281 lines
14 KiB
Python
281 lines
14 KiB
Python
import gradio as gr
|
||
import os
|
||
import shutil
|
||
from chains.local_doc_qa import LocalDocQA
|
||
from configs.model_config import *
|
||
import nltk
|
||
|
||
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(VS_ROOT_PATH):
|
||
return lst_default
|
||
lst = os.listdir(VS_ROOT_PATH)
|
||
if not lst:
|
||
return lst_default
|
||
lst.sort()
|
||
return lst_default + lst
|
||
|
||
|
||
vs_list = get_vs_list()
|
||
|
||
embedding_model_dict_list = list(embedding_model_dict.keys())
|
||
|
||
llm_model_dict_list = list(llm_model_dict.keys())
|
||
|
||
local_doc_qa = LocalDocQA()
|
||
|
||
flag_csv_logger = gr.CSVLogger()
|
||
|
||
def get_answer(query, vs_path, history, mode,
|
||
streaming: bool = STREAMING):
|
||
if mode == "知识库问答" and 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, ""
|
||
else:
|
||
for resp, history in local_doc_qa.llm._call(query, history,
|
||
streaming=streaming):
|
||
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():
|
||
try:
|
||
local_doc_qa.init_cfg()
|
||
local_doc_qa.llm._call("你好")
|
||
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)
|
||
return 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 reply
|
||
|
||
|
||
def reinit_model(llm_model, embedding_model, llm_history_len, use_ptuning_v2, use_lora, top_k, history):
|
||
try:
|
||
local_doc_qa.init_cfg(llm_model=llm_model,
|
||
embedding_model=embedding_model,
|
||
llm_history_len=llm_history_len,
|
||
use_ptuning_v2=use_ptuning_v2,
|
||
use_lora=use_lora,
|
||
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, history):
|
||
vs_path = os.path.join(VS_ROOT_PATH, vs_id)
|
||
filelist = []
|
||
if not os.path.exists(os.path.join(UPLOAD_ROOT_PATH, vs_id)):
|
||
os.makedirs(os.path.join(UPLOAD_ROOT_PATH, vs_id))
|
||
for file in files:
|
||
filename = os.path.split(file.name)[-1]
|
||
shutil.move(file.name, os.path.join(UPLOAD_ROOT_PATH, vs_id, filename))
|
||
filelist.append(os.path.join(UPLOAD_ROOT_PATH, vs_id, filename))
|
||
if local_doc_qa.llm and local_doc_qa.embeddings:
|
||
vs_path, loaded_files = local_doc_qa.init_knowledge_vector_store(filelist, vs_path)
|
||
if len(loaded_files):
|
||
file_status = f"已上传 {'、'.join([os.path.split(i)[-1] for i in loaded_files])} 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]]
|
||
|
||
|
||
|
||
def change_vs_name_input(vs_id, history):
|
||
if vs_id == "新建知识库":
|
||
return gr.update(visible=True), gr.update(visible=True), gr.update(visible=False), None, history
|
||
else:
|
||
file_status = f"已加载知识库{vs_id}，请开始提问"
|
||
return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), os.path.join(VS_ROOT_PATH,
|
||
vs_id), history + [
|
||
[None, file_status]]
|
||
|
||
|
||
def change_mode(mode):
|
||
if mode == "知识库问答":
|
||
return gr.update(visible=True)
|
||
else:
|
||
return gr.update(visible=False)
|
||
|
||
|
||
def add_vs_name(vs_name, vs_list, chatbot):
|
||
if vs_name in vs_list:
|
||
vs_status = "与已有知识库名称冲突，请重新选择其他名称后提交"
|
||
chatbot = chatbot + [[None, vs_status]]
|
||
return gr.update(visible=True), vs_list,gr.update(visible=True), gr.update(visible=True), gr.update(visible=False), chatbot
|
||
else:
|
||
vs_status = f"""已新增知识库"{vs_name}",将在上传文件并载入成功后进行存储。请在开始对话前，先完成文件上传。 """
|
||
chatbot = chatbot + [[None, vs_status]]
|
||
return gr.update(visible=True, choices= [vs_name] + vs_list, value=vs_name), [vs_name]+vs_list, gr.update(visible=False), gr.update(visible=False), gr.update(visible=True),chatbot
|
||
|
||
block_css = """.importantButton {
|
||
background: linear-gradient(45deg, #7e0570,#5d1c99, #6e00ff) !important;
|
||
border: none !important;
|
||
}
|
||
.importantButton:hover {
|
||
background: linear-gradient(45deg, #ff00e0,#8500ff, #6e00ff) !important;
|
||
border: none !important;
|
||
}"""
|
||
|
||
webui_title = """
|
||
# 🎉langchain-ChatGLM WebUI🎉
|
||
👍 [https://github.com/imClumsyPanda/langchain-ChatGLM](https://github.com/imClumsyPanda/langchain-ChatGLM)
|
||
"""
|
||
default_vs = vs_list[0] if len(vs_list) > 1 else "为空"
|
||
init_message = f"""欢迎使用 langchain-ChatGLM Web UI！
