mirror of
https://github.com/MODSetter/SurfSense.git
synced 2025-09-01 10:09:08 +00:00
941 lines
34 KiB
Python
941 lines
34 KiB
Python
# Force asyncio to use standard event loop before unstructured imports
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import asyncio
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from fastapi import APIRouter, BackgroundTasks, Depends, Form, HTTPException, UploadFile
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from litellm import atranscription
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy.future import select
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from sqlalchemy.orm import selectinload
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from app.config import config as app_config
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from app.db import (
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Chunk,
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Document,
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DocumentType,
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Log,
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SearchSpace,
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User,
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get_async_session,
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)
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from app.schemas import (
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DocumentRead,
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DocumentsCreate,
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DocumentUpdate,
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DocumentWithChunksRead,
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)
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from app.services.task_logging_service import TaskLoggingService
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from app.tasks.document_processors import (
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add_crawled_url_document,
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add_extension_received_document,
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add_received_file_document_using_docling,
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add_received_file_document_using_llamacloud,
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add_received_file_document_using_unstructured,
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add_received_markdown_file_document,
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add_youtube_video_document,
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)
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from app.users import current_active_user
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from app.utils.check_ownership import check_ownership
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try:
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asyncio.set_event_loop_policy(asyncio.DefaultEventLoopPolicy())
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except RuntimeError as e:
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print("Error setting event loop policy", e)
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pass
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import os
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os.environ["UNSTRUCTURED_HAS_PATCHED_LOOP"] = "1"
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router = APIRouter()
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@router.post("/documents/")
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async def create_documents(
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request: DocumentsCreate,
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session: AsyncSession = Depends(get_async_session),
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user: User = Depends(current_active_user),
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fastapi_background_tasks: BackgroundTasks = BackgroundTasks(),
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):
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try:
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# Check if the user owns the search space
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await check_ownership(session, SearchSpace, request.search_space_id, user)
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if request.document_type == DocumentType.EXTENSION:
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for individual_document in request.content:
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fastapi_background_tasks.add_task(
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process_extension_document_with_new_session,
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individual_document,
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request.search_space_id,
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str(user.id),
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)
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elif request.document_type == DocumentType.CRAWLED_URL:
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for url in request.content:
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fastapi_background_tasks.add_task(
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process_crawled_url_with_new_session,
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url,
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request.search_space_id,
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str(user.id),
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)
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elif request.document_type == DocumentType.YOUTUBE_VIDEO:
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for url in request.content:
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fastapi_background_tasks.add_task(
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process_youtube_video_with_new_session,
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url,
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request.search_space_id,
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str(user.id),
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)
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else:
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raise HTTPException(status_code=400, detail="Invalid document type")
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await session.commit()
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return {"message": "Documents processed successfully"}
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except HTTPException:
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raise
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except Exception as e:
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await session.rollback()
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raise HTTPException(
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status_code=500, detail=f"Failed to process documents: {e!s}"
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) from e
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@router.post("/documents/fileupload")
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async def create_documents_file_upload(
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files: list[UploadFile],
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search_space_id: int = Form(...),
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session: AsyncSession = Depends(get_async_session),
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user: User = Depends(current_active_user),
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fastapi_background_tasks: BackgroundTasks = BackgroundTasks(),
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):
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try:
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await check_ownership(session, SearchSpace, search_space_id, user)
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if not files:
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raise HTTPException(status_code=400, detail="No files provided")
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for file in files:
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try:
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# Save file to a temporary location to avoid stream issues
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import os
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import tempfile
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# Create temp file
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with tempfile.NamedTemporaryFile(
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delete=False, suffix=os.path.splitext(file.filename)[1]
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) as temp_file:
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temp_path = temp_file.name
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# Write uploaded file to temp file
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content = await file.read()
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with open(temp_path, "wb") as f:
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f.write(content)
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fastapi_background_tasks.add_task(
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process_file_in_background_with_new_session,
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temp_path,
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file.filename,
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search_space_id,
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str(user.id),
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)
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except Exception as e:
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raise HTTPException(
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status_code=422,
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detail=f"Failed to process file {file.filename}: {e!s}",
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) from e
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await session.commit()
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return {"message": "Files uploaded for processing"}
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except HTTPException:
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raise
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except Exception as e:
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await session.rollback()
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raise HTTPException(
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status_code=500, detail=f"Failed to upload files: {e!s}"
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) from e
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@router.get("/documents/", response_model=list[DocumentRead])
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async def read_documents(
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skip: int = 0,
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limit: int = 3000,
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search_space_id: int | None = None,
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session: AsyncSession = Depends(get_async_session),
