feat(llm): add json_output support for structured LLM responses

api/app/core/workflow/nodes/llm/config.py

@@ -116,6 +116,11 @@ class LLMNodeConfig(BaseNodeConfig):
         description="Top-p 采样参数"
     )
 
+    json_output: bool = Field(
+        default=False,
+        description="是否以 JSON 格式输出"
+    )
+
     frequency_penalty: float | None = Field(
         default=None,
         ge=-2.0,

api/app/core/workflow/nodes/llm/node.py

@@ -22,6 +22,7 @@ from app.db import get_db_context
 from app.models import ModelType
 from app.schemas.model_schema import ModelInfo
 from app.services.model_service import ModelConfigService
+from app.models.models_model import ModelProvider
 
 logger = logging.getLogger(__name__)
 
@@ -126,7 +127,11 @@ class LLMNode(BaseNode):
 
         # 4. 创建 LLM 实例（使用已提取的数据）
         # 注意：对于流式输出，需要在模型初始化时设置 streaming=True
-        extra_params = {"streaming": stream} if stream else {}
+        extra_params: dict[str, Any] = {"streaming": stream} if stream else {}
+        if self.typed_config.temperature is not None:
+            extra_params["temperature"] = self.typed_config.temperature
+        if self.typed_config.max_tokens is not None:
+            extra_params["max_tokens"] = self.typed_config.max_tokens
 
         llm = RedBearLLM(
             RedBearModelConfig(
@@ -135,7 +140,9 @@ class LLMNode(BaseNode):
                 api_key=model_info.api_key,
                 base_url=model_info.api_base,
                 extra_params=extra_params,
-                is_omni=model_info.is_omni
+                is_omni=model_info.is_omni,
+                capability=model_info.capability,
+                json_output=self.typed_config.json_output,
             ),
             type=model_info.model_type
         )
@@ -218,6 +225,16 @@ class LLMNode(BaseNode):
             rendered = self._render_template(prompt_template, variable_pool)
             self.messages = [{"role": "user", "content": rendered}]
 
+        # ChatTongyi 要求 messages 含 'json' 字样才能使用 response_format，在 system prompt 中注入
+        if (self.typed_config.json_output
+                and model_info.provider.lower() == ModelProvider.DASHSCOPE
+                and not model_info.is_omni):
+            system_msg = next((m for m in self.messages if m["role"] == "system"), None)
+            if system_msg:
+                system_msg["content"] += "\n请以JSON格式输出。"
+            else:
+                self.messages.insert(0, {"role": "system", "content": "请以JSON格式输出。"})
+
         return llm
 
     async def execute(self, state: WorkflowState, variable_pool: VariablePool) -> AIMessage: