Bypass Ragas scenario generation in direct mode

 from __future__ import annotations
 from __future__ import annotations
 
 
+import asyncio
+import inspect
 import json
 import json
 import logging
 import logging
-import inspect
 from typing import Any
 from typing import Any
 
 
 from langchain_core.documents import Document
 from langchain_core.documents import Document
 from langchain_openai import ChatOpenAI, OpenAIEmbeddings
 from langchain_openai import ChatOpenAI, OpenAIEmbeddings
 from ragas.run_config import RunConfig
 from ragas.run_config import RunConfig
-from ragas.testset.graph import KnowledgeGraph, Node, NodeType
-from ragas.testset.persona import Persona
 from ragas.testset import TestsetGenerator
 from ragas.testset import TestsetGenerator
-from ragas.testset.synthesizers.single_hop.specific import SingleHopSpecificQuerySynthesizer
+from ragas.testset.graph import Node, NodeType
+from ragas.testset.persona import Persona
+from ragas.testset.synthesizers.base import QueryLength, QueryStyle
+from ragas.testset.synthesizers.single_hop.base import (
+    SingleHopQuerySynthesizer,
+    SingleHopScenario,
+)
 
 
 from weknora_eval.config import require_config
 from weknora_eval.config import require_config
 from weknora_eval.loaders import read_jsonl, write_jsonl
 from weknora_eval.loaders import read_jsonl, write_jsonl
@@ -96,7 +101,11 @@ def generate_ragas_testset(
         max_workers=int(ragas_config.get("max_workers", 1)),
         max_workers=int(ragas_config.get("max_workers", 1)),
     )
     )
     if ragas_mode == "direct":
     if ragas_mode == "direct":
-        result = _generate_ragas_direct(llm, documents, size, run_config)
+        rows = _generate_ragas_direct_rows(
+            llm, documents, selected_source_rows, size, run_config
+        )
+        write_jsonl(output_path, rows)
+        return rows
     elif ragas_mode == "prechunked":
     elif ragas_mode == "prechunked":
         result = _generate_ragas_prechunked(
         result = _generate_ragas_prechunked(
             config, ragas_config, llm, documents, size, run_config
             config, ragas_config, llm, documents, size, run_config
@@ -114,57 +123,100 @@ def generate_ragas_testset(
     return rows
     return rows
 
 
 
 
-def _generate_ragas_direct(
+def _generate_ragas_direct_rows(
     llm: ChatOpenAI,
     llm: ChatOpenAI,
     documents: list[Document],
     documents: list[Document],
+    source_rows: list[dict[str, Any]],
     size: int,
     size: int,
     run_config: RunConfig,
     run_config: RunConfig,
-) -> Any:
+) -> list[dict[str, Any]]:
     ragas_llm = _wrap_langchain_llm(llm)
     ragas_llm = _wrap_langchain_llm(llm)
-    kg = KnowledgeGraph(
-        nodes=[
-            Node(
-                type=NodeType.CHUNK,
-                properties={
-                    "page_content": document.page_content,
-                    "document_metadata": document.metadata,
-                    "entities": _generation_terms(document),
-                    "themes": _generation_terms(document),
-                },
-            )
-            for document in documents
-            if document.page_content.strip()
-        ]
-    )
-    generator = TestsetGenerator(
-        llm=ragas_llm,
-        embedding_model=None,
-        knowledge_graph=kg,
-        persona_list=[
-            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。",
-            ),
-        ],
+    if hasattr(ragas_llm, "set_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。",
+        ),
+    ]
+    synthesizer = SingleHopQuerySynthesizer(llm=ragas_llm)
+    rows = asyncio.run(
+        _generate_direct_samples(synthesizer, documents, source_rows, personas, size)
     )
     )
-    generate_kwargs: dict[str, Any] = {
-        "testset_size": size,
-        "query_distribution": [(SingleHopSpecificQuerySynthesizer(llm=ragas_llm), 1.0)],
-        "num_personas": 3,
-        "run_config": run_config,
-        "raise_exceptions": False,
-    }
-    if "batch_size" in inspect.signature(generator.generate).parameters:
-        generate_kwargs["batch_size"] = 1
-    return generator.generate(**generate_kwargs)
+    logger.info("Generated %s Ragas direct QA samples", len(rows))
+    return rows
+
+
+async def _generate_direct_samples(
+    synthesizer: SingleHopQuerySynthesizer,
+    documents: list[Document],
+    source_rows: list[dict[str, Any]],
+    personas: list[Persona],
+    size: int,
+) -> list[dict[str, Any]]:
+    rows: list[dict[str, Any]] = []
+    styles = [QueryStyle.PERFECT_GRAMMAR, QueryStyle.WEB_SEARCH_LIKE]
+    lengths = [QueryLength.MEDIUM, QueryLength.SHORT]
+    for index, (document, source) in enumerate(zip(documents, source_rows), start=1):
+        if len(rows) >= size:
+            break
+        term = _generation_terms(document)[0]
+        node = Node(
+            type=NodeType.CHUNK,
+            properties={
+                "page_content": document.page_content,
+                "document_metadata": document.metadata,
+            },
+        )
+        scenario = SingleHopScenario(
+            nodes=[node],
+            term=term,
+            persona=personas[(index - 1) % len(personas)],
+            style=styles[(index - 1) % len(styles)],
+            length=lengths[(index - 1) % len(lengths)],
+        )
+        try:
+            sample = await synthesizer.generate_sample(scenario)
+        except Exception as exc:  # noqa: BLE001
+            logger.warning(
+                "Ragas direct QA generation failed for source_file=%s doc_id=%s: %s",
+                source.get("source_file"),
+                source.get("doc_id"),
+                exc,
+            )
+            continue
+
+        chunk_id = (source.get("metadata") or {}).get("chunk_id") or source.get(
+            "doc_id"
+        )
+        rows.append(
+            TestsetRecord(
+                sample_id=f"qa-{len(rows) + 1:04d}",
+                user_input=str(sample.user_input or "").strip(),
+                reference=str(sample.reference or "").strip(),
+                reference_contexts=[document.page_content],
+                source_file=source.get("source_file"),
+                gold_chunk_ids=[str(chunk_id)] if chunk_id else [],
+                question_type="ragas_single_hop_direct",
+                review_status="pending",
+            ).to_dict()
+        )
+    return [
+        row
+        for row in rows
+        if row.get("user_input")
+        and row.get("reference")
+        and row.get("reference_contexts")
+    ]
 
 
 
 
 def _generate_ragas_prechunked(
 def _generate_ragas_prechunked(