Installation
pip install openai
npm install openai
Configuration
The only changes needed are:- Set
base_url/baseURLtohttps://api.voidai.app/v1 - Use your VoidAI API key instead of OpenAI’s
from openai import OpenAI
client = OpenAI(
api_key="sk-voidai-your_key_here",
base_url="https://api.voidai.app/v1"
)
import OpenAI from 'openai';
const client = new OpenAI({
apiKey: 'sk-voidai-your_key_here',
baseURL: 'https://api.voidai.app/v1'
});
Supported Features
Chat Completions
Full support for chat completions including streaming, function calling, and tool use.# Basic completion
response = client.chat.completions.create(
model="gpt-5.1",
messages=[{"role": "user", "content": "Hello!"}]
)
# Streaming
stream = client.chat.completions.create(
model="gpt-5.1",
messages=[{"role": "user", "content": "Tell me a story"}],
stream=True
)
for chunk in stream:
if chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end="")
// Basic completion
const response = await client.chat.completions.create({
model: 'gpt-5.1',
messages: [{ role: 'user', content: 'Hello!' }]
});
// Streaming
const stream = await client.chat.completions.create({
model: 'gpt-5.1',
messages: [{ role: 'user', content: 'Tell me a story' }],
stream: true
});
for await (const chunk of stream) {
process.stdout.write(chunk.choices[0]?.delta?.content || '');
}
Function Calling / Tools
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 and state, e.g. San Francisco, CA"
}
},
"required": ["location"]
}
}
}
]
response = client.chat.completions.create(
model="gpt-5.1",
messages=[{"role": "user", "content": "What's the weather in Tokyo?"}],
tools=tools,
tool_choice="auto"
)
# Check if a tool was called
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 and state, e.g. San Francisco, CA'
}
},
required: ['location']
}
}
}
];
const response = await client.chat.completions.create({
model: 'gpt-5.1',
messages: [{ role: 'user', content: "What's the weather in Tokyo?" }],
tools,
tool_choice: 'auto'
});
// Check if a tool was called
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}`);
}
Image Generation
response = client.images.generate(
model="gpt-image-1",
prompt="A sunset over mountains",
size="1024x1024",
n=1
)
image_url = response.data[0].url
print(image_url)
const response = await client.images.generate({
model: 'gpt-image-1',
prompt: 'A sunset over mountains',
size: '1024x1024',
n: 1
});
const imageUrl = response.data[0].url;
console.log(imageUrl);
Audio Transcription
with open("audio.mp3", "rb") as audio_file:
transcript = client.audio.transcriptions.create(
model="whisper-1",
file=audio_file
)
print(transcript.text)
import fs from 'fs';
const transcript = await client.audio.transcriptions.create({
model: 'whisper-1',
file: fs.createReadStream('audio.mp3')
});
console.log(transcript.text);
Text-to-Speech
response = client.audio.speech.create(
model="tts-1",
voice="alloy",
input="Hello, this is a test of text to speech."
)
response.stream_to_file("output.mp3")
const response = await client.audio.speech.create({
model: 'tts-1',
voice: 'alloy',
input: 'Hello, this is a test of text to speech.'
});
const buffer = Buffer.from(await response.arrayBuffer());
fs.writeFileSync('output.mp3', buffer);
Embeddings
response = client.embeddings.create(
model="text-embedding-3-small",
input="Your text to embed"
)
embedding = response.data[0].embedding
print(f"Embedding dimension: {len(embedding)}")
const response = await client.embeddings.create({
model: 'text-embedding-3-small',
input: 'Your text to embed'
});
const embedding = response.data[0].embedding;
console.log(`Embedding dimension: ${embedding.length}`);
Using Different Providers
The main benefit of VoidAI is accessing multiple providers through one SDK. Simply change the model name:# OpenAI
client.chat.completions.create(model="gpt-5.1", ...)
# Anthropic
client.chat.completions.create(model="claude-sonnet-4-5-20250929", ...)
# Google
client.chat.completions.create(model="gemini-3-pro-preview", ...)
# DeepSeek
client.chat.completions.create(model="deepseek-v3", ...)
All providers use the same OpenAI-compatible request/response format. No code changes needed beyond the model name.
Environment Variables
For production, use environment variables:import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["VOIDAI_API_KEY"],
base_url="https://api.voidai.app/v1"
)
const client = new OpenAI({
apiKey: process.env.VOIDAI_API_KEY,
baseURL: 'https://api.voidai.app/v1'
});