684 lines
28 KiB
Python
684 lines
28 KiB
Python
import os
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import csv
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import io
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from typing import Any, Optional
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import uuid
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from fastapi import APIRouter, Depends, HTTPException, status, Query, UploadFile, File
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from fastapi.encoders import jsonable_encoder
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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.core.logging_config import get_api_logger
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from app.core.rag.common.settings import kg_retriever
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from app.core.rag.llm.chat_model import Base
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from app.core.rag.llm.cv_model import QWenCV
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from app.core.rag.llm.embedding_model import OpenAIEmbed
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from app.core.rag.models.chunk import DocumentChunk
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from app.core.rag.vdb.elasticsearch.elasticsearch_vector import ElasticSearchVectorFactory
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from app.core.response_utils import success
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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 import knowledge_model, knowledgeshare_model
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from app.models.document_model import Document
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from app.models.user_model import User
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from app.schemas import chunk_schema
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from app.schemas.response_schema import ApiResponse
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from app.services import knowledge_service, document_service, file_service, knowledgeshare_service
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from app.services.file_storage_service import FileStorageService, get_file_storage_service, generate_kb_file_key
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from app.services.model_service import ModelApiKeyService
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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="/chunks",
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tags=["chunks"],
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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}/{document_id}/previewchunks", response_model=ApiResponse)
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async def get_preview_chunks(
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kb_id: uuid.UUID,
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document_id: uuid.UUID,
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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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keywords: Optional[str] = Query(None, description="The keywords used to match chunk content"),
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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 block preview list
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- Support filtering by document_id
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- Support keyword search for segmented content
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- Return paging metadata + file list
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"""
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api_logger.info(f"Paged query document block preview list: kb_id={kb_id}, document_id={document_id}, page={page}, pagesize={pagesize}, keywords={keywords}, 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. Obtain knowledge base information
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db_knowledge = knowledge_service.get_knowledge_by_id(db, knowledge_id=kb_id, current_user=current_user)
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if not db_knowledge:
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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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# 3. Check if the document exists
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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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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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# 4. Check if the file exists
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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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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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# 5. Get file content from storage backend
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if not db_file.file_key:
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raise HTTPException(
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status_code=status.HTTP_404_NOT_FOUND,
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detail="File has no storage key (legacy data not migrated)"
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)
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from app.services.file_storage_service import FileStorageService
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import asyncio
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storage_service = FileStorageService()
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async def _download():
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return await storage_service.download_file(db_file.file_key)
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try:
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file_binary = asyncio.run(_download())
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except RuntimeError:
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loop = asyncio.new_event_loop()
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try:
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file_binary = loop.run_until_complete(_download())
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finally:
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loop.close()
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except Exception as e:
