234 lines
9.3 KiB
Python
234 lines
9.3 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 = [os.path.join(os.path.dirname(__file__), "nltk_data")] + nltk.data.path
|
||
|
||
# return top-k text chunk from vector store
|
||
VECTOR_SEARCH_TOP_K = 6
|
||
|
||
# LLM input history length
|
||
LLM_HISTORY_LEN = 3
|
||
|
||
|
||
def get_file_list():
|
||
if not os.path.exists("content"):
|
||
return []
|
||
return [f for f in os.listdir("content")]
|
||
|
||
|
||
def get_vs_list():
|
||
if not os.path.exists("vector_store"):
|
||
return []
|
||
return ["新建知识库"] + os.listdir("vector_store")
|
||
|
||
|
||
file_list = get_file_list()
|
||
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()
|
||
|
||
|
||
def upload_file(file, chatbot):
|
||
if not os.path.exists("content"):
|
||
os.mkdir("content")
|
||
filename = os.path.basename(file.name)
|
||
shutil.move(file.name, "content/" + filename)
|
||
# file_list首位插入新上传的文件
|
||
file_list.insert(0, filename)
|
||
status = "已将xx上传至xxx"
|
||
return chatbot + [None, status]
|
||
|
||
|
||
def get_answer(query, vs_path, history):
|
||
if vs_path:
|
||
resp, history = local_doc_qa.get_knowledge_based_answer(
|
||
query=query, vs_path=vs_path, chat_history=history)
|
||
source = "".join([f"""<details> <summary>出处 {i + 1}</summary>
|
||
{doc.page_content}
|
||
|
||
<b>所属文件：</b>{doc.metadata["source"]}
|
||
</details>""" for i, doc in enumerate(resp["source_documents"])])
|
||
history[-1][-1] += source
|
||
else:
|
||
resp = local_doc_qa.llm._call(query)
|
||
history = history + [[None, resp + "\n如需基于知识库进行问答，请先加载知识库后，再进行提问。"]]
|
||
return history, ""
|
||
|
||
|
||
def update_status(history, status):
|
||
history = history + [[None, status]]
|
||
print(status)
|
||
return history
|
||
|
||
|
||
def init_model():
|
||
try:
|
||
local_doc_qa.init_cfg()
|
||
local_doc_qa.llm._call("你好")
|
||
return """模型已成功加载，请选择文件后点击"加载文件"按钮"""
|
||
except Exception as e:
|
||
print(e)
|
||
return """模型未成功加载，请重新选择后点击"加载模型"按钮"""
|
||
|
||
|
||
def reinit_model(llm_model, embedding_model, llm_history_len, use_ptuning_v2, 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,
|
||
top_k=top_k)
|
||
model_status = """模型已成功重新加载，请选择文件后点击"加载文件"按钮"""
|
||
except Exception as e:
|
||
print(e)
|
||
model_status = """模型未成功重新加载，请重新选择后点击"加载模型"按钮"""
|
||
return history + [[None, model_status]]
|
||
|
||
|
||
def get_vector_store(filepath, history):
|
||
if local_doc_qa.llm and local_doc_qa.embeddings:
|
||
vs_path = local_doc_qa.init_knowledge_vector_store(["content/" + filepath])
|
||
if vs_path:
|
||
file_status = "文件已成功加载，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
|
||
return vs_path, history + [[None, file_status]]
|
||
|
||
|
||
def change_vs_name_input(vs):
|
||
if vs == "新建知识库":
|
||
return gr.update(lines=1, visible=True)
|
||
else:
|
||
return gr.update(visible=False)
|
||
|
||
|
||
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，开始提问前，请依次如下 3 个步骤：
|
||
1. 选择语言模型、Embedding 模型及相关参数，如果使用 ptuning-v2 方式微调过模型，将 PrefixEncoder 模型放在 ptuning-v2 文件夹里并勾选相关选项，然后点击"重新加载模型"，并等待加载完成提示
|
||
2. 上传或选择已有文件作为本地知识文档输入后点击"重新加载文档"，并等待加载完成提示
|
||
3. 输入要提交的问题后，点击回车提交 """
|
||
|
||
model_status = init_model()
|
||
|
||
with gr.Blocks(css=block_css) as demo:
|
||
vs_path, file_status, model_status = gr.State(""), gr.State(""), gr.State(model_status)
|
||
gr.Markdown(webui_title)
|
||
with gr.Tab("聊天"):
|
||
with gr.Row():
|
||
with gr.Column(scale=2):
|
||
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=1):
|
||
gr.Markdown("请选择使用模式")
|
||
gr.Radio(["默认", "知识库问答"],
|
||
label="请选择使用模式",
|
||
info="默认模式将不使用知识库")
|
||
with gr.Accordion("配置知识库"):
|
||
# gr.Markdown("配置知识库")
|
||
select_vs = gr.Dropdown(vs_list,
|
||
label="请选择要加载的知识库",
|
||
interactive=True,
|
||
value=vs_list[0] if len(vs_list) > 0 else None)
|
||
vs_name = gr.Textbox(label="请输入新建知识库名称",
|
||
lines=1,
|
||
interactive=True)
|
||
select_vs.change(fn=change_vs_name_input,
|
||
inputs=select_vs,
|
||
outputs=vs_name)
|
||
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")
|
||
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")
|
||
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 history len",
|
||
interactive=True)
|
||
use_ptuning_v2 = gr.Checkbox(USE_PTUNING_V2,
|
||
label="使用p-tuning-v2微调过的模型",
|
||
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, top_k, chatbot],
|
||
outputs=chatbot
|
||
)
|
||
# 将上传的文件保存到content文件夹下,并更新下拉框
|
||
files.upload(upload_file,
|
||
inputs=[files, chatbot],
|
||
outputs=chatbot)
|
||
load_file_button.click(get_vector_store,
|
||
show_progress=True,
|
||
inputs=[select_vs, chatbot],
|
||
outputs=[vs_path, chatbot],
|
||
)
|
||
query.submit(get_answer,
|
||
[query, vs_path, chatbot],
|
||
[chatbot, query],
|
||
)
|
||
|
||
demo.queue(concurrency_count=3
|
||
).launch(server_name='0.0.0.0',
|
||
server_port=7860,
|
||
show_api=False,
|
||
share=False,
|
||
inbrowser=False)
|