diff --git a/README.md b/README.md index 7cf32aa..c0cd76a 100644 --- a/README.md +++ b/README.md @@ -221,6 +221,6 @@ Web UI 可以实现如下功能： - [x] VUE 前端 ## 项目交流群 -![二维码](img/qqr_code_1.jpg) +![二维码](img/qr_code_23.jpg) 🎉 langchain-ChatGLM 项目交流群，如果你也对本项目感兴趣，欢迎加入群聊参与讨论交流。 diff --git a/img/qqr_code_1.jpg b/img/qqr_code_1.jpg deleted file mode 100644 index 9ae3f06..0000000 Binary files a/img/qqr_code_1.jpg and /dev/null differ diff --git a/img/qqr_code_2.jpg b/img/qqr_code_2.jpg new file mode 100644 index 0000000..2eb3c27 Binary files /dev/null and b/img/qqr_code_2.jpg differ diff --git a/img/qr_code_23.jpg b/img/qr_code_23.jpg new file mode 100644 index 0000000..812e600 Binary files /dev/null and b/img/qr_code_23.jpg differ diff --git a/webui_st.py b/webui_st.py new file mode 100644 index 0000000..74c426a --- /dev/null +++ b/webui_st.py @@ -0,0 +1,540 @@ +import streamlit as st +# from st_btn_select import st_btn_select +import tempfile +###### 从webui借用的代码 ##### +###### 做了少量修改 ##### +import os +import shutil + +from chains.local_doc_qa import LocalDocQA +from configs.model_config import * +import nltk +from models.base import (BaseAnswer, + AnswerResult, + AnswerResultStream, + AnswerResultQueueSentinelTokenListenerQueue) +import models.shared as shared +from models.loader.args import parser +from models.loader import LoaderCheckPoint + +nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path + + +def get_vs_list(): + lst_default = ["新建知识库"] + if not os.path.exists(VS_ROOT_PATH): + return lst_default + lst = os.listdir(VS_ROOT_PATH) + if not lst: + return lst_default + lst.sort() + return lst_default + lst + + +embedding_model_dict_list = list(embedding_model_dict.keys()) +llm_model_dict_list = list(llm_model_dict.keys()) +# flag_csv_logger = gr.CSVLogger() + + +def get_answer(query, vs_path, history, mode, score_threshold=VECTOR_SEARCH_SCORE_THRESHOLD, + vector_search_top_k=VECTOR_SEARCH_TOP_K, chunk_conent: bool = True, + chunk_size=CHUNK_SIZE, streaming: bool = STREAMING,): + if mode == "Bing搜索问答": + for resp, history in local_doc_qa.get_search_result_based_answer( + query=query, chat_history=history, streaming=streaming): + source = "\n\n" + source += "".join( + [f"""
出处 [{i + 1}] {doc.metadata["source"]} \n""" + f"""{doc.page_content}\n""" + f"""
""" + for i, doc in + enumerate(resp["source_documents"])]) + history[-1][-1] += source + yield history, "" + elif mode == "知识库问答" and vs_path is not None and os.path.exists(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"""
出处 [{i + 1}] {os.path.split(doc.metadata["source"])[-1]}\n""" + f"""{doc.page_content}\n""" + f"""
""" + for i, doc in + enumerate(resp["source_documents"])]) + history[-1][-1] += source + yield history, "" + elif mode == "知识库测试": + if os.path.exists(vs_path): + resp, prompt = local_doc_qa.get_knowledge_based_conent_test(query=query, vs_path=vs_path, + score_threshold=score_threshold, + vector_search_top_k=vector_search_top_k, + chunk_conent=chunk_conent, + chunk_size=chunk_size) + if not resp["source_documents"]: + yield history + [[query, + "根据您的设定，没有匹配到任何内容，请确认您设置的知识相关度 Score 阈值是否过小或其他参数是否正确。"]], "" + else: + source = "\n".join( + [ + f"""