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user: User = Depends(current_active_user),
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):
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try:
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query = (
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select(Document).join(SearchSpace).filter(SearchSpace.user_id == user.id)
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)
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# Filter by search_space_id if provided
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if search_space_id is not None:
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query = query.filter(Document.search_space_id == search_space_id)
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result = await session.execute(query.offset(skip).limit(limit))
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db_documents = result.scalars().all()
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# Convert database objects to API-friendly format
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api_documents = []
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for doc in db_documents:
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api_documents.append(
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DocumentRead(
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id=doc.id,
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title=doc.title,
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document_type=doc.document_type,
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document_metadata=doc.document_metadata,
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content=doc.content,
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created_at=doc.created_at,
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search_space_id=doc.search_space_id,
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)
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)
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return api_documents
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except Exception as e:
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raise HTTPException(
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status_code=500, detail=f"Failed to fetch documents: {e!s}"
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) from e
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@router.get("/documents/{document_id}", response_model=DocumentRead)
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async def read_document(
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document_id: int,
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session: AsyncSession = Depends(get_async_session),
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user: User = Depends(current_active_user),
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):
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try:
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result = await session.execute(
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select(Document)
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.join(SearchSpace)
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.filter(Document.id == document_id, SearchSpace.user_id == user.id)
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)
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document = result.scalars().first()
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if not document:
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raise HTTPException(
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status_code=404, detail=f"Document with id {document_id} not found"
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)
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# Convert database object to API-friendly format
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return DocumentRead(
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id=document.id,
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title=document.title,
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document_type=document.document_type,
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document_metadata=document.document_metadata,
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content=document.content,
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created_at=document.created_at,
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search_space_id=document.search_space_id,
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)
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except Exception as e:
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raise HTTPException(
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status_code=500, detail=f"Failed to fetch document: {e!s}"
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) from e
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@router.put("/documents/{document_id}", response_model=DocumentRead)
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async def update_document(
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document_id: int,
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document_update: DocumentUpdate,
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session: AsyncSession = Depends(get_async_session),
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user: User = Depends(current_active_user),
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):
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try:
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# Query the document directly instead of using read_document function
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result = await session.execute(
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select(Document)
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.join(SearchSpace)
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.filter(Document.id == document_id, SearchSpace.user_id == user.id)
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)
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db_document = result.scalars().first()
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if not db_document:
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raise HTTPException(
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status_code=404, detail=f"Document with id {document_id} not found"
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)
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update_data = document_update.model_dump(exclude_unset=True)
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for key, value in update_data.items():
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setattr(db_document, key, value)
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await session.commit()
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await session.refresh(db_document)
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# Convert to DocumentRead for response
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return DocumentRead(
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id=db_document.id,
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title=db_document.title,
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document_type=db_document.document_type,
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document_metadata=db_document.document_metadata,
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content=db_document.content,
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created_at=db_document.created_at,
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search_space_id=db_document.search_space_id,
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)
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except HTTPException:
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raise
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except Exception as e:
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await session.rollback()
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raise HTTPException(
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status_code=500, detail=f"Failed to update document: {e!s}"
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) from e
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@router.delete("/documents/{document_id}", response_model=dict)
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async def delete_document(
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document_id: int,
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session: AsyncSession = Depends(get_async_session),
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user: User = Depends(current_active_user),
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):
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try:
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# Query the document directly instead of using read_document function
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result = await session.execute(
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select(Document)
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.join(SearchSpace)
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.filter(Document.id == document_id, SearchSpace.user_id == user.id)
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)
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document = result.scalars().first()
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if not document:
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raise HTTPException(
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status_code=404, detail=f"Document with id {document_id} not found"
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)
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await session.delete(document)
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await session.commit()
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return {"message": "Document deleted successfully"}
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except HTTPException:
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raise
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except Exception as e:
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await session.rollback()
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raise HTTPException(
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status_code=500, detail=f"Failed to delete document: {e!s}"
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) from e
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@router.get("/documents/by-chunk/{chunk_id}", response_model=DocumentWithChunksRead)
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async def get_document_by_chunk_id(
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chunk_id: int,
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session: AsyncSession = Depends(get_async_session),
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user: User = Depends(current_active_user),
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):
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"""
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Retrieves a document based on a chunk ID, including all its chunks ordered by creation time.
