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Operation Ollama-First v5.0 / Phase 11.5 收尾（A4 已知 limitation 補完）
問題：Phase 11 A4 完成時揭露：
> Stage 3 dedup 需 episode 先 embed：目前 LearningPipeline.enqueue 寫入時
> embedding 為 NULL，所有 episode 都會略過 Stage 3 dedup
修補：
- learning_pipeline.enqueue 內 episode INSERT commit 後 enqueue embedding worker
- 用既有 _enqueue_embedding('learning_episodes', episode_id, distilled_text)
- ADR-007 retry queue worker 自動處理（_process_one_embedding 已動態 UPDATE
{target_table}，已支援 learning_episodes 表）
- distilled_text 截 4000 字避免 retry queue 表膨脹
- 失敗 swallow，僅 log debug（不阻擋 episode_id 回傳）
落地 ADR-033 護欄 #1 完整版：
Stage 1: quality_score >= 0.7 ✅ 既有
Stage 2: 無幻覺檢測（規則引擎） ✅ 既有
Stage 3: 與既有 insight cosine < 0.95 ✅ 解鎖 ⭐
Stage 4: weight >= 0.8 必經 👍/👎 ✅ 既有
regression: 70 unit tests 全綠（含修正 test_enqueue_returns_id_on_success
配合新增 _enqueue_embedding 的 commit 計數變化）
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
278 lines
12 KiB
Python
278 lines
12 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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tests/test_learning_pipeline.py
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Operation Ollama-First v5.0 / Phase 11 — Distiller + LearningPipeline 單元測試
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涵蓋：
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- Distiller 各 quality_score 規則（mcp / llm_response / user_feedback / manual_curated）
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- LearningPipeline.enqueue() DB 寫入路徑
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- expire_stale_reviews() 24h 自動降級
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- hash_human_approver() PII 保護
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"""
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from __future__ import annotations
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import json
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from unittest.mock import MagicMock, patch
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import pytest
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# ─────────────────────────────────────────────────────────────────────────────
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# Distiller 各規則
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# ─────────────────────────────────────────────────────────────────────────────
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class TestDistillerMcpResult:
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def test_long_with_keywords_high_quality(self):
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from services.learning_pipeline import Distiller
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d = Distiller()
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text = "本週業績分析顯示，建議聚焦保濕品類。" + "詳細說明 " * 80 # > 200 字
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result = d.distill(episode_type='mcp_result', raw_content=text)
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assert result is not None
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assert result.quality_score == 0.8
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assert result.episode_type == 'mcp_result'
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def test_long_no_keywords_medium_quality(self):
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from services.learning_pipeline import Distiller
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d = Distiller()
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text = "啦啦啦" * 100 # > 200 字但無關鍵字
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result = d.distill(episode_type='mcp_result', raw_content=text)
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assert result.quality_score == 0.65
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def test_short_low_quality(self):
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from services.learning_pipeline import Distiller
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d = Distiller()
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text = "短內容"
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result = d.distill(episode_type='mcp_result', raw_content=text)
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assert result.quality_score == 0.5
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def test_empty_returns_none(self):
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from services.learning_pipeline import Distiller
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d = Distiller()
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assert d.distill(episode_type='mcp_result', raw_content='') is None
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assert d.distill(episode_type='mcp_result', raw_content=' ') is None
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class TestDistillerLlmResponse:
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def test_json_structured_high_quality(self):
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from services.learning_pipeline import Distiller
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d = Distiller()
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text = json.dumps({"status": "ok", "summary": "本週重點"})
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result = d.distill(episode_type='llm_response', raw_content=text)
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assert result.quality_score == 0.9
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def test_json_array_non_empty_high(self):
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from services.learning_pipeline import Distiller
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d = Distiller()
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text = json.dumps([{"sku": "A001", "risk": "HIGH"}])
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result = d.distill(episode_type='llm_response', raw_content=text)
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assert result.quality_score == 0.9
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def test_json_dict_no_status_lower(self):
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from services.learning_pipeline import Distiller
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d = Distiller()
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text = json.dumps({"some_field": "value"})
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result = d.distill(episode_type='llm_response', raw_content=text)
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# dict 非空 → 0.9 (status_ok 條件含 "len(obj)>0")
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assert result.quality_score == 0.9
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def test_free_text_long_with_numbers(self):
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from services.learning_pipeline import Distiller
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d = Distiller()
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text = "本週業績漲了 15.3%。" + "詳細說明 " * 100 # > 500 字 + 數字
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result = d.distill(episode_type='llm_response', raw_content=text)
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assert result.quality_score == 0.65
