hankin
423baff73b
- Docker bridge 网络隔离（8000 端口封死） - Gunicorn 4 Worker 多进程 - Alembic 数据库迁移基线 - 日志轮转 20m×3 - JWT 密钥 + DB 密码 + CORS 收紧 - 3-2-1 备份链路（NAS + R740-B 冷备） - 连接池 pool_pre_ping + pool_recycle=3600
82 lines
2.4 KiB
Python
82 lines
2.4 KiB
Python
# -*- coding: utf-8 -*-
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"""
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客户沟通日志 API
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POST /api/v1/logs - 提交日志并触发后台 AI 标签提取
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"""
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import uuid
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from fastapi import APIRouter, BackgroundTasks, Depends, HTTPException, status
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from pydantic import BaseModel, Field
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.api.deps import get_current_user
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from app.core.database import get_db
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from app.models.crm_business import CustomerLog
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from app.services.ai_workflow import process_log_with_ai
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router = APIRouter()
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# ---- 请求/响应模型 ----
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class LogCreate(BaseModel):
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"""提交沟通日志请求"""
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customer_id: uuid.UUID = Field(..., description="关联客户 ID")
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content: str = Field(..., min_length=5, max_length=5000, description="沟通日志内容")
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class LogResponse(BaseModel):
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"""提交成功响应"""
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id: uuid.UUID
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message: str = "日志已提交，AI 正在后台分析标签和待办"
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# ---- 路由 ----
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@router.post(
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"",
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response_model=LogResponse,
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status_code=status.HTTP_200_OK,
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summary="提交客户沟通日志",
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tags=["客户日志"],
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)
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async def create_customer_log(
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body: LogCreate,
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background_tasks: BackgroundTasks,
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db: AsyncSession = Depends(get_db),
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current_user: dict = Depends(get_current_user),
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):
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"""
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接收前端提交的客户沟通日志：
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1. 立即存入 customer_logs 表
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2. 将 AI 标签提取任务加入 BackgroundTasks 后台队列
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3. 立即返回 200 OK（不等待 AI 处理完成）
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AI 后台任务会：
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- 调用 qwen3:14b 分析日志内容
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- 自动提取最多 3 个客户标签 → customer_tags
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- 自动生成 1 个跟进待办 → follow_up_todos
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"""
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# Step 1: 立即写入日志记录
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log = CustomerLog(
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customer_id=body.customer_id,
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content=body.content,
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)
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db.add(log)
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await db.flush()
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await db.refresh(log)
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# Step 2: 将 AI 处理加入后台队列
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# *** 关键：传入 log.id / body.content / body.customer_id 三个值 ***
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# process_log_with_ai 会创建独立的 DB Session，不与当前请求的 db 共享
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background_tasks.add_task(
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process_log_with_ai,
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log_id=log.id,
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content=body.content,
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customer_id=body.customer_id,
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)
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# Step 3: 立即返回（不等待 AI）
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return LogResponse(id=log.id)
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