AI & Machine Learning API Key

GenAI APIs REST API

Unified generative AI model access and orchestration

GenAI APIs provide a unified interface for accessing and orchestrating multiple generative AI models across text, image, and audio generation. Developers use GenAI to standardize model interactions, implement fallback strategies, and manage rate limits across different AI providers with a single API integration.

Base URL https://api.genai.rest/v1

API Endpoints

Method	Endpoint	Description
POST	`/completions`	Generate text completions using specified AI models with streaming support
POST	`/chat/completions`	Generate chat-based completions with conversation history and system prompts
POST	`/embeddings`	Generate vector embeddings for text inputs using various embedding models
POST	`/images/generate`	Generate images from text prompts using diffusion and other image generation models
POST	`/images/edit`	Edit existing images using AI models with inpainting and outpainting capabilities
POST	`/audio/transcribe`	Transcribe audio files to text using speech recognition models
POST	`/audio/translate`	Translate audio from one language to another with transcription
POST	`/audio/speech`	Generate speech audio from text input using text-to-speech models
GET	`/models`	List all available AI models across providers with capabilities and pricing
GET	`/models/{model_id}`	Retrieve detailed information about a specific AI model
POST	`/fine-tuning/jobs`	Create a fine-tuning job to customize models with training data
GET	`/fine-tuning/jobs/{job_id}`	Get the status and details of a fine-tuning job
GET	`/usage`	Retrieve usage statistics and token consumption across all API calls
POST	`/moderate`	Check content for safety and policy violations using moderation models
POST	`/chains/execute`	Execute multi-step AI workflows with chained model invocations

Code Examples

curl -X POST https://api.genai.rest/v1/chat/completions \
  -H 'Authorization: Bearer YOUR_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "gpt-4",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "Explain quantum computing in simple terms."}
    ],
    "temperature": 0.7,
    "max_tokens": 500
  }'

const response = await fetch('https://api.genai.rest/v1/chat/completions', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer YOUR_API_KEY',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    model: 'gpt-4',
    messages: [
      { role: 'system', content: 'You are a helpful assistant.' },
      { role: 'user', content: 'Explain quantum computing in simple terms.' }
    ],
    temperature: 0.7,
    max_tokens: 500
  })
});

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

import requests

url = 'https://api.genai.rest/v1/chat/completions'
headers = {
    'Authorization': 'Bearer YOUR_API_KEY',
    'Content-Type': 'application/json'
}
payload = {
    'model': 'gpt-4',
    'messages': [
        {'role': 'system', 'content': 'You are a helpful assistant.'},
        {'role': 'user', 'content': 'Explain quantum computing in simple terms.'}
    ],
    'temperature': 0.7,
    'max_tokens': 500
}

response = requests.post(url, headers=headers, json=payload)
result = response.json()
print(result['choices'][0]['message']['content'])

Connect GenAI APIs to AI

Deploy a GenAI APIs MCP server on IOX Cloud and connect it to Claude, ChatGPT, Cursor, or any AI client. Your AI assistant gets direct access to GenAI APIs through these tools:

generate_completion Generate text completions with automatic model selection and fallback handling for robust AI responses

create_embeddings Convert text into vector embeddings for semantic search, clustering, and similarity comparisons

generate_image Create images from text descriptions using the most appropriate available image generation model

transcribe_audio Transcribe audio files to text with automatic language detection and speaker diarization support

execute_ai_chain Run multi-step AI workflows that chain multiple model calls with conditional logic and data transformations

Deploy in 60 seconds

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GenAI APIs REST API

API Endpoints

Code Examples

Connect GenAI APIs to AI

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