Files
LocalAI/core/http/routes/openai.go
LocalAI [bot] 600dafd20b feat(ced): sound-event classification backend (CED audio tagger) (#10425)
* feat(ced): sketch sound-classification backend (CED audio tagger)

Wires ced.cpp (CED, 527-class AudioSet sound-event tagger; baby cry,
footsteps, glass, alarms, dog bark) into LocalAI as a Go/purego backend.

SKETCH (backend skeleton real; core REST wiring + CI/gallery is a checklist
in DESIGN.md):
- backend/backend.proto: new SoundDetection rpc + SoundClass messages
  (run `make protogen-go` to regenerate pkg/grpc/proto).
- backend/go/ced: main.go (purego dlopen libced.so + ced_capi.h),
  goced.go (Ced gRPC backend: Load + SoundDetection), Makefile
  (clone-at-pin CED_VERSION, ggml static-PIC shared build), run.sh,
  package.sh, .gitignore.
- DESIGN.md: REST /v1/audio/classification wiring (handler/route/capability
  registration checklist), gallery/index + CI registration, and a scoping
  note for the realtime/websocket live-recognition path (sliding-window
  classify over the existing ws transport + voicegate; the ced C-API
  per-PCM entry point is already window-friendly).

Backend code does not compile until protogen-go regenerates the pb types
and a libced.so is built (Makefile clones+builds it).

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* feat(ced): REST /v1/audio/classification endpoint + capability registration

Wires the ced sound-event classification backend (AudioSet audio tagger)
end to end through the REST surface, mirroring the transcription path.

- Handler: core/http/endpoints/openai/sound_classification.go parses the
  multipart audio upload, temp-files it, resolves the model config and
  calls the SoundDetection RPC; returns {model, detections[]} JSON.
- Backend wrapper: core/backend/sound_classification.go (ModelSoundDetection)
  loads the model and normalizes the proto response into schema types.
- Schema: core/schema/sound_classification.go (SoundClassificationResult).
- gRPC layer: SoundDetection wired through the LocalAI wrapper (interface,
  Backend client, Client, embed, server, base default) so the loader-typed
  client exposes the RPC; proto regenerated via make protogen-go.
- Route: POST /v1/audio/classification (+ /audio/classification alias) with
  the audio/multipart default-model middleware in routes/openai.go.
- Capability surfaces: swagger @Tags/@Router on the handler; FLAG_SOUND_
  CLASSIFICATION usecase flag + UsecaseSoundClassification + UsecaseInfoMap +
  GuessUsecases + ModalityGroups + GetAllModelConfigUsecases; meta usecase
  option; /api/instructions audio area updated; auth RouteFeatureRegistry +
  FeatureAudioClassification (APIFeatures, default ON) + FeatureMetas; UI
  usecaseFilters, capabilities.js CAP_SOUND_CLASSIFICATION, Models.jsx filter
  + i18n; docs page features/audio-classification.md + whats-new + crosslink.

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* feat(ced): realtime sound-event detection over the websocket API

When a realtime pipeline configures a sound-classification model, each
VAD-committed utterance (the same window the transcription path produces)
is also run through the CED sound-event classifier and the scored AudioSet
tags are emitted as a new server event. No new backend rpc is needed: the
SoundDetection gRPC method already exists on this branch.

- config: add Pipeline.SoundDetection (yaml/json sound_detection,omitempty)
  beside Transcription/VAD.
- realtime: add Model.SoundDetection(ctx, audio, topK, threshold) to the
  ModelInterface; implement it on wrappedModel and transcriptOnlyModel by
  calling backend.ModelSoundDetection with the session's sound-classification
  model config (mirrors how Transcribe dispatches). Load the optional config
  in newModel / newTranscriptionOnlyModel; nil config keeps it additive.
- types: add ConversationItemSoundDetectionEvent (item_id, content_index,
  detections[]{label,score,index}) with type conversation.item.sound_detection,
  its ServerEventType constant and MarshalJSON, mirroring the transcription
  completed event.
- realtime: add emitSoundDetection (unary path: classify the committed window,
  build the event, t.SendEvent) and wire it at the utterance-commit hook right
  after emitTranscription; gated on session.SoundDetectionEnabled (resolved
  from Pipeline.SoundDetection at session setup, defaults top_k=5, threshold=0).
  Its error is logged via xlog but never aborts the turn.
- test: Ginkgo specs for emitSoundDetection (tags emitted, empty detections,
  classifier error) plus a SoundDetection method on the fakeModel double.

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* fix(ced): implement SoundDetection in nodes backend test doubles

The SoundDetection method added to the grpc backend interface left two
test doubles (fakeBackendClient, fakeGRPCBackend) incomplete, so
core/services/nodes failed to compile under `go vet`/`go test` (go build
missed it: the doubles live in _test.go). Add the method to both,
mirroring their existing Detect mock. Repairs CI for the nodes package.

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* feat(ced): decouple realtime sound detection from VAD (sound-only sessions)

Sound-event detection must activate on sounds, not speech, so it no longer
runs through the voice VAD/transcription path. A sound-detection-only
pipeline (sound_detection set, no transcription/LLM) now:

- is accepted by prepareRealtimeConfig (sound_detection counts as a pipeline
  stage),
- builds a lightweight model via newSoundDetectionOnlyModel (no VAD/STT/LLM/TTS
  loaded), and
- defaults the session to turn_detection none (no VAD) with no transcription
  stage, so the client drives windowing via input_audio_buffer.commit
  (option A: client-side sliding window). The per-PCM C-API already supports
  arbitrary windows.

commitUtterance gains a sound-only branch: it emits the
conversation.item.sound_detection event (scored AudioSet tags) and stops -
no transcription, no LLM response. generateResponse is now guarded on a
transcription stage being present, so a sound-only turn never invokes the LLM.

Existing transcription/VAD sessions are unchanged (additive). Added a
commitUtterance sound-only Ginkgo spec asserting it emits the sound event and
neither transcribes nor generates a response. go vet + golangci-lint
(new-from-merge-base) clean; openai suite green.

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* feat(ced): register sound-classification backend in gallery + CI

Mechanical backend-image registration for the ced sound-event classifier,
mirroring the parakeet-cpp Go/purego backend everywhere it is wired up.

- .github/backend-matrix.yml: add the ced build matrix, field-for-field copies
  of the parakeet-cpp entries (cpu amd64/arm64, cublas cuda 12/13 amd64,
  l4t cuda-13 arm64, l4t-jetpack cuda-12 arm64, sycl f32/f16, vulkan
  amd64/arm64, rocm hipblas, and the metal darwin entry), changing only
  backend and tag-suffix. dockerfile stays ./backend/Dockerfile.golang.
- backend/index.yaml: add the &ced meta anchor (capabilities map per platform)
  plus ced-development and the per-arch image entries, each uri/mirror
  tag-suffix matching the matrix exactly. The model gallery (GGUF) entry is
  intentionally deferred pending the HuggingFace publish (TODO note inline).
- scripts/changed-backends.js: add an explicit item.backend === "ced" branch in
  inferBackendPath mapping to backend/go/ced/, same mechanism and ordering as
  the parakeet-cpp branch (before the generic golang fallthrough).
- .github/workflows/bump_deps.yaml: register mudler/ced.cpp -> CED_VERSION in
  backend/go/ced/Makefile so the daily bot bumps the pin.
- swagger/{docs.go,swagger.json,swagger.yaml}: regenerated via make swagger so
  the existing /v1/audio/classification annotations land in the generated spec.

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* feat(ced): server-side windowing for realtime sound detection (option B)

Adds an optional server-driven sliding-window classifier so a sound-only
realtime client only has to stream audio (no input_audio_buffer.commit):

- Pipeline.sound_detection_window_ms / sound_detection_hop_ms config knobs.
  When both > 0 on a sound-only session, the server classifies the last
  window of streamed audio every hop and emits a conversation.item.sound_
  detection event; the input buffer is trimmed to one window so a long
  stream stays bounded. When unset, the session stays client-driven
  (option A). Runs independent of VAD (sound events are not speech).
- handleSoundWindow (ticker) + classifySoundWindow (one tick, extracted so
  it is unit-testable) + writeWindowWAV, which declares the true
  InputSampleRate (NewWAVHeaderWithRate) so the classifier resamples
  correctly. Goroutine is started after toggleVAD and torn down with the
  session (close + wg.Wait).
- Register pipeline.sound_detection (+window_ms/hop_ms) in the config meta
  registry; the earlier realtime commit added pipeline.sound_detection
  without a registry entry, failing TestAllFieldsHaveRegistryEntries. This
  fixes that and covers the two new knobs.

