Complete Guide to datakraft API Integration

Integrating AI-powered document processing into your existing applications and workflows can transform how your organization handles documents. This comprehensive guide walks through the entire process of integrating with datakraft's API, from initial setup to advanced implementation patterns.

API Overview and Architecture

The datakraft API is built on REST principles with JSON payloads, making it easy to integrate with any programming language or platform. The API provides several key endpoints:

• Document Upload: Submit documents for processing
• Processing Status: Check the status of document processing jobs
• Results Retrieval: Get processed data and extracted information
• Webhook Configuration: Set up real-time notifications
• Batch Operations: Process multiple documents efficiently

Getting Started: Authentication and Setup

API Key Authentication

All API requests require authentication using an API key. Include your API key in the Authorization header:

Authorization: Bearer YOUR_API_KEY

Base URL and Endpoints

All API requests are made to the base URL:

https://api.datakraft.com/v1/

Rate Limiting

The API implements rate limiting to ensure fair usage:

• Standard tier: 100 requests per minute
• Professional tier: 500 requests per minute
• Enterprise tier: Custom limits based on agreement

Basic Document Processing Workflow

Step 1: Upload Document

Submit a document for processing using the upload endpoint:

POST /documents/upload
Content-Type: multipart/form-data

{
  "file": [binary file data],
  "document_type": "invoice",
  "processing_options": {
    "extract_tables": true,
    "ocr_language": "en",
    "confidence_threshold": 0.95
  }
}

Step 2: Check Processing Status

Monitor the processing status using the job ID returned from the upload:

GET /documents/{job_id}/status

Response:
{
  "job_id": "12345",
  "status": "processing",
  "progress": 75,
  "estimated_completion": "2024-01-15T10:30:00Z"
}

Step 3: Retrieve Results

Once processing is complete, retrieve the extracted data:

GET /documents/{job_id}/results

Response:
{
  "job_id": "12345",
  "status": "completed",
  "extracted_data": {
    "document_type": "invoice",
    "confidence_score": 0.98,
    "fields": {
      "invoice_number": "INV-2024-001",
      "date": "2024-01-15",
      "total_amount": 1250.00,
      "vendor_name": "Acme Corp",
      "line_items": [
        {
          "description": "Professional Services",
          "quantity": 10,
          "unit_price": 125.00,
          "total": 1250.00
        }
      ]
    },
    "tables": [...],
    "metadata": {
      "pages": 1,
      "processing_time": 2.3,
      "file_size": 245760
    }
  }
}

Advanced Integration Patterns

Webhook Integration

For real-time processing notifications, configure webhooks to receive updates when documents are processed:

POST /webhooks/configure
{
  "url": "https://your-app.com/webhook/datakraft",
  "events": ["document.completed", "document.failed"],
  "secret": "your_webhook_secret"
}

Your webhook endpoint will receive POST requests with processing updates:

{
  "event": "document.completed",
  "job_id": "12345",
  "timestamp": "2024-01-15T10:30:00Z",
  "data": {
    "status": "completed",
    "confidence_score": 0.98
  }
}

Batch Processing

Process multiple documents efficiently using batch operations:

POST /documents/batch
{
  "documents": [
    {
      "file_url": "https://your-storage.com/doc1.pdf",
      "document_type": "invoice"
    },
    {
      "file_url": "https://your-storage.com/doc2.pdf",
      "document_type": "receipt"
    }
  ],
  "processing_options": {
    "priority": "high",
    "callback_url": "https://your-app.com/batch-complete"
  }
}

Error Handling and Retry Logic

HTTP Status Codes

The API uses standard HTTP status codes:

• 200 OK: Request successful
• 202 Accepted: Document accepted for processing
• 400 Bad Request: Invalid request parameters
• 401 Unauthorized: Invalid or missing API key
• 429 Too Many Requests: Rate limit exceeded
• 500 Internal Server Error: Server error

Retry Strategy

Implement exponential backoff for handling temporary failures:

def retry_with_backoff(func, max_retries=3):
    for attempt in range(max_retries):
        try:
            return func()
        except requests.exceptions.RequestException as e:
            if attempt == max_retries - 1:
                raise e
            wait_time = (2 ** attempt) + random.uniform(0, 1)
            time.sleep(wait_time)

SDK and Client Libraries

Python SDK

pip install datakraft-python

from datakraft import DatakraftClient

client = DatakraftClient(api_key="your_api_key")

# Upload and process document
result = client.process_document(
    file_path="invoice.pdf",
    document_type="invoice",
    wait_for_completion=True
)

print(result.extracted_data)

JavaScript/Node.js SDK

npm install datakraft-js

const { DatakraftClient } = require('datakraft-js');

const client = new DatakraftClient('your_api_key');

// Process document with async/await
async function processDocument() {
  const result = await client.processDocument({
    filePath: 'invoice.pdf',
    documentType: 'invoice'
  });
  
  console.log(result.extractedData);
}

Integration Examples by Use Case

E-commerce Order Processing

Automatically process supplier invoices and update inventory systems:

// Webhook handler for completed invoice processing
app.post('/webhook/invoice-processed', (req, res) => {
  const { job_id, data } = req.body;
  
  if (data.status === 'completed') {
    const invoice = data.extracted_data;
    
    // Update inventory system
    updateInventory({
      supplier: invoice.fields.vendor_name,
      items: invoice.fields.line_items,
      total: invoice.fields.total_amount
    });
    
    // Create accounting entry
    createAccountingEntry({
      amount: invoice.fields.total_amount,
      date: invoice.fields.date,
      reference: invoice.fields.invoice_number
    });
  }
  
  res.status(200).send('OK');
});

