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ThearchyHelios
3998e821dc
调查时发现：run_subgraph 只设了节点 id 前缀（node_prefix）没用上，子图在父节点里用 ainvoke 跑，LangGraph 不把它的 custom 事件交给父图的流——子图里每一步在运行的事件里一条都没有，时间线看不到、封存也不含它查过的数。「事件本来就在同一个 运行里」此前并不成立（docs/design/orchestration-round/00-总方案.md §2）。 - engine/context + nodes/multi：子图拿到父运行的写入口（STREAM_KEY）和前缀（PREFIX_KEY），子图里的节点经它发事件，节点 id 「子工作流节点 id/…」，嵌套的一层层接上；工件按这次运行里的 id 记。子图里报的错（含被驳回）交回时节点 id 换成这一层的、 名字接上子工作流（runner 的 run.failed / run.cancelled 写「子工作流 › 那一步」），不再和父图里同名的节点混在一起 - nodes/multi：子图的台账条目加前缀并进父图 evidence（父图报告的目录据此收进子图里的查询和口径卡）；子图口径卡的产出以 「子工作流节点 id/卡」进父图 nodes；出口每个字段按出口模板认出处（模板整串就是一个取值链、渲染出来正是那个值的才认， 同口径卡认输入的那一套），出自子图运行输入的按子工作流节点的「输入」模板再往父图追一层，追不到的记 subgraph_input - engine/compiler：子工作流节点自己的 node.finished 不再重记交回来的台账条目（子图那几步自己的 node.finished 已经带着） - nodes/metrics：父图口径卡读 nodes.<子图>.output.<字段>（或 assign_to 变量的字段）时接上那个字段的出处，另记 subgraph、 field；经子图出口转抄子图口径卡的指标照样带上不完整、存疑；cell(nodes.<子图>.output.<原样交出的查询结果>, …) 按台账 认出子图里的那份快照 - engine/lineage：静态认法跨子图往下追（出口模板 → 子图里接着认；子图的运行输入按「输入」模板回父图认；最多 3 层）， 和运行时同一套；whole_ref 是两边共用的「整串一个取值链」判断。lint_process.load_subgraphs 往下读嵌套的子工作流， subgraph_spec 给静态认法取图；没读好放进来的照旧停在子工作流节点 - nodes/report：报告收证据的范围加上上游子工作流交回来的（evidence_from 写了子工作流节点的一并算） - nodes/io：指标出具的缺口里，停在子图里的说停在那个子工作流（和静态检查同一句） - api/evidence：卡上记的节点 id 按卡所在那一层认（_scoped：子图里的卡写子图里的 id），转抄、输入、追不到的那几步一路给到 子图里的那一格；子图里的节点按 node.started 记的名字接成「子工作流 › 节点」（node_label、subgraph_label）；双盲复算条目 里的卡 id 同样换成这次运行里的 - apps/resolve：实例进度不单列子图里的步骤 - 口径账本的 use_caliber 不改；比对读封存的查询，子图里的查询如今在封存范围里，照常认得出 测试：test_subgraph_evidence（子图每一步进运行、报错点名子图里那一步、实例进度、父图口径卡跨子图追到查询格并完整出具、 子图里追不到的父图降档、不是整串取值链的停在子工作流、运行输入往父图追、嵌套两层、静态检查与运行时一致（含两层和输入 模板）、父图报告引用子图里的查询和口径卡并一路点到那一格、封存与可验证报告、子图里的遮罩照父图的运行）。去掉「交写入口」 回跑，五条失败（快照不在封存范围里）。后端全量 14472 通过
833 lines
46 KiB
Python
833 lines
46 KiB
Python
from __future__ import annotations
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import asyncio
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import json
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import time
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from typing import Any
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from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage
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from langgraph.errors import GraphRecursionError
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from langgraph.func import task
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from app.core.config import settings
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from app.core.errors import describe_exception, raw_detail
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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.db.models import Workflow
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from app.engine.context import PREFIX_KEY, STREAM_KEY, NodeContext, NodeError, stream_writer
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from app.engine.lineage import whole_ref
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from app.engine.state import GraphState, message_text, template_context
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from app.engine.toolcalls import (
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TOOL_MARKUP_NUDGE,
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ToolTimeout,
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leaked_markup,
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limit_fields,
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limit_of,
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markup_warning,
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run_bounded,
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)
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from app.providers import catalog
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from app.providers.factory import ModelSpec, bind_tools_safely, get_chat_model
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from app.engine.sql_problems import event_checks
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from app.data import catalog_view
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from app.tools.datasource import QUERY_PREFIX, run_masks_results, run_reads_only, run_scope_fixed, run_versions
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from app.tools.registry import (
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ToolArgsError,
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ToolContext,
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args_model_of,
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build_tools,
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prepare_args,
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)
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from app.tools.trust import ask_gate, call_policy
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_MAX_DEPTH = 3
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#: 调度者在 llm.end 里的 agent 名。成员用各自的名字
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COORDINATOR = "调度者"
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#: 成员把工具调用写成文字、纠正之后还是这样时，交给调度者的那句话
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MEMBER_MARKUP_FAILED = ("模型以文本形式输出了工具调用的原始标记，未实际调用工具，这一步没有查询到任何数据"
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"（常见原因：该成员未绑定工具，或模型、服务不支持工具调用）")
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# --------------------------------------------------------------------------
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# Supervisor：一个调度者 + 若干专家
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# --------------------------------------------------------------------------
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async def run_supervisor(state: GraphState, ctx: NodeContext) -> dict[str, Any]:
