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ThearchyHelios
3a1e4aa816
用户拿到的是这么一段：
OpenAIAuthenticationError: Error code: 401 - {'error': {'code':
'invalid_model', 'message': 'The model does not exist or you do not have
access to it.'}}
流式失败，回退非流式：（同一段，再来一遍）
401 + Authentication 看着像 key 过期，实际上 key 好好的——是 model id 在这个
端点上不存在。照着原文去换 key 是白费功夫。而且整条轨迹里**没有任何地方**
记着试的是哪个模型：llm.start 只带了 message_count。
三处：
1. llm.start 带上 model。调用失败时至少知道试的是什么。
2. explain_model_error()：invalid_model 译成"模型「x」在这个供应商上不存在…
去设置→供应商改模型列表"；额度耗尽单独认；不认识的错误照抄原文（猜错了
比不猜更糟）。流式回退再失败时不再把同一段原文吐两遍，而是抛一条
NodeError。结构化输出那条路径也走同一个翻译——模型不存在不叫"结构化
输出失败"。
3. Copilot 提示里补上"model 字段不要填"。它原先只被告知 llm 节点有个 model
字段，却从不知道有哪些 model id，于是按供应商名字猜（看到 DeepSeek 就写
deepseek-chat）——猜出来的那个正好不存在。这是这次报错的真正来源，不修
的话每次生成都会再踩一遍。
顺带把那个供应商的模型列表改对了：列着的 deepseek-chat / deepseek-reasoner
两个都返回 invalid_model，而真正能用的 deepseek-v4-pro 反倒不在列表里（它是
default_model，所以"跟随默认"的节点一直是好的，手动选模型的节点必挂）。
逐个实测后换成 deepseek-v4-pro / deepseek-v3.2 / ernie-4.5-turbo-128k。
已确认没有已保存的工作流节点写死了那两个失效 id。
444 lines
18 KiB
Python
444 lines
18 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, BaseMessage, HumanMessage, SystemMessage, ToolMessage
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from langchain_core.tools import BaseTool
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from langgraph.func import task
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from langgraph.types import interrupt
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from sqlalchemy import select
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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 Skill
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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, thinking_text
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from app.providers import catalog
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from app.providers.factory import ModelSpec, ProviderNotConfigured, get_chat_model
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from app.tools.registry import ToolContext, build_tools, get_spec
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# --------------------------------------------------------------------------
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# 公共部分
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# --------------------------------------------------------------------------
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def _model_spec(ctx: NodeContext) -> ModelSpec:
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return ModelSpec(
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provider=ctx.cfg("provider"),
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model=ctx.cfg("model"),
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temperature=ctx.cfg("temperature"),
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max_tokens=ctx.cfg("max_tokens"),
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thinking=ctx.cfg("thinking"),
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effort=ctx.cfg("effort"),
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)
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async def _load_skills(names: list[str]) -> str:
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"""把挂载的 Skill 拼成一段 system 指令。
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Skill 把"怎么做事"从图结构里抽出来单独管理 —— 改方法论不用动编排。
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"""
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if not names:
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return ""
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async with SessionLocal() as session:
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rows = list(
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(
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await session.execute(
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select(Skill).where(Skill.name.in_(names), Skill.enabled.is_(True))
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)
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).scalars()
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)
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if not rows:
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return ""
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blocks: list[str] = []
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for skill in rows:
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block = f"## {skill.name}\n{skill.instructions.strip()}"
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if skill.examples:
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examples = "\n".join(
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f"- 输入：{e.get('input', '')}\n 输出：{e.get('output', '')}"
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for e in skill.examples
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if isinstance(e, dict)
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)
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if examples:
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block += f"\n\n示例：\n{examples}"
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blocks.append(block)
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return "# 适用的方法论\n\n" + "\n\n".join(blocks)
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async def _build_messages(state: GraphState, ctx: NodeContext) -> list[BaseMessage]:
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tctx = template_context(state)
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messages: list[BaseMessage] = []
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system_parts: list[str] = []
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system = ctx.render_str(ctx.cfg("system", ""), state)
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if system:
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system_parts.append(system)
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skill_text = await _load_skills(ctx.cfg("skills", []) or [])
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if skill_text:
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system_parts.append(skill_text)
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if system_parts:
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messages.append(SystemMessage(content="\n\n".join(system_parts)))
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# 是否把之前节点的对话历史带上
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if ctx.cfg("use_history", False):
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messages.extend(state.get("messages") or [])
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prompt = ctx.render_str(ctx.cfg("prompt", ""), state)
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if not prompt and not ctx.cfg("use_history", False):
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prompt = str(tctx.get("last_message") or json.dumps(tctx.get("input"), ensure_ascii=False))
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if prompt:
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messages.append(HumanMessage(content=prompt))
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if not messages:
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raise NodeError(ctx.node.id, "没有可发送的内容：system 和 prompt 都是空的")
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return messages
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def _usage_of(message: BaseMessage, model_id: str) -> dict[str, Any]:
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meta = getattr(message, "usage_metadata", None) or {}
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inp = int(meta.get("input_tokens") or 0)
