chore: track claude skills, tools, templates, reference code and research-wiki
- 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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"""
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test_pipeline.py — Pipeline 单元测试
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======================================
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使用 unittest.mock.MagicMock + patch 隔离所有外部依赖（无真实 API / 文件 IO）。
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"""
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from __future__ import annotations
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import os
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from pathlib import Path
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from unittest.mock import MagicMock, patch
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import numpy as np
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import pytest
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import torch
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from video_tree_trm.pipeline import Pipeline
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from video_tree_trm.tree_index import IndexMeta, L1Node, L2Node, L3Node, TreeIndex
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# ---------------------------------------------------------------------------
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# 辅助：构造最小 Config Mock
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# ---------------------------------------------------------------------------
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D = 8 # 嵌入维度（与 RetrieverConfig 一致）
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def _make_config(checkpoint: str | None = None) -> MagicMock:
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"""返回一个 Mock Config，字段值与实际 dataclass 对齐。"""
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cfg = MagicMock()
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cfg.embed.model_name = "test-embed"
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cfg.embed.embed_dim = D
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cfg.retriever.checkpoint = checkpoint
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cfg.retriever.embed_dim = D
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cfg.retriever.num_heads = 2
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cfg.retriever.L_layers = 2
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cfg.retriever.L_cycles = 2
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cfg.retriever.max_rounds = 2
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cfg.retriever.ffn_expansion = 2.0
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cfg.tree.cache_dir = "/tmp/test_pipeline_cache"
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return cfg
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def _make_small_tree() -> TreeIndex:
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"""构造最小 1×1×1 TreeIndex，用于 query() 测试。"""
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meta = IndexMeta(
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source_path="dummy",
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modality="text",
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embed_model="test",
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embed_dim=D,
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)
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l3 = L3Node(
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id="l3_0",
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description="节点描述",
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embedding=np.zeros(D, dtype=np.float32),
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raw_content="节点内容",
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)
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l2 = L2Node(
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id="l2_0",
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description="L2",
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embedding=np.zeros(D, dtype=np.float32),
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children=[l3],
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)
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l1 = L1Node(
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id="l1_0",
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summary="L1",
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embedding=np.zeros(D, dtype=np.float32),
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children=[l2],
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)
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return TreeIndex(metadata=meta, roots=[l1])
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# ---------------------------------------------------------------------------
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# Patch 工厂：将所有子模块构造函数替换为 MagicMock
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# ---------------------------------------------------------------------------
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_PATCHES = [
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"video_tree_trm.pipeline.EmbeddingModel",
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"video_tree_trm.pipeline.LLMClient",
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"video_tree_trm.pipeline.RecursiveRetriever",
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"video_tree_trm.pipeline.AnswerGenerator",
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"video_tree_trm.pipeline.TextTreeBuilder",
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"video_tree_trm.pipeline.VideoTreeBuilder",
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]
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# ---------------------------------------------------------------------------
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# Pipeline.__init__ 测试
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# ---------------------------------------------------------------------------
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def test_pipeline_init_components() -> None:
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"""__init__ 后各属性（embed_model/llm/vlm/retriever/generator）均存在。"""
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cfg = _make_config()
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with patch.multiple(
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"video_tree_trm.pipeline",
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EmbeddingModel=MagicMock(),
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LLMClient=MagicMock(),
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RecursiveRetriever=MagicMock(),
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AnswerGenerator=MagicMock(),
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):
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p = Pipeline(cfg)
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assert hasattr(p, "embed_model"), "缺少 embed_model 属性"
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assert hasattr(p, "llm"), "缺少 llm 属性"
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assert hasattr(p, "vlm"), "缺少 vlm 属性"
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assert hasattr(p, "retriever"), "缺少 retriever 属性"
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assert hasattr(p, "generator"), "缺少 generator 属性"
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def test_pipeline_init_no_checkpoint() -> None:
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"""checkpoint=None 时 load_state_dict 不被调用。"""
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cfg = _make_config(checkpoint=None)
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mock_retriever_instance = MagicMock()
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MockRetriever = MagicMock(return_value=mock_retriever_instance)
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with patch.multiple(
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"video_tree_trm.pipeline",
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EmbeddingModel=MagicMock(),
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LLMClient=MagicMock(),
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RecursiveRetriever=MockRetriever,
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AnswerGenerator=MagicMock(),
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):
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Pipeline(cfg)
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mock_retriever_instance.load_state_dict.assert_not_called()
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def test_pipeline_init_with_checkpoint(tmp_path: Path) -> None:
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"""checkpoint 非 None 时 load_state_dict 被调用一次。"""
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ckpt_file = tmp_path / "model.pt"
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ckpt_file.write_bytes(b"") # 创建空文件，使 os.path.isfile 返回 True
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cfg = _make_config(checkpoint=str(ckpt_file))
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mock_retriever_instance = MagicMock()
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MockRetriever = MagicMock(return_value=mock_retriever_instance)
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fake_state_dict = {"weight": torch.zeros(1)}
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with patch.multiple(
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"video_tree_trm.pipeline",
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EmbeddingModel=MagicMock(),
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LLMClient=MagicMock(),
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RecursiveRetriever=MockRetriever,
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AnswerGenerator=MagicMock(),
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), patch("video_tree_trm.pipeline.torch.load", return_value=fake_state_dict):
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Pipeline(cfg)
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mock_retriever_instance.load_state_dict.assert_called_once_with(fake_state_dict)
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# ---------------------------------------------------------------------------
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# Pipeline.build_index 测试
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# ---------------------------------------------------------------------------
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def test_build_index_text_calls_builder(tmp_path: Path) -> None:
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"""文本模式调用 TextTreeBuilder.build，参数含文件内容。"""
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src = tmp_path / "doc.txt"
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src.write_text("文档内容", encoding="utf-8")
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cfg = _make_config()
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cfg.tree.cache_dir = str(tmp_path / "cache")
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mock_tree = MagicMock(spec=TreeIndex)
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mock_builder_instance = MagicMock()
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mock_builder_instance.build.return_value = mock_tree
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MockTextBuilder = MagicMock(return_value=mock_builder_instance)
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with patch.multiple(
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"video_tree_trm.pipeline",
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EmbeddingModel=MagicMock(),
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LLMClient=MagicMock(),
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RecursiveRetriever=MagicMock(),
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AnswerGenerator=MagicMock(),
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TextTreeBuilder=MockTextBuilder,
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):
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p = Pipeline(cfg)
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result = p.build_index(str(src), modality="text")
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mock_builder_instance.build.assert_called_once()
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call_args = mock_builder_instance.build.call_args
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assert "文档内容" in call_args[0][0], "TextTreeBuilder.build 应传入文件内容"
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assert result is mock_tree
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def test_build_index_video_calls_builder(tmp_path: Path) -> None:
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"""视频模式调用 VideoTreeBuilder.build，参数为 source_path。"""
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cfg = _make_config()
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cfg.tree.cache_dir = str(tmp_path / "cache")
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mock_tree = MagicMock(spec=TreeIndex)
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mock_builder_instance = MagicMock()
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mock_builder_instance.build.return_value = mock_tree
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MockVideoBuilder = MagicMock(return_value=mock_builder_instance)
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video_path = "/fake/video.mp4"
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with patch.multiple(
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"video_tree_trm.pipeline",
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EmbeddingModel=MagicMock(),
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LLMClient=MagicMock(),
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RecursiveRetriever=MagicMock(),
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AnswerGenerator=MagicMock(),
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VideoTreeBuilder=MockVideoBuilder,
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):
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p = Pipeline(cfg)
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result = p.build_index(video_path, modality="video")
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mock_builder_instance.build.assert_called_once_with(video_path)
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assert result is mock_tree
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def test_build_index_cache_hit(tmp_path: Path) -> None:
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"""缓存文件存在时直接 TreeIndex.load，不重新构建。"""
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cfg = _make_config()
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cache_dir = tmp_path / "cache"
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cache_dir.mkdir()
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cfg.tree.cache_dir = str(cache_dir)
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# 手动创建缓存文件（空文件即可让 isfile 返回 True）
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cache_file = cache_dir / "doc_text.pkl"
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cache_file.write_bytes(b"")
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mock_tree = MagicMock(spec=TreeIndex)
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mock_text_builder = MagicMock()
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with patch.multiple(
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"video_tree_trm.pipeline",
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EmbeddingModel=MagicMock(),
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LLMClient=MagicMock(),
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RecursiveRetriever=MagicMock(),
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AnswerGenerator=MagicMock(),
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TextTreeBuilder=mock_text_builder,
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), patch("video_tree_trm.pipeline.TreeIndex.load", return_value=mock_tree) as mock_load:
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p = Pipeline(cfg)
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result = p.build_index(str(tmp_path / "doc.txt"), modality="text")
