Files
LocalAI/pkg/grpc/server.go
Richard Palethorpe 0245b33eab feat(realtime): Add Liquid Audio s2s model and assistant mode on talk page (#9801)
* feat(liquid-audio): add LFM2.5-Audio any-to-any backend + realtime_audio usecase

Wires LiquidAI's LFM2.5-Audio-1.5B as a self-contained Realtime API model:
single engine handles VAD, transcription, LLM, and TTS in one bidirectional
stream — drop-in alternative to a VAD+STT+LLM+TTS pipeline.

Backend
- backend/python/liquid-audio/ — new Python gRPC backend wrapping the
  `liquid-audio` package. Modes: chat / asr / tts / s2s, voice presets,
  Load/Predict/PredictStream/AudioTranscription/TTS/VAD/AudioToAudioStream/
  Free and StartFineTune/FineTuneProgress/StopFineTune. Runtime monkey-patch
  on `liquid_audio.utils.snapshot_download` so absolute local paths from
  LocalAI's gallery resolve without a HF round-trip. soundfile in place of
  torchaudio.load/save (torchcodec drags NVIDIA NPP we don't bundle).
- backend/backend.proto + pkg/grpc/{backend,client,server,base,embed,
  interface}.go — new AudioToAudioStream RPC mirroring AudioTransformStream
  (config/frame/control oneof in; typed event+pcm+meta out).
- core/services/nodes/{health_mock,inflight}_test.go — add stubs for the
  new RPC to the test fakes.

Config + capabilities
- core/config/backend_capabilities.go — UsecaseRealtimeAudio, MethodAudio
  ToAudioStream, UsecaseInfoMap entry, liquid-audio BackendCapability row.
- core/config/model_config.go — FLAG_REALTIME_AUDIO bitmask, ModalityGroups
  membership in both speech-input and audio-output groups so a lone flag
  still reads as multimodal, GetAllModelConfigUsecases entry, GuessUsecases
  branch.

Realtime endpoint
- core/http/endpoints/openai/realtime.go — extract prepareRealtimeConfig()
  so the gate is unit-testable; accept realtime_audio models and self-fill
  empty pipeline slots with the model's own name (user-pinned slots win).
- core/http/endpoints/openai/realtime_gate_test.go — six specs covering nil
  cfg, empty pipeline, legacy pipeline, self-contained realtime_audio,
  user-pinned VAD slot, and partial legacy pipeline.

UI + endpoints
- core/http/routes/ui.go — /api/pipeline-models accepts either a legacy
  VAD+STT+LLM+TTS pipeline or a realtime_audio model; surfaces a
  self_contained flag so the Talk page can collapse the four cards.
- core/http/routes/ui_api.go — realtime_audio in usecaseFilters.
- core/http/routes/ui_pipeline_models_test.go — covers both code paths.
- core/http/react-ui/src/pages/Talk.jsx — self-contained badge instead of
  the four-slot grid; rename Edit Pipeline → Edit Model Config; less
  pipeline-specific wording.
- core/http/react-ui/src/pages/Models.jsx + locales/en/models.json — new
  realtime_audio filter button + i18n.
- core/http/react-ui/src/utils/capabilities.js — CAP_REALTIME_AUDIO.
- core/http/react-ui/src/pages/FineTune.jsx — voice + validation-dataset
  fields, surfaced when backend === liquid-audio, plumbed via
  extra_options on submit/export/import.

Gallery + importer
- gallery/liquid-audio.yaml — config template with known_usecases:
  [realtime_audio, chat, tts, transcript, vad].
- gallery/index.yaml — four model entries (realtime/chat/asr/tts) keyed by
  mode option. Fixed pre-existing `transcribe` typo on the asr entry
  (loader silently dropped the unknown string → entry never surfaced as a
  transcript model).
- gallery/lfm.yaml — function block for the LFM2 Pythonic tool-call format
  `<|tool_call_start|>[name(k="v")]<|tool_call_end|>` matching
  common_chat_params_init_lfm2 in vendored llama.cpp.
- core/gallery/importers/{liquid-audio,liquid-audio_test}.go — detector
  matches LFM2-Audio HF repos (excludes -gguf mirrors); mode/voice
  preferences plumbed through to options.
- core/gallery/importers/importers.go — register LiquidAudioImporter
  before LlamaCPPImporter.
- pkg/functions/parse_lfm2_test.go — seven specs for the response/argument
  regex pair on the LFM2 pythonic format.

