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/pg.go

@@ -10,25 +10,25 @@ import (
 
 	"git.yeij.top/AskaEth/Cyrene_v2/internal/runtime/embedding"
 
-	_ "github.com/jackc/pgx/v5/stdlib" // Postgres driver, registers name "pgx"
+	_ "github.com/jackc/pgx/v5/stdlib" // Postgres 驱动，注册名为 "pgx"
 )
 
-// pgMaxOpenConns is the PostgreSQL connection pool size mandated by
-// DevDocs/02-development-constraints.md §2.2 for the pgvector enhancement.
+// pgMaxOpenConns 是 DevDocs/02-development-constraints.md §2.2 为
+// pgvector 增强规定的 PostgreSQL 连接池大小。
 const pgMaxOpenConns = 25
 
-// PGVectorIndex stores vectors in a PostgreSQL pgvector vector column and
-// answers similarity with the cosine-distance operator <=> in SQL. It is the
-// optional enhancement selected when DB_PG_URL is configured; when used,
-// semantic search is genuinely backed by pgvector and does not fall back to
-// keyword matching (DevDocs/design/03-记忆.md §3).
+// PGVectorIndex 把向量存入 PostgreSQL 的 pgvector vector 列，并
+// 用 SQL 中的余弦距离运算符 <=> 作答相似度。它是配置了
+// DB_PG_URL 时选中的可选增强；启用时，
+// 语义检索真正由 pgvector 支撑，不会退化为
+// 关键词匹配（DevDocs/design/03-记忆.md §3）。
 //
-// The schema is prepared by NewPG (vector extension + vec_items table). Search
-// issues ORDER BY vector <=> $1 LIMIT n, which returns exact cosine-distance-
-// ranked results. With no ANN index the query planner does a sequential scan,
-// which is the linear-scan behavior expected for milestone M4 (small datasets).
-// An HNSW index (CREATE INDEX ... USING hnsw (vector vector_cosine_ops)) is the
-// documented ANN upgrade for larger datasets and requires no change to Search.
+// schema 由 NewPG 准备（vector 扩展 + vec_items 表）。Search
+// 发出 ORDER BY vector <=> $1 LIMIT n，返回精确的余弦距离
+// 排名结果。没有 ANN 索引时，查询规划器做顺序扫描，
+// 这正是 M4 里程碑（小数据集）期望的线性扫描行为。
+// HNSW 索引（CREATE INDEX ... USING hnsw (vector vector_cosine_ops)）是
+// 大数据集下既定的 ANN 升级方案，且无需改动 Search。
 type PGVectorIndex struct {
 	db       *sql.DB
 	dim      int
@@ -36,16 +36,16 @@ type PGVectorIndex struct {
 	log      *slog.Logger
 }
 
-// NewPG connects to the PostgreSQL+pgvector database at pgURL using the pgx
-// driver and prepares the schema: the vector extension and a vec_items table
-// with a fixed-dimension vector column. It deliberately creates no ANN index:
-// for milestone M4 the <=> query runs as an exact sequential scan, and an HNSW
-// index is a documented, optional upgrade for larger data. A blank pgURL
-// returns ErrInvalidPGURL, and a non-positive dim returns ErrInvalidDim, both
-// before any network I/O. ctx controls the connection ping and schema setup.
+// NewPG 用 pgx 驱动连接到 pgURL 的 PostgreSQL+pgvector 数据库
+// 并准备 schema：vector 扩展与一张带固定维度 vector 列的
+// vec_items 表。它刻意不创建 ANN 索引：
+// M4 里程碑下 <=> 查询走精确顺序扫描，而 HNSW
+// 索引是既定的大数据量可选升级。pgURL 为空
+// 返回 ErrInvalidPGURL，dim 非正返回 ErrInvalidDim，两者都在
+// 任何网络 I/O 之前。ctx 控制连接 ping 与 schema 准备。
 //
-// The returned index has no embedder wired in; use New to build a fully wired
-// index, or wire one in before calling Embed.
+// 返回的索引没有接入 embedder；用 New 构建完全接好的
+// 索引，或在调用 Embed 前接入一个。
 func NewPG(ctx context.Context, pgURL string, dim int, log *slog.Logger) (*PGVectorIndex, error) {
 	if strings.TrimSpace(pgURL) == "" {
 		return nil, ErrInvalidPGURL
@@ -74,16 +74,16 @@ func NewPG(ctx context.Context, pgURL string, dim int, log *slog.Logger) (*PGVec
 	return idx, nil
 }
 
