diff --git a/metagpt/document_store/document.py b/metagpt/document_store/document.py
deleted file mode 100644
index 90abc54de..000000000
--- a/metagpt/document_store/document.py
+++ /dev/null
@@ -1,81 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-"""
-@Time    : 2023/6/8 14:03
-@Author  : alexanderwu
-@File    : document.py
-@Desc    : Classes and Operations Related to Vector Files in the Vector Database. Still under design.
-"""
-from pathlib import Path
-
-import pandas as pd
-from langchain.document_loaders import (
-    TextLoader,
-    UnstructuredPDFLoader,
-    UnstructuredWordDocumentLoader,
-)
-from langchain.text_splitter import CharacterTextSplitter
-from tqdm import tqdm
-
-
-def validate_cols(content_col: str, df: pd.DataFrame):
-    if content_col not in df.columns:
-        raise ValueError
-
-
-def read_data(data_path: Path):
-    suffix = data_path.suffix
-    if ".xlsx" == suffix:
-        data = pd.read_excel(data_path)
-    elif ".csv" == suffix:
-        data = pd.read_csv(data_path)
-    elif ".json" == suffix:
-        data = pd.read_json(data_path)
-    elif suffix in (".docx", ".doc"):
-        data = UnstructuredWordDocumentLoader(str(data_path), mode="elements").load()
-    elif ".txt" == suffix:
-        data = TextLoader(str(data_path)).load()
-        text_splitter = CharacterTextSplitter(separator="\n", chunk_size=256, chunk_overlap=0)
-        texts = text_splitter.split_documents(data)
-        data = texts
-    elif ".pdf" == suffix:
-        data = UnstructuredPDFLoader(str(data_path), mode="elements").load()
-    else:
-        raise NotImplementedError
-    return data
-
-
-class Document:
-    def __init__(self, data_path, content_col="content", meta_col="metadata"):
-        self.data = read_data(data_path)
-        if isinstance(self.data, pd.DataFrame):
-            validate_cols(content_col, self.data)
-        self.content_col = content_col
-        self.meta_col = meta_col
-
-    def _get_docs_and_metadatas_by_df(self) -> (list, list):
-        df = self.data
-        docs = []
-        metadatas = []
-        for i in tqdm(range(len(df))):
-            docs.append(df[self.content_col].iloc[i])
-            if self.meta_col:
-                metadatas.append({self.meta_col: df[self.meta_col].iloc[i]})
-            else:
-                metadatas.append({})
-
-        return docs, metadatas
-
-    def _get_docs_and_metadatas_by_langchain(self) -> (list, list):
-        data = self.data
-        docs = [i.page_content for i in data]
-        metadatas = [i.metadata for i in data]
-        return docs, metadatas
-
-    def get_docs_and_metadatas(self) -> (list, list):
-        if isinstance(self.data, pd.DataFrame):
-            return self._get_docs_and_metadatas_by_df()
-        elif isinstance(self.data, list):
-            return self._get_docs_and_metadatas_by_langchain()
-        else:
-            raise NotImplementedError