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ThearchyHelios
c02ef189bb
起因是一条没人看得懂的报错：
工具 db_query__shop 执行失败：
_make_query_tool.<locals>._run() got an unexpected keyword argument 'query'
参数名写成了 query（它叫 sql），而 call_tool 对动态工具是裸 **args 展开的，
跳过了 args_schema —— pydantic 那句"缺少 sql 字段"漏成了只剩内部闭包名的
TypeError，既指不到是哪个工具，也说不出该填什么。同一个函数里内置工具那条
分支本来是校验的，两条路径不一致。
registry 里补上 prepare_args / describe_args，两条路径共用：
- 候选唯一时（多一个不认识的、少一个必填的）改名重试，并发一条 warn 说破
—— 配置里那个错名还在，替它跑通一次却不吭声，下次照样踩
- 有歧义就不猜，报错改成"参数不对：query 不是它的参数；缺少必填参数 sql。
它接受：sql（必填，字符串）— 要执行的 SQL…"，模型照着这句就能改对。
这才是失败之后真正能自愈的重试
- 顺带堵了一类静默失败：pydantic 默认把认不出的键悄悄丢掉，全可选 schema 上
{"tabel": "orders"} 会"成功"执行一次什么都没查的调用
agent 循环默认改成串行：一轮只调一个工具，看到结果再想下一步。并行更快，但
一批调用中间没有任何新的思考——第二个是照着"还没看到第一个结果"时的判断发出
去的。节点上留了「并行执行工具」开关。两个必须守住的点：没执行的调用绝不发
tool.start（前端靠 call_id 配对，发了没结尾的 start 会永远转圈），但必须各有
一条配对的 tool_result（少一条下次调用直接 400）。
交错思考跟着补上。4.6 起 adaptive thinking 自带交错，不需要也不该挂 beta
header（Opus 4.6 上手动模式压根没有交错）；4/4.5 那一代要手动开 thinking +
interleaved-thinking beta，光挂 header 是一条什么都不做的空设置。开了 thinking
就不能再传 temperature，这一代本来是收的，漏了就是 400。
还有两处是串行带出来的，一并治了：
- 步数用完时收尾取的是 messages[-1]，而那往往是给模型看的 ToolMessage。
真漏到用户面前当过答案（run dd9927e6）：一句"本轮只执行了第一个工具"冒充
结论。改成只取模型自己说过的话，并把"步数用完了、去调大最大步数"附在后面
- db_schema 一次能问多张表。一次调用就是一步，逐张问 6 张表就是 6 步——
实测 8 步的运行里 7 步花在这上面，最后只剩一步真查数据
338 lines
14 KiB
Python
338 lines
14 KiB
Python
from __future__ import annotations
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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.func import task
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from app.core.config import settings
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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 NodeContext, NodeError
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from app.engine.state import GraphState, message_text, template_context
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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.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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_MAX_DEPTH = 3
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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, "supervisor 节点至少要配一个 agent")
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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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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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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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"next": {"type": "string", "enum": names + ["FINISH"]},
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"instruction": {"type": "string"},
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"reason": {"type": "string"},
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},
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"required": ["next"],
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}
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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 _accumulate(msg: Any, model_id: str) -> None:
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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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usage_total["input_tokens"] += i
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usage_total["output_tokens"] += o
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usage_total["calls"] += 1
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usage_total["cost_usd"] = round(
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usage_total["cost_usd"] + catalog.estimate_cost(model_id, i, o), 6
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)
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@task
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async def route(round_no: int, progress: str) -> dict[str, Any]:
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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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"决定下一步交给谁处理，并给出一句明确的指令。"
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"如果目标已经达成，next 返回 FINISH。"
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)
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try:
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value = await supervisor_model.with_structured_output(route_schema).ainvoke(prompt)
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if not isinstance(value, dict):
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value = json.loads(message_text(value))
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except Exception: # noqa: BLE001
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raw = message_text(await supervisor_model.ainvoke(prompt))
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picked = next((n for n in names if n in raw), "FINISH")
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value = {"next": picked, "instruction": raw[:500], "reason": ""}
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return value
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@task
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async def work(round_no: int, name: str, instruction: str, progress: str) -> dict[str, Any]:
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cfg = agent_map[name]
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async with SessionLocal() as session:
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model, model_id = await get_chat_model(
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session,
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ModelSpec(
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provider=cfg.get("provider") or ctx.cfg("provider"),
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model=cfg.get("model") or ctx.cfg("model"),
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max_tokens=int(cfg.get("max_tokens") or 4096),
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),
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)
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tool_ctx = ToolContext(
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run_id=ctx.run.run_id,
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node_id=f"{ctx.node.id}:{name}",
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sandbox_session=ctx.run.thread_id,
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memory_scope=ctx.run.memory_scope,
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collection=ctx.run.collection,
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)
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tools = await build_tools(cfg.get("tools", []) or [], tool_ctx, session=session)
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bound = bind_tools_safely(model, tools, parallel=False)
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tool_map = {t.name: t for t in tools}
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messages: list[Any] = [
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SystemMessage(content=cfg.get("system") or f"你是 {name}。"),
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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}"),
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]
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calls: list[dict[str, Any]] = []
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for _ in range(int(cfg.get("max_steps") or 4)):
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reply = await bound.ainvoke(messages)
