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Data Extraction Patterns

Extract structured information from documents, emails, and other unstructured text.

Contact Information Extraction​

class ContactInfo {
  name string?
  email string?
  phone string?
  company string?
}

function ExtractContact(text: string) -> ContactInfo {
  client GPT4
  prompt #"
    Extract contact information from this text:
    {{ text }}

    Return any contact details found.
  "#
}

Invoice Data Extraction​

class LineItem {
  description string
  quantity int
  unit_price float
  total float
}

class Invoice {
  invoice_number string
  date string
  vendor string
  items LineItem[]
  subtotal float
  tax float
  total float
}

function ExtractInvoice(document: string) -> Invoice {
  client GPT4
  prompt #"
    Extract all invoice data from this document:
    {{ document }}
  "#
}

Handling Missing Data​

Use optional fields (?) for data that may not be present:

class Person {
  name string           // Required
  email string?         // Optional
  age int?              // Optional
  address Address?      // Optional nested object
}

Best Practices​

  1. Make fields optional when data might be missing
  2. Use descriptive names that match how data appears in text
  3. Add @description for ambiguous fields