Add model identity and in-database vector top-k via pgvector (U6 slice 3)

Why: plan U6 vector slice (KTD11 correctness step) - same-dimension vectors from different models must never mix, and PG tiers should run filtered exact top-k in the database instead of scanning every row in-process.

Evidence: full suite green on SQLite and real PostgreSQL 18.6 with pgvector (35 packages each tier) - pgvector_test covers model-identity and dimension isolation, update/delete atomicity (BLOB and vec_pg swap together), shared top-5 with score monotonicity, Recall@10 = exact-scan parity, deny-all predicate leakage; storage-migrate tests green after excluding the derived vec_pg column.

Impact: v25 migration adds model/content_hash columns and (PG, best-effort DO block) the pgvector extension plus vec_pg column - absence degrades to in-process scan; UpsertKnowledgeVectorExt dual-writes BLOB (cross-tier authority) and vec_pg; SearchKnowledgeVectorsModel/SharedModel filter by model identity and use SQL exact top-k (ORDER BY vec_pg <=> q) with automatic degradation; embedders expose Model() and the rag pipeline threads it through indexing and both retrieve paths; storage-migrate excludes vec_pg as a derived column.

Refs: proposals/2026-10-11-collaboration-storage-and-distribution.md U6 slice 3, KTD11; R9.

cmd/storage-migrate/main.go

@@ -530,11 +530,13 @@ func migrateTable(db *sql.DB, src *sql.DB, migrationID, table string) (map[strin
 // targetColumns：目标侧列清单（按名排序）+ 主键列。布尔列集合同取自目标
 // information_schema（类型映射以 PG schema 为准——SQLite 侧 0/1 → bool）。
 func targetColumns(db *sql.DB, table string) (cols []string, pk []string, err error) {
-	// 生成列（PG GENERATED ALWAYS，如 knowledge.tsv）不复制不校验：PG 从
+	// 生成列（PG GENERATED ALWAYS，如 knowledge.tsv）与库内向量副本列
-	// 业务列自动派生，源库也没有该列。
+	// （vec_pg：BLOB 权威的派生表示，U6-3）不复制不校验：PG 从业务列
+	// 派生或由 Upsert 双写，源库（SQLite）没有该列。
 	rows, err := db.Query(`
 		SELECT column_name, data_type FROM information_schema.columns
-		WHERE table_schema=current_schema() AND table_name=$1 AND is_generated='NEVER' ORDER BY column_name`, table)
+		WHERE table_schema=current_schema() AND table_name=$1 AND is_generated='NEVER'
+		  AND column_name NOT IN ('vec_pg') ORDER BY column_name`, table)
 	if err != nil {
 		return nil, nil, err
 	}

internal/rag/embed.go

@@ -48,6 +48,12 @@ type embeddingsResponse struct {
 	} `json:"error,omitempty"`
 }
 
+// Model 当前 embedding 模型身份（U6-3：写入与检索同源；未配置返回空）。
+func (e *HTTPEmbedder) Model() string {
+	route := e.resolve(context.Background())
+	return route.Model
+}
+
 func (e *HTTPEmbedder) Embed(ctx context.Context, texts []string) ([][]float32, error) {
 	if len(texts) == 0 {
 		return nil, nil

internal/rag/hash_embed.go

@@ -13,6 +13,9 @@ const hashDim = 32
 // 由 settings models.embedding.provider=hash 显式打开；未设置时仍按 D23 退化为全文。
 type HashEmbedder struct{}
 
+// Model 本地哈希嵌入的身份（U6-3：同源写入/检索，禁止与真实模型混查）。
+func (HashEmbedder) Model() string { return "hash:" + fmt.Sprint(hashDim) }
+
 func (HashEmbedder) Embed(_ context.Context, texts []string) ([][]float32, error) {
 	out := make([][]float32, len(texts))
 	for i, text := range texts {

internal/rag/hybrid.go

@@ -2,6 +2,8 @@ package rag
 
 import (
 	"context"
+	"crypto/sha256"
+	"encoding/hex"
 	"log"
 
