354 lines
12 KiB
Go
354 lines
12 KiB
Go
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// Package service - 对话服务
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// 功能：处理RAGFlow响应、批量落库、卡片触发逻辑
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package service
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import (
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"context"
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"customer-server/dao"
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"customer-server/model/entity"
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"gitea.com/red-future/common/jaeger"
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"gitea.com/red-future/common/rabbitmq"
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"gitea.com/red-future/common/redis"
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"github.com/gogf/gf/v2/encoding/gjson"
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"github.com/gogf/gf/v2/frame/g"
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"github.com/gogf/gf/v2/os/glog"
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"github.com/gogf/gf/v2/os/gtime"
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)
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// conversationService 对话服务（操作 conversation 表）
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type conversationService struct{}
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// ConversationService 对话服务单例
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var ConversationService = new(conversationService)
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// ============== RabbitMQ 消费者（RAGFlow 响应消息）==============
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// ResponseConsumer RabbitMQ 响应消费者
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type ResponseConsumer struct {
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queueName string
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consumer *rabbitmq.Consumer
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}
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// NewResponseConsumer 创建响应消费者
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// 参数: ctx - 上下文
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// 返回: ResponseConsumer - 响应消费者实例
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// 功能: 创建当前实例的唯一响应队列，支持多实例部署
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func NewResponseConsumer(ctx context.Context) *ResponseConsumer {
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// 从配置读取基础队列名（用于生成唯一实例队列名）
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baseQueue := GetConfigString(ctx, "rabbitmq.responseQueue")
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// 生成当前实例的唯一队列名：ragflow.response.queue.{hostname}.{uuid8}
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// 支持多实例部署，每个实例有独立响应队列
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queueName := rabbitmq.GetInstanceQueueName(baseQueue)
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glog.Infof(ctx, "响应队列动态生成 - 实例队列: %s", queueName)
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return &ResponseConsumer{
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queueName: queueName,
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}
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}
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// Start 启动消费者
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// 参数: ctx - 上下文
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// 返回: err - 错误信息
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// 功能: 声明并绑定当前实例的响应队列，开始消费RAGFlow响应消息
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func (c *ResponseConsumer) Start(ctx context.Context) (err error) {
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glog.Infof(ctx, "RabbitMQ 响应消费者启动 - Queue: %s", c.queueName)
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// 声明当前实例的动态响应队列
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if err = rabbitmq.DeclareQueue(ctx, &rabbitmq.QueueConfig{
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Name: c.queueName,
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Durable: true,
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}); err != nil {
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glog.Errorf(ctx, "声明动态响应队列失败: %v", err)
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return err
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}
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// 绑定队列到 Exchange（使用队列名作为routing key，实现精确路由）
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// message发送消息时会使用队列名作为routing key
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if err = rabbitmq.BindQueue(ctx, &rabbitmq.BindingConfig{
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Queue: c.queueName,
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Exchange: "ragflow.response",
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RoutingKey: c.queueName, // 使用队列名，只接收发给自己的消息
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}); err != nil {
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glog.Errorf(ctx, "绑定动态响应队列失败: %v", err)
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return err
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}
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glog.Infof(ctx, "动态响应队列已绑定: %s -> ragflow.response (routingKey=#)", c.queueName)
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c.consumer = rabbitmq.NewConsumer(c.queueName, handleResponse)
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return c.consumer.Start(ctx)
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}
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// Stop 停止消费者
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// 参数: ctx - 上下文
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// 功能: 停止消费RAGFlow响应消息
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func (c *ResponseConsumer) Stop(ctx context.Context) {
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if c.consumer != nil {
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c.consumer.Stop(ctx)
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}
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}
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// ============== 卡片触发配置（待接入小红书卡片接口后修改）==============
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//
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// 【用户状态存储】
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// 使用 Redis Hash 存储用户会话状态（阶段+对话计数），统一5分钟TTL
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// Key: ragflow:user:state:{userId}_{platform}
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// Fields: stage（阶段）、count（对话计数）
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//
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// 【状态定义】
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// 状态0：走AI模型 | 状态1：打招呼 | 状态2：业务咨询 | 状态3：发卡片
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//
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// 【卡片触发逻辑】
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// 对话轮数>=配置值时，更新状态为3，并发送卡片消息
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// 配置项：config.yml中的card.triggerCount（默认5轮）
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//
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// 【待接入小红书卡片 API 后修改位置】
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// checkAndSendCard() 函数中的 cardMessage 变量
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const (
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// ConversationFlushDelaySeconds 对话缓存延时落库时间（秒）
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ConversationFlushDelaySeconds = 600 // 10分钟
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)
