fix: 修复 AI 回复无法送达发送者 + 重复消息 + action角色泄露 + OS环境支持
广播逻辑重构: - AI 回复 (stream_start/response/stream_segments/multi_message/stream_end) 改用 broadcastToUser 发送给所有客户端 - 用户消息回显保持 broadcastToUserExcept 排除发送者 消息去重与角色修复: - CacheMessage(user) 移至回复生成后，避免本轮 LLM 调用出现重复用户消息 - action 角色消息在 DB 存储时映射为 assistant，DeepSeek 等模型不支持自定义角色 - stream_end defer 机制确保错误路径也会终止客户端思考指示器 OS 完整环境支持: - host 包重构为 HostBackend 接口 + Direct/WSL/Docker 三种后端 - 新增 os_exec/os_file/os_system 工具供 AI 在完整 Linux 环境中自由操作 其他: - 视觉模型注入 + 图片预处理后清空 Images 避免传给 Chat 模型 - 图片 URL 相对路径→绝对 URL 转换 - DevTools 链路追踪页面 + 重启修复 - 记忆搜索模糊匹配增强 - 后台思考定时调度支持 - 管理后台页面 (模型配置/用户管理等) - docs/api 更新广播机制说明 Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
@@ -582,16 +582,7 @@ func (t *Thinker) performThink(triggerReason string) {
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return
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}
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// 2. 检索相关记忆
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var memories []memory.MemoryEntry
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if t.memRetriever != nil {
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memories, err = t.memRetriever.Retrieve(ctx, t.adminUserID, "最近发生了什么 重要的事情 用户偏好 个人信息")
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if err != nil {
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log.Printf("[后台思考] 记忆检索失败: %v", err)
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}
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}
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// 3. 获取当前活跃会话的对话历史（优先活跃会话，回退到管理员主会话）
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// 2. 获取当前活跃会话的对话历史（优先活跃会话，回退到管理员主会话）
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var convHistory []model.LLMMessage
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if t.convStore != nil {
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t.mu.Lock()
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@@ -608,6 +599,37 @@ func (t *Thinker) performThink(triggerReason string) {
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}
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}
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// 3. 检索相关记忆（精确检索 + 模糊搜索）
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var memories []memory.MemoryEntry
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if t.memRetriever != nil {
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memories, err = t.memRetriever.Retrieve(ctx, t.adminUserID, "最近发生了什么 重要的事情 用户偏好 个人信息")
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if err != nil {
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log.Printf("[后台思考] 记忆检索失败: %v", err)
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}
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// 模糊搜索：从对话历史提取话题，LLM 扩展关键词后语义搜索
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if t.memClient != nil && len(convHistory) > 0 {
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fuzzyQuery := lastUserMessage(convHistory)
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if fuzzyQuery == "" {
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fuzzyQuery = "最近对话 重要事件 用户状态"
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}
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fuzzyResults := t.fuzzyMemorySearch(ctx, t.adminUserID, fuzzyQuery)
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seen := make(map[string]bool)
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for _, m := range memories {
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seen[m.ID] = true
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}
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for _, m := range fuzzyResults {
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if !seen[m.ID] {
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seen[m.ID] = true
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memories = append(memories, m)
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}
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}
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if len(fuzzyResults) > 0 {
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log.Printf("[后台思考] 模糊搜索补充 %d 条记忆", len(fuzzyResults))
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}
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}
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}
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// 4. 查询 IoT 设备状态（每次都查询，无间隔限制）
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var deviceSummary string
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if t.iotClient != nil {
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@@ -750,10 +772,9 @@ func (t *Thinker) performThink(triggerReason string) {
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log.Printf("[后台思考] 完成 (触发原因=%s, 内容长度=%d, 工具调用=%d次)", triggerReason, len(finalContent), totalToolCalls)
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// 9. 周期性记忆维护（每 10 次思考触发一次）
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// 注：不再从思考结果中提取记忆——思考内容基于已有记忆生成，
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// 再次提取会造成"读取→思考→保存→再次读取"的重复循环。
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// 9. 记忆维护：机械合并(每10次) + LLM整理(每次)
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t.maybeMaintainMemories(currentCount)
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t.performMemoryConsolidation(ctx)
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}
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// buildThinkingSystemPrompt 构建思考用的系统提示词
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@@ -1196,6 +1217,375 @@ func (t *Thinker) maybeMaintainMemories(thinkCount int) {
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}
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}
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// consolidationAction is a parsed memory consolidation instruction from the LLM.