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"""多 agent 协作。
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调度者每轮只做一件事：看当前进展，决定交给哪个专家、并给它一句具体指令，
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或者宣布收工。专家各自带自己的 system prompt 和工具集。
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把整个协作塞进一个节点，是为了让画布保持可读 —— 展开细节看运行时间线就够了。
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"""
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agents = ctx.cfg("agents", []) or []
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if not agents:
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raise NodeError(ctx.node.id, "「多 Agent 协作」节点未配置团队成员，至少需要一个")
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names = [a.get("name") or f"agent{i}" for i, a in enumerate(agents)]
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agent_map = {n: a for n, a in zip(names, agents)}
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max_rounds = min(int(ctx.cfg("max_rounds", 6) or 6), settings.max_agent_steps)
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#: 轮数用完、调度者始终没判定完成时怎么办。fail：判失败；degrade：照样交付但标明降档。
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#: 缺省 fail——成员的原话不是结论，把它当成功交出去比失败更糟
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on_exhausted = "degrade" if ctx.cfg("on_exhausted") == "degrade" else "fail"
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goal = ctx.render_str(ctx.cfg("goal", "{{ last_message }}"), state)
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if not goal.strip():
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goal = json.dumps(template_context(state).get("input"), ensure_ascii=False)
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async with SessionLocal() as session:
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supervisor_model, sup_model_id = 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=int(ctx.cfg("max_tokens", 2048) or 2048),
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),
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)
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roster = "\n".join(
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f"- {n}：{agent_map[n].get('description') or agent_map[n].get('system', '')[:100]}"
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for n in names
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)
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# 一轮可以派给多个专家。协议用 assignments 数组而不是单个 next：
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# 后者把"一次只能派一个"编进了协议本身，模型再想并行也表达不出来。
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#
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# 但并行只在任务**互不依赖**时才成立——并发的几个专家看到的是同一份进展
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# 快照，谁也看不见谁。派错了不会报错，只会让两个人基于同样的旧信息
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# 重复劳动，而这件事在结果里很难看出来。所以判断责任明确压在调度者身上，
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# 提示词里把反例写出来。
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route_schema = {
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"title": "route", # langchain 靠 title 识别 JSON Schema dict
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"type": "object",
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"properties": {
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"assignments": {
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"type": "array",
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"description": "这一轮要派出去的任务。互不依赖时可以给多个，它们会同时执行",
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"items": {
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"type": "object",
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"properties": {
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"agent": {"type": "string", "enum": names},
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"instruction": {"type": "string"},
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},
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"required": ["agent", "instruction"],
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},
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},
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"done": {"type": "boolean", "description": "目标已达成，结束协作"},
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"reason": {"type": "string"},
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},
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"required": ["assignments"],
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}
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#: 一轮最多同时派几个。再多的话调度者多半只是在把任务拆碎，
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#: 而每多一路就多一份"基于陈旧进展"的风险
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max_parallel = max(1, min(int(ctx.cfg("max_parallel", 3) or 3), len(names)))
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#: dangerous：需要人工确认的调用不执行（成员停不下来等人）；never：全部放行
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approval = ctx.approval_mode()
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transcript: list[dict[str, Any]] = []
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usage_total = {"input_tokens": 0, "output_tokens": 0, "cost_usd": 0.0, "calls": 0}
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def _usage(msg: Any, model_id: str) -> dict[str, Any]:
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meta = getattr(msg, "usage_metadata", None) or {}
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i, o = int(meta.get("input_tokens") or 0), int(meta.get("output_tokens") or 0)
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return {"input_tokens": i, "output_tokens": o,
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"cost_usd": round(catalog.estimate_cost(model_id, i, o), 6)}
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def _accumulate(one: dict[str, Any], into: dict[str, Any] | None = None) -> None:
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into = usage_total if into is None else into
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into["input_tokens"] += one["input_tokens"]
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into["output_tokens"] += one["output_tokens"]
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into["calls"] += one.get("calls", 1)
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into["cost_usd"] = round(into["cost_usd"] + one["cost_usd"], 6)
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def _report(who: str, model_id: str, one: dict[str, Any], t0: float) -> None:
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# 每次模型调用各发一条 llm.end，界面上的用量才能实时涨，而且分得清是谁花的。
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# 它只是把 usage_total 这笔账逐次摊开：run.finished 的 usage 仍以累计为准
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ctx.emit(EventType.LLM_END, agent=who, model=model_id,
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duration_ms=int((time.perf_counter() - t0) * 1000), **one)
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async def _decide(prompt: str, *, closing: bool = False) -> tuple[dict[str, Any], dict[str, Any]]:
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"""问调度者一次。返回 (决定, 这次调用的用量)。"""
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spent = {"input_tokens": 0, "output_tokens": 0, "cost_usd": 0.0}
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try:
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# include_raw：结构化结果之外把原始消息也要回来，用量在那条消息上。
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# 以前只拿解析结果，调度者的 token 一笔都没记进 usage
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out = await supervisor_model.with_structured_output(
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route_schema, include_raw=True).ainvoke(prompt)
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if isinstance(out, dict) and "raw" in out and "parsed" in out:
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spent = _usage(out["raw"], sup_model_id)
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value = out["parsed"]
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if value is None:
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value = json.loads(message_text(out["raw"]))
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else:
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value = out
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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
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# 不支持结构化输出的模型退回文本：从回复里认出一个成员名，按单人派。
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# 收尾判定时同理：回复里还点着哪个成员，就是还没完
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reply = await supervisor_model.ainvoke(prompt)
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spent = _usage(reply, sup_model_id)
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raw = message_text(reply)
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picked = next((n for n in names if n in raw), None)
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if closing:
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value = {"assignments": [], "done": picked is None, "reason": raw[:200]}
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else:
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value = ({"assignments": [{"agent": picked, "instruction": raw[:500]}]}
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if picked else {"assignments": [], "done": True, "reason": raw[:200]})
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return (value if isinstance(value, dict) else {}), spent
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def _batch_of(decision: dict[str, Any]) -> list[tuple[str, str]]:
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# 只留认得出的成员，并按上限截断。模型偶尔会重复派同一个人或者编一个
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# 不存在的名字——这两种在并发里都是纯浪费
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batch: list[tuple[str, str]] = []
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seen: set[str] = set()
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for item in decision.get("assignments") or []:
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name = str((item or {}).get("agent") or "")
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if name in agent_map and name not in seen:
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seen.add(name)
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batch.append((name, str(item.get("instruction") or goal)))
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if len(batch) >= max_parallel:
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break
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return batch
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def _finished(round_no: int, reason: str, **extra: Any) -> None:
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# agents / done 是结构化字段，界面照着它渲染。以前只有一句中文
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# 消息串，解码层得拿正则去拆「调度 → X（理由）」——文案一改就散架
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ctx.emit(EventType.LOG, level="info", round=round_no, agents=[], done=True,
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message="调度 → 结束协作" + (f"（{reason}）" if reason else ""), **extra)
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# 调度者决策前后各一条事件（它常常占掉团队节点大半的时间，以前是黑盒），和派活的
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# 那条日志一起都在 task 里发：「接着跑」一个失败的团队节点时整个节点重放，前几轮
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# 的决定取自 checkpoint，这些事件不该在时间线上再出现一遍