【知识相关度 Score】：{doc.metadata["score"]} - 【出处{i + 1}】： {os.path.split(doc.metadata["source"])[-1]} \n""" + f"""{doc.page_content}\n""" + f"""
""" + for i, doc in + enumerate(resp["source_documents"])]) + history.append([query, "以下内容为知识库中满足设置条件的匹配结果：\n\n" + source]) + yield history, "" + else: + yield history + [[query, + "请选择知识库后进行测试，当前未选择知识库。"]], "" + else: + for answer_result in local_doc_qa.llm.generatorAnswer(prompt=query, history=history, + streaming=streaming): + + resp = answer_result.llm_output["answer"] + history = answer_result.history + 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, "" + logger.info(f"flagging: username={FLAG_USER_NAME},query={query},vs_path={vs_path},mode={mode},history={history}") + # flag_csv_logger.flag([query, vs_path, history, mode], username=FLAG_USER_NAME) + + +def init_model(llm_model: str = 'chat-glm-6b', embedding_model: str = 'text2vec'): + local_doc_qa = LocalDocQA() + # 初始化消息 + args = parser.parse_args() + args_dict = vars(args) + args_dict.update(model=llm_model) + shared.loaderCheckPoint = LoaderCheckPoint(args_dict) + llm_model_ins = shared.loaderLLM() + llm_model_ins.set_history_len(LLM_HISTORY_LEN) + + try: + local_doc_qa.init_cfg(llm_model=llm_model_ins, + embedding_model=embedding_model) + generator = local_doc_qa.llm.generatorAnswer("你好") + for answer_result in generator: + print(answer_result.llm_output) + 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""" + logger.info(reply) + except Exception as e: + logger.error(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": + logger.info("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: + logger.info(reply) + return local_doc_qa + + +# 暂未使用到，先保留 +# def reinit_model(llm_model, embedding_model, llm_history_len, no_remote_model, use_ptuning_v2, use_lora, top_k, history): +# try: +# llm_model_ins = shared.loaderLLM(llm_model, no_remote_model, use_ptuning_v2) +# llm_model_ins.history_len = llm_history_len +# local_doc_qa.init_cfg(llm_model=llm_model_ins, +# embedding_model=embedding_model, +# 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""" +# logger.info(model_status) +# except Exception as e: +# logger.error(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""" +# logger.info(model_status) +# return history + [[None, model_status]] + + +def get_vector_store(vs_id, files, sentence_size, history, one_conent, one_content_segmentation): + vs_path = os.path.join(VS_ROOT_PATH, vs_id) + filelist = [] + if not os.path.exists(os.path.join(UPLOAD_ROOT_PATH, vs_id)): + os.makedirs(os.path.join(UPLOAD_ROOT_PATH, vs_id)) + if local_doc_qa.llm and local_doc_qa.embeddings: + if isinstance(files, list): + for file in files: + filename = os.path.split(file.name)[-1] + shutil.move(file.name, os.path.join( + UPLOAD_ROOT_PATH, vs_id, filename)) + filelist.append(os.path.join( + UPLOAD_ROOT_PATH, vs_id, filename)) + vs_path, loaded_files = local_doc_qa.init_knowledge_vector_store( + filelist, vs_path, sentence_size) + else: + vs_path, loaded_files = local_doc_qa.one_knowledge_add(vs_path, files, one_conent, one_content_segmentation, + sentence_size) + if len(loaded_files): + file_status = f"已添加 {'、'.join([os.path.split(i)[-1] for i in loaded_files if i])} 内容至知识库，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 + logger.info(file_status) + return vs_path, None, history + [[None, file_status]] + + +knowledge_base_test_mode_info = ("【注意】\n\n" + "1. 您已进入知识库测试模式，您输入的任何对话内容都将用于进行知识库查询，" + "并仅输出知识库匹配出的内容及相似度分值和及输入的文本源路径，查询的内容并不会进入模型查询。\n\n" + "2. 知识相关度 Score 经测试，建议设置为 500 或更低，具体设置情况请结合实际使用调整。" + """3. 使用"添加单条数据"添加文本至知识库时，内容如未分段，则内容越多越会稀释各查询内容与之关联的score阈值。\n\n""" + "4. 单条内容长度建议设置在100-150左右。\n\n" + "5. 本界面用于知识入库及知识匹配相关参数设定，但当前版本中，" + "本界面中修改的参数并不会直接修改对话界面中参数，仍需前往`configs/model_config.py`修改后生效。" + "相关参数将在后续版本中支持本界面直接修改。") + + +webui_title = """ +# 🎉langchain-ChatGLM WebUI🎉 +👍 [https://github.com/imClumsyPanda/langchain-ChatGLM](https://github.com/imClumsyPanda/langchain-ChatGLM) +""" +###### ##### + + +###### todo ##### +# 1. streamlit运行方式与一般web服务器不同，使用模块是无法实现单例模式的，所以shared和local_doc_qa都需要进行全局化处理。 +# 目前已经实现了local_doc_qa的全局化，后面要考虑shared。 +# 2. 当前local_doc_qa是一个全局变量，一方面：任何一个session对其做出修改，都会影响所有session的对话;另一方面，如何处理所有session的请求竞争也是问题。 +# 这个暂时无法避免，在配置普通的机器上暂时也无需考虑。 +# 3. 目前只包含了get_answer对应的参数，以后可以添加其他参数，如temperature。 +###### ##### + + +###### 配置项 ##### +class ST_CONFIG: + user_bg_color = '#77ff77' + user_icon = 'https://tse2-mm.cn.bing.net/th/id/OIP-C.LTTKrxNWDr_k74wz6jKqBgHaHa?w=203&h=203&c=7&r=0&o=5&pid=1.7' + robot_bg_color = '#ccccee' + robot_icon = 'https://ts1.cn.mm.bing.net/th/id/R-C.5302e2cc6f5c7c4933ebb3394e0c41bc?rik=z4u%2b7efba5Mgxw&riu=http%3a%2f%2fcomic-cons.xyz%2fwp-content%2fuploads%2fStar-Wars-avatar-icon-C3PO.png&ehk=kBBvCvpJMHPVpdfpw1GaH%2brbOaIoHjY5Ua9PKcIs%2bAc%3d&risl=&pid=ImgRaw&r=0' + default_mode = '知识库问答' + defalut_kb = '' +###### ##### + + +class MsgType: + ''' + 目前仅支持文本类型的输入输出，为以后多模态模型预留图像、视频、音频支持。 + ''' + TEXT = 1 + IMAGE = 2 + VIDEO = 3 + AUDIO = 4 + + +class TempFile: + ''' + 为保持与get_vector_store的兼容性，需要将streamlit上传文件转化为其可以接受的方式 + ''' + + def __init__(self, path): + self.name = path + + +def init_session(): + st.session_state.setdefault('history', []) + + +# def get_query_params(): +# ''' +# 可以用url参数传递配置参数：llm_model, embedding_model, kb, mode。 +# 该参数将覆盖model_config中的配置。处于安全考虑，目前只支持kb和mode +# 方便将固定的配置分享给特定的人。 +# ''' +# params = st.experimental_get_query_params() +# return {k: v[0] for k, v in params.items() if v} + + +def robot_say(msg, kb=''): + st.session_state['history'].append( + {'is_user': False, 'type': MsgType.TEXT, 'content': msg, 'kb': kb}) + + +def user_say(msg): + st.session_state['history'].append( + {'is_user': True, 'type': MsgType.TEXT, 'content': msg}) + + +def format_md(msg, is_user=False, bg_color='', margin='10%'): + ''' + 将文本消息格式化为markdown文本 + ''' + if is_user: + bg_color = bg_color or ST_CONFIG.user_bg_color + text = f''' +
+ {msg} +
+ ''' + else: + bg_color = bg_color or ST_CONFIG.robot_bg_color + text = f''' +
+ {msg} +
