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-rw-r--r--rag_new/storage.go300
1 files changed, 300 insertions, 0 deletions
diff --git a/rag_new/storage.go b/rag_new/storage.go
new file mode 100644
index 0000000..2ab56fb
--- /dev/null
+++ b/rag_new/storage.go
@@ -0,0 +1,300 @@
+package rag_new
+
+import (
+ "gf-lt/models"
+ "gf-lt/storage"
+ "encoding/binary"
+ "fmt"
+ "log/slog"
+ "sort"
+ "strings"
+ "unsafe"
+
+ "github.com/jmoiron/sqlx"
+)
+
+// VectorStorage handles storing and retrieving vectors from SQLite
+type VectorStorage struct {
+ logger *slog.Logger
+ sqlxDB *sqlx.DB
+ store storage.FullRepo
+}
+
+func NewVectorStorage(logger *slog.Logger, store storage.FullRepo) *VectorStorage {
+ return &VectorStorage{
+ logger: logger,
+ sqlxDB: store.DB(), // Use the new DB() method
+ store: store,
+ }
+}
+
+// CreateTables creates the necessary tables for vector storage
+func (vs *VectorStorage) CreateTables() error {
+ // Create tables for different embedding dimensions
+ queries := []string{
+ `CREATE TABLE IF NOT EXISTS embeddings_384 (
+ id INTEGER PRIMARY KEY AUTOINCREMENT,
+ embeddings BLOB NOT NULL,
+ slug TEXT NOT NULL,
+ raw_text TEXT NOT NULL,
+ filename TEXT NOT NULL,
+ created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+ )`,
+ `CREATE TABLE IF NOT EXISTS embeddings_5120 (
+ id INTEGER PRIMARY KEY AUTOINCREMENT,
+ embeddings BLOB NOT NULL,
+ slug TEXT NOT NULL,
+ raw_text TEXT NOT NULL,
+ filename TEXT NOT NULL,
+ created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+ )`,
+ // Indexes for better performance
+ `CREATE INDEX IF NOT EXISTS idx_embeddings_384_filename ON embeddings_384(filename)`,
+ `CREATE INDEX IF NOT EXISTS idx_embeddings_5120_filename ON embeddings_5120(filename)`,
+ `CREATE INDEX IF NOT EXISTS idx_embeddings_384_slug ON embeddings_384(slug)`,
+ `CREATE INDEX IF NOT EXISTS idx_embeddings_5120_slug ON embeddings_5120(slug)`,
+
+ // Additional indexes that may help with searches
+ `CREATE INDEX IF NOT EXISTS idx_embeddings_384_created_at ON embeddings_384(created_at)`,
+ `CREATE INDEX IF NOT EXISTS idx_embeddings_5120_created_at ON embeddings_5120(created_at)`,
+ }
+
+ for _, query := range queries {
+ if _, err := vs.sqlxDB.Exec(query); err != nil {
+ return fmt.Errorf("failed to create table: %w", err)
+ }
+ }
+ return nil
+}
+
+// SerializeVector converts []float32 to binary blob
+func SerializeVector(vec []float32) []byte {
+ buf := make([]byte, len(vec)*4) // 4 bytes per float32
+ for i, v := range vec {
+ binary.LittleEndian.PutUint32(buf[i*4:], mathFloat32bits(v))
+ }
+ return buf
+}
+
+// DeserializeVector converts binary blob back to []float32
+func DeserializeVector(data []byte) []float32 {
+ count := len(data) / 4
+ vec := make([]float32, count)
+ for i := 0; i < count; i++ {
+ vec[i] = mathBitsToFloat32(binary.LittleEndian.Uint32(data[i*4:]))
+ }
+ return vec
+}
+
+// mathFloat32bits and mathBitsToFloat32 are helpers to convert between float32 and uint32
+func mathFloat32bits(f float32) uint32 {
+ return binary.LittleEndian.Uint32((*(*[4]byte)(unsafe.Pointer(&f)))[:4])
+}
+
+func mathBitsToFloat32(b uint32) float32 {
+ return *(*float32)(unsafe.Pointer(&b))
+}
+
+// WriteVector stores an embedding vector in the database
+func (vs *VectorStorage) WriteVector(row *models.VectorRow) error {
+ tableName, err := vs.getTableName(row.Embeddings)
+ if err != nil {
+ return err
+ }
+
+ // Serialize the embeddings to binary
+ serializedEmbeddings := SerializeVector(row.Embeddings)
+
+ query := fmt.Sprintf(
+ "INSERT INTO %s (embeddings, slug, raw_text, filename) VALUES (?, ?, ?, ?)",
+ tableName,
+ )
+
+ if _, err := vs.sqlxDB.Exec(query, serializedEmbeddings, row.Slug, row.RawText, row.FileName); err != nil {
+ vs.logger.Error("failed to write vector", "error", err, "slug", row.Slug)
+ return err
+ }
+
+ return nil
+}
+
+// getTableName determines which table to use based on embedding size
+func (vs *VectorStorage) getTableName(emb []float32) (string, error) {
+ switch len(emb) {
+ case 384:
+ return "embeddings_384", nil
+ case 5120:
+ return "embeddings_5120", nil
+ default:
+ return "", fmt.Errorf("no table for embedding size of %d", len(emb))
+ }
+}
+
+// SearchClosest finds vectors closest to the query vector using efficient cosine similarity calculation
+func (vs *VectorStorage) SearchClosest(query []float32) ([]models.VectorRow, error) {
