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LLM API

Direct API access to Large Language Model capabilities without requiring workflow execution.

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Chat Completion

Send a chat completion request to the configured LLM provider. 
curl -X POST http://localhost:8002/osm/api/llm/v1/chat/completions \
  -H "Authorization: Bearer $TOKEN" \
  -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
  }'
Request Body:
FieldTypeRequiredDescription
messagesarrayYesArray of message objects with role and content
modelstringNoModel to use (defaults to provider’s default model)
max_tokensintNoMaximum tokens in response
temperaturefloatNoSampling temperature (0.0-2.0)
top_pfloatNoTop-p sampling parameter
top_kintNoTop-k sampling parameter
nintNoNumber of completions to generate
streamboolNoEnable streaming (not yet supported)
toolsarrayNoTool definitions for function calling
tool_choicestring/objectNoTool selection strategy
response_formatobjectNoResponse format ({"type": "json_object"})
Message Roles:
  • system - System prompt to set assistant behavior
  • user - User message
  • assistant - Previous assistant response
  • tool - Tool call result
Response: 
{
  "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
  }
}

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With Tools (Function Calling)

curl -X POST http://localhost:8002/osm/api/llm/v1/chat/completions \
  -H "Authorization: Bearer $TOKEN" \
  -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"
  }'
Response with Tool Calls: 
{
  "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
  }
}

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Generate Embeddings

Generate vector embeddings for input text. 
curl -X POST http://localhost:8002/osm/api/llm/v1/embeddings \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "input": ["security analysis", "vulnerability assessment"],
    "model": "text-embedding-3-small"
  }'
Request Body:
FieldTypeRequiredDescription
inputarrayYesArray of strings to embed
modelstringNoEmbedding model (defaults to provider’s model)
Response: 
{
  "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
  }
}