Quick Start

Evaluate rules in Python with the datalogic-py binding.

Simple One-Shot Evaluation

Use apply for one-off evaluations:

from datalogic_py import apply

# Arithmetic
result = apply({"+": [1, 2, 3]}, {})
print(result) # 6

# Variable Access
result = apply(
    {"var": "user.age"},
    {"user": {"age": 25}}
)
print(result) # 25

Reusable Compiled Rules

For production loops, compile the rule once. The engine parses the rule a single time into a compiled node tree, so each evaluation skips parsing:

from datalogic_py import Engine

engine = Engine()

# 1. Compile once
rule = engine.compile({"if": [{">": [{"var": "score"}, 50]}, "pass", "fail"]})

# 2. Evaluate many times
for user in [{"score": 75}, {"score": 30}, {"score": 90}]:
    print(rule.evaluate(user)) # prints "pass", "fail", "pass"

Catching Rule Mistakes Before They Run

compile accepts a rule that calls an operator the engine doesn’t have; the call fails when it runs. compile_checked refuses such a rule up front and lists every problem, each located by a JSON Pointer into the rule:

from datalogic_py import CompileError, Engine

engine = Engine()
try:
    engine.compile_checked({"if": [True, {"vr": "x"}, "no"]})
except CompileError as e:
    for d in e.diagnostics:
        print(d["pointer"], d["message"])  # /if/1 unknown operator `vr`; did you mean `var`?

engine.check(rule) returns the same list without compiling. API & GIL Management covers the other introspection calls.

Parsing Performance: evaluate vs evaluate_str

• rule.evaluate(dict_data) accepts a Python dict or list and walks the Python objects straight into the engine’s arena (it falls back to pythonize only for unusual types such as subclasses and sets). In the boundary benchmark this is about 3 to 6 times faster than a json.dumps → evaluate_str → json.loads round trip.
• rule.evaluate_str(json_string) accepts a raw JSON string. If you already have a serialized JSON payload (e.g. read from a network socket or file), use this method to skip Python-to-Rust dictionary marshaling.

Next: Configuration & Errors covers engine configuration presets, the exception hierarchy, and type conversion.