🤖 每日自动备份 - 2026-04-18 08:00:01
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小溪
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memory/2026-04-17-request-timed-out-before-a-res.md
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memory/2026-04-17-request-timed-out-before-a-res.md
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# Session: 2026-04-17 06:31:58 UTC
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- **Session Key**: agent:main:feishu:direct:ou_e63ce6b760ad39382852472f28fbe2a2
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- **Session ID**: 88616d0d-a45b-42f6-b9de-7294e5acefb7
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- **Source**: feishu
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## Conversation Summary
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assistant: ✅ New session started · model: cloudsway/MaaS_Cl_Opus_4.6_20260205_cache (default: volcengine/doubao-seed-2-0-pro-260215)
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user: System: [2026-04-17 14:23:21 GMT+8] Feishu[default] DM | 李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2) [msg:om_x100b5129534530a4b393b181a373266]
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A new session was started via /new or /reset. Run your Session Startup sequence - read the required files before responding to the user. Then greet the user in your configured persona, if one is provided. Be yourself - use your defined voice, mannerisms, and mood. Keep it to 1-3 sentences and ask what they want to do. If the runtime model differs from default_model in the system prompt, mention the default model. Do not mention internal steps, files, tools, or reasoning.
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Current time: Friday, April 17th, 2026 — 2:23 PM (Asia/Shanghai) / 2026-04-17 06:23 UTC
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assistant:
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Let me initialize by reading the required files.
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assistant: 已了解近期上下文。当前对话用户为李承龙（`ou_e63ce6b760ad39382852472f28fbe2a2`），是第一业务负责人。
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承龙你好~ 我是小溪📊，刚重置了会话。上次聊到4月购课用户的U0完成情况和退款分析，有什么需要继续跟进的吗？
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user: System: [2026-04-17 14:24:18 GMT+8] Feishu[xiaoxi] DM | 李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2) [msg:om_x100b51296fd5a4b0b3b6638abde0ba7]
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Conversation info (untrusted metadata):
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```json
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{
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"message_id": "om_x100b51296fd5a4b0b3b6638abde0ba7",
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"sender_id": "ou_e63ce6b760ad39382852472f28fbe2a2",
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"sender": "李承龙",
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"timestamp": "Fri 2026-04-17 14:24 GMT+8"
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}
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```
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Sender (untrusted metadata):
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```json
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{
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"label": "李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2)",
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"id": "ou_e63ce6b760ad39382852472f28fbe2a2",
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"name": "李承龙"
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}
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```
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帮我统计一下，渠道“学霸老王”和“学霸三人行”这两个渠道中4月份退款的用户中，有多少人仍有未退费的有效订单，以及这些用户涉及多少订单
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assistant:
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我来查一下这两个渠道4月份退款用户中仍持有有效订单的情况。
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assistant: 再补充一下这两个渠道4月退款用户的总数，以便给出完整的比例。
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assistant: 注意到退款用户总数中合计419去重后小于 191+235=426，说明有7个用户同时在两个渠道都有退款订单。结果已经清晰了：
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**4月份退款用户中仍持有有效订单的统计：**
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| 渠道 | 退款用户总数 | 仍有有效订单的用户数 | 占比 | 涉及有效订单数 |
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|------|------------|-------------------|------|-------------|
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| 学霸老王 | 235 | 23 | 9.8% | 23 |
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| 学霸三人行 | 191 | 23 | 12.0% | 23 |
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| **合计（去重）** | **419** | **46** | **11.0%** | **46** |
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几个要点：
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- 两个渠道4月退款用户合计419人（去重），其中**46人仍持有至少1笔有效订单**，占比约11%
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- 这46人共涉及**46笔有效订单**，说明每人恰好还有1笔未退的订单
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- 学霸三人行的"仍有有效订单"占比略高（12.0% vs 9.8%）
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需要进一步看这46人的有效订单商品分布，或者其他维度的拆解吗？
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user: System: [2026-04-17 14:26:03 GMT+8] Feishu[default] DM | 李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2) [msg:om_x100b5129696a30b8b37bbd746f5b0ea]
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Conversation info (untrusted metadata):
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```json
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{
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"message_id": "om_x100b5129696a30b8b37bbd746f5b0ea",
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"sender_id": "ou_e63ce6b760ad39382852472f28fbe2a2",
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"sender": "李承龙",
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"timestamp": "Fri 2026-04-17 14:26 GMT+8"
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}
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```
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Sender (untrusted metadata):
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```json
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{
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"label": "李承龙 (ou_e63ce6b760ad39382852472f28fbe2a2)",
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"id": "ou_e63ce6b760ad39382852472f28fbe2a2",
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"name": "李承龙"
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}
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```
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加一个这两个渠道各自4月份的订单总量
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assistant: 补充完整后的统计如下：
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| 渠道 | 4月订单总量 | 4月购课用户数 | 退款用户数 | 退款率 | 仍有有效订单的用户数 | 占退款用户比 | 涉及有效订单数 |
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|------|-----------|------------|----------|-------|-------------------|------------|-------------|
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| 学霸老王 | 538 | 510 | 235 | 46.1% | 23 | 9.8% | 23 |
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| 学霸三人行 | 420 | 405 | 191 | 47.2% | 23 | 12.0% | 23 |
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| **合计** | **958** | **~880(去重)** | **419(去重)** | — | **46(去重)** | **11.0%** | **46** |
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两个渠道4月总订单量958单，退款率都在46%~47%左右。退款用户中约11%的人仍保留了一笔有效订单。
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还需要进一步拆解吗？
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@ -17,6 +17,7 @@ export PATH=$PATH:/root/.nvm/versions/node/v24.14.0/bin
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PGPASSWORD="${PG_ONLINE_PASSWORD}" psql -h bj-postgres-16pob4sg.sql.tencentcdb.com -p 28591 -U ai_member -d vala_bi -A -F "," -o "${CSV_FILE}" -c "
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select
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key_from as \"购课渠道\",
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goods_name as \"购买课包\",
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created_at as \"购课日期\",
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count(distinct trade_no) as \"总订单数\",
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count(distinct case when order_status = 3 then trade_no else NULL end) as \"未退费订单数\",
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@ -35,18 +36,21 @@ left join
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trade_no,
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date(created_at) as created_at,
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order_status,
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key_from
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key_from,
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goods_name
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from bi_vala_order
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group by
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account_id,
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trade_no,
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date(created_at),
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order_status,
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key_from
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key_from,
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goods_name
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) as b on a.id = b.account_id
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where key_from is not null
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group by
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key_from,
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goods_name,
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created_at
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order by
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created_at desc,
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