342 lines
14 KiB
Python
342 lines
14 KiB
Python
import os
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from typing import Optional
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import datetime
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import uuid
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from fastapi import APIRouter, Depends, HTTPException, status, Query
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from sqlalchemy.orm import Session
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from app.core.config import settings
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from app.db import get_db
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from app.dependencies import get_current_user
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from app.models.user_model import User
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from app.models import document_model
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from app.schemas import document_schema
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from app.schemas.response_schema import ApiResponse
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from app.core.response_utils import success
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from app.services import document_service, file_service, knowledge_service
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from app.controllers import file_controller
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from app.celery_app import celery_app
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from app.core.rag.vdb.elasticsearch.elasticsearch_vector import ElasticSearchVectorFactory
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from app.core.logging_config import get_api_logger
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# Obtain a dedicated API logger
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api_logger = get_api_logger()
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router = APIRouter(
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prefix="/documents",
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tags=["documents"],
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dependencies=[Depends(get_current_user)] # Apply auth to all routes in this controller
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)
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@router.get("/{kb_id}/documents", response_model=ApiResponse)
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async def get_documents(
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kb_id: uuid.UUID,
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parent_id: Optional[uuid.UUID] = Query(None, description="parent folder id when type is Folder"),
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page: int = Query(1, gt=0), # Default: 1, which must be greater than 0
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pagesize: int = Query(20, gt=0, le=100), # Default: 20 items per page, maximum: 100 items
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orderby: Optional[str] = Query(None, description="Sort fields, such as: created_at,updated_at"),
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desc: Optional[bool] = Query(False, description="Is it descending order"),
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keywords: Optional[str] = Query(None, description="Search keywords (file name)"),
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document_ids: Optional[str] = Query(None, description="document ids, separated by commas"),
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db: Session = Depends(get_db),
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current_user: User = Depends(get_current_user)
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):
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"""
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Paged query document list
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- Support filtering by kb_id and parent_id
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- Support keyword search for file names
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- Support dynamic sorting
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- Return paging metadata + file list
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"""
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api_logger.info(f"Query document list: kb_id={kb_id}, page={page}, pagesize={pagesize}, keywords={keywords}, document_ids={document_ids}, username: {current_user.username}")
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# 1. parameter validation
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if page < 1 or pagesize < 1:
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raise HTTPException(
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status_code=status.HTTP_400_BAD_REQUEST,
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detail="The paging parameter must be greater than 0"
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)
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# 2. Construct query conditions
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filters = [
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document_model.Document.kb_id == kb_id,
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document_model.Document.status == 1
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]
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if parent_id:
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files = file_service.get_files_by_parent_id(db=db, parent_id=parent_id, current_user=current_user)
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files_ids = [item.id for item in files]
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filters.append(document_model.Document.file_id.in_(files_ids))
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# Keyword search (fuzzy matching of file name)
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if keywords:
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api_logger.debug(f"Add keyword search criteria: {keywords}")
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filters.append(document_model.Document.file_name.ilike(f"%{keywords}%"))
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# document ids
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if document_ids:
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filters.append(document_model.Document.id.in_(document_ids.split(',')))
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# 3. Execute paged query
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try:
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api_logger.debug("Start executing document paging query")
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total, items = document_service.get_documents_paginated(
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db=db,
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filters=filters,
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page=page,
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pagesize=pagesize,
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orderby=orderby,
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desc=desc,
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current_user=current_user
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)
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api_logger.info(f"Document query successful: total={total}, returned={len(items)} records")
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except Exception as e:
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api_logger.error(f"Document query failed: {str(e)}")
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raise HTTPException(
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status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
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detail=f"Query failed: {str(e)}"
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)
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# 4. Return structured response
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result = {
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"items": items,
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"page": {
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"page": page,
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"pagesize": pagesize,
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"total": total,
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"has_next": True if page * pagesize < total else False
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}
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}
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return success(data=result, msg="Query of document list succeeded")
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@router.post("/document", response_model=ApiResponse)
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async def create_document(
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create_data: document_schema.DocumentCreate,
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db: Session = Depends(get_db),
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current_user: User = Depends(get_current_user)
