318 lines
8.4 KiB
Go
318 lines
8.4 KiB
Go
package rag
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import (
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"encoding/binary"
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"fmt"
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"gf-lt/models"
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"gf-lt/storage"
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"log/slog"
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"sort"
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"strings"
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"unsafe"
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"github.com/jmoiron/sqlx"
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)
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// VectorStorage handles storing and retrieving vectors from SQLite
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type VectorStorage struct {
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logger *slog.Logger
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sqlxDB *sqlx.DB
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store storage.FullRepo
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}
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func NewVectorStorage(logger *slog.Logger, store storage.FullRepo) *VectorStorage {
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return &VectorStorage{
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logger: logger,
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sqlxDB: store.DB(), // Use the new DB() method
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store: store,
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}
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}
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// CreateTables creates the necessary tables for vector storage
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func (vs *VectorStorage) CreateTables() error {
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// Create tables for common embedding dimensions
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embeddingSizes := []int{384, 768, 1024, 1536, 2048, 3072, 4096, 5120}
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// Pre-allocate queries slice: each embedding size needs 1 table + 3 indexes = 4 queries per size
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queries := make([]string, 0, len(embeddingSizes)*4)
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// Generate table creation queries for each embedding size
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for _, size := range embeddingSizes {
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tableName := fmt.Sprintf("embeddings_%d", size)
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queries = append(queries,
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fmt.Sprintf(`CREATE TABLE IF NOT EXISTS %s (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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embeddings BLOB NOT NULL,
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slug TEXT NOT NULL,
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raw_text TEXT NOT NULL,
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filename TEXT NOT NULL,
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
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)`, tableName),
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)
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}
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// Add indexes for all supported sizes
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for _, size := range embeddingSizes {
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tableName := fmt.Sprintf("embeddings_%d", size)
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queries = append(queries,
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fmt.Sprintf(`CREATE INDEX IF NOT EXISTS idx_%s_filename ON %s(filename)`, tableName, tableName),
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fmt.Sprintf(`CREATE INDEX IF NOT EXISTS idx_%s_slug ON %s(slug)`, tableName, tableName),
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fmt.Sprintf(`CREATE INDEX IF NOT EXISTS idx_%s_created_at ON %s(created_at)`, tableName, tableName),
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)
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}
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for _, query := range queries {
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if _, err := vs.sqlxDB.Exec(query); err != nil {
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return fmt.Errorf("failed to create table: %w", err)
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}
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}
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return nil
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}
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// SerializeVector converts []float32 to binary blob
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func SerializeVector(vec []float32) []byte {
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buf := make([]byte, len(vec)*4) // 4 bytes per float32
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for i, v := range vec {
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binary.LittleEndian.PutUint32(buf[i*4:], mathFloat32bits(v))
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}
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return buf
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}
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// DeserializeVector converts binary blob back to []float32
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func DeserializeVector(data []byte) []float32 {
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count := len(data) / 4
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vec := make([]float32, count)
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for i := 0; i < count; i++ {
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vec[i] = mathBitsToFloat32(binary.LittleEndian.Uint32(data[i*4:]))
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}
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return vec
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}
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// mathFloat32bits and mathBitsToFloat32 are helpers to convert between float32 and uint32
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func mathFloat32bits(f float32) uint32 {
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return binary.LittleEndian.Uint32((*(*[4]byte)(unsafe.Pointer(&f)))[:4])
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}
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func mathBitsToFloat32(b uint32) float32 {
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return *(*float32)(unsafe.Pointer(&b))
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}
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// WriteVector stores an embedding vector in the database
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func (vs *VectorStorage) WriteVector(row *models.VectorRow) error {
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tableName, err := vs.getTableName(row.Embeddings)
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if err != nil {
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return err
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}
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// Serialize the embeddings to binary
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serializedEmbeddings := SerializeVector(row.Embeddings)
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query := fmt.Sprintf(
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"INSERT INTO %s (embeddings, slug, raw_text, filename) VALUES (?, ?, ?, ?)",
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tableName,
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)
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if _, err := vs.sqlxDB.Exec(query, serializedEmbeddings, row.Slug, row.RawText, row.FileName); err != nil {
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vs.logger.Error("failed to write vector", "error", err, "slug", row.Slug)
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return err
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}
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return nil
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}
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// getTableName determines which table to use based on embedding size
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func (vs *VectorStorage) getTableName(emb []float32) (string, error) {
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size := len(emb)
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// Check if we support this embedding size
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supportedSizes := map[int]bool{
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384: true,
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768: true,
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1024: true,
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1536: true,
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2048: true,
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3072: true,
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4096: true,
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5120: true,
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}
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if supportedSizes[size] {
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return fmt.Sprintf("embeddings_%d", size), nil
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}
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return "", fmt.Errorf("no table for embedding size of %d", size)
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}
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// SearchClosest finds vectors closest to the query vector using efficient cosine similarity calculation
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func (vs *VectorStorage) SearchClosest(query []float32) ([]models.VectorRow, error) {
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tableName, err := vs.getTableName(query)
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if err != nil {
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return nil, err
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}
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// For better performance, instead of loading all vectors at once,
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// we'll implement batching and potentially add L2 distance-based pre-filtering
