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 def get_vs_list(): 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 embedding_model_dict_list = list(embedding_model_dict.keys()) llm_model_dict_list = list(llm_model_dict.keys()) local_doc_qa = LocalDocQA() flag_csv_logger = gr.CSVLogger() 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"""
出处 [{i + 1}] {doc.metadata["source"]} \n""" f"""{doc.page_content}\n""" f"""
""" for i, doc in enumerate(resp["source_documents"])]) history[-1][-1] += source yield history, "" 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): 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"""
出处 [{i + 1}] {os.path.split(doc.metadata["source"])[-1]}\n""" f"""{doc.page_content}\n""" f"""
""" for i, doc in enumerate(resp["source_documents"])]) history[-1][-1] += source yield history, "" elif mode == "知识库测试": if os.path.exists(vs_path): 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"""
【知识相关度 Score】:{doc.metadata["score"]} - 【出处{i + 1}】: {os.path.split(doc.metadata["source"])[-1]} \n""" f"""{doc.page_content}\n""" f"""
""" 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: for answer_result in local_doc_qa.llm.generatorAnswer(prompt=query, history=history, streaming=streaming): 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) def init_model(): args = parser.parse_args() args_dict = vars(args) shared.loaderCheckPoint = LoaderCheckPoint(args_dict) llm_model_ins = shared.loaderLLM() llm_model_ins.set_history_len(LLM_HISTORY_LEN) try: local_doc_qa.init_cfg(llm_model=llm_model_ins) generator = local_doc_qa.llm.generatorAnswer("你好") for answer_result in generator: 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 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) return history + [[None, model_status]] 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 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 []) 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: 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。") def change_mode(mode, history): 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 def change_chunk_conent(mode, label_conent, history): 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}】"]] def add_vs_name(vs_name, chatbot): if 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]] 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) # 自动化加载固定文件间中文件 def reinit_vector_store(vs_id, history): try: shutil.rmtree(os.path.join(KB_ROOT_PATH, vs_id, "vector_store")) vs_path = os.path.join(KB_ROOT_PATH, vs_id, "vector_store") sentence_size = gr.Number(value=SENTENCE_SIZE, precision=0, label="文本入库分句长度限制", interactive=True, visible=True) vs_path, loaded_files = local_doc_qa.init_knowledge_vector_store(os.path.join(KB_ROOT_PATH, vs_id, "content"), vs_path, sentence_size) model_status = """知识库构建成功""" except Exception as e: logger.error(e) model_status = """知识库构建未成功""" logger.info(model_status) return history + [[None, model_status]] def refresh_vs_list(): return gr.update(choices=get_vs_list()), gr.update(choices=get_vs_list()) 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) 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) """ 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'], ) with gr.Blocks(css=block_css, theme=gr.themes.Default(**default_theme_args)) as demo: 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) with gr.Tab("对话"): with gr.Row(): with gr.Column(scale=10): chatbot = gr.Chatbot([[None, init_message], [None, model_status.value]], elem_id="chat-box", show_label=False).style(height=750) 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): 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", ) knowledge_set = gr.Accordion("知识库设定", visible=False) vs_setting = gr.Accordion("配置知识库") mode.change(fn=change_mode, 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") 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") 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"): with gr.Row(): with gr.Column(scale=10): chatbot = gr.Chatbot([[None, knowledge_base_test_mode_info]], elem_id="chat-box", show_label=False).style(height=750) 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): 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) knowledge_set = gr.Accordion("知识库设定", visible=True) vs_setting = gr.Accordion("配置知识库", visible=True) mode.change(fn=change_mode, inputs=[mode, chatbot], outputs=[vs_setting, knowledge_set, chatbot]) with knowledge_set: 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") 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 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( fn=refresh_vs_list, inputs=None, outputs=[select_vs, select_vs_test], queue=True, show_progress=False, ) (demo .queue(concurrency_count=3) .launch(server_name='0.0.0.0', server_port=7860, show_api=False, share=False, inbrowser=False))