447 lines
20 KiB
Python
447 lines
20 KiB
Python
import os
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import urllib
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from fastapi import File, Form, Body, Query, UploadFile
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from pymilvus import connections
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from configs import (DEFAULT_VS_TYPE, EMBEDDING_MODEL,
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VECTOR_SEARCH_TOP_K, SCORE_THRESHOLD,
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CHUNK_SIZE, OVERLAP_SIZE, ZH_TITLE_ENHANCE,
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logger, log_verbose, DEFAULT_KNOWLEDGE_BASE, )
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from server.utils import BaseResponse, ListResponse, run_in_thread_pool
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from server.knowledge_base.utils import (validate_kb_name, list_files_from_folder, get_file_path,
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files2docs_in_thread, KnowledgeFile)
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from fastapi.responses import StreamingResponse, FileResponse
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from pydantic import Json
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import json
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from server.knowledge_base.kb_service.base import KBServiceFactory
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from server.db.repository.knowledge_file_repository import get_file_detail
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from typing import List
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from langchain.docstore.document import Document
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class DocumentWithScore(Document):
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score: float = None
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def search_docs(query: str = Body(..., description="用户输入", examples=["你好"]),
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knowledge_base_name: str = Body(..., description="知识库名称", examples=["samples"]),
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top_k: int = Body(VECTOR_SEARCH_TOP_K, description="匹配向量数"),
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score_threshold: float = Body(SCORE_THRESHOLD,
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description="知识库匹配相关度阈值，取值范围在0-1之间，SCORE越小，相关度越高，取到1相当于不筛选，建议设置在0.5左右",
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ge=0, le=1),
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) -> List[DocumentWithScore]:
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kb = KBServiceFactory.get_service_by_name(knowledge_base_name)
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print("kb", kb)
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if kb is None:
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return []
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docs = kb.search_docs(query, top_k, score_threshold)
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data = [DocumentWithScore(**x[0].dict(), score=x[1]) for x in docs]
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return data
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def list_files(
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knowledge_base_name: str
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) -> ListResponse:
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if not validate_kb_name(knowledge_base_name):
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return ListResponse(code=403, msg="Don't attack me", data=[])
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knowledge_base_name = urllib.parse.unquote(knowledge_base_name)
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kb = KBServiceFactory.get_service_by_name(knowledge_base_name)
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if kb is None:
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return ListResponse(code=404, msg=f"未找到知识库 {knowledge_base_name}", data=[])
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else:
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all_doc_names = kb.list_files()
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return ListResponse(data=all_doc_names)
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def _save_files_in_thread(files: List[UploadFile],
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knowledge_base_name: str,
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override: bool):
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'''
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通过多线程将上传的文件保存到对应知识库目录内。
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生成器返回保存结果：{"code":200, "msg": "xxx", "data": {"knowledge_base_name":"xxx", "file_name": "xxx"}}
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'''
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def save_file(file: UploadFile, knowledge_base_name: str, override: bool) -> dict:
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'''
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保存单个文件。
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'''
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try:
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filename = file.filename
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file_path = get_file_path(knowledge_base_name=knowledge_base_name, doc_name=filename)
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data = {"knowledge_base_name": knowledge_base_name, "file_name": filename}
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file_content = file.file.read() # 读取上传文件的内容
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if (os.path.isfile(file_path)
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and not override
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and os.path.getsize(file_path) == len(file_content)
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):
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# TODO: filesize 不同后的处理
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file_status = f"文件 {filename} 已存在。"
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logger.warn(file_status)
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return dict(code=404, msg=file_status, data=data)
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with open(file_path, "wb") as f:
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f.write(file_content)
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return dict(code=200, msg=f"成功上传文件 {filename}", data=data)
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except Exception as e:
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msg = f"{filename} 文件上传失败，报错信息为: {e}"
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logger.error(f'{e.__class__.__name__}: {msg}',
