Merge branch 'dev_fastchat' of github.com:chatchat-space/langchain-ChatGLM into dev_fastchat
This commit is contained in:
hzg0601
commit
82494aec00
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@ -1,407 +0,0 @@
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import os
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import sqlite3
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import datetime
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import shutil
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from langchain.vectorstores import FAISS
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from langchain.embeddings.huggingface import HuggingFaceEmbeddings
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from configs.model_config import (embedding_model_dict, EMBEDDING_MODEL, EMBEDDING_DEVICE,
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DB_ROOT_PATH, VECTOR_SEARCH_TOP_K, CACHED_VS_NUM)
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from server.utils import torch_gc
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from functools import lru_cache
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from server.knowledge_base.knowledge_file import KnowledgeFile
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from typing import List
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import numpy as np
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from server.knowledge_base.utils import (get_kb_path, get_doc_path, get_vs_path)
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SUPPORTED_VS_TYPES = ["faiss", "milvus"]
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_VECTOR_STORE_TICKS = {}
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@lru_cache(1)
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def load_embeddings(model: str, device: str):
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embeddings = HuggingFaceEmbeddings(model_name=embedding_model_dict[model],
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model_kwargs={'device': device})
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return embeddings
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@lru_cache(CACHED_VS_NUM)
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def load_vector_store(
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knowledge_base_name: str,
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embedding_model: str,
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embedding_device: str,
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tick: int, # tick will be changed by upload_doc etc. and make cache refreshed.
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):
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print(f"loading vector store in '{knowledge_base_name}' with '{embedding_model}' embeddings.")
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embeddings = load_embeddings(embedding_model, embedding_device)
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vs_path = get_vs_path(knowledge_base_name)
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search_index = FAISS.load_local(vs_path, embeddings)
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return search_index
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def refresh_vs_cache(kb_name: str):
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"""
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make vector store cache refreshed when next loading
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"""
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_VECTOR_STORE_TICKS[kb_name] = _VECTOR_STORE_TICKS.get(kb_name, 0) + 1
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def list_kbs_from_db():
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conn = sqlite3.connect(DB_ROOT_PATH)
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c = conn.cursor()
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c.execute('''CREATE TABLE if not exists knowledge_base
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(id INTEGER PRIMARY KEY AUTOINCREMENT,
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kb_name TEXT,
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vs_type TEXT,
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embed_model TEXT,
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file_count INTEGER,
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create_time DATETIME) ''')
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c.execute(f'''SELECT kb_name
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FROM knowledge_base
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WHERE file_count>0 ''')
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kbs = [i[0] for i in c.fetchall() if i]
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conn.commit()
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conn.close()
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return kbs
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def add_kb_to_db(kb_name, vs_type, embed_model):
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conn = sqlite3.connect(DB_ROOT_PATH)
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c = conn.cursor()
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# Create table
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c.execute('''CREATE TABLE if not exists knowledge_base
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(id INTEGER PRIMARY KEY AUTOINCREMENT,
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kb_name TEXT,
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vs_type TEXT,
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embed_model TEXT,
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file_count INTEGER,
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create_time DATETIME) ''')
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# Insert a row of data
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c.execute(f"""INSERT INTO knowledge_base
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(kb_name, vs_type, embed_model, file_count, create_time)
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VALUES
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('{kb_name}','{vs_type}','{embed_model}',
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0,'{datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")}')""")
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conn.commit()
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conn.close()
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def kb_exists(kb_name):
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conn = sqlite3.connect(DB_ROOT_PATH)
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c = conn.cursor()
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c.execute('''CREATE TABLE if not exists knowledge_base
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(id INTEGER PRIMARY KEY AUTOINCREMENT,
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kb_name TEXT,
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vs_type TEXT,
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embed_model TEXT,
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file_count INTEGER,
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create_time DATETIME) ''')
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c.execute(f'''SELECT COUNT(*)
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FROM knowledge_base
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WHERE kb_name="{kb_name}" ''')
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status = True if c.fetchone()[0] else False
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conn.commit()
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conn.close()
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return status
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def load_kb_from_db(kb_name):
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conn = sqlite3.connect(DB_ROOT_PATH)
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c = conn.cursor()
