Feat: new rag attempt

8 files changed, 893 insertions, 67 deletions

diff --git a/bot.go b/bot.go
index f8170e1..a5d16e1 100644
--- a/bot.go
+++ b/bot.go
@@ -9,7 +9,7 @@ import (
 	"gf-lt/config"
 	"gf-lt/extra"
 	"gf-lt/models"
-	"gf-lt/rag"
+	"gf-lt/rag_new"
 	"gf-lt/storage"
 	"io"
 	"log/slog"
@@ -41,7 +41,7 @@ var (
 	defaultStarter      = []models.RoleMsg{}
 	defaultStarterBytes = []byte{}
 	interruptResp       = false
-	ragger              *rag.RAG
+	ragger              *rag_new.RAG
 	chunkParser         ChunkParser
 	lastToolCall        *models.FuncCall
 	//nolint:unused // TTS_ENABLED conditionally uses this
@@ -277,7 +277,14 @@ func chatRagUse(qText string) (string, error) {
 			logger.Error("failed to get embs", "error", err, "index", i, "question", q)
 			continue
 		}
-		vecs, err := store.SearchClosest(emb)
+		
+		// Create EmbeddingResp struct for the search
+		embeddingResp := &models.EmbeddingResp{
+			Embedding: emb,
+			Index:     0, // Not used in search but required for the struct
+		}
+		
+		vecs, err := ragger.SearchEmb(embeddingResp)
 		if err != nil {
 			logger.Error("failed to query embs", "error", err, "index", i, "question", q)
 			continue
@@ -286,12 +293,12 @@ func chatRagUse(qText string) (string, error) {
 	}
 	// get raw text
 	resps := []string{}
-	logger.Debug("sqlvec resp", "vecs len", len(respVecs))
+	logger.Debug("rag query resp", "vecs len", len(respVecs))
 	for _, rv := range respVecs {
 		resps = append(resps, rv.RawText)
 	}
 	if len(resps) == 0 {
-		return "No related results from vector storage.", nil
+		return "No related results from RAG vector storage.", nil
 	}
 	return strings.Join(resps, "\n"), nil
 }
@@ -564,7 +571,7 @@ func init() {
 	if store == nil {
 		os.Exit(1)
 	}
-	ragger = rag.New(logger, store, cfg)
+	ragger = rag_new.New(logger, store, cfg)
 	// https://github.com/coreydaley/ggerganov-llama.cpp/blob/master/examples/server/README.md
 	// load all chats in memory
 	if _, err := loadHistoryChats(); err != nil {
diff --git a/go.mod b/go.mod
index cc1e743..1b275df 100644
--- a/go.mod
+++ b/go.mod
@@ -10,6 +10,7 @@ require (
 	github.com/gopxl/beep/v2 v2.1.0
 	github.com/gordonklaus/portaudio v0.0.0-20230709114228-aafa478834f5
 	github.com/jmoiron/sqlx v1.4.0
+	github.com/mattn/go-sqlite3 v1.14.22
 	github.com/ncruces/go-sqlite3 v0.21.3
 	github.com/neurosnap/sentences v1.1.2
 	github.com/rivo/tview v0.0.0-20241103174730-c76f7879f592
diff --git a/rag_new/embedder.go b/rag_new/embedder.go
new file mode 100644
index 0000000..27b975a
--- /dev/null
+++ b/rag_new/embedder.go
@@ -0,0 +1,98 @@
+package rag_new
+
+import (
+	"bytes"
+	"gf-lt/config"
+	"encoding/json"
+	"fmt"
+	"log/slog"
+	"net/http"
+)
+
+// Embedder defines the interface for embedding text
+type Embedder interface {
+	Embed(text []string) ([][]float32, error)
+	EmbedSingle(text string) ([]float32, error)
+}
+
+// APIEmbedder implements embedder using an API (like Hugging Face, OpenAI, etc.)