Build matrix
- .github/backend-matrix.yml — seven liquid-audio targets (cuda12, cuda13,
  l4t-cuda-13, hipblas, intel, cpu amd64, cpu arm64). Jetpack r36 cuda-12
  is skipped (Ubuntu 22.04 / Python 3.10 incompatible with liquid-audio's
  3.12 floor).
- backend/index.yaml — anchor + 13 image entries.
- Makefile — .NOTPARALLEL, prepare-test-extra, test-extra,
  docker-build-liquid-audio.

Docs
- .agents/plans/liquid-audio-integration.md — phased plan; PR-D (real
  any-to-any wiring via AudioToAudioStream), PR-E (mid-audio tool-call
  detector), PR-G (GGUF entries once upstream llama.cpp PR #18641 lands)
  remain.
- .agents/api-endpoints-and-auth.md — expand the capability-surface
  checklist with every place a new FLAG_* needs to be registered.

Assisted-by: claude-code:claude-opus-4-7-1m [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>

* feat(realtime): function calling + history cap for any-to-any models

Three pieces, all on the realtime_audio path that just landed:

1. liquid-audio backend (backend/python/liquid-audio/backend.py):
   - _build_chat_state grows a `tools_prelude` arg.
   - new _render_tools_prelude parses request.Tools (the OpenAI Chat
     Completions function array realtime.go already serialises) and
     emits an LFM2 `<|tool_list_start|>…<|tool_list_end|>` system turn
     ahead of the user history. Mirrors gallery/lfm.yaml's `function:`
     template so the model sees the same prompt shape whether served
     via llama-cpp or here. Without this the backend silently dropped
     tools — function calling was wired end-to-end on the Go side but
     the model never saw a tool list.

2. Realtime history cap (core/http/endpoints/openai/realtime.go):
   - Session grows MaxHistoryItems int; default picked by new
     defaultMaxHistoryItems(cfg) — 6 for realtime_audio models (LFM2.5
     1.5B degrades quickly past a handful of turns), 0/unlimited for
     legacy pipelines composing larger LLMs.
   - triggerResponse runs conv.Items through trimRealtimeItems before
     building conversationHistory. Helper walks the cut left if it
     would orphan a function_call_output, so tool result + call pairs
     stay intact.
   - realtime_gate_test.go: specs for defaultMaxHistoryItems and
     trimRealtimeItems (zero cap, under cap, over cap, tool-call pair
     preservation).

3. Talk page (core/http/react-ui/src/pages/Talk.jsx):
   - Reuses the chat page's MCP plumbing — useMCPClient hook,
     ClientMCPDropdown component, same auto-connect/disconnect effect
     pattern. No bespoke tool registry, no new REST endpoints; tools
     come from whichever MCP servers the user toggles on, exactly as
     on the chat page.
   - sendSessionUpdate now passes session.tools=getToolsForLLM(); the
     update re-fires when the active server set changes mid-session.
   - New response.function_call_arguments.done handler executes via
     the hook's executeTool (which round-trips through the MCP client
     SDK), then replies with conversation.item.create
     {type:function_call_output} + response.create so the model
     completes its turn with the tool output. Mirrors chat's
     client-side agentic loop, translated to the realtime wire shape.

UI changes require a LocalAI image rebuild (Dockerfile:308-313 bakes
react-ui/dist into the runtime image). Backend.py changes can be
swapped live in /backends/<id>/backend.py + /backend/shutdown.

Assisted-by: claude-code:claude-opus-4-7-1m [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>

* feat(realtime): LocalAI Assistant ("Manage Mode") for the Talk page

Mirrors the chat-page metadata.localai_assistant flow so users can ask the
realtime model what's loaded / installed / configured. Tools are run
server-side via the same in-process MCP holder that powers the chat
modality — no transport switch, no proxy, no new wire protocol.

