holo-llm-deepseek/main.py [24:59]:
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        print("start connecting")
        HOLO_ENDPOINT = self.config['holo_config']['HOLO_ENDPOINT']
        HOLO_PORT = self.config['holo_config']['HOLO_PORT']
        HOLO_DATABASE = self.config['holo_config']['HOLO_DATABASE']
        HOLO_USER = self.config['holo_config']['HOLO_USER']
        HOLO_PASSWORD = self.config['holo_config']['HOLO_PASSWORD']
        connection_string = Hologres.connection_string_from_db_params(
            HOLO_ENDPOINT, int(HOLO_PORT), HOLO_DATABASE, HOLO_USER, HOLO_PASSWORD)
        vectorstore = Hologres(
            connection_string=connection_string,
            embedding_function=self.embeddings,
            ndims=768,
            table_name='langchain_embedding',
            pre_delete_table=clear_db)
        return vectorstore

    def load_db(self, files: List[str]) -> None:
        # read docs
        documents = []
        for fname in files:
            loader = CSVLoader(fname)
            documents += loader.load()

        # split docs
        text_splitter = RecursiveCharacterTextSplitter(
            chunk_size=1000, chunk_overlap=100)
        documents = text_splitter.split_documents(documents)

        # store embedding in vectorstore
        start_time = time.time()
        self.vectorstore.add_documents(documents)
        end_time = time.time()
        print(
            "Store embedding into Hologres Success.Cost Time: {:.2f}s".format(
                end_time -
                start_time))
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holo-llm/main.py [24:59]:
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        print("start connecting")
        HOLO_ENDPOINT = self.config['holo_config']['HOLO_ENDPOINT']
        HOLO_PORT = self.config['holo_config']['HOLO_PORT']
        HOLO_DATABASE = self.config['holo_config']['HOLO_DATABASE']
        HOLO_USER = self.config['holo_config']['HOLO_USER']
        HOLO_PASSWORD = self.config['holo_config']['HOLO_PASSWORD']
        connection_string = Hologres.connection_string_from_db_params(
            HOLO_ENDPOINT, int(HOLO_PORT), HOLO_DATABASE, HOLO_USER, HOLO_PASSWORD)
        vectorstore = Hologres(
            connection_string=connection_string,
            embedding_function=self.embeddings,
            ndims=768,
            table_name='langchain_embedding',
            pre_delete_table=clear_db)
        return vectorstore

    def load_db(self, files: List[str]) -> None:
        # read docs
        documents = []
        for fname in files:
            loader = CSVLoader(fname)
            documents += loader.load()

        # split docs
        text_splitter = RecursiveCharacterTextSplitter(
            chunk_size=1000, chunk_overlap=100)
        documents = text_splitter.split_documents(documents)

        # store embedding in vectorstore
        start_time = time.time()
        self.vectorstore.add_documents(documents)
        end_time = time.time()
        print(
            "Store embedding into Hologres Success.Cost Time: {:.2f}s".format(
                end_time -
                start_time))
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