feat: 第四轮大版本更新 — 修复4个严重Bug、2个UI Bug，实现自主思考重构与主-子会话架构
## 🐛 Bug 修复 - 修复前端对话无响应：消除 ChatContainer 中的双重 WebSocket 连接，优化 sendMessage 失败提示 - 修复 Memory-Service 数据库迁移失败：ai-core 和 memory-service 均添加 ALTER TABLE ADD COLUMN IF NOT EXISTS 模式演化 - 修复语音/STT 不可用：添加 MediaRecorder API 降级方案，修复 whisper-cli 输出文件名错误 - 修复仪表盘数据库按钮失效：补充按钮 ID 属性，重写 controlDB() 控制逻辑 ## 🎨 UI 修复 - 修正用户消息头像位置：从 flex-row-reverse 改为 justify-end - 移除空聊天列表的 emoji 占位图标 ## ✨ 新功能 - devtools 新增 STT 处理日志面板（环形缓冲区 + WebSocket 广播 + 可视化表格） - 新增 ADMIN_NICKNAME 环境变量，支持自定义管理员昵称 ## 🔧 改进 - 注册流程增加昵称必填字段（前后端同步） ## 🏗️ 架构重构 - 重构自主思考逻辑：从定时器轮询改为事件驱动（对话后触发 + 静默检测），优化提示词使其更自然人性化 - 实现主-子会话架构：新增 4 种子会话类型（general/memory/iot/knowledge），意图分析 → 并行分发 → 结果合成流程 ## 📄 新增文档 - docs/architecture/main-session-sub-session-design.md — 子会话架构设计文档
This commit is contained in:
@@ -0,0 +1,193 @@
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package orchestrator
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import (
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"context"
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"encoding/json"
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"fmt"
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"log"
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"strings"
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"github.com/yourname/cyrene-ai/ai-core/internal/llm"
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"github.com/yourname/cyrene-ai/ai-core/internal/model"
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)
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// IntentAnalyzer 意图分析器
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// 使用轻量 LLM 调用判断用户消息的意图
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type IntentAnalyzer struct {
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llmAdapter *llm.Adapter
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enabled bool
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}
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// NewIntentAnalyzer 创建意图分析器
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func NewIntentAnalyzer(llmAdapter *llm.Adapter) *IntentAnalyzer {
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return &IntentAnalyzer{
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llmAdapter: llmAdapter,
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enabled: llmAdapter != nil,
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}
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}
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// Analyze 分析用户消息意图
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// 优先使用 LLM，失败时使用关键词规则降级
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func (a *IntentAnalyzer) Analyze(ctx context.Context, userMessage string) (*model.IntentResult, error) {
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// 如果 LLM 不可用，直接使用关键词匹配
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if !a.enabled || a.llmAdapter == nil {
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log.Printf("[intent] LLM 不可用，使用关键词规则分析意图")
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return a.keywordAnalyze(userMessage), nil
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}
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// 构建轻量意图分析提示词
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messages := []model.LLMMessage{
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{
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Role: model.RoleSystem,
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Content: intentAnalysisSystemPrompt,
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},
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{
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Role: model.RoleUser,
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Content: fmt.Sprintf("用户消息: %s", userMessage),
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},
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}
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// 调用 LLM (同步)
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resp, err := a.llmAdapter.Chat(ctx, messages)
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if err != nil {
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log.Printf("[intent] LLM 意图分析失败: %v，降级使用关键词规则", err)
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return a.keywordAnalyze(userMessage), nil
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}
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// 解析 JSON 响应
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intent, err := parseIntentResponse(resp.Content)
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if err != nil {
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log.Printf("[intent] 解析意图 JSON 失败: %v，降级使用关键词规则", err)
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return a.keywordAnalyze(userMessage), nil
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}
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log.Printf("[intent] 意图分析完成: primary=%s, iot=%v, memory=%v, sentiment=%s",
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intent.Primary, intent.NeedsIoT, intent.NeedsMemory, intent.Sentiment)
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return intent, nil
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}
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// keywordAnalyze 基于关键词的意图分析（降级方案）
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func (a *IntentAnalyzer) keywordAnalyze(userMessage string) *model.IntentResult {
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result := &model.IntentResult{
