chore: import upstream snapshot with attribution

2026-07-13 12:37:47 +08:00
commit 7653f56fed
1422 changed files with 359026 additions and 0 deletions
@@ -0,0 +1,112 @@
+from deepeval import evaluate
+from deepeval.metrics import GEval
+from deepeval.test_case import LLMTestCase, LLMTestCaseParams
+from deepeval.metrics.g_eval import Rubric
+from typing import Dict, Any
+
+def evaluate_code(generated_code: str, reference_code: str = None):
+    try:
+        # Initialize test case
+        test_case = LLMTestCase(
+            input="Code Generation Task",
+            actual_output=generated_code,
+            expected_output=reference_code if reference_code else ""
+        )
+
+        # Code Correctness Metric
+        correctness_metric = GEval(
+            name="Code Correctness",
+            criteria="Evaluate if the code is functionally correct, properly handles edge cases, and implements the required functionality completely.",
+            evaluation_steps=[
+                "Check if the code implements all required functionality",
+                "Verify proper handling of edge cases",
+                "Check for potential runtime errors",
+                "Assess if the code produces expected outputs"
+            ],
+            evaluation_params=[LLMTestCaseParams.ACTUAL_OUTPUT, LLMTestCaseParams.EXPECTED_OUTPUT],
+            rubric=[
+                Rubric(score_range=(0,2), expected_outcome="Code is non-functional or has critical errors"),
+                Rubric(score_range=(3,5), expected_outcome="Code works but misses key functionality"),
+                Rubric(score_range=(6,8), expected_outcome="Code is mostly correct with minor issues"),
+                Rubric(score_range=(9,10), expected_outcome="Code is completely correct")
+            ],
+            threshold=0.7
+        )
+
+        # Code Readability Metric
+        readability_metric = GEval(
+            name="Code Readability",
+            criteria="Evaluate code readability including proper naming, formatting, and documentation.",
+            evaluation_steps=[
+                "Check for clear and consistent naming conventions",
+                "Verify proper code formatting and indentation",
+                "Assess quality and completeness of comments and docstrings",
+                "Check for code organization and logical structure"
+            ],
+            evaluation_params=[LLMTestCaseParams.ACTUAL_OUTPUT],
+            rubric=[
+                Rubric(score_range=(0,2), expected_outcome="Code is poorly formatted and hard to read"),
+                Rubric(score_range=(3,5), expected_outcome="Code has basic formatting but lacks clarity"),
+                Rubric(score_range=(6,8), expected_outcome="Code is well formatted with minor issues"),
+                Rubric(score_range=(9,10), expected_outcome="Code is exceptionally readable and well documented")
+            ],
+            threshold=0.7
+        )
+
+        # Code Best Practices Metric
+        best_practices_metric = GEval(
+            name="Code Best Practices",
+            criteria="Evaluate adherence to coding best practices, including error handling, security, and efficiency.",
+            evaluation_steps=[
+                "Check for proper error handling and exceptions",
+                "Verify security best practices",
+                "Assess code efficiency and performance considerations",
+                "Check for code reusability and modularity"
+            ],
+            evaluation_params=[LLMTestCaseParams.ACTUAL_OUTPUT],
+            rubric=[
+                Rubric(score_range=(0,2), expected_outcome="Code ignores best practices"),
+                Rubric(score_range=(3,5), expected_outcome="Code follows basic practices with gaps"),
+                Rubric(score_range=(6,8), expected_outcome="Code mostly follows best practices"),
+                Rubric(score_range=(9,10), expected_outcome="Code perfectly follows all best practices")
+            ],
+            threshold=0.7
+        )
+
+        # Run evaluation
+        metrics = [correctness_metric, readability_metric, best_practices_metric]
+        for metric in metrics:
+            metric.measure(test_case)
+
+        # Calculate overall score
+        overall_score = (correctness_metric.score + readability_metric.score + best_practices_metric.score) / 3
+
+        # Prepare detailed metrics
+        detailed_metrics = {
+            "correctness": {
+                "score": correctness_metric.score,
+                "reason": correctness_metric.reason
+            },
+            "readability": {
+                "score": readability_metric.score,
+                "reason": readability_metric.reason
+            },
+            "best_practices": {
+                "score": best_practices_metric.score,
+                "reason": best_practices_metric.reason
+            }
+        }
+
+        return {
+            "overall_score": overall_score,
+            "detailed_metrics": detailed_metrics,
+            "passed": overall_score >= 0.7 
+        }
+
+    except Exception as e:
+        return {
+            "error": f"Error evaluating code: {str(e)}",
+            "overall_score": 0.0,
+            "detailed_metrics": {},
+            "passed": False
+        }