671 lines
28 KiB
Python
671 lines
28 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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services/rag_service.py
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Operation Ollama-First v5.0 / Phase 11 — RAG 查詢服務
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設計原則（憲法級）：
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1. 純讀 ai_insights + 寫 rag_query_log（不動 ai_insights schema）
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2. cosine similarity threshold 預設 0.85，太低不採用避免幻覺
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3. embedding 走 bge-m3:latest（與 ai_insights 一致簽名 — Phase 11.0 護欄 #3）
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4. feature flag RAG_ENABLED 預設 OFF（避免影響戰前行為）
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5. 失敗安全：DB 掛 / embedding 失敗 / threshold 不到 → 回 RAGResult(hits=[])
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caller 自行 fallback LLM
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6. fire-and-forget log：rag_query_log INSERT async daemon thread，不阻塞主流程
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7. PII 保護：query_text 寫入時截 4KB（CHECK constraint 也會擋）
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對應:
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- migrations/027_create_rag_query_log.sql
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- migrations/026_add_embedding_signature.sql
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- docs/adr/ADR-029-hermes-first-twin-tower.md
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- docs/phase0_audit_report_20260503.md Section 3 (BGE-M3 一致性護欄)
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主入口:
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- rag_service.query(...) : 主查詢介面
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- rag_service.feedback(log_id, ...) : Telegram 👍/👎 反饋寫回
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- rag_service.invalidate_by_caller(...) : 預留快取失效鉤（v5.0 暫無 cache 層）
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"""
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from __future__ import annotations
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import hashlib
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import logging
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import os
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import threading
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import time
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from dataclasses import dataclass, field
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from typing import Any, Dict, List, Optional
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logger = logging.getLogger(__name__)
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# ─────────────────────────────────────────────────────────────────────────────
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# Feature flag + 預設參數
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# ─────────────────────────────────────────────────────────────────────────────
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def is_rag_enabled() -> bool:
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"""環境變數即時讀取（允許 runtime toggle，與 ai_call_logger 一致風格）。
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feature flag 預設 OFF — 戰前部署後行為與 v4.x 完全相同。
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"""
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val = os.environ.get('RAG_ENABLED', 'false').strip().lower()
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return val in ('true', '1', 'yes', 'on')
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# 內部別名（沿用既有 _is_rag_enabled 命名相容性）
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_is_rag_enabled = is_rag_enabled
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RAG_DEFAULT_THRESHOLD = float(os.getenv('RAG_DEFAULT_THRESHOLD', '0.85'))
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RAG_DEFAULT_TOP_K = int(os.getenv('RAG_DEFAULT_TOP_K', '5'))
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# bge-m3 一致性參數（與 ai_insights 簽名計算同源）
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RAG_EMBED_MODEL = os.getenv('RAG_EMBED_MODEL', 'bge-m3:latest')
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RAG_EMBED_DIM = int(os.getenv('RAG_EMBED_DIM', '1024'))
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RAG_EMBED_NORMALIZE = os.getenv('RAG_EMBED_NORMALIZE', 'true').strip().lower() in (
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'true', '1', 'yes', 'on',
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)
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# query_text 寫入長度上限（與 027 CHECK octet_length<=4096 對齊；中文 1 字 3 byte → ~1300 字）
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_QUERY_TEXT_MAX_BYTES = 4096
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# 連續失敗門檻（與 ai_call_logger 同模式）
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_MAX_CONSECUTIVE_FAILURES = 10
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_failure_counter_lock = threading.Lock()
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_failure_state = {'count': 0, 'killed': False}
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def _record_failure() -> None:
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with _failure_counter_lock:
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_failure_state['count'] += 1
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if _failure_state['count'] >= _MAX_CONSECUTIVE_FAILURES and not _failure_state['killed']:
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_failure_state['killed'] = True
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logger.error(
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"[RAGService] consecutive write failures hit %d — kill-switch ON, "
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"downgrading rag_query_log writes to logger.info",
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_MAX_CONSECUTIVE_FAILURES,
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)
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def _record_success() -> None:
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with _failure_counter_lock:
