Chore: remove old rag

1 files changed, 0 insertions, 300 deletions

diff --git a/rag_new/storage.go b/rag_new/storage.go
deleted file mode 100644
index 2ab56fb..0000000
--- a/rag_new/storage.go
+++ /dev/null
@@ -1,300 +0,0 @@
-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