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| 1435 -\n .../typings/types/wx/lib.wx.miniapp.d.ts\" | 2606 -\n .../typings/types/wx/lib.wx.page.d.ts\" | 259 -\n apps/miniprogram/README.md | 4 +-\n apps/miniprogram/miniprogram/app.json | 21 +-\n apps/miniprogram/miniprogram/app.wxss | 173 +\n .../miniprogram/assets/icons/arrow-left.svg | 3 -\n .../miniprogram/miniprogram/assets/icons/chart.svg | 3 -\n .../miniprogram/assets/icons/chat-gray.svg | 3 -\n apps/miniprogram/miniprogram/assets/icons/chat.svg | 3 -\n .../miniprogram/assets/icons/check-bold.svg | 3 -\n .../miniprogram/assets/icons/check-circle.svg | 3 -\n .../miniprogram/miniprogram/assets/icons/clock.svg | 4 -\n .../miniprogram/assets/icons/forbidden.svg | 4 -\n .../miniprogram/assets/icons/help-circle.svg | 3 -\n .../miniprogram/assets/icons/icon-ai-float.png | Bin 70 -> 0 bytes\n .../miniprogram/assets/icons/icon-ai-inline.png | Bin 70 -> 0 bytes\n .../miniprogram/assets/icons/info-circle.svg | 3 -\n .../miniprogram/assets/icons/info-error.svg | 3 -\n 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| 29 -\n db/zqyy_app/seeds/admin_web_seed.sql | 18 -\n docs/DOCUMENTATION-MAP.md | 35 +-\n docs/audit/audit_dashboard.md | 33 +-\n .../BD_Manual_20260301_cleanup_and_fixes.md | 203 -\n .../BD_Manual_assistant_accounts_master.md | 106 -\n .../BD_Manual_assistant_service_records.md | 114 -\n .../BD_Manual_biz_date_function_and_mv_rebuild.md | 186 -\n ...Manual_dim_groupbuy_package_ex_detail_fields.md | 88 -\n .../BD_Manual_dws_assistant_order_contribution.md | 266 -\n docs/database/BD_Manual_dws_goods_stock_summary.md | 80 -\n .../BD_Manual_dws_member_spending_power_index.md | 251 -\n .../BD_Manual_fix_dim_staff_ex_rankname.md | 94 -\n ...BD_Manual_fix_dws_assistant_daily_table_area.md | 132 -\n docs/database/BD_Manual_goods_stock_movements.md | 62 -\n docs/database/BD_Manual_goods_stock_summary.md | 62 -\n .../database/BD_Manual_goods_stock_warning_info.md | 102 -\n .../BD_Manual_group_buy_package_details.md | 92 -\n docs/database/BD_Manual_member_balance_changes.md | 74 -\n 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.../table_fee_transactions.json | 669540 -----------\n .../table_fee_transactions.json | 654472 -----------\n .../table_fee_transactions.json | 654472 -----------\n .../table_fee_transactions.json | 647072 -----------\n .../tenant_goods_master.json | 6274 -\n .../tenant_goods_master.json | 6274 -\n .../tenant_goods_master.json | 6274 -\n .../tenant_goods_master.json | 6274 -\n .../tenant_goods_master.json | 6274 -\n scripts/README.md | 37 -\n scripts/ops/_daily_revenue_0305.py | 4 +-\n scripts/ops/anchor_compare.py | 866 +-\n scripts/ops/daily_revenue_report.py | 4 +-\n scripts/ops/extract_kiro_session.py | 112 +-\n scripts/ops/screenshot_h5_pages.py | 11 +-\n 737 files changed, 9706 insertions(+), 16108780 deletions(-)", "high_risk_diff": "--- /dev/null\n+++ b/apps/backend/app/ai/apps/app1_chat.py\n@@ -0,0 +1 @@\n\"\"\"应用 1：通用对话（SSE 流式）。\n\n每次进入 chat 页面新建 ai_conversations 记录（不复用），\n首条消息注入页面上下文，流式返回 AI 回复。\n\napp_id = \"app1_chat\"\n\"\"\"\n\nfrom __future__ import annotations\n\nimport json\nimport logging\nfrom typing import AsyncGenerator\n\nfrom app.ai.bailian_client import BailianClient\nfrom app.ai.cache_service import AICacheService\nfrom app.ai.conversation_service import ConversationService\nfrom app.ai.schemas import SSEEvent\n\nlogger = logging.getLogger(__name__)\n\nAPP_ID = \"app1_chat\"\n\n\nasync def chat_stream(\n *,\n message: str,\n user_id: int | str,\n nickname: str,\n role: str,\n site_id: int,\n source_page: str | None = None,\n page_context: dict | None = None,\n screen_content: str | None = None,\n bailian: BailianClient,\n conv_svc: ConversationService,\n) -> AsyncGenerator[SSEEvent, None]:\n \"\"\"流式对话入口，返回 SSEEvent 异步生成器。\n\n 流程：\n 1. 创建 conversation 记录\n 2. 写入 user message\n 3. 构建 system prompt（注入页面上下文）\n 4. 调用 bailian.chat_stream 流式获取回复\n 5. 逐 chunk yield SSEEvent(type=\"chunk\")\n 6. 完成后写入 assistant message，yield SSEEvent(type=\"done\")\n 7. 异常时 yield SSEEvent(type=\"error\")\n \"\"\"\n conversation_id: int | None = None\n\n try:\n # 1. 每次新建 conversation（不复用）\n source_ctx = _build_source_context(\n source_page=source_page,\n page_context=page_context,\n screen_content=screen_content,\n )\n conversation_id = conv_svc.create_conversation(\n user_id=user_id,\n nickname=nickname,\n app_id=APP_ID,\n site_id=site_id,\n source_page=source_page,\n source_context=source_ctx,\n )\n logger.info(\n \"App1 新建对话: conversation_id=%s user_id=%s site_id=%s\",\n conversation_id, user_id, site_id,\n )\n\n # 2. 