对齐报告生成步骤

@@ -115,7 +115,7 @@ def generate_summary_report(
         lines.append(
         lines.append(
             f"| {row.get('sample_id', '')} | {_cell(sample.get('user_input'))} | "
             f"| {row.get('sample_id', '')} | {_cell(sample.get('user_input'))} | "
             f"{_cell(sample.get('response'))} | {_cell(sample.get('reference'))} | "
             f"{_cell(sample.get('response'))} | {_cell(sample.get('reference'))} | "
-            f"{_score(row.get('faithfulness'))} | {_score(row.get('factual_correctness'))} |"
+            f"{_score(_metric_value(row, 'faithfulness'))} | {_score(_metric_value(row, 'factual_correctness'))} |"
         )
         )
 
 
     empty_retrievals = sum(1 for row in ragas_rows if not row.get("retrieved_contexts"))
     empty_retrievals = sum(1 for row in ragas_rows if not row.get("retrieved_contexts"))
@@ -149,9 +149,27 @@ def generate_summary_report(
 
 
 
 
 def _worst_rows(scores: pd.DataFrame, column: str, *, limit: int = 10) -> list[dict[str, Any]]:
 def _worst_rows(scores: pd.DataFrame, column: str, *, limit: int = 10) -> list[dict[str, Any]]:
-    if scores.empty or column not in scores.columns:
+    metric_column = _metric_column(scores, column)
+    if scores.empty or metric_column is None:
         return []
         return []
-    return scores.sort_values(column, ascending=True).head(limit).to_dict(orient="records")
+    return scores.sort_values(metric_column, ascending=True).head(limit).to_dict(orient="records")
+
+
+def _metric_column(scores: pd.DataFrame, name: str) -> str | None:
+    if name in scores.columns:
+        return name
+    prefix = f"{name}("
+    return next((column for column in scores.columns if column.startswith(prefix)), None)
+
+
+def _metric_value(row: dict[str, Any], name: str) -> Any:
+    if name in row:
+        return row[name]
+    prefix = f"{name}("
+    for key, value in row.items():
+        if str(key).startswith(prefix):
+            return value
+    return None
 
 
 
 
 def _sample_by_id(rows: list[dict[str, Any]], sample_id: Any) -> dict[str, Any]:
 def _sample_by_id(rows: list[dict[str, Any]], sample_id: Any) -> dict[str, Any]:

@@ -140,20 +140,7 @@ def _generate_ragas_direct_rows(
     if hasattr(ragas_llm, "set_run_config"):
     if hasattr(ragas_llm, "set_run_config"):
         ragas_llm.set_run_config(run_config)
         ragas_llm.set_run_config(run_config)
 
 
-    personas = [
-        Persona(
-            name="合同审核人员",
-            role_description="关注合同条款、权利归属、授权范围和履约义务。",
-        ),
-        Persona(
-            name="业务运营人员",
-            role_description="关注文档中可用于业务执行和信息核验的事实。",
-        ),
-        Persona(
-            name="法务合规人员",
-            role_description="关注协议、版权、授权、责任和风险表述。",
-        ),
-    ]
+    personas = _default_personas()
     synthesizer = SingleHopSpecificQuerySynthesizer(llm=ragas_llm)
     synthesizer = SingleHopSpecificQuerySynthesizer(llm=ragas_llm)
     rows = asyncio.run(
     rows = asyncio.run(
         _generate_direct_samples(synthesizer, documents, source_rows, personas, size)
         _generate_direct_samples(synthesizer, documents, source_rows, personas, size)
@@ -235,7 +222,11 @@ def _generate_ragas_prechunked(
 ) -> Any:
 ) -> Any:
     embeddings = _build_embeddings(config, ragas_config)
     embeddings = _build_embeddings(config, ragas_config)
     ragas_llm, ragas_embeddings = _wrap_langchain_models(llm, embeddings)
     ragas_llm, ragas_embeddings = _wrap_langchain_models(llm, embeddings)
-    generator = TestsetGenerator(llm=ragas_llm, embedding_model=ragas_embeddings)
+    generator = TestsetGenerator(
+        llm=ragas_llm,
+        embedding_model=ragas_embeddings,
+        persona_list=_default_personas(),
+    )
     return generator.generate_with_chunks(
     return generator.generate_with_chunks(
         documents,
         documents,
         testset_size=size,
         testset_size=size,
@@ -256,7 +247,11 @@ def _generate_ragas_langchain_docs(
 ) -> Any:
 ) -> Any:
     embeddings = _build_embeddings(config, ragas_config)
     embeddings = _build_embeddings(config, ragas_config)
     ragas_llm, ragas_embeddings = _wrap_langchain_models(llm, embeddings)
     ragas_llm, ragas_embeddings = _wrap_langchain_models(llm, embeddings)
-    generator = TestsetGenerator(llm=ragas_llm, embedding_model=ragas_embeddings)
+    generator = TestsetGenerator(
+        llm=ragas_llm,
+        embedding_model=ragas_embeddings,
+        persona_list=_default_personas(),
+    )
     generate_kwargs: dict[str, Any] = {
     generate_kwargs: dict[str, Any] = {
         "testset_size": size,
         "testset_size": size,
         "query_distribution": [(SingleHopSpecificQuerySynthesizer(llm=ragas_llm), 1.0)],
         "query_distribution": [(SingleHopSpecificQuerySynthesizer(llm=ragas_llm), 1.0)],
@@ -279,6 +274,23 @@ def _is_chunk_node(node: Any) -> bool:
     return getattr(getattr(node, "type", None), "name", "") == "CHUNK"
     return getattr(getattr(node, "type", None), "name", "") == "CHUNK"
 
 
 
 
+def _default_personas() -> list[Persona]:
+    return [
+        Persona(
+            name="合同审核人员",
+            role_description="关注合同条款、权利归属、授权范围和履约义务。",
+        ),
+        Persona(
+            name="业务运营人员",
+            role_description="关注文档中可用于业务执行和信息核验的事实。",
+        ),
+        Persona(
+            name="法务合规人员",
+            role_description="关注协议、版权、授权、责任和风险表述。",
+        ),
+    ]
+
+
 def _build_embeddings(
 def _build_embeddings(
     config: dict[str, Any], ragas_config: dict[str, Any]
     config: dict[str, Any], ragas_config: dict[str, Any]
 ) -> OpenAIEmbeddings:
 ) -> OpenAIEmbeddings: