feat(data-process): 支持思维链输出类型

backend/app/api/v1/endpoints/data_process.py

@@ -556,12 +556,16 @@ def _run_generation(
             if task["process_type"] == "unstructured"
             else _value(config, "qa_pairs_per_row", "qaPairsPerRow", 1)
         )
+        output_type = str(
+            _value(config, "output_type", "outputType", "standard")
+        ).strip().lower()
         if generation_model:
             runtime_config = {
                 **config,
                 "generation_prompt": _value(
                     config, "generation_prompt", "generationPrompt", ""
                 ),
+                "output_type": output_type,
                 "max_tokens": _value(config, "max_tokens", "maxTokens", 1024),
                 "json_mode": _value(config, "json_mode", "jsonMode", False),
             }
@@ -583,6 +587,8 @@ def _run_generation(
                 qa_pairs_per_item=int(pairs or 1),
                 on_progress=report_progress,
             )
+        elif output_type == "reasoning":
+            raise InvalidStateError("思维链输出必须配置可用的数据生成模型")
         else:
             generated = generate_standard_records(
                 preview_items,

backend/app/modules/data_process/generation.py

@@ -22,6 +22,11 @@ class ModelGenerationError(ValueError):
     """模型配置、响应或调用失败。"""
 
 
+OUTPUT_TYPE_STANDARD = "standard"
+OUTPUT_TYPE_REASONING = "reasoning"
+SUPPORTED_OUTPUT_TYPES = {OUTPUT_TYPE_STANDARD, OUTPUT_TYPE_REASONING}
+
+
 def chat_completions_url(value: str) -> str:
     """把域名、基础 URL 或完整地址统一为 chat completions 地址。"""
 
@@ -52,7 +57,9 @@ def _message_content(payload: Mapping[str, Any]) -> str:
     try:
         content = payload["choices"][0]["message"]["content"]
     except (KeyError, IndexError, TypeError) as exc:
-        raise ModelGenerationError("model response does not contain choices[0].message.content") from exc
+        raise ModelGenerationError(
+            "model response does not contain choices[0].message.content"
+        ) from exc
     if isinstance(content, str):
         return content
     if isinstance(content, list):
@@ -67,7 +74,14 @@ def _message_content(payload: Mapping[str, Any]) -> str:
 
 
 def _json_payload(content: str) -> Any:
-    cleaned = re.sub(r"<think>[\s\S]*?</think>", "", content, flags=re.IGNORECASE).strip()
+    # 只移除模型在 JSON 之前自行输出的思考过程，不能破坏 JSON 字段中的训练内容。
+    cleaned = re.sub(
+        r"^\s*<think>[\s\S]*?</think>\s*",
+        "",
+        content,
+        count=1,
+        flags=re.IGNORECASE,
+    ).strip()
     fenced = re.fullmatch(r"```(?:json)?\s*([\s\S]*?)\s*```", cleaned, flags=re.IGNORECASE)
     if fenced:
         cleaned = fenced.group(1).strip()
@@ -107,19 +121,27 @@ def _prompt_messages(
     *,
     start_index: int,
     total_count: int,
+    output_type: str,
 ) -> list[dict[str, str]]:
     end_index = start_index + count - 1
+    if output_type == OUTPUT_TYPE_REASONING:
+        schema = '{"items":[{"instruction":"...","input":"...","reasoning":"...","answer":"..."}]}'
+        output_rule = (
+            "instruction、reasoning 和 answer 均不得为空；reasoning 必须是基于来源内容、"
+            "可核对且简洁的推理步骤，answer 只写最终答案。"
+            "这是思维链输出模式，即使其他提示语要求省略分析，也不得省略 reasoning。"
+            "不要自行添加 <think> 标签，系统会在保存时统一组装。"
+        )
+    else:
+        schema = '{"items":[{"instruction":"...","input":"...","output":"..."}]}'
+        output_rule = "instruction 和 output 不得为空，不要输出分析过程。"
     schema_instruction = (
-        f"必须只返回 JSON 对象，格式为 {{\"items\":[{{\"instruction\":\"...\","
-        f"\"input\":\"...\",\"output\":\"...\"}}]}}；items 必须包含 {count} 条。"
+        f"必须只返回 JSON 对象，格式为 {schema}；items 必须包含 {count} 条。"
         f"这是总计 {total_count} 条中的第 {start_index}-{end_index} 条，"
         "各条必须使用不同的提问角度和表述，避免重复。"
-        "instruction 和 output 不得为空，不要输出 Markdown 代码围栏或分析过程。"
-    )
-    base_prompt = (
-        normalize_text(prompt)
-        or "请根据来源内容生成可用于监督微调的问答数据。"
+        f"{output_rule}不要输出 Markdown 代码围栏或 JSON 之外的说明。"
     )
+    base_prompt = normalize_text(prompt) or "请根据来源内容生成可用于监督微调的问答数据。"
     if "{{ content }}" in base_prompt:
         user_prompt = base_prompt.replace("{{ content }}", content)
         return [
@@ -150,9 +172,10 @@ def generate_model_records(
     """
 