立即写入 user message\n conv_svc.add_message(\n conversation_id=conversation_id,\n role=\"user\",\n content=message,\n )\n\n # 3. 构建消息列表（system prompt + user message）\n messages = _build_messages(\n message=message,\n user_id=user_id,\n nickname=nickname,\n role=role,\n source_page=source_page,\n page_context=page_context,\n screen_content=screen_content,\n )\n\n # 4-5. 流式调用百炼，逐 chunk yield\n full_reply_parts: list[str] = []\n async for chunk in bailian.chat_stream(messages):\n full_reply_parts.append(chunk)\n yield SSEEvent(type=\"chunk\", content=chunk)\n\n # 6. 流式完成，拼接完整回复并写入 assistant message\n full_reply = \"\".join(full_reply_parts)\n # 百炼流式模式不返回 tokens_used，按字符数估算（粗略）\n estimated_tokens = len(full_reply)\n conv_svc.add_message(\n conversation_id=conversation_id,\n role=\"assistant\",\n content=full_reply,\n tokens_used=estimated_tokens,\n )\n\n yield SSEEvent(\n type=\"done\",\n conversation_id=conversation_id,\n tokens_used=estimated_tokens,\n )\n\n except Exception as e:\n logger.error(\n \"App1 对话异常: conversation_id=%s error=%s\",\n conversation_id, e,\n exc_info=True,\n )\n yield SSEEvent(type=\"error\", message=str(e))\n\n\ndef _build_messages(\n *,\n message: str,\n user_id: int | str,\n nickname: str,\n role: str,\n source_page: str | None,\n page_context: dict | None,\n screen_content: str | None,\n) -> list[dict]:\n \"\"\"构建发送给百炼的消息列表。\n\n 首条 system 消息注入页面上下文和用户信息。\n \"\"\"\n system_content = _build_system_prompt(\n user_id=user_id,\n nickname=nickname,\n role=role,\n source_page=source_page,\n page_context=page_context,\n screen_content=screen_content,\n )\n return [\n {\"role\": \"system\", \"content\": json.dumps(system_content, ensure_ascii=False)},\n {\"role\": \"user\", \"content\": message},\n ]\n\n\ndef _build_system_prompt(\n *,\n user_id: int | str,\n nickname: str,\n role: str,\n source_page: str | None,\n page_context: dict | None,\n screen_content: str | None,\n) -> dict:\n \"\"\"构建 system prompt JSON。\n\n 通过 biz_params.user_prompt_params 传入用户信息，\n 注入页面上下文供 AI 理解当前场景。\n \"\"\"\n prompt: dict = {\n \"task\": \"你是台球门店的 AI 助手，根据用户的问题和当前页面上下文提供帮助。\",\n \"biz_params\": {\n \"user_prompt_params\": {\n \"User_ID\": str(user_id),\n \"Role\": role,\n \"Nickname\": nickname,\n },\n },\n }\n\n # 注入页面上下文（首条消息）\n page_ctx = _build_page_context(\n source_page=source_page,\n page_context=page_context,\n screen_content=screen_content,\n )\n if page_ctx:\n prompt[\"page_context\"] = page_ctx\n\n return prompt\n\n\ndef _build_page_context(\n *,\n source_page: str | None,\n page_context: dict | None,\n screen_content: str | None,\n) -> dict:\n \"\"\"构建页面上下文信息。\n\n P5-A 阶段：直接透传前端传入的上下文字段。\n P5-B 阶段：各页面逐步实现文本化工具，丰富 screen_content。\n \"\"\"\n # TODO: P5-B 各页面文本化工具细化\n ctx: dict = {}\n if source_page:\n ctx[\"source_page\"] = source_page\n if page_context:\n ctx[\"page_context\"] = page_context\n if screen_content:\n ctx[\"screen_content\"] = screen_content\n return ctx\n\n\ndef _build_source_context(\n *,\n source_page: str | None,\n page_context: dict | None,\n screen_content: str | None,\n) -> dict | None:\n \"\"\"构建存入 ai_conversations.source_context 的 JSON。\"\"\"\n ctx: dict = {}\n if source_page:\n ctx[\"source_page\"] = source_page\n if page_context:\n ctx[\"page_context\"] = page_context\n if screen_content:\n ctx[\"screen_content\"] = screen_content\n return ctx if ctx else None\n\n--- /dev/null\n+++ b/apps/backend/app/ai/apps/app2_finance.py\n@@ -0,0 +1 @@\n\"\"\"应用 2：财务洞察。\n\n8 个时间维度独立调用，每次调用结果写入 ai_cache，\n同时创建 ai_conversations + ai_messages 记录。\n\n营业日分界点：每日 08:00（BUSINESS_DAY_START_HOUR 环境变量，默认 8）。\n\napp_id = \"app2_finance\"\n\"\"\"\n\nfrom __future__ import annotations\n\nimport json\nimport logging\nimport os\nfrom datetime import date, datetime, timedelta\n\nfrom app.ai.bailian_client import BailianClient\nfrom app.ai.cache_service import AICacheService\nfrom app.ai.conversation_service import ConversationService\nfrom app.ai.prompts.app2_finance_prompt import build_prompt\nfrom app.ai.schemas import CacheTypeEnum\n\nlogger = logging.getLogger(__name__)\n\nAPP_ID = \"app2_finance\"\n\n# 8 个时间维度编码\nTIME_DIMENSIONS = (\n \"this_month\",\n \"last_month\",\n \"this_week\",\n \"last_week\",\n \"last_3_months\",\n \"this_quarter\",\n \"last_quarter\",\n \"last_6_months\",\n)\n\n\ndef get_business_date() -> date:\n \"\"\"根据营业日分界点计算当前营业日。