feat：All Chinese characters in the code have been converted to English.

Author: Liwenru88

src/backend/bisheng/__init__.py

@@ -6,7 +6,7 @@
 # from bisheng.processing.process import load_flow_from_json  # noqa: E402
 
 try:
-    # 通过ci去自动修改
+    # SetujuciGo to automatic modification
     __version__ = '2.3.0-beta3'
 except metadata.PackageNotFoundError:
     # Case where package metadata is not available.

src/backend/bisheng/api/services/assistant.py

@@ -32,7 +32,7 @@
 
 
 class AssistantAgent(AssistantUtils):
-    # cohere的模型需要的特殊prompt
+    # cohereThe special needs of the model prompt
     ASSISTANT_PROMPT_COHERE = """{preamble}|<instruct>|Carefully perform the following instructions, in order, starting each with a new line.
     Firstly, You may need to use complex and advanced reasoning to complete your task and answer the question. Think about how you can use the provided tools to answer the question and come up with a high level plan you will execute.
     Write 'Plan:' followed by an initial high level plan of how you will solve the problem including the tools and steps required.
@@ -47,7 +47,7 @@ class AssistantAgent(AssistantUtils):
 
     Additional instructions to note:
     - If the user's question is in Chinese, please answer it in Chinese.
-    - 当问题中有涉及到时间信息时，比如最近6个月、昨天、去年等，你需要用时间工具查询时间信息。
+    - When there is time information involved in a question, such as recently6Months, yesterday, last year, etc., you need to use the time tool to query the time information.
     """  # noqa
 
     def __init__(self, assistant_info: Assistant, chat_id: str, invoke_user_id: int):
@@ -67,7 +67,7 @@ def __init__(self, assistant_info: Assistant, chat_id: str, invoke_user_id: int)
         self.current_agent_executor = None
         self.llm: BaseLanguageModel | None = None
         self.llm_agent_executor = None
-        # 知识库检索相关参数
+        # Knowledge Base Retrieval Related Parameters
         self.knowledge_retriever = {'max_content': 15000, 'sort_by_source_and_index': False}
 
     async def init_assistant(self, callbacks: Callbacks = None):
@@ -76,7 +76,7 @@ async def init_assistant(self, callbacks: Callbacks = None):
         await self.init_agent()
 
     async def init_llm(self):
-        # 获取配置的助手模型列表
+        # Get a list of configured helper models
         assistant_llm = await LLMService.get_assistant_llm()
         if not assistant_llm.llm_list:
             raise AssistantModelEmptyError()
@@ -96,7 +96,7 @@ async def init_llm(self):
             'sort_by_source_and_index': default_llm.knowledge_sort_index
         }
 
-        # 初始化llm
+        # Inisialisasillm
         self.llm = await LLMService.get_bisheng_llm(model_id=default_llm.model_id,
                                                     temperature=self.assistant.temperature,
                                                     streaming=default_llm.streaming,
@@ -106,7 +106,7 @@ async def init_llm(self):
                                                     user_id=self.invoke_user_id)
 
     async def init_auto_update_llm(self):
-        """ 初始化自动优化prompt等信息的llm实例 """
+        """ Initialize Automatic Optimization prompt and other information.llmInstances """
         assistant_llm = await LLMService.get_assistant_llm()
         if not assistant_llm.auto_llm:
             raise AssistantAutoLLMError()
@@ -120,7 +120,7 @@ async def init_auto_update_llm(self):
                                                     user_id=self.invoke_user_id)
 
     async def init_tools(self, callbacks: Callbacks = None):
-        """通过名称获取tool 列表
+        """Get by nametool Vertical
            tools_name_param:: {name: params}
         """
         links: List[AssistantLink] = await AssistantLinkDao.get_assistant_link(
@@ -191,38 +191,38 @@ async def init_tools(self, callbacks: Callbacks = None):
 
