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ChatGLM / webui_st.py

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	import streamlit as st
	# from st_btn_select import st_btn_select
	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())
	# flag_csv_logger = gr.CSVLogger()


	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):
	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:
	for answer_result in local_doc_qa.llm.generatorAnswer(prompt=query, history=history,
	streaming=streaming):

	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}")
	# flag_csv_logger.flag([query, vs_path, history, mode], username=FLAG_USER_NAME)


	def init_model(llm_model: str = 'chat-glm-6b', embedding_model: str = 'text2vec'):
	local_doc_qa = LocalDocQA()
	# 初始化消息
	args = parser.parse_args()
	args_dict = vars(args)
	args_dict.update(model=llm_model)
	shared.loaderCheckPoint = LoaderCheckPoint(args_dict)
	llm_model_ins = shared.loaderLLM()
	llm_model_ins.set_history_len(LLM_HISTORY_LEN)

	try:
	local_doc_qa.init_cfg(llm_model=llm_model_ins,
	embedding_model=embedding_model)
	generator = local_doc_qa.llm.generatorAnswer("你好")
	for answer_result in generator:
	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


	# 暂未使用到，先保留
	# def reinit_model(llm_model, embedding_model, llm_history_len, no_remote_model, use_ptuning_v2, use_lora, top_k, history):
	# try:
	# llm_model_ins = shared.loaderLLM(llm_model, no_remote_model, use_ptuning_v2)
	# llm_model_ins.history_len = llm_history_len
	# local_doc_qa.init_cfg(llm_model=llm_model_ins,
	# embedding_model=embedding_model,
	# top_k=top_k)
	# model_status = """模型已成功重新加载，可以开始对话，或从右侧选择模式后开始对话"""
	# logger.info(model_status)
	# except Exception as e:
	# logger.error(e)
	# model_status = """模型未成功重新加载，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"""
	# logger.info(model_status)
	# return history + [[None, model_status]]


	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"))
	if local_doc_qa.llm and local_doc_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 = local_doc_qa.init_knowledge_vector_store(
	filelist, vs_path, sentence_size)
	else:
	vs_path, loaded_files = local_doc_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。\n\n"
	"5. 本界面用于知识入库及知识匹配相关参数设定，但当前版本中，"
	"本界面中修改的参数并不会直接修改对话界面中参数，仍需前往`configs/model_config.py`修改后生效。"
	"相关参数将在后续版本中支持本界面直接修改。")


	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的全局化，后面要考虑shared。
	# 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:
	user_bg_color = '#77ff77'
	user_icon = 'https://tse2-mm.cn.bing.net/th/id/OIP-C.LTTKrxNWDr_k74wz6jKqBgHaHa?w=203&h=203&c=7&r=0&o=5&pid=1.7'
	robot_bg_color = '#ccccee'
	robot_icon = 'https://ts1.cn.mm.bing.net/th/id/R-C.5302e2cc6f5c7c4933ebb3394e0c41bc?rik=z4u%2b7efba5Mgxw&riu=http%3a%2f%2fcomic-cons.xyz%2fwp-content%2fuploads%2fStar-Wars-avatar-icon-C3PO.png&ehk=kBBvCvpJMHPVpdfpw1GaH%2brbOaIoHjY5Ua9PKcIs%2bAc%3d&risl=&pid=ImgRaw&r=0'
	default_mode = '知识库问答'
	defalut_kb = ''
	###### #####


	class MsgType:
	'''
	目前仅支持文本类型的输入输出，为以后多模态模型预留图像、视频、音频支持。
	'''
	TEXT = 1
	IMAGE = 2
	VIDEO = 3
	AUDIO = 4


	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


	def init_session():
	st.session_state.setdefault('history', [])


	# def get_query_params():
	# '''
	# 可以用url参数传递配置参数：llm_model, embedding_model, kb, mode。
	# 该参数将覆盖model_config中的配置。处于安全考虑，目前只支持kb和mode
	# 方便将固定的配置分享给特定的人。
	# '''
	# params = st.experimental_get_query_params()
	# return {k: v[0] for k, v in params.items() if v}


	def robot_say(msg, kb=''):
	st.session_state['history'].append(
	{'is_user': False, 'type': MsgType.TEXT, 'content': msg, 'kb': kb})


	def user_say(msg):
	st.session_state['history'].append(
	{'is_user': True, 'type': MsgType.TEXT, 'content': msg})


	def format_md(msg, is_user=False, bg_color='', margin='10%'):
	'''
	将文本消息格式化为markdown文本
	'''
	if is_user:
	bg_color = bg_color or ST_CONFIG.user_bg_color
	text = f'''
	<div style="background:{bg_color};
	margin-left:{margin};
	word-break:break-all;
	float:right;
	padding:2%;
	border-radius:2%;">
	{msg}
	</div>
	'''
	else:
	bg_color = bg_color or ST_CONFIG.robot_bg_color
	text = f'''
	<div style="background:{bg_color};
	margin-right:{margin};
	word-break:break-all;
	padding:2%;
	border-radius:2%;">
	{msg}
	</div>
	'''
	return text


