fix(ci): 修復 docker socket 重複掛載 (1774793847)

apps/api/src/services/intent_classifier.py

@@ -21,18 +21,40 @@ K8s 操作意圖分類器，用於智能路由模型選擇
 
 from __future__ import annotations
 
+import json
 import re
+import time
 from dataclasses import dataclass, field
 from enum import Enum
 from typing import Protocol, runtime_checkable
 
+import httpx
 import structlog
 
+from src.core.config import settings
 from src.services.model_registry import get_model_registry
 
 logger = structlog.get_logger(__name__)
 
 
+# LLM 分類 Prompt 模板 (Phase 13.4)
+_LLM_CLASSIFY_PROMPT = """你是 K8s 操作意圖分類專家。根據以下輸入，判斷用戶的操作意圖。
+
+可選意圖類型:
+- restart: 重啟 Pod/Deployment/StatefulSet
+- scale: 擴縮容、HPA 調整
+- config: ConfigMap/Secret/ENV 變更
+- diagnose: 日誌查詢、健康檢查、RCA
+- delete: 刪除資源（高風險）
+- rollback: 回滾版本
+- unknown: 無法判斷
+
+輸入: {text}
+
+請以 JSON 格式回答，只輸出 JSON:
+{{"intent": "<類型>", "confidence": <0.0-1.0>, "reasoning": "<判斷依據>"}}"""
+
+
 # =============================================================================
 # 意圖類型定義 (Phase 13.3 #85)
 # =============================================================================
@@ -503,9 +525,9 @@ class IntentClassifier:
 
     async def _llm_classify(self, text: str) -> IntentResult:
         """
-        LLM 分類 (方案 B)
+        LLM 分類 (方案 B) - Phase 13.4
 
-        目標延遲: < 100ms (使用 qwen2.5:1b)
+        目標延遲: < 100ms (使用 qwen2.5:1b 或配置的 intent 模型)
 
         Args:
             text: 已轉小寫的輸入文字
@@ -513,20 +535,114 @@ class IntentClassifier:
         Returns:
             IntentResult: 分類結果
 
-        Note:
-            目前返回 UNKNOWN，待 Ollama qwen2.5:1b 部署後啟用
+        2026-03-30 Claude Code: 實作 Ollama 整合
         """
-        # TODO: 整合 Ollama qwen2.5:1b (Phase 13.4)
-        # 預計使用 text 呼叫 Ollama API 進行分類
-        # 目前先返回 UNKNOWN，規則引擎已能處理大部分情況
-        del text  # 預留給 LLM 分類使用，避免 unused-parameter 警告
+        start_time = time.time()
+
+        try:
+            # 建構 Prompt
+            prompt = _LLM_CLASSIFY_PROMPT.format(text=text)
+
+            # 取得模型配置
+            model_name = self.llm_model  # qwen2.5:1b 或配置值
+
+            # 呼叫 Ollama
+            async with httpx.AsyncClient() as client:
+                response = await client.post(
+                    f"{settings.OLLAMA_URL}/api/generate",
+                    json={
+                        "model": model_name,
+                        "prompt": prompt,
+                        "stream": False,
+                        "format": "json",
+                        "options": {
+                            "num_predict": 128,  # 意圖分類只需短回應
+                            "temperature": 0.0,  # 確定性輸出
+                            "top_p": 0.9,
+                        },
+                    },
+                    timeout=httpx.Timeout(5.0, connect=2.0),  # 嚴格超時
+                )
+
+                response.raise_for_status()
+                data = response.json()
+                result_text = data.get("response", "")
+
+            # 解析 JSON 回應
+            elapsed_ms = (time.time() - start_time) * 1000
+
+            try:
+                parsed = json.loads(result_text)
+                intent_str = parsed.get("intent", "unknown").lower()
+                confidence = float(parsed.get("confidence", 0.5))
+                reasoning = parsed.get("reasoning", "")
+
+                # 映射到 IntentType
+                intent = self._parse_intent_type(intent_str)
+
+                logger.info(
+                    "intent_llm_classified",
+                    intent=intent.value,
+                    confidence=confidence,
+                    elapsed_ms=round(elapsed_ms, 1),
+                    model=model_name,
+                )
+
+                return IntentResult(
+                    intent=intent,
+                    confidence=confidence,
+                    method="llm",
+                    matched_keywords=[],
+                    detected_resources=[],
+                    reasoning=reasoning,
+                )
+
+            except (json.JSONDecodeError, KeyError, ValueError) as e:
+                logger.warning(
+                    "intent_llm_parse_failed",
+                    error=str(e),
+                    response_preview=result_text[:100],
+                )
+                return self._llm_fallback_result("JSON 解析失敗")
+
+        except httpx.TimeoutException:
+            elapsed_ms = (time.time() - start_time) * 1000
+            logger.warning(
+                "intent_llm_timeout",
+                elapsed_ms=round(elapsed_ms, 1),
+            )
+            return self._llm_fallback_result("LLM 超時")
+
+        except Exception as e:
+            logger.warning(
+                "intent_llm_error",
+                error=str(e),
+                error_type=type(e).__name__,
+            )
+            return self._llm_fallback_result(f"LLM 錯誤: {type(e).__name__}")
+
+    def _parse_intent_type(self, intent_str: str) -> IntentType:
+        """解析意圖字串為 IntentType"""
+        intent_map = {
+            "restart": IntentType.RESTART,
+            "scale": IntentType.SCALE,
+            "config": IntentType.CONFIG,
+            "diagnose": IntentType.DIAGNOSE,
+            "delete": IntentType.DELETE,
+            "rollback": IntentType.ROLLBACK,
+            "unknown": IntentType.UNKNOWN,
+        }
+        return intent_map.get(intent_str.lower(), IntentType.UNKNOWN)
+
+    def _llm_fallback_result(self, reason: str) -> IntentResult:
+        """LLM 失敗時的 fallback 結果"""
         return IntentResult(
             intent=IntentType.UNKNOWN,
-            confidence=0.0,  # 🔴 LLM 未啟用，非 AI 分析
+            confidence=0.0,
             method="llm",
             matched_keywords=[],
             detected_resources=[],
-            reasoning="LLM 分類尚未啟用",
+            reasoning=reason,
         )
 
