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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
|