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The document's embedding and chunk embeddings are excluded from the response.
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"""
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try:
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# First, get the chunk and verify it exists
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chunk_result = await session.execute(select(Chunk).filter(Chunk.id == chunk_id))
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chunk = chunk_result.scalars().first()
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if not chunk:
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raise HTTPException(
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status_code=404, detail=f"Chunk with id {chunk_id} not found"
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)
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# Get the associated document and verify ownership
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document_result = await session.execute(
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select(Document)
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.options(selectinload(Document.chunks))
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.join(SearchSpace)
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.filter(Document.id == chunk.document_id, SearchSpace.user_id == user.id)
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)
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document = document_result.scalars().first()
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if not document:
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raise HTTPException(
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status_code=404,
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detail="Document not found or you don't have access to it",
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)
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# Sort chunks by creation time
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sorted_chunks = sorted(document.chunks, key=lambda x: x.created_at)
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# Return the document with its chunks
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return DocumentWithChunksRead(
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id=document.id,
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title=document.title,
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document_type=document.document_type,
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document_metadata=document.document_metadata,
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content=document.content,
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created_at=document.created_at,
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search_space_id=document.search_space_id,
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chunks=sorted_chunks,
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)
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except HTTPException:
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raise
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except Exception as e:
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raise HTTPException(
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status_code=500, detail=f"Failed to retrieve document: {e!s}"
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) from e
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async def process_extension_document_with_new_session(
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individual_document, search_space_id: int, user_id: str
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):
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"""Create a new session and process extension document."""
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from app.db import async_session_maker
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from app.services.task_logging_service import TaskLoggingService
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async with async_session_maker() as session:
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# Initialize task logging service
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task_logger = TaskLoggingService(session, search_space_id)
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# Log task start
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log_entry = await task_logger.log_task_start(
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task_name="process_extension_document",
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source="document_processor",
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message=f"Starting processing of extension document from {individual_document.metadata.VisitedWebPageTitle}",
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metadata={
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"document_type": "EXTENSION",
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"url": individual_document.metadata.VisitedWebPageURL,
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"title": individual_document.metadata.VisitedWebPageTitle,
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"user_id": user_id,
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},
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)
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try:
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result = await add_extension_received_document(
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session, individual_document, search_space_id, user_id
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)
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if result:
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await task_logger.log_task_success(
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log_entry,
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f"Successfully processed extension document: {individual_document.metadata.VisitedWebPageTitle}",
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{"document_id": result.id, "content_hash": result.content_hash},
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)
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else:
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await task_logger.log_task_success(
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log_entry,
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f"Extension document already exists (duplicate): {individual_document.metadata.VisitedWebPageTitle}",
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{"duplicate_detected": True},
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)
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except Exception as e:
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await task_logger.log_task_failure(
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log_entry,
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f"Failed to process extension document: {individual_document.metadata.VisitedWebPageTitle}",
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str(e),
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{"error_type": type(e).__name__},
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)
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import logging
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logging.error(f"Error processing extension document: {e!s}")
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|
|
|
|
async def process_crawled_url_with_new_session(
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url: str, search_space_id: int, user_id: str
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):
|
|
"""Create a new session and process crawled URL."""
