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app.py
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1 |
+
"""
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1. 完成了用Qwen通义千问作为知识库查询。
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1. 总共有三个区块:知识库回答,应用来源,相关问题。
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+
1. 在Huggingface的API上部署了一个在线BGE的模型,用于回答问题。OpenAI的Emebedding或者Langchain的Embedding都不可以用(会报错: self.d)。
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注意事项:
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1. langchain_KB.py中的代码是用来构建本地知识库的,里面的embeddings需要与rag_response_002.py中的embeddings一致。否则会出错!
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1. 如果报错sentence_transformer, 主要原因是与matlabplot等各种package的兼容性冲突。目前几个核心python文件的中的package不会冲突,可以看一下。
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"""
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##TODO:
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# -*- coding: utf-8 -*-
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import streamlit as st
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import openai
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import os
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import numpy as np
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import pandas as pd
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import csv
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import tempfile
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from tempfile import NamedTemporaryFile
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import pathlib
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from pathlib import Path
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import re
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from re import sub
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from itertools import product
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import time
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from time import sleep
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import streamlit_authenticator as stauth
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from langchain_community.vectorstores import FAISS
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from langchain.embeddings.huggingface import HuggingFaceEmbeddings
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.runnables import RunnablePassthrough
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from langchain.llms.base import LLM
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from langchain.llms.utils import enforce_stop_tokens
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from typing import Dict, List, Optional, Tuple, Union
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import requests
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import streamlit as st
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import qwen_response
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import rag_reponse_002
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import dashscope
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from dotenv import load_dotenv
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from datetime import datetime
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import pytz
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from pytz import timezone
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# def get_current_time():
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# beijing_tz = timezone('Asia/Shanghai')
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# beijing_time = datetime.now(beijing_tz)
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# current_time = beijing_time.strftime('%H:%M:%S')
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# return current_time
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load_dotenv()
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56 |
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### 设置openai的API key
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os.environ["OPENAI_API_KEY"] = os.environ['user_token']
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openai.api_key = os.environ['user_token']
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bing_search_api_key = os.environ['bing_api_key']
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dashscope.api_key = os.environ['dashscope_api_key']
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### Streamlit页面设定。
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st.set_page_config(layout="wide", page_icon="🚀", page_title="本地化国产大模型知识库查询演示")
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st.title("本地化国产大模型知识库查询演示")
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# st.title("大语言模型智能知识库查询中心")
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# st.title("大语言模型本地知识库问答系统")
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68 |
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# st.subheader("Large Language Model-based Knowledge Base QA System")
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# st.warning("_声明:内容由人工智能生成,仅供参考。如果您本人使用或对外传播本服务生成的输出,您应当主动核查输出内容的真实性、准确性,避免传播虚假信息。_")
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st.caption("_声明:内容由人工智能生成,仅供参考。您应当主动核查输出内容的真实性、准确性,避免传播虚假信息。_")
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# st.caption("_声明:内容由人工智能生成,仅供参考。如果您本人使用或对外传播本服务生成的输出,您应当主动核查输出内容的真实性、准确性,避免传播虚假信息。_")
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+
# st.info("_声明:内容由人工智能生成,仅供参考。如果您本人使用或对外传播本服务生成的输出,您应当主动核查输出内容的真实性、准确性,避免传播虚假信息。_")
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73 |
+
# st.divider()
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76 |
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### upload file
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# username = 'test'
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# path = f'./{username}/faiss_index/index.faiss'
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80 |
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# if os.path.exists(path):
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# print(f'{path} local KB exists')
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# database_info = pd.read_csv(f'./{username}/database_name.csv')
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# current_database_name = database_info.iloc[-1][0]
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# current_database_date = database_info.iloc[-1][1]
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# database_claim = f"当前知识库为:{current_database_name},创建于{current_database_date}。可以开始提问!"