\n\n 分界点前（如 07:59）视为前一天营业日，\n 分界点及之后（如 08:00）视为当天营业日。\n \"\"\"\n hour = int(os.environ.get(\"BUSINESS_DAY_START_HOUR\", \"8\"))\n now = datetime.now()\n if now.hour < hour:\n return (now - timedelta(days=1)).date()\n return now.date()\n\n\ndef compute_time_range(dimension: str, business_date: date) -> tuple[date, date]:\n \"\"\"计算时间维度对应的日期范围 [start, end]（闭区间）。\n\n Args:\n dimension: 时间维度编码\n business_date: 当前营业日\n\n Returns:\n (start_date, end_date) 元组\n \"\"\"\n y, m, d = business_date.year, business_date.month, business_date.day\n\n if dimension == \"this_month\":\n start = date(y, m, 1)\n return start, business_date\n\n if dimension == \"last_month\":\n prev = _month_offset(y, m, -1)\n start = date(prev[0], prev[1], 1)\n end = date(y, m, 1) - timedelta(days=1)\n return start, end\n\n if dimension == \"this_week\":\n # 周一起算\n weekday = business_date.weekday() # 0=周一\n start = business_date - timedelta(days=weekday)\n return start, business_date\n\n if dimension == \"last_week\":\n weekday = business_date.weekday()\n this_monday = business_date - timedelta(days=weekday)\n last_monday = this_monday - timedelta(days=7)\n last_sunday = this_monday - timedelta(days=1)\n return last_monday, last_sunday\n\n if dimension == \"last_3_months\":\n # 当前月 - 3 ~ 当前月 - 1\n end_ym = _month_offset(y, m, -1)\n start_ym = _month_offset(y, m, -3)\n start = date(start_ym[0], start_ym[1], 1)\n # end = 上月最后一天\n end = date(y, m, 1) - timedelta(days=1)\n return start, end\n\n if dimension == \"this_quarter\":\n q_start_month = ((m - 1) // 3) * 3 + 1\n start = date(y, q_start_month, 1)\n return start, business_date\n\n if dimension == \"last_quarter\":\n q_start_month = ((m - 1) // 3) * 3 + 1\n # 上季度结束 = 本季度第一天 - 1\n this_q_start = date(y, q_start_month, 1)\n end = this_q_start - timedelta(days=1)\n # 上季度开始\n ly, lm = end.year, end.month\n lq_start_month = ((lm - 1) // 3) * 3 + 1\n start = date(ly, lq_start_month, 1)\n return start, end\n\n if dimension == \"last_6_months\":\n # 当前月 - 6 ~ 当前月 - 1\n end_ym = _month_offset(y, m, -1)\n start_ym = _month_offset(y, m, -6)\n start = date(start_ym[0], start_ym[1], 1)\n end = date(y, m, 1) - timedelta(days=1)\n return start, end\n\n raise ValueError(f\"未知时间维度: {dimension}\")\n\n\nasync def run(\n context: dict,\n bailian: BailianClient,\n cache_svc: AICacheService,\n conv_svc: ConversationService,\n) -> dict:\n \"\"\"执行 App2 财务洞察调用。\n\n Args:\n context: 包含 site_id, time_dimension, user_id(默认'system'), nickname(默认'')\n bailian: 百炼客户端\n cache_svc: 缓存服务\n conv_svc: 对话服务\n\n Returns:\n 百炼返回的结构化 JSON（insights 数组）\n \"\"\"\n site_id = context[\"site_id\"]\n time_dimension = context[\"time_dimension\"]\n user_id = context.get(\"user_id\", \"system\")\n nickname = context.get(\"nickname\", \"\")\n\n # 构建 Prompt\n prompt_context = {\n \"site_id\": site_id,\n \"time_dimension\": time_dimension,\n \"current_data\": context.get(\"current_data\", {}),\n \"previous_data\": context.get(\"previous_data\", {}),\n }\n messages = build_prompt(prompt_context)\n\n # 创建对话记录\n conversation_id = conv_svc.create_conversation(\n user_id=user_id,\n nickname=nickname,\n app_id=APP_ID,\n site_id=site_id,\n source_context={\"time_dimension\": time_dimension},\n )\n\n # 写入 system prompt 消息\n conv_svc.add_message(\n conversation_id=conversation_id,\n role=\"system\",\n content=messages[0][\"content\"],\n )\n # 写入 user 消息\n conv_svc.add_message(\n conversation_id=conversation_id,\n role=\"user\",\n content=messages[1][\"content\"],\n )\n\n # 调用百炼 API\n result, tokens_used = await bailian.chat_json(messages)\n\n # 写入 assistant 消息\n conv_svc.add_message(\n conversation_id=conversation_id,\n role=\"assistant\",\n content=json.dumps(result, ensure_ascii=False),\n tokens_used=tokens_used,\n )\n\n # 写入缓存\n cache_svc.write_cache(\n cache_type=CacheTypeEnum.APP2_FINANCE.value,\n site_id=site_id,\n target_id=time_dimension,\n result_json=result,\n triggered_by=f\"user:{user_id}\",\n )\n\n logger.info(\n \"App2 财务洞察完成: site_id=%s dimension=%s conversation_id=%s tokens=%d\",\n site_id, time_dimension, conversation_id, tokens_used,\n )\n\n return result\n\n\ndef _month_offset(year: int, month: int, offset: int) -> tuple[int, int]:\n \"\"\"计算月份偏移，返回 (year, month)。\"\"\"\n # 转为 0-based 计算\n total = (year * 12 + (month - 1)) + offset\n return total // 12, total % 12 + 1\n\n--- /dev/null\n+++ b/apps/backend/app/ai/apps/app3_clue.py\n@@ -0,0 +1 @@\n\"\"\"应用 3：客户数据维客线索分析（骨架）。