feat(sidebar&conversation): 新增动态获取客户历史消息功能,完善参数传递

1. 为ScriptRecommend组件新增customerId参数,支持从父组件传入客户ID
2. 添加动态获取最后一条学员消息的逻辑,从后端API拉取会话历史
3. 为ArchiveMessageEvent类添加Jackson忽略未知属性注解
4. 新增部署文档和项目说明文档AGENTS.md、CLAUDE.md
This commit is contained in:
jiao 2026-06-01 10:48:50 +08:00
parent 6160691aa9
commit 4d8bc95063
9 changed files with 277 additions and 4 deletions

111
AGENTS.md Normal file
View File

@ -0,0 +1,111 @@
# Agent Instructions for 九艺AI坐席辅助系统
## 部署规则(核心)
### ⚠️ 任何代码修改后必须同步服务器部署
**无论前端、后台还是后端,任何代码修改完成后,都必须立即同步到服务器并重新部署,绝对不能只做本地修改就结束。**
---
## 一、前端 Sidebar 部署
**模块路径**: `frontend/sidebar/`
**服务器部署路径**: `/www/wwwroot/deploy-package/frontend/sidebar/`
**容器名称**: `ai-assistant-sidebar`
**访问地址**: `https://ai.9artedu.com/sidebar/`
### 部署流程
```bash
# 1. 本地构建
cd frontend/sidebar && npm run build
# 2. 同步到服务器
scp -r -o StrictHostKeyChecking=no -i root.pem frontend/sidebar/dist/* root@8.133.162.25:/www/wwwroot/deploy-package/frontend/sidebar/dist/
# 3. SSH 登录服务器,重新构建镜像并重启容器
ssh -o StrictHostKeyChecking=no -i root.pem root@8.133.162.25 << 'EOF'
cd /www/wwwroot/deploy-package/frontend/sidebar
docker build -t deploy-package-sidebar .
docker stop ai-assistant-sidebar
docker rm ai-assistant-sidebar
cd /www/wwwroot/deploy-package
docker-compose up -d sidebar
EOF
```
---
## 二、后台 Admin 部署
**模块路径**: `frontend/admin/`
**服务器部署路径**: `/www/wwwroot/deploy-package/frontend/admin/`
**容器名称**: `ai-assistant-admin`
**访问地址**: `https://ai.9artedu.com/admin/`
### 部署流程
```bash
# 1. 本地构建
cd frontend/admin && npm run build
# 2. 同步到服务器
scp -r -o StrictHostKeyChecking=no -i root.pem frontend/admin/dist/* root@8.133.162.25:/www/wwwroot/deploy-package/frontend/admin/dist/
# 3. SSH 登录服务器,重新构建镜像并重启容器
ssh -o StrictHostKeyChecking=no -i root.pem root@8.133.162.25 << 'EOF'
cd /www/wwwroot/deploy-package/frontend/admin
docker build -t deploy-package-admin .
docker stop ai-assistant-admin
docker rm ai-assistant-admin
cd /www/wwwroot/deploy-package
docker-compose up -d admin
EOF
```
---
## 三、后端微服务部署
**模块路径**: `backend/*-service/`
**服务器部署路径**: `/www/wwwroot/deploy-package/backend/*-service/`
**技术栈**: Spring Boot + Docker Compose
### 微服务列表
| 服务名 | 容器名 | 端口 | 部署路径 |
|--------|--------|------|----------|
| gateway | ai-assistant-gateway | 8080 | `backend/gateway/` |
| auth-service | ai-assistant-auth | 8081 | `backend/auth-service/` |
| archive-service | ai-assistant-archive | 8082 | `backend/archive-service/` |
| conversation-service | ai-assistant-conversation | 8083 | `backend/conversation-service/` |
| intent-service | ai-assistant-intent | 8084 | `backend/intent-service/` |
| recommend-service | ai-assistant-recommend | 8085 | `backend/recommendation-service/` |
| generation-service | ai-assistant-generation | 8086 | `backend/generation-service/` |
| kb-admin-service | ai-assistant-kbadmin | 8087 | `backend/kb-admin-service/` |
| analytics-service | ai-assistant-analytics | 8088 | `backend/analytics-service/` |
### 部署流程(以某个服务为例)
```bash
# 1. 本地构建 JAR 包(通常在对应服务目录下)
cd backend/auth-service && mvn clean package -DskipTests
# 2. 将 JAR 包同步到服务器的 deploy-package 对应目录
scp -o StrictHostKeyChecking=no -i root.pem target/*.jar root@8.133.162.25:/www/wwwroot/deploy-package/backend/auth-service/
# 3. SSH 登录服务器,重启对应容器
ssh -o StrictHostKeyChecking=no -i root.pem root@8.133.162.25 << 'EOF'
cd /www/wwwroot/deploy-package
docker-compose up -d --build auth-service
EOF
```
---
## 服务器信息
- **IP**: `8.133.162.25`
- **SSH 私钥**: `root.pem`(项目根目录)
- **部署根目录**: `/www/wwwroot/deploy-package/`
- **Docker Compose 文件**: `/www/wwwroot/deploy-package/docker-compose.yml`
- **前端站点**: `https://ai.9artedu.com/sidebar/`
- **后台站点**: `https://ai.9artedu.com/admin/`
- **代理**: 宝塔 Nginx → Docker Nginx (8089) → 各容器

