Chat Completions

The chat completions endpoint is the recommended way to generate text. It supports multi-turn conversations with system, user, and assistant messages.

Edge endpoints require a JWT. See Authentication for how to obtain one.

Create Chat Completion

POST /v1/chat/completions

Generate a chat completion for the given messages.

Request Body

Parameter	Type	Required	Default	Description
`model`	string	Yes	—	Model ID (e.g., `qwen-3.5-9b`)
`messages`	array	Yes	—	Array of message objects
`max_tokens`	integer	No	150	Maximum tokens to generate (1-4096)
`temperature`	number	No	0.7	Sampling temperature (0.0-2.0)
`top_p`	number	No	0.9	Nucleus sampling (0.0-1.0)
`top_k`	integer	No	—	Top-k sampling
`frequency_penalty`	number	No	0.0	Frequency penalty (-2.0 to 2.0)
`presence_penalty`	number	No	0.0	Presence penalty (-2.0 to 2.0)
`stop`	array	No	—	Up to 4 stop sequences
`stream`	boolean	No	false	Enable streaming
`user`	string	No	—	End-user identifier

Function / tool calling is not supported. The tools, functions, and function_call parameters are silently ignored by the chat completions endpoint. To use tools, parse the model’s text output and dispatch tool calls in your application code.

Message Format

{
  "role": "system" | "user" | "assistant",
  "content": "message text"
}

Example Request

# Edge endpoints require a JWT — see Authentication
curl -X POST https://api.yto-01.edge.polargrid.ai/v1/chat/completions \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen-3.5-9b",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "What is the capital of France?"}
    ],
    "max_tokens": 100,
    "temperature": 0.7
  }'

const response = await client.chatCompletion({
  model: 'qwen-3.5-9b',
  messages: [
    { role: 'system', content: 'You are a helpful assistant.' },
    { role: 'user', content: 'What is the capital of France?' }
  ],
  maxTokens: 100,
  temperature: 0.7,
});

console.log(response.choices[0].message.content);

response = await client.chat_completion({
    "model": "qwen-3.5-9b",
    "messages": [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is the capital of France?"}
    ],
    "max_tokens": 100,
    "temperature": 0.7,
})

print(response.choices[0].message.content)

Response

{
  "id": "chatcmpl-abc123",
  "object": "chat.completion",
  "created": 1234567890,
  "model": "qwen-3.5-9b",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "The capital of France is Paris."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 25,
    "completion_tokens": 8,
    "total_tokens": 33
  }
}

Streaming

For real-time responses, enable streaming:

for await (const chunk of client.chatCompletionStream({
  model: 'qwen-3.5-9b',
  messages: [{ role: 'user', content: 'Tell me a story' }],
})) {
  const content = chunk.choices[0]?.delta?.content;
  if (content) {
    process.stdout.write(content);
  }
}

async for chunk in client.chat_completion_stream({
    "model": "qwen-3.5-9b",
    "messages": [{"role": "user", "content": "Tell me a story"}],
}):
    content = chunk.choices[0].delta.content
    if content:
        print(content, end="", flush=True)

Stream Response Format

Each chunk is a Server-Sent Event:

data: {"id":"chatcmpl-abc","object":"chat.completion.chunk","choices":[{"delta":{"content":"The"}}]}

data: {"id":"chatcmpl-abc","object":"chat.completion.chunk","choices":[{"delta":{"content":" capital"}}]}

data: {"id":"chatcmpl-abc","object":"chat.completion.chunk","choices":[{"delta":{},"finish_reason":"stop"}]}

data: [DONE]

Finish Reasons

Reason	Description
`stop`	Natural completion or stop sequence hit
`length`	Max tokens reached
`content_filter`	Content filtered

Overview