Documentation Index
Fetch the complete documentation index at: https://www.edenai.co/docs/llms.txt
Use this file to discover all available pages before exploring further.
Endpoint
POST /v3/universal-ai (sync)
Model string pattern: ocr/ocr/{provider}[/{model}]
| Field | Type | Required | Description |
|---|
| file | file_input | Yes | PDF or image file ID from /v3/upload or direct file URL |
| language | string | Yes | Document language code |
Output
| Field | Type | Required | Description |
|---|
| text | string | Yes | |
| bounding_boxes | array[object] | No | |
| text | string | Yes | |
| left | float | Yes | |
| top | float | Yes | |
| width | float | Yes | |
| height | float | Yes | |
Available Providers
| Provider | Model String | Price |
|---|
| amazon | ocr/ocr/amazon | $1.5 per 1,000 pages |
| api4ai | ocr/ocr/api4ai | $3 per 1,000 requests |
| google | ocr/ocr/google | $1.5 per 1,000 pages |
| microsoft | ocr/ocr/microsoft | $1 per 1,000 pages |
| mistral | ocr/ocr/mistral | $1 per 1,000 pages |
| sentisight | ocr/ocr/sentisight | $0.75 per 1,000 files |
Quick Start
import requests
url = "https://api.edenai.run/v3/universal-ai"
headers = {
"Authorization": "Bearer YOUR_API_KEY",
"Content-Type": "application/json"
}
payload = {
"model": "ocr/ocr/amazon",
"input": {
"file": "YOUR_FILE_UUID_OR_URL",
"language": "en"
}
}
response = requests.post(url, headers=headers, json=payload)
print(response.json())