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raise HTTPException(
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status_code=status.HTTP_404_NOT_FOUND,
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detail=f"File not found in storage: {e}"
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)
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# 7. Document parsing & segmentation
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def progress_callback(prog=None, msg=None):
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print(f"prog: {prog} msg: {msg}\n")
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# Prepare to configure vision_model information
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vision_model = QWenCV(
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key=db_knowledge.image2text.api_keys[0].api_key,
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model_name=db_knowledge.image2text.api_keys[0].model_name,
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lang="Chinese",
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base_url=db_knowledge.image2text.api_keys[0].api_base
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)
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from app.core.rag.app.naive import chunk
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res = chunk(filename=db_file.file_name,
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binary=file_binary,
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from_page=0,
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to_page=5,
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callback=progress_callback,
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vision_model=vision_model,
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parser_config=db_document.parser_config,
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is_root=False)
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start_index = (page - 1) * pagesize
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end_index = start_index + pagesize
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# Use slicing to obtain the data of the current page
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paginated_chunk_str_list = res[start_index:end_index]
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chunks = []
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for idx, item in enumerate(paginated_chunk_str_list):
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metadata = {
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"doc_id": uuid.uuid4().hex,
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"file_id": str(db_document.file_id),
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"file_name": db_document.file_name,
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"file_created_at": int(db_document.created_at.timestamp() * 1000),
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"document_id": str(db_document.id),
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"knowledge_id": str(db_document.kb_id),
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"sort_id": idx,
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"status": 1,
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}
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chunks.append(DocumentChunk(page_content=item["content_with_weight"], metadata=metadata))
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# 8. Return structured response
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total = len(res)
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result = {
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"items": chunks,
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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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api_logger.info(f"Querying the document block preview list successful: total={total}, returned={len(chunks)} records")
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return success(data=jsonable_encoder(result), msg="Querying the document block preview list succeeded")
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@router.get("/{kb_id}/{document_id}/chunks", response_model=ApiResponse)
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async def get_chunks(
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kb_id: uuid.UUID,
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document_id: uuid.UUID,
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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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keywords: Optional[str] = Query(None, description="The keywords used to match chunk content"),
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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 chunk list
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- Support filtering by document_id
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- Support keyword search for segmented content
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- Return paging metadata + file list
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"""
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api_logger.info(f"Paged query document chunk list: kb_id={kb_id}, document_id={document_id}, page={page}, pagesize={pagesize}, keywords={keywords}, 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. Obtain knowledge base information
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db_knowledge = knowledge_service.get_knowledge_by_id(db, knowledge_id=kb_id, current_user=current_user)
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if not db_knowledge:
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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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# 3. Execute paged query
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try:
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api_logger.debug("Start executing document chunk query")
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vector_service = ElasticSearchVectorFactory().init_vector(knowledge=db_knowledge)
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total, items = vector_service.search_by_segment(document_id=str(document_id), query=keywords, pagesize=pagesize, page=page, asc=True)
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api_logger.info(f"Document chunk 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 chunk 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=jsonable_encoder(result), msg="Query of document chunk list succeeded")
