Version: 0.9.13.dev.260410

后端：
1. Memory Day1 链路打通（chat_history -> outbox -> memory_jobs）
   - 更新 service/events/chat_history_persist.go：聊天消息落库同事务追加 memory.extract.requested 事件（仅 user 消息，失败回滚后由 outbox 重试）
   - 新建 service/events/memory_extract_requested.go：消费 memory.extract.requested 并幂等入队 memory_jobs，补齐 payload 校验、文本截断与 idempotency key
   - 更新 cmd/start.go：注册 RegisterMemoryExtractRequestedHandler
2. Memory 模块骨架落地（先跑通状态机，再接入真实抽取）
   - 新建 memory/model、repo、service、orchestrator、worker、utils 目录与 Day1 mock 抽取执行链
   - 新建 model/memory.go：补齐 memory_items / memory_jobs / memory_audit_logs / memory_user_settings 与事件 payload 模型
   - 更新 inits/mysql.go：接入 4 张 memory 相关表 AutoMigrate
3. RAG 复用基础设施预埋（依赖可替换）
   - 新建 infra/rag：core pipeline + chunk/embed/retrieve/rerank/store/corpus/config 分层实现
   - 默认接入 MockEmbedder + InMemoryStore，预留 Milvus / Eino 适配实现
   - 新增 infra/rag/RAG复用接口实施计划.md
4. 本地依赖与交接文档同步
   - 更新 docker-compose.yml：新增 etcd / minio / milvus / attu 服务与数据卷
   - 删除 newAgent/HANDOFF_工具研究与运行态重置.md、newAgent/阶段3_上下文瘦身设计.md
   - 新增 newAgent/HANDOFF_WebSearch两阶段实施计划.md、memory/HANDOFF-RAG复用后续实施计划.md、memory/README.md
前端：无 仓库：无

backend/infra/rag/retrieve/vector_retriever.go

@@ -0,0 +1,90 @@
+package retrieve
+
+import (
+	"context"
+	"fmt"
+	"strings"
+
+	"github.com/LoveLosita/smartflow/backend/infra/rag/core"
+)
+
+// VectorRetriever 是通用检索器（embed + vector search）。
+type VectorRetriever struct {
+	embedder core.Embedder
+	store    core.VectorStore
+}
+
+func NewVectorRetriever(embedder core.Embedder, store core.VectorStore) *VectorRetriever {
+	return &VectorRetriever{
+		embedder: embedder,
+		store:    store,
+	}
+}
+
+func (r *VectorRetriever) Retrieve(ctx context.Context, req core.RetrieveRequest) ([]core.ScoredChunk, error) {
+	if r == nil || r.embedder == nil || r.store == nil {
+		return nil, core.ErrNilDependency
+	}
+	query := strings.TrimSpace(req.Query)
+	if query == "" {
+		return nil, core.ErrInvalidQuery
+	}
+	topK := req.TopK
+	if topK <= 0 {
+		topK = 8
+	}
+	action := strings.TrimSpace(req.Action)
+	if action == "" {
+		action = "search"
+	}
+
+	vectors, err := r.embedder.Embed(ctx, []string{query}, action)
+	if err != nil {
+		return nil, err
+	}
+	if len(vectors) != 1 {
+		return nil, fmt.Errorf("embedding query length mismatch: %d", len(vectors))
+	}
+
+	rows, err := r.store.Search(ctx, core.VectorSearchRequest{
+		QueryVector: vectors[0],
+		TopK:        topK,
+		Filter:      req.Filter,
+	})
+	if err != nil {
+		return nil, err
+	}
+
+	result := make([]core.ScoredChunk, 0, len(rows))
+	for _, row := range rows {
+		if row.Score < req.Threshold {
+			continue
+		}
+		result = append(result, core.ScoredChunk{
+			ChunkID:    row.Row.ID,
+			DocumentID: asString(row.Row.Metadata["document_id"]),
+			Text:       row.Row.Text,
+			Score:      row.Score,
+			Metadata:   cloneMap(row.Row.Metadata),
+		})
+	}
+	return result, nil
+}
+
+func cloneMap(src map[string]any) map[string]any {
+	if len(src) == 0 {
+		return map[string]any{}
+	}
+	dst := make(map[string]any, len(src))
+	for k, v := range src {
+		dst[k] = v
+	}
+	return dst
+}
+
+func asString(v any) string {
+	if v == nil {
+		return ""
+	}
+	return fmt.Sprintf("%v", v)
+}