     if not 1 <= qa_pairs_per_item <= MAX_QA_PAIRS_PER_ITEM:
-        raise ModelGenerationError(
-            f"qa_pairs_per_item must be in [1, {MAX_QA_PAIRS_PER_ITEM}]"
-        )
+        raise ModelGenerationError(f"qa_pairs_per_item must be in [1, {MAX_QA_PAIRS_PER_ITEM}]")
+    output_type = str(config.get("output_type") or OUTPUT_TYPE_STANDARD).strip().lower()
+    if output_type not in SUPPORTED_OUTPUT_TYPES:
+        raise ModelGenerationError(f"output_type must be one of {sorted(SUPPORTED_OUTPUT_TYPES)}")
     endpoint = chat_completions_url(str(model.get("api_url") or ""))
     model_name = str(model.get("online_model_name") or model.get("name") or "").strip()
     if not model_name:
@@ -193,6 +216,7 @@ def generate_model_records(
                         batch_count,
                         start_index=batch_start,
                         total_count=qa_pairs_per_item,
+                        output_type=output_type,
                     ),
                     "temperature": temperature,
                     "max_tokens": max_tokens,
@@ -212,12 +236,8 @@ def generate_model_records(
                         response.raise_for_status()
                         body = response.json()
                         if not isinstance(body, Mapping):
-                            raise ModelGenerationError(
-                                "model response body must be a JSON object"
-                            )
-                        candidate_items = _result_items(
-                            _json_payload(_message_content(body))
-                        )
+                            raise ModelGenerationError("model response body must be a JSON object")
+                        candidate_items = _result_items(_json_payload(_message_content(body)))
                         if len(candidate_items) < batch_count:
                             raise ModelGenerationError(
                                 "model response contains fewer result objects than requested: "
@@ -266,6 +286,36 @@ def generate_model_records(
                     input_text = normalize_text(
                         str(value.get("input") or value.get("context") or "")
                     )
+                    if output_type == OUTPUT_TYPE_REASONING:
+                        reasoning = normalize_text(
+                            re.sub(
+                                r"</?think>",
+                                "",
+                                str(value.get("reasoning") or value.get("analysis") or ""),
+                                flags=re.IGNORECASE,
+                            )
+                        )
+                        answer = normalize_text(
+                            re.sub(
+                                r"</?think>",
+                                "",
+                                str(
+                                    value.get("answer")
+                                    or value.get("final_answer")
+                                    or value.get("output")
+                                    or ""
+                                ),
+                                flags=re.IGNORECASE,
+                            )
+                        )
+                        output = (
+                            f"<think>\n{reasoning}\n</think>\n{answer}"
+                            if reasoning and answer
+                            else answer or (f"<think>\n{reasoning}\n</think>" if reasoning else "")
+                        )
+                        valid = bool(instruction and reasoning and answer)
+                        missing_error = "model result is missing instruction, reasoning or answer"
+                    else:
                         output = normalize_text(
                             str(
                                 value.get("output")
@@ -274,11 +324,18 @@ def generate_model_records(
                                 or ""
                             )
                         )
-                    raw_id = f"{preview_id}:{variant_index + 1}:{instruction}:{output}"
-                    result_id = (
-                        f"result_{hashlib.sha256(raw_id.encode()).hexdigest()[:16]}"
+                        output = normalize_text(
+                            re.sub(
+                                r"<think>[\s\S]*?(?:</think>|$)",
+                                "",
+                                output,
+                                flags=re.IGNORECASE,
+                            )
                         )
                         valid = bool(instruction and output)
+                        missing_error = "model result is missing instruction or output"
+                    raw_id = f"{preview_id}:{variant_index + 1}:{instruction}:{output}"
+                    result_id = f"result_{hashlib.sha256(raw_id.encode()).hexdigest()[:16]}"
                     results.append(
                         {
                             "id": result_id,
@@ -290,11 +347,7 @@ def generate_model_records(
                             "original_input": input_text,
                             "original_output": output,
                             "status": "valid" if valid else "invalid",
-                            "error": (
-                                None
-                                if valid
-                                else "model result is missing instruction or output"
-                            ),
+                            "error": (None if valid else missing_error),
                             "split": "train",
                         }
                     )

backend/app/modules/data_process/store.py

@@ -2,6 +2,7 @@ from __future__ import annotations
 
 import hashlib
 import json
+import re
 import uuid
 from collections.abc import Iterator, Sequence
 from contextlib import contextmanager
@@ -87,6 +88,31 @@ def _json_value(value: Any, default: Any) -> Any:
         return default
 
