diff --git a/chains/local_doc_qa.py b/chains/local_doc_qa.py index ec2440d..f9b7207 100644 --- a/chains/local_doc_qa.py +++ b/chains/local_doc_qa.py @@ -17,10 +17,6 @@ VECTOR_SEARCH_TOP_K = 6 # LLM input history length LLM_HISTORY_LEN = 3 -<<<<<<< HEAD -<<<<<<< HEAD -======= ->>>>>>> 7cc03c3 (feat: add api for knowledge_based QA) def load_file(filepath): if filepath.lower().endswith(".pdf"): @@ -33,11 +29,6 @@ def load_file(filepath): docs = loader.load_and_split(text_splitter=textsplitter) return docs -<<<<<<< HEAD -======= ->>>>>>> cba44ca (修复 webui.py 中 llm_history_len 和 vector_search_top_k 显示值与启动设置默认值不一致的问题) -======= ->>>>>>> 7cc03c3 (feat: add api for knowledge_based QA) class LocalDocQA: llm: object = None diff --git a/webui.py b/webui.py index f4eacaa..b9a1dd7 100644 --- a/webui.py +++ b/webui.py @@ -12,18 +12,7 @@ VECTOR_SEARCH_TOP_K = 6 # LLM input history length LLM_HISTORY_LEN = 3 -<<<<<<< HEAD -======= -<<<<<<< HEAD ->>>>>>> f87a5f5 (fix bug in webui.py) -======= -# return top-k text chunk from vector store -VECTOR_SEARCH_TOP_K = 6 - -# LLM input history length -LLM_HISTORY_LEN = 3 ->>>>>>> cba44ca (修复 webui.py 中 llm_history_len 和 vector_search_top_k 显示值与启动设置默认值不一致的问题) def get_file_list(): if not os.path.exists("content"): @@ -31,7 +20,14 @@ def get_file_list(): return [f for f in os.listdir("content")] +def get_vs_list(): + if not os.path.exists("vector_store"): + return [] + return [f for f in os.listdir("vector_store")] + + file_list = get_file_list() +vs_list = get_vs_list() embedding_model_dict_list = list(embedding_model_dict.keys()) @@ -40,22 +36,30 @@ llm_model_dict_list = list(llm_model_dict.keys()) local_doc_qa = LocalDocQA() -def upload_file(file): +def upload_file(file, chatbot): if not os.path.exists("content"): os.mkdir("content") filename = os.path.basename(file.name) shutil.move(file.name, "content/" + filename) # file_list首位插入新上传的文件 file_list.insert(0, filename) - return gr.Dropdown.update(choices=file_list, value=filename) + status = "已将xx上传至xxx" + return chatbot + [None, status] def get_answer(query, vs_path, history): if vs_path: resp, history = local_doc_qa.get_knowledge_based_answer( query=query, vs_path=vs_path, chat_history=history) + source = "".join([f"""
出处 {i + 1} +{doc.page_content} + +所属文件：{doc.metadata["source"]} +
""" for i, doc in enumerate(resp["source_documents"])]) + history[-1][-1] += source else: - history = history + [[None, "请先加载文件后，再进行提问。"]] + resp = local_doc_qa.llm._call(query) + 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。"]] return history, "" @@ -68,6 +72,7 @@ def update_status(history, status): def init_model(): try: local_doc_qa.init_cfg() + local_doc_qa.llm._call("你好") return """模型已成功加载，请选择文件后点击"加载文件"按钮""" except Exception as e: print(e) @@ -88,7 +93,6 @@ def reinit_model(llm_model, embedding_model, llm_history_len, use_ptuning_v2, to return history + [[None, model_status]] - def get_vector_store(filepath, history): if local_doc_qa.llm and local_doc_qa.embeddings: vs_path = local_doc_qa.init_knowledge_vector_store(["content/" + filepath]) @@ -120,71 +124,79 @@ webui_title = """ """ init_message = """欢迎使用 langchain-ChatGLM Web UI，开始提问前，请依次如下 3 个步骤： -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 +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 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 3. 输入要提交的问题后，点击回车提交 """ - model_status = init_model() with gr.Blocks(css=block_css) as demo: vs_path, file_status, model_status = gr.State(""), gr.State(""), gr.State(model_status) gr.Markdown(webui_title) - with gr.Row(): - with gr.Column(scale=2): - 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.Tab("聊天"): + with gr.Row(): + with gr.Column(scale=2): + 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=1): - 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 history len", - 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") - - # with gr.Column(): - with gr.Tab("select"): - selectFile = gr.Dropdown(file_list, - label="content file", + with gr.Column(scale=1): + # with gr.Column(): + # with gr.Tab("select"): + selectFile = gr.Dropdown(vs_list, + label="请选择要加载的知识库", interactive=True, - value=file_list[0] if len(file_list) > 0 else None) - with gr.Tab("upload"): - file = gr.File(label="content file", - file_types=['.txt', '.md', '.docx', '.pdf'] - ) # .style(height=100) - load_file_button = gr.Button("加载文件") + value=vs_list[0] if len(vs_list) > 0 else None) + # + gr.Markdown("向知识库中添加文件") + with gr.Tab("上传文件"): + files = gr.File(label="向知识库中添加文件", + file_types=['.txt', '.md', '.docx', '.pdf'], + file_count="multiple" + ) # .style(height=100) + with gr.Tab("上传文件夹"): + files = gr.File(label="向知识库中添加文件", + file_types=['.txt', '.md', '.docx', '.pdf'], + file_count="directory" + ) # .style(height=100) + load_file_button = gr.Button("加载知识库") + 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 history len", + 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 ) # 将上传的文件保存到content文件夹下,并更新下拉框 - file.upload(upload_file, - inputs=file, - outputs=selectFile) + files.upload(upload_file, + inputs=[files, chatbot], + outputs=chatbot) load_file_button.click(get_vector_store, show_progress=True, inputs=[selectFile, chatbot],