feat(synthesis): add functionality to archive synthesis tasks to existing datasets (#132)

frontend/src/pages/SynthesisTask/components/SynthesisTaskTab.tsx

@@ -16,7 +16,9 @@ import { formatDateTime } from "@/utils/unit";
 import {
   querySynthesisTasksUsingGet,
   deleteSynthesisTaskByIdUsingDelete,
+  archiveSynthesisTaskToDatasetUsingPost,
 } from "@/pages/SynthesisTask/synthesis-api";
+import { createDatasetUsingPost } from "@/pages/DataManagement/dataset.api";
 
 interface SynthesisTask {
   id: string;
@@ -183,6 +185,23 @@ export default function SynthesisTaskTab() {
               icon={<EyeOutlined />}
             />
           </Tooltip>
+          <Tooltip title="归档到数据集">
+            <Button
+              type="text"
+              className="hover:bg-green-50 p-1 h-7 w-7"
+              onClick={() => {
+                Modal.confirm({
+                  title: "确认归档该合成任务?",
+                  content: `任务名称：${task.name}`,
+                  okText: "归档",
+                  cancelText: "取消",
+                  onOk: () => handleArchiveTask(task),
+                });
+              }}
+            >
+              归档
+            </Button>
+          </Tooltip>
           <Tooltip title="删除任务">
             <Button
               danger
@@ -191,7 +210,7 @@ export default function SynthesisTaskTab() {
               icon={<DeleteOutlined />}
               onClick={() => {
                 Modal.confirm({
-                  title: `确认删除任务？`,
+                  title: `确认删除任务?`,
                   content: `任务名：${task.name}`,
                   okText: "删除",
                   okType: "danger",
@@ -214,6 +233,37 @@ export default function SynthesisTaskTab() {
     },
   ];
 
+  const handleArchiveTask = async (task: SynthesisTask) => {
+    try {
+      // 1. 创建目标数据集（使用简单的默认命名 + 随机后缀，可后续扩展为弹窗自定义）
+      const randomSuffix = Math.random().toString(36).slice(2, 8);
+      const datasetReq = {
+        name: `${task.name}-合成数据留用${randomSuffix}`,
+        description: `由合成任务 ${task.id} 留用生成`,
+        datasetType: "TEXT",
+        category: "SYNTHESIS",
+        format: "JSONL",
+        status: "DRAFT",
+      } as any;
+      const datasetRes = await createDatasetUsingPost(datasetReq);
+      const datasetId = datasetRes?.data?.id;
+      if (!datasetId) {
+        message.error("创建数据集失败");
+        return;
+      }
+
+      // 2. 调用后端归档接口，将合成数据写入该数据集
+      await archiveSynthesisTaskToDatasetUsingPost(task.id, datasetId);
+
+      message.success("归档成功");
+      // 3. 可选：跳转到数据集详情页
+      navigate(`/data/management/detail/${datasetId}`);
+    } catch (e) {
+      console.error(e);
+      message.error("归档失败");
+    }
+  };
+
   return (
     <div className="space-y-4">
       {/* 搜索和筛选 */}

frontend/src/pages/SynthesisTask/synthesis-api.ts

@@ -45,3 +45,8 @@ export function querySynthesisDataByChunkUsingGet(chunkId: string) {
 export function getPromptByTypeUsingGet(synthType: string) {
   return get(`/api/synthesis/gen/prompt`, { synth_type: synthType });
 }
+
+// 将合成任务数据归档到已存在的数据集中
+export function archiveSynthesisTaskToDatasetUsingPost(taskId: string, datasetId: string) {
+  return post(`/api/synthesis/gen/task/${taskId}/export-dataset/${datasetId}`);
+}

runtime/datamate-python/app/module/generation/interface/generation_api.py

@@ -28,6 +28,7 @@ from app.module.generation.schema.generation import (
 )
 from app.module.generation.service.generation_service import GenerationService
 from app.module.generation.service.prompt import get_prompt
+from app.module.generation.service.export_service import SynthesisDatasetExporter, SynthesisExportError
 from app.module.shared.schema import StandardResponse
 
