feat(core_text): enhance data validation in PromptedFilter and Text2MultiHopQAGenerator

dataflow/operators/core_text/filter/prompted_filter.py

@@ -18,6 +18,21 @@ def __init__(self, llm_serving: LLMServingABC, system_prompt: str = "Please eval
         self.prompted_evaluator = PromptedEvaluator(llm_serving, system_prompt)
         self.min_score = min_score
         self.max_score = max_score
+
+    @staticmethod
+    def _has_valid_content(value) -> bool:
+        if value is None:
+            return False
+        if isinstance(value, str):
+            return value.strip() != ""
+        if isinstance(value, (list, tuple, set, dict)):
+            return len(value) > 0
+        try:
+            if pd.isna(value):
+                return False
+        except TypeError:
+            pass
+        return bool(value)
 
     @staticmethod
     def get_desc(lang: str = "zh"):
@@ -71,7 +86,7 @@ def run(self, storage: DataFlowStorage, input_key: str = "raw_content", output_k
         self.logger.info(f"Loading, number of rows: {len(dataframe)}")
 
         # Drop rows where input_key is empty/null before evaluation
-        valid_mask = dataframe[input_key].notna() & (dataframe[input_key].astype(str).str.strip() != '')
+        valid_mask = dataframe[input_key].apply(self._has_valid_content)
         valid_dataframe = dataframe[valid_mask]
         self.logger.info(f"Skipping {(~valid_mask).sum()} rows with empty '{input_key}'")
 

dataflow/operators/core_text/generate/text2multihopqa_generator.py

@@ -225,13 +225,23 @@ def run(
         dataframe = storage.read("dataframe")
         self._validate_dataframe(dataframe)
         texts = dataframe[self.input_key].tolist()
-        outputs=self.process_batch(texts)
-        dataframe[self.output_key] = [
+        outputs = self.process_batch(texts)
+        qa_pairs_column = [
             output['qa_pairs'][:self.num_q] if len(output['qa_pairs']) >= self.num_q else output['qa_pairs']
             for output in outputs
         ]
+        metadata_column = [output['metadata'] for output in outputs]
+
+        dataframe = dataframe.copy()
+        dataframe[self.output_key] = qa_pairs_column
+        dataframe[self.output_meta_key] = metadata_column
+
+        valid_mask = dataframe[self.output_key].apply(lambda qa_pairs: isinstance(qa_pairs, list) and len(qa_pairs) > 0)
+        filtered_count = int((~valid_mask).sum())
+        if filtered_count:
+            self.logger.info(f"Filtering out {filtered_count} rows with empty '{self.output_key}'")
+        dataframe = dataframe[valid_mask].reset_index(drop=True)
 
-        dataframe[self.output_meta_key] = [output['metadata'] for output in outputs]
         output_file = storage.write(dataframe)
         self.logger.info(f"Results saved to {output_file}")