\n\n客户新增消费时自动触发，通过 AI 分析客户数据提取维客线索。\n线索 category 限定 3 个枚举值：客户基础、消费习惯、玩法偏好。\n线索提供者统一标记为\"系统\"。\n\n使用 items_sum 口径（= table_charge_money + goods_money\n+ assistant_pd_money + assistant_cx_money + electricity_money），\n禁止使用 consume_money。\n\napp_id = \"app3_clue\"\n\"\"\"\n\nfrom __future__ import annotations\n\nimport json\nimport logging\n\nfrom app.ai.bailian_client import BailianClient\nfrom app.ai.cache_service import AICacheService\nfrom app.ai.conversation_service import ConversationService\nfrom app.ai.schemas import CacheTypeEnum\n\nlogger = logging.getLogger(__name__)\n\nAPP_ID = \"app3_clue\"\n\n\ndef build_prompt(\n context: dict,\n cache_svc: AICacheService | None = None,\n) -> list[dict]:\n \"\"\"构建 Prompt 消息列表。\n\n P5-A 阶段：返回占位 Prompt，标注待细化字段。\n P5-B 阶段（P9-T1）：补充 consumption_records 等完整数据。\n\n Args:\n context: 包含 site_id, member_id, nickname 等\n cache_svc: 缓存服务，用于获取 reference 历史数据\n\n Returns:\n 消息列表 [{\"role\": \"system\", \"content\": ...}, {\"role\": \"user\", ...}]\n \"\"\"\n site_id = context[\"site_id\"]\n member_id = context[\"member_id\"]\n\n # 构建 reference：App6 线索 + 最近 2 套 App8 历史（附 generated_at）\n reference = _build_reference(site_id, member_id, cache_svc)\n\n system_content = {\n \"task\": \"分析客户消费数据，提取维客线索。\",\n \"app_id\": APP_ID,\n \"rules\": {\n \"category_enum\": [\"客户基础\", \"消费习惯\", \"玩法偏好\"],\n \"providers\": \"系统\",\n \"amount_caliber\": \"items_sum = table_charge_money + goods_money + assistant_pd_money + assistant_cx_money + electricity_money\",\n \"禁止使用\": \"consume_money\",\n },\n \"output_format\": {\n \"clues\": [\n {\n \"category\": \"枚举值（客户基础/消费习惯/玩法偏好）\",\n \"summary\": \"一句话摘要\",\n \"detail\": \"详细说明\",\n \"emoji\": \"表情符号\",\n }\n ]\n },\n # TODO: P9-T1 细化 - consumption_records 等客户消费数据\n \"data\": {\n \"consumption_records\": \"待 P9-T1 补充\",\n \"member_info\": \"待 P9-T1 补充\",\n },\n \"reference\": reference,\n }\n\n user_content = (\n f\"请分析会员 {member_id} 的消费数据，提取维客线索。\"\n \"每条线索包含 category、summary、detail、emoji 四个字段。\"\n \"category 必须是：客户基础、消费习惯、玩法偏好 之一。\"\n )\n\n return [\n {\"role\": \"system\", \"content\": json.dumps(system_content, ensure_ascii=False)},\n {\"role\": \"user\", \"content\": user_content},\n ]\n\n\ndef _build_reference(\n site_id: int,\n member_id: int,\n cache_svc: AICacheService | None,\n) -> dict:\n \"\"\"构建 Prompt reference 字段。\n\n 包含：\n - App6 备注分析线索（最新一条，如有）\n - 最近 2 套 App8 维客线索整理历史（附 generated_at）\n\n 缓存不存在时返回空对象 {}。\n \"\"\"\n if cache_svc is None:\n return {}\n\n reference: dict = {}\n target_id = str(member_id)\n\n # App6 备注分析线索\n app6_latest = cache_svc.get_latest(\n CacheTypeEnum.APP6_NOTE_ANALYSIS.value, site_id, target_id,\n )\n if app6_latest:\n reference[\"app6_note_clues\"] = {\n \"result_json\": app6_latest.get(\"result_json\"),\n \"generated_at\": app6_latest.get(\"created_at\"),\n }\n\n # 最近 2 套 App8 历史\n app8_history = cache_svc.get_history(\n CacheTypeEnum.APP8_CLUE_CONSOLIDATED.value, site_id, target_id, limit=2,\n )\n if app8_history:\n reference[\"app8_history\"] = [\n {\n \"result_json\": h.get(\"result_json\"),\n \"generated_at\": h.get(\"created_at\"),\n }\n for h in app8_history\n ]\n\n return reference\n\n\nasync def run(\n context: dict,\n bailian: BailianClient,\n cache_svc: AICacheService,\n conv_svc: ConversationService,\n) -> dict:\n \"\"\"执行 App3 客户数据维客线索分析。\n\n 流程：\n 1. build_prompt 构建 Prompt\n 2. bailian.chat_json 调用百炼\n 3. 写入 conversation + messages\n 4. 写入 ai_cache\n 5. 返回结果\n\n Args:\n context: site_id, member_id, user_id(默认'system'), nickname(默认'')\n bailian: 百炼客户端\n cache_svc: 缓存服务\n conv_svc: 对话服务\n\n Returns:\n 百炼返回的结构化 JSON（clues 数组）\n \"\"\"\n site_id = context[\"site_id\"]\n member_id = context[\"member_id\"]\n user_id = context.get(\"user_id\", \"system\")\n nickname = context.get(\"nickname\", \"\")\n\n # 1. 构建 Prompt\n messages = build_prompt(context, cache_svc)\n\n # 2. 创建对话记录\n conversation_id = conv_svc.create_conversation(\n user_id=user_id,\n nickname=nickname,\n app_id=APP_ID,\n site_id=site_id,\n source_context={\"member_id\": member_id},\n )\n\n # 写入 system + user 消息\n conv_svc.add_message(\n conversation_id=conversation_id,\n role=\"system\",\n content=messages[0][\"content\"],\n )\n conv_svc.add_message(\n conversation_id=conversation_id,\n role=\"user\",\n content=messages[1][\"content\"],\n )\n\n # 3. 调用百炼 API\n result, tokens_used = await bailian.chat_json(messages)\n\n # 4. 