import os
import shutil
from app_modules.overwrites import postprocess
from app_modules.presets import *
from clc.langchain_application import LangChainApplication
# 修改成自己的配置！！！
class LangChainCFG:
llm_model_name = 'THUDM/chatglm-6b-int4-qe' # 本地模型文件 or huggingface远程仓库
embedding_model_name = 'GanymedeNil/text2vec-large-chinese' # 检索模型文件 or huggingface远程仓库
vector_store_path = './cache'
docs_path = './docs'
kg_vector_stores = {
'中文维基百科': './cache/zh_wikipedia',
'大规模金融研报': './cache/financial_research_reports',
'初始化': './cache',
} # 可以替换成自己的知识库，如果没有需要设置为None
# kg_vector_stores=None
patterns = ['模型问答', '知识库问答'] #
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 set_knowledge(kg_name, history):
try:
application.source_service.load_vector_store(config.kg_vector_stores[kg_name])
msg_status = f'{kg_name}知识库已成功加载'
except Exception as e:
print(e)
msg_status = f'{kg_name}知识库未成功加载'
return history + [[None, msg_status]]
def clear_session():
return '', None
def predict(input,
large_language_model,
embedding_model,
top_k,
use_web,
use_pattern,
history=None):
# print(large_language_model, embedding_model)
print(input)
if history == None:
history = []
if use_web == '使用':
web_content = application.source_service.search_web(query=input)
else:
web_content = ''
search_text = ''
if use_pattern == '模型问答':
result = application.get_llm_answer(query=input, web_content=web_content)
history.append((input, result))
search_text += web_content
return '', history, history, search_text
else:
resp = application.get_knowledge_based_answer(
query=input,
history_len=1,
temperature=0.1,
top_p=0.9,
top_k=top_k,
web_content=web_content,
chat_history=history
)
history.append((input, resp['result']))
for idx, source in enumerate(resp['source_documents'][:4]):
sep = f'----------【搜索结果{idx + 1}：】---------------\n'
search_text += f'{sep}\n{source.page_content}\n\n'
print(search_text)
search_text += "----------【网络检索内容】-----------\n"
search_text += web_content
return '', history, history, search_text
with open("assets/custom.css", "r", encoding="utf-8") as f:
customCSS = f.read()
with gr.Blocks(css=customCSS, theme=small_and_beautiful_theme) as demo:
gr.Markdown("""Chinese-LangChain
""")
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=4,
step=1,
label="检索top-k文档",
interactive=True)
use_web = gr.Radio(["使用", "不使用"], label="web search",
info="是否使用网络搜索，使用时确保网络通常",
value="不使用"
)
use_pattern = gr.Radio(
[
'模型问答',
'知识库问答',
],
label="模式",
value='模型问答',
interactive=True)
kg_name = gr.Radio(list(config.kg_vector_stores.keys()),
label="知识库",
value=None,
info="使用知识库问答，请加载知识库",
interactive=True)
set_kg_btn = gr.Button("加载知识库")
file = gr.File(label="将文件上传到知识库库，内容要尽量匹配",
visible=True,
file_types=['.txt', '.md', '.docx', '.pdf']
)
with gr.Column(scale=4):
with gr.Row():
chatbot = gr.Chatbot(label='Chinese-LangChain').style(height=400)
with gr.Row():
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.Row():
gr.Markdown("""提醒：
[Chinese-LangChain](https://github.com/yanqiangmiffy/Chinese-LangChain)
有任何使用问题[Github Issue区](https://github.com/yanqiangmiffy/Chinese-LangChain)进行反馈.
""")
with gr.Column(scale=2):
search = gr.Textbox(label='搜索结果')
# ============= 触发动作=============
file.upload(upload_file,
inputs=file,
outputs=None)
set_kg_btn.click(
set_knowledge,
show_progress=True,
inputs=[kg_name, chatbot],
outputs=chatbot
)
# 发送按钮 提交
send.click(predict,
inputs=[
message,
large_language_model,
embedding_model,
top_k,
use_web,
use_pattern,
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,
top_k,
use_web,
use_pattern,
state
],
outputs=[message, chatbot, state, search])
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,
inbrowser=True,
)