feat(workflow): support context injection in LLM node

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

@@ -5,11 +5,13 @@ LLM 节点实现
 """
 
 import logging
+import re
 from typing import Any
 from langchain_core.messages import AIMessage, SystemMessage, HumanMessage
 
 from app.core.workflow.nodes.base_node import BaseNode, WorkflowState
 from app.core.models import RedBearLLM, RedBearModelConfig
+from app.core.workflow.nodes.llm.config import LLMNodeConfig
 from app.db import get_db_context
 from app.models import ModelType
 from app.services.model_service import ModelConfigService
@@ -63,6 +65,13 @@ class LLMNode(BaseNode):
     - user/human: 用户消息（HumanMessage）
     - ai/assistant: AI 消息（AIMessage）
     """
+    def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any]):
+        super().__init__(node_config, workflow_config)
+        self.typed_config = LLMNodeConfig(**self.config)
+
+    def _render_context(self, message,state):
+        context = f"<context>{self._render_template(self.typed_config.context, state)}</context>"
+        return re.sub(r"{{context}}", context, message)
 
     def _prepare_llm(self, state: WorkflowState, stream: bool = False) -> tuple[RedBearLLM, list | str]:
         """准备 LLM 实例（公共逻辑）
@@ -83,6 +92,7 @@ class LLMNode(BaseNode):
             for msg_config in messages_config:
                 role = msg_config.get("role", "user").lower()
                 content_template = msg_config.get("content", "")
+                content_template = self._render_context(content_template, state)
                 content = self._render_template(content_template, state)
 
                 # 根据角色创建对应的消息对象