import streamlit as st
from streamlit_chatbox import st_chatbox
import tempfile
###### 从webui借用的代码 #####
###### 做了少量修改 #####
import os
import shutil
from chains.local_doc_qa import LocalDocQA
from configs.model_config import *
import nltk
from models.base import (BaseAnswer,
AnswerResult,)
import models.shared as shared
from models.loader.args import parser
from models.loader import LoaderCheckPoint
nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path
def get_vs_list():
lst_default = ["新建知识库"]
if not os.path.exists(KB_ROOT_PATH):
return lst_default
lst = os.listdir(KB_ROOT_PATH)
if not lst:
return lst_default
lst.sort()
return lst_default + lst
embedding_model_dict_list = list(embedding_model_dict.keys())
llm_model_dict_list = list(llm_model_dict.keys())
def get_answer(query, vs_path, history, mode, score_threshold=VECTOR_SEARCH_SCORE_THRESHOLD,
vector_search_top_k=VECTOR_SEARCH_TOP_K, chunk_conent: bool = True,
chunk_size=CHUNK_SIZE, streaming: bool = STREAMING,):
if mode == "Bing搜索问答":
for resp, history in local_doc_qa.get_search_result_based_answer(
query=query, chat_history=history, streaming=streaming):
source = "\n\n"
source += "".join(
[f"""<details> <summary>出处 [{i + 1}] <a href="{doc.metadata["source"]}" target="_blank">{doc.metadata["source"]}</a> </summary>\n"""
f"""{doc.page_content}\n"""
f"""</details>"""
for i, doc in
enumerate(resp["source_documents"])])
history[-1][-1] += source
yield history, ""
elif mode == "知识库问答" and vs_path is not None and os.path.exists(vs_path):
local_doc_qa.top_k = vector_search_top_k
local_doc_qa.chunk_conent = chunk_conent
local_doc_qa.chunk_size = chunk_size
for resp, history in local_doc_qa.get_knowledge_based_answer(
query=query, vs_path=vs_path, chat_history=history, streaming=streaming):
source = "\n\n"
source += "".join(
[f"""<details> <summary>出处 [{i + 1}] {os.path.split(doc.metadata["source"])[-1]}</summary>\n"""
f"""{doc.page_content}\n"""
f"""</details>"""
for i, doc in
enumerate(resp["source_documents"])])
history[-1][-1] += source
yield history, ""
elif mode == "知识库测试":
if os.path.exists(vs_path):
resp, prompt = local_doc_qa.get_knowledge_based_conent_test(query=query, vs_path=vs_path,
score_threshold=score_threshold,
vector_search_top_k=vector_search_top_k,
chunk_conent=chunk_conent,
chunk_size=chunk_size)
if not resp["source_documents"]:
yield history + [[query,
"根据您的设定，没有匹配到任何内容，请确认您设置的知识相关度 Score 阈值是否过小或其他参数是否正确。"]], ""
else:
source = "\n".join(
[
f"""<details open> <summary>【知识相关度 Score】：{doc.metadata["score"]} - 【出处{i + 1}】： {os.path.split(doc.metadata["source"])[-1]} </summary>\n"""
f"""{doc.page_content}\n"""
f"""</details>"""
for i, doc in
enumerate(resp["source_documents"])])
history.append([query, "以下内容为知识库中满足设置条件的匹配结果：\n\n" + source])
yield history, ""
else:
yield history + [[query,
"请选择知识库后进行测试，当前未选择知识库。"]], ""
else:
answer_result_stream_result = local_doc_qa.llm_model_chain(
{"prompt": query, "history": history, "streaming": streaming})
for answer_result in answer_result_stream_result['answer_result_stream']:
resp = answer_result.llm_output["answer"]
history = answer_result.history
history[-1][-1] = resp + (
"\n\n当前知识库为空，如需基于知识库进行问答，请先加载知识库后，再进行提问。" if mode == "知识库问答" else "")
yield history, ""
logger.info(f"flagging: username={FLAG_USER_NAME},query={query},vs_path={vs_path},mode={mode},history={history}")
def get_vector_store(vs_id, files, sentence_size, history, one_conent, one_content_segmentation):
vs_path = os.path.join(KB_ROOT_PATH, vs_id, "vector_store")
filelist = []
if not os.path.exists(os.path.join(KB_ROOT_PATH, vs_id, "content")):
os.makedirs(os.path.join(KB_ROOT_PATH, vs_id, "content"))
qa = st.session_state.local_doc_qa
if qa.llm_model_chain and qa.embeddings:
if isinstance(files, list):
for file in files:
filename = os.path.split(file.name)[-1]
shutil.move(file.name, os.path.join(
KB_ROOT_PATH, vs_id, "content", filename))