     def get_supported_intents(self) -> list[dict]:

apps/api/tests/test_intent_classifier.py

@@ -0,0 +1,288 @@
+"""
+Intent Classifier Tests - Phase 13.4 Ollama 整合
+================================================
+2026-03-30 Claude Code: Intent Classifier LLM 整合測試
+
+測試範圍:
+- _llm_classify: LLM 分類邏輯
+- _parse_intent_type: 意圖解析
+- _llm_fallback_result: 失敗回退
+- classify: 完整分類流程
+"""
+
+import pytest
+from unittest.mock import AsyncMock, MagicMock, patch
+import json
+
+from src.services.intent_classifier import (
+    IntentClassifier,
+    IntentType,
+    IntentResult,
+    RiskLevel,
+    get_intent_classifier,
+)
+
+
+# =============================================================================
+# Fixtures
+# =============================================================================
+
+
+@pytest.fixture
+def classifier():
+    """測試用 IntentClassifier"""
+    return IntentClassifier()
+
+
+@pytest.fixture
+def mock_ollama_response():
+    """模擬 Ollama 回應"""
+    return {
+        "response": json.dumps({
+            "intent": "restart",
+            "confidence": 0.85,
+            "reasoning": "用戶要求重啟 Pod"
+        })
+    }
+
+
+# =============================================================================
+# Test Cases
+# =============================================================================
+
+
+class TestParseIntentType:
+    """_parse_intent_type 測試"""
+
+    def test_parse_restart(self, classifier):
+        """測試解析 restart"""
+        assert classifier._parse_intent_type("restart") == IntentType.RESTART
+        assert classifier._parse_intent_type("RESTART") == IntentType.RESTART
+
+    def test_parse_scale(self, classifier):
+        """測試解析 scale"""
+        assert classifier._parse_intent_type("scale") == IntentType.SCALE
+
+    def test_parse_config(self, classifier):
+        """測試解析 config"""
+        assert classifier._parse_intent_type("config") == IntentType.CONFIG
+
+    def test_parse_diagnose(self, classifier):
+        """測試解析 diagnose"""
+        assert classifier._parse_intent_type("diagnose") == IntentType.DIAGNOSE
+
+    def test_parse_delete(self, classifier):
+        """測試解析 delete"""
+        assert classifier._parse_intent_type("delete") == IntentType.DELETE
+
+    def test_parse_rollback(self, classifier):
+        """測試解析 rollback"""
+        assert classifier._parse_intent_type("rollback") == IntentType.ROLLBACK
+
+    def test_parse_unknown(self, classifier):
+        """測試解析 unknown"""
+        assert classifier._parse_intent_type("unknown") == IntentType.UNKNOWN
+        assert classifier._parse_intent_type("invalid") == IntentType.UNKNOWN
+        assert classifier._parse_intent_type("") == IntentType.UNKNOWN
+
+
+class TestLlmFallbackResult:
+    """_llm_fallback_result 測試"""
+
+    def test_fallback_result(self, classifier):
+        """測試 fallback 結果"""
+        result = classifier._llm_fallback_result("test error")
+
+        assert result.intent == IntentType.UNKNOWN
+        assert result.confidence == 0.0
+        assert result.method == "llm"
+        assert result.reasoning == "test error"
+        assert result.matched_keywords == []
+        assert result.detected_resources == []
+
+
+class TestKeywordClassify:
+    """關鍵字分類測試"""
+
+    def test_restart_keywords(self, classifier):
+        """測試重啟關鍵字"""
+        result = classifier.classify_sync("重啟 api-server pod")
+        assert result.intent == IntentType.RESTART
+        assert result.method == "keyword"
+        # 關鍵字匹配信心度為 0 (非 AI 分析)
+        assert result.confidence == 0.0
+        assert "重啟" in result.matched_keywords
+
+    def test_scale_keywords(self, classifier):
+        """測試擴縮容關鍵字"""
+        result = classifier.classify_sync("擴展 deployment 副本數到 5")
+        assert result.intent == IntentType.SCALE
+        assert result.method == "keyword"
+
+    def test_diagnose_keywords(self, classifier):
+        """測試診斷關鍵字"""
+        result = classifier.classify_sync("查看 pod 日誌")
+        assert result.intent == IntentType.DIAGNOSE
+        assert result.method == "keyword"
+
+    def test_delete_keywords(self, classifier):
+        """測試刪除關鍵字"""
+        result = classifier.classify_sync("刪除這個 pod")
+        assert result.intent == IntentType.DELETE
+        assert result.method == "keyword"
+
+    def test_unknown_text(self, classifier):
+        """測試無法識別的文字"""
+        result = classifier.classify_sync("今天天氣真好")
+        assert result.intent == IntentType.UNKNOWN
+
+
+class TestLlmClassify:
+    """_llm_classify 測試"""
+
+    @pytest.mark.asyncio
+    async def test_llm_success(self, classifier, mock_ollama_response):
+        """測試 LLM 成功分類"""
+        mock_response = MagicMock()
+        mock_response.status_code = 200
+        mock_response.json.return_value = mock_ollama_response
+        mock_response.raise_for_status = MagicMock()
+
+        with patch("httpx.AsyncClient") as mock_client:
+            mock_instance = AsyncMock()
+            mock_instance.post.return_value = mock_response
+            mock_instance.__aenter__.return_value = mock_instance
+            mock_instance.__aexit__.return_value = None
+            mock_client.return_value = mock_instance
+
+            result = await classifier._llm_classify("重啟 pod")
+
+            assert result.intent == IntentType.RESTART
+            assert result.confidence == 0.85
+            assert result.method == "llm"
+
+    @pytest.mark.asyncio
+    async def test_llm_timeout(self, classifier):
+        """測試 LLM 超時"""
+        import httpx
+
+        with patch("httpx.AsyncClient") as mock_client:
+            mock_instance = AsyncMock()
+            mock_instance.post.side_effect = httpx.TimeoutException("timeout")
+            mock_instance.__aenter__.return_value = mock_instance
+            mock_instance.__aexit__.return_value = None
+            mock_client.return_value = mock_instance
+
+            result = await classifier._llm_classify("重啟 pod")
+
+            assert result.intent == IntentType.UNKNOWN
+            assert result.confidence == 0.0
+            assert "超時" in result.reasoning
+
+    @pytest.mark.asyncio
+    async def test_llm_invalid_json(self, classifier):
+        """測試 LLM 返回無效 JSON"""
+        mock_response = MagicMock()
+        mock_response.status_code = 200
+        mock_response.json.return_value = {"response": "not valid json"}
+        mock_response.raise_for_status = MagicMock()
+
+        with patch("httpx.AsyncClient") as mock_client:
+            mock_instance = AsyncMock()
+            mock_instance.post.return_value = mock_response
+            mock_instance.__aenter__.return_value = mock_instance
+            mock_instance.__aexit__.return_value = None
+            mock_client.return_value = mock_instance
+
+            result = await classifier._llm_classify("重啟 pod")
+
+            assert result.intent == IntentType.UNKNOWN
+            assert "解析失敗" in result.reasoning
+
+
+class TestClassify:
+    """完整分類流程測試"""
+
+    @pytest.mark.asyncio
+    async def test_keyword_with_llm_fallback(self, classifier):
+        """測試關鍵字匹配 + LLM fallback"""
+        # 由於關鍵字信心度為 0，會嘗試 LLM
+        # LLM 可能超時或失敗，最終返回關鍵字結果
+        result = await classifier.classify("重啟 api pod")
+
+        # 意圖應該是 RESTART (來自關鍵字或 LLM)
+        assert result.intent == IntentType.RESTART
+        # method 可能是 keyword (LLM 超時) 或 llm (LLM 成功)
+        assert result.method in ["keyword", "llm"]
+
+    @pytest.mark.asyncio
+    async def test_llm_used_when_available(self, classifier, mock_ollama_response):
+        """測試 LLM 可用時使用 LLM 結果"""
+        mock_response = MagicMock()
+        mock_response.status_code = 200
+        mock_response.json.return_value = mock_ollama_response
+        mock_response.raise_for_status = MagicMock()
+
+        with patch("httpx.AsyncClient") as mock_client:
+            mock_instance = AsyncMock()
+            mock_instance.post.return_value = mock_response
+            mock_instance.__aenter__.return_value = mock_instance
+            mock_instance.__aexit__.return_value = None
+            mock_client.return_value = mock_instance
+
+            result = await classifier.classify("重啟 pod")
+
+            # LLM 成功時，應該使用 LLM 結果 (信心度 0.85 > 0)
+            assert result.intent == IntentType.RESTART
+            assert result.method == "llm"
+            assert result.confidence == 0.85
+
+
+class TestGetIntentClassifier:
+    """Singleton 測試"""
+
+    def test_singleton(self):
+        """測試 singleton 模式"""
+        c1 = get_intent_classifier()
+        c2 = get_intent_classifier()
+        assert c1 is c2
+
+
+class TestIntentResult:
+    """IntentResult 測試"""
+
+    def test_dataclass_fields(self):
+        """測試 dataclass 欄位"""
+        result = IntentResult(
+            intent=IntentType.RESTART,
+            confidence=0.9,
+            method="llm",
+            matched_keywords=["重啟", "pod"],
+            detected_resources=["api-server"],
+            reasoning="匹配重啟關鍵字",
+        )
+
+        assert result.intent == IntentType.RESTART
+        assert result.confidence == 0.9
+        assert result.method == "llm"
+        assert result.risk_level == RiskLevel.MEDIUM  # auto-set by __post_init__
+        assert "重啟" in result.matched_keywords
+        assert "api-server" in result.detected_resources
+
+    def test_risk_level_auto_set(self):
+        """測試風險等級自動設定"""
+        # DELETE 應該是 CRITICAL
+        delete_result = IntentResult(
+            intent=IntentType.DELETE,
+            confidence=0.8,
+            method="llm",
+        )
+        assert delete_result.risk_level == RiskLevel.CRITICAL
+
+        # DIAGNOSE 應該是 LOW
+        diagnose_result = IntentResult(
+            intent=IntentType.DIAGNOSE,
+            confidence=0.8,
+            method="llm",
+        )
+        assert diagnose_result.risk_level == RiskLevel.LOW

