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ThearchyHelios
e46a3f0d62
运行 87daca70：一个计时器，外层每一拍里内层跑两个 50ms 的子拍。5 秒该推进 50 拍， 结果推进了 138 拍。循环节点走 done 出口时计数原样留着，外层下一轮再把它带进来，就从 上次停下的地方接着往上数：内层 max_iterations 是 5，第一、二拍各跑 2 个子拍，第三拍 1 个，从第四拍起一进来就撞上限直接退出，子拍一次都不跑（撞上限的 warn 136 条）。 嵌套的 foreach 更糟：外层第二轮时内层游标已经在末尾，内层一项都不再跑，也不报错， 结果只是悄悄少了一大截。 改法：走 done 时记 finished，再被带进来时从 0 数。index 仍停在最后一轮，结束后读 loops.x.index 的照旧拿到轮数；画布上内层的"第 N/上限 轮"也随之每轮重新数起。 测试：test_nested_loops 两条——嵌套 while 合计 6 次（修复前 3 次）、嵌套 foreach 走完 3×2 项（修复前只有 1a、1b）。用 87daca70 的原图在临时库里真沙箱重跑：内层每拍都是 "跑、跑、结束"，撞上限 0 次。后端 473 passed ×2。
191 lines
7.1 KiB
Python
191 lines
7.1 KiB
Python
from __future__ import annotations
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import json
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from typing import Any
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from app.core.events import EventType
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from app.db.base import SessionLocal
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from app.engine.context import NodeContext, NodeError
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from app.engine.expressions import ExpressionError, eval_condition
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from app.engine.state import GraphState, message_text, template_context
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from app.providers.factory import ModelSpec, get_chat_model
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# 分支和循环的"决定"写在 nodes[node_id] 里，编译器的路由函数再读出来。
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# 路由函数本身不能有副作用，所以判断必须发生在节点执行阶段。
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DECISION_KEY = "__decision__"
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async def run_branch(state: GraphState, ctx: NodeContext) -> dict[str, Any]:
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"""条件分支。两种模式：表达式判断，或让模型做语义分类。"""
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cases = ctx.cfg("cases", []) or []
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if not cases:
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raise NodeError(ctx.node.id, "分支节点没有配置任何条件")
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mode = ctx.cfg("mode", "expression")
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tctx = template_context(state)
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chosen = "default"
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reason = ""
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if mode == "llm":
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chosen, reason = await _classify_with_llm(state, ctx, cases)
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else:
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for case in cases:
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expr = case.get("condition", "")
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key = case.get("key") or case.get("label") or "case"
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if not expr:
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continue
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try:
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if eval_condition(expr, tctx):
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chosen, reason = key, f"命中条件：{expr}"
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break
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except ExpressionError as e:
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raise NodeError(ctx.node.id, f"分支条件写错了（{expr}）：{e}") from e
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else:
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reason = "所有条件都不满足，走 default"
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ctx.emit(EventType.EDGE_TAKEN, branch=chosen, reason=reason, mode=mode)
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return {
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"nodes": {ctx.node.id: {DECISION_KEY: chosen, "branch": chosen, "reason": reason}},
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}
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async def _classify_with_llm(
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state: GraphState, ctx: NodeContext, cases: list[dict[str, Any]]
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) -> tuple[str, str]:
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"""让模型在给定选项里挑一个。用结构化输出保证它只会吐出合法的 key。"""
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options = [
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{"key": c.get("key") or f"case{i}", "描述": c.get("label") or c.get("condition") or ""}
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for i, c in enumerate(cases)
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]
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keys = [o["key"] for o in options] + ["default"]
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subject = ctx.render_str(ctx.cfg("input", "{{ last_message }}"), state)
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instruction = ctx.render_str(ctx.cfg("instruction", ""), state)
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prompt = (
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f"{instruction}\n\n" if instruction else ""
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) + (
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"请把下面的内容归入最合适的一类。\n\n"
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f"可选类别：\n{json.dumps(options, ensure_ascii=False, indent=2)}\n\n"
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f"内容：\n{subject}\n\n"
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"只返回 JSON：{\"key\": <类别key>, \"reason\": <一句话理由>}"
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)
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async with SessionLocal() as session:
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model, _ = await get_chat_model(
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session,
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ModelSpec(
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provider=ctx.cfg("provider"),
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model=ctx.cfg("model"),
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max_tokens=512,
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),
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)
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schema = {
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"title": "classify", # langchain 靠 title 识别 JSON Schema dict
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"type": "object",
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"properties": {
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"key": {"type": "string", "enum": keys},
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"reason": {"type": "string"},
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},
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"required": ["key"],
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}
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try:
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value = await model.with_structured_output(schema).ainvoke(prompt)