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out = int(meta.get("output_tokens") or 0)
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return {
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"input_tokens": inp,
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"output_tokens": out,
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"total_tokens": inp + out,
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"cost_usd": round(catalog.estimate_cost(model_id, inp, out), 6),
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"calls": 1,
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}
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def explain_model_error(exc: Exception, model_id: str) -> str:
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"""把供应商的原始报错翻成能照着做的一句话。
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最典型的是 `401 invalid_model`：网关用"认证失败"的状态码报"模型不存在"，
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原文长得像 key 过期，实际上 key 好好的、只是这个 model id 在这个端点上
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不存在（供应商配置里列了几个它根本不提供的模型，就会这样）。照着原文去
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换 key 是白费功夫，所以这里必须把话说对。
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"""
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text = str(exc)
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if "invalid_model" in text or "model does not exist" in text.lower():
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return (
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f"模型「{model_id}」在这个供应商上不存在，或者当前 key 没有它的权限。"
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f"去「设置 → 供应商」把模型列表改成它真正提供的 id，"
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f"或者在节点上换一个模型。"
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)
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if "insufficient" in text.lower() or "quota" in text.lower():
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return f"模型「{model_id}」的额度用完了：{text[:200]}"
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return f"调用模型「{model_id}」失败：{type(exc).__name__}: {text[:300]}"
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async def _invoke_streaming(
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model: Any, messages: list[BaseMessage], ctx: NodeContext, model_id: str = ""
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) -> AIMessage:
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"""流式调用并把 token 实时推给前端。
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失败时回退到非流式 —— 有些兼容网关不支持 SSE，不该因此让整个节点挂掉。
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但回退也失败的话，两次都是同一个原因，不能只把原始异常往上抛：用户看到的
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会是两段一模一样的供应商报错，而里面既没有模型名也没有下一步该干什么。
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"""
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# 模型 id 要跟着 llm.start 走。以前这条事件只有 message_count，于是调用失败时
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# 整条轨迹里**没有任何地方**记着试的是哪个模型——排查只能靠翻配置猜。
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ctx.emit(EventType.LLM_START, model=model_id or None, message_count=len(messages))
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chunks: list[Any] = []
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response: AIMessage | None = None
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try:
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async for chunk in model.astream(messages):
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chunks.append(chunk)
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text = message_text(chunk)
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if text:
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ctx.emit(EventType.LLM_TOKEN, delta=text)
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reasoning = thinking_text(chunk)
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if reasoning:
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# 增量只服务画布的实时滚动，不进轨迹（runner 标记为 ephemeral）
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ctx.emit(EventType.LLM_THINKING_DELTA, delta=reasoning)
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except NotImplementedError:
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response = await model.ainvoke(messages)
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except Exception as e: # noqa: BLE001
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ctx.emit(EventType.LOG, level="warn",
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message=f"流式失败，回退非流式：{explain_model_error(e, model_id)}")
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try:
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response = await model.ainvoke(messages)
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except Exception as retry_error: # noqa: BLE001
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raise NodeError(ctx.node.id, explain_model_error(retry_error, model_id)) from retry_error
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if response is None:
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if not chunks:
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response = await model.ainvoke(messages)
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else:
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merged = chunks[0]
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for chunk in chunks[1:]:
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merged = merged + chunk
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response = merged
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# 一次思考在轨迹里只占一条事件：调用收尾时落完整内容，
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# 刷新页面回放时也靠它一次性恢复，而不是重放几十条增量
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reasoning = thinking_text(response)
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if reasoning:
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ctx.emit(
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EventType.LLM_THINKING,
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text=reasoning[:6000],
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chars=len(reasoning),
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truncated=len(reasoning) > 6000,
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)
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return response
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# --------------------------------------------------------------------------
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# LLM 节点：单次调用
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# --------------------------------------------------------------------------
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async def run_llm(state: GraphState, ctx: NodeContext) -> dict[str, Any]:
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spec = _model_spec(ctx)
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async with SessionLocal() as session:
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try:
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model, model_id = await get_chat_model(session, spec)
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except ProviderNotConfigured as e:
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raise NodeError(ctx.node.id, str(e)) from e
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messages = await _build_messages(state, ctx)
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schema = ctx.cfg("output_schema")
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started = time.perf_counter()
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if schema:
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# 要结构化结果就走原生 structured output，这条路径拿不到 token 流
