YG-Datasets/backend/app/api/v1/chunks/__init__.py

"""
Chunks API Router
"""
import asyncio
from pathlib import Path
from typing import List, Optional
from uuid import UUID
from pydantic import BaseModel, Field
from fastapi import APIRouter, Depends, HTTPException, Query
from sqlalchemy.ext.asyncio import AsyncSession
from sqlalchemy import select

from app.api.response import ApiResponse, PaginatedResponse
from app.core.database import get_db, AsyncSessionLocal
from app.core.exceptions import NotFoundException
from app.core.crud import CRUDBase
from app.core.logging import log_success, log_failure
from app.models.models import Chunk, File
from app.schemas.chunk import ChunkResponse
from app.schemas.chunk import ChunkCreateSchema, ChunkUpdateSchema
from app.services.text_splitter.splitter import get_splitter
from markitdown import MarkItDown

router = APIRouter()

# Initialize CRUD
chunk_crud = CRUDBase(Chunk)

# Initialize markitdown
markitdown = MarkItDown()


def get_project_ready_dir(project_id: str) -> Path:
    """获取项目的 ready 文件目录"""
    base_dir = Path("/data/code/YG-Datasets/data") / project_id / "ready"
    base_dir.mkdir(parents=True, exist_ok=True)
    return base_dir


class SplitRequest(BaseModel):
    """Request model for splitting text"""
    file_id: UUID
    method: str = "recursive"
    chunk_size: int = Field(500, ge=50, le=5000)
    overlap: int = Field(50, ge=0, le=500)
    separator: Optional[str] = None
    # Embedding 相关参数（用于 semantic_embedding 方法）
    embedding_provider: Optional[str] = Field(None, description="embedding provider: openai, minimax")
    embedding_api_key: Optional[str] = Field(None, description="API key for embedding")
    embedding_base_url: Optional[str] = Field(None, description="API base URL")
    embedding_model: Optional[str] = Field(None, description="Embedding model name")
    # 语义分割参数
    similarity_threshold: float = Field(0.3, ge=0.0, le=1.0, description="Similarity threshold for semantic split")
    min_chunk_size: int = Field(100, ge=10, le=1000, description="Minimum chunk size")


async def process_file_by_type(file: File) -> str:
    """Process file based on its type, convert to markdown"""
    if not file.file_path:
        raise NotFoundException("File", file.id)

    # Supported types for markitdown
    markitdown_types = ["pdf", "docx", "doc", "pptx", "ppt", "xlsx", "xls", "htm", "html"]

    if file.file_type in markitdown_types:
        # Use markitdown to convert to markdown
        loop = asyncio.get_event_loop()
        result = await loop.run_in_executor(
            None,
            lambda: markitdown.convert(file.file_path)
        )
        return result.text_content

    # Return raw text for txt, md files
    loop = asyncio.get_event_loop()
    content = await loop.run_in_executor(
        None,
        lambda: open(file.file_path, 'r', encoding='utf-8').read()
    )
    return content


async def process_split_async(
    project_id: UUID,
    request: SplitRequest,
):
    """Run chunk splitting in background."""
    async with AsyncSessionLocal() as db:
        file = None
        try:
            result = await db.execute(
                select(File).where(File.id == request.file_id, File.project_id == project_id)
            )
            file = result.scalar_one_or_none()
            if not file:
                return

            text = await process_file_by_type(file)

            kwargs = {"chunk_size": request.chunk_size, "overlap": request.overlap}
            if request.method == "custom" and request.separator:
                kwargs["separator"] = request.separator

            if request.method == "semantic_embedding":
                kwargs["embedding_provider_type"] = request.embedding_provider or "openai"
                kwargs["embedding_api_key"] = request.embedding_api_key
                kwargs["embedding_base_url"] = request.embedding_base_url or "https://api.minimax.chat/v1"
                kwargs["embedding_model"] = request.embedding_model or "text-embedding-3-small"
                kwargs["similarity_threshold"] = request.similarity_threshold
                kwargs["min_chunk_size"] = request.min_chunk_size

            splitter = get_splitter(request.method, **kwargs)
            split_results = splitter.split(text)

            await db.execute(
                Chunk.__table__.delete().where(
                    Chunk.project_id == project_id,
                    Chunk.file_id == file.id
                )
            )

            chunks = []
            for chunk_data in split_results:
                db_chunk = Chunk(
                    project_id=project_id,
                    file_id=file.id,
                    name=chunk_data.get("name", f"Chunk {chunk_data['index'] + 1}"),
                    content=chunk_data["content"],
                    word_count=chunk_data.get("word_count", len(chunk_data["content"].split()))
                )
                db.add(db_chunk)
                chunks.append(db_chunk)

            await db.commit()

            ready_dir = get_project_ready_dir(str(project_id))