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Primary: "chat",
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NeedsMemory: true, // 默认检索记忆
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Sentiment: "neutral",
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Urgency: "low",
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}
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msgLower := strings.ToLower(userMessage)
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// IoT 关键词检测
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iotKeywords := []string{
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"灯", "空调", "窗帘", "电视", "设备", "开关",
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"打开", "关闭", "调到", "设置", "温度", "亮度",
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"传感器", "门锁", "插座", "风扇", "加湿器",
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}
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for _, kw := range iotKeywords {
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if strings.Contains(msgLower, kw) {
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result.NeedsIoT = true
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result.Primary = "iot_control"
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break
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}
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}
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// 情感检测
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positiveWords := []string{"开心", "高兴", "哈哈", "好棒", "喜欢", "爱", "谢谢", "棒", "赞", "太好了"}
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negativeWords := []string{"难过", "伤心", "生气", "烦", "累", "不开心", "讨厌", "恨", "糟糕", "烦死了"}
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for _, w := range positiveWords {
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if strings.Contains(msgLower, w) {
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result.Sentiment = "positive"
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break
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}
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}
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for _, w := range negativeWords {
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if strings.Contains(msgLower, w) {
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result.Sentiment = "negative"
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result.Primary = "emotional"
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break
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}
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}
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// 问题检测
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questionWords := []string{"什么", "怎么", "为什么", "如何", "谁", "哪里", "哪个", "多少", "能不能", "可以"}
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for _, w := range questionWords {
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if strings.Contains(msgLower, w) {
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result.Primary = "question"
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break
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}
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}
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return result
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}
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// intentAnalysisSystemPrompt 意图分析系统提示词 (轻量，快速返回)
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const intentAnalysisSystemPrompt = `分析以下用户消息的意图。只需返回 JSON，不要其他内容。
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返回格式:
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{
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"primary": "chat|iot_control|iot_query|question|emotional",
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"needs_iot": true/false,
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"needs_memory": true/false,
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"sentiment": "positive|neutral|negative",
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"urgency": "low|medium|high"
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}
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规则:
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- primary: 用户的主要意图
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- chat: 日常闲聊
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- iot_control: 需要控制智能设备
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- iot_query: 查询设备状态
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- question: 提问
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- emotional: 情绪表达/倾诉
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- needs_iot: 是否需要调用 IoT 相关功能
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- needs_memory: 是否需要检索用户记忆（大部分情况为 true）
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- sentiment: 用户情绪
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- urgency: low=普通闲聊, medium=需要回应, high=紧急求助`
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// parseIntentResponse 从 LLM 响应中解析意图 JSON
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func parseIntentResponse(content string) (*model.IntentResult, error) {
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// 尝试找到 JSON 块
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content = strings.TrimSpace(content)
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// 如果被 markdown 代码块包裹，提取内容
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if strings.HasPrefix(content, "```") {
