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A decisions node is designed to manage and execute decision table, which is composed of a set of rules. Each rules will be evaluated in order from top to bottom and the result of the first successful rule will be returned as the final result.

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Rule

Each rule includes two parts:
  • conditions: A set of conditions to evaluate.
  • actions: The results to return when all conditions are satisfied.

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Condition

Each condition includes a key and a value expression. To simplify condition building and ensure correct syntax, use the DecisionsCondition class. The following example checks whether the value of revenue_quote_ratio falls within the range from 0 (inclusive) to 50 (exclusive).
Python
"conditions": {
  "revenue_quote_ratio": "[0:50)"
}

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Action

An action defines the result when all conditions in a rule are satisfied. You can also define a default action to return when no rules match.

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Locale

You can specify a locale string (e.g., en-US, fr-FR, es-MX) to control how dates are parsed. Currently, locale only affects date parsing.

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Example

In this example (get_insurance_rate), you define a set of rules to calculate the insurance rate based on credit grade and loan amount:
Python
def build_decisions(aflow: Flow) -> DecisionsNode:
    """
    Build a Decisions Table programmatically as a node.
    e.g. read from an Excel spreadsheet or CSV and populate the decision table.
    """
    rules = []
    ## We will be build the rules programmatically.
    rule1 = DecisionsRule()
    rule1.condition("grade", DecisionsCondition().equal("A")).condition("loan_amount", DecisionsCondition().less_than(100000))
    rule1.action("insurance_required", False)
    rules.append(rule1)
    rule2 = DecisionsRule()
    rule2.condition("grade", DecisionsCondition().equal("A")).condition("loan_amount", DecisionsCondition().in_range(100000, 300000, True, False))
    rule2.action("insurance_required", True).action("insurance_rate", 0.001)
    rules.append(rule2)
    rule3 = DecisionsRule()
    rule3.condition("grade", DecisionsCondition().equal("A")).condition("loan_amount", DecisionsCondition().in_range(300000, 600000, True, False))
    rule3.action("insurance_required", True).action("insurance_rate", 0.003)
    rules.append(rule3)
    rule4 = DecisionsRule()
    rule4.condition("grade", DecisionsCondition().equal("A")).condition("loan_amount", DecisionsCondition().greater_than_or_equal(600000))
    rule4.action("insurance_required", True).action("insurance_rate", 0.005)
    rules.append(rule4)
    ## We will be build the rules programmatically.
    rule5 = DecisionsRule()
    rule5.condition("grade", DecisionsCondition().equal("B")).condition("loan_amount", DecisionsCondition().less_than(100000))
    rule5.action("insurance_required", False)
    rules.append(rule5)
    rule6 = DecisionsRule()
    rule6.condition("grade", DecisionsCondition().equal("B")).condition("loan_amount", DecisionsCondition().in_range(100000, 300000, True, False))
    rule6.action("insurance_required", True).action("insurance_rate", 0.0025)
    rules.append(rule6)
    rule7 = DecisionsRule()
    rule7.condition("grade", DecisionsCondition().equal("B")).condition("loan_amount", DecisionsCondition().in_range(300000, 600000, True, False))
    rule7.action("insurance_required", True).action("insurance_rate", 0.005)
    rules.append(rule7)
    rule8 = DecisionsRule()
    rule8.condition("grade", DecisionsCondition().equal("A")).condition("loan_amount", DecisionsCondition().greater_than_or_equal(600000))
    rule8.action("insurance_required", True).action("insurance_rate", 0.0075)
    rules.append(rule8)
    node = aflow.decisions(
        name="assess_insurance_rate",
        display_name="Assess insurance rate.",
        description="Based on credit rate and loan amount, assess insurance rate.",
        rules=rules,
        default_actions={
            "assessment_error": "Not assessed. Incorrect data submitted."
        },
        input_schema=AssessmentData,
        output_schema=Assessment
    )
    return node