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ctx.emit(EventType.LLM_START, model=model_id, structured=True,
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message_count=len(messages))
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try:
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# langchain 靠 "title" 识别裸 JSON Schema dict，缺了会抛 Unsupported function
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if isinstance(schema, dict) and "title" not in schema:
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schema = {"title": "output", **schema}
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structured = model.with_structured_output(schema)
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value = await structured.ainvoke(messages)
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except Exception as e: # noqa: BLE001
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# 模型压根不存在也会走到这里，那不是"结构化输出失败"
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if "invalid_model" in str(e) or "model does not exist" in str(e).lower():
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raise NodeError(ctx.node.id, explain_model_error(e, model_id)) from e
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raise NodeError(ctx.node.id, f"结构化输出失败：{type(e).__name__}: {e}") from e
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payload = value if isinstance(value, (dict, list)) else getattr(value, "model_dump", lambda: value)()
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output = {"data": payload, "text": json.dumps(payload, ensure_ascii=False, indent=2)}
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response: BaseMessage = AIMessage(content=output["text"])
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else:
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response = await _invoke_streaming(model, messages, ctx, model_id)
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text = message_text(response)
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reasoning = thinking_text(response)
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if not text and reasoning:
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# 思考型模型把额度全花在推理上了，正文是空的 —— 说清楚而不是抛个空结果给下游
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ctx.emit(EventType.LOG, level="warn",
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message="模型只输出了思考内容，正文为空。把 max_tokens 调大，或把 thinking 设为 off。")
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output = {"text": text, "thinking": reasoning}
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usage = _usage_of(response, model_id)
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elapsed = int((time.perf_counter() - started) * 1000)
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ctx.emit(
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EventType.LLM_END,
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model=model_id,
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duration_ms=elapsed,
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**usage,
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)
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result = {**output, "model": model_id, "duration_ms": elapsed}
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updates: dict[str, Any] = {
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"nodes": {ctx.node.id: result},
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"usage": usage,
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}
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if ctx.cfg("emit_message", True):
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updates["messages"] = [response]
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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.get("data", output.get("text"))}
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return updates
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# --------------------------------------------------------------------------
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# Agent 节点：带工具循环
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# --------------------------------------------------------------------------
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async def _resolve_tools(ctx: NodeContext, state: GraphState) -> list[BaseTool]:
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names = ctx.cfg("tools", []) or []
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tool_ctx = ToolContext(
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run_id=ctx.run.run_id,
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node_id=ctx.node.id,
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sandbox_session=ctx.run.thread_id,
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memory_scope=ctx.cfg("memory_scope") or ctx.run.memory_scope,
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collection=ctx.cfg("collection") or ctx.run.collection,
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)
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async with SessionLocal() as session:
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return await build_tools(names, tool_ctx, session=session)
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def _needs_approval(ctx: NodeContext, tool_name: str) -> bool:
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mode = ctx.cfg("approval", "dangerous") # never | dangerous | always
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if mode == "always":
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return True
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if mode == "never":
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return False
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spec = get_spec(tool_name)
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return bool(spec and spec.dangerous)
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async def run_agent(state: GraphState, ctx: NodeContext) -> dict[str, Any]:
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"""ReAct 循环：模型想调工具就调，拿到结果继续想，直到给出最终答案。
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这里没有用 prebuilt 的 create_react_agent，因为需要在循环内部做三件它不做的事：
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每步工具调用都往画布推事件、对危险工具逐个弹人工审批、以及步数护栏。
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"""
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model_spec = _model_spec(ctx)
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async with SessionLocal() as session:
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try:
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base_model, model_id = await get_chat_model(session, model_spec)
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except ProviderNotConfigured as e:
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raise NodeError(ctx.node.id, str(e)) from e
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tools = await _resolve_tools(ctx, state)
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tool_map = {t.name: t for t in tools}
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model = base_model.bind_tools(tools) if tools else base_model
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messages = await _build_messages(state, ctx)
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max_steps = min(int(ctx.cfg("max_steps", 8) or 8), settings.max_agent_steps)
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total_usage: dict[str, Any] = {"input_tokens": 0, "output_tokens": 0, "cost_usd": 0.0, "calls": 0}
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transcript: list[dict[str, Any]] = []
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# @task 的返回值会进 checkpoint：人工审批导致节点重放时，
