Version: 0.9.76.dev.260505
后端： 1.阶段 6 agent / memory 服务化收口 - 新增 cmd/agent 独立进程入口，承载 agent zrpc server、agent outbox relay / consumer 和运行时依赖初始化 - 补齐 services/agent/rpc 的 Chat stream 与 conversation meta/list/timeline、schedule-preview、context-stats、schedule-state unary RPC - 新增 gateway/client/agent 与 shared/contracts/agent，将 /api/v1/agent chat 和非 chat 门面切到 agent zrpc - 收缩 gateway 本地 AgentService 装配，双 RPC 开关开启时不再初始化本地 agent 编排、LLM、RAG 和 memory reader fallback - 将 backend/memory 物理迁入 services/memory，私有实现收入 internal，保留 module/model/observe 作为 memory 服务门面 - 调整 memory outbox、memory reader 和 agent 记忆渲染链路的 import 与服务边界，cmd/memory 独占 memory worker / consumer - 关闭 gateway 侧 agent outbox worker 所有权，agent relay / consumer 由 cmd/agent 独占，gateway 仅保留 HTTP/SSE 门面与迁移期开关回退 - 更新阶段 6 文档，记录 agent / memory 当前切流点、smoke 结果，以及 backend/client 与 gateway/shared 的目录收口口径
This commit is contained in:
Losita
483
backend/services/memory/internal/worker/runner.go
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483
backend/services/memory/internal/worker/runner.go
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@@ -0,0 +1,483 @@
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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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"github.com/LoveLosita/smartflow/backend/model"
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memoryorchestrator "github.com/LoveLosita/smartflow/backend/services/memory/internal/orchestrator"
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memoryrepo "github.com/LoveLosita/smartflow/backend/services/memory/internal/repo"
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memoryutils "github.com/LoveLosita/smartflow/backend/services/memory/internal/utils"
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memoryvectorsync "github.com/LoveLosita/smartflow/backend/services/memory/internal/vectorsync"
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memorymodel "github.com/LoveLosita/smartflow/backend/services/memory/model"
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memoryobserve "github.com/LoveLosita/smartflow/backend/services/memory/observe"
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ragservice "github.com/LoveLosita/smartflow/backend/services/rag"
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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 ragservice.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 ragservice.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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||||
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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,
|
||||
) {
|
||||
if r == nil {
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return
|
||||
}
|
||||
|
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level := memoryobserve.LevelInfo
|
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if !success || err != nil {
|
||||
level = memoryobserve.LevelWarn
|
||||
}
|
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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)
|
||||
fields["user_id"] = payload.UserID
|
||||
fields["conversation_id"] = strings.TrimSpace(payload.ConversationID)
|
||||
}
|
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|
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r.observer.Observe(ctx, memoryobserve.Event{
|
||||
Level: level,
|
||||
Component: memoryobserve.ComponentWrite,
|
||||
Operation: "job",
|
||||
Fields: fields,
|
||||
})
|
||||
r.metrics.AddCounter(memoryobserve.MetricJobTotal, 1, map[string]string{
|
||||
"status": strings.TrimSpace(status),
|
||||
})
|
||||
}
|
||||
|
||||
func (r *Runner) recordDecisionObservation(
|
||||
ctx context.Context,
|
||||
job *model.MemoryJob,
|
||||
payload memorymodel.ExtractJobPayload,
|
||||
fact memorymodel.NormalizedFact,
|
||||
candidateCount int,
|
||||
finalAction string,
|
||||
fallbackMode string,
|
||||
success bool,
|
||||
err error,
|
||||
) {
|
||||
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
|
||||
}
|
||||
Reference in New Issue
Block a user