Chat Completions

Create a model response for a conversation. This endpoint is OpenAI-compatible, including message roles, tool calling, and streaming.

Create a chat completion

POST /v1/chat/completions

Request body

Required

• model (string). The model ID to use.

• messages (array). A list of messages that make up the conversation so far.

Messages

Each message has a role plus content. Supported roles include system, developer, user, assistant, tool, and function.

content can be either a simple string or a structured array for multimodal inputs. For example, user content can include typed blocks like text, image_url, input_audio, document, video_url, or file.

Basic example

curl https://proxy.alfnrl.io/v1/chat/completions \
  -H "Authorization: Bearer $ALPHANEURAL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3",
    "messages": [
      { "role": "user", "content": "Write a haiku about routers." }
    ]
  }'

Multimodal example (text + image)

curl https://proxy.alfnrl.io/v1/chat/completions \
  -H "Authorization: Bearer $ALPHANEURAL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3",
    "messages": [
      {
        "role": "user",
        "content": [
          { "type": "text", "text": "What is in this image?" },
          { "type": "image_url", "image_url": { "url": "https://example.com/cat.png" } }
        ]
      }
    ]
  }'

Multimodal content blocks are part of the message schema supported by the proxy.

Common parameters

These follow the OpenAI Chat Completions shape.

• Sampling and length: temperature, top_p, max_tokens, stop, n, seed

• Penalties and biasing: presence_penalty, frequency_penalty, logit_bias

• Structured outputs: response_format

• Logging: logprobs, top_logprobs

• Streaming: stream, stream_options

• Tool calling: tools, tool_choice, parallel_tool_calls

• Legacy function calling: functions, function_call

• Metadata: metadata, user

Tool calling

Provide tool definitions in tools. When the model decides to call a tool, it will return tool_calls on the assistant message, and you should respond by sending a tool role message referencing the matching tool_call_id.

Tool calling example

curl https://proxy.alfnrl.io/v1/chat/completions \
  -H "Authorization: Bearer $ALPHANEURAL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3",
    "messages": [
      { "role": "user", "content": "What is the weather in Paris right now?" }
    ],
    "tools": [
      {
        "type": "function",
        "function": {
          "name": "get_weather",
          "description": "Get the current weather for a city",
          "parameters": {
            "type": "object",
            "properties": { "city": { "type": "string" } },
            "required": ["city"]
          }
        }
      }
    ],
    "tool_choice": "auto"
  }'

Streaming

Set stream: true to receive server-sent events (SSE). Each event contains a delta. The stream ends with a final event.

curl https://proxy.alfnrl.io/v1/chat/completions \
  -H "Authorization: Bearer $ALPHANEURAL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3",
    "stream": true,
    "messages": [{ "role": "user", "content": "Explain TCP slow start in one paragraph." }]
  }'

Response

The response follows the OpenAI Chat Completions format (for example, choices with an assistant message, plus token usage).

AlphaNeural proxy extensions

The proxy accepts a few optional routing and reliability fields that do not exist in the upstream OpenAI API, such as guardrails, caching, num_retries, fallbacks, and context_window_fallback_dict. Use these only if you need proxy-level behaviour controls.