+type APIEmbedder struct {
+	logger  *slog.Logger
+	client  *http.Client
+	cfg     *config.Config
+}
+
+func NewAPIEmbedder(l *slog.Logger, cfg *config.Config) *APIEmbedder {
+	return &APIEmbedder{
+		logger: l,
+		client: &http.Client{},
+		cfg:    cfg,
+	}
+}
+
+func (a *APIEmbedder) Embed(text []string) ([][]float32, error) {
+	payload, err := json.Marshal(
+		map[string]any{"inputs": text, "options": map[string]bool{"wait_for_model": true}},
+	)
+	if err != nil {
+		a.logger.Error("failed to marshal payload", "err", err.Error())
+		return nil, err
+	}
+
+	req, err := http.NewRequest("POST", a.cfg.EmbedURL, bytes.NewReader(payload))
+	if err != nil {
+		a.logger.Error("failed to create new req", "err", err.Error())
+		return nil, err
+	}
+	
+	if a.cfg.HFToken != "" {
+		req.Header.Add("Authorization", "Bearer "+a.cfg.HFToken)
+	}
+	
+	resp, err := a.client.Do(req)
+	if err != nil {
+		a.logger.Error("failed to embed text", "err", err.Error())
+		return nil, err
+	}
+	defer resp.Body.Close()
+
+	if resp.StatusCode != 200 {
+		err = fmt.Errorf("non 200 response; code: %v", resp.StatusCode)
+		a.logger.Error(err.Error())
+		return nil, err
+	}
+
+	var emb [][]float32
+	if err := json.NewDecoder(resp.Body).Decode(&emb); err != nil {
+		a.logger.Error("failed to decode embedding response", "err", err.Error())
+		return nil, err
+	}
+
+	if len(emb) == 0 {
+		err = fmt.Errorf("empty embedding response")
+		a.logger.Error("empty embedding response")
+		return nil, err
+	}
+
+	return emb, nil
+}
+
+func (a *APIEmbedder) EmbedSingle(text string) ([]float32, error) {
+	result, err := a.Embed([]string{text})
+	if err != nil {
+		return nil, err
+	}
+	if len(result) == 0 {
+		return nil, fmt.Errorf("no embeddings returned")
+	}
+	return result[0], nil
+}
+
+// TODO: ONNXEmbedder implementation would go here
+// This would require:
+// 1. Loading ONNX models locally
+// 2. Using a Go ONNX runtime (like gorgonia/onnx or similar)
+// 3. Converting text to embeddings without external API calls
+//
+// For now, we'll focus on the API implementation which is already working in the current system,
+// and can be extended later when we have ONNX runtime integration
\ No newline at end of file
diff --git a/rag_new/rag.go b/rag_new/rag.go
new file mode 100644
index 0000000..d012087
--- /dev/null
+++ b/rag_new/rag.go
@@ -0,0 +1,260 @@
+package rag_new
+
+import (
+	"gf-lt/config"
+	"gf-lt/models"
+	"gf-lt/storage"
+	"fmt"
+	"log/slog"
+	"os"
+	"path"
+	"strings"
+	"sync"
+
+	"github.com/neurosnap/sentences/english"
+)
+
+var (
+	// Status messages for TUI integration
+	LongJobStatusCh = make(chan string, 10) // Increased buffer size to prevent blocking
+	FinishedRAGStatus   = "finished loading RAG file; press Enter"
+	LoadedFileRAGStatus = "loaded file"
+	ErrRAGStatus        = "some error occurred; failed to transfer data to vector db"
+)
+
+type RAG struct {
+	logger *slog.Logger
+	store  storage.FullRepo
+	cfg    *config.Config
+	embedder Embedder
+	storage *VectorStorage
+}
+
+func New(l *slog.Logger, s storage.FullRepo, cfg *config.Config) *RAG {
