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先说一个和名字不符的事实：这个节点叫「多 Agent 协作」，但在此之前它是一条
严格的串行流水线。调度协议写死成 `next: string`——"一次只能派一个"被编进了
协议本身，模型再想并行也表达不出来。三个专家各花 10 秒，总是 30 秒。
（真并行一直在图级 fan-out：实测 3 个各 sleep 2 秒的节点连在同一个上游，
全部 +0.00s 开始、+2.37s 结束，墙钟 2.6 秒。只是协作团队走不到那条路。）
**协议改成 assignments 数组**，一轮可以派多个互不依赖的专家，asyncio.gather
并发执行，总耗时按最慢的那个算。max_parallel 可配，设成 1 即退回严格串行。
**并发只在任务真的互不依赖时才成立**，这件事的成本必须说清楚：同一轮发出去的
专家拿到的是**同一份进展快照**，谁也看不见谁这一轮干了什么。派错了不报错，
只会让两个人基于一样的旧信息重复劳动，而这在结果里很难看出来。所以判断责任
压在调度者身上，提示词里把反例写出来：
可以同时派：「查 A 的资料」和「查 B 的资料」
不能同时派：「查资料」和「根据资料写报告」
不能同时派：「写初稿」和「校对初稿」
并明确告诉它拿不准就分两轮——代价只是慢一点。
**每个人单独计时**，而不是事后拿整轮墙钟去摊。界面要显示"并行省下多少"，
那个数必须是量出来的：按 N×墙钟 推算等于假设每个人都跑满了最慢那条，
而实际上快的那个可能只花了三分之一。测试里专门有个快慢不对称的用例守这条。
**调度日志改成结构化字段**（agents / done / reason / parallel）。以前只有一句
中文消息串，解码层得拿正则去拆「调度 → X（理由）」——后端文案一改就散架，
而这次正是改了文案。老运行的事件没有这些字段，正则那条路留着兜底。
**界面加泳道。** 每个成员一行、每轮一格，条形宽度按耗时归一化，所以一眼看得出
谁和谁是同时干的、谁是这一轮的瓶颈。顶上一行汇总"3 名成员 · 3 轮 · 2 轮并行，
省下 11.8s"；点任意一格展开那个人这一轮的任务和回复。全程串行时不装作有并行，
而是说"本次任务互相依赖，逐轮进行"。
泳道排在子步骤**前面**：先给一眼看清的形状，再往下是每一步的内容。反过来的话，
得把十几行步骤读完才知道这个团队是怎么分工的。窄栏（画布右栏 360px）下隐去
"N 人同时"的文字——并行与否看条形本来就一目了然，而缩写成"并2"没人看得懂。
验证：后端 276 passed（新增 test_supervisor.py 7 条，核心那条用**墙钟**判定
并发而不是看事件顺序——顺序只能说明"发出去了"，说明不了"同时跑"）；
check-stream 新增 10 条泳道断言，含"同一轮里慢的那个条更长"和两种栏宽不溢出；
tsc 干净，三个 check 不回归。
311 lines
12 KiB
Python
311 lines
12 KiB
Python
"""协作团队：一轮可以同时派多个互不依赖的专家。
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在此之前调度协议是 `next: string`——"一次只能派一个"被编进了协议本身，模型
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再想并行也表达不出来。于是这个节点叫「多 Agent 协作」，实际是一条严格的串行
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流水线：三个专家各花 10 秒，总是 30 秒。
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改成 assignments 数组之后，互不依赖的任务同一轮发出去，总耗时按最慢的那个算。
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**但并发只在任务真的互不依赖时才成立。** 同一轮发出去的专家拿到的是同一份
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进展快照，谁也看不见谁这一轮干了什么。派错了不会报错，只会让两个人基于一样的
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旧信息重复劳动——所以判断责任压在调度者身上，提示词里写了反例，而这里守的是
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机制本身：说好并发就真的并发、串行的路径一步没坏、单个专家挂掉不拖垮整轮。
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"""
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from __future__ import annotations
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import asyncio
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import json
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import pytest
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from langchain_core.messages import AIMessage
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from sqlalchemy import select
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from app.db.base import SessionLocal
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from app.db.models import Run, RunEvent
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from app.engine.runner import run_manager
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#: 每个专家假装干这么久。并发时整轮 ≈ 这个数，串行时 ≈ 这个数 × 人数
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WORK_SECONDS = 0.4
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@pytest.fixture
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async def runner():
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await run_manager.setup()
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yield run_manager
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await run_manager.shutdown()
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def _graph(*, max_parallel: int = 3, members=("researcher", "analyst", "writer")) -> dict:
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agents = [
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{"name": n, "description": f"{n} 的职责", "system": f"你是 {n}", "max_steps": 1}
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for n in members
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]
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return {
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"nodes": [
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{"id": "in", "type": "input", "data": {"config": {}}},
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{"id": "team", "type": "supervisor", "data": {"config": {
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"goal": "比较三种向量库",
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"agents": agents,
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"max_rounds": 4,
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"max_parallel": max_parallel,
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}}},
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{"id": "out", "type": "output", "data": {"config": {}}},
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],
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"edges": [
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{"source": "in", "target": "team"},
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{"source": "team", "target": "out"},
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],
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}
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def script(monkeypatch, decisions: list[dict]) -> None:
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"""让调度者按剧本派活，让专家干活花真实时间。
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专家那一边必须是**异步**等待：同步 sleep 会把事件循环堵死，于是并发测试
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永远"通过"（因为根本没并发过，墙钟也就成了串行时间）。
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"""
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from app.providers import mock_model
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turns = {"n": 0}
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def _decide(self, messages):
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# response_format 有值 = 调度者在做结构化路由；没有 = 专家在干活