Tests: classifySoundWindow emits an event + trims the buffer to one window,
no-ops on too-little audio; writeWindowWAV declares the given sample rate.
go build/vet + golangci-lint (new-from-merge-base) clean; config + openai
suites green.

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* feat(ced): add ced-base GGUF model gallery entries (f16 + q8_0)

The ced-base weights are now published at mudler/ced-base-gguf (Apache-2.0,
converted from mispeech/ced-base). Adds gallery/ced.yaml (backend: ced +
known_usecases: sound_classification) and two gallery/index.yaml entries
(ced-base-f16 default, ced-base-q8 smallest) with sha256-pinned files, and
removes the now-resolved TODO from backend/index.yaml.

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* feat(ced): add tiny/mini/small GGUF model gallery entries

Publishes the rest of the CED family (same architecture, metadata-driven port
verified end-to-end on ced-tiny) to mudler/ced-{tiny,mini,small}-gguf and adds
their f16 + q8_0 gallery entries:

  ced-tiny  (5.5M, edge/Pi-class)  f16 11MB / q8_0 6MB
  ced-mini  (9.6M)                 f16 19MB / q8_0 11MB
  ced-small (22M)                  f16 42MB / q8_0 23MB

All sha256-pinned. ced-base remains the accuracy default.

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* chore(ced): point gallery entries at the consolidated mudler/ced-gguf repo

All CED quantizations (tiny/mini/small/base, f16/q8_0) now live in a single
HuggingFace repo, mudler/ced-gguf, instead of per-model repos. Repoint the 8
gallery model entries' urls + file uris accordingly. sha256 and filenames are
unchanged.

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* chore(ced): bump CED_VERSION to the short-clip fix

Pin the ced backend to ced.cpp 99c6ed3, which fixes a crash on any clip
shorter than target_length (~10.11s): time_pos_embed was added at its full
63-frame grid instead of being sliced to the clip's actual time grid, tripping
ggml_can_repeat in ggml_add. Surfaced by the live realtime e2e (sub-10s
windows) and gated with a short-clip parity test upstream.

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* docs(ced): list ced.cpp as a LocalAI-team engine + backend-guide directive

- README.md: add ced.cpp to the "native C/C++/GGML engines developed and
  maintained by the LocalAI project" table.
- docs/content/features/backends.md: add a Sound Classification backend
  category (sound-event classification / audio tagging) listing ced.cpp.
- .agents/adding-backends.md: add a "Documenting the backend" section and two
  verification-checklist items requiring new backends to be documented in the
  backends.md category list, and in-house native engines to be added to the
  README maintained-engines table. This directive was missing.

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* chore(ced): repin CED_VERSION to the v0.1.0 release commit

ced.cpp history was squashed into a single release commit (tagged v0.1.0), so
the previous pin (99c6ed3) no longer exists upstream. Pin to c04ac14, the
v0.1.0 release commit, so the backend builds against a commit that exists.

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* fix(ced): silence gosec G304/G103 + govet unsafeptr on audited paths

- sound_classification.go: os.Create(dst) where dst = temp dir + path.Base of
  the upload (no traversal). #nosec G304, matching the depth-anything-cpp handler.
- goced.go: reading a NUL-terminated C string from a libced-owned buffer.
  #nosec G103 (gosec) + //nolint:govet (golangci-lint's unsafeptr check), since
  the uintptr is a C-owned malloc'd buffer, not Go-GC memory.

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

---------

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
2026-06-22 01:00:28 +02:00

261 lines
12 KiB
Go

package routes
import (
"github.com/labstack/echo/v4"
"github.com/mudler/LocalAI/core/application"
"github.com/mudler/LocalAI/core/config"
"github.com/mudler/LocalAI/core/http/endpoints/localai"
mcpTools "github.com/mudler/LocalAI/core/http/endpoints/mcp"
"github.com/mudler/LocalAI/core/http/endpoints/openai"
"github.com/mudler/LocalAI/core/http/middleware"
"github.com/mudler/LocalAI/core/schema"
"github.com/mudler/LocalAI/core/services/routing/pii"
"github.com/mudler/LocalAI/core/services/routing/piiadapter"
"github.com/mudler/LocalAI/core/services/routing/router"
)
func RegisterOpenAIRoutes(app *echo.Echo,
re *middleware.RequestExtractor,
application *application.Application,
) {
// openAI compatible API endpoint
traceMiddleware := middleware.TraceMiddleware(application)
usageMiddleware := middleware.UsageMiddleware(application.StatsRecorder(), application.FallbackUser())
// X-LocalAI-Node attribution middleware: wraps the response writer and
// stamps the header on first write when --expose-node-header is on. No-op
// otherwise. Applied to every inference path that routes through
// ml.Load (chat, completion, embeddings, audio transcriptions/speech,
// image generation/inpainting) so distributed-mode operators can observe
// which worker served each request.
nodeHeaderMiddleware := middleware.ExposeNodeHeader(application.ApplicationConfig())
// realtime
// TODO: Modify/disable the API key middleware for this endpoint to allow ephemeral keys created by sessions
app.GET("/v1/realtime", openai.Realtime(application))
app.POST("/v1/realtime/sessions", openai.RealtimeTranscriptionSession(application), traceMiddleware)
app.POST("/v1/realtime/transcription_session", openai.RealtimeTranscriptionSession(application), traceMiddleware)
app.POST("/v1/realtime/calls", openai.RealtimeCalls(application), traceMiddleware)
// NATS client for distributed MCP tool routing (nil when not in distributed mode)
var natsClient mcpTools.MCPNATSClient
if d := application.Distributed(); d != nil {
natsClient = d.Nats
}
// chat
chatHandler := openai.ChatEndpoint(application.ModelConfigLoader(), application.ModelLoader(), application.TemplatesEvaluator(), application.ApplicationConfig(), natsClient, application.LocalAIAssistant())
chatMiddleware := []echo.MiddlewareFunc{
nodeHeaderMiddleware,
usageMiddleware,
traceMiddleware,
re.BuildFilteredFirstAvailableDefaultModel(config.BuildUsecaseFilterFn(config.FLAG_CHAT)),
re.SetModelAndConfig(func() schema.LocalAIRequest { return new(schema.OpenAIRequest) }),
func(next echo.HandlerFunc) echo.HandlerFunc {
return func(c echo.Context) error {
if err := re.SetOpenAIRequest(c); err != nil {
return err
}
return next(c)
}
},
// RouteModel runs AFTER the schema-specific request parser so
// the classifier sees a populated *schema.OpenAIRequest. When
// the resolved model has a Router config, the middleware
// rewrites input.Model to the chosen candidate, swaps
// MODEL_CONFIG, and stamps RequestedModel/ServedModel for the
// usage log. Models without a Router pass through.
middleware.RouteModel(
application.ModelConfigLoader(),
application.ApplicationConfig(),
application.RouterDecisions(),
application.FallbackUser(),
middleware.OpenAIProbe,
router.SourceChat,
middleware.ClassifierDeps{
Scorer: application.Scorer,
TokenCounter: application.TokenCounter,
Embedder: application.Embedder,
VectorStore: application.VectorStore,
Reranker: application.Reranker,
ModelLookup: application.ModelConfigLookup(),
Registry: application.RouterClassifierRegistry(),
Evaluator: application.TemplatesEvaluator(),
},
),
// Admission control runs after RouteModel so the SERVED
// model's limits apply — a router fanout that lands on a
// saturated downstream gets rejected even when the requested
