log user name during chat

.gitignore

@@ -161,3 +161,5 @@ temp.*
 objdump*
 *.min.*.js
 TODO
+experimental_mods
+search_results

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)

shared_utils/handle_upload.py

@@ -104,17 +104,27 @@ def extract_archive(file_path, dest_dir):
             logger.info("Successfully extracted zip archive to {}".format(dest_dir))
 
     elif file_extension in [".tar", ".gz", ".bz2"]:
-        with tarfile.open(file_path, "r:*") as tarobj:
-            # 清理提取路径，移除任何不安全的元素
-            for member in tarobj.getmembers():
-                member_path = os.path.normpath(member.name)
-                full_path = os.path.join(dest_dir, member_path)
-                full_path = os.path.abspath(full_path)
-                if not full_path.startswith(os.path.abspath(dest_dir) + os.sep):
-                    raise Exception(f"Attempted Path Traversal in {member.name}")
+        try:
+            with tarfile.open(file_path, "r:*") as tarobj:
+                # 清理提取路径，移除任何不安全的元素
+                for member in tarobj.getmembers():
+                    member_path = os.path.normpath(member.name)
+                    full_path = os.path.join(dest_dir, member_path)
+                    full_path = os.path.abspath(full_path)
+                    if not full_path.startswith(os.path.abspath(dest_dir) + os.sep):
+                        raise Exception(f"Attempted Path Traversal in {member.name}")
 
-            tarobj.extractall(path=dest_dir)
-            logger.info("Successfully extracted tar archive to {}".format(dest_dir))
+                tarobj.extractall(path=dest_dir)
+                logger.info("Successfully extracted tar archive to {}".format(dest_dir))
+        except tarfile.ReadError as e:
+            if file_extension == ".gz":
+                # 一些特别奇葩的项目，是一个gz文件，里面不是tar，只有一个tex文件
+                import gzip
+                with gzip.open(file_path, 'rb') as f_in:
+                    with open(os.path.join(dest_dir, 'main.tex'), 'wb') as f_out:
+                        f_out.write(f_in.read())
+            else:
+                raise e
 
     # 第三方库，需要预先pip install rarfile
     # 此外，Windows上还需要安装winrar软件，配置其Path环境变量，如"C:\Program Files\WinRAR"才可以

shared_utils/key_pattern_manager.py

@@ -14,6 +14,7 @@ openai_regex = re.compile(
     r"sk-[a-zA-Z0-9_-]{92}$|" +
     r"sk-proj-[a-zA-Z0-9_-]{48}$|"+
     r"sk-proj-[a-zA-Z0-9_-]{124}$|"+
+    r"sk-proj-[a-zA-Z0-9_-]{156}$|"+ #新版apikey位数不匹配故修改此正则表达式
     r"sess-[a-zA-Z0-9]{40}$"
 )
 def is_openai_api_key(key):

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())