docs/LOGBOOK.md

@@ -5,10 +5,11 @@
 
 ---
 
-## 📍 當前狀態 (2026-03-30 00:30 台北)
+## 📍 當前狀態 (2026-03-30 01:00 台北)
 
 | 項目 | 狀態 |
 |------|------|
+| **Intent Classifier** | ✅ **Ollama 整合完成** (21 測試通過) |
 | **Learning Service** | ✅ **Playbook 信心度調整完成** (13 測試通過) |
 | **🔴 ADR-039 Gitea 遷移** | 🔄 **執行中** (方案 B - GitHub → Gitea CI/CD) |
 | **Gitea CI/CD** | ✅ **已設置** (cd.yaml + e2e-health.yaml) |
@@ -45,6 +46,35 @@
 | **Wave 2 Worker HPA** | ✅ **已部署** (min:1 max:3, CPU 70%) |
 | **Wave C-D 監控** | ✅ **全部完成** (generate + discover + coverage_report) |
 
+## ✅ Intent Classifier Ollama 整合 (2026-03-30 01:00 台北)
+
+### 實作內容 (Phase 13.4)
+
+| 功能 | 說明 |
+|------|------|
+| `_llm_classify` | 呼叫 Ollama 進行意圖分類 |
+| `_parse_intent_type` | 解析 LLM 返回的意圖字串 |
+| `_llm_fallback_result` | LLM 失敗時的 fallback |
+| `_LLM_CLASSIFY_PROMPT` | 結構化 Prompt 模板 |
+
+### 設計特點
+
+| 特性 | 說明 |
+|------|------|
+| **超時控制** | 5 秒超時 (目標 < 100ms) |
+| **JSON Mode** | 強制 Ollama 返回 JSON |
+| **Fallback** | LLM 失敗時使用關鍵字結果 |
+| **確定性** | temperature: 0.0 |
+
+### 測試覆蓋
+
+- **21 測試案例全部通過**
+- LLM 成功/超時/解析失敗測試
+- 關鍵字分類測試
+- 完整流程測試
+
+---
+
 ## ✅ Learning Service 信心度調整 (2026-03-30 00:30 台北)
 
 ### 實作內容