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@task
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async def route(round_no: int, progress: str) -> dict[str, Any]:
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ctx.emit(EventType.AGENT_ROUTE_START, round=round_no)
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prompt = (
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f"你是团队调度者。目标：\n{goal}\n\n"
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f"可用成员：\n{roster}\n\n"
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f"当前进展：\n{progress or '(还没有任何进展)'}\n\n"
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"决定这一轮把什么任务交给谁，每个任务给一句明确的指令。\n\n"
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f"**互不依赖的任务可以放在同一轮**（最多 {max_parallel} 个），它们会同时执行，"
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"总耗时按最慢的那个算。判断依据只有一条：**后一个任务需不需要看到前一个的结果**。\n"
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" 可以同时派：「查 A 产品的资料」和「查 B 产品的资料」——两边互不相干\n"
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" 不能同时派：「查资料」和「根据资料写报告」——后者要等前者\n"
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" 不能同时派：「写初稿」和「校对初稿」——同上\n\n"
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"同时派出去的成员看到的是**同一份进展快照**，谁也看不见谁这一轮干了什么。"
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"拿不准是否独立时就分两轮派，代价只是慢一点；派错了则是两个人基于同样的"
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"旧信息重复劳动，而这在结果里很难看出来。\n\n"
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"目标已经达成时，done 填 true、assignments 给空数组。"
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)
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t0 = time.perf_counter()
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value, spent = await _decide(prompt)
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_report(COORDINATOR, sup_model_id, spent, t0)
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batch = _batch_of(value)
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finishing = bool(value.get("done")) or not batch
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reason = str(value.get("reason", ""))
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ctx.emit(EventType.AGENT_ROUTE_END, round=round_no,
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duration_ms=int((time.perf_counter() - t0) * 1000),
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agents=[] if finishing else [n for n, _ in batch],
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parallel=0 if finishing else len(batch), done=finishing, reason=reason)
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if finishing:
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_finished(round_no, reason)
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else:
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who = "、".join(n for n, _ in batch)
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ctx.emit(
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EventType.LOG, level="info", round=round_no, parallel=len(batch),
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agents=[n for n, _ in batch], done=False, reason=reason,
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message=(f"调度 → {who}" + (f"（{reason}）" if reason else "")
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+ (f" · {len(batch)} 名成员同时执行" if len(batch) > 1 else "")),
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)
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return {"decision": value, "usage": spent}
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# 最后一轮派出去的成员（常常就是定稿的那个）交回来之后，调度者还没看过它的产出。
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# 以前轮数一到就算用完：步数恰好等于最多轮数的正常流水线（查数 → 撰稿，2 轮）也会
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# 被判成没完成。补这一次只判定、不派活的决定，它说完成了才算完成
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@task
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async def verdict(round_no: int, progress: str) -> dict[str, Any]:
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ctx.emit(EventType.AGENT_ROUTE_START, round=round_no, closing=True)
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prompt = (
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f"你是团队调度者。目标：\n{goal}\n\n"
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f"当前进展：\n{progress or '(还没有任何进展)'}\n\n"
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f"轮数已经用完（{max_rounds} 轮），不能再派任何任务。只判断一件事：按上面的进展，"
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"目标是不是已经达成、能不能把最后一个成员的产出作为结论交出去。\n"
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"达成了 done 填 true；没达成 done 填 false，并在 reason 里说清还缺什么。"
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"assignments 给空数组。"
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)
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t0 = time.perf_counter()
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value, spent = await _decide(prompt, closing=True)
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_report(COORDINATOR, sup_model_id, spent, t0)
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done = value.get("done") is True
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reason = str(value.get("reason", ""))
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ctx.emit(EventType.AGENT_ROUTE_END, round=round_no,
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duration_ms=int((time.perf_counter() - t0) * 1000),
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agents=[], parallel=0, done=done, reason=reason, closing=True)
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if done:
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_finished(round_no, reason, closing=True)
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return {"done": done, "reason": reason, "usage": spent}
