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