg"
image_media_type = "image/jpeg"
image_data = base64.standard_b64encode(httpx.get(image_url).content).decode("utf-8")

message = anthropic.Anthropic().messages.create(
    model="claude-opus-4-8",
    max_tokens=1024,
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "source": {
                        "type": "base64",
                        "media_type": image_media_type,
                        "data": image_data,
                    },
                },
                {"type": "text", "text": "What is in the above image?"},
            ],
        }
    ],
)
print(message.content[0].text)

# 选项 2：URL 引用的图片
message_from_url = anthropic.Anthropic().messages.create(
    model="claude-opus-4-8",
    max_tokens=1024,
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "source": {
                        "type": "url",
                        "url": "https://upload.wikimedia.org/wikipedia/commons/a/a7/Camponotus_flavomarginatus_ant.jpg",
                    },
                },
                {"type": "text", "text": "What is in the above image?"},
            ],
        }
    ],
)
print(message_from_url.content[0].text)

import anthropic

client = anthropic.Anthropic()

response = client.messages.create(
    model="claude-opus-4-8",
    max_tokens=16000,
    thinking={"type": "adaptive", "display": "summarized"},
    messages=[
        {
            "role": "user",
            "content": "Are there an infinite number of prime numbers such that n mod 4 == 3?",
        }
    ],
)

# 响应将包含摘要化的思考块和文本块
for block in response.content:
    if block.type == "thinking":
        print(f"\nThinking summary: {block.thinking}")
    elif block.type == "text":
        print(f"\nResponse: {block.text}")

import anthropic

client = anthropic.Anthropic()

weather_tool = {
    "name": "get_weather",
    "description": "Get the current weather for a location.",
    "input_schema": {
        "type": "object",
        "properties": {"location": {"type": "string", "description": "The city name."}},
        "required": ["location"],
    },
}

weather_data = {"temperature": 72}

# 第一个请求 - Claude 返回思考内容和工具请求
response = client.messages.create(
    model="claude-opus-4-8",
    max_tokens=16000,
    thinking={"type": "adaptive", "display": "summarized"},
    tools=[weather_tool],
    messages=[{"role": "user", "content": "What's the weather in Paris?"}],
)

# 提取思考块和工具使用块
thinking_block = next(
    (block for block in response.content if block.type == "thinking"), None
)
tool_use_block = next(
    (block for block in response.content if block.type == "tool_use"), None
)

# 第二个请求 - 包含思考块和工具结果
continuation = client.messages.create(
    model="claude-opus-4-8",
    max_tokens=16000,
    thinking={"type": "adaptive", "display": "summarized"},
    tools=[weather_tool],
    messages=[
        {"role": "user", "content": "What's the weather in Paris?"},
        # 请注意，thinking_block 和 tool_use_block 都被一并传入
        {"role": "assistant", "content": [thinking_block, tool_use_block]},
        {
            "role": "user",
            "content": [
                {
                    "type": "tool_result",
                    "tool_use_id": tool_use_block.id,
                    "content": f"Current temperature: {weather_data['temperature']}°F",
                }
            ],
        },
    ],
)

for block in continuation.content:
    if block.type == "text":
        print(block.text)

import anthropic

client = anthropic.Anthropic()

calculator_tool = {
    "name": "calculator",
    "description": "Perform arithmetic calculations.",
    "input_schema": {
        "type": "object",
        "properties": {
            "expression": {
                "type": "string",
                "description": "The math expression to evaluate.",
            }
        },
        "required": ["expression"],
    },
}

database_tool = {
    "name": "database_query",
    "description": "Query the product database.",
    "input_schema": {
        "type": "object",
        "properties": {
            "query": {"type": "string", "description": "The database query."}
        },
        "required": ["query"],
    },
}

response = client.beta.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=16000,
    thinking={"type": "enabled", "budget_tokens": 10000},
    tools=[calculator_tool, database_tool],
    messages=[
        {
            "role": "user",
            "content": "What's the total revenue if we sold 150 units of product A at $50 each?",
        }
    ],
    betas=["interleaved-thinking-2025-05-14"],
)

for block in response.content:
    if block.type == "thinking":
        print(f"Thinking: {block.thinking}")
    elif block.type == "tool_use":
        print(f"Tool call: {block.name}({block.input})")
    elif block.type == "text":
        print(f"Response: {block.text}")

import anthropic

client = anthropic.Anthropic()

with client.messages.stream(
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello"}],
    model="claude-opus-4-8",
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)