97 lines
4.7 KiB
Python
97 lines
4.7 KiB
Python
"""分析失败动作，生成下一次修正建议"""
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import json
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from langchain.agents.structured_output import StructuredOutputError
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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 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 FIELD_QUERY_PROMPT, uses_material_fields
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from app.prompt.reflection import REFLECTION_SYSTEM_PROMPT
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from app.schemas.action_repair import ActionRepairPlan
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class ReflectionNode(BaseNode):
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name = "reflection_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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# 汇总原始上下文、需求和失败信息，让模型基于实际错误提出修正建议
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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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# 动作可能在生成或解析阶段就失败，没有动作时传入空列表
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"actions": [
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action.model_dump(mode="json") for action in state.get("actions") or []
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],
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# 将校验错误转成字典，保留错误类型、动作下标和具体原因
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"validation_errors": [
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error.model_dump() for error in state["validation_errors"]
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],
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# 如果已有修正建议，一并提供给模型，帮助分析上次修正后仍失败的原因
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"previous_repair_plan": state["repair_plan"].model_dump()
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if state.get("repair_plan")
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else None,
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}
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# 如果动作来自大屏规划，补充规划和新增组件物料，避免修正时偏离规划或物料定义
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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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# 涉及组件属性时，修正建议须基于查询到的字段定义，与动作生成使用同一组工具
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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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# 用户消息承载本轮待分析的具体资料，序列化时保留中文
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content = json.dumps(payload, ensure_ascii=False)
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# 只捕获输出结构和解析错误，配置、网络等异常继续向上抛出
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try:
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# 异步生成修正建议，本节点不直接修改动作
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if with_fields:
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plan = await invoke_with_field_tools(
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ActionRepairPlan,
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REFLECTION_SYSTEM_PROMPT + "\n" + FIELD_QUERY_PROMPT,
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content,
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)
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else:
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plan = await get_llm(ActionRepairPlan).ainvoke(
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[
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SystemMessage(content=REFLECTION_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 plan is None:
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raise OutputParserException("模型未返回结构化结果")
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except (ValidationError, OutputParserException, StructuredOutputError):
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# 如果未能得到合法建议，清空旧修正计划，避免再次使用已失效的建议
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updated_state["repair_plan"] = None
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# 记录无法继续修正的原因，交给后续路由和失败处理流程
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updated_state["failure_reason"] = "未能生成有效的动作修正建议"
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return updated_state
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# 保存通过校验的修正建议，供后续路由判断及动作重新生成使用
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updated_state["repair_plan"] = plan
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# 仅在建议允许重试时将次数加一，缺少历史计数时从零开始，不可重试时保持原次数
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updated_state["retry_count"] = state.get("retry_count", 0) + int(plan.retryable)
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# 可以重试时清空旧失败原因；不可重试时保留分析，由失败收尾交给模型向用户说明
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updated_state["failure_reason"] = (
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None if plan.retryable else f"修正分析判断无法完成：{plan.reason}"
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)
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# 返回修正建议、重试次数和失败原因，由后续路由决定重新生成动作还是结束
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return updated_state
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