diff options
Diffstat (limited to 'rag')
| -rw-r--r-- | rag/embedder.go | 5 | ||||
| -rw-r--r-- | rag/rag.go | 5 | ||||
| -rw-r--r-- | rag/storage.go | 8 |
3 files changed, 10 insertions, 8 deletions
diff --git a/rag/embedder.go b/rag/embedder.go index 1804019..4849941 100644 --- a/rag/embedder.go +++ b/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 } @@ -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 diff --git a/rag/storage.go b/rag/storage.go index 26ca0e3..64d54f7 100644 --- a/rag/storage.go +++ b/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 |