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exc_info=e if log_verbose else None)
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return dict(code=500, msg=msg, data=data)
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params = [{"file": file, "knowledge_base_name": knowledge_base_name, "override": override} for file in files]
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for result in run_in_thread_pool(save_file, params=params):
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yield result
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# 似乎没有单独增加一个文件上传API接口的必要
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# def upload_files(files: List[UploadFile] = File(..., description="上传文件，支持多文件"),
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# knowledge_base_name: str = Form(..., description="知识库名称", examples=["samples"]),
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# override: bool = Form(False, description="覆盖已有文件")):
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# '''
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# API接口：上传文件。流式返回保存结果：{"code":200, "msg": "xxx", "data": {"knowledge_base_name":"xxx", "file_name": "xxx"}}
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# '''
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# def generate(files, knowledge_base_name, override):
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# for result in _save_files_in_thread(files, knowledge_base_name=knowledge_base_name, override=override):
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# yield json.dumps(result, ensure_ascii=False)
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# return StreamingResponse(generate(files, knowledge_base_name=knowledge_base_name, override=override), media_type="text/event-stream")
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# TODO: 等langchain.document_loaders支持内存文件的时候再开通
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# def files2docs(files: List[UploadFile] = File(..., description="上传文件，支持多文件"),
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# knowledge_base_name: str = Form(..., description="知识库名称", examples=["samples"]),
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# override: bool = Form(False, description="覆盖已有文件"),
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# save: bool = Form(True, description="是否将文件保存到知识库目录")):
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# def save_files(files, knowledge_base_name, override):
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# for result in _save_files_in_thread(files, knowledge_base_name=knowledge_base_name, override=override):
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# yield json.dumps(result, ensure_ascii=False)
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# def files_to_docs(files):
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# for result in files2docs_in_thread(files):
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# yield json.dumps(result, ensure_ascii=False)
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def upload_docs(files: List[UploadFile] = File(..., description="上传文件，支持多文件"),
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knowledge_base_name: str = Form(..., description="知识库名称", examples=["samples"]),
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override: bool = Form(False, description="覆盖已有文件"),
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to_vector_store: bool = Form(True, description="上传文件后是否进行向量化"),
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chunk_size: int = Form(CHUNK_SIZE, description="知识库中单段文本最大长度"),
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chunk_overlap: int = Form(OVERLAP_SIZE, description="知识库中相邻文本重合长度"),
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zh_title_enhance: bool = Form(ZH_TITLE_ENHANCE, description="是否开启中文标题加强"),
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docs: Json = Form({}, description="自定义的docs，需要转为json字符串",
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examples=[{"test.txt": [Document(page_content="custom doc")]}]),
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not_refresh_vs_cache: bool = Form(False, description="暂不保存向量库（用于FAISS）"),
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) -> BaseResponse:
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'''
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API接口：上传文件，并/或向量化
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'''
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if not validate_kb_name(knowledge_base_name):
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return BaseResponse(code=403, msg="Don't attack me")
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kb = KBServiceFactory.get_service_by_name(knowledge_base_name)
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if kb is None:
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return BaseResponse(code=404, msg=f"未找到知识库 {knowledge_base_name}")
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failed_files = {}
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file_names = list(docs.keys())
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# 先将上传的文件保存到磁盘
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for result in _save_files_in_thread(files, knowledge_base_name=knowledge_base_name, override=override):
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filename = result["data"]["file_name"]
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if result["code"] != 200:
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failed_files[filename] = result["msg"]
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if filename not in file_names:
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file_names.append(filename)
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# 对保存的文件进行向量化
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if to_vector_store:
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result = update_docs(
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knowledge_base_name=knowledge_base_name,
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file_names=file_names,
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override_custom_docs=True,
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chunk_size=chunk_size,
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chunk_overlap=chunk_overlap,
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zh_title_enhance=zh_title_enhance,
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docs=docs,
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not_refresh_vs_cache=True,
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)
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failed_files.update(result.data["failed_files"])
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if not not_refresh_vs_cache:
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kb.save_vector_store()
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return BaseResponse(code=200, msg="文件上传与向量化完成", data={"failed_files": failed_files})
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def delete_docs(knowledge_base_name: str = Body(..., examples=["samples"]),
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file_names: List[str] = Body(..., examples=[["file_name.md", "test.txt"]]),
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delete_content: bool = Body(False),
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not_refresh_vs_cache: bool = Body(False, description="暂不保存向量库（用于FAISS）"),
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) -> BaseResponse:
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if not validate_kb_name(knowledge_base_name):
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return BaseResponse(code=403, msg="Don't attack me")
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knowledge_base_name = urllib.parse.unquote(knowledge_base_name)
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kb = KBServiceFactory.get_service_by_name(knowledge_base_name)
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if kb is None:
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return BaseResponse(code=404, msg=f"未找到知识库 {knowledge_base_name}")
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failed_files = {}
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for file_name in file_names:
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if not kb.exist_doc(file_name):
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failed_files[file_name] = f"未找到文件 {file_name}"
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try:
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kb_file = KnowledgeFile(filename=file_name,
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knowledge_base_name=knowledge_base_name)
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kb.delete_doc(kb_file, delete_content, not_refresh_vs_cache=True)
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except Exception as e:
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msg = f"{file_name} 文件删除失败，错误信息：{e}"
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logger.error(f'{e.__class__.__name__}: {msg}',
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exc_info=e if log_verbose else None)
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failed_files[file_name] = msg
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if not not_refresh_vs_cache:
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kb.save_vector_store()
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return BaseResponse(code=200, msg=f"文件删除完成", data={"failed_files": failed_files})
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def update_info(knowledge_base_name: str = Body(..., description="知识库名称", examples=["samples"]),
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kb_info: str = Body(..., description="知识库介绍", examples=["这是一个知识库"]),
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):
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if not validate_kb_name(knowledge_base_name):
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return BaseResponse(code=403, msg="Don't attack me")
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kb = KBServiceFactory.get_service_by_name(knowledge_base_name)
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if kb is None:
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return BaseResponse(code=404, msg=f"未找到知识库 {knowledge_base_name}")
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kb.update_info(kb_info)
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return BaseResponse(code=200, msg=f"知识库介绍修改完成", data={"kb_info": kb_info})
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def update_docs(
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knowledge_base_name: str = Body(..., description="知识库名称", examples=["samples"]),
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file_names: List[str] = Body(..., description="文件名称，支持多文件", examples=[["file_name1", "text.txt"]]),
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chunk_size: int = Body(CHUNK_SIZE, description="知识库中单段文本最大长度"),
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chunk_overlap: int = Body(OVERLAP_SIZE, description="知识库中相邻文本重合长度"),
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zh_title_enhance: bool = Body(ZH_TITLE_ENHANCE, description="是否开启中文标题加强"),
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override_custom_docs: bool = Body(False, description="是否覆盖之前自定义的docs"),
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docs: Json = Body({}, description="自定义的docs，需要转为json字符串",
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examples=[{"test.txt": [Document(page_content="custom doc")]}]),
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not_refresh_vs_cache: bool = Body(False, description="暂不保存向量库（用于FAISS）"),
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) -> BaseResponse:
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'''
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更新知识库文档
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'''
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if not validate_kb_name(knowledge_base_name):
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return BaseResponse(code=403, msg="Don't attack me")
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kb = KBServiceFactory.get_service_by_name(knowledge_base_name)
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if kb is None:
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return BaseResponse(code=404, msg=f"未找到知识库 {knowledge_base_name}")
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failed_files = {}
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kb_files = []
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# 生成需要加载docs的文件列表
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for file_name in file_names:
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file_detail = get_file_detail(kb_name=knowledge_base_name, filename=file_name)
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# 如果该文件之前使用了自定义docs，则根据参数决定略过或覆盖
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if file_detail.get("custom_docs") and not override_custom_docs:
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continue
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if file_name not in docs:
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try:
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kb_files.append(KnowledgeFile(filename=file_name, knowledge_base_name=knowledge_base_name))
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except Exception as e:
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msg = f"加载文档 {file_name} 时出错：{e}"