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):
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"""
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create document
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"""
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api_logger.info(f"Create document request: file_name={create_data.file_name}, kb_id={create_data.kb_id}, username: {current_user.username}")
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try:
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api_logger.debug(f"Start creating a document: {create_data.file_name}")
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db_document = document_service.create_document(db=db, document=create_data, current_user=current_user)
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api_logger.info(f"Document created successfully: {db_document.file_name} (ID: {db_document.id})")
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return success(data=document_schema.Document.model_validate(db_document), msg="Document creation successful")
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except Exception as e:
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api_logger.error(f"Document creation failed: {create_data.file_name} - {str(e)}")
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raise
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@router.get("/{document_id}", response_model=ApiResponse)
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async def get_document(
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document_id: uuid.UUID,
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db: Session = Depends(get_db),
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current_user: User = Depends(get_current_user)
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):
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"""
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Retrieve document information based on document_id
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"""
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api_logger.info(f"Obtain document information: document_id={document_id}, username: {current_user.username}")
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try:
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# 1. Query document information from the database
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api_logger.debug(f"query documentation: {document_id}")
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db_document = document_service.get_document_by_id(db, document_id=document_id, current_user=current_user)
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if not db_document:
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api_logger.warning(f"The document does not exist or you do not have access: document_id={document_id}")
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raise HTTPException(
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status_code=status.HTTP_404_NOT_FOUND,
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detail="The document does not exist or you do not have access"
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)
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api_logger.info(f"Document query successful: {db_document.file_name} (ID: {db_document.id})")
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return success(data=document_schema.Document.model_validate(db_document), msg="Successfully obtained document information")
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except HTTPException:
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raise
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except Exception as e:
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api_logger.error(f"Document query failed: document_id={document_id} - {str(e)}")
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raise
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@router.put("/{document_id}", response_model=ApiResponse)
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async def update_document(
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document_id: uuid.UUID,
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update_data: document_schema.DocumentUpdate,
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db: Session = Depends(get_db),
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current_user: User = Depends(get_current_user)
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):
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"""
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Update document information
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"""
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# 1. Check if the document exists
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api_logger.debug(f"Query the document to be updated: {document_id}")
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db_document = document_service.get_document_by_id(db, document_id=document_id, current_user=current_user)
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if not db_document:
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api_logger.warning(f"The document does not exist or you do not have permission to access it: document_id={document_id}")
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raise HTTPException(
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status_code=status.HTTP_404_NOT_FOUND,
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detail="The document does not exist or you do not have permission to access it"
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)
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# 2. If updating the status, synchronize the document status switch to whether it can be retrieved from the vector database
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update_dict = update_data.dict(exclude_unset=True)
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if "status" in update_dict:
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new_status = update_dict["status"]
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if new_status != db_document.status:
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db_knowledge = knowledge_service.get_knowledge_by_id(db, knowledge_id=db_document.kb_id, current_user=current_user)
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vector_service = ElasticSearchVectorFactory().init_vector(knowledge=db_knowledge)
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vector_service.change_status_by_document_id(document_id=str(document_id), status=new_status)
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# 3. Update fields (only update non-null fields)
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api_logger.debug(f"Start updating the document fields: {document_id}")
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updated_fields = []
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for field, value in update_dict.items():
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if hasattr(db_document, field):
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old_value = getattr(db_document, field)
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if old_value != value:
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# update value
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setattr(db_document, field, value)
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updated_fields.append(f"{field}: {old_value} -> {value}")
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if updated_fields:
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api_logger.debug(f"updated fields: {', '.join(updated_fields)}")
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db_document.updated_at = datetime.datetime.now()
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# 4. Save to database
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try:
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db.commit()
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db.refresh(db_document)
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api_logger.info(f"The document has been successfully updated: {db_document.file_name} (ID: {db_document.id})")
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except Exception as e:
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db.rollback()
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api_logger.error(f"Document update failed: document_id={document_id} - {str(e)}")
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raise HTTPException(
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status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
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detail=f"Document update failed: {str(e)}"
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)
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# 5. Return the updated document
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return success(data=document_schema.Document.model_validate(db_document), msg="Document information updated successfully")
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@router.delete("/{document_id}", response_model=ApiResponse)
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async def delete_document(
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document_id: uuid.UUID,
