langchain-ChatGLM源码阅读：webui.py

样式定制

使用gradio设置页面的视觉组件和交互逻辑，位于webui.py

import gradio as gr
import shutil

from chains.local_doc_qa import LocalDocQA
from configs.model_config import *
import nltk
import models.shared as shared
from models.loader.args import parser
from models.loader import LoaderCheckPoint
import os

nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path
embedding_model_dict_list = list(embedding_model_dict.keys())

llm_model_dict_list = list(llm_model_dict.keys())

local_doc_qa = LocalDocQA()
# 记录运行日志到 CSV文件
flag_csv_logger = gr.CSVLogger()

block_css = """.importantButton {
    background: linear-gradient(45deg, #7e0570,#5d1c99, #6e00ff) !important;
    border: none !important;
}
.importantButton:hover {
    background: linear-gradient(45deg, #ff00e0,#8500ff, #6e00ff) !important;
    border: none !important;
}"""

webui_title = """
# 🎉langchain-ChatGLM WebUI🎉
👍 [https://github.com/imClumsyPanda/langchain-ChatGLM](https://github.com/imClumsyPanda/langchain-ChatGLM)
"""
# 检索知识库
default_vs = get_vs_list()[0] if len(get_vs_list()) > 1 else "为空"
init_message = f"""欢迎使用 langchain-ChatGLM Web UI！

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。

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。

知识库暂不支持文件删除，该功能将在后续版本中推出。
"""

# 初始化消息
model_status = init_model()

default_theme_args = dict(
    font=["Source Sans Pro", 'ui-sans-serif', 'system-ui', 'sans-serif'],
    font_mono=['IBM Plex Mono', 'ui-monospace', 'Consolas', 'monospace'],
)

