1

apps/backend/app/ai/apps/app1_chat.py

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+"""应用 1：通用对话（SSE 流式）。
+
+每次进入 chat 页面新建 ai_conversations 记录（不复用），
+首条消息注入页面上下文，流式返回 AI 回复。
+
+app_id = "app1_chat"
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from typing import AsyncGenerator
+
+from app.ai.bailian_client import BailianClient
+from app.ai.cache_service import AICacheService
+from app.ai.conversation_service import ConversationService
+from app.ai.schemas import SSEEvent
+
+logger = logging.getLogger(__name__)
+
+APP_ID = "app1_chat"
+
+
+async def chat_stream(
+    *,
+    message: str,
+    user_id: int | str,
+    nickname: str,
+    role: str,
+    site_id: int,
+    source_page: str | None = None,
+    page_context: dict | None = None,
+    screen_content: str | None = None,
+    bailian: BailianClient,
+    conv_svc: ConversationService,
+) -> AsyncGenerator[SSEEvent, None]:
+    """流式对话入口，返回 SSEEvent 异步生成器。
+
+    流程：
+    1. 创建 conversation 记录
+    2. 写入 user message
+    3. 构建 system prompt（注入页面上下文）
+    4. 调用 bailian.chat_stream 流式获取回复
+    5. 逐 chunk yield SSEEvent(type="chunk")
+    6. 完成后写入 assistant message，yield SSEEvent(type="done")
+    7. 异常时 yield SSEEvent(type="error")
+    """
+    conversation_id: int | None = None
+
+    try:
+        # 1. 每次新建 conversation（不复用）
+        source_ctx = _build_source_context(
+            source_page=source_page,
+            page_context=page_context,
+            screen_content=screen_content,
+        )
+        conversation_id = conv_svc.create_conversation(
+            user_id=user_id,
+            nickname=nickname,
+            app_id=APP_ID,
+            site_id=site_id,
+            source_page=source_page,
+            source_context=source_ctx,
+        )
+        logger.info(
+            "App1 新建对话: conversation_id=%s user_id=%s site_id=%s",
+            conversation_id, user_id, site_id,
+        )
+
+        # 2. 立即写入 user message
+        conv_svc.add_message(
+            conversation_id=conversation_id,
+            role="user",
+            content=message,
+        )
+
+        # 3. 构建消息列表（system prompt + user message）
+        messages = _build_messages(
+            message=message,
+            user_id=user_id,
+            nickname=nickname,
+            role=role,
+            source_page=source_page,
+            page_context=page_context,
+            screen_content=screen_content,
+        )
+
+        # 4-5. 流式调用百炼，逐 chunk yield
+        full_reply_parts: list[str] = []
+        async for chunk in bailian.chat_stream(messages):
+            full_reply_parts.append(chunk)
+            yield SSEEvent(type="chunk", content=chunk)
+
+        # 6. 流式完成，拼接完整回复并写入 assistant message
+        full_reply = "".join(full_reply_parts)
+        # 百炼流式模式不返回 tokens_used，按字符数估算（粗略）
+        estimated_tokens = len(full_reply)
+        conv_svc.add_message(
+            conversation_id=conversation_id,
+            role="assistant",
+            content=full_reply,
+            tokens_used=estimated_tokens,
+        )
+
+        yield SSEEvent(
+            type="done",
+            conversation_id=conversation_id,
+            tokens_used=estimated_tokens,
+        )
+
+    except Exception as e:
+        logger.error(
+            "App1 对话异常: conversation_id=%s error=%s",
+            conversation_id, e,
+            exc_info=True,
+        )
+        yield SSEEvent(type="error", message=str(e))
+
+
+def _build_messages(
+    *,
+    message: str,
+    user_id: int | str,
+    nickname: str,
+    role: str,
+    source_page: str | None,
+    page_context: dict | None,
+    screen_content: str | None,
+) -> list[dict]:
+    """构建发送给百炼的消息列表。
+
+    首条 system 消息注入页面上下文和用户信息。
+    """
+    system_content = _build_system_prompt(
+        user_id=user_id,
+        nickname=nickname,
+        role=role,
+        source_page=source_page,
+        page_context=page_context,
+        screen_content=screen_content,
+    )
+    return [
+        {"role": "system", "content": json.dumps(system_content, ensure_ascii=False)},
+        {"role": "user", "content": message},
+    ]
+
+
+def _build_system_prompt(
+    *,
+    user_id: int | str,
+    nickname: str,
+    role: str,
+    source_page: str | None,
+    page_context: dict | None,
+    screen_content: str | None,
+) -> dict:
+    """构建 system prompt JSON。
+
+    通过 biz_params.user_prompt_params 传入用户信息，
+    注入页面上下文供 AI 理解当前场景。
+    """
+    prompt: dict = {
+        "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。",
+        "biz_params": {
+            "user_prompt_params": {
+                "User_ID": str(user_id),
+                "Role": role,
+                "Nickname": nickname,
+            },
+        },
+    }
+
+    # 注入页面上下文（首条消息）
+    page_ctx = _build_page_context(
+        source_page=source_page,
+        page_context=page_context,
+        screen_content=screen_content,
+    )
+    if page_ctx:
+        prompt["page_context"] = page_ctx
+
+    return prompt
+
+
+def _build_page_context(
+    *,
+    source_page: str | None,
+    page_context: dict | None,
+    screen_content: str | None,
+) -> dict:
+    """构建页面上下文信息。
+
+    P5-A 阶段：直接透传前端传入的上下文字段。
+    P5-B 阶段：各页面逐步实现文本化工具，丰富 screen_content。
+    """
+    # TODO: P5-B 各页面文本化工具细化
+    ctx: dict = {}
+    if source_page:
+        ctx["source_page"] = source_page
+    if page_context:
+        ctx["page_context"] = page_context
+    if screen_content:
+        ctx["screen_content"] = screen_content
+    return ctx
+
+
+def _build_source_context(
+    *,
+    source_page: str | None,
+    page_context: dict | None,
+    screen_content: str | None,
+) -> dict | None:
+    """构建存入 ai_conversations.source_context 的 JSON。"""
+    ctx: dict = {}
+    if source_page:
+        ctx["source_page"] = source_page
+    if page_context:
+        ctx["page_context"] = page_context
+    if screen_content:
+        ctx["screen_content"] = screen_content
+    return ctx if ctx else None

