Files
LocalAI/pkg/grpc/base/base.go
T
mudler-agentandEttore Di Giacinto c60a2e47d6 feat(backends): timestral.cpp forecast backend and /v1/forecast API (#12885)
* feat(grpc): add a Forecast RPC for time-series models

Add Forecast to the backend service with columnar request and response
messages: flat row-major arrays, NaN for missing values, and one message
per series so a caller can send series of different shapes in one call.
The response keeps the request order.

Wire the RPC through the server, client, embedded client, connection
evicting client, in-flight tracking client and the base backend, which
answers unimplemented. core/backend gains a Forecast helper that stamps
the model identity and records a trace, like the other single-purpose
RPCs.

Assisted-by: Claude:claude-sonnet-5-5 go-vet

* feat(config): add the forecast usecase

FLAG_FORECAST marks a model as a time-series forecaster. The timestral-cpp
backend declares it as its default usecase, so a config with that backend
needs no extra lines. The backend is also listed as a non-text backend, so
a forecast model is never offered as a chat model. It shows up in the
gallery filter and in the capability list.

Assisted-by: Claude:claude-sonnet-5-5 go-vet

* feat(api): add POST /v1/forecast

Take one or more numeric series and return quantile trajectories for the
next prediction_length steps. Targets are [variates][steps] or a flat
array for one variate; JSON null marks a missing value. Past and known
covariates and categorical codes are checked for length before the
backend sees them. Quantiles default to 0.1, 0.5 and 0.9 in the backend.

The route is gated by a new default-on forecast API feature. The endpoint
is listed in the swagger spec, the /api/instructions index, the UI model
filter and the capability symbols.

Assisted-by: Claude:claude-sonnet-5-5 go-vet

* feat(backends): add the timestral-cpp forecast backend

Wrap the timestral.cpp C ABI with purego, the way the other ggml
backends do: libtimestral is built from a pinned commit with ggml
embedded, and the Go binary loads it at start.

Forecast validates the counts against the array lengths, groups the
series by shape, makes one library call per group and puts the results
back in the order the caller sent them. Quantiles default to 0.1, 0.5
and 0.9, and a level the model does not have is refused with the list of
valid ones. A mutex guards the handle because the library is not
reentrant. Request size is bounded by two model options.

The unit specs use a fake library. The integration specs and a gRPC
spec run against the 5M F32 model that test.sh downloads and checks
against a fixed SHA-256.

Assisted-by: Claude:claude-sonnet-5-5 go-vet

* build(backends): register timestral-cpp in the build and the library

Add the CPU image to the backend matrix for amd64 and arm64, plus an
Apple silicon entry that is not built or tested yet. The image has no GPU
keys because the engine runs on the CPU only. Wire the make targets, the
path filter and the dependency bump bot.

The library entries were generated with backendcatalog. The images are
not published yet, so the release entries are unpinned until a
maintainer runs --repin. The gallery reader test now counts the names in
the library instead of a fixed number, so adding a backend does not
break it.

Assisted-by: Claude:claude-sonnet-5-5 go-vet

* feat(gallery): add the Timestral forecast models

Add Timestral 5M, 28M, 123M and 209M as F32 entries, each with its F16
build as a variant. Q8_0 and the K-quants are left out until the quality
question is settled. Each entry runs on the timestral-cpp backend, on
the CPU only.

The weights use Mistral's Modified MIT licence, which bars use by
companies above a monthly revenue threshold. The first paragraph after
the summary says so, the licence file is installed next to the weights,
and the revenue-restricted-license tag makes the entries filterable.

The memory each build needs has not been measured, so no size is set.

Assisted-by: Claude:claude-sonnet-5-5

* docs(forecast): document /v1/forecast and the Timestral models

Add a feature page with the request and response shapes, the error
cases, the model options and the licence note. List the engine in the
README, the compatibility table and the backend categories.

