rui40000andClaude Opus 5
205a7c0e31
线上跑批时最常见的失败不是参数错，而是链路抖动，典型报错是 SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol'))。 此前这两个节点只有针对 HTTP 400 的「自适应参数重试」（业务层）， 网络异常直接落到 except ConnectionError 返回错误字符串——而 SSLError 正是 ConnectionError 的子类，等于一次失败就放弃。 新增 max_retries（默认 3）与 timeout（默认 180）两个参数： 会重连：SSL 握手被打断、连接被重置、连接/读超时、响应体截断 （ChunkedEncodingError / JSONDecodeError），以及 429 限流与 5xx 临时故障。 不重连：参数类 400、鉴权类 401/403、404——重试结果相同，只会拖慢报错。 退避 1s→2s→4s→8s→16s 指数增长并叠加 ±25% 抖动。抖动是必要的：一条 工作流里常有多个 API 节点同时失败，无抖动会让它们在同一毫秒一起重连， 把刚缓过来的服务端再打垮一次。服务端给了 Retry-After 时以它为准。 与既有的自适应参数重试分属两层、互不消耗额度；参数被剔除后的 payload 在后续重连中保留，不会重蹈覆辙。HTTPError 报错信息补上已重连次数， 以免用户误以为没有重试。 mock 验证 24 项全通过：失败序列后成功、重试耗尽、max_retries=0/5、 退避递增与抖动、Retry-After、429/5xx/401/404 分流、读超时、响应体截断、 连接重置、参数自适应不受影响、旧工作流不传新参数的向后兼容、ZenMux 同样生效。 Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
542 lines
24 KiB
Python
542 lines
24 KiB
Python
# -*- coding: utf-8 -*-
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"""
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越光 API 连接节点
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=================
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通过越光（Nebula）聚合平台调用其收录的文本类模型。
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接口为 OpenAI 兼容协议，chat 端点：
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https://llm.ai-nebula.com/v1/chat/completions
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与 ZenMux 节点的差异：
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- 越光的可用模型与价格由官方规范文档给定，没有可枚举的模型接口，
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因此模型清单内置在 model_registry.py 里，不做在线快照；
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- 越光的 model id **不带厂商前缀**（gpt-4o 而非 openai/gpt-4o），
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下拉里同厂商靠排序聚在一起，搜索时输 gpt / claude / deepseek / kimi 过滤。
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其余行为与 ZenMux 节点一致，包括那套「自适应参数重试」——
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部分模型弃用 temperature、或要求用 max_completion_tokens 取代 max_tokens，
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命中这类 400 报错时会剔除/改名对应参数后自动重试，正常请求零额外开销。
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"""
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import base64
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import io
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import json
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import os
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import random
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import re
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import time
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import numpy as np
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import requests
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from PIL import Image
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from .model_registry import (
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DEFAULT_MODEL_ID,
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all_model_labels,
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default_model_label,
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label_to_model_id,
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model_label_by_id,
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model_prices,
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model_vendor,
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)
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# 越光平台固定地址（OpenAI 兼容）
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DEFAULT_BASE_URL = "https://llm.ai-nebula.com/v1"
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# 输入框默认值不带 "://"——ComfyUI 前端会吞掉文本框里的协议片段
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# （本仓库为此修过多次），协议由 _build_chat_url 自动补全。
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DEFAULT_BASE_URL_INPUT = "llm.ai-nebula.com/v1"
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def _clear_proxy_env():
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"""清除可能干扰 requests 的代理环境变量（仅本模块加载时执行一次）。"""
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for key in ("HTTP_PROXY", "HTTPS_PROXY", "http_proxy", "https_proxy"):
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os.environ.pop(key, None)
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_clear_proxy_env()
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def _build_chat_url(base_url: str) -> str:
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"""由 base_url 拼出 chat/completions 端点，容忍结尾斜杠有无。"""
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s = (base_url or "").strip()
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if not s:
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s = DEFAULT_BASE_URL
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if not re.match(r"^https?://", s, flags=re.IGNORECASE):
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s = "https://" + s.lstrip("/")
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s = s.rstrip("/")
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if s.lower().endswith("/chat/completions"):
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return s
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return s + "/chat/completions"
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# ── 网络层自动重连 ──
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# 可重试的 HTTP 状态：限流与服务端临时故障，换个时间再来往往就好了。
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# 4xx（除 408/429）是参数或鉴权问题，重试纯属浪费，不列入。
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_RETRIABLE_STATUS = {408, 425, 429, 500, 502, 503, 504, 520, 521, 522, 523, 524}
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# 这几类异常的共同点是「没拿到完整响应」，重连通常能恢复：
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# ConnectionError —— 含 SSLError/SSLEOFError（握手被打断）、连接被重置
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# Timeout —— 连接超时与读超时
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# ChunkedEncodingError / JSONDecodeError —— 响应体传到一半断了
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_RETRIABLE_EXC = (
