Overview The Extract Tables endpoint processes PDF documents and extracts structured table data using advanced AI-powered table detection and parsing. This endpoint is specifically designed to identify, extract, and structure tabular content from documents with high accuracy.
The endpoint returns a job ID for asynchronous processing. Use the job management endpoints to check status and retrieve results.
Request The PDF file to process for table extraction. Maximum file size: 100MB
Response Unique identifier for the table extraction job
Initial job status (typically “queued”)
Descriptive message about the job creation
Number of credits remaining in your quota
curl -X POST "https://prod.visionapi.unsiloed.ai/tables" \ -H "accept: application/json" \ -H "api-key: your-api-key" \ -H "Content-Type: multipart/form-data" \ -F "pdf_file=@document.pdf;type=application/pdf"
Success Response
Error Response
{ "job_id" : "e4a73f3c-869e-4129-bdef-614a6a51b043" , "status" : "queued" , "message" : "PDF table extraction started" , "quota_remaining" : 48960 }
Job Management Integration After creating a table extraction job, use the job management endpoints to monitor progress and retrieve results:
import time import requests def wait_for_table_extraction_completion ( job_id , api_key ): """Poll job status until completion""" headers = { "api-key" : api_key} status_url = f "https://prod.visionapi.unsiloed.ai/jobs/ { job_id } " while True : response = requests.get(status_url, headers = headers) if response.status_code == 200 : status_data = response.json() print ( f "Job status: { status_data[ 'status' ] } " ) if status_data[ 'status' ] == 'completed' : # Get results results_url = f "https://prod.visionapi.unsiloed.ai/jobs/ { job_id } /results" results_response = requests.get(results_url, headers = headers) if results_response.status_code == 200 : return results_response.json() else : raise Exception ( f "Failed to get results: { results_response.text } " ) elif status_data[ 'status' ] == 'failed' : raise Exception ( f "Job failed: { status_data.get( 'error' , 'Unknown error' ) } " ) time.sleep( 5 ) # Wait 5 seconds before next check # Usage job_id = "e4a73f3c-869e-4129-bdef-614a6a51b043" results = wait_for_table_extraction_completion(job_id, "your-api-key" ) print ( "Table extraction completed:" , results) Expected Results Structure When the job completes, the results will contain structured table data:
{ "job_id" : "e4a73f3c-869e-4129-bdef-614a6a51b043" , "status" : "completed" , "results" : { "tables" : [ { "table_id" : "table_1" , "page_number" : 1 , "bbox" : { "x1" : 50 , "y1" : 100 , "x2" : 550 , "y2" : 300 }, "confidence" : 0.95 , "headers" : [ "Product" , "Quantity" , "Price" , "Total" ], "rows" : [ [ "Widget A" , "10" , "$25.00" , "$250.00" ], [ "Widget B" , "5" , "$40.00" , "$200.00" ], [ "Widget C" , "8" , "$15.00" , "$120.00" ] ], "structured_data" : { "headers" : [ "Product" , "Quantity" , "Price" , "Total" ], "data" : [ { "Product" : "Widget A" , "Quantity" : "10" , "Price" : "$25.00" , "Total" : "$250.00" }, { "Product" : "Widget B" , "Quantity" : "5" , "Price" : "$40.00" , "Total" : "$200.00" }, { "Product" : "Widget C" , "Quantity" : "8" , "Price" : "$15.00" , "Total" : "$120.00" } ] }, "table_type" : "data_table" , "column_count" : 4 , "row_count" : 3 } ], "summary" : { "total_tables" : 1 , "pages_processed" : 1 , "extraction_method" : "ai_vision" , "processing_time" : 4.2 } } } The PDF file to process for table extraction. Maximum file size: 100MB
Unique identifier for the table extraction job
Descriptive message about the job creation
Number of credits remaining in your quota