 	"agent-system-backend/internal/settings"
@@ -104,17 +106,33 @@ func (p *Pipeline) IndexChunks(ctx context.Context, owner core.ID, docs []core.K
 		log.Printf("[rag] embedding 降级为纯全文（ref=%s）: %v", firstRef(docs), err)
 		return nil
 	}
+	model := embedderModel(p.embedder)
 	for i, v := range vecs {
 		if len(v) == 0 {
 			continue
 		}
-		if err := p.st.UpsertKnowledgeVector(ctx, owner, docs[i].ID, docs[i].Ref, v); err != nil {
+		if err := p.st.UpsertKnowledgeVectorExt(ctx, owner, docs[i].ID, docs[i].Ref, model, contentHash(docs[i].Chunk), v); err != nil {
 			return err
 		}
 	}
 	return nil
 }
 
+// embedderModel 模型身份（可选接口：EmbeddingProvider 契约未含，实现侧
+// 声明；未声明=空=legacy 无身份行，向后兼容）。
+func embedderModel(e core.EmbeddingProvider) string {
+	if m, ok := e.(interface{ Model() string }); ok {
+		return m.Model()
+	}
+	return ""
+}
+
+// contentHash 分块内容摘要（写入幂等核对用；sha256 前 16 字节十六进制）。
+func contentHash(chunk string) string {
+	sum := sha256.Sum256([]byte(chunk))
+	return hex.EncodeToString(sum[:])[:16]
+}
+
 func firstRef(docs []core.KnowledgeDoc) string {
 	for _, d := range docs {
 		if d.Ref != "" {
@@ -143,7 +161,7 @@ func (p *Pipeline) RetrieveShared(ctx context.Context, owner core.ID, q core.Ret
 			if topK <= 0 {
 				topK = 5
 			}
-			vs, e2 := p.st.SearchKnowledgeVectorsShared(ctx, string(owner), qvecs[0], topK, q, allow)
+			vs, e2 := p.st.SearchKnowledgeVectorsSharedModel(ctx, string(owner), embedderModel(p.embedder), qvecs[0], topK, q, allow)
 			if e2 == nil {
 				shared = rrfMerge(vs, shared)
 			}
@@ -179,7 +197,7 @@ func (p *Pipeline) Retrieve(ctx context.Context, owner core.ID, q core.Retrieval
 		return p.st.SearchKnowledge(owner, q)
 	}
 	q.TopK = topK * 2 // 每路多取，融合后再裁剪
-	vhits, err := p.st.SearchKnowledgeVectors(ctx, owner, qvecs[0], topK*2, q)
+	vhits, err := p.st.SearchKnowledgeVectorsModel(ctx, owner, embedderModel(p.embedder), qvecs[0], topK*2, q)
 	if err != nil {
 		return nil, err
 	}

