feat: enhance token management and normalize content handling in chat model

apps/application/chat_pipeline/step/chat_step/impl/base_chat_step.py

@@ -93,6 +93,7 @@ def event_content(response,
                 reasoning_content_chunk = chunk.additional_kwargs.get('reasoning_content', '')
             else:
                 reasoning_content_chunk = reasoning_chunk.get('reasoning_content')
+            content_chunk = reasoning._normalize_content(content_chunk)
             all_text += content_chunk
             if reasoning_content_chunk is None:
                 reasoning_content_chunk = ''
@@ -191,13 +192,15 @@ class BaseChatStep(IChatStep):
                                        manage, padding_problem_text, chat_user_id, chat_user_type,
                                        no_references_setting,
                                        model_setting,
-                                       mcp_enable, mcp_tool_ids, mcp_servers, mcp_source, tool_enable, tool_ids, mcp_output_enable)
+                                       mcp_enable, mcp_tool_ids, mcp_servers, mcp_source, tool_enable, tool_ids,
+                                       mcp_output_enable)
         else:
             return self.execute_block(message_list, chat_id, problem_text, post_response_handler, chat_model,
                                       paragraph_list,
                                       manage, padding_problem_text, chat_user_id, chat_user_type, no_references_setting,
                                       model_setting,
-                                      mcp_enable, mcp_tool_ids, mcp_servers, mcp_source, tool_enable, tool_ids, mcp_output_enable)
+                                      mcp_enable, mcp_tool_ids, mcp_servers, mcp_source, tool_enable, tool_ids,
+                                      mcp_output_enable)
 
     def get_details(self, manage, **kwargs):
         return {
@@ -264,7 +267,6 @@ class BaseChatStep(IChatStep):
 
         return None
 
-
     def get_stream_result(self, message_list: List[BaseMessage],
                           chat_model: BaseChatModel = None,
                           paragraph_list=None,
@@ -294,7 +296,8 @@ class BaseChatStep(IChatStep):
         else:
             # 处理 MCP 请求
             mcp_result = self._handle_mcp_request(
-                mcp_enable, tool_enable, mcp_source, mcp_servers, mcp_tool_ids, tool_ids, mcp_output_enable, chat_model, message_list,
+                mcp_enable, tool_enable, mcp_source, mcp_servers, mcp_tool_ids, tool_ids, mcp_output_enable, chat_model,
+                message_list,
             )
             if mcp_result:
                 return mcp_result, True
@@ -319,7 +322,8 @@ class BaseChatStep(IChatStep):
                        tool_ids=None,
                        mcp_output_enable=True):
         chat_result, is_ai_chat = self.get_stream_result(message_list, chat_model, paragraph_list,
-                                                         no_references_setting, problem_text, mcp_enable, mcp_tool_ids, mcp_servers, mcp_source, tool_enable, tool_ids,
+                                                         no_references_setting, problem_text, mcp_enable, mcp_tool_ids,
+                                                         mcp_servers, mcp_source, tool_enable, tool_ids,
                                                          mcp_output_enable)
         chat_record_id = uuid.uuid7()
         r = StreamingHttpResponse(
@@ -394,7 +398,9 @@ class BaseChatStep(IChatStep):
         # 调用模型
         try:
             chat_result, is_ai_chat = self.get_block_result(message_list, chat_model, paragraph_list,
-                                                            no_references_setting, problem_text, mcp_enable, mcp_tool_ids, mcp_servers, mcp_source, tool_enable, tool_ids, mcp_output_enable)
+                                                            no_references_setting, problem_text, mcp_enable,
+                                                            mcp_tool_ids, mcp_servers, mcp_source, tool_enable,
+                                                            tool_ids, mcp_output_enable)
             if is_ai_chat:
                 request_token = chat_model.get_num_tokens_from_messages(message_list)
                 response_token = chat_model.get_num_tokens(chat_result.content)

apps/application/flow/tools.py

@@ -48,6 +48,21 @@ class Reasoning:
             return r
         return {'content': '', 'reasoning_content': ''}
 