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type consolidationAction struct {
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Action string `json:"action"`
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IDs []string `json:"ids,omitempty"`
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ID string `json:"id,omitempty"`
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Content string `json:"content,omitempty"`
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Category string `json:"category,omitempty"`
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Importance int `json:"importance,omitempty"`
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Priority int `json:"priority,omitempty"`
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Keywords []string `json:"keywords,omitempty"`
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Reason string `json:"reason,omitempty"`
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}
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// performMemoryConsolidation uses LLM to review and clean up the memory store.
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// It identifies duplicates, contradictions, outdated info, and low-quality memories,
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// then executes merge/delete/update actions.
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func (t *Thinker) performMemoryConsolidation(ctx context.Context) {
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if t.memClient == nil {
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return
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}
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allMemories, err := t.memClient.Query(ctx, model.MemoryQuery{
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UserID: t.adminUserID,
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Limit: 200,
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})
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if err != nil {
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log.Printf("[记忆整理] 获取记忆失败: %v", err)
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return
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}
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if len(allMemories) < 5 {
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return
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}
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log.Printf("[记忆整理] LLM 审查 %d 条记忆...", len(allMemories))
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systemPrompt := t.buildConsolidationPrompt(allMemories)
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messages := []model.LLMMessage{
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{Role: model.RoleSystem, Content: systemPrompt},
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{Role: model.RoleUser, Content: "请审查以上记忆库，找出重复、矛盾、过时和低质量的记忆，输出 JSON 整理方案。如果没有需要整理的，输出空数组 []。"},
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}
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resp, err := t.toolAdapter.Chat(ctx, messages)
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if err != nil {
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log.Printf("[记忆整理] LLM 调用失败: %v", err)
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return
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}
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actions := parseConsolidationActions(resp.Content)
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if len(actions) == 0 {
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log.Printf("[记忆整理] 记忆库状态良好，无需整理")
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return
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}
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log.Printf("[记忆整理] LLM 建议 %d 项操作", len(actions))
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executed := t.executeConsolidationActions(ctx, actions, allMemories)
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log.Printf("[记忆整理] 完成: 执行了 %d 项操作", executed)
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}
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// buildConsolidationPrompt formats all memories as a structured list for LLM review.
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func (t *Thinker) buildConsolidationPrompt(memories []model.MemoryEntry) string {
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var sb strings.Builder
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sb.WriteString("你是记忆库管理助手。审查以下用户记忆，找出问题并输出 JSON 操作清单。\n\n")
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sb.WriteString("## 需要识别的问题\n")
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sb.WriteString("1. 重复记忆 — 多条记忆记录了相同信息 → merge 合并为一条\n")
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sb.WriteString("2. 矛盾记忆 — 两条记忆互相矛盾（如\"喜欢猫\"vs\"讨厌猫\"）→ delete 删除过时的、update 修正错误的\n")
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sb.WriteString("3. 过时记忆 — 信息已被新记忆取代 → delete 或 update\n")
