log best optimization id

Author: aseembits93

codeflash/api/aiservice.py

@@ -286,6 +286,7 @@ def log_results(  # noqa: D417
         optimized_runtime: dict[str, float | None] | None,
         is_correct: dict[str, bool] | None,
         optimized_line_profiler_results: dict[str, str] | None,
+        metadata: dict[str, any] | None,
     ) -> None:
         """Log features to the database.
 
@@ -297,7 +298,7 @@ def log_results(  # noqa: D417
         - optimized_runtime (Optional[Dict[str, float]]): The optimized runtime.
         - is_correct (Optional[Dict[str, bool]]): Whether the optimized code is correct.
         - optimized_line_profiler_results: line_profiler results for every candidate mapped to their optimization_id
-
+        - metadata: contains the best optimization id
         """
         payload = {
             "trace_id": function_trace_id,
@@ -307,6 +308,7 @@ def log_results(  # noqa: D417
             "is_correct": is_correct,
             "codeflash_version": codeflash_version,
             "optimized_line_profiler_results": optimized_line_profiler_results,
+            "metadata": metadata,
         }
         try:
             self.make_ai_service_request("/log_features", payload=payload, timeout=5)

codeflash/optimization/function_optimizer.py

@@ -556,14 +556,8 @@ def determine_best_candidate(
                             original_code_baseline=original_code_baseline,
                             code_context=code_context,
                             trace_id=trace_id,
-                            experiment_metadata=ExperimentMetadata(
-                                id=self.experiment_id, group="control" if exp_type == "EXP0" else "experiment"
-                            )
-                            if self.experiment_id
-                            else None,
                             ai_service_client=ai_service_client,
                             executor=executor,
-                            fto_name=self.function_to_optimize.qualified_name,
                         )
                         candidates.extend(refinement_response)
                         print("Added candidates from refinement")
@@ -599,6 +593,7 @@ def determine_best_candidate(
             optimized_runtime=optimized_runtimes,
             is_correct=is_correct,
             optimized_line_profiler_results=optimized_line_profiler_results,
+            metadata={"best_optimization_id": best_optimization.candidate.optimization_id}
         )
         return best_optimization
 
@@ -608,10 +603,8 @@ def refine_optimizations(
         original_code_baseline: OriginalCodeBaseline,
         code_context: CodeOptimizationContext,
         trace_id: str,
-        experiment_metadata: ExperimentMetadata | None,
         ai_service_client: AiServiceClient,
         executor: concurrent.futures.ThreadPoolExecutor,
-        fto_name: str,
     ) -> list[OptimizedCandidate]:
         request = [
             AIServiceRefinerRequest(
@@ -626,8 +619,6 @@ def refine_optimizations(
                 trace_id=trace_id,
                 original_line_profiler_results=original_code_baseline.line_profile_results["str_out"],
                 optimized_line_profiler_results=opt.line_profiler_test_results["str_out"],
-                experiment_metadata=experiment_metadata,
-                fto_name=fto_name,
             )
             for opt in valid_optimizations
         ]  # TODO: multiple workers for this?