Wire:
- core/http/endpoints/openai/realtime.go:
  - RealtimeSessionOptions{LocalAIAssistant,IsAdmin}; isCurrentUserAdmin
    helper mirrors chat.go's requireAssistantAccess (no-op when auth
    disabled, else requires auth.RoleAdmin).
  - Session grows AssistantExecutor mcpTools.ToolExecutor.
  - runRealtimeSession, when opts.LocalAIAssistant is set: gate on admin,
    fail closed if DisableLocalAIAssistant or the holder has no tools,
    DiscoverTools and inject into session.Tools, prepend
    holder.SystemPrompt() to instructions.
  - Tool-call dispatch loop: when AssistantExecutor.IsTool(name), run
    ExecuteTool inproc, append a FunctionCallOutput to conv.Items, skip
    the function_call_arguments client emit (the client can't execute
    these — it doesn't know about them). After the loop, if any
    assistant tool ran, trigger another response so the model speaks the
    result. Mirrors chat's agentic loop, driven server-side rather than
    via client round-trip.

- core/http/endpoints/openai/realtime_webrtc.go: RealtimeCallRequest
  gains `localai_assistant` (JSON omitempty). Handshake calls
  isCurrentUserAdmin and builds RealtimeSessionOptions.

- core/http/react-ui/src/pages/Talk.jsx: admin-only "Manage Mode"
  checkbox under the Tools dropdown; passes localai_assistant: true to
  realtimeApi.call's body, captured in the connect callback's deps.

Mirroring chat's pattern means the in-process MCP tools surface "just
works" for the Talk page without exposing a Streamable-HTTP MCP endpoint
(which was the alternative). Clients with their own MCP servers can
still use the existing ClientMCPDropdown path in parallel; the realtime
handler distinguishes them by AssistantExecutor.IsTool() at dispatch
time.

Assisted-by: claude-code:claude-opus-4-7-1m [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>

* feat(realtime): render Manage Mode tool calls in the Talk transcript

Previously the realtime endpoint only emitted response.output_item.added
for the FunctionCall item, and Talk.jsx's switch ignored the event — so
server-side tool runs were invisible in the UI. The model would speak
the result but the user had no way to see what tool was actually
called.

realtime.go: after executing an assistant tool inproc, emit a second
output_item.added/.done pair for the FunctionCallOutput item. Mirrors
the way the chat page displays tool_call + tool_result blocks.

Talk.jsx: handle both response.output_item.added and .done. Render
FunctionCall (with arguments) and FunctionCallOutput (pretty-printed
JSON when possible) as two transcript entries — `tool_call` with the
wrench icon, `tool_result` with the clipboard icon, both in mono-space
secondary-colour. Resets streamingRef after the result so the next
assistant text delta starts a fresh transcript entry instead of
appending to the previous turn.

Assisted-by: claude-code:claude-opus-4-7-1m [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>

* refactor(realtime): bound the Manage Mode tool-loop + preserve assistant tools

Fallout from a review pass on the Manage Mode patches:

- Bound the server-side agentic loop. triggerResponse used to recurse on
  executedAssistantTool with no cap — a model that kept calling tools
  would blow the goroutine stack. New maxAssistantToolTurns = 10 (mirrors
  useChat.js's maxToolTurns). Public triggerResponse is now a thin shim
  over triggerResponseAtTurn(toolTurn int); recursion increments the
  counter and stops at the cap with an xlog.Warn.

- Preserve Manage Mode tools across client session.update. The handler
  used to blindly overwrite session.Tools, so toggling a client MCP
  server mid-session silently wiped the in-process admin tools. Session
  now caches the original AssistantTools slice at session creation and
  the session.update handler merges them back in (client names win on
  collision — the client is explicit).

- strconv.ParseBool for the localai_assistant query param instead of
  hand-rolled "1" || "true". Mirrors LocalAIAssistantFromMetadata.

- Talk.jsx: render both tool_call and tool_result on
  response.output_item.done instead of splitting them across .added and
  .done. The server's event pairing (added → done) stays correct; the
  UI just doesn't need to inspect both phases of the same item. One
  switch case instead of two, no behavioural change.

Out of scope (noted for follow-ups): extract a shared assistant-tools
helper between chat.go and realtime.go (duplication is small enough
that two parallel implementations stay readable for now), and an i18n
key for the Manage Mode helper text (Talk.jsx doesn't use i18n
anywhere else yet).

Assisted-by: claude-code:claude-opus-4-7-1m [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>

* ci(test-extra): wire liquid-audio backend smoke test

The backend ships test.py + a `make test` target and is listed in
backend-matrix.yml, so scripts/changed-backends.js already writes a
`liquid-audio=true|false` output when files under backend/python/liquid-audio/
change. The workflow just wasn't reading it.