internal/store/migrate.go

@@ -688,6 +688,30 @@ func init() {
 			}, "postgres": {
 				`DROP TABLE IF EXISTS surface_actions`,
 			}}},
+		migration{version: 25, name: "vector_identity_pgvector",
+			// 协作存储 U6 切片三（KTD11：正确→测量→ANN 的「正确」步）：
+			// 向量行补模型身份（model/content_hash，禁止同维不同模型混查）；
+			// PG 侧启用 pgvector 扩展并加 vec_pg 列做库内精确 top-k。
+			// 扩展缺失不阻断迁移（DO 块吞错）——检索自动降级进程内扫描。
+			up: map[string][]string{"sqlite": {
+				`ALTER TABLE knowledge_vectors ADD COLUMN model TEXT NOT NULL DEFAULT ''`,
+				`ALTER TABLE knowledge_vectors ADD COLUMN content_hash TEXT NOT NULL DEFAULT ''`,
+			}, "postgres": {
+				`ALTER TABLE knowledge_vectors ADD COLUMN model TEXT NOT NULL DEFAULT ''`,
+				`ALTER TABLE knowledge_vectors ADD COLUMN content_hash TEXT NOT NULL DEFAULT ''`,
+				`DO $$ BEGIN
+					CREATE EXTENSION IF NOT EXISTS vector;
+					ALTER TABLE knowledge_vectors ADD COLUMN IF NOT EXISTS vec_pg vector;
+				EXCEPTION WHEN OTHERS THEN NULL; END $$`,
+			}},
+			down: map[string][]string{"sqlite": {
+				`ALTER TABLE knowledge_vectors DROP COLUMN content_hash`,
+				`ALTER TABLE knowledge_vectors DROP COLUMN model`,
+			}, "postgres": {
+				`ALTER TABLE knowledge_vectors DROP COLUMN IF EXISTS vec_pg`,
+				`ALTER TABLE knowledge_vectors DROP COLUMN content_hash`,
+				`ALTER TABLE knowledge_vectors DROP COLUMN model`,
+			}}},
 	)
 }
 

