439 lines
13 KiB
Go
439 lines
13 KiB
Go
package pet
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import (
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"context"
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"fmt"
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"time"
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"github.com/flipped-aurora/gin-vue-admin/server/global"
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"github.com/flipped-aurora/gin-vue-admin/server/model/pet"
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petRequest "github.com/flipped-aurora/gin-vue-admin/server/model/pet/request"
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petResponse "github.com/flipped-aurora/gin-vue-admin/server/model/pet/response"
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"github.com/flipped-aurora/gin-vue-admin/server/utils"
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"github.com/google/uuid"
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"go.uber.org/zap"
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)
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// 使用新定义的模型结构
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type ChatRequest = petRequest.ChatRequest
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type ChatResponse = petResponse.ChatResponse
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type StreamEvent = petResponse.StreamEvent
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// PetChatService 宠物聊天服务
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type PetChatService struct{}
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// SendMessage 发送消息（非流式）
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func (p *PetChatService) SendMessage(ctx context.Context, userId uint, req ChatRequest) (*ChatResponse, error) {
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startTime := time.Now()
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// 1. 敏感词检测
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sensitiveUtil := utils.GetSensitiveWordUtil()
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filtered, hasSensitive, err := sensitiveUtil.FilterText(req.Message)
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if err != nil {
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global.GVA_LOG.Error("敏感词检测失败", zap.Error(err))
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return nil, fmt.Errorf("敏感词检测失败: %v", err)
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}
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// 如果包含敏感词，记录并返回提示
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if hasSensitive {
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global.GVA_LOG.Warn("用户消息包含敏感词", zap.Uint("userId", userId), zap.String("original", req.Message), zap.String("filtered", filtered))
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// 保存用户消息（包含敏感词标记）
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if err := p.saveUserMessage(ctx, userId, req.SessionId, req.Message, true); err != nil {
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global.GVA_LOG.Error("保存用户消息失败", zap.Error(err))
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}
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return &ChatResponse{
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Message: "您的消息包含不当内容，请重新输入。",
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SessionId: req.SessionId,
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IsSensitive: true,
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TokenCount: 0,
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ResponseTime: time.Since(startTime).Milliseconds(),
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}, nil
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}
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// 2. 生成会话ID（如果没有提供）
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sessionId := req.SessionId
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if sessionId == "" {
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sessionId = uuid.New().String()
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}
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// 3. 获取对话历史构建上下文
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history, err := p.GetChatHistory(ctx, userId, sessionId, 10) // 获取最近10条消息
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if err != nil {
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global.GVA_LOG.Error("获取对话历史失败", zap.Error(err))
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return nil, fmt.Errorf("获取对话历史失败: %v", err)
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}
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// 4. 构建LLM请求
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messages := p.buildMessages(history, req.Message)
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llmReq := utils.LLMRequest{
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Messages: messages,
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Model: req.Model,
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Stream: false,
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Temperature: req.Temperature,
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MaxTokens: req.MaxTokens,
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RequestID: uuid.New().String(),
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}
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// 5. 调用LLM服务
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llmUtil := utils.GetVolcengineLLMUtil()
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llmResp, err := llmUtil.ChatCompletion(llmReq)
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if err != nil {
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global.GVA_LOG.Error("LLM调用失败", zap.Error(err))
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return nil, fmt.Errorf("LLM调用失败: %v", err)
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}
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// 6. 提取AI回复
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var aiMessage string
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if len(llmResp.Choices) > 0 {
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aiMessage = llmResp.Choices[0].Message.Content
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}
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// 7. 对AI回复进行敏感词检测
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aiFiltered, aiHasSensitive, err := sensitiveUtil.FilterText(aiMessage)
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if err != nil {
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global.GVA_LOG.Error("AI回复敏感词检测失败", zap.Error(err))
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aiFiltered = aiMessage // 如果检测失败，使用原始消息
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}
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if aiHasSensitive {
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global.GVA_LOG.Warn("AI回复包含敏感词", zap.String("original", aiMessage), zap.String("filtered", aiFiltered))
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aiMessage = aiFiltered
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}
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// 8. 保存对话记录
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responseTime := time.Since(startTime).Milliseconds()
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tokenCount := 0
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if llmResp.Usage != nil {
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tokenCount = llmResp.Usage.TotalTokens
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}
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// 保存用户消息
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if err := p.saveUserMessage(ctx, userId, sessionId, req.Message, false); err != nil {
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global.GVA_LOG.Error("保存用户消息失败", zap.Error(err))
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}
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// 保存AI回复
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if err := p.saveAssistantMessage(ctx, userId, sessionId, aiMessage, aiHasSensitive, tokenCount, responseTime); err != nil {
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global.GVA_LOG.Error("保存AI回复失败", zap.Error(err))
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}
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return &ChatResponse{
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Message: aiMessage,
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SessionId: sessionId,
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IsSensitive: aiHasSensitive,
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TokenCount: tokenCount,
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ResponseTime: responseTime,
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RequestId: llmReq.RequestID,
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}, nil
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}
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// StreamChat 流式聊天
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func (p *PetChatService) StreamChat(ctx context.Context, userId uint, req ChatRequest, eventChan chan<- StreamEvent) error {
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startTime := time.Now()
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// 1. 敏感词检测
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sensitiveUtil := utils.GetSensitiveWordUtil()
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filtered, hasSensitive, err := sensitiveUtil.FilterText(req.Message)
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if err != nil {
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global.GVA_LOG.Error("敏感词检测失败", zap.Error(err))
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eventChan <- StreamEvent{
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Event: "error",
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Data: map[string]interface{}{