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@router.post("/{kb_id}/{document_id}/chunk", response_model=ApiResponse)
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async def create_chunk(
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kb_id: uuid.UUID,
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document_id: uuid.UUID,
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create_data: chunk_schema.ChunkCreate,
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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 chunk
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"""
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# Obtain the actual content
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content = create_data.chunk_content
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api_logger.info(f"Create chunk request: kb_id={kb_id}, document_id={document_id}, content={content}, username: {current_user.username}")
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# 1. Obtain knowledge base information
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db_knowledge = knowledge_service.get_knowledge_by_id(db, knowledge_id=kb_id, current_user=current_user)
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if not db_knowledge:
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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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# 1. Obtain document information
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db_document = db.query(Document).filter(Document.id == document_id).first()
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if not db_document:
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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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vector_service = ElasticSearchVectorFactory().init_vector(knowledge=db_knowledge)
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# 2. Get the sort ID
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sort_id = 0
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total, items = vector_service.search_by_segment(document_id=str(document_id), pagesize=1, page=1, asc=False)
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if items:
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sort_id = items[0].metadata["sort_id"]
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sort_id = sort_id + 1
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doc_id = uuid.uuid4().hex
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metadata = {
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"doc_id": doc_id,
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"file_id": str(db_document.file_id),
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"file_name": db_document.file_name,
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"file_created_at": int(db_document.created_at.timestamp() * 1000),
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"document_id": str(document_id),
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"knowledge_id": str(kb_id),
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"sort_id": sort_id,
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"status": 1,
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}
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# QA chunk: 注入 chunk_type/question/answer 到 metadata
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if create_data.is_qa:
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metadata.update(create_data.qa_metadata)
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chunk = DocumentChunk(page_content=content, metadata=metadata)
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# 3. Segmented vector storage
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vector_service.add_chunks([chunk])
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# 4.update chunk_num
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db_document.chunk_num += 1
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db.commit()
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return success(data=jsonable_encoder(chunk), msg="Document chunk creation successful")
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@router.post("/{kb_id}/{document_id}/chunk/batch", response_model=ApiResponse)
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async def create_chunks_batch(
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kb_id: uuid.UUID,
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document_id: uuid.UUID,
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batch_data: chunk_schema.ChunkBatchCreate,
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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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Batch create chunks (max 8)
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"""
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api_logger.info(f"Batch create chunks: kb_id={kb_id}, document_id={document_id}, count={len(batch_data.items)}, username: {current_user.username}")
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if len(batch_data.items) > settings.MAX_CHUNK_BATCH_SIZE:
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raise HTTPException(
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status_code=status.HTTP_400_BAD_REQUEST,
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detail=f"Batch size exceeds limit: max {settings.MAX_CHUNK_BATCH_SIZE}, got {len(batch_data.items)}"
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)
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db_knowledge = knowledge_service.get_knowledge_by_id(db, knowledge_id=kb_id, current_user=current_user)
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if not db_knowledge:
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raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="The knowledge base does not exist or access is denied")
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db_document = db.query(Document).filter(Document.id == document_id).first()
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if not db_document:
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raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="The document does not exist or you do not have permission to access it")
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vector_service = ElasticSearchVectorFactory().init_vector(knowledge=db_knowledge)