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// 找到第一行换行符
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idx := strings.Index(content, "\n")
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if idx >= 0 {
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content = content[idx+1:]
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}
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// 找到结尾的 ```
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lastIdx := strings.LastIndex(content, "```")
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if lastIdx >= 0 {
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content = content[:lastIdx]
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}
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content = strings.TrimSpace(content)
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}
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// 尝试找到 JSON 对象
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startIdx := strings.Index(content, "{")
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endIdx := strings.LastIndex(content, "}")
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if startIdx >= 0 && endIdx > startIdx {
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content = content[startIdx : endIdx+1]
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}
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var result model.IntentResult
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if err := json.Unmarshal([]byte(content), &result); err != nil {
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return nil, fmt.Errorf("JSON 解析失败: %w", err)
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}
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// 设置默认值
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if result.Primary == "" {
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result.Primary = "chat"
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}
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if result.Sentiment == "" {
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result.Sentiment = "neutral"
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}
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if result.Urgency == "" {
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result.Urgency = "low"
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}
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return &result, nil
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}
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@@ -1,143 +1,297 @@
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package orchestrator
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import (
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"context"
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"fmt"
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"strings"
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"context"
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"fmt"
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"log"
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"time"
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"github.com/yourname/cyrene-ai/ai-core/internal/llm"
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"github.com/yourname/cyrene-ai/ai-core/internal/memory"
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"github.com/yourname/cyrene-ai/ai-core/internal/persona"
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ctxt "github.com/yourname/cyrene-ai/ai-core/internal/context"
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ctxbuild "github.com/yourname/cyrene-ai/ai-core/internal/context"
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"github.com/yourname/cyrene-ai/ai-core/internal/llm"
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"github.com/yourname/cyrene-ai/ai-core/internal/memory"
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"github.com/yourname/cyrene-ai/ai-core/internal/model"
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"github.com/yourname/cyrene-ai/ai-core/internal/persona"
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"github.com/yourname/cyrene-ai/ai-core/internal/subsession"
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)
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// Orchestrator 对话编排器 —— 核心组件
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// 当前MVP阶段由 main.go 内联处理，此结构体作为未来重构的基础
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// Orchestrator 对话编排器 v2.0
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// 负责：意图分析 → 子会话分派 → 结果汇总 → 综合生成回复
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type Orchestrator struct {
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personaLoader *persona.Loader
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contextBuilder *ctxt.Builder
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llmAdapter *llm.Adapter
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memoryExtractor *memory.Extractor
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memoryRetriever *memory.Retriever
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personaLoader *persona.Loader
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contextBuilder *ctxbuild.Builder
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llmAdapter *llm.Adapter
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subManager *subsession.Manager
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intentAnalyzer *IntentAnalyzer
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synthesizer *Synthesizer
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memoryRetriever *memory.Retriever
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memoryExtractor *memory.Extractor
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}
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// ProcessInput 处理用户输入的主流程
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// NewOrchestrator 创建编排器
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func NewOrchestrator(
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personaLoader *persona.Loader,
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contextBuilder *ctxbuild.Builder,
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llmAdapter *llm.Adapter,
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subManager *subsession.Manager,
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memoryRetriever *memory.Retriever,
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memoryExtractor *memory.Extractor,
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) *Orchestrator {
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return &Orchestrator{
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personaLoader: personaLoader,
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contextBuilder: contextBuilder,
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llmAdapter: llmAdapter,
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subManager: subManager,
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intentAnalyzer: NewIntentAnalyzer(llmAdapter),
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synthesizer: NewSynthesizer(llmAdapter),
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memoryRetriever: memoryRetriever,
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memoryExtractor: memoryExtractor,
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}
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}
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// ProcessParams 处理参数
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type ProcessParams struct {
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UserID string
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SessionID string
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Message string
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Mode string // text / voice_msg / voice_assistant
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Nickname string
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}
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// ProcessResult 处理结果
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type ProcessResult struct {
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FullContent string // 完整回复文本
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Mode string // 回复模式
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Segments []model.Segment // 断句片段
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Intent *model.IntentResult // 意图分析结果
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}
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// ProcessInput 处理用户输入 — 新的主入口
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// 返回流式事件通道
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func (o *Orchestrator) ProcessInput(
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ctx context.Context,
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userID string,
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sessionID string,
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userMessage string,
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mode string, // text / voice_msg / voice_assistant
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) (*Response, error) {
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ctx context.Context,
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params ProcessParams,
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) (<-chan model.StreamEvent, error) {
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// 步骤1: 检索相关记忆
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memories, err := o.memoryRetriever.Retrieve(ctx, userID, userMessage)
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if err != nil {
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// 记忆检索失败不阻断对话
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memories = nil
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}
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eventCh := make(chan model.StreamEvent, 100)
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// 步骤2: 加载人格配置
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personaConfig, err := o.personaLoader.Get("cyrene")
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if err != nil {
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return nil, fmt.Errorf("加载人格配置失败: %w", err)
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}
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if params.Mode == "" {
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params.Mode = "text"
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}
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// 步骤3: 构建对话上下文
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llmMessages, err := o.contextBuilder.Build(ctx, ctxt.BuildParams{
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UserID: userID,
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SessionID: sessionID,
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UserMessage: userMessage,
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Persona: personaConfig,