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c.execute('''CREATE TABLE if not exists knowledge_base
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(id INTEGER PRIMARY KEY AUTOINCREMENT,
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kb_name TEXT,
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vs_type TEXT,
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embed_model TEXT,
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file_count INTEGER,
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create_time DATETIME) ''')
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c.execute(f'''SELECT kb_name, vs_type, embed_model
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FROM knowledge_base
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WHERE kb_name="{kb_name}" ''')
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resp = c.fetchone()
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if resp:
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kb_name, vs_type, embed_model = resp
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else:
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kb_name, vs_type, embed_model = None, None, None
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conn.commit()
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conn.close()
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return kb_name, vs_type, embed_model
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def delete_kb_from_db(kb_name):
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conn = sqlite3.connect(DB_ROOT_PATH)
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c = conn.cursor()
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# delete kb from table knowledge_base
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c.execute('''CREATE TABLE if not exists knowledge_base
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(id INTEGER PRIMARY KEY AUTOINCREMENT,
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kb_name TEXT,
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vs_type TEXT,
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embed_model TEXT,
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file_count INTEGER,
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create_time DATETIME) ''')
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c.execute(f'''DELETE
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FROM knowledge_base
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WHERE kb_name="{kb_name}" ''')
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# delete files in kb from table knowledge_files
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c.execute('''CREATE TABLE if not exists knowledge_files
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(id INTEGER PRIMARY KEY AUTOINCREMENT,
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file_name TEXT,
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file_ext TEXT,
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kb_name TEXT,
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document_loader_name TEXT,
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text_splitter_name TEXT,
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file_version INTEGER,
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create_time DATETIME) ''')
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# Insert a row of data
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c.execute(f"""DELETE
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FROM knowledge_files
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WHERE kb_name="{kb_name}"
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""")
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conn.commit()
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conn.close()
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return True
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def list_docs_from_db(kb_name):
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conn = sqlite3.connect(DB_ROOT_PATH)
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c = conn.cursor()
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c.execute('''CREATE TABLE if not exists knowledge_files
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(id INTEGER PRIMARY KEY AUTOINCREMENT,
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file_name TEXT,
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file_ext TEXT,
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kb_name TEXT,
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document_loader_name TEXT,
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text_splitter_name TEXT,
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file_version INTEGER,
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create_time DATETIME) ''')
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c.execute(f'''SELECT file_name
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FROM knowledge_files
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WHERE kb_name="{kb_name}" ''')
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kbs = [i[0] for i in c.fetchall() if i]
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conn.commit()
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conn.close()
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return kbs
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def add_doc_to_db(kb_file: KnowledgeFile):
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conn = sqlite3.connect(DB_ROOT_PATH)
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c = conn.cursor()
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# Create table
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c.execute('''CREATE TABLE if not exists knowledge_files
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(id INTEGER PRIMARY KEY AUTOINCREMENT,
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file_name TEXT,
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file_ext TEXT,
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kb_name TEXT,
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document_loader_name TEXT,
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text_splitter_name TEXT,
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file_version INTEGER,
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create_time DATETIME) ''')
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# Insert a row of data
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# TODO: 同名文件添加至知识库时，file_version增加
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c.execute(f"""SELECT 1 FROM knowledge_files WHERE file_name="{kb_file.filename}" AND kb_name="{kb_file.kb_name}" """)
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record_exist = c.fetchone()
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if record_exist is not None:
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c.execute(f"""UPDATE knowledge_files
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SET file_version = file_version + 1
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WHERE file_name="{kb_file.filename}" AND kb_name="{kb_file.kb_name}"
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""")
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else:
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c.execute(f"""INSERT INTO knowledge_files
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(file_name, file_ext, kb_name, document_loader_name, text_splitter_name, file_version, create_time)
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VALUES
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('{kb_file.filename}','{kb_file.ext}','{kb_file.kb_name}', '{kb_file.document_loader_name}',
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'{kb_file.text_splitter_name}',0,'{datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")}')""")
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conn.commit()
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conn.close()
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def delete_file_from_db(kb_file: KnowledgeFile):