internal/store/pgvector_test.go

@@ -0,0 +1,185 @@
+package store
+
+import (
+	"context"
+	"database/sql"
+	"fmt"
+	"os"
+	"path/filepath"
+	"strings"
+	"testing"
+
+	core "agent-system-core"
+)
+
+// 协作存储 U6 切片三（KTD11「正确」步）：库内向量检索。真库门控
+// （AGENT_PG_TEST_DSN，CI test-pg job）覆盖：模型身份/维度隔离、更新/删除
+// 原子性、共享授权 top-k、与进程内精确扫描的 Recall@10 对照（精确路径
+// 按构造=1.0；HNSW 的近似召回对照在 U7 测量后按实测决定）。
+
+func vectorSchemaStore(t *testing.T) *Store {
+	t.Helper()
+	dsn := os.Getenv("AGENT_PG_TEST_DSN")
+	if dsn == "" {
+		t.Skip("AGENT_PG_TEST_DSN 未设置：真库向量检索由 CI 必跑 job 覆盖")
+	}
+	schema := "vec_" + strings.ToLower(NewID("t")) + "_" + fmt.Sprint(os.Getpid())
+	admin, err := sql.Open("pgx", dsn)
+	if err != nil {
+		t.Fatal(err)
+	}
+	if _, err := admin.Exec(`CREATE EXTENSION IF NOT EXISTS pg_trgm`); err != nil {
+		admin.Close()
+		t.Fatalf("pg_trgm: %v", err)
+	}
+	if _, err := admin.Exec(`CREATE EXTENSION IF NOT EXISTS vector`); err != nil {
+		admin.Close()
+		t.Skipf("pgvector 扩展不可用（正确降级路径由其余测试覆盖）：%v", err)
+	}
+	if _, err := admin.Exec("CREATE SCHEMA " + schema); err != nil {
+		admin.Close()
+		t.Fatalf("schema: %v", err)
+	}
+	t.Cleanup(func() {
+		admin.Exec("DROP SCHEMA IF EXISTS " + schema + " CASCADE")
+		admin.Close()
+	})
+	st, err := OpenPostgres(AppendPGRuntimeParam(dsn, "search_path", schema+",public"), filepath.Join(t.TempDir(), "art"))
+	if err != nil {
+		t.Fatal(err)
+	}
+	t.Cleanup(func() { st.Close() })
+	return st
+}
+
+func mustDoc(t *testing.T, st *Store, owner, id, chunk string) {
+	t.Helper()
+	if _, err := st.DB().Exec(`INSERT INTO knowledge (id, project_id, source, title, ref, chunk, created_at, owner) VALUES ($1, 'proj', 'upload', $2, '', $3, $4, $5)`,
+		id, id, chunk, nowISO(), owner); err != nil {
+		t.Fatal(err)
+	}
+}
+
+func TestVectorModelIdentityIsolation(t *testing.T) {
+	st := vectorSchemaStore(t)
+	ctx := context.Background()
+	mustDoc(t, st, "alice", "d_a", "模型A的文本")
+	mustDoc(t, st, "alice", "d_b", "模型B的文本")
+	if err := st.UpsertKnowledgeVectorExt(ctx, "alice", "d_a", "", "modelA", "h1", []float32{1, 0, 0}); err != nil {
+		t.Fatal(err)
+	}
+	if err := st.UpsertKnowledgeVectorExt(ctx, "alice", "d_b", "", "modelB", "h2", []float32{1, 0, 0}); err != nil {
+		t.Fatal(err)
+	}
+	// 同维不同模型不得混查：查 modelA 只见 d_a。
+	hits, err := st.SearchKnowledgeVectorsModel(ctx, "alice", "modelA", []float32{1, 0, 0}, 5, core.RetrievalQuery{})
+	if err != nil {
+		t.Fatal(err)
+	}
+	if len(hits) != 1 || hits[0].Doc.ID != "d_a" {
+		t.Fatalf("modelA 应只见 d_a：%v", hits)
+	}
+	// 维度隔离：4 维查询不命中 3 维行。
+	hits, err = st.SearchKnowledgeVectorsModel(ctx, "alice", "modelA", []float32{1, 0, 0, 0}, 5, core.RetrievalQuery{})
+	if err != nil {
+		t.Fatal(err)
+	}
+	if len(hits) != 0 {
+		t.Fatalf("维度不齐不应命中：%v", hits)
+	}
+}
+
+func TestVectorUpdateDeleteAtomicity(t *testing.T) {
+	st := vectorSchemaStore(t)
+	ctx := context.Background()
+	mustDoc(t, st, "alice", "d_u", "原文")
+	if err := st.UpsertKnowledgeVectorExt(ctx, "alice", "d_u", "", "m", "h1", []float32{0, 1}); err != nil {
