refactor: migrate workflow to v2 core and archive legacy modules
This commit is contained in:
2
legacy/evolution/__init__.py
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2
legacy/evolution/__init__.py
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"""Self-evolution MVP utilities for the customer service agent."""
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591
legacy/evolution/mvp.py
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591
legacy/evolution/mvp.py
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from __future__ import annotations
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import json
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import os
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import sqlite3
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from dataclasses import asdict, dataclass
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from datetime import datetime, timedelta
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from pathlib import Path
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from typing import Any, Dict, Iterable, List, Optional, Tuple
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ROOT = Path(__file__).resolve().parent.parent
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ARTIFACT_DIR = ROOT / "evolution" / "artifacts"
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DEFAULT_POLICY_PATH = ROOT / "config" / "evolution_policy.json"
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DEFAULT_CANDIDATE_PATH = ROOT / "config" / "evolution_candidate.json"
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RISK_KEYWORDS = (
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"退款",
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"退货",
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"投诉",
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"差评",
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"举报",
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"欺骗",
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"骗人",
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"不满意",
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"生气",
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"法院",
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"起诉",
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)
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TRANSFER_HINTS = ("转人工", "人工", "为您转接", "专员", "稍后联系")
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WEAK_REPLY_HINTS = ("不清楚", "不知道", "稍后", "晚点", "我再看下", "等会")
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EMPATHY_HINTS = ("抱歉", "不好意思", "理解", "辛苦", "感谢反馈")
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@dataclass
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class Sample:
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customer_id: str
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acc_id: str
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in_ts: str
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in_text: str
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out_ts: str
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out_text: str
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latency_sec: int
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@dataclass
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class Finding:
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kind: str
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severity: str
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customer_id: str
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acc_id: str
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in_ts: str
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in_text: str
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out_text: str
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detail: str
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@dataclass
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class ChatSourceConfig:
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source: str = "auto" # auto | sqlite | mysql
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sqlite_path: str = str(ROOT / "db" / "chat_log_db" / "chats.db")
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mysql_host: str = os.getenv("MYSQL_HOST", "127.0.0.1")
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mysql_port: int = int(os.getenv("MYSQL_PORT", "3306"))
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mysql_user: str = os.getenv("MYSQL_USER", "root")
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mysql_password: str = os.getenv("MYSQL_PASSWORD", "")
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mysql_database: str = os.getenv("MYSQL_DATABASE", "ai_cs")
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def _parse_ts(ts_text: str) -> Optional[datetime]:
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if not ts_text:
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return None
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try:
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return datetime.strptime(ts_text, "%Y-%m-%d %H:%M:%S")
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except ValueError:
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return None
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def _to_ts_text(value: Any) -> str:
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if isinstance(value, datetime):
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return value.strftime("%Y-%m-%d %H:%M:%S")
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if value is None:
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return ""
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return str(value)
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def _iter_recent_conversations_sqlite(
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cfg: ChatSourceConfig,
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hours: int,
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max_customers: int,
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max_messages_per_customer: int,
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) -> Iterable[Tuple[str, List[Dict[str, Any]]]]:
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cutoff_dt = datetime.now() - timedelta(hours=hours)
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cutoff_text = cutoff_dt.strftime("%Y-%m-%d %H:%M:%S")
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db_path = Path(cfg.sqlite_path)
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if not db_path.exists():
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return
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conn = sqlite3.connect(f"file:{db_path.as_posix()}?mode=ro", uri=True)
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conn.row_factory = sqlite3.Row
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try:
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cur = conn.execute(
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"""
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SELECT customer_id, MAX(timestamp) AS last_ts
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FROM chat_logs
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WHERE timestamp >= ?
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GROUP BY customer_id
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ORDER BY last_ts DESC
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LIMIT ?
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""",
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(cutoff_text, max_customers),
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)
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customers = [dict(r) for r in cur.fetchall()]
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for c in customers:
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customer_id = str(c.get("customer_id") or "").strip()
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if not customer_id:
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continue
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rows_cur = conn.execute(
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"""
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SELECT direction, message, timestamp, acc_id
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FROM chat_logs
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WHERE customer_id = ? AND timestamp >= ?
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ORDER BY timestamp ASC, id ASC
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LIMIT ?
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""",
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(customer_id, cutoff_text, max_messages_per_customer),
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)
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rows = [dict(r) for r in rows_cur.fetchall()]
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if rows:
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yield customer_id, rows
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finally:
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conn.close()
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def _iter_recent_conversations_mysql(
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cfg: ChatSourceConfig,
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hours: int,
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max_customers: int,
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max_messages_per_customer: int,
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) -> Iterable[Tuple[str, List[Dict[str, Any]]]]:
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try:
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import pymysql
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except Exception:
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return
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cutoff_dt = datetime.now() - timedelta(hours=hours)
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try:
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conn = pymysql.connect(
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host=cfg.mysql_host,
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port=cfg.mysql_port,
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user=cfg.mysql_user,
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password=cfg.mysql_password,
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database=cfg.mysql_database,
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charset="utf8mb4",
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cursorclass=pymysql.cursors.DictCursor,
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autocommit=True,
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)
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except Exception:
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return
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try:
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with conn.cursor() as cur:
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cur.execute(
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"""
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SELECT customer_id, MAX(timestamp) AS last_ts
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FROM chat_logs
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WHERE timestamp >= %s
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GROUP BY customer_id
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ORDER BY last_ts DESC
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LIMIT %s
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""",
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(cutoff_dt, max_customers),
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)
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customers = cur.fetchall() or []
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for c in customers:
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customer_id = str(c.get("customer_id") or "").strip()
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if not customer_id:
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continue
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with conn.cursor() as cur:
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cur.execute(
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"""
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SELECT direction, message, timestamp, acc_id
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FROM chat_logs
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WHERE customer_id = %s AND timestamp >= %s
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ORDER BY timestamp ASC, id ASC
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LIMIT %s
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""",
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(customer_id, cutoff_dt, max_messages_per_customer),
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)
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rows = cur.fetchall() or []
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normalized = []
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for r in rows:
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normalized.append(
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{
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"direction": r.get("direction"),
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"message": r.get("message"),
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"timestamp": _to_ts_text(r.get("timestamp")),
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"acc_id": r.get("acc_id"),
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}
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)
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if normalized:
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yield customer_id, normalized
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finally:
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conn.close()
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def _iter_recent_conversations(
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cfg: ChatSourceConfig,
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hours: int,
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max_customers: int,
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max_messages_per_customer: int,
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) -> Iterable[Tuple[str, List[Dict[str, Any]]]]:
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source = (cfg.source or "auto").strip().lower()
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if source == "sqlite":
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yield from _iter_recent_conversations_sqlite(cfg, hours, max_customers, max_messages_per_customer)
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return
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if source == "mysql":
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yield from _iter_recent_conversations_mysql(cfg, hours, max_customers, max_messages_per_customer)
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return
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# auto: prefer mysql when DB_TYPE=mysql, otherwise sqlite
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db_type = os.getenv("DB_TYPE", "").strip().lower()
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if db_type in ("mysql", "mariadb"):
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got_any = False