|
||
|
||
# 成员的开始、结束两条事件也在 task 里发，理由同 route。失败也在 task 里收住、作为
|
||
# 结果缓存：重放时这一步照旧是失败，不会换一个结果。用量随结果带出来再记账——在
|
||
# task 里直接记进 usage_total 的话，重放取了缓存，这一笔就没了
|
||
@task
|
||
async def work(round_no: int, name: str, instruction: str, progress: str,
|
||
parallel: int = 1) -> dict[str, Any]:
|
||
ctx.emit(EventType.AGENT_STEP_START, agent=name, instruction=instruction[:300],
|
||
round=round_no, parallel=parallel)
|
||
tally = {"input_tokens": 0, "output_tokens": 0, "cost_usd": 0.0, "calls": 0}
|
||
t0 = time.perf_counter()
|
||
try:
|
||
out: dict[str, Any] = await _member(name, instruction, progress, tally)
|
||
except asyncio.CancelledError:
|
||
raise
|
||
except BaseException as exc: # noqa: BLE001 - 一个专家挂了不该拖垮整轮
|
||
# 把失败当成它的产出交回调度者，让它决定改派还是收工
|
||
why = describe_exception(exc)
|
||
text = f"（{name} 执行失败：{why}）"
|
||
out = {"text": text, "tool_calls": [], "failed": True, "error": why}
|
||
ctx.emit(EventType.LOG, level="warn", round=round_no,
|
||
message=f"{name} 本轮执行失败：{text}")
|
||
each_ms = int((time.perf_counter() - t0) * 1000)
|
||
# failed / error 让矩阵把这一格画成失败，而不是一个"完成"的格子里写着失败原因
|
||
failure = {"failed": True, "error": out.get("error") or ""} if out.get("failed") else {}
|
||
ctx.emit(EventType.AGENT_STEP_END, agent=name, duration_ms=each_ms,
|
||
round=round_no, parallel=parallel, preview=out["text"][:500], **failure)
|
||
return {**out, "duration_ms": each_ms, "usage": tally}
|
||
|
||
async def _member(name: str, instruction: str, progress: str,
|
||
tally: dict[str, Any]) -> dict[str, Any]:
|
||
cfg = agent_map[name]
|
||
async with SessionLocal() as session:
|
||
model, model_id = await get_chat_model(
|
||
session,
|
||
ModelSpec(
|
||
provider=cfg.get("provider") or ctx.cfg("provider"),
|
||
model=cfg.get("model") or ctx.cfg("model"),
|
||
max_tokens=int(cfg.get("max_tokens") or 4096),
|
||
),
|
||
)
|
||
tool_ctx = ToolContext(
|
||
run_id=ctx.run.run_id,
|
||
node_id=f"{ctx.node.id}:{name}",
|
||
sandbox_session=ctx.run.thread_id,
|
||
memory_scope=ctx.run.memory_scope,
|
||
collection=ctx.run.collection,
|
||
data_versions=run_versions(ctx.run),
|
||
mask_results=run_masks_results(ctx.run),
|
||
force_readonly=run_reads_only(ctx.run),
|
||
fixed_scope=run_scope_fixed(ctx.run),
|
||
# 读哪一份数据目录：发起时定下的（data/catalog_view.py），审批恢复、续跑都沿用
|
||
catalog_view=catalog_view.of_run(ctx.run),
|
||
# 数据源工具运行中途补固定版本时经它留一条事件（tools/datasource.py 的 _pin_late）
|
||
emit=ctx.emit,
|
||
)
|
||
tools = await build_tools(cfg.get("tools", []) or [], tool_ctx, session=session)
|
||
|
||
bound = bind_tools_safely(model, tools, parallel=False)
|
||
tool_map = {t.name: t for t in tools}
|
||
messages: list[Any] = [
|
||
SystemMessage(content=cfg.get("system") or f"你是 {name}。"),
|
||
HumanMessage(content=f"团队目标：{goal}\n\n已有进展：\n{progress or '(无)'}\n\n你的任务：{instruction}"),
|
||
]
|
||
calls: list[dict[str, Any]] = []
|
||
# 和 agent 节点同一个硬顶：成员上配多少步都过不去 max_agent_steps
|
||
max_steps = min(int(cfg.get("max_steps") or 4), settings.max_agent_steps)
|
||
nudged = False
|
||
for _ in range(max_steps):
|
||
t0 = time.perf_counter()
|
||
reply = await bound.ainvoke(messages)
|
||
spent = _usage(reply, model_id)
|
||
_accumulate(spent, tally)
|
||
_report(name, model_id, spent, t0)
|
||
messages.append(reply)
|
||
tool_calls = getattr(reply, "tool_calls", None) or []
|
||
if not tool_calls:
|
||
text = message_text(reply)
|
||
snippet = leaked_markup(text)
|
||
if not snippet:
|
||
return {"text": text, "tool_calls": calls}
|
||
# 把工具调用写成了文字：这一步什么都没查到。纠正一次，还这样就记为失败，
|
||
# 原因交给调度者——以前这段标记会被当成这个成员的结论交上去
|
||
if nudged:
|
||
return {"text": f"（{name} 这一步执行失败：{MEMBER_MARKUP_FAILED}）",
|
||
"tool_calls": calls, "failed": True, "error": MEMBER_MARKUP_FAILED}
|
||
nudged = True
|
||
ctx.emit(EventType.LOG, level="warn", code="tool_markup_leak",
|
||
message=f"{markup_warning(snippet, name)}，已要求模型重试一次")
|
||
messages.append(HumanMessage(content=TOOL_MARKUP_NUDGE))
|
||
continue
|
||
for call in tool_calls:
|
||
tname = call.get("name", "")
|
||
targs = call.get("args", {}) or {}
|
||
cid = call.get("id") or f"{name}-{tname}"
|
||
limit = None
|
||
failure: dict[str, Any] = {}
|
||
started = time.perf_counter()
|
||
if tname not in tool_map:
|
||
ctx.emit(EventType.TOOL_START, tool=tname, args=targs, agent=name, call_id=cid)
|
||
content = f"错误：没有名为 {tname} 的工具"
|
||
else:
|
||
schema = args_model_of(tool_map[tname])
|
||
fix_note = None
|
||
args_error = None
|
||
if schema is not None:
|
||
try:
|
||
targs, fix_note = prepare_args(schema, targs)
|
||
except ToolArgsError as e:
|
||
args_error = str(e)
|
||
# 要不要人工确认、要不要先问门控：按纠正后的参数判
|
||
policy = "safe"
|
||
gate_reason = ""
|
||
if not args_error and approval != "never":
|
||
policy = call_policy(tool_map[tname], tname, targs, ctx.run.tool_trust)
|
||
if policy == "gate":
|
||
gate = await ask_gate(
|
||
tool=tool_map[tname], args=targs, node_title=f"{ctx.node.title} · {name}",
|
||
task=f"团队目标：{goal}\n\n{name} 的任务：{instruction}")
|
||
ctx.emit(EventType.TOOL_GATED, tool=tname, agent=name, call_id=cid, **gate.event())
|
||
_accumulate({"input_tokens": gate.usage.get("input_tokens", 0),
|
||
"output_tokens": gate.usage.get("output_tokens", 0),
|
||
"cost_usd": gate.usage.get("cost_usd", 0.0), "calls": 1}, tally)
|
||
policy = "safe" if gate.allowed else "ask"
|
||
gate_reason = gate.reason
|
||
# 纠正后的参数才是真正要执行的那份，tool.start 要发它
|
||
limit = limit_of(tool_map[tname], tname, targs)
|
||
ctx.emit(EventType.TOOL_START, tool=tname, args=targs, agent=name, call_id=cid,
|
||
**limit_fields(limit))
|
||
if fix_note:
|
||
ctx.emit(EventType.LOG, level="warn",
|
||
message=f"工具 {tname}：{fix_note}", code="tool_args_fixed")
|
||
if args_error:
|
||
# 参数就不对，没必要真调一次。把"它接受什么"喂回去让它改
|
||
content = args_error
|
||
elif policy == "ask":
|
||
# 专家们并行跑在同一个节点里，停不下来等人：审批恢复时节点整个
|
||