            # 删除旧的 markdown 文件（可能有两种命名格式）
            old_md_files = list(ready_dir.glob(f"{file.id}*.md"))
            for old_file in old_md_files:
                try:
                    old_file.unlink()
                except Exception:
                    pass

            md_filename = f"{file.id}.md"
            md_path = ready_dir / md_filename

            loop = asyncio.get_event_loop()
            await loop.run_in_executor(
                None,
                lambda: md_path.write_text(text, encoding='utf-8')
            )

            file.file_path = str(md_path)
            file.status = "completed"
            await db.commit()

            log_success(
                "文件分割完成",
                project_id=str(project_id),
                file_id=str(file.id),
                filename=file.filename,
                method=request.method,
                chunk_count=len(chunks),
                text_length=len(text),
                ready_path=str(md_path)
            )
        except Exception as e:
            if file:
                file.status = "failed"
                await db.commit()

            log_failure(
                "文件分割失败",
                project_id=str(project_id),
                file_id=str(request.file_id),
                method=request.method,
                error=str(e)
            )


@router.post("/split", response_model=ApiResponse)
async def split_text(
    project_id: UUID,
    request: SplitRequest,
    db: AsyncSession = Depends(get_db)
):
    """Split text into chunks"""
    try:
        result = await db.execute(
            select(File).where(File.id == request.file_id, File.project_id == project_id)
        )
        file = result.scalar_one_or_none()
        if not file:
            raise NotFoundException("File", request.file_id)

        # 记录开始处理
        log_success(
            "开始处理文件",
            project_id=str(project_id),
            file_id=str(file.id),
            filename=file.filename,
            method=request.method,
            chunk_size=request.chunk_size,
            overlap=request.overlap
        )

        file.status = "processing"
        await db.commit()

        asyncio.create_task(
            process_split_async(
                project_id=project_id,
                request=request,
            )
        )

        return ApiResponse.ok(
            data={"file_id": str(file.id), "status": file.status},
            message="Split task started, processing in background"
        )
    except Exception as e:
        if 'file' in locals() and file:
            file.status = "failed"
            await db.commit()

        log_failure(
            "分割任务启动失败",
            project_id=str(project_id),
            file_id=str(request.file_id),
            error=str(e)
        )
        raise


@router.get("", response_model=ApiResponse)
async def list_chunks(
    project_id: UUID,
    file_id: Optional[UUID] = Query(None),
    page: int = Query(1, ge=1),
    page_size: int = Query(20, ge=1, le=100),
    db: AsyncSession = Depends(get_db)
):
    """List chunks for a project"""
    filters = {"project_id": project_id}
    if file_id:
        filters["file_id"] = file_id

    skip = (page - 1) * page_size
    chunks, total = await chunk_crud.get_multi(
        db,
        skip=skip,
        limit=page_size,
        filters=filters,
        order_by="created_at",
        descending=False
    )

    chunk_responses = [ChunkResponse.model_validate(c) for c in chunks]
    return PaginatedResponse.ok(
        items=chunk_responses,
        page=page,
        page_size=page_size,
        total=total
    )


@router.get("/{chunk_id}", response_model=ApiResponse)
async def get_chunk(
    project_id: UUID,
    chunk_id: UUID,
    db: AsyncSession = Depends(get_db)
):
    """Get chunk by ID"""
    chunk = await chunk_crud.get(db, chunk_id)
    if not chunk or chunk.project_id != project_id:
        raise NotFoundException("Chunk", chunk_id)

    return ApiResponse.ok(data=ChunkResponse.model_validate(chunk))


@router.put("/{chunk_id}", response_model=ApiResponse)
async def update_chunk(
    project_id: UUID,
    chunk_id: UUID,
    chunk: ChunkUpdateSchema,
    db: AsyncSession = Depends(get_db)
):
    """Update chunk"""
    db_chunk = await chunk_crud.get(db, chunk_id)
    if not db_chunk or db_chunk.project_id != project_id:
        raise NotFoundException("Chunk", chunk_id)

    updated_chunk = await chunk_crud.update(db, db_chunk, chunk)
    return ApiResponse.ok(
        data=ChunkResponse.model_validate(updated_chunk),
        message="Chunk updated successfully"
    )


@router.delete("/{chunk_id}", response_model=ApiResponse)
async def delete_chunk(
    project_id: UUID,
    chunk_id: UUID,
    db: AsyncSession = Depends(get_db)
):
    """Delete chunk"""
    chunk = await chunk_crud.get(db, chunk_id)
    if not chunk or chunk.project_id != project_id:
        raise NotFoundException("Chunk", chunk_id)

    await chunk_crud.delete(db, chunk_id)
    return ApiResponse.ok(message="Chunk deleted successfully")