// router-model has slack.
middleware.AdmissionControl(application.AdmissionLimiter(), application.PIIEvents()),
// PII redaction runs INNERMOST, after RouteModel has resolved
// the actual served model. This is what makes per-model PII
// configs honour the routed target (e.g., a router fans out to
// claude-strict; that model's pii block applies, not the
// router model's).
pii.RequestMiddleware(application.PIIRedactor(), application.PIIEvents(), piiadapter.OpenAI(), application.FallbackUser(), pii.WithNERResolver(application.PIINERResolver()), pii.WithPolicyResolver(application.PIIPolicyResolver())),
}
app.POST("/v1/chat/completions", chatHandler, chatMiddleware...)
app.POST("/chat/completions", chatHandler, chatMiddleware...)
// edit
editHandler := openai.EditEndpoint(application.ModelConfigLoader(), application.ModelLoader(), application.TemplatesEvaluator(), application.ApplicationConfig())
editMiddleware := []echo.MiddlewareFunc{
usageMiddleware,
traceMiddleware,
re.BuildFilteredFirstAvailableDefaultModel(config.BuildUsecaseFilterFn(config.FLAG_EDIT)),
re.BuildConstantDefaultModelNameMiddleware("gpt-4o"),
re.SetModelAndConfig(func() schema.LocalAIRequest { return new(schema.OpenAIRequest) }),
func(next echo.HandlerFunc) echo.HandlerFunc {
return func(c echo.Context) error {
if err := re.SetOpenAIRequest(c); err != nil {
return err
}
return next(c)
}
},
pii.RequestMiddleware(application.PIIRedactor(), application.PIIEvents(), piiadapter.OpenAICompletion(), application.FallbackUser(), pii.WithNERResolver(application.PIINERResolver()), pii.WithPolicyResolver(application.PIIPolicyResolver())),
}
app.POST("/v1/edits", editHandler, editMiddleware...)
app.POST("/edits", editHandler, editMiddleware...)
// completion
completionHandler := openai.CompletionEndpoint(application.ModelConfigLoader(), application.ModelLoader(), application.TemplatesEvaluator(), application.ApplicationConfig())
completionMiddleware := []echo.MiddlewareFunc{
nodeHeaderMiddleware,
usageMiddleware,
traceMiddleware,
re.BuildFilteredFirstAvailableDefaultModel(config.BuildUsecaseFilterFn(config.FLAG_COMPLETION)),
re.BuildConstantDefaultModelNameMiddleware("gpt-4o"),
re.SetModelAndConfig(func() schema.LocalAIRequest { return new(schema.OpenAIRequest) }),
func(next echo.HandlerFunc) echo.HandlerFunc {
return func(c echo.Context) error {
if err := re.SetOpenAIRequest(c); err != nil {
return err
}
return next(c)
}
},
pii.RequestMiddleware(application.PIIRedactor(), application.PIIEvents(), piiadapter.OpenAICompletion(), application.FallbackUser(), pii.WithNERResolver(application.PIINERResolver()), pii.WithPolicyResolver(application.PIIPolicyResolver())),
}
app.POST("/v1/completions", completionHandler, completionMiddleware...)
app.POST("/completions", completionHandler, completionMiddleware...)
app.POST("/v1/engines/:model/completions", completionHandler, completionMiddleware...)
// embeddings
embeddingHandler := openai.EmbeddingsEndpoint(application.ModelConfigLoader(), application.ModelLoader(), application.ApplicationConfig())
embeddingMiddleware := []echo.MiddlewareFunc{
nodeHeaderMiddleware,
usageMiddleware,
traceMiddleware,
re.BuildFilteredFirstAvailableDefaultModel(config.BuildUsecaseFilterFn(config.FLAG_EMBEDDINGS)),
re.BuildConstantDefaultModelNameMiddleware("gpt-4o"),
re.SetModelAndConfig(func() schema.LocalAIRequest { return new(schema.OpenAIRequest) }),
func(next echo.HandlerFunc) echo.HandlerFunc {
return func(c echo.Context) error {
if err := re.SetOpenAIRequest(c); err != nil {
return err
}
return next(c)
}
},
pii.RequestMiddleware(application.PIIRedactor(), application.PIIEvents(), piiadapter.OpenAICompletion(), application.FallbackUser(), pii.WithNERResolver(application.PIINERResolver()), pii.WithPolicyResolver(application.PIIPolicyResolver())),
}
app.POST("/v1/embeddings", embeddingHandler, embeddingMiddleware...)
app.POST("/embeddings", embeddingHandler, embeddingMiddleware...)
