log user name during chat

crazy_functional.py

@@ -17,7 +17,7 @@ def get_crazy_functions():
     from crazy_functions.SourceCode_Analyse import 解析一个前端项目
     from crazy_functions.高级功能函数模板 import 高阶功能模板函数
     from crazy_functions.高级功能函数模板 import Demo_Wrap
-    from crazy_functions.Latex_Project_Polish import Latex英文润色
+    from crazy_functions.Latex全文润色 import Latex英文润色
     from crazy_functions.询问多个大语言模型 import 同时问询
     from crazy_functions.SourceCode_Analyse import 解析一个Lua项目
     from crazy_functions.SourceCode_Analyse import 解析一个CSharp项目
@@ -33,8 +33,8 @@ def get_crazy_functions():
     from crazy_functions.PDF_Translate import 批量翻译PDF文档
     from crazy_functions.谷歌检索小助手 import 谷歌检索小助手
     from crazy_functions.理解PDF文档内容 import 理解PDF文档内容标准文件输入
-    from crazy_functions.Latex_Project_Polish import Latex中文润色
-    from crazy_functions.Latex_Project_Polish import Latex英文纠错
+    from crazy_functions.Latex全文润色 import Latex中文润色
+    from crazy_functions.Latex全文润色 import Latex英文纠错
     from crazy_functions.Markdown_Translate import Markdown中译英
     from crazy_functions.虚空终端 import 虚空终端
     from crazy_functions.生成多种Mermaid图表 import Mermaid_Gen
@@ -740,6 +740,19 @@ def get_crazy_functions():
     #     logger.error(trimmed_format_exc())
     #     print('Load function plugin failed')
 
+    # try:
+    #     from crazy_functions.chatglm微调工具 import 微调数据集生成
+    #     function_plugins.update({
+    #         "黑盒模型学习: 微调数据集生成 (先上传数据集)": {
+    #             "Color": "stop",
+    #             "AsButton": False,
+    #             "AdvancedArgs": True,
+    #             "ArgsReminder": "针对数据集输入（如 绿帽子*深蓝色衬衫*黑色运动裤）给出指令，例如您可以将以下命令复制到下方: --llm_to_learn=azure-gpt-3.5 --prompt_prefix='根据下面的服装类型提示，想象一个穿着者，对这个人外貌、身处的环境、内心世界、过去经历进行描写。要求：100字以内，用第二人称。' --system_prompt=''",
+    #             "Function": HotReload(微调数据集生成)
+    #         }
+    #     })
+    # except:
+    #     print('Load function plugin failed')
 
     """
     设置默认值:

crazy_functions/Dynamic_Load_Plugin.py

@@ -1,62 +0,0 @@
-
-from toolbox import get_conf, update_ui
-from crazy_functions.Image_Generate import 图片生成_DALLE2, 图片生成_DALLE3, 图片修改_DALLE2
-from crazy_functions.plugin_template.plugin_class_template import GptAcademicPluginTemplate, ArgProperty
-
-
-
-def update_js_plugin_info():
-    # encode_plugin_info
-    ...
-
-
-
-class ImageGen_Wrap(GptAcademicPluginTemplate):
-    def __init__(self):
-        """
-        请注意`execute`会执行在不同的线程中，因此您在定义和使用类变量时，应当慎之又慎！
-        """
-        pass
-
-    def define_arg_selection_menu(self):
-        """
-        定义插件的二级选项菜单
-
-        第一个参数，名称`main_input`，参数`type`声明这是一个文本框，文本框上方显示`title`，文本框内部显示`description`，`default_value`为默认值；
-        第二个参数，名称`advanced_arg`，参数`type`声明这是一个文本框，文本框上方显示`title`，文本框内部显示`description`，`default_value`为默认值；
-
-        """
-        gui_definition = {
-            "main_input":
-                ArgProperty(title="输入图片描述", description="需要生成图像的文本描述，尽量使用英文", default_value="", type="string").model_dump_json(), # 主输入，自动从输入框同步
-            "model_name":
-                ArgProperty(title="模型", options=["DALLE2", "DALLE3"], default_value="DALLE3", description="无", type="dropdown").model_dump_json(),
-            "resolution":
-                ArgProperty(title="分辨率", options=["256x256(限DALLE2)", "512x512(限DALLE2)", "1024x1024", "1792x1024(限DALLE3)", "1024x1792(限DALLE3)"], default_value="1024x1024", description="无", type="dropdown").model_dump_json(),
-            "quality (仅DALLE3生效)":
-                ArgProperty(title="质量", options=["standard", "hd"], default_value="standard", description="无", type="dropdown").model_dump_json(),
-            "style (仅DALLE3生效)":
-                ArgProperty(title="风格", options=["vivid", "natural"], default_value="vivid", description="无", type="dropdown").model_dump_json(),
-        }
-        return gui_definition
-
-    def execute(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
-        """
-        执行插件
-        """
-        # 分辨率
-        resolution = plugin_kwargs["resolution"].replace("(限DALLE2)", "").replace("(限DALLE3)", "")
-
-        if plugin_kwargs["model_name"] == "DALLE2":
-            plugin_kwargs["advanced_arg"] = resolution
-            yield from 图片生成_DALLE2(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)
-
-        elif plugin_kwargs["model_name"] == "DALLE3":
-            quality = plugin_kwargs["quality (仅DALLE3生效)"]
-            style = plugin_kwargs["style (仅DALLE3生效)"]
-            plugin_kwargs["advanced_arg"] = f"{resolution}-{quality}-{style}"
-            yield from 图片生成_DALLE3(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)
-
-        else:
-            chatbot.append([None, "抱歉，找不到该模型"])
-            yield from update_ui(chatbot=chatbot, history=history) # 刷新界面

