feat(api): ADR-030 Phase 3 Playbook RAG 向量搜尋
實作 Playbook 語意搜尋能力： 1. playbook_rag.py - RAG 向量服務 - Ollama nomic-embed-text 生成 embedding - Redis 儲存向量 (JSON 格式) - 餘弦相似度搜尋 - 混合搜尋 (Vector 60% + Jaccard 40%) 2. playbook_service.py - 整合 RAG - extract_from_incident 後自動建立向量索引 - get_recommendations 支援混合搜尋 - RAG 失敗時 fallback 到純 Jaccard 功能: - embed_text(): 文字向量化 - embed_playbook(): Playbook 向量化 - search_similar(): 向量相似度搜尋 - hybrid_search(): 混合搜尋 Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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apps/api/src/services/playbook_rag.py
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624
apps/api/src/services/playbook_rag.py
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"""
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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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版本: v1.0
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建立: 2026-03-26 (台北時區)
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"""
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import json
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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 structlog
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from src.core.config import settings
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from src.core.redis_client import get_redis
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from src.models.playbook import Playbook, SymptomPattern
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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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# Redis Keys
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PLAYBOOK_EMBEDDING_PREFIX = "playbook:embedding:"
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PLAYBOOK_EMBEDDING_INDEX = "playbook:embedding:index"
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# Cache TTL: 30 天 (Embedding 不常變化)
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EMBEDDING_TTL_SECONDS = 30 * 24 * 60 * 60
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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))
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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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"""
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def __init__(self):
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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 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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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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import httpx
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async with httpx.AsyncClient(timeout=30.0) as 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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)
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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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if playbook.repair_steps:
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steps_text = "; ".join(
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f"{s.sequence}. {s.description}"
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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
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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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"""
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儲存 Playbook 向量到 Redis
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存儲格式:
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- playbook:embedding:{id} -> {vector: [...], metadata: {...}}
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- playbook:embedding:index -> Set of playbook_ids
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"""
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try:
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redis = get_redis()
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key = f"{PLAYBOOK_EMBEDDING_PREFIX}{playbook_id}"
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data = {
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"vector": embedding,
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"metadata": metadata or {},
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"updated_at": datetime.now(UTC).isoformat(),
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}
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await redis.set(
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key,
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json.dumps(data),
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ex=EMBEDDING_TTL_SECONDS,
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)
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# 更新索引
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await redis.sadd(PLAYBOOK_EMBEDDING_INDEX, playbook_id)
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logger.debug(
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"playbook_embedding_stored",
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playbook_id=playbook_id,
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vector_dim=len(embedding),
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)
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return True
