Chore: linter complaints

.gitignore

@@ -3,6 +3,8 @@
 testlog
 history/
 *.db
+*.db-shm
+*.db-wal
 config.toml
 sysprompts/*
 !sysprompts/alice_bob_carl.json

rag/embedder.go

@@ -213,7 +213,6 @@ func NewONNXEmbedder(modelPath, tokenizerPath string, dims int, logger *slog.Log
 	if cudaLibPath == "" {
 		fmt.Println("WARNING: CUDA provider library not found, will use CPU")
 	}
-
 	emb := &ONNXEmbedder{
 		tokenizerPath: tokenizerPath,
 		dims:          dims,
@@ -232,7 +231,6 @@ func (e *ONNXEmbedder) ensureInitialized() error {
 	if e.session != nil {
 		return nil
 	}
-
 	// Load tokenizer lazily
 	if e.tokenizer == nil {
 		tok, err := pretrained.FromFile(e.tokenizerPath)
@@ -241,7 +239,6 @@ func (e *ONNXEmbedder) ensureInitialized() error {
 		}
 		e.tokenizer = tok
 	}
-
 	onnxInitOnce.Do(func() {
 		onnxruntime_go.SetSharedLibraryPath(onnxLibPath)
 		if err := onnxruntime_go.InitializeEnvironment(); err != nil {
@@ -260,13 +257,14 @@ func (e *ONNXEmbedder) ensureInitialized() error {
 	if !onnxReady {
 		return errors.New("ONNX runtime not ready")
 	}
-
 	// Create session options
 	opts, err := onnxruntime_go.NewSessionOptions()
 	if err != nil {
 		return fmt.Errorf("failed to create session options: %w", err)
 	}
-	defer opts.Destroy()
+	defer func() {
+		_ = opts.Destroy()
+	}()
 
 	// Try to add CUDA provider
 	useCUDA := cudaLibPath != ""
@@ -276,7 +274,9 @@ func (e *ONNXEmbedder) ensureInitialized() error {
 			e.logger.Warn("failed to create CUDA provider options, falling back to CPU", "error", err)
 			useCUDA = false
 		} else {
-			defer cudaOpts.Destroy()
+			defer func() {
+				_ = cudaOpts.Destroy()
+			}()
 			if err := cudaOpts.Update(map[string]string{"device_id": "0"}); err != nil {
 				e.logger.Warn("failed to update CUDA options, falling back to CPU", "error", err)
 				useCUDA = false
@@ -286,7 +286,6 @@ func (e *ONNXEmbedder) ensureInitialized() error {
 			}
 		}
 	}
-
 	if useCUDA {
 		e.logger.Info("Using CUDA for ONNX inference")
 	} else {

rag/rag.go

@@ -19,10 +19,7 @@ import (
 	"github.com/neurosnap/sentences/english"
 )
 
-const (
+const ()
-	// batchTimeout is the maximum time allowed for embedding a single batch
-	batchTimeout = 2 * time.Minute
-)
 
 var (
 	// Status messages for TUI integration
@@ -102,10 +99,6 @@ func New(l *slog.Logger, s storage.FullRepo, cfg *config.Config) (*RAG, error) {
 	return rag, nil
 }
 
-func wordCounter(sentence string) int {
-	return len(strings.Split(strings.TrimSpace(sentence), " "))
-}
-
 func createChunks(sentences []string, wordLimit, overlapWords uint32) []string {
 	if len(sentences) == 0 {
 		return nil
@@ -181,7 +174,6 @@ func (r *RAG) LoadRAG(fpath string) error {
 func (r *RAG) LoadRAGWithContext(ctx context.Context, fpath string) error {
 	r.mu.Lock()
 	defer r.mu.Unlock()
-
 	fileText, err := ExtractText(fpath)
 	if err != nil {
 		return err
@@ -190,7 +182,6 @@ func (r *RAG) LoadRAGWithContext(ctx context.Context, fpath string) error {
 
 	// Send initial status (non-blocking with retry)
 	r.sendStatusNonBlocking(LoadedFileRAGStatus)
-
 	tokenizer, err := english.NewSentenceTokenizer(nil)
 	if err != nil {
 		return err
@@ -210,7 +201,6 @@ func (r *RAG) LoadRAGWithContext(ctx context.Context, fpath string) error {
 	if len(paragraphs) == 0 {
 		return errors.New("no valid paragraphs found in file")
 	}
-
 	totalBatches := (len(paragraphs) + r.cfg.RAGBatchSize - 1) / r.cfg.RAGBatchSize
 	r.logger.Debug("starting parallel embedding", "total_batches", totalBatches, "batch_size", r.cfg.RAGBatchSize)
 