crazy_functions/chatglm微调工具.py

@@ -0,0 +1,141 @@
+from toolbox import CatchException, update_ui, promote_file_to_downloadzone
+from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
+import datetime, json
+
+def fetch_items(list_of_items, batch_size):
+    for i in range(0, len(list_of_items), batch_size):
+        yield list_of_items[i:i + batch_size]
+
+def string_to_options(arguments):
+    import argparse
+    import shlex
+
+    # Create an argparse.ArgumentParser instance
+    parser = argparse.ArgumentParser()
+
+    # Add command-line arguments
+    parser.add_argument("--llm_to_learn", type=str, help="LLM model to learn", default="gpt-3.5-turbo")
+    parser.add_argument("--prompt_prefix", type=str, help="Prompt prefix", default='')
+    parser.add_argument("--system_prompt", type=str, help="System prompt", default='')
+    parser.add_argument("--batch", type=int, help="System prompt", default=50)
+    parser.add_argument("--pre_seq_len", type=int, help="pre_seq_len", default=50)
+    parser.add_argument("--learning_rate", type=float, help="learning_rate", default=2e-2)
+    parser.add_argument("--num_gpus", type=int, help="num_gpus", default=1)
+    parser.add_argument("--json_dataset", type=str, help="json_dataset", default="")
+    parser.add_argument("--ptuning_directory", type=str, help="ptuning_directory", default="")
+
+
+
+    # Parse the arguments
+    args = parser.parse_args(shlex.split(arguments))
+
+    return args
+
+@CatchException
+def 微调数据集生成(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
+    """
+    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
+    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
+    plugin_kwargs   插件模型的参数
+    chatbot         聊天显示框的句柄，用于显示给用户
+    history         聊天历史，前情提要
+    system_prompt   给gpt的静默提醒
+    user_request    当前用户的请求信息（IP地址等）
+    """
+    history = []    # 清空历史，以免输入溢出
+    chatbot.append(("这是什么功能？", "[Local Message] 微调数据集生成"))
+    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
+    args = plugin_kwargs.get("advanced_arg", None)
+    if args is None:
+        chatbot.append(("没给定指令", "退出"))
+        yield from update_ui(chatbot=chatbot, history=history); return
+    else:
+        arguments = string_to_options(arguments=args)
+
+    dat = []
+    with open(txt, 'r', encoding='utf8') as f:
+        for line in f.readlines():
+            json_dat = json.loads(line)
+            dat.append(json_dat["content"])
+
+    llm_kwargs['llm_model'] = arguments.llm_to_learn
+    for batch in fetch_items(dat, arguments.batch):
+        res = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
+            inputs_array=[f"{arguments.prompt_prefix}\n\n{b}" for b in (batch)],
+            inputs_show_user_array=[f"Show Nothing" for _ in (batch)],
+            llm_kwargs=llm_kwargs,
+            chatbot=chatbot,
+            history_array=[[] for _ in (batch)],
+            sys_prompt_array=[arguments.system_prompt for _ in (batch)],
+            max_workers=10  # OpenAI所允许的最大并行过载
+        )
+
+        with open(txt+'.generated.json', 'a+', encoding='utf8') as f:
+            for b, r in zip(batch, res[1::2]):
+                f.write(json.dumps({"content":b, "summary":r}, ensure_ascii=False)+'\n')
+
+    promote_file_to_downloadzone(txt+'.generated.json', rename_file='generated.json', chatbot=chatbot)
+    return
+
+
+
+@CatchException
+def 启动微调(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
+    """
+    txt             输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
+    llm_kwargs      gpt模型参数，如温度和top_p等，一般原样传递下去就行
+    plugin_kwargs   插件模型的参数
+    chatbot         聊天显示框的句柄，用于显示给用户
+    history         聊天历史，前情提要
+    system_prompt   给gpt的静默提醒
+    user_request    当前用户的请求信息（IP地址等）
+    """
+    import subprocess
+    history = []    # 清空历史，以免输入溢出
+    chatbot.append(("这是什么功能？", "[Local Message] 微调数据集生成"))
+    if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
+    args = plugin_kwargs.get("advanced_arg", None)
+    if args is None:
+        chatbot.append(("没给定指令", "退出"))
+        yield from update_ui(chatbot=chatbot, history=history); return
+    else:
+        arguments = string_to_options(arguments=args)
+
+
+
+    pre_seq_len = arguments.pre_seq_len             # 128
+    learning_rate = arguments.learning_rate                               # 2e-2
+    num_gpus = arguments.num_gpus                   # 1
+    json_dataset = arguments.json_dataset                 # 't_code.json'
+    ptuning_directory = arguments.ptuning_directory       # '/home/hmp/ChatGLM2-6B/ptuning'
+
+    command = f"torchrun --standalone --nnodes=1 --nproc-per-node={num_gpus} main.py \
+        --do_train \
+        --train_file AdvertiseGen/{json_dataset} \
+        --validation_file AdvertiseGen/{json_dataset} \
+        --preprocessing_num_workers 20 \
+        --prompt_column content \
+        --response_column summary \
+        --overwrite_cache \
+        --model_name_or_path THUDM/chatglm2-6b \
+        --output_dir output/clothgen-chatglm2-6b-pt-{pre_seq_len}-{learning_rate} \
+        --overwrite_output_dir \
+        --max_source_length 256 \
+        --max_target_length 256 \
+        --per_device_train_batch_size 1 \
+        --per_device_eval_batch_size 1 \
+        --gradient_accumulation_steps 16 \
+        --predict_with_generate \
+        --max_steps 100 \
+        --logging_steps 10 \
+        --save_steps 20 \
+        --learning_rate {learning_rate} \
+        --pre_seq_len {pre_seq_len} \
+        --quantization_bit 4"
+
+    process = subprocess.Popen(command, shell=True, cwd=ptuning_directory)
+    try:
+        process.communicate(timeout=3600*24)
+    except subprocess.TimeoutExpired:
+        process.kill()
+    return

docs/plugin_with_secondary_menu.md

@@ -149,19 +149,6 @@
         DEFINE_ARG_INPUT_INTERFACE = json.dumps(define_arg_selection)
         return base64.b64encode(DEFINE_ARG_INPUT_INTERFACE.encode('utf-8')).decode('utf-8')
     ```
-1-2. 预留4个动态插件按钮（常规状态隐藏）
-
-点击 “+插件按钮”：跳转到插件市场
-
-点击 加载插件：
-    - 下载文件
-    - 注册 exe_dynamic_plugin开始执行
-    - 执行浏览器js函数
-
-点击动态插件按钮
-    - js： 检查register_advanced_plugin_init_code_arr，如果为空，提示
-    - 如果非空，先跳二级菜单，设定诸元后，执行一个隐藏的按钮 -- 关联 exe_dynamic_plugin
-    - exe_dynamic_plugin开始执行
 
