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@@ -14,19 +14,12 @@ VECTOR_SEARCH_TOP_K = 6
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LLM_HISTORY_LEN = 3
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-def get_file_list():
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- if not os.path.exists("content"):
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- return []
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- return [f for f in os.listdir("content")]
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-
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-
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def get_vs_list():
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- if not os.path.exists("vector_store"):
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+ if not os.path.exists(VS_ROOT_PATH):
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return []
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- return [f for f in os.listdir("vector_store")]
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+ return ["新建知识库"] + os.listdir(VS_ROOT_PATH)
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-file_list = get_file_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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@@ -36,19 +29,8 @@ llm_model_dict_list = list(llm_model_dict.keys())
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local_doc_qa = LocalDocQA()
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-def upload_file(file, chatbot):
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- if not os.path.exists("content"):
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- os.mkdir("content")
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- filename = os.path.basename(file.name)
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- shutil.move(file.name, "content/" + filename)
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- # file_list首位插入新上传的文件
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- file_list.insert(0, filename)
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- status = "已将xx上传至xxx"
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- return chatbot + [None, status]
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-
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-
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-def get_answer(query, vs_path, history):
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- if vs_path:
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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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@@ -59,7 +41,7 @@ def get_answer(query, vs_path, history):
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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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+ history = history + [[query, 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 "")]]
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return history, ""
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@@ -73,10 +55,10 @@ 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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+ return """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"""
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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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+ return """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"""
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def reinit_model(llm_model, embedding_model, llm_history_len, use_ptuning_v2, top_k, history):
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@@ -86,24 +68,54 @@ def reinit_model(llm_model, embedding_model, llm_history_len, use_ptuning_v2, to
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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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+ 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"""
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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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+ 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"""
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return history + [[None, model_status]]
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-def get_vector_store(filepath, history):
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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 = local_doc_qa.init_knowledge_vector_store(["content/" + filepath])
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- if vs_path:
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- file_status = "文件已成功加载，请开始提问"
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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, history + [[None, file_status]]
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+ return vs_path, None, history + [[None, file_status]]
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+
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+
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+def change_vs_name_input(vs_id):
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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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+ else:
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+ return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), VS_ROOT_PATH + vs_id
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+
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+
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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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+
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+
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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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@@ -123,46 +135,88 @@ webui_title = """
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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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+init_message = """欢迎使用 langchain-ChatGLM Web UI！
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+
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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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+
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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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+
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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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with gr.Blocks(css=block_css) as demo:
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- vs_path, file_status, model_status = gr.State(""), gr.State(""), gr.State(model_status)
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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("聊天"):
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+ with gr.Tab("对话"):
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with gr.Row():
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- with gr.Column(scale=2):
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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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-
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- with gr.Column(scale=1):
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- # with gr.Column():
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- # with gr.Tab("select"):
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- selectFile = gr.Dropdown(vs_list,
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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[0] if len(vs_list) > 0 else None)
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- #
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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="向知识库中添加文件",
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- file_types=['.txt', '.md', '.docx', '.pdf'],
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- file_count="multiple"
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- ) # .style(height=100)
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- with gr.Tab("上传文件夹"):
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- files = gr.File(label="向知识库中添加文件",
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- file_types=['.txt', '.md', '.docx', '.pdf'],
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- file_count="directory"
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- ) # .style(height=100)
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- load_file_button = gr.Button("加载知识库")
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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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+
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+ file2vs = gr.Column(visible=False)
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+ with file2vs:
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+ # load_vs = gr.Button("加载知识库")
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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("上传文件并加载知识库")
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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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+ # 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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@@ -172,7 +226,7 @@ with gr.Blocks(css=block_css) as demo:
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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 history len",
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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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@@ -193,19 +247,6 @@ with gr.Blocks(css=block_css) as demo:
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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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- # 将上传的文件保存到content文件夹下,并更新下拉框
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- files.upload(upload_file,
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- inputs=[files, chatbot],
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- outputs=chatbot)
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- load_file_button.click(get_vector_store,
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- show_progress=True,
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- inputs=[selectFile, chatbot],
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- outputs=[vs_path, chatbot],
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- )
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- query.submit(get_answer,
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- [query, vs_path, chatbot],
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- [chatbot, query],
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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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imClumsyPanda