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// ============== 延时落库消费者 ==============
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// DelayedFlushMessage 延时落库消息（按sessionId）
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type DelayedFlushMessage struct {
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SessionId string `json:"sessionId"`
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}
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// DelayedFlushConsumer 延时落库消费者
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type DelayedFlushConsumer struct {
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queueName string
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consumer *rabbitmq.Consumer
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}
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// NewDelayedFlushConsumer 创建延时落库消费者
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func NewDelayedFlushConsumer(ctx context.Context) *DelayedFlushConsumer {
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return &DelayedFlushConsumer{
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queueName: "conversation.flush.queue",
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}
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}
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// Start 启动消费者
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func (c *DelayedFlushConsumer) Start(ctx context.Context) (err error) {
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glog.Infof(ctx, "延时落库消费者启动 - Queue: %s", c.queueName)
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c.consumer = rabbitmq.NewConsumer(c.queueName, handleDelayedFlush)
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return c.consumer.Start(ctx)
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}
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// Stop 停止消费者
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func (c *DelayedFlushConsumer) Stop(ctx context.Context) {
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if c.consumer != nil {
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c.consumer.Stop(ctx)
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}
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}
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// handleDelayedFlush 处理延时落库消息
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func handleDelayedFlush(ctx context.Context, body []byte) error {
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var msg DelayedFlushMessage
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if err := gjson.DecodeTo(body, &msg); err != nil {
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glog.Errorf(ctx, "解析延时落库消息失败: %v", err)
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return err
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}
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glog.Infof(ctx, "收到延时落库消息 - SessionId: %s", msg.SessionId)
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// 检查是否有未落库的缓存
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count, err := redis.GetCachedConversationCount(ctx, msg.SessionId)
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if err != nil {
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glog.Errorf(ctx, "获取缓存数量失败: %v", err)
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return err
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}
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if count == 0 {
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glog.Debugf(ctx, "无需落库（缓存为空或已落库）- SessionId: %s", msg.SessionId)
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return nil
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}
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// 执行落库
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if err = flushConversationCache(ctx, msg.SessionId); err != nil {
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glog.Errorf(ctx, "延时落库失败: %v", err)
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return err
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}
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glog.Infof(ctx, "延时落库完成 - SessionId: %s", msg.SessionId)
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return nil
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}
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// 延时落库发布器（单例）
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var delayedFlushPublisher *rabbitmq.Publisher
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// getDelayedFlushPublisher 获取延时落库发布器
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func getDelayedFlushPublisher() *rabbitmq.Publisher {
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if delayedFlushPublisher == nil {
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delayedFlushPublisher = rabbitmq.NewPublisher("conversation.flush.delayed", "flush")
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}
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return delayedFlushPublisher
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}
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// sendDelayedFlushMessage 发送延时落库消息
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func sendDelayedFlushMessage(ctx context.Context, sessionId string) error {
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msg := &DelayedFlushMessage{
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SessionId: sessionId,
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}
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return getDelayedFlushPublisher().PublishDelayed(ctx, msg, ConversationFlushDelaySeconds)
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}
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// handleResponse 处理 RabbitMQ 消息（幂等）
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// 落库逻辑：前5句缓存到Redis，第5句时批量落库MongoDB，超过5句不落库
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func handleResponse(ctx context.Context, body []byte) error {
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ctx, span := jaeger.NewSpan(ctx, "consumer.response")
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defer span.End()
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glog.Infof(ctx, ">>> handleResponse 被调用，消息长度: %d", len(body))
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// 解析消息到结构体
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var msg redis.ResponseStreamMessage
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if err := gjson.DecodeTo(body, &msg); err != nil {
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jaeger.RecordError(ctx, err, "解析响应消息失败")
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return err
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}
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glog.Infof(ctx, "收到 RAGFlow 响应 - 用户: %s, MessageId: %s", msg.UserId, msg.MessageId)
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// 1. 获取当前对话轮数
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state, err := redis.GetUserState(ctx, msg.UserId, msg.Platform)
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if err != nil {
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jaeger.RecordError(ctx, err, "获取用户状态失败")
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}
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count := state.Count
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// 2. 根据轮数决定落库策略
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cardTriggerCount := g.Cfg().MustGet(ctx, "card.triggerCount", 5).Int64()
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if count <= cardTriggerCount {
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// 前N句：缓存到Redis（按sessionId）
