Langchain-Chatchat/server/knowledge_base/kb_doc_api.py

120 lines
4.6 KiB
Python

import os
import urllib
from fastapi import File, Form, UploadFile
from server.utils import BaseResponse, ListResponse
from server.knowledge_base.utils import (validate_kb_name)
from fastapi.responses import StreamingResponse
import json
from server.knowledge_base.utils import KnowledgeFile, list_docs_from_folder
from server.knowledge_base.kb_service.base import KBServiceFactory
from server.knowledge_base.kb_service.base import SupportedVSType
from server.knowledge_base.kb_service.faiss_kb_service import refresh_vs_cache
async def list_docs(knowledge_base_name: str):
if not validate_kb_name(knowledge_base_name):
return ListResponse(code=403, msg="Don't attack me", data=[])
knowledge_base_name = urllib.parse.unquote(knowledge_base_name)
kb = KBServiceFactory.get_service_by_name(knowledge_base_name)
if kb is None:
return ListResponse(code=404, msg=f"未找到知识库 {knowledge_base_name}", data=[])
else:
all_doc_names = kb.list_docs()
return ListResponse(data=all_doc_names)
async def upload_doc(file: UploadFile = File(description="上传文件"),
knowledge_base_name: str = Form(..., description="知识库名称", example="kb1"),
override: bool = Form(False, description="覆盖已有文件", example=False),
):
if not validate_kb_name(knowledge_base_name):
return BaseResponse(code=403, msg="Don't attack me")
kb = KBServiceFactory.get_service_by_name(knowledge_base_name)
if kb is None:
return BaseResponse(code=404, msg=f"未找到知识库 {knowledge_base_name}")
file_content = await file.read() # 读取上传文件的内容
kb_file = KnowledgeFile(filename=file.filename,
knowledge_base_name=knowledge_base_name)
if (os.path.exists(kb_file.filepath)
and not override
and os.path.getsize(kb_file.filepath) == len(file_content)
):
# TODO: filesize 不同后的处理
file_status = f"文件 {kb_file.filename} 已存在。"
return BaseResponse(code=404, msg=file_status)
try:
with open(kb_file.filepath, "wb") as f:
f.write(file_content)
except Exception as e:
return BaseResponse(code=500, msg=f"{kb_file.filename} 文件上传失败,报错信息为: {e}")
kb.add_doc(kb_file)
return BaseResponse(code=200, msg=f"成功上传文件 {kb_file.filename}")
async def delete_doc(knowledge_base_name: str,
doc_name: str,
):
if not validate_kb_name(knowledge_base_name):
return BaseResponse(code=403, msg="Don't attack me")
knowledge_base_name = urllib.parse.unquote(knowledge_base_name)
kb = KBServiceFactory.get_service_by_name(knowledge_base_name)
if kb is None:
return BaseResponse(code=404, msg=f"未找到知识库 {knowledge_base_name}")
if not kb.exist_doc(doc_name):
return BaseResponse(code=404, msg=f"未找到文件 {doc_name}")
kb_file = KnowledgeFile(filename=doc_name,
knowledge_base_name=knowledge_base_name)
kb.delete_doc(kb_file)
return BaseResponse(code=200, msg=f"{kb_file.filename} 文件删除成功")
# return BaseResponse(code=500, msg=f"{kb_file.filename} 文件删除失败")
async def update_doc():
# TODO: 替换文件
# refresh_vs_cache(knowledge_base_name)
pass
async def download_doc():
# TODO: 下载文件
pass
async def recreate_vector_store(knowledge_base_name: str):
'''
recreate vector store from the content.
this is usefull when user can copy files to content folder directly instead of upload through network.
'''
kb = KBServiceFactory.get_service_by_name(knowledge_base_name)
if kb is None:
return BaseResponse(code=404, msg=f"未找到知识库 {knowledge_base_name}")
async def output(kb):
kb.clear_vs()
print(f"start to recreate vector store of {kb.kb_name}")
docs = list_docs_from_folder(knowledge_base_name)
print(docs)
for i, filename in enumerate(docs):
yield json.dumps({
"total": len(docs),
"finished": i,
"doc": filename,
})
kb_file = KnowledgeFile(filename=filename,
knowledge_base_name=kb.kb_name)
print(f"processing {kb_file.filepath} to vector store.")
kb.add_doc(kb_file)
if kb.vs_type == SupportedVSType.FAISS:
refresh_vs_cache(knowledge_base_name)
return StreamingResponse(output(kb), media_type="text/event-stream")