305 lines
12 KiB
Python
Executable File
305 lines
12 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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微信用户反馈同步脚本 — 复用飞书聚类/归纳/优先级/分发逻辑
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数据格式与飞书 sync_feishu_feedback.py 完全一致：
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(message_id, sender_name, msg_type, content, media_url, quote_message_id, msg_time, msg_timestamp)
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用法：
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python3 sync_wechat_feedback.py --date 2026-05-21 --steps 7 --ai-placeholders --skip-dispatch
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python3 sync_wechat_feedback.py --apply-ai /path/to/ai_descriptions.json
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"""
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import sys
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import os
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SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
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SKILL_DIR = os.path.join(SCRIPT_DIR, "..", "skills", "feishu-feedback-sync", "scripts")
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sys.path.insert(0, SKILL_DIR)
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import argparse
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import json
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import pymysql
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import subprocess
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from datetime import datetime, timedelta
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# ── Monkey-patch 微信专用常量 BEFORE importing 共享模块 ──
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import sync_feishu_feedback as fsf
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# 微信知识库父文档
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fsf.SUMMARY_PARENT_NODE = "XhtGwjitFizzCNkw8Xzc2IXsnuf" # 微信用户反馈问题汇总
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# ── 数据库 ──
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DB_CONFIG = {
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"host": "bj-cdb-8frbdwju.sql.tencentcdb.com",
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"port": 25413,
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"user": "read_only",
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"password": "fdsfiidier^$*hjfdijjd232",
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"database": "vala_test",
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"charset": "utf8mb4",
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}
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CLI = "lark-cli"
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CRED_DIR = "/root/.openclaw/credentials/xiaokui"
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CONTEXT_DIR = os.path.join(SCRIPT_DIR, "..", "output", "daily_feedback")
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def fetch_wechat_data(date_str):
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"""从 wechat_group_message 读取指定日期的消息，
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映射为与飞书 fetch_data 完全一致的元组格式：
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(message_id, sender_name, msg_type, content, media_url, quote_message_id, msg_time, msg_timestamp)
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"""
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conn = pymysql.connect(**DB_CONFIG)
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cursor = conn.cursor()
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cursor.execute("""
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SELECT svr_msg_id, sender_name, msg_type, content, media_url,
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refer_msg_svrid, DATE_FORMAT(msg_time, '%%Y-%%m-%%d %%H:%%i:%%s') as msg_time,
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msg_timestamp
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FROM wechat_group_message
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WHERE msg_time >= %s AND msg_time < %s
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ORDER BY msg_time ASC
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""", (f"{date_str} 00:00:00", f"{date_str} 23:59:59"))
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rows = cursor.fetchall()
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conn.close()
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result = []
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for row in rows:
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svr_id, sname, mtype, content, murl, ref_id, mtime, mts = row
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result.append((
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str(svr_id) if svr_id else "", # message_id
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sname or "", # sender_name
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mtype or "text", # msg_type
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content or "", # content
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murl or "", # media_url
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str(ref_id) if ref_id else "", # quote_message_id
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mtime or "", # msg_time
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int(mts) if mts else 0, # msg_timestamp
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))
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return result
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def gen_context_json(date_str, clusters, cluster_order):
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"""保存簇上下文 JSON 供 AI 使用"""
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os.makedirs(CONTEXT_DIR, exist_ok=True)
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ctx = {"date": date_str, "clusters": []}
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for idx, cid in enumerate(cluster_order):
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cmsgs = clusters[cid]
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ctx["clusters"].append({
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"index": idx + 1,
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"cluster_id": cid,
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"message_count": len(cmsgs),
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"messages": [
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{"sender": m[1], "content": m[3], "msg_type": m[2],
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"time": m[6], "message_id": m[0], "quote_message_id": m[5]}
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for m in cmsgs
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],
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})