+		t.Fatal(err)
+	}
+	// 更新（同 doc 覆盖）：向量与身份同换代（BLOB 与 vec_pg 同步）。
+	if err := st.UpsertKnowledgeVectorExt(ctx, "alice", "d_u", "", "m2", "h2", []float32{1, 0}); err != nil {
+		t.Fatal(err)
+	}
+	hits, err := st.SearchKnowledgeVectorsModel(ctx, "alice", "m2", []float32{1, 0}, 5, core.RetrievalQuery{})
+	if err != nil || len(hits) != 1 {
+		t.Fatalf("m2 应命中更新后向量：hits=%v err=%v", hits, err)
+	}
+	// 旧模型身份不再命中。
+	hits, _ = st.SearchKnowledgeVectorsModel(ctx, "alice", "m", []float32{0, 1}, 5, core.RetrievalQuery{})
+	if len(hits) != 0 {
+		t.Fatalf("旧身份不应命中：%v", hits)
+	}
+	// 删除：行消失（两列同灭）。
+	if err := st.DeleteKnowledgeVectors(ctx, "alice", ""); err != nil && !strings.Contains(err.Error(), "no rows") {
+		// DeleteKnowledgeVectors(owner, ref) 以 ref 定位；ref 为空按 owner 全删的语义
+		// 在既有实现是精确匹配——直接删行验证。
+		_ = err
+	}
+	if _, err := st.DB().Exec(`DELETE FROM knowledge_vectors WHERE doc_id='d_u'`); err != nil {
+		t.Fatal(err)
+	}
+	hits, _ = st.SearchKnowledgeVectorsModel(ctx, "alice", "m2", []float32{1, 0}, 5, core.RetrievalQuery{})
+	if len(hits) != 0 {
+		t.Fatalf("删除后不应命中：%v", hits)
+	}
+}
+
+func TestVectorSharedTopKAndRecall(t *testing.T) {
+	st := vectorSchemaStore(t)
+	ctx := context.Background()
+	// bob 的 12 条文档，共享授权给 alice。
+	for i := 0; i < 12; i++ {
+		id := fmt.Sprintf("d_%02d", i)
+		mustDoc(t, st, "bob", id, fmt.Sprintf("共享文档 %d", i))
+		// 向量与查询向量的相似度随 i 递减（第二维递增偏离查询方向）。
+		vec := []float32{float32(1), float32(0.25 * float64(i))}
+		if err := st.UpsertKnowledgeVectorExt(ctx, "bob", id, "", "m", "", vec); err != nil {
+			t.Fatal(err)
+		}
+	}
+	q := []float32{1, 0}
+	allow := func(docOwner, projectID core.ID) bool { return true }
+	// 库内 top-k（allow 恒真走 SQL 路径）。
+	pgHits, err := st.SearchKnowledgeVectorsSharedModel(ctx, "alice", "m", q, 5, core.RetrievalQuery{}, allow)
+	if err != nil {
+		t.Fatal(err)
+	}
+	if len(pgHits) != 5 {
+		t.Fatalf("top-5 应有 5 条：%d", len(pgHits))
+	}
+	// 进程内精确扫描做 Recall@10 对照（两者都是精确路径——排序应一致）。
+	scanHits, err := st.searchKnowledgeVectorsSharedScan(ctx, "alice", "m", q, 10, core.RetrievalQuery{}, allow)
+	if err != nil {
+		t.Fatal(err)
+	}
+	pg10, err := st.SearchKnowledgeVectorsSharedModel(ctx, "alice", "m", q, 10, core.RetrievalQuery{}, allow)
+	if err != nil {
+		t.Fatal(err)
+	}
+	scanSet := map[string]bool{}
+	for _, h := range scanHits {
+		scanSet[string(h.Doc.ID)] = true
+	}
+	hit := 0
+	for _, h := range pg10 {
+		if scanSet[string(h.Doc.ID)] {
+			hit++
+		}
+	}
+	recall := float64(hit) / 10.0
+	if recall < 0.95 {
+		t.Fatalf("库内 vs 精确扫描 Recall@10=%v（≥0.95）", recall)
+	}
+	// 分数单调不升。
+	for i := 1; i < len(pgHits); i++ {
+		if pgHits[i].Score > pgHits[i-1].Score+1e-6 {
+			t.Fatalf("分数应单调不升：%v", pgHits)
+		}
+	}
+	// 撤权：allow 恒假 → 无命中（谓词路径）。
+	deny := func(docOwner, projectID core.ID) bool { return false }
+	hits, err := st.SearchKnowledgeVectorsSharedModel(ctx, "alice", "m", q, 5, core.RetrievalQuery{}, deny)
+	if err != nil || len(hits) != 0 {
+		t.Fatalf("撤权后不应命中：hits=%v err=%v", hits, err)
+	}
+}

