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webui.py
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webui.py
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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():
if not os.path.exists(VS_ROOT_PATH):
return []
return os.listdir(VS_ROOT_PATH)
vs_list = ["新建知识库"] + 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()
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, ""
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"""
print(reply)
return reply
except Exception as e:
print(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":
print("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:
print(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"""
print(model_status)
except Exception as e:
print(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"""
print(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
print(file_status)
return vs_path, None, history + [[None, file_status]]
def change_vs_name_input(vs_id):
if vs_id == "新建知识库":
return gr.update(visible=True), gr.update(visible=True), gr.update(visible=False), None
else:
return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), os.path.join(VS_ROOT_PATH, vs_id)
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, 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_list + [vs_name], value=vs_name), vs_list + [vs_name], 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)
"""
init_message = """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。
知识库问答模式中，选择知识库名称后，即可开始问答，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()
with gr.Blocks(css=block_css) as demo:
vs_path, file_status, model_status, vs_list = gr.State(""), 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)
vs_add = gr.Button(value="添加至知识库选项")
vs_add.click(fn=add_vs_name,
inputs=[vs_name, vs_list, chatbot],
outputs=[select_vs, vs_list, chatbot])
file2vs = gr.Column(visible=False)
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=)
select_vs.change(fn=change_vs_name_input,
inputs=select_vs,
outputs=[vs_name, vs_add, file2vs, vs_path])
# 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],
)
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))