apps/backend/app/ai/apps/app2_finance.py

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+"""应用 2：财务洞察。
+
+8 个时间维度独立调用，每次调用结果写入 ai_cache，
+同时创建 ai_conversations + ai_messages 记录。
+
+营业日分界点：每日 08:00（BUSINESS_DAY_START_HOUR 环境变量，默认 8）。
+
+app_id = "app2_finance"
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import os
+from datetime import date, datetime, timedelta
+
+from app.ai.bailian_client import BailianClient
+from app.ai.cache_service import AICacheService
+from app.ai.conversation_service import ConversationService
+from app.ai.prompts.app2_finance_prompt import build_prompt
+from app.ai.schemas import CacheTypeEnum
+
+logger = logging.getLogger(__name__)
+
+APP_ID = "app2_finance"
+
+# 8 个时间维度编码
+TIME_DIMENSIONS = (
+    "this_month",
+    "last_month",
+    "this_week",
+    "last_week",
+    "last_3_months",
+    "this_quarter",
+    "last_quarter",
+    "last_6_months",
+)
+
+
+def get_business_date() -> date:
+    """根据营业日分界点计算当前营业日。
+
+    分界点前（如 07:59）视为前一天营业日，
+    分界点及之后（如 08:00）视为当天营业日。
+    """
+    hour = int(os.environ.get("BUSINESS_DAY_START_HOUR", "8"))
+    now = datetime.now()
+    if now.hour < hour:
+        return (now - timedelta(days=1)).date()
+    return now.date()
+
+
+def compute_time_range(dimension: str, business_date: date) -> tuple[date, date]:
+    """计算时间维度对应的日期范围 [start, end]（闭区间）。
+
+    Args:
+        dimension: 时间维度编码
+        business_date: 当前营业日
+
+    Returns:
+        (start_date, end_date) 元组
+    """
+    y, m, d = business_date.year, business_date.month, business_date.day
+
+    if dimension == "this_month":
+        start = date(y, m, 1)
+        return start, business_date
+
+    if dimension == "last_month":
+        prev = _month_offset(y, m, -1)
+        start = date(prev[0], prev[1], 1)
+        end = date(y, m, 1) - timedelta(days=1)
+        return start, end
+
+    if dimension == "this_week":
+        # 周一起算
+        weekday = business_date.weekday()  # 0=周一
+        start = business_date - timedelta(days=weekday)
+        return start, business_date
+
+    if dimension == "last_week":
+        weekday = business_date.weekday()
+        this_monday = business_date - timedelta(days=weekday)
+        last_monday = this_monday - timedelta(days=7)
+        last_sunday = this_monday - timedelta(days=1)
+        return last_monday, last_sunday
+
+    if dimension == "last_3_months":
+        # 当前月 - 3 ~ 当前月 - 1
+        end_ym = _month_offset(y, m, -1)
+        start_ym = _month_offset(y, m, -3)
+        start = date(start_ym[0], start_ym[1], 1)
+        # end = 上月最后一天
+        end = date(y, m, 1) - timedelta(days=1)
+        return start, end
+
+    if dimension == "this_quarter":
+        q_start_month = ((m - 1) // 3) * 3 + 1
+        start = date(y, q_start_month, 1)
+        return start, business_date
+
+    if dimension == "last_quarter":
+        q_start_month = ((m - 1) // 3) * 3 + 1
+        # 上季度结束 = 本季度第一天 - 1
+        this_q_start = date(y, q_start_month, 1)
+        end = this_q_start - timedelta(days=1)
+        # 上季度开始
+        ly, lm = end.year, end.month
+        lq_start_month = ((lm - 1) // 3) * 3 + 1
+        start = date(ly, lq_start_month, 1)
+        return start, end
+
+    if dimension == "last_6_months":
+        # 当前月 - 6 ~ 当前月 - 1
+        end_ym = _month_offset(y, m, -1)
+        start_ym = _month_offset(y, m, -6)
+        start = date(start_ym[0], start_ym[1], 1)
+        end = date(y, m, 1) - timedelta(days=1)
+        return start, end
+
+    raise ValueError(f"未知时间维度: {dimension}")
+
+
+async def run(
+    context: dict,
+    bailian: BailianClient,
+    cache_svc: AICacheService,
+    conv_svc: ConversationService,
+) -> dict:
+    """执行 App2 财务洞察调用。
+
+    Args:
+        context: 包含 site_id, time_dimension, user_id(默认'system'), nickname(默认'')
+        bailian: 百炼客户端
+        cache_svc: 缓存服务
+        conv_svc: 对话服务
+
+    Returns:
+        百炼返回的结构化 JSON（insights 数组）
+    """
+    site_id = context["site_id"]
+    time_dimension = context["time_dimension"]
+    user_id = context.get("user_id", "system")
+    nickname = context.get("nickname", "")
+
+    # 构建 Prompt
+    prompt_context = {
+        "site_id": site_id,
+        "time_dimension": time_dimension,
+        "current_data": context.get("current_data", {}),
+        "previous_data": context.get("previous_data", {}),
+    }
+    messages = build_prompt(prompt_context)
+
+    # 创建对话记录
+    conversation_id = conv_svc.create_conversation(
+        user_id=user_id,
+        nickname=nickname,
+        app_id=APP_ID,
+        site_id=site_id,
+        source_context={"time_dimension": time_dimension},
+    )
+
+    # 写入 system prompt 消息
+    conv_svc.add_message(
+        conversation_id=conversation_id,
+        role="system",
+        content=messages[0]["content"],
+    )
+    # 写入 user 消息
+    conv_svc.add_message(
+        conversation_id=conversation_id,
+        role="user",
+        content=messages[1]["content"],
+    )
+
+    # 调用百炼 API
+    result, tokens_used = await bailian.chat_json(messages)
+
+    # 写入 assistant 消息
+    conv_svc.add_message(
+        conversation_id=conversation_id,
+        role="assistant",
+        content=json.dumps(result, ensure_ascii=False),
+        tokens_used=tokens_used,
+    )
+
+    # 写入缓存
+    cache_svc.write_cache(
+        cache_type=CacheTypeEnum.APP2_FINANCE.value,
+        site_id=site_id,
+        target_id=time_dimension,
+        result_json=result,
+        triggered_by=f"user:{user_id}",
+    )
+
+    logger.info(
+        "App2 财务洞察完成: site_id=%s dimension=%s conversation_id=%s tokens=%d",
+        site_id, time_dimension, conversation_id, tokens_used,
+    )
+
+    return result
+
+
+def _month_offset(year: int, month: int, offset: int) -> tuple[int, int]:
+    """计算月份偏移，返回 (year, month)。"""
+    # 转为 0-based 计算
+    total = (year * 12 + (month - 1)) + offset
+    return total // 12, total % 12 + 1

apps/backend/app/ai/apps/app3_clue.py

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+"""应用 3：客户数据维客线索分析（骨架）。
+
+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。
+线索 category 限定 3 个枚举值：客户基础、消费习惯、玩法偏好。
+线索提供者统一标记为"系统"。
+
+使用 items_sum 口径（= table_charge_money + goods_money
++ assistant_pd_money + assistant_cx_money + electricity_money），
+禁止使用 consume_money。
+
+app_id = "app3_clue"
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+
+from app.ai.bailian_client import BailianClient
+from app.ai.cache_service import AICacheService
+from app.ai.conversation_service import ConversationService
+from app.ai.schemas import CacheTypeEnum
+
+logger = logging.getLogger(__name__)
+
+APP_ID = "app3_clue"
+
+
+def build_prompt(
+    context: dict,
+    cache_svc: AICacheService | None = None,
+) -> list[dict]:
+    """构建 Prompt 消息列表。
+
+    P5-A 阶段：返回占位 Prompt，标注待细化字段。
+    P5-B 阶段（P9-T1）：补充 consumption_records 等完整数据。
+
+    Args:
+        context: 包含 site_id, member_id, nickname 等
+        cache_svc: 缓存服务，用于获取 reference 历史数据
+
+    Returns:
+        消息列表 [{"role": "system", "content": ...}, {"role": "user", ...}]
+    """
+    site_id = context["site_id"]
+    member_id = context["member_id"]
+
+    # 构建 reference：App6 线索 + 最近 2 套 App8 历史（附 generated_at）
+    reference = _build_reference(site_id, member_id, cache_svc)
+
+    system_content = {
+        "task": "分析客户消费数据，提取维客线索。",
+        "app_id": APP_ID,
+        "rules": {
+            "category_enum": ["客户基础", "消费习惯", "玩法偏好"],
+            "providers": "系统",
+            "amount_caliber": "items_sum = table_charge_money + goods_money + assistant_pd_money + assistant_cx_money + electricity_money",
+            "禁止使用": "consume_money",
+        },
+        "output_format": {
+            "clues": [
+                {
+                    "category": "枚举值（客户基础/消费习惯/玩法偏好）",
+                    "summary": "一句话摘要",
+                    "detail": "详细说明",
+                    "emoji": "表情符号",
+                }
+            ]
+        },
+        # TODO: P9-T1 细化 - consumption_records 等客户消费数据
+        "data": {
+            "consumption_records": "待 P9-T1 补充",
+            "member_info": "待 P9-T1 补充",
+        },
+        "reference": reference,
+    }
+
+    user_content = (
+        f"请分析会员 {member_id} 的消费数据，提取维客线索。"
+        "每条线索包含 category、summary、detail、emoji 四个字段。"
+        "category 必须是：客户基础、消费习惯、玩法偏好 之一。"
+    )
+
+    return [
+        {"role": "system", "content": json.dumps(system_content, ensure_ascii=False)},
+        {"role": "user", "content": user_content},
+    ]
+
+
+def _build_reference(
+    site_id: int,
+    member_id: int,
+    cache_svc: AICacheService | None,
+) -> dict:
+    """构建 Prompt reference 字段。
+
+    包含：
+    - App6 备注分析线索（最新一条，如有）
+    - 最近 2 套 App8 维客线索整理历史（附 generated_at）
+
+    缓存不存在时返回空对象 {}。
+    """
+    if cache_svc is None:
+        return {}
+
+    reference: dict = {}
+    target_id = str(member_id)
+
+    # App6 备注分析线索
+    app6_latest = cache_svc.get_latest(
+        CacheTypeEnum.APP6_NOTE_ANALYSIS.value, site_id, target_id,
+    )
+    if app6_latest:
+        reference["app6_note_clues"] = {
+            "result_json": app6_latest.get("result_json"),
+            "generated_at": app6_latest.get("created_at"),
+        }
+
+    # 最近 2 套 App8 历史
+    app8_history = cache_svc.get_history(
+        CacheTypeEnum.APP8_CLUE_CONSOLIDATED.value, site_id, target_id, limit=2,
+    )
+    if app8_history:
+        reference["app8_history"] = [
+            {
+                "result_json": h.get("result_json"),
+                "generated_at": h.get("created_at"),
+            }
+            for h in app8_history
+        ]
+
+    return reference
+
+
+async def run(
+    context: dict,
+    bailian: BailianClient,
+    cache_svc: AICacheService,
+    conv_svc: ConversationService,
+) -> dict:
+    """执行 App3 客户数据维客线索分析。
+
+    流程：
+    1. build_prompt 构建 Prompt
+    2. bailian.chat_json 调用百炼
+    3. 写入 conversation + messages
+    4. 写入 ai_cache
+    5. 返回结果
+
+    Args:
+        context: site_id, member_id, user_id(默认'system'), nickname(默认'')
+        bailian: 百炼客户端
+        cache_svc: 缓存服务
+        conv_svc: 对话服务
+
+    Returns:
+        百炼返回的结构化 JSON（clues 数组）
+    """
+    site_id = context["site_id"]
+    member_id = context["member_id"]
+    user_id = context.get("user_id", "system")
+    nickname = context.get("nickname", "")
+
+    # 1. 构建 Prompt
+    messages = build_prompt(context, cache_svc)
+
+    # 2. 创建对话记录
+    conversation_id = conv_svc.create_conversation(
+        user_id=user_id,
+        nickname=nickname,
+        app_id=APP_ID,
+        site_id=site_id,
+        source_context={"member_id": member_id},
+    )
+
+    # 写入 system + user 消息
+    conv_svc.add_message(
+        conversation_id=conversation_id,
+        role="system",
+        content=messages[0]["content"],
+    )
+    conv_svc.add_message(
+        conversation_id=conversation_id,
+        role="user",
+        content=messages[1]["content"],
+    )
+
+    # 3. 调用百炼 API
+    result, tokens_used = await bailian.chat_json(messages)
+
+    # 4. 写入 assistant 消息
+    conv_svc.add_message(
+        conversation_id=conversation_id,
+        role="assistant",
+        content=json.dumps(result, ensure_ascii=False),
+        tokens_used=tokens_used,
+    )
+
+    # 5. 写入缓存
+    cache_svc.write_cache(
+        cache_type=CacheTypeEnum.APP3_CLUE.value,
+        site_id=site_id,
+        target_id=str(member_id),
+        result_json=result,
+        triggered_by=f"user:{user_id}",
+    )
+
+    logger.info(
+        "App3 线索分析完成: site_id=%s member_id=%s conversation_id=%s tokens=%d",
+        site_id, member_id, conversation_id, tokens_used,
+    )
+
+    return result