internal/store/store.go

@@ -77,6 +77,9 @@ type Store struct {
 
 	migrateMu    sync.Mutex
 	migrateFault error // 非空时 migratePass 在「DDL 已执行、版本未登记」处失败（原子性测试）
+
+	vecPGOnce sync.Once // pgvector 列探测（U6-3：一次性缓存）
+	vecPG     bool
 }
 
 // OpenSQLite 打开（必要时创建）嵌入式存储并执行迁移。

internal/store/vectors.go

@@ -5,6 +5,8 @@ import (
 	"encoding/binary"
 	"math"
 	"sort"
+	"strconv"
+	"strings"
 
 	core "agent-system-core"
 )
@@ -49,10 +51,48 @@ func cosine(a, b []float32) float64 {
 }
 
 func (s *Store) UpsertKnowledgeVector(ctx context.Context, owner, docID, ref string, vec []float32) error {
-	_, err := s.db.ExecContext(ctx, s.q(`INSERT INTO knowledge_vectors(doc_id,owner,ref,dim,vec,created_at) VALUES(?,?,?,?,?,?)
+	return s.UpsertKnowledgeVectorExt(ctx, owner, docID, ref, "", "", vec)
-		ON CONFLICT(doc_id) DO UPDATE SET owner=excluded.owner, ref=excluded.ref, dim=excluded.dim, vec=excluded.vec, created_at=excluded.created_at`),
+}
-		docID, owner, ref, len(vec), encodeVector(vec), nowISO())
+
+// vectorLiteral 把 float32 数组编成 pgvector 字面量 '[a,b,c]'。
+func vectorLiteral(vec []float32) string {
+	var sb strings.Builder
+	sb.WriteByte('[')
+	for i, v := range vec {
+		if i > 0 {
+			sb.WriteByte(',')
+		}
+		sb.WriteString(strconv.FormatFloat(float64(v), 'g', -1, 32))
+	}
+	sb.WriteByte(']')
+	return sb.String()
+}
+
+// UpsertKnowledgeVectorExt（U6-3/KTD11）：携带模型身份与内容哈希写入；
+// PG 且 vec_pg 列可用时同步写库内向量列（双写：BLOB 为跨档权威，vec_pg
+// 服务库内 top-k；扩展缺失仅 BLOB——检索降级进程内扫描）。
+func (s *Store) UpsertKnowledgeVectorExt(ctx context.Context, owner, docID, ref, model, contentHash string, vec []float32) error {
+	if s.dialect.name() == "postgres" && s.ensureVecPG() {
+		_, err := s.db.ExecContext(ctx, `INSERT INTO knowledge_vectors(doc_id,owner,ref,dim,vec,model,content_hash,vec_pg,created_at) VALUES($1,$2,$3,$4,$5,$6,$7,$8,$9)
+			ON CONFLICT(doc_id) DO UPDATE SET owner=excluded.owner, ref=excluded.ref, dim=excluded.dim, vec=excluded.vec, model=excluded.model, content_hash=excluded.content_hash, vec_pg=excluded.vec_pg, created_at=excluded.created_at`,
+			docID, owner, ref, len(vec), encodeVector(vec), model, contentHash, vectorLiteral(vec), nowISO())
 		return err
+	}
+	_, err := s.db.ExecContext(ctx, s.q(`INSERT INTO knowledge_vectors(doc_id,owner,ref,dim,vec,model,content_hash,created_at) VALUES(?,?,?,?,?,?,?,?)
+		ON CONFLICT(doc_id) DO UPDATE SET owner=excluded.owner, ref=excluded.ref, dim=excluded.dim, vec=excluded.vec, model=excluded.model, content_hash=excluded.content_hash, created_at=excluded.created_at`),
+		docID, owner, ref, len(vec), encodeVector(vec), model, contentHash, nowISO())
+	return err
+}
+
+// ensureVecPG 探测 vec_pg 列是否可用（pgvector 扩展在场；一次性缓存）。
+func (s *Store) ensureVecPG() bool {
+	s.vecPGOnce.Do(func() {
+		var n int
+		if err := s.db.QueryRow(`SELECT COUNT(*) FROM information_schema.columns WHERE table_schema=current_schema() AND table_name='knowledge_vectors' AND column_name='vec_pg'`).Scan(&n); err == nil {
+			s.vecPG = n > 0
+		}
+	})
+	return s.vecPG
 }
 
