Files
Aether/tests/services/test_model_fetch_scheduler.py
fawney19 6984984c22 feat(provider): 重构模型测试对话框,加固 Vertex AI 传输层
模型测试:
- 将消息输入替换为完整 JSON 请求体编辑器,支持格式化和校验
- 新增端点选择面板,测试前可选择目标端点
- 新增调试检查器,可查看每次尝试的请求/响应头和体
- 结果视图改用 HorizontalRequestTimeline 组件展示请求追踪
- endpoint_checker 返回完整调试数据,通过 candidate extra_data 持久化

Vertex AI:
- 改进上下文检测逻辑,不再仅依赖 provider_type,支持从 base_url 推断
- Service Account 密钥现支持自动拉取模型(使用 auth_config 而非 api_key)
- 移除 Gemini Developer API 回退,API Key 仅走 Express 模式
- 端点表单为 Vertex AI 显示格式特定的默认路径模板
- 密钥格式校验仅在 auth_type/api_formats 变更时执行

其他:
- 禁用 ClaudeCode 提供商类型创建入口
- Dialog 组件新增 closeOnBackdrop 属性
2026-03-19 23:52:17 +08:00

180 lines
5.8 KiB
Python

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 (
EndpointFetchConfig,
ModelFetchScheduler,
PreparedModelsFetchContext,
_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"]]
@pytest.mark.asyncio
async def test_fetch_models_for_key_by_id_vertex_service_account_uses_auth_config(
monkeypatch: pytest.MonkeyPatch,
) -> None:
scheduler = ModelFetchScheduler()
captured: dict[str, object] = {}
prepared = PreparedModelsFetchContext(
key_id="key-vertex-sa",
provider_id="provider-vertex",
provider_name="Vertex",
provider_type="vertex_ai",
auth_type="service_account",
encrypted_api_key="ENC_PLACEHOLDER",
encrypted_auth_config="ENC_AUTH_CONFIG",
format_to_endpoint={
"gemini:chat": EndpointFetchConfig(base_url="https://aiplatform.googleapis.com"),
},
proxy_config=None,
)
monkeypatch.setattr(scheduler, "_prepare_fetch_context", lambda key_id: prepared)
async def fake_fetch_models_for_key(ctx, *, timeout_seconds: float):
captured["api_key_value"] = ctx.api_key_value
captured["auth_config"] = ctx.auth_config
captured["timeout_seconds"] = timeout_seconds
return ([], [], True, None)
async def fake_update_key_after_fetch(
key_id: str,
provider_id: str,
provider_name: str,
all_models: list[dict],
errors: list[str],
has_success: bool,
upstream_metadata=None,
) -> str:
captured["update_key_id"] = key_id
captured["update_provider_id"] = provider_id
return "success"
def fake_decrypt(value: str) -> str:
if value == "ENC_AUTH_CONFIG":
return (
'{"project_id":"demo-project","client_email":"svc@example.com",'
'"private_key":"-----BEGIN PRIVATE KEY-----\\nTEST\\n-----END PRIVATE KEY-----\\n"}'
)
raise AssertionError(f"unexpected decrypt call for {value}")
monkeypatch.setattr(fetch_scheduler_module, "fetch_models_for_key", fake_fetch_models_for_key)
monkeypatch.setattr(scheduler, "_update_key_after_fetch", fake_update_key_after_fetch)
monkeypatch.setattr(fetch_scheduler_module.crypto_service, "decrypt", fake_decrypt)
result = await scheduler._fetch_models_for_key_by_id("key-vertex-sa")
assert result == "success"
assert captured["api_key_value"] == "__placeholder__"
assert captured["auth_config"] == {
"project_id": "demo-project",
"client_email": "svc@example.com",
"private_key": "-----BEGIN PRIVATE KEY-----\nTEST\n-----END PRIVATE KEY-----\n",
}
assert captured["update_key_id"] == "key-vertex-sa"
assert captured["update_provider_id"] == "provider-vertex"