ba8e8e2a82
后端： 1. 记忆系统移除 todo_hint 类型——随口记已由 Task 系统承接，todo_hint 语义重叠且无完成追踪 - 全链路清理：常量、校验、默认重要度、30 天 TTL、读取预算、LLM 抽取提示词枚举 - 总预算从四类收缩为三类（preference / constraint / fact） 2. 记忆抽取触发点从 chat-persist 移至 graph-completion——避免随口记消息被误提取为 constraint/preference - chat-persist consumer 不再自动入队 memory.extract.requested，仅负责聊天历史落库 - graph 完成后新增条件发布：检测 UsedQuickNote 标记，调用过 quick_note_create 则跳过记忆抽取 - ResetForNextRun 重置 UsedQuickNote，防止跨轮残留导致后续正常消息记忆抽取被误跳过 3. 任务类查询接口返回 items 补充数据库主键 ID（前端拖拽编排依赖此字段） 前端： 4. 排程视图新增手动编排模式——侧边栏任务块拖拽入周课表 + 悬浮删除热区 + 建议块虚线标识 - TaskClassSidebar 拖拽发起 + 预览态嵌入时间格式化（含周次/星期） - WeekPlanningBoard 外部拖入 / 内部移动 / 悬浮删除区交互 - ScheduleView 手动编排状态机（进入/退出/取消/覆盖确认）+ apply 时同步处理新增与删除
484 lines
15 KiB
Go
484 lines
15 KiB
Go
package worker
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import (
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"context"
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"encoding/json"
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"errors"
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"fmt"
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"log"
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"strings"
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"time"
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infrarag "github.com/LoveLosita/smartflow/backend/infra/rag"
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memorymodel "github.com/LoveLosita/smartflow/backend/memory/model"
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memoryobserve "github.com/LoveLosita/smartflow/backend/memory/observe"
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memoryorchestrator "github.com/LoveLosita/smartflow/backend/memory/orchestrator"
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memoryrepo "github.com/LoveLosita/smartflow/backend/memory/repo"
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memoryutils "github.com/LoveLosita/smartflow/backend/memory/utils"
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memoryvectorsync "github.com/LoveLosita/smartflow/backend/memory/vectorsync"
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"github.com/LoveLosita/smartflow/backend/model"
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"gorm.io/gorm"
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)
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// RunOnceResult 描述单次手工触发执行的结果。
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type RunOnceResult struct {
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Claimed bool
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JobID int64
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Status string
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Facts int
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}
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// Runner 负责把 memory_jobs 推进成 memory_items 和审计日志。
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//
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// 职责边界：
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// 1. 负责任务抢占、抽取、落库和状态推进；
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// 2. 不负责 outbox 消费，也不负责 LLM prompt 组装；
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// 3. 失败时只做可恢复的状态回写，避免把业务错误直接抛到启动层。
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type Runner struct {
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db *gorm.DB
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jobRepo *memoryrepo.JobRepo
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itemRepo *memoryrepo.ItemRepo
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auditRepo *memoryrepo.AuditRepo
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settingsRepo *memoryrepo.SettingsRepo
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extractor Extractor
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ragRuntime infrarag.Runtime
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logger *log.Logger
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vectorSyncer *memoryvectorsync.Syncer
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observer memoryobserve.Observer
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metrics memoryobserve.MetricsRecorder
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// 决策层依赖。
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// 说明：
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// 1. cfg 提供决策层配置（是否启用、TopK、MinScore、FallbackMode）；
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// 2. decisionOrchestrator 在决策启用时负责 LLM 逐对比较，为 nil 时走旧路径。
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cfg memorymodel.Config
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decisionOrchestrator *memoryorchestrator.LLMDecisionOrchestrator
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}
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// NewRunner 构造记忆 worker 执行器。
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func NewRunner(
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db *gorm.DB,
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jobRepo *memoryrepo.JobRepo,
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itemRepo *memoryrepo.ItemRepo,
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auditRepo *memoryrepo.AuditRepo,
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settingsRepo *memoryrepo.SettingsRepo,
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extractor Extractor,
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ragRuntime infrarag.Runtime,
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cfg memorymodel.Config,
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decisionOrchestrator *memoryorchestrator.LLMDecisionOrchestrator,
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vectorSyncer *memoryvectorsync.Syncer,
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observer memoryobserve.Observer,
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metrics memoryobserve.MetricsRecorder,
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) *Runner {
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if observer == nil {
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observer = memoryobserve.NewNopObserver()
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}
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if metrics == nil {
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metrics = memoryobserve.NewNopMetrics()
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}
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return &Runner{
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db: db,
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jobRepo: jobRepo,
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itemRepo: itemRepo,
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auditRepo: auditRepo,
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settingsRepo: settingsRepo,