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logger.error(f'{e.__class__.__name__}: {msg}',
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exc_info=e if log_verbose else None)
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failed_files[file_name] = msg
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# 从文件生成docs，并进行向量化。
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# 这里利用了KnowledgeFile的缓存功能，在多线程中加载Document，然后传给KnowledgeFile
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for status, result in files2docs_in_thread(kb_files,
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chunk_size=chunk_size,
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chunk_overlap=chunk_overlap,
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zh_title_enhance=zh_title_enhance):
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if status:
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kb_name, file_name, new_docs = result
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kb_file = KnowledgeFile(filename=file_name,
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knowledge_base_name=knowledge_base_name)
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kb_file.splited_docs = new_docs
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kb.update_doc(kb_file, not_refresh_vs_cache=True)
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else:
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kb_name, file_name, error = result
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failed_files[file_name] = error
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# 将自定义的docs进行向量化
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for file_name, v in docs.items():
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try:
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v = [x if isinstance(x, Document) else Document(**x) for x in v]
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kb_file = KnowledgeFile(filename=file_name, knowledge_base_name=knowledge_base_name)
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kb.update_doc(kb_file, docs=v, not_refresh_vs_cache=True)
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except Exception as e:
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msg = f"为 {file_name} 添加自定义docs时出错：{e}"
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logger.error(f'{e.__class__.__name__}: {msg}',
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exc_info=e if log_verbose else None)
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failed_files[file_name] = msg
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if not not_refresh_vs_cache:
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kb.save_vector_store()
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return BaseResponse(code=200, msg=f"更新文档完成", data={"failed_files": failed_files})
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def download_doc(
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knowledge_base_name: str = Query(..., description="知识库名称", examples=["samples"]),
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file_name: str = Query(..., description="文件名称", examples=["test.txt"]),
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preview: bool = Query(False, description="是：浏览器内预览；否：下载"),
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):
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'''
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下载知识库文档
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'''
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if not validate_kb_name(knowledge_base_name):
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return BaseResponse(code=403, msg="Don't attack me")
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kb = KBServiceFactory.get_service_by_name(knowledge_base_name)
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if kb is None:
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return BaseResponse(code=404, msg=f"未找到知识库 {knowledge_base_name}")
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if preview:
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content_disposition_type = "inline"
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else:
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content_disposition_type = None
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try:
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kb_file = KnowledgeFile(filename=file_name,
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knowledge_base_name=knowledge_base_name)
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if os.path.exists(kb_file.filepath):
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return FileResponse(
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path=kb_file.filepath,
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filename=kb_file.filename,
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media_type="multipart/form-data",
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content_disposition_type=content_disposition_type,
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)
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except Exception as e:
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msg = f"{kb_file.filename} 读取文件失败，错误信息是：{e}"
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logger.error(f'{e.__class__.__name__}: {msg}',
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exc_info=e if log_verbose else None)
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return BaseResponse(code=500, msg=msg)
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return BaseResponse(code=500, msg=f"{kb_file.filename} 读取文件失败")
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def recreate_vector_store(
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knowledge_base_name: str = Body(..., examples=["samples"]),
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allow_empty_kb: bool = Body(True),
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vs_type: str = Body(DEFAULT_VS_TYPE),
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embed_model: str = Body(EMBEDDING_MODEL),
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chunk_size: int = Body(CHUNK_SIZE, description="知识库中单段文本最大长度"),
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chunk_overlap: int = Body(OVERLAP_SIZE, description="知识库中相邻文本重合长度"),
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zh_title_enhance: bool = Body(ZH_TITLE_ENHANCE, description="是否开启中文标题加强"),
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not_refresh_vs_cache: bool = Body(False, description="暂不保存向量库（用于FAISS）"),
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):
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'''
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recreate vector store from the content.