filelist.append(os.path.join(
KB_ROOT_PATH, vs_id, "content", filename))
vs_path, loaded_files = qa.init_knowledge_vector_store(
filelist, vs_path, sentence_size)
else:
vs_path, loaded_files = qa.one_knowledge_add(vs_path, files, one_conent, one_content_segmentation,
sentence_size)
if len(loaded_files):
file_status = f"已添加 {'、'.join([os.path.split(i)[-1] for i in loaded_files if i])} 内容至知识库，并已加载知识库，请开始提问"
else:
file_status = "文件未成功加载，请重新上传文件"
else:
file_status = "模型未完成加载，请先在加载模型后再导入文件"
vs_path = None
logger.info(file_status)
return vs_path, None, history + [[None, file_status]]
knowledge_base_test_mode_info = ("【注意】\n\n"
"1. 您已进入知识库测试模式，您输入的任何对话内容都将用于进行知识库查询，"
"并仅输出知识库匹配出的内容及相似度分值和及输入的文本源路径，查询的内容并不会进入模型查询。\n\n"
"2. 知识相关度 Score 经测试，建议设置为 500 或更低，具体设置情况请结合实际使用调整。"
"""3. 使用"添加单条数据"添加文本至知识库时，内容如未分段，则内容越多越会稀释各查询内容与之关联的score阈值。\n\n"""
"4. 单条内容长度建议设置在100-150左右。")
webui_title = """
# 🎉langchain-ChatGLM WebUI🎉
👍 [https://github.com/imClumsyPanda/langchain-ChatGLM](https://github.com/imClumsyPanda/langchain-ChatGLM)
"""
###### #####
###### todo #####
# 1. streamlit运行方式与一般web服务器不同，使用模块是无法实现单例模式的，所以shared和local_doc_qa都需要进行全局化处理。
# 目前已经实现了local_doc_qa和shared.loaderCheckPoint的全局化。
# 2. 当前local_doc_qa是一个全局变量，一方面：任何一个session对其做出修改，都会影响所有session的对话;另一方面，如何处理所有session的请求竞争也是问题。
# 这个暂时无法避免，在配置普通的机器上暂时也无需考虑。
# 3. 目前只包含了get_answer对应的参数，以后可以添加其他参数，如temperature。
###### #####
###### 配置项 #####
class ST_CONFIG:
default_mode = "知识库问答"
default_kb = ""
###### #####
class TempFile:
'''
为保持与get_vector_store的兼容性，需要将streamlit上传文件转化为其可以接受的方式
'''
def __init__(self, path):
self.name = path
@st.cache_resource(show_spinner=False, max_entries=1)
def load_model(
llm_model: str = LLM_MODEL,
embedding_model: str = EMBEDDING_MODEL,
use_ptuning_v2: bool = USE_PTUNING_V2,
):
'''
对应init_model，利用streamlit cache避免模型重复加载
'''
local_doc_qa = LocalDocQA()
# 初始化消息
args = parser.parse_args()
args_dict = vars(args)
args_dict.update(model=llm_model)
if shared.loaderCheckPoint is None: # avoid checkpoint reloading when reinit model
shared.loaderCheckPoint = LoaderCheckPoint(args_dict)
# shared.loaderCheckPoint.model_name is different by no_remote_model.
# if it is not set properly error occurs when reinit llm model(issue#473).
# as no_remote_model is removed from model_config, need workaround to set it automaticlly.
local_model_path = llm_model_dict.get(llm_model, {}).get('local_model_path') or ''
no_remote_model = os.path.isdir(local_model_path)
llm_model_ins = shared.loaderLLM(llm_model, no_remote_model, use_ptuning_v2)
llm_model_ins.history_len = LLM_HISTORY_LEN
try:
local_doc_qa.init_cfg(llm_model=llm_model_ins,
embedding_model=embedding_model)
answer_result_stream_result = local_doc_qa.llm_model_chain(
{"prompt": "你好", "history": [], "streaming": False})
for answer_result in answer_result_stream_result['answer_result_stream']:
print(answer_result.llm_output)
reply = """模型已成功加载，可以开始对话，或从右侧选择模式后开始对话"""
logger.info(reply)
except Exception as e:
logger.error(e)
reply = """模型未成功加载，请到页面左上角"模型配置"选项卡中重新选择后点击"加载模型"按钮"""
if str(e) == "Unknown platform: darwin":
logger.info("该报错可能因为您使用的是 macOS 操作系统，需先下载模型至本地后执行 Web UI，具体方法请参考项目 README 中本地部署方法及常见问题："
" https://github.com/imClumsyPanda/langchain-ChatGLM")
else:
logger.info(reply)
return local_doc_qa
# @st.cache_data
def answer(query, vs_path='', history=[], mode='', score_threshold=0,
vector_search_top_k=5, chunk_conent=True, chunk_size=100
):
'''
对应get_answer，--利用streamlit cache缓存相同问题的答案--
'''
return get_answer(query, vs_path, history, mode, score_threshold,
vector_search_top_k, chunk_conent, chunk_size)
def use_kb_mode(m):
return m in ["知识库问答", "知识库测试"]
# main ui
st.set_page_config(webui_title, layout='wide')
chat_box = st_chatbox(greetings=["模型已成功加载，可以开始对话，或从左侧选择模式后开始对话。"])
# 使用 help(st_chatbox) 查看自定义参数
# sidebar
modes = ['LLM 对话', '知识库问答', 'Bing搜索问答', '知识库测试']
with st.sidebar:
def on_mode_change():
m = st.session_state.mode
