update webui.py
This commit is contained in:
imClumsyPanda
parent
57d78bd1f6
commit
635aa2d2ac
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@ -17,10 +17,6 @@ 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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<<<<<<< HEAD
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<<<<<<< HEAD
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=======
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>>>>>>> 7cc03c3 (feat: add api for knowledge_based QA)
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def load_file(filepath):
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if filepath.lower().endswith(".pdf"):
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@ -33,11 +29,6 @@ def load_file(filepath):
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docs = loader.load_and_split(text_splitter=textsplitter)
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return docs
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<<<<<<< HEAD
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=======
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>>>>>>> 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)
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=======
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>>>>>>> 7cc03c3 (feat: add api for knowledge_based QA)
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class LocalDocQA:
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llm: object = None
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140
webui.py
140
webui.py
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@ -12,18 +12,7 @@ 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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<<<<<<< HEAD
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=======
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<<<<<<< HEAD
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>>>>>>> f87a5f5 (fix bug in webui.py)
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=======
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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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>>>>>>> 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)
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def get_file_list():
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if not os.path.exists("content"):
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@ -31,7 +20,14 @@ def get_file_list():
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return [f for f in os.listdir("content")]
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def get_vs_list():
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if not os.path.exists("vector_store"):
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return []
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return [f for f in os.listdir("vector_store")]
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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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@ -40,22 +36,30 @@ 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):
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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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return gr.Dropdown.update(choices=file_list, value=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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def get_answer(query, vs_path, history):
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if vs_path:
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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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history = history + [[None, "请先加载文件后，再进行提问。"]]
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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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@ -68,6 +72,7 @@ def update_status(history, status):
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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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@ -88,7 +93,6 @@ def reinit_model(llm_model, embedding_model, llm_history_len, use_ptuning_v2, to
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return history + [[None, model_status]]
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def get_vector_store(filepath, history):
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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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@ -120,71 +124,79 @@ 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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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 = gr.State(""), gr.State(""), gr.State(model_status)
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gr.Markdown(webui_title)
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with gr.Row():
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with gr.Column(scale=2):
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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.Tab("聊天"):
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with gr.Row():
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with gr.Column(scale=2):
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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=1):
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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 history len",
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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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# with gr.Column():
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with gr.Tab("select"):
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selectFile = gr.Dropdown(file_list,
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label="content file",
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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=file_list[0] if len(file_list) > 0 else None)
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with gr.Tab("upload"):
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file = gr.File(label="content file",
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file_types=['.txt', '.md', '.docx', '.pdf']
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) # .style(height=100)
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load_file_button = gr.Button("加载文件")
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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.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 history len",
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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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# 将上传的文件保存到content文件夹下,并更新下拉框
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file.upload(upload_file,
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inputs=file,
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outputs=selectFile)
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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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