     async def init_agent(self):
         """
-        初始化智能体的agent
+        Initialize agentagent
         """
-        # 引入agent执行参数
+        # Introductionagentexecution parameter
         agent_executor_type = self.llm_agent_executor
         self.current_agent_executor = agent_executor_type
-        # 做转换
+        # Do the Conversion
         agent_executor_type = self.agent_executor_dict.get(agent_executor_type,
                                                            agent_executor_type)
 
         prompt = self.assistant.prompt
         if getattr(self.llm, 'model_name', '').startswith('command-r'):
             prompt = self.ASSISTANT_PROMPT_COHERE.format(preamble=prompt)
         if self.current_agent_executor == 'ReAct':
-            # 初始化agent
+            # Inisialisasiagent
             self.agent = ConfigurableAssistant(agent_executor_type=agent_executor_type,
                                                tools=self.tools,
                                                llm=self.llm,
                                                assistant_message=prompt)
         else:
-            # function-calling模式，也添加递归限制
+            # function-callingpattern, but also add recursive constraints
             logger.info(f'Creating LangGraph agent with {len(self.tools)} tools, llm type: {type(self.llm)}')
             logger.info(f'LLM streaming capability: {getattr(self.llm, "streaming", "unknown")}')
 
             self.agent = create_react_agent(self.llm, self.tools, prompt=prompt, checkpointer=False)
             logger.info(f'LangGraph agent created: {type(self.agent)}')
 
-            # 为agent添加递归限制配置
+            # areagentAdd Recursive Limit Configuration
             self.agent = self.agent.with_config({'recursion_limit': 100})
             logger.info(f'Agent config applied: recursion_limit=100')
 
     async def optimize_assistant_prompt(self):
-        """ 自动优化生成prompt """
+        """ Automatically optimize generationprompt """
         chain = ({
                      'assistant_name': lambda x: x['assistant_name'],
                      'assistant_description': lambda x: x['assistant_description'],
@@ -239,16 +239,16 @@ def sync_optimize_assistant_prompt(self):
         return optimize_assistant_prompt(self.llm, self.assistant.name, self.assistant.desc)
 
     def generate_guide(self, prompt: str):
-        """ 生成开场对话和开场问题 """
+        """ Generate opening dialogue and opening questions """
         return generate_opening_dialog(self.llm, prompt)
 
     def generate_description(self, prompt: str):
-        """ 生成描述对话 """
+        """ Generate description dialog """
         return generate_breif_description(self.llm, prompt)
 
     def choose_tools(self, tool_list: List[Dict[str, str]], prompt: str) -> List[str]:
         """
-         选择工具
+         Choose A Tool
          tool_list: [{name: xxx, description: xxx}]
         """
         tool_list = [
@@ -261,7 +261,7 @@ def choose_tools(self, tool_list: List[Dict[str, str]], prompt: str) -> List[str
     async def fake_callback(self, callback: Callbacks):
         if not callback:
             return
-        # 假回调，将已下线的技能回调给前端
+        # False callback to call back skills that are offline to the front-end
         for one in self.offline_flows:
             run_id = uuid.uuid4()
             await callback[0].on_tool_start({
@@ -272,7 +272,7 @@ async def fake_callback(self, callback: Callbacks):
             await callback[0].on_tool_end(output='flow is offline', name=one, run_id=run_id)
 
     async def record_chat_history(self, message: List[Any]):
-        # 记录助手的聊天历史
+        # Record Assistant Chat History
         if not os.getenv('BISHENG_RECORD_HISTORY'):
             return
         try:
@@ -292,11 +292,11 @@ async def record_chat_history(self, message: List[Any]):
             logger.error(f'record assistant history error: {str(e)}')
 
     async def trim_messages(self, messages: List[Any]) -> List[Any]:
-        # 获取encoding
+        # Dapatkanencoding
         enc = self.cl100k_base()
 
         def get_finally_message(new_messages: List[Any]) -> List[Any]:
-            # 修剪到只有一条记录则不再处理
+            # No more processing until only one record has been trimmed
             if len(new_messages) == 1:
                 return new_messages
             total_count = 0
@@ -319,7 +319,7 @@ def get_finally_message(new_messages: List[Any]) -> List[Any]:
 
     async def run(self, query: str, chat_history: List = None, callback: Callbacks = None) -> List[BaseMessage]:
         """
-        运行智能体对话
+        Run Agent Conversation
         """
         await self.fake_callback(callback)
 
