feat：接入mcp，为模型提供图表信息

app/processor/nodes/action_node.py

@@ -2,6 +2,10 @@
 
 import json
 
+from langchain.agents.structured_output import (
+    StructuredOutputError,
+    StructuredOutputValidationError,
+)
 from langchain_core.exceptions import OutputParserException
 from langchain_core.messages import HumanMessage, SystemMessage
 from pydantic import ValidationError
@@ -9,13 +13,18 @@ from pydantic import ValidationError
 from app.actions.plan import assemble_plan_actions, plan_block_reason, plan_materials
 from app.clients.llm import get_llm
 from app.processor.base import BaseNode
+from app.processor.material_agent import field_group_directory, invoke_with_field_tools
 from app.processor.state import QueryState
-from app.prompt.action import build_action_prompt
-from app.resources.material_fields import editable_fields
+from app.prompt.action import build_action_prompt, uses_material_fields
 from app.schemas.action_validation import ActionValidationError, schema_errors
 from app.schemas.actions import ActionBatch, AddAction
 from app.schemas.operations import is_direct_operation
 
+GENERATION_ERROR = ActionValidationError(
+    code="generation_error",
+    message="模型输出无法解析为 ActionBatch，请按协议重新生成",
+)
+
 
 def can_generate_actions(state: QueryState) -> bool:
     # 没有阻塞原因时才允许生成动作，路由和节点入口复用同一套能力检查
@@ -59,28 +68,6 @@ def action_block_reason(state: QueryState) -> str | None:
     return None
 
 
-def update_fields(state: QueryState) -> dict[str, list[dict]]:
-    """按组件 ID 汇总可能被属性更新的已有组件的可编辑字段，没有字段的组件省略"""
-    plan = state.get("screen_plan")
-    if plan is None:
-        target_ids = [
-            target
-            for change in state["requirement"].changes
-            if change.target_scope == "nodes"
-            for target in change.target_ids
-        ]
-    else:
-        target_ids = [
-            item.target_id for item in plan.components if item.disposition == "modify"
-        ]
-    nodes = {node.id: node for node in state["input_context"].screen.nodes}
-    result = {}
-    for target in dict.fromkeys(target_ids):
-        if target in nodes and (fields := editable_fields(nodes[target].type)):
-            result[target] = fields
-    return result
-
-
 class ActionNode(BaseNode):
     name = "action_node"
 
@@ -115,10 +102,6 @@ class ActionNode(BaseNode):
             payload["screen_plan"] = state["screen_plan"].model_dump(mode="json")
             payload["component_materials"] = plan_materials(state)
 
-        # 提供待修改组件的属性面板字段，让模型把用户说的“标题”“颜色”等对应到真实路径
-        if fields := update_fields(state):
-            payload["editable_fields"] = fields
-
         # 如果本轮是动作修正，提供修正建议、上一轮动作和校验错误供模型参考
         if state.get("repair_plan") is not None:
             payload.update(
@@ -133,7 +116,17 @@ class ActionNode(BaseNode):
                 ],
             )
 
-        llm_client = get_llm(ActionBatch)
+        system_prompt = build_action_prompt(
+            state["requirement"],
+            state.get("screen_plan"),
+            repairing=state.get("repair_plan") is not None,
+        )
+        # 需要写组件属性时提供相关组件的字段分组，模型再按需查询字段定义
+        with_fields = uses_material_fields(state["requirement"], state.get("screen_plan"))
+        if with_fields:
+            payload["field_groups"] = field_group_directory(state)
+        # 将本次具体资料放入用户消息，保留中文以便阅读和排查
+        content = json.dumps(payload, ensure_ascii=False)
 
         # 旧动作及错误已放入模型输入，现在清空输出状态，避免本轮失败后残留旧结果
         updated_state["actions"] = None
@@ -146,19 +139,12 @@ class ActionNode(BaseNode):
         # 捕获输出结构和解析错误，转换成统一校验结果供后续修正流程使用
         try:
             # 异步调用模型，根据需求、规划及是否正在修正选择对应的系统规则
-            result = await llm_client.ainvoke(
-                [
-                    SystemMessage(
-                        content=build_action_prompt(
-                            state["requirement"],
-                            state.get("screen_plan"),
-                            repairing=state.get("repair_plan") is not None,
-                        )
-                    ),
-                    # 将本次具体资料放入用户消息，保留中文以便阅读和排查
-                    HumanMessage(content=json.dumps(payload, ensure_ascii=False)),
-                ]
-            )
+            if with_fields:
+                result = await invoke_with_field_tools(ActionBatch, system_prompt, content)
+            else:
+                result = await get_llm(ActionBatch).ainvoke(
+                    [SystemMessage(content=system_prompt), HumanMessage(content=content)]
+                )
 
             # 响应中没有工具调用时解析器会返回 None
             if result is None:
@@ -184,15 +170,19 @@ class ActionNode(BaseNode):
             updated_state["validation_passed"] = False
             updated_state["validation_errors"] = schema_errors(error)
             return updated_state
-        except OutputParserException:
+        except StructuredOutputValidationError as error:
+            # 查询字段后的输出不符合结构，与直接输出时的处理一致
+            updated_state["validation_passed"] = False
+            updated_state["validation_errors"] = (
+                schema_errors(error.source)
+                if isinstance(error.source, ValidationError)
+                else [GENERATION_ERROR]
+            )
+            return updated_state
+        except (OutputParserException, StructuredOutputError):
             # 如果模型输出无法解析，记录生成错误，提示后续按 ActionBatch 协议重新生成
             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",
-                )
-            ]
+            updated_state["validation_errors"] = [GENERATION_ERROR]
             return updated_state
 
         # 保存生成的动作，实际目标、规划一致性及模拟执行结果仍由后续校验节点检查