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

storage/vector.go

@@ -75,9 +75,7 @@ func (p ProviderSQL) WriteVector(row *models.VectorRow) error {
 	return err
 }
 
-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) {
 	// TODO: This function has been temporarily disabled to avoid deprecated library usage. 

tools.go

@@ -4,7 +4,6 @@ import (
 	"context"
 	"encoding/json"
 	"fmt"
-	"gf-lt/config"
 	"gf-lt/extra"
 	"gf-lt/models"
 	"regexp"
@@ -82,29 +81,29 @@ After that you are free to respond to the user.
 	sysLabels = []string{"basic_sys", "tool_sys"}
 )
 
-func populateTools(cfg config.Config) {
+// func populateTools(cfg config.Config) {
-	// if we have access to some server with funcs we can populate funcs (tools|toolbelt?) with it
+// 	// if we have access to some server with funcs we can populate funcs (tools|toolbelt?) with it
-	// there must be a better way
+// 	// there must be a better way
-	if cfg.SearchAPI == "" || cfg.SearchDescribe == "" {
+// 	if cfg.SearchAPI == "" || cfg.SearchDescribe == "" {
-		return
+// 		return
-	}
+// 	}
-	resp, err := httpClient.Get(cfg.SearchDescribe)
+// 	resp, err := httpClient.Get(cfg.SearchDescribe)
-	if err != nil {
+// 	if err != nil {
-		logger.Error("failed to get websearch tool description",
+// 		logger.Error("failed to get websearch tool description",
-			"link", cfg.SearchDescribe, "error", err)
+// 			"link", cfg.SearchDescribe, "error", err)
-		return
+// 		return
-	}
+// 	}
-	descResp := models.Tool{}
+// 	defer resp.Body.Close()
-	if err := json.NewDecoder(resp.Body).Decode(&descResp); err != nil {
+// 	descResp := models.Tool{}
-		logger.Error("failed to unmarshal websearch tool description",
+// 	if err := json.NewDecoder(resp.Body).Decode(&descResp); err != nil {
-			"link", cfg.SearchDescribe, "error", err)
+// 		logger.Error("failed to unmarshal websearch tool description",
-		return
+// 			"link", cfg.SearchDescribe, "error", err)
-	}
+// 		return
-	fnMap["web_search"] = websearch
+// 	}
-	baseTools = append(baseTools, descResp)
+// 	fnMap["web_search"] = websearch
-	logger.Info("added web_search tool", "tool", descResp)
+// 	baseTools = append(baseTools, descResp)
-	return
+// 	logger.Info("added web_search tool", "tool", descResp)
-}
+// }
 
 // {"type":"function","function":{"name":"web_search","description":"Perform a web search to find information on varioust topics","parameters":{"type":"object","properties":{"num_results":{"type":"integer","description":"Maximum number of results to return (default: 10)"},"query":{"type":"string","description":"The search query to find information about"},"search_type":{"type":"string","description":"Type of search to perform: 'api' for SearXNG API search or 'scraper' for web scraping (default: 'scraper')"}},"required":["query"]}}}
 

tui.go

@@ -950,7 +950,7 @@ func init() {
 					persona = cfg.WriteNextMsgAs
 				}
 				// check if plain text
-				if injectRole == false {
+				if !injectRole {
 					matches := roleRE.FindStringSubmatch(msgText)
 					if len(matches) > 1 {
 						persona = matches[1]