⚡️ Speed up function build_eval_function by 10%

[codeflash-ai]

📄 10% (0.10x) speedup for build_eval_function in inference/core/workflows/core_steps/common/query_language/evaluation_engine/core.py

⏱️ Runtime : 86.2 microseconds → 78.1 microseconds (best of 6 runs)

📝 Explanation and details

The optimized code achieves a 10% speedup through several key micro-optimizations that reduce function call overhead and attribute lookups:

Key optimizations:

1. Replaced isinstance() with direct type() comparisons in build_eval_function: The original code used isinstance(definition, BinaryStatement) which is more expensive than type(definition) is BinaryStatement_type. This optimization assumes no subclassing is used, which is common in query language evaluation engines.

2. Eliminated redundant loops with list comprehension for StatementGroup processing: Instead of explicitly creating an empty list and appending in a loop, the optimized version uses a list comprehension that's more efficient in Python.

3. Reduced repeated attribute lookups in build_binary_statement and build_unary_statement: The original code called definition.comparator.type and type(definition.comparator).model_fields multiple times. The optimized version stores these in local variables (comparator, model_fields) to avoid repeated lookups.

4. Minor optimization in compound_eval: Eliminated an intermediate variable assignment by directly passing fun(values) to operator_fun().

Performance impact: The line profiler shows that build_eval_function execution time dropped from 940.64μs to 821.41μs, with the most significant gains in the type checking logic (31.4% → 12.9% of total time for the first isinstance check).

Context benefits: Since build_eval_function is called recursively for nested statement groups and is used in the main evaluate() function that processes query language definitions, these micro-optimizations compound when processing complex nested queries. The function appears to be in a hot path for query language evaluation, making these small gains meaningful for overall system performance.

Test case suitability: These optimizations are particularly effective for workloads with deeply nested statement groups or frequent query evaluations, where the recursive calls to build_eval_function amplify the performance benefits.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 6 Passed
🌀 Generated Regression Tests	🔘 None Found
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	88.9%

⚙️ Existing Unit Tests and Runtime

Test File::Test Function	Original ⏱️	Optimized ⏱️	Speedup
workflows/unit_tests/core_steps/control_flow/test_control_flow.py::test_continue_if_evaluation	86.2μs	78.1μs	10.4%✅

To edit these changes git checkout codeflash/optimize-build_eval_function-miqkpu8h and push.