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Integrate with HyperAPI for financial document processing - OCR text extraction, document classification, PDF splitting, and structured data extraction from invoices, receipts, and financial documents. Use when the user needs to parse PDFs, extract text from documents, classify document types, split multi-document PDFs, or extract structured entities like invoice numbers, vendor names, line items. Keywords: hyperapi, hyperbots, document parsing, OCR, PDF processing, invoice extraction, receipt processing, document classification, VLM, vision language model.
npx skill4agent add guglxni/hyperbots-agent-skills hyperbots-api| Endpoint | Task | Use Case | Cost | Latency |
|---|---|---|---|---|
| OCR Text Extraction | Extract raw text from any document | $0.05/page | ~1s |
| Structured Data Extraction | Get entities and line items from invoices | $0.08/page | ~2s |
| Document Classification | Categorize documents automatically | $0.03/page | ~0.8s |
| PDF Segmentation | Split multi-document PDFs logically | $0.02/page | ~0.5s |
https://api.hyperapi.devX-API-Keyhk_live_...hk_test_...pip install hyperapifrom hyperapi import HyperAPIClient
client = HyperAPIClient(api_key="hk_live_your_key_here")
# Parse - extract OCR text
result = client.parse("document.pdf")
print(result["result"]["ocr"])
# Extract - get structured data
result = client.extract("invoice.pdf")
print(result["result"]["entities"])
print(result["result"]["line_items"])
client.close()curl -X POST https://api.hyperapi.dev/v1/parse \
-H "X-API-Key: hk_live_..." \
-F "file=@document.pdf"{
"status": "success",
"request_id": "req_01j9x...",
"task": "parse",
"model_used": "hyperbots_vlm_ocr",
"result": {
"ocr": "Invoice\n\nBill To: Acme Corp\nDate: 2024-01-15..."
},
"duration_ms": 843,
"metadata": {
"pages": 2,
"file_type": "pdf"
}
}curl -X POST https://api.hyperapi.dev/v1/extract \
-H "X-API-Key: hk_live_..." \
-F "file=@invoice.pdf"{
"status": "success",
"request_id": "req_01ja0...",
"task": "extract",
"model_used": "hyperbots_vlm_extract",
"result": {
"entities": {
"invoice_number": "INV-2024-0042",
"date": "2024-01-15",
"due_date": "2024-02-15",
"vendor_name": "Acme Supplies Ltd",
"total_amount": "1,250.00",
"currency": "USD"
},
"line_items": [
{
"description": "Widget A",
"quantity": 10,
"unit_price": "100.00",
"total": "1,000.00"
}
]
},
"duration_ms": 1820,
"metadata": { "pages": 2 }
}curl -X POST https://api.hyperapi.dev/v1/classify \
-H "X-API-Key: hk_live_..." \
-F "file=@document.pdf"{
"status": "success",
"request_id": "req_01j9y...",
"task": "classify",
"model_used": "hyperbots_vlm_ocr",
"result": {
"label": "invoice",
"confidence": 0.98,
"candidates": [
{ "label": "invoice", "confidence": 0.98 },
{ "label": "receipt", "confidence": 0.01 },
{ "label": "contract", "confidence": 0.01 }
]
},
"duration_ms": 612,
"metadata": { "pages": 1 }
}curl -X POST https://api.hyperapi.dev/v1/split \
-H "X-API-Key: hk_live_..." \
-F "file=@batch.pdf"{
"status": "success",
"request_id": "req_01j9z...",
"task": "split",
"model_used": "hyperbots_vlm_ocr",
"result": {
"segments": [
{ "document_index": 0, "start_page": 1, "end_page": 3, "type": "invoice" },
{ "document_index": 1, "start_page": 4, "end_page": 5, "type": "receipt" }
]
},
"duration_ms": 490,
"metadata": { "pages": 5 }
}| Status | Code | Description |
|---|---|---|
| 401 | Unauthorized | Missing or invalid X-API-Key |
| 402 | Payment Required | Insufficient credit balance |
| 413 | Payload Too Large | File exceeds 50 MB limit |
| 422 | Unprocessable Entity | Unsupported file type |
| 429 | Too Many Requests | Rate limit exceeded |
| 503 | Service Unavailable | Backend unavailable (circuit open) |
{
"error": {
"code": "INSUFFICIENT_CREDITS",
"message": "Your credit balance is too low to process this request."
}
}| Tier | Requests/min | Concurrency | Priority |
|---|---|---|---|
| Free | 10 | 1 | 1 |
| Pro | 100 | 10 | 10 |
| Enterprise | Unlimited | 100 | 100 |
from hyperapi import HyperAPIClient
client = HyperAPIClient(api_key="hk_live_...")
# All available methods
client.parse(file_path) # OCR extraction
client.extract(file_path) # Structured data extraction
client.classify(file_path) # Document classification
client.split(file_path) # PDF segmentation
client.process(file_path) # Parse + Extract combined
client.upload_document(file_path) # S3 presigned upload
client.close()client.upload_document()parseextractclassifysplitexport HYPERAPI_KEY=hk_live_...
python3 scripts/hyperbots_cli.py extract invoice.pdf