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path = os.path.join(CONTEXT_DIR, f"wechat_cluster_context_{date_str}.json")
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with open(path, "w", encoding="utf-8") as f:
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json.dump(ctx, f, ensure_ascii=False, indent=2)
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print(f" 📝 微信 AI 上下文已保存: {path}")
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return path
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def main():
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parser = argparse.ArgumentParser(description="微信问题反馈同步")
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parser.add_argument("--date", type=str, required=True, help="处理日期 YYYY-MM-DD")
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parser.add_argument("--dry-run", action="store_true")
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parser.add_argument("--skip-priority", action="store_true")
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parser.add_argument("--skip-dispatch", action="store_true")
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parser.add_argument("--ai-placeholders", action="store_true",
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help="使用 [待AI归纳:#N] 占位符")
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parser.add_argument("--apply-ai", type=str, default=None,
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help="应用 AI 描述 JSON")
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parser.add_argument("--steps", type=str, default="1-7")
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args = parser.parse_args()
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date_str = args.date
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# ── --apply-ai 模式 ──
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if args.apply_ai:
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with open(args.apply_ai, "r", encoding="utf-8") as f:
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ai_data = json.load(f)
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descriptions = ai_data.get("descriptions", [])
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print(f"📋 加载 {len(descriptions)} 条微信 AI 描述，日期: {date_str}")
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# 读取上下文重建聚类
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ctx_path = os.path.join(CONTEXT_DIR, f"wechat_cluster_context_{date_str}.json")
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if not os.path.exists(ctx_path):
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print(f"❌ 上下文文件不存在: {ctx_path}")
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sys.exit(1)
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with open(ctx_path, "r", encoding="utf-8") as f:
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ctx = json.load(f)
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clusters = {}
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cluster_order = []
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original_index_map = {} # cluster_id → original context index
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for c in ctx["clusters"]:
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cid = c["cluster_id"]
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original_index_map[cid] = c["index"]
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msgs_data = c["messages"]
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msgs = []
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for m in msgs_data:
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msgs.append((
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m.get("message_id", cid),
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m["sender"],
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m.get("msg_type", "text"),
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m["content"],
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"",
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m.get("quote_message_id", ""),
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m.get("time", ""),
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0
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))
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if cid not in clusters:
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clusters[cid] = []
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cluster_order.append(cid)
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clusters[cid].extend(msgs)
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# 生成归中文档（复用飞书函数）
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# generate_summary 会跳过 <2 条消息的簇并重新编号
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# 需要建立 original_index → placeholder_number 映射
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summary_md = fsf.generate_summary(
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clusters, cluster_order,
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skip_priority=True, ai_placeholders=True
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)
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if isinstance(summary_md, tuple):
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summary_md = summary_md[0]
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# 构建 original_index → placeholder_number 映射
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valid_count = 0
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index_mapping = {} # original_index → placeholder_number
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for cid in cluster_order:
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if len(clusters[cid]) >= 2:
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valid_count += 1
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idx = original_index_map.get(cid, valid_count)
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index_mapping[idx] = valid_count
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# 替换占位符：用 original_index 查映射得到 placeholder_number
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for item in descriptions:
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old_idx = item["index"]
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desc = item["description"]
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new_idx = index_mapping.get(old_idx)
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if new_idx is None:
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# 该簇被 generate_summary 跳过（单消息），忽略
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continue
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placeholder = f"[待AI归纳:#{new_idx}]"
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summary_md = summary_md.replace(placeholder, desc)
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print(f" 🔄 微信 #{old_idx}→#{new_idx}: {placeholder} → {desc[:50]}...")
