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