- Expose the `liquid-audio` output on the detect-changes job
- Add a tests-liquid-audio job that runs `make` + `make test` in
  backend/python/liquid-audio, gated on the per-backend detect flag

The smoke covers Health() and LoadModel(mode:finetune); fine-tune mode
short-circuits before any HuggingFace download (backend.py:192), so the
job needs neither weights nor a GPU. The full-inference path remains
gated on LIQUID_AUDIO_MODEL_ID, which CI doesn't set.

The four new Go test files (core/gallery/importers/liquid-audio_test.go,
core/http/endpoints/openai/realtime_gate_test.go,
core/http/routes/ui_pipeline_models_test.go, pkg/functions/parse_lfm2_test.go)
are already picked up by the existing test.yml workflow via `make test` →
`ginkgo -r ./pkg/... ./core/...`; their packages all carry RunSpecs entries.

Assisted-by: Claude:claude-opus-4-7
Signed-off-by: Richard Palethorpe <io@richiejp.com>

---------

Signed-off-by: Richard Palethorpe <io@richiejp.com>
2026-05-13 21:57:27 +02:00

781 lines
19 KiB
Go

package grpc
import (
"context"
"crypto/subtle"
"errors"
"fmt"
"io"
"log"
"net"
"os"
"strings"
pb "github.com/mudler/LocalAI/pkg/grpc/proto"
"google.golang.org/grpc"
"google.golang.org/grpc/codes"
"google.golang.org/grpc/metadata"
"google.golang.org/grpc/status"
)
// 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 AIModel
}
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) {
if s.llm.Locking() {
s.llm.Lock()
defer s.llm.Unlock()
}
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) {
if s.llm.Locking() {
s.llm.Lock()
defer s.llm.Unlock()
}
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) {
if s.llm.Locking() {
s.llm.Lock()
defer s.llm.Unlock()
}
result, err := s.llm.Predict(in)
return newReply(result), err
}
func (s *server) GenerateImage(ctx context.Context, in *pb.GenerateImageRequest) (*pb.Result, error) {
if s.llm.Locking() {
s.llm.Lock()
defer s.llm.Unlock()
}
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) GenerateVideo(ctx context.Context, in *pb.GenerateVideoRequest) (*pb.Result, error) {
if s.llm.Locking() {
s.llm.Lock()
defer s.llm.Unlock()
}
err := s.llm.GenerateVideo(in)
if err != nil {
return &pb.Result{Message: fmt.Sprintf("Error generating video: %s", err.Error()), Success: false}, err
}
return &pb.Result{Message: "Video generated", Success: true}, nil
}
func (s *server) TTS(ctx context.Context, in *pb.TTSRequest) (*pb.Result, error) {
if s.llm.Locking() {
s.llm.Lock()
defer s.llm.Unlock()
}
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: "TTS audio generated", Success: true}, nil
}
func (s *server) TTSStream(in *pb.TTSRequest, stream pb.Backend_TTSStreamServer) error {
if s.llm.Locking() {
s.llm.Lock()
defer s.llm.Unlock()
}
audioChan := make(chan []byte)
done := make(chan bool)
go func() {
for audioChunk := range audioChan {
stream.Send(&pb.Reply{Audio: audioChunk})
}
done <- true
}()
err := s.llm.TTSStream(in, audioChan)
<-done
return err
}
func (s *server) SoundGeneration(ctx context.Context, in *pb.SoundGenerationRequest) (*pb.Result, error) {
if s.llm.Locking() {
s.llm.Lock()
defer s.llm.Unlock()
}
err := s.llm.SoundGeneration(in)
if err != nil {
return &pb.Result{Message: fmt.Sprintf("Error generating audio: %s", err.Error()), Success: false}, err
}
return &pb.Result{Message: "Sound Generation audio generated", Success: true}, nil
}
func (s *server) Detect(ctx context.Context, in *pb.DetectOptions) (*pb.DetectResponse, error) {
if s.llm.Locking() {
s.llm.Lock()
defer s.llm.Unlock()
}
res, err := s.llm.Detect(in)
