🔥 Quickstart Guide

Welcome! This guide will help you make your first call to the AI Assistant API in under 5 minutes.

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✅ Requirements

• An API key (example): api_key_1234567890abcdef
• A tool like curl, Postman, or a script in Python/JavaScript

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🛠️ Make a POST /chat/send request

Request

POST /api/v1/chat/send
Content-Type: application/json
Authorization: Bearer api_key_1234567890abcdef

{
  "message": "I want to return my order",
  "user_id": "user_123456789",
  "context": {
    "order_id": "ORD-456",
    "purchase_date": "2025-01-15"
  }
}

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Example response

{
  "reply": "Sure, I can help you return your order. Please follow this link: https://ecommerce.ai/returns/ORD-456",
  "conversation_id": "conv_789",
  "timestamp": "2025-06-09T10:12:00Z"
}

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Code Examples

Using cURL

curl -X POST https://api.ecommerce.ai/v1/chat/send \
  -H "Authorization: Bearer api_key_1234567890abcdef" \
  -H "Content-Type: application/json" \
  -d '{
    "message": "I want to return my order",
    "user_id": "user_123",
    "context": {
      "order_id": "ORD-456",
      "purchase_date": "2025-01-15"
    }'

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Python (requests)

import requests

url = "https://api.ecommerce.ai/v1/chat/send"
headers = {
    "Authorization": "Bearer api_key_1234567890abcdef",
    "Content-Type": "application/json"
}
payload = {
    "message": "I want to return my order",
    "user_id": "user_123",
    "context": {
        "order_id": "ORD-456",
        "purchase_date": "2025-01-15"
    }
}

response = requests.post(url, json=payload, headers=headers)
print(response.json())

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JavaScript (Fetch API)

fetch("https://api.ecommerce.ai/v1/chat/send", {
  method: "POST",
  headers: {
    "Authorization": "Bearer api_key_1234567890abcdef",
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    message: "I want to return my order",
    user_id: "user_123",
    context: {
      order_id: "ORD-456",
      purchase_date: "2025-01-15"
    }
  })
})
  .then(res => res.json())
  .then(console.log);

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Results

You’ll receive a structured reply based on the user’s intent and context — ready to be integrated into your support system or chatbot.

👉 Next: check out the API reference or explore more code samples.