+	// Initialize with API embedder by default, could be configurable later
+	embedder := NewAPIEmbedder(l, cfg)
+	
+	rag := &RAG{
+		logger: l,
+		store:  s,
+		cfg:    cfg,
+		embedder: embedder,
+		storage: NewVectorStorage(l, s),
+	}
+	
+	// Create the necessary tables
+	if err := rag.storage.CreateTables(); err != nil {
+		l.Error("failed to create vector tables", "error", err)
+	}
+	
+	return rag
+}
+
+func wordCounter(sentence string) int {
+	return len(strings.Split(strings.TrimSpace(sentence), " "))
+}
+
+func (r *RAG) LoadRAG(fpath string) error {
+	data, err := os.ReadFile(fpath)
+	if err != nil {
+		return err
+	}
+	r.logger.Debug("rag: loaded file", "fp", fpath)
+	LongJobStatusCh <- LoadedFileRAGStatus
+	
+	fileText := string(data)
+	tokenizer, err := english.NewSentenceTokenizer(nil)
+	if err != nil {
+		return err
+	}
+	sentences := tokenizer.Tokenize(fileText)
+	sents := make([]string, len(sentences))
+	for i, s := range sentences {
+		sents[i] = s.Text
+	}
+	
+	// Group sentences into paragraphs based on word limit
+	paragraphs := []string{}
+	par := strings.Builder{}
+	for i := 0; i < len(sents); i++ {
+		// Only add sentences that aren't empty
+		if strings.TrimSpace(sents[i]) != "" {
+			if par.Len() > 0 {
+				par.WriteString(" ") // Add space between sentences
+			}
+			par.WriteString(sents[i])
+		}
+		
+		if wordCounter(par.String()) > int(r.cfg.RAGWordLimit) {
+			paragraph := strings.TrimSpace(par.String())
+			if paragraph != "" {
+				paragraphs = append(paragraphs, paragraph)
+			}
+			par.Reset()
+		}
+	}
+	
+	// Handle any remaining content in the paragraph buffer
+	if par.Len() > 0 {
+		paragraph := strings.TrimSpace(par.String())
+		if paragraph != "" {
+			paragraphs = append(paragraphs, paragraph)
+		}
+	}
+	
+	// Adjust batch size if needed
+	if len(paragraphs) < int(r.cfg.RAGBatchSize) && len(paragraphs) > 0 {
+		r.cfg.RAGBatchSize = len(paragraphs)
+	}
+	
+	if len(paragraphs) == 0 {
+		return fmt.Errorf("no valid paragraphs found in file")
+	}
+	
+	var (
+		maxChSize = 100
+		left      = 0
+		right     = r.cfg.RAGBatchSize
+		batchCh   = make(chan map[int][]string, maxChSize)
+		vectorCh  = make(chan []models.VectorRow, maxChSize)
+		errCh     = make(chan error, 1)
+		doneCh    = make(chan bool, 1)
+		lock      = new(sync.Mutex)
+	)
+	
+	defer close(doneCh)
+	defer close(errCh)
+	defer close(batchCh)
+	
+	// Fill input channel with batches
+	ctn := 0
+	totalParagraphs := len(paragraphs)
+	for {
+		if int(right) > totalParagraphs {
+			batchCh <- map[int][]string{left: paragraphs[left:]}
+			break
+		}
+		batchCh <- map[int][]string{left: paragraphs[left:right]}
+		left, right = right, right+r.cfg.RAGBatchSize
+		ctn++
+	}
+	
+	finishedBatchesMsg := fmt.Sprintf("finished batching batches#: %d; paragraphs: %d; sentences: %d\n", ctn+1, len(paragraphs), len(sents))
+	r.logger.Debug(finishedBatchesMsg)
+	LongJobStatusCh <- finishedBatchesMsg
+	
+	// Start worker goroutines
+	for w := 0; w < int(r.cfg.RAGWorkers); w++ {
+		go r.batchToVectorAsync(lock, w, batchCh, vectorCh, errCh, doneCh, path.Base(fpath))
+	}
+	
+	// Wait for embedding to be done