# Blocks API用于构建交互式界面，创建一个名为demo的块。该块包含三个状态变量：
# vs_path、file_status和model_status
with gr.Blocks(css=block_css, theme=gr.themes.Default(**default_theme_args)) as demo:
# gr.State是Gradio提供的一种不可见控件，用于存储同一用户运行演示时的会话状态。
# 当用户刷新页面时，State变量的值被清除。它的目的是在后台存储一些变量方便访问和交互。
    vs_path, file_status, model_status = gr.State(
        os.path.join(KB_ROOT_PATH, get_vs_list()[0], "vector_store") if len(get_vs_list()) > 1 else ""), gr.State(
        ""), gr.State(
        model_status)
    gr.Markdown(webui_title)
    # Tab用来控制功能切换的标签
    with gr.Tab("对话"):
        # gr.Row() 会创建一个水平方向的行,之后的组件默认会按顺序水平排列在这一行内
        # 这里是将对话框和选择框放在同一行，做了一个大对齐
        with gr.Row():
            # 这一列的宽度是默认列宽的 10 倍
            with gr.Column(scale=10):
                # 最上面的框
                chatbot = gr.Chatbot([[None, init_message], [None, model_status.value]],
                                     elem_id="chat-box",
                                     show_label=False).style(height=750)
                # 最下面的输入框，注意可通过调用.style()方法来设置组件的 CSS 样式
                query = gr.Textbox(show_label=False,
                                   placeholder="请输入提问内容，按回车进行提交").style(container=False)
            with gr.Column(scale=5):
                # Radio 组件实现了一个单选按钮组，可以通过mode变量得到用户的选择
                mode = gr.Radio(["LLM 对话", "知识库问答", "Bing搜索问答"],
                                label="请选择使用模式",
                                value="知识库问答", )
                # 使用 Grace 的 Accordion 组件创建了一个折叠面板
                # visible 属性来显示或隐藏这个面板
                knowledge_set = gr.Accordion("知识库设定", visible=False)
                vs_setting = gr.Accordion("配置知识库")
                # 为单选按钮绑定 change 事件处理函数 change_mode
                mode.change(fn=change_mode,
                            # change_mode函数的输入输出
                            inputs=[mode, chatbot],
                            outputs=[vs_setting, knowledge_set, chatbot])
                with vs_setting:
                    vs_refresh = gr.Button("更新已有知识库选项")
                    # 下拉框
                    # interactive=True时,组件变为交互可用状态,用户可以修改或选择
                    select_vs = gr.Dropdown(get_vs_list(),
                                            label="请选择要加载的知识库",
                                            interactive=True,
                                            value=get_vs_list()[0] if len(get_vs_list()) > 0 else None
                                            )
                    vs_name = gr.Textbox(label="请输入新建知识库名称，当前知识库命名暂不支持中文",
                                         lines=1,
                                         interactive=True,
                                         visible=True)
                    vs_add = gr.Button(value="添加至知识库选项", visible=True)
                    vs_delete = gr.Button("删除本知识库", visible=False)
                    file2vs = gr.Column(visible=False)
                    with file2vs:
                        # load_vs = gr.Button("加载知识库")
                        gr.Markdown("向知识库中添加文件")
                        sentence_size = gr.Number(value=SENTENCE_SIZE, precision=0,
                                                  label="文本入库分句长度限制",
                                                  interactive=True, visible=True)
                        with gr.Tab("上传文件"):
                            files = gr.File(label="添加文件",
                                            file_types=['.txt', '.md', '.docx', '.pdf', '.png', '.jpg', ".csv"],
                                            file_count="multiple",
                                            show_label=False)
                            load_file_button = gr.Button("上传文件并加载知识库")
                        with gr.Tab("上传文件夹"):
                            folder_files = gr.File(label="添加文件",
                                                   file_count="directory",
                                                   show_label=False)
                            load_folder_button = gr.Button("上传文件夹并加载知识库")
                        with gr.Tab("删除文件"):
                            files_to_delete = gr.CheckboxGroup(choices=[],
                                                               label="请从知识库已有文件中选择要删除的文件",
                                                               interactive=True)
                            delete_file_button = gr.Button("从知识库中删除选中文件")
                    # 这里绑定了select_vs，后面还有一个test，因此绑定的refresh_vs_list函数要返回两个值
                    # select_vs此时的值被改变了，因此触发了change动作绑定的change_vs_name_input函数
                    vs_refresh.click(fn=refresh_vs_list,
                                     inputs=[],
                                     outputs=select_vs)
                    vs_add.click(fn=add_vs_name,
                                 inputs=[vs_name, chatbot],
                                 outputs=[select_vs, vs_name, vs_add, file2vs, chatbot, vs_delete])
                    vs_delete.click(fn=delete_vs,
                                    inputs=[select_vs, chatbot],
                                    outputs=[select_vs, vs_name, vs_add, file2vs, chatbot, vs_delete])
                    select_vs.change(fn=change_vs_name_input,
                                      inputs=[select_vs, chatbot],
                                      outputs=[vs_name, vs_add, file2vs, vs_path, chatbot, files_to_delete, vs_delete])
