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Critical #1: KM write task 移出 try/except - _trigger_learning 的 KM 寫入原在 try 內，learning 失敗時不寫 KM - 移至 except 後確保成功/失敗都寫入 - 移除冗餘 import asyncio（已在頂層 import） - Minor: approval.incident_id or None 防空字串 Important #2: migration 加 PRIMARY KEY - playbook_id 從 UNIQUE 升為 PRIMARY KEY - prod DB 已執行 ALTER TABLE ADD PRIMARY KEY Important #3: s.sequence→s.step_number, s.description→s.command - embed_playbook() 使用不存在的欄位名，RAG 向量索引靜默失敗 - RepairStep 正確欄位: step_number, command Important #1: PlaybookService._get_rag_service 不再 Service 層快取 - 改為每次呼叫工廠 get_playbook_rag_service() - 避免舊實例繞過工廠的 is_closed 重建邏輯 冷啟動修復 (首席架構師建議B+C): - _trigger_playbook_extraction 執行成功後自動設定 execution_success=True, effectiveness_score=4, status=RESOLVED - skip 路徑 logger.debug → logger.info 提升可觀測性 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
600 lines
18 KiB
Python
600 lines
18 KiB
Python
"""
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Playbook RAG Service - Phase 3 向量化語意搜尋
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=============================================
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ADR-030: 智能自動修復系統
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使用 Embedding 進行 Playbook 語意搜尋:
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1. Ollama nomic-embed-text 生成向量
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2. Redis 儲存向量 (JSON 格式)
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3. 餘弦相似度搜尋
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設計原則:
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- Embedding 快取，避免重複計算
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- 混合搜尋 (向量 + Jaccard)
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- Fallback: Embedding 失敗時用 Jaccard
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- 2026-03-27 ogt: 模組化改造 (P1 違規修復)
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- Repository Pattern for Redis
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- DI httpx client from Lifespan
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版本: v1.1
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建立: 2026-03-26 (台北時區)
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修改: 2026-03-27 (模組化改造)
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"""
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import math
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from dataclasses import dataclass, field
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from datetime import UTC, datetime
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from typing import Any
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import httpx
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import structlog
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from src.core.config import settings
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from src.models.playbook import Playbook, SymptomPattern
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from src.repositories.interfaces import IEmbeddingCacheRepository
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logger = structlog.get_logger(__name__)
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# =============================================================================
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# Constants
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# =============================================================================
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# Embedding Model (Ollama 本地)
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EMBEDDING_MODEL = "nomic-embed-text"
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EMBEDDING_DIM = 768 # nomic-embed-text 向量維度
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# =============================================================================
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# Data Models
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# =============================================================================
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@dataclass
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class PlaybookMatch:
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"""Playbook 匹配結果"""
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playbook_id: str
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similarity_score: float # 0.0 ~ 1.0
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match_type: str # "vector", "jaccard", "hybrid"
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matched_keywords: list[str] = field(default_factory=list)
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def to_dict(self) -> dict[str, Any]:
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return {
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"playbook_id": self.playbook_id,
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"similarity_score": round(self.similarity_score, 4),
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"match_type": self.match_type,
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"matched_keywords": self.matched_keywords,
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}
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@dataclass
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class EmbeddingResult:
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"""Embedding 結果"""
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text: str
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vector: list[float]
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model: str
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created_at: datetime = field(default_factory=lambda: datetime.now(UTC))
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def to_dict(self) -> dict[str, Any]:
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return {
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"text_hash": hash(self.text) % 2**32, # 不存完整 text
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"vector": self.vector,
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"model": self.model,
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"created_at": self.created_at.isoformat(),
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}
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# =============================================================================
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# Vector Utilities
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# =============================================================================
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def cosine_similarity(vec_a: list[float], vec_b: list[float]) -> float:
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"""計算餘弦相似度"""
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if len(vec_a) != len(vec_b):
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return 0.0
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dot_product = sum(a * b for a, b in zip(vec_a, vec_b, strict=True))
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norm_a = math.sqrt(sum(a * a for a in vec_a))
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norm_b = math.sqrt(sum(b * b for b in vec_b))
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if norm_a == 0 or norm_b == 0:
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return 0.0
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return dot_product / (norm_a * norm_b)
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def normalize_vector(vec: list[float]) -> list[float]:
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"""正規化向量 (L2 norm)"""
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norm = math.sqrt(sum(v * v for v in vec))
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if norm == 0:
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return vec
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return [v / norm for v in vec]
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# =============================================================================
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# Playbook RAG Service
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# =============================================================================
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class PlaybookRAGService:
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"""
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Playbook RAG 服務
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功能:
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1. 將 Playbook 向量化並存入 Redis
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2. 語意搜尋相似的 Playbook
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3. 混合搜尋 (向量 + Jaccard)
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2026-03-27 ogt: 模組化改造
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- 使用 DI 注入 http_client 和 embedding_cache