apps/backend/app/ai/apps/app4_analysis.py

@@ -0,0 +1,200 @@
+"""应用 4：关系分析/任务建议（骨架）。
+
+助教参与新结算或被分配召回任务时自动触发，
+生成关系分析和任务建议。
+
+Prompt reference 包含 App8 最新 + 最近 2 套历史（附 generated_at）。
+缓存不存在时 reference 传空对象，标注"暂无历史线索"。
+
+app_id = "app4_analysis"
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+
+from app.ai.bailian_client import BailianClient
+from app.ai.cache_service import AICacheService
+from app.ai.conversation_service import ConversationService
+from app.ai.schemas import CacheTypeEnum
+
+logger = logging.getLogger(__name__)
+
+APP_ID = "app4_analysis"
+
+
+def build_prompt(
+    context: dict,
+    cache_svc: AICacheService | None = None,
+) -> list[dict]:
+    """构建 Prompt 消息列表。
+
+    P5-A 阶段：返回占位 Prompt，标注待细化字段。
+    P5-B 阶段（P6-T4）：补充 service_history、assistant_info 等完整数据。
+
+    Args:
+        context: 包含 site_id, assistant_id, member_id
+        cache_svc: 缓存服务，用于获取 reference 历史数据
+
+    Returns:
+        消息列表
+    """
+    site_id = context["site_id"]
+    assistant_id = context["assistant_id"]
+    member_id = context["member_id"]
+
+    # 构建 reference：App8 最新 + 最近 2 套历史
+    reference = _build_reference(site_id, member_id, cache_svc)
+
+    system_content = {
+        "task": "分析助教与客户的关系，生成任务建议。",
+        "app_id": APP_ID,
+        "output_format": {
+            "task_description": "任务描述文本",
+            "action_suggestions": ["建议1", "建议2"],
+            "one_line_summary": "一句话总结",
+        },
+        # TODO: P6-T4 细化 - service_history、assistant_info
+        "data": {
+            "service_history": "待 P6-T4 补充",
+            "assistant_info": "待 P6-T4 补充",
+        },
+        "reference": reference,
+    }
+
+    # 缓存不存在时在 user prompt 中标注
+    no_history_hint = ""
+    if not reference:
+        no_history_hint = "（暂无历史线索，请基于现有信息分析）"
+
+    user_content = (
+        f"请分析助教 {assistant_id} 与会员 {member_id} 的关系，"
+        f"生成任务建议。{no_history_hint}"
+        "返回 task_description、action_suggestions、one_line_summary 三个字段。"
+    )
+
+    return [
+        {"role": "system", "content": json.dumps(system_content, ensure_ascii=False)},
+        {"role": "user", "content": user_content},
+    ]
+
+
+def _build_reference(
+    site_id: int,
+    member_id: int,
+    cache_svc: AICacheService | None,
+) -> dict:
+    """构建 Prompt reference 字段。
+
+    包含：
+    - App8 最新维客线索（如有）
+    - 最近 2 套 App8 历史（附 generated_at）
+
+    缓存不存在时返回空对象 {}。
+    """
+    if cache_svc is None:
+        return {}
+
+    reference: dict = {}
+    target_id = str(member_id)
+
+    # App8 最新
+    app8_latest = cache_svc.get_latest(
+        CacheTypeEnum.APP8_CLUE_CONSOLIDATED.value, site_id, target_id,
+    )
+    if app8_latest:
+        reference["app8_latest"] = {
+            "result_json": app8_latest.get("result_json"),
+            "generated_at": app8_latest.get("created_at"),
+        }
+
+    # 最近 2 套 App8 历史
+    app8_history = cache_svc.get_history(
+        CacheTypeEnum.APP8_CLUE_CONSOLIDATED.value, site_id, target_id, limit=2,
+    )
+    if app8_history:
+        reference["app8_history"] = [
+            {
+                "result_json": h.get("result_json"),
+                "generated_at": h.get("created_at"),
+            }
+            for h in app8_history
+        ]
+
+    return reference
+
+
+async def run(
+    context: dict,
+    bailian: BailianClient,
+    cache_svc: AICacheService,
+    conv_svc: ConversationService,
+) -> dict:
+    """执行 App4 关系分析。
+
+    Args:
+        context: site_id, assistant_id, member_id
+        bailian: 百炼客户端
+        cache_svc: 缓存服务
+        conv_svc: 对话服务
+
+    Returns:
+        百炼返回的结构化 JSON（task_description, action_suggestions, one_line_summary）
+    """
+    site_id = context["site_id"]
+    assistant_id = context["assistant_id"]
+    member_id = context["member_id"]
+    user_id = context.get("user_id", "system")
+    nickname = context.get("nickname", "")
+
+    # 1. 构建 Prompt
+    messages = build_prompt(context, cache_svc)
+
+    # 2. 创建对话记录
+    conversation_id = conv_svc.create_conversation(
+        user_id=user_id,
+        nickname=nickname,
+        app_id=APP_ID,
+        site_id=site_id,
+        source_context={"assistant_id": assistant_id, "member_id": member_id},
+    )
+
+    # 写入 system + user 消息
+    conv_svc.add_message(
+        conversation_id=conversation_id,
+        role="system",
+        content=messages[0]["content"],
+    )
+    conv_svc.add_message(
+        conversation_id=conversation_id,
+        role="user",
+        content=messages[1]["content"],
+    )
+
+    # 3. 调用百炼 API
+    result, tokens_used = await bailian.chat_json(messages)
+
+    # 4. 写入 assistant 消息
+    conv_svc.add_message(
+        conversation_id=conversation_id,
+        role="assistant",
+        content=json.dumps(result, ensure_ascii=False),
+        tokens_used=tokens_used,
+    )
+
+    # 5. 写入缓存（target_id = {assistant_id}_{member_id}）
+    cache_svc.write_cache(
+        cache_type=CacheTypeEnum.APP4_ANALYSIS.value,
+        site_id=site_id,
+        target_id=f"{assistant_id}_{member_id}",
+        result_json=result,
+        triggered_by=f"user:{user_id}",
+    )
+
+    logger.info(
+        "App4 关系分析完成: site_id=%s assistant=%s member=%s conversation_id=%s tokens=%d",
+        site_id, assistant_id, member_id, conversation_id, tokens_used,
+    )
+
+    return result