120
CLAUDE.md Normal file
View File

@ -0,0 +1,120 @@
# CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
## Project Overview
九艺AI坐席辅助系统 — A real-time script recommendation system for course consultants/sales reps at 9artedu.com. It captures WeCom (企业微信) conversations, analyzes intent, builds customer profiles, and recommends sales scripts to agents via a sidebar H5 interface.
## Architecture
The system uses a microservices architecture with 10 backend services, all sharing a `common` module. An API Gateway routes requests by path prefix to individual services.
```
nginx (port 80)
→ gateway (port 8080) — Spring Cloud Gateway, routes by /api/v1/*
→ auth-service (8081) — WeCom OAuth, JWT, JS-SDK signing
→ archive-service (8082) — WeCom archive C SDK via JNA, RSA decrypt, callbacks
→ conversation-service (8083) — Session management, context windows (Redis List)
→ intent-service (8084) — Intent recognition (40 scenarios), customer profiles
→ recommendation-svc (8085) — 3-layer recall, multi-factor ranking, MMR reranking
→ generation-service (8086) — Tongyi Qianwen (DashScope) API, prompt engine
→ kb-admin-service (8087) — Script CRUD, category management, import/export
→ analytics-service (8088) — Statistics, script effectiveness analysis
→ admin frontend (port 5174) — React + Ant Design (script library management, dashboards)
→ sidebar frontend (port 5173) — React + Ant Design Mobile (agent-facing H5 sidebar)
```
### Key Design Patterns
- **Unified API response**: All services use `Result<T>` from `common` module (`code: 0` = success, `code: -1` = failure, with `message` and `timestamp`)
- **Pagination**: `PageResult<T>` for list responses
- **Database**: MyBatis-Plus with logical delete (`deleted` field: 0/1), auto-fill handler for timestamps
- **Config**: All services use environment variables for infra hosts (`MYSQL_HOST`, `REDIS_HOST`, `RABBITMQ_HOST`), allowing local dev with `localhost` and containerized with service names
- **Data pipeline**: WeCom callback → archive-service → RabbitMQ → conversation-service → intent-service → recommendation-service → WebSocket push to sidebar
## Development Commands
### Backend (Java 8 / Spring Boot 2.7.18)
```bash
# Build all services from backend/
cd backend && mvn clean package -DskipTests
# Run a single service locally (e.g., conversation-service)
cd backend/conversation-service && mvn spring-boot:run
# Run a specific test
cd backend/conversation-service && mvn test -Dtest=ConversationControllerTest
# Run all tests
cd backend && mvn test
```
### Frontend
```bash
# Sidebar (agent-facing H5)
cd frontend/sidebar && npm install && npm run dev
# Admin dashboard
cd frontend/admin && npm install && npm run dev
# Build for production
cd frontend/sidebar && npm run build
cd frontend/admin && npm run build
```
### Docker (full stack)
```bash
# Start all services
docker compose up -d
# Start only infrastructure (MySQL, Redis, RabbitMQ)
docker compose up -d mysql redis rabbitmq
# View logs for a service
docker compose logs -f conversation-service
# Rebuild and restart a specific service
docker compose up -d --build recommendation-service
```
### Database
- `init.sql` — Full schema (15+ tables) + seed data. Loaded into MySQL on first `docker compose up`.
- `backend/utterances_cg.sql` — CG training script seed data
- `add_utterances.sql` — Additional utterance data
## Service Ports Reference
| Service | Port |
|---------|------|
| Gateway | 8080 |
| Auth | 8081 |
| Archive | 8082 |
| Conversation | 8083 |
| Intent | 8084 |
| Recommendation | 8085 |
| Generation | 8086 |
| KB Admin | 8087 |
| Analytics | 8088 |
| Admin Frontend | 5174 |
| Sidebar Frontend | 5173 |
| Nginx (unified entry) | 80 |
## Tech Stack
- **Backend**: Spring Boot 2.7.18, Spring Cloud 2021.0.8, MyBatis-Plus 3.5.5, Java 8
- **Infra**: MySQL 5.7, Redis 6.x, RabbitMQ 3.x
- **Frontend**: React 18, TypeScript, Vite 5, Ant Design 5 / Ant Design Mobile 5
- **LLM**: DashScope (Tongyi Qianwen qwen-turbo/qwen-plus)
- **Auth**: JWT (jjwt 0.11.5), WeCom OAuth2
- **Deployment**: Docker + Docker Compose, Nginx
## Important Constraints
- **MySQL 5.7**: No native JSON functions beyond basics, no vector types. Vectors are stored in Redis.
- **Java 8**: All services compile to Java 1.8 (not the Dockerfile's Temurin 17 JRE, which is forward-compatible)
- **No Spring Cloud Discovery**: Services use static gateway routing, not Eureka/Consul