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"""
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def __init__(
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self,
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http_client: httpx.AsyncClient,
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embedding_cache: IEmbeddingCacheRepository,
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):
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"""
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初始化 RAG Service
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Args:
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http_client: httpx AsyncClient (DI 注入，來自 Lifespan)
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embedding_cache: Embedding Cache Repository (DI 注入)
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"""
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self._http_client = http_client
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self._embedding_cache = embedding_cache
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self.ollama_url = settings.OLLAMA_URL
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self.embedding_model = EMBEDDING_MODEL
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# =========================================================================
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# Embedding Operations
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# =========================================================================
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async def _get_http_client(self) -> httpx.AsyncClient:
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"""
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取得有效的 HTTP Client，若已關閉則重建
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2026-04-04 ogt: 修復滾動重啟後 is_closed=True 導致 embedding 失敗
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"""
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if self._http_client.is_closed:
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logger.warning("playbook_rag_http_client_closed_rebuilding")
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from src.core.http_client import get_general_client
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self._http_client = await get_general_client()
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return self._http_client
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async def embed_text(self, text: str) -> list[float] | None:
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"""
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使用 Ollama 生成文字 embedding
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2026-03-27 ogt: 改用 DI 注入的 http_client (P1 違規修復)
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2026-04-04 ogt: 加入 is_closed 自動重建機制
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Args:
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text: 要向量化的文字
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Returns:
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向量 (768 維) 或 None (失敗時)
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"""
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try:
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client = await self._get_http_client()
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response = await client.post(
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f"{self.ollama_url}/api/embeddings",
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json={
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"model": self.embedding_model,
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"prompt": text,
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},
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timeout=30.0, # 單次請求 timeout
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)
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if response.status_code != 200:
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logger.warning(
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"ollama_embedding_failed",
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status_code=response.status_code,
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text_preview=text[:50],
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)
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return None
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result = response.json()
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embedding = result.get("embedding", [])
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if not embedding:
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logger.warning("ollama_embedding_empty", text_preview=text[:50])
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return None
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return normalize_vector(embedding)
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except Exception as e:
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logger.warning(
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"ollama_embedding_error",
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error=str(e),
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text_preview=text[:50],
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)
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return None
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async def embed_playbook(self, playbook: Playbook) -> list[float] | None:
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"""
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將 Playbook 向量化
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結合症狀模式和修復步驟生成向量
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"""
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# 構建文字表示
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text_parts = []
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# 症狀
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if playbook.symptom_pattern:
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sp = playbook.symptom_pattern
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if sp.alert_names:
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text_parts.append(f"告警: {', '.join(sp.alert_names)}")
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if sp.affected_services:
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text_parts.append(f"服務: {', '.join(sp.affected_services)}")
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if sp.keywords:
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text_parts.append(f"關鍵字: {', '.join(sp.keywords)}")
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# 名稱和描述
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text_parts.append(f"名稱: {playbook.name}")
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if playbook.description:
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text_parts.append(f"描述: {playbook.description}")
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# 修復步驟
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# 2026-04-04 ogt: 修正欄位名稱 s.sequence→s.step_number, s.description→s.command
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if playbook.repair_steps:
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steps_text = "; ".join(
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f"{s.step_number}. {s.command}"
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for s in playbook.repair_steps[:5] # 最多 5 步
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)
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text_parts.append(f"步驟: {steps_text}")
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text = "\n".join(text_parts)
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return await self.embed_text(text)
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async def embed_incident_query(
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self,
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alert_names: list[str],
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affected_services: list[str],
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description: str | None = None,
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) -> list[float] | None:
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"""
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為 Incident 查詢生成 embedding
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用於搜尋相似 Playbook
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"""
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text_parts = []
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if alert_names:
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text_parts.append(f"告警: {', '.join(alert_names)}")
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if affected_services:
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text_parts.append(f"服務: {', '.join(affected_services)}")