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this is usefull when user can copy files to content folder directly instead of upload through network.
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by default, get_service_by_name only return knowledge base in the info.db and having document files in it.
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set allow_empty_kb to True make it applied on empty knowledge base which it not in the info.db or having no documents.
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'''
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def output():
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kb = KBServiceFactory.get_service(knowledge_base_name, vs_type, embed_model)
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if not kb.exists() and not allow_empty_kb:
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yield {"code": 404, "msg": f"未找到知识库 ‘{knowledge_base_name}’"}
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else:
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if kb.exists():
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kb.clear_vs()
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kb.create_kb()
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files = list_files_from_folder(knowledge_base_name)
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kb_files = [(file, knowledge_base_name) for file in files]
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i = 0
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for status, result in files2docs_in_thread(kb_files,
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chunk_size=chunk_size,
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chunk_overlap=chunk_overlap,
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zh_title_enhance=zh_title_enhance):
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if status:
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kb_name, file_name, docs = result
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kb_file = KnowledgeFile(filename=file_name, knowledge_base_name=kb_name)
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kb_file.splited_docs = docs
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yield json.dumps({
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"code": 200,
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"msg": f"({i + 1} / {len(files)}): {file_name}",
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"total": len(files),
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"finished": i,
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"doc": file_name,
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}, ensure_ascii=False)
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kb.add_doc(kb_file, not_refresh_vs_cache=True)
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else:
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kb_name, file_name, error = result
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msg = f"添加文件‘{file_name}’到知识库‘{knowledge_base_name}’时出错：{error}。已跳过。"
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logger.error(msg)
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yield json.dumps({
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"code": 500,
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"msg": msg,
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})
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i += 1
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if not not_refresh_vs_cache:
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kb.save_vector_store()
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return StreamingResponse(output(), media_type="text/event-stream")
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# get all param
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def search_docs_all(query, knowledge_base_name=DEFAULT_KNOWLEDGE_BASE, top_k=VECTOR_SEARCH_TOP_K,
|
||
score_threshold=SCORE_THRESHOLD):
|
||
kb = KBServiceFactory.get_service_by_name(knowledge_base_name)
|
||
if kb is None:
|
||
return []
|
||
data = kb.search_docs_all(query, top_k, score_threshold)
|
||
|
||
return data
|
||
|
||
|
||
def do_delete_text_by_pk(kb, pk):
|
||
kb.do_delete_one_doc(pk)
|
||
|
||
|
||
def do_insert_text(kb, source, query, answer):
|
||
# 执行插入操作
|
||
from server.knowledge_base.kb_service.base import EmbeddingsFunAdapter
|
||
embedding_function = EmbeddingsFunAdapter(kb.embeddings)
|
||
new_vector = embedding_function.embed_query(query)
|
||
from pymilvus import connections
|
||
connections.connect("default", host="localhost", port="19530")
|
||
c = kb.get_collection(kb.kb_name)
|
||
data = [
|
||
[source],
|
||
[query],
|
||
[new_vector]
|
||
]
|
||
mr = c.insert(data)
|
||
return mr
|
||
|
||
|
||
def update_text(query, answer, kb_name):
|
||
from server.knowledge_base.kb_service.base import KBServiceFactory
|
||
kb = KBServiceFactory.get_service_by_name(kb_name)
|
||
results = kb.search_docs_all(query)
|
||
# get the value of an output field specified in the search request.
|
||
hit = results[0][0]
|
||
source = hit.entity.get('source')
|
||
# 插入操作
|
||
return do_insert_text(kb, source, query, answer)
|
||
|
||
|
||
if __name__ == '__main__':
|
||
connections.connect("default", host="localhost", port="19530")
|
||
kb = KBServiceFactory.get_service_by_name("xiaoshuo")
|
||
print(kb)
|
||
do_delete_text_by_pk(kb, "445534446841640594")
|