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Memories: memories,
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HistoryLimit: 20,
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})
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if err != nil {
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return nil, fmt.Errorf("构建上下文失败: %w", err)
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}
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go func() {
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defer close(eventCh)
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// 步骤4: 调用LLM生成回复
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llmResponse, err := o.llmAdapter.Chat(ctx, llmMessages)
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if err != nil {
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return nil, fmt.Errorf("LLM调用失败: %w", err)
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}
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// 1. 意图分析
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intent, err := o.intentAnalyzer.Analyze(ctx, params.Message)
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if err != nil || intent == nil {
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log.Printf("[orchestrator] 意图分析失败: %v，使用默认值", err)
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intent = &model.IntentResult{
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Primary: "chat",
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NeedsMemory: true,
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Sentiment: "neutral",
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Urgency: "low",
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}
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}
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// 步骤5: 提取并存储新的记忆
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go o.memoryExtractor.ExtractAndStore(
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context.Background(),
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userID, sessionID,
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userMessage, llmResponse.Content,
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)
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// 2. 加载人格配置
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personaConfig, err := o.personaLoader.Get("cyrene")
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if err != nil {
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eventCh <- model.StreamEvent{
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Type: model.StreamError,
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Error: fmt.Errorf("加载人格配置失败: %w", err),
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}
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return
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}
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// 步骤6: 构建响应
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response := &Response{
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Text: llmResponse.Content,
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ResponseMode: mode,
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}
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// 确定用户名
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userName := params.Nickname
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if userName == "" {
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userName = params.UserID
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}
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// 步骤7: 如果是语音助手模式，进行断句处理
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if mode == "voice_assistant" {
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response.Segments = splitIntoSegments(llmResponse.Content)
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}
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// 3. 分派子会话（并行执行）
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createParams := subsession.CreateContextParams{
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UserID: params.UserID,
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SessionID: params.SessionID,
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UserMessage: params.Message,
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PersonaConfig: personaConfig,
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Intent: intent,
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Nickname: userName,
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}
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return response, nil
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// 注入 userID 到 context 供 MemoryProvider 使用
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subCtx := context.WithValue(ctx, "userID", params.UserID)
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resultCh := o.subManager.Dispatch(subCtx, intent, params.Message, createParams)
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// 4. 收集子会话结果
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var results []model.SubSessionResult
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for result := range resultCh {
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results = append(results, result)
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}
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log.Printf("[orchestrator] 子会话全部完成: 收集到 %d 个结果", len(results))
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// 5. 汇总子会话结果