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if self.response_format:
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i = turns["n"]
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turns["n"] += 1
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payload = decisions[i] if i < len(decisions) else {"assignments": [], "done": True}
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return AIMessage(content=json.dumps(payload, ensure_ascii=False))
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return AIMessage(content="（干完了）")
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async def _agenerate(self, messages, stop=None, run_manager=None, **kw):
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if not self.response_format:
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await asyncio.sleep(WORK_SECONDS)
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return self._generate(messages, stop, **kw)
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monkeypatch.setattr(mock_model.MockChatModel, "_decide", _decide)
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monkeypatch.setattr(mock_model.MockChatModel, "_agenerate", _agenerate)
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async def _run(graph: dict):
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run = await run_manager.start(graph=graph, input_payload={"question": "比较三种向量库"})
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for _ in range(300):
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await asyncio.sleep(0.05)
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async with SessionLocal() as session:
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row = (await session.execute(select(Run).where(Run.id == run.id))).scalar_one()
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if row.status in ("succeeded", "failed", "cancelled"):
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break
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else:
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pytest.fail("运行没有在 15 秒内结束")
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async with SessionLocal() as session:
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events = list((await session.execute(
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select(RunEvent).where(RunEvent.run_id == run.id).order_by(RunEvent.seq)
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)).scalars())
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return events, row
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def steps(events, kind):
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return [e.data for e in events if e.type == kind]
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# --------------------------------------------------------------------------
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@pytest.mark.asyncio
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async def test_independent_tasks_really_run_at_the_same_time(runner, monkeypatch):
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"""这块改动的全部意义：说好并发就真的并发。
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用墙钟判定而不是看事件顺序——顺序只能说明"发出去了"，说明不了"同时跑"。
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"""
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script(monkeypatch, [{
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"assignments": [
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{"agent": "researcher", "instruction": "查 A 的资料"},
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{"agent": "analyst", "instruction": "查 B 的资料"},
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],
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"reason": "两边互不相干",
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}, {"assignments": [], "done": True}])
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events, run = await _run(_graph())
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assert run.status == "succeeded", run.error
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starts = steps(events, "agent.step.start")
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assert {d["agent"] for d in starts} == {"researcher", "analyst"}
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assert all(d["parallel"] == 2 for d in starts), starts
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# 两个人都开跑了，第一个才结束——这是"同时"的事件层证据
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first_end = next(e.ts for e in events if e.type == "agent.step.end")
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assert all(e.ts <= first_end for e in events if e.type == "agent.step.start")
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# 而这是墙钟证据：串行要 2×WORK_SECONDS，并发约 1×
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sched = (run.output or {}).get("_schedule") or _schedule_of(events)
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assert sched, "调度信息要能拿得到"
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r0 = sched[0]
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assert r0["parallel"] == 2
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assert r0["wall_ms"] < WORK_SECONDS * 2000 * 0.8, f"看着像串行：{r0}"
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assert r0["sum_ms"] > r0["wall_ms"], "并发就该省出时间"
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def _schedule_of(events):
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"""schedule 在节点输出里，测试从事件流兜一份等价的出来。"""
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from collections import defaultdict
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rounds = defaultdict(lambda: {"agents": [], "parallel": 0, "wall_ms": 0, "sum_ms": 0})
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for d in (e.data for e in events if e.type == "agent.step.end"):