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requests.exceptions.ConnectionError,
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requests.exceptions.Timeout,
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requests.exceptions.ChunkedEncodingError,
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json.JSONDecodeError,
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)
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def _retry_wait(attempt: int, resp=None) -> float:
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"""指数退避 + 随机抖动，返回本次该等几秒。attempt 从 0 起。
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抖动不是可有可无的装饰：一条工作流里常有多个 API 节点同时失败，
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没有抖动它们会在同一毫秒一起重连，把刚缓过来的服务端再打垮一次。
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服务端明确给了 Retry-After 时以它为准。
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"""
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if resp is not None:
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ra = resp.headers.get("Retry-After")
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if ra:
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try:
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return max(0.5, min(float(ra), 60.0))
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except (TypeError, ValueError):
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pass
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base = min(2.0 ** attempt, 16.0) # 1s → 2s → 4s → 8s → 16s 封顶
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return round(base * random.uniform(0.75, 1.25), 2)
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def _build_proxy_url(raw_proxy: str) -> str:
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"""把 '127.0.0.1:7890' 或 'http://127.0.0.1:7890' 统一成带协议的地址。"""
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s = (raw_proxy or "").strip()
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if not s:
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return ""
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if re.match(r"^https?://", s, flags=re.IGNORECASE):
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return s
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s = re.sub(r"^https?\s*:\s*/*/?\s*", "", s, flags=re.IGNORECASE).strip("/")
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return ("http://" + s) if s else ""
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# 遇到「参数不被支持 / 已弃用」的 400 时，可安全剔除的采样参数
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# （绝不触碰 model / messages 等核心字段）
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_ADJUSTABLE_PARAMS = ("temperature", "top_p", "seed", "max_tokens",
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"max_completion_tokens", "frequency_penalty",
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"presence_penalty", "top_k")
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# 判定「这条 400 是参数问题」的关键词（命中才尝试剔除重试）
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_PARAM_ERR_HINTS = ("deprecat", "unsupport", "not support", "not allowed",
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"invalid", "unexpected", "unknown", "must be", "cannot",
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"not permitted", "removed")
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def _diagnose_param(resp, payload):
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"""
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从 400 响应里判断是哪个采样参数导致失败，返回处理指令：
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("drop", 参数名) —— 从 payload 剔除该参数后重试
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("rename_mct", ...) —— 把 max_tokens 改名为 max_completion_tokens 后重试
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None —— 非参数类错误，不重试
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各模型的参数规则不一（新版 claude 弃用 temperature、OpenAI reasoning 系
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要求 max_completion_tokens 等），靠错误消息动态识别，免维护静态清单。
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"""
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try:
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msg = (resp.json().get("error", {}) or {}).get("message", "") or resp.text or ""
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except Exception:
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msg = getattr(resp, "text", "") or ""
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low = msg.lower()
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if "max_completion_tokens" in low and "max_tokens" in payload:
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return ("rename_mct", "max_tokens")
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for name in re.findall(r"""[`'"]([a-zA-Z_]+)[`'"]""", msg):
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if name in payload and name in _ADJUSTABLE_PARAMS:
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return ("drop", name)
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if any(h in low for h in _PARAM_ERR_HINTS):
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for name in _ADJUSTABLE_PARAMS:
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if name in payload and name in low:
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return ("drop", name)
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return None
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class YueGuangNode:
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"""越光 API 连接节点。"""
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@classmethod
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def INPUT_TYPES(cls):
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model_labels = all_model_labels()
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default_label = default_model_label()
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return {
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"required": {
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"api_key": ("STRING", {
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"default": "",
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"multiline": False,