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def test_free_text_long_no_numbers(self):
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from services.learning_pipeline import Distiller
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d = Distiller()
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text = "本週業績趨勢上升。" + "詳細說明 " * 100 # > 500 字無數字
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result = d.distill(episode_type='llm_response', raw_content=text)
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assert result.quality_score == 0.55
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def test_free_text_short_below_quality_gate(self):
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from services.learning_pipeline import Distiller
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d = Distiller()
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text = "本週業績有變化" # 短文本
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result = d.distill(episode_type='llm_response', raw_content=text)
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# 0.4 → Stage 1 會 reject
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assert result.quality_score == 0.4
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class TestDistillerUserFeedback:
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def test_score_5_high_quality(self):
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from services.learning_pipeline import Distiller
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d = Distiller()
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result = d.distill(
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episode_type='user_feedback',
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raw_content='這個建議幫我增加了 8% 銷量',
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user_feedback_score=5,
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)
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assert result.quality_score == 1.0
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assert result.weight == 0.9 # 高權重 → Stage 4 人工驗收
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def test_score_1_negative_sample(self):
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from services.learning_pipeline import Distiller
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d = Distiller()
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result = d.distill(
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episode_type='user_feedback',
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raw_content='完全沒幫助',
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user_feedback_score=1,
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)
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assert result.quality_score == 0.0 # Stage 1 reject
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def test_default_score_3_mid(self):
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from services.learning_pipeline import Distiller
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d = Distiller()
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result = d.distill(
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episode_type='user_feedback',
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raw_content='普通',
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user_feedback_score=None,
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)
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# 預設 3 → (3-1)/4 = 0.5
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assert result.quality_score == 0.5
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class TestDistillerManualCurated:
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def test_max_quality_and_weight(self):
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from services.learning_pipeline import Distiller
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d = Distiller()
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result = d.distill(episode_type='manual_curated', raw_content='手動入庫')
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assert result.quality_score == 1.0
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assert result.weight == 1.0
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class TestDistillerInvalidType:
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def test_unknown_type_returns_none(self):
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from services.learning_pipeline import Distiller
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d = Distiller()
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result = d.distill(episode_type='garbage', raw_content='whatever')
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assert result is None
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class TestDistillerLengthGuard:
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def test_distilled_text_truncated_to_16kb(self):
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from services.learning_pipeline import Distiller, DISTILLED_TEXT_MAX_BYTES
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d = Distiller()
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text = '建議分析 ' * 5000 # 遠超 16KB
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result = d.distill(episode_type='mcp_result', raw_content=text)
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encoded = result.distilled_text.encode('utf-8')
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assert len(encoded) <= DISTILLED_TEXT_MAX_BYTES
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# ─────────────────────────────────────────────────────────────────────────────
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# LearningPipeline.enqueue
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# ─────────────────────────────────────────────────────────────────────────────
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class TestLearningPipelineEnqueue:
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def test_enqueue_returns_id_on_success(self, monkeypatch):
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from services.learning_pipeline import learning_pipeline
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fake_session = MagicMock()
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fake_row = MagicMock()
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fake_row.__getitem__.return_value = 42
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fake_session.execute.return_value.fetchone.return_value = fake_row
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monkeypatch.setattr('database.manager.get_session', lambda: fake_session)
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new_id = learning_pipeline.enqueue(
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episode_type='manual_curated',
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raw_content='手動入庫測試內容',
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)
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assert new_id == 42
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# Phase 11.5: enqueue 後 _enqueue_embedding 也用同一個 fake session → commit 2 次
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# （1: episode INSERT, 2: embedding_retry_queue INSERT）
