mirror of
https://github.com/RYDE-WORK/Langchain-Chatchat.git
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57c3cac3bb
1、自动读取knowledge_based_chatglm.py中LLM及embedding模型枚举，选择后点击setting进行模型加载，可随时切换模型进行测试 2、可手动调节保留对话历史长度，可根据显存大小自行调节 3、添加上传文件功能，通过下拉框选择已上传的文件，点击loading加载文件，过程中可随时更换加载的文件 4、底部添加use via API可对接到自己系统 TODO： 1、添加模型加载进度条 2、添加输出内容及错误提示 3、国家化语言切换 4、引用标注 5、添加插件系统（可基础lora训练等）
121 lines
4.4 KiB
Python
121 lines
4.4 KiB
Python
import gradio as gr
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import os
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import shutil
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import knowledge_based_chatglm as kb
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# class kb:
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# def __init__(self):
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# pass
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# def init_knowledge_vector_store(filepath):
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# return filepath
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# def get_knowledge_based_answer(*args):
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# return args
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def get_file_list():
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if not os.path.exists("content"):
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return []
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return [f for f in os.listdir("content")]
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file_list = get_file_list()
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embedding_model_dict_list = list(kb.embedding_model_dict.keys())
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llm_model_dict_list = list(kb.llm_model_dict.keys())
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def upload_file(file):
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if not os.path.exists("content"):
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os.mkdir("content")
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filename = os.path.basename(file.name)
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shutil.move(file.name, "content/"+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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return gr.Dropdown.update(choices=file_list, value=filename)
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def getAnswer(q, v, h):
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resp, history = kb.get_knowledge_based_answer(
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query=q, vector_store=v, chat_history=h)
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return history, history
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with gr.Blocks(css="""
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.importantButton {
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background: linear-gradient(45deg, #7e0570,#5d1c99, #6e00ff) !important;
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border: none !important;
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}
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.importantButton:hover {
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background: linear-gradient(45deg, #ff00e0,#8500ff, #6e00ff) !important;
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border: none !important;
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}
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""") as demo:
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gr.Markdown(
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f"""
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# 🎉langchain-ChatGLM WebUI🎉
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👍 [https://github.com/imClumsyPanda/langchain-ChatGLM](https://github.com/imClumsyPanda/langchain-ChatGLM)
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""")
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with gr.Row():
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with gr.Column(scale=2):
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chatbot = gr.Chatbot(elem_id="chat-box",
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show_label=False).style(height=600)
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with gr.Column(scale=1):
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with gr.Column():
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llm_model = gr.Radio(
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llm_model_dict_list, label="llm model",
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value="chatglm-6b", interactive=True)
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LLM_HISTORY_LEN = gr.Slider(
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1, 10, value=3, step=1, label="LLM history len", interactive=True)
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embedding_model = gr.Radio(
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embedding_model_dict_list, label="embedding model",
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value="text2vec", interactive=True)
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VECTOR_SEARCH_TOP_K = gr.Slider(
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1, 20, value=6, step=1, label="vector search top k", interactive=True)
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load_model_button = gr.Button("step.1：setting")
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load_model_button.click(lambda *args:
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kb.init_cfg(args[0], args[1], args[2], args[3]),
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show_progress=True,
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api_name="init_cfg",
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inputs=[llm_model, embedding_model, VECTOR_SEARCH_TOP_K, LLM_HISTORY_LEN])
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with gr.Column():
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with gr.Tab("select"):
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selectFile = gr.Dropdown(
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file_list, label="content file", interactive=True, value=file_list[0] if len(file_list) > 0 else None)
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with gr.Tab("upload"):
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file = gr.File(label="content file", file_types=[
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'.txt', '.md', '.docx']).style(height=100)
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# 将上传的文件保存到content文件夹下,并更新下拉框
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file.upload(upload_file, inputs=file, outputs=selectFile)
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history = gr.State([])
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vector_store = gr.State()
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load_button = gr.Button("step.2：loading")
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load_button.click(lambda fileName:
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kb.init_knowledge_vector_store(
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"content/"+fileName),
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show_progress=True,
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api_name="init_knowledge_vector_store",
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inputs=selectFile, outputs=vector_store)
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with gr.Row():
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with gr.Column(scale=2):
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query = gr.Textbox(show_label=False, placeholder="Prompts", lines=1, value="用200字总结一下").style(
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container=False)
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with gr.Column(scale=1):
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generate_button = gr.Button(
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"step.3：asking", elem_classes="importantButton")
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generate_button.click(getAnswer, [query, vector_store, history],
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[chatbot, history],api_name="get_knowledge_based_answer")
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demo.queue(concurrency_count=3).launch(
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server_name='0.0.0.0', share=False, inbrowser=False)
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