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chore: track claude skills, tools, templates, reference code and research-wiki

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- Add all claude skills (brainstorming, commit, debugging, TDD, etc.)
- Add claude hooks (pre-commit-guard, post-edit-quality)
- Add research templates (experiment plan, research brief, etc.)
- Add claude tools (arxiv/semantic_scholar/openalex fetch, wiki, exa)
- Add TRM4 reference implementation as algorithm fidelity baseline
- Add research-wiki content (plans, index, graph, query_pack)
- Update .gitignore to exclude .graphify_version runtime state
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iomgaa
2026-07-06 20:59:03 -04:00
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reference/tests/unit/test_config.py
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"""
配置管理模块单元测试
====================
覆盖: YAML 加载、.env 覆盖、CLI 覆盖、缺字段报错、优先级验证、embed_dim 一致性。
"""
from __future__ import annotations
from pathlib import Path
import pytest
import yaml
from video_tree_trm.config import Config, TreeConfig
# ---------------------------------------------------------------------------
# Fixtures
# ---------------------------------------------------------------------------
_FULL_YAML = {
"tree": {
"max_paragraphs_per_l2": 5,
"l1_segment_duration": 600.0,
"l2_clip_duration": 20.0,
"l3_fps": 1.0,
"l2_representative_frames": 3,
"cache_dir": "cache/trees",
},
"embed": {
"backend": "local",
"model_name": "test-model",
"embed_dim": 2560,
"device": "cpu",
"api_key": "",
"api_url": "",
},
"llm": {
"backend": "qwen",
"api_key": "yaml-llm-key",
"model": "qwen-plus",
"api_url": "https://example.com/llm",
"max_tokens": 256,
"temperature": 0.1,
},
"vlm": {
"backend": "qwen",
"api_key": "yaml-vlm-key",
"model": "qwen-vl-plus",
"api_url": "https://example.com/vlm",
"max_tokens": 256,
"temperature": 0.1,
},
"retriever": {
"embed_dim": 2560,
"num_heads": 4,
"L_layers": 2,
"L_cycles": 4,
"max_rounds": 5,
"ffn_expansion": 2.0,
"checkpoint": None,
},
"train": {
"lr": 1e-4,
"weight_decay": 1e-5,
"batch_size": 1,
"max_epochs_phase1": 30,
"max_epochs_phase2": 20,
"nav_loss_weight": 1.0,
"act_loss_weight": 0.1,
"act_lambda_step": 0.1,
"act_gamma": 0.9,
"eval_interval": 5,
"save_dir": "checkpoints",
"dataset": "longbench",
"dataset_path": "data/longbench",
},
}
@pytest.fixture()
def yaml_path(tmp_path: Path) -> Path:
"""创建完整配置的临时 YAML 文件。"""
p = tmp_path / "config" / "default.yaml"
p.parent.mkdir(parents=True, exist_ok=True)
with open(p, "w", encoding="utf-8") as f:
yaml.dump(_FULL_YAML, f, allow_unicode=True)
return p
@pytest.fixture()
def env_path(tmp_path: Path) -> Path:
"""创建临时 .env 文件。"""
p = tmp_path / ".env"
p.write_text(
"LLM_API_KEY=env-llm-key\n"
"LLM_MODEL=env-llm-model\n"
"LLM_API_URL=https://env.example.com/llm\n"
"VLM_API_KEY=env-vlm-key\n"
"VLM_MODEL=env-vlm-model\n"
"VLM_API_URL=https://env.example.com/vlm\n"
"EMBED_BACKEND=remote\n"
"EMBED_MODEL=env-embed-model\n"
"EMBED_API_KEY=env-embed-key\n"
"EMBED_API_URL=https://env.example.com/embed\n"
)
return p
# ---------------------------------------------------------------------------
# 测试: YAML 加载
# ---------------------------------------------------------------------------
class TestYAMLLoad:
"""YAML 基础加载测试。"""
def test_load_full_yaml(self, yaml_path: Path) -> None:
"""完整 YAML 应成功加载所有字段。"""
cfg = Config.load(
str(yaml_path), env_path=str(yaml_path.parent / ".env.nonexist")
)
assert isinstance(cfg.tree, TreeConfig)
assert cfg.tree.max_paragraphs_per_l2 == 5
assert cfg.tree.l1_segment_duration == 600.0
assert cfg.embed.embed_dim == 2560
assert cfg.retriever.checkpoint is None
assert cfg.train.dataset == "longbench"
def test_file_not_found(self, tmp_path: Path) -> None:
"""不存在的 YAML 应抛出 FileNotFoundError。"""
with pytest.raises(FileNotFoundError, match="配置文件不存在"):
Config.load(str(tmp_path / "nonexist.yaml"))
# ---------------------------------------------------------------------------
# 测试: 缺字段报错
# ---------------------------------------------------------------------------
class TestMissingField:
