389 lines
12 KiB
Python
Executable File
389 lines
12 KiB
Python
Executable File
import operator
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from abc import ABC, abstractmethod
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import os
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import numpy as np
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from langchain.embeddings.base import Embeddings
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from langchain.docstore.document import Document
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from server.db.repository.knowledge_base_repository import (
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add_kb_to_db, delete_kb_from_db, list_kbs_from_db, kb_exists,
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load_kb_from_db, get_kb_detail,
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)
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from server.db.repository.knowledge_file_repository import (
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add_file_to_db, delete_file_from_db, delete_files_from_db, file_exists_in_db,
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count_files_from_db, list_files_from_db, get_file_detail, delete_file_from_db,
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list_docs_from_db,
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)
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from configs import (kbs_config, VECTOR_SEARCH_TOP_K, SCORE_THRESHOLD,
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EMBEDDING_MODEL, KB_INFO)
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from server.knowledge_base.utils import (
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get_kb_path, get_doc_path, KnowledgeFile,
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list_kbs_from_folder, list_files_from_folder,
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)
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from typing import List, Union, Dict, Optional
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from server.embeddings_api import embed_texts
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from server.embeddings_api import embed_documents
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def normalize(embeddings: List[List[float]]) -> np.ndarray:
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'''
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sklearn.preprocessing.normalize 的替代（使用 L2），避免安装 scipy, scikit-learn
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'''
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norm = np.linalg.norm(embeddings, axis=1)
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norm = np.reshape(norm, (norm.shape[0], 1))
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norm = np.tile(norm, (1, len(embeddings[0])))
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return np.divide(embeddings, norm)
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class SupportedVSType:
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FAISS = 'faiss'
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MILVUS = 'milvus'
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DEFAULT = 'default'
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ZILLIZ = 'zilliz'
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PG = 'pg'
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ES = 'es'
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class KBService(ABC):
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def __init__(self,
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knowledge_base_name: str,
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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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self.kb_info = KB_INFO.get(knowledge_base_name, f"关于{knowledge_base_name}的知识库")
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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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self.do_init()
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def __repr__(self) -> str:
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return f"{self.kb_name} @ {self.embed_model}"
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def save_vector_store(self):
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'''
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保存向量库:FAISS保存到磁盘，milvus保存到数据库。PGVector暂未支持
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'''
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pass
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def create_kb(self):
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"""
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创建知识库
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"""
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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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self.do_create_kb()
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status = add_kb_to_db(self.kb_name, self.kb_info, self.vs_type(), self.embed_model)
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return status
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def clear_vs(self):
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"""
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删除向量库中所有内容
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"""
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self.do_clear_vs()
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status = delete_files_from_db(self.kb_name)
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return status
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def drop_kb(self):
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"""
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删除知识库
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"""
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self.do_drop_kb()
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status = delete_kb_from_db(self.kb_name)
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return status
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def _docs_to_embeddings(self, docs: List[Document]) -> Dict:
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'''
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将 List[Document] 转化为 VectorStore.add_embeddings 可以接受的参数
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'''
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return embed_documents(docs=docs, embed_model=self.embed_model, to_query=False)
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def add_doc(self, kb_file: KnowledgeFile, docs: List[Document] = [], **kwargs):
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"""
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向知识库添加文件
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如果指定了docs，则不再将文本向量化，并将数据库对应条目标为custom_docs=True
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"""
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if docs:
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custom_docs = True
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for doc in docs:
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doc.metadata.setdefault("source", kb_file.filepath)
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else:
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docs = kb_file.file2text()
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custom_docs = False
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if docs:
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self.delete_doc(kb_file)
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doc_infos = self.do_add_doc(docs, **kwargs)
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status = add_file_to_db(kb_file,
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custom_docs=custom_docs,
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docs_count=len(docs),
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doc_infos=doc_infos)
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else:
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status = False
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return status
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def delete_doc(self, kb_file: KnowledgeFile, delete_content: bool = False, **kwargs):
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"""
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从知识库删除文件
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"""
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self.do_delete_doc(kb_file, **kwargs)
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status = delete_file_from_db(kb_file)
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if delete_content and os.path.exists(kb_file.filepath):
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os.remove(kb_file.filepath)
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return status
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def update_info(self, kb_info: str):
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"""
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更新知识库介绍
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"""
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self.kb_info = kb_info
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status = add_kb_to_db(self.kb_name, self.kb_info, self.vs_type(), self.embed_model)
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return status
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def update_doc(self, kb_file: KnowledgeFile, docs: List[Document] = [], **kwargs):
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"""
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使用content中的文件更新向量库
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如果指定了docs，则使用自定义docs，并将数据库对应条目标为custom_docs=True
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"""
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if os.path.exists(kb_file.filepath):
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self.delete_doc(kb_file, **kwargs)
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return self.add_doc(kb_file, docs=docs, **kwargs)
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def exist_doc(self, file_name: str):
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return file_exists_in_db(KnowledgeFile(knowledge_base_name=self.kb_name,
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filename=file_name))
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def list_files(self):
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return list_files_from_db(self.kb_name)
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def count_files(self):
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return count_files_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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score_threshold: float = SCORE_THRESHOLD,
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):
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docs = self.do_search(query, top_k, score_threshold)
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return docs
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def get_doc_by_id(self, id: str) -> Optional[Document]:
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return None
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def list_docs(self, file_name: str = None, metadata: Dict = {}) -> List[Document]:
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'''
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通过file_name或metadata检索Document
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'''
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doc_infos = list_docs_from_db(kb_name=self.kb_name, file_name=file_name, metadata=metadata)
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docs = [self.get_doc_by_id(x["id"]) for x in doc_infos]
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return docs
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@abstractmethod
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def do_create_kb(self):
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"""
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创建知识库子类实自己逻辑
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"""
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pass
