package grpc import ( "context" "fmt" "log" "net" pb "github.com/go-skynet/LocalAI/pkg/grpc/proto" "google.golang.org/grpc" ) // A GRPC Server that allows to run LLM inference. // It is used by the LLMServices to expose the LLM functionalities that are called by the client. // The GRPC Service is general, trying to encompass all the possible LLM options models. // It depends on the real implementer then what can be done or not. // // The server is implemented as a GRPC service, with the following methods: // - Predict: to run the inference with options // - PredictStream: to run the inference with options and stream the results // server is used to implement helloworld.GreeterServer. type server struct { pb.UnimplementedBackendServer llm LLM } func (s *server) Health(ctx context.Context, in *pb.HealthMessage) (*pb.Reply, error) { return newReply("OK"), nil } func (s *server) Embedding(ctx context.Context, in *pb.PredictOptions) (*pb.EmbeddingResult, error) { embeds, err := s.llm.Embeddings(in) if err != nil { return nil, err } return &pb.EmbeddingResult{Embeddings: embeds}, nil } func (s *server) LoadModel(ctx context.Context, in *pb.ModelOptions) (*pb.Result, error) { err := s.llm.Load(in) if err != nil { return &pb.Result{Message: fmt.Sprintf("Error loading model: %s", err.Error()), Success: false}, err } return &pb.Result{Message: "Loading succeeded", Success: true}, nil } func (s *server) Predict(ctx context.Context, in *pb.PredictOptions) (*pb.Reply, error) { result, err := s.llm.Predict(in) return newReply(result), err } func (s *server) GenerateImage(ctx context.Context, in *pb.GenerateImageRequest) (*pb.Result, error) { err := s.llm.GenerateImage(in) if err != nil { return &pb.Result{Message: fmt.Sprintf("Error generating image: %s", err.Error()), Success: false}, err } return &pb.Result{Message: "Image generated", Success: true}, nil } func (s *server) TTS(ctx context.Context, in *pb.TTSRequest) (*pb.Result, error) { err := s.llm.TTS(in) if err != nil { return &pb.Result{Message: fmt.Sprintf("Error generating audio: %s", err.Error()), Success: false}, err } return &pb.Result{Message: "Audio generated", Success: true}, nil } func (s *server) AudioTranscription(ctx context.Context, in *pb.TranscriptRequest) (*pb.TranscriptResult, error) { result, err := s.llm.AudioTranscription(in) if err != nil { return nil, err } tresult := &pb.TranscriptResult{} for _, s := range result.Segments { tks := []int32{} for _, t := range s.Tokens { tks = append(tks, int32(t)) } tresult.Segments = append(tresult.Segments, &pb.TranscriptSegment{ Text: s.Text, Id: int32(s.Id), Start: int64(s.Start), End: int64(s.End), Tokens: tks, }) } tresult.Text = result.Text return tresult, nil } func (s *server) PredictStream(in *pb.PredictOptions, stream pb.Backend_PredictStreamServer) error { resultChan := make(chan string) done := make(chan bool) go func() { for result := range resultChan { stream.Send(newReply(result)) } done <- true }() s.llm.PredictStream(in, resultChan) <-done return nil } func StartServer(address string, model LLM) error { lis, err := net.Listen("tcp", address) if err != nil { return err } s := grpc.NewServer() pb.RegisterBackendServer(s, &server{llm: model}) log.Printf("gRPC Server listening at %v", lis.Addr()) if err := s.Serve(lis); err != nil { return err } return nil }