+    def _normalize_content(self, content):
+        """将不同类型的内容统一转换为字符串"""
+        if isinstance(content, str):
+            return content
+        elif isinstance(content, list):
+            # 处理包含多种内容类型的列表
+            normalized_parts = []
+            for item in content:
+                if isinstance(item, dict):
+                    if item.get('type') == 'text':
+                        normalized_parts.append(item.get('text', ''))
+            return ''.join(normalized_parts)
+        else:
+            return str(content)
+
     def get_reasoning_content(self, chunk):
         # 如果没有开始思考过程标签那么就全是结果
         if self.reasoning_content_start_tag is None or len(self.reasoning_content_start_tag) == 0:
@@ -56,6 +71,7 @@ class Reasoning:
         # 如果没有结束思考过程标签那么就全部是思考过程
         if self.reasoning_content_end_tag is None or len(self.reasoning_content_end_tag) == 0:
             return {'content': '', 'reasoning_content': chunk.content}
+        chunk.content = self._normalize_content(chunk.content)
         self.all_content += chunk.content
         if not self.reasoning_content_is_start and len(self.all_content) >= self.reasoning_content_start_tag_len:
             if self.all_content.startswith(self.reasoning_content_start_tag):
@@ -202,6 +218,7 @@ def to_stream_response_simple(stream_event):
     r['Cache-Control'] = 'no-cache'
     return r
 
+
 tool_message_json_template = """
 ```json
 %s

apps/models_provider/impl/azure_model_provider/credential/llm.py

@@ -66,7 +66,8 @@ class AzureLLMModelCredential(BaseForm, BaseModelCredential):
                     return False
         try:
             model = provider.get_model(model_type, model_name, model_credential, **model_params)
-            model.invoke([HumanMessage(content=gettext('Hello'))])
+            res = model.invoke([HumanMessage(content=gettext('Hello'))])
+            print( res)
         except Exception as e:
             traceback.print_exc()
             if isinstance(e, AppApiException) or isinstance(e, BadRequestError):

apps/models_provider/impl/azure_model_provider/model/azure_chat_model.py

@@ -7,9 +7,11 @@
     @desc:
 """
 
-from typing import List, Dict
+from typing import List, Dict, Optional, Any
 
+from langchain_core.language_models import LanguageModelInput
 from langchain_core.messages import BaseMessage, get_buffer_string
+from langchain_core.runnables import RunnableConfig
 from langchain_openai import AzureChatOpenAI
 
 from common.config.tokenizer_manage_config import TokenizerManage
@@ -36,16 +38,42 @@ class AzureChatModel(MaxKBBaseModel, AzureChatOpenAI):
             streaming=True,
         )
 
+    def get_last_generation_info(self) -> Optional[Dict[str, Any]]:
+        return self.__dict__.get('_last_generation_info')
+
     def get_num_tokens_from_messages(self, messages: List[BaseMessage]) -> int:
         try:
-            return super().get_num_tokens_from_messages(messages)
+            return self.get_last_generation_info().get('input_tokens', 0)
         except Exception as e:
             tokenizer = TokenizerManage.get_tokenizer()
             return sum([len(tokenizer.encode(get_buffer_string([m]))) for m in messages])
 
     def get_num_tokens(self, text: str) -> int:
         try:
-            return super().get_num_tokens(text)
+            return self.get_last_generation_info().get('output_tokens', 0)
         except Exception as e:
             tokenizer = TokenizerManage.get_tokenizer()
             return len(tokenizer.encode(text))
+
+    def invoke(
+            self,
+            input: LanguageModelInput,
+            config: Optional[RunnableConfig] = None,
+            *,
+            stop: Optional[list[str]] = None,
+            **kwargs: Any,
+    ) -> BaseMessage:
+        message = super().invoke(input, config, stop=stop, **kwargs)
+        if isinstance(message.content, str):
+            return message
+        elif isinstance(message.content, list):
+            # 构造新的响应消息返回
+            content = message.content
+            normalized_parts = []
+            for item in content:
+                if isinstance(item, dict):
+                    if item.get('type') == 'text':
+                        normalized_parts.append(item.get('text', ''))
+            message.content = ''.join(normalized_parts)
+            self.__dict__.setdefault('_last_generation_info', {}).update(message.usage_metadata)
+            return message