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"tooltip": "越光的 API Key（形如 sk-xxxxxxxx）。\n"
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"⚠ 工作流会连同此值一起保存，分享 json 前记得清空。"
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}),
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"model": (model_labels, {
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"default": default_label,
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"tooltip": "模型，标签里直接带了输入/输出单价（USD/百万 token）。\n"
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"越光的 model id 不带厂商前缀，下拉里同厂商是靠排序\n"
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"聚在一起的 —— 在下拉的搜索框输 gpt / claude /\n"
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"deepseek / kimi 即可快速过滤。\n"
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"默认 deepseek-v4-flash 是表里最便宜的（$0.1/$0.3）。"
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}),
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"system_prompt": ("STRING", {
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"default": "You are a helpful assistant.",
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"multiline": True,
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"tooltip": "系统提示词：设定模型的角色与总体行为准则。\n"
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"输出格式要求（如「只返回 JSON」）写在这里最稳定。"
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}),
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"user_prompt": ("STRING", {
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"default": "",
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"multiline": True,
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"tooltip": "用户提示词：这一次具体要模型做什么。"
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}),
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"seed": ("INT", {
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"default": 0,
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"min": 0,
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"max": 0xffffffffffffffff,
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"tooltip": "随机种子。多数模型并不真正支持复现，\n"
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"这里主要用于强制节点重新执行（改了它就不会走缓存）。"
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}),
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},
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"optional": {
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"temperature": ("FLOAT", {
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"default": 0.7,
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"min": 0.0,
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"max": 2.0,
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"step": 0.1,
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"tooltip": "采样温度：越低越稳定保守，越高越发散。\n"
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"结构化输出用 0~0.3，创意文案用 0.7~1.0。\n"
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"部分新模型已弃用该参数，节点会自动剔除后重试。"
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}),
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"top_p": ("FLOAT", {
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"default": 1.0,
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"min": 0.0,
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"max": 1.0,
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"step": 0.05,
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"tooltip": "核采样：只在累计概率前 top_p 的词里挑。\n"
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"与温度作用重叠，一般固定 1.0 只调温度，别两个一起动。"
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}),
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"max_tokens": ("INT", {
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"default": 1024,
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"min": 1,
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"max": 200000,
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"tooltip": "回复的最大长度上限。设小了会把回答从中间截断。\n"
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"注意它同时是费用上限的重要因素。"
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}),
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"image_1": ("IMAGE", {
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"tooltip": "要一并发给模型的图像 1（需所选模型支持视觉）。\n"
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"会按下方最大边长压缩后转 base64 提交。"
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}),
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"image_2": ("IMAGE", {"tooltip": "图像 2。"}),
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"image_3": ("IMAGE", {"tooltip": "图像 3。"}),
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"image_4": ("IMAGE", {"tooltip": "图像 4。"}),
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"image_5": ("IMAGE", {"tooltip": "图像 5。"}),
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"image_6": ("IMAGE", {"tooltip": "图像 6。图越多越贵、越慢。"}),
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"detail": (["auto", "low", "high"], {
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"default": "auto",
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"tooltip": "图像细节级别：\n"
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"low 便宜快速，只看大致内容；\n"
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"high 切块细看，认小字/细节更准但更贵；\n"
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"auto 由服务端决定。"
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}),
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"image_max_size": ("INT", {
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"default": 1024,
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"min": 256,
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"max": 4096,
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"step": 64,
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"tooltip": "上传前把图缩放到的最大边长。\n"
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"调小可显著省钱提速，但小字与细节会看不清。"
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}),
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"base_url": ("STRING", {
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"default": DEFAULT_BASE_URL_INPUT,