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extractor: extractor,
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ragRuntime: ragRuntime,
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logger: log.Default(),
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vectorSyncer: vectorSyncer,
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observer: observer,
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metrics: metrics,
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cfg: cfg,
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decisionOrchestrator: decisionOrchestrator,
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}
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}
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// RunOnce 手工执行一轮任务处理。
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//
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// 返回语义：
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// 1. Claimed=false 表示当前没有可执行任务；
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// 2. Claimed=true 且 Status=success/failed/dead 表示本轮已经推进过一个任务；
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// 3. 只有初始化缺失或数据库级错误才返回 error。
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func (r *Runner) RunOnce(ctx context.Context) (*RunOnceResult, error) {
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if r == nil || r.db == nil || r.jobRepo == nil || r.itemRepo == nil || r.auditRepo == nil || r.settingsRepo == nil || r.extractor == nil {
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return nil, errors.New("memory worker runner is not initialized")
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}
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// 1. 先抢占一条可执行任务，避免多个 worker 重复处理同一条记录。
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job, err := r.jobRepo.ClaimNextRunnableExtractJob(ctx, time.Now())
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if err != nil {
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return nil, err
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}
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if job == nil {
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return &RunOnceResult{Claimed: false}, nil
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}
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if job.RetryCount > 0 {
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r.metrics.AddCounter(memoryobserve.MetricJobRetryTotal, 1, map[string]string{
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"job_type": strings.TrimSpace(job.JobType),
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})
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}
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result := &RunOnceResult{
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Claimed: true,
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JobID: job.ID,
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Status: model.MemoryJobStatusProcessing,
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Facts: 0,
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}
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// 2. 解析任务载荷。这里属于数据质量问题，解析失败就直接标记为可重试失败。
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var payload memorymodel.ExtractJobPayload
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if err = json.Unmarshal([]byte(job.PayloadJSON), &payload); err != nil {
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failReason := fmt.Sprintf("解析任务载荷失败: %v", err)
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_ = r.jobRepo.MarkFailed(ctx, job.ID, failReason)
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result.Status = model.MemoryJobStatusFailed
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r.recordJobOutcome(ctx, job, nil, result.Status, false, err)
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return result, nil
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}
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// 3. 先读取用户记忆设置。总开关关闭时，任务直接成功结束，不再继续抽取和落库。
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setting, err := r.settingsRepo.GetByUserID(ctx, payload.UserID)
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if err != nil {
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r.recordJobOutcome(ctx, job, &payload, model.MemoryJobStatusFailed, false, err)
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return nil, err
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}
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effectiveSetting := memoryutils.EffectiveUserSetting(setting, payload.UserID)
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if !effectiveSetting.MemoryEnabled {
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if err = r.jobRepo.MarkSuccess(ctx, job.ID); err != nil {
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r.recordJobOutcome(ctx, job, &payload, model.MemoryJobStatusFailed, false, err)
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return nil, err
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}
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result.Status = model.MemoryJobStatusSuccess
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r.logger.Printf("memory worker skipped by user setting: job_id=%d user_id=%d", job.ID, payload.UserID)
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r.recordJobOutcome(ctx, job, &payload, result.Status, true, nil)
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return result, nil
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}
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// 4. 调用抽取器。LLM 失败时由编排器做保守 fallback，worker 只关心最终结果。
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facts, extractErr := r.extractor.ExtractFacts(ctx, payload)
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if extractErr != nil {
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failReason := fmt.Sprintf("抽取执行失败: %v", extractErr)
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_ = r.jobRepo.MarkFailed(ctx, job.ID, failReason)
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result.Status = model.MemoryJobStatusFailed
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r.recordJobOutcome(ctx, job, &payload, result.Status, false, extractErr)
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return result, nil
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}
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facts = memoryutils.FilterFactsBySetting(facts, effectiveSetting)
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facts = memoryutils.FilterFactsByConfidence(facts, r.cfg.WriteMinConfidence)
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if len(facts) == 0 {
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if err = r.jobRepo.MarkSuccess(ctx, job.ID); err != nil {