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conn = sqlite3.connect(DB_ROOT_PATH)
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c = conn.cursor()
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# delete files in kb from table knowledge_files
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c.execute('''CREATE TABLE if not exists knowledge_files
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(id INTEGER PRIMARY KEY AUTOINCREMENT,
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file_name TEXT,
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file_ext TEXT,
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kb_name TEXT,
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document_loader_name TEXT,
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text_splitter_name TEXT,
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file_version INTEGER,
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create_time DATETIME) ''')
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# Insert a row of data
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c.execute(f"""DELETE
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FROM knowledge_files
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WHERE file_name="{kb_file.filename}"
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AND kb_name="{kb_file.kb_name}"
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""")
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conn.commit()
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conn.close()
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return True
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def doc_exists(kb_file: KnowledgeFile):
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conn = sqlite3.connect(DB_ROOT_PATH)
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c = conn.cursor()
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c.execute('''CREATE TABLE if not exists knowledge_files
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(id INTEGER PRIMARY KEY AUTOINCREMENT,
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file_name TEXT,
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file_ext TEXT,
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kb_name TEXT,
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document_loader_name TEXT,
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text_splitter_name TEXT,
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file_version INTEGER,
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create_time DATETIME) ''')
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c.execute(f'''SELECT COUNT(*)
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FROM knowledge_files
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WHERE file_name="{kb_file.filename}"
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AND kb_name="{kb_file.kb_name}" ''')
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status = True if c.fetchone()[0] else False
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conn.commit()
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conn.close()
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return status
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def delete_doc_from_faiss(vector_store: FAISS, ids: List[str]):
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overlapping = set(ids).intersection(vector_store.index_to_docstore_id.values())
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if not overlapping:
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raise ValueError("ids do not exist in the current object")
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_reversed_index = {v: k for k, v in vector_store.index_to_docstore_id.items()}
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index_to_delete = [_reversed_index[i] for i in ids]
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vector_store.index.remove_ids(np.array(index_to_delete, dtype=np.int64))
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for _id in index_to_delete:
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del vector_store.index_to_docstore_id[_id]
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# Remove items from docstore.
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overlapping2 = set(ids).intersection(vector_store.docstore._dict)
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if not overlapping2:
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raise ValueError(f"Tried to delete ids that does not exist: {ids}")
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for _id in ids:
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vector_store.docstore._dict.pop(_id)
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return vector_store
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class KnowledgeBase:
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def __init__(self,
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knowledge_base_name: str,
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vector_store_type: str = "faiss",
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embed_model: str = EMBEDDING_MODEL,
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):
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self.kb_name = knowledge_base_name
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if vector_store_type not in SUPPORTED_VS_TYPES:
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raise ValueError(f"暂未支持向量库类型 {vector_store_type}")
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self.vs_type = vector_store_type
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if embed_model not in embedding_model_dict.keys():
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raise ValueError(f"暂未支持embedding模型 {embed_model}")
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self.embed_model = embed_model
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self.kb_path = get_kb_path(self.kb_name)
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self.doc_path = get_doc_path(self.kb_name)
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if self.vs_type in ["faiss"]:
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self.vs_path = get_vs_path(self.kb_name)
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elif self.vs_type in ["milvus"]:
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pass
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def create(self):
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if not os.path.exists(self.doc_path):
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os.makedirs(self.doc_path)
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if self.vs_type in ["faiss"]:
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if not os.path.exists(self.vs_path):
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os.makedirs(self.vs_path)
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add_kb_to_db(self.kb_name, self.vs_type, self.embed_model)
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elif self.vs_type in ["milvus"]:
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# TODO: 创建milvus库
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pass
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return True
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def recreate_vs(self):
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if self.vs_type in ["faiss"]:
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shutil.rmtree(self.vs_path)
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self.create()
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def add_doc(self, kb_file: KnowledgeFile):
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docs = kb_file.file2text()
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embeddings = load_embeddings(self.embed_model, EMBEDDING_DEVICE)
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if self.vs_type in ["faiss"]:
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if os.path.exists(self.vs_path) and "index.faiss" in os.listdir(self.vs_path):