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for item in _iter_recent_conversations_mysql(cfg, hours, max_customers, max_messages_per_customer):
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got_any = True
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yield item
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if got_any:
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return
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yield from _iter_recent_conversations_sqlite(cfg, hours, max_customers, max_messages_per_customer)
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def build_samples(
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hours: int = 24,
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max_customers: int = 200,
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max_messages_per_customer: int = 80,
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chat_source: Optional[ChatSourceConfig] = None,
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) -> List[Sample]:
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cfg = chat_source or ChatSourceConfig()
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samples: List[Sample] = []
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for customer_id, rows in _iter_recent_conversations(
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cfg=cfg,
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hours=hours,
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max_customers=max_customers,
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max_messages_per_customer=max_messages_per_customer,
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):
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pending_in: Optional[Dict[str, Any]] = None
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for row in rows:
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direction = str(row.get("direction") or "")
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if direction == "in":
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pending_in = row
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continue
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if direction != "out" or pending_in is None:
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continue
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in_text = str(pending_in.get("message") or "").strip()
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out_text = str(row.get("message") or "").strip()
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if not in_text:
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pending_in = None
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continue
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in_ts = _parse_ts(str(pending_in.get("timestamp") or ""))
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out_ts = _parse_ts(str(row.get("timestamp") or ""))
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latency = 0
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if in_ts and out_ts:
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latency = int((out_ts - in_ts).total_seconds())
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samples.append(
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Sample(
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customer_id=customer_id,
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acc_id=str(row.get("acc_id") or pending_in.get("acc_id") or ""),
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in_ts=str(pending_in.get("timestamp") or ""),
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in_text=in_text,
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out_ts=str(row.get("timestamp") or ""),
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out_text=out_text,
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latency_sec=max(0, latency),
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)
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)
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pending_in = None
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return samples
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def evaluate_samples(samples: List[Sample]) -> List[Finding]:
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findings: List[Finding] = []
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for s in samples:
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in_text = s.in_text
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out_text = s.out_text
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inbound_risky = any(k in in_text for k in RISK_KEYWORDS)
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if not out_text:
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findings.append(
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Finding(
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kind="empty_reply",
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severity="high",
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customer_id=s.customer_id,
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acc_id=s.acc_id,
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in_ts=s.in_ts,
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in_text=s.in_text,
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out_text=s.out_text,
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detail="收到消息但回复为空",
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)
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)
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continue
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if s.latency_sec > 600:
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findings.append(
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Finding(
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kind="slow_reply",
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severity="medium",
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customer_id=s.customer_id,
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acc_id=s.acc_id,
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in_ts=s.in_ts,
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in_text=s.in_text,
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out_text=s.out_text,
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detail=f"回复耗时 {s.latency_sec}s (>600s)",
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)
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)
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if inbound_risky:
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has_transfer = any(k in out_text for k in TRANSFER_HINTS)
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has_empathy = any(k in out_text for k in EMPATHY_HINTS)
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if not has_transfer:
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findings.append(
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Finding(
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kind="risk_not_transferred",