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# st.markdown(database_claim)
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87 |
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88 |
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# uploaded_file = st.file_uploader(
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# "选择上传一个新知识库", type=(["pdf"]))
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90 |
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# # 默认状态下没有上传文件,None,会报错。需要判断。
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91 |
+
# if uploaded_file is not None:
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92 |
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# # uploaded_file_path = upload_file(uploaded_file)
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93 |
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# upload_file(uploaded_file)
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94 |
+
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95 |
+
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96 |
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# # ## 创建向量数据库
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97 |
+
# from langchain.embeddings.openai import OpenAIEmbeddings
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98 |
+
# embeddings = OpenAIEmbeddings(disallowed_special=()) ## 这里是联网情况下,部署在Huggingface上后使用。
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99 |
+
# print('embeddings:', embeddings)
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100 |
+
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101 |
+
# embedding_model_name = 'GanymedeNil/text2vec-large-chinese'
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102 |
+
# # embeddings = HuggingFaceEmbeddings(model_name=embedding_model_name) ## 这里是联网情况下连接huggingface后使用。
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103 |
+
# embeddings = HuggingFaceEmbeddings(model_name='/Users/yunshi/Downloads/360Data/Data Center/Working-On Task/演讲与培训/2023ChatGPT/Coding/RAG/bge-large-zh') ## 切换成BGE的embedding。
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104 |
+
# embeddings = HuggingFaceEmbeddings(model_name='/Users/yunshi/Downloads/360Data/Data Center/Working-On Task/演讲与培训/2023ChatGPT/RAG/bge-large-zh/') ## 切换成BGE的embedding。
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105 |
+
# embeddings = HuggingFaceEmbeddings(model_name='/Users/yunshi/Downloads/chatGLM/My_LocalKB_Project/GanymedeNil_text2vec-large-chinese/') ## 这里会有个“No sentence-transformers model found with name“的warning,但不是error,不影响使用。
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106 |
+
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107 |
+
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108 |
+
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109 |
+
### authentication with a local yaml file.
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110 |
+
import yaml
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111 |
+
from yaml.loader import SafeLoader
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112 |
+
with open('./config.yaml') as file:
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113 |
+
config = yaml.load(file, Loader=SafeLoader)
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114 |
+
authenticator = stauth.Authenticate(
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115 |
+
config['credentials'],
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116 |
+
config['cookie']['name'],
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117 |
+
config['cookie']['key'],
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118 |
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config['cookie']['expiry_days'],
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119 |
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config['preauthorized']
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120 |
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)
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121 |
+
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122 |
+
user, authentication_status, username = authenticator.login('用户登录', 'main')
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123 |
+
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124 |
+
if authentication_status:
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125 |
+
with st.sidebar:
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126 |
+
st.markdown(
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127 |
+
"""
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128 |
+
<style>
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129 |
+
[data-testid="stSidebar"][aria-expanded="true"]{
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130 |
+
min-width: 450px;
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131 |
+
max-width: 450px;
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132 |
+
}
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133 |
+
""",
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134 |
+
unsafe_allow_html=True,
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135 |
+
)
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136 |
+
### siderbar的题目。
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137 |
+
### siderbar的题目。
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138 |
+
# st.header(f'**大语言模型专家系统工作设定区**')
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139 |
+
st.header(f'**欢迎 **{username}** 使用本系统** ')
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140 |
+
st.write(f'_Large Language Model Expert System Working Environment_')
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141 |
+
# st.write(f'_Welcome and Hope U Enjoy Staying Here_')
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142 |
+
authenticator.logout('登出', 'sidebar')
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143 |
+
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144 |
+
### upload模块
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145 |
+
def upload_file(uploaded_file):
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146 |
+
if uploaded_file is not None:
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147 |
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# filename = uploaded_file.name
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148 |
+
# st.write(filename) # print out the whole file name to validate. not to show in the final version.
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149 |
+
try:
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150 |
+
# if '.pdf' in filename: ### original code here.
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151 |
+
if '.pdf' in uploaded_file.name:
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152 |
+
pdf_filename = uploaded_file.name ### original code here.