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sb.WriteString("4. 低质量记忆 — 太模糊、不完整、无实际信息量 → delete\n\n")
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sb.WriteString("## JSON 操作格式\n")
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sb.WriteString("```json\n[\n")
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sb.WriteString(" {\"action\":\"merge\", \"ids\":[\"id1\",\"id2\"], \"content\":\"合并后的内容\", \"category\":\"personal_info\", \"importance\":8, \"reason\":\"两条记录同一件事\"},\n")
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sb.WriteString(" {\"action\":\"delete\", \"id\":\"id3\", \"reason\":\"完全被 id1 覆盖\"},\n")
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sb.WriteString(" {\"action\":\"update\", \"id\":\"id4\", \"content\":\"修正后的内容\", \"importance\":7, \"reason\":\"纠正过时信息\"},\n")
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sb.WriteString(" {\"action\":\"create\", \"content\":\"需要补充的记忆\", \"category\":\"knowledge\", \"importance\":6, \"reason\":\"从已有记忆推断\"}\n")
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sb.WriteString("]\n```\n\n")
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sb.WriteString("## 规则\n")
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sb.WriteString("- 只输出 JSON 数组，可以用 ```json``` 包裹，不要输出其他解释文字\n")
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sb.WriteString("- 确实有问题才建议操作，不要强行找问题\n")
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sb.WriteString("- merge 时保留最重要的那条的 ID，合并内容应包含各条的关键信息\n")
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sb.WriteString("- 不确定时宁可保守（不操作）\n")
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sb.WriteString("- importance 范围 1-10，数字越大越重要\n")
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sb.WriteString("- category 可选: personal_info, user_preference, conversation, knowledge, event, task, relationship\n\n")
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sb.WriteString(fmt.Sprintf("## 当前记忆库 (%d 条)\n\n", len(memories)))
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for i, m := range memories {
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sb.WriteString(fmt.Sprintf("%d. [%s] **%s** | cat=%s imp=%d pri=%d | src=%s\n",
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i+1, m.ID[:min(8, len(m.ID))], m.Content,
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m.Category, m.Importance, m.Priority, m.Source))
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}
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return sb.String()
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}
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// parseConsolidationActions extracts JSON actions from LLM response text.
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func parseConsolidationActions(text string) []consolidationAction {
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// Try to extract from ```json fences first
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jsonStr := text
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if idx := strings.Index(text, "```json"); idx >= 0 {
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start := idx + 7
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if end := strings.Index(text[start:], "```"); end >= 0 {
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jsonStr = text[start : start+end]
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}
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} else if idx := strings.Index(text, "```"); idx >= 0 {
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start := idx + 3
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if end := strings.Index(text[start:], "```"); end >= 0 {
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jsonStr = text[start : start+end]
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}
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}
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// Find the JSON array
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arrStart := strings.Index(jsonStr, "[")
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arrEnd := strings.LastIndex(jsonStr, "]")
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if arrStart < 0 || arrEnd <= arrStart {
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return nil
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}
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jsonStr = jsonStr[arrStart : arrEnd+1]
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var actions []consolidationAction
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if err := json.Unmarshal([]byte(jsonStr), &actions); err != nil {
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log.Printf("[记忆整理] JSON 解析失败: %v", err)
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return nil
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}
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return actions
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}
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// executeConsolidationActions runs the parsed consolidation actions against the memory store.