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"multiline": False,
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"tooltip": "接口地址，一般不用改。\n"
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"**不要写 https://** —— ComfyUI 前端会吞掉 \"://\"，\n"
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"协议由后端自动补全，这里只填域名和路径。"
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}),
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"proxy_url": ("STRING", {
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"default": "",
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"multiline": False,
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"tooltip": "HTTP 代理，同样不要带协议前缀，只填 IP:端口，\n"
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"例如 127.0.0.1:7890。留空表示直连。"
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}),
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"max_retries": ("INT", {
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"default": 3,
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"min": 0,
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"max": 10,
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"step": 1,
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"tooltip": "网络失败后的自动重连次数，0 表示不重连。\n\n"
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"会触发重连的情况：SSL 握手被打断\n"
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"（SSLEOFError）、连接被重置、读超时、响应体\n"
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"截断，以及 429 限流和 5xx 服务端临时故障。\n\n"
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"不会重连的情况：参数类 400、鉴权类 401/403 ——\n"
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"这些重试多少次都是同样的结果。\n\n"
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"退避按 1s→2s→4s 指数增长并带随机抖动，避免\n"
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"多个节点同时重连再次压垮服务端；服务端给了\n"
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"Retry-After 时以它为准。"
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}),
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"timeout": ("INT", {
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"default": 180,
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"min": 10,
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"max": 1800,
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"step": 10,
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"tooltip": "单次请求的超时秒数。\n"
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"长文本或多图推理较慢时可调大。\n"
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"超时会计入上面的重连次数。"
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}),
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"usd_to_cny": ("FLOAT", {
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"default": 7.2,
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"min": 0.1,
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"max": 100.0,
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"step": 0.01,
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"tooltip": "美元兑人民币汇率，仅用于把 usage_stats 输出里的\n"
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"费用换算成人民币显示，不影响实际计费。\n"
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"可按当日牌价自行调整。"
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}),
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},
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}
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RETURN_TYPES = ("STRING", "STRING", "STRING")
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RETURN_NAMES = ("text", "model_id", "usage_stats")
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FUNCTION = "generate"
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CATEGORY = "Rui-Node🐶/AI模型🤖"
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@classmethod
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def VALIDATE_INPUTS(cls, model):
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"""
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接管 model 下拉的校验：价格表更新后，旧工作流里保存的标签
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（带旧价格）不再逐字匹配新列表，但只要能解析出 model id 就应放行，
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避免整个工作流被判为无效。
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"""
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if label_to_model_id(model) is None:
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return f"无法从 '{model}' 解析出越光模型 id"
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return True
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@staticmethod
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def _encode_image(img_tensor, max_size):
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"""把单张图像张量 [H,W,C]（0~1）编码为 base64 JPEG。"""
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img_np = np.clip(img_tensor.cpu().numpy(), 0, 1)
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if img_np.shape[-1] == 4: # 带 alpha 的输入丢掉 alpha
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img_np = img_np[..., :3]
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pil = Image.fromarray((img_np * 255).astype(np.uint8), "RGB")
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w, h = pil.size
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if max(w, h) > max_size:
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r = max_size / max(w, h)
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pil = pil.resize((max(1, int(w * r)), max(1, int(h * r))), Image.LANCZOS)
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buf = io.BytesIO()
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pil.save(buf, format="JPEG", quality=85)
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return base64.b64encode(buf.getvalue()).decode("utf-8")
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@staticmethod
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def _fmt_cost(v):
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"""费用格式化：最多 6 位小数并去尾零；未知为 '?'。"""
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if v is None:
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return "?"
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s = f"{v:.6f}".rstrip("0").rstrip(".")
