Build with AI
Leverage EzenIA's powerful APIs to integrate AI solutions into your applications seamlessly.
Getting Started
AUTH
Authentication
curl -X POST "https://api.ezenia.com/auth/token" \
-H "Content-Type: application/json" \
-d '{"client_id": "your_client_id", "client_secret": "your_secret"}'
This endpoint returns your access token, which needs to be included in all API requests in the Authorization header.
1
Install SDK
npm install ezenia-sdk
2
Configure Client
import { EzenClient } from 'ezenia-sdk'
const client = new EzenClient({
apiKey: 'your-api-key',
endpoint: 'https://api.ezenia.com'
})
API Endpoints
ðŸ§
AI Models
List Available Models
GET /api/v1/models
Public
{
"models": [
{
"id": "llm-001",
"name": "Ezen-12B",
"type": "conversation",
"description": "General purpose language model for enterprise use",
"capabilities": ["text", "code", "audio"]
}
]
}
Generate AI Response
POST /api/v1/models/{id}/generate
Protected
{
"prompt": "Explain quantum computing in simple terms"
}
{
"response": "Quantum computing uses qubits that can exist in multiple states simultaneously...",
"model_used": "llm-001",
"tokens_used": 357
}
📊
Data Analysis
Upload Data
POST /api/v1/data/upload
Protected
{
"data": [{"column1": 123}, {"column2": "example"}],
"format": "json"
}
Run Analysis
POST /api/v1/analysis
Protected
{
"analysis_type": "regression",
"parameters": { "confidence": 0.95 }
}
{
"coefficient": 1.23,
"p_value": 0.002,
"confidence_interval": [0.89, 1.57]
}
âš¡
Real-time Inference
Start Inference Session
POST /api/v1/session
Protected
{
"model_id": "llm-001",
"temperature": 0.7
}
Stream Response
POST /api/v1/session/{id}/stream
Protected
{
"input": "Analyze this customer sentiment data"
}
Type: positive
Score: 92%
Confidence: 95.4%
Score: 92%
Confidence: 95.4%
Build AI Chatbot
Here's how to implement a basic AI conversation interface with streaming responses
React Example
// src/App.jsx
import { useState, useRef } from 'react';
function Chatbot() {
const [messages, setMessages] = useState([]);
const session = useRef();
const startSession = async () => {
const res = await fetch('/api/v1/session', {
method: "POST",
headers: { "Authorization": "Bearer YOUR_TOKEN" },
body: JSON.stringify({ model_id: "llm-001" })
});
session.current = await res.json();
};
const sendMessage = async (text) => {
const res = await fetch(`/api/v1/session/${session.current.id}/stream`, {
method: "POST",
headers: { "Authorization": "Bearer YOUR_TOKEN" },
body: JSON.stringify({ input: text })
});
const parser = new ReadableStream(res.body);
let response = '';
parser.on('data', (chunk) => {
response += chunk;
setMessages(prev => [...prev, { from: 'ai', text: response }]);
});
};
return (
{/* Chat UI would go here */}
);
}
Node.js Example
const fetch = require('node-fetch');
async function getAIResponse(prompt) {
const sessionRes = await fetch('https://api.ezenia.com/api/v1/session', {
method: 'POST',
headers: {
'Authorization': `Bearer ${apiKey}`,
'Content-Type': 'application/json'
},
body: JSON.stringify({ model_id: 'llm-001' })
});
const sessionData = await sessionRes.json();
const streamRes = await fetch(
`https://api.ezenia.com/api/v1/session/${sessionData.id}/stream`,
{
method: 'POST',
headers: {
'Authorization': `Bearer ${apiKey}`,
'Content-Type': 'application/json'
},
body: JSON.stringify({ input: prompt })
}
);
return new Promise((resolve) => {
let chunks = '';
const reader = streamRes.body.getReader();
function read() {
reader.read().then(({ value, done }) => {
if (value) {
chunks += new TextDecoder().decode(value);
}
if (done) {
resolve(chunks);
} else {
read();
}
});
}
read();
});
}