feat(ctxmgr): 增加离线确定性高维嵌入生成器与向量字面量工具

Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
WeiCHEN
2026-09-13 02:50:19 +00:00
committed by weichen
co-authored by Cursor
parent 0e49892075
commit edf2f759ae
2 changed files with 179 additions and 0 deletions
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package ctxmgr
import (
"context"
"crypto/sha256"
"encoding/binary"
"fmt"
"math"
"strings"
)
// VectorEmbedder 统一向量化接口
type VectorEmbedder interface {
Embed(ctx context.Context, texts []string) ([][]float32, error)
}
// DeterministicEmbedder 确定性特征高维投影嵌入器(无外部网络依赖,测试与离线兜底)
type DeterministicEmbedder struct {
Dim int
}
// NewDeterministicEmbedder 创建确定性嵌入生成器
func NewDeterministicEmbedder(dim int) *DeterministicEmbedder {
if dim <= 0 {
dim = 1024
}
return &DeterministicEmbedder{Dim: dim}
}
// Embed 为文本切片生成归一化的特征向量
func (e *DeterministicEmbedder) Embed(ctx context.Context, texts []string) ([][]float32, error) {
if len(texts) == 0 {
return nil, nil
}
results := make([][]float32, len(texts))
for idx, text := range texts {
vec := make([]float32, e.Dim)
cleaned := strings.TrimSpace(strings.ToLower(text))
if cleaned == "" {
vec[0] = 1.0
results[idx] = vec
continue
}
// 分词提取(简单按空格或中文按单字/双字切分)
tokens := tokenizeForEmbedding(cleaned)
for _, tok := range tokens {
h := sha256.Sum256([]byte(tok))
for i := 0; i < e.Dim; i++ {
offset := (i * 4) % (len(h) - 4)
val := binary.BigEndian.Uint32(h[offset : offset+4])
// 映射到 [-1.0, 1.0]
fVal := float32((float64(val)/float64(math.MaxUint32))*2.0 - 1.0)
vec[i] += fVal
}
}
// L2 归一化 (Unit Normalization)
var sumSq float64
for _, v := range vec {
sumSq += float64(v * v)
}
norm := float32(math.Sqrt(sumSq))
if norm > 1e-9 {
for i := range vec {
vec[i] /= norm
}
} else {
vec[0] = 1.0
}
results[idx] = vec
}
return results, nil
}
// tokenizeForEmbedding 字符级与词级特征切分
func tokenizeForEmbedding(s string) []string {
var tokens []string
words := strings.Fields(s)
if len(words) > 1 {
tokens = append(tokens, words...)
}
runes := []rune(s)
// 提取 2-gram 和单字符
for i := 0; i < len(runes); i++ {
tokens = append(tokens, string(runes[i]))
if i+1 < len(runes) {
tokens = append(tokens, string(runes[i:i+2]))
}
}
return tokens
}
// VectorLiteral 将 float32 向量序列化为 pgvector 接受的字面量字符串 [0.1,0.2,...]
func VectorLiteral(vec []float32) string {
if len(vec) == 0 {
return ""
}
var sb strings.Builder
sb.WriteByte('[')
for i, f := range vec {
if i > 0 {
sb.WriteByte(',')
}
sb.WriteString(fmt.Sprintf("%g", f))
}
sb.WriteByte(']')
return sb.String()
}
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package ctxmgr_test
import (
"context"
"math"
"testing"
"chat-agent-system/internal/ctxmgr"
)
func TestDeterministicEmbedder_EmbeddingProperties(t *testing.T) {
emb := ctxmgr.NewDeterministicEmbedder(1024)
ctx := context.Background()
texts := []string{
"超声软组织切割止血手术设备",
"微创外科硬管内窥镜摄像系统",
"超声软组织切割止血手术设备", // 重复文本验证确定性
}
vecs, err := emb.Embed(ctx, texts)
if err != nil {
t.Fatalf("embed failed: %v", err)
}
if len(vecs) != 3 {
t.Fatalf("expected 3 vectors, got %d", len(vecs))
}
// 1. 验证维度
for i, v := range vecs {
if len(v) != 1024 {
t.Fatalf("vector %d length expected 1024, got %d", i, len(v))
}
// 验证单位化模长 (L2 norm ≈ 1.0)
var sumSq float64
for _, val := range v {
sumSq += float64(val * val)
}
norm := math.Sqrt(sumSq)
if math.Abs(norm-1.0) > 1e-4 {
t.Fatalf("vector %d not normalized, norm=%f", i, norm)
}
}
// 2. 验证确定性:相同文本向量完全一致
for i := 0; i < 1024; i++ {
if vecs[0][i] != vecs[2][i] {
t.Fatalf("deterministic mismatch at index %d: %v vs %v", i, vecs[0][i], vecs[2][i])
}
}
// 3. 验证语义区别:不同文本余弦相似度应小于 0.95
var dotProduct float32
for i := 0; i < 1024; i++ {
dotProduct += vecs[0][i] * vecs[1][i]
}
if dotProduct > 0.95 {
t.Fatalf("distinct texts should not have near-identical embeddings: dotProduct=%f", dotProduct)
}
// 4. 验证 VectorLiteral
lit := ctxmgr.VectorLiteral(vecs[0][:3])
if lit == "" || lit[0] != '[' || lit[len(lit)-1] != ']' {
t.Fatalf("invalid vector literal: %s", lit)
}
}