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return s if s else "0"
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@staticmethod
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def _build_usage_stats(model_id, usage, usd_to_cny, out_text=None):
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"""
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由 API 响应的 usage、输出文本与内置单价生成五行消耗统计：
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token消耗，输入：XXX，输出：XXX
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输出文字数量：XXX
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厂商：OpenAI
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模型类型：gpt-4o [入$2.5/M 出$10/M]
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价格换算，美元：XXX，人民币：XXX
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usage 缺失/单价未知的项以 '?' 呈现；请求未发生时传 usage=None 记为 0 消耗。
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"""
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||
if not isinstance(usage, dict):
|
||
usage = {"prompt_tokens": 0, "completion_tokens": 0}
|
||
in_tok = usage.get("prompt_tokens")
|
||
out_tok = usage.get("completion_tokens")
|
||
in_price, out_price = model_prices(model_id)
|
||
|
||
usd = None
|
||
if (isinstance(in_tok, (int, float)) and isinstance(out_tok, (int, float))
|
||
and in_price is not None and out_price is not None):
|
||
usd = in_tok / 1e6 * in_price + out_tok / 1e6 * out_price
|
||
cny = usd * usd_to_cny if usd is not None else None
|
||
|
||
n_chars = len(out_text) if isinstance(out_text, str) else 0
|
||
tok = lambda t: str(int(t)) if isinstance(t, (int, float)) else "?" # noqa: E731
|
||
return (f"token消耗，输入：{tok(in_tok)}，输出：{tok(out_tok)}\n"
|
||
f"输出文字数量：{n_chars}\n"
|
||
f"厂商：{model_vendor(model_id)}\n"
|
||
f"模型类型：{model_label_by_id(model_id)}\n"
|
||
f"价格换算，美元：{YueGuangNode._fmt_cost(usd)}，"
|
||
f"人民币：{YueGuangNode._fmt_cost(cny)}")
|
||
|
||
def generate(
|
||
self,
|
||
api_key,
|
||
model,
|
||
system_prompt,
|
||
user_prompt,
|
||
seed,
|
||
temperature=0.7,
|
||
top_p=1.0,
|
||
max_tokens=1024,
|
||
image_1=None,
|
||
image_2=None,
|
||
image_3=None,
|
||
image_4=None,
|
||
image_5=None,
|
||
image_6=None,
|
||
detail="auto",
|
||
image_max_size=1024,
|
||
base_url=DEFAULT_BASE_URL_INPUT,
|
||
proxy_url="",
|
||
usd_to_cny=7.2,
|
||
max_retries=3,
|
||
timeout=180,
|
||
):
|
||
model_id = label_to_model_id(model) or DEFAULT_MODEL_ID
|
||
chat_url = _build_chat_url(base_url)
|
||
print(f"[Rui-Node] 越光 -> {chat_url} model={model_id}")
|
||
|
||
zero_stats = self._build_usage_stats(model_id, None, usd_to_cny)
|
||
|
||
if not (api_key or "").strip():
|
||
return ("（错误：未填写 api_key，请在节点里填入越光的 API Key）",
|
||
model_id, zero_stats)
|
||
|
||
images = [img for img in (image_1, image_2, image_3, image_4,
|
||
image_5, image_6) if img is not None]
|
||
|
||
messages = []
|
||
if system_prompt and system_prompt.strip():
|
||
messages.append({"role": "system", "content": system_prompt})
|
||
|
||
if images:
|
||
parts = []
|
||
if user_prompt and user_prompt.strip():
|
||
parts.append({"type": "text", "text": user_prompt})
|
||
for img in images:
|
||
b64 = self._encode_image(img[0], image_max_size)
|
||
parts.append({
|
||
"type": "image_url",
|
||
"image_url": {
|
||
"url": f"data:image/jpeg;base64,{b64}",
|
||
"detail": detail,
|
||
},
|
||
})
|
||
if not parts:
|
||
parts.append({"type": "text", "text": " "})
|
||
messages.append({"role": "user", "content": parts})
|
||
else:
|
||
text = (user_prompt or "").strip()
|
||
if not text:
|
||
return ("（错误：未提供图片也未提供提示词，请至少填写 user_prompt）",
|
||
model_id, zero_stats)
|
||
messages.append({"role": "user", "content": text})
|
||
|
||
headers = {
|
||
"Content-Type": "application/json",
|
||
"Authorization": f"Bearer {api_key.strip()}",
|
||
}
|
||
payload = {
|
||
"model": model_id,
|
||
"messages": messages,
|
||
"seed": seed,
|
||
"temperature": temperature,
|
||
"top_p": top_p,
|
||
"max_tokens": max_tokens,
|
||
}
|
||
|
||
proxies = None
|
||
p = _build_proxy_url(proxy_url)
|
||
if p:
|
||
proxies = {"http": p, "https": p}
|
||
|
||
resp = None
|
||
dropped = []
|
||
last_net_err = ""
|
||
total_tries = max(1, int(max_retries) + 1)
|
||
|
||
for attempt in range(total_tries):
|
||
try:
|
||
# 自适应参数重试：部分模型弃用/不支持某些采样参数，
|
||
# 命中即剔除或改名后重试；正常请求不受影响、零额外开销。
|
||
# 这层不消耗网络重连次数——两者是性质不同的问题。
|
||
for _ in range(len(_ADJUSTABLE_PARAMS) + 2):
|
||
resp = requests.post(chat_url, headers=headers, json=payload,
|
||
proxies=proxies, timeout=timeout)
|
||
if resp.status_code == 400:
|
||
fix = _diagnose_param(resp, payload)
|
||
if fix:
|
||
action, param = fix
|
||
if action == "rename_mct":
|
||
payload["max_completion_tokens"] = payload.pop("max_tokens")
|
||
dropped.append("max_tokens→max_completion_tokens")
|
||
print("[Rui-Node] 越光: 该模型要求 max_completion_tokens，"
|
||
"已改名重试")
|
||
else:
|
||
payload.pop(param, None)
|
||
dropped.append(param)
|
||
print(f"[Rui-Node] 越光: 该模型不支持参数 '{param}'，"
|
||
"已剔除后重试")
|
||
continue
|
||
break
|
||
|
||
# 限流 / 服务端临时故障：计入网络重连
|
||
if resp.status_code in _RETRIABLE_STATUS and attempt < total_tries - 1:
|
||
wait = _retry_wait(attempt, resp)
|
||
print(f"[Rui-Node] 越光: 服务端返回 {resp.status_code}，"
|
||
f"{wait:.1f}s 后重试"
|
||
f"（第 {attempt + 1}/{total_tries - 1} 次重连）")
|
||
time.sleep(wait)
|
||
continue
|
||
|
||
resp.raise_for_status()
|
||
data = resp.json()
|
||
if data.get("choices"):
|
||
msg = data["choices"][0].get("message", {})
|
||
content = msg.get("content", "")
|
||
if isinstance(content, list): # 少数模型返回分段内容
|
||
content = "".join(
|
||
seg.get("text", "") for seg in content
|
||
if isinstance(seg, dict)
|
||
)
|
||
stats = self._build_usage_stats(model_id, data.get("usage"),
|
||
usd_to_cny, content or "")
|
||
if attempt:
|
||
print(f"[Rui-Node] 越光: 第 {attempt} 次重连后成功")
|
||
return (content or "", model_id, stats)
|
||
stats = self._build_usage_stats(model_id, data.get("usage"), usd_to_cny)
|
||
return (f"API 返回格式异常: {json.dumps(data, ensure_ascii=False)[:800]}",
|
||
model_id, stats)
|
||
|
||
except _RETRIABLE_EXC as e:
|
||
last_net_err = f"{type(e).__name__}: {e}"
|
||
if attempt < total_tries - 1:
|
||
wait = _retry_wait(attempt)
|
||
print(f"[Rui-Node] 越光: 连接失败（{type(e).__name__}），"
|
||
f"{wait:.1f}s 后重连"
|
||
f"（第 {attempt + 1}/{total_tries - 1} 次）")
|
||
time.sleep(wait)
|
||
continue
|
||
tail = (f"（已自动重连 {max_retries} 次仍失败；"
|
||
f"可调大 max_retries，或检查网络/代理）"
|
||
if max_retries else "（max_retries=0，未重连）")
|
||
return (f"连接失败（请检查网络/代理）: {last_net_err}{tail}",
|
||
model_id, zero_stats)
|
||
|
||
except requests.exceptions.HTTPError:
|
||
# 参数与鉴权类错误重试无意义，直接报错
|
||
code = resp.status_code if resp is not None else "?"
|
||
body = resp.text[:600] if resp is not None else ""
|
||
hint = (f"（已尝试剔除参数 {', '.join(dropped)} 仍失败）"
|
||
if (code == 400 and dropped) else "")
|
||
if attempt: # 重连过仍是这个状态，明说，省得用户以为没重试
|
||
hint += f"（已自动重连 {attempt} 次仍返回该状态）"
|
||
return (f"HTTP 错误 {code}: {body}{hint}", model_id, zero_stats)
|
||
|
||
except Exception as e:
|
||
return (f"请求异常: {type(e).__name__}: {e}", model_id, zero_stats)
|
||
|
||
# 循环内必定 return；这条仅作兜底，避免异常路径返回 None
|
||
return ("请求未能完成，请重试或检查服务状态。", model_id, zero_stats)
|
||
|
||
|
||
NODE_CLASS_MAPPINGS = {
|
||
"YueGuangAPINode": YueGuangNode,
|
||
}
|
||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||
"YueGuangAPINode": "越光 API 连接 / YueGuang API Connector",
|
||
}
|