docs(comments): 注释中文化 · internal/runtime/memory（31 文件，纯替换）

- 只翻注释/文档字符串：820 增 / 820 删 严格 1:1；剥掉整行注释与行尾注释尾巴后，
  逐文件与 HEAD 比对「代码部分差异 = 0」；裁决/design 编号计数逐项与 HEAD 一致
- 保留可执行 SQL 样例、SQLite 报错原文、pgvector/HNSW/ANN/BLOB 等技术词原文
- 复核：go build 绿 + memory/projectindex/vector 三包测试绿；gofmt 仅剩 2 个既有未格式化文件（改动前即如此，未顺手修）

internal/runtime/memory/vector/index.go

@@ -1,22 +1,22 @@
-// Package vector implements dense-vector similarity search for Cyrene's
-// long-term memory layer (DevDocs/design/03-记忆.md §3).
+// vector 包为 Cyrene 的长期记忆层（DevDocs/design/03-记忆.md §3）
+// 实现稠密向量相似度检索。
 //
-// It exposes an Index abstraction over two storage backends:
+// 它在两种存储后端之上暴露 Index 抽象：
 //
-//   - SQLiteIndex (the default): vectors are stored as BLOBs in a SQLite
-//     vec_items table and similarity is computed in Go as cosine similarity via
-//     a linear scan over the persisted vectors. This is the zero-extra-
-//     dependency path used when DB_PG_URL is empty.
-//   - PGVectorIndex (optional enhancement): uses PostgreSQL + pgvector, storing
-//     vectors in a vector column and answering similarity with the cosine-
-//     distance operator <=> in SQL. It is selected only when DB_PG_URL is set;
-//     when it is, semantic search is genuinely backed by pgvector and does not
-//     silently fall back to keyword matching.
+//   - SQLiteIndex（默认）：向量以 BLOB 存在 SQLite 的
+//     vec_items 表中，相似度在 Go 侧通过对已持久化向量
+//     线性扫描计算余弦相似度。这是 DB_PG_URL 为空时使用的
+//     零额外依赖路径。
+//   - PGVectorIndex（可选增强）：使用 PostgreSQL + pgvector，把
+//     向量存入 vector 列，并用 SQL 中的余弦
+//     距离运算符 <=> 作答相似度。仅当设置了 DB_PG_URL 时才选中；
+//     此时语义检索真正由 pgvector 支撑，不会
+//     静默退化为关键词匹配。
 //
-// Both backends are linear-scan for milestone M4 (small datasets, no ANN
-// training). The PostgreSQL path may later be upgraded to an HNSW index as data
-// grows; the Search SQL is unchanged. Vectors are produced by an
-// embedding.Embedder wired in via New.
+// 两个后端在 M4 里程碑都是线性扫描（数据集小，不做 ANN
+// 训练）。随着数据增长，PostgreSQL 路径日后可升级为 HNSW 索引；
+// Search SQL 不变。向量由经 New 接入的
+// embedding.Embedder 产出。
 package vector
 
 import (
@@ -33,22 +33,22 @@ import (
 	"git.yeij.top/AskaEth/Cyrene_v2/internal/runtime/embedding"
 )
 
-// Vector is a dense embedding: one float32 per dimension.
+// Vector 是一个稠密向量：每维一个 float32。
 type Vector []float32
 
-// Hit is a single similarity result: a stored id and its similarity score.
-// Higher Score means more similar, so results sort in descending Score order.
+// Hit 是一条相似度结果：一个已存 id 及其相似度得分。
+// Score 越高越相似，因此结果按 Score 降序排列。
 type Hit struct {
 	ID    string
 	Score float64
 }
 
-// Index is a dense-vector similarity index over stored (id, vector) pairs.
+// Index 是建立在已存 (id, vector) 对上的稠密向量相似度索引。
 //
-// Embed turns a batch of texts into vectors using the wired embedder. Add
-// persists an (id, vector) pair, upserting on a repeated id. Search returns the
-// topK ids most similar to q, ranked by descending similarity. Dim reports the
-// fixed vector dimension. Close releases the underlying database.
+// Embed 用接入的 embedder 把一批文本转成向量。Add
+// 持久化一个 (id, vector) 对，id 重复时 upsert。Search 返回与 q
+// 最相似的前 topK 个 id，按相似度降序排列。Dim 报告
+// 固定向量维度。Close 释放底层数据库。
 type Index interface {
 	Embed(ctx context.Context, texts []string) ([]Vector, error)
 	Add(ctx context.Context, id string, v Vector) error
@@ -57,51 +57,51 @@ type Index interface {
 	Close() error
 }
 
-// Package-level sentinel errors. Each is wrapped with %w at the call site so
-// callers can use errors.Is.
+// 包级哨兵错误。每个都在调用点用 %w 包裹，调用方
+// 可用 errors.Is 判断。
 var (
-	// ErrNoEmbedder is returned when an index is asked to Embed but no embedder
-	// has been wired in (e.g. a constructor called directly rather than via New).
+	// ErrNoEmbedder 在索引被要求 Embed 却没有接入 embedder
+	// 时返回（例如直接调用构造函数而非经 New）。
 	ErrNoEmbedder = errors.New("memory/vector: no embedder configured")
 