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severity="high",
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customer_id=s.customer_id,
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acc_id=s.acc_id,
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in_ts=s.in_ts,
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in_text=s.in_text,
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out_text=s.out_text,
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detail="高风险诉求未出现转人工提示",
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)
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)
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if not has_empathy:
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findings.append(
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Finding(
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kind="risk_no_empathy",
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severity="medium",
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customer_id=s.customer_id,
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acc_id=s.acc_id,
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in_ts=s.in_ts,
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in_text=s.in_text,
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out_text=s.out_text,
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detail="高风险诉求回复缺少安抚语气",
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)
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)
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if any(k in out_text for k in WEAK_REPLY_HINTS):
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findings.append(
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Finding(
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kind="weak_reply",
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severity="medium",
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customer_id=s.customer_id,
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acc_id=s.acc_id,
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in_ts=s.in_ts,
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in_text=s.in_text,
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out_text=s.out_text,
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detail="回复存在低置信度兜底话术",
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)
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)
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return findings
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def summarize_findings(findings: List[Finding]) -> Dict[str, Any]:
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by_kind: Dict[str, int] = {}
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by_severity: Dict[str, int] = {}
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for f in findings:
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by_kind[f.kind] = by_kind.get(f.kind, 0) + 1
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by_severity[f.severity] = by_severity.get(f.severity, 0) + 1
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return {"total": len(findings), "by_kind": by_kind, "by_severity": by_severity}
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def make_proposals(findings: List[Finding], sample_count: int) -> List[Dict[str, Any]]:
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summary = summarize_findings(findings)
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by_kind = summary["by_kind"]
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proposals: List[Dict[str, Any]] = []
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if by_kind.get("risk_not_transferred", 0) > 0:
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proposals.append(
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{
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"id": "policy-risk-transfer",
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"priority": "p0",
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"module": "policy/prompt",
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"title": "风险关键词触发后强制转人工",
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"suggestion": "在风险路由的系统提示词中增加硬规则：遇到退款/投诉/法律威胁类诉求必须调用 transfer_to_human。",
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"evidence_count": by_kind["risk_not_transferred"],
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}
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)
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if by_kind.get("risk_no_empathy", 0) > 0:
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proposals.append(
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{
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"id": "tone-empathy-pack",
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"priority": "p1",
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"module": "policy/prompt",
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"title": "高风险场景补充安抚模板",
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"suggestion": "为投诉类回复追加一段安抚模板，降低激化概率。",
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"evidence_count": by_kind["risk_no_empathy"],
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}
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)
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if by_kind.get("weak_reply", 0) > 0:
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proposals.append(
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{
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"id": "fallback-reduction",
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"priority": "p1",
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"module": "intent/router",
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"title": "减少低置信度兜底话术",
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"suggestion": "出现“不清楚/稍后”等兜底词时，优先触发澄清问题或转人工而非直接结束。",
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"evidence_count": by_kind["weak_reply"],
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}
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)
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if by_kind.get("slow_reply", 0) > 0:
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proposals.append(
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{
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"id": "slow-path-timeout",
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"priority": "p2",
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"module": "tools/workflow",
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"title": "慢链路超时与短回复兜底",
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"suggestion": "当工具调用超过阈值时先发短确认回复，避免长时间无响应。",
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"evidence_count": by_kind["slow_reply"],
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}
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)
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proposals.append(
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{