 
+def _task_output_type(task: dict[str, Any]) -> str:
+    config = _json_value(task.get("config"), {})
+    if not isinstance(config, dict):
+        return "standard"
+    return str(config.get("output_type") or config.get("outputType") or "standard")
+
+
+def _reasoning_output_is_valid(value: Any) -> bool:
+    match = re.fullmatch(
+        r"\s*<think>\s*(?P<reasoning>[\s\S]*?)\s*</think>\s*(?P<answer>[\s\S]+?)\s*",
+        str(value or ""),
+        flags=re.IGNORECASE,
+    )
+    return bool(
+        match
+        and match.group("reasoning").strip()
+        and match.group("answer").strip()
+        and all(
+            tag not in part.lower()
+            for tag in ("<think", "</think")
+            for part in (match.group("reasoning"), match.group("answer"))
+        )
+    )
+
+
 def _preview_config_value(config: dict[str, Any], key: str, default: Any) -> Any:
     if key in config:
         return config[key]
@@ -1565,10 +1591,13 @@ class DataProcessStore:
                 raise ConflictError("data process result was modified by another request")
             merged = {**current, **values}
             quality = payload.get("quality_score") or {}
-            hard_valid = bool(
-                str(merged.get("instruction") or "").strip()
-                and str(merged.get("output") or "").strip()
+            instruction_valid = bool(str(merged.get("instruction") or "").strip())
+            output_valid = bool(str(merged.get("output") or "").strip())
+            reasoning_valid = (
+                _task_output_type(task) != "reasoning"
+                or _reasoning_output_is_valid(merged.get("output"))
             )
+            hard_valid = instruction_valid and output_valid and reasoning_valid
             quality_valid = bool(quality.get("is_valid", hard_valid))
             changed = any(
                 str(merged.get(field) or "")
@@ -1580,8 +1609,16 @@ class DataProcessStore:
             )
             values["status"] = status
             flags = quality.get("flags") if isinstance(quality, dict) else None
+            format_error = (
+                "思维链输出必须包含非空的 <think>...</think> 推理过程和最终答案"
+                if instruction_valid and output_valid and not reasoning_valid
+                else "Instruction 和 Output 不能为空"
+                if not instruction_valid or not output_valid
+                else None
+            )
             values["error"] = ", ".join(str(flag) for flag in flags or []) or (
-                "quality validation failed" if status == "invalid" else None
+                format_error
+                or ("quality validation failed" if status == "invalid" else None)
             )
             values["updated_at"] = utcnow()
             assignments = ", ".join(f"{key}=%s" for key in values)
@@ -1671,6 +1708,10 @@ class DataProcessStore:
                 if row["status"] == "invalid"
                 or not str(row.get("instruction") or "").strip()
                 or not str(row.get("output") or "").strip()
+                or (
+                    _task_output_type(task) == "reasoning"
+                    and not _reasoning_output_is_valid(row.get("output"))
+                )
             )
             if invalid_count:
                 raise InvalidStateError(f"task contains {invalid_count} invalid results")
@@ -1731,6 +1772,7 @@ class DataProcessStore:
                 "source": "data_process",
                 "storage_backend": "database",
                 "source_task_id": task_id,
+                "output_type": _task_output_type(task),
                 "source_file_ids": [item["id"] for item in self._source_ids(conn, task_id)],
                 "source_result_ids": source_result_ids,
                 "format": payload.get("format") or "alpaca_jsonl",

backend/tests/test_data_process_api.py

@@ -1405,6 +1405,40 @@ def test_stale_generation_worker_cannot_overwrite_new_run(monkeypatch: Any) -> N
     assert store.tasks[task_id]["status"] == "running"
 
 
+def test_reasoning_output_requires_generation_model() -> None:
+    store = FakeDataProcessStore()
+    task = store.create_task(
+        {
+            "name": "思维链模型校验",
+            "process_type": "structured",
+            "config": {"output_type": "reasoning"},
+        }
+    )
+    task_id = task["id"]
+    store.replace_preview_items(
+        task_id,
+        [
+            {
+                "source_file_id": None,
+                "original_content": "需要推理的来源内容",
+                "edited_content": "需要推理的来源内容",
+                "status": "manual",
+            }
+        ],
+    )
+    started = store.start_generation(task_id, replace_existing=True)
+
+    data_process_endpoint._run_generation(
+        store,
+        task_id,
+        started["generation_run_id"],
+    )
+
+    failed = store.get_task(task_id)
+    assert failed["status"] == "failed"
+    assert failed["failure_reason"] == "思维链输出必须配置可用的数据生成模型"
+
+
 def test_result_status_cannot_be_forged_by_client(tmp_path: Path) -> None:
     client, _, _ = make_client(tmp_path)
     task_id = client.post(

backend/tests/test_data_process_generation.py

@@ -86,6 +86,119 @@ def test_generate_model_records_uses_prompt_auth_and_stable_split() -> None:
     assert progress_updates == [(1, 1)]
 