写入 assistant 消息\n conv_svc.add_message(\n conversation_id=conversation_id,\n role=\"assistant\",\n content=json.dumps(result, ensure_ascii=False),\n tokens_used=tokens_used,\n )\n\n # 5. 写入缓存\n cache_svc.write_cache(\n cache_type=CacheTypeEnum.APP3_CLUE.value,\n site_id=site_id,\n target_id=str(member_id),\n result_json=result,\n triggered_by=f\"user:{user_id}\",\n )\n\n logger.info(\n \"App3 线索分析完成: site_id=%s member_id=%s conversation_id=%s tokens=%d\",\n site_id, member_id, conversation_id, tokens_used,\n )\n\n return result\n\n--- /dev/null\n+++ b/apps/backend/app/ai/apps/app4_analysis.py\n@@ -0,0 +1 @@\n\"\"\"应用 4：关系分析/任务建议（骨架）。\n\n助教参与新结算或被分配召回任务时自动触发，\n生成关系分析和任务建议。\n\nPrompt reference 包含 App8 最新 + 最近 2 套历史（附 generated_at）。\n缓存不存在时 reference 传空对象，标注\"暂无历史线索\"。\n\napp_id = \"app4_analysis\"\n\"\"\"\n\nfrom __future__ import annotations\n\nimport json\nimport logging\n\nfrom app.ai.bailian_client import BailianClient\nfrom app.ai.cache_service import AICacheService\nfrom app.ai.conversation_service import ConversationService\nfrom app.ai.schemas import CacheTypeEnum\n\nlogger = logging.getLogger(__name__)\n\nAPP_ID = \"app4_analysis\"\n\n\ndef build_prompt(\n context: dict,\n cache_svc: AICacheService | None = None,\n) -> list[dict]:\n \"\"\"构建 Prompt 消息列表。\n\n P5-A 阶段：返回占位 Prompt，标注待细化字段。\n P5-B 阶段（P6-T4）：补充 service_history、assistant_info 等完整数据。\n\n Args:\n context: 包含 site_id, assistant_id, member_id\n cache_svc: 缓存服务，用于获取 reference 历史数据\n\n Returns:\n 消息列表\n \"\"\"\n site_id = context[\"site_id\"]\n assistant_id = context[\"assistant_id\"]\n member_id = context[\"member_id\"]\n\n # 构建 reference：App8 最新 + 最近 2 套历史\n reference = _build_reference(site_id, member_id, cache_svc)\n\n system_content = {\n \"task\": \"分析助教与客户的关系，生成任务建议。\",\n \"app_id\": APP_ID,\n \"output_format\": {\n \"task_description\": \"任务描述文本\",\n \"action_suggestions\": [\"建议1\", \"建议2\"],\n \"one_line_summary\": \"一句话总结\",\n },\n # TODO: P6-T4 细化 - service_history、assistant_info\n \"data\": {\n \"service_history\": \"待 P6-T4 补充\",\n \"assistant_info\": \"待 P6-T4 补充\",\n },\n \"reference\": reference,\n }\n\n # 缓存不存在时在 user prompt 中标注\n no_history_hint = \"\"\n if not reference:\n no_history_hint = \"（暂无历史线索，请基于现有信息分析）\"\n\n user_content = (\n f\"请分析助教 {assistant_id} 与会员 {member_id} 的关系，\"\n f\"生成任务建议。{no_history_hint}\"\n \"返回 task_description、action_suggestions、one_line_summary 三个字段。\"\n )\n\n return [\n {\"role\": \"system\", \"content\": json.dumps(system_content, ensure_ascii=False)},\n {\"role\": \"user\", \"content\": user_content},\n ]\n\n\ndef _build_reference(\n site_id: int,\n member_id: int,\n cache_svc: AICacheService | None,\n) -> dict:\n \"\"\"构建 Prompt reference 字段。\n\n 包含：\n - App8 最新维客线索（如有）\n - 最近 2 套 App8 历史（附 generated_at）\n\n 缓存不存在时返回空对象 {}。\n \"\"\"\n if cache_svc is None:\n return {}\n\n reference: dict = {}\n target_id = str(member_id)\n\n # App8 最新\n app8_latest = cache_svc.get_latest(\n CacheTypeEnum.APP8_CLUE_CONSOLIDATED.value, site_id, target_id,\n )\n if app8_latest:\n reference[\"app8_latest\"] = {\n \"result_json\": app8_latest.get(\"result_json\"),\n \"generated_at\": app8_latest.get(\"created_at\"),\n }\n\n # 最近 2 套 App8 历史\n app8_history = cache_svc.get_history(\n CacheTypeEnum.APP8_CLUE_CONSOLIDATED.value, site_id, target_id, limit=2,\n )\n if app8_history:\n reference[\"app8_history\"] = [\n {\n \"result_json\": h.get(\"result_json\"),\n \"generated_at\": h.get(\"created_at\"),\n }\n for h in app8_history\n ]\n\n return reference\n\n\nasync def run(\n context: dict,\n bailian: BailianClient,\n cache_svc: AICacheService,\n conv_svc: ConversationService,\n) -> dict:\n \"\"\"执行 App4 关系分析。\n\n Args:\n context: site_id, assistant_id, member_id\n bailian: 百炼客户端\n cache_svc: 缓存服务\n conv_svc: 对话服务\n\n Returns:\n 百炼返回的结构化 JSON（task_description, action_suggestions, one_line_summary）\n \"\"\"\n site_id = context[\"site_id\"]\n assistant_id = context[\"assistant_id\"]\n member_id = context[\"member_id\"]\n user_id = context.get(\"user_id\", \"system\")\n nickname = context.get(\"nickname\", \"\")\n\n # 1. 构建 Prompt\n messages = build_prompt(context, cache_svc)\n\n # 2. 创建对话记录\n conversation_id = conv_svc.create_conversation(\n user_id=user_id,\n nickname=nickname,\n app_id=APP_ID,\n site_id=site_id,\n source_context={\"assistant_id\": assistant_id, \"member_id\": member_id},\n )\n\n # 写入 system + user 消息\n conv_svc.add_message(\n conversation_id=conversation_id,\n role=\"system\",\n content=messages[0][\"content\"],\n )\n conv_svc.add_message(\n conversation_id=conversation_id,\n role=\"user\",\n content=messages[1][\"content\"],\n )\n\n # 3. 