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- import gradio as gr
- import os
- import shutil
- from chains.local_doc_qa import LocalDocQA
- from configs.model_config import *
- import nltk
- nltk.data.path = [os.path.join(os.path.dirname(__file__), "nltk_data")] + nltk.data.path
- # return top-k text chunk from vector store
- VECTOR_SEARCH_TOP_K = 6
- # LLM input history length
- LLM_HISTORY_LEN = 3
- def get_vs_list():
- if not os.path.exists(VS_ROOT_PATH):
- return []
- return os.listdir(VS_ROOT_PATH)
- vs_list = ["新建知识库"] + get_vs_list()
- embedding_model_dict_list = list(embedding_model_dict.keys())
- llm_model_dict_list = list(llm_model_dict.keys())
- local_doc_qa = LocalDocQA()
- def get_answer(query, vs_path, history, mode,
- streaming: bool = STREAMING):
- if mode == "知识库问答" and 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"""<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
- yield history, ""
- else:
- for resp, history in local_doc_qa.llm._call(query, history,
- streaming=streaming):
- history[-1][-1] = resp + (
- "\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 "")
- yield history, ""
- def update_status(history, status):
- history = history + [[None, status]]
- print(status)
- return history
- def init_model():
- try:
- local_doc_qa.init_cfg()
- local_doc_qa.llm._call("你好")
- 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"""
- print(reply)
- return reply
- except Exception as e:
- print(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":
- print("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:
- print(reply)
- return reply
- def reinit_model(llm_model, embedding_model, llm_history_len, use_ptuning_v2, top_k, history):
- try:
- local_doc_qa.init_cfg(llm_model=llm_model,
- embedding_model=embedding_model,
- llm_history_len=llm_history_len,
- use_ptuning_v2=use_ptuning_v2,
- 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"""
- print(model_status)
- except Exception as e:
- print(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"""
- print(model_status)
- return history + [[None, model_status]]
- def get_vector_store(vs_id, files, history):
- vs_path = VS_ROOT_PATH + vs_id
- filelist = []
- for file in files:
- filename = os.path.split(file.name)[-1]
- shutil.move(file.name, UPLOAD_ROOT_PATH + filename)
- filelist.append(UPLOAD_ROOT_PATH + filename)
- if local_doc_qa.llm and local_doc_qa.embeddings:
- vs_path, loaded_files = local_doc_qa.init_knowledge_vector_store(filelist, vs_path)
- if len(loaded_files):
- file_status = f"已上传 {'、'.join([os.path.split(i)[-1] for i in loaded_files])} 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
- print(file_status)
- return vs_path, None, history + [[None, file_status]]
- def change_vs_name_input(vs_id):
- if vs_id == "新建知识库":
- return gr.update(visible=True), gr.update(visible=True), gr.update(visible=False), None
- else:
- return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), VS_ROOT_PATH + vs_id
- def change_mode(mode):
- if mode == "知识库问答":
- return gr.update(visible=True)
- else:
- return gr.update(visible=False)
- def add_vs_name(vs_name, vs_list, chatbot):
- if vs_name in 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), vs_list, chatbot
- else:
- 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=vs_list + [vs_name], value=vs_name), vs_list + [vs_name], chatbot
- 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)
- """
- init_message = """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。
- 知识库暂不支持文件删除,该功能将在后续版本中推出。
- """
- model_status = init_model()
- with gr.Blocks(css=block_css) as demo:
- vs_path, file_status, model_status, vs_list = gr.State(""), gr.State(""), gr.State(model_status), gr.State(vs_list)
- 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"],
- label="请选择使用模式",
- value="知识库问答", )
- vs_setting = gr.Accordion("配置知识库")
- mode.change(fn=change_mode,
- inputs=mode,
- outputs=vs_setting)
- with vs_setting:
- select_vs = gr.Dropdown(vs_list.value,
- label="请选择要加载的知识库",
- interactive=True,
- value=vs_list.value[0] if len(vs_list.value) > 0 else None
- )
- vs_name = gr.Textbox(label="请输入新建知识库名称",
- lines=1,
- interactive=True)
- vs_add = gr.Button(value="添加至知识库选项")
- vs_add.click(fn=add_vs_name,
- inputs=[vs_name, vs_list, chatbot],
- outputs=[select_vs, vs_list, chatbot])
- 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")
- 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")
- # load_vs.click(fn=)
- select_vs.change(fn=change_vs_name_input,
- inputs=select_vs,
- outputs=[vs_name, vs_add, file2vs, vs_path])
- # 将上传的文件保存到content文件夹下,并更新下拉框
- load_file_button.click(get_vector_store,
- show_progress=True,
- inputs=[select_vs, files, chatbot],
- outputs=[vs_path, files, chatbot],
- )
- load_folder_button.click(get_vector_store,
- show_progress=True,
- inputs=[select_vs, folder_files, chatbot],
- outputs=[vs_path, folder_files, chatbot],
- )
- query.submit(get_answer,
- [query, vs_path, chatbot, mode],
- [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)
- 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)
- 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, use_ptuning_v2, top_k, chatbot],
- outputs=chatbot
- )
- (demo
- .queue(concurrency_count=3)
- .launch(server_name='0.0.0.0',
- server_port=7860,
- show_api=False,
- share=False,
- inbrowser=False))
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