wehub-resource-sync
200 lines
6.7 KiB
Python
200 lines
6.7 KiB
Python
"""模型缓存服务 - 基于 Redis 的跨进程模型信息缓存。
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本模块将数据库中的 model_providers 表数据序列化到 Redis，
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供 API 和 Worker 等多进程同步读取，避免在同步函数中查询异步数据库。
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模型 spec 格式: provider_id:model_id（冒号分隔）。model_id 允许包含斜杠。
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"""
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from __future__ import annotations
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import json
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import time
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from dataclasses import dataclass, field
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from typing import Any
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from yuxi.storage.redis import sync_redis_client
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from yuxi.utils.logging_config import logger
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REDIS_CACHE_KEY = "yuxi:model_cache"
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_CACHE_TTL_SECONDS = 5
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@dataclass(frozen=True)
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class ModelInfo:
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"""不可变的模型信息，供运行时使用。"""
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provider_id: str
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model_id: str
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model_type: str # chat / embedding / rerank
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display_name: str
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# 运行时配置
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api_key: str
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base_url: str
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provider_type: str # openai / anthropic / gemini / openrouter
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# 可选配置
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headers: dict[str, str] = field(default_factory=dict)
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extra: dict[str, Any] = field(default_factory=dict)
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# Embedding 专属
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dimension: int | None = None
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batch_size: int = 40
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@property
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def spec(self) -> str:
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return f"{self.provider_id}:{self.model_id}"
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def to_dict(self) -> dict:
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return {
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"provider_id": self.provider_id,
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"model_id": self.model_id,
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"model_type": self.model_type,
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"display_name": self.display_name,
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"api_key": self.api_key,
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"base_url": self.base_url,
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"provider_type": self.provider_type,
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"headers": self.headers,
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"extra": self.extra,
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"dimension": self.dimension,
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"batch_size": self.batch_size,
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}
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@classmethod
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def from_dict(cls, data: dict) -> ModelInfo:
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return cls(
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provider_id=data["provider_id"],
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model_id=data["model_id"],
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model_type=data["model_type"],
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display_name=data["display_name"],
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api_key=data["api_key"],
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base_url=data["base_url"],
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provider_type=data["provider_type"],
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headers=data.get("headers", {}),
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extra=data.get("extra", {}),
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dimension=data.get("dimension"),
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batch_size=data.get("batch_size", 40),
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)
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class ModelCache:
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"""基于 Redis 的模型缓存，所有写入均走 Redis，保证跨进程一致。"""
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def __init__(self) -> None:
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self._local_cache: dict[str, ModelInfo] | None = None
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self._local_cache_at: float = 0.0
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def _load_cache(self) -> dict[str, ModelInfo]:
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now = time.monotonic()
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if self._local_cache is not None and (now - self._local_cache_at) < _CACHE_TTL_SECONDS:
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return self._local_cache
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try:
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with sync_redis_client() as redis_client:
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raw = redis_client.get(REDIS_CACHE_KEY)
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if not raw:
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self._local_cache = {}
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self._local_cache_at = now
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return {}
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items = json.loads(raw)
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cache = {spec: ModelInfo.from_dict(data) for spec, data in items.items()}
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except Exception as e:
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logger.warning(f"Failed to load model cache from Redis: {e}")
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return {}
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self._local_cache = cache
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self._local_cache_at = now
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return cache
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def _invalidate_local(self) -> None:
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self._local_cache = None
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self._local_cache_at = 0.0
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def get_model_info(self, spec: str) -> ModelInfo | None:
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cache = self._load_cache()
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return cache.get(spec)
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def get_all_specs(self, model_type: str | None = None) -> list[ModelInfo]:
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cache = self._load_cache()
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if model_type is None:
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return list(cache.values())
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return [info for info in cache.values() if info.model_type == model_type]
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def get_specs_grouped_by_provider(self, model_type: str = "chat") -> dict[str, list[ModelInfo]]:
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cache = self._load_cache()
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grouped: dict[str, list[ModelInfo]] = {}
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for info in cache.values():
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if info.model_type != model_type:
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continue
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grouped.setdefault(info.provider_id, []).append(info)
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return grouped
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def rebuild(self, providers: list[Any]) -> None:
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from yuxi.models.providers.service import resolve_api_key
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new_cache: dict[str, ModelInfo] = {}
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for provider in providers:
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if not provider.is_enabled:
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continue
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api_key = resolve_api_key(provider)
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for model in provider.enabled_models or []:
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model_type = model.get("type", "chat")
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base_url = model.get("base_url_override") or self._get_base_url_for_type(provider, model_type)
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info = ModelInfo(
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provider_id=provider.provider_id,
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model_id=model["id"],
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model_type=model_type,
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display_name=model.get("display_name", model["id"]),
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api_key=api_key or "",
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base_url=base_url,
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provider_type=provider.provider_type,
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headers=dict(provider.headers_json or {}),
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extra=dict(provider.extra_json or {}),
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dimension=model.get("dimension"),
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batch_size=model.get("batch_size", 40),
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)
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new_cache[info.spec] = info
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self._save_cache(new_cache)
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self._invalidate_local()
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logger.info(f"Model cache rebuilt: {len(new_cache)} models → Redis")
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def _save_cache(self, cache: dict[str, ModelInfo]) -> None:
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try:
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data = {spec: info.to_dict() for spec, info in cache.items()}
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with sync_redis_client() as redis_client:
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redis_client.set(REDIS_CACHE_KEY, json.dumps(data, ensure_ascii=False))
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except Exception as e:
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logger.error(f"Failed to save model cache to Redis: {e}")
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@staticmethod
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def _get_base_url_for_type(provider: Any, model_type: str) -> str:
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if model_type == "embedding" and provider.embedding_base_url:
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return provider.embedding_base_url
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if model_type == "rerank" and provider.rerank_base_url:
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return provider.rerank_base_url
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return provider.base_url
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model_cache = ModelCache()
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def resolve_model_spec(spec: str) -> ModelInfo:
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"""根据 spec 返回 ModelInfo。"""
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if not spec:
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raise ValueError("model spec 不能为空")
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info = model_cache.get_model_info(spec)
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if info:
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return info
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all_specs = model_cache.get_all_specs()
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available = [item.spec for item in all_specs[:10]]
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raise ValueError(f"未找到模型: '{spec}'。可用模型 ({len(all_specs)}): {available}")
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