LocalAI/core/backend/embeddings.go

package backend

import (
	"fmt"

	"github.com/mudler/LocalAI/core/config"

	"github.com/mudler/LocalAI/pkg/grpc"
	model "github.com/mudler/LocalAI/pkg/model"
)

func ModelEmbedding(s string, tokens []int, loader *model.ModelLoader, backendConfig config.BackendConfig, appConfig *config.ApplicationConfig) (func() ([]float32, error), error) {

	var inferenceModel interface{}
	var err error

	opts := ModelOptions(backendConfig, appConfig, []model.Option{})

	if backendConfig.Backend == "" {
		inferenceModel, err = loader.GreedyLoader(opts...)
	} else {
		opts = append(opts, model.WithBackendString(backendConfig.Backend))
		inferenceModel, err = loader.BackendLoader(opts...)
	}
	if err != nil {
		return nil, err
	}

	var fn func() ([]float32, error)
	switch model := inferenceModel.(type) {
	case grpc.Backend:
		fn = func() ([]float32, error) {
			predictOptions := gRPCPredictOpts(backendConfig, loader.ModelPath)
			if len(tokens) > 0 {
				embeds := []int32{}

				for _, t := range tokens {
					embeds = append(embeds, int32(t))
				}
				predictOptions.EmbeddingTokens = embeds

				res, err := model.Embeddings(appConfig.Context, predictOptions)
				if err != nil {
					return nil, err
				}

				return res.Embeddings, nil
			}
			predictOptions.Embeddings = s

			res, err := model.Embeddings(appConfig.Context, predictOptions)
			if err != nil {
				return nil, err
			}

			return res.Embeddings, nil
		}
	default:
		fn = func() ([]float32, error) {
			return nil, fmt.Errorf("embeddings not supported by the backend")
		}
	}

	return func() ([]float32, error) {
		embeds, err := fn()
		if err != nil {
			return embeds, err
		}
		// Remove trailing 0s
		for i := len(embeds) - 1; i >= 0; i-- {
			if embeds[i] == 0.0 {
				embeds = embeds[:i]
			} else {
				break
			}
		}
		return embeds, nil
	}, nil
}