256 lines
6.1 KiB
Go
256 lines
6.1 KiB
Go
package storage
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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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"unsafe"
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"github.com/jmoiron/sqlx"
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)
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type VectorRepo interface {
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WriteVector(*models.VectorRow) error
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SearchClosest(q []float32) ([]models.VectorRow, error)
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ListFiles() ([]string, error)
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RemoveEmbByFileName(filename string) error
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DB() *sqlx.DB
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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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func fetchTableName(emb []float32) (string, error) {
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switch len(emb) {
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case 384:
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return "embeddings_384", nil
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case 768:
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return "embeddings_768", nil
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case 1024:
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return "embeddings_1024", nil
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case 1536:
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return "embeddings_1536", nil
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case 2048:
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return "embeddings_2048", nil
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case 3072:
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return "embeddings_3072", nil
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case 4096:
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return "embeddings_4096", nil
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case 5120:
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return "embeddings_5120", nil
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default:
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return "", fmt.Errorf("no table for the size of %d", len(emb))
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}
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}
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func (p ProviderSQL) WriteVector(row *models.VectorRow) error {
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tableName, err := fetchTableName(row.Embeddings)
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if err != nil {
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return err
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}
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serializedEmbeddings := SerializeVector(row.Embeddings)
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query := fmt.Sprintf("INSERT INTO %s(embeddings, slug, raw_text, filename) VALUES (?, ?, ?, ?)", tableName)
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_, err = p.db.Exec(query, serializedEmbeddings, row.Slug, row.RawText, row.FileName)
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return err
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}
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func (p ProviderSQL) SearchClosest(q []float32) ([]models.VectorRow, error) {
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tableName, err := fetchTableName(q)
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if err != nil {
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return nil, err
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}
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querySQL := "SELECT embeddings, slug, raw_text, filename FROM " + tableName
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rows, err := p.db.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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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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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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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(q, 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 results
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topResults = append(topResults, result)
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// Sort and keep only top results
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// We'll keep the top 3 closest vectors
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if len(topResults) > 3 {
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// Simple sort and truncate to maintain only 3 best matches
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for i := 0; i < len(topResults); i++ {
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for j := i + 1; j < len(topResults); j++ {
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if topResults[i].distance > topResults[j].distance {
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topResults[i], topResults[j] = topResults[j], topResults[i]
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}
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}
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}
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topResults = topResults[: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, len(topResults))
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for i, result := range topResults {
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result.vector.Distance = result.distance
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results[i] = result.vector
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}
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return results, 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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func (p ProviderSQL) 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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tableNames := []string{
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"embeddings_384", "embeddings_768", "embeddings_1024", "embeddings_1536",
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"embeddings_2048", "embeddings_3072", "embeddings_4096", "embeddings_5120",
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}
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for _, table := range tableNames {
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query := "SELECT DISTINCT filename FROM " + table
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rows, err := p.db.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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func (p ProviderSQL) RemoveEmbByFileName(filename string) error {
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var errors []string
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tableNames := []string{
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"embeddings_384", "embeddings_768", "embeddings_1024", "embeddings_1536",
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"embeddings_2048", "embeddings_3072", "embeddings_4096", "embeddings_5120",
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}
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for _, table := range tableNames {
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query := fmt.Sprintf("DELETE FROM %s WHERE filename = ?", table)
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if _, err := p.db.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: %v", errors)
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}
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return nil
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}
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