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# 失敗安全：_enqueue_embedding 失敗會 swallow 不影響 episode_id 回傳
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assert fake_session.commit.call_count >= 1
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def test_enqueue_returns_none_when_distill_fails(self):
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from services.learning_pipeline import learning_pipeline
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# 空內容 → distill 回 None → enqueue 回 None
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result = learning_pipeline.enqueue(
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episode_type='mcp_result',
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raw_content='',
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)
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assert result is None
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def test_enqueue_db_failure_returns_none(self, monkeypatch):
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from services.learning_pipeline import learning_pipeline
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fake_session = MagicMock()
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fake_session.execute.side_effect = RuntimeError("db down")
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monkeypatch.setattr('database.manager.get_session', lambda: fake_session)
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result = learning_pipeline.enqueue(
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episode_type='manual_curated',
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raw_content='測試內容',
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)
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assert result is None
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# ─────────────────────────────────────────────────────────────────────────────
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# expire_stale_reviews
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# ─────────────────────────────────────────────────────────────────────────────
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class TestExpireStaleReviews:
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def test_expire_uses_correct_sql(self, monkeypatch):
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from services.learning_pipeline import expire_stale_reviews
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fake_session = MagicMock()
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fake_result = MagicMock()
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fake_result.rowcount = 3
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fake_session.execute.return_value = fake_result
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monkeypatch.setattr('database.manager.get_session', lambda: fake_session)
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count = expire_stale_reviews(hours=24)
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assert count == 3
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# 確認 commit 跑了
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fake_session.commit.assert_called_once()
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def test_expire_db_failure_returns_zero(self, monkeypatch):
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from services.learning_pipeline import expire_stale_reviews
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fake_session = MagicMock()
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fake_session.execute.side_effect = RuntimeError("db down")
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monkeypatch.setattr('database.manager.get_session', lambda: fake_session)
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count = expire_stale_reviews(hours=24)
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assert count == 0
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# ─────────────────────────────────────────────────────────────────────────────
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# hash_human_approver
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# ─────────────────────────────────────────────────────────────────────────────
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class TestHashHumanApprover:
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def test_returns_8_char_hex(self):
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from services.learning_pipeline import hash_human_approver
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h = hash_human_approver('owen.tsai')
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assert len(h) == 8
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assert all(c in '0123456789abcdef' for c in h)
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def test_empty_returns_empty(self):
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from services.learning_pipeline import hash_human_approver
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assert hash_human_approver('') == ''
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assert hash_human_approver(None) == '' # type: ignore
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def test_deterministic(self):
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from services.learning_pipeline import hash_human_approver
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a = hash_human_approver('alice')
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b = hash_human_approver('alice')
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c = hash_human_approver('bob')
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assert a == b
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assert a != c
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# ─────────────────────────────────────────────────────────────────────────────
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# 工具函式：_detect_simple_contradiction
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# ─────────────────────────────────────────────────────────────────────────────
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class TestContradictionDetector:
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def test_no_contradiction_returns_none(self):
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from services.learning_pipeline import _detect_simple_contradiction
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text = "業績是上升。市場是競爭。"
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# subject=業績→上升, subject=市場→競爭，沒矛盾
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assert _detect_simple_contradiction(text) is None
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def test_contradiction_detected(self):
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from services.learning_pipeline import _detect_simple_contradiction
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text = "A是黑色。A是白色。"
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result = _detect_simple_contradiction(text)
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assert result is not None
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assert 'A' in result
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