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"error": "敏感词检测失败",
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},
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}
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return err
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}
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// 如果包含敏感词，返回提示并结束
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if hasSensitive {
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global.GVA_LOG.Warn("用户消息包含敏感词", zap.Uint("userId", userId), zap.String("original", req.Message), zap.String("filtered", filtered))
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// 保存用户消息（包含敏感词标记）
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if err := p.saveUserMessage(ctx, userId, req.SessionId, req.Message, true); err != nil {
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global.GVA_LOG.Error("保存用户消息失败", zap.Error(err))
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}
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eventChan <- StreamEvent{
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Event: "message",
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Data: ChatResponse{
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Message: "您的消息包含不当内容，请重新输入。",
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SessionId: req.SessionId,
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IsSensitive: true,
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TokenCount: 0,
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ResponseTime: time.Since(startTime).Milliseconds(),
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},
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}
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eventChan <- StreamEvent{
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Event: "done",
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Data: map[string]interface{}{"finished": true},
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}
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return nil
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}
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// 2. 生成会话ID（如果没有提供）
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sessionId := req.SessionId
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if sessionId == "" {
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sessionId = uuid.New().String()
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}
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// 3. 获取对话历史构建上下文
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history, err := p.GetChatHistory(ctx, userId, sessionId, 10)
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if err != nil {
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global.GVA_LOG.Error("获取对话历史失败", zap.Error(err))
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eventChan <- StreamEvent{
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Event: "error",
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Data: map[string]interface{}{
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"error": "获取对话历史失败",
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},
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}
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return err
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}
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// 4. 构建LLM请求
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messages := p.buildMessages(history, req.Message)
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llmReq := utils.LLMRequest{
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Messages: messages,
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Model: req.Model,
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Stream: true,
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Temperature: req.Temperature,
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MaxTokens: req.MaxTokens,
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RequestID: uuid.New().String(),
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}
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// 5. 创建LLM流式响应通道
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llmEventChan := make(chan utils.LLMStreamEvent, 100)
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defer close(llmEventChan)
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// 6. 启动LLM流式调用
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llmUtil := utils.GetVolcengineLLMUtil()
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go func() {
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if err := llmUtil.StreamChatCompletion(llmReq, llmEventChan); err != nil {
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global.GVA_LOG.Error("LLM流式调用失败", zap.Error(err))
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eventChan <- StreamEvent{
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Event: "error",
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Data: map[string]interface{}{
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"error": "LLM调用失败",
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},
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}
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}
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}()
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// 7. 处理流式响应
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var fullMessage string
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var tokenCount int
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for {
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select {
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case <-ctx.Done():
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return ctx.Err()
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case llmEvent, ok := <-llmEventChan:
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if !ok {
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// 通道关闭，流式响应结束
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return nil
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}
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switch llmEvent.Event {
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case "message":
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// 处理消息事件
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if len(llmEvent.Data.Choices) > 0 {
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delta := llmEvent.Data.Choices[0].Delta.Content
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fullMessage += delta
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// 转发消息给客户端
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eventChan <- StreamEvent{
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Event: "message",
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Data: map[string]interface{}{
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"delta": delta,
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"sessionId": sessionId,
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},
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}
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}
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case "done":
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// 流式响应完成
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responseTime := time.Since(startTime).Milliseconds()
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if llmEvent.Data.Usage != nil {
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tokenCount = llmEvent.Data.Usage.TotalTokens
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}
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// 对完整消息进行敏感词检测
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aiFiltered, aiHasSensitive, err := sensitiveUtil.FilterText(fullMessage)
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if err != nil {
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global.GVA_LOG.Error("AI回复敏感词检测失败", zap.Error(err))
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aiFiltered = fullMessage
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}
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if aiHasSensitive {
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global.GVA_LOG.Warn("AI回复包含敏感词", zap.String("original", fullMessage), zap.String("filtered", aiFiltered))
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fullMessage = aiFiltered
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}
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// 保存对话记录
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if err := p.saveUserMessage(ctx, userId, sessionId, req.Message, false); err != nil {
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global.GVA_LOG.Error("保存用户消息失败", zap.Error(err))
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}
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if err := p.saveAssistantMessage(ctx, userId, sessionId, fullMessage, aiHasSensitive, tokenCount, responseTime); err != nil {
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global.GVA_LOG.Error("保存AI回复失败", zap.Error(err))
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}
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// 发送完成事件
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eventChan <- StreamEvent{
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Event: "done",
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Data: ChatResponse{
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Message: fullMessage,
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SessionId: sessionId,
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IsSensitive: aiHasSensitive,
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TokenCount: tokenCount,
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ResponseTime: responseTime,
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RequestId: llmReq.RequestID,
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},
