v6ole
5b38246801
后端: - 新增 ai_summaries 表 (week_start/week_end/generated_by/summary) - get_cached_summary(): 按周+用户查缓存 - delete_cached_summary(): 生成前清理旧缓存 - generate_summary(): 自动保存到 DB - API: GET /ai/summary(加载) POST(生成) DELETE(清除) 前端: - onMounted 自动 GET 缓存摘要, 有则直接展示 - 按钮: 无缓存→'AI 生成摘要', 有缓存→'重新生成' - 标题栏显示生成时间戳
270 lines
9.1 KiB
Python
270 lines
9.1 KiB
Python
"""AI-powered weekly report summary using an OpenAI-compatible LLM."""
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import json
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import httpx
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from app.config import settings
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from app.services.dashboard import get_weekly_report, get_week_range
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from app.services.light_board import get_light_board
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from app.models.ai_summary import AISummary
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from sqlalchemy import select
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from sqlalchemy.ext.asyncio import AsyncSession
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from uuid import UUID
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from datetime import date, datetime
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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。
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## 约束
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- 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
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- 使用正式但不生硬的中文，适合放入政企工作周报
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- 每条分析简洁有力，1-2句话即可，避免空泛套话
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- 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
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- 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
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## 输出格式（使用 Markdown）
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### 一、本周概况
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[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]
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### 二、拜访重点与客户需求
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[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]
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### 三、客户覆盖分析
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[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]
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### 四、下周建议
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[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]"""
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def build_summary_prompt(
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weekly_report: dict,
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light_board: dict,
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period: str,
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reference_date: str,
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) -> str:
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"""Build the user prompt with structured visit data for the LLM."""
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# ── Summary stats ──
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visits = weekly_report.get("visits", [])
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daily_notes = weekly_report.get("daily_notes", [])
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managers_involved: set[str] = set()
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customers_visited: set[str] = set()
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methods: dict[str, int] = {}
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demands: list[str] = []
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for v in visits:
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managers_involved.add(v.get("manager_name", ""))
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customers_visited.add(v.get("customer_name", ""))
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method = v.get("visit_method", "")
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methods[method] = methods.get(method, 0) + 1
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demand = v.get("customer_demand", "")
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if demand and demand.strip():
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demands.append(f"{v.get('customer_name', '未知')}: {demand.strip()}")
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# Manager breakdown
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manager_visits: dict[str, list] = {}
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for v in visits:
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mn = v.get("manager_name", "未知")
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if mn not in manager_visits:
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manager_visits[mn] = []
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manager_visits[mn].append({
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"client": v.get("customer_name", ""),
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"method": v.get("visit_method", ""),
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"content": (v.get("communication_content", "") or "")[:120],
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"demand": v.get("customer_demand", "") or "",
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})
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# Build the data block
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data_block = f"""## 基本信息
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- 分析周期：{period}
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- 参考日期：{reference_date}
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- 周范围：{weekly_report.get('week_start', '')} — {weekly_report.get('week_end', '')}
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## 拜访总览
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- 拜访记录总数：{len(visits)}
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- 覆盖客户数：{len(customers_visited)}
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- 参与经理数：{len(managers_involved)}
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- 拜访方式分布：{json.dumps(methods, ensure_ascii=False)}
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## 各客户经理拜访明细
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"""
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for mn, items in manager_visits.items():
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data_block += f"\n### {mn}（{len(items)}条）\n"
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for item in items[:10]: # cap per manager
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data_block += f"- {item['method']}拜访 {item['client']}"
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if item['content']:
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data_block += f" — {item['content'][:100]}"
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if item['demand']:
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data_block += f" [需求: {item['demand'][:80]}]"
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data_block += "\n"
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# Customer demands
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if demands:
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data_block += "\n## 客户需求汇总\n"
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for d in demands[:15]:
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data_block += f"- {d[:200]}\n"
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# Daily notes summary
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notes_by_cat: dict[str, int] = {}
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for n in daily_notes:
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cat = n.get("category", "其他")
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notes_by_cat[cat] = notes_by_cat.get(cat, 0) + 1
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if notes_by_cat:
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data_block += "\n## 纪要分类统计\n"
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data_block += json.dumps(notes_by_cat, ensure_ascii=False) + "\n"
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# Light board data
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team = light_board.get("team_summary", {})
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data_block += f"""
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## 客户覆盖数据（亮灯表）
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- 团队总客户数：{team.get('total_customers', 0)}
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- 本月已拜访（绿灯）：{team.get('visited_this_month', 0)}
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- 仅上月拜访（黄灯）：{team.get('visited_last_month_only', 0)}
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- 连续未拜访（红灯）：{team.get('not_visited_2months', 0)}
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- 未分配客户：{team.get('unassigned', 0)}
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- 整体覆盖率：{team.get('coverage_rate', 0) * 100:.1f}%
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### 各经理覆盖率
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"""
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for m in light_board.get("managers", []):
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data_block += (
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f"- {m['manager_name']}: {m['coverage_rate'] * 100:.0f}% "
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f"({m['visited_this_month']}/{m['total_customers']}) "
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f"🟢{m['visited_this_month']} 🟡{m['visited_last_month_only']} 🔴{m['not_visited_2months']}\n"
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)
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# List red customers
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red_customers = [c for c in m.get("customers", []) if c["status"] == "red"]
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if red_customers:
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data_block += " 红灯客户：\n"
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for rc in red_customers[:5]:
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lvd = rc.get("last_visit_date") or "从未"
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data_block += f" - {rc['customer_name']}（上次拜访: {lvd}）\n"
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return data_block
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async def get_cached_summary(
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db: AsyncSession,
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user_id: UUID,
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reference_date: date | None = None,
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period: str = "week",
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) -> dict | None:
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"""Load a previously generated summary for this week/user."""
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ref = reference_date or date.today()
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monday, sunday = get_week_range(ref)
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result = await db.execute(
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select(AISummary)
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.where(
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AISummary.week_start == monday,
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AISummary.period == period,
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AISummary.generated_by == user_id,
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)
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.order_by(AISummary.created_at.desc())
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.limit(1)
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)
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row = result.scalar()
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if not row:
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return None
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return {
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"summary": row.summary,
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"week_start": str(row.week_start),
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"week_end": str(row.week_end),
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"period": row.period,
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"created_at": str(row.created_at),
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"cached": True,
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}
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async def delete_cached_summary(
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db: AsyncSession,
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user_id: UUID,
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reference_date: date | None = None,
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period: str = "week",
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) -> bool:
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"""Delete a cached summary so it can be regenerated."""
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ref = reference_date or date.today()
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monday, sunday = get_week_range(ref)
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result = await db.execute(
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select(AISummary).where(
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AISummary.week_start == monday,
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AISummary.period == period,
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AISummary.generated_by == user_id,
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)
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)
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rows = result.scalars().all()
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for row in rows:
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await db.delete(row)
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if rows:
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await db.commit()
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return len(rows) > 0
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async def generate_summary(
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db: AsyncSession,
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user_id: UUID,
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role: str,
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reference_date: date | None = None,
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period: str = "week",
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) -> dict:
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"""Generate an AI-powered weekly summary, save to DB, return it.
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Raises ValueError if AI config is missing, httpx.HTTPError on API failure.
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"""
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if not settings.AI_API_URL:
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raise ValueError("AI_API_URL not configured")
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# Gather data
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ref = reference_date or date.today()
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monday, sunday = get_week_range(ref)
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weekly_report = await get_weekly_report(
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db=db, user_id=user_id, role=role, reference_date=ref,
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)
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light_board = await get_light_board(db, ref, user_id, role)
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user_prompt = build_summary_prompt(weekly_report, light_board, period, str(ref))
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# Call LLM
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headers = {"Content-Type": "application/json"}
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if settings.AI_API_KEY:
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headers["Authorization"] = f"Bearer {settings.AI_API_KEY}"
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payload = {
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"model": settings.AI_MODEL,
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"messages": [
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{"role": "system", "content": SUMMARY_SYSTEM_PROMPT},
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{"role": "user", "content": user_prompt},
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],
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"max_tokens": settings.AI_MAX_TOKENS,
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"temperature": 0.3,
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}
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async with httpx.AsyncClient(timeout=90.0) as client:
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resp = await client.post(settings.AI_API_URL, json=payload, headers=headers)
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resp.raise_for_status()
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result = resp.json()
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content = result.get("choices", [{}])[0].get("message", {}).get("content", "")
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if not content:
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raise ValueError("AI returned empty response")
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# Delete old cached entry for this week/user, then save new
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await delete_cached_summary(db, user_id, reference_date=ref, period=period)
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row = AISummary(
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week_start=monday,
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week_end=sunday,
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period=period,
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generated_by=user_id,
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role=role,
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summary=content,
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)
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db.add(row)
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await db.commit()
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return {
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"summary": content,
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"week_start": str(monday),
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"week_end": str(sunday),
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"period": period,
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"created_at": str(row.created_at),
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"cached": False,
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}
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