fix(data-process): 发布三个独立切分数据集

backend/app/db/platform_store.py

@@ -1226,18 +1226,26 @@ class PlatformStore:
 
     def training_dataset_files(self, dataset_id: str) -> list[dict[str, Any]]:
         with self.connect() as conn:
-            dataset = conn.execute("SELECT id FROM datasets WHERE id=?", (dataset_id,)).fetchone()
+            dataset = conn.execute(
+                "SELECT id, metadata FROM datasets WHERE id=?", (dataset_id,)
+            ).fetchone()
             if not dataset:
                 raise KeyError(dataset_id)
+            dataset_metadata = json_loads(dataset.get("metadata"), {})
+            related_ids = dataset_metadata.get("split_dataset_ids") or {}
+            runtime_dataset_ids = [dataset_id]
+            validation_dataset_id = related_ids.get("validation")
+            if dataset_metadata.get("dataset_split") == "train" and validation_dataset_id:
+                runtime_dataset_ids.append(str(validation_dataset_id))
             rows = conn.execute(
                 """
                 SELECT id, dataset_id, name, size, content, active_version_id,
                        create_time, record_count, metadata
                 FROM dataset_files
-                WHERE dataset_id=?
-                ORDER BY create_time
+                WHERE dataset_id = ANY(%s)
+                ORDER BY CASE WHEN dataset_id=%s THEN 0 ELSE 1 END, create_time, id
                 """,
-                (dataset_id,),
+                (runtime_dataset_ids, dataset_id),
             ).fetchall()
             return [
                 {
@@ -1350,6 +1358,18 @@ class PlatformStore:
             raise ValueError("base_model or base_model_id is required")
         if not train_dataset_id:
             raise ValueError("train_dataset_id is required")
+        with self.connect() as conn:
+            train_dataset = conn.execute(
+                "SELECT id, type, metadata FROM datasets WHERE id=?",
+                (train_dataset_id,),
+            ).fetchone()
+        if not train_dataset:
+            raise ValueError("training dataset not found")
+        train_metadata = json_loads(train_dataset.get("metadata"), {})
+        if train_dataset.get("type") != "train" or train_metadata.get(
+            "dataset_split"
+        ) in {"validation", "test"}:
+            raise ValueError("train_dataset_id must reference a training dataset")
         now = utcnow()
         task = {
             "id": task_id,
@@ -1535,7 +1555,24 @@ class PlatformStore:
                    FROM dataset_files WHERE dataset_id=? ORDER BY create_time, id""",
                 (dataset_id,),
             ).fetchall()
+            dataset_metadata = json_loads(dataset.get("metadata"), {}) if dataset else {}
+            related_ids = dataset_metadata.get("split_dataset_ids") or {}
+            validation_dataset_id = related_ids.get("validation")
+            if dataset_metadata.get("dataset_split") == "train" and validation_dataset_id:
+                files = [
+                    *files,
+                    *conn.execute(
+                        """SELECT id, name, size, active_version_id, create_time, metadata
+                           FROM dataset_files WHERE dataset_id=? ORDER BY create_time, id""",
+                        (str(validation_dataset_id),),
+                    ).fetchall(),
+                ]
         model_path = (model and model.get("path")) or task.get("model_name_or_path") or base_model_id
+        dataset_metadata = json_loads(dataset.get("metadata"), {}) if dataset else {}
+        if not dataset or dataset.get("type") != "train" or dataset_metadata.get(
+            "dataset_split"
+        ) in {"validation", "test"}:
+            raise RuntimeError(f"training dataset is invalid: {dataset_id}")
         if not files:
             raise RuntimeError(f"dataset has no uploaded file: {dataset_id}")
         dataset_key = str(task.get("dataset_key") or llama_dataset_key(dataset_id))