apps/backend/app/ai/apps/app5_tactics.py

@@ -0,0 +1,182 @@
+"""应用 5：话术参考（骨架）。
+
+App4 完成后自动联动触发，接收 App4 完整返回结果
+作为 Prompt 中的 task_suggestion 字段。
+
+Prompt reference 包含最近 2 套 App8 历史（附 generated_at）。
+
+app_id = "app5_tactics"
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+
+from app.ai.bailian_client import BailianClient
+from app.ai.cache_service import AICacheService
+from app.ai.conversation_service import ConversationService
+from app.ai.schemas import CacheTypeEnum
+
+logger = logging.getLogger(__name__)
+
+APP_ID = "app5_tactics"
+
+
+def build_prompt(
+    context: dict,
+    cache_svc: AICacheService | None = None,
+) -> list[dict]:
+    """构建 Prompt 消息列表。
+
+    P5-A 阶段：返回占位 Prompt，标注待细化字段。
+    P5-B 阶段（P6-T4）：补充 service_history、assistant_info（随 App4 同步）。
+
+    Args:
+        context: 包含 site_id, assistant_id, member_id, app4_result(dict)
+        cache_svc: 缓存服务，用于获取 reference 历史数据
+
+    Returns:
+        消息列表
+    """
+    site_id = context["site_id"]
+    assistant_id = context["assistant_id"]
+    member_id = context["member_id"]
+    app4_result = context.get("app4_result", {})
+
+    # 构建 reference：最近 2 套 App8 历史
+    reference = _build_reference(site_id, member_id, cache_svc)
+
+    system_content = {
+        "task": "基于关系分析和任务建议，生成沟通话术参考。",
+        "app_id": APP_ID,
+        "task_suggestion": app4_result,
+        "output_format": {
+            "tactics": [
+                {"scenario": "场景描述", "script": "话术内容"}
+            ]
+        },
+        # TODO: P6-T4 细化 - service_history、assistant_info（随 App4 同步）
+        "data": {
+            "service_history": "待 P6-T4 补充",
+            "assistant_info": "待 P6-T4 补充",
+        },
+        "reference": reference,
+    }
+
+    user_content = (
+        f"请为助教 {assistant_id} 生成与会员 {member_id} 沟通的话术参考。"
+        "返回 tactics 数组，每条包含 scenario 和 script 字段。"
+    )
+
+    return [
+        {"role": "system", "content": json.dumps(system_content, ensure_ascii=False)},
+        {"role": "user", "content": user_content},
+    ]
+
+
+def _build_reference(
+    site_id: int,
+    member_id: int,
+    cache_svc: AICacheService | None,
+) -> dict:
+    """构建 Prompt reference 字段。
+
+    包含最近 2 套 App8 历史（附 generated_at）。
+    缓存不存在时返回空对象 {}。
+    """
+    if cache_svc is None:
+        return {}
+
+    reference: dict = {}
+    target_id = str(member_id)
+
+    # 最近 2 套 App8 历史
+    app8_history = cache_svc.get_history(
+        CacheTypeEnum.APP8_CLUE_CONSOLIDATED.value, site_id, target_id, limit=2,
+    )
+    if app8_history:
+        reference["app8_history"] = [
+            {
+                "result_json": h.get("result_json"),
+                "generated_at": h.get("created_at"),
+            }
+            for h in app8_history
+        ]
+
+    return reference
+
+
+async def run(
+    context: dict,
+    bailian: BailianClient,
+    cache_svc: AICacheService,
+    conv_svc: ConversationService,
+) -> dict:
+    """执行 App5 话术参考。
+
+    Args:
+        context: site_id, assistant_id, member_id, app4_result(dict)
+        bailian: 百炼客户端
+        cache_svc: 缓存服务
+        conv_svc: 对话服务
+
+    Returns:
+        百炼返回的结构化 JSON（tactics 数组）
+    """
+    site_id = context["site_id"]
+    assistant_id = context["assistant_id"]
+    member_id = context["member_id"]
+    user_id = context.get("user_id", "system")
+    nickname = context.get("nickname", "")
+
+    # 1. 构建 Prompt
+    messages = build_prompt(context, cache_svc)
+
+    # 2. 创建对话记录
+    conversation_id = conv_svc.create_conversation(
+        user_id=user_id,
+        nickname=nickname,
+        app_id=APP_ID,
+        site_id=site_id,
+        source_context={"assistant_id": assistant_id, "member_id": member_id},
+    )
+
+    # 写入 system + user 消息
+    conv_svc.add_message(
+        conversation_id=conversation_id,
+        role="system",
+        content=messages[0]["content"],
+    )
+    conv_svc.add_message(
+        conversation_id=conversation_id,
+        role="user",
+        content=messages[1]["content"],
+    )
+
+    # 3. 调用百炼 API
+    result, tokens_used = await bailian.chat_json(messages)
+
+    # 4. 写入 assistant 消息
+    conv_svc.add_message(
+        conversation_id=conversation_id,
+        role="assistant",
+        content=json.dumps(result, ensure_ascii=False),
+        tokens_used=tokens_used,
+    )
+
+    # 5. 写入缓存（target_id = {assistant_id}_{member_id}）
+    cache_svc.write_cache(
+        cache_type=CacheTypeEnum.APP5_TACTICS.value,
+        site_id=site_id,
+        target_id=f"{assistant_id}_{member_id}",
+        result_json=result,
+        triggered_by=f"user:{user_id}",
+    )
+
+    logger.info(
+        "App5 话术参考完成: site_id=%s assistant=%s member=%s conversation_id=%s tokens=%d",
+        site_id, assistant_id, member_id, conversation_id, tokens_used,
+    )
+
+    return result

apps/backend/app/ai/apps/app6_note.py

@@ -0,0 +1,217 @@
+"""应用 6：备注分析（骨架）。
+
+助教提交备注后自动触发，通过 AI 分析备注内容，
+提取维客线索并评分。
+
+返回 score（1-10）+ clues 数组。
+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。
+线索 category 限定 6 个枚举值。
+线索提供者标记为当前备注提供人（context.noted_by_name）。
+
+app_id = "app6_note"
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+
+from app.ai.bailian_client import BailianClient
+from app.ai.cache_service import AICacheService
+from app.ai.conversation_service import ConversationService
+from app.ai.schemas import CacheTypeEnum
+
+logger = logging.getLogger(__name__)
+
+APP_ID = "app6_note"
+
+
+def build_prompt(
+    context: dict,
+    cache_svc: AICacheService | None = None,
+) -> list[dict]:
+    """构建 Prompt 消息列表。
+
+    P5-A 阶段：返回占位 Prompt，标注待细化字段。
+    P5-B 阶段（P9-T1）：补充 consumption_data 等完整数据。
+
+    Args:
+        context: 包含 site_id, member_id, note_content, noted_by_name
+        cache_svc: 缓存服务，用于获取 reference 历史数据
+
+    Returns:
+        消息列表
+    """
+    site_id = context["site_id"]
+    member_id = context["member_id"]
+    note_content = context.get("note_content", "")
+    noted_by_name = context.get("noted_by_name", "")
+
+    # 构建 reference：App3 线索 + 最近 2 套 App8 历史
+    reference = _build_reference(site_id, member_id, cache_svc)
+
+    system_content = {
+        "task": "分析备注内容，提取维客线索并评分。",
+        "app_id": APP_ID,
+        "rules": {
+            "category_enum": [
+                "客户基础", "消费习惯", "玩法偏好",
+                "促销偏好", "社交关系", "重要反馈",
+            ],
+            "providers": noted_by_name,
+            "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",
+            "score_range": "1-10",
+        },
+        "output_format": {
+            "score": "1-10 整数",
+            "clues": [
+                {
+                    "category": "枚举值（6 选 1）",
+                    "summary": "一句话摘要",
+                    "detail": "详细说明",
+                    "emoji": "表情符号",
+                }
+            ],
+        },
+        "note_content": note_content,
+        "noted_by_name": noted_by_name,
+        # TODO: P9-T1 细化 - consumption_data 等客户消费数据
+        "data": {
+            "consumption_data": "待 P9-T1 补充",
+        },
+        "reference": reference,
+    }
+
+    user_content = (
+        f"请分析以下备注内容，提取维客线索并评分。\n"
+        f"备注提供人：{noted_by_name}\n"
+        f"备注内容：{note_content}\n"
+        "返回 score（1-10 整数）和 clues 数组。"
+        "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。"
+    )
+
+    return [
+        {"role": "system", "content": json.dumps(system_content, ensure_ascii=False)},
+        {"role": "user", "content": user_content},
+    ]
+
+
+def _build_reference(
+    site_id: int,
+    member_id: int,
+    cache_svc: AICacheService | None,
+) -> dict:
+    """构建 Prompt reference 字段。
+
+    包含：
+    - App3 客户数据线索（最新一条，如有）
+    - 最近 2 套 App8 维客线索整理历史（附 generated_at）
+
+    缓存不存在时返回空对象 {}。
+    """
+    if cache_svc is None:
+        return {}
+
+    reference: dict = {}
+    target_id = str(member_id)
+
+    # App3 客户数据线索
+    app3_latest = cache_svc.get_latest(
+        CacheTypeEnum.APP3_CLUE.value, site_id, target_id,
+    )
+    if app3_latest:
+        reference["app3_clues"] = {
+            "result_json": app3_latest.get("result_json"),
+            "generated_at": app3_latest.get("created_at"),
+        }
+
+    # 最近 2 套 App8 历史
+    app8_history = cache_svc.get_history(
+        CacheTypeEnum.APP8_CLUE_CONSOLIDATED.value, site_id, target_id, limit=2,
+    )
+    if app8_history:
+        reference["app8_history"] = [
+            {
+                "result_json": h.get("result_json"),
+                "generated_at": h.get("created_at"),
+            }
+            for h in app8_history
+        ]
+
+    return reference
+
+
+async def run(
+    context: dict,
+    bailian: BailianClient,
+    cache_svc: AICacheService,
+    conv_svc: ConversationService,
+) -> dict:
+    """执行 App6 备注分析。
+
+    Args:
+        context: site_id, member_id, note_content, noted_by_name
+        bailian: 百炼客户端
+        cache_svc: 缓存服务
+        conv_svc: 对话服务
+
+    Returns:
+        百炼返回的结构化 JSON（score + clues 数组）
+    """
+    site_id = context["site_id"]
+    member_id = context["member_id"]
+    user_id = context.get("user_id", "system")
+    nickname = context.get("nickname", "")
+
+    # 1. 构建 Prompt
+    messages = build_prompt(context, cache_svc)
+
+    # 2. 创建对话记录
+    conversation_id = conv_svc.create_conversation(
+        user_id=user_id,
+        nickname=nickname,
+        app_id=APP_ID,
+        site_id=site_id,
+        source_context={"member_id": member_id},
+    )
+
+    # 写入 system + user 消息
+    conv_svc.add_message(
+        conversation_id=conversation_id,
+        role="system",
+        content=messages[0]["content"],
+    )
+    conv_svc.add_message(
+        conversation_id=conversation_id,
+        role="user",
+        content=messages[1]["content"],
+    )
+
+    # 3. 调用百炼 API
+    result, tokens_used = await bailian.chat_json(messages)
+
+    # 4. 写入 assistant 消息
+    conv_svc.add_message(
+        conversation_id=conversation_id,
+        role="assistant",
+        content=json.dumps(result, ensure_ascii=False),
+        tokens_used=tokens_used,
+    )
+
+    # 5. 写入缓存（score 存入 ai_cache.score）
+    score = result.get("score")
+    cache_svc.write_cache(
+        cache_type=CacheTypeEnum.APP6_NOTE_ANALYSIS.value,
+        site_id=site_id,
+        target_id=str(member_id),
+        result_json=result,
+        triggered_by=f"user:{user_id}",
+        score=score,
+    )
+
+    logger.info(
+        "App6 备注分析完成: site_id=%s member_id=%s score=%s conversation_id=%s tokens=%d",
+        site_id, member_id, score, conversation_id, tokens_used,
+    )
+
+    return result