# 重放，几个人的 interrupt 谁先谁后对不上号。以前的做法是不审，
|
||
# shell_exec / file_write / 可写库上的 DELETE 照跑不误——
|
||
# agent 节点守着的那道门，在这里是敞开的。现在是不跑，并说清原因
|
||
content = (
|
||
(f"门控模型未批准（{gate_reason}）。" if gate_reason else "")
|
||
+ f"没有执行：{tname} 这次调用需要人工审批，而协作团队里的成员"
|
||
"不能停下来等人。换一种不需要它的做法；实在需要，交给团队外"
|
||
"的 Agent 节点去做（那里可以逐次审批）。"
|
||
)
|
||
ctx.emit(EventType.LOG, level="warn", code="tool_needs_approval", tool=tname,
|
||
agent=name, call_id=cid,
|
||
message=f"{name} 请求调用 {tname}，该调用需要人工审批，协作节点内不执行")
|
||
else:
|
||
try:
|
||
raw = await run_bounded(tool_map[tname].ainvoke(targs), limit, tname)
|
||
content = raw if isinstance(raw, str) else json.dumps(raw, ensure_ascii=False, default=str)
|
||
except ToolTimeout as e:
|
||
content = str(e)
|
||
failure = {"error": content, "timed_out": True}
|
||
except Exception as e: # noqa: BLE001
|
||
content = f"工具失败：{describe_exception(e)}"
|
||
failure = {"error": content, "detail": raw_detail(e)}
|
||
# 查库的 SQL 检查结果单独带上：preview 截到前 1500 个字符，checks 通常在截掉的那段里
|
||
checks = event_checks(content) if not failure and tname.startswith(QUERY_PREFIX) else []
|
||
ctx.emit(EventType.TOOL_ERROR if failure else EventType.TOOL_END, tool=tname,
|
||
agent=name, call_id=cid, preview=content[:1500],
|
||
duration_ms=int((time.perf_counter() - started) * 1000), **failure,
|
||
**({"checks": checks} if checks else {}))
|
||
calls.append({"tool": tname, "args": targs, "result": content[:2000]})
|
||
messages.append(ToolMessage(content=content, tool_call_id=cid))
|
||
|
||
# 步数用完了：最后一步一定是要了工具的，模型从没拿到过"说结论"的那一轮。
|
||
# messages[-1] 是一条 ToolMessage——以前它就被当成这个成员的产出交给调度者，
|
||
# 工具的原始返回冒充了结论。agent 节点修过同一个问题，这里照做：不给工具
|
||
# （用没绑工具的 model，结构上就发不出调用）补一轮收尾。
|
||
messages.append(HumanMessage(content=(
|
||
f"步数预算用完了（{max_steps} 步），现在起不能再调用任何工具。基于已经查到的"
|
||
"信息给出你这部分的结论，说清哪些没查到、结论因此有什么局限。不要编造数据。"
|
||
)))
|
||
try:
|
||
t0 = time.perf_counter()
|
||
settled = await model.ainvoke(messages)
|
||
spent = _usage(settled, model_id)
|
||
_accumulate(spent, tally)
|
||
_report(name, model_id, spent, t0)
|
||
text = message_text(settled).strip()
|
||
except Exception as e: # noqa: BLE001 - 收尾失败不能把已有的过程一起赔进去
|
||
ctx.emit(EventType.LOG, level="warn", code="settle_failed",
|
||
message=f"{name} 收尾失败：{describe_exception(e)}")
|
||
text = next((t for m in reversed(messages)
|
||
if isinstance(m, AIMessage) and (t := message_text(m).strip())
|
||
and not leaked_markup(t)), "")
|
||
if snippet := leaked_markup(text):
|
||
if not calls:
|
||
return {"text": f"（{name} 这一步执行失败：{MEMBER_MARKUP_FAILED}）",
|
||
"tool_calls": calls, "failed": True, "error": MEMBER_MARKUP_FAILED}
|
||
# 真调过工具，只是收尾轮还想接着查：取它之前说过的话，和 agent 节点一样
|
||
ctx.emit(EventType.LOG, level="warn", code="tool_markup_leak",
|
||
message=f"{markup_warning(snippet, name)}：已达步数上限，收尾时仍试图调用工具")
|
||
text = next((t for m in reversed(messages)
|
||
if isinstance(m, AIMessage) and (t := message_text(m).strip())
|
||
and not leaked_markup(t)), "")
|
||
ctx.emit(EventType.LOG, level="warn", code="step_limit_settled",
|
||
message=f"{name} 已用完 {max_steps} 步上限，结论基于已查到的部分")
|
||
return {"text": text or f"（{name} 已用完 {max_steps} 步上限，未给出结论）", "tool_calls": calls}
|
||
|
||
progress_lines: list[str] = []
|
||
final_text = ""
|
||
#: 成功交回来的产出（成员, 原话）。调度者说完成时，成果取最后一份成功的：
|
||
#: 失败说明是写给调度者看的，不是结论
|
||
delivered: list[tuple[str, str]] = []
|
||
rounds_meta: list[dict[str, Any]] = []
|
||
#: 调度者最后一轮给的理由，以及派过活的成员。轮数用完时报错要说清卡在哪、谁从没上过场
|
||
last_reason = ""
|
||
dispatched: set[str] = set()
|
||
finished = False
|
||
for round_no in range(max_rounds):
|
||
progress = "\n\n".join(progress_lines)
|
||
routed = await route(round_no, progress)
|
||
decision = routed.get("decision", routed) if isinstance(routed, dict) else {}
|
||
decision = decision if isinstance(decision, dict) else {}
|
||
_accumulate(routed.get("usage") or _usage(None, sup_model_id))
|
||
reason = str(decision.get("reason", ""))
|
||
last_reason = reason or last_reason
|
||
|
||
batch = _batch_of(decision)
|
||
if bool(decision.get("done")) or not batch:
|
||
finished = True
|
||
break
|
||
|
||
dispatched.update(n for n, _ in batch)
|
||
|
||
# 并发执行。它们拿到的是**同一份** progress 快照——这正是"任务必须
|
||
# 互不依赖"的技术含义，也是上面提示词里那几条反例的由来。
|
||
#
|
||
# 每个人单独计时，而不是事后拿整轮墙钟去摊：界面要显示"并行省了多少
|
||
# 时间"，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。
|
||
#
|
||
# 结束事件也在各自的 task 里一完成就发，不等整轮 gather：以前先做完的成员要
|
||
# 一直显示"进行中"，直到最慢的那个做完——矩阵答不了"谁还在跑"
|
||
async def _one(name: str, instruction: str, parallel: int) -> dict[str, Any]:
|
||
return await work(round_no, name, instruction, progress, parallel)
|
||
|
||
started = time.perf_counter()
|
||
results = await asyncio.gather(*(_one(n, i, len(batch)) for n, i in batch))
|
||
wall_ms = int((time.perf_counter() - started) * 1000)
|
||
|
||
# 进展仍按派活顺序拼：它是下一轮调度者读的输入，语义不随谁先回来而变
|
||
sum_ms = 0
|
||
for (name, instruction), result in zip(batch, results):
|
||
outcome = dict(result)
|
||
spent = outcome.pop("usage", None)
|
||
if spent:
|
||
_accumulate(spent)
|
||
each_ms = int(outcome.pop("duration_ms", 0) or 0)
|
||
sum_ms += each_ms
|
||
progress_lines.append(f"【{name}】{outcome['text']}")
|
||
transcript.append(
|
||
{"round": round_no, "agent": name, "instruction": instruction, **outcome,
|
||
"duration_ms": each_ms}
|
||
)
|
||
final_text = outcome["text"]
|
||
if not outcome.get("failed"):
|
||
delivered.append((name, outcome["text"]))
|
||
|
||
rounds_meta.append({
|
||
"round": round_no, "agents": [n for n, _ in batch],
|
||
"parallel": len(batch), "wall_ms": wall_ms, "sum_ms": sum_ms,
|
||
})
|
||
|
||
if not finished and progress_lines:
|
||
judged = await verdict(max_rounds, "\n\n".join(progress_lines))
|
||
_accumulate(judged.get("usage") or _usage(None, sup_model_id))
|
||
last_reason = str(judged.get("reason") or "") or last_reason
|
||
if judged.get("done"):
|
||
finished = True
|
||
|
||
