251 lines
11 KiB
Python
251 lines
11 KiB
Python
import gradio as gr
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import os
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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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nltk.data.path = [os.path.join(os.path.dirname(__file__), "nltk_data")] + nltk.data.path
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# return top-k text chunk from vector store
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VECTOR_SEARCH_TOP_K = 6
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# LLM input history length
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LLM_HISTORY_LEN = 3
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def get_vs_list():
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if not os.path.exists(VS_ROOT_PATH):
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return []
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return ["新建知识库"] + os.listdir(VS_ROOT_PATH)
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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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llm_model_dict_list = list(llm_model_dict.keys())
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local_doc_qa = LocalDocQA()
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def get_answer(query, vs_path, history, mode):
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if vs_path and mode == "知识库问答":
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resp, history = local_doc_qa.get_knowledge_based_answer(
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query=query, vs_path=vs_path, chat_history=history)
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source = "".join([f"""<details> <summary>出处 {i + 1}</summary>
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{doc.page_content}
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<b>所属文件：</b>{doc.metadata["source"]}
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</details>""" for i, doc in enumerate(resp["source_documents"])])
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history[-1][-1] += source
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else:
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resp = local_doc_qa.llm._call(query)
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history = history + [[None, resp + "\n如需基于知识库进行问答，请先加载知识库后，再进行提问。"]]
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return history, ""
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def update_status(history, status):
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history = history + [[None, status]]
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print(status)
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return history
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def init_model():
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try:
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local_doc_qa.init_cfg()
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local_doc_qa.llm._call("你好")
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return """模型已成功加载，请选择文件后点击"加载文件"按钮"""
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except Exception as e:
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print(e)
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return """模型未成功加载，请重新选择后点击"加载模型"按钮"""
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def reinit_model(llm_model, embedding_model, llm_history_len, use_ptuning_v2, top_k, history):
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try:
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local_doc_qa.init_cfg(llm_model=llm_model,
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embedding_model=embedding_model,
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llm_history_len=llm_history_len,
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use_ptuning_v2=use_ptuning_v2,
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top_k=top_k)
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model_status = """模型已成功重新加载，请选择文件后点击"加载文件"按钮"""
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except Exception as e:
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print(e)
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model_status = """模型未成功重新加载，请重新选择后点击"加载模型"按钮"""
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return history + [[None, model_status]]
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def get_vector_store(vs_id, files, history):
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vs_path = VS_ROOT_PATH + vs_id
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filelist = []
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for file in files:
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filename = os.path.split(file.name)[-1]
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shutil.move(file.name, UPLOAD_ROOT_PATH + filename)
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filelist.append(UPLOAD_ROOT_PATH + filename)
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if local_doc_qa.llm and local_doc_qa.embeddings:
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vs_path, loaded_files = local_doc_qa.init_knowledge_vector_store(filelist, vs_path)
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if len(loaded_files):
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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"
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else:
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file_status = "文件未成功加载，请重新上传文件"
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else:
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file_status = "模型未完成加载，请先在加载模型后再导入文件"
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vs_path = None
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return vs_path, None, history + [[None, file_status]]
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def change_vs_name_input(vs):
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if vs == "新建知识库":
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return gr.update(visible=True), gr.update(visible=True), gr.update(visible=False)
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else:
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return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)
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def change_mode(mode):
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if mode == "知识库问答":
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return gr.update(visible=True)
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else:
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return gr.update(visible=False)
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def add_vs_name(vs_name, vs_list, chatbot):
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if vs_name in vs_list:
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chatbot = chatbot + [[None, "与已有知识库名称冲突，请重新选择其他名称后提交"]]
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return gr.update(visible=True), vs_list, chatbot
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else:
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chatbot = chatbot + [
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[None, 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。 """]]
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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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}
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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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webui_title = """
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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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init_message = """欢迎使用 langchain-ChatGLM Web UI，开始提问前，请依次如下 3 个步骤：
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1. 选择语言模型、Embedding 模型及相关参数，如果使用 ptuning-v2 方式微调过模型，将 PrefixEncoder 模型放在 ptuning-v2 文件夹里并勾选相关选项，然后点击"重新加载模型"，并等待加载完成提示
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2. 上传或选择已有文件作为本地知识文档输入后点击"重新加载文档"，并等待加载完成提示
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3. 输入要提交的问题后，点击回车提交 """
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model_status = init_model()
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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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gr.Markdown(webui_title)
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with gr.Tab("对话"):
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with gr.Row():
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with gr.Column(scale=10):
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chatbot = gr.Chatbot([[None, init_message], [None, model_status.value]],
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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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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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value="知识库问答", )
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vs_setting = gr.Accordion("配置知识库")
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mode.change(fn=change_mode,
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inputs=mode,
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outputs=vs_setting)
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with vs_setting:
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select_vs = gr.Dropdown(vs_list.value,
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label="请选择要加载的知识库",
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interactive=True,
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value=vs_list.value[0] if len(vs_list.value) > 0 else None
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)
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vs_name = gr.Textbox(label="请输入新建知识库名称",
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lines=1,
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interactive=True)
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vs_add = gr.Button(value="添加至知识库选项")
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vs_add.click(fn=add_vs_name,
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inputs=[vs_name, vs_list, chatbot],
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outputs=[select_vs, vs_list, chatbot])
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file2vs = gr.Box(visible=False)
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with file2vs:
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gr.Markdown("向知识库中添加文件")
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with gr.Tab("上传文件"):
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files = gr.File(label="添加文件",
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file_types=['.txt', '.md', '.docx', '.pdf'],
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file_count="multiple",
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show_label=False
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)
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load_file_button = gr.Button("上传文件")
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with gr.Tab("上传文件夹"):
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folder_files = gr.File(label="添加文件",
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# file_types=['.txt', '.md', '.docx', '.pdf'],
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file_count="directory",
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show_label=False
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)
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load_folder_button = gr.Button("上传文件夹")
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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])
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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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inputs=[select_vs, files, chatbot],
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outputs=[vs_path, files, chatbot],
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)
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load_folder_button.click(get_vector_store,
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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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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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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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value=LLM_MODEL,
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interactive=True)
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llm_history_len = gr.Slider(0,
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10,
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value=LLM_HISTORY_LEN,
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step=1,
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label="LLM 对话轮数",
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interactive=True)
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use_ptuning_v2 = gr.Checkbox(USE_PTUNING_V2,
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label="使用p-tuning-v2微调过的模型",
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interactive=True)
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embedding_model = gr.Radio(embedding_model_dict_list,
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label="Embedding 模型",
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value=EMBEDDING_MODEL,
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interactive=True)
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top_k = gr.Slider(1,
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20,
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value=VECTOR_SEARCH_TOP_K,
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step=1,
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label="向量匹配 top k",
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interactive=True)
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load_model_button = gr.Button("重新加载模型")
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load_model_button.click(reinit_model,
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show_progress=True,
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inputs=[llm_model, embedding_model, llm_history_len, use_ptuning_v2, top_k, chatbot],
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outputs=chatbot
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)
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demo.queue(concurrency_count=3
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).launch(server_name='0.0.0.0',
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server_port=7860,
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show_api=False,
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share=False,
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inbrowser=False)
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