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except Exception as e:
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logger.warning(
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"playbook_embedding_store_failed",
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playbook_id=playbook_id,
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error=str(e),
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)
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return False
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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"""
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try:
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redis = get_redis()
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key = f"{PLAYBOOK_EMBEDDING_PREFIX}{playbook_id}"
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data = await redis.get(key)
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if not data:
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return None
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parsed = json.loads(data)
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return parsed.get("vector")
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except Exception as e:
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logger.warning(
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"playbook_embedding_get_failed",
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playbook_id=playbook_id,
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error=str(e),
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)
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return None
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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"""
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try:
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redis = get_redis()
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# 取得所有 playbook_id
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playbook_ids = await redis.smembers(PLAYBOOK_EMBEDDING_INDEX)
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if not playbook_ids:
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return {}
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# 批次取得向量
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result = {}
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for pid in playbook_ids:
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pid_str = pid.decode() if isinstance(pid, bytes) else pid
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vec = await self.get_playbook_embedding(pid_str)
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if vec:
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result[pid_str] = vec
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return result
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except Exception as e:
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logger.warning(
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"playbook_embeddings_get_all_failed",
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error=str(e),
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)
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return {}
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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 = {pid: score for pid, score in 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)
|
||||
j_score = jaccard_scores.get(pid, 0.0)
|
||||
|
||||
hybrid_score = (v_score * vector_weight) + (j_score * jaccard_weight)
|
||||
|
||||
# 決定主要匹配類型
|
||||
if v_score > 0 and j_score > 0:
|
||||
match_type = "hybrid"
|
||||
elif v_score > 0:
|
||||
match_type = "vector"
|
||||
else:
|
||||
match_type = "jaccard"
|
||||
|
||||
hybrid_results.append((pid, hybrid_score, match_type))
|
||||
|
||||
# 5. 排序並返回 Top K
|
||||
hybrid_results.sort(key=lambda x: x[1], reverse=True)
|
||||
|
||||
results = [
|
||||
PlaybookMatch(
|
||||
playbook_id=pid,
|
||||
similarity_score=score,
|
||||
match_type=match_type,
|
||||
)
|
||||
for pid, score, match_type in hybrid_results[:top_k]
|
||||
]
|
||||
|
||||
logger.info(
|
||||
"playbook_hybrid_search",
|
||||
vector_count=len(vector_scores),
|
||||
jaccard_count=len(jaccard_scores),
|
||||
hybrid_count=len(results),
|
||||
top_score=results[0].similarity_score if results else 0,
|
||||
)
|
||||
|
||||
return results
|
||||
|
||||
# =========================================================================
|
||||
# Index Management
|
||||
# =========================================================================
|
||||
|
||||
async def index_playbook(self, playbook: Playbook) -> bool:
|
||||
"""
|
||||
為 Playbook 建立向量索引
|
||||
|
||||
呼叫時機: Playbook 建立或更新時
|
||||
"""
|
||||
embedding = await self.embed_playbook(playbook)
|
||||
|
||||
if not embedding:
|
||||
logger.warning(
|
||||
"playbook_index_embedding_failed",
|
||||
playbook_id=playbook.playbook_id,
|
||||
)
|
||||
return False
|
||||
|
||||
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"""
|
||||
try:
|
||||
redis = get_redis()
|
||||
key = f"{PLAYBOOK_EMBEDDING_PREFIX}{playbook_id}"
|
||||
|
||||
await redis.delete(key)
|
||||
await redis.srem(PLAYBOOK_EMBEDDING_INDEX, playbook_id)
|
||||
|
||||
logger.info("playbook_index_removed", playbook_id=playbook_id)
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(