|
|
from app.db import async_session_maker
|
|
from app.services.task_logging_service import TaskLoggingService
|
|
|
|
async with async_session_maker() as session:
|
|
# Initialize task logging service
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|
task_logger = TaskLoggingService(session, search_space_id)
|
|
|
|
# Log task start
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|
log_entry = await task_logger.log_task_start(
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task_name="process_crawled_url",
|
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source="document_processor",
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message=f"Starting URL crawling and processing for: {url}",
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|
metadata={"document_type": "CRAWLED_URL", "url": url, "user_id": user_id},
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)
|
|
|
|
try:
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|
result = await add_crawled_url_document(
|
|
session, url, search_space_id, user_id
|
|
)
|
|
|
|
if result:
|
|
await task_logger.log_task_success(
|
|
log_entry,
|
|
f"Successfully crawled and processed URL: {url}",
|
|
{
|
|
"document_id": result.id,
|
|
"title": result.title,
|
|
"content_hash": result.content_hash,
|
|
},
|
|
)
|
|
else:
|
|
await task_logger.log_task_success(
|
|
log_entry,
|
|
f"URL document already exists (duplicate): {url}",
|
|
{"duplicate_detected": True},
|
|
)
|
|
except Exception as e:
|
|
await task_logger.log_task_failure(
|
|
log_entry,
|
|
f"Failed to crawl URL: {url}",
|
|
str(e),
|
|
{"error_type": type(e).__name__},
|
|
)
|
|
import logging
|
|
|
|
logging.error(f"Error processing crawled URL: {e!s}")
|
|
|
|
|
|
async def process_file_in_background_with_new_session(
|
|
file_path: str, filename: str, search_space_id: int, user_id: str
|
|
):
|
|
"""Create a new session and process file."""
|
|
from app.db import async_session_maker
|
|
from app.services.task_logging_service import TaskLoggingService
|
|
|
|
async with async_session_maker() as session:
|
|
# Initialize task logging service
|
|
task_logger = TaskLoggingService(session, search_space_id)
|
|
|
|
# Log task start
|
|
log_entry = await task_logger.log_task_start(
|
|
task_name="process_file_upload",
|
|
source="document_processor",
|
|
message=f"Starting file processing for: {filename}",
|
|
metadata={
|
|
"document_type": "FILE",
|
|
"filename": filename,
|
|
"file_path": file_path,
|
|
"user_id": user_id,
|
|
},
|
|
)
|
|
|
|
try:
|
|
await process_file_in_background(
|
|
file_path,
|
|
filename,
|
|
search_space_id,
|
|
user_id,
|
|
session,
|
|
task_logger,
|
|
log_entry,
|
|
)
|
|
|
|
# Note: success/failure logging is handled within process_file_in_background
|
|
except Exception as e:
|
|
await task_logger.log_task_failure(
|
|
log_entry,
|
|
f"Failed to process file: {filename}",
|
|
str(e),
|
|
{"error_type": type(e).__name__},
|
|
)
|
|
import logging
|
|
|
|
logging.error(f"Error processing file: {e!s}")
|
|
|
|
|
|
async def process_youtube_video_with_new_session(
|
|
url: str, search_space_id: int, user_id: str
|
|
):
|
|
"""Create a new session and process YouTube video."""
|
|
from app.db import async_session_maker
|
|
from app.services.task_logging_service import TaskLoggingService
|
|
|
|
async with async_session_maker() as session:
|
|
# Initialize task logging service
|
|
task_logger = TaskLoggingService(session, search_space_id)
|
|
|
|
# Log task start
|
|
log_entry = await task_logger.log_task_start(
|
|
task_name="process_youtube_video",
|
|
source="document_processor",
|
|