 
 2. 用户加载阶段（主javascript程序`common.js`中），浏览器加载`register_advanced_plugin_init_code_arr`，存入本地的字典`advanced_plugin_init_code_lib`：

request_llms/bridge_chatgpt.py

@@ -341,7 +341,7 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
                     # 前者是API2D的结束条件，后者是OPENAI的结束条件
                     if ('data: [DONE]' in chunk_decoded) or (len(chunkjson['choices'][0]["delta"]) == 0):
                         # 判定为数据流的结束，gpt_replying_buffer也写完了
-                        log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer)
+                        log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer, user_name=chatbot.get_user())
                         break
                     # 处理数据流的主体
                     status_text = f"finish_reason: {chunkjson['choices'][0].get('finish_reason', 'null')}"
@@ -375,7 +375,7 @@ def handle_o1_model_special(response, inputs, llm_kwargs, chatbot, history):
     try:
         chunkjson = json.loads(response.content.decode())
         gpt_replying_buffer = chunkjson['choices'][0]["message"]["content"]
-        log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer)
+        log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer, user_name=chatbot.get_user())
         history[-1] = gpt_replying_buffer
         chatbot[-1] = (history[-2], history[-1])
         yield from update_ui(chatbot=chatbot, history=history) # 刷新界面

request_llms/bridge_chatgpt_vision.py

@@ -184,7 +184,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
                         # 判定为数据流的结束，gpt_replying_buffer也写完了
                         lastmsg = chatbot[-1][-1] + f"\n\n\n\n「{llm_kwargs['llm_model']}调用结束，该模型不具备上下文对话能力，如需追问，请及时切换模型。」"
                         yield from update_ui_lastest_msg(lastmsg, chatbot, history, delay=1)
-                        log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer)
+                        log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer, user_name=chatbot.get_user())
                         break
                     # 处理数据流的主体
                     status_text = f"finish_reason: {chunkjson['choices'][0].get('finish_reason', 'null')}"

request_llms/bridge_claude.py

@@ -216,7 +216,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
                 if need_to_pass:
                     pass
                 elif is_last_chunk:
-                    log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer)
+                    log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer, user_name=chatbot.get_user())
                     # logger.info(f'[response] {gpt_replying_buffer}')
                     break
                 else:

request_llms/bridge_cohere.py

@@ -223,7 +223,7 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
                         chatbot[-1] = (history[-2], history[-1])
                         yield from update_ui(chatbot=chatbot, history=history, msg="正常") # 刷新界面
                     if chunkjson['event_type'] == 'stream-end':
-                        log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer)
+                        log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer, user_name=chatbot.get_user())
                         history[-1] = gpt_replying_buffer
                         chatbot[-1] = (history[-2], history[-1])
                         yield from update_ui(chatbot=chatbot, history=history, msg="正常") # 刷新界面

request_llms/bridge_google_gemini.py

@@ -109,7 +109,7 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
             gpt_replying_buffer += paraphrase['text']    # 使用 json 解析库进行处理
             chatbot[-1] = (inputs, gpt_replying_buffer)
             history[-1] = gpt_replying_buffer
-            log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer)
+            log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer, user_name=chatbot.get_user())
             yield from update_ui(chatbot=chatbot, history=history)
         if error_match:
             history = history[-2]  # 错误的不纳入对话

request_llms/bridge_moonshot.py

@@ -166,7 +166,7 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
             history = history[:-2]
             yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
             break
-    log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_bro_result)
+    log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_bro_result, user_name=chatbot.get_user())
 
 def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None,
                                   console_slience=False):

request_llms/bridge_openrouter.py

@@ -337,7 +337,7 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
                     # 前者是API2D的结束条件，后者是OPENAI的结束条件
                     if ('data: [DONE]' in chunk_decoded) or (len(chunkjson['choices'][0]["delta"]) == 0):
                         # 判定为数据流的结束，gpt_replying_buffer也写完了
-                        log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer)
+                        log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer, user_name=chatbot.get_user())
                         break
                     # 处理数据流的主体
                     status_text = f"finish_reason: {chunkjson['choices'][0].get('finish_reason', 'null')}"
@@ -371,7 +371,7 @@ def handle_o1_model_special(response, inputs, llm_kwargs, chatbot, history):
     try:
         chunkjson = json.loads(response.content.decode())
         gpt_replying_buffer = chunkjson['choices'][0]["message"]["content"]
-        log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer)
+        log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer, user_name=chatbot.get_user())
         history[-1] = gpt_replying_buffer
         chatbot[-1] = (history[-2], history[-1])
         yield from update_ui(chatbot=chatbot, history=history) # 刷新界面

request_llms/bridge_qwen.py

@@ -59,7 +59,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         chatbot[-1] = (inputs, response)
         yield from update_ui(chatbot=chatbot, history=history)
 