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msgTime := gtime.NewFromTimeStamp(msg.Timestamp).Time
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conversation := &entity.Conversation{
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UserId: msg.UserId,
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Platform: msg.Platform,
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SessionId: msg.SessionId,
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Question: msg.Question,
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Answer: msg.Content,
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MessageId: msg.MessageId,
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MsgTime: &msgTime, // 取地址赋值给指针类型
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}
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conversation.TenantId = msg.TenantId
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// 序列化后缓存（使用sessionId作为key）
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data, _ := gjson.Encode(conversation)
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if cacheErr := redis.CacheConversation(ctx, msg.SessionId, data); cacheErr != nil {
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jaeger.RecordError(ctx, cacheErr, "缓存对话记录失败")
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} else {
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glog.Debugf(ctx, "对话已缓存到 Redis - SessionId: %s, 第 %d 轮", msg.SessionId, count)
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}
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// 第1句时：发送10分钟延时落库消息（兜底）
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if count == 1 {
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if delayErr := sendDelayedFlushMessage(ctx, msg.SessionId); delayErr != nil {
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glog.Warningf(ctx, "发送延时落库消息失败: %v", delayErr)
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}
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}
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// 第N句时：立即批量落库
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if count == cardTriggerCount {
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if flushErr := flushConversationCache(ctx, msg.SessionId); flushErr != nil {
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jaeger.RecordError(ctx, flushErr, "批量落库失败")
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}
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}
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} else {
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// 超过N句：不落库（已发卡片）
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glog.Debugf(ctx, "第 %d 轮（>%d），跳过落库 - 用户: %s", count, cardTriggerCount, msg.UserId)
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}
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// 3. 推送给 WebSocket 用户（无论是否落库都推送）
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glog.Infof(ctx, "准备推送 WebSocket - 用户: %s_%s, 内容长度: %d", msg.UserId, msg.Platform, len(msg.Content))
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if err = WebSocket.PushRAGFlowResponse(ctx, msg.TenantId, msg.UserId, msg.Platform, msg.Content); err != nil {
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jaeger.RecordError(ctx, err, "推送 WebSocket 失败")
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} else {
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glog.Infof(ctx, "WebSocket 推送成功 - 用户: %s_%s", msg.UserId, msg.Platform)
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}
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return nil
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}
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// flushConversationCache 将Redis缓存的对话批量落库到MongoDB
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func flushConversationCache(ctx context.Context, sessionId string) error {
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// 获取缓存的对话列表
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cached, err := redis.GetCachedConversations(ctx, sessionId)
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if err != nil {
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return err
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}
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if len(cached) == 0 {
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return nil
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}
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// 反序列化
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list := make([]*entity.Conversation, 0, len(cached))
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for _, data := range cached {
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var conv entity.Conversation
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if decErr := gjson.DecodeTo([]byte(data), &conv); decErr != nil {
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glog.Warningf(ctx, "反序列化对话失败: %v", decErr)
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continue
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}
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list = append(list, &conv)
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}
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// 批量插入MongoDB
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if len(list) > 0 {
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if insertErr := dao.Conversation.BatchInsert(ctx, list); insertErr != nil {
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return insertErr
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}
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glog.Infof(ctx, "批量落库成功 - SessionId: %s, 共 %d 条", sessionId, len(list))
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}
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return nil
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}
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// checkCardBeforeProcess 检查对话轮数，达到阈值时发卡片
|
|||
|
|
// 返回 handled=true 表示已处理（发送卡片），调用方应跳过后续话术处理
|
|||
|
|
func checkCardBeforeProcess(ctx context.Context, tenantId, userId, platform string) (handled bool, err error) {
|
|||
|
|
// 获取用户当前状态
|
|||
|
|
state, err := redis.GetUserState(ctx, userId, platform)
|
|||
|
|
if err != nil {
|
|||
|
|
return
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// 状态5（未选择方向）时不计数，等用户选择方向后再开始计数
|
|||
|
|
if state.Stage == 5 {
|
|||
|
|
glog.Debugf(ctx, "用户 %s_%s 处于状态5（未选择方向），跳过计数", userId, platform)
|
|||
|
|
return false, nil
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// 增加对话计数
|
|||
|
|
count, err := redis.IncrUserCount(ctx, userId, platform)
|
|||
|
|
if err != nil {
|
|||
|
|
return
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
glog.Infof(ctx, "用户 %s_%s 当前对话轮数: %d", userId, platform, count)
|
|||
|
|
|
|||
|
|
// 对话>=配置轮数，发卡片并跳过话术（从配置值开始就发卡片，不再调用AI）
|
|||
|
|
cardTriggerCount := g.Cfg().MustGet(ctx, "card.triggerCount", 5).Int64()
|
|||
|
|
if count >= cardTriggerCount {
|
|||
|
|
glog.Infof(ctx, "用户 %s_%s 对话第 %d 轮（>=%d），触发发送卡片", userId, platform, count, cardTriggerCount)
|
|||
|
|
|
|||
|
|
// 更新用户状态为3（发卡片状态）
|
|||
|
|
if updateErr := redis.SetUserStage(ctx, userId, platform, 3); updateErr != nil {
|
|||
|
|
jaeger.RecordError(ctx, updateErr, "更新用户状态为3失败")
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
cardMessage := "请加一下卡片的联系方式,进行更专业的咨询" // TODO: 替换为实际卡片发送逻辑
|
|||
|
|
if pushErr := WebSocket.PushRAGFlowResponse(ctx, tenantId, userId, platform, cardMessage); pushErr != nil {
|
|||
|
|
jaeger.RecordError(ctx, pushErr, "推送卡片消息失败")
|
|||
|
|
glog.Errorf(ctx, "推送卡片失败 - 用户: %s_%s, 错误: %v", userId, platform, pushErr)
|
|||
|
|
err = pushErr
|
|||
|
|
return
|
|||
|
|
}
|
|||
|
|
glog.Infof(ctx, "卡片消息已推送 - 用户: %s_%s", userId, platform)
|
|||
|
|
handled = true
|
|||
|
|
}
|
|||
|
|
return
|
|||
|
|
}
|