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POST
/
api
/
v1
/
chat
/
completions
curl https://aireiter.com/api/v1/chat/completions \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-sonnet-4-5-20250929",
    "messages": [
      {"role": "user", "content": "你好，世界"}
    ]
  }'

{
  "id": "chatcmpl-9vKqnMf3Ax8ZpRdTw2LsYe7b",
  "object": "chat.completion",
  "created": 1741680000,
  "model": "claude-sonnet-4-5-20250929",
  "task_id": "task_abc123",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "你好！有什么我可以帮你的吗？"
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 12,
    "completion_tokens": 15,
    "total_tokens": 27
  }
}

​
Authorizations

​
Authorization
string
必填
API 密钥，Bearer Token 格式获取 API Key：访问 API Key 管理页面 获取您的 API Key
Authorization: Bearer YOUR_API_KEY

​
Body

​
model
string
必填
模型名称OpenAI 兼容模型（推荐用于工具调用）：
  • gpt-4o-mini — 轻量快速，适合高频简单任务
  • gpt-4o — 综合推荐，性能与成本平衡
  • gpt-5.2 — 高性能版本，完整支持工具调用
  • 更多模型请查询 GET /api/v1/models
Claude 模型（Anthropic 协议转发）：
  • claude-haiku-4-5-20251001 — 轻量快速
  • claude-sonnet-4-5-20250929 — 综合推荐
  • claude-sonnet-4-6 — Sonnet 新版
  • claude-opus-4-5-20251101 — 旗舰推理模型
  • claude-opus-4-6 — Opus 新版，能力最强
完整模型列表请查询：GET /api/v1/models
​
messages
array
必填
消息列表模型根据消息历史生成下一条回复。每条消息包含 role 和 content。纯文本消息：
[{"role": "user", "content": "你好"}]
多轮对话：
[
  {"role": "system",    "content": "你是一位专业的代码审查员。"},
  {"role": "user",      "content": "帮我看下这段代码"},
  {"role": "assistant", "content": "我来分析一下..."},
  {"role": "user",      "content": "有没有性能问题？"}
]
多轮工具调用：
[
  {"role": "user", "content": "北京今天天气如何？"},
  {
    "role": "assistant",
    "content": null,
    "tool_calls": [
      {
        "id": "call_001",
        "type": "function",
        "function": {"name": "get_weather", "arguments": "{\"city\": \"北京\"}"}
      }
    ]
  },
  {
    "role": "tool",
    "tool_call_id": "call_001",
    "content": "北京：25°C，晴"
  }
]
​
max_tokens
integer
最大输出 Token 数控制模型最多生成的 token 数量，模型可能在达到上限前自然结束。最小值：1。 默认不限制（受模型上下文窗口约束）。
​
max_completion_tokens
integer
最大输出 Token 数（max_tokens 的新名称）与 max_tokens 完全等价，两者提供其一即可，max_tokens 优先级更高。
​
stream
boolean
是否启用流式输出设为 true 时，通过 SSE（Server-Sent Events）实时流式返回。默认 true。如需非流式响应，需显式传入 "stream": false。设为 false 时，等待生成完成后一次性返回完整响应。
​
temperature
number
温度，范围 0–2
  • 低值（如 0.2）：输出更确定、保守
  • 高值（如 0.8）：输出更随机、有创意
默认 1.0。不建议与 top_p 同时使用。
​
top_p
number
核采样参数，范围 0–1从累积概率达到 top_p 的 token 集合中采样。默认 1.0。 不建议与 temperature 同时使用。
​
frequency_penalty
number
频率惩罚，范围 -2.0–2.0正值会根据 token 在已生成文本中的出现频率对其进行惩罚，降低重复输出的概率。默认 0。
​
presence_penalty
number
存在惩罚，范围 -2.0–2.0正值会对已出现过的 token 进行惩罚，鼓励模型探索新话题。默认 0。
​
seed
integer
随机种子设置后，相同 seed + 相同请求参数将尽量产生确定性输出，便于结果复现。
​
stop
string | array
停止序列遇到此字符串（或数组中任意一个）时，模型立即停止生成。最多 4 个。
{"stop": ["\n\nUser:", "###END###"]}
​
tools
array
工具定义列表定义模型可调用的函数工具，每个工具包含名称、描述和参数 JSON Schema。
{
  "tools": [
    {
      "type": "function",
      "function": {
        "name": "get_weather",
        "description": "获取指定城市的当前天气",
        "parameters": {
          "type": "object",
          "properties": {
            "city": {
              "type": "string",
              "description": "城市名称，例如：北京"
            },
            "unit": {
              "type": "string",
              "enum": ["celsius", "fahrenheit"]
            }
          },
          "required": ["city"]
        }
      }
    }
  ]
}
​
tool_choice
string | object
工具选择策略
  • "auto" — 模型自行决定是否调用工具（默认）
  • "required" — 强制模型必须调用某个工具
  • "none" — 禁止调用任何工具
  • {"type": "function", "function": {"name": "get_weather"}} — 强制调用指定工具
{"tool_choice": "required"}
​
parallel_tool_calls
boolean
是否允许并行工具调用设为 false 时，每次只调用一个工具。默认 true（允许并行）。
​
response_format
object
响应格式控制模型输出格式。JSON 对象模式：
{"response_format": {"type": "json_object"}}
JSON Schema 模式：
{
  "response_format": {
    "type": "json_schema",
    "json_schema": {
      "name": "analysis_result",
      "strict": true,
      "schema": {
        "type": "object",
        "properties": {
          "summary":    {"type": "string"},
          "score":      {"type": "number"},
          "tags":       {"type": "array", "items": {"type": "string"}}
        },
        "required": ["summary", "score", "tags"]
      }
    }
  }
}
​
reasoning_effort
string
推理强度适用于支持推理的模型（如 gpt-5.2 及以上），控制模型推理深度。
  • "low" — 快速推理，节省 token
  • "medium" — 均衡推理
  • "high" — 深度推理，更准确但消耗更多 token