View File

@ -257,6 +257,7 @@ public class ConversationManager {
return content.length() > maxLength ? content.substring(0, maxLength) + "..." : content;
}
@com.fasterxml.jackson.annotation.JsonIgnoreProperties(ignoreUnknown = true)
public static class ArchiveMessageEvent {
private String msgid;
private String corpId;

View File

@ -215,7 +215,7 @@ function App() {
<Tabs activeKey={activeTab} onChange={setActiveTab}>
<Tabs.Tab title="话术推荐" key="recommend">
<ScriptRecommend userInfo={userInfo} />
<ScriptRecommend userInfo={userInfo} customerId={currentCustomerId} />
</Tabs.Tab>
<Tabs.Tab title="对话分析" key="analysis">
<ConversationAnalysis userInfo={userInfo} />

View File

@ -4,25 +4,66 @@ import type { Recommendation } from '../types'
interface Props {
userInfo?: any
customerId?: string
}
export default function ScriptRecommend({ userInfo }: Props) {
interface ConversationTurn {
turnNumber: number
studentContent: string
seatContent: string
}
export default function ScriptRecommend({ userInfo, customerId: propCustomerId }: Props) {
const [scripts, setScripts] = useState<Recommendation[]>([])
const [loading, setLoading] = useState(false)
const [generating, setGenerating] = useState(false)
const [customerMsg, setCustomerMsg] = useState('我想学游戏美术,零基础可以吗?')
const [customerMsg, setCustomerMsg] = useState('')
const [generated, setGenerated] = useState('')
// 从 localStorage 或企微环境获取真实身份信息
const sessionId = localStorage.getItem('current_session_id') || `session_${Date.now()}`
const customerId = localStorage.getItem('current_customer_id') || 'wx_001'
const customerId = propCustomerId || localStorage.getItem('current_customer_id') || 'wx_001'
const staffId = userInfo?.userId || 'staff_001'
const corpId = userInfo?.corpId || 'wwd483c2fba24ae30a'
// 动态获取最后一条学员消息
const fetchLastCustomerMessage = async () => {
if (!customerId || customerId === 'wx_001') return
try {
const res = await fetch(`/api/v1/conversations?customerId=${customerId}&corpId=${corpId}`)
const data = await res.json()
if (data.code === 0 && data.data && data.data.length > 0) {
// 取最新的会话
const sessions = data.data as Array<{ sessionId: string; startTime: string }>
const latestSession = sessions.sort(
(a, b) => new Date(b.startTime).getTime() - new Date(a.startTime).getTime()
)[0]
const turnsRes = await fetch(`/api/v1/conversations/${latestSession.sessionId}/turns`)
const turnsData = await turnsRes.json()
if (turnsData.code === 0 && turnsData.data && turnsData.data.length > 0) {
const turns = turnsData.data as ConversationTurn[]
// 取最后一条有内容的学员消息
for (let i = turns.length - 1; i >= 0; i--) {
if (turns[i].studentContent?.trim()) {
setCustomerMsg(turns[i].studentContent.trim())
break
}
}
}
}
} catch (e) {
console.error('获取最后一条学员消息失败:', e)
}
}
useEffect(() => {
loadRecommendations()
}, [])
useEffect(() => {
fetchLastCustomerMessage()
}, [customerId])
const loadRecommendations = async () => {
setLoading(true)
try {