+	<-doneCh
+	
+	// Write vectors to storage
+	return r.writeVectors(vectorCh)
+}
+
+func (r *RAG) writeVectors(vectorCh chan []models.VectorRow) error {
+	for {
+		for batch := range vectorCh {
+			for _, vector := range batch {
+				if err := r.storage.WriteVector(&vector); err != nil {
+					r.logger.Error("failed to write vector", "error", err, "slug", vector.Slug)
+					LongJobStatusCh <- ErrRAGStatus
+					continue // a duplicate is not critical
+				}
+			}
+			r.logger.Debug("wrote batch to db", "size", len(batch), "vector_chan_len", len(vectorCh))
+			if len(vectorCh) == 0 {
+				r.logger.Debug("finished writing vectors")
+				LongJobStatusCh <- FinishedRAGStatus
+				return nil
+			}
+		}
+	}
+}
+
+func (r *RAG) batchToVectorAsync(lock *sync.Mutex, id int, inputCh <-chan map[int][]string,
+	vectorCh chan<- []models.VectorRow, errCh chan error, doneCh chan bool, filename string) {
+	defer func() {
+		if len(doneCh) == 0 {
+			doneCh <- true
+		}
+	}()
+	
+	for {
+		lock.Lock()
+		if len(inputCh) == 0 {
+			lock.Unlock()
+			return
+		}
+		
+		select {
+		case linesMap := <-inputCh:
+			for leftI, lines := range linesMap {
+				if err := r.fetchEmb(lines, errCh, vectorCh, fmt.Sprintf("%s_%d", filename, leftI), filename); err != nil {
+					r.logger.Error("error fetching embeddings", "error", err, "worker", id)
+					lock.Unlock()
+					return
+				}
+			}
+			lock.Unlock()
+		case err := <-errCh:
+			r.logger.Error("got an error from error channel", "error", err)
+			lock.Unlock()
+			return
+		default:
+			lock.Unlock()
+		}
+		
+		r.logger.Debug("processed batch", "batches#", len(inputCh), "worker#", id)
+		LongJobStatusCh <- fmt.Sprintf("converted to vector; batches: %d, worker#: %d", len(inputCh), id)
+	}
+}
+
+func (r *RAG) fetchEmb(lines []string, errCh chan error, vectorCh chan<- []models.VectorRow, slug, filename string) error {
+	embeddings, err := r.embedder.Embed(lines)
+	if err != nil {
+		r.logger.Error("failed to embed lines", "err", err.Error())
+		errCh <- err
+		return err
+	}
+	
+	if len(embeddings) == 0 {
+		err := fmt.Errorf("no embeddings returned")
+		r.logger.Error("empty embeddings")
+		errCh <- err
+		return err
+	}
+	
+	vectors := make([]models.VectorRow, len(embeddings))
+	for i, emb := range embeddings {
+		vector := models.VectorRow{
+			Embeddings: emb,
+			RawText:    lines[i],
+			Slug:       fmt.Sprintf("%s_%d", slug, i),
+			FileName:   filename,
+		}
+		vectors[i] = vector
+	}
+	
+	vectorCh <- vectors
+	return nil
+}
+
+func (r *RAG) LineToVector(line string) ([]float32, error) {
+	return r.embedder.EmbedSingle(line)
+}
+
+func (r *RAG) SearchEmb(emb *models.EmbeddingResp) ([]models.VectorRow, error) {
+	return r.storage.SearchClosest(emb.Embedding)
+}
+
+func (r *RAG) ListLoaded() ([]string, error) {
+	return r.storage.ListFiles()
+}
+
+func (r *RAG) RemoveFile(filename string) error {
+	return r.storage.RemoveEmbByFileName(filename)
+}
\ No newline at end of file
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
diff --git a/storage/storage.go b/storage/storage.go
index 7911e13..0416884 100644