 router = APIRouter(
@@ -443,3 +444,35 @@ async def list_synthesis_data_by_chunk(
         message="Success",
         data=items,
     )
+
+
+@router.post("/task/{task_id}/export-dataset/{dataset_id}", response_model=StandardResponse[str])
+async def export_synthesis_task_to_dataset(
+    task_id: str,
+    dataset_id: str,
+    db: AsyncSession = Depends(get_db),
+):
+    """将指定合成任务的全部合成数据归档到已有数据集中。
+
+    规则：
+    - 以原始文件为维度，每个原始文件生成一个 JSONL 文件；
+    - JSONL 文件名称与原始文件名称完全一致；
+    - 仅写入文件，不再创建数据集。
+    """
+    exporter = SynthesisDatasetExporter(db)
+    try:
+        dataset = await exporter.export_task_to_dataset(task_id, dataset_id)
+    except SynthesisExportError as e:
+        logger.error(
+            "Failed to export synthesis task %s to dataset %s: %s",
+            task_id,
+            dataset_id,
+            e,
+        )
+        raise HTTPException(status_code=400, detail=str(e))
+
+    return StandardResponse(
+        code=200,
+        message="success",
+        data=dataset.id,
+    )

runtime/datamate-python/app/module/generation/service/export_service.py

@@ -0,0 +1,160 @@
+import datetime
+import json
+import os
+import time
+from typing import Iterable, List, Sequence, cast
+
+from sqlalchemy import select
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.core.logging import get_logger
+from app.db.models.data_synthesis import (
+    DataSynthesisInstance,
+    DataSynthesisFileInstance,
+    SynthesisData,
+)
+from app.db.models.dataset_management import Dataset, DatasetFiles
+
+logger = get_logger(__name__)
+
+
+class SynthesisExportError(Exception):
+    """Raised when exporting synthesis data to dataset fails."""
+
+
+class SynthesisDatasetExporter:
+    """Export synthesis data of a task into an existing dataset.
+
+    Export rules:
+    - Dimension: original file (DatasetFiles)
+    - One JSONL file per original file
+    - JSONL file name is exactly the same as the original file name
+    """
+
+    def __init__(self, db: AsyncSession):
+        self._db = db
+
+    async def export_task_to_dataset(
+        self,
+        task_id: str,
+        dataset_id: str,
+    ) -> Dataset:
+        """Export the full synthesis data of the given task into an existing dataset.
+
+        Optimized to process one file at a time to reduce memory usage.
+        """
+        task = await self._db.get(DataSynthesisInstance, task_id)
+        if not task:
+            raise SynthesisExportError(f"Synthesis task {task_id} not found")
+
+        dataset = await self._db.get(Dataset, dataset_id)
+        if not dataset:
+            raise SynthesisExportError(f"Dataset {dataset_id} not found")
+
+        file_instances = await self._load_file_instances(task_id)
+        if not file_instances:
+            raise SynthesisExportError("No synthesis file instances found for task")
+
+        base_path = self._ensure_dataset_path(dataset)
+
+        created_files: list[DatasetFiles] = []
+        total_size = 0
+
+        # 一个文件一个文件处理，避免一次性加载所有合成数据
+        for file_instance in file_instances:
+            records = await self._load_synthesis_data_for_file(file_instance.id)
+            if not records:
+                continue
+
+            # 归档文件名称：原始文件名称.xxx -> 原始文件名称.jsonl
+            original_name = file_instance.file_name or "unknown"
+            base_name, _ = os.path.splitext(original_name)
+            archived_file_name = f"{base_name}.jsonl"
+
+            file_path = os.path.join(base_path, archived_file_name)
+            os.makedirs(os.path.dirname(file_path), exist_ok=True)
+            self._write_jsonl(file_path, records)
+
+            # 计算文件大小
+            try:
+                file_size = os.path.getsize(file_path)
+            except OSError:
+                file_size = 0
+
+            df = DatasetFiles(
+                dataset_id=dataset.id,
+                file_name=archived_file_name,
+                file_path=file_path,
+                file_type="jsonl",
+                file_size=file_size,
+                last_access_time=datetime.datetime.now(datetime.UTC),
+            )
+            self._db.add(df)
+            created_files.append(df)
+            total_size += file_size
+
+        # 更新数据集的文件数、总大小和状态
+        if created_files:
+            dataset.file_count = (dataset.file_count or 0) + len(created_files)
+            dataset.size_bytes = (dataset.size_bytes or 0) + total_size
+            dataset.status = "ACTIVE"
+
+        await self._db.commit()
+
+        logger.info(
+            "Exported synthesis task %s to dataset %s with %d files (total %d bytes)",
+            task_id,
+            dataset.id,
+            len(created_files),
+            total_size,
+        )
+
+        return dataset
+
+    async def _load_file_instances(self, task_id: str) -> Sequence[DataSynthesisFileInstance]:
+        result = await self._db.execute(
+            select(DataSynthesisFileInstance).where(
+                DataSynthesisFileInstance.synthesis_instance_id == task_id
+            )
+        )
+        return result.scalars().all()
+
+    async def _load_synthesis_data_for_file(
+        self, file_instance_id: str
+    ) -> List[dict]:
+        """Load all synthesis data records for a single file instance.
+
+        Each returned item is a plain JSON-serialisable dict based on SynthesisData.data.
+        """
+        result = await self._db.execute(
+            select(SynthesisData).where(
+                SynthesisData.synthesis_file_instance_id == file_instance_id
+            )
+        )
+        rows: Sequence[SynthesisData] = result.scalars().all()
+
+        records: List[dict] = []
+        for row in rows:
+            payload = row.data or {}
+            records.append(payload)
+        return records
+
+    @staticmethod
+    def _write_jsonl(path: str, records: Iterable[dict]) -> None:
+        with open(path, "w", encoding="utf-8") as f:
+            for record in records:
+                f.write(json.dumps(record, ensure_ascii=False))
+                f.write("\n")
+
+    @staticmethod
+    def _ensure_dataset_path(dataset: Dataset) -> str:
+        """Ensure dataset.path is available and the directory exists.
+
+        The actual value of dataset.path should come from Dataset's default
+        path generation logic or external configuration, not from the
+        synthesis task's result_data_location.
+        """
+        if not dataset.path:
+            raise SynthesisExportError("Dataset path is empty")
+        os.makedirs(dataset.path, exist_ok=True)
+        return dataset.path

