Examples

Real-world examples showing how to extract structured data from common document types.

Invoice Processing

Extract vendor information, amounts, dates, and line items from invoices.

{
  "schema": {
    "type": "object",
    "properties": {
      "invoice_number": {
        "type": "string",
        "description": "Unique invoice identifier, often prefixed with 'INV-' or similar"
      },
      "vendor_name": {
        "type": "string",
        "description": "Name of the company or person who issued the invoice"
      },
      "total_amount": {
        "type": "number",
        "description": "Final amount due including all taxes and fees"
      },
      "date": {
        "type": "string",
        "description": "Invoice date in YYYY-MM-DD format"
      },
      "line_items": {
        "type": "array",
        "description": "Individual products or services listed on the invoice",
        "items": {
          "type": "object",
          "properties": {
            "description": {
              "type": "string",
              "description": "Product or service description"
            },
            "quantity": {
              "type": "number",
              "description": "Number of units"
            },
            "price": {
              "type": "number",
              "description": "Price per unit"
            }
          }
        }
      }
    }
  }
}

Receipt Parsing

Get merchant details, totals, and itemized purchases from receipts.

{
  "schema": {
    "type": "object",
    "properties": {
      "merchant_name": {
        "type": "string",
        "description": "Name of the store or business"
      },
      "total_amount": {
        "type": "number",
        "description": "Final total amount paid"
      },
      "tax_amount": {
        "type": "number",
        "description": "Total tax amount"
      },
      "date": {
        "type": "string",
        "description": "Date of purchase in YYYY-MM-DD format"
      },
      "items": {
        "type": "array",
        "description": "Individual items purchased",
        "items": {
          "type": "object",
          "properties": {
            "name": {
              "type": "string",
              "description": "Product name or description"
            },
            "price": {
              "type": "number",
              "description": "Price of the item"
            }
          }
        }
      }
    }
  }
}

Business Card OCR

Extract contact information from business card images.

{
  "schema": {
    "type": "object",
    "properties": {
      "name": {
        "type": "string",
        "description": "Full name of the person"
      },
      "title": {
        "type": "string",
        "description": "Job title or position"
      },
      "company": {
        "type": "string",
        "description": "Company or organization name"
      },
      "email": {
        "type": "string",
        "description": "Email address",
        "format": "email"
      },
      "phone": {
        "type": "string",
        "description": "Phone number"
      },
      "address": {
        "type": "string",
        "description": "Business address"
      }
    }
  }
}

Contract Analysis

Extract key terms, dates, and parties from legal contracts.

{
  "schema": {
    "type": "object",
    "properties": {
      "contract_title": {
        "type": "string",
        "description": "Title or type of contract (e.g., 'Service Agreement', 'Employment Contract')"
      },
      "parties": {
        "type": "array",
        "description": "Names of all parties involved in the contract",
        "items": {"type": "string"}
      },
      "effective_date": {
        "type": "string",
        "description": "Date the contract becomes effective in YYYY-MM-DD format"
      },
      "expiration_date": {
        "type": "string",
        "description": "Date the contract expires in YYYY-MM-DD format"
      },
      "key_terms": {
        "type": "array",
        "description": "Important terms, conditions, or clauses mentioned",
        "items": {"type": "string"}
      }
    }
  }
}

Form Data Extraction

Parse structured data from filled forms and applications.

{
  "schema": {
    "type": "object",
    "properties": {
      "applicant_name": {
        "type": "string",
        "description": "Full name of the applicant"
      },
      "email": {
        "type": "string",
        "description": "Email address",
        "format": "email"
      },
      "phone": {
        "type": "string",
        "description": "Phone number"
      },
      "address": {
        "type": "string",
        "description": "Home address"
      },
      "date_of_birth": {
        "type": "string",
        "description": "Date of birth in YYYY-MM-DD format"
      },
      "employment_status": {
        "type": "string",
        "description": "Current employment status (employed, unemployed, student, etc.)"
      }
    }
  }
}

Medical Records

Extract patient information and medical data from documents.

{
  "schema": {
    "type": "object",
    "properties": {
      "patient_name": {
        "type": "string",
        "description": "Full name of the patient"
      },
      "date_of_birth": {
        "type": "string",
        "description": "Patient's date of birth in YYYY-MM-DD format"
      },
      "medical_record_number": {
        "type": "string",
        "description": "Unique medical record identifier"
      },
      "diagnosis": {
        "type": "string",
        "description": "Primary diagnosis or medical condition"
      },
      "medications": {
        "type": "array",
        "description": "List of prescribed medications",
        "items": {"type": "string"}
      },
      "appointment_date": {
        "type": "string",
        "description": "Date of appointment in YYYY-MM-DD format"
      }
    }
  }
}

Getting Started

1. Choose your use case from the examples above
2. Copy the schema that matches your needs
3. Replace the placeholder data with your actual file
4. Make your API request using the quickstart guide

Custom Schemas

Don’t see your use case? Create a custom schema that matches your specific data structure. The API is flexible and can extract any structured data you define.

Next Steps

• Copy a schema from the examples above that matches your use case
• Follow the quickstart guide to make your first API call
• Learn schema design best practices to customize schemas for your needs
• Start with our free tier - 100 pages at no cost