if err != nil {
return nil, err
}
return &res, nil
}
func (s *server) FaceVerify(ctx context.Context, in *pb.FaceVerifyRequest) (*pb.FaceVerifyResponse, error) {
if s.llm.Locking() {
s.llm.Lock()
defer s.llm.Unlock()
}
res, err := s.llm.FaceVerify(in)
if err != nil {
return nil, err
}
return &res, nil
}
func (s *server) FaceAnalyze(ctx context.Context, in *pb.FaceAnalyzeRequest) (*pb.FaceAnalyzeResponse, error) {
if s.llm.Locking() {
s.llm.Lock()
defer s.llm.Unlock()
}
res, err := s.llm.FaceAnalyze(in)
if err != nil {
return nil, err
}
return &res, nil
}
func (s *server) VoiceVerify(ctx context.Context, in *pb.VoiceVerifyRequest) (*pb.VoiceVerifyResponse, error) {
if s.llm.Locking() {
s.llm.Lock()
defer s.llm.Unlock()
}
res, err := s.llm.VoiceVerify(in)
if err != nil {
return nil, err
}
return &res, nil
}
func (s *server) VoiceAnalyze(ctx context.Context, in *pb.VoiceAnalyzeRequest) (*pb.VoiceAnalyzeResponse, error) {
if s.llm.Locking() {
s.llm.Lock()
defer s.llm.Unlock()
}
res, err := s.llm.VoiceAnalyze(in)
if err != nil {
return nil, err
}
return &res, nil
}
func (s *server) VoiceEmbed(ctx context.Context, in *pb.VoiceEmbedRequest) (*pb.VoiceEmbedResponse, error) {
if s.llm.Locking() {
s.llm.Lock()
defer s.llm.Unlock()
}
res, err := s.llm.VoiceEmbed(in)
if err != nil {
return nil, err
}
return &res, nil
}
func (s *server) AudioTranscription(ctx context.Context, in *pb.TranscriptRequest) (*pb.TranscriptResult, error) {
if s.llm.Locking() {
s.llm.Lock()
defer s.llm.Unlock()
}
result, err := s.llm.AudioTranscription(ctx, 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,
Speaker: s.Speaker,
})
}
tresult.Text = result.Text
tresult.Language = result.Language
tresult.Duration = result.Duration
return tresult, nil
}
func (s *server) AudioTranscriptionStream(in *pb.TranscriptRequest, stream pb.Backend_AudioTranscriptionStreamServer) error {
if s.llm.Locking() {
s.llm.Lock()
defer s.llm.Unlock()
}
resultChan := make(chan *pb.TranscriptStreamResponse)
done := make(chan bool)
go func() {
for chunk := range resultChan {
stream.Send(chunk)
}
done <- true
}()
err := s.llm.AudioTranscriptionStream(stream.Context(), in, resultChan)
<-done
return err
}
func (s *server) PredictStream(in *pb.PredictOptions, stream pb.Backend_PredictStreamServer) error {
if s.llm.Locking() {
s.llm.Lock()
defer s.llm.Unlock()
}
resultChan := make(chan string)
done := make(chan bool)
go func() {
for result := range resultChan {
stream.Send(newReply(result))
}
done <- true
}()
err := s.llm.PredictStream(in, resultChan)
<-done
return err
}
func (s *server) TokenizeString(ctx context.Context, in *pb.PredictOptions) (*pb.TokenizationResponse, error) {
if s.llm.Locking() {
s.llm.Lock()
defer s.llm.Unlock()
}
res, err := s.llm.TokenizeString(in)
if err != nil {
return nil, err
}
castTokens := make([]int32, len(res.Tokens))
for i, v := range res.Tokens {
castTokens[i] = int32(v)
}
return &pb.TokenizationResponse{
Length: int32(res.Length),
Tokens: castTokens,
}, err
}
func (s *server) Status(ctx context.Context, in *pb.HealthMessage) (*pb.StatusResponse, error) {
res, err := s.llm.Status()
if err != nil {
return nil, err
}
return &res, nil
}
func (s *server) StoresSet(ctx context.Context, in *pb.StoresSetOptions) (*pb.Result, error) {
if s.llm.Locking() {
s.llm.Lock()
defer s.llm.Unlock()
}
err := s.llm.StoresSet(in)
if err != nil {
return &pb.Result{Message: fmt.Sprintf("Error setting entry: %s", err.Error()), Success: false}, err
}
return &pb.Result{Message: "Set key", Success: true}, nil
}
