185 lines
7.5 KiB
Python
185 lines
7.5 KiB
Python
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from configs import (
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EMBEDDING_MODEL, DEFAULT_VS_TYPE, ZH_TITLE_ENHANCE,
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CHUNK_SIZE, OVERLAP_SIZE,
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logger, log_verbose
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)
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from server.knowledge_base.utils import (
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get_file_path, list_kbs_from_folder,
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list_files_from_folder, files2docs_in_thread,
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KnowledgeFile
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)
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from server.knowledge_base.kb_service.base import KBServiceFactory
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from server.db.models.chat_history_model import ChatHistoryModel
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from server.db.repository.knowledge_file_repository import add_file_to_db # ensure Models are imported
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from server.db.base import Base, engine
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from server.db.session import session_scope
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import os
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from dateutil.parser import parse
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from typing import Literal, List
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def create_tables():
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Base.metadata.create_all(bind=engine)
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def reset_tables():
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Base.metadata.drop_all(bind=engine)
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create_tables()
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def import_from_db(
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sqlite_path: str = None,
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# csv_path: str = None,
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) -> bool:
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"""
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在知识库与向量库无变化的情况下，从备份数据库中导入数据到 info.db。
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适用于版本升级时，info.db 结构变化，但无需重新向量化的情况。
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请确保两边数据库表名一致，需要导入的字段名一致
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当前仅支持 sqlite
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"""
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import sqlite3 as sql
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from pprint import pprint
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models = list(Base.registry.mappers)
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try:
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con = sql.connect(sqlite_path)
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con.row_factory = sql.Row
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cur = con.cursor()
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tables = [x["name"] for x in cur.execute("select name from sqlite_master where type='table'").fetchall()]
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for model in models:
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table = model.local_table.fullname
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if table not in tables:
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continue
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print(f"processing table: {table}")
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with session_scope() as session:
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for row in cur.execute(f"select * from {table}").fetchall():
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data = {k: row[k] for k in row.keys() if k in model.columns}
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if "create_time" in data:
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data["create_time"] = parse(data["create_time"])
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pprint(data)
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session.add(model.class_(**data))
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con.close()
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return True
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except Exception as e:
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print(f"无法读取备份数据库：{sqlite_path}。错误信息：{e}")
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return False
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def file_to_kbfile(kb_name: str, files: List[str]) -> List[KnowledgeFile]:
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kb_files = []
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for file in files:
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try:
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kb_file = KnowledgeFile(filename=file, knowledge_base_name=kb_name)
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kb_files.append(kb_file)
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except Exception as e:
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msg = f"{e}，已跳过"
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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 kb_files
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def folder2db(
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kb_names: List[str],
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mode: Literal["recreate_vs", "update_in_db", "increament"],
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vs_type: Literal["faiss", "milvus", "pg", "chromadb"] = DEFAULT_VS_TYPE,
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embed_model: str = EMBEDDING_MODEL,
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chunk_size: int = CHUNK_SIZE,
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chunk_overlap: int = OVERLAP_SIZE,
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zh_title_enhance: bool = ZH_TITLE_ENHANCE,
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):
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"""
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use existed files in local folder to populate database and/or vector store.
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set parameter `mode` to:
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recreate_vs: recreate all vector store and fill info to database using existed files in local folder
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fill_info_only(disabled): do not create vector store, fill info to db using existed files only
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update_in_db: update vector store and database info using local files that existed in database only
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increament: create vector store and database info for local files that not existed in database only
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"""
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def files2vs(kb_name: str, kb_files: List[KnowledgeFile]):
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for success, 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 success:
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_, filename, docs = result
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print(f"正在将 {kb_name}/{filename} 添加到向量库，共包含{len(docs)}条文档")
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kb_file = KnowledgeFile(filename=filename, knowledge_base_name=kb_name)
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kb_file.splited_docs = docs
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kb.add_doc(kb_file=kb_file, not_refresh_vs_cache=True)
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else:
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print(result)
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kb_names = kb_names or list_kbs_from_folder()
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for kb_name in kb_names:
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kb = KBServiceFactory.get_service(kb_name, vs_type, embed_model)
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if not kb.exists():
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kb.create_kb()
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# 清除向量库，从本地文件重建
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if mode == "recreate_vs":
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kb.clear_vs()
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kb.create_kb()
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kb_files = file_to_kbfile(kb_name, list_files_from_folder(kb_name))
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files2vs(kb_name, kb_files)
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kb.save_vector_store()
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# # 不做文件内容的向量化，仅将文件元信息存到数据库
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# # 由于现在数据库存了很多与文本切分相关的信息，单纯存储文件信息意义不大，该功能取消。
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# elif mode == "fill_info_only":
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# files = list_files_from_folder(kb_name)
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# kb_files = file_to_kbfile(kb_name, files)
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# for kb_file in kb_files:
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# add_file_to_db(kb_file)
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# print(f"已将 {kb_name}/{kb_file.filename} 添加到数据库")
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# 以数据库中文件列表为基准，利用本地文件更新向量库
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elif mode == "update_in_db":
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files = kb.list_files()
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kb_files = file_to_kbfile(kb_name, files)
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files2vs(kb_name, kb_files)
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kb.save_vector_store()
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# 对比本地目录与数据库中的文件列表，进行增量向量化
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elif mode == "increament":
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db_files = kb.list_files()
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folder_files = list_files_from_folder(kb_name)
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files = list(set(folder_files) - set(db_files))
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kb_files = file_to_kbfile(kb_name, files)
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files2vs(kb_name, kb_files)
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kb.save_vector_store()
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else:
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print(f"unspported migrate mode: {mode}")
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def prune_db_docs(kb_names: List[str]):
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"""
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delete docs in database that not existed in local folder.
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it is used to delete database docs after user deleted some doc files in file browser
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"""
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for kb_name in kb_names:
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kb = KBServiceFactory.get_service_by_name(kb_name)
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if kb is not None:
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files_in_db = kb.list_files()
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files_in_folder = list_files_from_folder(kb_name)
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files = list(set(files_in_db) - set(files_in_folder))
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kb_files = file_to_kbfile(kb_name, files)
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for kb_file in kb_files:
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kb.delete_doc(kb_file, not_refresh_vs_cache=True)
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print(f"success to delete docs for file: {kb_name}/{kb_file.filename}")
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kb.save_vector_store()
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def prune_folder_files(kb_names: List[str]):
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"""
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delete doc files in local folder that not existed in database.
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it is used to free local disk space by delete unused doc files.
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"""
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for kb_name in kb_names:
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kb = KBServiceFactory.get_service_by_name(kb_name)
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if kb is not None:
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files_in_db = kb.list_files()
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files_in_folder = list_files_from_folder(kb_name)
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files = list(set(files_in_folder) - set(files_in_db))
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for file in files:
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os.remove(get_file_path(kb_name, file))
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print(f"success to delete file: {kb_name}/{file}")
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