	def message(msg,
	is_user=False,
	msg_type=MsgType.TEXT,
	icon='',
	bg_color='',
	margin='10%',
	kb='',
	):
	'''
	渲染单条消息。目前仅支持文本
	'''
	cols = st.columns([1, 10, 1])
	empty = cols[1].empty()
	if is_user:
	icon = icon or ST_CONFIG.user_icon
	bg_color = bg_color or ST_CONFIG.user_bg_color
	cols[2].image(icon, width=40)
	if msg_type == MsgType.TEXT:
	text = format_md(msg, is_user, bg_color, margin)
	empty.markdown(text, unsafe_allow_html=True)
	else:
	raise RuntimeError('only support text message now.')
	else:
	icon = icon or ST_CONFIG.robot_icon
	bg_color = bg_color or ST_CONFIG.robot_bg_color
	cols[0].image(icon, width=40)
	if kb:
	cols[0].write(f'({kb})')
	if msg_type == MsgType.TEXT:
	text = format_md(msg, is_user, bg_color, margin)
	empty.markdown(text, unsafe_allow_html=True)
	else:
	raise RuntimeError('only support text message now.')
	return empty


	def output_messages(
	user_bg_color='',
	robot_bg_color='',
	user_icon='',
	robot_icon='',
	):
	with chat_box.container():
	last_response = None
	for msg in st.session_state['history']:
	bg_color = user_bg_color if msg['is_user'] else robot_bg_color
	icon = user_icon if msg['is_user'] else robot_icon
	empty = message(msg['content'],
	is_user=msg['is_user'],
	icon=icon,
	msg_type=msg['type'],
	bg_color=bg_color,
	kb=msg.get('kb', '')
	)
	if not msg['is_user']:
	last_response = empty
	return last_response


	@st.cache_resource(show_spinner=False, max_entries=1)
	def load_model(llm_model: str, embedding_model: str):
	'''
	对应init_model，利用streamlit cache避免模型重复加载
	'''
	local_doc_qa = init_model(llm_model, embedding_model)
	robot_say('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。\n请尽量不要刷新页面，以免模型出错或重复加载。')
	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, qa=None
	):
	'''
	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 load_vector_store(
	vs_id,
	files,
	sentence_size=100,
	history=[],
	one_conent=None,
	one_content_segmentation=None,
	):
	return get_vector_store(
	local_doc_qa,
	vs_id,
	files,
	sentence_size,
	history,
	one_conent,
	one_content_segmentation,
	)


	# main ui
	st.set_page_config(webui_title, layout='wide')
	init_session()
	# params = get_query_params()
	# llm_model = params.get('llm_model', LLM_MODEL)
	# embedding_model = params.get('embedding_model', EMBEDDING_MODEL)

	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)


	def use_kb_mode(m):
	return m in ['知识库问答', '知识库测试']


	# 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
	robot_say(f'已切换到"{m}"模式')
	if m == '知识库测试':
	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', '知识' not in 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)

	no_remote_model = st.checkbox('加载本地模型', False)
	use_ptuning_v2 = st.checkbox('使用p-tuning-v2微调过的模型', False)
	use_lora = st.checkbox('使用lora微调的权重', 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)

	if mode in ['知识库问答', '知识库测试']:
	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():
	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')
	cols[1].button('新建知识库', on_click=on_new_kb)
	vs_path = st.selectbox(
	'选择知识库', vs_list, 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)
	history_len = st.slider(
	'LLM对话轮数', 1, 50, LLM_HISTORY_LEN) # 也许要跟知识库分开设置
	local_doc_qa.llm.set_history_len(history_len)
	chunk_conent = st.checkbox('启用上下文关联', False)
	st.text('')
	# chunk_conent = st.checkbox('分割文本', True) # 知识库文本分割入库
	chunk_size = st.slider('上下文关联长度', 1, 1000, CHUNK_SIZE)
	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 = load_vector_store(
	vs_path, file_list, sentence_size, [], None, None)
	st.session_state.files = []


	# main body
	chat_box = st.empty()

	with st.form('my_form', clear_on_submit=True):
	cols = st.columns([8, 1])
	question = cols[0].text_input(
	'temp', key='input_question', label_visibility='collapsed')

	def on_send():
	q = st.session_state.input_question
	if q:
	user_say(q)

	if mode == 'LLM 对话':
	robot_say('正在思考...')
	last_response = output_messages()
	for history, _ in answer(q,
	history=[],
	mode=mode):
	last_response.markdown(
	format_md(history[-1][-1], False),
	unsafe_allow_html=True
	)
	elif use_kb_mode(mode):
	robot_say('正在思考...', vs_path)
	last_response = output_messages()
	for history, _ in answer(q,
	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):
	last_response.markdown(
	format_md(history[-1][-1], False, 'ligreen'),
	unsafe_allow_html=True
	)
	else:
	robot_say('正在思考...')
	last_response = output_messages()
	st.session_state['history'][-1]['content'] = history[-1][-1]
	submit = cols[1].form_submit_button('发送', on_click=on_send)

	output_messages()

	# st.write(st.session_state['history'])