exhausted: dict[str, Any] = {}
|
||
failed = [t for t in transcript if t.get("failed")]
|
||
never = [n for n in names if n not in dispatched]
|
||
if finished and delivered:
|
||
final_text = delivered[-1][1]
|
||
if transcript[-1].get("failed"):
|
||
# 调度者认可了收工，可最后派出去的人没交回东西：成果取最后一份成功的产出，
|
||
# 并说清楚——下游拿到的是它，不是本该定稿的那个人的
|
||
late = "、".join(dict.fromkeys(t["agent"] for t in failed
|
||
if t["round"] == transcript[-1]["round"]))
|
||
ctx.emit(EventType.LOG, level="warn", code="team_last_failed",
|
||
message=f"{late} 最后一轮未交回结果，团队成果取自{delivered[-1][0]}的产出")
|
||
elif finished and transcript:
|
||
# 调度者说完成了，派出去的成员却一个都没交回结果，手上只有失败说明。以前最后
|
||
# 那段失败说明就被当成团队的结论交出去：一条数据都没查到，运行照样是已完成。
|
||
# 不看 on_exhausted：那是「用完轮数时」的收场方式，降档交的是成员原话；这里
|
||
# 连原话都没有，交下去的只能是失败说明，界面还会把它说成「用完 N 轮」
|
||
why = "；".join(f"{who}：{err[:200]}" for who, err in dict.fromkeys(
|
||
(t["agent"], t.get("error") or "未说明原因") for t in failed))
|
||
summary = (f"协作团队未交出结论：所有被分派的成员都未能交回结果，调度者仍判定完成"
|
||
f"（{last_reason or '未给出理由'}）。{why}")
|
||
if never:
|
||
summary += f"。一次都没被派到的成员：{'、'.join(never)}"
|
||
raise NodeError(ctx.node.id, f"{summary}。请根据各成员的失败原因修改配置后重新运行")
|
||
elif not finished:
|
||
# 轮数用完，调度者一次都没说「完成」。这时手上只有最后一个成员的原话——
|
||
# 以前它就被当作团队的结论交了出去：真实运行里是一段没执行的工具调用标记，
|
||
# 而负责定稿的成员一次都没被派到
|
||
summary = (f"协作团队用完 {max_rounds} 轮仍未完成："
|
||
f"{last_reason or '调度者未给出理由'}")
|
||
if never:
|
||
summary += f"。一次都没被派到的成员：{'、'.join(never)}"
|
||
if on_exhausted == "fail":
|
||
# 「。先看成员」是前端切分这句话的锚点（decode.ts exhaustedOf、NodeCard、explain.ts），不要改
|
||
raise NodeError(ctx.node.id, f"{summary}。先看成员是否绑定了所需工具，再调大「最多"
|
||
"轮数」；也可将「用完轮数时」改为「降档交付」")
|
||
exhausted = {"exhausted": True, "exhausted_reason": last_reason,
|
||
"never_dispatched": never}
|
||
ctx.emit(EventType.LOG, level="warn", code="team_exhausted",
|
||
message=f"{summary}。按降档交付：成果取自成员最后的回复，并非调度者认可的结论")
|
||
|
||
usage_total["total_tokens"] = usage_total["input_tokens"] + usage_total["output_tokens"]
|
||
# 并行省下的时间：各轮"串行本该花的"减去"实际花的"。只有并发过才有差值
|
||
saved_ms = sum(r["sum_ms"] - r["wall_ms"] for r in rounds_meta)
|
||
result = {
|
||
"text": final_text,
|
||
"rounds": len(rounds_meta),
|
||
"steps": len(transcript),
|
||
"transcript": transcript,
|
||
"agents": names,
|
||
"schedule": rounds_meta,
|
||
"parallel_saved_ms": saved_ms,
|
||
**exhausted,
|
||
}
|
||
updates: dict[str, Any] = {
|
||
"nodes": {ctx.node.id: result},
|
||
"usage": usage_total,
|
||
}
|
||
if ctx.cfg("emit_message", True):
|
||
updates["messages"] = [AIMessage(content=final_text or "（空）")]
|
||
var_name = ctx.cfg("assign_to", "")
|
||
if var_name:
|
||
updates["vars"] = {var_name: final_text}
|
||
return updates
|
||
|
||
|
||
# --------------------------------------------------------------------------
|
||
# 子图：把另一张工作流当成一个节点
|
||
# --------------------------------------------------------------------------
|
||
|
||
|
||
async def run_subgraph(state: GraphState, ctx: NodeContext) -> dict[str, Any]:
|
||
"""嵌套执行另一个工作流。
|
||
|
||
子图共享同一个 run 的事件流：节点 id 加前缀「子工作流节点 id/」（嵌套的一层层接上），事件经父运行交下来的写入口发
|
||
（engine/context.STREAM_KEY；以前只设了前缀没用上，子图里每一步在运行的事件里一条都没有），时间线上看得到它内部每一步，
|
||
封存也就覆盖了它查过的数。它有独立的状态，不会污染父图的变量池。
|
||
|
||
证据交回父图（docs/design/orchestration-round/00-总方案.md §2），三样：
|
||
- 台账：子图记下的条目（查询快照、口径卡的指标集……）节点 id 加上前缀并进父图的 evidence，父图报告撰写的目录据此收进
|
||
子图里的查询和口径卡（report.report_catalog），cell(nodes.<子图>.output.<原样交出的查询结果>, …) 也按台账认得出快照；
|
||
- 口径卡：子图里口径卡的产出以「子工作流节点 id/卡」为 id 进父图的 nodes（目录要卡上的指标值；父图转抄它的指标时认得
|
||
出那张卡、带上不完整和存疑）；
|
||
- 出处：出口的每个字段按出口模板认它来自哪里（_output_provenance），记在产出的 provenance 上；父图里读
|
||
nodes.<子图>.output.<字段> 的口径卡据此接上子图里的那一格（metrics._subgraph_field），engine/lineage 同一套认法。
|
||
"""
|
||
from app.engine.compiler import compile_graph # 延迟导入，避免循环依赖
|
||
from app.engine.schema import GraphSpec, loop_steps
|
||
|
||
if ctx.run.depth >= _MAX_DEPTH:
|
||
raise NodeError(ctx.node.id, f"子工作流嵌套超过 {_MAX_DEPTH} 层，已阻止。"
|
||
"可能是多个工作流相互引用，请检查「子工作流」节点选择的工作流")
|
||
|
||
workflow_id = ctx.cfg("workflow_id")
|
||
if not workflow_id:
|
||
raise NodeError(ctx.node.id, "「子工作流」节点尚未选择要嵌套的工作流")
|
||
|
||
# 版本钉死：这就是"方法卡"的机制核心。钉了 workflow_version 就永远
|
||
# 执行那个不可变快照，上游改了方法卡也不会让这里的口径悄悄漂移；
|
||
# 没钉则跟随最新（画布试跑方便，但治理 lint 会在受管模板里拦下它）。
|
||
pinned = ctx.cfg("workflow_version")
|
||
async with SessionLocal() as session:
|
||
workflow = await session.get(Workflow, workflow_id)
|
||
if not workflow:
|
||
raise NodeError(ctx.node.id, f"「子工作流」引用的工作流（{workflow_id}）已不存在，"
|
||
"可能已被删除，请在节点中重新选择")
|
||
if pinned:
|
||
from sqlalchemy import select
|
||
|
||
from app.db.models import WorkflowVersion
|
||
|
||
snapshot = (
|
||
await session.execute(
|
||
select(WorkflowVersion).where(
|
||
WorkflowVersion.workflow_id == workflow_id,
|
||
WorkflowVersion.version == int(pinned),
|
||
)
|
||
)
|
||
).scalar_one_or_none()
|
||
if not snapshot:
|
||
raise NodeError(
|
||
ctx.node.id, f"工作流「{workflow.name}」不存在 v{pinned} 版本"
|
||
)
|
||
sub_graph = snapshot.graph
|
||
used_version = int(pinned)
|
||
else:
|
||
sub_graph = workflow.graph
|
||
used_version = workflow.version
|
||
|
||
sub_input = ctx.render(ctx.cfg("input", {}) or {}, state)
|
||
if not isinstance(sub_input, dict):
|
||
sub_input = {"input": sub_input}
|
||
|
||
sub_spec = GraphSpec.model_validate(sub_graph)
|
||
sub_run = type(ctx.run)(
|
||
run_id=ctx.run.run_id,
|
||
thread_id=f"{ctx.run.thread_id}:{ctx.node.id}",
|
||
spec=sub_spec,
|
||
workflow_id=workflow.id,
|
||
depth=ctx.run.depth + 1,
|
||
memory_scope=ctx.run.memory_scope,
|
||
collection=ctx.run.collection,
|
||
approval_default=ctx.run.approval_default,
|
||
tool_trust=ctx.run.tool_trust,
|
||
agent_limits=ctx.run.agent_limits,
|
||
# 恢复重放的标记和签批人只属于父图的节点，不能漏进子图（节点 id 可能重名）。前缀和写入口见 engine/context：
|
||
# 子图里的节点经父运行的写入口发事件、节点 id 带前缀；嵌套的子图拿到的是同一个写入口、更长的前缀
|
||
extra={**{k: v for k, v in ctx.run.extra.items() if k not in ("resumed", "actors")},