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r = rounds[d["round"]]
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r["round"] = d["round"]
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r["agents"].append(d["agent"])
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r["parallel"] = d.get("parallel", 1)
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r["wall_ms"] = max(r["wall_ms"], d["duration_ms"])
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r["sum_ms"] += d["duration_ms"]
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return [rounds[k] for k in sorted(rounds)]
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@pytest.mark.asyncio
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async def test_saved_time_is_measured_not_assumed(runner, monkeypatch):
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"""界面要显示"并行省了 X 秒"，那个数必须是量出来的。
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两个人快慢不同时才区分得出来：按"整轮墙钟 × 人数"推算的话，快的那个会被
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记成和慢的一样长，省下的时间就被夸大了。所以让一个人明显更快，
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再看各自的耗时是不是真的不一样。
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"""
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from app.providers import mock_model
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script(monkeypatch, [{
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"assignments": [
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{"agent": "researcher", "instruction": "慢活"},
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{"agent": "analyst", "instruction": "快活"},
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],
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}, {"assignments": [], "done": True}])
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async def _uneven(self, messages, stop=None, run_manager=None, **kw):
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if not self.response_format:
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text = " ".join(str(getattr(m, "content", "")) for m in messages)
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await asyncio.sleep(0.05 if "快活" in text else WORK_SECONDS)
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return self._generate(messages, stop, **kw)
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monkeypatch.setattr(mock_model.MockChatModel, "_agenerate", _uneven)
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events, run = await _run(_graph())
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assert run.status == "succeeded", run.error
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took = {d["agent"]: d["duration_ms"] for d in steps(events, "agent.step.end")}
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assert took["analyst"] < took["researcher"] / 2, f"快慢该有差别：{took}"
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# 整轮墙钟按最慢的那个算，而不是两个人加起来
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assert took["researcher"] < WORK_SECONDS * 2000 * 0.8, took
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@pytest.mark.asyncio
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async def test_dependent_tasks_stay_sequential(runner, monkeypatch):
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"""分两轮派就该老老实实跑两轮——并行是可选项，不是强加的。"""
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script(monkeypatch, [
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{"assignments": [{"agent": "researcher", "instruction": "查资料"}]},
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{"assignments": [{"agent": "writer", "instruction": "根据资料写报告"}]},
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{"assignments": [], "done": True},
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])
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events, run = await _run(_graph())
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assert run.status == "succeeded", run.error
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starts = steps(events, "agent.step.start")
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assert [d["agent"] for d in starts] == ["researcher", "writer"]
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assert all(d["parallel"] == 1 for d in starts)
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# 第二个人开跑必须在第一个人结束之后
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first_end = next(e.ts for e in events if e.type == "agent.step.end")
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second_start = [e for e in events if e.type == "agent.step.start"][1].ts
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assert second_start >= first_end
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@pytest.mark.asyncio
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async def test_max_parallel_caps_the_batch(runner, monkeypatch):
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"""上限设成 1 就退回严格串行——给不放心的人一个明确的开关。"""
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script(monkeypatch, [{
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"assignments": [
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{"agent": "researcher", "instruction": "a"},
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{"agent": "analyst", "instruction": "b"},
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{"agent": "writer", "instruction": "c"},
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],
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}, {"assignments": [], "done": True}])
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events, run = await _run(_graph(max_parallel=1))
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assert run.status == "succeeded", run.error
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starts = steps(events, "agent.step.start")
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assert len(starts) == 1, "上限 1 就只该派一个"
