Skip to main content
POST
/
extract
curl -X POST "https://prod.visionapi.unsiloed.ai/extract" \
  -H "accept: application/json" \
  -H "api-key: your-api-key" \
  -H "Content-Type: multipart/form-data" \
  -F "[email protected];type=application/pdf" \
  -F "schema_data={\"type\":\"object\",\"properties\":{\"title\":{\"type\":\"string\",\"description\":\"Document title\"},\"date\":{\"type\":\"string\",\"description\":\"Document date\"}},\"required\":[\"title\",\"date\"],\"additionalProperties\":false}"

{
  "job_id": "945b4578-691f-4c74-8184-dde654093b11",
  "status": "queued",
  "message": "PDF citation processing started",
  "quota_remaining": 48988
}

​
Overview

The Extract Data endpoint processes PDF documents and extracts structured information based on custom schemas. This is ideal for extracting specific data points, citations, references, and structured content from documents using AI-powered analysis.
The endpoint returns a job ID for asynchronous processing. Use the job management endpoints to check status and retrieve results.

​
Request

​
pdf_file
file
required
The PDF file to process for data extraction. Maximum file size: 100MB
​
schema_data
string
required
JSON schema defining the structure and fields to extract from the document. Must be a valid JSON Schema format with type definitions, properties, and required fields.
​
citation_config
string
JSON object controlling citation detail level. Configure what citation references are returned with extracted data.Properties:
  • semantic_citations (boolean): Enable segment-level citations. Default: false
  • ocr_citations (boolean): Enable word-level citations with OCR coordinates. Default: false
Example:
{
  "semantic_citations": true,
  "ocr_citations": true
}
When to use:
  • Set both to true for segment-level and word-level citations
  • Set semantic_citations: true, ocr_citations: false for segment-level citations only
  • Set both to false (default) when citations aren’t needed

​
Response

​
job_id
string
Unique identifier for the extraction job
​
status
string
Initial job status (typically “queued”)
​
message
string
Descriptive message about the job creation
​
quota_remaining
number
Number of Credits remaining in your quota

​
Extraction Results Format

Once the job is completed, the results will contain the extracted data with additional metadata:
​
[field_name]
object
Each extracted field returns an object with the following structure:
​
[field_name].value
string|number|array|object
The extracted value matching the schema type
​
[field_name].score
number
Confidence score between 0 and 1 indicating extraction accuracy
​
[field_name].bboxes
array
Array of bounding box coordinates where the data was found in the document. Only included when semantic_citations or ocr_citations is enabled in citation_config.
​
[field_name].bboxes[].bbox
array
Bounding box coordinates [x1, y1, x2, y2] in pixels. Only included when citations are enabled.
​
[field_name].page_no
number
Page number where the data was extracted (1-indexed). Only included when citations are enabled via citation_config.
​
min_confidence_score
number
Minimum confidence score across all extracted fields

​
Example Extraction Results

For a simple financial document schema:
Schema
{
  "type": "object",
  "properties": {
    "Individuals": {
      "type": "string",
      "description": "Percentage Holding"
    },
    "LIC of India": {
      "type": "string",
      "description": "No of Shares Held"
    },
    "United bank of india": {
      "type": "string",
      "description": "No of shares held by United bank of india"
    }
  },
  "required": [
    "Individuals",
    "LIC of India",
    "United bank of india"
  ],
  "additionalProperties": false
}
Extraction Results
{
  "Individuals": {
    "score": 0.9998314521743098,
    "value": "10.57",
    "bboxes": [
      {
        "bbox": [
          79,
          381,
          524,
          565
        ]
      }
    ],
    "page_no": 2
  },
  "LIC of India": {
    "score": 0.9999889986487799,
    "value": "1515000",
    "bboxes": [
      {
        "bbox": [
          79,
          381,
          524,
          565
        ]
      }
    ],
    "page_no": 2
  },
  "United bank of india": {
    "score": 0.999984548437705,
    "value": "500000",
    "bboxes": [
      {
        "bbox": [
          79,
          381,
          524,
          565
        ]
      }
    ],
    "page_no": 2
  },
  "min_confidence_score": 0.9998314521743098
}