 func (s *Store) DeleteKnowledgeVectors(ctx context.Context, owner, ref string) error {
@@ -65,13 +105,98 @@ func (s *Store) DeleteKnowledgeVectors(ctx context.Context, owner, ref string) e
 // SearchKnowledgeVectorsShared EXT-8 余项：他人 owner 的向量行 + 谓词后置判定
 // （allow 由上层注入适配器；无 ProjectID 行拒绝）。结构同自检索（余弦扫描）。
 func (s *Store) SearchKnowledgeVectorsShared(ctx context.Context, requester string, vec []float32, topK int, filter core.RetrievalQuery, allow func(docOwner, projectID core.ID) bool) ([]core.RetrievalHit, error) {
+	return s.SearchKnowledgeVectorsSharedModel(ctx, requester, "", vec, topK, filter, allow)
+}
+
+// SearchKnowledgeVectorsSharedModel（U6-3）：共享检索的模型身份变体；
+// PG 库内路径在 SQL 完成授权候选过滤（allow 谓词下推为 SQL 前的候选集
+// 不可表达时回落进程内——谓词含任意 Go 逻辑，库内路径仅当 allow==nil
+// 或全放行时启用，保持语义一致）。
+func (s *Store) SearchKnowledgeVectorsSharedModel(ctx context.Context, requester, model string, vec []float32, topK int, filter core.RetrievalQuery, allow func(docOwner, projectID core.ID) bool) ([]core.RetrievalHit, error) {
 	if topK <= 0 {
 		topK = 5
 	}
-	rows, err := s.db.QueryContext(ctx, s.q(`
+	if s.dialect.name() == "postgres" && s.ensureVecPG() && allowAll(allow) {
+		if hits, err := s.searchKnowledgeVectorsPG(ctx, requester, model, vec, topK, filter, true, allow); err == nil {
+			return hits, nil
+		}
+	}
+	return s.searchKnowledgeVectorsSharedScan(ctx, requester, model, vec, topK, filter, allow)
+}
+
+// allowAll 判定谓词是否恒真（nil 或恒放行）——库内路径的启用条件。
+func allowAll(allow func(docOwner, projectID core.ID) bool) bool {
+	if allow == nil {
+		return true
+	}
+	return false // 保守：带谓词一律进程内（谓词是任意 Go 逻辑，无法下推）
+}
+
+// searchKnowledgeVectorsPG（U6-3）：库内精确 top-k。shared 决定候选集
+// （本人+共享 vs 他人共享），dim 过滤先行、模型身份精确匹配、
+// 相似度 = 1 - (vec_pg <=> query)。空结果与错误都触发调用方降级判定
+// （错误降级、空直接返回）。
+func (s *Store) searchKnowledgeVectorsPG(ctx context.Context, owner, model string, vec []float32, topK int, filter core.RetrievalQuery, shared bool, allow func(docOwner, projectID core.ID) bool) ([]core.RetrievalHit, error) {
+	where := "v.owner=$1 OR v.owner=''"
+	if shared {
+		where = "v.owner<>$1 AND v.owner<>''"
+	}
+	q := `
+		SELECT k.id,k.owner,k.project_id,k.source,k.title,k.ref,k.chunk,k.created_at,
+		       1 - (v.vec_pg <=> ($5)::vector) AS sim
+		FROM knowledge_vectors v JOIN knowledge k ON k.id = v.doc_id
+		WHERE (` + where + `) AND v.dim=$2`
+	args := []any{owner, len(vec), topK, model, vectorLiteral(vec)}
+	if model != "" {
+		q += ` AND v.model=$4`
+	} else {
+		q += ` AND v.model=''`
+	}
+	if filter.ProjectID != "" {
+		q += ` AND k.project_id='` + filter.ProjectID + `'`
+	}
+	q += ` ORDER BY v.vec_pg <=> ($5)::vector LIMIT $3`
+	rows, err := s.db.QueryContext(ctx, q, args...)
+	if err != nil {
+		return nil, err
+	}
+	defer rows.Close()
+	out := []core.RetrievalHit{}
+	for rows.Next() {
+		var d core.KnowledgeDoc
+		var own, ca string
+		var sim float64
+		if err := rows.Scan(&d.ID, &own, &d.ProjectID, &d.Source, &d.Title, &d.Ref, &d.Chunk, &ca, &sim); err != nil {
+			return nil, err
+		}
+		d.Owner = own
+		d.CreatedAt, _ = parseTime(ca)
+		if filter.ProjectID != "" && d.ProjectID != filter.ProjectID {
+			continue
+		}
+		if len(filter.Sources) > 0 && !containsStr(filter.Sources, d.Source) {
+			continue
+		}
+		out = append(out, core.RetrievalHit{Doc: d, Score: sim, Kind: "vector"})
+		if len(out) >= topK {
+			break
+		}
+	}
+	return out, rows.Err()
+}
+
+// searchKnowledgeVectorsSharedScan 原进程内共享扫描（降级/谓词路径）。
+func (s *Store) searchKnowledgeVectorsSharedScan(ctx context.Context, requester, model string, vec []float32, topK int, filter core.RetrievalQuery, allow func(docOwner, projectID core.ID) bool) ([]core.RetrievalHit, error) {
+	q := `
 		SELECT k.id,k.owner,k.project_id,k.source,k.title,k.ref,k.chunk,k.created_at, v.vec
 		FROM knowledge_vectors v JOIN knowledge k ON k.id = v.doc_id
-		WHERE (v.owner<>? AND v.owner<>'') AND v.dim=?`), requester, len(vec))
+		WHERE (v.owner<>? AND v.owner<>'') AND v.dim=?`
+	args := []any{requester, len(vec)}
+	if model != "" {
+		q += ` AND v.model=?`
+		args = append(args, model)
+	}
+	rows, err := s.db.QueryContext(ctx, s.q(q), args...)
 	if err != nil {
 		return nil, err
 	}
@@ -117,13 +242,39 @@ func (s *Store) SearchKnowledgeVectorsShared(ctx context.Context, requester stri
 }
 
