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