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func (t *Thinker) executeConsolidationActions(ctx context.Context, actions []consolidationAction, memories []model.MemoryEntry) int {
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// Index memories by their short ID prefix for lookup
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memByShortID := make(map[string]*model.MemoryEntry)
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for i := range memories {
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short := memories[i].ID[:min(8, len(memories[i].ID))]
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memByShortID[short] = &memories[i]
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}
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memByFullID := make(map[string]*model.MemoryEntry)
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for i := range memories {
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memByFullID[memories[i].ID] = &memories[i]
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}
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executed := 0
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for _, a := range actions {
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switch a.Action {
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case "delete":
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id := resolveID(a.ID, memByShortID, memByFullID)
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if id == "" {
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log.Printf("[记忆整理] 跳过 delete: 找不到记忆 %s", a.ID)
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continue
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}
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if err := t.memClient.Delete(ctx, id); err != nil {
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log.Printf("[记忆整理] 删除 %s 失败: %v", a.ID, err)
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continue
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}
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log.Printf("[记忆整理] 已删除: %s (原因: %s)", a.ID, a.Reason)
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executed++
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case "merge":
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if len(a.IDs) < 2 {
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continue
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}
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// Resolve all IDs, use first as the keeper
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var resolved []string
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for _, mid := range a.IDs {
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if rid := resolveID(mid, memByShortID, memByFullID); rid != "" {
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resolved = append(resolved, rid)
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}
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}
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if len(resolved) < 2 {
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continue
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}
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keeper := resolved[0]
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// Update the keeper with merged content
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cat := model.MemoryCategory(a.Category)
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if cat == "" {
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if m, ok := memByFullID[keeper]; ok {
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cat = m.Category
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}
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}
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imp := a.Importance
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if imp == 0 {
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if m, ok := memByFullID[keeper]; ok {
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imp = m.Importance + 1
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}
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}
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if imp > 10 {
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imp = 10
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}
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pri := a.Priority
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if pri == 0 {
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if m, ok := memByFullID[keeper]; ok {
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pri = int(m.Priority)
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}
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}
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if err := t.memClient.Update(ctx, &model.MemoryEntry{
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ID: keeper,
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Content: a.Content,
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Category: cat,
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Importance: imp,
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Priority: model.MemoryPriority(pri),
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Keywords: a.Keywords,
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Source: "consolidated",
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}); err != nil {
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log.Printf("[记忆整理] 更新合并目标 %s 失败: %v", keeper, err)
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continue
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}
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// Delete the discarded ones
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for _, did := range resolved[1:] {
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if err := t.memClient.Delete(ctx, did); err != nil {
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log.Printf("[记忆整理] 删除被合并记忆 %s 失败: %v", did, err)
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}
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}
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log.Printf("[记忆整理] 已合并: %v -> %s (原因: %s)", resolved, keeper, a.Reason)
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executed++
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case "update":
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id := resolveID(a.ID, memByShortID, memByFullID)
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if id == "" {
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log.Printf("[记忆整理] 跳过 update: 找不到记忆 %s", a.ID)
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continue
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}
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existing := memByFullID[id]
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cat := model.MemoryCategory(a.Category)
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if cat == "" && existing != nil {
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cat = existing.Category
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}
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imp := a.Importance
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if imp == 0 && existing != nil {
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imp = existing.Importance
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}
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pri := a.Priority
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if pri == 0 && existing != nil {
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pri = int(existing.Priority)
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}
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if err := t.memClient.Update(ctx, &model.MemoryEntry{
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ID: id,
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Content: a.Content,
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Category: cat,
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Importance: imp,
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Priority: model.MemoryPriority(pri),
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Keywords: a.Keywords,
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Source: "consolidated",
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}); err != nil {
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log.Printf("[记忆整理] 更新 %s 失败: %v", id, err)
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continue
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}
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log.Printf("[记忆整理] 已更新: %s (原因: %s)", id, a.Reason)
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executed++
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case "create":
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cat := model.MemoryCategory(a.Category)
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if cat == "" {
|
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cat = model.CategoryKnowledge
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}
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imp := a.Importance
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if imp == 0 {
|
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imp = 5
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}
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if err := t.memClient.Save(ctx, &model.MemoryEntry{
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UserID: t.adminUserID,
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Content: a.Content,
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Category: cat,
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Importance: imp,
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Priority: model.MemoryNormal,
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Keywords: a.Keywords,
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Source: "consolidation",
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}); err != nil {
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log.Printf("[记忆整理] 创建记忆失败: %v", err)
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continue
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}
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log.Printf("[记忆整理] 已创建: %s (原因: %s)", a.Content, a.Reason)
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executed++
|
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}
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}
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return executed
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}
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// resolveID tries to match a short or full ID to an existing memory.