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assert starts[0]["parallel"] == 1
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@pytest.mark.asyncio
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async def test_unknown_and_duplicate_agents_are_dropped(runner, monkeypatch):
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"""模型偶尔会重复派同一个人或者编一个不存在的名字，两种在并发里都是纯浪费。"""
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script(monkeypatch, [{
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"assignments": [
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{"agent": "researcher", "instruction": "a"},
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{"agent": "researcher", "instruction": "又派一次"},
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{"agent": "根本没这个人", "instruction": "c"},
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],
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}, {"assignments": [], "done": True}])
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events, run = await _run(_graph())
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assert run.status == "succeeded", run.error
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starts = steps(events, "agent.step.start")
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assert [d["agent"] for d in starts] == ["researcher"]
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@pytest.mark.asyncio
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async def test_one_failing_member_does_not_sink_the_round(runner, monkeypatch):
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"""并发里一个人挂了，另一个的成果不该跟着丢。"""
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from app.providers import mock_model
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script(monkeypatch, [{
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"assignments": [
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{"agent": "researcher", "instruction": "会挂的那个"},
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{"agent": "analyst", "instruction": "正常的那个"},
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],
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}, {"assignments": [], "done": True}])
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original = mock_model.MockChatModel._agenerate
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async def _boom(self, messages, stop=None, run_manager=None, **kw):
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text = " ".join(str(getattr(m, "content", "")) for m in messages)
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if "会挂的那个" in text:
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raise RuntimeError("这个专家炸了")
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return await original(self, messages, stop, run_manager, **kw)
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monkeypatch.setattr(mock_model.MockChatModel, "_agenerate", _boom)
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events, run = await _run(_graph())
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assert run.status == "succeeded", run.error
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# 失败要说出来，而不是安静地少一个人的产出
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warns = [d["message"] for d in steps(events, "log") if d.get("level") == "warn"]
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assert any("researcher" in w for w in warns), warns
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# 另一个人的结束事件照常有
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ends = {d["agent"] for d in steps(events, "agent.step.end")}
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assert "analyst" in ends
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@pytest.mark.asyncio
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async def test_schedule_is_reported_for_the_ui(runner, monkeypatch):
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"""界面要画泳道，就得拿得到每轮谁干了活、并发几路、省了多少时间。"""
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script(monkeypatch, [{
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"assignments": [
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{"agent": "researcher", "instruction": "a"},
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{"agent": "analyst", "instruction": "b"},
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],
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}, {
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"assignments": [{"agent": "writer", "instruction": "汇总"}],
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}, {"assignments": [], "done": True}])
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events, run = await _run(_graph())
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assert run.status == "succeeded", run.error
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rounds = sorted({d["round"] for d in steps(events, "agent.step.end")})
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assert rounds == [0, 1]
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by_round = {r: [d["agent"] for d in steps(events, "agent.step.end") if d["round"] == r]
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for r in rounds}
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assert set(by_round[0]) == {"researcher", "analyst"}
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assert by_round[1] == ["writer"]
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# 调度决策本身也要在事件流里，否则界面上这个节点就是个跑了 N 秒的黑盒
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dispatches = [d["message"] for d in steps(events, "log") if d.get("round") is not None]
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assert any("同时进行" in m for m in dispatches), dispatches
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