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if description:
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text_parts.append(f"描述: {description}")
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if not text_parts:
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return None
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text = "\n".join(text_parts)
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return await self.embed_text(text)
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# =========================================================================
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# Storage Operations (委派給 Repository)
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# 2026-03-27 ogt: 改用 DI 注入的 embedding_cache (P1 違規修復)
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# =========================================================================
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async def store_playbook_embedding(
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self,
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playbook_id: str,
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embedding: list[float],
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metadata: dict | None = None,
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) -> bool:
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"""儲存 Playbook 向量到 Redis (委派給 Repository)"""
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return await self._embedding_cache.store(playbook_id, embedding, metadata)
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async def get_playbook_embedding(
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self,
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playbook_id: str,
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) -> list[float] | None:
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"""取得 Playbook 向量 (委派給 Repository)"""
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return await self._embedding_cache.get(playbook_id)
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async def get_all_playbook_embeddings(self) -> dict[str, list[float]]:
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"""取得所有 Playbook 向量 (委派給 Repository)"""
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return await self._embedding_cache.get_all()
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# =========================================================================
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# Search Operations
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# =========================================================================
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async def search_similar(
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self,
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query_embedding: list[float],
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top_k: int = 5,
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min_similarity: float = 0.5,
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) -> list[PlaybookMatch]:
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"""
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向量相似度搜尋
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使用餘弦相似度在所有 Playbook 向量中搜尋
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Args:
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query_embedding: 查詢向量
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top_k: 返回前 K 個結果
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min_similarity: 最小相似度閾值
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Returns:
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排序後的 PlaybookMatch 列表
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"""
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# 取得所有 Playbook 向量
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all_embeddings = await self.get_all_playbook_embeddings()
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if not all_embeddings:
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logger.debug("search_similar_no_embeddings")
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return []
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# 計算相似度
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similarities: list[tuple[str, float]] = []
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for playbook_id, embedding in all_embeddings.items():
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sim = cosine_similarity(query_embedding, embedding)
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if sim >= min_similarity:
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similarities.append((playbook_id, sim))
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# 排序 (降序)
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similarities.sort(key=lambda x: x[1], reverse=True)
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# 返回 Top K
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results = [
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PlaybookMatch(
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playbook_id=pid,
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similarity_score=sim,
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match_type="vector",
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)
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for pid, sim in similarities[:top_k]
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]
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logger.info(
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"playbook_vector_search",
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total_embeddings=len(all_embeddings),
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matches_found=len(results),
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top_score=results[0].similarity_score if results else 0,
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)
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return results
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async def search_by_incident(
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self,
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alert_names: list[str],
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affected_services: list[str],
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description: str | None = None,
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top_k: int = 5,
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min_similarity: float = 0.5,
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) -> list[PlaybookMatch]:
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"""
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根據 Incident 資訊搜尋相似 Playbook
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Convenience 方法，結合 embed + search
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"""
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# 生成查詢向量
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query_embedding = await self.embed_incident_query(
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alert_names=alert_names,
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affected_services=affected_services,
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description=description,
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)
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if not query_embedding:
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logger.warning(
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"search_by_incident_embedding_failed",
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alert_names=alert_names,
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)
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return []
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return await self.search_similar(
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query_embedding=query_embedding,
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top_k=top_k,
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min_similarity=min_similarity,
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)
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# =========================================================================
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# Hybrid Search (Vector + Jaccard)
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# =========================================================================
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async def hybrid_search(
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self,
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symptoms: SymptomPattern,
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jaccard_results: list[tuple[str, float]], # (playbook_id, jaccard_score)
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top_k: int = 5,
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vector_weight: float = 0.6,
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jaccard_weight: float = 0.4,
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) -> list[PlaybookMatch]:
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"""
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混合搜尋 (向量 + Jaccard)
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結合向量語意相似度和 Jaccard 精確匹配
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Args:
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symptoms: 症狀模式
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jaccard_results: Jaccard 匹配結果
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top_k: 返回前 K 個
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vector_weight: 向量分數權重
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jaccard_weight: Jaccard 分數權重
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Returns:
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混合排序後的結果
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"""
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# 1. 向量搜尋
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query_embedding = await self.embed_incident_query(
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alert_names=symptoms.alert_names,
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affected_services=symptoms.affected_services,
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description=None,
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)
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vector_scores: dict[str, float] = {}
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if query_embedding:
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vector_matches = await self.search_similar(
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query_embedding=query_embedding,
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top_k=top_k * 2,
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min_similarity=0.3,
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)
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vector_scores = {m.playbook_id: m.similarity_score for m in vector_matches}
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# 2. Jaccard 分數
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jaccard_scores = dict(jaccard_results)
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# 3. 合併所有 playbook_id
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all_ids = set(vector_scores.keys()) | set(jaccard_scores.keys())
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# 4. 計算混合分數
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hybrid_results: list[tuple[str, float, str]] = []
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for pid in all_ids:
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v_score = vector_scores.get(pid, 0.0)
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j_score = jaccard_scores.get(pid, 0.0)
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hybrid_score = (v_score * vector_weight) + (j_score * jaccard_weight)
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# 決定主要匹配類型
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if v_score > 0 and j_score > 0:
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match_type = "hybrid"
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elif v_score > 0:
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match_type = "vector"
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else:
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match_type = "jaccard"
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hybrid_results.append((pid, hybrid_score, match_type))
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# 5. 排序並返回 Top K
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hybrid_results.sort(key=lambda x: x[1], reverse=True)
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results = [
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PlaybookMatch(
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playbook_id=pid,
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similarity_score=score,
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match_type=match_type,
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)
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for pid, score, match_type in hybrid_results[:top_k]
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]
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logger.info(
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"playbook_hybrid_search",
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vector_count=len(vector_scores),
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jaccard_count=len(jaccard_scores),
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hybrid_count=len(results),
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top_score=results[0].similarity_score if results else 0,
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)
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return results
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# =========================================================================
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# Index Management
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# =========================================================================
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|
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async def index_playbook(self, playbook: Playbook) -> bool:
|
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"""
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為 Playbook 建立向量索引
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呼叫時機: Playbook 建立或更新時
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"""
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embedding = await self.embed_playbook(playbook)
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if not embedding:
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logger.warning(
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"playbook_index_embedding_failed",
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playbook_id=playbook.playbook_id,
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)
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return False
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|
||
return await self.store_playbook_embedding(
|
||
playbook_id=playbook.playbook_id,
|
||
embedding=embedding,
|
||
metadata={
|
||
"name": playbook.name,
|
||
"status": playbook.status.value if playbook.status else None,
|
||
"tags": playbook.tags,
|
||
},
|
||
)
|
||
|
||
async def remove_playbook_index(self, playbook_id: str) -> bool:
|
||
"""移除 Playbook 向量索引 (委派給 Repository)"""
|
||
return await self._embedding_cache.remove(playbook_id)
|
||
|
||
async def reindex_all_playbooks(
|
||
self,
|
||
playbooks: list[Playbook],
|
||
) -> tuple[int, int]:
|
||
"""
|
||
重建所有 Playbook 向量索引
|
||
|
||
Returns:
|
||
(成功數, 失敗數)
|
||
"""
|
||
success = 0
|
||
failed = 0
|
||
|
||
for playbook in playbooks:
|
||
if await self.index_playbook(playbook):
|
||
success += 1
|
||
else:
|
||
failed += 1
|
||
|
||
logger.info(
|
||
"playbook_reindex_complete",
|
||
success=success,
|
||
failed=failed,
|
||
total=len(playbooks),
|
||
)
|
||
|
||
return success, failed
|
||
|
||
|
||
# =============================================================================
|
||
# Factory (DI-aware)
|
||
# 2026-03-27 ogt: 模組化改造 - 支援 DI 注入
|
||
# =============================================================================
|
||
|
||
_rag_service: PlaybookRAGService | None = None
|
||
|
||
|
||
async def get_playbook_rag_service() -> PlaybookRAGService:
|
||
"""
|
||
取得 Playbook RAG 服務 singleton (lazy initialization)
|
||
|
||
2026-03-27 ogt: 改用 DI 注入，從 Lifespan 取得 http_client 和 Redis
|
||
"""
|
||
global _rag_service
|
||
# 2026-04-04 ogt: 滾動重啟後 http_client is_closed，需重建 singleton
|
||
if _rag_service is None or _rag_service._http_client.is_closed:
|
||
# 延遲導入避免循環依賴
|
||
from src.core.http_client import get_general_client
|
||
from src.core.redis_client import get_redis
|
||
from src.repositories.embedding_repository import EmbeddingCacheRepository
|
||
|
||
http_client = await get_general_client()
|
||
redis = get_redis()
|
||
embedding_cache = EmbeddingCacheRepository(redis)
|
||
|
||
_rag_service = PlaybookRAGService(
|
||
http_client=http_client,
|
||
embedding_cache=embedding_cache,
|
||
)
|
||
return _rag_service
|
||
|
||
|
||
def create_playbook_rag_service(
|
||
http_client: httpx.AsyncClient,
|
||
embedding_cache: IEmbeddingCacheRepository,
|
||
) -> PlaybookRAGService:
|
||
"""
|
||
建立 PlaybookRAGService 實例 (工廠函數)
|
||
|
||
用於測試或需要自訂依賴的場景
|
||
|
||
Args:
|
||
http_client: httpx AsyncClient
|
||
embedding_cache: Embedding Cache Repository
|
||
|
||
Returns:
|
||
PlaybookRAGService 實例
|
||
"""
|
||
return PlaybookRAGService(
|
||
http_client=http_client,
|
||
embedding_cache=embedding_cache,
|
||
)
|