qiji/backend/app/services/ai_summary.py

"""AI-powered weekly report summary using an OpenAI-compatible LLM."""

import json
import httpx
from app.config import settings
from app.services.dashboard import get_weekly_report, get_week_range
from app.services.light_board import get_light_board
from sqlalchemy.ext.asyncio import AsyncSession
from uuid import UUID
from datetime import date


SUMMARY_SYSTEM_PROMPT = """你是一位经验丰富的政企客户经理团队的周报分析助手。你的分析将被直接提交给支局长作为工作周报的文字摘要。

## 约束
- 严格基于提供的数据进行分析，绝不编造数据中不存在的信息
- 使用正式但不生硬的中文，适合放入政企工作周报
- 每条分析简洁有力，1-2句话即可，避免空泛套话
- 如果某个结论是基于数据推断的，请使用"数据显示""从本周情况看"等表述
- 对于覆盖不足的情况，请明确指出具体客户名称和负责人，方便支局长跟进

## 输出格式（使用 Markdown）

### 一、本周概况
[2-3句话，涵盖：拜访总量、覆盖客户数、团队参与情况、拜访方式分布]

### 二、拜访重点与客户需求
[2-3个值得关注的客户需求或沟通内容要点，有具体客户名称]

### 三、客户覆盖分析
[引用覆盖数据，明确指出：覆盖率、低于60%的经理、红灯客户名单、需要关注的客户]

### 四、下周建议
[2-3条针对性的工作建议，基于数据中暴露的问题和客户需求]"""


def build_summary_prompt(
    weekly_report: dict,
    light_board: dict,
    period: str,
    reference_date: str,
) -> str:
    """Build the user prompt with structured visit data for the LLM."""

    # ── Summary stats ──
    visits = weekly_report.get("visits", [])
    daily_notes = weekly_report.get("daily_notes", [])
    managers_involved: set[str] = set()
    customers_visited: set[str] = set()
    methods: dict[str, int] = {}
    demands: list[str] = []

    for v in visits:
        managers_involved.add(v.get("manager_name", ""))
        customers_visited.add(v.get("customer_name", ""))
        method = v.get("visit_method", "")
        methods[method] = methods.get(method, 0) + 1
        demand = v.get("customer_demand", "")
        if demand and demand.strip():
            demands.append(f"{v.get('customer_name', '未知')}: {demand.strip()}")

    # Manager breakdown
    manager_visits: dict[str, list] = {}
    for v in visits:
        mn = v.get("manager_name", "未知")
        if mn not in manager_visits:
            manager_visits[mn] = []
        manager_visits[mn].append({
            "client": v.get("customer_name", ""),
            "method": v.get("visit_method", ""),
            "content": (v.get("communication_content", "") or "")[:120],
            "demand": v.get("customer_demand", "") or "",
        })

    # Build the data block
    data_block = f"""## 基本信息
- 分析周期：{period}
- 参考日期：{reference_date}
- 周范围：{weekly_report.get('week_start', '')} — {weekly_report.get('week_end', '')}

## 拜访总览
- 拜访记录总数：{len(visits)}
- 覆盖客户数：{len(customers_visited)}
- 参与经理数：{len(managers_involved)}
- 拜访方式分布：{json.dumps(methods, ensure_ascii=False)}

## 各客户经理拜访明细
"""
    for mn, items in manager_visits.items():
        data_block += f"\n### {mn}（{len(items)}条）\n"
        for item in items[:10]:  # cap per manager
            data_block += f"- {item['method']}拜访 {item['client']}"
            if item['content']:
                data_block += f" — {item['content'][:100]}"
            if item['demand']:
                data_block += f" [需求: {item['demand'][:80]}]"
            data_block += "\n"

    # Customer demands
    if demands:
        data_block += "\n## 客户需求汇总\n"
        for d in demands[:15]:
            data_block += f"- {d[:200]}\n"

    # Daily notes summary
    notes_by_cat: dict[str, int] = {}
    for n in daily_notes:
        cat = n.get("category", "其他")
        notes_by_cat[cat] = notes_by_cat.get(cat, 0) + 1
    if notes_by_cat:
        data_block += "\n## 纪要分类统计\n"
        data_block += json.dumps(notes_by_cat, ensure_ascii=False) + "\n"

    # Light board data
    team = light_board.get("team_summary", {})
    data_block += f"""
## 客户覆盖数据（亮灯表）
- 团队总客户数：{team.get('total_customers', 0)}
- 本月已拜访（绿灯）：{team.get('visited_this_month', 0)}
- 仅上月拜访（黄灯）：{team.get('visited_last_month_only', 0)}
- 连续未拜访（红灯）：{team.get('not_visited_2months', 0)}
- 未分配客户：{team.get('unassigned', 0)}
- 整体覆盖率：{team.get('coverage_rate', 0) * 100:.1f}%

### 各经理覆盖率
"""
    for m in light_board.get("managers", []):
        data_block += (
            f"- {m['manager_name']}: {m['coverage_rate'] * 100:.0f}% "
            f"({m['visited_this_month']}/{m['total_customers']}) "
            f"🟢{m['visited_this_month']} 🟡{m['visited_last_month_only']} 🔴{m['not_visited_2months']}\n"
        )
        # List red customers
        red_customers = [c for c in m.get("customers", []) if c["status"] == "red"]
        if red_customers:
            data_block += "  红灯客户：\n"
            for rc in red_customers[:5]:
                lvd = rc.get("last_visit_date") or "从未"
                data_block += f"    - {rc['customer_name']}（上次拜访: {lvd}）\n"

    return data_block


async def generate_summary(
    db: AsyncSession,
    user_id: UUID,
    role: str,
    reference_date: date | None = None,
    period: str = "week",
) -> str:
    """Generate an AI-powered weekly summary.

    Raises ValueError if AI config is missing, httpx.HTTPError on API failure.
    """
    if not settings.AI_API_URL:
        raise ValueError("AI_API_URL not configured")

    # Gather data
    ref = reference_date or date.today()
    weekly_report = await get_weekly_report(
        db=db, user_id=user_id, role=role, reference_date=ref,
    )
    light_board = await get_light_board(db, ref)
    user_prompt = build_summary_prompt(weekly_report, light_board, period, str(ref))

    # Call LLM
    headers = {"Content-Type": "application/json"}
    if settings.AI_API_KEY:
        headers["Authorization"] = f"Bearer {settings.AI_API_KEY}"

    payload = {
        "model": settings.AI_MODEL,
        "messages": [
            {"role": "system", "content": SUMMARY_SYSTEM_PROMPT},
            {"role": "user", "content": user_prompt},
        ],
        "max_tokens": settings.AI_MAX_TOKENS,
        "temperature": 0.3,
    }

    async with httpx.AsyncClient(timeout=90.0) as client:
        resp = await client.post(settings.AI_API_URL, json=payload, headers=headers)
        resp.raise_for_status()
        result = resp.json()

    content = result.get("choices", [{}])[0].get("message", {}).get("content", "")
    if not content:
        raise ValueError("AI returned empty response")

    return content