func (s *server) StoresDelete(ctx context.Context, in *pb.StoresDeleteOptions) (*pb.Result, error) {
if s.llm.Locking() {
s.llm.Lock()
defer s.llm.Unlock()
}
err := s.llm.StoresDelete(in)
if err != nil {
return &pb.Result{Message: fmt.Sprintf("Error deleting entry: %s", err.Error()), Success: false}, err
}
return &pb.Result{Message: "Deleted key", Success: true}, nil
}
func (s *server) StoresGet(ctx context.Context, in *pb.StoresGetOptions) (*pb.StoresGetResult, error) {
if s.llm.Locking() {
s.llm.Lock()
defer s.llm.Unlock()
}
res, err := s.llm.StoresGet(in)
if err != nil {
return nil, err
}
return &res, nil
}
func (s *server) StoresFind(ctx context.Context, in *pb.StoresFindOptions) (*pb.StoresFindResult, error) {
if s.llm.Locking() {
s.llm.Lock()
defer s.llm.Unlock()
}
res, err := s.llm.StoresFind(in)
if err != nil {
return nil, err
}
return &res, nil
}
func (s *server) VAD(ctx context.Context, in *pb.VADRequest) (*pb.VADResponse, error) {
if s.llm.Locking() {
s.llm.Lock()
defer s.llm.Unlock()
}
res, err := s.llm.VAD(in)
if err != nil {
return nil, err
}
return &res, nil
}
func (s *server) Diarize(ctx context.Context, in *pb.DiarizeRequest) (*pb.DiarizeResponse, error) {
if s.llm.Locking() {
s.llm.Lock()
defer s.llm.Unlock()
}
res, err := s.llm.Diarize(in)
if err != nil {
return nil, err
}
return &res, nil
}
func (s *server) AudioEncode(ctx context.Context, in *pb.AudioEncodeRequest) (*pb.AudioEncodeResult, error) {
if s.llm.Locking() {
s.llm.Lock()
defer s.llm.Unlock()
}
res, err := s.llm.AudioEncode(in)
if err != nil {
return nil, err
}
return res, nil
}
func (s *server) AudioDecode(ctx context.Context, in *pb.AudioDecodeRequest) (*pb.AudioDecodeResult, error) {
if s.llm.Locking() {
s.llm.Lock()
defer s.llm.Unlock()
}
res, err := s.llm.AudioDecode(in)
if err != nil {
return nil, err
}
return res, nil
}
func (s *server) AudioTransform(ctx context.Context, in *pb.AudioTransformRequest) (*pb.AudioTransformResult, error) {
if s.llm.Locking() {
s.llm.Lock()
defer s.llm.Unlock()
}
res, err := s.llm.AudioTransform(in)
if err != nil {
return nil, err
}
return res, nil
}
func (s *server) AudioTransformStream(stream pb.Backend_AudioTransformStreamServer) error {
if s.llm.Locking() {
s.llm.Lock()
defer s.llm.Unlock()
}
in := make(chan *pb.AudioTransformFrameRequest, 4)
out := make(chan *pb.AudioTransformFrameResponse, 4)
// Pump incoming frames from the gRPC stream into `in`. EOF closes the
// channel, which signals the backend that the client is done sending.
recvErrCh := make(chan error, 1)
go func() {
defer close(in)
for {
req, err := stream.Recv()
if err != nil {
if errors.Is(err, io.EOF) {
recvErrCh <- nil
return
}
recvErrCh <- err
return
}
select {
case in <- req:
case <-stream.Context().Done():
recvErrCh <- stream.Context().Err()
return
}
}
}()
// Pump outgoing frames from `out` to the gRPC stream. The backend closes
// `out` on completion.
sendDone := make(chan error, 1)
go func() {
for resp := range out {
if err := stream.Send(resp); err != nil {
sendDone <- err
// Drain `out` so the backend can finish.
for range out {
}
return
}
}
sendDone <- nil
}()
backendErr := s.llm.AudioTransformStream(in, out)
sendErr := <-sendDone
recvErr := <-recvErrCh
if backendErr != nil {
return backendErr
}
if sendErr != nil {
return sendErr
}
return recvErr
}
// AudioToAudioStream is the bidirectional any-to-any S2S handler. The
// shape mirrors AudioTransformStream exactly (recv → in chan, out chan →
// send) so backends can implement either via the same goroutine idiom.