​
Response

​
id
string
补全唯一标识符示例："chatcmpl-9vKqnMf3Ax8ZpRdTw2LsYe7b"
​
object
string
对象类型，固定为 "chat.completion"（非流式）或 "chat.completion.chunk"（流式）
​
created
integer
创建时间，Unix 时间戳（秒）
​
model
string
请求时传入的模型名称
​
task_id
string
计费任务 ID（项目扩展字段），用于追踪本次调用的积分消耗记录
​
choices
array
生成结果数组（始终只有 1 条）
​
usage
object
Token 使用统计（非流式中在响应体顶层，流式中在最后一帧）
curl https://aireiter.com/api/v1/chat/completions \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-sonnet-4-5-20250929",
    "messages": [
      {"role": "user", "content": "你好，世界"}
    ]
  }'

{
  "id": "chatcmpl-9vKqnMf3Ax8ZpRdTw2LsYe7b",
  "object": "chat.completion",
  "created": 1741680000,
  "model": "claude-sonnet-4-5-20250929",
  "task_id": "task_abc123",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "你好！有什么我可以帮你的吗？"
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 12,
    "completion_tokens": 15,
    "total_tokens": 27
  }
}

​
使用示例

​
基础对话

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://aireiter.com/api/v1"
)

response = client.chat.completions.create(
    model="claude-sonnet-4-5-20250929",
    messages=[
        {"role": "user", "content": "用 Python 实现快速排序"}
    ]
)

print(response.choices[0].message.content)

​
系统提示词 + 多轮对话

response = client.chat.completions.create(
    model="claude-sonnet-4-5-20250929",
    messages=[
        {"role": "system",    "content": "你是一位资深 Python 开发专家，擅长代码审查和性能优化。"},
        {"role": "user",      "content": "什么是 GIL？"},
        {"role": "assistant", "content": "GIL（全局解释器锁）是..."},
        {"role": "user",      "content": "怎么绕过 GIL 实现真正的并行？"}
    ],
    temperature=0.3
)

​
流式响应

stream = client.chat.completions.create(
    model="claude-sonnet-4-5-20250929",
    messages=[{"role": "user", "content": "写一篇关于量子计算的技术博客"}],
    stream=True
)

for chunk in stream:
    delta = chunk.choices[0].delta
    if delta.content:
        print(delta.content, end="", flush=True)

​
工具调用（完整多轮流程）

import json

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_stock_price",
            "description": "获取股票实时价格",
            "parameters": {
                "type": "object",
                "properties": {
                    "ticker": {"type": "string", "description": "股票代码，例如：AAPL"}
                },
                "required": ["ticker"]
            }
        }
    }
]

messages = [{"role": "user", "content": "特斯拉股价是多少？"}]

# 第一轮：模型决定调用工具
response = client.chat.completions.create(
    model="claude-sonnet-4-5-20250929",
    messages=messages,
    tools=tools
)

# 处理工具调用
if response.choices[0].finish_reason == "tool_calls":
    tool_call = response.choices[0].message.tool_calls[0]

    # 执行工具（业务逻辑）
    tool_result = {"price": 245.80, "currency": "USD"}

    # 第二轮：将结果返回给模型
    messages += [
        response.choices[0].message,
        {
            "role": "tool",
            "tool_call_id": tool_call.id,
            "content": json.dumps(tool_result)
        }
    ]

    final = client.chat.completions.create(
        model="claude-sonnet-4-5-20250929",
        messages=messages,
        tools=tools
    )
    print(final.choices[0].message.content)

