webui优化:解决每次页面刷新后都需要重新选择知识库的问题,增加Flagging收集问答效果。 (#250)
Co-authored-by: Bob Chang <bob.chang@amway.com>
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52
webui.py
52
webui.py
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@ -4,17 +4,21 @@ import shutil
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from chains.local_doc_qa import LocalDocQA
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from configs.model_config import *
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import nltk
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import uuid
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nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path
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def get_vs_list():
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lst_default = ["新建知识库"]
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if not os.path.exists(VS_ROOT_PATH):
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return []
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return os.listdir(VS_ROOT_PATH)
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return lst_default
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lst= os.listdir(VS_ROOT_PATH)
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if not lst:
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return lst_default
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lst.sort(reverse=True)
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return lst+ lst_default
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vs_list = ["新建知识库"] + get_vs_list()
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vs_list =get_vs_list()
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embedding_model_dict_list = list(embedding_model_dict.keys())
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@ -22,6 +26,8 @@ llm_model_dict_list = list(llm_model_dict.keys())
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local_doc_qa = LocalDocQA()
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logger = gr.CSVLogger()
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username = uuid.uuid4().hex
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def get_answer(query, vs_path, history, mode,
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streaming: bool = STREAMING):
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@ -45,8 +51,8 @@ def get_answer(query, vs_path, history, mode,
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streaming=streaming):
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history[-1][-1] = resp + (
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"\n\n当前知识库为空,如需基于知识库进行问答,请先加载知识库后,再进行提问。" if mode == "知识库问答" else "")
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yield history, ""
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yield history, ""
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logger.flag([query, vs_path, history, mode],username=username)
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def init_model():
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try:
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@ -105,11 +111,12 @@ def get_vector_store(vs_id, files, history):
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return vs_path, None, history + [[None, file_status]]
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def change_vs_name_input(vs_id):
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def change_vs_name_input(vs_id,history):
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if vs_id == "新建知识库":
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return gr.update(visible=True), gr.update(visible=True), gr.update(visible=False), None
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return gr.update(visible=True), gr.update(visible=True), gr.update(visible=False), None,history
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else:
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return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), os.path.join(VS_ROOT_PATH, vs_id)
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file_status = f"已加载知识库{vs_id},请开始提问"
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return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), os.path.join(VS_ROOT_PATH, vs_id),history + [[None, file_status]]
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def change_mode(mode):
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@ -129,7 +136,6 @@ def add_vs_name(vs_name, vs_list, chatbot):
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chatbot = chatbot + [[None, vs_status]]
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return gr.update(visible=True, choices=vs_list + [vs_name], value=vs_name), vs_list + [vs_name], chatbot
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block_css = """.importantButton {
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background: linear-gradient(45deg, #7e0570,#5d1c99, #6e00ff) !important;
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border: none !important;
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@ -146,20 +152,21 @@ webui_title = """
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👍 [https://github.com/imClumsyPanda/langchain-ChatGLM](https://github.com/imClumsyPanda/langchain-ChatGLM)
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"""
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init_message = """欢迎使用 langchain-ChatGLM Web UI!
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default_vs = vs_list[0] if len(vs_list) > 1 else "为空"
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init_message = f"""欢迎使用 langchain-ChatGLM Web UI!
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请在右侧切换模式,目前支持直接与 LLM 模型对话或基于本地知识库问答。
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知识库问答模式中,选择知识库名称后,即可开始问答,如有需要可以在选择知识库名称后上传文件/文件夹至知识库。
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知识库问答模式,选择知识库名称后,即可开始问答,当前知识库{default_vs},如有需要可以在选择知识库名称后上传文件/文件夹至知识库。
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知识库暂不支持文件删除,该功能将在后续版本中推出。
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"""
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model_status = init_model()
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default_path = os.path.join(VS_ROOT_PATH, vs_list[0]) if len(vs_list) > 1 else ""
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with gr.Blocks(css=block_css) as demo:
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vs_path, file_status, model_status, vs_list = gr.State(""), gr.State(""), gr.State(model_status), gr.State(vs_list)
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vs_path, file_status, model_status, vs_list = gr.State(default_path), gr.State(""), gr.State(model_status), gr.State(vs_list)
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gr.Markdown(webui_title)
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with gr.Tab("对话"):
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with gr.Row():
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@ -168,8 +175,7 @@ with gr.Blocks(css=block_css) as demo:
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elem_id="chat-box",
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show_label=False).style(height=750)
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query = gr.Textbox(show_label=False,
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placeholder="请输入提问内容,按回车进行提交",
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).style(container=False)
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placeholder="请输入提问内容,按回车进行提交").style(container=False)
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with gr.Column(scale=5):
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mode = gr.Radio(["LLM 对话", "知识库问答"],
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label="请选择使用模式",
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@ -212,8 +218,8 @@ with gr.Blocks(css=block_css) as demo:
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load_folder_button = gr.Button("上传文件夹并加载知识库")
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# load_vs.click(fn=)
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select_vs.change(fn=change_vs_name_input,
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inputs=select_vs,
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outputs=[vs_name, vs_add, file2vs, vs_path])
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inputs=[select_vs,chatbot],
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outputs=[vs_name, vs_add, file2vs, vs_path, chatbot])
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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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load_file_button.click(get_vector_store,
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show_progress=True,
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@ -224,11 +230,11 @@ with gr.Blocks(css=block_css) as demo:
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show_progress=True,
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inputs=[select_vs, folder_files, chatbot],
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outputs=[vs_path, folder_files, chatbot],
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)
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)
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logger.setup([query, vs_path, chatbot, mode], "flagged")
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query.submit(get_answer,
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[query, vs_path, chatbot, mode],
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[chatbot, query],
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)
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[query, vs_path, chatbot, mode],
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[chatbot, query])
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with gr.Tab("模型配置"):
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llm_model = gr.Radio(llm_model_dict_list,
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label="LLM 模型",
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