curl -X POST "https://prod.visionapi.unsiloed.ai/extract" \
  -H "accept: application/json" \
  -H "api-key: your-api-key" \
  -H "Content-Type: multipart/form-data" \
  -F "[email protected];type=application/pdf" \
  -F "schema_data={\"type\":\"object\",\"properties\":{\"title\":{\"type\":\"string\",\"description\":\"Document title\"},\"date\":{\"type\":\"string\",\"description\":\"Document date\"}},\"required\":[\"title\",\"date\"],\"additionalProperties\":false}"

{
  "job_id": "945b4578-691f-4c74-8184-dde654093b11",
  "status": "queued",
  "message": "PDF citation processing started",
  "quota_remaining": 48988
}

​
Citation Configuration

The citation_config parameter controls the level of citation detail returned with extracted data. Citations provide references back to the source document, allowing you to trace where each extracted value was found.

​
Configuration Options

​
semantic_citations
boolean
default:"false"
Enabled (true):
  • Returns segment-level citations with bounding boxes
  • Provides contextual text spans for each extracted field
  • Useful for tracing data back to document sections
Disabled (false):
  • No segment-level citations included
  • Smaller response payload
​
ocr_citations
boolean
default:"false"
Enabled (true):
  • Returns word-level citations with precise coordinates
  • Includes OCR-level bounding boxes for individual words
  • Enables character-level text mapping
Disabled (false):
  • No word-level citations included
  • Faster processing and smaller response size

​
Use Cases

Segment-Level and Word-Level Citations 
{
  "semantic_citations": true,
  "ocr_citations": true
}
Use when you need both segment-level context and word-level precision for detailed auditing or UI highlighting. Segment-Level Citations Only 
{
  "semantic_citations": true,
  "ocr_citations": false
}
Use when you need to trace data back to document sections without word-level detail. Balanced performance with moderate response size. No Citations (Values Only) 
{
  "semantic_citations": false,
  "ocr_citations": false
}
Use when you only need extracted values without citation references. Fastest processing with minimal response payload.
When both semantic_citations and ocr_citations are set to false (default), the response will only contain value and score for each field. The bboxes and page_no fields will be omitted.

​
JSON Schema Definition

The schema_data parameter must be a valid JSON Schema that defines the structure of data to extract. All schemas must follow the JSON Schema specification with proper type definitions, properties, and constraints.

​
Basic Schema Structure

All extraction schemas must include:
  • type: “object” (root level)
  • properties: Object defining the fields to extract
  • required: Array of required field names
  • additionalProperties: Set to false for strict validation

​
Financial Document Schema Example

This example demonstrates extracting shareholding patterns and board information from financial documents: 
{
  "type": "object",
  "properties": {
    "Individuals": {
      "type": "string",
      "description": "Percentage Holding"
    },
    "LIC of India": {
      "type": "number",
      "description": "No of Shares Held"
    },
    "board of directors": {
      "type": "array",
      "description": "list of names of board of directors",
      "items": {
        "type": "object",
        "required": [
          "names of board of directors"
        ],
        "properties": {
          "names of board of directors": {
            "type": "string",
            "description": "names of all the members of board of directors of ACRE"
          }
        },
        "additionalProperties": false
      }
    },
    "shareholding pattern": {
      "type": "array",
      "description": "shareholding pattern",
      "items": {
        "type": "object",
        "required": [
          "name of shareholders",
          "number of shares held"
        ],
        "properties": {
          "name of shareholders": {
            "type": "string",
            "description": "name of the shareholders in ACRE Table"
          },
          "number of shares held": {
            "type": "string",
            "description": "numbers of shares held by shareholders in ACRE Table"
          }
        },
        "additionalProperties": false
      }
    }
  },
  "required": [
    "Individuals",
    "LIC of India",
    "board of directors",
    "shareholding pattern"
  ],
  "additionalProperties": false
}