@@ -223,7 +213,7 @@ func (r *RAG) LoadRAGWithContext(ctx context.Context, fpath string) error {
 		concurrency = 1
 	}
 	// If using ONNX embedder, limit concurrency to 1 due to mutex serialization
-	isONNX := false
+	var isONNX bool
 	if _, isONNX = r.embedder.(*ONNXEmbedder); isONNX {
 		concurrency = 1
 	}
@@ -258,7 +248,6 @@ func (r *RAG) LoadRAGWithContext(ctx context.Context, fpath string) error {
 		// Ensure task channel is closed when this goroutine exits
 		defer close(taskCh)
 		r.logger.Debug("task distributor started", "total_batches", totalBatches)
-
 		for i := 0; i < totalBatches; i++ {
 			start := i * r.cfg.RAGBatchSize
 			end := start + r.cfg.RAGBatchSize
@@ -304,7 +293,6 @@ func (r *RAG) LoadRAGWithContext(ctx context.Context, fpath string) error {
 	resultsBuffer := make(map[int]batchResult)
 	filename := path.Base(fpath)
 	batchesProcessed := 0
-
 	for {
 		select {
 		case <-ctx.Done():
@@ -382,7 +370,6 @@ func (r *RAG) LoadRAGWithContext(ctx context.Context, fpath string) error {
 			break
 		}
 	}
-
 	r.logger.Debug("finished writing vectors", "batches", batchesProcessed)
 	r.resetIdleTimer()
 	r.sendStatusNonBlocking(FinishedRAGStatus)
@@ -406,7 +393,6 @@ func (r *RAG) embeddingWorker(ctx context.Context, workerID int, taskCh <-chan b
 			}
 		}
 	}()
-
 	for task := range taskCh {
 		select {
 		case <-ctx.Done():
@@ -432,7 +418,6 @@ func (r *RAG) embeddingWorker(ctx context.Context, workerID int, taskCh <-chan b
 			r.logger.Debug("worker sent empty batch", "worker", workerID, "batch", task.batchIndex)
 			continue
 		}
-
 		// Embed with retry for API embedder
 		embeddings, err := r.embedWithRetry(ctx, task.paragraphs, 3)
 		if err != nil {
@@ -444,7 +429,6 @@ func (r *RAG) embeddingWorker(ctx context.Context, workerID int, taskCh <-chan b
 			}
 			return
 		}
-
 		// Send result with context awareness
 		select {
 		case resultCh <- batchResult{
@@ -465,7 +449,6 @@ func (r *RAG) embeddingWorker(ctx context.Context, workerID int, taskCh <-chan b
 // embedWithRetry attempts embedding with exponential backoff for API embedder
 func (r *RAG) embedWithRetry(ctx context.Context, paragraphs []string, maxRetries int) ([][]float32, error) {
 	var lastErr error
-
 	for attempt := 0; attempt < maxRetries; attempt++ {
 		if attempt > 0 {
 			// Exponential backoff
@@ -473,13 +456,11 @@ func (r *RAG) embedWithRetry(ctx context.Context, paragraphs []string, maxRetrie
 			if backoff > 10*time.Second {
 				backoff = 10 * time.Second
 			}
-
 			select {
 			case <-time.After(backoff):
 			case <-ctx.Done():
 				return nil, ctx.Err()
 			}
-
 			r.logger.Debug("retrying embedding", "attempt", attempt, "max_retries", maxRetries)
 		}
 
@@ -499,7 +480,6 @@ func (r *RAG) embedWithRetry(ctx context.Context, paragraphs []string, maxRetrie
 			break
 		}
 	}
-
 	return nil, fmt.Errorf("embedding failed after %d attempts: %w", maxRetries, lastErr)
 }
 
@@ -509,7 +489,6 @@ func (r *RAG) writeBatchToStorage(ctx context.Context, result batchResult, filen
 		// Empty batch, skip
 		return nil
 	}
-
 	// Check context before starting
 	select {
 	case <-ctx.Done():
@@ -534,7 +513,6 @@ func (r *RAG) writeBatchToStorage(ctx context.Context, result batchResult, filen
 		r.sendStatusNonBlocking(ErrRAGStatus)
 		return fmt.Errorf("failed to write vectors batch: %w", err)
 	}
-
 	r.logger.Debug("wrote batch to db", "batch", result.batchIndex+1, "size", len(result.paragraphs))
 	return nil
 }

rag/storage.go

@@ -64,7 +64,6 @@ func (vs *VectorStorage) WriteVector(row *models.VectorRow) error {
 		return err
 	}
 	embeddingSize := len(row.Embeddings)
-
 	// Start transaction
 	tx, err := vs.sqlxDB.Beginx()
 	if err != nil {
@@ -72,7 +71,7 @@ func (vs *VectorStorage) WriteVector(row *models.VectorRow) error {
 	}
 	defer func() {
 		if err != nil {
-			tx.Rollback()
+			_ = tx.Rollback()
 		}
 	}()
 
