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fix(backend): 部署韧性——模型自愈/就绪门禁/全局代理/启动诊断

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- whisper: model.bin 截断/损坏时删目录重下重试一次，修「Unable to
  open file model.bin」死循环；mlx 同样按 config.json 判完整性
- /generate_note 加就绪门禁：本地转写引擎模型没下好直接拦截，返回
  reason=transcriber_model_not_ready，不让任务静默卡在首次下载
- 全局代理：新增 ProxyConfigManager（JSON 配置 + HTTP_PROXY env 兜底）
  + build_openai_client，统一注入代理到 LLM/Groq 客户端；yt-dlp 与
  youtube-transcript-api 也走代理
- build_openai_client 校验 api_key 非空，空 key 给「xxx 的 API Key
  未配置」而不是天书般的 Illegal header value b'Bearer '
- universal_gpt: 模型拒绝自定义 temperature（o1/o3/gpt-5 系列）时
  就地去掉参数重试，不消耗重试预算
- connect_test 改用真实 chat completion 而非 /v1/models 探测
- main.py: lifespan 拆 [startup 1/5..5/5] 分段日志 + 异常清晰定位
- /sys_health 重构为结构化返回 {backend,ffmpeg,db,whisper_model}

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
huangjianwu
2026-05-14 19:01:14 +08:00
co-authored by Claude Opus 4.7
parent 88d25f8cc1
commit 41f17592c2
16 changed files with 534 additions and 92 deletions
Download Patch File Download Diff File Expand all files Collapse all files
backend/app/routers/config.py
+155 -29
View File
@@ -80,6 +80,36 @@ def update_transcriber_config(data: TranscriberConfigRequest):
return R.success(data=config)
# ---- 全局代理配置（作用于 LLM API + 转写 API + yt-dlp 下载）----
class ProxyConfigRequest(BaseModel):
enabled: bool
url: Optional[str] = None
@router.get("/proxy_config")
def get_proxy_config():
from app.services.proxy_config_manager import ProxyConfigManager
mgr = ProxyConfigManager()
cfg = mgr.get_config()
# effective 给前端展示「当前实际生效的代理」——可能来自配置，也可能来自 env 兜底
return R.success(data={
**cfg,
"effective": mgr.get_proxy_url() or "",
})
@router.post("/proxy_config")
def update_proxy_config(data: ProxyConfigRequest):
from app.services.proxy_config_manager import ProxyConfigManager
mgr = ProxyConfigManager()
cfg = mgr.update_config(enabled=data.enabled, url=data.url)
return R.success(data={
**cfg,
"effective": mgr.get_proxy_url() or "",
})
# ---- Whisper 模型下载状态 & 下载触发 ----
# 用于跟踪正在进行的下载任务
@@ -87,10 +117,33 @@ _downloading: dict[str, str] = {} # model_size -> status ("downloading" | "done
def _check_whisper_model_exists(model_size: str, subdir: str = "whisper") -> bool:
"""检查指定 whisper 模型是否已下载到本地。"""
"""检查指定 whisper 模型是否已下载完整到本地。
必须 model.bin 落盘才算完成，仅有空目录或半成品不能算「已下载」——
否则监控页会显示绿勾但加载时报「Unable to open file 'model.bin'」。
"""
model_dir = get_model_dir(subdir)
model_path = os.path.join(model_dir, f"whisper-{model_size}")
return Path(model_path).exists()
return (Path(model_path) / "model.bin").exists()
def _check_mlx_whisper_model_exists(model_size: str) -> bool:
"""检查 mlx-whisper 模型是否已下载完整到本地。
与 fast-whisper 的目录布局不同：mlx 模型按 HuggingFace repo_id
（如 mlx-community/whisper-tiny-mlx）落盘，且没有 model.bin，
用 config.json 作为「下载完成」的判据，和 mlx_whisper_transcriber.py 保持一致。
"""
try:
from app.transcriber.mlx_whisper_transcriber import MLX_MODEL_MAP
except Exception:
return False
repo_id = MLX_MODEL_MAP.get(model_size)
if not repo_id:
return False
model_dir = get_model_dir("mlx-whisper")
model_path = os.path.join(model_dir, repo_id)
return (Path(model_path) / "config.json").exists()
@router.get("/transcriber_models_status")
@@ -113,11 +166,9 @@ def get_transcriber_models_status():
from app.transcriber.mlx_whisper_transcriber import MLX_MODEL_MAP
for size in WHISPER_MODEL_SIZES:
mlx_key = f"mlx-{size}"
model_dir = get_model_dir("mlx-whisper")
repo_id = MLX_MODEL_MAP.get(size)
# 模型在本地按 repo_id（如 mlx-community/whisper-small-mlx）落盘
model_path = os.path.join(model_dir, repo_id) if repo_id else None
downloaded = bool(model_path and Path(model_path).exists())
# 用 config.json 判定，和 _check_mlx_whisper_model_exists / 加载逻辑保持一致
downloaded = _check_mlx_whisper_model_exists(size)
mlx_statuses.append({
"model_size": size,
"downloaded": downloaded,
@@ -146,7 +197,8 @@ def _do_download_whisper(model_size: str):
_downloading[model_size] = "downloading"
model_dir = get_model_dir("whisper")
model_path = os.path.join(model_dir, f"whisper-{model_size}")
if Path(model_path).exists():
# 用 model.bin 判定而非目录存在：半成品目录不能算「已下载」
if (Path(model_path) / "model.bin").exists():