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}
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return nil
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case "error":
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// 处理错误事件
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global.GVA_LOG.Error("LLM流式响应错误", zap.Any("error", llmEvent.Data.Error))
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eventChan <- StreamEvent{
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Event: "error",
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Data: map[string]interface{}{
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"error": "LLM响应错误",
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},
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}
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return fmt.Errorf("LLM响应错误")
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}
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}
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}
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}
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// GetChatHistory 获取对话历史
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func (p *PetChatService) GetChatHistory(ctx context.Context, userId uint, sessionId string, limit int) ([]pet.PetAiAssistantConversations, error) {
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var conversations []pet.PetAiAssistantConversations
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query := global.GVA_DB.WithContext(ctx).Where("user_id = ?", userId)
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// 如果提供了会话ID，则按会话ID过滤
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if sessionId != "" {
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query = query.Where("session_id = ?", sessionId)
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}
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// 按创建时间倒序，限制数量
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if err := query.Order("created_at DESC").Limit(limit).Find(&conversations).Error; err != nil {
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return nil, err
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}
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// 反转切片，使其按时间正序排列
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for i, j := 0, len(conversations)-1; i < j; i, j = i+1, j-1 {
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conversations[i], conversations[j] = conversations[j], conversations[i]
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}
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return conversations, nil
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}
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// SaveConversation 保存对话记录
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func (p *PetChatService) SaveConversation(ctx context.Context, conversation *pet.PetAiAssistantConversations) error {
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return global.GVA_DB.WithContext(ctx).Create(conversation).Error
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}
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// ClearChatHistory 清空对话历史
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func (p *PetChatService) ClearChatHistory(ctx context.Context, userId uint, sessionId string) error {
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query := global.GVA_DB.WithContext(ctx).Where("user_id = ?", userId)
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// 如果提供了会话ID，则只清空指定会话
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if sessionId != "" {
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query = query.Where("session_id = ?", sessionId)
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}
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return query.Delete(&pet.PetAiAssistantConversations{}).Error
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}
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// GetChatSessions 获取用户的聊天会话列表
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func (p *PetChatService) GetChatSessions(ctx context.Context, userId uint) ([]map[string]interface{}, error) {
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var sessions []map[string]interface{}
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// 查询用户的所有会话，按最后更新时间分组
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err := global.GVA_DB.WithContext(ctx).
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Model(&pet.PetAiAssistantConversations{}).
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Select("session_id, MAX(updated_at) as last_updated, COUNT(*) as message_count").
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Where("user_id = ? AND session_id IS NOT NULL AND session_id != ''", userId).
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Group("session_id").
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Order("last_updated DESC").
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Scan(&sessions).Error
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return sessions, err
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}
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// buildMessages 构建LLM请求消息
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func (p *PetChatService) buildMessages(history []pet.PetAiAssistantConversations, userMessage string) []utils.LLMMessage {
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messages := []utils.LLMMessage{
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{
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Role: "system",
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Content: "你是一个专业的宠物助手，专门为宠物主人提供关于宠物护理、健康、训练和日常生活的建议。请用友善、专业的语气回答问题。",
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},
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}
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// 添加历史对话
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for _, conv := range history {
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if conv.MessageContent != nil && conv.Role != nil {
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messages = append(messages, utils.LLMMessage{
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Role: *conv.Role,
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Content: *conv.MessageContent,
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})
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}
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}
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// 添加当前用户消息
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messages = append(messages, utils.LLMMessage{
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Role: "user",
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Content: userMessage,
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})
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return messages
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}
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// saveUserMessage 保存用户消息
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func (p *PetChatService) saveUserMessage(ctx context.Context, userId uint, sessionId, message string, isSensitive bool) error {
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userIdPtr := int(userId)
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rolePtr := "user"
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messagePtr := message
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sessionIdPtr := sessionId
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isSensitivePtr := isSensitive
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conversation := &pet.PetAiAssistantConversations{
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UserId: &userIdPtr,
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MessageContent: &messagePtr,
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Role: &rolePtr,
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SessionId: &sessionIdPtr,
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IsSensitive: &isSensitivePtr,
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}
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return p.SaveConversation(ctx, conversation)
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}
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// saveAssistantMessage 保存AI助手消息
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func (p *PetChatService) saveAssistantMessage(ctx context.Context, userId uint, sessionId, message string, isSensitive bool, tokenCount int, responseTime int64) error {
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userIdPtr := int(userId)
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rolePtr := "assistant"
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messagePtr := message
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sessionIdPtr := sessionId
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isSensitivePtr := isSensitive
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tokenCountPtr := tokenCount
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responseTimePtr := int(responseTime)
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conversation := &pet.PetAiAssistantConversations{
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UserId: &userIdPtr,
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MessageContent: &messagePtr,
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Role: &rolePtr,
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SessionId: &sessionIdPtr,
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IsSensitive: &isSensitivePtr,
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TokenCount: &tokenCountPtr,
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ResponseTime: &responseTimePtr,
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}
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return p.SaveConversation(ctx, conversation)
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}
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