-	// ErrNilDB is returned when a constructor receives a nil *sql.DB.
+	// ErrNilDB 在构造函数收到 nil *sql.DB 时返回。
 	ErrNilDB = errors.New("memory/vector: nil db")
 
-	// ErrInvalidDim is returned when a dimension is not positive.
+	// ErrInvalidDim 在维度非正时返回。
 	ErrInvalidDim = errors.New("memory/vector: dimension must be positive")
 
-	// ErrDimMismatch is returned when an added or queried vector's length does
-	// not match the index dimension.
+	// ErrDimMismatch 在新增或查询的向量长度与
+	// 索引维度不匹配时返回。
 	ErrDimMismatch = errors.New("memory/vector: vector dimension mismatch")
 
-	// ErrMissingID is returned when an id is blank.
+	// ErrMissingID 在 id 为空时返回。
 	ErrMissingID = errors.New("memory/vector: id is required")
 
-	// ErrInvalidPGURL is returned when the PostgreSQL URL is blank.
+	// ErrInvalidPGURL 在 PostgreSQL URL 为空时返回。
 	ErrInvalidPGURL = errors.New("memory/vector: invalid postgres url")
 )
 
-// New builds the Index selected by cfg: PGVectorIndex when cfg.PGURL is set,
-// otherwise SQLiteIndex. embedder must be non-nil and report a positive Dim();
-// it is wired into the returned index so Index.Embed produces vectors with it.
-// log may be nil; logs are tagged with source "memory/vector".
+// New 构建 cfg 选定的 Index：设置了 cfg.PGURL 时为 PGVectorIndex，
+// 否则为 SQLiteIndex。embedder 必须非 nil 且 Dim() 为正；
+// 它被接入返回的索引，使 Index.Embed 用它产向量。
+// log 可为 nil；日志带 source "memory/vector" 标签。
 //
-// SQLiteIndex is created on a connection opened at cfg.SQLitePath (":memory:"
-// for an isolated test database or a file path). PGVectorIndex connects to
-// cfg.PGURL with the pgx driver and requires a PostgreSQL server with the
-// pgvector extension installed.
+// SQLiteIndex 建立在 cfg.SQLitePath 打开的连接上（":memory:"
+// 用于隔离的测试数据库，或一个文件路径）。PGVectorIndex 用
+// pgx 驱动连接 cfg.PGURL，要求已安装
+// pgvector 扩展的 PostgreSQL 服务。
 //
-// New performs one-time setup with a background context and has no ctx
-// parameter by design; every subsequent operation accepts a context.Cancel.
+// New 用后台 context 做一次性准备，按设计不接收 ctx
+// 参数；后续每个操作都接受 context.Cancel。
 func New(cfg config.DBConfig, embedder embedding.Embedder, log *slog.Logger) (Index, error) {
 	if embedder == nil {
 		return nil, ErrNoEmbedder
 	}
 	dim := embedder.Dim()
 	if dim <= 0 {
-		// API-backed embedders do not know their vector dimension until the first
-		// successful response. Probe once so the index is built with the real
-		// dimension instead of erroring out and falling back to in-memory.
+		// 走 API 的 embedder 在首次成功响应前不知道自己的向量维度。
+		// 探测一次，使索引以真实
+		// 维度构建，而不是报错并回退到内存内实现。
 		if _, err := embedder.Embed(context.Background(), []string{"probe"}); err != nil {
 			return nil, fmt.Errorf("memory/vector: probe embedder dim: %w", err)
 		}
@@ -118,8 +118,8 @@ func New(cfg config.DBConfig, embedder embedding.Embedder, log *slog.Logger) (In
 		idx.withEmbedder(embedder)
 		return idx, nil
 	}
-	// db.Open logs internally; hand it a sourced logger so its records carry
-	// provenance, while the raw log goes to NewSQLite which sources it itself.
+	// db.Open 内部会记日志；交给它一个带来源的 logger，使其记录携带
+	// 出处，而原始 log 传给 NewSQLite，由后者自行加来源。
 	d, err := db.Open(context.Background(), cfg.SQLitePath, sourcedLogger(log))
 	if err != nil {
 		return nil, fmt.Errorf("memory/vector: open sqlite: %w", err)
@@ -133,16 +133,16 @@ func New(cfg config.DBConfig, embedder embedding.Embedder, log *slog.Logger) (In
 	return idx, nil
 }
 
-// usePG reports whether cfg should select the PostgreSQL+pgvector backend.
-// Surrounding whitespace counts as unset.
+// usePG 报告 cfg 是否应选择 PostgreSQL+pgvector 后端。
+// 首尾空白视为未设置。
 func usePG(pgURL string) bool {
 	return strings.TrimSpace(pgURL) != ""
 }
 
-// sourcedLogger returns log, or a discard logger when nil, tagged with source
-// "memory/vector" so every record carries provenance. It is idempotent in
-// output: re-applying the same source value on an already-sourced logger yields
-// the same emitted attribute.
+// sourcedLogger 返回 log，nil 时返回丢弃型 logger，并打上 source
+// "memory/vector" 标签，使每条记录都携带出处。它在
+// 输出上幂等：对已带来源的 logger 再次施加相同 source 值，
+// 得到的输出属性相同。
 func sourcedLogger(log *slog.Logger) *slog.Logger {
 	if log == nil {
 		log = slog.New(slog.NewTextHandler(io.Discard, nil))