HR Document Management

Process employee onboarding documents and update HR systems:

async function processOnboardingDocuments(employeeId, documents) {
  const results = [];
  
  for (const doc of documents) {
    const result = await client.processDocument({
      filePath: doc.path,
      documentType: doc.type,
      processingOptions: {
        extractTables: true,
        confidenceThreshold: 0.95
      }
    });
    
    // Update employee record based on document type
    switch (doc.type) {
      case 'tax_form':
        await updateTaxInformation(employeeId, result.extractedData);
        break;
      case 'bank_details':
        await updatePayrollInformation(employeeId, result.extractedData);
        break;
      case 'emergency_contact':
        await updateEmergencyContacts(employeeId, result.extractedData);
        break;
    }
    
    results.push(result);
  }
  
  return results;
}

Performance Optimization

Parallel Processing

Process multiple documents concurrently to improve throughput:

import asyncio
import aiohttp

async def process_documents_parallel(documents):
    async with aiohttp.ClientSession() as session:
        tasks = []
        for doc in documents:
            task = process_single_document(session, doc)
            tasks.append(task)
        
        results = await asyncio.gather(*tasks)
        return results

async def process_single_document(session, document):
    async with session.post(
        'https://api.datakraft.com/v1/documents/upload',
        headers={'Authorization': f'Bearer {API_KEY}'},
        data={'file': document}
    ) as response:
        return await response.json()

Caching Strategies

Implement caching to avoid reprocessing identical documents:

import hashlib
import redis

redis_client = redis.Redis(host='localhost', port=6379, db=0)

def get_document_hash(file_content):
    return hashlib.sha256(file_content).hexdigest()

def process_with_cache(file_content, document_type):
    doc_hash = get_document_hash(file_content)
    cache_key = f"doc:{doc_hash}:{document_type}"
    
    # Check cache first
    cached_result = redis_client.get(cache_key)
    if cached_result:
        return json.loads(cached_result)
    
    # Process document if not in cache
    result = client.process_document(file_content, document_type)
    
    # Cache result for 24 hours
    redis_client.setex(cache_key, 86400, json.dumps(result))
    
    return result

Monitoring and Analytics

Usage Tracking

Monitor API usage and performance:

GET /analytics/usage?start_date=2024-01-01&end_date=2024-01-31

Response:
{
  "period": {
    "start": "2024-01-01",
    "end": "2024-01-31"
  },
  "metrics": {
    "total_documents": 15420,
    "successful_processing": 15180,
    "failed_processing": 240,
    "average_processing_time": 2.3,
    "api_calls": 18650,
    "data_processed_gb": 45.2
  },
  "top_document_types": [
    {"type": "invoice", "count": 8920},
    {"type": "receipt", "count": 3240},
    {"type": "contract", "count": 2180}
  ]
}

Error Monitoring

Track and analyze processing errors:

GET /analytics/errors?start_date=2024-01-01&end_date=2024-01-31

Response:
{
  "error_summary": {
    "total_errors": 240,
    "error_rate": 1.56
  },
  "error_types": [
    {
      "type": "low_quality_image",
      "count": 120,
      "percentage": 50.0
    },
    {
      "type": "unsupported_format",
      "count": 80,
      "percentage": 33.3
    },
    {
      "type": "processing_timeout",
      "count": 40,
      "percentage": 16.7
    }
  ]
}

Security Best Practices

API Key Management

• Store API keys securely using environment variables or secret management systems
• Rotate API keys regularly
• Use different API keys for different environments (dev, staging, production)
• Monitor API key usage for suspicious activity

Data Protection

• Use HTTPS for all API communications
• Implement request signing for additional security
• Validate webhook signatures to ensure authenticity
• Sanitize and validate all input data

Testing and Development

Sandbox Environment

Use the sandbox environment for development and testing:

Base URL: https://sandbox-api.datakraft.com/v1/
API Key: Use sandbox-specific API keys

Unit Testing

Mock API responses for reliable unit testing:

import unittest
from unittest.mock import patch, Mock

class TestDatakraftIntegration(unittest.TestCase):
    
    @patch('requests.post')
    def test_document_upload(self, mock_post):
        mock_response = Mock()
        mock_response.json.return_value = {
            'job_id': '12345',
            'status': 'accepted'
        }
        mock_response.status_code = 202
        mock_post.return_value = mock_response
        
        result = upload_document('test.pdf', 'invoice')
        
        self.assertEqual(result['job_id'], '12345')
        self.assertEqual(result['status'], 'accepted')

Troubleshooting Common Issues

Document Quality Issues

• Low OCR accuracy: Ensure documents are high resolution (300+ DPI)
• Poor table extraction: Use documents with clear table borders
• Missing text: Check for sufficient contrast between text and background

API Integration Issues

• Timeout errors: Implement proper retry logic with exponential backoff
• Rate limiting: Implement request queuing and respect rate limits
• Authentication failures: Verify API key validity and permissions

Migration and Deployment

Production Deployment Checklist

• ✅ API keys configured in production environment
• ✅ Webhook endpoints secured and tested
• ✅ Error handling and retry logic implemented
• ✅ Monitoring and alerting configured
• ✅ Rate limiting and throttling implemented
• ✅ Security review completed
• ✅ Performance testing completed
• ✅ Backup and recovery procedures documented

Support and Resources

Getting Help

• API Documentation: https://docs.datakraft.com
• Developer Support: support@datakraft.com
• Community Forum: https://community.datakraft.com
• Status Page: https://status.datakraft.com

Additional Resources

• Sample code repositories on GitHub
• Postman collection for API testing
• Video tutorials and webinars
• Integration templates for popular platforms

This comprehensive guide provides the foundation for successfully integrating datakraft's document processing capabilities into your applications. Start with basic document upload and processing, then gradually implement more advanced features like webhooks, batch processing, and performance optimizations as your needs grow.