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# 之前已经完成的模型调用和工具执行不会重跑，也就不会重复计费。
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@task
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async def llm_step(step: int, payload: list[BaseMessage]) -> BaseMessage:
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return await _invoke_streaming(model, payload, ctx, model_id)
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@task
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async def tool_step(step: int, name: str, args: dict[str, Any]) -> str:
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tool = tool_map[name]
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result = await tool.ainvoke(args)
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return result if isinstance(result, str) else json.dumps(result, ensure_ascii=False, default=str)
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final_text = ""
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for step in range(max_steps):
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response = await llm_step(step, messages)
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messages.append(response)
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usage = _usage_of(response, model_id)
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for key in ("input_tokens", "output_tokens", "calls"):
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total_usage[key] += usage[key]
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total_usage["cost_usd"] = round(total_usage["cost_usd"] + usage["cost_usd"], 6)
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tool_calls = getattr(response, "tool_calls", None) or []
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if not tool_calls:
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final_text = message_text(response)
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break
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for call in tool_calls:
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name = call.get("name", "")
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args = call.get("args", {}) or {}
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call_id = call.get("id") or f"{ctx.node.id}-{step}-{name}"
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if name not in tool_map:
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messages.append(
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ToolMessage(content=f"错误：没有名为 {name} 的工具", tool_call_id=call_id)
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)
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continue
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if _needs_approval(ctx, name):
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ctx.emit(
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EventType.HUMAN_REQUESTED,
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mode="approve",
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tool=name,
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args=args,
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title=f"Agent 想调用工具 {name}",
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)
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decision = interrupt(
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{
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"kind": "tool_approval",
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"node_id": ctx.node.id,
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"tool": name,
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"args": args,
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"title": f"是否允许调用 {name}？",
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}
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)
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approved, note, override = _parse_decision(decision)
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ctx.emit(EventType.HUMAN_RESOLVED, tool=name, approved=approved, note=note)
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if not approved:
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messages.append(
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ToolMessage(
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content=f"用户拒绝了这次调用。原因：{note or '未说明'}。请换一种方式。",
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tool_call_id=call_id,
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)
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)
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transcript.append({"tool": name, "args": args, "denied": True, "note": note})
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continue
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if isinstance(override, dict) and override:
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args = override
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ctx.emit(EventType.TOOL_START, tool=name, args=args, call_id=call_id)
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started = time.perf_counter()
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try:
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content = await tool_step(step, name, args)
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ok = True
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except Exception as e: # noqa: BLE001 - 工具失败要喂回模型，让它自己纠错
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content = f"工具执行失败：{type(e).__name__}: {e}"
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ok = False
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elapsed = int((time.perf_counter() - started) * 1000)
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snapshot_id: str | None = None
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if ok:
|
||
from app.core.artifact_store import put_json
|
||
|
||
try:
|
||
snapshot_id = await put_json(
|
||
{"tool": name, "args": args, "result": content},
|
||
kind="tool_snapshot", run_id=ctx.run.run_id, node_id=ctx.node.id,
|
||
)
|
||
except Exception: # noqa: BLE001
|
||
snapshot_id = None
|
||
ctx.emit(
|
||
EventType.TOOL_END if ok else EventType.TOOL_ERROR,
|
||
tool=name,
|
||
call_id=call_id,
|
||
duration_ms=elapsed,
|
||
preview=content[:2000],
|
||
artifact=snapshot_id,
|
||
)
|
||
transcript.append(
|
||
{"tool": name, "args": args, "ok": ok, "duration_ms": elapsed, "result": content[:4000]}
|
||
)
|
||
messages.append(ToolMessage(content=content, tool_call_id=call_id))
|
||
else:
|
||
final_text = message_text(messages[-1]) if messages else ""
|
||
ctx.emit(
|
||
EventType.LOG,
|
||
level="warn",
|
||
message=f"agent 达到最大步数 {max_steps}，提前收尾",
|
||
)
|
||
|
||
total_usage["total_tokens"] = total_usage["input_tokens"] + total_usage["output_tokens"]
|
||
result = {
|
||
"text": final_text,
|
||
"steps": len(transcript),
|
||
"tool_calls": transcript,
|
||
"model": model_id,
|
||
}
|
||
updates: dict[str, Any] = {
|
||
"nodes": {ctx.node.id: result},
|
||
"usage": total_usage,
|
||
}
|
||
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
|
||
|
||
|
||
def _parse_decision(decision: Any) -> tuple[bool, str, dict[str, Any] | None]:
|
||
"""人工审批的返回值兼容几种写法：布尔、字符串、或带备注的结构体。"""
|
||
if isinstance(decision, bool):
|
||
return decision, "", None
|
||
if isinstance(decision, str):
|
||
return decision.lower() in ("yes", "y", "true", "approve", "ok", "同意"), decision, None
|
||
if isinstance(decision, dict):
|
||
approved = decision.get("approved")
|
||
if approved is None:
|
||
approved = decision.get("decision") in ("approve", "yes", True)
|
||
return bool(approved), str(decision.get("note", "")), decision.get("args")
|
||
return bool(decision), "", None
|