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agg := AggregateResults(results)
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// 6. 构建对话历史
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history := o.contextBuilder.GetHistory(params.SessionID, 20)
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// 7. 构建完整人格提示词
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systemPrompt := personaConfig.BuildSystemPrompt(userName, 1)
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// 8. 构建综合参数
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synthParams := SynthesizeParams{
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UserID: params.UserID,
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SessionID: params.SessionID,
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UserMessage: params.Message,
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Nickname: userName,
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PersonaPrompt: systemPrompt,
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DialogHistory: history,
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MemorySummary: agg.MemorySummary,
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ThoughtOutline: agg.ThoughtOutline,
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IoTSummary: agg.IoTSummary,
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Mode: params.Mode,
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}
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// 9. 调用 Synthesizer 流式生成最终回复
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chunkCh, err := o.synthesizer.Synthesize(ctx, synthParams)
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if err != nil {
|
||||
log.Printf("[orchestrator] 综合器启动失败: %v", err)
|
||||
eventCh <- model.StreamEvent{
|
||||
Type: model.StreamError,
|
||||
Error: fmt.Errorf("生成回复失败: %w", err),
|
||||
}
|
||||
return
|
||||
}
|
||||
|
||||
// 10. 流式输出 delta
|
||||
var fullContent string
|
||||
segmenter := llm.NewSegmenter()
|
||||
var segments []model.Segment
|
||||
|
||||
for chunk := range chunkCh {
|
||||
if chunk.Error != nil {
|
||||
log.Printf("[orchestrator] 流式错误: %v", chunk.Error)
|
||||
eventCh <- model.StreamEvent{
|
||||
Type: model.StreamError,
|
||||
Error: chunk.Error,
|
||||
}
|
||||
return
|
||||
}
|
||||
|
||||
if chunk.Done {
|
||||
if remaining := segmenter.Flush(); remaining != nil {
|
||||
segments = append(segments, model.Segment{
|
||||
Index: remaining.Index,
|
||||
Text: remaining.Text,
|
||||
})
|
||||
}
|
||||
break
|
||||
}
|
||||
|
||||
if chunk.Content != "" {
|
||||
fullContent += chunk.Content
|
||||
|
||||
// 实时断句
|
||||
newSegs := segmenter.Feed(chunk.Content)
|
||||
for _, s := range newSegs {
|
||||
segments = append(segments, model.Segment{
|
||||
Index: s.Index,
|
||||
Text: s.Text,
|
||||
})
|
||||
}
|
||||
|
||||
eventCh <- model.StreamEvent{
|
||||
Type: model.StreamDelta,
|
||||
Delta: chunk.Content,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// 11. 发送断句信息
|
||||
if len(segments) > 0 {
|
||||
eventCh <- model.StreamEvent{
|
||||
Type: model.StreamSegments,
|
||||
Segments: segments,
|
||||
}
|
||||
}
|
||||
|
||||
// 12. 完成
|
||||
eventCh <- model.StreamEvent{
|
||||
Type: model.StreamDone,
|
||||
}
|
||||
|
||||
// 13. 后处理：缓存回复
|
||||
if fullContent != "" {
|
||||
o.contextBuilder.CacheMessage(params.SessionID, model.RoleAssistant, fullContent)
|
||||
}
|
||||
|
||||
// 14. 异步提取记忆
|
||||
if o.memoryExtractor != nil && fullContent != "" {
|
||||
go o.memoryExtractor.ExtractAndStore(
|
||||
context.Background(),
|
||||
params.UserID,
|
||||
params.SessionID,
|
||||
params.Message,
|
||||
fullContent,
|
||||
)
|
||||
}
|
||||
|
||||
log.Printf("[orchestrator] 处理完成: intent=%s, content_len=%d, sub_results=%d",
|
||||
intent.Primary, len(fullContent), len(results))
|
||||
}()
|
||||
|
||||
return eventCh, nil
|
||||
}
|
||||
|
||||
// Response 回复结构
|
||||
type Response struct {
|
||||
Text string
|
||||
Segments []Segment
|
||||
ResponseMode string
|
||||
ToolCalls []ToolCall
|
||||
// ProcessInputSync 同步处理用户输入（兼容旧接口）
|
||||
func (o *Orchestrator) ProcessInputSync(
|
||||
ctx context.Context,
|
||||
params ProcessParams,
|
||||
) (*ProcessResult, error) {
|
||||
|
||||
eventCh, err := o.ProcessInput(ctx, params)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
result := &ProcessResult{
|
||||
Mode: params.Mode,
|
||||
}
|
||||
|
||||
for event := range eventCh {
|
||||
switch event.Type {
|
||||
case model.StreamError:
|
||||
return nil, event.Error
|
||||
case model.StreamDelta:
|
||||
result.FullContent += event.Delta
|
||||
case model.StreamSegments:
|
||||
result.Segments = event.Segments
|
||||
case model.StreamDone:
|
||||
// 完成
|
||||
}
|
||||
}
|
||||
|
||||
return result, nil
|
||||
}
|
||||
|
||||
// ToolCall 工具调用
|
||||
type ToolCall struct {
|
||||
Name string
|
||||
Arguments map[string]interface{}
|
||||
Result interface{}
|
||||
// GetHistory 获取会话历史（暴露给外部使用）
|
||||
func (o *Orchestrator) GetHistory(sessionID string, limit int) []model.LLMMessage {
|
||||
if o.contextBuilder == nil {
|
||||
return nil
|
||||
}
|
||||
return o.contextBuilder.GetHistory(sessionID, limit)
|
||||
}
|
||||
|
||||
// Segment 语音片段
|
||||
type Segment struct {
|
||||
Index int
|
||||
Text string
|
||||
}
|
||||
|
||||
// splitIntoSegments 按句号断句
|
||||
func splitIntoSegments(text string) []Segment {
|
||||
var segments []Segment
|
||||
runes := []rune(text)
|
||||
start := 0
|
||||
index := 0
|
||||
|
||||
for i, r := range runes {
|
||||
if isSentenceEnd(r) {
|
||||
segText := strings.TrimSpace(string(runes[start : i+1]))
|
||||
if segText != "" {