func (s *server) AudioToAudioStream(stream pb.Backend_AudioToAudioStreamServer) error {
if s.llm.Locking() {
s.llm.Lock()
defer s.llm.Unlock()
}
in := make(chan *pb.AudioToAudioRequest, 8)
out := make(chan *pb.AudioToAudioResponse, 8)
recvErrCh := make(chan error, 1)
go func() {
defer close(in)
for {
req, err := stream.Recv()
if err != nil {
if errors.Is(err, io.EOF) {
recvErrCh <- nil
return
}
recvErrCh <- err
return
}
select {
case in <- req:
case <-stream.Context().Done():
recvErrCh <- stream.Context().Err()
return
}
}
}()
sendDone := make(chan error, 1)
go func() {
for resp := range out {
if err := stream.Send(resp); err != nil {
sendDone <- err
for range out {
}
return
}
}
sendDone <- nil
}()
backendErr := s.llm.AudioToAudioStream(in, out)
sendErr := <-sendDone
recvErr := <-recvErrCh
if backendErr != nil {
return backendErr
}
if sendErr != nil {
return sendErr
}
return recvErr
}
func (s *server) StartFineTune(ctx context.Context, in *pb.FineTuneRequest) (*pb.FineTuneJobResult, error) {
if s.llm.Locking() {
s.llm.Lock()
defer s.llm.Unlock()
}
res, err := s.llm.StartFineTune(in)
if err != nil {
return &pb.FineTuneJobResult{Success: false, Message: fmt.Sprintf("Error starting fine-tune: %s", err.Error())}, err
}
return res, nil
}
func (s *server) FineTuneProgress(in *pb.FineTuneProgressRequest, stream pb.Backend_FineTuneProgressServer) error {
if s.llm.Locking() {
s.llm.Lock()
defer s.llm.Unlock()
}
updateChan := make(chan *pb.FineTuneProgressUpdate)
done := make(chan bool)
go func() {
for update := range updateChan {
stream.Send(update)
}
done <- true
}()
err := s.llm.FineTuneProgress(in, updateChan)
<-done
return err
}
func (s *server) StopFineTune(ctx context.Context, in *pb.FineTuneStopRequest) (*pb.Result, error) {
if s.llm.Locking() {
s.llm.Lock()
defer s.llm.Unlock()
}
err := s.llm.StopFineTune(in)
if err != nil {
return &pb.Result{Message: fmt.Sprintf("Error stopping fine-tune: %s", err.Error()), Success: false}, err
}
return &pb.Result{Message: "Fine-tune stopped", Success: true}, nil
}
func (s *server) ListCheckpoints(ctx context.Context, in *pb.ListCheckpointsRequest) (*pb.ListCheckpointsResponse, error) {
if s.llm.Locking() {
s.llm.Lock()
defer s.llm.Unlock()
}
res, err := s.llm.ListCheckpoints(in)
if err != nil {
return nil, err
}
return res, nil
}
func (s *server) ExportModel(ctx context.Context, in *pb.ExportModelRequest) (*pb.Result, error) {
if s.llm.Locking() {
s.llm.Lock()
defer s.llm.Unlock()
}
err := s.llm.ExportModel(in)
if err != nil {
return &pb.Result{Message: fmt.Sprintf("Error exporting model: %s", err.Error()), Success: false}, err
}
return &pb.Result{Message: "Model exported", Success: true}, nil
}
func (s *server) StartQuantization(ctx context.Context, in *pb.QuantizationRequest) (*pb.QuantizationJobResult, error) {
if s.llm.Locking() {
s.llm.Lock()
defer s.llm.Unlock()
}
res, err := s.llm.StartQuantization(in)
if err != nil {
return &pb.QuantizationJobResult{Success: false, Message: fmt.Sprintf("Error starting quantization: %s", err.Error())}, err
}
return res, nil
}
func (s *server) QuantizationProgress(in *pb.QuantizationProgressRequest, stream pb.Backend_QuantizationProgressServer) error {
if s.llm.Locking() {
s.llm.Lock()
defer s.llm.Unlock()
}
updateChan := make(chan *pb.QuantizationProgressUpdate)
done := make(chan bool)
go func() {
for update := range updateChan {
stream.Send(update)
}
done <- true
}()
err := s.llm.QuantizationProgress(in, updateChan)
<-done
return err
}
func (s *server) StopQuantization(ctx context.Context, in *pb.QuantizationStopRequest) (*pb.Result, error) {
