feat: improve agent runtime observability

backend-fastapi/ai_platform/services/agent_service.py

@@ -43,6 +43,16 @@ class AgentService:
     def _attach_agent_collaboration(self, event: Dict[str, Any], agent: Agent) -> Dict[str, Any]:
         enriched = dict(event)
         collaboration = dict(enriched.get("collaboration") or {})
+        for key in (
+            "model_id",
+            "model",
+            "model_name",
+            "provider_id",
+            "provider_name",
+            "provider_type",
+        ):
+            if enriched.get(key):
+                collaboration.setdefault(key, enriched[key])
         for key, value in self._agent_collaboration_metadata(agent).items():
             collaboration.setdefault(key, value)
         enriched["collaboration"] = collaboration
@@ -55,6 +65,10 @@ class AgentService:
             "collaboration_role",
             "collaboration_mode",
             "model_id",
+            "model",
+            "model_name",
+            "provider_name",
+            "provider_type",
         ):
             if collaboration.get(key):
                 actor.setdefault(key, collaboration[key])
@@ -247,6 +261,34 @@ class AgentService:
             model.id,
         )
         return str(model.id)
+
+    async def _get_model_runtime_meta(self, model_id: Optional[str]) -> Dict[str, Any]:
+        if not self._db or not model_id:
+            return {}
+
+        result = await self._db.execute(
+            select(LLMModel, LLMProvider)
+            .join(LLMProvider, LLMProvider.id == LLMModel.provider_id)
+            .where(
+                LLMModel.id == model_id,
+                LLMModel.is_deleted == False,
+                LLMProvider.is_deleted == False,
+            )
+        )
+        row = result.first()
+        if not row:
+            return {"model_id": str(model_id)}
+
+        model, provider = row[0], row[1]
+        display_model = model.display_name or model.model_name or str(model.id)
+        return {
+            "model_id": str(model.id),
+            "model": display_model,
+            "model_name": model.model_name or display_model,
+            "provider_id": str(provider.id),
+            "provider_name": provider.name or provider.provider_type,
+            "provider_type": provider.provider_type,
+        }
     
     async def _chat_autonomous_function_calling(
         self,
@@ -264,6 +306,8 @@ class AgentService:
         from ai_platform.providers.base import LLMMessage
         
         start_time = time.time()
+        model_id: Optional[str] = None
+        model_meta: Dict[str, Any] = {}
         
         if not self._db:
             yield {'type': 'error', 'content': '数据库会话未初始化'}
@@ -315,11 +359,13 @@ class AgentService:
             model_id = await self._resolve_agent_model_id(agent)
             if not model_id:
                 raise ValueError('未找到可用的 chat 模型，请先在模型配置中启用一个模型')
+            model_meta = await self._get_model_runtime_meta(model_id)
 
             event = self._attach_agent_collaboration({
                 'type': 'thought',
                 'content': '智能体开始分析用户需求并调用默认 chat 模型',
                 'model_id': model_id,
+                **model_meta,
             }, agent)
             step = self._event_to_reasoning_step(event)
             if step:
@@ -390,6 +436,7 @@ class AgentService:
             model_id = await self._resolve_agent_model_id(agent)
             if not model_id:
                 raise ValueError('未找到可用的 chat 模型，请先在模型配置中启用一个模型')
+            model_meta = await self._get_model_runtime_meta(model_id)
             
             total_tokens = 0
             final_answer = ''
@@ -410,6 +457,7 @@ class AgentService:
                             'content': chunk.content,
                             'accumulated_content': accumulated_content,
                             'model_id': model_id,
+                            **model_meta,
                         }
                     
                     if chunk.is_finished:
@@ -431,6 +479,7 @@ class AgentService:
                     'type': 'answer',
                     'content': final_answer,
                     'model_id': model_id,
+                    **model_meta,
                 }
             
             # 更新助手消息
@@ -461,21 +510,20 @@ class AgentService:
                 'conversation_id': str(conversation.id),
                 'tokens_used': total_tokens,
                 'elapsed_time': elapsed_time,
+                **model_meta,
             }
             
         except Exception as e:
             logger.exception(f'Agent chat error: {e}')
             assistant_msg.status = 'failed'
             assistant_msg.error_message = str(e)
-            error_step = self._event_to_reasoning_step({'type': 'error', 'content': str(e)})
+            error_event = {'type': 'error', 'content': str(e), **model_meta}
+            error_step = self._event_to_reasoning_step(error_event)
             if error_step:
                 reasoning_steps.append(error_step)
                 assistant_msg.reasoning_steps = reasoning_steps.copy()
             await self._db.commit()
-            yield {
-                'type': 'error',
-                'content': str(e),
-            }
+            yield error_event
     
     async def _chat_autonomous_react(
         self,