                    load_file_button.click(get_vector_store,
                                           show_progress=True,
                                           inputs=[select_vs, files, sentence_size, chatbot, vs_add, vs_add],
                                           outputs=[vs_path, files, chatbot, files_to_delete], )
                    load_folder_button.click(get_vector_store,
                                             show_progress=True,
                                             inputs=[select_vs, folder_files, sentence_size, chatbot, vs_add,
                                                     vs_add],
                                             outputs=[vs_path, folder_files, chatbot, files_to_delete], )
                    flag_csv_logger.setup([query, vs_path, chatbot, mode], "flagged")
                    query.submit(get_answer,
                                 [query, vs_path, chatbot, mode],
                                 [chatbot, query])
                    delete_file_button.click(delete_file,
                                             show_progress=True,
                                             inputs=[select_vs, files_to_delete, chatbot],
                                             outputs=[files_to_delete, chatbot])
    with gr.Tab("知识库测试 Beta"):
        # gr.Row() 会创建一个水平方向的行,之后的组件默认会按顺序水平排列在这一行内
        # 这里是将对话框和选择框放在同一行，做了一个大对齐
        with gr.Row():
            # 这一列的宽度是默认列宽的 10 倍
            with gr.Column(scale=10):
                # 最上面的框
                chatbot = gr.Chatbot([[None, knowledge_base_test_mode_info]],
                                     elem_id="chat-box",
                                     show_label=False).style(height=750)
                # 最下面的输入框
                query = gr.Textbox(show_label=False,
                                   placeholder="请输入提问内容，按回车进行提交").style(container=False)
            with gr.Column(scale=5):
                # Radio 组件实现了一个单选按钮组，可以通过mode变量得到用户的选择，这里直接设不可见
                mode = gr.Radio(["知识库测试"],  # "知识库问答",
                                label="请选择使用模式",
                                value="知识库测试",
                                visible=False)
                # 使用 Grace 的 Accordion 组件创建了一个折叠面板
                knowledge_set = gr.Accordion("知识库设定", visible=True)
                vs_setting = gr.Accordion("配置知识库", visible=True)
                # 为单选按钮绑定 change 事件处理函数 change_mode
                mode.change(fn=change_mode,
                            inputs=[mode, chatbot],
                            outputs=[vs_setting, knowledge_set, chatbot])
                with knowledge_set:
                    # 数字输入组件
                    score_threshold = gr.Number(value=VECTOR_SEARCH_SCORE_THRESHOLD,
                                                label="知识相关度 Score 阈值，分值越低匹配度越高",
                                                precision=0,
                                                interactive=True)
                    vector_search_top_k = gr.Number(value=VECTOR_SEARCH_TOP_K, precision=0,
                                                    label="获取知识库内容条数", interactive=True)
                    chunk_conent = gr.Checkbox(value=False,
                                               label="是否启用上下文关联",
                                               interactive=True)
                    chunk_sizes = gr.Number(value=CHUNK_SIZE, precision=0,
                                            label="匹配单段内容的连接上下文后最大长度",
                                            interactive=True, visible=False)
                    chunk_conent.change(fn=change_chunk_conent,
                                        inputs=[chunk_conent, gr.Textbox(value="chunk_conent", visible=False), chatbot],
                                        outputs=[chunk_sizes, chatbot])
                with vs_setting:
                    vs_refresh = gr.Button("更新已有知识库选项")
                    # 不同Tab下标志同一动作开始的刷新键可以共用一个名字，但是各Tab下动作影响的
                    # 组件需要起不同的名字，并且在函数返回时依次赋值给各个组件并更新
                    select_vs_test = gr.Dropdown(get_vs_list(),
                                                 label="请选择要加载的知识库",
                                                 interactive=True,
                                                 value=get_vs_list()[0] if len(get_vs_list()) > 0 else None)
                    vs_name = gr.Textbox(label="请输入新建知识库名称，当前知识库命名暂不支持中文",
                                         lines=1,
                                         interactive=True,
                                         visible=True)
                    vs_add = gr.Button(value="添加至知识库选项", visible=True)
                    file2vs = gr.Column(visible=False)
                    with file2vs:
                        # load_vs = gr.Button("加载知识库")
                        gr.Markdown("向知识库中添加单条内容或文件")
                        sentence_size = gr.Number(value=SENTENCE_SIZE, precision=0,
                                                  label="文本入库分句长度限制",
                                                  interactive=True, visible=True)
                        with gr.Tab("上传文件"):
                            files = gr.File(label="添加文件",
                                            file_types=['.txt', '.md', '.docx', '.pdf'],
                                            file_count="multiple",
                                            show_label=False
                                            )
                            load_file_button = gr.Button("上传文件并加载知识库")
                        with gr.Tab("上传文件夹"):
                            folder_files = gr.File(label="添加文件",
                                                   # file_types=['.txt', '.md', '.docx', '.pdf'],
                                                   file_count="directory",
                                                   show_label=False)
                            load_folder_button = gr.Button("上传文件夹并加载知识库")
                        with gr.Tab("添加单条内容"):
                            one_title = gr.Textbox(label="标题", placeholder="请输入要添加单条段落的标题", lines=1)
                            one_conent = gr.Textbox(label="内容", placeholder="请输入要添加单条段落的内容", lines=5)
                            one_content_segmentation = gr.Checkbox(value=True, label="禁止内容分句入库",
                                                                   interactive=True)
                            load_conent_button = gr.Button("添加内容并加载知识库")
                    # 将上传的文件保存到content文件夹下,并更新下拉框，注意这里是select_vs_test
                    vs_refresh.click(fn=refresh_vs_list,
                                     inputs=[],
                                     outputs=select_vs_test)
                    vs_add.click(fn=add_vs_name,
                                 inputs=[vs_name, chatbot],
                                 outputs=[select_vs_test, vs_name, vs_add, file2vs, chatbot])
                    select_vs_test.change(fn=change_vs_name_input,
                                          inputs=[select_vs_test, chatbot],
                                          outputs=[vs_name, vs_add, file2vs, vs_path, chatbot])
                    load_file_button.click(get_vector_store,
                                           show_progress=True,
                                           inputs=[select_vs_test, files, sentence_size, chatbot, vs_add, vs_add],
                                           outputs=[vs_path, files, chatbot], )
                    load_folder_button.click(get_vector_store,
                                             show_progress=True,
                                             inputs=[select_vs_test, folder_files, sentence_size, chatbot, vs_add,
                                                     vs_add],
                                             outputs=[vs_path, folder_files, chatbot], )
                    load_conent_button.click(get_vector_store,
                                             show_progress=True,
                                             inputs=[select_vs_test, one_title, sentence_size, chatbot,
                                                     one_conent, one_content_segmentation],
                                             outputs=[vs_path, files, chatbot], )
                    flag_csv_logger.setup([query, vs_path, chatbot, mode], "flagged")
                    query.submit(get_answer,
                                 [query, vs_path, chatbot, mode, score_threshold, vector_search_top_k, chunk_conent,
                                  chunk_sizes],
                                 [chatbot, query])
    with gr.Tab("模型配置"):
        llm_model = gr.Radio(llm_model_dict_list,
                             label="LLM 模型",
                             value=LLM_MODEL,
                             interactive=True)
        no_remote_model = gr.Checkbox(shared.LoaderCheckPoint.no_remote_model,
                                      label="加载本地模型",
                                      interactive=True)
        llm_history_len = gr.Slider(0, 10,
                                    value=LLM_HISTORY_LEN,
                                    step=1,
                                    label="LLM 对话轮数",
                                    interactive=True)
        use_ptuning_v2 = gr.Checkbox(USE_PTUNING_V2,
                                     label="使用p-tuning-v2微调过的模型",
                                     interactive=True)
        use_lora = gr.Checkbox(USE_LORA,
                               label="使用lora微调的权重",
                               interactive=True)
        embedding_model = gr.Radio(embedding_model_dict_list,
                                   label="Embedding 模型",
                                   value=EMBEDDING_MODEL,
                                   interactive=True)
        top_k = gr.Slider(1, 20, value=VECTOR_SEARCH_TOP_K, step=1,
                          label="向量匹配 top k", interactive=True)
        load_model_button = gr.Button("重新加载模型")
        load_model_button.click(reinit_model, show_progress=True,
                                inputs=[llm_model, embedding_model, llm_history_len, no_remote_model, use_ptuning_v2,
                                        use_lora, top_k, chatbot], outputs=chatbot)
        # load_knowlege_button = gr.Button("重新构建知识库")
        # load_knowlege_button.click(reinit_vector_store, show_progress=True,
        #                            inputs=[select_vs, chatbot], outputs=chatbot)
    demo.load(
        # 加载初始化逻辑refresh_vs_list,并传入输出组件
        fn=refresh_vs_list,
        inputs=None,
        outputs=[select_vs, select_vs_test],
        # 加入后台执行队列
        queue=True,
        show_progress=False,
    )

(demo
    # 限制回调函数的最多并发执行数为3以避免应用过载
 .queue(concurrency_count=3)
 .launch(server_name='172.20.63.134',
         server_port=7860,
         show_api=False,
         share=False,
         inbrowser=False))

回调函数具体实现

监听到前端的事件后调用的回调函数，负责实现前后端交互。需要注意的一点是，chatbot中显示新的聊天内容并不是在原来的基础上添加，而是从头到尾的重新打印，所以基本上每个函数都要传旧的history和返回新的history。文章来源地址https://www.toymoban.com/news/detail-622911.html

获取知识库列表

def get_vs_list():
    # 返回当前最新的经排序后的知识库列表
    lst_default = ["新建知识库"]
    if not os.path.exists(KB_ROOT_PATH):
        return lst_default
    lst = os.listdir(KB_ROOT_PATH)
    if not lst:
        return lst_default
    lst.sort()
    return lst_default + lst

获取不同模式下的回答

• query.submit动作绑定的函数

def get_answer(query, vs_path, history, mode, score_threshold=VECTOR_SEARCH_SCORE_THRESHOLD,
               vector_search_top_k=VECTOR_SEARCH_TOP_K, chunk_conent: bool = True,
               chunk_size=CHUNK_SIZE, streaming: bool = STREAMING):
    if mode == "Bing搜索问答":
        for resp, history in local_doc_qa.get_search_result_based_answer(
                query=query, chat_history=history, streaming=streaming):
            source = "\n\n"
            source += "".join(
                [
                    f"""<details> <summary>出处 [{i + 1}] <a href="{doc.metadata["source"]}" target="_blank">{doc.metadata["source"]}</a> </summary>\n"""
                    f"""{doc.page_content}\n"""
                    f"""</details>"""
                    for i, doc in
                    enumerate(resp["source_documents"])])
            history[-1][-1] += source
            yield history, ""
    # "index.faiss"是向量索引的文件名，可以根据文件存在性,来判断向量索引是否需要构建。
    elif mode == "知识库问答" and vs_path is not None and os.path.exists(vs_path) and "index.faiss" in os.listdir(
            vs_path):
        # 注意score_threshold, vector_search_top_k, chunk_conent,chunk_size
        # 这几个参数压根没传进get_knowledge_based_answer
        for resp, history in local_doc_qa.get_knowledge_based_answer(
                query=query, vs_path=vs_path, chat_history=history, streaming=streaming):
            source = "\n\n"
            source += "".join(
                [f"""<details> <summary>出处 [{i + 1}] {os.path.split(doc.metadata["source"])[-1]}</summary>\n"""
                 f"""{doc.page_content}\n"""
                 f"""</details>"""
                 for i, doc in
                 enumerate(resp["source_documents"])])
            history[-1][-1] += source
            # 分别赋值给chatbot和query
            yield history, ""
    elif mode == "知识库测试":
        if os.path.exists(vs_path):
            # 使用了全部传入参数，但是只是用于测试文件匹配度不能回答
            resp, prompt = local_doc_qa.get_knowledge_based_conent_test(query=query, vs_path=vs_path,
                                                                        score_threshold=score_threshold,
                                                                        vector_search_top_k=vector_search_top_k,
                                                                        chunk_conent=chunk_conent,
                                                                        chunk_size=chunk_size)
            if not resp["source_documents"]:
                yield history + [[query,
                                  "根据您的设定，没有匹配到任何内容，请确认您设置的知识相关度 Score 阈值是否过小或其他参数是否正确。"]], ""
            else:
                source = "\n".join(
                    [
                        f"""<details open> <summary>【知识相关度 Score】：{doc.metadata["score"]} - 【出处{i + 1}】：  {os.path.split(doc.metadata["source"])[-1]} </summary>\n"""
                        f"""{doc.page_content}\n"""
                        f"""</details>"""
                        for i, doc in
                        enumerate(resp["source_documents"])])
                history.append([query, "以下内容为知识库中满足设置条件的匹配结果：\n\n" + source])
                yield history, ""
        else:
            yield history + [[query,
                              "请选择知识库后进行测试，当前未选择知识库。"]], ""
    else:

        answer_result_stream_result = local_doc_qa.llm_model_chain(
            {"prompt": query, "history": history, "streaming": streaming})

        for answer_result in answer_result_stream_result['answer_result_stream']:
            resp = answer_result.llm_output["answer"]
            history = answer_result.history
            history[-1][-1] = resp
            yield history, ""
    logger.info(f"flagging: username={FLAG_USER_NAME},query={query},vs_path={vs_path},mode={mode},history={history}")
    flag_csv_logger.flag([query, vs_path, history, mode], username=FLAG_USER_NAME)

模型初始化

• 初始化model_status

def init_model():
    args = parser.parse_args()

    args_dict = vars(args)
    shared.loaderCheckPoint = LoaderCheckPoint(args_dict)
    llm_model_ins = shared.loaderLLM()
    llm_model_ins.history_len = LLM_HISTORY_LEN
    try:
        local_doc_qa.init_cfg(llm_model=llm_model_ins)
        answer_result_stream_result = local_doc_qa.llm_model_chain(
            {"prompt": "你好", "history": [], "streaming": False})

        for answer_result in answer_result_stream_result['answer_result_stream']:
            print(answer_result.llm_output)
        reply = """模型已成功加载，可以开始对话，或从右侧选择模式后开始对话"""
        logger.info(reply)
        return reply
    except Exception as e:
        logger.error(e)
        reply = """模型未成功加载，请到页面左上角"模型配置"选项卡中重新选择后点击"加载模型"按钮"""
        if str(e) == "Unknown platform: darwin":
            logger.info("该报错可能因为您使用的是 macOS 操作系统，需先下载模型至本地后执行 Web UI，具体方法请参考项目 README 中本地部署方法及常见问题："
                        " https://github.com/imClumsyPanda/langchain-ChatGLM")
        else:
            logger.info(reply)
        return reply

模型重加载

• load_model_button.click动作绑定的函数

def reinit_model(llm_model, embedding_model, llm_history_len, no_remote_model, use_ptuning_v2, use_lora, top_k,
                 history):
    try:
        llm_model_ins = shared.loaderLLM(llm_model, no_remote_model, use_ptuning_v2)
        llm_model_ins.history_len = llm_history_len
        local_doc_qa.init_cfg(llm_model=llm_model_ins,
                              embedding_model=embedding_model,
                              top_k=top_k)
        model_status = """模型已成功重新加载，可以开始对话，或从右侧选择模式后开始对话"""
        logger.info(model_status)
    except Exception as e:
        logger.error(e)
        model_status = """模型未成功重新加载，请到页面左上角"模型配置"选项卡中重新选择后点击"加载模型"按钮"""
        logger.info(model_status)
    # 更新chatbot的值
    return history + [[None, model_status]]