message=f"Starting YouTube video processing for: {url}",
|
|
metadata={"document_type": "YOUTUBE_VIDEO", "url": url, "user_id": user_id},
|
|
)
|
|
|
|
try:
|
|
result = await add_youtube_video_document(
|
|
session, url, search_space_id, user_id
|
|
)
|
|
|
|
if result:
|
|
await task_logger.log_task_success(
|
|
log_entry,
|
|
f"Successfully processed YouTube video: {result.title}",
|
|
{
|
|
"document_id": result.id,
|
|
"video_id": result.document_metadata.get("video_id"),
|
|
"content_hash": result.content_hash,
|
|
},
|
|
)
|
|
else:
|
|
await task_logger.log_task_success(
|
|
log_entry,
|
|
f"YouTube video document already exists (duplicate): {url}",
|
|
{"duplicate_detected": True},
|
|
)
|
|
except Exception as e:
|
|
await task_logger.log_task_failure(
|
|
log_entry,
|
|
f"Failed to process YouTube video: {url}",
|
|
str(e),
|
|
{"error_type": type(e).__name__},
|
|
)
|
|
import logging
|
|
|
|
logging.error(f"Error processing YouTube video: {e!s}")
|
|
|
|
|
|
async def process_file_in_background(
|
|
file_path: str,
|
|
filename: str,
|
|
search_space_id: int,
|
|
user_id: str,
|
|
session: AsyncSession,
|
|
task_logger: TaskLoggingService,
|
|
log_entry: Log,
|
|
):
|
|
try:
|
|
# Check if the file is a markdown or text file
|
|
if filename.lower().endswith((".md", ".markdown", ".txt")):
|
|
await task_logger.log_task_progress(
|
|
log_entry,
|
|
f"Processing markdown/text file: {filename}",
|
|
{"file_type": "markdown", "processing_stage": "reading_file"},
|
|
)
|
|
|
|
# For markdown files, read the content directly
|
|
with open(file_path, encoding="utf-8") as f:
|
|
markdown_content = f.read()
|
|
|
|
# Clean up the temp file
|
|
import os
|
|
|
|
try:
|
|
os.unlink(file_path)
|
|
except Exception as e:
|
|
print("Error deleting temp file", e)
|
|
pass
|
|
|
|
await task_logger.log_task_progress(
|
|
log_entry,
|
|
f"Creating document from markdown content: {filename}",
|
|
{
|
|
"processing_stage": "creating_document",
|
|
"content_length": len(markdown_content),
|
|
},
|
|
)
|
|
|
|
# Process markdown directly through specialized function
|
|
result = await add_received_markdown_file_document(
|
|
session, filename, markdown_content, search_space_id, user_id
|
|
)
|
|
|
|
if result:
|
|
await task_logger.log_task_success(
|
|
log_entry,
|
|
f"Successfully processed markdown file: {filename}",
|
|
{
|
|
"document_id": result.id,
|
|
"content_hash": result.content_hash,
|
|
"file_type": "markdown",
|
|
},
|
|
)
|
|
else:
|
|
await task_logger.log_task_success(
|
|
log_entry,
|
|
f"Markdown file already exists (duplicate): {filename}",
|
|
{"duplicate_detected": True, "file_type": "markdown"},
|
|
)
|
|
|
|
# Check if the file is an audio file
|
|
elif filename.lower().endswith(
|
|
(".mp3", ".mp4", ".mpeg", ".mpga", ".m4a", ".wav", ".webm")
|
|
):
|
|
await task_logger.log_task_progress(
|
|
log_entry,
|
|
f"Processing audio file for transcription: {filename}",
|
|
{"file_type": "audio", "processing_stage": "starting_transcription"},
|
|
)
|
|
|
|
# Open the audio file for transcription
|
|
with open(file_path, "rb") as audio_file:
|
|
# Use LiteLLM for audio transcription
|
|
if app_config.STT_SERVICE_API_BASE:
|
|
transcription_response = await atranscription(
|
|
model=app_config.STT_SERVICE,
|
|
file=audio_file,
|
|
api_base=app_config.STT_SERVICE_API_BASE,
|
|
api_key=app_config.STT_SERVICE_API_KEY,
|
|
)
|
|
else:
|
|
transcription_response = await atranscription(
|
|
model=app_config.STT_SERVICE,
|
|
api_key=app_config.STT_SERVICE_API_KEY,
|
|
file=audio_file,
|
|
)
|
|
|
|
# Extract the transcribed text
|
|
transcribed_text = transcription_response.get("text", "")