@@ -86,14 +85,12 @@ func (vs *VectorStorage) WriteVector(row *models.VectorRow) error {
 		vs.logger.Error("failed to write vector", "error", err, "slug", row.Slug)
 		return err
 	}
-
 	// Insert into FTS table
 	ftsQuery := `INSERT INTO fts_embeddings (slug, raw_text, filename, embedding_size) VALUES (?, ?, ?, ?)`
 	if _, err := tx.Exec(ftsQuery, row.Slug, row.RawText, row.FileName, embeddingSize); err != nil {
 		vs.logger.Error("failed to write to FTS table", "error", err, "slug", row.Slug)
 		return err
 	}
-
 	err = tx.Commit()
 	if err != nil {
 		vs.logger.Error("failed to commit transaction", "error", err)
@@ -133,7 +130,6 @@ func (vs *VectorStorage) WriteVectors(rows []*models.VectorRow) error {
 	if err != nil {
 		return err
 	}
-
 	// Start transaction
 	tx, err := vs.sqlxDB.Beginx()
 	if err != nil {
@@ -141,7 +137,7 @@ func (vs *VectorStorage) WriteVectors(rows []*models.VectorRow) error {
 	}
 	defer func() {
 		if err != nil {
-			tx.Rollback()
+			_ = tx.Rollback()
 		}
 	}()
 
@@ -161,7 +157,6 @@ func (vs *VectorStorage) WriteVectors(rows []*models.VectorRow) error {
 		vs.logger.Error("failed to write vectors batch", "error", err, "batch_size", len(rows))
 		return err
 	}
-
 	// Build batch insert for FTS table
 	ftsPlaceholders := make([]string, 0, len(rows))
 	ftsArgs := make([]any, 0, len(rows)*4)
@@ -170,15 +165,12 @@ func (vs *VectorStorage) WriteVectors(rows []*models.VectorRow) error {
 		ftsPlaceholders = append(ftsPlaceholders, "(?, ?, ?, ?)")
 		ftsArgs = append(ftsArgs, row.Slug, row.RawText, row.FileName, embeddingSize)
 	}
-	ftsQuery := fmt.Sprintf(
+	ftsQuery := "INSERT INTO fts_embeddings (slug, raw_text, filename, embedding_size) VALUES " +
-		"INSERT INTO fts_embeddings (slug, raw_text, filename, embedding_size) VALUES %s",
+		strings.Join(ftsPlaceholders, ", ")
-		strings.Join(ftsPlaceholders, ", "),
-	)
 	if _, err := tx.Exec(ftsQuery, ftsArgs...); err != nil {
 		vs.logger.Error("failed to write FTS batch", "error", err, "batch_size", len(rows))
 		return err
 	}
-
 	err = tx.Commit()
 	if err != nil {
 		vs.logger.Error("failed to commit transaction", "error", err)
@@ -218,14 +210,12 @@ func (vs *VectorStorage) SearchClosest(query []float32, limit int) ([]models.Vec
 	if err != nil {
 		return nil, err
 	}
-
 	querySQL := "SELECT embeddings, slug, raw_text, filename FROM " + tableName
 	rows, err := vs.sqlxDB.Query(querySQL)
 	if err != nil {
 		return nil, err
 	}
 	defer rows.Close()
-
 	type SearchResult struct {
 		vector   models.VectorRow
 		distance float32
@@ -241,7 +231,6 @@ func (vs *VectorStorage) SearchClosest(query []float32, limit int) ([]models.Vec
 			vs.logger.Error("failed to scan row", "error", err)
 			continue
 		}
-
 		storedEmbeddings := DeserializeVector(embeddingsBlob)
 		similarity := cosineSimilarity(query, storedEmbeddings)
 		distance := 1 - similarity
@@ -264,7 +253,6 @@ func (vs *VectorStorage) SearchClosest(query []float32, limit int) ([]models.Vec
 			topResults = topResults[:limit]
 		}
 	}
-
 	results := make([]models.VectorRow, 0, len(topResults))
 	for _, result := range topResults {
 		result.vector.Distance = result.distance