first-update

backend/app/api/v1/datasets/__init__.py

@@ -0,0 +1,126 @@
+"""
+Datasets API Router
+"""
+from typing import List, Optional
+from uuid import UUID
+from pydantic import BaseModel
+from fastapi import APIRouter, Depends, HTTPException, Query
+from sqlalchemy.ext.asyncio import AsyncSession
+from sqlalchemy import select, func
+from app.core.database import get_db
+from app.models.models import Dataset, Question
+from app.schemas.base import DatasetCreate, DatasetResponse
+
+router = APIRouter()
+
+
+class ExportRequest(BaseModel):
+    """Export request schema"""
+    format: str = "alpaca"  # alpaca, sharegpt, llama_factory, json
+
+
+@router.get("/", response_model=dict)
+async def list_datasets(project_id: UUID, db: AsyncSession = Depends(get_db)):
+    """List datasets for a project"""
+    result = await db.execute(
+        select(Dataset).where(Dataset.project_id == project_id).order_by(Dataset.created_at.desc())
+    )
+    datasets = result.scalars().all()
+
+    # Get question count for each dataset
+    dataset_list = []
+    for dataset in datasets:
+        dataset_data = DatasetResponse.model_validate(dataset)
+        # TODO: Count questions in dataset
+        dataset_data.question_count = 0
+        dataset_list.append(dataset_data)
+
+    return {"datasets": dataset_list}
+
+
+@router.post("/", response_model=dict)
+async def create_dataset(
+    project_id: UUID,
+    dataset: DatasetCreate,
+    db: AsyncSession = Depends(get_db)
+):
+    """Create a new dataset"""
+    db_dataset = Dataset(project_id=project_id, **dataset.model_dump())
+    db.add(db_dataset)
+    await db.commit()
+    await db.refresh(db_dataset)
+
+    return {"id": str(db_dataset.id)}
+
+
+@router.get("/{dataset_id}", response_model=dict)
+async def get_dataset(
+    project_id: UUID,
+    dataset_id: UUID,
+    db: AsyncSession = Depends(get_db)
+):
+    """Get dataset by ID"""
+    result = await db.execute(
+        select(Dataset).where(Dataset.id == dataset_id, Dataset.project_id == project_id)
+    )
+    dataset = result.scalar_one_or_none()
+    if not dataset:
+        raise HTTPException(status_code=404, detail="Dataset not found")
+
+    return DatasetResponse.model_validate(dataset)
+
+
+@router.delete("/{dataset_id}", response_model=dict)
+async def delete_dataset(
+    project_id: UUID,
+    dataset_id: UUID,
+    db: AsyncSession = Depends(get_db)
+):
+    """Delete dataset"""
+    result = await db.execute(
+        select(Dataset).where(Dataset.id == dataset_id, Dataset.project_id == project_id)
+    )
+    dataset = result.scalar_one_or_none()
+    if not dataset:
+        raise HTTPException(status_code=404, detail="Dataset not found")
+
+    await db.delete(dataset)
+    await db.commit()
+
+    return {"message": "Dataset deleted successfully"}
+
+
+@router.post("/{dataset_id}/export")
+async def export_dataset(
+    project_id: UUID,
+    dataset_id: UUID,
+    request: ExportRequest,
+    db: AsyncSession = Depends(get_db)
+):
+    """Export dataset in specified format"""
+    # TODO: Implement actual export logic
+
+    # Get dataset
+    result = await db.execute(
+        select(Dataset).where(Dataset.id == dataset_id, Dataset.project_id == project_id)
+    )
+    dataset = result.scalar_one_or_none()
+    if not dataset:
+        raise HTTPException(status_code=404, detail="Dataset not found")
+
+    # Get questions for this dataset (placeholder)
+    # In real implementation, would link questions to datasets
+
+    # Return sample data based on format
+    sample_data = [
+        {
+            "instruction": "这是一个示例指令",
+            "input": "",
+            "output": "这是一个示例输出"
+        }
+    ]
+
+    if request.format == "json":
+        return sample_data
+
+    return {"data": sample_data, "format": request.format}