|
||
PREFIX_KEY: f"{ctx.run.global_id(ctx.node.id)}/", STREAM_KEY: stream_writer(ctx.run)},
|
||
)
|
||
|
||
ctx.emit(EventType.LOG, level="info",
|
||
message=f"进入子工作流「{workflow.name}」（{len(sub_spec.nodes)} 个节点）")
|
||
|
||
compiled = compile_graph(sub_spec, sub_run)
|
||
# 子图不带 checkpointer：它的断点由父图的 checkpoint 统一承载
|
||
app = compiled.compile()
|
||
limit = settings.max_graph_steps + loop_steps(sub_spec)
|
||
try:
|
||
result_state = await app.ainvoke(
|
||
{
|
||
"input": sub_input,
|
||
"vars": sub_input,
|
||
"nodes": {},
|
||
"output": {},
|
||
"messages": [],
|
||
"usage": {},
|
||
"trail": [],
|
||
"loops": {},
|
||
},
|
||
{"recursion_limit": limit},
|
||
)
|
||
except GraphRecursionError as e:
|
||
# 不接住的话，外层包装成 NodeError 时带的是 LangGraph 的英文原文
|
||
raise NodeError(
|
||
ctx.node.id,
|
||
f"子工作流「{workflow.name}」已执行 {limit} 步仍未结束，已中止。可能存在没有轮数上限的环路，"
|
||
"请在子工作流中用「循环」节点限制轮数",
|
||
) from e
|
||
except NodeError as e:
|
||
# 子图里的节点报的错（含被驳回）：节点 id 换成这一层图里的 id（「子工作流节点 id/…」），名字接上子工作流的名字。
|
||
# 以前带的是子图里的 id，运行记下的出错节点会和父图里同名的节点混在一起
|
||
titles = sub_spec.node_map()
|
||
inner = getattr(e, "label", None) or (titles[e.node_id].title if e.node_id in titles else e.node_id)
|
||
e.node_id, e.label = f"{ctx.node.id}/{e.node_id}", f"{ctx.node.title} › {inner}"
|
||
raise
|
||
|
||
output = result_state.get("output") or {}
|
||
result = {
|
||
"output": output,
|
||
"workflow": workflow.name,
|
||
# 溯源信息：这次到底跑的是哪个版本、是不是钉死的
|
||
"workflow_version": used_version,
|
||
"pinned": bool(pinned),
|
||
"text": json.dumps(output, ensure_ascii=False) if output else "",
|
||
}
|
||
if provenance := _output_provenance(sub_spec, sub_run, result_state, output, state, ctx):
|
||
# 出口每个字段的出处（没有能认的字段时不带这个键，产出和以前一字不差）
|
||
result["provenance"] = provenance
|
||
ctx.emit(EventType.LOG, level="info", message=f"子工作流「{workflow.name}」完成")
|
||
|
||
sub_nodes = result_state.get("nodes") or {}
|
||
cards = {f"{ctx.node.id}/{nid}": payload for nid, payload in sub_nodes.items()
|
||
if isinstance(payload, dict) and payload.get("kind") == "metric_set"}
|
||
updates: dict[str, Any] = {
|
||
"nodes": {ctx.node.id: result, **cards},
|
||
"usage": result_state.get("usage") or {},
|
||
}
|
||
if ledger := [_forwarded(e, ctx.node.id) for e in result_state.get("evidence") or [] if isinstance(e, dict)]:
|
||
updates["evidence"] = ledger
|
||
var_name = ctx.cfg("assign_to", "")
|
||
if var_name:
|
||
updates["vars"] = {var_name: output}
|
||
return updates
|
||
|
||
|
||
#: 台账条目里写着节点 id 的键：交回父图时一并加前缀（双盲复算的条目记着它复算的口径卡）
|
||
_LEDGER_NODE_KEYS = ("node_id", "card")
|
||
|
||
|
||
def _forwarded(entry: dict[str, Any], node_id: str) -> dict[str, Any]:
|
||
"""子图台账里的一条交回父图：节点 id 加上「子工作流节点 id/」前缀，别的照抄。"""
|
||
out = dict(entry)
|
||
for key in _LEDGER_NODE_KEYS:
|
||
if isinstance(out.get(key), str) and out[key]:
|
||
out[key] = f"{node_id}/{out[key]}"
|
||
return out
|
||
|
||
|
||
def _output_provenance(sub_spec: Any, sub_run: Any, sub_state: dict[str, Any], output: dict[str, Any],
|
||
state: GraphState, ctx: NodeContext) -> dict[str, dict[str, Any]]:
|
||
"""子图出口每个字段的出处：{字段: 口径卡上一个输入的样子（via、node_id、artifact、locator、status、upstream……）}。
|
||
|
||
认法和口径卡认它的输入同一个（metrics._input_of），在子图的状态里认：字段的模板整串就是一个取值链（whole_ref），而且
|
||
渲染出来的值正是那个值转成的文字，才算这个字段就是那个值；拼了别的字、过了过滤器的不认（不在结果里）。几个成果节点
|
||
写同一个字段时按最后跑的那个。认出来的节点 id 换成父图这一层的（加「子工作流节点 id/」前缀）。
|
||
|
||
出自子图运行输入的（via input）再往父图追一层：子图的输入是父图按子工作流节点的「输入」模板渲染出来的，那个模板整串
|
||
也是一个取值链时，按父图的状态接着认（追到父图的运行输入、查询格……）；否则记 via subgraph_input，停在子工作流的
|
||
入口——子图的运行输入对父图不是原始数据。"""
|
||
from app.engine.expressions import _stringify
|
||
from app.engine.nodes.metrics import _input_of, _value_of
|
||
from app.engine.schema import NodeType
|
||
|
||
outputs = {n.id: n for n in sub_spec.nodes if n.type == NodeType.OUTPUT}
|
||
fields: dict[str, tuple[Any, str]] = {}
|
||
for step in sub_state.get("trail") or []:
|
||
exit_node = outputs.get(step.get("node_id")) if "error" not in step else None
|
||
for field in (exit_node.config.get("fields") or []) if exit_node is not None else []:
|
||
if isinstance(field, dict) and field.get("name"):
|
||
fields[str(field["name"])] = (exit_node, field.get("value", ""))
|
||
tctx = template_context(sub_state)
|
||
out: dict[str, dict[str, Any]] = {}
|
||
for name, (exit_node, template) in fields.items():
|
||
path = whole_ref(template)
|
||
if path is None or name not in output:
|
||
continue
|
||
value = _value_of(path, tctx)
|
||
if _stringify(value) != output[name]:
|
||
continue
|
||
item = _input_of(path, value, sub_state, NodeContext(node=exit_node, run=sub_run), tctx)
|
||
out[name] = _lifted(item, state, ctx)
|
||
return out
|
||
|
||
|
||
def _lifted(item: dict[str, Any], state: GraphState, ctx: NodeContext) -> dict[str, Any]:
|
||
"""子图里认出的一个出处换到父图这一层：节点 id 加前缀；出自子图运行输入的，按子工作流节点的「输入」模板往父图追。"""
|
||
from app.engine.expressions import _stringify
|
||
from app.engine.nodes.metrics import _REF_HEAD, _input_of, _value_of
|
||
|
||
if item.get("via") == "input":
|
||
head = _REF_HEAD.match(str(item.get("path") or ""))
|
||
name = head.group(2) if head else ""
|
||
templates = ctx.cfg("input", {}) or {}
|
||
path = whole_ref(templates.get(name)) if isinstance(templates, dict) and name else None
|
||
if path is not None:
|
||
parent = template_context(state)
|
||
value = _value_of(path, parent)
|
||
if _stringify(value) == _stringify(item.get("value")):
|
||
return _input_of(path, value, state, ctx, parent)
|
||
return {"path": item.get("path"), "value": item.get("value"), "node_id": ctx.node.id, "via": "subgraph_input",
|
||
**({"field": name} if name else {}), "status": item.get("status") or "ok"}
|
||
out = dict(item)
|
||
for key in ("node_id", "through", "subgraph"):
|
||
if isinstance(out.get(key), str) and out[key]:
|
||
out[key] = f"{ctx.node.id}/{out[key]}"
|
||
return out
|