Extraction

​

Overview

• Invoice processing — Extract invoice numbers, dates, amounts, and line items
• Contract analysis — Pull key terms, parties, dates, and obligations
• Resume parsing — Extract contact info, skills, experience, and education
• Product catalogs — Capture product names, prices, descriptions, and specifications
• Research papers — Extract titles, authors, abstracts, and citations

​

How It Works

1. Parse your document — First, ingest and parse your document using any parsing method
2. Define your schema — Specify the fields you want to extract with types and descriptions
3. Add instructions — Provide optional natural language guidance for the extraction
4. Run extraction — The LLM processes the document and extracts matching data
5. Review results — View extracted data with page-level provenance

​

Accessing Data Extraction

1. Navigate to Sources in the left sidebar
2. Double-click on a processed document to open Source details
3. Click the Extraction tab at the top center of the page

​

Defining Your Schema

​

Field Name

• invoice_number
• total_amount
• customer_name
• line_items

​

Field Type

​

Object Type

• Addresses (street, city, zip, country)
• Contact information (name, email, phone)
• Specifications (weight, dimensions, material)

​

Array Type

​

Field Description

• What the field represents
• Expected format (if applicable)
• Any special instructions

• “The unique invoice identifier, usually starting with ‘INV-’”
• “Total amount due in USD, as a number without currency symbols”
• “List of all product names mentioned in the document”

• “The number”
• “Amount”
• “Items”

​

Writing Effective Instructions

• Scope — What parts of the document to focus on
• Format — How to format the extracted data
• Edge cases — How to handle ambiguous situations
• Multiple items — How to handle documents with multiple extractable entities

​

Example Instructions

Extract all invoice information from the document. If multiple invoices 
are present, extract each one as a separate item. Use USD for currency 
values and convert dates to YYYY-MM-DD format. If a field is not found, 
leave it empty rather than guessing.

Focus on extracting the main contractual terms. For dates, use ISO format 
(YYYY-MM-DD). For monetary values, include the currency. If there are 
multiple parties, list all of them in the parties field.

Extract each product as a separate item. Prices should be numeric values 
in the document's currency. Include all product variants as separate items.

​

Running an Extraction

1. Review your field definitions in the schema builder
2. Add your instructions in the instructions text area
3. Click Extract to start the extraction process
4. Wait for the extraction to complete (processing time varies by document size)

​

Viewing Results

​

Extracted Data Table

• Each row representing one extracted entity
• Columns for each field in your schema
• Values extracted from the document

​

Page References

• Traceability — Know exactly where each piece of data came from
• Verification — Quickly check the source for accuracy
• Context — Understand the surrounding content

​

Export Options

• JSON — Structured data for programmatic use
• CSV — Tabular format for spreadsheets and databases

​

Schema Examples

​

Invoice Extraction

​

Resume Parsing

​

Product Catalog

​

Best Practices

​

Schema Design

1. Start simple — Begin with a few essential fields, then expand
2. Be specific — Detailed descriptions produce better results
3. Use appropriate types — Match the field type to expected data
4. Consider edge cases — Think about what happens when data is missing
5. Use objects for structured data — Group related fields (address, contact info) using Object type
6. Use arrays for repeating items — Line items, work history, and skills are perfect for Array type
7. Keep nesting shallow — Avoid deeply nested structures for better extraction accuracy
8. Choose the right array items type — Use Text arrays for simple lists (tags, skills), Object arrays for complex items (line items, experience)

​

Instructions

1. Be explicit about format — Specify date formats, currency handling, etc.
2. Handle multiples — Clarify how to handle multiple items (e.g., multiple invoices)
3. Set defaults — Explain what to do when information isn’t found
4. Provide context — Mention the document type if relevant

​

Parsing Method Selection

• Complex layouts — Use MAI or Graphor parsing methods
• Scanned documents — Use Hi-Res or Hi-Res FT for better OCR
• Simple text documents — Fast method is usually sufficient

​

API Integration

​

Basic Extraction

curl -X POST "https://sources.graphorlm.com/run-extraction" \
  -H "Authorization: Bearer YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "file_name": "invoice.pdf",
    "user_instruction": "Extract all invoice information. Use YYYY-MM-DD for dates.",
    "output_schema_fields": [
      {
        "key": "invoice_number",
        "type": "string",
        "description": "The unique invoice identifier"
      },
      {
        "key": "total_amount",
        "type": "number",
        "description": "Total amount due"
      }
    ]
  }'

​

Extraction with Object and Array Types

curl -X POST "https://sources.graphorlm.com/run-extraction" \
  -H "Authorization: Bearer YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "file_name": "invoice.pdf",
    "user_instruction": "Extract invoice with line items and billing address.",
    "output_schema_fields": [
      {
        "key": "invoice_number",
        "type": "string",
        "description": "The unique invoice identifier"
      },
      {
        "key": "billing_address",
        "type": "object",
        "description": "Billing address details",
        "nested_fields": [
          { "key": "street", "type": "string", "description": "Street address" },
          { "key": "city", "type": "string", "description": "City name" },
          { "key": "country", "type": "string", "description": "Country name" }
        ]
      },
      {
        "key": "tags",
        "type": "array",
        "description": "Invoice tags",
        "items_type": "string"
      },
      {
        "key": "line_items",
        "type": "array",
        "description": "Invoice line items",
        "items_type": "object",
        "nested_fields": [
          { "key": "description", "type": "string", "description": "Item description" },
          { "key": "quantity", "type": "number", "description": "Quantity" },
          { "key": "unit_price", "type": "number", "description": "Price per unit" }
        ]
      }
    ]
  }'

​

Troubleshooting

​

Next Steps