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mock_load.assert_called_once_with(str(cache_file))
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mock_text_builder.return_value.build.assert_not_called()
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assert result is mock_tree
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def test_build_index_saves_cache(tmp_path: Path) -> None:
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"""缓存不存在时构建后调用 tree.save。"""
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cfg = _make_config()
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cfg.tree.cache_dir = str(tmp_path / "cache")
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src = tmp_path / "doc.txt"
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src.write_text("内容", encoding="utf-8")
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mock_tree = MagicMock(spec=TreeIndex)
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mock_builder_instance = MagicMock()
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mock_builder_instance.build.return_value = mock_tree
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with patch.multiple(
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"video_tree_trm.pipeline",
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EmbeddingModel=MagicMock(),
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LLMClient=MagicMock(),
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RecursiveRetriever=MagicMock(),
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AnswerGenerator=MagicMock(),
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TextTreeBuilder=MagicMock(return_value=mock_builder_instance),
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):
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p = Pipeline(cfg)
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p.build_index(str(src), modality="text")
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mock_tree.save.assert_called_once()
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saved_path: str = mock_tree.save.call_args[0][0]
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assert "doc_text.pkl" in saved_path, f"保存路径应含 'doc_text.pkl'，实际={saved_path}"
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# ---------------------------------------------------------------------------
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# Pipeline.query 测试
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# ---------------------------------------------------------------------------
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def test_query_embeds_question() -> None:
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"""query() 调用 embed_model.embed_tensor(question)。"""
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cfg = _make_config()
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tree = _make_small_tree()
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mock_embed = MagicMock()
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mock_embed.embed_tensor.return_value = torch.zeros(1, D)
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MockEmbed = MagicMock(return_value=mock_embed)
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mock_retriever_instance = MagicMock()
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mock_retriever_instance.return_value = {"paths": [(0, 0, 0)], "num_rounds": 1}
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with patch.multiple(
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"video_tree_trm.pipeline",
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EmbeddingModel=MockEmbed,
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LLMClient=MagicMock(),
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RecursiveRetriever=MagicMock(return_value=mock_retriever_instance),
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AnswerGenerator=MagicMock(),
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):
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p = Pipeline(cfg)
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p.query("测试问题", tree)
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mock_embed.embed_tensor.assert_called_once_with("测试问题")
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def test_query_calls_retriever() -> None:
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"""query() 调用 retriever(q, tree)。"""
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cfg = _make_config()
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tree = _make_small_tree()
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q_tensor = torch.zeros(1, D)
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mock_embed = MagicMock()
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mock_embed.embed_tensor.return_value = q_tensor
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mock_retriever_instance = MagicMock()
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mock_retriever_instance.return_value = {"paths": [(0, 0, 0)], "num_rounds": 1}
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with patch.multiple(
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"video_tree_trm.pipeline",
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EmbeddingModel=MagicMock(return_value=mock_embed),
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LLMClient=MagicMock(),
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RecursiveRetriever=MagicMock(return_value=mock_retriever_instance),
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AnswerGenerator=MagicMock(),
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):
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p = Pipeline(cfg)
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p.query("测试问题", tree)
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mock_retriever_instance.assert_called_once()
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call_args = mock_retriever_instance.call_args
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# 第一个位置参数应为嵌入 Tensor，第二个为 tree
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assert call_args[0][1] is tree, "retriever 第二个参数应为 tree"
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def test_query_returns_answer() -> None:
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"""query() 返回 generator.generate 的返回值。"""
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cfg = _make_config()
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tree = _make_small_tree()
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mock_embed = MagicMock()
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mock_embed.embed_tensor.return_value = torch.zeros(1, D)
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mock_retriever_instance = MagicMock()
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mock_retriever_instance.return_value = {"paths": [(0, 0, 0)], "num_rounds": 1}
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mock_generator_instance = MagicMock()
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mock_generator_instance.generate.return_value = "生成的答案"
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with patch.multiple(
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"video_tree_trm.pipeline",
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EmbeddingModel=MagicMock(return_value=mock_embed),
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LLMClient=MagicMock(),
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RecursiveRetriever=MagicMock(return_value=mock_retriever_instance),
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AnswerGenerator=MagicMock(return_value=mock_generator_instance),
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):
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p = Pipeline(cfg)
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answer = p.query("问题", tree)
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assert answer == "生成的答案", f"query() 应返回 generator 的结果，实际='{answer}'"
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mock_generator_instance.generate.assert_called_once_with(
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"问题", [(0, 0, 0)], tree
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)
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