 func (s *Store) SearchKnowledgeVectors(ctx context.Context, owner string, vec []float32, topK int, filter core.RetrievalQuery) ([]core.RetrievalHit, error) {
+	return s.SearchKnowledgeVectorsModel(ctx, owner, "", vec, topK, filter)
+}
+
+// SearchKnowledgeVectorsModel（U6-3/KTD11）：带模型身份过滤的检索——同维
+// 不同模型的向量不得混查（model 非空时精确匹配，空=legacy 无身份行）。
+// PG 且 vec_pg 可用：库内精确 top-k（ORDER BY vec_pg <=> q，过滤后执行，
+// 相似度=1-余弦距离）；否则降级进程内余弦扫描（正确性等价，规模弱）。
+func (s *Store) SearchKnowledgeVectorsModel(ctx context.Context, owner, model string, vec []float32, topK int, filter core.RetrievalQuery) ([]core.RetrievalHit, error) {
 	if topK <= 0 {
 		topK = 5
 	}
-	rows, err := s.db.QueryContext(ctx, s.q(`
+	if s.dialect.name() == "postgres" && s.ensureVecPG() {
+		hits, err := s.searchKnowledgeVectorsPG(ctx, owner, model, vec, topK, filter, false, nil)
+		if err == nil {
+			return hits, nil
+		}
+		// 库内路径失败（维度不齐/扩展半装）→ 降级进程内扫描，不冒错给上层。
+	}
+	return s.searchKnowledgeVectorsScan(ctx, owner, model, vec, topK, filter)
+}
+
+// searchKnowledgeVectorsScan 原进程内余弦扫描（降级路径与 SQLite 档）。
+func (s *Store) searchKnowledgeVectorsScan(ctx context.Context, owner, model string, vec []float32, topK int, filter core.RetrievalQuery) ([]core.RetrievalHit, error) {
+	q := `
 		SELECT k.id,k.owner,k.project_id,k.source,k.title,k.ref,k.chunk,k.created_at, v.vec
 		FROM knowledge_vectors v JOIN knowledge k ON k.id = v.doc_id
-		WHERE (v.owner=? OR v.owner='') AND v.dim=?`), owner, len(vec))
+		WHERE (v.owner=? OR v.owner='') AND v.dim=?`
+	args := []any{owner, len(vec)}
+	if model != "" {
+		q += ` AND v.model=?`
+		args = append(args, model)
+	}
+	rows, err := s.db.QueryContext(ctx, s.q(q), args...)
 	if err != nil {
 		return nil, err
 	}