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_accumulate(reply, model_id)
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messages.append(reply)
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tool_calls = getattr(reply, "tool_calls", None) or []
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if not tool_calls:
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return {"text": message_text(reply), "tool_calls": calls}
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for call in tool_calls:
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tname = call.get("name", "")
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targs = call.get("args", {}) or {}
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cid = call.get("id") or f"{name}-{tname}"
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if tname not in tool_map:
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ctx.emit(EventType.TOOL_START, tool=tname, args=targs, agent=name, call_id=cid)
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content = f"错误：没有名为 {tname} 的工具"
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else:
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schema = args_model_of(tool_map[tname])
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fix_note = None
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args_error = None
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if schema is not None:
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try:
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targs, fix_note = prepare_args(schema, targs)
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except ToolArgsError as e:
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args_error = str(e)
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# 纠正后的参数才是真正要执行的那份，tool.start 要发它
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ctx.emit(EventType.TOOL_START, tool=tname, args=targs, agent=name, call_id=cid)
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if fix_note:
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ctx.emit(EventType.LOG, level="warn",
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message=f"工具 {tname}：{fix_note}")
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if args_error:
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# 参数就不对，没必要真调一次。把"它接受什么"喂回去让它改
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content = args_error
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else:
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try:
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raw = await tool_map[tname].ainvoke(targs)
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content = raw if isinstance(raw, str) else json.dumps(raw, ensure_ascii=False, default=str)
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except Exception as e: # noqa: BLE001
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content = f"工具失败：{type(e).__name__}: {e}"
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ctx.emit(EventType.TOOL_END, tool=tname, agent=name, call_id=cid, preview=content[:1500])
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calls.append({"tool": tname, "args": targs, "result": content[:2000]})
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messages.append(ToolMessage(content=content, tool_call_id=cid))
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return {"text": message_text(messages[-1]), "tool_calls": calls}
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progress_lines: list[str] = []
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final_text = ""
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for round_no in range(max_rounds):
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progress = "\n\n".join(progress_lines)
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decision = await route(round_no, progress)
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_accumulate(AIMessage(content=""), sup_model_id) # route 用了一次调用
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nxt = str(decision.get("next") or "FINISH")
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reason = str(decision.get("reason", ""))
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ctx.emit(
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EventType.LOG, level="info", round=round_no,
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message=f"调度 → {nxt}" + (f"（{reason}）" if reason else ""),
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)
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if nxt == "FINISH" or nxt not in agent_map:
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final_text = progress_lines[-1].split("：", 1)[-1] if progress_lines else ""
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break
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instruction = str(decision.get("instruction") or goal)
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ctx.emit(EventType.AGENT_STEP_START, agent=nxt, instruction=instruction[:300],
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round=round_no)
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started = time.perf_counter()
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outcome = await work(round_no, nxt, instruction, progress)
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elapsed = int((time.perf_counter() - started) * 1000)
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ctx.emit(EventType.AGENT_STEP_END, agent=nxt, duration_ms=elapsed,
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round=round_no, preview=outcome["text"][:500])
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progress_lines.append(f"【{nxt}】{outcome['text']}")
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transcript.append(
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{"round": round_no, "agent": nxt, "instruction": instruction, **outcome,
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"duration_ms": elapsed}
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)
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final_text = outcome["text"]
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usage_total["total_tokens"] = usage_total["input_tokens"] + usage_total["output_tokens"]
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result = {
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"text": final_text,
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"rounds": len(transcript),
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"transcript": transcript,
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"agents": names,
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}
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updates: dict[str, Any] = {
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"nodes": {ctx.node.id: result},
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"usage": usage_total,
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}
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if ctx.cfg("emit_message", True):
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updates["messages"] = [AIMessage(content=final_text or "(空)")]
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var_name = ctx.cfg("assign_to", "")
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if var_name:
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updates["vars"] = {var_name: final_text}
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return updates
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# --------------------------------------------------------------------------