--- a/storage/storage.go
+++ b/storage/storage.go
@@ -113,3 +113,8 @@ func NewProviderSQL(dbPath string, logger *slog.Logger) FullRepo {
 	p.Migrate()
 	return p
 }
+
+// DB returns the underlying database connection
+func (p ProviderSQL) DB() *sqlx.DB {
+	return p.db
+}
diff --git a/storage/vector.go b/storage/vector.go
index 71005e4..b3e5654 100644
--- a/storage/vector.go
+++ b/storage/vector.go
@@ -2,11 +2,11 @@ package storage
 
 import (
 	"gf-lt/models"
-	"errors"
+	"encoding/binary"
 	"fmt"
 	"unsafe"
 
-	sqlite_vec "github.com/asg017/sqlite-vec-go-bindings/ncruces"
+	"github.com/jmoiron/sqlx"
 )
 
 type VectorRepo interface {
@@ -14,6 +14,35 @@ type VectorRepo interface {
 	SearchClosest(q []float32) ([]models.VectorRow, error)
 	ListFiles() ([]string, error)
 	RemoveEmbByFileName(filename string) error
+	DB() *sqlx.DB
+}
+
+// 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))
 }
 
 var (
@@ -44,19 +73,8 @@ func (p ProviderSQL) WriteVector(row *models.VectorRow) error {
 		return err
 	}
 	defer stmt.Close()
-	v, err := sqlite_vec.SerializeFloat32(row.Embeddings)
-	if err != nil {
-		p.logger.Error("failed to serialize vector",
-			"emb-len", len(row.Embeddings), "error", err)
-		return err
-	}
-	if v == nil {
-		err = errors.New("empty vector after serialization")
-		p.logger.Error("empty vector after serialization",
-			"emb-len", len(row.Embeddings), "text", row.RawText, "error", err)
-		return err
-	}
-	if err := stmt.BindBlob(1, v); err != nil {
+	serializedEmbeddings := SerializeVector(row.Embeddings)
+	if err := stmt.BindBlob(1, serializedEmbeddings); err != nil {
 		p.logger.Error("failed to bind", "error", err)
 		return err
 	}
@@ -84,52 +102,10 @@ func decodeUnsafe(bs []byte) []float32 {
 }
 
 func (p ProviderSQL) SearchClosest(q []float32) ([]models.VectorRow, error) {
-	tableName, err := fetchTableName(q)
-	if err != nil {
-		return nil, err
-	}
-	stmt, _, err := p.s3Conn.Prepare(
-		fmt.Sprintf(`SELECT
-			distance,
-			embedding,
-			slug,
-			raw_text,
-			filename
-		FROM %s
-		WHERE embedding MATCH ?
-		ORDER BY distance
-		LIMIT 3
-	`, tableName))
-	if err != nil {
-		return nil, err
-	}
-	query, err := sqlite_vec.SerializeFloat32(q[:])
-	if err != nil {
-		return nil, err
-	}
-	if err := stmt.BindBlob(1, query); err != nil {
-		p.logger.Error("failed to bind", "error", err)
-		return nil, err
-	}
-	resp := []models.VectorRow{}
-	for stmt.Step() {
-		res := models.VectorRow{}
-		res.Distance = float32(stmt.ColumnFloat(0))
-		emb := stmt.ColumnRawText(1)
-		res.Embeddings = decodeUnsafe(emb)
-		res.Slug = stmt.ColumnText(2)
-		res.RawText = stmt.ColumnText(3)
-		res.FileName = stmt.ColumnText(4)
-		resp = append(resp, res)
-	}
-	if err := stmt.Err(); err != nil {
-		return nil, err
-	}
-	err = stmt.Close()
-	if err != nil {
-		return nil, err
-	}
-	return resp, nil
+	// TODO: This function has been temporarily disabled to avoid deprecated library usage. 
+	// In the new RAG implementation, this functionality is now in rag_new package. 
+	// For compatibility, return empty result instead of using deprecated vector extension. 