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if _failure_state['count'] > 0:
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_failure_state['count'] = 0
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def _is_killed() -> bool:
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with _failure_counter_lock:
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return _failure_state['killed']
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def _reset_kill_switch() -> None:
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"""測試專用：重置 kill-switch 狀態。"""
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with _failure_counter_lock:
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_failure_state['count'] = 0
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_failure_state['killed'] = False
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# ─────────────────────────────────────────────────────────────────────────────
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# BGE-M3 一致性簽名（v5.0 護欄 #3）
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# 與 migration 026 註解一致：SHA1({model}|{normalize}|{dim}|{ollama_digest})[:12]
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# Python 端不查 ollama digest（避免每次 query 都 GET /api/show），
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# 改用 model+normalize+dim 三元組已足以擋住「升級 bge-m3 / 改 normalize」雙寫漂移。
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# ─────────────────────────────────────────────────────────────────────────────
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def get_embedding_signature(
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model: str = RAG_EMBED_MODEL,
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dim: int = RAG_EMBED_DIM,
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normalize: bool = RAG_EMBED_NORMALIZE,
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) -> str:
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"""產生 12 碼 BGE-M3 一致性簽名。
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與 ai_insights.embedding_signature 比對；不一致 → log warning + 不採該筆。
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"""
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raw = f"{model}|{str(normalize).lower()}|{dim}"
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return hashlib.sha1(raw.encode('utf-8')).hexdigest()[:12]
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# ─────────────────────────────────────────────────────────────────────────────
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# Phase 11.0 護欄 #3：BGE-M3 跨主機一致性啟動驗證（ADR-033）
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# ─────────────────────────────────────────────────────────────────────────────
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EMBED_CONSISTENCY_TEST_TEXT = "momo電商競品分析測試向量一致性檢查"
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EMBED_CONSISTENCY_MAX_DIFF = 1e-4 # cosine 距離上限（浮點誤差容忍）
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EMBED_CONSISTENCY_TIMEOUT_SEC = 10.0 # 各主機 embedding 探測 timeout
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def _cosine_distance(vec_a: List[float], vec_b: List[float]) -> float:
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"""純 Python cosine distance（不依賴 numpy 避免額外 import）"""
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if not vec_a or not vec_b or len(vec_a) != len(vec_b):
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return 1.0
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dot = sum(a * b for a, b in zip(vec_a, vec_b))
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norm_a = sum(a * a for a in vec_a) ** 0.5
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norm_b = sum(b * b for b in vec_b) ** 0.5
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if norm_a == 0 or norm_b == 0:
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return 1.0
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return max(0.0, 1.0 - dot / (norm_a * norm_b))
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def verify_embedding_consistency(
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test_text: str = EMBED_CONSISTENCY_TEST_TEXT,
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max_diff: float = EMBED_CONSISTENCY_MAX_DIFF,
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) -> Dict[str, Any]:
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"""跨三主機（GCP Primary / Secondary / 111）BGE-M3 embedding 一致性驗證。
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Owen v5.0 護欄 #3（ADR-033）— RAG 啟動時驗證；不一致則 log warning。
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fail-safe：任何主機失敗（連線、超時）都跳過，只比對能拿到的 embeddings。
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最少 2 個主機可達才能比對；只有 1 個 → 回 ok=True + warning「無法比對」。
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回傳：
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{
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'ok': bool,
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'signature': str,
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'reachable': [...], # ['gcp_ollama', 'ollama_secondary', 'ollama_111']
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'max_diff': float, # 跨主機最大 cosine 距離
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'errors': [...],
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}
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"""
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import time
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from services.ollama_service import (
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OLLAMA_HOST_PRIMARY, OLLAMA_HOST_SECONDARY, OLLAMA_HOST_FALLBACK,
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ollama_service,
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)
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hosts = {
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'gcp_ollama': OLLAMA_HOST_PRIMARY,
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'ollama_secondary': OLLAMA_HOST_SECONDARY,
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'ollama_111': OLLAMA_HOST_FALLBACK,
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}
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embeddings: Dict[str, List[float]] = {}