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153 |
+
filename = uploaded_file.name
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154 |
+
# print('PDF file:', pdf_filename)
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155 |
+
# with st.status('正在为您解析新知识库...', expanded=False, state='running') as status:
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156 |
+
spinner = st.spinner('正在为您解析新知识库...请耐心等待')
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157 |
+
with spinner:
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158 |
+
### 以下是langchain方案。
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159 |
+
import langchain_KB
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160 |
+
import save_database_info
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161 |
+
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162 |
+
uploaded_file_name = "File_provided"
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163 |
+
temp_dir = tempfile.TemporaryDirectory()
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164 |
+
# ! working.
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165 |
+
uploaded_file_path = pathlib.Path(temp_dir.name) / uploaded_file_name
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166 |
+
with open(pdf_filename, 'wb') as output_temporary_file:
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167 |
+
# with open(f'./{username}_upload.pdf', 'wb') as output_temporary_file: ### original code here. 可能会造成在引用信息来源时文件名不对的问题。
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168 |
+
# ! 必须用这种格式读入内容,然后才可以写入temporary文件夹中。
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169 |
+
# output_temporary_file.write(uploaded_file.getvalue())
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170 |
+
output_temporary_file.write(uploaded_file.getvalue())
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171 |
+
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172 |
+
langchain_KB.langchain_localKB_construct(output_temporary_file, username)
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173 |
+
## 在屏幕上展示当前知识库的信息,包括名字和加载日期。
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174 |
+
save_database_info.save_database_info(f'./{username}/database_name.csv', pdf_filename, str(datetime.now(pytz.timezone('Asia/Shanghai')).strftime("%Y-%m-%d %H:%M")))
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175 |
+
st.markdown('新知识库解析成功,请务必刷新页面,然后开启对话 🔃')
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176 |
+
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177 |
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return pdf_filename
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178 |
+
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179 |
+
else:
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180 |
+
# if '.csv' in filename: ### original code here.
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181 |
+
if '.csv' in uploaded_file.name:
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182 |
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print('start the csv file processing...')
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183 |
+
csv_filename = uploaded_file.name
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184 |
+
filename = uploaded_file.name
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185 |
+
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186 |
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csv_file = pd.read_csv(uploaded_file)
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187 |
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csv_file.to_csv(f'./{username}/{username}_upload.csv', encoding='utf-8', index=False)
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188 |
+
st.write(csv_file[:3]) # 这里只是显示文件,后面需要定位文件所在的绝对路径。
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189 |
+
else:
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190 |
+
xls_file = pd.read_excel(uploaded_file)
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191 |
+
xls_file.to_csv(f'./{username}_upload.csv', index=False)
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192 |
+
st.write(xls_file[:3])
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193 |
+
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194 |
+
print('end the csv file processing...')
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195 |
+
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196 |
+
# uploaded_file_name = "File_provided"
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197 |
+
# temp_dir = tempfile.TemporaryDirectory()
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198 |
+
# ! working.
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199 |
+
# uploaded_file_path = pathlib.Path(temp_dir.name) / uploaded_file_name
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200 |
+
# with open('./upload.csv', 'wb') as output_temporary_file:
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201 |
+
# with open(f'./{username}_upload.csv', 'wb') as output_temporary_file:
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202 |
+
# print(f'./{name}_upload.csv')
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203 |
+
# ! 必须用这种格式读入内容,然后才可以写入temporary文件夹中。
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204 |
+
# output_temporary_file.write(uploaded_file.getvalue())
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205 |
+
# st.write(uploaded_file_path) #* 可以查看文件是否真实存在,然后是否可以
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206 |
+
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207 |
+
except Exception as e:
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208 |
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st.write(e)