 
+def test_generate_model_records_builds_reasoning_output_with_think_tags() -> None:
+    def handler(request: httpx.Request) -> httpx.Response:
+        payload = json.loads(request.content)
+        system_prompt = payload["messages"][0]["content"]
+        assert '"reasoning":"...","answer":"..."' in system_prompt
+        assert "系统会在保存时统一组装" in system_prompt
+        content = "<think>模型接口自己的分析</think>" + json.dumps(
+            {
+                "items": [
+                    {
+                        "instruction": "计算两项费用合计",
+                        "input": "交通费 30 元，餐费 20 元",
+                        "reasoning": "先识别两项费用，再计算 30 + 20。",
+                        "answer": "合计 50 元。",
+                    }
+                ]
+            },
+            ensure_ascii=False,
+        )
+        return httpx.Response(
+            200,
+            json={"choices": [{"message": {"content": content}}]},
+        )
+
+    records = generate_model_records(
+        [{"id": "preview-reasoning", "edited_content": "交通费 30 元，餐费 20 元"}],
+        model={"name": "model", "api_url": "https://model.example/v1"},
+        config={"output_type": "reasoning"},
+        task_id="task-reasoning",
+        split={"train": 100, "validation": 0, "test": 0},
+        qa_pairs_per_item=1,
+        client=httpx.Client(transport=httpx.MockTransport(handler)),
+    )
+
+    assert records[0]["status"] == "valid"
+    assert records[0]["output"] == (
+        "<think>\n先识别两项费用,再计算 30 + 20。\n</think>\n合计 50 元。"
+    )
+
+
+def test_generate_model_records_marks_reasoning_without_reasoning_field_invalid() -> None:
+    response = {
+        "choices": [
+            {
+                "message": {
+                    "content": json.dumps(
+                        {
+                            "items": [
+                                {
+                                    "instruction": "问题",
+                                    "answer": "只有最终答案",
+                                }
+                            ]
+                        },
+                        ensure_ascii=False,
+                    )
+                }
+            }
+        ]
+    }
+    client = httpx.Client(
+        transport=httpx.MockTransport(lambda _: httpx.Response(200, json=response))
+    )
+
+    records = generate_model_records(
+        [{"id": "preview-missing-reasoning", "edited_content": "来源正文"}],
+        model={"name": "model", "api_url": "https://model.example/v1"},
+        config={"output_type": "reasoning"},
+        task_id="task-missing-reasoning",
+        split={"train": 100, "validation": 0, "test": 0},
+        qa_pairs_per_item=1,
+        client=client,
+    )
+
+    assert records[0]["status"] == "invalid"
+    assert records[0]["output"] == "只有最终答案"
+    assert "reasoning" in records[0]["error"]
+
+
+def test_standard_output_removes_model_think_block() -> None:
+    content = json.dumps(
+        {
+            "items": [
+                {
+                    "instruction": "问题",
+                    "output": "<think>不应保存的分析</think>最终答案",
+                }
+            ]
+        },
+        ensure_ascii=False,
+    )
+    client = httpx.Client(
+        transport=httpx.MockTransport(
+            lambda _: httpx.Response(
+                200,
+                json={"choices": [{"message": {"content": content}}]},
+            )
+        )
+    )
+
+    records = generate_model_records(
+        [{"id": "preview-standard", "edited_content": "来源正文"}],
+        model={"name": "model", "api_url": "https://model.example/v1"},
+        config={"output_type": "standard"},
+        task_id="task-standard",
+        split={"train": 100, "validation": 0, "test": 0},
+        qa_pairs_per_item=1,
+        client=client,
+    )
+
+    assert records[0]["output"] == "最终答案"
+
+
 def test_generate_model_records_keeps_partial_failure_for_manual_repair() -> None:
     client = httpx.Client(
         transport=httpx.MockTransport(
@@ -267,3 +380,15 @@ def test_generate_model_records_rejects_out_of_range_count(
             split={"train": 100, "validation": 0, "test": 0},
             qa_pairs_per_item=qa_pairs_per_item,
         )
+
+
+def test_generate_model_records_rejects_unknown_output_type() -> None:
+    with pytest.raises(ModelGenerationError, match="output_type"):
+        generate_model_records(
+            [],
+            model={"name": "model", "api_url": "https://model.example/v1"},
+            config={"output_type": "unknown"},
+            task_id="task-invalid-output",
+            split={"train": 100, "validation": 0, "test": 0},
+            qa_pairs_per_item=1,
+        )

backend/tests/test_data_process_store.py

@@ -15,6 +15,7 @@ from app.modules.data_process.store import (
     InvalidStateError,
     _decode_row,
     _preview_config_changed,
+    _reasoning_output_is_valid,
     _source_storage_descriptor,
 )
 