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errors: List[str] = []
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for label, host in hosts.items():
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try:
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t0 = time.monotonic()
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vec = ollama_service.generate_embedding(
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text=test_text,
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model=RAG_EMBED_MODEL,
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host=host, # 顯式指定（避免 retry 鏈干擾驗證）
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timeout=int(EMBED_CONSISTENCY_TIMEOUT_SEC),
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)
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elapsed = time.monotonic() - t0
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if vec and len(vec) == RAG_EMBED_DIM:
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embeddings[label] = vec
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logger.info(f"[EmbedVerify] {label} ({host}) ok in {elapsed:.2f}s, dim={len(vec)}")
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else:
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errors.append(f"{label}: empty or wrong dim ({len(vec) if vec else 0})")
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logger.warning(f"[EmbedVerify] {label} returned empty/wrong-dim vector")
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except Exception as exc:
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errors.append(f"{label}: {type(exc).__name__}: {str(exc)[:200]}")
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logger.warning(f"[EmbedVerify] {label} failed: {exc}")
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signature = get_embedding_signature()
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reachable = list(embeddings.keys())
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if len(embeddings) < 2:
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msg = f"only {len(embeddings)} host reachable, cannot cross-verify"
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logger.warning(f"[EmbedVerify] {msg}")
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return {
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'ok': True, # fail-safe：1 主機可達不算錯（戰時可能 2 主機暫斷）
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'signature': signature,
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'reachable': reachable,
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'max_diff': 0.0,
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'errors': errors + [msg],
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}
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# 兩兩比對 cosine 距離
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import itertools
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max_diff_observed = 0.0
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for label_a, label_b in itertools.combinations(embeddings, 2):
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d = _cosine_distance(embeddings[label_a], embeddings[label_b])
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max_diff_observed = max(max_diff_observed, d)
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logger.debug(f"[EmbedVerify] {label_a} vs {label_b}: cosine_distance={d:.6f}")
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consistent = max_diff_observed <= max_diff
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if not consistent:
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logger.error(
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f"[EmbedVerify] ⚠️ INCONSISTENT! max cosine distance {max_diff_observed:.6f} > {max_diff} "
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f"(signature={signature}, reachable={reachable}). "
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f"模型版本可能漂移；RAG 召回率將下降。"
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)
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else:
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logger.info(
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f"[EmbedVerify] ✅ consistent across {len(reachable)} hosts "
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f"(max_diff={max_diff_observed:.2e}, signature={signature})"
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)
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return {
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'ok': consistent,
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'signature': signature,
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'reachable': reachable,
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'max_diff': max_diff_observed,
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'errors': errors,
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}
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# ─────────────────────────────────────────────────────────────────────────────
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# 結果容器
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# ─────────────────────────────────────────────────────────────────────────────
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@dataclass
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class RAGResult:
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"""RAG 查詢結果。caller 透過 has_high_confidence / synthesize() 決定是否走 LLM。"""
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query: str
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embedding_signature: str
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hits: List[Dict[str, Any]] = field(default_factory=list)
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threshold: float = RAG_DEFAULT_THRESHOLD
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saved_call: bool = False # 是否成功避免 LLM 呼叫（caller 確認後設定）
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duration_ms: int = 0
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log_id: Optional[int] = None # rag_query_log.id（fire-and-forget，可能為 None）
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@property
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def has_high_confidence(self) -> bool:
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"""有至少 1 個 hit 且 top-1 score >= threshold。"""
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if not self.hits:
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return False
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top_score = self.hits[0].get('score', 0.0) or 0.0
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return float(top_score) >= self.threshold
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def synthesize(self) -> str:
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"""組合前 3 筆 hits.content（用 \\n\\n---\\n\\n 分隔，與 OCLearn 既有風格一致）。
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caller 拿到後可直接當 LLM 回覆呈現給用戶；避免再次 LLM 呼叫。
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"""
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if not self.hits:
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return ""
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parts = []
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for h in self.hits[:3]:
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content = h.get('content') or ""
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if content:
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parts.append(content)
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return "\n\n---\n\n".join(parts)
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# ─────────────────────────────────────────────────────────────────────────────
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# 主類別
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# ─────────────────────────────────────────────────────────────────────────────
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class RAGService:
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"""RAG 查詢主入口 — 雙寫 rag_query_log + 回傳 hits。
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使用範例:
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from services.rag_service import rag_service
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result = rag_service.query("本週業績趨勢", caller='openclaw_qa')
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if result.has_high_confidence:
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return result.synthesize()
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# 否則走既有 LLM 路徑
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"""
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def query(
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self,
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text: str,
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caller: str,
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top_k: int = RAG_DEFAULT_TOP_K,
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threshold: float = RAG_DEFAULT_THRESHOLD,
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request_id: Optional[str] = None,
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insight_type: Optional[str] = None,
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mark_saved_call: bool = False,
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) -> RAGResult:
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"""執行 RAG 召回。
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Args:
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text: 查詢文本（用戶問題或 LLM prompt）
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caller: 與 ai_calls.caller 同白名單（hermes_qa / openclaw_qa / ...）
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top_k: 召回筆數（1-50）
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threshold: cosine similarity 門檻（0-1，預設 0.85）
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request_id: 與 ai_calls.request_id 串鏈
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insight_type: 限制 ai_insights.insight_type（None = 全類型）
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mark_saved_call:
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True 時，若本次結果 has_high_confidence，rag_query_log.saved_call
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會寫 True。只給「命中後直接跳過 LLM」的 RAG-first caller 使用，
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觀測台相似案例查詢等輔助用途應保留 False。
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Returns:
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RAGResult。失敗時 hits=[] + duration_ms 仍記錄。
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"""
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signature = get_embedding_signature()
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start = time.monotonic()
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# ── 路徑 1：feature flag OFF → 短路（不查 DB / 不寫 log）──
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if not _is_rag_enabled():
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return RAGResult(
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query=text or "",
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embedding_signature=signature,
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threshold=threshold,
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duration_ms=0,
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)
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# ── 路徑 2：empty text → 早退（避免無謂 embedding 呼叫）──
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if not text or not text.strip():
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return RAGResult(
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query="",
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embedding_signature=signature,
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threshold=threshold,
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duration_ms=int((time.monotonic() - start) * 1000),
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)
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# 護欄：top_k / threshold 範圍夾擠（與 027 CHECK 對齊）
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top_k = max(1, min(int(top_k or RAG_DEFAULT_TOP_K), 50))
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threshold = max(0.0, min(float(threshold or RAG_DEFAULT_THRESHOLD), 1.0))
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# ── 路徑 3：embedding ──
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query_vec: Optional[List[float]] = None
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try:
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from services.ollama_service import ollama_service
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query_vec = ollama_service.generate_embedding(
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text,
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model=RAG_EMBED_MODEL,
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allow_111_fallback=False,
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)
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if not query_vec:
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logger.warning(
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"[RAGService] embedding empty (caller=%s, len=%d) — fallback LLM",
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caller, len(text),
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)
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except Exception as exc:
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logger.warning(
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"[RAGService] embedding failed (caller=%s): %s — fallback LLM",
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caller, exc,
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)
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hits: List[Dict[str, Any]] = []
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# ── 路徑 4：DB 召回（只在 embedding 成功時）──
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if query_vec:
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try:
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hits = self._select_hits(
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query_vec=query_vec,
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threshold=threshold,
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top_k=top_k,
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insight_type=insight_type,
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expected_signature=signature,
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)
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except Exception as exc:
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logger.warning(
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"[RAGService] DB select failed (caller=%s): %s — fallback LLM",
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caller, exc,
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)
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hits = []
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duration_ms = int((time.monotonic() - start) * 1000)
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result = RAGResult(
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query=text,
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embedding_signature=signature,
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hits=hits,
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threshold=threshold,
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duration_ms=duration_ms,
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)
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result.saved_call = bool(mark_saved_call and result.has_high_confidence)
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# ── 路徑 5：fire-and-forget rag_query_log ──
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self._async_log(
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caller=caller,
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text=text,
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query_vec=query_vec,
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top_k=top_k,
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threshold=threshold,
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hits=hits,
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saved_call=result.saved_call,
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request_id=request_id,
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)
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return result
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# ──────────────────────────────────────────────────────────────────────
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# DB 召回
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# ──────────────────────────────────────────────────────────────────────
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def _select_hits(
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self,
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query_vec: List[float],
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threshold: float,
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top_k: int,
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insight_type: Optional[str],
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expected_signature: str,
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) -> List[Dict[str, Any]]:
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"""從 ai_insights 召回 top_k 筆（cosine similarity >= threshold）。
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embedding_signature 不一致的列：log warning + 不採該筆（v5.0 護欄 #3）。
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||
"""
|
||
from sqlalchemy import text as sa_text
|
||
from database.manager import get_session
|
||
|
||
# cosine_distance = embedding <=> qvec； similarity = 1 - distance
|
||
# 多取 top_k * 2 緩衝給簽名漂移過濾，最終裁回 top_k
|
||
fetch_limit = max(top_k * 2, top_k)
|
||
filters = [
|
||
"embedding IS NOT NULL",
|
||
"status IN ('approved', 'active', 'executed')",
|
||
]
|
||
params: Dict[str, Any] = {
|
||
'qvec': str(query_vec),
|
||
'lim': fetch_limit,
|
||
'max_distance': 1.0 - threshold,
|
||
}
|
||
if insight_type:
|
||
filters.append("insight_type = :insight_type")
|
||
params['insight_type'] = insight_type
|
||
|
||
sql = sa_text(f"""
|
||
SELECT id, insight_type, period, content,
|
||
embedding_signature,
|
||
embedding <=> CAST(:qvec AS vector) AS distance
|
||
FROM ai_insights
|
||
WHERE {' AND '.join(filters)}
|
||
AND (embedding <=> CAST(:qvec AS vector)) <= :max_distance
|
||
ORDER BY distance ASC
|
||
LIMIT :lim
|
||
""")
|
||
|
||
session = get_session()
|
||
try:
|
||
rows = session.execute(sql, params).fetchall()
|
||
finally:
|
||
session.close()
|
||
|
||
hits: List[Dict[str, Any]] = []
|
||
signature_mismatch = 0
|
||
for row in rows:
|
||
if len(hits) >= top_k:
|
||
break
|
||
row_signature = getattr(row, 'embedding_signature', None)
|
||
# v5.0 護欄 #3：簽名漂移檢查（NULL = 既有未回填資料，暫時放行避免戰前資料完全失效）
|
||
if row_signature and row_signature != expected_signature:
|
||
signature_mismatch += 1
|
||
continue
|
||
distance = float(row.distance or 1.0)
|
||
similarity = 1.0 - distance
|
||
hits.append({
|
||
'id': int(row.id),
|
||
'insight_type': row.insight_type,
|
||
'period': row.period,
|
||
'content': row.content or '',
|
||
'score': round(similarity, 4),
|
||
'distance': round(distance, 4),
|
||
'embedding_signature': row_signature,
|
||
})
|
||
|
||
if signature_mismatch:
|
||
logger.warning(
|
||
"[RAGService] %d hits skipped due to embedding_signature mismatch "
|
||
"(expected=%s); 建議跑批次回填腳本",
|
||
signature_mismatch, expected_signature,
|
||
)
|
||
|
||
return hits
|
||
|
||
# ──────────────────────────────────────────────────────────────────────
|
||
# 反饋（Telegram 👍/👎）
|
||
# ──────────────────────────────────────────────────────────────────────
|
||
def feedback(self, rag_query_log_id: int, score: int) -> bool:
|
||
"""寫回 rag_query_log.feedback_score。
|
||
|
||
Args:
|
||
rag_query_log_id: rag_query_log.id
|
||
score: 1-5（1=很沒用，5=非常有用；常用：5=👍，1=👎）
|
||
|
||
Returns:
|
||
True 寫入成功；False 寫入失敗（不 raise，靜默 log warning）。
|
||
"""
|
||
if not rag_query_log_id or not isinstance(rag_query_log_id, int):
|
||
return False
|
||
score = max(1, min(int(score or 0), 5))
|
||
|
||
try:
|
||
from sqlalchemy import text as sa_text
|
||
from database.manager import get_session
|
||
|
||
session = get_session()
|
||
try:
|
||
session.execute(
|
||
sa_text("""
|
||
UPDATE rag_query_log
|
||
SET feedback_score = :score
|
||
WHERE id = :id
|
||
"""),
|
||
{'score': score, 'id': rag_query_log_id},
|
||
)
|
||
session.commit()
|
||
return True
|
||
except Exception:
|
||
session.rollback()
|
||
raise
|
||
finally:
|
||
session.close()
|
||
except Exception as exc:
|
||
logger.warning(
|
||
"[RAGService] feedback write failed (id=%s, score=%s): %s",
|
||
rag_query_log_id, score, exc,
|
||
)
|
||
return False
|
||
|
||
# ──────────────────────────────────────────────────────────────────────
|
||
# 預留：caller 級失效（v5.0 暫無 in-memory cache 層）
|
||
# ──────────────────────────────────────────────────────────────────────
|
||
def invalidate_by_caller(self, caller: str) -> None:
|
||
"""預留鉤：caller 的 prompt 模板更新時呼叫。
|
||
|
||
v5.0 RAG 主要靠 ai_insights 寫入時的 embedding 自動更新，
|
||
無 in-memory cache 層 → 此函式為 no-op，留 API 一致性給後續 cache layer 啟用。
|
||
"""
|
||
if caller:
|
||
logger.debug("[RAGService] invalidate_by_caller(%s) — no-op (no cache layer yet)", caller)
|
||
|
||
# ──────────────────────────────────────────────────────────────────────
|
||
# fire-and-forget log
|
||
# ──────────────────────────────────────────────────────────────────────
|
||
def _async_log(
|
||
self,
|
||
caller: str,
|
||
text: str,
|
||
query_vec: Optional[List[float]],
|
||
top_k: int,
|
||
threshold: float,
|
||
hits: List[Dict[str, Any]],
|
||
request_id: Optional[str] = None,
|
||
saved_call: bool = False,
|
||
) -> None:
|
||
"""放到 daemon thread 寫入 rag_query_log，主流程不阻塞。
|
||
|
||
kill-switch 觸發 → 退化為 logger.info。
|
||
"""
|
||
if _is_killed():
|
||
logger.info(
|
||
"[RAGQuery|killed] caller=%s hits=%d threshold=%.3f request_id=%s",
|
||
caller, len(hits), threshold, request_id,
|
||
)
|
||
return
|
||
|
||
threading.Thread(
|
||
target=self._write_log,
|
||
args=(caller, text, query_vec, top_k, threshold, hits, request_id, saved_call),
|
||
name=f"rag-query-log-{caller}",
|
||
daemon=True,
|
||
).start()
|
||
|
||
def _write_log(
|
||
self,
|
||
caller: str,
|
||
text: str,
|
||
query_vec: Optional[List[float]],
|
||
top_k: int,
|
||
threshold: float,
|
||
hits: List[Dict[str, Any]],
|
||
request_id: Optional[str] = None,
|
||
saved_call: bool = False,
|
||
) -> None:
|
||
"""try/except 全包；DB 掛了只 log warning 不爆炸。"""
|
||
try:
|
||
from sqlalchemy import text as sa_text
|
||
from database.manager import get_session
|
||
|
||
# PII 保護：query_text 截 4KB（與 027 CHECK 對齊）
|
||
safe_text = (text or '')
|
||
encoded = safe_text.encode('utf-8', errors='replace')
|
||
if len(encoded) > _QUERY_TEXT_MAX_BYTES:
|
||
# 截到 byte 邊界後 decode 容錯（errors='ignore' 避免 UTF-8 multi-byte 截斷）
|
||
safe_text = encoded[:_QUERY_TEXT_MAX_BYTES].decode('utf-8', errors='ignore')
|
||
|
||
used_results = [int(h['id']) for h in hits if h.get('id')]
|
||
embedding_str = str(query_vec) if query_vec else None
|
||
embedding_signature = (
|
||
get_embedding_signature(dim=len(query_vec))
|
||
if query_vec else None
|
||
)
|
||
|
||
session = get_session()
|
||
try:
|
||
session.execute(
|
||
sa_text("""
|
||
INSERT INTO rag_query_log (
|
||
caller, query_text, query_embedding,
|
||
embedding_signature,
|
||
top_k, threshold,
|
||
hit_count, used_results,
|
||
saved_call, request_id
|
||
) VALUES (
|
||
:caller, :query_text,
|
||
CAST(:embedding AS vector),
|
||
:embedding_signature,
|
||
:top_k, :threshold,
|
||
:hit_count, CAST(:used_results AS BIGINT[]),
|
||
:saved_call, :request_id
|
||
)
|
||
"""),
|
||
{
|
||
'caller': (caller or 'unknown')[:64],
|
||
'query_text': safe_text,
|
||
'embedding': embedding_str,
|
||
'embedding_signature': embedding_signature,
|
||
'top_k': int(top_k),
|
||
'threshold': round(float(threshold), 3),
|
||
'hit_count': len(hits),
|
||
'used_results': used_results if used_results else None,
|
||
'saved_call': bool(saved_call),
|
||
'request_id': (request_id or None),
|
||
},
|
||
)
|
||
session.commit()
|
||
_record_success()
|
||
except Exception:
|
||
session.rollback()
|
||
raise
|
||
finally:
|
||
session.close()
|
||
except Exception as exc:
|
||
_record_failure()
|
||
logger.warning(
|
||
"[RAGService] rag_query_log write failed (caller=%s): %s",
|
||
caller, exc,
|
||
)
|
||
|
||
|
||
# 全域單例（與 ollama_service / ai_call_logger 同模式）
|
||
rag_service = RAGService()
|
||
|
||
|
||
__all__ = [
|
||
'RAGService',
|
||
'RAGResult',
|
||
'rag_service',
|
||
'get_embedding_signature',
|
||
'verify_embedding_consistency',
|
||
'is_rag_enabled',
|
||
]
|