-    log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=response)
+    log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=response, user_name=chatbot.get_user())
     # 总结输出
     if response == f"[Local Message] 等待{model_name}响应中 ...":
         response = f"[Local Message] {model_name}响应异常 ..."

request_llms/bridge_taichu.py

@@ -68,5 +68,5 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
         chatbot[-1] = [inputs, response]
         yield from update_ui(chatbot=chatbot, history=history)
     history.extend([inputs, response])
-    log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=response)
+    log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=response, user_name=chatbot.get_user())
     yield from update_ui(chatbot=chatbot, history=history)

request_llms/bridge_zhipu.py

@@ -97,5 +97,5 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
         chatbot[-1] = [inputs, response]
         yield from update_ui(chatbot=chatbot, history=history)
     history.extend([inputs, response])
-    log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=response)
+    log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=response, user_name=chatbot.get_user())
     yield from update_ui(chatbot=chatbot, history=history)

tests/test_plugins.py

@@ -36,7 +36,7 @@ if __name__ == "__main__":
 
     # plugin_test(plugin='crazy_functions.SourceCode_Analyse->解析一个C项目', main_input="crazy_functions/test_project/cpp/cppipc")
 
-    # plugin_test(plugin='crazy_functions.Latex_Project_Polish->Latex英文润色', main_input="crazy_functions/test_project/latex/attention")
+    # plugin_test(plugin='crazy_functions.Latex全文润色->Latex英文润色', main_input="crazy_functions/test_project/latex/attention")
 
     # plugin_test(plugin='crazy_functions.Markdown_Translate->Markdown中译英', main_input="README.md")
 
@@ -65,3 +65,8 @@ if __name__ == "__main__":
 
     # plugin_test(plugin='crazy_functions.Latex_Function->Latex翻译中文并重新编译PDF', main_input="2210.03629")
 
+    # advanced_arg = {"advanced_arg":"--llm_to_learn=gpt-3.5-turbo --prompt_prefix='根据下面的服装类型提示，想象一个穿着者，对这个人外貌、身处的环境、内心世界、人设进行描写。要求：100字以内，用第二人称。' --system_prompt=''" }
+    # plugin_test(plugin='crazy_functions.chatglm微调工具->微调数据集生成', main_input='build/dev.json', advanced_arg=advanced_arg)
+
+    # advanced_arg = {"advanced_arg":"--pre_seq_len=128 --learning_rate=2e-2 --num_gpus=1 --json_dataset='t_code.json' --ptuning_directory='/home/hmp/ChatGLM2-6B/ptuning'     " }
+    # plugin_test(plugin='crazy_functions.chatglm微调工具->启动微调', main_input='build/dev.json', advanced_arg=advanced_arg)

toolbox.py

@@ -1029,7 +1029,7 @@ def check_repeat_upload(new_pdf_path, pdf_hash):
     # 如果所有页的内容都相同，返回 True
     return False, None
 
-def log_chat(llm_model: str, input_str: str, output_str: str):
+def log_chat(llm_model: str, input_str: str, output_str: str, user_name: str=default_user_name):
     try:
         if output_str and input_str and llm_model:
             uid = str(uuid.uuid4().hex)
@@ -1038,8 +1038,8 @@ def log_chat(llm_model: str, input_str: str, output_str: str):
             logger.bind(chat_msg=True).info(dedent(
             """
             ╭──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
-            [UID]
-            {uid}
+            [UID/USER]
+            {uid}/{user_name}
             [Model]
             {llm_model}
             [Query]
@@ -1047,6 +1047,6 @@ def log_chat(llm_model: str, input_str: str, output_str: str):
             [Response]
             {output_str}
             ╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
-            """).format(uid=uid, llm_model=llm_model, input_str=input_str, output_str=output_str))
+            """).format(uid=uid, user_name=user_name, llm_model=llm_model, input_str=input_str, output_str=output_str))
     except:
         logger.error(trimmed_format_exc())