200 lines
9.2 KiB
Python
200 lines
9.2 KiB
Python
"""将简单修改或包含新增、调整的规划转换为动作列表"""
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import json
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from langchain.agents.structured_output import (
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StructuredOutputError,
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StructuredOutputValidationError,
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)
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from langchain_core.exceptions import OutputParserException
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from langchain_core.messages import HumanMessage, SystemMessage
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from pydantic import ValidationError
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from app.actions.plan import assemble_plan_actions, plan_block_reason, plan_materials
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from app.clients.llm import get_llm
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from app.processor.base import BaseNode
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from app.processor.material_agent import field_group_directory, invoke_with_field_tools
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from app.processor.state import QueryState
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from app.prompt.action import build_action_prompt, uses_material_fields
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from app.schemas.action_validation import ActionValidationError, schema_errors
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from app.schemas.actions import ActionBatch, AddAction
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from app.schemas.operations import is_direct_operation
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GENERATION_ERROR = ActionValidationError(
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code="generation_error",
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message="模型输出无法解析为 ActionBatch，请按协议重新生成",
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)
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def can_generate_actions(state: QueryState) -> bool:
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# 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
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return action_block_reason(state) is None
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def action_block_reason(state: QueryState) -> str | None:
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# 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
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if state.get("screen_plan") is not None:
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return plan_block_reason(state)
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# 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
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requirement = state["requirement"]
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scope = state["change_scope"]
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# 如果需求尚未澄清完整，不能猜测用户意图并生成动作
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if requirement.needs_clarification:
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return "需求尚未澄清完整，不能生成动作"
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# 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
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if scope.needs_planning or requirement.intent != "modify_screen":
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return "当前需求需要先完成规划，不能直接生成动作"
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# 如果仍需查询额外资源，当前直接操作流程无法满足需求
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if scope.resource_needs:
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return "尚未接入所需资源查询：" + "；".join(scope.resource_needs)
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# 如果没有具体变更，就没有可转换为动作的需求
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if not requirement.changes:
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return "需求中没有可执行的变更"
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# 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
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# 直接操作仅支持下列已有节点修改和画布属性修改
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unsupported = [
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f"{change.target_scope}/{change.operation}"
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for change in requirement.changes
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if not is_direct_operation(change.target_scope, change.operation)
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]
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# 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
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if unsupported:
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return "当前尚未支持的直接操作：" + "、".join(dict.fromkeys(unsupported))
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# 所有检查通过，返回 None 表示没有阻塞原因
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return None
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class ActionNode(BaseNode):
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name = "action_node"
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async def process(self, state: QueryState) -> QueryState:
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# 浅拷贝状态
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updated_state: QueryState = state.copy()
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# 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
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if state.get("screen_plan") is not None and (
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reason := plan_block_reason(state)
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):
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# 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
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updated_state.update(
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actions=None,
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validation_passed=False,
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validation_errors=[],
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simulated_screen=None,
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failure_reason=reason,
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status="failed",
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)
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return updated_state
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# 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
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payload = {
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"input_context": state["input_context"].model_dump(mode="json"),
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"requirement": state["requirement"].model_dump(mode="json"),
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"change_scope": state["change_scope"].model_dump(mode="json"),
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}
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# 如果已有规划，补充规划内容及新增组件需要的物料配置
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if state.get("screen_plan") is not None:
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payload["screen_plan"] = state["screen_plan"].model_dump(mode="json")
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payload["component_materials"] = plan_materials(state)
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# 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
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if state.get("repair_plan") is not None:
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payload.update(
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repair_plan=state["repair_plan"].model_dump(mode="json"),
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# 上一轮可能在解析阶段就失败，没有动作时按空列表传入
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previous_actions=[
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action.model_dump(mode="json")
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for action in state.get("actions") or []
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],
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validation_errors=[
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error.model_dump() for error in state.get("validation_errors", [])
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],
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)
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system_prompt = build_action_prompt(
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state["requirement"],
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state.get("screen_plan"),
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repairing=state.get("repair_plan") is not None,
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)
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# 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
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with_fields = uses_material_fields(
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state["requirement"], state.get("screen_plan")
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)
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if with_fields:
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payload["field_groups"] = field_group_directory(state)
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# 将本次具体资料放入用户消息，保留中文以便阅读和排查
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content = json.dumps(payload, ensure_ascii=False)
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# 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
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updated_state["actions"] = None
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updated_state["validation_passed"] = None
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updated_state["validation_errors"] = []
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updated_state["simulated_screen"] = None
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updated_state["failure_reason"] = None
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updated_state["status"] = "running"
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# 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
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try:
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# 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
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if with_fields:
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result = await invoke_with_field_tools(
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ActionBatch, system_prompt, content
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)
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else:
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result = await get_llm(ActionBatch).ainvoke(
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[
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SystemMessage(content=system_prompt),
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HumanMessage(content=content),
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]
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)
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# 响应中没有工具调用时解析器会返回 None
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if result is None:
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raise OutputParserException("模型未返回结构化结果")
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raw = result.model_dump()
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# 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
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if state.get("screen_plan") is not None:
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# 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
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for source, target in zip(result.actions, raw["actions"]):
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if isinstance(source, AddAction):
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target["node"] = source.node.model_dump(exclude_unset=True)
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# 统一校验整批动作的字段和结构，格式不合法时抛出 ValidationError
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batch = ActionBatch.model_validate(raw)
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# 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
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if state.get("screen_plan") is not None:
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batch = assemble_plan_actions(state, batch)
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except ValidationError as error:
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# 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
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updated_state["validation_passed"] = False
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updated_state["validation_errors"] = schema_errors(error)
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return updated_state
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except StructuredOutputValidationError as error:
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# 查询字段后的输出不符合结构，与直接输出时的处理一致
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updated_state["validation_passed"] = False
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updated_state["validation_errors"] = (
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schema_errors(error.source)
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if isinstance(error.source, ValidationError)
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else [GENERATION_ERROR]
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)
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return updated_state
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except (OutputParserException, StructuredOutputError):
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# 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
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updated_state["validation_passed"] = False
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updated_state["validation_errors"] = [GENERATION_ERROR]
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return updated_state
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# 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
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updated_state["actions"] = batch.actions
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# 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
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return updated_state
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