调用百炼 API\n result, tokens_used = await bailian.chat_json(messages)\n\n # 4. 写入 assistant 消息\n conv_svc.add_message(\n conversation_id=conversation_id,\n role=\"assistant\",\n content=json.dumps(result, ensure_ascii=False),\n tokens_used=tokens_used,\n )\n\n # 5. 写入缓存（target_id = {assistant_id}_{member_id}）\n cache_svc.write_cache(\n cache_type=CacheTypeEnum.APP4_ANALYSIS.value,\n site_id=site_id,\n target_id=f\"{assistant_id}_{member_id}\",\n result_json=result,\n triggered_by=f\"user:{user_id}\",\n )\n\n logger.info(\n \"App4 关系分析完成: site_id=%s assistant=%s member=%s conversation_id=%s tokens=%d\",\n site_id, assistant_id, member_id, conversation_id, tokens_used,\n )\n\n return result\n\n--- /dev/null\n+++ b/apps/backend/app/ai/apps/app5_tactics.py\n@@ -0,0 +1 @@\n\"\"\"应用 5：话术参考（骨架）。\n\nApp4 完成后自动联动触发，接收 App4 完整返回结果\n作为 Prompt 中的 task_suggestion 字段。\n\nPrompt reference 包含最近 2 套 App8 历史（附 generated_at）。\n\napp_id = \"app5_tactics\"\n\"\"\"\n\nfrom __future__ import annotations\n\nimport json\nimport logging\n\nfrom app.ai.bailian_client import BailianClient\nfrom app.ai.cache_service import AICacheService\nfrom app.ai.conversation_service import ConversationService\nfrom app.ai.schemas import CacheTypeEnum\n\nlogger = logging.getLogger(__name__)\n\nAPP_ID = \"app5_tactics\"\n\n\ndef build_prompt(\n context: dict,\n cache_svc: AICacheService | None = None,\n) -> list[dict]:\n \"\"\"构建 Prompt 消息列表。\n\n P5-A 阶段：返回占位 Prompt，标注待细化字段。\n P5-B 阶段（P6-T4）：补充 service_history、assistant_info（随 App4 同步）。\n\n Args:\n context: 包含 site_id, assistant_id, member_id, app4_result(dict)\n cache_svc: 缓存服务，用于获取 reference 历史数据\n\n Returns:\n 消息列表\n \"\"\"\n site_id = context[\"site_id\"]\n assistant_id = context[\"assistant_id\"]\n member_id = context[\"member_id\"]\n app4_result = context.get(\"app4_result\", {})\n\n # 构建 reference：最近 2 套 App8 历史\n reference = _build_reference(site_id, member_id, cache_svc)\n\n system_content = {\n \"task\": \"基于关系分析和任务建议，生成沟通话术参考。\",\n \"app_id\": APP_ID,\n \"task_suggestion\": app4_result,\n \"output_format\": {\n \"tactics\": [\n {\"scenario\": \"场景描述\", \"script\": \"话术内容\"}\n ]\n },\n # TODO: P6-T4 细化 - service_history、assistant_info（随 App4 同步）\n \"data\": {\n \"service_history\": \"待 P6-T4 补充\",\n \"assistant_info\": \"待 P6-T4 补充\",\n },\n \"reference\": reference,\n }\n\n user_content = (\n f\"请为助教 {assistant_id} 生成与会员 {member_id} 沟通的话术参考。\"\n \"返回 tactics 数组，每条包含 scenario 和 script 字段。\"\n )\n\n return [\n {\"role\": \"system\", \"content\": json.dumps(system_content, ensure_ascii=False)},\n {\"role\": \"user\", \"content\": user_content},\n ]\n\n\ndef _build_reference(\n site_id: int,\n member_id: int,\n cache_svc: AICacheService | None,\n) -> dict:\n \"\"\"构建 Prompt reference 字段。\n\n 包含最近 2 套 App8 历史（附 generated_at）。\n 缓存不存在时返回空对象 {}。\n \"\"\"\n if cache_svc is None:\n return {}\n\n reference: dict = {}\n target_id = str(member_id)\n\n # 最近 2 套 App8 历史\n app8_history = cache_svc.get_history(\n CacheTypeEnum.APP8_CLUE_CONSOLIDATED.value, site_id, target_id, limit=2,\n )\n if app8_history:\n reference[\"app8_history\"] = [\n {\n \"result_json\": h.get(\"result_json\"),\n \"generated_at\": h.get(\"created_at\"),\n }\n for h in app8_history\n ]\n\n return reference\n\n\nasync def run(\n context: dict,\n bailian: BailianClient,\n cache_svc: AICacheService,\n conv_svc: ConversationService,\n) -> dict:\n \"\"\"执行 App5 话术参考。\n\n Args:\n context: site_id, assistant_id, member_id, app4_result(dict)\n bailian: 百炼客户端\n cache_svc: 缓存服务\n conv_svc: 对话服务\n\n Returns:\n 百炼返回的结构化 JSON（tactics 数组）\n \"\"\"\n site_id = context[\"site_id\"]\n assistant_id = context[\"assistant_id\"]\n member_id = context[\"member_id\"]\n user_id = context.get(\"user_id\", \"system\")\n nickname = context.get(\"nickname\", \"\")\n\n # 1. 构建 Prompt\n messages = build_prompt(context, cache_svc)\n\n # 2. 创建对话记录\n conversation_id = conv_svc.create_conversation(\n user_id=user_id,\n nickname=nickname,\n app_id=APP_ID,\n site_id=site_id,\n source_context={\"assistant_id\": assistant_id, \"member_id\": member_id},\n )\n\n # 写入 system + user 消息\n conv_svc.add_message(\n conversation_id=conversation_id,\n role=\"system\",\n content=messages[0][\"content\"],\n )\n conv_svc.add_message(\n conversation_id=conversation_id,\n role=\"user\",\n content=messages[1][\"content\"],\n )\n\n # 3. 