apps/backend/app/ai/apps/app7_customer.py

@@ -0,0 +1,200 @@
+"""应用 7：客户分析（骨架）。
+
+消费事件链中 App8 完成后串行触发，生成客户全量分析与运营建议。
+
+使用 items_sum 口径（= table_charge_money + goods_money
++ assistant_pd_money + assistant_cx_money + electricity_money），
+禁止使用 consume_money。
+
+对主观信息（来自备注）标注【来源：XXX，请甄别信息真实性】。
+
+app_id = "app7_customer"
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+
+from app.ai.bailian_client import BailianClient
+from app.ai.cache_service import AICacheService
+from app.ai.conversation_service import ConversationService
+from app.ai.schemas import CacheTypeEnum
+
+logger = logging.getLogger(__name__)
+
+APP_ID = "app7_customer"
+
+
+def build_prompt(
+    context: dict,
+    cache_svc: AICacheService | None = None,
+) -> list[dict]:
+    """构建 Prompt 消息列表。
+
+    P5-A 阶段：返回占位 Prompt，标注待细化字段。
+    P5-B 阶段（P9-T1）：补充 objective_data 等完整数据。
+
+    Args:
+        context: 包含 site_id, member_id
+        cache_svc: 缓存服务，用于获取 reference 历史数据
+
+    Returns:
+        消息列表
+    """
+    site_id = context["site_id"]
+    member_id = context["member_id"]
+
+    # 构建 reference：最新 + 最近 2 套 App8 历史
+    reference = _build_reference(site_id, member_id, cache_svc)
+
+    system_content = {
+        "task": "综合分析客户数据，生成运营策略建议。",
+        "app_id": APP_ID,
+        "rules": {
+            "amount_caliber": "items_sum = table_charge_money + goods_money + assistant_pd_money + assistant_cx_money + electricity_money",
+            "禁止使用": "consume_money",
+            "subjective_info_label": "对主观信息（来自备注）标注【来源：XXX，请甄别信息真实性】",
+        },
+        "output_format": {
+            "strategies": [
+                {"title": "策略标题", "content": "策略内容"}
+            ],
+            "summary": "一句话总结",
+        },
+        # TODO: P9-T1 细化 - objective_data 等客户消费数据
+        "data": {
+            "objective_data": "待 P9-T1 补充",
+        },
+        "reference": reference,
+    }
+
+    user_content = (
+        f"请综合分析会员 {member_id} 的客户数据，生成运营策略建议。"
+        "返回 strategies 数组（每条含 title 和 content）和 summary 字段。"
+        "对来自备注的主观信息，请标注【来源：XXX，请甄别信息真实性】。"
+    )
+
+    return [
+        {"role": "system", "content": json.dumps(system_content, ensure_ascii=False)},
+        {"role": "user", "content": user_content},
+    ]
+
+
+def _build_reference(
+    site_id: int,
+    member_id: int,
+    cache_svc: AICacheService | None,
+) -> dict:
+    """构建 Prompt reference 字段。
+
+    包含：
+    - App8 最新维客线索（如有）
+    - 最近 2 套 App8 历史（附 generated_at）
+
+    缓存不存在时返回空对象 {}。
+    """
+    if cache_svc is None:
+        return {}
+
+    reference: dict = {}
+    target_id = str(member_id)
+
+    # App8 最新
+    app8_latest = cache_svc.get_latest(
+        CacheTypeEnum.APP8_CLUE_CONSOLIDATED.value, site_id, target_id,
+    )
+    if app8_latest:
+        reference["app8_latest"] = {
+            "result_json": app8_latest.get("result_json"),
+            "generated_at": app8_latest.get("created_at"),
+        }
+
+    # 最近 2 套 App8 历史
+    app8_history = cache_svc.get_history(
+        CacheTypeEnum.APP8_CLUE_CONSOLIDATED.value, site_id, target_id, limit=2,
+    )
+    if app8_history:
+        reference["app8_history"] = [
+            {
+                "result_json": h.get("result_json"),
+                "generated_at": h.get("created_at"),
+            }
+            for h in app8_history
+        ]
+
+    return reference
+
+
+async def run(
+    context: dict,
+    bailian: BailianClient,
+    cache_svc: AICacheService,
+    conv_svc: ConversationService,
+) -> dict:
+    """执行 App7 客户分析。
+
+    Args:
+        context: site_id, member_id
+        bailian: 百炼客户端
+        cache_svc: 缓存服务
+        conv_svc: 对话服务
+
+    Returns:
+        百炼返回的结构化 JSON（strategies 数组 + summary）
+    """
+    site_id = context["site_id"]
+    member_id = context["member_id"]
+    user_id = context.get("user_id", "system")
+    nickname = context.get("nickname", "")
+
+    # 1. 构建 Prompt
+    messages = build_prompt(context, cache_svc)
+
+    # 2. 创建对话记录
+    conversation_id = conv_svc.create_conversation(
+        user_id=user_id,
+        nickname=nickname,
+        app_id=APP_ID,
+        site_id=site_id,
+        source_context={"member_id": member_id},
+    )
+
+    # 写入 system + user 消息
+    conv_svc.add_message(
+        conversation_id=conversation_id,
+        role="system",
+        content=messages[0]["content"],
+    )
+    conv_svc.add_message(
+        conversation_id=conversation_id,
+        role="user",
+        content=messages[1]["content"],
+    )
+
+    # 3. 调用百炼 API
+    result, tokens_used = await bailian.chat_json(messages)
+
+    # 4. 写入 assistant 消息
+    conv_svc.add_message(
+        conversation_id=conversation_id,
+        role="assistant",
+        content=json.dumps(result, ensure_ascii=False),
+        tokens_used=tokens_used,
+    )
+
+    # 5. 写入缓存
+    cache_svc.write_cache(
+        cache_type=CacheTypeEnum.APP7_CUSTOMER_ANALYSIS.value,
+        site_id=site_id,
+        target_id=str(member_id),
+        result_json=result,
+        triggered_by=f"user:{user_id}",
+    )
+
+    logger.info(
+        "App7 客户分析完成: site_id=%s member_id=%s conversation_id=%s tokens=%d",
+        site_id, member_id, conversation_id, tokens_used,
+    )
+
+    return result

