chore: bump version 4.3.8

pkg/pipeline/pipelinemgr.py

@@ -213,7 +213,7 @@ class RuntimePipeline:
             await self._execute_from_stage(0, query)
         except Exception as e:
             inst_name = query.current_stage_name if query.current_stage_name else 'unknown'
-            self.ap.logger.error(f'处理请求时出错 query_id={query.query_id} stage={inst_name} : {e}')
+            self.ap.logger.error(f'Error processing query {query.query_id} stage={inst_name} : {e}')
             self.ap.logger.error(f'Traceback: {traceback.format_exc()}')
         finally:
             self.ap.logger.debug(f'Query {query.query_id} processed')

pkg/pipeline/preproc/preproc.py

@@ -35,11 +35,17 @@ class PreProcessor(stage.PipelineStage):
         session = await self.ap.sess_mgr.get_session(query)
 
         # When not local-agent, llm_model is None
-        llm_model = (
-            await self.ap.model_mgr.get_model_by_uuid(query.pipeline_config['ai']['local-agent']['model'])
-            if selected_runner == 'local-agent'
-            else None
-        )
+        try:
+            llm_model = (
+                await self.ap.model_mgr.get_model_by_uuid(query.pipeline_config['ai']['local-agent']['model'])
+                if selected_runner == 'local-agent'
+                else None
+            )
+        except ValueError:
+            self.ap.logger.warning(
+                f'LLM model {query.pipeline_config["ai"]["local-agent"]["model"] + " "}not found or not configured'
+            )
+            llm_model = None
 
         conversation = await self.ap.sess_mgr.get_conversation(
             query,
@@ -54,7 +60,7 @@ class PreProcessor(stage.PipelineStage):
         query.prompt = conversation.prompt.copy()
         query.messages = conversation.messages.copy()
 
-        if selected_runner == 'local-agent':
+        if selected_runner == 'local-agent' and llm_model:
             query.use_funcs = []
             query.use_llm_model_uuid = llm_model.model_entity.uuid
 
@@ -72,7 +78,11 @@ class PreProcessor(stage.PipelineStage):
 
         # Check if this model supports vision, if not, remove all images
         # TODO this checking should be performed in runner, and in this stage, the image should be reserved
-        if selected_runner == 'local-agent' and not llm_model.model_entity.abilities.__contains__('vision'):
+        if (
+            selected_runner == 'local-agent'
+            and llm_model
+            and not llm_model.model_entity.abilities.__contains__('vision')
+        ):
             for msg in query.messages:
                 if isinstance(msg.content, list):
                     for me in msg.content:
@@ -89,7 +99,9 @@ class PreProcessor(stage.PipelineStage):
                 content_list.append(provider_message.ContentElement.from_text(me.text))
                 plain_text += me.text
             elif isinstance(me, platform_message.Image):
-                if selected_runner != 'local-agent' or llm_model.model_entity.abilities.__contains__('vision'):
+                if selected_runner != 'local-agent' or (
+                    llm_model and llm_model.model_entity.abilities.__contains__('vision')
+                ):
                     if me.base64 is not None:
                         content_list.append(provider_message.ContentElement.from_image_base64(me.base64))
             elif isinstance(me, platform_message.File):
@@ -100,7 +112,9 @@ class PreProcessor(stage.PipelineStage):
                     if isinstance(msg, platform_message.Plain):
                         content_list.append(provider_message.ContentElement.from_text(msg.text))
                     elif isinstance(msg, platform_message.Image):
-                        if selected_runner != 'local-agent' or llm_model.model_entity.abilities.__contains__('vision'):
+                        if selected_runner != 'local-agent' or (
+                            llm_model and llm_model.model_entity.abilities.__contains__('vision')
+                        ):
                             if msg.base64 is not None:
                                 content_list.append(provider_message.ContentElement.from_image_base64(msg.base64))
 

pkg/platform/sources/wechatpad.py

@@ -139,7 +139,7 @@ class WeChatPadMessageConverter(abstract_platform_adapter.AbstractMessageConvert
             pattern = r'@\S{1,20}'
             content_no_preifx = re.sub(pattern, '', content_no_preifx)
 