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r.recordJobOutcome(ctx, job, &payload, model.MemoryJobStatusFailed, false, err)
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return nil, err
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}
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result.Status = model.MemoryJobStatusSuccess
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r.logger.Printf("memory worker run once noop: job_id=%d", job.ID)
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r.recordJobOutcome(ctx, job, &payload, result.Status, true, nil)
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return result, nil
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}
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items := buildMemoryItems(job, payload, facts)
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if len(items) == 0 {
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if err = r.jobRepo.MarkSuccess(ctx, job.ID); err != nil {
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r.recordJobOutcome(ctx, job, &payload, model.MemoryJobStatusFailed, false, err)
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return nil, err
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}
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result.Status = model.MemoryJobStatusSuccess
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r.logger.Printf("memory worker run once empty-after-normalize: job_id=%d", job.ID)
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r.recordJobOutcome(ctx, job, &payload, result.Status, true, nil)
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return result, nil
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}
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// 5. 根据配置选择写入路径：决策层 or 旧路径。
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if r.cfg.DecisionEnabled && r.decisionOrchestrator != nil {
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// 5a. 决策路径：召回→比对→汇总→执行。
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outcome, decisionErr := r.executeDecisionFlow(ctx, job, payload, facts)
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if decisionErr != nil {
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// 决策流程整体失败，根据 FallbackMode 决定是否退回旧路径。
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r.logger.Printf("[WARN][去重] 决策流程整体失败: job_id=%d user_id=%d facts_count=%d fallback=%s err=%v", job.ID, payload.UserID, len(facts), r.cfg.DecisionFallbackMode, decisionErr)
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if r.cfg.DecisionFallbackMode == "legacy_add" {
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if err = r.persistMemoryWrite(ctx, job.ID, items); err != nil {
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failReason := fmt.Sprintf("决策降级后记忆落库失败: %v", err)
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_ = r.jobRepo.MarkFailed(ctx, job.ID, failReason)
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result.Status = model.MemoryJobStatusFailed
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r.recordJobOutcome(ctx, job, &payload, result.Status, false, err)
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return result, nil
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}
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result.Status = model.MemoryJobStatusSuccess
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result.Facts = len(items)
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r.syncMemoryVectors(ctx, items)
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r.recordJobOutcome(ctx, job, &payload, result.Status, true, nil)
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return result, nil
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}
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// FallbackMode=drop：丢弃本轮抽取结果，直接标记 job 成功。
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_ = r.jobRepo.MarkSuccess(ctx, job.ID)
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result.Status = model.MemoryJobStatusSuccess
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r.recordJobOutcome(ctx, job, &payload, result.Status, true, nil)
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return result, nil
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}
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// 5b. 决策成功：同步向量（新增/更新）和删除过期向量。
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result.Status = model.MemoryJobStatusSuccess
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result.Facts = outcome.AddCount + outcome.UpdateCount + outcome.DeleteCount
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r.syncMemoryVectors(ctx, outcome.ItemsToSync)
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r.syncVectorDeletes(ctx, outcome.VectorDeletes)
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r.logger.Printf("[去重] 决策流程完成: job_id=%d user_id=%d 新增=%d 更新=%d 删除=%d 跳过=%d",
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job.ID, payload.UserID, outcome.AddCount, outcome.UpdateCount, outcome.DeleteCount, outcome.NoneCount)
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r.recordJobOutcome(ctx, job, &payload, result.Status, true, nil)
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return result, nil
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}
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// 5c. 旧路径：和现在完全一样 — 先在事务里写入记忆条目和审计日志，再统一确认 job 成功。
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if err = r.persistMemoryWrite(ctx, job.ID, items); err != nil {
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failReason := fmt.Sprintf("记忆落库失败: %v", err)
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_ = r.jobRepo.MarkFailed(ctx, job.ID, failReason)
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result.Status = model.MemoryJobStatusFailed
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r.recordJobOutcome(ctx, job, &payload, result.Status, false, err)
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return result, nil
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}
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result.Status = model.MemoryJobStatusSuccess
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result.Facts = len(items)
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r.syncMemoryVectors(ctx, items)
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r.logger.Printf("memory worker run once success: job_id=%d extracted_facts=%d", job.ID, len(items))
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r.recordJobOutcome(ctx, job, &payload, result.Status, true, nil)
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return result, nil
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}
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func (r *Runner) persistMemoryWrite(ctx context.Context, jobID int64, items []model.MemoryItem) error {
