import { Callout, Tabs } from "@/docs/components";

Chat Completions

<div className="status-badge status-stable">Stable</div>

OpenAI-compatible chat completions endpoint for conversational AI.

Create Chat Completion

<div className="api-endpoint"> <span className="method-badge method-badge-post">POST</span> <span className="path">/api/v1/chat/completions</span> </div>

Generate a chat completion response from the model.

Request

bash

curl -X POST "https://elizacloud.ai/api/v1/chat/completions" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4o",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "Hello!"}
    ],
    "temperature": 0.7
  }'

</Tabs.Tab> <Tabs.Tab>

javascript

const response = await fetch('https://elizacloud.ai/api/v1/chat/completions', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer YOUR_API_KEY',
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    model: 'gpt-4o',
    messages: [
      { role: 'system', content: 'You are a helpful assistant.' },
      { role: 'user', content: 'Hello!' }
    ],
    temperature: 0.7,
  }),
});

const data = await response.json();
console.log(data.choices[0].message.content);

</Tabs.Tab> <Tabs.Tab>

python

import requests

response = requests.post(
    'https://elizacloud.ai/api/v1/chat/completions',
    headers={
        'Authorization': 'Bearer YOUR_API_KEY',
        'Content-Type': 'application/json',
    },
    json={
        'model': 'gpt-4o',
        'messages': [
            {'role': 'system', 'content': 'You are a helpful assistant.'},
            {'role': 'user', 'content': 'Hello!'}
        ],
        'temperature': 0.7,
    }
)

data = response.json()
print(data['choices'][0]['message']['content'])

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Parameters

Parameter	Type	Required	Description
`model`	string	✓	Model ID or agent ID
`messages`	array	✓	Array of message objects
`temperature`	number		Sampling temperature (0-2). Default: 1
`max_tokens`	integer		Maximum tokens to generate
`stream`	boolean		Stream response chunks. Default: false
`top_p`	number		Nucleus sampling parameter (0-1)
`frequency_penalty`	number		Frequency penalty (-2 to 2)
`presence_penalty`	number		Presence penalty (-2 to 2)
`stop`	string/array		Stop sequences
`user`	string		Unique user identifier

Message Object

Field	Type	Required	Description
`role`	string	✓	`system`, `user`, `assistant`, or `tool`
`content`	string	✓	Message content
`name`	string		Optional name for the participant

Response

json

{
  "id": "chatcmpl-abc123",
  "object": "chat.completion",
  "created": 1705312800,
  "model": "gpt-4o",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Hello! How can I help you today?"
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 20,
    "completion_tokens": 10,
    "total_tokens": 30
  }
}

Streaming

Enable streaming for real-time responses by setting stream: true.

Streaming Request

javascript

const response = await fetch("https://elizacloud.ai/api/v1/chat/completions", {
  method: "POST",
  headers: {
    Authorization: "Bearer YOUR_API_KEY",
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    model: "gpt-4o",
    messages: [{ role: "user", content: "Tell me a story" }],
    stream: true,
  }),
});

const reader = response.body.getReader();
const decoder = new TextDecoder();

while (true) {
  const { done, value } = await reader.read();
  if (done) break;

  const chunk = decoder.decode(value);
  const lines = chunk.split("\n").filter((line) => line.startsWith("data: "));

  for (const line of lines) {
    const data = line.slice(6);
    if (data === "[DONE]") continue;
    const parsed = JSON.parse(data);
    process.stdout.write(parsed.choices[0]?.delta?.content || "");
  }
}

Using with Agents

You can use an agent ID as the model to chat with your custom AI agents:

json

{
  "model": "agent_abc123",
  "messages": [{ "role": "user", "content": "Hello!" }]
}

<Callout type="info"> The agent's personality, system prompt, and configured model will be used automatically. </Callout>

Available Models

Model availability changes as provider catalogs and account configuration change. See the Models API or API Explorer for the current catalog and defaults.