if s.llm.Locking() {
s.llm.Lock()
defer s.llm.Unlock()
}
err := s.llm.StopQuantization(in)
if err != nil {
return &pb.Result{Message: fmt.Sprintf("Error stopping quantization: %s", err.Error()), Success: false}, err
}
return &pb.Result{Message: "Quantization stopped", Success: true}, nil
}
func (s *server) ModelMetadata(ctx context.Context, in *pb.ModelOptions) (*pb.ModelMetadataResponse, error) {
if s.llm.Locking() {
s.llm.Lock()
defer s.llm.Unlock()
}
res, err := s.llm.ModelMetadata(in)
if err != nil {
return nil, err
}
return res, nil
}
func (s *server) Free(ctx context.Context, in *pb.HealthMessage) (*pb.Result, error) {
if err := s.llm.Free(); err != nil {
return &pb.Result{Success: false, Message: err.Error()}, nil
}
return &pb.Result{Success: true}, nil
}
// NewBackendServer creates a pb.BackendServer.
func NewBackendServer(model AIModel) pb.BackendServer {
return &server{llm: model}
}
// AuthTokenEnvVar is the environment variable used to configure gRPC bearer token auth.
const AuthTokenEnvVar = "LOCALAI_GRPC_AUTH_TOKEN"
// validateToken extracts the bearer token from gRPC metadata and validates it.
func validateToken(ctx context.Context, expected string) error {
md, ok := metadata.FromIncomingContext(ctx)
if !ok {
return status.Error(codes.Unauthenticated, "missing metadata")
}
values := md.Get("authorization")
if len(values) == 0 {
return status.Error(codes.Unauthenticated, "missing authorization header")
}
raw := values[0]
if !strings.HasPrefix(raw, "Bearer ") {
return status.Error(codes.Unauthenticated, "authorization must use Bearer scheme")
}
token := strings.TrimPrefix(raw, "Bearer ")
if subtle.ConstantTimeCompare([]byte(token), []byte(expected)) != 1 {
return status.Error(codes.Unauthenticated, "invalid token")
}
return nil
}
func tokenUnaryInterceptor(token string) grpc.UnaryServerInterceptor {
return func(ctx context.Context, req any, info *grpc.UnaryServerInfo, handler grpc.UnaryHandler) (any, error) {
if err := validateToken(ctx, token); err != nil {
return nil, err
}
return handler(ctx, req)
}
}
func tokenStreamInterceptor(token string) grpc.StreamServerInterceptor {
return func(srv any, ss grpc.ServerStream, info *grpc.StreamServerInfo, handler grpc.StreamHandler) error {
if err := validateToken(ss.Context(), token); err != nil {
return err
}
return handler(srv, ss)
}
}
// serverOpts returns the common gRPC server options, including auth interceptors
// when LOCALAI_GRPC_AUTH_TOKEN is set.
func serverOpts() []grpc.ServerOption {
opts := []grpc.ServerOption{
grpc.MaxRecvMsgSize(maxGRPCMessageSize),
grpc.MaxSendMsgSize(maxGRPCMessageSize),
}
if token := os.Getenv(AuthTokenEnvVar); token != "" {
opts = append(opts,
grpc.UnaryInterceptor(tokenUnaryInterceptor(token)),
grpc.StreamInterceptor(tokenStreamInterceptor(token)),
)
log.Printf("gRPC auth enabled via %s", AuthTokenEnvVar)
}
return opts
}
func StartServer(address string, model AIModel) error {
lis, err := net.Listen("tcp", address)
if err != nil {
return err
}
s := grpc.NewServer(serverOpts()...)
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
}
func RunServer(address string, model AIModel) (func() error, error) {
lis, err := net.Listen("tcp", address)
if err != nil {
return nil, err
}
s := grpc.NewServer(serverOpts()...)
pb.RegisterBackendServer(s, &server{llm: model})
log.Printf("gRPC Server listening at %v", lis.Addr())
if err = s.Serve(lis); err != nil {
return func() error {
return lis.Close()
}, err
}
return func() error {
s.GracefulStop()
return nil
}, nil
}