|
||||
index++
|
||||
segments = append(segments, Segment{Index: index, Text: segText})
|
||||
}
|
||||
start = i + 1
|
||||
}
|
||||
}
|
||||
|
||||
if start < len(runes) {
|
||||
remaining := strings.TrimSpace(string(runes[start:]))
|
||||
if remaining != "" {
|
||||
index++
|
||||
segments = append(segments, Segment{Index: index, Text: remaining})
|
||||
}
|
||||
}
|
||||
|
||||
return segments
|
||||
}
|
||||
|
||||
func isSentenceEnd(r rune) bool {
|
||||
switch r {
|
||||
case '。', '！', '？', '.', '!', '?', '\n':
|
||||
return true
|
||||
}
|
||||
return false
|
||||
// CacheMessage 缓存消息
|
||||
func (o *Orchestrator) CacheMessage(sessionID string, role model.Role, content string) {
|
||||
if o.contextBuilder != nil {
|
||||
o.contextBuilder.CacheMessage(sessionID, role, content)
|
||||
}
|
||||
}
|
||||
|
||||
// Ensure time, memory are used
|
||||
var _ = time.Now
|
||||
var _ = memory.NewRetriever
|
||||
|
||||
@@ -0,0 +1,157 @@
|
||||
package orchestrator
|
||||
|
||||
import (
|
||||
"context"
|
||||
"fmt"
|
||||
"log"
|
||||
"strings"
|
||||
|
||||
"github.com/yourname/cyrene-ai/ai-core/internal/llm"
|
||||
"github.com/yourname/cyrene-ai/ai-core/internal/model"
|
||||
)
|
||||
|
||||
// Synthesizer 主会话综合器
|
||||
// 汇总子会话结果，生成最终回复
|
||||
type Synthesizer struct {
|
||||
llmAdapter *llm.Adapter
|
||||
}
|
||||
|
||||
// NewSynthesizer 创建综合器
|
||||
func NewSynthesizer(llmAdapter *llm.Adapter) *Synthesizer {
|
||||
return &Synthesizer{
|
||||
llmAdapter: llmAdapter,
|
||||
}
|
||||
}
|
||||
|
||||
// SynthesizeParams 综合参数
|
||||
type SynthesizeParams struct {
|
||||
UserID string
|
||||
SessionID string
|
||||
UserMessage string
|
||||
Nickname string
|
||||
PersonaPrompt string // 完整人格提示词
|
||||
DialogHistory []model.LLMMessage // 对话历史
|
||||
MemorySummary string // 记忆检索摘要
|
||||
ThoughtOutline string // 通用对话思考
|
||||
IoTSummary string // IoT 操作摘要
|
||||
DeviceContext string // 设备状态上下文
|
||||
Mode string // text / voice_assistant
|
||||
}
|
||||
|
||||
// Synthesize 综合所有子会话结果，流式生成最终回复
|
||||
func (s *Synthesizer) Synthesize(ctx context.Context, params SynthesizeParams) (<-chan llm.StreamChunk, error) {
|
||||
messages := s.buildSynthesizeMessages(params)
|
||||
|
||||
log.Printf("[synthesizer] 开始综合 (上下文 %d 条消息)", len(messages))
|
||||
|
||||
// 流式调用 LLM
|
||||
return s.llmAdapter.ChatStream(ctx, messages)
|
||||
}
|
||||
|
||||
// buildSynthesizeMessages 构建综合用的 LLM 消息列表
|
||||
func (s *Synthesizer) buildSynthesizeMessages(params SynthesizeParams) []model.LLMMessage {
|
||||
var messages []model.LLMMessage
|
||||
|
||||
userName := params.Nickname
|
||||
if userName == "" {
|
||||
userName = params.UserID
|
||||
}
|
||||
|
||||
// 构建综合系统提示词
|
||||
systemPrompt := params.PersonaPrompt
|
||||
|
||||
// 注入设备上下文
|
||||
if params.DeviceContext != "" {
|
||||
systemPrompt += "\n\n" + params.DeviceContext
|
||||
}
|
||||
|
||||
messages = append(messages, model.LLMMessage{
|
||||
Role: model.RoleSystem,
|
||||
Content: systemPrompt,
|
||||
})
|
||||
|
||||
// 注入记忆摘要
|
||||
if params.MemorySummary != "" && !strings.Contains(params.MemorySummary, "没有找到") {
|
||||
messages = append(messages, model.LLMMessage{
|
||||
Role: model.RoleSystem,
|
||||
Content: fmt.Sprintf("【你回忆起的关于%s的事】\n%s", userName, params.MemorySummary),
|
||||
})
|
||||
}
|
||||
|
||||
// 注入通用对话思考
|
||||
if params.ThoughtOutline != "" && params.ThoughtOutline != "思考完成，等待主会话综合" {
|
||||
messages = append(messages, model.LLMMessage{
|
||||
Role: model.RoleSystem,
|
||||
Content: fmt.Sprintf("【你对%s这句话的理解】\n%s", userName, params.ThoughtOutline),
|
||||
})
|
||||
}
|
||||
|
||||
// 注入 IoT 操作摘要
|
||||
if params.IoTSummary != "" && !strings.Contains(params.IoTSummary, "未匹配") && !strings.Contains(params.IoTSummary, "未执行") {
|
||||
messages = append(messages, model.LLMMessage{
|
||||
Role: model.RoleSystem,
|
||||
Content: fmt.Sprintf("【IoT 设备操作结果】\n%s", params.IoTSummary),
|
||||
})
|
||||
}
|
||||
|
||||
// 注入对话历史
|
||||
if len(params.DialogHistory) > 0 {
|
||||
messages = append(messages, params.DialogHistory...)
|
||||
}
|
||||
|
||||
// 当前用户消息
|
||||
messages = append(messages, model.LLMMessage{
|
||||
Role: model.RoleUser,
|
||||
Content: params.UserMessage,
|
||||
})
|
||||
|
||||
return messages
|
||||
}
|
||||
|
||||
// AggregateResults 汇总子会话结果
|
||||
func AggregateResults(results []model.SubSessionResult) *AggregatedContext {
|
||||
agg := &AggregatedContext{
|
||||
MemorySummary: "",
|
||||
ThoughtOutline: "",
|
||||
IoTSummary: "",
|
||||
}
|
||||
|
||||
for _, r := range results {
|
||||
if r.Error != "" {
|
||||
log.Printf("[aggregate] 子会话 %s 出错: %s", r.Type, r.Error)
|
||||
continue
|
||||
}
|
||||
|
||||
switch r.Type {
|
||||
case model.SubSessionMemory:
|
||||
agg.MemorySummary = r.Summary
|
||||
if r.Details != "" {
|
||||
agg.MemorySummary += "\n" + r.Details
|
||||
}
|
||||
agg.MemorySnippets = r.Memories
|
||||
|
||||
case model.SubSessionGeneral:
|
||||
agg.ThoughtOutline = r.Summary
|
||||
if r.Details != "" {
|
||||
agg.ThoughtOutline += "\n" + r.Details
|
||||
}
|
||||
|
||||
case model.SubSessionIoT:
|
||||
agg.IoTSummary = r.Summary
|
||||
|
||||
case model.SubSessionKnowledge:
|
||||
agg.KnowledgeInfo = r.Summary
|
||||
}
|
||||
}
|
||||
|
||||
return agg
|
||||
}
|
||||
|
||||
// AggregatedContext 汇总后的上下文
|
||||
type AggregatedContext struct {
|
||||
MemorySummary string `json:"memory_summary"`
|
||||
ThoughtOutline string `json:"thought_outline"`
|
||||
IoTSummary string `json:"iot_summary"`
|
||||
KnowledgeInfo string `json:"knowledge_info"`
|
||||
MemorySnippets []model.MemorySnippet `json:"memory_snippets"`
|
||||
}
|
||||
Reference in New Issue
Block a user