Fix Dockerfile build issue

autorag/evaluation/retrieval_contents.py

@@ -0,0 +1,65 @@
+import functools
+from typing import List, Callable, Any, Tuple
+
+import pandas as pd
+
+from autorag.evaluation.metric import (
+	retrieval_token_f1,
+	retrieval_token_precision,
+	retrieval_token_recall,
+)
+from autorag.schema.metricinput import MetricInput
+
+
+def evaluate_retrieval_contents(metric_inputs: List[MetricInput], metrics: List[str]):
+	def decorator_evaluate_retrieval_contents(
+		func: Callable[
+			[Any], Tuple[List[List[str]], List[List[str]], List[List[float]]]
+		],
+	):
+		"""
+		Decorator for evaluating retrieval contents.
+		You can use this decorator to any method that returns (contents, scores, ids),
+		which is the output of conventional retrieval modules.
+
+		:param func: Must return (contents, scores, ids)
+		:return: pd.DataFrame, which is the evaluation result and function result.
+		"""
+
+		@functools.wraps(func)
+		def wrapper(*args, **kwargs) -> pd.DataFrame:
+			contents, pred_ids, scores = func(*args, **kwargs)
+			metric_funcs = {
+				retrieval_token_recall.__name__: retrieval_token_recall,
+				retrieval_token_precision.__name__: retrieval_token_precision,
+				retrieval_token_f1.__name__: retrieval_token_f1,
+			}
+			for metric_input, content in zip(metric_inputs, contents):
+				metric_input.retrieved_contents = content
+
+			metrics_scores = {}
+			for metric in metrics:
+				if metric not in metric_funcs:
+					raise ValueError(
+						f"metric {metric} is not in supported metrics: {metric_funcs.keys()}"
+					)
+				else:
+					metric_func = metric_funcs[metric]
+					# Extract each required field from all payloads
+					metric_scores = metric_func(metric_inputs=metric_inputs)
+					metrics_scores[metric] = metric_scores
+
+			metric_result_df = pd.DataFrame(metrics_scores)
+			execution_result_df = pd.DataFrame(
+				{
+					"retrieved_contents": contents,
+					"retrieved_ids": pred_ids,
+					"retrieve_scores": scores,
+				}
+			)
+			result_df = pd.concat([execution_result_df, metric_result_df], axis=1)
+			return result_df
+
+		return wrapper
+
+	return decorator_evaluate_retrieval_contents