AI API Documentation | ceuadellhes

Getting Started

Welcome to our AI API documentation. Here you'll find comprehensive information to integrate our AI capabilities into your application efficiently. We use modern API practices with interactive elements to help you explore the features of our AI API.

POST /api/v1/ai/analyze

Analyze text, images, or data with our AI models.

Authentication

Use Bearer token authentication with the Authorization header. Obtain your API key from the dashboard.


                                Authorization: Bearer {api-key}

API keys expire after 24 hours. Use the token refresh endpoint to extend access.

Versioning

Current stable version: v1.2.0

Stable - v1.2.0

Released June 2024

Recommended for production use with quarterly updates available via our dashboard.

New features in 1.2.0: Contextual embeddings for multilingual NLP, automatic bias detection, and model explainability reports.

AI Capabilities

Natural Language

Sentiment analysis, entity extraction, summarization, and conversation generation using transformer-based models.

• Text classification
• Multilingual support
• Real-time inference

Computer Vision

Image recognition, object detection, and visual search using state-of-the-art convolutional neural networks.

• OCR with layout analysis
• Image captioning
• Deepfake detection

Machine Learning

Custom model deployment, automated feature engineering, and real-time model monitoring for continuous improvement.

• AutoML pipelines
• Feature store
• Model explainability

API Endpoints

/ai/analyze

Process text, images, or data inputs through the AI engine.

POST

Request Body

{
  "input": "Sample text for analysis",
  "model": "nlp-v2"
}

/ai/models

List available AI models or retrieve model metadata.

get

Query Parameters

model_type
version
provider

Code Examples

JavaScript


fetch('https://api.example.com/ai/analyze', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer ' + apiKey,
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    input: 'What is quantum computing?',
    model: 'question-answer'
  })
})
.then(response => response.json())
.then(data => console.log(data));

Python


import requests

response = requests.post(
    'https://api.example.com/ai/analyze',
    headers={
        'Authorization': f'Bearer {api_key}',
        'Content-Type': 'application/json'
    },
    json={"input": "Analyze this image", "model": "vision-v1"}
)

print(response.json())