LLM API

无需执行工作流即可直接访问大语言模型功能。

聊天补全

向配置的 LLM 供应商发送聊天补全请求。

curl -X POST http://localhost:8002/osm/api/llm/v1/chat/completions \
  -H "Authorization: Bearer ***" \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      {"role": "system", "content": "You are a security analyst."},
      {"role": "user", "content": "Analyze the security posture of example.com"}
    ],
    "max_tokens": 1000,
    "temperature": 0.7
  }'

请求体：

字段	类型	必填	描述
messages	array	是	包含 role 和 content 的消息对象数组
model	string	否	使用的模型（默认为供应商的默认模型）
max_tokens	int	否	响应中的最大令牌数
temperature	float	否	采样温度（0.0-2.0）
top_p	float	否	Top-p 采样参数
top_k	int	否	Top-k 采样参数
n	int	否	生成的补全数量
stream	bool	否	启用流式传输（暂不支持）
tools	array	否	用于函数调用的工具定义
tool_choice	string/object	否	工具选择策略
response_format	object	否	响应格式（{"type": "json_object"}）

消息角色： - system - 设置助手行为的系统提示 - user - 用户消息 - assistant - 之前的助手响应 - tool - 工具调用结果

响应：

{
  "id": "chatcmpl-abc123",
  "model": "gpt-4",
  "content": "Based on my analysis of example.com...",
  "finish_reason": "stop",
  "usage": {
    "prompt_tokens": 50,
    "completion_tokens": 200,
    "total_tokens": 250
  }
}

---

带工具（函数调用）

curl -X POST http://localhost:8002/osm/api/llm/v1/chat/completions \
  -H "Authorization: Bearer ***" \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      {"role": "user", "content": "What DNS records exist for example.com?"}
    ],
    "tools": [
      {
        "type": "function",
        "function": {
          "name": "dns_lookup",
          "description": "Look up DNS records for a domain",
          "parameters": {
            "type": "object",
            "properties": {
              "domain": {"type": "string", "description": "Domain to look up"},
              "record_type": {"type": "string", "enum": ["A", "AAAA", "MX", "TXT", "NS"]}
            },
            "required": ["domain"]
          }
        }
      }
    ],
    "tool_choice": "auto"
  }'

带工具调用的响应：

{
  "id": "chatcmpl-xyz789",
  "model": "gpt-4",
  "content": null,
  "finish_reason": "tool_calls",
  "tool_calls": [
    {
      "id": "call_abc123",
      "type": "function",
      "function": {
        "name": "dns_lookup",
        "arguments": "{\"domain\": \"example.com\", \"record_type\": \"A\"}"
      }
    }
  ],
  "usage": {
    "prompt_tokens": 100,
    "completion_tokens": 25,
    "total_tokens": 125
  }
}

---

生成嵌入

为输入文本生成向量嵌入。

curl -X POST http://localhost:8002/osm/api/llm/v1/embeddings \
  -H "Authorization: Bearer ***" \
  -H "Content-Type: application/json" \
  -d '{
    "input": ["security analysis", "vulnerability assessment"],
    "model": "text-embedding-3-small"
  }'

请求体：

字段	类型	必填	描述
input	array	是	要嵌入的字符串数组
model	string	否	嵌入模型（默认为供应商的模型）

响应：

{
  "model": "text-embedding-3-small",
  "embeddings": [
    [0.0023, -0.0045, 0.0178, ...],
    [0.0112, -0.0067, 0.0234, ...]
  ],
  "usage": {
    "prompt_tokens": 10,
    "total_tokens": 10
  }
}