runtime/datamate-python/app/module/generation/service/prompt.py

@@ -20,7 +20,7 @@ QA_PROMPT="""# 角色
 5.  **答案质量**：答案应准确、简洁、完整。
 
 # 输出格式
-请严格按照以下JSON格式输出，确保没有额外的解释或标记:
+请严格按照以下JSON格式输出，保持字段顺序，确保没有额外的解释或标记:
 [
 {{"instruction": "问题1","input": "参考内容1","output": "答案1"}},
 {{"instruction": "问题2","input": "参考内容1","output": "答案2"}},
@@ -53,7 +53,7 @@ COT_PROMPT="""# 角色
     * 请根据输入文档的主要语言进行提问和回答。
 
 # 输出格式
-请严格按照以下 JSON 格式输出，确保没有额外的解释或标记，每条 COT 数据独立成项：
+请严格按照以下 JSON 格式输出，保持字段顺序，确保没有额外的解释或标记，每条 COT 数据独立成项：
 [
 {{"question": "具体问题","chain_of_thought": "步骤 1：明确问题核心，定位文档中相关信息范围；步骤 2：提取文档中与问题相关的关键信息 1；步骤 3：结合关键信息 1 推导中间结论 1；步骤 4：提取文档中与问题相关的关键信息 2；步骤 5：结合中间结论 1 和关键信息 2 推导中间结论 2；...（逐步推进）；步骤 N：汇总所有中间结论，得出最终结论","conclusion": "简洁准确的最终结论"}},