Chore: fix linter complaints

rag/embedder.go

@@ -3,6 +3,7 @@ package rag
 import (
 	"bytes"
 	"encoding/json"
+	"errors"
 	"fmt"
 	"gf-lt/config"
 	"log/slog"
@@ -69,7 +70,7 @@ func (a *APIEmbedder) Embed(text []string) ([][]float32, error) {
 	}
 
 	if len(emb) == 0 {
-		err = fmt.Errorf("empty embedding response")
+		err = errors.New("empty embedding response")
 		a.logger.Error("empty embedding response")
 		return nil, err
 	}
@@ -83,7 +84,7 @@ func (a *APIEmbedder) EmbedSingle(text string) ([]float32, error) {
 		return nil, err
 	}
 	if len(result) == 0 {
-		return nil, fmt.Errorf("no embeddings returned")
+		return nil, errors.New("no embeddings returned")
 	}
 	return result[0], nil
 }

rag/rag.go

@@ -1,6 +1,7 @@
 package rag
 
 import (
+	"errors"
 	"fmt"
 	"gf-lt/config"
 	"gf-lt/models"
@@ -108,7 +109,7 @@ func (r *RAG) LoadRAG(fpath string) error {
 	}
 
 	if len(paragraphs) == 0 {
-		return fmt.Errorf("no valid paragraphs found in file")
+		return errors.New("no valid paragraphs found in file")
 	}
 
 	var (
@@ -222,7 +223,7 @@ func (r *RAG) fetchEmb(lines []string, errCh chan error, vectorCh chan<- []model
 	}
 
 	if len(embeddings) == 0 {
-		err := fmt.Errorf("no embeddings returned")
+		err := errors.New("no embeddings returned")
 		r.logger.Error("empty embeddings")
 		errCh <- err
 		return err

rag/storage.go

@@ -141,7 +141,7 @@ func (vs *VectorStorage) SearchClosest(query []float32) ([]models.VectorRow, err
 	// 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)
+	querySQL := "SELECT embeddings, slug, raw_text, filename FROM " + tableName
 	rows, err := vs.sqlxDB.Query(querySQL)
 	if err != nil {
 		return nil, err
@@ -198,7 +198,7 @@ func (vs *VectorStorage) SearchClosest(query []float32) ([]models.VectorRow, err
 	}
 
 	// Convert back to VectorRow slice
-	var results []models.VectorRow
+	results := make([]models.VectorRow, 0, len(topResults))
 	for _, result := range topResults {
 		result.vector.Distance = result.distance
 		results = append(results, result.vector)
@@ -209,11 +209,11 @@ func (vs *VectorStorage) SearchClosest(query []float32) ([]models.VectorRow, err
 
 // ListFiles returns a list of all loaded files
 func (vs *VectorStorage) ListFiles() ([]string, error) {
-	var fileLists [][]string
+	fileLists := make([][]string, 0)
 
 	// Query both tables and combine results
 	for _, table := range []string{"embeddings_384", "embeddings_5120"} {
-		query := fmt.Sprintf("SELECT DISTINCT filename FROM %s", table)
+		query := "SELECT DISTINCT filename FROM " + table
 		rows, err := vs.sqlxDB.Query(query)
 		if err != nil {
 			// Continue if one table doesn't exist