@@ -114,14 +115,25 @@ class _PublishConnection:
             )
         if normalized.startswith("INSERT INTO dataset_records"):
             self.records.append(
-                {"dataset_id": params[1], "line_no": params[4], "split": params[5]}
+                {
+                    "dataset_id": params[1],
+                    "line_no": params[4],
+                    "split": params[5],
+                    "output": params[8],
+                    "raw": json.loads(params[9]),
+                }
             )
         return _Result()
 
 
 class _PublishStore(DataProcessStore):
-    def __init__(self, conn: _PublishConnection):
+    def __init__(
+        self,
+        conn: _PublishConnection,
+        task_config: dict[str, Any] | None = None,
+    ):
         self._conn = conn
+        self._task_config = task_config or {}
 
     @contextmanager
     def connect(self) -> Iterator[_PublishConnection]:
@@ -135,7 +147,7 @@ class _PublishStore(DataProcessStore):
             "id": task_id,
             "status": "completed",
             "description": "",
-            "config": {},
+            "config": self._task_config,
             "output_dataset_id": train_dataset and train_dataset["id"],
         }
 
@@ -967,7 +979,11 @@ def test_publish_creates_three_independent_datasets_with_exact_counts() -> None:
             "status": "valid",
             "instruction": f"问题 {index}",
             "input": "",
-            "output": f"答案 {index}",
+            "output": (
+                "<think>\n先读取制度条款。\n</think>\n答案 0"
+                if index == 0
+                else f"答案 {index}"
+            ),
             "preview_item_id": f"preview-{index}",
         }
         for index in range(28)
@@ -993,6 +1009,10 @@ def test_publish_creates_three_independent_datasets_with_exact_counts() -> None:
         item["id"] for item in conn.datasets
     }
     assert len(conn.records) == 28
+    reasoning_record = next(
+        item for item in conn.records if item["output"].startswith("<think>")
+    )
+    assert reasoning_record["raw"]["output"] == reasoning_record["output"]
     assert published["dataset"]["type"] == "train"
     assert len(published["datasets"]) == 3
     assert published["split_counts"] == {"train": 22, "validation": 3, "test": 3}
@@ -1078,6 +1098,49 @@ def test_publish_keeps_all_three_datasets_when_a_small_split_is_empty() -> None:
     assert len(conn.files) == 3
 
 
+@pytest.mark.parametrize(
+    ("output", "expected"),
+    [
+        ("<think>\n推理步骤\n</think>\n最终答案", True),
+        ("<think></think>\n最终答案", False),
+        ("<think>只有推理</think>", False),
+        ("没有标签的最终答案", False),
+        ("<think>外层<think>嵌套</think></think>答案", False),
+    ],
+)
+def test_reasoning_output_validator_requires_one_complete_pair(
+    output: str,
+    expected: bool,
+) -> None:
+    assert _reasoning_output_is_valid(output) is expected
+
+
+def test_publish_rejects_invalid_reasoning_output_format() -> None:
+    conn = _PublishConnection(
+        [
+            {
+                "id": "result-reasoning-invalid",
+                "status": "valid",
+                "instruction": "需要推理的问题",
+                "input": "",
+                "output": "只有最终答案",
+                "preview_item_id": "preview-reasoning-invalid",
+            }
+        ]
+    )
+
+    with pytest.raises(InvalidStateError, match="1 invalid results"):
+        _PublishStore(conn, {"output_type": "reasoning"}).publish(
+            "task-reasoning-invalid",
+            {
+                "dataset_name": "无效思维链",
+                "storage_type": "local",
+                "format": "alpaca_jsonl",
+                "split": {"train": 80, "validation": 10, "test": 10},
+            },
+        )
+
+
 def test_source_storage_descriptor_accepts_owned_local_and_legacy_db_references() -> None:
     task_id = "dpt_task"
     source_file_id = "dpsf_source"

frontend/scripts/regression-data-process-detail.mjs

@@ -86,6 +86,8 @@ assert.doesNotMatch(detailSource, /^\s*max-width:\s*\d+px/m, '详情页不应使
 assert.match(detailSource, /!\/\(\?:password\|secret\|token\|api_key\)\/i\.test\(key\)/, '处理配置没有过滤敏感凭据字段')
 assert.match(detailSource, /key !== 'generation_model_snapshot'/, '处理配置仍直接展示内部模型快照')
 assert.match(detailSource, /preprocessOptionLabelMap/, '处理配置没有把预处理内部枚举转换为中文')
+assert.match(detailSource, /output_type:\s*'输出类型'/, '处理配置没有显示输出类型名称')
+assert.match(detailSource, /value === 'reasoning' \? '思维链回答' : '标准回答'/, '处理配置没有转换输出类型枚举')
 assert.match(detailSource, /const parsed = typeof value === 'number' \? value : Number\(value\)/, '详情页不兼容 PostgreSQL 数字字符串')
 assert.match(detailSource, /new Date\(startTime\.value\)\.getTime\(\)/, '详情页没有在后端耗时缺失时按开始、完成时间回算')
 assert.match(detailSource, /outputCount \+ numeric\(detail\.value\?\.filtered_count\)/, '结果保留率没有使用输出和过滤结果的同口径分母')