|
||
|
||
请在右侧切换模式，目前支持直接与 LLM 模型对话或基于本地知识库问答。
|
||
|
||
知识库问答模式，选择知识库名称后，即可开始问答，当前知识库{default_vs}，如有需要可以在选择知识库名称后上传文件/文件夹至知识库。
|
||
|
||
知识库暂不支持文件删除，该功能将在后续版本中推出。
|
||
"""
|
||
|
||
model_status = init_model()
|
||
default_path = os.path.join(VS_ROOT_PATH, vs_list[0]) if len(vs_list) > 1 else ""
|
||
|
||
with gr.Blocks(css=block_css) as demo:
|
||
vs_path, file_status, model_status, vs_list = gr.State(default_path), gr.State(""), gr.State(
|
||
model_status), gr.State(vs_list)
|
||
|
||
gr.Markdown(webui_title)
|
||
with gr.Tab("对话"):
|
||
with gr.Row():
|
||
with gr.Column(scale=10):
|
||
chatbot = gr.Chatbot([[None, init_message], [None, model_status.value]],
|
||
elem_id="chat-box",
|
||
show_label=False).style(height=750)
|
||
query = gr.Textbox(show_label=False,
|
||
placeholder="请输入提问内容，按回车进行提交").style(container=False)
|
||
with gr.Column(scale=5):
|
||
mode = gr.Radio(["LLM 对话", "知识库问答"],
|
||
label="请选择使用模式",
|
||
value="知识库问答", )
|
||
vs_setting = gr.Accordion("配置知识库")
|
||
mode.change(fn=change_mode,
|
||
inputs=mode,
|
||
outputs=vs_setting)
|
||
with vs_setting:
|
||
select_vs = gr.Dropdown(vs_list.value,
|
||
label="请选择要加载的知识库",
|
||
interactive=True,
|
||
value=vs_list.value[0] if len(vs_list.value) > 0 else None
|
||
)
|
||
vs_name = gr.Textbox(label="请输入新建知识库名称",
|
||
lines=1,
|
||
interactive=True,
|
||
visible=True if default_path=="" else False)
|
||
vs_add = gr.Button(value="添加至知识库选项", visible=True if default_path=="" else False)
|
||
file2vs = gr.Column(visible=False if default_path=="" else True)
|
||
with file2vs:
|
||
# load_vs = gr.Button("加载知识库")
|
||
gr.Markdown("向知识库中添加文件")
|
||
with gr.Tab("上传文件"):
|
||
files = gr.File(label="添加文件",
|
||
file_types=['.txt', '.md', '.docx', '.pdf'],
|
||
file_count="multiple",
|
||
show_label=False
|
||
)
|
||
load_file_button = gr.Button("上传文件并加载知识库")
|
||
with gr.Tab("上传文件夹"):
|
||
folder_files = gr.File(label="添加文件",
|
||
# file_types=['.txt', '.md', '.docx', '.pdf'],
|
||
file_count="directory",
|
||
show_label=False
|
||
)
|
||
load_folder_button = gr.Button("上传文件夹并加载知识库")
|
||
# load_vs.click(fn=)
|
||
vs_add.click(fn=add_vs_name,
|
||
inputs=[vs_name, vs_list, chatbot],
|
||
outputs=[select_vs, vs_list,vs_name,vs_add, file2vs,chatbot])
|
||
select_vs.change(fn=change_vs_name_input,
|
||
inputs=[select_vs, chatbot],
|
||
outputs=[vs_name, vs_add, file2vs, vs_path, chatbot])
|
||
# 将上传的文件保存到content文件夹下,并更新下拉框
|
||
load_file_button.click(get_vector_store,
|
||
show_progress=True,
|
||
inputs=[select_vs, files, chatbot],
|
||
outputs=[vs_path, files, chatbot],
|
||
)
|
||
load_folder_button.click(get_vector_store,
|
||
show_progress=True,
|
||
inputs=[select_vs, folder_files, chatbot],
|
||
outputs=[vs_path, folder_files, chatbot],
|
||
)
|
||
flag_csv_logger.setup([query, vs_path, chatbot, mode], "flagged")
|
||
query.submit(get_answer,
|
||
[query, vs_path, chatbot, mode],
|
||
[chatbot, query])
|
||
with gr.Tab("模型配置"):
|
||
llm_model = gr.Radio(llm_model_dict_list,
|
||
label="LLM 模型",
|
||
value=LLM_MODEL,
|
||
interactive=True)
|
||
llm_history_len = gr.Slider(0,
|
||
10,
|
||
value=LLM_HISTORY_LEN,
|
||
step=1,
|
||
label="LLM 对话轮数",
|
||
interactive=True)
|
||
use_ptuning_v2 = gr.Checkbox(USE_PTUNING_V2,
|
||
label="使用p-tuning-v2微调过的模型",
|
||
interactive=True)
|
||
use_lora = gr.Checkbox(USE_LORA,
|
||
label="使用lora微调的权重",
|
||
interactive=True)
|
||
embedding_model = gr.Radio(embedding_model_dict_list,
|
||
label="Embedding 模型",
|
||
value=EMBEDDING_MODEL,
|
||
interactive=True)
|
||
top_k = gr.Slider(1,
|
||
20,
|
||
value=VECTOR_SEARCH_TOP_K,
|
||
step=1,
|
||
label="向量匹配 top k",
|
||
interactive=True)
|
||
load_model_button = gr.Button("重新加载模型")
|
||
load_model_button.click(reinit_model,
|
||
show_progress=True,
|
||
inputs=[llm_model, embedding_model, llm_history_len, use_ptuning_v2, use_lora, top_k,
|
||
chatbot],
|
||
outputs=chatbot
|
||
)
|
||
|
||
(demo
|
||
.queue(concurrency_count=3)
|
||
.launch(server_name='0.0.0.0',
|
||
server_port=7860,
|
||
show_api=False,
|
||
share=False,
|
||
inbrowser=False)) |