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209 |
+
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210 |
+
## 以下代码是为了解决上传文件后,文件路径和文件名不对的问题。
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211 |
+
# uploaded_file_name = "File_provided"
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212 |
+
# temp_dir = tempfile.TemporaryDirectory()
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213 |
+
# # ! working.
|
214 |
+
# uploaded_file_path = pathlib.Path(temp_dir.name) / uploaded_file_name
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215 |
+
# # with open('./upload.csv', 'wb') as output_temporary_file:
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216 |
+
# with open(f'./{name}_upload.csv', 'wb') as output_temporary_file:
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217 |
+
# # print(f'./{name}_upload.csv')
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218 |
+
# # ! 必须用这种格式读入内容,然后才可以写入temporary文件夹中。
|
219 |
+
# # output_temporary_file.write(uploaded_file.getvalue())
|
220 |
+
# output_temporary_file.write(uploaded_file.getvalue())
|
221 |
+
# # st.write(uploaded_file_path) # * 可以查看文件是否真实存在,然后是否可以
|
222 |
+
# # st.write('Now file saved successfully.')
|
223 |
+
|
224 |
+
# return pdf_filename, csv_filename
|
225 |
+
return filename
|
226 |
+
|
227 |
+
path = f'./{username}/faiss_index/index.faiss'
|
228 |
+
if os.path.exists(path):
|
229 |
+
print(f'{path} local KB exists')
|
230 |
+
database_info = pd.read_csv(f'./{username}/database_name.csv', encoding='utf-8', header=None) ## 不加encoding的话,中文名字的PDF会报错。
|
231 |
+
print(database_info)
|
232 |
+
current_database_name = database_info.iloc[-1][0]
|
233 |
+
current_database_date = database_info.iloc[-1][1]
|
234 |
+
database_claim = f"当前知识库为:{current_database_name},创建于{current_database_date}。可以开始提问!"
|
235 |
+
st.warning(database_claim)
|
236 |
+
# st.markdown(database_claim)
|
237 |
+
|
238 |
+
try:
|
239 |
+
uploaded_file = st.file_uploader(
|
240 |
+
"选择上传一个新知识库", type=(["pdf"]))
|
241 |
+
# 默认状态下没有上传文件,None,会报错。需要判断。
|
242 |
+
if uploaded_file is not None:
|
243 |
+
# uploaded_file_path = upload_file(uploaded_file)
|
244 |
+
upload_file(uploaded_file)
|
245 |
+
except Exception as e:
|
246 |
+
print(e)
|
247 |
+
pass
|
248 |
+
|
249 |
+
## 在sidebar上的三个分页显示,用st.tabs实现。
|
250 |
+
tab_1, tab_2, tab_3, tab_4 = st.tabs(['使用须知', '模型参数', '提示词模板', '系统角色设定'])
|
251 |
+
|
252 |
+
# with st.expander(label='**使用须知**', expanded=False):
|
253 |
+
with tab_1:
|
254 |
+
# st.markdown("#### 快速上手指南")
|
255 |
+
# with st.text(body="说明"):
|
256 |
+
# st.markdown("* 重启一轮新对话时,只需要刷新页面(按Ctrl/Command + R)即可。")
|
257 |
+
with st.text(body="说明"):
|
258 |
+
st.markdown("* 为了保护数据与隐私,所有对话均不会被保存,刷新页面立即删除。敬请放心。")
|
259 |
+