​
Advanced Financial Schema Example

This example shows a more complex schema for extracting detailed shareholding information: 
{
  "type": "object",
  "properties": {
    "shares held by Punjab National bank": {
      "type": "string",
      "description": "shares held by Punjab National bank"
    },
    "shares held by IFCI": {
      "type": "string",
      "description": "shares held by IFCI"
    },
    "shareholding pattern": {
      "type": "object",
      "description": "shareholding pattern",
      "properties": {
        "Percentage holding": {
          "type": "array",
          "description": "percentage holding of shareholders in ACRE",
          "items": {
            "type": "string",
            "description": "percentage holding of shareholders in ACRE"
          }
        },
        "Name of shareholders": {
          "type": "array",
          "description": "Names of shareholders in ACRE",
          "items": {
            "type": "string",
            "description": "Names of shareholders in ACRE"
          }
        }
      },
      "required": ["Percentage holding", "Name of shareholders"],
      "additionalProperties": false
    },
    "names of board of directors": {
      "type": "array",
      "description": "list of names of members of board of directors in ACRE",
      "items": {
        "type": "object",
        "properties": {
          "names of board of directors": {
            "type": "string",
            "description": "list of names of members of board of directors in ACRE"
          }
        },
        "required": ["names of board of directors"],
        "additionalProperties": false
      }
    }
  },
  "required": [
    "shares held by Punjab National bank", 
    "shares held by IFCI", 
    "shareholding pattern", 
    "names of board of directors"
  ],
  "additionalProperties": false
}

​
Citation Extraction Schema

{
  "type": "object",
  "properties": {
    "title": {
      "type": "string",
      "description": "Document title or paper title"
    },
    "authors": {
      "type": "array",
      "description": "List of author names",
      "items": {
        "type": "string"
      }
    },
    "publication_date": {
      "type": "string",
      "description": "Publication date in YYYY-MM-DD format"
    },
    "journal_name": {
      "type": "string",
      "description": "Name of journal or publication venue"
    },
    "doi": {
      "type": "string",
      "description": "Digital Object Identifier"
    },
    "abstract": {
      "type": "string",
      "description": "Document abstract or summary"
    },
    "keywords": {
      "type": "array",
      "description": "Key terms and subject keywords",
      "items": {
        "type": "string"
      }
    },
    "references": {
      "type": "array",
      "description": "List of cited references",
      "items": {
        "type": "string"
      }
    }
  },
  "required": ["title", "authors"],
  "additionalProperties": false
}

{
  "type": "object",
  "properties": {
    "document_type": {
      "type": "string",
      "description": "Type of legal document (contract, agreement, etc.)"
    },
    "parties": {
      "type": "array",
      "description": "Names of parties involved",
      "items": {
        "type": "object",
        "properties": {
          "name": {
            "type": "string",
            "description": "Party name"
          },
          "role": {
            "type": "string",
            "description": "Party role (e.g., buyer, seller, contractor)"
          }
        },
        "required": ["name", "role"],
        "additionalProperties": false
      }
    },
    "effective_date": {
      "type": "string",
      "description": "Document effective date"
    },
    "key_terms": {
      "type": "array",
      "description": "Important terms and conditions",
      "items": {
        "type": "string"
      }
    },
    "obligations": {
      "type": "array",
      "description": "Key obligations and responsibilities",
      "items": {
        "type": "object",
        "properties": {
          "party": {
            "type": "string",
            "description": "Party responsible for the obligation"
          },
          "obligation": {
            "type": "string",
            "description": "Description of the obligation"
          }
        },
        "required": ["party", "obligation"],
        "additionalProperties": false
      }
    }
  },
  "required": ["document_type", "parties", "effective_date"],
  "additionalProperties": false
}