|
|
|
|
# Add metadata about the transcription
|
|
transcribed_text = (
|
|
f"# Transcription of {filename}\n\n{transcribed_text}"
|
|
)
|
|
|
|
await task_logger.log_task_progress(
|
|
log_entry,
|
|
f"Transcription completed, creating document: {filename}",
|
|
{
|
|
"processing_stage": "transcription_complete",
|
|
"transcript_length": len(transcribed_text),
|
|
},
|
|
)
|
|
|
|
# Clean up the temp file
|
|
try:
|
|
os.unlink(file_path)
|
|
except Exception as e:
|
|
print("Error deleting temp file", e)
|
|
pass
|
|
|
|
# Process transcription as markdown document
|
|
result = await add_received_markdown_file_document(
|
|
session, filename, transcribed_text, search_space_id, user_id
|
|
)
|
|
|
|
if result:
|
|
await task_logger.log_task_success(
|
|
log_entry,
|
|
f"Successfully transcribed and processed audio file: {filename}",
|
|
{
|
|
"document_id": result.id,
|
|
"content_hash": result.content_hash,
|
|
"file_type": "audio",
|
|
"transcript_length": len(transcribed_text),
|
|
},
|
|
)
|
|
else:
|
|
await task_logger.log_task_success(
|
|
log_entry,
|
|
f"Audio file transcript already exists (duplicate): {filename}",
|
|
{"duplicate_detected": True, "file_type": "audio"},
|
|
)
|
|
|
|
else:
|
|
if app_config.ETL_SERVICE == "UNSTRUCTURED":
|
|
await task_logger.log_task_progress(
|
|
log_entry,
|
|
f"Processing file with Unstructured ETL: {filename}",
|
|
{
|
|
"file_type": "document",
|
|
"etl_service": "UNSTRUCTURED",
|
|
"processing_stage": "loading",
|
|
},
|
|
)
|
|
|
|
from langchain_unstructured import UnstructuredLoader
|
|
|
|
# Process the file
|
|
loader = UnstructuredLoader(
|
|
file_path,
|
|
mode="elements",
|
|
post_processors=[],
|
|
languages=["eng"],
|
|
include_orig_elements=False,
|
|
include_metadata=False,
|
|
strategy="auto",
|
|
)
|
|
|
|
docs = await loader.aload()
|
|
|
|
await task_logger.log_task_progress(
|
|
log_entry,
|
|
f"Unstructured ETL completed, creating document: {filename}",
|
|
{"processing_stage": "etl_complete", "elements_count": len(docs)},
|
|
)
|
|
|
|
# Clean up the temp file
|
|
import os
|
|
|
|
try:
|
|
os.unlink(file_path)
|
|
except Exception as e:
|
|
print("Error deleting temp file", e)
|
|
pass
|
|
|
|
# Pass the documents to the existing background task
|
|
result = await add_received_file_document_using_unstructured(
|
|
session, filename, docs, search_space_id, user_id
|
|
)
|
|
|
|
if result:
|
|
await task_logger.log_task_success(
|
|
log_entry,
|
|
f"Successfully processed file with Unstructured: {filename}",
|
|
{
|
|
"document_id": result.id,
|
|
"content_hash": result.content_hash,
|
|
"file_type": "document",
|
|
"etl_service": "UNSTRUCTURED",
|
|
},
|
|
)
|
|
else:
|
|
await task_logger.log_task_success(
|
|
log_entry,
|
|
f"Document already exists (duplicate): {filename}",
|
|
{
|
|
"duplicate_detected": True,
|
|
"file_type": "document",
|
|
"etl_service": "UNSTRUCTURED",
|
|
},
|
|
)
|
|
|
|
elif app_config.ETL_SERVICE == "LLAMACLOUD":
|
|
await task_logger.log_task_progress(
|
|
log_entry,
|
|
f"Processing file with LlamaCloud ETL: {filename}",
|
|
{
|
|
"file_type": "document",
|
|
"etl_service": "LLAMACLOUD",
|
|
"processing_stage": "parsing",
|
|
},
|
|
)
|
|
|
|
from llama_cloud_services import LlamaParse
|
|
from llama_cloud_services.parse.utils import ResultType
|
|
|
|
# Create LlamaParse parser instance
|
|
parser = LlamaParse(
|
|
api_key=app_config.LLAMA_CLOUD_API_KEY,
|
|
num_workers=1, # Use single worker for file processing
|
|
verbose=True,
|
|
language="en",
|
|
result_type=ResultType.MD,
|
|
)
|
|
|
|
# Parse the file asynchronously
|
|