@@ -338,14 +338,14 @@ async def run(self, query: str, chat_history: List = None, callback: Callbacks =
             result = await self.agent.ainvoke({'messages': inputs}, config=RunnableConfig(callbacks=callback))
             result = result['messages']
 
-        # 记录聊天历史
+        # Record Chat History
         await self.record_chat_history([one.to_json() for one in result])
 
         return result
 
     async def astream(self, query: str, chat_history: List = None, callback: Callbacks = None):
         """
-        运行智能体对话 - 流式版本
+        Run Agent Conversation - Streaming version
         """
         await self.fake_callback(callback)
 
@@ -359,13 +359,13 @@ async def astream(self, query: str, chat_history: List = None, callback: Callbac
         inputs = await self.trim_messages(inputs)
 
         if self.current_agent_executor == 'ReAct':
-            # ReAct模式暂时不支持流式，降级到非流式
+            # ReActMode temporarily does not support streaming, downgrade to non streaming
             result = await self.react_run(inputs, callback)
-            # 记录聊天历史
+            # Record Chat History
             await self.record_chat_history([one.to_json() for one in result])
             yield result
         else:
-            # 使用流式调用
+            # Use Streaming Calls
             config = RunnableConfig(callbacks=callback)
             final_messages = []
 
@@ -374,21 +374,21 @@ async def astream(self, query: str, chat_history: List = None, callback: Callbac
             chunk_count = 0
 
             try:
-                # 使用messages模式的LangGraph streaming获得token级别的流式输出
+                # UsemessagesPatternedLangGraph streamingattaintokenLevel of Streaming Output
                 async for chunk in self.agent.astream({'messages': inputs}, config=config, stream_mode="messages"):
                     chunk_count += 1
 
-                    # stream_mode="messages" 返回 (message, metadata) 元组
+                    # stream_mode="messages" Return (message, metadata) Meta Group
                     message = None
                     if isinstance(chunk, tuple) and len(chunk) >= 2:
                         message, metadata = chunk[:2]
                     elif hasattr(chunk, 'content'):
-                        # 直接是消息对象
+                        # Directly to the message object
                         message = chunk
 
                     if message:
-                        # stream_mode="messages"返回的是独立chunk，直接使用其内容
-                        final_messages = [message]  # 保存消息用于历史记录
+                        # stream_mode="messages"Returns Independencechunk, use its content directly
+                        final_messages = [message]  # Save message for history
                         yield [message]
 
             except Exception as astream_error:
@@ -400,12 +400,12 @@ async def astream(self, query: str, chat_history: List = None, callback: Callbac
             if chunk_count == 0:
                 logger.warning(f'No chunks received from agent.astream()! This indicates a streaming issue.')
 
-            # 记录聊天历史
+            # Record Chat History
             if final_messages:
                 await self.record_chat_history([one.to_json() for one in final_messages])
 
     async def react_run(self, inputs: List, callback: Callbacks = None):
-        """ react 模式的输入和执行 """
+        """ react Mode input and execution """
         result = await self.agent.ainvoke({
             'input': inputs[-1].content,
             'chat_history': inputs[:-1],

src/backend/bisheng/api/services/assistant_agent.py

@@ -5,7 +5,7 @@
 
 
 class AssistantUtils:
-    # 忽略助手配置已从系统配置中移除，暂不需要此类的方法
+    # Ignore assistant configuration has been removed from the system configuration, no such method is required for now
 
     @staticmethod
     def cl100k_base() -> TikTokenEncoding: