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ThearchyHelios
66cc96b463
多条边汇入同一个节点时，编译器对每条入边各 add_edge 一次。LangGraph 对这种 接法的语义是"任一上游跑完就触发一次"：两条支路一样长时，两次触发落在同一步 被合并，看不出问题；一长一短时，汇合节点先拿短支路的结果跑一次，长支路到了 再跑一次——模型调用、工具副作用、下游全部翻倍。循环体里更糟：每轮汇合两次， 循环节点就多推进一格，done 出口在循环跑到一半时就放行了，成果节点跑了 3 次。 README 写的"自动等待汇聚"只在支路等长时成立。 修法是给汇合节点加 defer（等图里其余待执行的任务都跑完再跑一次），而不是 屏障 add_edge([a, b], j)：分支之后的二选一汇合，没走的那条永远不会到，屏障 会一直等，而 LangGraph 没有可执行任务时会安静结束——汇合节点和下游就被无声 跳过。代价写在 _join_nodes 的注释和 README 里：它也会等图里无关、恰好在跑的支路。 汇合 = 两个以上前向上游，循环回边不算。回边识别因此从 layout.py 挪到 schema.back_edges，加了 roots 参数：执行语义要从入口起 DFS，否则循环体里的 前向边可能被误判成回边；排版照旧按节点顺序起算，行为不变（check-canvas-layout 全过）。 新增 test_fan_in.py 5 例：长短支路、循环体内汇合（修前分别跑了 2 次和 4 次）； 二选一汇合、被分支剪掉一路的汇合（守着别把修法做成屏障）；回边不算汇合。 292 → 297 passed。内置模板里 ③④ 的成果节点是二选一汇合，行为不变。
391 lines
16 KiB
Python
391 lines
16 KiB
Python
"""画布自动排版。
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模型生成的图只有拓扑、没有坐标，全堆在原点就没法看；用户手拖过之后点
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「自动排版」也得重新摆一遍。这里做的是分层布局（Sugiyama 那条路）的简化版，
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四步：
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1. **分层**：按最长路径定层。环上的回边先剔掉——不剔的话 loop 节点的回边
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会把整张图压成一层，循环体跑到循环节点左边去。
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2. **层内排序**：中位数启发式上下扫，取交叉最少的那一版。并行分支因此会按
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上游的相对位置排好，而不是按 id 字典序乱排（以前 `out_no` 排在 `out_yes`
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上面，因为 "n" < "y"——分支的 yes/no 就反着画）。
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3. **定 y**：先均匀堆叠，再按邻居重心迭代松弛。这一步是"同一列的节点和上下游
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对齐"的关键，也是长边能走直、少拐弯的前提。
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4. **定 x**：层与层之间留一条走廊，走廊宽度按穿过它的连线在竖直方向的最大
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重叠数算。前端拿这个宽度给每条线分配独立的竖直车道，线才不会叠在一起。
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——走廊留窄了，前端再聪明也只能把线叠起来。
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坐标的约定和前端 `canvas/routing.ts` 是一份契约，改这里要同步改那边：
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节点宽 238（NodeCard 写死的），同列节点最小间距 ≥ MIN_ROW_GAP，走廊里
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竖直车道间距 LANE。
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"""
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from __future__ import annotations
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from collections import defaultdict, deque
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from statistics import median
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from app.engine.schema import GraphNode, GraphSpec, NodeType, back_edges
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#: 画布节点的宽度。前端 NodeCard 的 `width: 238` 是写死的，这里必须跟着它——
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#: 排版算出来的走廊是给连线用的，宽度估错了走廊就白留。
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NODE_W = 238
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#: 节点高度估算：标题栏 + 两行摘要。真实高度由前端量，这里只要不低估到让
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#: 同列节点撞上就行（前端按实测高度铺端口，能自己吸收这点误差）。
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NODE_H = 76
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#: 同列相邻节点的最小空隙。除了不撞，还要给横向走线留出能穿过去的空档。
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MIN_ROW_GAP = 56
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#: 连线离开节点后的第一段水平长度。
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STUB = 14
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#: 同一条走廊里相邻竖直车道的间距。
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LANE = 18
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#: 竖直区间重叠多少以内算"同一条道"。共用扇出点的两条线首尾相接，不该各占一条。
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LANE_OVERLAP_TOLERANCE = 6.0
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#: 走廊宽度下限／上限。上限是防止一个 20 路扇出的节点把整张图撑到屏幕外；
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#: 顶到上限时前端会按可用宽度压缩车道间距。
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CORRIDOR_MIN = 96
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CORRIDOR_MAX = 420
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X_ORIGIN = 80
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#: 分支节点的兜底出口，和前端 `sourceHandles()` 里的写法一致
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BRANCH_FALLBACK = "default"
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def _height(node: GraphNode) -> float:
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"""节点高度估算。
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多 agent 节点跑起来会在卡片里展开一块协作矩阵（前端 canvas/TeamMatrix）：
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名册有几个人就多几行。排版必须按**展开后**的高度留位置——它会盖住下面那张
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卡，而且运行结束后矩阵不会收起来（那正是要回看的东西），重叠是永久的。
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"""
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if node.type == NodeType.SUPERVISOR:
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n = len(node.config.get("agents") or [])
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if n:
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# 标题 34 + 摘要 28 + 矩阵（表头 19 + n 行 21 + 页脚 25 + 内外边距 14）
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# + 正在跑的那个人多半还会带一行当前任务 26
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return 146 + 21 * n + 26
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return NODE_H
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def _dedupe_edges(spec: GraphSpec, nodes: dict[str, GraphNode]) -> list:
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"""丢掉悬空边和重复边，顺便给每条边定一个稳定的身份。
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自环（source == target）留到最后单独处理，它不参与分层。
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"""
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seen: set[tuple[str, str, str]] = set()
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out = []
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for e in spec.edges:
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if e.source not in nodes or e.target not in nodes:
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continue
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key = (e.source, e.target, e.sourceHandle or "")
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if key in seen:
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continue
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seen.add(key)
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out.append(e)
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return out
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def _ranks(
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nodes: dict[str, GraphNode], edges: list, back: set[tuple[str, str, str]],
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) -> dict[str, int]:
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"""最长路径分层。并行分支会落在同一层，串行的才会被推开。"""
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forward = [e for e in edges if (e.source, e.target, e.sourceHandle or "") not in back]
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succ: dict[str, list[str]] = defaultdict(list)
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indeg: dict[str, int] = dict.fromkeys(nodes, 0)
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for e in forward:
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succ[e.source].append(e.target)
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indeg[e.target] += 1
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rank = dict.fromkeys(nodes, 0)
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queue = deque(sorted(n for n in nodes if indeg[n] == 0))
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while queue:
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node_id = queue.popleft()
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for target in succ[node_id]:
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rank[target] = max(rank[target], rank[node_id] + 1)
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indeg[target] -= 1
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if indeg[target] == 0:
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queue.append(target)
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return rank
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def _handle_order(node: GraphNode) -> list[str]:
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"""节点出口在画布上从上到下的顺序。分支的 yes/no 就该是这个顺序。
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前端 `sourceHandles()` 决定出口怎么画，这里决定它们怎么排。两份必须一致，
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否则分支标签的上下关系会和连线对不上。
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"""
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cfg = node.config
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if node.type == NodeType.BRANCH:
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keys = [
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str(c.get("key"))
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for c in (cfg.get("cases") or [])
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if isinstance(c, dict) and c.get("key")
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]
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return keys + [BRANCH_FALLBACK]
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if node.type == NodeType.LOOP:
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return ["body", "done"]
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if node.type == NodeType.HUMAN and cfg.get("mode", "approve") == "approve":
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return ["approved", "rejected"]
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return ["out"]
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def _initial_orders(
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spec: GraphSpec, nodes: dict[str, GraphNode], rank: dict[str, int], edges: list,
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) -> list[list[str]]:
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"""第 0 层先按声明顺序（入口排前面），后面的层按上游位置摆一遍。"""
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max_rank = max(rank.values(), default=0)
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orders: list[list[str]] = [[] for _ in range(max_rank + 1)]
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for node in spec.nodes:
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if node.id in rank:
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orders[rank[node.id]].append(node.id)
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preds: dict[str, list[str]] = defaultdict(list)
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for e in edges:
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preds[e.target].append(e.source)
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for r in range(1, max_rank + 1):
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pos = {nid: i for i, nid in enumerate(orders[r - 1])}
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here = {nid: i for i, nid in enumerate(orders[r])}
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def key(nid: str) -> tuple[float, int]:
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ps = [pos[p] for p in preds[nid] if p in pos]
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return (median(ps) if ps else here[nid], here[nid])
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orders[r].sort(key=key)
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return orders
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def _crossings(orders: list[list[str]], rank: dict[str, int], edges: list) -> int:
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"""相邻两层之间的连线交叉数。只数跨一层的边——跨多层的长边没有虚拟节点，
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数不准，不如不数，免得把排序带偏。"""
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total = 0
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by_rank: dict[int, list] = defaultdict(list)
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for e in edges:
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rs, rt = rank[e.source], rank[e.target]
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if rt == rs + 1:
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by_rank[rs].append(e)
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for r, es in by_rank.items():
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if r + 1 >= len(orders):
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continue
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pos_s = {nid: i for i, nid in enumerate(orders[r])}
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pos_t = {nid: i for i, nid in enumerate(orders[r + 1])}
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pairs = [(pos_s[e.source], pos_t[e.target]) for e in es]
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for i in range(len(pairs)):
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for j in range(i + 1, len(pairs)):
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a, b = pairs[i], pairs[j]
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if (a[0] - b[0]) * (a[1] - b[1]) < 0:
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total += 1
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return total
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def _sweep(
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orders: list[list[str]], neigh: dict[str, list[str]], down: bool,
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) -> list[list[str]]:
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"""一轮中位数扫描。没邻居的节点按当前位置排，不被扫到队尾去。"""
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result = [list(ids) for ids in orders]
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seq = range(1, len(result)) if down else range(len(result) - 2, -1, -1)
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for r in seq:
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ref_rank = r - 1 if down else r + 1
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pos = {nid: i for i, nid in enumerate(result[ref_rank])}
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here = {nid: i for i, nid in enumerate(result[r])}
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def key(nid: str) -> tuple[float, int]:
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ps = [pos[p] for p in neigh[nid] if p in pos]
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return (median(ps) if ps else here[nid], here[nid])
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result[r].sort(key=key)
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return result
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def _minimize_crossings(
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orders: list[list[str]], rank: dict[str, int], edges: list,
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preds: dict[str, list[str]], succs: dict[str, list[str]],
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) -> list[list[str]]:
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best = [list(ids) for ids in orders]
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best_score = _crossings(best, rank, edges)
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for iteration in range(6):
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orders = _sweep(orders, preds if iteration % 2 == 0 else succs, iteration % 2 == 0)
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score = _crossings(orders, rank, edges)
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if score < best_score:
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best, best_score = [list(ids) for ids in orders], score
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if best_score == 0:
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break
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return best
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def _apply_handle_order(
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orders: list[list[str]], spec: GraphSpec, nodes: dict[str, GraphNode],
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rank: dict[str, int],
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) -> None:
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"""分支/循环的出口有语义顺序，交叉再少也得让 yes 排在 no 上面。
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做法是"占位重排"：这些后继本来占着层里的哪几个格子，就把它们按出口顺序
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填回那几个格子——层里其他节点的相对次序不受影响。
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"""
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outgoing: dict[str, list] = defaultdict(list)
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for e in spec.edges:
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outgoing[e.source].append(e)
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for node in spec.nodes:
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handles = _handle_order(node)
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if len(handles) < 2 or node.id not in nodes:
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continue
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outs = outgoing.get(node.id, [])
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if not outs:
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continue
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groups: list[str] = []
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for handle in handles:
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for e in outs:
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if e.target not in nodes or e.target == node.id:
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continue
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if (e.sourceHandle or handles[0]) == handle and e.target not in groups:
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groups.append(e.target)
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# 同一层里按出口顺序重排；跨层的没法重排，跳过
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by_rank: dict[int, list[str]] = defaultdict(list)
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for target in groups:
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by_rank[rank[target]].append(target)
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for r, targets in by_rank.items():
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if len(targets) < 2 or r >= len(orders):
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continue
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slots = sorted(orders[r].index(t) for t in targets if t in orders[r])
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if len(slots) != len(targets):
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continue
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for slot, target in zip(slots, targets):
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orders[r][slot] = target
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def _stack(orders: list[list[str]], nodes: dict[str, GraphNode], row_gap: float) -> dict[str, float]:
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"""每层先均匀堆叠，整层以 y=0 为中心。"""
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y: dict[str, float] = {}
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for ids in orders:
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heights = [_height(nodes[i]) for i in ids]
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total = sum(heights) + row_gap * (len(ids) - 1)
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cursor = -total / 2
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for node_id, h in zip(ids, heights):
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y[node_id] = cursor + h / 2
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cursor += h + row_gap
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return y
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def _relax(
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y: dict[str, float], orders: list[list[str]], nodes: dict[str, GraphNode],
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preds: dict[str, list[str]], succs: dict[str, list[str]], row_gap: float,
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rounds: int = 6,