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"id": "ops-regression-gate",
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"priority": "p0",
|
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"module": "eval/pipeline",
|
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"title": "上线前回归门禁",
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"suggestion": "新增候选策略必须在离线评测集上通过，再灰度 5% 流量后扩大。",
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"evidence_count": sample_count,
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}
|
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)
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return proposals
|
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|
||||
|
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def load_policy(path: Path = DEFAULT_POLICY_PATH) -> Dict[str, Any]:
|
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if not path.exists():
|
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return {
|
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"publish_gate": {
|
||||
"min_sample_count": 30,
|
||||
"max_high_findings_rate": 0.08,
|
||||
"max_ai_fail_rate": 5.0,
|
||||
"max_transfer_rate": 45.0,
|
||||
}
|
||||
}
|
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return json.loads(path.read_text(encoding="utf-8"))
|
||||
|
||||
|
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def can_publish_candidate(samples: List[Sample], findings: List[Finding], runtime_hours: int, policy: Dict[str, Any]) -> Tuple[bool, Dict[str, Any]]:
|
||||
try:
|
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from utils.metrics_tracker import get_runtime_summary
|
||||
except Exception:
|
||||
def get_runtime_summary(hours: int = 24) -> Dict[str, Any]:
|
||||
return {"window_hours": hours, "counts": {}, "rates": {"ai_fail_rate": 0.0, "transfer_rate": 0.0}}
|
||||
|
||||
gate = (policy or {}).get("publish_gate", {})
|
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min_sample_count = int(gate.get("min_sample_count", 30))
|
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max_high_rate = float(gate.get("max_high_findings_rate", 0.08))
|
||||
max_ai_fail_rate = float(gate.get("max_ai_fail_rate", 5.0))
|
||||
max_transfer_rate = float(gate.get("max_transfer_rate", 45.0))
|
||||
|
||||
high_cnt = sum(1 for f in findings if f.severity == "high")
|
||||
sample_count = max(1, len(samples))
|
||||
high_rate = high_cnt / sample_count
|
||||
runtime = get_runtime_summary(hours=runtime_hours)
|
||||
ai_fail_rate = float(runtime.get("rates", {}).get("ai_fail_rate", 0.0))
|
||||
transfer_rate = float(runtime.get("rates", {}).get("transfer_rate", 0.0))
|
||||
|
||||
reasons = []
|
||||
ok = True
|
||||
if len(samples) < min_sample_count:
|
||||
ok = False
|
||||
reasons.append(f"样本不足: {len(samples)} < {min_sample_count}")
|
||||
if high_rate > max_high_rate:
|
||||
ok = False
|
||||
reasons.append(f"高危发现占比过高: {high_rate:.2%} > {max_high_rate:.2%}")
|
||||
if ai_fail_rate > max_ai_fail_rate:
|
||||
ok = False
|
||||
reasons.append(f"AI失败率过高: {ai_fail_rate:.2f}% > {max_ai_fail_rate:.2f}%")
|
||||
if transfer_rate > max_transfer_rate:
|
||||
ok = False
|
||||
reasons.append(f"转人工率过高: {transfer_rate:.2f}% > {max_transfer_rate:.2f}%")
|
||||
|
||||
return ok, {
|
||||
"sample_count": len(samples),
|
||||
"high_findings": high_cnt,
|
||||
"high_findings_rate": round(high_rate, 4),
|
||||
"runtime": runtime,
|
||||
"policy_gate": gate,
|
||||
"reasons": reasons,
|
||||
}
|
||||
|
||||
|
||||
def _write_json(path: Path, payload: Dict[str, Any]) -> None:
|
||||
path.parent.mkdir(parents=True, exist_ok=True)
|
||||
path.write_text(json.dumps(payload, ensure_ascii=False, indent=2), encoding="utf-8")
|
||||
|
||||
|
||||
def _write_jsonl(path: Path, rows: Iterable[Dict[str, Any]]) -> None:
|
||||
path.parent.mkdir(parents=True, exist_ok=True)
|
||||
with path.open("w", encoding="utf-8") as f:
|
||||
for row in rows:
|
||||
f.write(json.dumps(row, ensure_ascii=False) + "\n")
|
||||
|
||||
|
||||
def run_cycle(
|
||||
hours: int = 24,
|
||||
max_customers: int = 200,
|
||||
max_messages_per_customer: int = 80,
|
||||
runtime_hours: int = 24,
|
||||
publish: bool = False,
|
||||
chat_source: Optional[ChatSourceConfig] = None,
|
||||
policy_path: Path = DEFAULT_POLICY_PATH,
|
||||
candidate_path: Path = DEFAULT_CANDIDATE_PATH,
|
||||
) -> Dict[str, Any]:
|
||||
ARTIFACT_DIR.mkdir(parents=True, exist_ok=True)
|
||||
now_tag = datetime.now().strftime("%Y%m%d_%H%M%S")
|
||||
source_error = ""
|
||||
|
||||
try:
|
||||
samples = build_samples(
|
||||
hours=hours,
|
||||
max_customers=max_customers,
|
||||
max_messages_per_customer=max_messages_per_customer,
|
||||
chat_source=chat_source,
|
||||
)
|
||||
except Exception as e:
|
||||
samples = []
|
||||
source_error = str(e)
|
||||
findings = evaluate_samples(samples)
|
||||
proposals = make_proposals(findings=findings, sample_count=len(samples))
|
||||
policy = load_policy(path=policy_path)
|
||||
publish_ok, gate_report = can_publish_candidate(
|
||||
samples=samples,
|
||||
findings=findings,
|
||||
runtime_hours=runtime_hours,
|
||||
policy=policy,
|
||||
)
|
||||
|
||||
sample_file = ARTIFACT_DIR / f"samples_{now_tag}.jsonl"
|
||||
eval_file = ARTIFACT_DIR / f"eval_report_{now_tag}.json"
|
||||
proposal_file = ARTIFACT_DIR / f"proposals_{now_tag}.json"
|
||||
|
||||
_write_jsonl(sample_file, (asdict(s) for s in samples))
|
||||
_write_json(
|
||||
eval_file,
|
||||
{
|
||||
"generated_at": datetime.now().isoformat(timespec="seconds"),
|
||||
"sample_count": len(samples),
|
||||
"finding_summary": summarize_findings(findings),
|
||||
"publish_gate_report": gate_report,
|
||||
},
|
||||
)
|
||||
_write_json(
|
||||
proposal_file,
|
||||
{
|
||||
"generated_at": datetime.now().isoformat(timespec="seconds"),
|
||||
"proposals": proposals,
|
||||
},
|
||||
)
|
||||
|
||||
published = False
|
||||
candidate_payload: Dict[str, Any] = {}
|
||||
if publish and publish_ok:
|
||||
candidate_payload = {
|
||||
"version": f"candidate-{now_tag}",
|
||||
"created_at": datetime.now().isoformat(timespec="seconds"),
|
||||
"sample_file": str(sample_file),
|
||||
"eval_file": str(eval_file),
|
||||
"proposal_file": str(proposal_file),
|
||||
"gate_report": gate_report,
|
||||
"proposals": proposals,
|
||||
"status": "ready_for_gray_5_percent",
|
||||
}
|
||||
_write_json(candidate_path, candidate_payload)
|
||||
published = True
|
||||
|
||||
source_view = asdict(chat_source) if chat_source else asdict(ChatSourceConfig())
|
||||
if source_view.get("mysql_password"):
|
||||
source_view["mysql_password"] = "***"
|
||||
|
||||
return {
|
||||
"samples": len(samples),
|
||||
"findings": len(findings),
|
||||
"publish_ok": publish_ok,
|
||||
"published": published,
|
||||
"chat_source": source_view,
|
||||
"source_error": source_error,
|
||||
"artifacts": {
|
||||
"samples": str(sample_file),
|
||||
"evaluation": str(eval_file),
|
||||
"proposals": str(proposal_file),
|
||||
"candidate": str(candidate_path) if published else "",
|
||||
},
|
||||
"gate_report": gate_report,
|
||||
"top_proposals": proposals[:3],
|
||||
}
|
||||
Reference in New Issue
Block a user