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-rw-r--r--rag_new/embedder.go98
-rw-r--r--rag_new/rag.go260
-rw-r--r--rag_new/storage.go300
3 files changed, 0 insertions, 658 deletions
diff --git a/rag_new/embedder.go b/rag_new/embedder.go
deleted file mode 100644
index 27b975a..0000000
--- a/rag_new/embedder.go
+++ /dev/null
@@ -1,98 +0,0 @@
-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
deleted file mode 100644
index d012087..0000000
--- a/rag_new/rag.go
+++ /dev/null
@@ -1,260 +0,0 @@
-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
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
generated by cgit v1.2.3 (git 2.25.1) at 2025-10-22 23:50:42 +0000