-        return platform_message.MessageChain([platform_message.Plain(content_no_preifx)])
+        return platform_message.MessageChain([platform_message.Plain(text=content_no_preifx)])
 
     async def _handler_image(self, message: Optional[dict], content_no_preifx: str) -> platform_message.MessageChain:
         """处理图像消息 (msg_type=3)"""
@@ -265,7 +265,7 @@ class WeChatPadMessageConverter(abstract_platform_adapter.AbstractMessageConvert
             # 文本消息
             try:
                 if '<msg>' not in quote_data:
-                    quote_data_message_list.append(platform_message.Plain(quote_data))
+                    quote_data_message_list.append(platform_message.Plain(text=quote_data))
                 else:
                     # 引用消息展开
                     quote_data_xml = ET.fromstring(quote_data)
@@ -280,7 +280,7 @@ class WeChatPadMessageConverter(abstract_platform_adapter.AbstractMessageConvert
                         quote_data_message_list.extend(await self._handler_compound(None, quote_data))
             except Exception as e:
                 self.logger.error(f'处理引用消息异常 expcetion:{e}')
-                quote_data_message_list.append(platform_message.Plain(quote_data))
+                quote_data_message_list.append(platform_message.Plain(text=quote_data))
             message_list.append(
                 platform_message.Quote(
                     sender_id=sender_id,
@@ -290,7 +290,7 @@ class WeChatPadMessageConverter(abstract_platform_adapter.AbstractMessageConvert
             if len(user_data) > 0:
                 pattern = r'@\S{1,20}'
                 user_data = re.sub(pattern, '', user_data)
-                message_list.append(platform_message.Plain(user_data))
+                message_list.append(platform_message.Plain(text=user_data))
 
         return platform_message.MessageChain(message_list)
 
@@ -543,7 +543,6 @@ class WeChatPadAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter)
     ] = {}
 
     def __init__(self, config: dict, logger: EventLogger):
-
         quart_app = quart.Quart(__name__)
 
         message_converter = WeChatPadMessageConverter(config, logger)
@@ -551,15 +550,14 @@ class WeChatPadAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter)
         bot = WeChatPadClient(config['wechatpad_url'], config['token'])
         super().__init__(
             config=config,
-            logger = logger,
-            quart_app = quart_app,
-            message_converter =message_converter,
-            event_converter = event_converter,
+            logger=logger,
+            quart_app=quart_app,
+            message_converter=message_converter,
+            event_converter=event_converter,
             listeners={},
-            bot_account_id ='',
-            name="WeChatPad",
+            bot_account_id='',
+            name='WeChatPad',
             bot=bot,
-
         )
 
     async def ws_message(self, data):

pkg/plugin/connector.py

@@ -215,7 +215,7 @@ class PluginRuntimeConnector:
         if not self.is_enable_plugin:
             return event_ctx
 
-        event_ctx_result = await self.handler.emit_event(event_ctx.model_dump(serialize_as_any=True))
+        event_ctx_result = await self.handler.emit_event(event_ctx.model_dump(serialize_as_any=False))
 
         event_ctx = context.EventContext.model_validate(event_ctx_result['event_context'])
 

pkg/utils/constants.py

@@ -1,4 +1,4 @@
-semantic_version = 'v4.3.7'
+semantic_version = 'v4.3.8'
 
 required_database_version = 8
 """Tag the version of the database schema, used to check if the database needs to be migrated"""

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "langbot"
-version = "4.3.7"
+version = "4.3.8"
 description = "Easy-to-use global IM bot platform designed for LLM era"
 readme = "README.md"
 requires-python = ">=3.10.1,<4.0"