result = await parser.aparse(file_path)
|
|
|
|
# Clean up the temp file
|
|
import os
|
|
|
|
try:
|
|
os.unlink(file_path)
|
|
except Exception as e:
|
|
print("Error deleting temp file", e)
|
|
pass
|
|
|
|
# Get markdown documents from the result
|
|
markdown_documents = await result.aget_markdown_documents(
|
|
split_by_page=False
|
|
)
|
|
|
|
await task_logger.log_task_progress(
|
|
log_entry,
|
|
f"LlamaCloud parsing completed, creating documents: {filename}",
|
|
{
|
|
"processing_stage": "parsing_complete",
|
|
"documents_count": len(markdown_documents),
|
|
},
|
|
)
|
|
|
|
for doc in markdown_documents:
|
|
# Extract text content from the markdown documents
|
|
markdown_content = doc.text
|
|
|
|
# Process the documents using our LlamaCloud background task
|
|
doc_result = await add_received_file_document_using_llamacloud(
|
|
session,
|
|
filename,
|
|
llamacloud_markdown_document=markdown_content,
|
|
search_space_id=search_space_id,
|
|
user_id=user_id,
|
|
)
|
|
|
|
if doc_result:
|
|
await task_logger.log_task_success(
|
|
log_entry,
|
|
f"Successfully processed file with LlamaCloud: {filename}",
|
|
{
|
|
"document_id": doc_result.id,
|
|
"content_hash": doc_result.content_hash,
|
|
"file_type": "document",
|
|
"etl_service": "LLAMACLOUD",
|
|
},
|
|
)
|
|
else:
|
|
await task_logger.log_task_success(
|
|
log_entry,
|
|
f"Document already exists (duplicate): {filename}",
|
|
{
|
|
"duplicate_detected": True,
|
|
"file_type": "document",
|
|
"etl_service": "LLAMACLOUD",
|
|
},
|
|
)
|
|
|
|
elif app_config.ETL_SERVICE == "DOCLING":
|
|
await task_logger.log_task_progress(
|
|
log_entry,
|
|
f"Processing file with Docling ETL: {filename}",
|
|
{
|
|
"file_type": "document",
|
|
"etl_service": "DOCLING",
|
|
"processing_stage": "parsing",
|
|
},
|
|
)
|
|
|
|
# Use Docling service for document processing
|
|
from app.services.docling_service import create_docling_service
|
|
|
|
# Create Docling service
|
|
docling_service = create_docling_service()
|
|
|
|
# Process the document
|
|
result = await docling_service.process_document(file_path, filename)
|
|
|
|
# Clean up the temp file
|
|
import os
|
|
|
|
try:
|
|
os.unlink(file_path)
|
|
except Exception as e:
|
|
print("Error deleting temp file", e)
|
|
pass
|
|
|
|
await task_logger.log_task_progress(
|
|
log_entry,
|
|
f"Docling parsing completed, creating document: {filename}",
|
|
{
|
|
"processing_stage": "parsing_complete",
|
|
"content_length": len(result["content"]),
|
|
},
|
|
)
|
|
|
|
# Process the document using our Docling background task
|
|
doc_result = await add_received_file_document_using_docling(
|
|
session,
|
|
filename,
|
|
docling_markdown_document=result["content"],
|
|
search_space_id=search_space_id,
|
|
user_id=user_id,
|
|
)
|
|
|
|
if doc_result:
|
|
await task_logger.log_task_success(
|
|
log_entry,
|
|
f"Successfully processed file with Docling: {filename}",
|
|
{
|
|
"document_id": doc_result.id,
|
|
"content_hash": doc_result.content_hash,
|
|
"file_type": "document",
|
|
"etl_service": "DOCLING",
|
|
},
|
|
)
|
|
else:
|
|
await task_logger.log_task_success(
|
|
log_entry,
|
|
f"Document already exists (duplicate): {filename}",
|
|
{
|
|
"duplicate_detected": True,
|
|
"file_type": "document",
|
|
"etl_service": "DOCLING",
|
|
},
|
|
)
|
|
except Exception as e:
|
|
await task_logger.log_task_failure(
|
|
log_entry,
|
|
f"Failed to process file: {filename}",
|
|
str(e),
|
|
{"error_type": type(e).__name__, "filename": filename},
|
|
)
|
|
import logging
|
|
|
|
logging.error(f"Error processing file in background: {e!s}")
|
|
raise # Re-raise so the wrapper can also handle it
|