POST /v1/chat/completions

OpenAI 兼容聊天补全接口，支持流式输出。

请求参数

参数	类型	必填	默认值	说明
model	string	是	-	模型名称，如 llama3.2、kimi-k2.6:cloud
messages	array	是	-	对话历史，每项含 role 和 content
temperature	float	否	0.5	温度 (0-2)，控制随机性
max_tokens	int	否	无限制	最大生成 token 数
stream	bool	否	false	是否流式输出
top_p	float	否	-	Top-p 采样 (0-1)
frequency_penalty	float	否	-	频率惩罚 (-2 到 2)
presence_penalty	float	否	-	存在惩罚 (-2 到 2)
repetition_penalty	float	否	1.2	重复惩罚 (Ollama 特有)
stop	array	否	-	停止序列列表

请求示例

curl ${location.origin}/v1/chat/completions \
  -H "Authorization: Bearer sk-your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama3.2",
    "messages": [{"role": "user", "content": "Hello!"}],
    "temperature": 0.7,
    "max_tokens": 1024,
    "stream": false
  }'

响应格式

{
  "id": "chatcmpl-xxx",
  "object": "chat.completion",
  "model": "llama3.2",
  "choices": [{
    "index": 0,
    "message": {"role": "assistant", "content": "..."},
    "finish_reason": "stop"
  }],
  "usage": {"prompt_tokens": 5, "completion_tokens": 10, "total_tokens": 15}
}

流式响应 (stream: true)

data: {"choices":[{"delta":{"role":"assistant"}}]}
data: {"choices":[{"delta":{"content":"Hello"}}]}
data: {"choices":[{"delta":{"content":"!"}}]}
data: {"choices":[{"delta":{},"finish_reason":"stop"}]}
data: [DONE]

扩展：支持推理的模型会发送 reasoning_content 字段：

data: {"choices":[{"delta":{"reasoning_content":"让我思考..."}}]}

GET /v1/models

curl ${location.origin}/v1/models \
  -H "Authorization: Bearer sk-your-api-key"

{
  "object": "list",
  "data": [
    {"id": "llama3.2", "object": "model", "owned_by": "ollama"},
    {"id": "kimi-k2.6:cloud", "object": "model", "owned_by": "cloud"}
  ]
}

Python SDK 示例

from openai import OpenAI

client = OpenAI(
    api_key="sk-your-api-key",
    base_url="${location.origin}/v1",
)

# 非流式
resp = client.chat.completions.create(
    model="llama3.2",
    messages=[{"role": "user", "content": "Hello!"}],
    temperature=0.7,
    max_tokens=1024,
)
print(resp.choices[0].message.content)

# 流式
stream = client.chat.completions.create(
    model="llama3.2",
    messages=[{"role": "user", "content": "Hello!"}],
    stream=True,
)
for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")
    if chunk.choices[0].delta.reasoning_content:
        print(chunk.choices[0].delta.reasoning_content, end="")

POST /v1/messages

Anthropic Messages API 兼容接口，支持流式输出和扩展思考。

请求头

Authorization: Bearer sk-your-api-key
Content-Type: application/json
anthropic-version: 2023-06-01

或使用 x-api-key: sk-your-api-key 代替 Authorization

请求参数

参数	类型	必填	默认值	说明
model	string	是	-	模型名称
messages	array	是	-	对话历史，每项含 role 和 content
max_tokens	int	是	1024	最大生成 token 数
temperature	float	否	0.5	温度 (0-1)
top_p	float	否	-	Top-p 采样 (0-1)
top_k	int	否	-	Top-k 采样
stream	bool	否	false	是否流式输出
stop_sequences	array	否	-	停止序列列表
system	string	否	-	系统提示词
thinking	object	否	-	扩展思考配置，如 {"type": "enabled", "budget_tokens": 10000}

请求示例

curl ${location.origin}/v1/messages \
  -H "Authorization: Bearer sk-your-api-key" \
  -H "anthropic-version: 2023-06-01" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-3-sonnet",
    "max_tokens": 1024,
    "messages": [{"role": "user", "content": "Hello!"}],
    "system": "你是一个友好的助手",
    "temperature": 0.7
  }'

响应格式

{
  "id": "msg-xxx",
  "type": "message",
  "role": "assistant",
  "model": "claude-3-sonnet",
  "content": [{"type": "text", "text": "..."}],
  "stop_reason": "end_turn",
  "stop_sequence": null,
  "usage": {"input_tokens": 10, "output_tokens": 15}
}

流式响应 (stream: true)

完整 SSE 事件序列：

event: message_start
data: {"type":"message_start","message":{"id":"msg-xxx","role":"assistant","content":[],"usage":{"input_tokens":10}}}

event: content_block_start
data: {"type":"content_block_start","index":0,"content_block":{"type":"text","text":""}}

event: content_block_delta
data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"Hello"}}

event: content_block_stop
data: {"type":"content_block_stop","index":0}

event: message_delta
data: {"type":"message_delta","delta":{"stop_reason":"end_turn"},"usage":{"output_tokens":15}}

event: message_stop
data: {"type":"message_stop"}

扩展思考 (thinking_delta)

支持推理的模型会先发送 thinking 块：

event: content_block_start
data: {"type":"content_block_start","index":0,"content_block":{"type":"thinking","thinking":""}}

event: content_block_delta
data: {"type":"content_block_delta","index":0,"delta":{"type":"thinking_delta","thinking":"让我分析..."}}

event: content_block_stop
data: {"type":"content_block_stop","index":0}

event: content_block_start
data: {"type":"content_block_start","index":1,"content_block":{"type":"text","text":""}}

event: content_block_delta
data: {"type":"content_block_delta","index":1,"delta":{"type":"text_delta","text":"答案..."}}

Python SDK 示例

import anthropic

client = anthropic.Anthropic(
    api_key="sk-your-api-key",
    base_url="${location.origin}",
)

# 非流式
msg = client.messages.create(
    model="claude-3-sonnet",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello!"}],
    system="你是一个友好的助手",
)
print(msg.content[0].text)

# 流式
with client.messages.stream(
    model="claude-3-sonnet",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello!"}],
) as stream:
    for event in stream:
        if event.type == "content_block_delta":
            if event.delta.type == "thinking_delta":
                print(event.delta.thinking, end="")
            elif event.delta.type == "text_delta":
                print(event.delta.text, end="")