Langchain-Chatchat/server/knowledge_base/utils.py

from typing import Union
import os
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from configs.model_config import (embedding_model_dict, KB_ROOT_PATH, EMBEDDING_MODEL, kbs_config)
from functools import lru_cache
from server.knowledge_base.kb_service.base import KBService, SupportedVSType
from server.db.repository.knowledge_base_repository import load_kb_from_db
from server.knowledge_base.kb_service.default_kb_service import DefaultKBService


def validate_kb_name(knowledge_base_id: str) -> bool:
    # 检查是否包含预期外的字符或路径攻击关键字
    if "../" in knowledge_base_id:
        return False
    return True

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)

def list_docs_from_folder(kb_name: str):
    doc_path = get_doc_path(kb_name)
    return [file for file in os.listdir(doc_path)
            if os.path.isfile(os.path.join(doc_path, file))]

@lru_cache(1)
def load_embeddings(model: str, device: str):
    embeddings = HuggingFaceEmbeddings(model_name=embedding_model_dict[model],
                                       model_kwargs={'device': device})
    return embeddings


LOADER_DICT = {"UnstructuredFileLoader": ['.eml', '.html', '.json', '.md', '.msg', '.rst',
                                          '.rtf', '.txt', '.xml',
                                          '.doc', '.docx', '.epub', '.odt', '.pdf',
                                          '.ppt', '.pptx', '.tsv'],  # '.pdf', '.xlsx', '.csv'
               "CSVLoader": [".csv"],
               }
SUPPORTED_EXTS = [ext for sublist in LOADER_DICT.values() for ext in sublist]

def get_LoaderClass(file_extension):
    for LoaderClass, extensions in LOADER_DICT.items():
        if file_extension in extensions:
            return LoaderClass


class KnowledgeFile:
    def __init__(
            self,
            filename: str,
            knowledge_base_name: str
    ):
        self.kb_name = knowledge_base_name
        self.filename = filename
        self.ext = os.path.splitext(filename)[-1]
        if self.ext not in SUPPORTED_EXTS:
            raise ValueError(f"暂未支持的文件格式 {self.ext}")
        self.filepath = get_file_path(knowledge_base_name, filename)
        self.docs = None
        self.document_loader_name = get_LoaderClass(self.ext)

        # TODO: 增加依据文件格式匹配text_splitter
        self.text_splitter_name = "CharacterTextSplitter"

    def file2text(self):
        DocumentLoader = getattr(sys.modules['langchain.document_loaders'], self.document_loader_name)
        loader = DocumentLoader(self.filepath)

        # TODO: 增加依据文件格式匹配text_splitter
        TextSplitter = getattr(sys.modules['langchain.text_splitter'], self.text_splitter_name)
        text_splitter = TextSplitter(chunk_size=500, chunk_overlap=200)
        return loader.load_and_split(text_splitter)


class KBServiceFactory:

    @staticmethod
    def get_service(kb_name: str,
                    vector_store_type: Union[str, SupportedVSType],
                    embed_model: str = EMBEDDING_MODEL,
                    ) -> KBService:
        if isinstance(vector_store_type, str):
            vector_store_type = getattr(SupportedVSType, vector_store_type.upper())
        if SupportedVSType.FAISS == vector_store_type:
            from server.knowledge_base.kb_service.faiss_kb_service import FaissKBService
            return FaissKBService(kb_name, embed_model=embed_model)
        elif SupportedVSType.MILVUS == vector_store_type:
            from server.knowledge_base.kb_service.milvus_kb_service import MilvusKBService
            return MilvusKBService(kb_name, embed_model=embed_model) # other milvus parameters are set in model_config.kbs_config
        elif SupportedVSType.DEFAULT == vector_store_type: # kb_exists of default kbservice is False, to make validation easier.
            return DefaultKBService(kb_name)

    @staticmethod
    def get_service_by_name(kb_name: str
                            ) -> KBService:
        kb_name, vs_type, embed_model = load_kb_from_db(kb_name)
        return KBServiceFactory.get_service(kb_name, vs_type, embed_model)

    @staticmethod
    def get_default():
        return KBServiceFactory.get_service("default", SupportedVSType.DEFAULT)