+ tableName, err := vs.getTableName(query)
+ if err != nil {
+ return nil, err
+ }
+
+ // For better performance, instead of loading all vectors at once,
+ // we'll implement batching and potentially add L2 distance-based pre-filtering
+ // since cosine similarity is related to L2 distance for normalized vectors
+
+ querySQL := fmt.Sprintf("SELECT embeddings, slug, raw_text, filename FROM %s", tableName)
+ rows, err := vs.sqlxDB.Query(querySQL)
+ if err != nil {
+ return nil, err
+ }
+ defer rows.Close()
+
+ // Use a min-heap or simple slice to keep track of top 3 closest vectors
+ type SearchResult struct {
+ vector models.VectorRow
+ distance float32
+ }
+
+ var topResults []SearchResult
+
+ // Process vectors one by one to avoid loading everything into memory
+ for rows.Next() {
+ var (
+ embeddingsBlob []byte
+ slug, rawText, fileName string
+ )
+
+ if err := rows.Scan(&embeddingsBlob, &slug, &rawText, &fileName); err != nil {
+ vs.logger.Error("failed to scan row", "error", err)
+ continue
+ }
+
+ storedEmbeddings := DeserializeVector(embeddingsBlob)
+
+ // Calculate cosine similarity (returns value between -1 and 1, where 1 is most similar)
+ similarity := cosineSimilarity(query, storedEmbeddings)
+ distance := 1 - similarity // Convert to distance where 0 is most similar
+
+ result := SearchResult{
+ vector: models.VectorRow{
+ Embeddings: storedEmbeddings,
+ Slug: slug,
+ RawText: rawText,
+ FileName: fileName,
+ },
+ distance: distance,
+ }
+
+ // Add to top results and maintain only top 3
+ topResults = append(topResults, result)
+
+ // Sort and keep only top 3
+ sort.Slice(topResults, func(i, j int) bool {
+ return topResults[i].distance < topResults[j].distance
+ })
+
+ if len(topResults) > 3 {
+ topResults = topResults[:3] // Keep only closest 3
+ }
+ }
+
+ // Convert back to VectorRow slice
+ var results []models.VectorRow
+ for _, result := range topResults {
+ result.vector.Distance = result.distance
+ results = append(results, result.vector)
+ }
+
+ return results, nil
+}
+
+// ListFiles returns a list of all loaded files
+func (vs *VectorStorage) ListFiles() ([]string, error) {
+ var fileLists [][]string
+
+ // Query both tables and combine results
+ for _, table := range []string{"embeddings_384", "embeddings_5120"} {
+ query := fmt.Sprintf("SELECT DISTINCT filename FROM %s", table)
+ rows, err := vs.sqlxDB.Query(query)
+ if err != nil {
+ // Continue if one table doesn't exist
+ continue
+ }
+
+ var files []string
+ for rows.Next() {
+ var filename string
+ if err := rows.Scan(&filename); err != nil {
+ continue
+ }
+ files = append(files, filename)
+ }
+ rows.Close()
+
+ fileLists = append(fileLists, files)
+ }
+
+ // Combine and deduplicate
+ fileSet := make(map[string]bool)
+ var allFiles []string
+ for _, files := range fileLists {
+ for _, file := range files {
+ if !fileSet[file] {
+ fileSet[file] = true
+ allFiles = append(allFiles, file)
+ }
+ }
+ }
+
+ return allFiles, nil
+}
+
+// RemoveEmbByFileName removes all embeddings associated with a specific filename
+func (vs *VectorStorage) RemoveEmbByFileName(filename string) error {
+ var errors []string
+
+ for _, table := range []string{"embeddings_384", "embeddings_5120"} {
+ query := fmt.Sprintf("DELETE FROM %s WHERE filename = ?", table)
+ if _, err := vs.sqlxDB.Exec(query, filename); err != nil {
+ errors = append(errors, err.Error())
+ }
+ }
+
+ if len(errors) > 0 {
+ return fmt.Errorf("errors occurred: %s", strings.Join(errors, "; "))
+ }
+
+ return nil
+}
+
+// cosineSimilarity calculates the cosine similarity between two vectors
+func cosineSimilarity(a, b []float32) float32 {
+ if len(a) != len(b) {
+ return 0.0
+ }
+
+ var dotProduct, normA, normB float32
+ for i := 0; i < len(a); i++ {
+ dotProduct += a[i] * b[i]
+ normA += a[i] * a[i]
+ normB += b[i] * b[i]
+ }
+
+ if normA == 0 || normB == 0 {
+ return 0.0
+ }
+
+ return dotProduct / (sqrt(normA) * sqrt(normB))
+}
+
+// sqrt returns the square root of a float32
+func sqrt(f float32) float32 {
+ // A simple implementation of square root using Newton's method
+ if f == 0 {
+ return 0
+ }
+ guess := f / 2
+ for i := 0; i < 10; i++ { // 10 iterations should be enough for good precision
+ guess = (guess + f/guess) / 2
+ }
+ return guess
+} \ No newline at end of file
generated by cgit v1.2.3 (git 2.25.1) at 2025-10-22 18:42:04 +0000