apps/backend/app/ai/apps/app8_consolidation.py

@@ -0,0 +1,211 @@
+"""应用 8：维客线索整理。
+
+接收 App3（消费分析）和 App6（备注分析）的线索，
+通过百炼 AI 整合去重，然后全量替换写入 member_retention_clue 表。
+
+app_id = "app8_consolidation"
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+
+from app.ai.bailian_client import BailianClient
+from app.ai.cache_service import AICacheService
+from app.ai.conversation_service import ConversationService
+from app.ai.prompts.app8_consolidation_prompt import build_prompt
+from app.ai.schemas import CacheTypeEnum
+from app.database import get_connection
+
+logger = logging.getLogger(__name__)
+
+APP_ID = "app8_consolidation"
+
+
+class ClueWriter:
+    """维客线索全量替换写入器。
+
+    DELETE source IN ('ai_consumption', 'ai_note') → INSERT 新线索（事务）。
+    人工线索（source='manual'）不受影响。
+    """
+
+    def replace_ai_clues(
+        self,
+        member_id: int,
+        site_id: int,
+        clues: list[dict],
+    ) -> int:
+        """全量替换该客户的 AI 来源线索，返回写入数量。
+
+        在单个事务中执行 DELETE + INSERT，失败时回滚保留原有线索。
+
+        字段映射：
+        - category → category
+        - emoji + " " + summary → summary（如 "📅 偏好周末下午时段消费"）
+        - detail → detail
+        - providers → recorded_by_name
+        - source: 根据 providers 判断（见 _determine_source）
+        - recorded_by_assistant_id: NULL（系统触发）
+        """
+        conn = get_connection()
+        try:
+            with conn.cursor() as cur:
+                # 1. 删除该客户所有 AI 来源线索
+                cur.execute(
+                    """
+                    DELETE FROM member_retention_clue
+                    WHERE member_id = %s AND site_id = %s
+                      AND source IN ('ai_consumption', 'ai_note')
+                    """,
+                    (member_id, site_id),
+                )
+
+                # 2. 插入新线索
+                for clue in clues:
+                    emoji = clue.get("emoji", "")
+                    raw_summary = clue.get("summary", "")
+                    summary = f"{emoji} {raw_summary}" if emoji else raw_summary
+                    source = _determine_source(clue.get("providers", ""))
+
+                    cur.execute(
+                        """
+                        INSERT INTO member_retention_clue
+                            (member_id, site_id, category, summary, detail,
+                             source, recorded_by_name, recorded_by_assistant_id)
+                        VALUES (%s, %s, %s, %s, %s, %s, %s, NULL)
+                        """,
+                        (
+                            member_id,
+                            site_id,
+                            clue.get("category", ""),
+                            summary,
+                            clue.get("detail", ""),
+                            source,
+                            clue.get("providers", ""),
+                        ),
+                    )
+
+                conn.commit()
+                return len(clues)
+        except Exception:
+            conn.rollback()
+            raise
+        finally:
+            conn.close()
+
+
+def _determine_source(providers: str) -> str:
+    """根据 providers 判断 source 值。
+
+    - 纯 App3（providers 仅含"系统"）→ ai_consumption
+    - 纯 App6（providers 不含"系统"）→ ai_note
+    - 混合来源 → ai_consumption
+    """
+    if not providers:
+        return "ai_consumption"
+    provider_list = [p.strip() for p in providers.split(",")]
+    has_system = "系统" in provider_list
+    has_human = any(p != "系统" for p in provider_list if p)
+    if has_system and not has_human:
+        # 纯 App3（系统自动分析）
+        return "ai_consumption"
+    elif has_human and not has_system:
+        # 纯 App6（人工备注分析）
+        return "ai_note"
+    else:
+        # 混合来源
+        return "ai_consumption"
+
+
+async def run(
+    context: dict,
+    bailian: BailianClient,
+    cache_svc: AICacheService,
+    conv_svc: ConversationService,
+) -> dict:
+    """执行 App8 维客线索整理。
+
+    流程：
+    1. build_prompt 构建 Prompt
+    2. bailian.chat_json 调用百炼
+    3. 写入 conversation + messages
+    4. 写入 ai_cache
+    5. ClueWriter 全量替换 member_retention_clue
+    6. 返回结果
+
+    Args:
+        context: site_id, member_id, app3_clues, app6_clues,
+                 app3_generated_at, app6_generated_at
+        bailian: 百炼客户端
+        cache_svc: 缓存服务
+        conv_svc: 对话服务
+
+    Returns:
+        百炼返回的结构化 JSON（clues 数组）
+    """
+    site_id = context["site_id"]
+    member_id = context["member_id"]
+    user_id = context.get("user_id", "system")
+    nickname = context.get("nickname", "")
+
+    # 1. 构建 Prompt
+    messages = build_prompt(context)
+
+    # 2. 创建对话记录
+    conversation_id = conv_svc.create_conversation(
+        user_id=user_id,
+        nickname=nickname,
+        app_id=APP_ID,
+        site_id=site_id,
+        source_context={"member_id": member_id},
+    )
+
+    # 写入 system + user 消息
+    conv_svc.add_message(
+        conversation_id=conversation_id,
+        role="system",
+        content=messages[0]["content"],
+    )
+    conv_svc.add_message(
+        conversation_id=conversation_id,
+        role="user",
+        content=messages[1]["content"],
+    )
+
+    # 3. 调用百炼 API
+    result, tokens_used = await bailian.chat_json(messages)
+
+    # 4. 写入 assistant 消息
+    conv_svc.add_message(
+        conversation_id=conversation_id,
+        role="assistant",
+        content=json.dumps(result, ensure_ascii=False),
+        tokens_used=tokens_used,
+    )
+
+    # 5. 写入缓存
+    cache_svc.write_cache(
+        cache_type=CacheTypeEnum.APP8_CLUE_CONSOLIDATED.value,
+        site_id=site_id,
+        target_id=str(member_id),
+        result_json=result,
+        triggered_by=f"user:{user_id}",
+    )
+
+    # 6. 全量替换 member_retention_clue
+    clues = result.get("clues", [])
+    if clues:
+        writer = ClueWriter()
+        written = writer.replace_ai_clues(member_id, site_id, clues)
+        logger.info(
+            "App8 线索写入完成: site_id=%s member_id=%s written=%d",
+            site_id, member_id, written,
+        )
+
+    logger.info(
+        "App8 线索整理完成: site_id=%s member_id=%s conversation_id=%s tokens=%d",
+        site_id, member_id, conversation_id, tokens_used,
+    )
+
+    return result