+ ''' + return text + + +def message(msg, + is_user=False, + msg_type=MsgType.TEXT, + icon='', + bg_color='', + margin='10%', + kb='', + ): + ''' + 渲染单条消息。目前仅支持文本 + ''' + cols = st.columns([1, 10, 1]) + empty = cols[1].empty() + if is_user: + icon = icon or ST_CONFIG.user_icon + bg_color = bg_color or ST_CONFIG.user_bg_color + cols[2].image(icon, width=40) + if msg_type == MsgType.TEXT: + text = format_md(msg, is_user, bg_color, margin) + empty.markdown(text, unsafe_allow_html=True) + else: + raise RuntimeError('only support text message now.') + else: + icon = icon or ST_CONFIG.robot_icon + bg_color = bg_color or ST_CONFIG.robot_bg_color + cols[0].image(icon, width=40) + if kb: + cols[0].write(f'({kb})') + if msg_type == MsgType.TEXT: + text = format_md(msg, is_user, bg_color, margin) + empty.markdown(text, unsafe_allow_html=True) + else: + raise RuntimeError('only support text message now.') + return empty + + +def output_messages( + user_bg_color='', + robot_bg_color='', + user_icon='', + robot_icon='', +): + with chat_box.container(): + last_response = None + for msg in st.session_state['history']: + bg_color = user_bg_color if msg['is_user'] else robot_bg_color + icon = user_icon if msg['is_user'] else robot_icon + empty = message(msg['content'], + is_user=msg['is_user'], + icon=icon, + msg_type=msg['type'], + bg_color=bg_color, + kb=msg.get('kb', '') + ) + if not msg['is_user']: + last_response = empty + return last_response + + +@st.cache_resource(show_spinner=False, max_entries=1) +def load_model(llm_model: str, embedding_model: str): + ''' + 对应init_model，利用streamlit cache避免模型重复加载 + ''' + local_doc_qa = init_model(llm_model, embedding_model) + robot_say('模型已成功加载，可以开始对话，或从左侧选择模式后开始对话。\n请尽量不要刷新页面，以免模型出错或重复加载。') + return local_doc_qa + + +# @st.cache_data +def answer(query, vs_path='', history=[], mode='', score_threshold=0, + vector_search_top_k=5, chunk_conent=True, chunk_size=100, qa=None + ): + ''' + 对应get_answer，--利用streamlit cache缓存相同问题的答案-- + ''' + return get_answer(query, vs_path, history, mode, score_threshold, + vector_search_top_k, chunk_conent, chunk_size) + + +def load_vector_store( + vs_id, + files, + sentence_size=100, + history=[], + one_conent=None, + one_content_segmentation=None, +): + return get_vector_store( + local_doc_qa, + vs_id, + files, + sentence_size, + history, + one_conent, + one_content_segmentation, + ) + + +# main ui +st.set_page_config(webui_title, layout='wide') +init_session() +# params = get_query_params() +# llm_model = params.get('llm_model', LLM_MODEL) +# embedding_model = params.get('embedding_model', EMBEDDING_MODEL) + +with st.spinner(f'正在加载模型({LLM_MODEL} + {EMBEDDING_MODEL})，请耐心等候...'): + local_doc_qa = load_model(LLM_MODEL, EMBEDDING_MODEL) + + +def use_kb_mode(m): + return m in ['知识库问答', '知识库测试'] + + +# sidebar +modes = ['LLM 对话', '知识库问答', 'Bing搜索问答', '知识库测试'] +with st.sidebar: + def on_mode_change(): + m = st.session_state.mode + robot_say(f'已切换到"{m}"模式') + if m == '知识库测试': + robot_say(knowledge_base_test_mode_info) + + index = 0 + try: + index = modes.index(ST_CONFIG.default_mode) + except: + pass + mode = st.selectbox('对话模式', modes, index, + on_change=on_mode_change, key='mode') + + with st.expander('模型配置', '知识' not in mode): + with st.form('model_config'): + index = 0 + try: + index = llm_model_dict_list.index(LLM_MODEL) + except: + pass + llm_model = st.selectbox('LLM模型', llm_model_dict_list, index) + + no_remote_model = st.checkbox('加载本地模型', False) + use_ptuning_v2 = st.checkbox('使用p-tuning-v2微调过的模型', False) + use_lora = st.checkbox('使用lora微调的权重', False) + try: + index = embedding_model_dict_list.index(EMBEDDING_MODEL) + except: + pass + embedding_model = st.selectbox( + 'Embedding模型', embedding_model_dict_list, index) + + btn_load_model = st.form_submit_button('重新加载模型') + if btn_load_model: + local_doc_qa = load_model(llm_model, embedding_model) + + if mode in ['知识库问答', '知识库测试']: + vs_list = get_vs_list() + vs_list.remove('新建知识库') + + def on_new_kb(): + name = st.session_state.kb_name + if name in vs_list: + st.error(f'名为“{name}”的知识库已存在。') + else: + vs_list.append(name) + st.session_state.vs_path = name + + def on_vs_change(): + robot_say(f'已加载知识库： {st.session_state.vs_path}') + with st.expander('知识库配置', True): + cols = st.columns([12, 10]) + kb_name = cols[0].text_input( + '新知识库名称', placeholder='新知识库名称', label_visibility='collapsed') + cols[1].button('新建知识库', on_click=on_new_kb) + vs_path = st.selectbox( + '选择知识库', vs_list, on_change=on_vs_change, key='vs_path') + + st.text('') + + score_threshold = st.slider( + '知识相关度阈值', 0, 1000, VECTOR_SEARCH_SCORE_THRESHOLD) + top_k = st.slider('向量匹配数量', 1, 20, VECTOR_SEARCH_TOP_K) + history_len = st.slider( + 'LLM对话轮数', 1, 50, LLM_HISTORY_LEN) # 也许要跟知识库分开设置 + local_doc_qa.llm.set_history_len(history_len) + chunk_conent = st.checkbox('启用上下文关联', False) + st.text('') + # chunk_conent = st.checkbox('分割文本', True) # 知识库文本分割入库 + chunk_size = st.slider('上下文关联长度', 1, 1000, CHUNK_SIZE) + sentence_size = st.slider('文本入库分句长度限制', 1, 1000, SENTENCE_SIZE) + files = st.file_uploader('上传知识文件', + ['docx', 'txt', 'md', 'csv', 'xlsx', 'pdf'], + accept_multiple_files=True) + if st.button('添加文件到知识库'): + temp_dir = tempfile.mkdtemp() + file_list = [] + for f in files: + file = os.path.join(temp_dir, f.name) + with open(file, 'wb') as fp: + fp.write(f.getvalue()) + file_list.append(TempFile(file)) + _, _, history = load_vector_store( + vs_path, file_list, sentence_size, [], None, None) + st.session_state.files = [] + + +# main body +chat_box = st.empty() + +with st.form('my_form', clear_on_submit=True): + cols = st.columns([8, 1]) + question = cols[0].text_input( + 'temp', key='input_question', label_visibility='collapsed') + + def on_send(): + q = st.session_state.input_question + if q: + user_say(q) + + if mode == 'LLM 对话': + robot_say('正在思考...') + last_response = output_messages() + for history, _ in answer(q, + history=[], + mode=mode): + last_response.markdown( + format_md(history[-1][-1], False), + unsafe_allow_html=True + ) + elif use_kb_mode(mode): + robot_say('正在思考...', vs_path) + last_response = output_messages() + for history, _ in answer(q, + vs_path=os.path.join( + VS_ROOT_PATH, vs_path), + history=[], + mode=mode, + score_threshold=score_threshold, + vector_search_top_k=top_k, + chunk_conent=chunk_conent, + chunk_size=chunk_size): + last_response.markdown( + format_md(history[-1][-1], False, 'ligreen'), + unsafe_allow_html=True + ) + else: + robot_say('正在思考...') + last_response = output_messages() + st.session_state['history'][-1]['content'] = history[-1][-1] + submit = cols[1].form_submit_button('发送', on_click=on_send) + +output_messages() + +# st.write(st.session_state['history'])