|
||||
"playbook_index_remove_failed",
|
||||
playbook_id=playbook_id,
|
||||
error=str(e),
|
||||
)
|
||||
return False
|
||||
|
||||
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
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Singleton
|
||||
# =============================================================================
|
||||
|
||||
_rag_service: PlaybookRAGService | None = None
|
||||
|
||||
|
||||
def get_playbook_rag_service() -> PlaybookRAGService:
|
||||
"""取得 Playbook RAG 服務 singleton"""
|
||||
global _rag_service
|
||||
if _rag_service is None:
|
||||
_rag_service = PlaybookRAGService()
|
||||
return _rag_service
|
||||
@@ -4,6 +4,7 @@ Playbook Service - #7 Playbook 萃取
|
||||
Playbook 業務邏輯層
|
||||
|
||||
Phase 7.3: Service 實作
|
||||
Phase 3 ADR-030: RAG 向量搜尋整合
|
||||
建立時間: 2026-03-26 (台北時區)
|
||||
建立者: Claude Code (#7 Playbook 萃取)
|
||||
|
||||
@@ -30,6 +31,7 @@ from src.models.playbook import (
|
||||
)
|
||||
from src.repositories.interfaces import IPlaybookRepository
|
||||
from src.repositories.playbook_repository import get_playbook_repository
|
||||
from src.services.playbook_rag import get_playbook_rag_service
|
||||
from src.utils.timezone import now_taipei
|
||||
|
||||
logger = structlog.get_logger(__name__)
|
||||
@@ -78,12 +80,14 @@ class PlaybookService:
|
||||
|
||||
職責:
|
||||
- 從 Incident 萃取 Playbook
|
||||
- 提供 Playbook 推薦
|
||||
- 提供 Playbook 推薦 (混合搜尋: Jaccard + RAG)
|
||||
- 管理 Playbook 生命週期
|
||||
- 維護向量索引
|
||||
"""
|
||||
|
||||
def __init__(self, repository: IPlaybookRepository | None = None):
|
||||
self._repository = repository or get_playbook_repository()
|
||||
self._rag_service = get_playbook_rag_service()
|
||||
|
||||
# === Core Operations ===
|
||||
|
||||
@@ -161,6 +165,10 @@ class PlaybookService:
|
||||
# 7. 儲存
|
||||
playbook = await self._repository.create(playbook)
|
||||
|
||||
# 8. ADR-030 Phase 3: 建立向量索引 (非阻塞，失敗不影響主流程)
|
||||
import asyncio
|
||||
asyncio.create_task(self._index_playbook_async(playbook))
|
||||
|
||||
logger.info(
|
||||
"playbook_extracted",
|
||||
playbook_id=playbook.playbook_id,
|
||||
@@ -171,33 +179,94 @@ class PlaybookService:
|
||||
|
||||
return playbook
|
||||
|
||||
async def _index_playbook_async(self, playbook: Playbook) -> None:
|
||||
"""非同步建立 Playbook 向量索引 (ADR-030 Phase 3)"""
|
||||
try:
|
||||
success = await self._rag_service.index_playbook(playbook)
|
||||
if success:
|
||||
logger.debug(
|
||||
"playbook_indexed",
|
||||
playbook_id=playbook.playbook_id,
|
||||
)
|
||||
except Exception as e:
|
||||
logger.warning(
|
||||
"playbook_index_failed",
|
||||
playbook_id=playbook.playbook_id,
|
||||
error=str(e),
|
||||
)
|
||||
|
||||
async def get_recommendations(
|
||||
self,
|
||||
symptoms: SymptomPattern,
|
||||
top_k: int = 3,
|
||||
use_rag: bool = True,
|
||||
) -> list[PlaybookRecommendation]:
|
||||
"""
|
||||
取得 Playbook 推薦
|
||||
|
||||
策略:
|
||||
1. 從 Repository 找相似症狀的 Playbook
|
||||
2. 按 similarity_score * success_rate 排序
|
||||
3. 返回 Top K 推薦
|
||||
ADR-030 Phase 3 策略:
|
||||
1. Jaccard 精確匹配 (Repository)
|
||||
2. RAG 向量語意搜尋 (可選)
|
||||
3. 混合排序 (Jaccard 40% + Vector 60%)
|
||||
4. 按 similarity_score * success_rate 排序
|
||||
"""
|
||||
# 查詢相似 Playbook
|
||||
# Step 1: Jaccard 精確匹配
|
||||
similar_playbooks = await self._repository.find_by_symptoms(
|
||||
symptoms=symptoms,
|
||||
top_k=top_k * 2, # 多取一些用於後續過濾
|
||||
min_similarity=0.4,
|
||||
)
|
||||
|
||||
if not similar_playbooks:
|
||||
jaccard_results = [(pb.playbook_id, sim) for pb, sim in similar_playbooks]
|
||||
playbook_map = {pb.playbook_id: pb for pb, _ in similar_playbooks}
|
||||
|
||||
# Step 2: RAG 混合搜尋 (如果啟用)
|
||||
if use_rag and symptoms.alert_names:
|
||||
try:
|
||||
hybrid_matches = await self._rag_service.hybrid_search(
|
||||
symptoms=symptoms,
|
||||
jaccard_results=jaccard_results,
|
||||
top_k=top_k * 2,
|
||||
vector_weight=0.6,
|
||||
jaccard_weight=0.4,
|
||||
)
|
||||
|
||||
# 補充 playbook_map (RAG 可能找到 Jaccard 沒找到的)
|
||||
for match in hybrid_matches:
|
||||
if match.playbook_id not in playbook_map:
|
||||
pb = await self._repository.get_by_id(match.playbook_id)
|
||||
if pb:
|
||||
playbook_map[match.playbook_id] = pb
|
||||
|
||||
# 使用混合結果
|
||||
final_results = [
|
||||
(playbook_map[m.playbook_id], m.similarity_score)
|
||||
for m in hybrid_matches
|
||||
if m.playbook_id in playbook_map
|
||||
]
|
||||
|
||||
logger.info(
|
||||
"playbook_recommendation_hybrid",
|
||||
jaccard_count=len(jaccard_results),
|
||||
hybrid_count=len(final_results),
|
||||
)
|
||||
except Exception as e:
|
||||
# RAG 失敗時 fallback 到純 Jaccard
|
||||
logger.warning(
|
||||
"playbook_rag_fallback",
|
||||
error=str(e),
|
||||
)
|
||||
final_results = similar_playbooks
|
||||
else:
|
||||
final_results = similar_playbooks
|
||||
|
||||
if not final_results:
|
||||
return []
|
||||
|
||||
# 建立推薦列表
|
||||
# Step 3: 建立推薦列表
|
||||
recommendations: list[PlaybookRecommendation] = []
|
||||
|
||||
for playbook, similarity in similar_playbooks:
|
||||
for playbook, similarity in final_results:
|
||||
# 找出匹配的症狀
|
||||
matched_symptoms = self._find_matched_symptoms(symptoms, playbook.symptom_pattern)
|
||||
|
||||
@@ -217,7 +286,7 @@ class PlaybookService:
|
||||
)
|
||||
)
|
||||
|
||||
# 按綜合分數排序
|
||||
# Step 4: 按綜合分數排序 (similarity * success_rate)
|
||||
recommendations.sort(
|
||||
key=lambda r: r.similarity_score * (0.5 + 0.5 * r.playbook.success_rate),
|
||||
reverse=True,
|
||||
|
||||
Reference in New Issue
Block a user