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@staticmethod
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def list_kbs_type():
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return list(kbs_config.keys())
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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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def exists(self, kb_name: str = None):
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kb_name = kb_name or self.kb_name
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return kb_exists(kb_name)
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@abstractmethod
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def vs_type(self) -> str:
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pass
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@abstractmethod
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def do_init(self):
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pass
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@abstractmethod
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def do_drop_kb(self):
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"""
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删除知识库子类实自己逻辑
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"""
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pass
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@abstractmethod
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def do_search(self,
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query: str,
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top_k: int,
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score_threshold: float,
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) -> List[Document]:
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"""
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搜索知识库子类实自己逻辑
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"""
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pass
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@abstractmethod
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def do_add_doc(self,
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docs: List[Document],
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) -> List[Dict]:
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"""
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向知识库添加文档子类实自己逻辑
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"""
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pass
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@abstractmethod
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def do_delete_doc(self,
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kb_file: KnowledgeFile):
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"""
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从知识库删除文档子类实自己逻辑
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"""
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pass
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@abstractmethod
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def do_clear_vs(self):
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"""
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从知识库删除全部向量子类实自己逻辑
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"""
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pass
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class KBServiceFactory:
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@staticmethod
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def get_service(kb_name: str,
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vector_store_type: Union[str, SupportedVSType],
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embed_model: str = EMBEDDING_MODEL,
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) -> KBService:
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if isinstance(vector_store_type, str):
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vector_store_type = getattr(SupportedVSType, vector_store_type.upper())
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if SupportedVSType.FAISS == vector_store_type:
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from server.knowledge_base.kb_service.faiss_kb_service import FaissKBService
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return FaissKBService(kb_name, embed_model=embed_model)
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@staticmethod
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def get_service_by_name(kb_name: str) -> KBService:
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_, vs_type, embed_model = load_kb_from_db(kb_name)
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if _ is None: # kb not in db, just return None
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return None
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return KBServiceFactory.get_service(kb_name, vs_type, embed_model)
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@staticmethod
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def get_default():
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return KBServiceFactory.get_service("default", SupportedVSType.DEFAULT)
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def get_kb_details() -> List[Dict]:
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kbs_in_folder = list_kbs_from_folder()
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kbs_in_db = KBService.list_kbs()
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result = {}
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for kb in kbs_in_folder:
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result[kb] = {
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"kb_name": kb,
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"vs_type": "",
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"kb_info": "",
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"embed_model": "",
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"file_count": 0,
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"create_time": None,
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"in_folder": True,
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"in_db": False,
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}
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for kb in kbs_in_db:
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kb_detail = get_kb_detail(kb)
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if kb_detail:
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kb_detail["in_db"] = True
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if kb in result:
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result[kb].update(kb_detail)
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else:
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kb_detail["in_folder"] = False
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result[kb] = kb_detail
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data = []
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for i, v in enumerate(result.values()):
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v['No'] = i + 1
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data.append(v)
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return data
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def get_kb_file_details(kb_name: str) -> List[Dict]:
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kb = KBServiceFactory.get_service_by_name(kb_name)
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if kb is None:
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return []
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files_in_folder = list_files_from_folder(kb_name)
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files_in_db = kb.list_files()
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result = {}
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for doc in files_in_folder:
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result[doc] = {
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"kb_name": kb_name,
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"file_name": doc,
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"file_ext": os.path.splitext(doc)[-1],
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"file_version": 0,
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"document_loader": "",
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"docs_count": 0,
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"text_splitter": "",
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"create_time": None,
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"in_folder": True,
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"in_db": False,
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}
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for doc in files_in_db:
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doc_detail = get_file_detail(kb_name, doc)
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if doc_detail:
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doc_detail["in_db"] = True
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if doc in result:
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result[doc].update(doc_detail)
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else:
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doc_detail["in_folder"] = False
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result[doc] = doc_detail
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data = []
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for i, v in enumerate(result.values()):
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v['No'] = i + 1
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data.append(v)
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return data
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class EmbeddingsFunAdapter(Embeddings):
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def __init__(self, embed_model: str = EMBEDDING_MODEL):
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self.embed_model = embed_model
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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embeddings = embed_texts(texts=texts, embed_model=self.embed_model, to_query=False).data
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return normalize(embeddings).tolist()
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def embed_query(self, text: str) -> List[float]:
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embeddings = embed_texts(texts=[text], embed_model=self.embed_model, to_query=True).data
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query_embed = embeddings[0]
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query_embed_2d = np.reshape(query_embed, (1, -1)) # 将一维数组转换为二维数组
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normalized_query_embed = normalize(query_embed_2d)
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return normalized_query_embed[0].tolist() # 将结果转换为一维数组并返回
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# TODO: 暂不支持异步
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# async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
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# return normalize(await self.embeddings.aembed_documents(texts))
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# async def aembed_query(self, text: str) -> List[float]:
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# return normalize(await self.embeddings.aembed_query(text))
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def score_threshold_process(score_threshold, k, docs):
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if score_threshold is not None:
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cmp = (
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operator.le
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)
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docs = [
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(doc, similarity)
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for doc, similarity in docs
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if cmp(similarity, score_threshold)
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]
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return docs[:k]
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