消息接口 (Anthropic)

POST https://gateway.mytokengate.com/v1/messages

curl --request POST \
  --url https://gateway.mytokengate.com/v1/messages \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '{
    "model": "claude-sonnet-4-6",
    "messages": [
      {
        "role": "user",
        "content": "中国大模型行业2025年将会迎来哪些机遇和挑战？"
      }
    ],
    "max_tokens": 8192
  }'

Authorization: Bearer YOUR_API_KEY

{
  "model": "claude-sonnet-4-6",
  "system": "You are a helpful coding assistant.",
  "messages": [...],
  "max_tokens": 4096
}

{
  "system": [
    {
      "type": "text",
      "text": "You are a helpful assistant with access to the following documents...",
      "cache_control": { "type": "ephemeral" }
    }
  ]
}

{ "role": "user", "content": "你好！" }

{
  "role": "user",
  "content": [
    {
      "type": "image",
      "source": {
        "type": "base64",
        "media_type": "image/png",
        "data": "<base64 编码的图片>"
      }
    },
    {
      "type": "image",
      "source": {
        "type": "url",
        "url": "https://example.com/photo.jpg"
      }
    },
    { "type": "text", "text": "描述这张图片。" }
  ]
}

{
  "role": "user",
  "content": [
    {
      "type": "document",
      "source": {
        "type": "base64",
        "media_type": "application/pdf",
        "data": "<base64 编码的 PDF>"
      }
    },
    { "type": "text", "text": "总结这份文档。" }
  ]
}

{
  "role": "assistant",
  "content": [
    { "type": "text", "text": "让我查一下天气。" },
    {
      "type": "tool_use",
      "id": "toolu_abc123",
      "name": "get_weather",
      "input": { "location": "Paris", "unit": "celsius" }
    }
  ]
}

{
  "role": "user",
  "content": [
    {
      "type": "tool_result",
      "tool_use_id": "toolu_abc123",
      "content": "巴黎22°C，晴天",
      "is_error": false
    }
  ]
}

{
  "id": "msg_01XFDUDYJgAACzvnptvVoYEL",
  "type": "message",
  "role": "assistant",
  "content": [
    {
      "type": "text",
      "text": "响应内容..."
    }
  ],
  "model": "claude-sonnet-4-6",
  "stop_reason": "end_turn",
  "stop_sequence": null,
  "usage": {
    "input_tokens": 2095,
    "output_tokens": 503,
    "cache_creation_input_tokens": 0,
    "cache_read_input_tokens": 0
  }
}

import anthropic
 
client = anthropic.Anthropic(
    api_key="YOUR_API_KEY",
    base_url="https://gateway.mytokengate.com/v1"
)
 
with client.messages.stream(
    model="claude-sonnet-4-6",
    max_tokens=4096,
    messages=[{"role": "user", "content": "你好！"}]
) as stream:
    for text in stream.text_stream:
        print(text, end="")

1. message_start          → 消息元数据（id、model、role、初始 usage）
2. content_block_start   → 新内容块开始（text、tool_use 或 thinking）
3. content_block_delta   → 增量内容（text_delta、input_json_delta、thinking_delta）
4. content_block_stop    → 内容块结束
5. message_delta          → 停止原因和最终输出 token 用量
6. message_stop           → 流式结束

with client.messages.stream(
    model="claude-sonnet-4-6",
    max_tokens=4096,
    messages=[{"role": "user", "content": "解释量子计算"}],
    thinking={"type": "enabled", "budget_tokens": 10000}
) as stream:
    for event in stream:
        if event.type == "content_block_delta":
            if event.delta.type == "thinking_delta":
                print(f"[思考] {event.delta.thinking}")
            elif event.delta.type == "text_delta":
                print(event.delta.text, end="")

{
  "name": "get_weather",
  "description": "获取指定城市的当前天气",
  "input_schema": {
    "type": "object",
    "properties": {
      "location": {
        "type": "string",
        "description": "城市和州/省"
      },
      "unit": {
        "type": "string",
        "enum": ["celsius", "fahrenheit"]
      }
    },
    "required": ["location"]
  }
}

import anthropic
import json
 
client = anthropic.Anthropic(
    api_key="YOUR_API_KEY",
    base_url="https://gateway.mytokengate.com/v1"
)
 
tools = [
    {
        "name": "get_weather",
        "description": "获取指定城市的当前天气",
        "input_schema": {
            "type": "object",
            "properties": {
                "city": {"type": "string", "description": "城市名称"}
            },
            "required": ["city"]
        }
    }
]
 
# 第 1 步：模型决定调用工具
response = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=4096,
    messages=[{"role": "user", "content": "巴黎天气怎么样？"}],
    tools=tools
)
 
# 第 2 步：提取工具调用并执行
tool_use_block = next(b for b in response.content if b.type == "tool_use")
tool_result = get_weather(**tool_use_block.input)
 
# 第 3 步：将工具结果回传
response = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=4096,
    messages=[
        {"role": "user", "content": "巴黎天气怎么样？"},
        {"role": "assistant", "content": response.content},
        {
            "role": "user",
            "content": [{
                "type": "tool_result",
                "tool_use_id": tool_use_block.id,
                "content": tool_result
            }]
        }
    ],
    tools=tools
)
 
print(response.content[0].text)

{
  "model": "claude-sonnet-4-6",
  "max_tokens": 16000,
  "thinking": { "type": "enabled", "budget_tokens": 10000 },
  "messages": [{"role": "user", "content": "一步步解这道数学题..."}]
}

{
  "content": [
    {
      "type": "thinking",
      "thinking": "让我逐步分析..."
    },
    {
      "type": "text",
      "text": "根据我的分析..."
    }
  ],
  "stop_reason": "end_turn"
}

{
  "system": [
    {
      "type": "text",
      "text": "You are a helpful assistant with access to these documents: ...",
      "cache_control": { "type": "ephemeral" }
    }
  ],
  "messages": [
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "文档中关于 X 是怎么说的？",
          "cache_control": { "type": "ephemeral" }
        }
      ]
    }
  ]
}