# with st.text(body="说明"):
|
260 |
+
# st.markdown("* “GPT-4”回答质量极佳,但速度缓慢,建议适当使用。")
|
261 |
+
with st.text(body="说明"):
|
262 |
+
st.markdown("* 查询知识库模式与所有的搜索引擎或者数据库检索方式一样,仅限一轮对话,将不会保持之前的会话记录。")
|
263 |
+
with st.text(body="说明"):
|
264 |
+
st.markdown("""* 系统的工作流程如下:
|
265 |
+
1. 用户输入问题。
|
266 |
+
1. 系统将问题转换为机器可理解的格式。
|
267 |
+
1. 系统使用大语言模型来生成与问题相关的候选答案。
|
268 |
+
1. 系统使用本地知识库来评估候选答案的准确性。
|
269 |
+
1. 系统返回最准确的答案。""")
|
270 |
+
|
271 |
+
## 大模型参数
|
272 |
+
# with st.expander(label='**大语言模型参数**', expanded=True):
|
273 |
+
with tab_2:
|
274 |
+
max_tokens = st.slider(label='Max_Token(生成结果时最大字数)', min_value=100, max_value=8096, value=4096,step=100)
|
275 |
+
temperature = st.slider(label='Temperature (温度)', min_value=0.0, max_value=1.0, value=0.8, step=0.1)
|
276 |
+
top_p = st.slider(label='Top_P (核采样)', min_value=0.0, max_value=1.0, value=0.6, step=0.1)
|
277 |
+
frequency_penalty = st.slider(label='Frequency Penalty (重复度惩罚因子)', min_value=-2.0, max_value=2.0, value=1.0, step=0.1)
|
278 |
+
presence_penalty = st.slider(label='Presence Penalty (控制主题的重复度)', min_value=-2.0, max_value=2.0, value=1.0, step=0.1)
|
279 |
+
|
280 |
+
with tab_3:
|
281 |
+
# st.markdown("#### Prompt提示词参考资料")
|
282 |
+
# with st.expander(label="**大语言模型基础提示词Prompt示例**", expanded=False):
|
283 |
+
st.code(
|
284 |
+
body="我是一个企业主,我需要关注哪些“存货”相关的数据资源规则?", language='plaintext')
|
285 |
+
st.code(
|
286 |
+
body="作为零售商,了解哪些关键的库存管理指标对我至关重要?", language='plaintext')
|
287 |
+
st.code(body="企业主在监控库存时,应如何确保遵守行业法规和最佳实践?",
|
288 |
+
language='plaintext')
|
289 |
+
st.code(body="在数字化时代,我应该关注哪些技术工具或平台来优化我的库存数据流程?", language='plaintext')
|
290 |
+
st.code(body="我应该如何定期审查和分析这些库存数据以提高运营效率?", language='plaintext')
|
291 |
+
st.code(body="如何设置预警系统来避免过度库存或缺货情况?", language='plaintext')
|
292 |
+
|
293 |
+
with tab_4:
|
294 |
+
st.text_area(label='系统角色设定', value='你是一个人工智能,你需要回答我提出的问题,或者完成我交代的任务。你需要使用我提问的语言(如中文、英文)来回答。', height=200, label_visibility='hidden')
|
295 |
+
|
296 |
+
|
297 |
+
elif authentication_status == False:
|
298 |
+
st.error('⛔ 用户名或密码错误!')
|
299 |
+
elif authentication_status == None:
|
300 |
+
st.warning('⬆ 请先登录!')
|
301 |
+
|
302 |
+
|
303 |
+
### 上传文件的模块
|
304 |
+
|
305 |
+
|
306 |
+
|
307 |
+
|
308 |
+
#### start: 主程序
|
309 |
+
## 清楚所有对话记录。
|
310 |
+
def clear_all():
|
311 |
+
st.session_state.conversation = None
|
312 |
+
st.session_state.chat_history = None
|
313 |
+
st.session_state.messages = []
|
314 |
+
message_placeholder = st.empty()
|
315 |
+
|
316 |
+
## 只用这一个就可以了。
|
317 |
+
st.rerun()
|
318 |
+
|
319 |
+
return None
|
320 |
+
|
321 |
+
if "copied" not in st.session_state:
|
322 |
+
st.session_state.copied = []
|
323 |
+
|
324 |
+
if "llm_response" not in st.session_state:
|
325 |
+
st.session_state.llm_response = ""
