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import os
import gradio as gr
import nltk
import sentence_transformers
import torch
from duckduckgo_search import ddg
from duckduckgo_search.utils import SESSION
from langchain.chains import RetrievalQA
from langchain.document_loaders import UnstructuredFileLoader
from langchain.embeddings import JinaEmbeddings
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from langchain.prompts import PromptTemplate
from langchain.prompts.prompt import PromptTemplate
from langchain.vectorstores import FAISS
from chatllm import ChatLLM
from chinese_text_splitter import ChineseTextSplitter
def load_files(filepaths):
docs = []
for filepath in filepaths:
docs += load_file(filepath)
return docs
def init_knowledge_vector_store(embedding_model, filepaths):
embeddings = HuggingFaceEmbeddings(
model_name=embedding_model_dict[embedding_model], )
embeddings.client = sentence_transformers.SentenceTransformer(
embeddings.model_name, device=DEVICE)
docs = load_files(filepaths)
vector_store = FAISS.from_documents(docs, embeddings)
return vector_store
def predict(input,
large_language_model,
embedding_model,
file_objs,
VECTOR_SEARCH_TOP_K,
history_len,
temperature,
top_p,
use_web,
history=None):
if history == None:
history = []
filepaths = [file_obj.name for file_obj in file_objs]
vector_store = init_knowledge_vector_store(embedding_model, filepaths)
if use_web == 'True':
web_content = search_web(query=input)
else:
web_content = ''
resp = get_knowledge_based_answer(
query=input,
large_language_model=large_language_model,
vector_store=vector_store,
VECTOR_SEARCH_TOP_K=VECTOR_SEARCH_TOP_K,
web_content=web_content,
chat_history=history,
history_len=history_len,
temperature=temperature,
top_p=top_p,
)
history.append((input, resp['result']))
return '', history, history
if __name__ == "__main__":
block = gr.Blocks()
with block as demo:
gr.Markdown("""<h1><center>Knowledge Based-ChatGLM-DongHeng</center></h1>
<center><font size=3>
本软件是Dongheng自用中文大模型软件. <br>
项目内容是基于GLM-6B大模型对本地上传知识库内容进行应答 <br>
禁止任何商业用途，谢谢！
</center></font>
""")
with gr.Row():
with gr.Column(scale=1):
model_choose = gr.Accordion("模型选择")
with model_choose:
large_language_model = gr.Dropdown(
list(llm_model_dict.keys()),
label="large language model",
value="ChatGLM-6B-int4")
embedding_model = gr.Dropdown(list(
embedding_model_dict.keys()),
label="Embedding model",
value="text2vec-base")
files = gr.Files(label='请上传知识库文件, 目前支持txt、docx、md格式',
file_types=['.txt', '.md', '.docx'])
use_web = gr.Radio(["True", "False"],
label="Web Search",
value="False")
model_argument = gr.Accordion("模型参数配置")
with model_argument:
VECTOR_SEARCH_TOP_K = gr.Slider(
1,
10,
value=6,
step=1,
label="vector search top k",
interactive=True)
HISTORY_LEN = gr.Slider(0,
3,
value=0,
step=1,
label="history len",
interactive=True)
temperature = gr.Slider(0,
1,
value=0.01,
step=0.01,
label="temperature",
interactive=True)
top_p = gr.Slider(0,
1,
value=0.9,
step=0.1,
label="top_p",
interactive=True)
with gr.Column(scale=4):
chatbot = gr.Chatbot(label='ChatLLM').style(height=600)
message = gr.Textbox(label='请输入问题')
state = gr.State()
with gr.Row():
clear_history = gr.Button("🧹 清除历史对话")
send = gr.Button("🚀 发送")
send.click(predict,
inputs=[
message, large_language_model,
embedding_model, files, VECTOR_SEARCH_TOP_K,
HISTORY_LEN, temperature, top_p, use_web,
state
],
outputs=[message, chatbot, state])
clear_history.click(fn=clear_session,
inputs=[],
outputs=[chatbot, state],
queue=False)
message.submit(predict,
inputs=[
message, large_language_model,
embedding_model, files,
VECTOR_SEARCH_TOP_K, HISTORY_LEN,
temperature, top_p, use_web, state
],
outputs=[message, chatbot, state])
gr.Markdown("""提醒：<br>
1. 使用时请先上传自己的知识文件，并且文件中不含某些特殊字符，否则将返回error. <br>
2. 有任何使用请注意这里有一些关键的改动：
1. 我将 `file = gr.File(label='请上传知识库文件, 目前支持txt、docx、md格式', file_types=['.txt', '.md', '.docx'])` 更改为 `files = gr.Files(label='请上传知识库文件, 目前支持txt、docx、md格式', file_types=['.txt', '.md', '.docx'])`。这意味着现在您可以上传多个文件。
2. 在 `send.click` 和 `message.submit` 的 `inputs` 参数中，我将 `file` 改成了 `files`。
这样一来，用户就能够在 Gradio 界面中选择并上传多个文件了。这些文件会被传递给 `predict` 函数，然后被合并在一起，送到模型进行处理。
请注意，由于我并不了解你的全部代码和环境，这个修改可能需要一些额外的调整才能在你的环境中正常运行。我推荐你在实际应用这段代码之前，先在一个安全的环境中进行测试。
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