文件向量化

• load_file_button.click和load_folder_button.click动作绑定的函数

def get_vector_store(vs_id, files, sentence_size, history, one_conent, one_content_segmentation):
    vs_path = os.path.join(KB_ROOT_PATH, vs_id, "vector_store")
    filelist = []
    if local_doc_qa.llm_model_chain and local_doc_qa.embeddings:
        if isinstance(files, list):
            for file in files:
                filename = os.path.split(file.name)[-1]
                shutil.move(file.name, os.path.join(KB_ROOT_PATH, vs_id, "content", filename))
                filelist.append(os.path.join(KB_ROOT_PATH, vs_id, "content", filename))
            vs_path, loaded_files = local_doc_qa.init_knowledge_vector_store(filelist, vs_path, sentence_size)
        else:
            vs_path, loaded_files = local_doc_qa.one_knowledge_add(vs_path, files, one_conent, one_content_segmentation,
                                                                   sentence_size)
        if len(loaded_files):
            file_status = f"已添加 {'、'.join([os.path.split(i)[-1] for i in loaded_files if i])} 内容至知识库，并已加载知识库，请开始提问"
        else:
            file_status = "文件未成功加载，请重新上传文件"
    else:
        file_status = "模型未完成加载，请先在加载模型后再导入文件"
        vs_path = None
    logger.info(file_status)
    return vs_path, None, history + [[None, file_status]], \
           gr.update(choices=local_doc_qa.list_file_from_vector_store(vs_path) if vs_path else [])

选择知识库

• select_vs.change和select_vs_test.change动作所绑定的函数，如果刷新知识库会有bug

def change_vs_name_input(vs_id, history):
    if vs_id == "新建知识库":
        return gr.update(visible=True), gr.update(visible=True), gr.update(visible=False), None, history, \
               gr.update(choices=[]), gr.update(visible=False)
    else:
        # 刷新时这地方有bug，直接传了个列表过去，逆天
        vs_path = os.path.join(KB_ROOT_PATH, vs_id, "vector_store")
        if "index.faiss" in os.listdir(vs_path):
            file_status = f"已加载知识库{vs_id}，请开始提问"
            return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), \
                   vs_path, history + [[None, file_status]], \
                   gr.update(choices=local_doc_qa.list_file_from_vector_store(vs_path), value=[]), \
                   gr.update(visible=True)
        else:
            file_status = f"已选择知识库{vs_id}，当前知识库中未上传文件，请先上传文件后，再开始提问"
            return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), \
                   vs_path, history + [[None, file_status]], \
                   gr.update(choices=[], value=[]), gr.update(visible=True, value=[])


knowledge_base_test_mode_info = ("【注意】\n\n"
                                 "1. 您已进入知识库测试模式，您输入的任何对话内容都将用于进行知识库查询，"
                                 "并仅输出知识库匹配出的内容及相似度分值和及输入的文本源路径，查询的内容并不会进入模型查询。\n\n"
                                 "2. 知识相关度 Score 经测试，建议设置为 500 或更低，具体设置情况请结合实际使用调整。"
                                 """3. 使用"添加单条数据"添加文本至知识库时，内容如未分段，则内容越多越会稀释各查询内容与之关联的score阈值。\n\n"""
                                 "4. 单条内容长度建议设置在100-150左右。\n\n"
                                 "5. 本界面用于知识入库及知识匹配相关参数设定，但当前版本中，"
                                 "本界面中修改的参数并不会直接修改对话界面中参数，仍需前往`configs/model_config.py`修改后生效。"
                                 "相关参数将在后续版本中支持本界面直接修改。")

切换模型

• mode.change动作绑定的函数

def change_mode(mode, history):
    # 调整vs_setting, knowledge_set, chatbot的可见性
    if mode == "知识库问答":
        return gr.update(visible=True), gr.update(visible=False), history
        # + [[None, "【注意】：您已进入知识库问答模式，您输入的任何查询都将进行知识库查询，然后会自动整理知识库关联内容进入模型查询！！！"]]
    elif mode == "知识库测试":
        return gr.update(visible=True), gr.update(visible=True), [[None,
                                                                   knowledge_base_test_mode_info]]
    else:
        return gr.update(visible=False), gr.update(visible=False), history

启用上下文

• chunk_conent.change动作绑定的函数

def change_chunk_conent(mode, label_conent, history):
    # 更新chunk_sizes, chatbot
    conent = ""
    if "chunk_conent" in label_conent:
        conent = "搜索结果上下文关联"
    elif "one_content_segmentation" in label_conent:  # 这里没用上，可以先留着
        conent = "内容分段入库"

    if mode:
        return gr.update(visible=True), history + [[None, f"【已开启{conent}】"]]
    else:
        return gr.update(visible=False), history + [[None, f"【已关闭{conent}】"]]