|
326 |
+
|
327 |
+
# ## copy to clipboard function with a button.
|
328 |
+
# def copy_to_clipboard(text):
|
329 |
+
# st.session_state.copied.append(text)
|
330 |
+
# clipboard.copy(text)
|
331 |
+
|
332 |
+
|
333 |
+
def main():
|
334 |
+
# llm = ChatGLM() ## 启动一个实例。
|
335 |
+
col1, col2 = st.columns([2, 1])
|
336 |
+
# st.markdown('### 数据库查询区')
|
337 |
+
# with st.expander(label='**查询企业内部知识库**', expanded=True):
|
338 |
+
with col1:
|
339 |
+
KB_mode = True
|
340 |
+
user_input = st.text_input(label='**📶 大模型数据库对话区**', placeholder='请输入您的问题', label_visibility='visible')
|
341 |
+
if user_input:
|
342 |
+
## 非stream输出,原始状态,不需要改变api.py中的内容。
|
343 |
+
# with st.status('检索中...', expanded=True, state='running') as status:
|
344 |
+
spinner = st.spinner('思考中...请耐心等待')
|
345 |
+
with spinner:
|
346 |
+
if KB_mode == True:
|
347 |
+
# import rag_reponse_001
|
348 |
+
# clear_all()
|
349 |
+
# response = rag_reponse_001.rag_response(user_input=user_input, k=5) ## working.
|
350 |
+
# print('user_input:', user_input)
|
351 |
+
response, source = rag_reponse_002.rag_response(username=username, user_input=user_input, k=3)
|
352 |
+
print('llm response:', response)
|
353 |
+
sim_prompt = f"""你需要根据以下的问题来提出5个可能的后续问题{user_input}
|
354 |
+
"""
|
355 |
+
# sim_questions = chatgpt.chatgpt(user_prompt=sim_prompt) ## chatgpt to get similar questions.
|
356 |
+
sim_questions = qwen_response.call_with_messages(sim_prompt)
|
357 |
+
if len(user_input) != 0:
|
358 |
+
sim_prompt = f"""你需要根据以下的初始问题来提出3个相似的问题和3个后续问题。
|
359 |
+
初始问题是:{user_input}
|
360 |
+
--------------------
|
361 |
+
你回答的时候,需要使用如下格式:
|
362 |
+
**相似问题:**
|
363 |
+
**后续问题:**
|
364 |
+
|
365 |
+
"""
|
366 |
+
# sim_prompt = f"""你需要根据以下的问题来提出5个可能的后续问题{user_input}"""
|
367 |
+
|
368 |
+
### 这里用chatgpt来生成相似问题。
|
369 |
+
# sim_questions = chatgpt.chatgpt(user_prompt=sim_prompt)
|
370 |
+
|
371 |
+
### 这里用Qwen来生成相似问题。
|
372 |
+
sim_questions = qwen_response.call_with_messages(sim_prompt)
|
373 |
+
|
374 |
+
|
375 |
+
st.markdown(response)
|
376 |
+
# st_copy_to_clipboard(text=str(response), show_text=True, before_copy_label="📋", after_copy_label="✅")
|
377 |
+
|
378 |
+
## 如果这样使用,每次按button都会重新提交问题。
|
379 |
+
# st.button(label="📃", on_click=copy_to_clipboard, args=(response,))
|
380 |
+
|
381 |
+
st.divider()
|
382 |
+
st.caption(source)
|
383 |
+
st.divider()
|
384 |
+
|
385 |
+
## 初始状态下response未被定义。
|
386 |
+
try:
|
387 |
+
if response:
|
388 |
+
with col2:
|
389 |
+
with st.expander(label='## **您可能还会关注以下内容**', expanded=True):
|
390 |
+
st.info(sim_questions)
|
391 |
+
except:
|
392 |
+
pass
|
393 |
+
|
394 |
+
# st.stop()
|
395 |
+
|
396 |
+
return None
|
397 |
+
|
398 |
+
#### End: 主程序
|
399 |
+
|
400 |
+
if __name__ == '__main__':
|
401 |
+
main()
|
402 |
+
|