Refinement

[misrasaurabh1]

PR Type

Enhancement

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Description

• Introduce AIServiceRefinerRequest and client refinement API

• Integrate AI-powered refinement in optimize flow

• Add helper methods for diff and ranking

• Refactor test env and line profiling steps

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Changes diagram

flowchart LR
  A["Initial optimize candidates"] --> B["Collect valid optimizations"]
  B --> C["Call optimize_python_code_refinement"]
  C --> D["Rank by diff length + runtime"]
  D --> E["Return best optimization"]

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Changes walkthrough 📝

Relevant files

Enhancement

aiservice.py Add AIService refinement request method                                    codeflash/api/aiservice.py Import ExperimentMetadata and pydantic dataclass Add AIServiceRefinerRequest dataclass Implement optimize_python_code_refinement method Handle API success and error logging +76/-1    models.py Update BestOptimization fields                                                      codeflash/models/models.py Replace helper_functions with code_context in BestOptimization Add line_profiler_test_results field +2/-1      function_optimizer.py Integrate optimization refinement and helpers                        codeflash/optimization/function_optimizer.py Track valid_optimizations and integrate refinement flow Add refine_optimizations, diff_length, ranking helpers Extend determine_best_candidate for refinement and ranking Refactor test env and line profiling into methods +193/-48 critic.py Allow speedup critic no-best-runtime handling                        codeflash/result/critic.py Allow best_runtime_until_now to be None in speedup_critic Adjust logic to handle no prior best runtime scenario +4/-1

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[github-actions]

PR Reviewer Guide 🔍

Here are some key observations to aid the review process:

⏱️ Estimated effort to review: 4 🔵🔵🔵🔵⚪

🧪 No relevant tests

🔒 No security concerns identified

⚡ Recommended focus areas for review Payload Field Mismatch The payload uses the key "original_read_only_dependency_code" whereas the dataclass defines "read_only_dependency_code". This naming mismatch may result in missing or incorrect data being sent to the refinement endpoint. def optimize_python_code_refinement(self, request: list[AIServiceRefinerRequest]) -> list[str]: payload = [ { "original_source_code": opt.original_source_code, "original_read_only_dependency_code": opt.original_read_only_dependency_code, "original_line_profiler_results": opt.original_line_profiler_results, "original_code_runtime": opt.original_code_runtime, "optimized_source_code": opt.optimized_source_code, "optimized_explanation": opt.optimized_explanation, "optimized_line_profiler_results": opt.optimized_line_profiler_results, "optimized_code_runtime": opt.optimized_code_runtime, "speedup": opt.speedup, "trace_id": opt.trace_id, "python_version": platform.python_version(), "experiment_metadata": opt.experiment_metadata, "codeflash_version": codeflash_version, "lsp_mode": is_LSP_enabled(), } for opt in request ExperimentMetadata Serialization The code passes the ExperimentMetadata dataclass directly in the payload. Ensure it is converted to a JSON-serializable form (e.g., via .dict() or pydantic_encoder) before sending, otherwise serialization may fail. "speedup": opt.speedup, "trace_id": opt.trace_id, "python_version": platform.python_version(), "experiment_metadata": opt.experiment_metadata, "codeflash_version": codeflash_version, "lsp_mode": is_LSP_enabled(), } Diff Performance The diff_length implementation computes full unified diffs on possibly large code strings. This may be slow or memory-intensive. Consider optimizing or limiting diff size for large inputs. def diff_length(a: str, b: str) -> int: """Compute the length (in characters) of the unified diff between two strings. Parameters ---------- a (str): Original string. b (str): Modified string. Returns ------- int: Total number of characters in the diff. """ # Split input strings into lines for line-by-line diff a_lines = a.splitlines(keepends=True) b_lines = b.splitlines(keepends=True) # Compute unified diff diff_lines = list(difflib.unified_diff(a_lines, b_lines, lineterm="")) # Join all lines with newline to calculate total diff length diff_text = "\n".join(diff_lines) return len(diff_text)

[github-actions]

PR Code Suggestions ✨

Explore these optional code suggestions:

Category	Suggestion	Impact
Possible issue	Fix payload field name The payload uses a non-existent field original_read_only_dependency_code; it should match the dataclass field read_only_dependency_code. Rename both the key and attribute reference to keep them consistent. codeflash/api/aiservice.py [241-242] { "original_source_code": opt.original_source_code, - "original_read_only_dependency_code": opt.original_read_only_dependency_code, + "read_only_dependency_code": opt.read_only_dependency_code, ... } Suggestion importance[1-10]: 9 __ Why: The payload key "original_read_only_dependency_code" and attribute opt.original_read_only_dependency_code do not exist on AIServiceRefinerRequest, so renaming to read_only_dependency_code is essential to avoid runtime errors.	High
General	Move refinement block This block is inside the while candidates: loop and will never run because the loop exits when len(candidates)==0. Move it outside the loop so refinement runs once after processing all candidates. codeflash/optimization/function_optimizer.py [543-571] -if len(candidates) == 0 and len(self.valid_optimizations) > 0 and not refinement_done: +# after the while-candidates loop ends +if len(self.valid_optimizations) > 0 and not refinement_done: # ... refine_optimizations ... candidates.extend(more_opt_candidates) refinement_done = True Suggestion importance[1-10]: 9 __ Why: The if len(candidates) == 0 check is inside the while candidates: loop and thus never executes, so relocating it after the loop is critical for the refinement step to run.	High
	Serialize metadata field Sending a Pydantic dataclass instance directly may fail JSON serialization. Convert the experiment_metadata to a dict (or JSON) before adding it to the payload. codeflash/api/aiservice.py [252] -"experiment_metadata": opt.experiment_metadata, +"experiment_metadata": opt.experiment_metadata.dict() if opt.experiment_metadata else None, Suggestion importance[1-10]: 6 __ Why: Passing a Pydantic dataclass directly may not serialize with requests.json, so converting opt.experiment_metadata to a dict ensures the payload is JSON-serializable.	Low

[codeflash-ai]

⚡️ Codeflash found optimizations for this PR

📄 15% (0.15x) speedup for speedup_critic in codeflash/result/critic.py

⏱️ Runtime : 1.84 milliseconds → 1.60 milliseconds (best of 56 runs)

I created a new dependent PR with the suggested changes. Please review:

• ⚡️ Speed up function speedup_critic by 15% in PR #555 (refinement) #557

If you approve, it will be merged into this PR (branch refinement).