​
结构化输出（JSON Schema）

response = client.chat.completions.create(
    model="claude-sonnet-4-5-20250929",
    messages=[
        {"role": "user", "content": "分析以下产品评论的情感：'这款耳机音质出色，但续航一般'"}
    ],
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "sentiment_analysis",
            "strict": True,
            "schema": {
                "type": "object",
                "properties": {
                    "sentiment":   {"type": "string", "enum": ["positive", "negative", "mixed"]},
                    "score":       {"type": "number", "description": "情感分值 -1.0 到 1.0"},
                    "highlights":  {"type": "array", "items": {"type": "string"}}
                },
                "required": ["sentiment", "score", "highlights"]
            }
        }
    }
)

result = json.loads(response.choices[0].message.content)
print(result)
# {"sentiment": "mixed", "score": 0.2, "highlights": ["音质出色", "续航一般"]}

​
采样参数控制

# 创意写作：高温度 + 频率惩罚
response = client.chat.completions.create(
    model="claude-sonnet-4-5-20250929",
    messages=[{"role": "user", "content": "写一首关于秋天的现代诗"}],
    temperature=0.9,
    frequency_penalty=0.5,
    presence_penalty=0.3
)

# 精确输出：低温度 + 固定种子
response = client.chat.completions.create(
    model="claude-sonnet-4-5-20250929",
    messages=[{"role": "user", "content": "计算 1234 × 5678 的结果"}],
    temperature=0.0,
    seed=42
)

​
流式响应事件格式

data: {"id":"chatcmpl-xxx","object":"chat.completion.chunk","created":1741680000,"model":"claude-sonnet-4-5-20250929","task_id":"task_abc123","choices":[{"index":0,"delta":{"role":"assistant"},"finish_reason":null}]}

data: {"id":"chatcmpl-xxx","object":"chat.completion.chunk","created":1741680000,"model":"claude-sonnet-4-5-20250929","choices":[{"index":0,"delta":{"content":"你好"},"finish_reason":null}]}

data: {"id":"chatcmpl-xxx","object":"chat.completion.chunk","created":1741680000,"model":"claude-sonnet-4-5-20250929","choices":[{"index":0,"delta":{"content":"！"},"finish_reason":null}]}

data: {"id":"chatcmpl-xxx","object":"chat.completion.chunk","created":1741680000,"model":"claude-sonnet-4-5-20250929","choices":[{"index":0,"delta":{},"finish_reason":"stop"}],"usage":{"prompt_tokens":12,"completion_tokens":5,"total_tokens":17}}

data: [DONE]
工具调用流式事件： 
data: {"id":"chatcmpl-xxx","object":"chat.completion.chunk","created":1741680000,"model":"claude-sonnet-4-5-20250929","choices":[{"index":0,"delta":{"tool_calls":[{"index":0,"id":"call_abc","type":"function","function":{"name":"get_weather","arguments":""}}]},"finish_reason":null}]}

data: {"id":"chatcmpl-xxx","object":"chat.completion.chunk","created":1741680000,"model":"claude-sonnet-4-5-20250929","choices":[{"index":0,"delta":{"tool_calls":[{"index":0,"function":{"arguments":"{\"city\""}}]},"finish_reason":null}]}

data: {"id":"chatcmpl-xxx","object":"chat.completion.chunk","created":1741680000,"model":"claude-sonnet-4-5-20250929","choices":[{"index":0,"delta":{"tool_calls":[{"index":0,"function":{"arguments":": \"北京\"}"}}]},"finish_reason":null}]}

data: {"id":"chatcmpl-xxx","object":"chat.completion.chunk","created":1741680000,"model":"claude-sonnet-4-5-20250929","choices":[{"index":0,"delta":{},"finish_reason":"tool_calls"}],"usage":{"prompt_tokens":85,"completion_tokens":22,"total_tokens":107}}

data: [DONE]

​
注意事项

  1. 认证方式：仅支持 Authorization: Bearer <api_key> 格式，使用 OpenAI SDK 时直接设置 api_key 即可。
  2. 默认流式：stream 参数默认为 true。如需非流式响应，需显式传入 "stream": false。
  3. 积分不足：余额不足时返回 HTTP 402，请充值后重试。
  4. stop 与停止序列：stop 参数目前不生效，底层供应商暂不支持该功能，传入参数不会报错但也不会在指定序列处停止生成。
  5. response_format 注意：当前底层供应商对 response_format 支持有限——json_object 模式下模型可能仍输出 Markdown 代码块而非纯 JSON；json_schema 模式下 Schema 约束可能不被遵守。如需结构化输出，建议在 prompt 中明确描述所需格式。
  6. 工具参数：parameters 字段必须是合法的 JSON Schema，required 数组决定哪些参数为必填项。
  7. 模型选择建议：
    • Haiku — 高频简单问答，成本最低
    • Sonnet — 代码生成、文档处理，综合推荐
    • Opus — 复杂推理、长文分析，能力最强
    • -thinking 系列 — 数学证明、逻辑推导等需要深度思考的场景