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func resolveID(id string, byShort, byFull map[string]*model.MemoryEntry) string {
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if _, ok := byFull[id]; ok {
|
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return id
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}
|
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if m, ok := byShort[id]; ok {
|
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return m.ID
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}
|
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// Try prefix match
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for fullID := range byFull {
|
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if strings.HasPrefix(fullID, id) {
|
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return fullID
|
||||
}
|
||||
}
|
||||
return ""
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||||
}
|
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|
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// fuzzyMemorySearch expands the query via LLM keyword extraction and performs semantic search.
|
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func (t *Thinker) fuzzyMemorySearch(ctx context.Context, userID, query string) []memory.MemoryEntry {
|
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if t.memClient == nil {
|
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return nil
|
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}
|
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|
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keywords := t.expandMemoryKeywords(ctx, query)
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if len(keywords) == 0 {
|
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return nil
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}
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|
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log.Printf("[后台思考] 模糊记忆关键词: %v", keywords)
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|
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var allResults []memory.MemoryEntry
|
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seen := make(map[string]bool)
|
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|
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for _, kw := range keywords {
|
||||
results, err := t.memClient.QueryByText(ctx, userID, kw, "", 0, 5)
|
||||
if err != nil {
|
||||
log.Printf("[后台思考] 模糊搜索 '%s' 失败: %v", kw, err)
|
||||
continue
|
||||
}
|
||||
for _, m := range results {
|
||||
if !seen[m.ID] {
|
||||
seen[m.ID] = true
|
||||
allResults = append(allResults, m)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return allResults
|
||||
}
|
||||
|
||||
// expandMemoryKeywords uses LLM to generate fuzzy/related keywords for memory search.
|
||||
func (t *Thinker) expandMemoryKeywords(ctx context.Context, message string) []string {
|
||||
prompt := fmt.Sprintf(
|
||||
"从以下对话消息中提取 3-5 个可用于模糊搜索记忆的关键词。这些关键词应该是：\n"+
|
||||
"- 与话题相关的抽象概念\n- 同义词和相关词\n- 更宽泛或更具体的相关概念\n"+
|
||||
"- 不要包含消息中已经出现的原词\n\n"+
|
||||
"用户消息：「%s」\n\n"+
|
||||
"只输出 JSON 字符串数组，例如：[\"关键词1\",\"关键词2\"]", message)
|
||||
|
||||
resp, err := t.llmAdapter.Chat(ctx, []model.LLMMessage{
|
||||
{Role: model.RoleSystem, Content: "你是记忆搜索专家。输出 JSON 字符串数组。"},
|
||||
{Role: model.RoleUser, Content: prompt},
|
||||
})
|
||||
if err != nil {
|
||||
log.Printf("[后台思考] 关键词扩展失败: %v", err)
|
||||
return nil
|
||||
}
|
||||
|
||||
text := strings.TrimSpace(resp.Content)
|
||||
if idx := strings.Index(text, "["); idx >= 0 {
|
||||
if end := strings.LastIndex(text, "]"); end > idx {
|
||||
text = text[idx : end+1]
|
||||
}
|
||||
}
|
||||
|
||||
var keywords []string
|
||||
if err := json.Unmarshal([]byte(text), &keywords); err != nil {
|
||||
log.Printf("[后台思考] 解析关键词 JSON 失败: %v (raw=%s)", err, resp.Content)
|
||||
return nil
|
||||
}
|
||||
|
||||
return keywords
|
||||
}
|
||||
|
||||
// lastUserMessage extracts the last user message from conversation history.
|
||||
func lastUserMessage(history []model.LLMMessage) string {
|
||||
for i := len(history) - 1; i >= 0; i-- {
|
||||
if history[i].Role == model.RoleUser {
|
||||
runes := []rune(history[i].Content)
|
||||
if len(runes) > 200 {
|
||||
return string(runes[:200])
|
||||
}
|
||||
return history[i].Content
|
||||
}
|
||||
}
|
||||
return ""
|
||||
}
|
||||
|
||||
// formatDeviceContext 格式化设备状态为文本
|
||||
func formatDeviceContext(devices []tools.IoTDevice) string {
|
||||
if len(devices) == 0 {
|
||||
|
||||
Reference in New Issue
Block a user