package rag import ( "bytes" "encoding/json" "errors" "fmt" "gf-lt/config" "log/slog" "net/http" ) // Embedder defines the interface for embedding text type Embedder interface { Embed(text []string) ([][]float32, error) EmbedSingle(text string) ([]float32, error) } // APIEmbedder implements embedder using an API (like Hugging Face, OpenAI, etc.) type APIEmbedder struct { logger *slog.Logger client *http.Client cfg *config.Config } func NewAPIEmbedder(l *slog.Logger, cfg *config.Config) *APIEmbedder { return &APIEmbedder{ logger: l, client: &http.Client{}, cfg: cfg, } } func (a *APIEmbedder) Embed(text []string) ([][]float32, error) { payload, err := json.Marshal( map[string]any{"inputs": text, "options": map[string]bool{"wait_for_model": true}}, ) if err != nil { a.logger.Error("failed to marshal payload", "err", err.Error()) return nil, err } req, err := http.NewRequest("POST", a.cfg.EmbedURL, bytes.NewReader(payload)) if err != nil { a.logger.Error("failed to create new req", "err", err.Error()) return nil, err } if a.cfg.HFToken != "" { req.Header.Add("Authorization", "Bearer "+a.cfg.HFToken) } resp, err := a.client.Do(req) if err != nil { a.logger.Error("failed to embed text", "err", err.Error()) return nil, err } defer resp.Body.Close() if resp.StatusCode != 200 { err = fmt.Errorf("non 200 response; code: %v", resp.StatusCode) a.logger.Error(err.Error()) return nil, err } var emb [][]float32 if err := json.NewDecoder(resp.Body).Decode(&emb); err != nil { a.logger.Error("failed to decode embedding response", "err", err.Error()) return nil, err } if len(emb) == 0 { err = errors.New("empty embedding response") a.logger.Error("empty embedding response") return nil, err } return emb, nil } func (a *APIEmbedder) EmbedSingle(text string) ([]float32, error) { result, err := a.Embed([]string{text}) if err != nil { return nil, err } if len(result) == 0 { return nil, errors.New("no embeddings returned") } return result[0], nil } // TODO: ONNXEmbedder implementation would go here // This would require: // 1. Loading ONNX models locally // 2. Using a Go ONNX runtime (like gorgonia/onnx or similar) // 3. Converting text to embeddings without external API calls // // For now, we'll focus on the API implementation which is already working in the current system, // and can be extended later when we have ONNX runtime integration