创建新的知识库

• vs_add.click动作所绑定的函数

def add_vs_name(vs_name, chatbot):
    if vs_name is None or vs_name.strip() == "":
        vs_status = "知识库名称不能为空，请重新填写知识库名称"
        chatbot = chatbot + [[None, vs_status]]
        return gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), gr.update(
            visible=False), chatbot, gr.update(visible=False)
    elif vs_name in get_vs_list():
        vs_status = "与已有知识库名称冲突，请重新选择其他名称后提交"
        chatbot = chatbot + [[None, vs_status]]
        return gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), gr.update(
            visible=False), chatbot, gr.update(visible=False)
    else:
        # 新建上传文件存储路径
        if not os.path.exists(os.path.join(KB_ROOT_PATH, vs_name, "content")):
            os.makedirs(os.path.join(KB_ROOT_PATH, vs_name, "content"))
        # 新建向量库存储路径
        if not os.path.exists(os.path.join(KB_ROOT_PATH, vs_name, "vector_store")):
            os.makedirs(os.path.join(KB_ROOT_PATH, vs_name, "vector_store"))
        vs_status = f"""已新增知识库"{vs_name}",将在上传文件并载入成功后进行存储。请在开始对话前，先完成文件上传。 """
        chatbot = chatbot + [[None, vs_status]]
        # 更新select_vs, vs_name, vs_add, file2vs, chatbot, vs_delete这几个组件
        return gr.update(visible=True, choices=get_vs_list(), value=vs_name), gr.update(
            visible=False), gr.update(visible=False), gr.update(visible=True), chatbot, gr.update(visible=True)

更新知识库

• vs_refresh.click和demo.load动作绑定的事件

def refresh_vs_list():
	# 更新select_vs, select_vs_test两个组件
    return gr.update(choices=get_vs_list()), gr.update(choices=get_vs_list())

删除文件及整个知识库

def delete_file(vs_id, files_to_delete, chatbot):
    vs_path = os.path.join(KB_ROOT_PATH, vs_id, "vector_store")
    content_path = os.path.join(KB_ROOT_PATH, vs_id, "content")
    docs_path = [os.path.join(content_path, file) for file in files_to_delete]
    status = local_doc_qa.delete_file_from_vector_store(vs_path=vs_path,
                                                        filepath=docs_path)
    if "fail" not in status:
        for doc_path in docs_path:
            if os.path.exists(doc_path):
                os.remove(doc_path)
    rested_files = local_doc_qa.list_file_from_vector_store(vs_path)
    if "fail" in status:
        vs_status = "文件删除失败。"
    elif len(rested_files) > 0:
        vs_status = "文件删除成功。"
    else:
        vs_status = f"文件删除成功，知识库{vs_id}中无已上传文件，请先上传文件后，再开始提问。"
    logger.info(",".join(files_to_delete) + vs_status)
    chatbot = chatbot + [[None, vs_status]]
    return gr.update(choices=local_doc_qa.list_file_from_vector_store(vs_path), value=[]), chatbot


def delete_vs(vs_id, chatbot):
    try:
        shutil.rmtree(os.path.join(KB_ROOT_PATH, vs_id))
        status = f"成功删除知识库{vs_id}"
        logger.info(status)
        chatbot = chatbot + [[None, status]]
        return gr.update(choices=get_vs_list(), value=get_vs_list()[0]), gr.update(visible=True), gr.update(
            visible=True), \
               gr.update(visible=False), chatbot, gr.update(visible=False)
    except Exception as e:
        logger.error(e)
        status = f"删除知识库{vs_id}失败"
        chatbot = chatbot + [[None, status]]
        return gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), \
               gr.update(visible=True), chatbot, gr.update(visible=True)

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