frontend/scripts/regression-data-process-wizard.mjs

@@ -452,6 +452,7 @@ assert.match(apiSource, /regenerateDataProcessTask[\s\S]*?\/regenerate`/, '重
 for (const field of [
   'generationModelId',
   'generationPrompt',
+  'outputType',
   'qualityFilterEnabled',
   'filterLowQuality',
   'filterShortContent',
@@ -472,11 +473,14 @@ assert.match(modelSelectionSource, /class="form-section"/, '大模型选择步
 assert.doesNotMatch(modelSelectionSource, /max-width:\s*980px/, '大模型选择步骤不应使用比第一步更窄的固定内容宽度')
 assert.match(taskSetupFeatureSource, /section="quality"/, '质量筛选没有保留在生成选项分类中')
 assert.doesNotMatch(generationControlSource, /<h4>大模型<\/h4>/, '大模型不应继续作为生成选项内部子分类')
-for (const label of ['大模型', '数据生成模型', '默认提示语', '质量筛选', '过滤低质量内容', '过滤过短内容', '最少字数']) {
+for (const label of ['大模型', '数据生成模型', '默认提示语', '输出类型', '标准回答', '思维链回答', '质量筛选', '过滤低质量内容', '过滤过短内容', '最少字数']) {
   assert.ok(generationControlSource.includes(label), `生成控制界面缺少：${label}`)
 }
 assert.match(generationControlSource, /filterable/, '数据生成模型下拉必须支持搜索')
 assert.match(generationControlSource, /maxlength="500"/, '默认提示语缺少合理的长度限制')
+assert.match(generationControlSource, /aria-label="输出类型"/, '输出类型选项缺少可访问名称')
+assert.match(generationControlSource, /&lt;think&gt;推理过程&lt;\/think&gt;/, '思维链选项没有说明最终保存格式')
+assert.match(generationControlSource, /\.output-type-options[\s\S]*?min-height:\s*44px/, '输出类型选项的点击区域不足 44px')
 assert.match(generationControlSource, /\.model-field\s*\{[\s\S]*?display:\s*flex[\s\S]*?flex-direction:\s*column/, '大模型字段没有使用稳定的纵向表单布局')
 assert.match(generationControlSource, /\.generation-config-group\s*\{[\s\S]*?border:\s*1px solid #e2e5ec/, '大模型配置没有保留统一配置面板边框')
 assert.match(generationControlSource, /\.model-config-group\s*\{[\s\S]*?padding:\s*0[\s\S]*?border:\s*0/, '独立大模型步骤仍存在嵌套卡片挤压')
@@ -487,6 +491,7 @@ assert.match(
 )
 assert.match(stateSource, /const DEFAULT_GENERATION_PROMPT\s*=\s*['"][^'"]{40,}['"]/, '大模型配置缺少可直接使用的默认提示语')
 assert.equal((stateSource.match(/generationPrompt:\s*DEFAULT_GENERATION_PROMPT/g) || []).length, 2, '结构化与非结构化任务必须共用默认提示语')
+assert.equal((stateSource.match(/outputType:\s*'standard'/g) || []).length, 2, '结构化与非结构化任务应默认生成标准回答')
 assert.match(generationControlSource, /v-if="options\.qualityFilterEnabled"/, '质量规则没有随总开关渐进显示')
 assert.match(generationControlSource, /v-if="options\.filterShortContent"/, '最少字数没有随短内容规则显示')
 assert.match(generationControlSource, /:min="1"[\s\S]*:max="1000"/, '最少字数缺少 1 到 1000 的边界限制')
@@ -659,6 +664,7 @@ for (const [backendField, frontendField] of [
   ['semantic_enrichment', 'semanticEnrichment'],
   ['generation_model_id', 'generationModelId'],
   ['generation_prompt', 'generationPrompt'],
+  ['output_type', 'outputType'],
   ['temperature', 'temperature'],
   ['max_tokens', 'maxTokens'],
   ['json_mode', 'jsonMode'],
@@ -702,6 +708,7 @@ assert.match(stateSource, /Object\.prototype\.hasOwnProperty\.call\(config, key\
 assert.match(stateSource, /Number\.isFinite\(value\) \? value : fallback/, '配置反向映射没有保留合法数字 0')
 assert.match(stateSource, /qaPairsPerRow:\s*normalizeQaPairsGenerationCount\([\s\S]*?qa_pairs_per_row[\s\S]*?defaults\.qaPairsPerRow/, '结构化生成数量回填没有按 1 到 50 归一化')
 assert.match(stateSource, /qaPairsPerChunk:\s*normalizeQaPairsGenerationCount\([\s\S]*?qa_pairs_per_chunk[\s\S]*?defaults\.qaPairsPerChunk/, '非结构化生成数量回填没有按 1 到 50 归一化')
+assert.match(stateSource, /outputType:\s*configValue\(config, 'output_type', defaults\.outputType\) === 'reasoning'[\s\S]*?\? 'reasoning'[\s\S]*?: 'standard'/, '输出类型没有从任务配置安全回填')
 assert.match(stateSource, /createStructuredOptionsFromConfig/, '结构化配置缺少后端到表单的反向映射')
 assert.match(stateSource, /createUnstructuredOptionsFromConfig/, '非结构化配置缺少后端到表单的反向映射')
 assert.match(stateSource, /configValue<unknown>\(config, 'preprocess_options', \[\]\)/, '历史任务缺少预处理配置时必须按后端空列表语义回填')
@@ -778,6 +785,7 @@ for (const field of [
   'datasetSplit',
   'generationModelId',
   'generationPrompt',
+  'outputType',
   'qualityFilterEnabled',
   'filterLowQuality',
   'filterShortContent',

