import streamlit as st from webui_pages.dialogue.utils import process_files from webui_pages.utils import * from streamlit_chatbox import * from streamlit_modal import Modal from datetime import datetime import os import re import time from configs import (TOOL_CONFIG, LLM_MODEL_CONFIG) from server.knowledge_base.utils import LOADER_DICT import uuid from typing import List, Dict chat_box = ChatBox( assistant_avatar=os.path.join( "img", "chatchat_icon_blue_square_v2.png" ) ) def get_messages_history(history_len: int, content_in_expander: bool = False) -> List[Dict]: ''' 返回消息历史。 content_in_expander控制是否返回expander元素中的内容，一般导出的时候可以选上，传入LLM的history不需要 ''' def filter(msg): content = [x for x in msg["elements"] if x._output_method in ["markdown", "text"]] if not content_in_expander: content = [x for x in content if not x._in_expander] content = [x.content for x in content] return { "role": msg["role"], "content": "\n\n".join(content), } return chat_box.filter_history(history_len=history_len, filter=filter) @st.cache_data def upload_temp_docs(files, _api: ApiRequest) -> str: ''' 将文件上传到临时目录，用于文件对话 返回临时向量库ID ''' return _api.upload_temp_docs(files).get("data", {}).get("id") def parse_command(text: str, modal: Modal) -> bool: ''' 检查用户是否输入了自定义命令，当前支持： /new {session_name}。如果未提供名称，默认为“会话X” /del {session_name}。如果未提供名称，在会话数量>1的情况下，删除当前会话。 /clear {session_name}。如果未提供名称，默认清除当前会话 /stop {session_name}。如果未提供名称，默认停止当前会话 /help。查看命令帮助 返回值：输入的是命令返回True，否则返回False ''' if m := re.match(r"/([^\s]+)\s*(.*)", text): cmd, name = m.groups() name = name.strip() conv_names = chat_box.get_chat_names() if cmd == "help": modal.open() elif cmd == "new": if not name: i = 1 while True: name = f"会话{i}" if name not in conv_names: break i += 1 if name in st.session_state["conversation_ids"]: st.error(f"该会话名称 “{name}” 已存在") time.sleep(1) else: st.session_state["conversation_ids"][name] = uuid.uuid4().hex st.session_state["cur_conv_name"] = name elif cmd == "del": name = name or st.session_state.get("cur_conv_name") if len(conv_names) == 1: st.error("这是最后一个会话，无法删除") time.sleep(1) elif not name or name not in st.session_state["conversation_ids"]: st.error(f"无效的会话名称：“{name}”") time.sleep(1) else: st.session_state["conversation_ids"].pop(name, None) chat_box.del_chat_name(name) st.session_state["cur_conv_name"] = "" elif cmd == "clear": chat_box.reset_history(name=name or None) return True return False def dialogue_page(api: ApiRequest, is_lite: bool = False): st.session_state.setdefault("conversation_ids", {}) st.session_state["conversation_ids"].setdefault(chat_box.cur_chat_name, uuid.uuid4().hex) st.session_state.setdefault("file_chat_id", None) default_model = api.get_default_llm_model()[0] if not chat_box.chat_inited: st.toast( f"欢迎使用 [`Langchain-Chatchat`](https://github.com/chatchat-space/Langchain-Chatchat) ! \n\n" f"当前运行的模型`{default_model}`, 您可以开始提问了." ) chat_box.init_session() # 弹出自定义命令帮助信息 modal = Modal("自定义命令", key="cmd_help", max_width="500") if modal.is_open(): with modal.container(): cmds = [x for x in parse_command.__doc__.split("\n") if x.strip().startswith("/")] st.write("\n\n".join(cmds)) with st.sidebar: conv_names = list(st.session_state["conversation_ids"].keys()) index = 0 tools = list(TOOL_CONFIG.keys()) selected_tool_configs = {} with st.expander("工具栏"): for tool in tools: is_selected = st.checkbox(tool, value=TOOL_CONFIG[tool]["use"], key=tool) if is_selected: selected_tool_configs[tool] = TOOL_CONFIG[tool] if st.session_state.get("cur_conv_name") in conv_names: index = conv_names.index(st.session_state.get("cur_conv_name")) conversation_name = st.selectbox("当前会话", conv_names, index=index) chat_box.use_chat_name(conversation_name) conversation_id = st.session_state["conversation_ids"][conversation_name] # def on_mode_change(): # mode = st.session_state.dialogue_mode # text = f"已切换到 {mode} 模式。" # st.toast(text) # dialogue_modes = ["智能对话", # "文件对话", # ] # dialogue_mode = st.selectbox("请选择对话模式：", # dialogue_modes, # index=0, # on_change=on_mode_change, # key="dialogue_mode", # ) def on_llm_change(): if llm_model: config = api.get_model_config(llm_model) if not config.get("online_api"): # 只有本地model_worker可以切换模型 st.session_state["prev_llm_model"] = llm_model st.session_state["cur_llm_model"] = st.session_state.llm_model def llm_model_format_func(x): if x in running_models: return f"{x} (Running)" return x running_models = list(api.list_running_models()) available_models = [] config_models = api.list_config_models() if not is_lite: for k, v in config_models.get("local", {}).items(): # 列出配置了有效本地路径的模型 if (v.get("model_path_exists") and k not in running_models): available_models.append(k) for k, v in config_models.get("online", {}).items(): if not v.get("provider") and k not in running_models and k in LLM_MODELS: available_models.append(k) llm_models = running_models + available_models + ["openai-api"] cur_llm_model = st.session_state.get("cur_llm_model", default_model) if cur_llm_model in llm_models: index = llm_models.index(cur_llm_model) else: index = 0 llm_model = st.selectbox("选择LLM模型", llm_models, index, format_func=llm_model_format_func, on_change=on_llm_change, key="llm_model", ) # 传入后端的内容 model_config = {key: {} for key in LLM_MODEL_CONFIG.keys()} for key in LLM_MODEL_CONFIG: if key == 'llm_model': continue if LLM_MODEL_CONFIG[key]: first_key = next(iter(LLM_MODEL_CONFIG[key])) model_config[key][first_key] = LLM_MODEL_CONFIG[key][first_key] if llm_model is not None: model_config['llm_model'][llm_model] = LLM_MODEL_CONFIG['llm_model'][llm_model] files = st.file_uploader("上传附件", type=[i for ls in LOADER_DICT.values() for i in ls], accept_multiple_files=True) files_upload = process_files(files=files) if files else None print(len(files_upload)) if files_upload else None # if dialogue_mode == "文件对话": # with st.expander("文件对话配置", True): # files = st.file_uploader("上传知识文件：", # [i for ls in LOADER_DICT.values() for i in ls], # accept_multiple_files=True, # ) # kb_top_k = st.number_input("匹配知识条数：", 1, 20, VECTOR_SEARCH_TOP_K) # score_threshold = st.slider("知识匹配分数阈值：", 0.0, 2.0, float(SCORE_THRESHOLD), 0.01) # if st.button("开始上传", disabled=len(files) == 0): # st.session_state["file_chat_id"] = upload_temp_docs(files, api) # Display chat messages from history on app rerun chat_box.output_messages() chat_input_placeholder = "请输入对话内容，换行请使用Shift+Enter。输入/help查看自定义命令 " def on_feedback( feedback, message_id: str = "", history_index: int = -1, ): reason = feedback["text"] score_int = chat_box.set_feedback(feedback=feedback, history_index=history_index) api.chat_feedback(message_id=message_id, score=score_int, reason=reason) st.session_state["need_rerun"] = True feedback_kwargs = { "feedback_type": "thumbs", "optional_text_label": "欢迎反馈您打分的理由", } if prompt := st.chat_input(chat_input_placeholder, key="prompt"): if parse_command(text=prompt, modal=modal): # 用户输入自定义命令 st.rerun() else: history = get_messages_history( model_config["llm_model"][next(iter(model_config["llm_model"]))]["history_len"]) chat_box.user_say(prompt) chat_box.ai_say("正在思考...") text = "" message_id = "" element_index = 0 for d in api.chat_chat(query=prompt, metadata=files_upload, history=history, model_config=model_config, conversation_id=conversation_id, tool_config=selected_tool_configs, ): try: d = json.loads(d) except: pass message_id = d.get("message_id", "") metadata = { "message_id": message_id, } if error_msg := check_error_msg(d): st.error(error_msg) if chunk := d.get("agent_action"): chat_box.insert_msg(Markdown("...", in_expander=True, title="Tools", state="complete")) element_index = 1 formatted_data = { "action": chunk["tool_name"], "action_input": chunk["tool_input"] } formatted_json = json.dumps(formatted_data, indent=2, ensure_ascii=False) text += f"\n```\nInput Params:\n" + formatted_json + f"\n```\n" chat_box.update_msg(text, element_index=element_index, metadata=metadata) if chunk := d.get("text"): text += chunk chat_box.update_msg(text, element_index=element_index, metadata=metadata) if chunk := d.get("agent_finish"): element_index = 0 text = chunk chat_box.update_msg(text, streaming=False, element_index=element_index, metadata=metadata) chat_box.show_feedback(**feedback_kwargs, key=message_id, on_submit=on_feedback, kwargs={"message_id": message_id, "history_index": len(chat_box.history) - 1}) # elif dialogue_mode == "文件对话": # if st.session_state["file_chat_id"] is None: # st.error("请先上传文件再进行对话") # st.stop() # chat_box.ai_say([ # f"正在查询文件 `{st.session_state['file_chat_id']}` ...", # Markdown("...", in_expander=True, title="文件匹配结果", state="complete"), # ]) # text = "" # for d in api.file_chat(prompt, # knowledge_id=st.session_state["file_chat_id"], # top_k=kb_top_k, # score_threshold=score_threshold, # history=history, # model=llm_model, # prompt_name=prompt_template_name, # temperature=temperature): # if error_msg := check_error_msg(d): # st.error(error_msg) # elif chunk := d.get("answer"): # text += chunk # chat_box.update_msg(text, element_index=0) # chat_box.update_msg(text, element_index=0, streaming=False) # chat_box.update_msg("\n\n".join(d.get("docs", [])), element_index=1, streaming=False) if st.session_state.get("need_rerun"): st.session_state["need_rerun"] = False st.rerun() now = datetime.now() with st.sidebar: cols = st.columns(2) export_btn = cols[0] if cols[1].button( "清空对话", use_container_width=True, ): chat_box.reset_history() st.rerun() export_btn.download_button( "导出记录", "".join(chat_box.export2md()), file_name=f"{now:%Y-%m-%d %H.%M}_对话记录.md", mime="text/markdown", use_container_width=True, )