apps/backend/app/ai/dispatcher.py

@@ -0,0 +1,338 @@
+"""AI 事件调度与调用链编排器。
+
+根据业务事件（消费、备注、任务分配）编排 AI 应用调用链，
+确保执行顺序和数据依赖正确。
+
+调用链：
+- 消费事件（无助教）：App3 → App8 → App7
+- 消费事件（有助教）：App3 → App8 → App7 + App4 → App5
+- 备注事件：App6 → App8
+- 任务分配事件：App4 → App5（读已有 App8 缓存）
+
+容错策略：
+- 某步失败记录错误日志，后续应用使用已有缓存继续
+- 失败应用写入失败 conversation 记录
+- 整条链后台异步执行，不阻塞业务请求
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from typing import Any, Callable, Coroutine
+
+from app.ai.bailian_client import BailianClient
+from app.ai.cache_service import AICacheService
+from app.ai.conversation_service import ConversationService
+from app.ai.schemas import CacheTypeEnum
+
+logger = logging.getLogger(__name__)
+
+
+class AIDispatcher:
+    """AI 应用调用链编排器。"""
+
+    def __init__(
+        self,
+        bailian: BailianClient,
+        cache_svc: AICacheService,
+        conv_svc: ConversationService,
+    ) -> None:
+        self.bailian = bailian
+        self.cache_svc = cache_svc
+        self.conv_svc = conv_svc
+
+    async def handle_consumption_event(
+        self,
+        member_id: int,
+        site_id: int,
+        settle_id: int,
+        assistant_id: int | None = None,
+    ) -> None:
+        """消费事件链：App3 → App8 → App7（+ App4 → App5 如有助教）。"""
+        from app.ai.apps.app3_clue import run as app3_run
+        from app.ai.apps.app4_analysis import run as app4_run
+        from app.ai.apps.app5_tactics import run as app5_run
+        from app.ai.apps.app7_customer import run as app7_run
+        from app.ai.apps.app8_consolidation import run as app8_run
+
+        context: dict[str, Any] = {
+            "member_id": member_id,
+            "site_id": site_id,
+            "settle_id": settle_id,
+            "user_id": "system",
+            "nickname": "",
+        }
+
+        # 步骤 1：App3 线索分析
+        app3_result = await self._run_step("app3_clue", app3_run, context)
+
+        # 步骤 2：App8 线索整理（需要 App3 的 clues）
+        app8_context = {**context}
+        # 从 App3 结果提取 clues；同时从缓存获取 App6 已有线索
+        if app3_result:
+            app8_context["app3_clues"] = app3_result.get("clues", [])
+            app8_context["app3_generated_at"] = None  # 刚生成，无需时间戳
+        else:
+            app8_context["app3_clues"] = []
+            app8_context["app3_generated_at"] = None
+
+        # 从缓存获取 App6 已有线索
+        app6_cache = self.cache_svc.get_latest(
+            CacheTypeEnum.APP6_NOTE_ANALYSIS.value, site_id, str(member_id),
+        )
+        if app6_cache:
+            app6_result_json = app6_cache.get("result_json", {})
+            if isinstance(app6_result_json, str):
+                try:
+                    app6_result_json = json.loads(app6_result_json)
+                except (json.JSONDecodeError, TypeError):
+                    app6_result_json = {}
+            app8_context["app6_clues"] = app6_result_json.get("clues", [])
+            app8_context["app6_generated_at"] = app6_cache.get("created_at")
+        else:
+            app8_context["app6_clues"] = []
+            app8_context["app6_generated_at"] = None
+
+        await self._run_step("app8_consolidation", app8_run, app8_context)
+
+        # 步骤 3：App7 客户分析
+        await self._run_step("app7_customer", app7_run, context)
+
+        # 步骤 4（可选）：如有助教，App4 → App5
+        if assistant_id is not None:
+            app4_context = {**context, "assistant_id": assistant_id}
+            app4_result = await self._run_step("app4_analysis", app4_run, app4_context)
+
+            app5_context = {
+                **context,
+                "assistant_id": assistant_id,
+                "app4_result": app4_result or {},
+            }
+            await self._run_step("app5_tactics", app5_run, app5_context)
+
+    async def handle_note_event(
+        self,
+        member_id: int,
+        site_id: int,
+        note_id: int,
+        note_content: str,
+        noted_by_name: str,
+    ) -> None:
+        """备注事件链：App6 → App8。"""
+        from app.ai.apps.app6_note import run as app6_run
+        from app.ai.apps.app8_consolidation import run as app8_run
+
+        context: dict[str, Any] = {
+            "member_id": member_id,
+            "site_id": site_id,
+            "note_id": note_id,
+            "note_content": note_content,
+            "noted_by_name": noted_by_name,
+            "user_id": "system",
+            "nickname": "",
+        }
+
+        # 步骤 1：App6 备注分析
+        app6_result = await self._run_step("app6_note", app6_run, context)
+
+        # 步骤 2：App8 线索整理（需要 App6 的 clues）
+        app8_context: dict[str, Any] = {
+            "member_id": member_id,
+            "site_id": site_id,
+            "user_id": "system",
+            "nickname": "",
+        }
+
+        if app6_result:
+            app8_context["app6_clues"] = app6_result.get("clues", [])
+            app8_context["app6_generated_at"] = None
+        else:
+            app8_context["app6_clues"] = []
+            app8_context["app6_generated_at"] = None
+
+        # 从缓存获取 App3 已有线索
+        app3_cache = self.cache_svc.get_latest(
+            CacheTypeEnum.APP3_CLUE.value, site_id, str(member_id),
+        )
+        if app3_cache:
+            app3_result_json = app3_cache.get("result_json", {})
+            if isinstance(app3_result_json, str):
+                try:
+                    app3_result_json = json.loads(app3_result_json)
+                except (json.JSONDecodeError, TypeError):
+                    app3_result_json = {}
+            app8_context["app3_clues"] = app3_result_json.get("clues", [])
+            app8_context["app3_generated_at"] = app3_cache.get("created_at")
+        else:
+            app8_context["app3_clues"] = []
+            app8_context["app3_generated_at"] = None
+
+        await self._run_step("app8_consolidation", app8_run, app8_context)
+
+    async def handle_task_assign_event(
+        self,
+        assistant_id: int,
+        member_id: int,
+        site_id: int,
+        task_type: str,
+    ) -> None:
+        """任务分配事件链：App4 → App5（读已有 App8 缓存）。"""
+        from app.ai.apps.app4_analysis import run as app4_run
+        from app.ai.apps.app5_tactics import run as app5_run
+
+        context: dict[str, Any] = {
+            "assistant_id": assistant_id,
+            "member_id": member_id,
+            "site_id": site_id,
+            "task_type": task_type,
+            "user_id": "system",
+            "nickname": "",
+        }
+
+        # 步骤 1：App4 关系分析
+        app4_result = await self._run_step("app4_analysis", app4_run, context)
+
+        # 步骤 2：App5 话术参考
+        app5_context = {
+            **context,
+            "app4_result": app4_result or {},
+        }
+        await self._run_step("app5_tactics", app5_run, app5_context)
+
+    async def _run_chain(
+        self,
+        chain: list[tuple[str, Callable[..., Coroutine], dict]],
+    ) -> None:
+        """串行执行调用链，某步失败记录日志后继续。
+
+        Args:
+            chain: [(app_name, run_func, context), ...] 的列表
+        """
+        for app_name, run_func, ctx in chain:
+            await self._run_step(app_name, run_func, ctx)
+
+    async def _run_step(
+        self,
+        app_name: str,
+        run_func: Callable[..., Coroutine],
+        context: dict,
+    ) -> dict | None:
+        """执行单个应用步骤，失败时记录日志并写入失败 conversation。
+
+        Returns:
+            应用返回结果，失败时返回 None
+        """
+        try:
+            result = await run_func(
+                context,
+                self.bailian,
+                self.cache_svc,
+                self.conv_svc,
+            )
+            logger.info("调用链步骤成功: %s", app_name)
+            return result
+        except Exception:
+            logger.exception("调用链步骤失败: %s", app_name)
+            # 写入失败 conversation 记录
+            try:
+                site_id = context.get("site_id", 0)
+                conv_id = self.conv_svc.create_conversation(
+                    user_id="system",
+                    nickname="",
+                    app_id=app_name,
+                    site_id=site_id,
+                    source_context={"error": True, "chain_step": app_name},
+                )
+                self.conv_svc.add_message(
+                    conversation_id=conv_id,
+                    role="system",
+                    content=f"调用链步骤 {app_name} 执行失败",
+                )
+            except Exception:
+                logger.exception("写入失败 conversation 记录也失败: %s", app_name)
+            return None
+
+def _create_ai_event_handlers(dispatcher: AIDispatcher) -> dict[str, Callable]:
+    """创建 AI 事件处理器，用于注册到 trigger_scheduler。
+
+    每个处理器从 payload 提取参数，通过 asyncio.create_task 后台执行，
+    不阻塞同步的 fire_event 调用。
+
+    Returns:
+        {event_job_type: handler_func} 映射
+    """
+    import asyncio
+
+    def _get_or_create_loop() -> asyncio.AbstractEventLoop:
+        """获取当前事件循环，兼容同步调用场景。"""
+        try:
+            return asyncio.get_running_loop()
+        except RuntimeError:
+            return asyncio.new_event_loop()
+
+    def handle_consumption_settled(payload: dict | None = None, **_kw: Any) -> None:
+        """消费结算事件处理器（同步入口，内部异步执行）。"""
+        if not payload:
+            logger.warning("consumption_settled 事件缺少 payload")
+            return
+        loop = _get_or_create_loop()
+        loop.create_task(
+            dispatcher.handle_consumption_event(
+                member_id=payload["member_id"],
+                site_id=payload["site_id"],
+                settle_id=payload["settle_id"],
+                assistant_id=payload.get("assistant_id"),
+            )
+        )
+
+    def handle_note_created(payload: dict | None = None, **_kw: Any) -> None:
+        """备注创建事件处理器。"""
+        if not payload:
+            logger.warning("note_created 事件缺少 payload")
+            return
+        loop = _get_or_create_loop()
+        loop.create_task(
+            dispatcher.handle_note_event(
+                member_id=payload["member_id"],
+                site_id=payload["site_id"],
+                note_id=payload["note_id"],
+                note_content=payload.get("note_content", ""),
+                noted_by_name=payload.get("noted_by_name", ""),
+            )
+        )
+
+    def handle_task_assigned(payload: dict | None = None, **_kw: Any) -> None:
+        """任务分配事件处理器。"""
+        if not payload:
+            logger.warning("task_assigned 事件缺少 payload")
+            return
+        loop = _get_or_create_loop()
+        loop.create_task(
+            dispatcher.handle_task_assign_event(
+                assistant_id=payload["assistant_id"],
+                member_id=payload["member_id"],
+                site_id=payload["site_id"],
+                task_type=payload.get("task_type", ""),
+            )
+        )
+
+    return {
+        "ai_consumption_settled": handle_consumption_settled,
+        "ai_note_created": handle_note_created,
+        "ai_task_assigned": handle_task_assigned,
+    }
+
+
+def register_ai_handlers(dispatcher: AIDispatcher) -> None:
+    """将 AI 事件处理器注册到 trigger_scheduler。
+
+    在 FastAPI lifespan 中调用，将三个 AI 事件处理器
+    注册为 trigger_scheduler 的 job handler。
+    """
+    from app.services.trigger_scheduler import register_job
+
+    handlers = _create_ai_event_handlers(dispatcher)
+    for job_type, handler in handlers.items():
+        register_job(job_type, handler)
+        logger.info("已注册 AI 事件处理器: %s", job_type)

