"""将简单修改或包含新增、调整的规划转换为动作列表""" 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 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, 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 return action_block_reason(state) is None def action_block_reason(state: QueryState) -> str | None: # 如果已有大屏规划,按规划检查资源、目标和操作是否受支持 if state.get("screen_plan") is not None: return plan_block_reason(state) # 没有规划时,读取需求和范围,检查是否可以直接生成简单修改动作 requirement = state["requirement"] scope = state["change_scope"] # 如果需求尚未澄清完整,不能猜测用户意图并生成动作 if requirement.needs_clarification: return "需求尚未澄清完整,不能生成动作" # 如果需要规划或不是修改已有大屏,则必须先完成规划流程 if scope.needs_planning or requirement.intent != "modify_screen": return "当前需求需要先完成规划,不能直接生成动作" # 如果仍需查询额外资源,当前直接操作流程无法满足需求 if scope.resource_needs: return "尚未接入所需资源查询:" + ";".join(scope.resource_needs) # 如果没有具体变更,就没有可转换为动作的需求 if not requirement.changes: return "需求中没有可执行的变更" # 收集不支持的目标范围与操作组合,防止只执行需求中受支持的部分 # 直接操作仅支持下列已有节点修改和画布属性修改 unsupported = [ f"{change.target_scope}/{change.operation}" for change in requirement.changes if not is_direct_operation(change.target_scope, change.operation) ] # 如果存在不支持的操作,按出现顺序去重后返回原因,便于展示失败信息 if unsupported: return "当前尚未支持的直接操作:" + "、".join(dict.fromkeys(unsupported)) # 所有检查通过,返回 None 表示没有阻塞原因 return None class ActionNode(BaseNode): name = "action_node" async def process(self, state: QueryState) -> QueryState: # 浅拷贝状态 updated_state: QueryState = state.copy() # 如果已有规划但不满足执行条件,记录原因并直接结束本节点,避免无效模型调用 if state.get("screen_plan") is not None and ( reason := plan_block_reason(state) ): # 清空旧动作及模拟结果,标记失败,防止后续误用上一轮生成内容 updated_state.update( actions=None, validation_passed=False, validation_errors=[], simulated_screen=None, failure_reason=reason, status="failed", ) return updated_state # 将输入上下文、结构化需求和修改范围转成字典,作为本次模型输入 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"), } # 如果已有规划,补充规划内容及新增组件需要的物料配置 if state.get("screen_plan") is not None: payload["screen_plan"] = state["screen_plan"].model_dump(mode="json") payload["component_materials"] = plan_materials(state) # 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( repair_plan=state["repair_plan"].model_dump(mode="json"), # 上一轮可能在解析阶段就失败,没有动作时按空列表传入 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", []) ], ) 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 updated_state["validation_passed"] = None updated_state["validation_errors"] = [] updated_state["simulated_screen"] = None updated_state["failure_reason"] = None updated_state["status"] = "running" # 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 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: raise OutputParserException("模型未返回结构化结果") raw = result.model_dump() # 规划生成的新增节点只保留显式提供的字段,避免模型默认值覆盖物料默认配置 if state.get("screen_plan") is not None: # 将原动作对象与序列化结果逐项对应,只调整新增动作的节点数据 for source, target in zip(result.actions, raw["actions"]): if isinstance(source, AddAction): target["node"] = source.node.model_dump(exclude_unset=True) # 统一校验整批动作的字段和结构,格式不合法时抛出 ValidationError batch = ActionBatch.model_validate(raw) # 如果已有规划,使用物料默认配置补全新增节点,并同步示例数据标记 if state.get("screen_plan") is not None: batch = assemble_plan_actions(state, batch) except ValidationError as error: # 将结构校验异常转换为统一错误列表,返回状态以进入后续修正判断 updated_state["validation_passed"] = False updated_state["validation_errors"] = schema_errors(error) return updated_state 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"] = [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 updated_state["actions"] = batch.actions # 返回待校验状态,此时 validation_passed 仍为 None,不代表动作已执行或通过校验 return updated_state