A simple Decision tree based Rule Engine described using json files. Rules are executed according to decision tree. Create a set of rules (make them nested as you like) and based on set of inputs run the rules.
- Rules expressed in huaman readable JSON
- Create new set of inputs or change existing inputs as you traverse rules tree
- Do multiple executions or rules set
npm install nested-rules-engine --save
const {executeEngine} = require('nested-rules-engine');
// Step1: Define your conditional rules
const rules = {
"you_are_a_human": {
"you_are_kind": "help_me_find_my_book",
"you_are_smart": "please_do_my_homework",
},
"default": "please_do_my_homework"
};
// Step2: make set of inputs collection
const inputs = {
"type" : "human",
"kindnessLevel": 0,
"intelligence": 10
}
// Step3: Make your custom Functions
const functions = {
default : () => true,
you_are_a_human: ({type}) => type === 'human',
you_are_kind: ({kindnessLevel}) => kindnessLevel > 300,
you_are_smart: ({intelligence}) => intelligence > 5,
help_me_find_my_book: () => ({
payload: 'lets help someone',
effort: 'finding the book'
}),
please_do_my_homework: () => ({
payload: 'doing homework',
effort: 'im getting sick'
})
};
// Step4: Execute Engine
const res = executeEngine(inputs, functions, rules);
// Output res:
/*
{
result: { payload: 'doing homework', effort: 'im getting sick' },
logs: []
}
*/
Engine Execution Signature:
executeEngine(variables, functions, rules, options);
-
variablesCollection of values on which rule engine will execute You can change these collection of variables (Add/Edit/Delte them) as you traverse the decision tree of rules. -
functionsCollection of functions that decide which way the tree should be traversed.- In case the function indicates a final decision in tree (leaf of decision tree): Output can be anything that you want to see as
result - In case the function is makes an intermediate decision (branch of decision tree):
- if output is
true: this means this branch should be traversed - else: the function will be executed
- if output is
- In case the function indicates a final decision in tree (leaf of decision tree): Output can be anything that you want to see as
-
rulesDecision Tree that will be traversed by this Rule Engine -
optionsthere are different options that you can provide to customize the execution nature- verbose (boolean): Makes Sure you get enough logs while engine goes through all decision tree
- multiple (boolean): You can run multiple Decision Trees based on same inputs. Input sets are shared between each tree
result: Result of the engine execution. format of Result will be defined by you throughfunctionslogs: Detailed logs while engine got executed (by default its disabled)
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