Version: 0.9.21.dev.260416
后端： 1. Memory 写入链路新增"召回→比对→汇总"去重决策层 - 新增决策流程：Runner 根据decision.enabled 配置走决策路径（语义召回候选 → Hash 精确命中 → LLM 逐对比对 → 汇总决策 → 执行 ADD/UPDATE/DELETE/NONE），默认关闭，旧路径完全保留 - 新增 LLMDecisionOrchestrator：单对关系判断编排器，输出 duplicate/update/conflict/unrelated 四种关系 - 新增 decision_flow / apply_actions：决策流程主循环与动作落地（新增、更新内容、软删除、跳过） - 新增 aggregate_decision / decision_validate：汇总规则（按优先级判定动作）与 LLM 输出校验 - 新增 decision model：CandidateSnapshot / ComparisonResult / FinalDecision 等决策层核心类型 - ItemRepo 新增 FindActiveByHash / UpdateContentByID / SoftDeleteByID 三个决策层专用方法 - RAG Runtime / Pipeline / Service 新增 DeleteMemory 向量删除能力，MilvusStore 补充 duplicate collection 错误识别 - Runner 新增 syncVectorDeletes 处理决策层 DELETE 动作的向量清理 - config 新增 decision（enabled/candidateTopK/candidateMinScore/fallbackMode）和 write.mode 配置项，config_loader 增加默认值兜底 - 删除 HANDOFF-RAG复用后续实施计划.md 和旧 log.txt，新增 Log.txt 记录决策流程调试日志 - normalize_facts 导出 HashContent 供决策层复用，audit 新增 update 操作常量 前端：无 仓库：无
This commit is contained in:
Losita
@@ -12,6 +12,7 @@ import (
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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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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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"github.com/LoveLosita/smartflow/backend/model"
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@@ -41,6 +42,13 @@ type Runner struct {
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extractor Extractor
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ragRuntime infrarag.Runtime
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logger *log.Logger
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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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@@ -52,16 +60,20 @@ func NewRunner(
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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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) *Runner {
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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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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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cfg: cfg,
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decisionOrchestrator: decisionOrchestrator,
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}
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}
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@@ -145,7 +157,42 @@ func (r *Runner) RunOnce(ctx context.Context) (*RunOnceResult, error) {
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return result, nil
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}
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// 5. 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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// 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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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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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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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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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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@@ -251,7 +298,7 @@ func (r *Runner) syncMemoryVectors(ctx context.Context, items []model.MemoryItem
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Items: requestItems,
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})
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if err != nil {
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r.logger.Printf("memory vector sync failed: err=%v", err)
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r.logger.Printf("[WARN][去重] 记忆向量同步失败: count=%d err=%v", len(items), err)
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for _, item := range items {
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_ = r.itemRepo.UpdateVectorStateByID(ctx, item.ID, "failed", nil)
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}
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@@ -273,6 +320,42 @@ func (r *Runner) syncMemoryVectors(ctx context.Context, items []model.MemoryItem
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}
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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 || len(memoryIDs) == 0 {
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return
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}
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// 1. 构造 documentID 列表。
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documentIDs := make([]string, 0, len(memoryIDs))
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for _, id := range memoryIDs {
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documentIDs = append(documentIDs, fmt.Sprintf("memory:%d", id))
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}
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// 2. 调 Runtime 删除向量。
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if r.ragRuntime != nil {
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if err := r.ragRuntime.DeleteMemory(ctx, documentIDs); err != nil {
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r.logger.Printf("[WARN][去重] Milvus 向量删除失败，标记为 pending 等待后续清理: count=%d ids=%v err=%v", len(memoryIDs), memoryIDs, err)
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} else {
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r.logger.Printf("[去重] Milvus 向量删除完成: count=%d ids=%v", len(memoryIDs), memoryIDs)
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}
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}
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// 3. 更新 MySQL vector_status。
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for _, memoryID := range memoryIDs {
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if updateErr := r.itemRepo.UpdateVectorStateByID(ctx, memoryID, "deleted", nil); updateErr != nil {
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if r.logger != nil {
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r.logger.Printf("[WARN] 向量状态更新失败: memory_id=%d err=%v", memoryID, updateErr)
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}
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}
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}
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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.MemoryTypeTodoHint:
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