-// setup creates the pgvector extension and the vec_items table idempotently.
-// For milestone M4 no ANN index is created — <->/<=> queries fall back to an
-// exact sequential scan, matching the linear-scan semantics of the SQLite path.
-// To scale, add:
+// setup 幂等地创建 pgvector 扩展与 vec_items 表。
+// M4 里程碑不创建 ANN 索引 —— <->/<=> 查询退化为
+// 精确顺序扫描，与 SQLite 路径的线性扫描语义一致。
+// 要扩容，可添加：
 //
 //	CREATE INDEX IF NOT EXISTS idx_vec_items_vector
 //	    ON vec_items USING hnsw (vector vector_cosine_ops)
 //
-// which requires no change to Search. Each statement is parameterized where it
-// takes input; the table DDL is static after dimension substitution.
+// 且无需改动 Search。每条语句在取输入处都参数化；
+// 表 DDL 在维度替换后是静态的。
 func (p *PGVectorIndex) setup(ctx context.Context) error {
 	statements := []string{
 		"CREATE EXTENSION IF NOT EXISTS vector",
@@ -101,16 +101,16 @@ func (p *PGVectorIndex) setup(ctx context.Context) error {
 	return nil
 }
 
-// withEmbedder wires the vector-producing embedder into the index. Used by New;
-// tests may also call it directly.
+// withEmbedder 把产向量的 embedder 接入索引。New 使用它；
+// 测试也可直接调用。
 func (p *PGVectorIndex) withEmbedder(e embedding.Embedder) { p.embedder = e }
 
-// Dim returns the fixed vector dimension.
+// Dim 返回固定向量维度。
 func (p *PGVectorIndex) Dim() int { return p.dim }
 
-// Embed turns texts into vectors with the wired embedder, returning one Vector
-// per input text aligned by index. With no embedder wired it returns
-// ErrNoEmbedder.
+// Embed 用接入的 embedder 把 texts 转成向量，每段输入文本
+// 对应一个按索引对齐的 Vector。未接入 embedder 时返回
+// ErrNoEmbedder。
 func (p *PGVectorIndex) Embed(ctx context.Context, texts []string) ([]Vector, error) {
 	if p.embedder == nil {
 		return nil, ErrNoEmbedder
@@ -126,10 +126,10 @@ func (p *PGVectorIndex) Embed(ctx context.Context, texts []string) ([]Vector, er
 	return out, nil
 }
 
-// Add persists the (id, vector) pair, upserting on a repeated id. The vector is
-// passed to Postgres as a vector literal bound to a $n::vector parameter. It
-// rejects a blank id (ErrMissingID) and a vector whose length differs from the
-// index dimension (ErrDimMismatch).
+// Add 持久化 (id, vector) 对，id 重复时 upsert。向量以
+// 绑定到 $n::vector 参数的向量字面量传给 Postgres。它
+// 拒绝空 id（ErrMissingID）以及与索引维度不一致的
+// 向量（ErrDimMismatch）。
 func (p *PGVectorIndex) Add(ctx context.Context, id string, v Vector) error {
 	if strings.TrimSpace(id) == "" {
 		return ErrMissingID
@@ -147,10 +147,10 @@ func (p *PGVectorIndex) Add(ctx context.Context, id string, v Vector) error {
 	return nil
 }
 
-// Search returns up to topK ids most similar to q, ranked by descending cosine
-// similarity (1 - cosine distance), computed by pgvector in SQL. A non-positive
-// topK returns an empty result; a query whose length differs from the index
-// dimension returns ErrDimMismatch.
+// Search 返回与 q 最相似的最多 topK 个 id，按余弦相似度
+// （1 - 余弦距离）降序排列，由 pgvector 在 SQL 中计算。topK 非正
+// 返回空结果；长度与索引维度不一致的查询
+// 返回 ErrDimMismatch。
 func (p *PGVectorIndex) Search(ctx context.Context, q Vector, topK int) ([]Hit, error) {
 	if topK <= 0 {
 		return []Hit{}, nil
@@ -184,7 +184,7 @@ func (p *PGVectorIndex) Search(ctx context.Context, q Vector, topK int) ([]Hit,
 	return hits, nil
 }
 
-// Close releases the underlying PostgreSQL connection pool.
+// Close 释放底层的 PostgreSQL 连接池。
 func (p *PGVectorIndex) Close() error {
 	if p.db == nil {
 		return nil
@@ -196,9 +196,9 @@ func (p *PGVectorIndex) Close() error {
 	return nil
 }
 
-// pgVectorLiteral renders v in pgvector's text form "[a,b,c]" so it can be
-// bound as a vector parameter ($n::vector). Values are formatted with enough
-// precision to round-trip a float32.
+// pgVectorLiteral 把 v 渲染成 pgvector 的文本形式 "[a,b,c]"，
+// 以便绑定为向量参数（$n::vector）。取值的格式化精度足以
+// 往返一个 float32。
 func pgVectorLiteral(v Vector) string {
 	var b strings.Builder
 	b.WriteByte('[')