apps/backend/app/ai/prompts/app2_finance_prompt.py

@@ -0,0 +1,145 @@
+"""应用 2 财务洞察 Prompt 模板。
+
+构建包含当期和上期收入结构的完整 Prompt，供百炼 API 生成财务洞察。
+
+收入字段映射（严格遵守 items_sum 口径）：
+- table_fee = table_charge_money（台费）
+- assistant_pd = assistant_pd_money（陪打费）
+- assistant_cx = assistant_cx_money（超休费）
+- goods = goods_money（商品收入）
+- recharge = 充值 pay_amount settle_type=5（充值收入）
+
+禁止使用 consume_money，统一使用：
+items_sum = table_charge_money + goods_money + assistant_pd_money
+            + assistant_cx_money + electricity_money
+"""
+
+from __future__ import annotations
+
+import json
+
+
+def build_prompt(context: dict) -> list[dict]:
+    """构建 App2 财务洞察 Prompt 消息列表。
+
+    Args:
+        context: 包含以下字段：
+            - site_id: int，门店 ID
+            - time_dimension: str，时间维度编码
+            - current_data: dict，当期数据
+            - previous_data: dict，上期数据
+
+    Returns:
+        messages 列表（system + user），供 BailianClient.chat_json 调用
+    """
+    site_id = context.get("site_id", 0)
+    time_dimension = context.get("time_dimension", "")
+    current_data = context.get("current_data", {})
+    previous_data = context.get("previous_data", {})
+
+    system_content = _build_system_content(
+        site_id=site_id,
+        time_dimension=time_dimension,
+        current_data=current_data,
+        previous_data=previous_data,
+    )
+
+    user_content = (
+        f"请根据以上数据，为门店 {site_id} 生成 {_dimension_label(time_dimension)} 的财务洞察分析。"
+        "以 JSON 格式返回，包含 insights 数组，每项含 seq（序号）、title（标题）、body（正文）。"
+    )
+
+    return [
+        {"role": "system", "content": json.dumps(system_content, ensure_ascii=False)},
+        {"role": "user", "content": user_content},
+    ]
+
+
+def _build_system_content(
+    *,
+    site_id: int,
+    time_dimension: str,
+    current_data: dict,
+    previous_data: dict,
+) -> dict:
+    """构建 system prompt JSON 结构。"""
+    return {
+        "task": (
+            "你是台球门店的财务分析 AI 助手。"
+            "根据提供的当期和上期经营数据，生成结构化的财务洞察。"
+            "分析维度包括：收入结构变化、各收入项占比、环比趋势、异常波动。"
+            "输出 JSON 格式：{\"insights\": [{\"seq\": 1, \"title\": \"...\", \"body\": \"...\"}]}"
+        ),
+        "data": {
+            "site_id": site_id,
+            "time_dimension": time_dimension,
+            "time_dimension_label": _dimension_label(time_dimension),
+            "current_period": _build_period_data(current_data),
+            "previous_period": _build_period_data(previous_data),
+        },
+        "reference": {
+            "field_mapping": {
+                "items_sum": (
+                    "table_charge_money + goods_money + assistant_pd_money"
+                    " + assistant_cx_money + electricity_money"
+                ),
+                "table_fee": "table_charge_money（台费收入）",
+                "assistant_pd": "assistant_pd_money（陪打费）",
+                "assistant_cx": "assistant_cx_money（超休费）",
+                "goods": "goods_money（商品收入）",
+                "recharge": "充值 pay_amount（settle_type=5，充值收入）",
+                "electricity": "electricity_money（电费，当前未启用，全为 0）",
+            },
+            "rules": [
+                "统一使用 items_sum 口径计算营收总额",
+                "助教费用必须拆分为 assistant_pd_money（陪打）和 assistant_cx_money（超休）",
+                "支付渠道恒等式：balance_amount = recharge_card_amount + gift_card_amount",
+                "金额单位：元（CNY），保留两位小数",
+            ],
+        },
+    }
+
+
+def _build_period_data(data: dict) -> dict:
+    """构建单期数据结构，确保字段名遵守 items_sum 口径。"""
+    return {
+        # 收入结构（items_sum 口径）
+        "table_charge_money": data.get("table_charge_money", 0),
+        "goods_money": data.get("goods_money", 0),
+        "assistant_pd_money": data.get("assistant_pd_money", 0),
+        "assistant_cx_money": data.get("assistant_cx_money", 0),
+        "electricity_money": data.get("electricity_money", 0),
+        # 充值收入
+        "recharge_income": data.get("recharge_income", 0),
+        # 储值资产
+        "balance_pay": data.get("balance_pay", 0),
+        "recharge_card_pay": data.get("recharge_card_pay", 0),
+        "gift_card_pay": data.get("gift_card_pay", 0),
+        # 费用汇总
+        "discount_amount": data.get("discount_amount", 0),
+        "adjust_amount": data.get("adjust_amount", 0),
+        # 平台结算
+        "platform_settlement_amount": data.get("platform_settlement_amount", 0),
+        "groupbuy_pay_amount": data.get("groupbuy_pay_amount", 0),
+        # 汇总
+        "order_count": data.get("order_count", 0),
+        "member_count": data.get("member_count", 0),
+    }
+
+
+# 时间维度编码 → 中文标签
+_DIMENSION_LABELS: dict[str, str] = {
+    "this_month": "本月",
+    "last_month": "上月",
+    "this_week": "本周",
+    "last_week": "上周",
+    "last_3_months": "近三个月",
+    "this_quarter": "本季度",
+    "last_quarter": "上季度",
+    "last_6_months": "近六个月",
+}
+
+
+def _dimension_label(dimension: str) -> str:
+    """将时间维度编码转为中文标签。"""
+    return _DIMENSION_LABELS.get(dimension, dimension)

apps/backend/app/ai/prompts/app8_consolidation_prompt.py

@@ -0,0 +1,93 @@
+"""应用 8：维客线索整理 Prompt 模板。
+
+接收 App3（消费分析）和 App6（备注分析）的全部线索，
+整合去重后输出统一维客线索。
+
+分类标签限定 6 个枚举值（与 member_retention_clue CHECK 约束一致）：
+客户基础、消费习惯、玩法偏好、促销偏好、社交关系、重要反馈。
+
+合并规则：
+- 相似线索合并，providers 以逗号分隔
+- 其余线索原文返回
+- 最小改动原则
+"""
+
+from __future__ import annotations
+
+import json
+
+
+def build_prompt(context: dict) -> list[dict]:
+    """构建 App8 维客线索整理 Prompt。
+
+    Args:
+        context: 包含以下字段：
+            - site_id: int
+            - member_id: int
+            - app3_clues: list[dict] — App3 产出的线索列表
+            - app6_clues: list[dict] — App6 产出的线索列表
+            - app3_generated_at: str | None — App3 线索生成时间
+            - app6_generated_at: str | None — App6 线索生成时间
+
+    Returns:
+        消息列表 [{"role": "system", ...}, {"role": "user", ...}]
+    """
+    member_id = context["member_id"]
+    app3_clues = context.get("app3_clues", [])
+    app6_clues = context.get("app6_clues", [])
+    app3_generated_at = context.get("app3_generated_at")
+    app6_generated_at = context.get("app6_generated_at")
+
+    system_content = {
+        "task": "整合去重来自消费分析和备注分析的维客线索，输出统一线索列表。",
+        "app_id": "app8_consolidation",
+        "rules": {
+            "category_enum": [
+                "客户基础", "消费习惯", "玩法偏好",
+                "促销偏好", "社交关系", "重要反馈",
+            ],
+            "merge_strategy": (
+                "相似线索合并为一条，providers 以逗号分隔（如 '系统,张三'）；"
+                "不相似的线索原文保留，不做修改。最小改动原则。"
+            ),
+            "output_format": {
+                "clues": [
+                    {
+                        "category": "枚举值（6 选 1）",
+                        "summary": "一句话摘要",
+                        "detail": "详细说明",
+                        "emoji": "表情符号",
+                        "providers": "提供者（逗号分隔）",
+                    }
+                ]
+            },
+        },
+        "input": {
+            "app3_clues": {
+                "source": "消费数据分析（App3）",
+                "generated_at": app3_generated_at,
+                "clues": app3_clues,
+            },
+            "app6_clues": {
+                "source": "备注分析（App6）",
+                "generated_at": app6_generated_at,
+                "clues": app6_clues,
+            },
+        },
+    }
+
+    user_content = (
+        f"请整合会员 {member_id} 的维客线索。\n"
+        "输入包含两个来源的线索：App3（消费数据分析）和 App6（备注分析）。\n"
+        "规则：\n"
+        "1. 相似线索合并为一条，providers 字段以逗号分隔多个提供者\n"
+        "2. 不相似的线索原文保留\n"
+        "3. 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"
+        "4. 每条线索包含 category、summary、detail、emoji、providers 五个字段\n"
+        "5. 最小改动原则，尽量保留原始表述"
+    )
+
+    return [
+        {"role": "system", "content": json.dumps(system_content, ensure_ascii=False)},
+        {"role": "user", "content": user_content},
+    ]