"""缺少必需字段时应抛出 TypeError。"""
def test_missing_tree_field(self, tmp_path: Path) -> None:
"""tree 节缺少字段应报 TypeError。"""
bad_yaml = _FULL_YAML.copy()
bad_yaml = {
k: (v.copy() if isinstance(v, dict) else v) for k, v in _FULL_YAML.items()
}
del bad_yaml["tree"]["cache_dir"]
p = tmp_path / "bad.yaml"
with open(p, "w") as f:
yaml.dump(bad_yaml, f)
with pytest.raises(TypeError):
Config.load(str(p), env_path=str(tmp_path / ".env.nonexist"))
def test_missing_section(self, tmp_path: Path) -> None:
"""缺少整个配置节应报 TypeError。"""
bad_yaml = {k: v for k, v in _FULL_YAML.items() if k != "train"}
p = tmp_path / "bad2.yaml"
with open(p, "w") as f:
yaml.dump(bad_yaml, f)
with pytest.raises(TypeError):
Config.load(str(p), env_path=str(tmp_path / ".env.nonexist"))
# ---------------------------------------------------------------------------
# 测试: .env 覆盖
# ---------------------------------------------------------------------------
class TestEnvOverride:
""".env 文件应覆盖 YAML 中的 api_key。"""
def test_env_overrides_api_keys(self, yaml_path: Path, env_path: Path) -> None:
"""api_key/model/api_url 应优先使用 .env 中的值。"""
cfg = Config.load(str(yaml_path), env_path=str(env_path))
assert cfg.llm.api_key == "env-llm-key"
assert cfg.llm.model == "env-llm-model"
assert cfg.llm.api_url == "https://env.example.com/llm"
assert cfg.vlm.api_key == "env-vlm-key"
assert cfg.vlm.model == "env-vlm-model"
assert cfg.vlm.api_url == "https://env.example.com/vlm"
def test_env_overrides_embed(self, yaml_path: Path, env_path: Path) -> None:
"""embed 相关字段应优先使用 .env 中的值。"""
cfg = Config.load(str(yaml_path), env_path=str(env_path))
assert cfg.embed.backend == "remote"
assert cfg.embed.model_name == "env-embed-model"
assert cfg.embed.api_key == "env-embed-key"
assert cfg.embed.api_url == "https://env.example.com/embed"
def test_yaml_fallback_when_no_env(self, yaml_path: Path) -> None:
"""无 .env 时应使用 YAML 中的值。"""
cfg = Config.load(
str(yaml_path), env_path=str(yaml_path.parent / ".env.nonexist")
)
assert cfg.llm.api_key == "yaml-llm-key"
assert cfg.vlm.api_key == "yaml-vlm-key"
# ---------------------------------------------------------------------------
# 测试: CLI 覆盖
# ---------------------------------------------------------------------------
class TestCLIOverride:
"""CLI args 应覆盖 YAML 和 .env 的值。"""
def test_cli_overrides_yaml(self, yaml_path: Path) -> None:
"""CLI 点路径覆盖应生效。"""
cfg = Config.load(
str(yaml_path),
cli_args={"retriever.num_heads": 8, "train.lr": 0.001},
env_path=str(yaml_path.parent / ".env.nonexist"),
)
assert cfg.retriever.num_heads == 8
assert cfg.train.lr == 0.001
def test_cli_overrides_env(self, yaml_path: Path, env_path: Path) -> None:
"""CLI 应覆盖 .env 中的 api_key。"""
cfg = Config.load(
str(yaml_path),
cli_args={"llm.api_key": "cli-key"},
env_path=str(env_path),
)
assert cfg.llm.api_key == "cli-key"
# ---------------------------------------------------------------------------
# 测试: 优先级
# ---------------------------------------------------------------------------
class TestPriority:
"""三层优先级: CLI > .env > YAML。"""
def test_full_priority_chain(self, yaml_path: Path, env_path: Path) -> None:
"""CLI > .env > YAML 的完整优先级链。"""
cfg = Config.load(
str(yaml_path),
cli_args={"llm.api_key": "cli-key"},
env_path=str(env_path),
)
# CLI 覆盖 .env
assert cfg.llm.api_key == "cli-key"
# .env 覆盖 YAML（vlm 未被 CLI 覆盖）
assert cfg.vlm.api_key == "env-vlm-key"
# ---------------------------------------------------------------------------
# 测试: embed_dim 一致性校验
# ---------------------------------------------------------------------------
class TestEmbedDimConsistency:
"""embed.embed_dim 与 retriever.embed_dim 必须一致。"""
def test_inconsistent_embed_dim(self, tmp_path: Path) -> None:
"""embed_dim 不一致应抛出 ValueError。"""
bad_yaml = {
k: (v.copy() if isinstance(v, dict) else v) for k, v in _FULL_YAML.items()
}
bad_yaml["retriever"] = bad_yaml["retriever"].copy()
bad_yaml["retriever"]["embed_dim"] = 512 # 与 embed.embed_dim=768 不一致
p = tmp_path / "bad_dim.yaml"
with open(p, "w") as f:
yaml.dump(bad_yaml, f)
with pytest.raises(ValueError, match="不一致"):
Config.load(str(p), env_path=str(tmp_path / ".env.nonexist"))