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# Get current max sort_id
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sort_id = 0
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total, items = vector_service.search_by_segment(document_id=str(document_id), pagesize=1, page=1, asc=False)
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if items:
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sort_id = items[0].metadata["sort_id"]
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chunks = []
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for create_data in batch_data.items:
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sort_id += 1
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doc_id = uuid.uuid4().hex
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metadata = {
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"doc_id": doc_id,
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"file_id": str(db_document.file_id),
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"file_name": db_document.file_name,
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"file_created_at": int(db_document.created_at.timestamp() * 1000),
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"document_id": str(document_id),
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"knowledge_id": str(kb_id),
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"sort_id": sort_id,
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"status": 1,
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}
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if create_data.is_qa:
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metadata.update(create_data.qa_metadata)
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chunks.append(DocumentChunk(page_content=create_data.chunk_content, metadata=metadata))
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vector_service.add_chunks(chunks)
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db_document.chunk_num += len(chunks)
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db.commit()
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return success(data=jsonable_encoder(chunks), msg=f"Batch created {len(chunks)} chunks successfully")
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@router.post("/{kb_id}/import_qa", response_model=ApiResponse)
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async def import_qa_new_doc(
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kb_id: uuid.UUID,
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file: UploadFile = File(..., description="CSV 或 Excel 文件（第一行标题跳过，第一列问题，第二列答案）"),
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db: Session = Depends(get_db),
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current_user: User = Depends(get_current_user),
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storage_service: FileStorageService = Depends(get_file_storage_service),
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):
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"""
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导入 QA 问答对并新建文档（CSV/Excel），异步处理
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"""
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from app.schemas import file_schema, document_schema
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api_logger.info(f"Import QA (new doc): kb_id={kb_id}, file={file.filename}, username: {current_user.username}")
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# 1. 校验文件格式
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filename = file.filename or ""
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if not (filename.endswith(".csv") or filename.endswith(".xlsx") or filename.endswith(".xls")):
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raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="仅支持 CSV (.csv) 或 Excel (.xlsx) 格式")
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# 2. 校验知识库
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db_knowledge = knowledge_service.get_knowledge_by_id(db, knowledge_id=kb_id, current_user=current_user)
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if not db_knowledge:
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raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="知识库不存在或无权访问")
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# 3. 读取文件
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contents = await file.read()
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file_size = len(contents)
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if file_size == 0:
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raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="文件为空")
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_, file_extension = os.path.splitext(filename)
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file_ext = file_extension.lower()
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# 4. 创建 File 记录
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file_data = file_schema.FileCreate(
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kb_id=kb_id, created_by=current_user.id,
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parent_id=uuid.UUID("00000000-0000-0000-0000-000000000000"),
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file_name=filename, file_ext=file_ext, file_size=file_size,
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)
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db_file = file_service.create_file(db=db, file=file_data, current_user=current_user)
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# 5. 上传文件到存储后端
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file_key = generate_kb_file_key(kb_id=kb_id, file_id=db_file.id, file_ext=file_ext)
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try:
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await storage_service.storage.upload(file_key=file_key, content=contents, content_type=file.content_type)
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except Exception as e:
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api_logger.error(f"Storage upload failed: {e}")
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raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=f"文件存储失败: {str(e)}")
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db_file.file_key = file_key
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db.commit()
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db.refresh(db_file)