frontend/src/types/dataProcess.ts

@@ -4,6 +4,7 @@ export type DataProcessStatus = 'pending' | 'running' | 'completed' | 'failed' |
 export type DataProcessType = 'structured' | 'unstructured' | 'external'
 export type DataProcessResultStatus = 'valid' | 'modified' | 'invalid'
 export type DataProcessSplit = 'train' | 'validation' | 'test'
+export type DataProcessOutputType = 'standard' | 'reasoning'
 
 export interface DataProcessPage<T> {
   items: T[]
@@ -20,6 +21,7 @@ export interface DataProcessDatasetSplit {
 
 export type DataProcessConfig = Record<string, unknown> & {
   dataset_split?: DataProcessDatasetSplit
+  output_type?: DataProcessOutputType
 }
 
 export interface DataProcessTask {

frontend/src/views/data-process/DataProcessCreateView.vue

@@ -195,13 +195,13 @@ function toBackendConfig(): DataProcessConfig {
   const options = processType.value === 'unstructured'
     ? unstructuredOptions.value
     : structuredOptions.value
-
   const common = {
     preprocess_options: [...options.preprocessOptions],
     semantic_enrichment: options.semanticEnrichment,
     dataset_split: { ...options.datasetSplit },
     generation_model_id: options.generationModelId,
     generation_prompt: options.generationPrompt,
+    output_type: options.outputType,
     temperature: options.temperature,
     max_tokens: options.maxTokens,
     json_mode: options.jsonMode,

frontend/src/views/data-process/DataProcessDetailView.vue

@@ -70,6 +70,7 @@ const configLabelMap: Record<string, string> = {
   dataset_split: '数据集划分',
   generation_model_id: '数据生成模型',
   generation_prompt: '生成提示语',
+  output_type: '输出类型',
   temperature: '生成温度',
   max_tokens: '最大输出长度',
   json_mode: 'JSON 输出',
@@ -245,6 +246,9 @@ function formatConfigValue(key: string, value: unknown) {
   if (key === 'chunk_method' && typeof value === 'string') {
     return chunkMethodLabelMap[value] || value
   }
+  if (key === 'output_type') {
+    return value === 'reasoning' ? '思维链回答' : '标准回答'
+  }
   if (key === 'dataset_split' && value && typeof value === 'object') {
     const split = value as Partial<DataProcessDatasetSplit>
     return `训练集 ${split.train ?? 0}% / 验证集 ${split.validation ?? 0}% / 测试集 ${split.test ?? 0}%`

frontend/src/views/data-process/create/GenerationOptionsPanel.vue

@@ -30,6 +30,10 @@ function updateQualityRules(value: Array<string | number>) {
   })
 }
 
+function updateOutputType(value: string | number | boolean | undefined) {
+  updateField('outputType', value === 'reasoning' ? 'reasoning' : 'standard')
+}
+
 const selectedQualityRules = () => [
   props.options.filterLowQuality ? 'low_quality' : '',
   props.options.filterShortContent ? 'short_content' : '',
@@ -106,6 +110,28 @@ function modelMeta(model: ModelItem) {
       </div>
     </div>
 