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db: Session = Depends(get_db),
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current_user: User = Depends(get_current_user)
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):
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"""
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Delete document
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"""
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api_logger.info(f"Request to delete document: document_id={document_id}, username: {current_user.username}")
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try:
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# 1. Check if the document exists
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api_logger.debug(f"Check whether the document exists: {document_id}")
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db_document = document_service.get_document_by_id(db, document_id=document_id, current_user=current_user)
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if not db_document:
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api_logger.warning(f"The document does not exist or you do not have permission to access it: document_id={document_id}")
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raise HTTPException(
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status_code=status.HTTP_404_NOT_FOUND,
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detail="The document does not exist or you do not have permission to access it"
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)
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file_id = db_document.file_id
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# 2. Delete document
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api_logger.debug(f"Perform document delete: {db_document.file_name} (ID: {document_id})")
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db.delete(db_document)
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db.commit()
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# 3. Delete file
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await file_controller._delete_file(db=db, file_id=file_id, current_user=current_user)
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# 4. Delete vector index
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db_knowledge = knowledge_service.get_knowledge_by_id(db, knowledge_id=db_document.kb_id, current_user=current_user)
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vector_service = ElasticSearchVectorFactory().init_vector(knowledge=db_knowledge)
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vector_service.delete_by_metadata_field(key="document_id", value=str(document_id))
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api_logger.info(f"The document has been successfully deleted: {db_document.file_name} (ID: {document_id})")
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return success(msg="The document has been successfully deleted")
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except Exception as e:
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api_logger.error(f"Failed to delete from the document: document_id={document_id} - {str(e)}")
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raise
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@router.post("/{document_id}/chunks", response_model=ApiResponse)
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async def parse_documents(
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document_id: uuid.UUID,
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db: Session = Depends(get_db),
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current_user: User = Depends(get_current_user)
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):
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"""
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parse document
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"""
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api_logger.info(f"Request to parse document: document_id={document_id}, username: {current_user.username}")
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try:
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# 1. Check if the document exists
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api_logger.debug(f"Check whether the document exists: {document_id}")
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db_document = document_service.get_document_by_id(db, document_id=document_id, current_user=current_user)
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if not db_document:
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api_logger.warning(f"The document does not exist or you do not have permission to access it: document_id={document_id}")
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raise HTTPException(
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status_code=status.HTTP_404_NOT_FOUND,
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detail="The document does not exist or you do not have permission to access it"
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)
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# 2. Check if the file exists
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api_logger.debug(f"Check whether the file exists: {db_document.file_id}")
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db_file = file_service.get_file_by_id(db, file_id=db_document.file_id)
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if not db_file:
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api_logger.warning(f"The file does not exist or you do not have permission to access it: file_id={db_document.file_id}")
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raise HTTPException(
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status_code=status.HTTP_404_NOT_FOUND,
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detail="The file does not exist or you do not have permission to access it"
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)
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# 3. Construct file path：/files/{kb_id}/{parent_id}/{file.id}{file.file_ext}
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file_path = os.path.join(
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settings.FILE_PATH,
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str(db_file.kb_id),
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str(db_file.parent_id),
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f"{db_file.id}{db_file.file_ext}"
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)
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# 4. Check if the file exists
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if not os.path.exists(file_path):
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api_logger.warning(f"File not found (possibly deleted): file_path={file_path}")
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raise HTTPException(
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status_code=status.HTTP_404_NOT_FOUND,
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detail="File not found (possibly deleted)"
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)
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# 5. Obtain knowledge base information
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api_logger.info( f"Obtain details of the knowledge base: knowledge_id={db_document.kb_id}")
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db_knowledge = knowledge_service.get_knowledge_by_id(db, knowledge_id=db_document.kb_id, current_user=current_user)
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if not db_knowledge:
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api_logger.warning(f"The knowledge base does not exist or access is denied: knowledge_id={db_document.kb_id}")
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raise HTTPException(
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status_code=status.HTTP_404_NOT_FOUND,
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detail="The knowledge base does not exist or access is denied"
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)
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# 6. Task: Document parsing, vectorization, and storage
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# from app.tasks import parse_document
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# parse_document(file_path, document_id)
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task = celery_app.send_task("app.core.rag.tasks.parse_document", args=[file_path, document_id])
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result = {
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"task_id": task.id
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}
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return success(data=result, msg="Task accepted. The document is being processed in the background.")
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except Exception as e:
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api_logger.error(f"Failed to parse document: document_id={document_id} - {str(e)}")
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raise
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