Create Chat Completion

POST /v1/chat/completions

Creates a model response for the given chat conversation. Supports streaming, function calling, and multiple AI providers through a unified interface.

Request Body

string

required

ID of the model to use (e.g., gpt-4, claude-3-5-sonnet, gemini-2.0-flash)

array

required

A list of messages comprising the conversation so far.

Show Message object

string

required

The role of the message author. One of system, user, assistant, tool, or function.

string | array

The contents of the message. Can be a string or array of content parts for multimodal input.

string

An optional name for the participant.

array

Tool calls generated by the model (for assistant messages).

string

The ID of the tool call this message is responding to (for tool messages).

boolean

default:"false"

If set to true, partial message deltas will be sent as server-sent events.

object

Options for streaming responses.

Show properties

boolean

If set, includes usage statistics in the final streamed chunk.

number

default:"1"

Sampling temperature between 0 and 2. Higher values make output more random.

integer

Maximum number of tokens to generate in the completion.

integer

An upper bound for the number of tokens that can be generated.

string | array

Up to 4 sequences where the API will stop generating tokens.

number

default:"0"

Number between -2.0 and 2.0. Positive values penalize new tokens based on presence in text.

number

default:"0"

Number between -2.0 and 2.0. Positive values penalize new tokens based on frequency in text.

array

A list of tools the model may call.

Show Tool object

string

required

The type of tool. Currently only function is supported.

object

required

The function definition including name, description, and parameters schema.

string | object

Controls which tool is called. auto lets the model decide, none prevents tool calls, or specify a tool.

boolean

default:"true"

Whether to enable parallel function calling during tool use.

object

Specify the output format.

Show properties

string

Either text (default) or json_object for JSON mode.

string

For reasoning models, controls the effort level: low, medium, or high.

Response

string

A unique identifier for the chat completion.

string

The object type, always chat.completion.

integer

Unix timestamp of when the completion was created.

string

The model used for completion.

array

A list of chat completion choices.

Show Choice object

integer

The index of this choice.

object

The generated message.

string

The reason the model stopped generating: stop, length, tool_calls, etc.

object

Token usage statistics.

Show properties

integer

Tokens in the prompt.

integer

Tokens in the completion.

integer

Total tokens used.

Examples

Basic Completion

from openai import OpenAI

client = OpenAI(
    api_key="sk-voidai-your_key_here",
    base_url="https://api.voidai.app/v1"
)

response = client.chat.completions.create(
    model="gpt-4",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is the capital of France?"}
    ]
)

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

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: 'sk-voidai-your_key_here',
  baseURL: 'https://api.voidai.app/v1'
});

const response = await client.chat.completions.create({
  model: 'gpt-4',
  messages: [
    { role: 'system', content: 'You are a helpful assistant.' },
    { role: 'user', content: 'What is the capital of France?' }
  ]
});

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

curl https://api.voidai.app/v1/chat/completions \
  -H "Authorization: Bearer sk-voidai-your_key_here" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "What is the capital of France?"}
    ]
  }'

Streaming

stream = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Tell me a short story"}],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

const stream = await client.chat.completions.create({
  model: 'gpt-4',
  messages: [{ role: 'user', content: 'Tell me a short story' }],
  stream: true
});

for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content || '');
}

curl https://api.voidai.app/v1/chat/completions \
  -H "Authorization: Bearer sk-voidai-your_key_here" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4",
    "messages": [{"role": "user", "content": "Tell me a short story"}],
    "stream": true
  }'

Function Calling

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the current weather in a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city name"
                    },
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"]
                    }
                },
                "required": ["location"]
            }
        }
    }
]

response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "What's the weather in Tokyo?"}],
    tools=tools,
    tool_choice="auto"
)

# Check if the model wants to call a function
if response.choices[0].message.tool_calls:
    tool_call = response.choices[0].message.tool_calls[0]
    print(f"Function: {tool_call.function.name}")
    print(f"Arguments: {tool_call.function.arguments}")

const tools = [
  {
    type: 'function' as const,
    function: {
      name: 'get_weather',
      description: 'Get the current weather in a location',
      parameters: {
        type: 'object',
        properties: {
          location: { type: 'string', description: 'The city name' },
          unit: { type: 'string', enum: ['celsius', 'fahrenheit'] }
        },
        required: ['location']
      }
    }
  }
];

const response = await client.chat.completions.create({
  model: 'gpt-4',
  messages: [{ role: 'user', content: "What's the weather in Tokyo?" }],
  tools,
  tool_choice: 'auto'
});

if (response.choices[0].message.tool_calls) {
  const toolCall = response.choices[0].message.tool_calls[0];
  console.log(`Function: ${toolCall.function.name}`);
  console.log(`Arguments: ${toolCall.function.arguments}`);
}

Vision (Multimodal)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What's in this image?"},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://example.com/image.jpg",
                        "detail": "high"
                    }
                }
            ]
        }
    ]
)

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

const response = await client.chat.completions.create({
  model: 'gpt-4o',
  messages: [
    {
      role: 'user',
      content: [
        { type: 'text', text: "What's in this image?" },
        {
          type: 'image_url',
          image_url: {
            url: 'https://example.com/image.jpg',
            detail: 'high'
          }
        }
      ]
    }
  ]
});

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

JSON Mode

response = client.chat.completions.create(
    model="gpt-4",
    messages=[
        {"role": "system", "content": "You are a helpful assistant that responds in JSON."},
        {"role": "user", "content": "List 3 programming languages with their year of creation"}
    ],
    response_format={"type": "json_object"}
)

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

const response = await client.chat.completions.create({
  model: 'gpt-4',
  messages: [
    { role: 'system', content: 'You are a helpful assistant that responds in JSON.' },
    { role: 'user', content: 'List 3 programming languages with their year of creation' }
  ],
  response_format: { type: 'json_object' }
});

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

Response Example

{
  "id": "chatcmpl-abc123",
  "object": "chat.completion",
  "created": 1701691200,
  "model": "gpt-4",
  "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 Response

When stream: true, responses are sent as server-sent events:

data: {"id":"chatcmpl-abc123","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"role":"assistant"},"finish_reason":null}]}

data: {"id":"chatcmpl-abc123","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"content":"The"},"finish_reason":null}]}

data: {"id":"chatcmpl-abc123","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"content":" capital"},"finish_reason":null}]}

data: [DONE]

Chat

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Audio

Video

Embeddings

Moderations

Models

Discounts

Create Chat Completion

Request Body

Response

Examples

Basic Completion

Streaming

Function Calling

Vision (Multimodal)

JSON Mode

Response Example

Streaming Response

​Request Body

​Response

​Examples

​Basic Completion

​Streaming

​Function Calling

​Vision (Multimodal)

​JSON Mode

​Response Example

​Streaming Response

Request Body

Response

Examples

Basic Completion

Streaming

Function Calling

Vision (Multimodal)

JSON Mode

Response Example

Streaming Response