_downloading[model_size] = "done"
return
repo_id = MODEL_MAP.get(model_size)
@@ -179,7 +231,8 @@ def _do_download_mlx_whisper(model_size: str):
model_dir = get_model_dir("mlx-whisper")
model_path = os.path.join(model_dir, repo_id)
if Path(model_path).exists():
# 用 config.json 判定而非目录存在：半成品目录不能算「已下载」
if (Path(model_path) / "config.json").exists():
_downloading[key] = "done"
return
logger.info(f"开始下载 mlx-whisper 模型: {model_size} ← {repo_id}")
@@ -214,46 +267,119 @@ def download_transcriber_model(data: ModelDownloadRequest, background_tasks: Bac
@router.get("/sys_health")
async def sys_health():
"""结构化健康状态——任何子项异常都不应让整个 endpoint 5xx。
每个字段：'ok' | 'missing' | 'error'。
前端 useCheckBackend 用 /sys_check 做存活判定（不依赖外部依赖），
/sys_health 用来在设置页区分「后端没起」vs「后端起了但 ffmpeg 缺」vs「DB 写不进去」等更细的状态。
"""
ffmpeg_status = "ok"
try:
ensure_ffmpeg_or_raise()
return R.success()
except EnvironmentError:
return R.error(msg="系统未安装 ffmpeg 请先进行安装")
except Exception:
ffmpeg_status = "missing"
db_status = "ok"
try:
from app.db.engine import engine
from sqlalchemy import text
with engine.connect() as conn:
conn.execute(text("SELECT 1"))
except Exception:
db_status = "error"
# 当前转写器配置 + 模型是否已下载（用 model.bin 落盘判定，与 transcriber 加载逻辑一致）
whisper_info: dict = {"size": None, "type": None, "downloaded": False, "checked": False}
try:
cfg = transcriber_config_manager.get_config()
size = cfg["whisper_model_size"]
ttype = cfg["transcriber_type"]
whisper_info["size"] = size
whisper_info["type"] = ttype
# 只有本地引擎才有「下载」概念；groq / bcut / kuaishou 在线引擎跳过
if ttype == "fast-whisper":
whisper_info["downloaded"] = _check_whisper_model_exists(size, "whisper")
whisper_info["checked"] = True
elif ttype == "mlx-whisper":
whisper_info["downloaded"] = _check_mlx_whisper_model_exists(size)
whisper_info["checked"] = True
except Exception:
pass
return R.success(data={
"backend": "ok",
"ffmpeg": ffmpeg_status,
"db": db_status,
"whisper_model": whisper_info,
})
@router.get("/sys_check")
async def sys_check():
"""轻量存活判定：后端进程能响应这个 endpoint 就算「起来了」，不查外部依赖。
给桌面端 useCheckBackend / Tauri ready-probe 用。
"""
return R.success()
@router.get("/deploy_status")
async def deploy_status():
"""返回部署监控所需的所有状态信息"""
import torch
"""返回部署监控所需的所有状态信息。
所有子项都用 try 包起来——监控页本身不应该被任何一个子项打死。
特别是 torch：它只在 fast-whisper 路径用得到，用 Groq / 必剪 / 快手在线
引擎的轻量部署完全可以不装，那种情况这个 endpoint 不应该 500。
"""
import os
# CUDA 状态
cuda_available = torch.cuda.is_available()
cuda_info = {
"available": cuda_available,
"version": torch.version.cuda if cuda_available else None,
"gpu_name": torch.cuda.get_device_name(0) if cuda_available else None,
}
# Whisper 模型状态（从配置文件读取，与前端设置同步）
transcriber_cfg = transcriber_config_manager.get_config()
model_size = transcriber_cfg["whisper_model_size"]
transcriber_type = transcriber_cfg["transcriber_type"]
try:
import torch
cuda_available = torch.cuda.is_available()
cuda_info = {
"available": cuda_available,
"torch_installed": True,
"version": torch.version.cuda if cuda_available else None,
"gpu_name": torch.cuda.get_device_name(0) if cuda_available else None,
}
except Exception:
cuda_info = {
"available": False,
"torch_installed": False,
"version": None,
"gpu_name": None,
}
# Whisper 模型 / 转写器配置 + 本地下载状态
try:
transcriber_cfg = transcriber_config_manager.get_config()
size = transcriber_cfg["whisper_model_size"]
ttype = transcriber_cfg["transcriber_type"]
if ttype == "fast-whisper":
downloaded = _check_whisper_model_exists(size, "whisper")
elif ttype == "mlx-whisper":
downloaded = _check_mlx_whisper_model_exists(size)
else:
downloaded = False # 在线引擎无下载概念
whisper_info = {
"model_size": size,
"transcriber_type": ttype,
"downloaded": downloaded,
}
except Exception:
whisper_info = {"model_size": None, "transcriber_type": None, "downloaded": False}
# FFmpeg 状态
try:
ensure_ffmpeg_or_raise()
ffmpeg_ok = True
except:
except Exception:
ffmpeg_ok = False
return R.success(data={
"backend": {"status": "running", "port": int(os.getenv("BACKEND_PORT", 8483))},
"cuda": cuda_info,
"whisper": {"model_size": model_size, "transcriber_type": transcriber_type},
"whisper": whisper_info,
"ffmpeg": {"available": ffmpeg_ok},
})