+    <div class="model-field output-type-field">
+      <div class="field-copy">
+        <strong>输出类型</strong>
+        <small>控制答案是否包含可用于推理模型训练的思维链内容</small>
+      </div>
+      <el-radio-group
+        class="output-type-options"
+        :model-value="options.outputType"
+        aria-label="输出类型"
+        @update:model-value="updateOutputType"
+      >
+        <el-radio-button value="standard">标准回答</el-radio-button>
+        <el-radio-button value="reasoning">思维链回答</el-radio-button>
+      </el-radio-group>
+      <p class="output-type-hint">
+        <template v-if="options.outputType === 'reasoning'">
+          生成结果将按 <code>&lt;think&gt;推理过程&lt;/think&gt;</code> 加最终答案的格式保存。
+        </template>
+        <template v-else>仅保存最终答案，不包含推理过程。</template>
+      </p>
+    </div>
+
     <div class="advanced-settings-grid">
       <label class="config-field">
         <span class="config-field-label">生成温度 (Temperature)</span>
@@ -304,6 +330,31 @@ function modelMeta(model: ModelItem) {
   }
 }
 
+.output-type-options {
+  align-self: flex-start;
+
+  :deep(.el-radio-button__inner) {
+    min-height: 44px;
+    padding: 13px 22px;
+  }
+}
+
+.output-type-hint {
+  min-height: 20px;
+  margin: 0;
+  color: #667085;
+  font-size: 12px;
+  line-height: 1.6;
+
+  code {
+    padding: 2px 5px;
+    color: #5b50f2;
+    background: #f0f0ff;
+    border-radius: 4px;
+    font-family: inherit;
+  }
+}
+
 .advanced-settings-grid {
   display: grid;
   grid-template-columns: repeat(2, minmax(0, 1fr));

frontend/src/views/data-process/create/dataProcessCreateState.ts

@@ -9,7 +9,7 @@ import type {
 } from './types'
 import { normalizeQaPairsGenerationCount } from './types'
 
-export const DEFAULT_GENERATION_PROMPT = '你是一名专业的数据生成助手。请根据输入内容生成准确、完整、可直接用于模型训练的问答数据。仅输出符合目标格式的内容，答案应事实清晰、语言自然，不要添加分析过程、说明或无关内容。'
+export const DEFAULT_GENERATION_PROMPT = '你是一名专业的数据生成助手。请根据输入内容生成准确、完整、可直接用于模型训练的问答数据。仅输出符合所选输出类型和目标格式的内容，答案应事实清晰、语言自然，不要添加无关说明。'
 
 export function createDefaultStructuredOptions(): StructuredProcessOptions {
   return {
@@ -19,6 +19,7 @@ export function createDefaultStructuredOptions(): StructuredProcessOptions {
     datasetSplit: { train: 80, validation: 10, test: 10 },
     generationModelId: '',
     generationPrompt: DEFAULT_GENERATION_PROMPT,
+    outputType: 'standard',
     temperature: 0.7,
     maxTokens: 1024,
     jsonMode: false,
@@ -52,6 +53,7 @@ export function createDefaultUnstructuredOptions(): UnstructuredProcessOptions {
     datasetSplit: { train: 80, validation: 10, test: 10 },
     generationModelId: '',
     generationPrompt: DEFAULT_GENERATION_PROMPT,
+    outputType: 'standard',
     temperature: 0.7,
     maxTokens: 1024,
     jsonMode: false,
@@ -93,6 +95,9 @@ function generationOptionsFromConfig(
   return {
     generationModelId: configValue(config, 'generation_model_id', defaults.generationModelId),
     generationPrompt: String(configValue(config, 'generation_prompt', defaults.generationPrompt)),
+    outputType: configValue(config, 'output_type', defaults.outputType) === 'reasoning'
+      ? 'reasoning'
+      : 'standard',
     temperature: numberValue(config, 'temperature', defaults.temperature),
     maxTokens: numberValue(config, 'max_tokens', defaults.maxTokens),
     jsonMode: Boolean(configValue(config, 'json_mode', defaults.jsonMode)),
@@ -218,6 +223,7 @@ export function generationAffectingOptionsFor(
     datasetSplit: options.datasetSplit,
     generationModelId: options.generationModelId,
     generationPrompt: options.generationPrompt,
+    outputType: options.outputType,
     temperature: options.temperature,
     maxTokens: options.maxTokens,
     jsonMode: options.jsonMode,

frontend/src/views/data-process/create/types.ts

@@ -1,4 +1,4 @@
-import type { DataProcessPreviewFileStatus } from '@/types/dataProcess'
+import type { DataProcessOutputType, DataProcessPreviewFileStatus } from '@/types/dataProcess'
 
 export type ProcessType = 'structured' | 'unstructured' | 'external'
 
@@ -32,6 +32,7 @@ export interface DatasetSplitOptions {
 export interface GenerationControlOptions {
   generationModelId: string | number | ''
   generationPrompt: string
+  outputType: DataProcessOutputType
   temperature: number
   maxTokens: number
   jsonMode: boolean