API Endpoints

AI Foundation Services provides an OpenAI-compatible REST API. All endpoints use the base URL:

https://llm-server.llmhub.t-systems.net/v2

For the complete OpenAPI specification, see the interactive API docs (Redoc).

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Endpoints

Method	Endpoint	Description
GET	/models	List all available models
GET	/models/{model_id}	Get model details and metadata
POST	/chat/completions	Create a chat completion
POST	/completions	Create a text completion
POST	/embeddings	Create embeddings
POST	/audio/transcriptions	Transcribe audio to text
POST	/audio/translations	Translate audio to English
GET	/audio/models	List available audio models
POST	/images/generations	Generate images from text
POST	/responses	Create a response (Responses API)
POST	/fine_tuning/jobs	Create a fine-tuning job
GET	/fine_tuning/jobs	List fine-tuning jobs
POST	/fine_tuning/jobs/{id}/cancel	Cancel a fine-tuning job
GET	/fine_tuning/jobs/{id}/events	List fine-tuning events
POST	/files	Upload a file
GET	/files	List uploaded files
DELETE	/files/{id}	Delete a file

Queue Endpoints (Asynchronous)

All major endpoints have /queue variants for asynchronous processing. See the Asynchronous Requests guide for details.

Method	Endpoint	Description
POST	/queue/chat/completions	Async chat completion
POST	/queue/completions	Async text completion
POST	/queue/embeddings	Async embeddings
POST	/queue/audio/transcriptions	Async audio transcription
POST	/queue/audio/translations	Async audio translation
POST	/queue/images/generations	Async image generation
POST	/queue/images/edits	Async image editing
GET	/queue/models	List models (queue)

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API Versioning

The API uses versioned paths:

Path Prefix	Purpose	Examples
/v2	Default — LLM inference, embeddings, audio, images	/v2/chat/completions, /v2/embeddings
/v1	Visual RAG, vector stores, file management	/v1/vector_stores, /v1/files
/queue	Asynchronous processing (mirrors /v2 endpoints)	/queue/chat/completions

• Use /v2 for all standard LLM API calls (this is the default when using OpenAI SDKs with our base URL)
• Use /v1 for Visual RAG and file management operations
• Use /queue for long-running or batch workloads

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Authentication

All requests require an API key in the Authorization header:

Authorization: Bearer YOUR_API_KEY

See Authentication for setup details.

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Request Format

All POST requests use JSON:

curl -X POST "https://llm-server.llmhub.t-systems.net/v2/chat/completions" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "Llama-3.3-70B-Instruct", "messages": [{"role": "user", "content": "Hello"}]}'

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Response Format

Responses follow the OpenAI response format. For chat completions:

{
  "id": "chatcmpl-abc123",
  "object": "chat.completion",
  "created": 1710000000,
  "model": "Llama-3.3-70B-Instruct",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Hello! How can I help you today?"
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 10,
    "completion_tokens": 9,
    "total_tokens": 19
  }
}