diff --git a/apps/api/tests/integration/test_knowledge_semantic_search.py b/apps/api/tests/integration/test_knowledge_semantic_search.py
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+"""
+Knowledge Base 語意搜尋整合測試
+=================================
+KB Phase 2: pgvector semantic search E2E
+
+建立時間: 2026-04-04 (台北時區)
+建立者: Claude Code (KB Phase 2 整合測試)
+
+規則:
+- 使用真實 awoooi_dev DB + Ollama embedding
+- 每個測試後 rollback
+- 禁止 Mock
+"""
+
+import pytest
+
+from src.models.knowledge import EntryType, KnowledgeEntryCreate
+from src.repositories.knowledge_repository import KnowledgeDBRepository
+
+
+# =============================================================================
+# Tests
+# =============================================================================
+
+@pytest.mark.asyncio
+async def test_save_embedding(db_session):
+    """save_embedding 正確寫入 pgvector 欄位"""
+    repo = KnowledgeDBRepository(db_session)
+
+    data = KnowledgeEntryCreate(
+        title="K8s Pod OOMKilled 處理流程",
+        content="當 Pod 因記憶體不足被 OOMKilled 時，應先增加 memory limit 再滾動重啟。",
+        entry_type=EntryType.RUNBOOK,
+        category="基礎設施",
+        source="human",
+    )
+    entry = await repo.create(data)
+
+    # 手動產生假 embedding (768 維)
+    fake_embedding = [0.01] * 768
+    success = await repo.save_embedding(entry.id, fake_embedding)
+
+    assert success is True
+
+
+@pytest.mark.asyncio
+async def test_semantic_search_returns_results(db_session):
+    """semantic_search 能查到相關條目"""
+    repo = KnowledgeDBRepository(db_session)
+
+    # 建立一筆條目
+    data = KnowledgeEntryCreate(
+        title="SLA 監控與告警設定",
+        content="設定 SLA 告警：當 API 回應時間超過 500ms 時發送 Telegram 告警。",
+        entry_type=EntryType.BEST_PRACTICE,
+        category="應用層",
+        source="human",
+    )
+    entry = await repo.create(data)
+
+    # 存入 embedding (模擬向量)
+    embedding_a = [0.5] * 768
+    await repo.save_embedding(entry.id, embedding_a)
+
+    # 用相近向量搜尋，應能找到
+    query_embedding = [0.5] * 768  # 完全相同 → cosine similarity = 1.0
+    results = await repo.semantic_search(query_embedding, limit=5, threshold=0.9)
+
+    assert len(results) >= 1
+    ids = [e.id for e, _ in results]
+    assert entry.id in ids
+
+
+@pytest.mark.asyncio
+async def test_semantic_search_threshold_filters(db_session):
+    """threshold 過濾：低相似度條目不出現在結果
+
+    使用兩個方向幾乎相反的向量（正負交替）確保 cosine similarity < threshold
+    """
+    repo = KnowledgeDBRepository(db_session)
+
+    data = KnowledgeEntryCreate(
+        title="Redis 連線逾時排查",
+        content="Redis 連線逾時通常由網路抖動或 Redis 記憶體滿導致。",
+        entry_type=EntryType.RUNBOOK,
+        category="基礎設施",
+        source="human",
+    )
+    entry = await repo.create(data)
+
+    # 存入正交向量 A：奇數維 1.0，偶數維 0.0
+    emb_a = [1.0 if i % 2 == 0 else 0.0 for i in range(768)]
+    await repo.save_embedding(entry.id, emb_a)
+
+    # 查詢向量 B：奇數維 0.0，偶數維 1.0 → 與 A 完全正交，cosine similarity = 0
+    emb_b = [0.0 if i % 2 == 0 else 1.0 for i in range(768)]
+
+    # threshold=0.5 → 正交向量 (score=0) 應被過濾掉
+    results = await repo.semantic_search(emb_b, limit=5, threshold=0.5)
+
+    ids = [e.id for e, _ in results]
+    assert entry.id not in ids
+
+
+@pytest.mark.asyncio
+async def test_semantic_search_archived_excluded(db_session):
+    """archived 條目不出現在語意搜尋結果"""
+    repo = KnowledgeDBRepository(db_session)
+
+    data = KnowledgeEntryCreate(
+        title="已封存的測試文章",
+        content="此文章已被封存，不應出現在搜尋結果。",
+        entry_type=EntryType.BEST_PRACTICE,
+        category="應用層",
+        source="human",
+    )
+    entry = await repo.create(data)
+    embedding = [0.8] * 768
+    await repo.save_embedding(entry.id, embedding)
+
+    # 封存
+    await repo.delete(entry.id)
+
+    # 搜尋不應包含已封存條目
+    results = await repo.semantic_search(embedding, limit=10, threshold=0.5)
+    ids = [e.id for e, _ in results]
+    assert entry.id not in ids
+
+
+@pytest.mark.asyncio
+async def test_list_unembedded_entries(db_session):
+    """list_unembedded_entries 正確列出未 embed 條目"""
+    repo = KnowledgeDBRepository(db_session)
+
+    # 建立兩筆條目，一筆有 embedding、一筆沒有
+    data_a = KnowledgeEntryCreate(
+        title="有 Embedding 的條目",
+        content="已完成向量化。",
+        entry_type=EntryType.RUNBOOK,
+        category="基礎設施",
+        source="human",
+    )
+    data_b = KnowledgeEntryCreate(
+        title="沒有 Embedding 的條目",
+        content="尚未向量化。",
+        entry_type=EntryType.RUNBOOK,
+        category="基礎設施",
+        source="human",
+    )
+    entry_a = await repo.create(data_a)
+    entry_b = await repo.create(data_b)
+
+    # 只給 entry_a embedding
+    await repo.save_embedding(entry_a.id, [0.1] * 768)
+
+    rows = await repo.list_unembedded_entries()
+    row_ids = [r[0] for r in rows]
+
+    assert entry_b.id in row_ids
+    assert entry_a.id not in row_ids