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import os
import shutil
import gradio as gr
from clc.langchain_application import LangChainApplication
os.environ["CUDA_VISIBLE_DEVICES"] = '1'
# 修改成自己的配置！！！
class LangChainCFG:
llm_model_name = '../../pretrained_models/chatglm-6b-int4-qe' # 本地模型文件 or huggingface远程仓库
embedding_model_name = '../../pretrained_models/text2vec-large-chinese' # 检索模型文件 or huggingface远程仓库
vector_store_path = './cache'
docs_path = './docs'
config = LangChainCFG()
application = LangChainApplication(config)
def get_file_list():
if not os.path.exists("docs"):
return []
return [f for f in os.listdir("docs")]
file_list = get_file_list()
def upload_file(file):
if not os.path.exists("docs"):
os.mkdir("docs")
filename = os.path.basename(file.name)
shutil.move(file.name, "docs/" + filename)
# file_list首位插入新上传的文件
file_list.insert(0, filename)
application.source_service.add_document("docs/" + filename)
return gr.Dropdown.update(choices=file_list, value=filename)
def clear_session():
return '', None
def predict(input,
large_language_model,
embedding_model,
history=None):
# print(large_language_model, embedding_model)
print(input)
if history == None:
history = []
resp = application.get_knowledge_based_answer(
query=input,
history_len=1,
temperature=0.1,
top_p=0.9,
chat_history=history
)
history.append((input, resp['result']))
search_text = ''
for idx, source in enumerate(resp['source_documents'][:2]):
sep = f'----------【搜索结果{idx}：】---------------\n'
search_text += f'{sep}\n{source.page_content}\n\n'
print(search_text)
return '', history, history, search_text
block = gr.Blocks()
with block as demo:
gr.Markdown("""<h1><center>Chinese-LangChain</center></h1>
<center><font size=3>
</center></font>
""")
state = gr.State()
with gr.Row():
with gr.Column(scale=1):
embedding_model = gr.Dropdown([
"text2vec-base"
],
label="Embedding model",
value="text2vec-base")
large_language_model = gr.Dropdown(
[
"ChatGLM-6B-int4",
],
label="large language model",
value="ChatGLM-6B-int4")
top_k = gr.Slider(1,
20,
value=2,
step=1,
label="向量匹配 top k",
interactive=True)
kg_name = gr.Radio(['中文维基百科', '百度百科数据', '坦克世界'],
label="知识库",
value='中文维基百科',
interactive=True)
file = gr.File(label="将文件上传到数据库",
visible=True,
file_types=['.txt', '.md', '.docx', '.pdf']
)
file.upload(upload_file,
inputs=file,
outputs=None)
with gr.Column(scale=4):
with gr.Row():
with gr.Column(scale=4):
chatbot = gr.Chatbot(label='Chinese-LangChain').style(height=400)
message = gr.Textbox(label='请输入问题')
with gr.Row():
clear_history = gr.Button("🧹 清除历史对话")
send = gr.Button("🚀 发送")
with gr.Column(scale=2):
search = gr.Textbox(label='搜索结果')
# 发送按钮 提交
send.click(predict,
inputs=[
message, large_language_model,
embedding_model, state
],
outputs=[message, chatbot, state, search])
# 清空历史对话按钮 提交
clear_history.click(fn=clear_session,
inputs=[],
outputs=[chatbot, state],
queue=False)
# 输入框 回车
message.submit(predict,
inputs=[
message, large_language_model,
embedding_model, state
],
outputs=[message, chatbot, state, search])
gr.Markdown("""提醒：<br>
有任何使用问题[Github Issue区](https://github.com/yanqiangmiffy/Chinese-LangChain)进行反馈. <br>
""")
demo.queue(concurrency_count=2).launch(
server_name='0.0.0.0',
server_port=8888,
share=False,
show_error=True,
debug=True,
enable_queue=True
)
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