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# 6. 创建 Document 记录（标记为 QA 类型）
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doc_data = document_schema.DocumentCreate(
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kb_id=kb_id, created_by=current_user.id, file_id=db_file.id,
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file_name=filename, file_ext=file_ext, file_size=file_size,
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file_meta={}, parser_id="qa",
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parser_config={"doc_type": "qa", "auto_questions": 0}
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)
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db_document = document_service.create_document(db=db, document=doc_data, current_user=current_user)
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api_logger.info(f"Created doc for QA import: file_id={db_file.id}, document_id={db_document.id}, file_key={file_key}")
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# 7. 派发异步任务
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from app.celery_app import celery_app
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task = celery_app.send_task(
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"app.core.rag.tasks.import_qa_chunks",
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args=[str(kb_id), str(db_document.id), filename, contents],
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queue="qa_import"
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)
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return success(data={
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"task_id": task.id,
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"document_id": str(db_document.id),
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"file_id": str(db_file.id),
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}, msg="QA 导入任务已提交，后台处理中")
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@router.post("/{kb_id}/{document_id}/import_qa", response_model=ApiResponse)
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async def import_qa_chunks(
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kb_id: uuid.UUID,
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document_id: uuid.UUID,
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file: UploadFile = File(..., description="CSV 或 Excel 文件（第一行标题跳过，第一列问题，第二列答案）"),
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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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||
"""
|
||
导入 QA 问答对（CSV/Excel），异步处理
|
||
"""
|
||
api_logger.info(f"Import QA chunks: kb_id={kb_id}, document_id={document_id}, file={file.filename}, username: {current_user.username}")
|
||
|
||
# 1. 校验文件格式
|
||
filename = file.filename or ""
|
||
if not (filename.endswith(".csv") or filename.endswith(".xlsx") or filename.endswith(".xls")):
|
||
raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="仅支持 CSV (.csv) 或 Excel (.xlsx) 格式")
|
||
|
||
# 2. 校验知识库和文档
|
||
db_knowledge = knowledge_service.get_knowledge_by_id(db, knowledge_id=kb_id, current_user=current_user)
|
||
if not db_knowledge:
|
||
raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="知识库不存在或无权访问")
|
||
|
||
db_document = db.query(Document).filter(Document.id == document_id).first()
|
||
if not db_document:
|
||
raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="文档不存在或无权访问")
|
||
|
||
# 3. 读取文件内容，派发异步任务
|
||
contents = await file.read()
|
||
|
||
from app.celery_app import celery_app
|
||
task = celery_app.send_task(
|
||
"app.core.rag.tasks.import_qa_chunks",
|
||
args=[str(kb_id), str(document_id), filename, contents],
|
||
queue="qa_import"
|
||
)
|
||
|
||
return success(data={"task_id": task.id}, msg="QA 导入任务已提交，后台处理中")
|
||
|
||
|
||
@router.get("/{kb_id}/{document_id}/{doc_id}", response_model=ApiResponse)
|
||
async def get_chunk(
|
||
kb_id: uuid.UUID,
|
||
document_id: uuid.UUID,
|
||
doc_id: str,
|
||
db: Session = Depends(get_db),
|
||
current_user: User = Depends(get_current_user)
|
||
):
|
||
"""
|
||
Retrieve document chunk information based on doc_id
|
||
"""
|
||
api_logger.info(f"Obtain document chunk information: kb_id={kb_id}, document_id={document_id}, doc_id={doc_id}, username: {current_user.username}")
|
||
|
||
# 1. Obtain knowledge base information
|
||
db_knowledge = knowledge_service.get_knowledge_by_id(db, knowledge_id=kb_id, current_user=current_user)
|
||
if not db_knowledge:
|
||
raise HTTPException(
|
||
status_code=status.HTTP_404_NOT_FOUND,
|
||
detail="The knowledge base does not exist or access is denied"
|
||
)
|
||
|
||
vector_service = ElasticSearchVectorFactory().init_vector(knowledge=db_knowledge)
|
||
total, items = vector_service.get_by_segment(doc_id=doc_id)
|
||
if total:
|
||
return success(data=jsonable_encoder(items[0]), msg="Document chunk query successful")
|
||
else:
|
||
raise HTTPException(
|
||
status_code=status.HTTP_404_NOT_FOUND,
|
||
detail="The document chunk does not exist or you do not have access"
|
||
)
|
||
|
||
|
||
@router.put("/{kb_id}/{document_id}/{doc_id}", response_model=ApiResponse)
|
||
async def update_chunk(
|
||
kb_id: uuid.UUID,
|
||
document_id: uuid.UUID,
|
||
doc_id: str,
|
||
update_data: chunk_schema.ChunkUpdate,
|
||
db: Session = Depends(get_db),
|
||
current_user: User = Depends(get_current_user)
|
||
):
|
||
"""
|
||
Update document chunk content
|
||
"""
|
||
# Obtain the actual content
|
||
content = update_data.chunk_content
|
||
api_logger.info(f"Update document chunk content: kb_id={kb_id}, document_id={document_id}, doc_id={doc_id}, content={content}, username: {current_user.username}")
|
||
|
||
db_knowledge = knowledge_service.get_knowledge_by_id(db, knowledge_id=kb_id, current_user=current_user)
|
||
if not db_knowledge:
|
||
raise HTTPException(
|
||
status_code=status.HTTP_404_NOT_FOUND,
|
||
detail="The knowledge base does not exist or access is denied"
|
||
)
|
||
|
||
vector_service = ElasticSearchVectorFactory().init_vector(knowledge=db_knowledge)
|
||
total, items = vector_service.get_by_segment(doc_id=doc_id)
|
||
if total:
|
||
chunk = items[0]
|
||
chunk.page_content = content
|
||
# QA chunk: 更新 metadata 中的 question/answer
|
||
if update_data.is_qa:
|
||
chunk.metadata.update(update_data.qa_metadata)
|
||
vector_service.update_by_segment(chunk)
|
||
return success(data=jsonable_encoder(chunk), msg="The document chunk has been successfully updated")
|
||
else:
|
||
raise HTTPException(
|
||
status_code=status.HTTP_404_NOT_FOUND,
|
||
