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API FAQ

Technical questions and answers for developers integrating with Unsiloed AI’s APIs.

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Getting Started with the API

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How do I get API access?

1. Create an account at unsiloed-ai.com
2. Navigate to your dashboard after signing up
3. Generate API keys in the API section
4. Start making requests using our endpoints

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What is the base URL for the API?

The base URL for all API endpoints is:

https://visionapi.unsiloed.ai

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How do I authenticate API requests?

We use API key authentication. Include your API key in the request headers:

curl -H "Authorization: Bearer YOUR_API_KEY" \
     -H "Content-Type: application/json" \
     https://visionapi.unsiloed.ai/endpoint

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API Endpoints and Usage

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What are the main API endpoints?

Our core endpoints include:

• Document Extraction: /extraction - Extract text and data from documents
• Table Extraction: /tables - Extract structured table data
• Document Parsing: /parsing - Parse and structure document content
• Document Splitting: /splitter/split-pdf - Split documents by classification
• Document Classification: /classification - Classify document types
• Batch Processing: /batch - Process multiple documents

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What HTTP methods are supported?

Most endpoints use POST requests for document processing:

• POST - Submit documents for processing
• GET - Retrieve job status and results (for async operations)
• DELETE - Cancel processing jobs

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How do I handle file uploads?

Use multipart/form-data for file uploads:

import requests

files = {"file": ("document.pdf", open("document.pdf", "rb"), "application/pdf")}
response = requests.post(
    "https://visionapi.unsiloed.ai/extraction",
    files=files,
    headers={"Authorization": "Bearer YOUR_API_KEY"}
)

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Rate Limits and Quotas

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What are the API rate limits?

Default rate limits:

• 100 requests per minute for standard plans
• 1000 requests per minute for professional plans
• Custom limits for enterprise plans

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How do I handle rate limiting?

When you exceed rate limits, you’ll receive a 429 Too Many Requests response. Implement exponential backoff:

import time
import requests

def make_request_with_retry(url, **kwargs):
    max_retries = 3
    for attempt in range(max_retries):
        response = requests.post(url, **kwargs)
        if response.status_code != 429:
            return response
        
        wait_time = 2 ** attempt  # Exponential backoff
        time.sleep(wait_time)
    
    return response

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What happens if I exceed my quota?

• Free tier: Processing stops until next billing cycle
• Paid plans: Overage charges apply
• Enterprise: Custom arrangements available

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Error Handling

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What HTTP status codes should I expect?

Common status codes:

• 200 - Success
• 400 - Bad Request (invalid parameters)
• 401 - Unauthorized (invalid API key)
• 413 - Payload Too Large (file size exceeded)
• 429 - Too Many Requests (rate limited)
• 500 - Internal Server Error

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How should I handle errors?

Always check the response status and handle errors appropriately:

response = requests.post(url, files=files, headers=headers)

if response.status_code == 200:
    result = response.json()
    # Process successful response
elif response.status_code == 400:
    error = response.json()
    print(f"Bad request: {error.get('detail', 'Unknown error')}")
elif response.status_code == 401:
    print("Invalid API key")
elif response.status_code == 413:
    print("File too large")
else:
    print(f"Unexpected error: {response.status_code}")

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What error information is provided?

Error responses include:

{
  "detail": "Descriptive error message",
  "error_code": "SPECIFIC_ERROR_CODE",
  "timestamp": "2024-01-01T12:00:00Z"
}

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Document Processing

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What file formats are supported via API?

Supported formats:

• PDF: Including scanned PDFs
• Images: PNG, JPEG, TIFF, WebP
• Documents: DOCX, TXT

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What’s the maximum file size?

• Standard: 100MB per file
• Enterprise: Custom limits available

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How do I process multiple files?

Use the batch processing endpoint:

files = [
    ("files", ("doc1.pdf", open("doc1.pdf", "rb"), "application/pdf")),
    ("files", ("doc2.pdf", open("doc2.pdf", "rb"), "application/pdf"))
]

response = requests.post(
    "https://visionapi.unsiloed.ai/batch",
    files=files,
    headers={"Authorization": "Bearer YOUR_API_KEY"}
)

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Response Formats

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What format do API responses use?

All responses are in JSON format:

{
  "status": "success",
  "data": {
    "extracted_text": "Document content...",
    "confidence": 0.95,
    "metadata": {
      "pages": 5,
      "processing_time": 2.3
    }
  }
}

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How do I handle binary responses?

Some endpoints (like document splitting) return ZIP files:

response = requests.post(url, files=files, headers=headers)

if response.headers.get('content-type') == 'application/zip':
    with open('result.zip', 'wb') as f:
        f.write(response.content)

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Webhooks and Async Processing

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Do you support webhooks?

Yes! Configure webhooks in your dashboard to receive notifications when processing completes:

{
  "job_id": "12345",
  "status": "completed",
  "result_url": "https://api.unsiloed.ai/results/12345",
  "timestamp": "2024-01-01T12:00:00Z"
}

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How do I handle long-running processes?

For large documents, use async processing:

1. Submit job: Receive a job_id
2. Poll status: Check /jobs/{job_id}/status
3. Retrieve results: Get results when status is completed

# Submit job
response = requests.post(url, files=files, headers=headers)
job_id = response.json()['job_id']

# Poll status
while True:
    status_response = requests.get(f"{base_url}/jobs/{job_id}/status", headers=headers)
    status = status_response.json()['status']
    
    if status == 'completed':
        results = requests.get(f"{base_url}/jobs/{job_id}/results", headers=headers)
        break
    elif status == 'failed':
        print("Job failed")
        break
    
    time.sleep(5)  # Wait 5 seconds before checking again

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SDK and Libraries

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Do you provide SDKs?

We provide official SDKs for:

• Python: pip install unsiloed-ai
• JavaScript/Node.js: npm install unsiloed-ai
• More languages: Coming soon

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How do I use the Python SDK?

from unsiloed_ai import UnsiloedAI

client = UnsiloedAI(api_key="your-api-key")

# Extract text from document
result = client.extract_text("document.pdf")
print(result.text)

# Parse structured data
parsed = client.parse_document("financial_report.pdf")
print(parsed.tables)