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return r.db.WithContext(ctx).Transaction(func(tx *gorm.DB) error {
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jobRepo := r.jobRepo.WithTx(tx)
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itemRepo := r.itemRepo.WithTx(tx)
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auditRepo := r.auditRepo.WithTx(tx)
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if err := itemRepo.UpsertItems(ctx, items); err != nil {
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return err
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}
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for i := range items {
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audit := memoryutils.BuildItemAuditLog(
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items[i].ID,
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items[i].UserID,
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memoryutils.AuditOperationCreate,
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"system",
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"LLM 提取入库",
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nil,
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&items[i],
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)
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if err := auditRepo.Create(ctx, audit); err != nil {
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return err
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}
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}
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return jobRepo.MarkSuccess(ctx, jobID)
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})
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}
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func buildMemoryItems(job *model.MemoryJob, payload memorymodel.ExtractJobPayload, facts []memorymodel.NormalizedFact) []model.MemoryItem {
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if job == nil || len(facts) == 0 {
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return nil
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}
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items := make([]model.MemoryItem, 0, len(facts))
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for _, fact := range facts {
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items = append(items, model.MemoryItem{
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UserID: payload.UserID,
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ConversationID: strPtrOrNil(payload.ConversationID),
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AssistantID: strPtrOrNil(payload.AssistantID),
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RunID: strPtrOrNil(payload.RunID),
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MemoryType: fact.MemoryType,
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Title: fact.Title,
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Content: fact.Content,
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NormalizedContent: strPtrFromValue(fact.NormalizedContent),
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ContentHash: strPtrFromValue(fact.ContentHash),
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Confidence: fact.Confidence,
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Importance: fact.Importance,
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SensitivityLevel: fact.SensitivityLevel,
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SourceMessageID: int64PtrOrNil(payload.SourceMessageID),
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SourceEventID: job.SourceEventID,
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IsExplicit: fact.IsExplicit,
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Status: model.MemoryItemStatusActive,
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TTLAt: resolveMemoryTTLAt(payload.OccurredAt, fact.MemoryType),
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VectorStatus: "pending",
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})
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}
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return items
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}
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func (r *Runner) syncMemoryVectors(ctx context.Context, items []model.MemoryItem) {
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if r == nil || r.vectorSyncer == nil || len(items) == 0 {
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return
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}
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r.vectorSyncer.Upsert(ctx, "", items)
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}
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// syncVectorDeletes 处理决策层 DELETE 动作产出的向量清理需求。
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//
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// 步骤：
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// 1. 将 memoryID 转为 Milvus documentID（"memory:{id}" 格式）；
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// 2. 调 Runtime.DeleteMemory 真正从 Milvus 删除对应向量；
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// 3. 更新 MySQL vector_status 标记删除结果。
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func (r *Runner) syncVectorDeletes(ctx context.Context, memoryIDs []int64) {
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if r == nil || r.vectorSyncer == nil || len(memoryIDs) == 0 {
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return
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}
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r.vectorSyncer.Delete(ctx, "", memoryIDs)
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}
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func resolveMemoryTTLAt(base time.Time, memoryType string) *time.Time {
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switch memoryType {
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case memorymodel.MemoryTypeFact:
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t := base.Add(180 * 24 * time.Hour)
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return &t
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default:
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return nil
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}
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}
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func strPtrFromValue(v string) *string {
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v = strings.TrimSpace(v)
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if v == "" {
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return nil
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}