Assisted-by: Claude:claude-sonnet-5-5

---------

Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
2026-10-24 22:47:04 +02:00

262 lines
7.8 KiB
Go

package base
// This is a wrapper to satisfy the GRPC service interface
// It is meant to be used by the main executable that is the server for the specific backend type (falcon, gpt3, etc)
import (
"context"
"fmt"
"os"
"github.com/mudler/LocalAI/pkg/grpc/grpcerrors"
pb "github.com/mudler/LocalAI/pkg/grpc/proto"
gopsutil "github.com/shirou/gopsutil/v3/process"
)
// Base is a base class for all backends to implement
// Note: the backends that does not support multiple requests
// should use SingleThread instead
type Base struct {
}
func (llm *Base) Locking() bool {
return false
}
func (llm *Base) Lock() {
panic("not implemented")
}
func (llm *Base) Unlock() {
panic("not implemented")
}
func (llm *Base) Busy() bool {
return false
}
func (llm *Base) Load(opts *pb.ModelOptions) error {
return fmt.Errorf("unimplemented")
}
func (llm *Base) Predict(opts *pb.PredictOptions) (string, error) {
return "", fmt.Errorf("unimplemented")
}
func (llm *Base) PredictStream(opts *pb.PredictOptions, results chan string) error {
close(results)
return fmt.Errorf("unimplemented")
}
func (llm *Base) Embeddings(opts *pb.PredictOptions) ([]float32, error) {
return []float32{}, fmt.Errorf("unimplemented")
}
func (llm *Base) GenerateImage(*pb.GenerateImageRequest) error {
return fmt.Errorf("unimplemented")
}
func (llm *Base) GenerateVideo(*pb.GenerateVideoRequest) error {
return fmt.Errorf("unimplemented")
}
func (llm *Base) Generate3D(*pb.Generate3DRequest) error {
return fmt.Errorf("unimplemented")
}
func (llm *Base) Animate3D(*pb.Animate3DRequest) error {
return fmt.Errorf("unimplemented")
}
func (llm *Base) UpscaleImage(*pb.UpscaleImageRequest) error {
return fmt.Errorf("unimplemented")
}
func (llm *Base) AudioTranscription(context.Context, *pb.TranscriptRequest) (pb.TranscriptResult, error) {
return pb.TranscriptResult{}, fmt.Errorf("unimplemented")
}
func (llm *Base) AudioTranscriptionStream(context.Context, *pb.TranscriptRequest, chan *pb.TranscriptStreamResponse) error {
return fmt.Errorf("unimplemented")
}
func (llm *Base) TTS(*pb.TTSRequest) error {
return fmt.Errorf("unimplemented")
}
func (llm *Base) TTSStream(*pb.TTSRequest, chan []byte) error {
return fmt.Errorf("unimplemented")
}
func (llm *Base) SoundGeneration(*pb.SoundGenerationRequest) error {
return fmt.Errorf("unimplemented")
}
func (llm *Base) Detect(*pb.DetectOptions) (pb.DetectResponse, error) {
return pb.DetectResponse{}, fmt.Errorf("unimplemented")
}
func (llm *Base) Depth(*pb.DepthRequest) (pb.DepthResponse, error) {
return pb.DepthResponse{}, fmt.Errorf("unimplemented")
}
func (llm *Base) Forecast(*pb.ForecastRequest) (pb.ForecastResponse, error) {
return pb.ForecastResponse{}, fmt.Errorf("unimplemented")
}
func (llm *Base) FaceVerify(*pb.FaceVerifyRequest) (pb.FaceVerifyResponse, error) {
return pb.FaceVerifyResponse{}, fmt.Errorf("unimplemented")
}
func (llm *Base) FaceAnalyze(*pb.FaceAnalyzeRequest) (pb.FaceAnalyzeResponse, error) {
return pb.FaceAnalyzeResponse{}, fmt.Errorf("unimplemented")
}
func (llm *Base) VoiceVerify(*pb.VoiceVerifyRequest) (pb.VoiceVerifyResponse, error) {
return pb.VoiceVerifyResponse{}, fmt.Errorf("unimplemented")
}
func (llm *Base) VoiceAnalyze(*pb.VoiceAnalyzeRequest) (pb.VoiceAnalyzeResponse, error) {
return pb.VoiceAnalyzeResponse{}, fmt.Errorf("unimplemented")
}
func (llm *Base) VoiceEmbed(*pb.VoiceEmbedRequest) (pb.VoiceEmbedResponse, error) {
return pb.VoiceEmbedResponse{}, fmt.Errorf("unimplemented")
}
func (llm *Base) Diarize(*pb.DiarizeRequest) (pb.DiarizeResponse, error) {
return pb.DiarizeResponse{}, fmt.Errorf("unimplemented")
}
func (llm *Base) SoundDetection(context.Context, *pb.SoundDetectionRequest) (*pb.SoundDetectionResponse, error) {
return nil, fmt.Errorf("unimplemented")
}
func (llm *Base) TokenizeString(opts *pb.PredictOptions) (pb.TokenizationResponse, error) {
return pb.TokenizationResponse{}, fmt.Errorf("unimplemented")
}
func (llm *Base) Detokenize(req *pb.DetokenizeRequest) (pb.DetokenizeResponse, error) {
return pb.DetokenizeResponse{}, fmt.Errorf("unimplemented")
}
func (llm *Base) ModelMetadata(opts *pb.ModelOptions) (*pb.ModelMetadataResponse, error) {
return nil, fmt.Errorf("unimplemented")
}
// backends may wish to call this to capture the gopsutil info, then enhance with additional memory usage details?
func (llm *Base) Status() (pb.StatusResponse, error) {
return pb.StatusResponse{
Memory: memoryUsage(),
}, nil
}
func (llm *Base) StoresSet(*pb.StoresSetOptions) error {
return fmt.Errorf("unimplemented")
}
func (llm *Base) StoresGet(*pb.StoresGetOptions) (pb.StoresGetResult, error) {
return pb.StoresGetResult{}, fmt.Errorf("unimplemented")
}
func (llm *Base) StoresDelete(*pb.StoresDeleteOptions) error {
return fmt.Errorf("unimplemented")
}
func (llm *Base) StoresFind(*pb.StoresFindOptions) (pb.StoresFindResult, error) {
return pb.StoresFindResult{}, fmt.Errorf("unimplemented")
}
func (llm *Base) VAD(*pb.VADRequest) (pb.VADResponse, error) {
return pb.VADResponse{}, fmt.Errorf("unimplemented")
}
func (llm *Base) AudioEncode(*pb.AudioEncodeRequest) (*pb.AudioEncodeResult, error) {
return nil, fmt.Errorf("unimplemented")
}
func (llm *Base) AudioDecode(*pb.AudioDecodeRequest) (*pb.AudioDecodeResult, error) {
return nil, fmt.Errorf("unimplemented")
}
func (llm *Base) AudioTransform(*pb.AudioTransformRequest) (*pb.AudioTransformResult, error) {
return nil, fmt.Errorf("unimplemented")
}
func (llm *Base) AudioTransformStream(in <-chan *pb.AudioTransformFrameRequest, out chan<- *pb.AudioTransformFrameResponse) error {
close(out)
return fmt.Errorf("unimplemented")
}
func (llm *Base) AudioTranscriptionLive(in <-chan *pb.TranscriptLiveRequest, out chan<- *pb.TranscriptLiveResponse) error {
close(out)
return grpcerrors.LiveTranscriptionUnsupported("base", "not implemented by this backend")
}
func (llm *Base) AudioToAudioStream(in <-chan *pb.AudioToAudioRequest, out chan<- *pb.AudioToAudioResponse) error {
close(out)
return fmt.Errorf("unimplemented")
}
func (llm *Base) Forward(ctx context.Context, in <-chan *pb.ForwardRequest, out chan<- *pb.ForwardReply) error {
close(out)
return fmt.Errorf("unimplemented")
}
func (llm *Base) StartFineTune(*pb.FineTuneRequest) (*pb.FineTuneJobResult, error) {
return nil, fmt.Errorf("unimplemented")
}
func (llm *Base) FineTuneProgress(*pb.FineTuneProgressRequest, chan *pb.FineTuneProgressUpdate) error {
return fmt.Errorf("unimplemented")
}
func (llm *Base) StopFineTune(*pb.FineTuneStopRequest) error {
return fmt.Errorf("unimplemented")
}
func (llm *Base) ListCheckpoints(*pb.ListCheckpointsRequest) (*pb.ListCheckpointsResponse, error) {
return nil, fmt.Errorf("unimplemented")
}
func (llm *Base) ExportModel(*pb.ExportModelRequest) error {
return fmt.Errorf("unimplemented")
}
func (llm *Base) StartQuantization(*pb.QuantizationRequest) (*pb.QuantizationJobResult, error) {
return nil, fmt.Errorf("unimplemented")
}
func (llm *Base) QuantizationProgress(*pb.QuantizationProgressRequest, chan *pb.QuantizationProgressUpdate) error {
return fmt.Errorf("unimplemented")
}
func (llm *Base) StopQuantization(*pb.QuantizationStopRequest) error {
return fmt.Errorf("unimplemented")
}
func memoryUsage() *pb.MemoryUsageData {
mud := pb.MemoryUsageData{
Breakdown: make(map[string]uint64),
}
pid := int32(os.Getpid())
backendProcess, err := gopsutil.NewProcess(pid)
if err == nil {
memInfo, err := backendProcess.MemoryInfo()
if err == nil {
mud.Total = memInfo.VMS // TEST, but rss seems reasonable first guess. Does include swap, but we might care about that.
mud.Breakdown["gopsutil-RSS"] = memInfo.RSS
}
}
return &mud
}
func (llm *Base) Free() error {
return nil
}
func (*Base) MotionStream(context.Context, func() (*pb.MotionRequest, error), func(*pb.MotionResponse) error) error {
return fmt.Errorf("motion streaming is not supported")
}