detail="The document chunk does not exist or you do not have access to it"
|
||
)
|
||
|
||
|
||
@router.delete("/{kb_id}/{document_id}/{doc_id}", response_model=ApiResponse)
|
||
async def delete_chunk(
|
||
kb_id: uuid.UUID,
|
||
document_id: uuid.UUID,
|
||
doc_id: str,
|
||
db: Session = Depends(get_db),
|
||
current_user: User = Depends(get_current_user)
|
||
):
|
||
"""
|
||
delete document chunk
|
||
"""
|
||
api_logger.info(f"Request to delete document chunk: kb_id={kb_id}, document_id={document_id}, doc_id={doc_id}, username: {current_user.username}")
|
||
|
||
db_knowledge = knowledge_service.get_knowledge_by_id(db, knowledge_id=kb_id, current_user=current_user)
|
||
if not db_knowledge:
|
||
raise HTTPException(
|
||
status_code=status.HTTP_404_NOT_FOUND,
|
||
detail="The knowledge base does not exist or access is denied"
|
||
)
|
||
|
||
vector_service = ElasticSearchVectorFactory().init_vector(knowledge=db_knowledge)
|
||
if vector_service.text_exists(doc_id):
|
||
vector_service.delete_by_ids([doc_id])
|
||
# 更新 chunk_num
|
||
db_document = db.query(Document).filter(Document.id == document_id).first()
|
||
db_document.chunk_num -= 1
|
||
db.commit()
|
||
return success(msg="The document chunk has been successfully deleted")
|
||
else:
|
||
raise HTTPException(
|
||
status_code=status.HTTP_404_NOT_FOUND,
|
||
detail="The document chunk does not exist or you do not have access to it"
|
||
)
|
||
|
||
|
||
@router.get("/retrieve_type", response_model=ApiResponse)
|
||
def get_retrieve_types():
|
||
return success(msg="Successfully obtained the retrieval type", data=list(chunk_schema.RetrieveType))
|
||
|
||
|
||
@router.post("/retrieval", response_model=Any, status_code=status.HTTP_200_OK)
|
||
async def retrieve_chunks(
|
||
retrieve_data: chunk_schema.ChunkRetrieve,
|
||
db: Session = Depends(get_db),
|
||
current_user: User = Depends(get_current_user)
|
||
):
|
||
"""
|
||
retrieve chunk
|
||
"""
|
||
api_logger.info(f"retrieve chunk: query={retrieve_data.query}, username: {current_user.username}")
|
||
|
||
filters = [
|
||
knowledge_model.Knowledge.id.in_(retrieve_data.kb_ids),
|
||
knowledge_model.Knowledge.permission_id == knowledge_model.PermissionType.Private,
|
||
knowledge_model.Knowledge.chunk_num > 0,
|
||
knowledge_model.Knowledge.status == 1
|
||
]
|
||
private_items = knowledge_service.get_chunked_knowledgeids(
|
||
db=db,
|
||
filters=filters,
|
||
current_user=current_user
|
||
)
|
||
private_kb_ids = [item[0] for item in private_items]
|
||
private_workspace_ids = [item[1] for item in private_items]
|
||
filters = [
|
||
knowledge_model.Knowledge.id.in_(retrieve_data.kb_ids),
|
||
knowledge_model.Knowledge.permission_id == knowledge_model.PermissionType.Share,
|
||
knowledge_model.Knowledge.chunk_num > 0,
|
||
knowledge_model.Knowledge.status == 1
|
||
]
|
||
items = knowledge_service.get_chunked_knowledgeids(
|
||
db=db,
|
||
filters=filters,
|
||
current_user=current_user
|
||
)
|
||
if items:
|
||
filters = [
|
||
knowledgeshare_model.KnowledgeShare.target_kb_id.in_(retrieve_data.kb_ids)
|
||
]
|
||
share_items = knowledgeshare_service.get_source_kb_ids_by_target_kb_id(
|
||
db=db,
|
||
filters=filters,
|
||
current_user=current_user
|
||
)
|
||
share_kb_ids = [item[0] for item in share_items]
|
||
share_workspace_ids = [item[1] for item in share_items]
|
||
private_kb_ids.extend(share_kb_ids)
|
||
private_workspace_ids.extend(share_workspace_ids)
|
||
if not private_kb_ids:
|
||
return success(data=[], msg="retrieval successful")
|
||
kb_id = private_kb_ids[0]
|
||
uuid_strs = [f"Vector_index_{kb_id}_Node".lower() for kb_id in private_kb_ids]
|
||
indices = ",".join(uuid_strs)
|
||
db_knowledge = knowledge_service.get_knowledge_by_id(db, knowledge_id=kb_id, current_user=current_user)
|
||
if not db_knowledge:
|
||
raise HTTPException(
|
||
status_code=status.HTTP_404_NOT_FOUND,
|
||
detail="The knowledge base does not exist or access is denied"
|
||
)
|
||
|
||
vector_service = ElasticSearchVectorFactory().init_vector(knowledge=db_knowledge)
|
||
|
||
# 1 participle search, 2 semantic search, 3 hybrid search
|
||
match retrieve_data.retrieve_type:
|
||
case chunk_schema.RetrieveType.PARTICIPLE:
|
||
rs = vector_service.search_by_full_text(query=retrieve_data.query, top_k=retrieve_data.top_k, indices=indices, score_threshold=retrieve_data.similarity_threshold, file_names_filter=retrieve_data.file_names_filter)
|
||
return success(data=jsonable_encoder(rs), msg="retrieval successful")
|
||
case chunk_schema.RetrieveType.SEMANTIC:
|
||
rs = vector_service.search_by_vector(query=retrieve_data.query, top_k=retrieve_data.top_k, indices=indices, score_threshold=retrieve_data.vector_similarity_weight, file_names_filter=retrieve_data.file_names_filter)
|
||
return success(data=jsonable_encoder(rs), msg="retrieval successful")
|
||
case _:
|
||
rs1 = vector_service.search_by_vector(query=retrieve_data.query, top_k=retrieve_data.top_k, indices=indices, score_threshold=retrieve_data.vector_similarity_weight, file_names_filter=retrieve_data.file_names_filter)
|
||
rs2 = vector_service.search_by_full_text(query=retrieve_data.query, top_k=retrieve_data.top_k, indices=indices, score_threshold=retrieve_data.similarity_threshold, file_names_filter=retrieve_data.file_names_filter)
|
||
# Efficient deduplication
|
||
seen_ids = set()
|
||
unique_rs = []
|
||
for doc in rs1 + rs2:
|
||
if doc.metadata["doc_id"] not in seen_ids:
|
||
seen_ids.add(doc.metadata["doc_id"])
|
||
unique_rs.append(doc)
|
||
rs = vector_service.rerank(query=retrieve_data.query, docs=unique_rs, top_k=retrieve_data.top_k) if unique_rs else []
|
||
if retrieve_data.retrieve_type == chunk_schema.RetrieveType.Graph:
|
||
kb_ids = [str(kb_id) for kb_id in private_kb_ids]
|
||
workspace_ids = [str(workspace_id) for workspace_id in private_workspace_ids]
|
||
llm_key = ModelApiKeyService.get_available_api_key(db, db_knowledge.llm_id)
|
||
emb_key = ModelApiKeyService.get_available_api_key(db, db_knowledge.embedding_id)
|
||
# Prepare to configure chat_mdl、embedding_model、vision_model information
|
||
chat_model = Base(
|
||
key=llm_key.api_key,
|
||
model_name=llm_key.model_name,
|
||
base_url=llm_key.api_base
|
||
)
|
||
embedding_model = OpenAIEmbed(
|
||
key=emb_key.api_key,
|
||
model_name=emb_key.model_name,
|
||
base_url=emb_key.api_base
|
||
)
|
||
doc = kg_retriever.retrieval(question=retrieve_data.query, workspace_ids=workspace_ids, kb_ids=kb_ids, emb_mdl=embedding_model, llm=chat_model)
|
||
if doc:
|
||
rs.insert(0, doc)
|
||
return success(data=jsonable_encoder(rs), msg="retrieval successful") |