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) -> None:
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"""按邻居重心松弛，再把同层节点推开到互不重叠。
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推开的正向扫描会把整层往下带，所以每层收尾要整体平移回重心——平移不改变
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层内间距，重叠约束依然成立。
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"""
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for iteration in range(rounds):
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down = iteration % 2 == 0
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neigh = preds if down else succs
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seq = range(len(orders)) if down else range(len(orders) - 1, -1, -1)
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for r in seq:
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ids = orders[r]
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if not ids:
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continue
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desired = []
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for node_id in ids:
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ys = [y[p] for p in neigh[node_id] if p in y]
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desired.append(sum(ys) / len(ys) if ys else y[node_id])
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cursor: float | None = None
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for i, node_id in enumerate(ids):
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if cursor is None:
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cursor = desired[i]
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else:
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gap = _height(nodes[ids[i - 1]]) / 2 + row_gap + _height(nodes[node_id]) / 2
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cursor = max(desired[i], cursor + gap)
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y[node_id] = cursor
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shift = sum(desired) / len(desired) - sum(y[i] for i in ids) / len(ids)
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for node_id in ids:
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y[node_id] += shift
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def _lane_count(intervals: list[tuple[float, float]]) -> int:
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"""竖直走线至少要几条车道。
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就是区间图上的首次适应染色，按起点排序即最优。前端 `routing.ts` 用的是
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同一套贪心，所以两边算出来的车道数一致——否则走廊留窄了，线还是会叠。
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"""
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lanes: list[float] = []
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for start, end in sorted((min(a, b), max(a, b)) for a, b in intervals):
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for i, lane_end in enumerate(lanes):
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# 首尾相接（共用扇出点）不算冲突：那正是一条总线的样子。
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# 真叠在一起（重叠超过 TOLERANCE）才换一条道。
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if start >= lane_end - LANE_OVERLAP_TOLERANCE:
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lanes[i] = max(lane_end, end)
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break
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else:
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lanes.append(end)
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return len(lanes)
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def _corridor_width(
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boundary: int, rank: dict[str, int], edges: list, y: dict[str, float],
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) -> float:
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"""第 boundary 层和第 boundary+1 层之间要留多宽。
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穿过这条走廊的边在竖直方向最多重叠几条，就至少要几条车道。
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"""
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intervals = [
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(y[e.source], y[e.target])
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for e in edges
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if rank[e.source] <= boundary < rank[e.target]
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]
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lanes = _lane_count(intervals)
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if lanes <= 1:
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return CORRIDOR_MIN
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width = STUB * 2 + LANE * (lanes - 1) + 28
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return float(min(CORRIDOR_MAX, max(CORRIDOR_MIN, width)))
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def auto_layout(
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spec: GraphSpec, *, x_gap: int | None = None, y_gap: int | None = None,
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) -> GraphSpec:
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"""给整张图重新分配坐标，返回同一个 spec（原地改坐标）。
|
||
|
||
`x_gap` / `y_gap` 是旧接口留下的旋钮，现在只当"走廊最窄多少、同列最少隔
|
||
多少"用：实际间距由拓扑算出来的车道需求决定，不再是固定 300/150。
|
||
"""
|
||
if not spec.nodes:
|
||
return spec
|
||
|
||
nodes = spec.node_map()
|
||
edges = _dedupe_edges(spec, nodes)
|
||
# 自环不参与分层：它不跨层，留着只会让"自己的前驱是自己"这种噪音渗进排序
|
||
edges = [e for e in edges if e.source != e.target]
|
||
|
||
back = back_edges(nodes, edges)
|
||
rank = _ranks(nodes, edges, back)
|
||
|
||
preds: dict[str, list[str]] = defaultdict(list)
|
||
succs: dict[str, list[str]] = defaultdict(list)
|
||
for e in edges:
|
||
preds[e.target].append(e.source)
|
||
succs[e.source].append(e.target)
|
||
|
||
orders = _initial_orders(spec, nodes, rank, edges)
|
||
orders = _minimize_crossings(orders, rank, edges, preds, succs)
|
||
_apply_handle_order(orders, spec, nodes, rank)
|
||
|
||
row_gap = float(y_gap) if y_gap else MIN_ROW_GAP
|
||
corridor_min = max(CORRIDOR_MIN, float(x_gap) - NODE_W) if x_gap else CORRIDOR_MIN
|
||
|
||
y = _stack(orders, nodes, row_gap)
|
||
_relax(y, orders, nodes, preds, succs, row_gap)
|
||
|
||
# 每个节点都有层，也就都在这两个表里；.get 的兜底是防"层算漏了"这种
|
||
# 不该发生的情况——真漏了也把节点摆在图末尾，而不是让它掉在原点压住别人
|
||
x: dict[str, float] = {}
|
||
cursor = float(X_ORIGIN)
|
||
for r, ids in enumerate(orders):
|
||
for node_id in ids:
|
||
x[node_id] = cursor
|
||
if r < len(orders) - 1:
|
||
width = _corridor_width(r, rank, edges, y)
|
||
cursor += NODE_W + max(width, corridor_min)
|
||
|
||
for node in spec.nodes:
|
||
node.position.x = round(x.get(node.id, cursor), 1)
|
||
node.position.y = round(y.get(node.id, 0.0), 1)
|
||
return spec
|