feat: 完成资源接入

app/processor/nodes/action_node.py

@@ -3,12 +3,15 @@
 import json
 
 from langchain_core.messages import HumanMessage, SystemMessage
+from langchain_core.exceptions import OutputParserException
+from pydantic import BaseModel, ValidationError
 
 from app.clients.llm import get_llm
 from app.processor.base import BaseNode
 from app.processor.state import QueryState
 from app.prompt.action import ACTION_SYSTEM_PROMPT
 from app.schemas.actions import ActionBatch
+from app.schemas.action_validation import ActionValidationError, schema_errors
 
 
 def can_generate_actions(state: QueryState) -> bool:
@@ -38,57 +41,63 @@ class ActionNode(BaseNode):
     name = "action_node"
 
     async def process(self, state: QueryState) -> QueryState:
+        updated_state: QueryState = state.copy()
         if not can_generate_actions(state):
             raise ValueError(
                 "ActionNode 当前只支持无需规划和资源的 move、resize、update 需求"
             )
-        batch = await get_llm(ActionBatch).ainvoke(
-            [
-                SystemMessage(content=ACTION_SYSTEM_PROMPT),
-                HumanMessage(
-                    content=json.dumps(
-                        {
-                            "input_context": state["input_context"].model_dump(
-                                mode="json"
-                            ),
-                            "requirement": state["requirement"].model_dump(mode="json"),
-                            "change_scope": state["change_scope"].model_dump(
-                                mode="json"
-                            ),
-                        },
-                        ensure_ascii=False,
-                    )
-                ),
-            ]
-        )
-        batch = ActionBatch.model_validate(batch.model_dump())
-        allowed = {}
-        for change in state["requirement"].changes:
-            for target_id in change.target_ids:
-                allowed.setdefault(target_id, set()).add(change.operation)
-        existing = {node.id for node in state["input_context"].screen.nodes}
-        for action in batch.actions:
-            if action.target_id not in existing:
-                raise ValueError(f"动作引用了不存在的组件: {action.target_id}")
-            if action.type not in allowed.get(action.target_id, set()):
-                raise ValueError(
-                    f"动作超出需求范围: {action.target_id} / {action.type}"
-                )
-        expected = {
-            (target, operation) for target, ops in allowed.items() for operation in ops
+        payload = {
+            "input_context": state["input_context"].model_dump(mode="json"),
+            "requirement": state["requirement"].model_dump(mode="json"),
+            "change_scope": state["change_scope"].model_dump(mode="json"),
         }
-        actual = {(action.target_id, action.type) for action in batch.actions}
-        if expected - actual:
-            raise ValueError(
-                f"动作遗漏了需求中的目标或操作: {sorted(expected - actual)}"
+        if state.get("repair_plan") is not None:
+            payload.update(
+                repair_plan=state["repair_plan"].model_dump(mode="json"),
+                previous_actions=[
+                    action.model_dump(mode="json")
+                    for action in state.get("actions") or []
+                ],
+                validation_errors=[
+                    error.model_dump() for error in state.get("validation_errors", [])
+                ],
             )
-        return {
-            **state,
-            "actions": batch.actions,
-            "validation_passed": None,
-            "validation_errors": [],
-            "simulated_screen": None,
-        }
+        # 配置和网络错误继续抛出，仅模型输出错误进入内容修正循环
+        llm_client = get_llm(ActionBatch)
+
+        updated_state["actions"] = None
+        updated_state["validation_passed"] = None
+        updated_state["validation_errors"] = []
+        updated_state["simulated_screen"] = None
+        updated_state["failure_reason"] = None
+        updated_state["status"] = "running"
+
+        try:
+            result = await llm_client.ainvoke(
+                [
+                    SystemMessage(content=ACTION_SYSTEM_PROMPT),
+                    HumanMessage(content=json.dumps(payload, ensure_ascii=False)),
+                ]
+            )
+            batch = ActionBatch.model_validate(
+                result.model_dump() if isinstance(result, BaseModel) else result
+            )
+        except ValidationError as error:
+            updated_state["validation_passed"] = False
+            updated_state["validation_errors"] = schema_errors(error)
+            return updated_state
+        except OutputParserException:
+            updated_state["validation_passed"] = False
+            updated_state["validation_errors"] = [
+                ActionValidationError(
+                    code="generation_error",
+                    message="模型输出无法解析为 ActionBatch，请按协议重新生成",
+                )
+            ]
+            return updated_state
+
+        updated_state["actions"] = batch.actions
+        return updated_state
 
 
 if __name__ == "__main__":