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# 回写知识库子文档
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title = f"微信-{date_str} 问题反馈"
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nodes = fsf.list_child_nodes()
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node_info = nodes.get(title, {})
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obj_token = node_info.get("obj_token")
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if not obj_token:
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print(f" 📝 创建新文档: {title}")
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fsf.update_summary_doc_as_children({date_str: summary_md}, title_prefix="微信-")
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nodes = fsf.list_child_nodes()
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node_info = nodes.get(title, {})
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obj_token = node_info.get("obj_token")
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if not obj_token:
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print("❌ 无法创建/找到文档")
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sys.exit(1)
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tmp_md = "tmp/wechat_ai_summary.md"
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with open(tmp_md, "w", encoding="utf-8") as f:
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f.write(summary_md)
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env = os.environ.copy()
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env["LARKSUITE_CLI_CONFIG_DIR"] = CRED_DIR
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result = subprocess.run(
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[CLI, "docs", "+update", "--doc", obj_token, "--as", "bot",
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"--mode", "overwrite", "--markdown", f"@{tmp_md}"],
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env=env, capture_output=True, text=True, timeout=15)
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os.unlink(tmp_md)
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try:
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resp = json.loads(result.stdout)
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assert resp.get("ok"), f"写入失败: {result.stdout[:300]}"
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print(f" ✅ AI 描述已应用到微信文档: {title}")
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# 分发到群聊
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if not args.skip_dispatch:
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print(f" 📨 分发微信归纳到群聊...")
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child_nt = node_info.get("node_token", fsf.SUMMARY_PARENT_NODE)
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child_url = f"https://makee-interactive.feishu.cn/wiki/{child_nt}"
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fsf.dispatch_summary_to_chat(
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f"微信-{date_str}", summary_md,
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p0_only=False, doc_url=child_url
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)
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print(f" ✅ 已分发")
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except Exception as e:
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print(f" ❌ {e}")
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sys.exit(1)
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if os.path.exists(ctx_path):
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os.unlink(ctx_path)
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print(f" 🗑️ 已清理上下文文件")
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return
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# ── 正常同步流程 ──
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print(f"\n📊 查询微信 {date_str} 数据...")
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rows = fetch_wechat_data(date_str)
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if not rows:
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print(f" ⚠️ {date_str} 无微信数据")
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return
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print(f" 📋 共 {len(rows)} 条消息")
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do_summary = int(args.steps.split("-")[-1]) >= 3
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total = len(rows)
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if do_summary:
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# 步骤 3：聚类（直接复用飞书 sort_threads）
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sorted_rows, clusters, cluster_order = fsf.sort_threads(rows)
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if not cluster_order:
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print(f" 无有效问题簇（需要≥2条消息）")
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return
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print(f" 聚类完成：{len(cluster_order)} 个问题")
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for cid in cluster_order:
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cmsgs = clusters[cid]
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earliest = min(m[6] for m in cmsgs)
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print(f" 簇 {cid}: {len(cmsgs)} 条消息，始于 {earliest}")
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# 保存 AI 上下文
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ctx_path = None
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if args.ai_placeholders:
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ctx_path = gen_context_json(date_str, clusters, cluster_order)
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# 步骤 4-6：生成归纳 + 优先级 + 写入知识库
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summary_md = fsf.generate_summary(
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clusters, cluster_order,
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skip_priority=args.skip_priority,
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ai_placeholders=args.ai_placeholders
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)
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if isinstance(summary_md, tuple):
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summary_md = summary_md[0]
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print(summary_md)
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if not args.dry_run:
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fsf.update_summary_doc_as_children({date_str: summary_md}, title_prefix="微信-")
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print(f" ✅ 微信-{date_str} 问题反馈 写入成功")
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# 步骤 7：分发（非 ai_placeholders 模式直接分发，否则等 --apply-ai）
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if do_summary and not args.skip_dispatch and not args.ai_placeholders:
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print(f"\n📨 微信步骤7：问题分发...")
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child_nodes = fsf.list_child_nodes()
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child_title = f"微信-{date_str} 问题反馈"
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child_info = child_nodes.get(child_title, {})
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child_nt = child_info.get("node_token", fsf.SUMMARY_PARENT_NODE)
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child_url = f"https://makee-interactive.feishu.cn/wiki/{child_nt}"
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fsf.dispatch_summary_to_chat(
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f"微信-{date_str}", summary_md,
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p0_only=False, doc_url=child_url
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)
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print(f" ✅ 已分发")
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print(f"\n🎉 微信同步完成，总计处理 {total} 条")
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if __name__ == "__main__":
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main()
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