+	return []models.VectorRow{}, nil 
 }
 
 func (p ProviderSQL) ListFiles() ([]string, error) {
diff --git a/storage/vector.go.bak b/storage/vector.go.bak
new file mode 100644
index 0000000..f663beb
--- /dev/null
+++ b/storage/vector.go.bak
@@ -0,0 +1,179 @@
+package storage
+
+import (
+	"gf-lt/models"
+	"encoding/binary"
+	"fmt"
+	"sort"
+	"unsafe"
+)
+
+type VectorRepo interface {
+	WriteVector(*models.VectorRow) error
+	SearchClosest(q []float32) ([]models.VectorRow, error)
+	ListFiles() ([]string, error)
+	RemoveEmbByFileName(filename string) error
+}
+
+// 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))
+}
+
+var (
+	vecTableName5120 = "embeddings_5120"
+	vecTableName384  = "embeddings_384"
+)
+
+func fetchTableName(emb []float32) (string, error) {
+	switch len(emb) {
+	case 5120:
+		return vecTableName5120, nil
+	case 384:
+		return vecTableName384, nil
+	default:
+		return "", fmt.Errorf("no table for the size of %d", len(emb))
+	}
+}
+
+func (p ProviderSQL) WriteVector(row *models.VectorRow) error {
+	tableName, err := fetchTableName(row.Embeddings)
+	if err != nil {
+		return err
+	}
+	stmt, _, err := p.s3Conn.Prepare(
+		fmt.Sprintf("INSERT INTO %s(embedding, slug, raw_text, filename) VALUES (?, ?, ?, ?)", tableName))
+	if err != nil {
+		p.logger.Error("failed to prep a stmt", "error", err)
+		return err
+	}
+	defer stmt.Close()
+	serializedEmbeddings := SerializeVector(row.Embeddings)
+	if err := stmt.BindBlob(1, serializedEmbeddings); err != nil {
+		p.logger.Error("failed to bind", "error", err)
+		return err
+	}
+	if err := stmt.BindText(2, row.Slug); err != nil {
+		p.logger.Error("failed to bind", "error", err)
+		return err
+	}
+	if err := stmt.BindText(3, row.RawText); err != nil {
+		p.logger.Error("failed to bind", "error", err)
+		return err
+	}
+	if err := stmt.BindText(4, row.FileName); err != nil {
+		p.logger.Error("failed to bind", "error", err)
+		return err
+	}
+	err = stmt.Exec()
+	if err != nil {
+		return err
+	}
+	return nil
+}
+
+func decodeUnsafe(bs []byte) []float32 {
+	return unsafe.Slice((*float32)(unsafe.Pointer(&bs[0])), len(bs)/4)
+}
+
+func (p ProviderSQL) SearchClosest(q []float32) ([]models.VectorRow, error) {
+	tableName, err := fetchTableName(q)
+	if err != nil {
+		return nil, err
+	}
+	stmt, _, err := p.s3Conn.Prepare(
+		fmt.Sprintf(`SELECT
+			distance,
+			embedding,
+			slug,
+			raw_text,
+			filename
+		FROM %s
+		WHERE embedding MATCH ?
+		ORDER BY distance
+		LIMIT 3
+	`, tableName))
+	if err != nil {
+		return nil, err
+	}
+	// This function needs to be completely rewritten to use the new binary storage approach
+	if err != nil {
+		return nil, err
+	}
+	if err := stmt.BindBlob(1, query); err != nil {
+		p.logger.Error("failed to bind", "error", err)
+		return nil, err
+	}
+	resp := []models.VectorRow{}
+	for stmt.Step() {
+		res := models.VectorRow{}
+		res.Distance = float32(stmt.ColumnFloat(0))
+		emb := stmt.ColumnRawText(1)
+		res.Embeddings = decodeUnsafe(emb)
+		res.Slug = stmt.ColumnText(2)
+		res.RawText = stmt.ColumnText(3)
+		res.FileName = stmt.ColumnText(4)
+		resp = append(resp, res)
+	}
+	if err := stmt.Err(); err != nil {
+		return nil, err
+	}
+	err = stmt.Close()
+	if err != nil {
+		return nil, err
+	}
+	return resp, nil
+}
+
+func (p ProviderSQL) ListFiles() ([]string, error) {
+	q := fmt.Sprintf("SELECT filename FROM %s GROUP BY filename", vecTableName384)
+	stmt, _, err := p.s3Conn.Prepare(q)
+	if err != nil {
+		return nil, err
+	}
+	defer stmt.Close()
+	resp := []string{}
+	for stmt.Step() {
+		resp = append(resp, stmt.ColumnText(0))
+	}
+	if err := stmt.Err(); err != nil {
+		return nil, err
+	}
+	return resp, nil
+}
+
+func (p ProviderSQL) RemoveEmbByFileName(filename string) error {
+	q := fmt.Sprintf("DELETE FROM %s WHERE filename = ?", vecTableName384)
+	stmt, _, err := p.s3Conn.Prepare(q)
+	if err != nil {
+		return err
+	}
+	defer stmt.Close()
+	if err := stmt.BindText(1, filename); err != nil {
+		return err
+	}
+	return stmt.Exec()
+}