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vector_store = FAISS.load_local(self.vs_path, embeddings)
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vector_store.add_documents(docs)
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torch_gc()
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else:
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if not os.path.exists(self.vs_path):
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os.makedirs(self.vs_path)
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vector_store = FAISS.from_documents(docs, embeddings) # docs 为Document列表
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torch_gc()
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vector_store.save_local(self.vs_path)
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add_doc_to_db(kb_file)
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refresh_vs_cache(self.kb_name)
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elif self.vs_type in ["milvus"]:
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# TODO: 向milvus库中增加文件
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pass
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def delete_doc(self, kb_file: KnowledgeFile):
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if os.path.exists(kb_file.filepath):
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os.remove(kb_file.filepath)
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if self.vs_type in ["faiss"]:
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# TODO: 从FAISS向量库中删除文档
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embeddings = load_embeddings(self.embed_model, EMBEDDING_DEVICE)
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if os.path.exists(self.vs_path) and "index.faiss" in os.listdir(self.vs_path):
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vector_store = FAISS.load_local(self.vs_path, embeddings)
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ids = [k for k, v in vector_store.docstore._dict.items() if v.metadata["source"] == kb_file.filepath]
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if len(ids) == 0:
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return None
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print(len(ids))
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vector_store = delete_doc_from_faiss(vector_store, ids)
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vector_store.save_local(self.vs_path)
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refresh_vs_cache(self.kb_name)
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delete_file_from_db(kb_file)
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return True
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def exist_doc(self, file_name: str):
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return doc_exists(KnowledgeFile(knowledge_base_name=self.kb_name,
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filename=file_name))
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def list_docs(self):
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return list_docs_from_db(self.kb_name)
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def search_docs(self,
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query: str,
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top_k: int = VECTOR_SEARCH_TOP_K,
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embedding_device: str = EMBEDDING_DEVICE, ):
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search_index = load_vector_store(self.kb_name,
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self.embed_model,
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embedding_device,
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_VECTOR_STORE_TICKS.get(self.kb_name))
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docs = search_index.similarity_search(query, k=top_k)
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return docs
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@classmethod
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def exists(cls,
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knowledge_base_name: str):
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return kb_exists(knowledge_base_name)
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@classmethod
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def load(cls,
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knowledge_base_name: str):
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kb_name, vs_type, embed_model = load_kb_from_db(knowledge_base_name)
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return cls(kb_name, vs_type, embed_model)
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@classmethod
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def delete(cls,
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knowledge_base_name: str):
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kb = cls.load(knowledge_base_name)
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if kb.vs_type in ["faiss"]:
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shutil.rmtree(kb.kb_path)
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elif kb.vs_type in ["milvus"]:
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# TODO: 删除milvus库
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pass
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status = delete_kb_from_db(knowledge_base_name)
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return status
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@classmethod
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def list_kbs(cls):
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return list_kbs_from_db()
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if __name__ == "__main__":
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# kb = KnowledgeBase("123", "faiss")
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# kb.create()
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kb = KnowledgeBase.load(knowledge_base_name="123")
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kb.delete_doc(KnowledgeFile(knowledge_base_name="123", filename="README.md"))
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print()
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@ -1,5 +1,8 @@
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import os.path
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from configs.model_config import KB_ROOT_PATH
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import os
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from langchain.embeddings.huggingface import HuggingFaceEmbeddings
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from configs.model_config import (embedding_model_dict, KB_ROOT_PATH)
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from functools import lru_cache
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def validate_kb_name(knowledge_base_id: str) -> bool:
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# 检查是否包含预期外的字符或路径攻击关键字
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|
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@ -18,3 +21,9 @@ def get_vs_path(knowledge_base_name: str):
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def get_file_path(knowledge_base_name: str, doc_name: str):
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return os.path.join(get_doc_path(knowledge_base_name), doc_name)
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@lru_cache(1)
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def load_embeddings(model: str, device: str):
|
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embeddings = HuggingFaceEmbeddings(model_name=embedding_model_dict[model],
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model_kwargs={'device': device})
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return embeddings
|
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|
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