​
JSON Schema Field Types

​
string
string
Text content, single values. Use for names, descriptions, dates as text.
​
number
number
Numeric values, amounts, quantities. Use for counts, percentages, monetary values.
​
integer
integer
Whole numbers only. Use for counts, IDs, years.
​
boolean
boolean
True/false values. Use for yes/no questions, flags.
​
array
array
Lists of items. Must include items property defining the type of array elements.
​
object
object
Structured data with nested fields. Must include properties defining nested structure.
​
null
null
Null values. Can be combined with other types using array notation: ["string", "null"]

​
Job Management Integration

After creating an extraction job, you can poll for completion using the job status endpoints: 
import requests
import time

# After creating the extraction job, you receive a job_id
job_id = "945b4578-691f-4c74-8184-dde654093b11"

headers = {
    "accept": "application/json",
    "api-key": "your-api-key"
}

# Poll for job completion
while True:
    response = requests.get(
        f"https://prod.visionapi.unsiloed.ai/jobs/{job_id}",
        headers=headers
    )

    if response.status_code == 200:
        result = response.json()
        print(f"Job status: {result['status']}")

        if result['status'] == 'Succeeded':
            print("Extraction completed!")
            print("Extracted data:", result['result'])
            break
        elif result['status'] == 'Failed':
            print(f"Job failed: {result.get('error', 'Unknown error')}")
            break
    else:
        print(f"Error checking status: {response.status_code}")
        break

    time.sleep(5)  # Wait 5 seconds before checking again

​
Advanced Schema Patterns

​
Nested Object Structures

For complex documents with hierarchical data: 
{
  "type": "object",
  "properties": {
    "company_info": {
      "type": "object",
      "description": "Company identification and basic information",
      "properties": {
        "name": {
          "type": "string",
          "description": "Full company name"
        },
        "ticker": {
          "type": "string",
          "description": "Stock ticker symbol"
        },
        "sector": {
          "type": "string",
          "description": "Business sector"
        }
      },
      "required": ["name"],
      "additionalProperties": false
    },
    "financial_data": {
      "type": "object",
      "description": "Financial metrics and performance data",
      "properties": {
        "revenue": {
          "type": "number",
          "description": "Total revenue"
        },
        "profit_margin": {
          "type": "number",
          "description": "Profit margin percentage"
        }
      },
      "required": ["revenue"],
      "additionalProperties": false
    }
  },
  "required": ["company_info", "financial_data"],
  "additionalProperties": false
}

​
Array of Complex Objects

For extracting lists of structured data: 
{
  "type": "object",
  "properties": {
    "transactions": {
      "type": "array",
      "description": "List of financial transactions",
      "items": {
        "type": "object",
        "properties": {
          "date": {
            "type": "string",
            "description": "Transaction date"
          },
          "amount": {
            "type": "number",
            "description": "Transaction amount"
          },
          "description": {
            "type": "string",
            "description": "Transaction description"
          },
          "category": {
            "type": "string",
            "description": "Transaction category"
          }
        },
        "required": ["date", "amount", "description"],
        "additionalProperties": false
      }
    }
  },
  "required": ["transactions"],
  "additionalProperties": false
}

​
Error Handling

​
400
Bad Request
Invalid JSON schema format or missing required parameters
​
401
Unauthorized
Invalid or missing API key
​
413
Payload Too Large
File size exceeds 100MB limit
​
422
Unprocessable Entity
Invalid file format, malformed JSON schema, or processing error
​
429
Too Many Requests
Rate limit exceeded or quota exhausted
​
500
Internal Server Error
Server error during processing

Authorizations

​
api-key
string
header
required

Body

multipart/form-data
​
pdf_file
file
required

The PDF file to process for data extraction. Maximum file size: 100MB

​
schema_data
string
required

JSON schema defining the structure and fields to extract from the document. Example: {"type":"object","properties":{"invoice_number":{"type":"string","description":"The invoice number"}},"required":["invoice_number"],"additionalProperties":false}

Response

200 - application/json

Successful response

​
job_id
string

Unique identifier for the extraction job

​
status
string

Initial job status (typically 'queued')

​
message
string

Descriptive message about the job creation

​
quota_remaining
number

Number of Credits remaining in your quota