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# 子图：把另一张工作流当成一个节点
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# --------------------------------------------------------------------------
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async def run_subgraph(state: GraphState, ctx: NodeContext) -> dict[str, Any]:
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"""嵌套执行另一个工作流。
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子图共享同一个 run 的事件流（节点 id 会加前缀），所以在时间线上能看到它内部
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每一步；但它有独立的状态，不会污染父图的变量池。
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"""
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from app.engine.compiler import compile_graph # 延迟导入，避免循环依赖
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from app.engine.schema import GraphSpec
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if ctx.run.depth >= _MAX_DEPTH:
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raise NodeError(ctx.node.id, f"子图嵌套超过 {_MAX_DEPTH} 层，已阻止")
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workflow_id = ctx.cfg("workflow_id")
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if not workflow_id:
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raise NodeError(ctx.node.id, "子图节点没有选择工作流")
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# 版本钉死：这就是"方法卡"的机制核心。钉了 workflow_version 就永远
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# 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；
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# 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）。
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pinned = ctx.cfg("workflow_version")
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async with SessionLocal() as session:
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workflow = await session.get(Workflow, workflow_id)
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if not workflow:
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raise NodeError(ctx.node.id, f"找不到工作流 {workflow_id}")
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if pinned:
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from sqlalchemy import select
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from app.db.models import WorkflowVersion
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snapshot = (
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await session.execute(
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select(WorkflowVersion).where(
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WorkflowVersion.workflow_id == workflow_id,
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WorkflowVersion.version == int(pinned),
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)
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)
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).scalar_one_or_none()
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if not snapshot:
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raise NodeError(
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ctx.node.id, f"工作流「{workflow.name}」没有 v{pinned} 这个版本"
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)
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sub_graph = snapshot.graph
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used_version = int(pinned)
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else:
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sub_graph = workflow.graph
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used_version = workflow.version
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sub_input = ctx.render(ctx.cfg("input", {}) or {}, state)
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if not isinstance(sub_input, dict):
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sub_input = {"input": sub_input}
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sub_spec = GraphSpec.model_validate(sub_graph)
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sub_run = type(ctx.run)(
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run_id=ctx.run.run_id,
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thread_id=f"{ctx.run.thread_id}:{ctx.node.id}",
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spec=sub_spec,
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workflow_id=workflow.id,
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depth=ctx.run.depth + 1,
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memory_scope=ctx.run.memory_scope,
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collection=ctx.run.collection,
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extra={**ctx.run.extra, "node_prefix": f"{ctx.node.id}/"},
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)
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ctx.emit(EventType.LOG, level="info",
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message=f"进入子图「{workflow.name}」（{len(sub_spec.nodes)} 个节点）")
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compiled = compile_graph(sub_spec, sub_run)
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# 子图不带 checkpointer：它的断点由父图的 checkpoint 统一承载
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app = compiled.compile()
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result_state = await app.ainvoke(
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{
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"input": sub_input,
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"vars": sub_input,
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"nodes": {},
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"output": {},
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"messages": [],
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"usage": {},
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"trail": [],
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"loops": {},
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},
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{"recursion_limit": settings.max_graph_steps},
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)
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output = result_state.get("output") or {}
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result = {
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"output": output,
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"workflow": workflow.name,
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# 溯源信息：这次到底跑的是哪个版本、是不是钉死的
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"workflow_version": used_version,
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"pinned": bool(pinned),
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"text": json.dumps(output, ensure_ascii=False) if output else "",
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}
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ctx.emit(EventType.LOG, level="info", message=f"子图「{workflow.name}」完成")
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updates: dict[str, Any] = {
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"nodes": {ctx.node.id: result},
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"usage": result_state.get("usage") or {},
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}
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var_name = ctx.cfg("assign_to", "")
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if var_name:
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updates["vars"] = {var_name: output}
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return updates
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