Schema Reference

Schemas define the structured outputs that DELM extracts from your documents. The schema system supports progressive complexity levels, from simple key‑value extraction to complex nested structures.

Table of Contents

• Simple Schema (Level 1)
• Nested Schema (Level 2)
• Multiple Schemas (Level 3)
• Variable Configuration

Imports

All schema classes are available directly from the main package:

from delm import Schema, ExtractionVariable

Schema Types

DELM supports three levels of schema complexity, each building on the previous level.

Simple Schema (Level 1)

The simplest form of extraction: individual key‑value pairs found once per chunk.

schema = Schema.simple(
    ExtractionVariable(
        name="company_names",
        description="Company names mentioned in the text",
        data_type="[string]",
        required=False
    ),
    ExtractionVariable(
        name="revenue_numbers",
        description="Revenue figures mentioned",
        data_type="[number]",
        required=False
    ),
    ExtractionVariable(
        name="forecast_year",
        description="Year for which forecast is made",
        data_type="integer",
        required=True,
        validate_in_text=True
    )
)

Output Format:

{
  "company_names": ["Apple", "Microsoft"],
  "revenue_numbers": [1500000000, 2000000000],
  "forecast_year": 2024
}

Nested Schema (Level 2)

Extract structured objects with multiple related fields (a list of dictionaries).

schema = Schema.nested(
    container_name="companies",
    variables_list=[
        ExtractionVariable(
            name="name",
            description="Company name",
            data_type="string",
            required=True
        ),
        ExtractionVariable(
            name="revenue",
            description="Revenue figure in USD",
            data_type="number",
            required=False
        ),
        ExtractionVariable(
            name="sector",
            description="Business sector",
            data_type="string",
            required=False,
            allowed_values=["technology", "finance", "healthcare", "energy", "retail"]
        ),
        ExtractionVariable(
            name="growth_rate",
            description="Annual growth rate percentage",
            data_type="number",
            required=False,
            validate_in_text=True  # Only extract if explicitly mentioned
        ),
        ExtractionVariable(
            name="products",
            description="List of products offered by the company",
            data_type="[string]",
            required=False
        )
    ]
)

Output Format:

{
  "companies": [
    {
      "name": "Apple",
      "revenue": 1500000000,
      "sector": "technology",
      "growth_rate": 12.5,
      "products": ["iPhone", "MacBook", "iPad"]
    },
    {
      "name": "Microsoft",
      "revenue": 2000000000,
      "sector": "technology",
      "growth_rate": null,
      "products": ["Windows", "Office", "Azure"]
    }
  ]
}

Multiple Schemas (Level 3)

Extract multiple independent structured objects simultaneously. These can be simple, nested, or even deep multi‑schemas.

# Define sub-schemas first
companies_schema = Schema.nested(
    container_name="companies",
    variables_list=[
        ExtractionVariable(name="name", description="Company name", data_type="string", required=True),
        ExtractionVariable(name="revenue", description="Revenue figure", data_type="number", required=False)
    ]
)

products_schema = Schema.nested(
    container_name="products",
    variables_list=[
        ExtractionVariable(name="name", description="Product name", data_type="string", required=True),
        ExtractionVariable(name="price", description="Product price in USD", data_type="number", required=False),
        ExtractionVariable(
            name="category", 
            description="Product category", 
            data_type="string", 
            allowed_values=["software", "hardware", "service", "consulting"]
        )
    ]
)

trends_schema = Schema.nested(
    container_name="trends",
    variables_list=[
        ExtractionVariable(name="trend_name", description="Market trend description", data_type="string", required=True),
        ExtractionVariable(
            name="impact", 
            description="Expected impact", 
            data_type="string", 
            allowed_values=["positive", "negative", "neutral"]
        )
    ]
)

# Combine into multiple schema
schema = Schema.multiple(
    companies=companies_schema,
    products=products_schema,
    market_trends=trends_schema
)

Output Format:

{
  "companies": [
    { "name": "Apple", "revenue": 1500000000 }
  ],
  "products": [
    { "name": "iPhone 15", "price": 999, "category": "hardware" }
  ],
  "trends": [
    { "trend_name": "AI adoption acceleration", "impact": "positive" }
  ]
}

Variable Configuration

Each ExtractionVariable can be configured with these arguments.

Required Arguments

Argument	Type	Description
name	string	Variable name (used as JSON key)
description	string	Human‑readable description for LLM
data_type	string	Data type (see supported types below)

Optional Arguments

Argument	Type	Default	Description
required	boolean	False	Whether field must be present
allowed_values	list	None	List of valid string values (enums)
validate_in_text	boolean	False	Only extract if value literally appears in text

Supported Data Types

Type String	Description	Example Values
"string"	Text values	"Apple", "technology"
"number"	Floating‑point numbers	1500000000, 12.5
"integer"	Whole numbers	2024, 100
"boolean"	True/false values	True, False
"date"	Date strings	"2025-09-15"
"[string]"	List of strings	["Apple", "Google"]
"[number]"	List of numbers	[12.5, 42, 100]
"[integer]"	List of integers	[2024, 100, 7]
"[boolean]"	List of booleans	[True, False, True]