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value := v
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return &value
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}
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func strPtrOrNil(v string) *string {
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v = strings.TrimSpace(v)
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if v == "" {
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return nil
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}
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value := v
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return &value
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}
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func int64PtrOrNil(v int64) *int64 {
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if v <= 0 {
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return nil
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}
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value := v
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return &value
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}
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func (r *Runner) recordJobOutcome(
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ctx context.Context,
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job *model.MemoryJob,
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payload *memorymodel.ExtractJobPayload,
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status string,
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success bool,
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err error,
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) {
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if r == nil {
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return
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}
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level := memoryobserve.LevelInfo
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if !success || err != nil {
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level = memoryobserve.LevelWarn
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}
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fields := map[string]any{
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"job_id": jobIDValue(job),
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"status": strings.TrimSpace(status),
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"success": success && err == nil,
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"error": err,
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"error_code": memoryobserve.ClassifyError(err),
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}
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if payload != nil {
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fields["trace_id"] = strings.TrimSpace(payload.TraceID)
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fields["user_id"] = payload.UserID
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fields["conversation_id"] = strings.TrimSpace(payload.ConversationID)
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}
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r.observer.Observe(ctx, memoryobserve.Event{
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Level: level,
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Component: memoryobserve.ComponentWrite,
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Operation: "job",
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Fields: fields,
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})
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r.metrics.AddCounter(memoryobserve.MetricJobTotal, 1, map[string]string{
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"status": strings.TrimSpace(status),
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})
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}
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func (r *Runner) recordDecisionObservation(
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ctx context.Context,
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job *model.MemoryJob,
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payload memorymodel.ExtractJobPayload,
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fact memorymodel.NormalizedFact,
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candidateCount int,
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finalAction string,
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fallbackMode string,
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success bool,
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err error,
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) {
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if r == nil {
|
||
return
|
||
}
|
||
|
||
level := memoryobserve.LevelInfo
|
||
status := "success"
|
||
if !success || err != nil {
|
||
level = memoryobserve.LevelWarn
|
||
status = "error"
|
||
}
|
||
fallbackMode = strings.TrimSpace(fallbackMode)
|
||
if fallbackMode == "" {
|
||
fallbackMode = "none"
|
||
}
|
||
|
||
r.observer.Observe(ctx, memoryobserve.Event{
|
||
Level: level,
|
||
Component: memoryobserve.ComponentWrite,
|
||
Operation: memoryobserve.OperationDecision,
|
||
Fields: map[string]any{
|
||
"trace_id": strings.TrimSpace(payload.TraceID),
|
||
"user_id": payload.UserID,
|
||
"conversation_id": strings.TrimSpace(payload.ConversationID),
|
||
"job_id": jobIDValue(job),
|
||
"fact_type": strings.TrimSpace(fact.MemoryType),
|
||
"candidate_count": candidateCount,
|
||
"final_action": strings.TrimSpace(finalAction),
|
||
"fallback_mode": fallbackMode,
|
||
"success": success && err == nil,
|
||
"error": err,
|
||
"error_code": memoryobserve.ClassifyError(err),
|
||
},
|
||
})
|
||
r.metrics.AddCounter(memoryobserve.MetricDecisionTotal, 1, map[string]string{
|
||
"action": strings.TrimSpace(finalAction),
|
||
"status": status,
|
||
})
|
||
if fallbackMode != "none" && fallbackMode != "hash_exact" && fallbackMode != "rag" {
|
||
r.metrics.AddCounter(memoryobserve.MetricDecisionFallbackTotal, 1, map[string]string{
|
||
"mode": fallbackMode,
|
||
})
|
||
}
|
||
}
|
||
|
||
func jobIDValue(job *model.MemoryJob) int64 {
|
||
if job == nil {
|
||
return 0
|
||
}
|
||
return job.ID
|
||
}
|
||
|
||
func parseMemoryID(documentID string) int64 {
|
||
documentID = strings.TrimSpace(documentID)
|
||
if !strings.HasPrefix(documentID, "memory:") {
|
||
return 0
|
||
}
|
||
raw := strings.TrimPrefix(documentID, "memory:")
|
||
if strings.HasPrefix(raw, "uid:") {
|
||
return 0
|
||
}
|
||
|
||
var value int64
|
||
for _, ch := range raw {
|
||
if ch < '0' || ch > '9' {
|
||
return 0
|
||
}
|
||
value = value*10 + int64(ch-'0')
|
||
}
|
||
return value
|
||
}
|