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package rag
import (
"errors"
"fmt"
"gf-lt/config"
"gf-lt/models"
"gf-lt/storage"
"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
mu sync.Mutex
}
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),
}
// Note: Vector tables are created via database migrations, not at runtime
return rag
}
func wordCounter(sentence string) int {
return len(strings.Split(strings.TrimSpace(sentence), " "))
}
func (r *RAG) LoadRAG(fpath string) error {
r.mu.Lock()
defer r.mu.Unlock()
data, err := os.ReadFile(fpath)
if err != nil {
return err
}
r.logger.Debug("rag: loaded file", "fp", fpath)
select {
case LongJobStatusCh <- LoadedFileRAGStatus:
default:
r.logger.Warn("LongJobStatusCh channel is full or closed, dropping status message", "message", 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++ {
if strings.TrimSpace(sents[i]) != "" {
if par.Len() > 0 {
par.WriteString(" ")
}
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) < r.cfg.RAGBatchSize && len(paragraphs) > 0 {
r.cfg.RAGBatchSize = len(paragraphs)
}
if len(paragraphs) == 0 {
return errors.New("no valid paragraphs found in file")
}
// Process paragraphs in batches synchronously
batchCount := 0
for i := 0; i < len(paragraphs); i += r.cfg.RAGBatchSize {
end := i + r.cfg.RAGBatchSize
if end > len(paragraphs) {
end = len(paragraphs)
}
batch := paragraphs[i:end]
batchCount++
// Filter empty paragraphs
nonEmptyBatch := make([]string, 0, len(batch))
for _, p := range batch {
if strings.TrimSpace(p) != "" {
nonEmptyBatch = append(nonEmptyBatch, strings.TrimSpace(p))
}
}
if len(nonEmptyBatch) == 0 {
continue
}
// Embed the batch
embeddings, err := r.embedder.EmbedSlice(nonEmptyBatch)
if err != nil {
r.logger.Error("failed to embed batch", "error", err, "batch", batchCount)
select {
case LongJobStatusCh <- ErrRAGStatus:
default:
r.logger.Warn("LongJobStatusCh channel full, dropping message")
}
return fmt.Errorf("failed to embed batch %d: %w", batchCount, err)
}
if len(embeddings) != len(nonEmptyBatch) {
err := errors.New("embedding count mismatch")
r.logger.Error("embedding mismatch", "expected", len(nonEmptyBatch), "got", len(embeddings))
return err
}
// Write vectors to storage
filename := path.Base(fpath)
for j, text := range nonEmptyBatch {
vector := models.VectorRow{
Embeddings: embeddings[j],
RawText: text,
Slug: fmt.Sprintf("%s_%d_%d", filename, batchCount, j),
FileName: filename,
}
if err := r.storage.WriteVector(&vector); err != nil {
r.logger.Error("failed to write vector to DB", "error", err, "slug", vector.Slug)
select {
case LongJobStatusCh <- ErrRAGStatus:
default:
r.logger.Warn("LongJobStatusCh channel full, dropping message")
}
return fmt.Errorf("failed to write vector: %w", err)
}
}
r.logger.Debug("wrote batch to db", "batch", batchCount, "size", len(nonEmptyBatch))
// Send progress status
statusMsg := fmt.Sprintf("processed batch %d/%d", batchCount, (len(paragraphs)+r.cfg.RAGBatchSize-1)/r.cfg.RAGBatchSize)
select {
case LongJobStatusCh <- statusMsg:
default:
r.logger.Warn("LongJobStatusCh channel full, dropping message")
}
}
r.logger.Debug("finished writing vectors", "batches", batchCount)
select {
case LongJobStatusCh <- FinishedRAGStatus:
default:
r.logger.Warn("LongJobStatusCh channel is full or closed, dropping status message", "message", FinishedRAGStatus)
}
return nil
}
func (r *RAG) LineToVector(line string) ([]float32, error) {
return r.embedder.Embed(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)
}
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