---
title: "LLM API"
description: "OpenAI-compatible chat completions and embeddings"
---

# LLM API

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

## Chat Completion

Send a chat completion request to the configured LLM provider.

```bash
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:**

| Field | Type | Required | Description |
|-------|------|----------|-------------|
| `messages` | array | Yes | Array of message objects with `role` and `content` |
| `model` | string | No | Model to use (defaults to provider's default model) |
| `max_tokens` | int | No | Maximum tokens in response |
| `temperature` | float | No | Sampling temperature (0.0-2.0) |
| `top_p` | float | No | Top-p sampling parameter |
| `top_k` | int | No | Top-k sampling parameter |
| `n` | int | No | Number of completions to generate |
| `stream` | bool | No | Enable streaming (not yet supported) |
| `tools` | array | No | Tool definitions for function calling |
| `tool_choice` | string/object | No | Tool selection strategy |
| `response_format` | object | No | Response 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:**
```json
{
  "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
  }
}
```

---

## With Tools (Function Calling)

```bash
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:**
```json
{
  "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
  }
}
```

---

## Generate Embeddings

Generate vector embeddings for input text.

```bash
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:**

| Field | Type | Required | Description |
|-------|------|----------|-------------|
| `input` | array | Yes | Array of strings to embed |
| `model` | string | No | Embedding model (defaults to provider's model) |

**Response:**
```json
{
  "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
  }
}
```