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if not isinstance(value, dict):
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value = json.loads(message_text(value))
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except Exception: # noqa: BLE001 - 结构化不支持就退回自由文本再解析
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raw = message_text(await model.ainvoke(prompt))
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value = {}
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for key in keys:
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if key in raw:
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value = {"key": key, "reason": raw[:200]}
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break
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chosen = str(value.get("key") or "default")
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if chosen not in keys:
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chosen = "default"
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return chosen, str(value.get("reason", ""))
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async def run_loop(state: GraphState, ctx: NodeContext) -> dict[str, Any]:
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"""循环控制。
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画布上的接法：loop 节点的 `body` 出口连向循环体，循环体最后一个节点再连回 loop 节点；
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`done` 出口连向循环结束后要走的分支。每进一次这个节点就推进一次计数。
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"""
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mode = ctx.cfg("mode", "foreach")
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node_id = ctx.node.id
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loops = dict(state.get("loops") or {})
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prev = loops.get(node_id) if isinstance(loops.get(node_id), dict) else {}
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# 上次已经走过 done 出口还又进来：是外层循环的下一轮把它重新带进来，从头数。
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# 以前计数原样留着接着往上加，内层的 max_iterations 于是成了整次运行合计的
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# 上限——运行 87daca70 的内层每拍该跑两个子拍，第四拍起一进来就撞上限直接
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# 退出；嵌套的 foreach 更糟，外层第二轮起内层一项都不跑，也不报错
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cursor = 0 if prev.get("finished") else int(prev.get("index", 0))
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max_iter = int(ctx.cfg("max_iterations", 10) or 10)
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tctx = template_context(state)
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item_var = ctx.cfg("item_var", "item")
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updates_vars: dict[str, Any] = {}
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keep_going = False
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current_item: Any = None
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total = None
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if mode == "foreach":
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items = ctx.render(ctx.cfg("items", ""), state)
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if isinstance(items, str):
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try:
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items = json.loads(items)
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except json.JSONDecodeError:
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items = [line for line in items.splitlines() if line.strip()]
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if not isinstance(items, list):
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items = [items] if items not in (None, "") else []
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total = len(items)
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keep_going = cursor < len(items) and cursor < max_iter
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if keep_going:
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current_item = items[cursor]
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updates_vars = {item_var: current_item, f"{item_var}_index": cursor}
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else: # while
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condition = ctx.cfg("condition", "")
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if cursor >= max_iter:
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keep_going = False
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elif not condition:
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keep_going = False
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else:
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try:
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keep_going = eval_condition(condition, tctx)
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except ExpressionError as e:
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raise NodeError(ctx.node.id, f"循环条件写错了：{e}") from e
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hit_limit = cursor >= max_iter
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if hit_limit:
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ctx.emit(
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EventType.LOG, level="warn",
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message=f"循环达到上限 {max_iter} 次，强制退出", code="loop_limit",
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)
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decision = "body" if keep_going else "done"
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ctx.emit(
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EventType.EDGE_TAKEN,
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branch=decision,
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iteration=cursor,
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total=total,
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mode=mode,
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)
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result = {
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DECISION_KEY: decision,
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"iteration": cursor,
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"total": total,
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"item": current_item,
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"finished": not keep_going,
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}
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updates: dict[str, Any] = {
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"nodes": {node_id: result},
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# 走完了 index 留在最后一轮，读 loops.x.index 的照旧拿到轮数；finished 让下次进来重数
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"loops": {node_id: {"index": cursor + 1 if keep_going else cursor, "finished": not keep_going}},
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}
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if updates_vars:
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updates["vars"] = updates_vars
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return updates
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