feat(harness): add factory.py — InferenceDeps dataclass + build_inference_deps
组装一次推理所需的全套依赖的工厂函数： - TreeIndex 加载（FileNotFoundError if missing） - TreeEnvironment 构建 - SkillRegistry 按需发现 - SearchToolDispatcher 装配 - PromptManager + prompt_builder 闭包 测试覆盖：正常路径、缺失树文件、skills 注入、frozen 不可变性。 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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"""app/harness/factory.py 的单元测试。
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验证 build_inference_deps 的返回类型、字段连接、以及错误路径。
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"""
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from __future__ import annotations
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import json
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from typing import TYPE_CHECKING
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from unittest.mock import AsyncMock, MagicMock
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if TYPE_CHECKING:
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from pathlib import Path
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import numpy as np
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import pytest
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from app.harness.factory import InferenceDeps, build_inference_deps
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from core.types import GeneratedQuestion
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class TestBuildInferenceDeps:
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"""build_inference_deps 工厂函数测试。"""
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def test_returns_inference_deps(self, tmp_path: Path) -> None:
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"""用 fake adapters 验证返回类型和字段非 None。"""
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# 准备一棵最小树
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vid_dir = tmp_path / "videos" / "test_vid"
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vid_dir.mkdir(parents=True)
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(vid_dir / "frames").mkdir()
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minimal_tree = {
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"metadata": {"source_path": "test", "modality": "video"},
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"roots": [
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{
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"id": "L1_000",
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"card": {
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"scene_summary": "s",
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"main_setting": "s",
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"key_entities": [],
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"main_actions": [],
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"topic_keywords": [],
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"visible_text": [],
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"temporal_flow": "s",
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},
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"time_range": [0, 10],
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"children": [],
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}
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],
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}
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(vid_dir / "tree.json").write_text(json.dumps(minimal_tree))
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# prompts
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prompts_dir = tmp_path / "prompts"
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prompts_dir.mkdir()
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(prompts_dir / "system.md").write_text("You are a search agent.")
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fake_llm = AsyncMock()
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fake_vlm = AsyncMock()
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fake_embed = MagicMock()
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fake_embed.dim = 4
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fake_embed.embed = lambda t: np.zeros((1, 4), dtype=np.float32)
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deps = build_inference_deps(
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store_dir=tmp_path,
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video_id="test_vid",
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prompts_dir=prompts_dir,
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skills_dir=None,
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skill_mode="none",
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embed_provider=fake_embed,
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llm=fake_llm,
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vlm=fake_vlm,
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ocr=None,
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verify_vision=False,
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anchor=False,
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assemble_mode="ids",
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)
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assert isinstance(deps, InferenceDeps)
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assert deps.llm is fake_llm
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assert callable(deps.tool_dispatch_fn)
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assert callable(deps.prompt_builder)
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# 验证 prompt_builder 实际可用（连接正确）
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fake_q = GeneratedQuestion(
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question_id="q1",
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video_id="test_vid",
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task_type="Object Recognition",
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question="What?",
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options=("A. X", "B. Y", "C. Z", "D. W"),
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answer="A",
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source_nodes=(),
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difficulty="medium",
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)
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system, user = deps.prompt_builder(fake_q)
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assert isinstance(system, str) and len(system) > 0
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assert isinstance(user, str) and "What?" in user
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def test_missing_tree_raises(self, tmp_path: Path) -> None:
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"""tree.json 不存在时应抛出 FileNotFoundError。"""
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prompts_dir = tmp_path / "prompts"
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prompts_dir.mkdir()
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(prompts_dir / "system.md").write_text("x")
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vid_dir = tmp_path / "videos" / "nonexist"
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vid_dir.mkdir(parents=True)
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with pytest.raises(FileNotFoundError):
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build_inference_deps(
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store_dir=tmp_path,
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video_id="nonexist",
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prompts_dir=prompts_dir,
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skills_dir=None,
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skill_mode="none",
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embed_provider=MagicMock(),
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llm=AsyncMock(),
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vlm=AsyncMock(),
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ocr=None,
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verify_vision=False,
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anchor=False,
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assemble_mode="ids",
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)
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def test_with_skills_dir(self, tmp_path: Path) -> None:
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"""提供 skills_dir 时 skill 信息应正确注入到 prompt_builder 输出。"""
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# 准备树
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vid_dir = tmp_path / "videos" / "vid1"
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vid_dir.mkdir(parents=True)
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(vid_dir / "frames").mkdir()
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minimal_tree = {
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"metadata": {"source_path": "test", "modality": "video"},
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"roots": [
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{
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"id": "L1_000",
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"card": {
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"scene_summary": "test scene",
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"main_setting": "indoor",
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"key_entities": [],
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"main_actions": [],
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"topic_keywords": [],
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"visible_text": [],
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"temporal_flow": "linear",
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},
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"time_range": [0, 5],
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"children": [],
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}
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],
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}
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(vid_dir / "tree.json").write_text(json.dumps(minimal_tree))
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# prompts
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prompts_dir = tmp_path / "prompts"
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prompts_dir.mkdir()
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(prompts_dir / "system.md").write_text("Base system prompt.")
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# skills
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skills_dir = tmp_path / "skills"
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skills_dir.mkdir()
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(skills_dir / "always_nav.md").write_text(
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"---\nname: always_nav\nalways: true\n---\nAlways navigate broadly."
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)
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(skills_dir / "action_skill.md").write_text(
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"---\nname: action_skill\ntask_type: Action Reasoning\n---\nFocus on actions."
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)
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fake_llm = AsyncMock()
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fake_vlm = AsyncMock()
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fake_embed = MagicMock()
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fake_embed.dim = 4
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fake_embed.embed = lambda t: np.zeros((1, 4), dtype=np.float32)
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deps = build_inference_deps(
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store_dir=tmp_path,
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video_id="vid1",
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prompts_dir=prompts_dir,
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skills_dir=skills_dir,
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skill_mode="auto",
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embed_provider=fake_embed,
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llm=fake_llm,
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vlm=fake_vlm,
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ocr=None,
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verify_vision=False,
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anchor=False,
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assemble_mode="ids",
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)
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fake_q = GeneratedQuestion(
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question_id="q2",
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video_id="vid1",
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task_type="Action Reasoning",
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question="What happened?",
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options=("A. X", "B. Y", "C. Z", "D. W"),
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answer="B",
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source_nodes=(),
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difficulty="easy",
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)
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system, user = deps.prompt_builder(fake_q)
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# always skill 文本和 task_type skill 文本应出现在 system prompt 中
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assert "Always navigate broadly" in system
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assert "Focus on actions" in system
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assert "What happened?" in user
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def test_frozen_dataclass(self) -> None:
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"""InferenceDeps 是 frozen dataclass，不可修改属性。"""
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deps = InferenceDeps(
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llm=AsyncMock(),
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tool_dispatch_fn=lambda: None,
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prompt_builder=lambda q: ("", ""),
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)
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with pytest.raises(AttributeError):
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deps.llm = AsyncMock() # type: ignore[misc]
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