refactor: 优化模型获取调度器并发模型和缓存格式

- 用固定 worker 数的队列消费模式替换 Semaphore+gather，避免大号池一次性创建大量协程
- 分批扫描 Key ID（keyset pagination），避免一次性加载全量 ID
- 上游模型缓存从 per-api_format 去重改为 per-model-id 聚合 api_formats 列表，减少 Redis 占用
- 启动阶段支持环境变量控制（MODEL_FETCH_STARTUP_ENABLED / MODEL_FETCH_STARTUP_DELAY_SECONDS）
- 新增单元测试覆盖聚合逻辑和并发上限验证

tests/services/test_model_fetch_scheduler.py

@@ -0,0 +1,110 @@
+from __future__ import annotations
+
+import asyncio
+
+import pytest
+
+import src.services.model.fetch_scheduler as fetch_scheduler_module
+from src.services.model.fetch_scheduler import (
+    ModelFetchScheduler,
+    _aggregate_models_for_cache,
+    _run_key_fetch_workers,
+)
+
+
+def test_aggregate_models_for_cache_merges_formats_by_model_id() -> None:
+    models: list[dict] = [
+        {"id": "gpt-4.1", "api_format": "openai:chat", "label": "GPT 4.1"},
+        {"id": "gpt-4.1", "api_format": "openai:cli", "extra": {"tier": "pro"}},
+        {"id": "claude-sonnet", "api_format": "claude:chat", "label": "Sonnet"},
+        {"id": "", "api_format": "ignored"},
+    ]
+
+    aggregated = _aggregate_models_for_cache(models)
+
+    assert len(aggregated) == 2
+
+    by_id = {item["id"]: item for item in aggregated}
+    assert by_id["gpt-4.1"]["api_formats"] == ["openai:chat", "openai:cli"]
+    assert by_id["gpt-4.1"]["label"] == "GPT 4.1"
+    assert by_id["gpt-4.1"]["extra"] == {"tier": "pro"}
+    assert "api_format" not in by_id["gpt-4.1"]
+
+    assert by_id["claude-sonnet"]["api_formats"] == ["claude:chat"]
+
+
+@pytest.mark.asyncio
+async def test_run_key_fetch_workers_caps_inflight_tasks() -> None:
+    inflight = 0
+    max_inflight = 0
+
+    async def fetch_one(key_id: str) -> str:
+        nonlocal inflight, max_inflight
+        inflight += 1
+        max_inflight = max(max_inflight, inflight)
+        await asyncio.sleep(0.01)
+        inflight -= 1
+        return "success"
+
+    timed_out: list[str] = []
+    failed: list[tuple[str, str]] = []
+
+    result = await _run_key_fetch_workers(
+        [f"key-{index}" for index in range(8)],
+        max_concurrent=3,
+        timeout_seconds=1.0,
+        running_predicate=lambda: True,
+        fetch_one=fetch_one,
+        on_timeout=timed_out.append,
+        on_error=lambda key_id, message: failed.append((key_id, message)),
+    )
+
+    assert result == (8, 0, 0)
+    assert max_inflight <= 3
+    assert timed_out == []
+    assert failed == []
+
+
+@pytest.mark.asyncio
+async def test_perform_fetch_all_keys_scans_in_batches(monkeypatch: pytest.MonkeyPatch) -> None:
+    scheduler = ModelFetchScheduler()
+    scheduler._running = True
+
+    batch_requests: list[str | None] = []
+    processed_batches: list[list[str]] = []
+
+    pages = {
+        None: ["a", "b"],
+        "b": ["c"],
+        "c": [],
+    }
+
+    def fake_list_batch(*, after_id: str | None = None, limit: int = 0) -> list[str]:
+        batch_requests.append(after_id)
+        return list(pages.get(after_id, []))
+
+    async def fake_run_key_fetch_workers(
+        key_ids: list[str],
+        *,
+        max_concurrent: int,
+        timeout_seconds: float,
+        running_predicate,
+        fetch_one,
+        on_timeout,
+        on_error,
+    ) -> tuple[int, int, int]:
+        processed_batches.append(list(key_ids))
+        return len(key_ids), 0, 0
+
+    monkeypatch.setattr(fetch_scheduler_module, "AUTO_FETCH_KEY_BATCH_SIZE", 2)
+    monkeypatch.setattr(scheduler, "_list_auto_fetch_key_id_batch", fake_list_batch)
+    monkeypatch.setattr(
+        fetch_scheduler_module,
+        "_run_key_fetch_workers",
+        fake_run_key_fetch_workers,
+    )
+
+    await scheduler._perform_fetch_all_keys()
+
+    assert batch_requests == [None, "b"]
+    assert processed_batches == [["a", "b"], ["c"]]