调用百炼 API\n result, tokens_used = await bailian.chat_json(messages)\n\n # 4. 写入 assistant 消息\n conv_svc.add_message(\n conversation_id=conversation_id,\n role=\"assistant\",\n content=json.dumps(result, ensure_ascii=False),\n tokens_used=tokens_used,\n )\n\n # 5. 写入缓存（target_id = {assistant_id}_{member_id}）\n cache_svc.write_cache(\n cache_type=CacheTypeEnum.APP5_TACTICS.value,\n site_id=site_id,\n target_id=f\"{assistant_id}_{member_id}\",\n result_json=result,\n triggered_by=f\"user:{user_id}\",\n )\n\n logger.info(\n \"App5 话术参考完成: site_id=%s assistant=%s member=%s conversation_id=%s tokens=%d\",\n site_id, assistant_id, member_id, conversation_id, tokens_used,\n )\n\n return result\n\n--- /dev/null\n+++ b/apps/backend/app/ai/apps/app6_note.py\n@@ -0,0 +1 @@\n\"\"\"应用 6：备注分析（骨架）。\n\n助教提交备注后自动触发，通过 AI 分析备注内容，\n提取维客线索并评分。\n\n返回 score（1-10）+ clues 数组。\n评分规则：6 分为标准分，重复/低价值/时效性低酌情扣分，高价值信息酌情加分。\n线索 category 限定 6 个枚举值。\n线索提供者标记为当前备注提供人（context.noted_by_name）。\n\napp_id = \"app6_note\"\n\"\"\"\n\nfrom __future__ import annotations\n\nimport json\nimport logging\n\nfrom app.ai.bailian_client import BailianClient\nfrom app.ai.cache_service import AICacheService\nfrom app.ai.conversation_service import ConversationService\nfrom app.ai.schemas import CacheTypeEnum\n\nlogger = logging.getLogger(__name__)\n\nAPP_ID = \"app6_note\"\n\n\ndef build_prompt(\n context: dict,\n cache_svc: AICacheService | None = None,\n) -> list[dict]:\n \"\"\"构建 Prompt 消息列表。\n\n P5-A 阶段：返回占位 Prompt，标注待细化字段。\n P5-B 阶段（P9-T1）：补充 consumption_data 等完整数据。\n\n Args:\n context: 包含 site_id, member_id, note_content, noted_by_name\n cache_svc: 缓存服务，用于获取 reference 历史数据\n\n Returns:\n 消息列表\n \"\"\"\n site_id = context[\"site_id\"]\n member_id = context[\"member_id\"]\n note_content = context.get(\"note_content\", \"\")\n noted_by_name = context.get(\"noted_by_name\", \"\")\n\n # 构建 reference：App3 线索 + 最近 2 套 App8 历史\n reference = _build_reference(site_id, member_id, cache_svc)\n\n system_content = {\n \"task\": \"分析备注内容，提取维客线索并评分。\",\n \"app_id\": APP_ID,\n \"rules\": {\n \"category_enum\": [\n \"客户基础\", \"消费习惯\", \"玩法偏好\",\n \"促销偏好\", \"社交关系\", \"重要反馈\",\n ],\n \"providers\": noted_by_name,\n \"scoring\": \"6 分为标准分，重复/低价值/时效性低酌情扣分，高价值信息酌情加分\",\n \"score_range\": \"1-10\",\n },\n \"output_format\": {\n \"score\": \"1-10 整数\",\n \"clues\": [\n {\n \"category\": \"枚举值（6 选 1）\",\n \"summary\": \"一句话摘要\",\n \"detail\": \"详细说明\",\n \"emoji\": \"表情符号\",\n }\n ],\n },\n \"note_content\": note_content,\n \"noted_by_name\": noted_by_name,\n # TODO: P9-T1 细化 - consumption_data 等客户消费数据\n \"data\": {\n \"consumption_data\": \"待 P9-T1 补充\",\n },\n \"reference\": reference,\n }\n\n user_content = (\n f\"请分析以下备注内容，提取维客线索并评分。\\n\"\n f\"备注提供人：{noted_by_name}\\n\"\n f\"备注内容：{note_content}\\n\"\n \"返回 score（1-10 整数）和 clues 数组。\"\n \"category 必须是：客户基础、消费习惯、玩法偏好、促销偏好、社交关系、重要反馈 之一。\"\n )\n\n return [\n {\"role\": \"system\", \"content\": json.dumps(system_content, ensure_ascii=False)},\n {\"role\": \"user\", \"content\": user_content},\n ]\n\n\ndef _build_reference(\n site_id: int,\n member_id: int,\n cache_svc: AICacheService | None,\n) -> dict:\n \"\"\"构建 Prompt reference 字段。\n\n 包含：\n - App3 客户数据线索（最新一条，如有）\n - 最近 2 套 App8 维客线索整理历史（附 generated_at）\n\n 缓存不存在时返回空对象 {}。\n \"\"\"\n if cache_svc is None:\n return {}\n\n reference: dict = {}\n target_id = str(member_id)\n\n # App3 客户数据线索\n app3_latest = cache_svc.get_latest(\n CacheTypeEnum.APP3_CLUE.value, site_id, target_id,\n )\n if app3_latest:\n reference[\"app3_clues\"] = {\n \"result_json\": app3_latest.get(\"result_json\"),\n \"generated_at\": app3_latest.get(\"created_at\"),\n }\n\n # 最近 2 套 App8 历史\n app8_history = cache_svc.get_history(\n CacheTypeEnum.APP8_CLUE_CONSOLIDATED.value, site_id, target_id, limit=2,\n )\n if app8_history:\n reference[\"app8_history\"] = [\n {\n \"result_json\": h.get(\"result_json\"),\n \"generated_at\": h.get(\"created_at\"),\n }\n for h in app8_history\n ]\n\n return reference\n\n\nasync def run(\n context: dict,\n bailian: BailianClient,\n cache_svc: AICacheService,\n conv_svc: ConversationService,\n) -> dict:\n \"\"\"执行 App6 备注分析。\n\n Args:\n context: site_id, member_id, note_content, noted_by_name\n bailian: 百炼客户端\n cache_svc: 缓存服务\n conv_svc: 对话服务\n\n Returns:\n 百炼返回的结构化 JSON（score + clues 数组）\n \"\"\"\n site_id = context[\"site_id\"]\n member_id = context[\"member_id\"]\n user_id = context.get(\"user_id\", \"system\")\n nickname = context.get(\"nickname\", \"\")\n\n # 1. 