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// since cosine similarity is related to L2 distance for normalized vectors
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querySQL := "SELECT embeddings, slug, raw_text, filename FROM " + tableName
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rows, err := vs.sqlxDB.Query(querySQL)
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if err != nil {
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return nil, err
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}
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defer rows.Close()
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// Use a min-heap or simple slice to keep track of top 3 closest vectors
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type SearchResult struct {
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vector models.VectorRow
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distance float32
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}
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var topResults []SearchResult
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// Process vectors one by one to avoid loading everything into memory
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for rows.Next() {
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var (
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embeddingsBlob []byte
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slug, rawText, fileName string
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)
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if err := rows.Scan(&embeddingsBlob, &slug, &rawText, &fileName); err != nil {
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vs.logger.Error("failed to scan row", "error", err)
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continue
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}
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storedEmbeddings := DeserializeVector(embeddingsBlob)
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// Calculate cosine similarity (returns value between -1 and 1, where 1 is most similar)
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similarity := cosineSimilarity(query, storedEmbeddings)
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distance := 1 - similarity // Convert to distance where 0 is most similar
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result := SearchResult{
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vector: models.VectorRow{
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Embeddings: storedEmbeddings,
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Slug: slug,
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RawText: rawText,
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FileName: fileName,
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},
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distance: distance,
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}
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// Add to top results and maintain only top 3
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topResults = append(topResults, result)
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// Sort and keep only top 3
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sort.Slice(topResults, func(i, j int) bool {
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return topResults[i].distance < topResults[j].distance
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})
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if len(topResults) > 3 {
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topResults = topResults[:3] // Keep only closest 3
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}
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}
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// Convert back to VectorRow slice
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results := make([]models.VectorRow, 0, len(topResults))
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for _, result := range topResults {
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result.vector.Distance = result.distance
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results = append(results, result.vector)
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}
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return results, nil
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}
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// ListFiles returns a list of all loaded files
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func (vs *VectorStorage) ListFiles() ([]string, error) {
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fileLists := make([][]string, 0)
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// Query all supported tables and combine results
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embeddingSizes := []int{384, 768, 1024, 1536, 2048, 3072, 4096, 5120}
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for _, size := range embeddingSizes {
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table := fmt.Sprintf("embeddings_%d", size)
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query := "SELECT DISTINCT filename FROM " + table
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rows, err := vs.sqlxDB.Query(query)
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if err != nil {
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// Continue if one table doesn't exist
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continue
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}
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var files []string
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for rows.Next() {
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var filename string
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if err := rows.Scan(&filename); err != nil {
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continue
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}
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files = append(files, filename)
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}
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rows.Close()
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fileLists = append(fileLists, files)
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}
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// Combine and deduplicate
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fileSet := make(map[string]bool)
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var allFiles []string
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for _, files := range fileLists {
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for _, file := range files {
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if !fileSet[file] {
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fileSet[file] = true
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allFiles = append(allFiles, file)
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}
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}
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}
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return allFiles, nil
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}
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// RemoveEmbByFileName removes all embeddings associated with a specific filename
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func (vs *VectorStorage) RemoveEmbByFileName(filename string) error {
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var errors []string
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embeddingSizes := []int{384, 768, 1024, 1536, 2048, 3072, 4096, 5120}
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for _, size := range embeddingSizes {
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table := fmt.Sprintf("embeddings_%d", size)
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query := fmt.Sprintf("DELETE FROM %s WHERE filename = ?", table)
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if _, err := vs.sqlxDB.Exec(query, filename); err != nil {
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errors = append(errors, err.Error())
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}
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}
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if len(errors) > 0 {
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return fmt.Errorf("errors occurred: %s", strings.Join(errors, "; "))
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}
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return nil
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}
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// cosineSimilarity calculates the cosine similarity between two vectors
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func cosineSimilarity(a, b []float32) float32 {
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if len(a) != len(b) {
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return 0.0
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}
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var dotProduct, normA, normB float32
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for i := 0; i < len(a); i++ {
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dotProduct += a[i] * b[i]
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normA += a[i] * a[i]
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normB += b[i] * b[i]
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}
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if normA == 0 || normB == 0 {
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return 0.0
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}
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return dotProduct / (sqrt(normA) * sqrt(normB))
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}
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// sqrt returns the square root of a float32
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func sqrt(f float32) float32 {
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// A simple implementation of square root using Newton's method
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if f == 0 {
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return 0
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}
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guess := f / 2
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for i := 0; i < 10; i++ { // 10 iterations should be enough for good precision
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guess = (guess + f/guess) / 2
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}
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return guess
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}
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