chat_box.robot_say(f'已切换到"{m}"模式')
if m == '知识库测试':
chat_box.robot_say(knowledge_base_test_mode_info)
index = 0
try:
index = modes.index(ST_CONFIG.default_mode)
except:
pass
mode = st.selectbox('对话模式', modes, index,
on_change=on_mode_change, key='mode')
with st.expander('模型配置', not use_kb_mode(mode)):
with st.form('model_config'):
index = 0
try:
index = llm_model_dict_list.index(LLM_MODEL)
except:
pass
llm_model = st.selectbox('LLM模型', llm_model_dict_list, index)
use_ptuning_v2 = st.checkbox('使用p-tuning-v2微调过的模型', False)
try:
index = embedding_model_dict_list.index(EMBEDDING_MODEL)
except:
pass
embedding_model = st.selectbox(
'Embedding模型', embedding_model_dict_list, index)
btn_load_model = st.form_submit_button('重新加载模型')
if btn_load_model:
local_doc_qa = load_model(llm_model, embedding_model, use_ptuning_v2)
history_len = st.slider(
"LLM对话轮数", 1, 50, LLM_HISTORY_LEN)
if use_kb_mode(mode):
vs_list = get_vs_list()
vs_list.remove('新建知识库')
def on_new_kb():
name = st.session_state.kb_name
if name in vs_list:
st.error(f'名为“{name}”的知识库已存在。')
else:
vs_list.append(name)
st.session_state.vs_path = name
def on_vs_change():
chat_box.robot_say(f'已加载知识库： {st.session_state.vs_path}')
with st.expander('知识库配置', True):
cols = st.columns([12, 10])
kb_name = cols[0].text_input(
'新知识库名称', placeholder='新知识库名称', label_visibility='collapsed')
if 'kb_name' not in st.session_state:
st.session_state.kb_name = kb_name
cols[1].button('新建知识库', on_click=on_new_kb)
index = 0
try:
index = vs_list.index(ST_CONFIG.default_kb)
except:
pass
vs_path = st.selectbox(
'选择知识库', vs_list, index, on_change=on_vs_change, key='vs_path')
st.text('')
score_threshold = st.slider(
'知识相关度阈值', 0, 1000, VECTOR_SEARCH_SCORE_THRESHOLD)
top_k = st.slider('向量匹配数量', 1, 20, VECTOR_SEARCH_TOP_K)
chunk_conent = st.checkbox('启用上下文关联', False)
chunk_size = st.slider('上下文关联长度', 1, 1000, CHUNK_SIZE)
st.text('')
sentence_size = st.slider('文本入库分句长度限制', 1, 1000, SENTENCE_SIZE)
files = st.file_uploader('上传知识文件',
['docx', 'txt', 'md', 'csv', 'xlsx', 'pdf'],
accept_multiple_files=True)
if st.button('添加文件到知识库'):
temp_dir = tempfile.mkdtemp()
file_list = []
for f in files:
file = os.path.join(temp_dir, f.name)
with open(file, 'wb') as fp:
fp.write(f.getvalue())
file_list.append(TempFile(file))
_, _, history = get_vector_store(
vs_path, file_list, sentence_size, [], None, None)
st.session_state.files = []
# load model after params rendered
with st.spinner(f"正在加载模型({llm_model} + {embedding_model})，请耐心等候..."):
local_doc_qa = load_model(
llm_model,
embedding_model,
use_ptuning_v2,
)
local_doc_qa.llm_model_chain.history_len = history_len
if use_kb_mode(mode):
local_doc_qa.chunk_conent = chunk_conent
local_doc_qa.chunk_size = chunk_size
# local_doc_qa.llm_model_chain.temperature = temperature # 这样设置temperature似乎不起作用
st.session_state.local_doc_qa = local_doc_qa
# input form
with st.form("my_form", clear_on_submit=True):
cols = st.columns([8, 1])
question = cols[0].text_area(
'temp', key='input_question', label_visibility='collapsed')
if cols[1].form_submit_button("发送"):
chat_box.user_say(question)
history = []
if mode == "LLM 对话":
chat_box.robot_say("正在思考...")
chat_box.output_messages()
for history, _ in answer(question,
history=[],
mode=mode):
chat_box.update_last_box_text(history[-1][-1])
elif use_kb_mode(mode):
chat_box.robot_say(f"正在查询 [{vs_path}] ...")
chat_box.output_messages()
for history, _ in answer(question,
vs_path=os.path.join(
KB_ROOT_PATH, vs_path, 'vector_store'),
history=[],
mode=mode,
score_threshold=score_threshold,
vector_search_top_k=top_k,
chunk_conent=chunk_conent,
chunk_size=chunk_size):
chat_box.update_last_box_text(history[-1][-1])
else:
chat_box.robot_say(f"正在执行Bing搜索...")
chat_box.output_messages()
for history, _ in answer(question,
history=[],
mode=mode):
chat_box.update_last_box_text(history[-1][-1])
# st.write(chat_box.history)
chat_box.output_messages()