122 lines
3.8 KiB
Python
Executable File
122 lines
3.8 KiB
Python
Executable File
import sys
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sys.path.append(".")
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from server.knowledge_base.migrate import (create_tables, reset_tables, import_from_db,
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folder2db, prune_db_docs, prune_folder_files)
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from configs.model_config import NLTK_DATA_PATH, EMBEDDING_MODEL
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import nltk
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nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path
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from datetime import datetime
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import sys
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if __name__ == "__main__":
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import argparse
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parser = argparse.ArgumentParser(description="please specify only one operate method once time.")
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parser.add_argument(
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"-r",
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"--recreate-vs",
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action="store_true",
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help=('''
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recreate vector store.
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use this option if you have copied document files to the content folder, but vector store has not been populated or DEFAUL_VS_TYPE/EMBEDDING_MODEL changed.
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'''
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)
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)
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parser.add_argument(
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"--create-tables",
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action="store_true",
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help=("create empty tables if not existed")
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)
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parser.add_argument(
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"--clear-tables",
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action="store_true",
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help=("create empty tables, or drop the database tables before recreate vector stores")
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)
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parser.add_argument(
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"--import-db",
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help="import tables from specified sqlite database"
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)
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parser.add_argument(
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"-u",
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"--update-in-db",
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action="store_true",
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help=('''
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update vector store for files exist in database.
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use this option if you want to recreate vectors for files exist in db and skip files exist in local folder only.
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'''
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)
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)
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parser.add_argument(
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"-i",
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"--increament",
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action="store_true",
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help=('''
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update vector store for files exist in local folder and not exist in database.
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use this option if you want to create vectors increamentally.
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'''
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)
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)
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parser.add_argument(
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"--prune-db",
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action="store_true",
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help=('''
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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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)
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)
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parser.add_argument(
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"--prune-folder",
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action="store_true",
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help=('''
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delete doc files in local folder that not existed in database.
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is is used to free local disk space by delete unused doc files.
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'''
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)
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)
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parser.add_argument(
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"-n",
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"--kb-name",
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type=str,
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nargs="+",
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default=[],
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help=("specify knowledge base names to operate on. default is all folders exist in KB_ROOT_PATH.")
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)
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parser.add_argument(
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"-e",
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"--embed-model",
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type=str,
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default=EMBEDDING_MODEL,
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help=("specify embeddings model.")
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)
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args = parser.parse_args()
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start_time = datetime.now()
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if args.create_tables:
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create_tables() # confirm tables exist
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if args.clear_tables:
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reset_tables()
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print("database talbes reseted")
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if args.recreate_vs:
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create_tables()
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print("recreating all vector stores")
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folder2db(kb_names=args.kb_name, mode="recreate_vs", embed_model=args.embed_model)
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elif args.import_db:
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import_from_db(args.import_db)
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elif args.update_in_db:
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folder2db(kb_names=args.kb_name, mode="update_in_db", embed_model=args.embed_model)
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elif args.increament:
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folder2db(kb_names=args.kb_name, mode="increament", embed_model=args.embed_model)
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elif args.prune_db:
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prune_db_docs(args.kb_name)
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elif args.prune_folder:
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prune_folder_files(args.kb_name)
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end_time = datetime.now()
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print(f"总计用时： {end_time-start_time}")
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