构建 Prompt\n messages = build_prompt(context, cache_svc)\n\n # 2. 创建对话记录\n conversation_id = conv_svc.create_conversation(\n user_id=user_id,\n nickname=nickname,\n app_id=APP_ID,\n site_id=site_id,\n source_context={\"member_id\": member_id},\n )\n\n # 写入 system + user 消息\n conv_svc.add_message(\n conversation_id=conversation_id,\n role=\"system\",\n content=messages[0][\"content\"],\n )\n conv_svc.add_message(\n conversation_id=conversation_id,\n role=\"user\",\n content=messages[1][\"content\"],\n )\n\n # 3. 调用百炼 API\n result, tokens_used = await bailian.chat_json(messages)\n\n # 4. 写入 assistant 消息\n conv_svc.add_message(\n conversation_id=conversation_id,\n role=\"assistant\",\n content=json.dumps(result, ensure_ascii=False),\n tokens_used=tokens_used,\n )\n\n # 5. 写入缓存（score 存入 ai_cache.score）\n score = result.get(\"score\")\n cache_svc.write_cache(\n cache_type=CacheTypeEnum.APP6_NOTE_ANALYSIS.value,\n site_id=site_id,\n target_id=str(member_id),\n result_json=result,\n triggered_by=f\"user:{user_id}\",\n score=score,\n )\n\n logger.info(\n \"App6 备注分析完成: site_id=%s member_id=%s score=%s conversation_id=%s tokens=%d\",\n site_id, member_id, score, conversation_id, tokens_used,\n )\n\n return result\n\n\n[TRUNCATED: diff exceeds 30KB]", "latest_prompt_log": "- [P20260315-095422] 2026-03-15 09:54:22 +0800\n - summary: CONTEXT TRANSFER: We are continuing a conversation that had gotten too long. Here is a summary: --- ## TASK 1: Consolida…\n - prompt:\n```text\nCONTEXT TRANSFER: We are continuing a conversation that had gotten too long. Here is a summary:\n\n---\n## TASK 1: Consolidate DDL files from database to `docs/database/ddl/`\n\n**STATUS**: done\n\n**USER QUERIES**: 1 (\"依据现在数据库现状，整理所有DDL文件\")\n\n**DETAILS**:\n- Ran `python scripts/ops/gen_consolidated_ddl.py` from `C:\\NeoZQYY` to export fresh DDL from test databases (`test_etl_feiqiu` and `test_zqyy_app`)\n- Generated 10 DDL files in `docs/database/ddl/`, merging all migrations from 2026-02-27 through 2026-03-09\n- Key changes reflected: dwd 44→42 tables (trash_event cleanup), dws 34→36 tables (project tags), biz 4→7 tables (AI tables), tenant_id int→bigint, DWS numeric precision expansion, cfg_area_category table-level mapping, card pay field unification\n- Verified all key changes with grep searches\n- Updated `db/README.md` and `docs/database/README.md` with new statistics\n\n**FILEPATHS**: `docs/database/ddl/`, `scripts/ops/gen_consolidated_ddl.py`, `db/README.md`, `docs/database/README.md`\n\n## TASK 2: Merge seed data into DDL files and delete migration/seed SQL files\n\n**STATUS**: done\n\n**USER QUERIES**: 2 (\"把已经纳入的sql文件删除吧\"), 3 (\"种子数据也合并到相应的sql文件。4 个 FDW 配置脚本保留\")\n\n**DETAILS**:\n- Merged 7 seed data files into corresponding DDL files:\n - `seed_ods_tasks.sql` + `seed_scheduler_tasks.sql` → `etl_feiqiu__meta.sql`\n - `seed_dws_config.sql` + `seed_index_parameters.sql` → `etl_feiqiu__dws.sql`\n - `admin_web_seed.sql` → `zqyy_app__public.sql`\n - `p3_seed_roles_permissions.sql` → `zqyy_app__auth.sql`\n - `p4_seed_trigger_jobs.sql` → `zqyy_app__biz.sql`\n- Deleted all 22 etl_feiqiu migration scripts + 4 seed files\n- Deleted all 15 zqyy_app migration scripts + 1 seed file\n- Preserved 4 FDW config scripts in `db/fdw/` and all `.gitkeep` files\n- Updated `db/README.md` directory structure and migration management section\n- Updated `docs/database/README.md` related resources section\n\n**FILEPATHS**: `docs/database/ddl/etl_feiqiu__meta.sql`, `docs/database/ddl/etl_feiqiu__dws.sql`, `docs/database/ddl/zqyy_app__public.sql`, `docs/database/ddl/zqyy_app__auth.sql`, `docs/database/ddl/zqyy_app__biz.sql`, `db/README.md`, `docs/database/README.md`\n\n## TASK 3: Analyze relationship between `docs/database/` and `apps/etl/connectors/feiqiu/docs/database/` and determine if they can be merged\n\n**STATUS**: in-progress\n\n**USER QUERIES**: 4 (\"docs\\database 下的文档和apps\\etl\\connectors\\feiqiu\\docs\\database下的文档 什么关系？能合并么？\")\n\n**DETAILS**:\n- Analysis completed via context-gatherer subagent. Key findings:\n - `docs/database/`: Project-level — DDL baseline + ODS→DWD cross-layer mappings + migration change records + business DB docs (auth/biz/public)\n - `apps/etl/.../docs/database/`: ETL module-specific — per-table field docs organized by schema layer (ODS 24 + DWD 41 + DWS 31 + ETL_Admin 3), API→OD\n[TRUNCATED]" }