app.POST("/v1/engines/:model/embeddings", embeddingHandler, embeddingMiddleware...)
audioHandler := openai.TranscriptEndpoint(application.ModelConfigLoader(), application.ModelLoader(), application.ApplicationConfig())
audioMiddleware := []echo.MiddlewareFunc{
nodeHeaderMiddleware,
traceMiddleware,
re.BuildFilteredFirstAvailableDefaultModel(config.BuildUsecaseFilterFn(config.FLAG_TRANSCRIPT)),
re.SetModelAndConfig(func() schema.LocalAIRequest { return new(schema.OpenAIRequest) }),
func(next echo.HandlerFunc) echo.HandlerFunc {
return func(c echo.Context) error {
if err := re.SetOpenAIRequest(c); err != nil {
return err
}
return next(c)
}
},
}
// audio
app.POST("/v1/audio/transcriptions", audioHandler, audioMiddleware...)
app.POST("/audio/transcriptions", audioHandler, audioMiddleware...)
diarizationHandler := openai.DiarizationEndpoint(application.ModelConfigLoader(), application.ModelLoader(), application.ApplicationConfig())
diarizationMiddleware := []echo.MiddlewareFunc{
traceMiddleware,
re.BuildFilteredFirstAvailableDefaultModel(config.BuildUsecaseFilterFn(config.FLAG_DIARIZATION)),
re.SetModelAndConfig(func() schema.LocalAIRequest { return new(schema.OpenAIRequest) }),
func(next echo.HandlerFunc) echo.HandlerFunc {
return func(c echo.Context) error {
if err := re.SetOpenAIRequest(c); err != nil {
return err
}
return next(c)
}
},
}
app.POST("/v1/audio/diarization", diarizationHandler, diarizationMiddleware...)
app.POST("/audio/diarization", diarizationHandler, diarizationMiddleware...)
soundClassificationHandler := openai.SoundClassificationEndpoint(application.ModelConfigLoader(), application.ModelLoader(), application.ApplicationConfig())
soundClassificationMiddleware := []echo.MiddlewareFunc{
traceMiddleware,
re.BuildFilteredFirstAvailableDefaultModel(config.BuildUsecaseFilterFn(config.FLAG_SOUND_CLASSIFICATION)),
re.SetModelAndConfig(func() schema.LocalAIRequest { return new(schema.OpenAIRequest) }),
func(next echo.HandlerFunc) echo.HandlerFunc {
return func(c echo.Context) error {
if err := re.SetOpenAIRequest(c); err != nil {
return err
}
return next(c)
}
},
}
app.POST("/v1/audio/classification", soundClassificationHandler, soundClassificationMiddleware...)
app.POST("/audio/classification", soundClassificationHandler, soundClassificationMiddleware...)
audioSpeechHandler := localai.TTSEndpoint(application.ModelConfigLoader(), application.ModelLoader(), application.ApplicationConfig())
audioSpeechMiddleware := []echo.MiddlewareFunc{
nodeHeaderMiddleware,
traceMiddleware,
re.BuildFilteredFirstAvailableDefaultModel(config.BuildUsecaseFilterFn(config.FLAG_TTS)),
re.SetModelAndConfig(func() schema.LocalAIRequest { return new(schema.TTSRequest) }),
}
app.POST("/v1/audio/speech", audioSpeechHandler, audioSpeechMiddleware...)
app.POST("/audio/speech", audioSpeechHandler, audioSpeechMiddleware...)
// images
imageHandler := openai.ImageEndpoint(application.ModelConfigLoader(), application.ModelLoader(), application.ApplicationConfig())
imageMiddleware := []echo.MiddlewareFunc{
nodeHeaderMiddleware,
traceMiddleware,
// Default: use the first available image generation model
re.BuildFilteredFirstAvailableDefaultModel(config.BuildUsecaseFilterFn(config.FLAG_IMAGE)),
re.SetModelAndConfig(func() schema.LocalAIRequest { return new(schema.OpenAIRequest) }),
func(next echo.HandlerFunc) echo.HandlerFunc {
return func(c echo.Context) error {
if err := re.SetOpenAIRequest(c); err != nil {
return err
}
return next(c)
}
},
}
app.POST("/v1/images/generations", imageHandler, imageMiddleware...)
app.POST("/images/generations", imageHandler, imageMiddleware...)
// inpainting endpoint (image + mask) - reuse same middleware config as images
inpaintingHandler := openai.InpaintingEndpoint(application.ModelConfigLoader(), application.ModelLoader(), application.ApplicationConfig())
app.POST("/v1/images/inpainting", inpaintingHandler, imageMiddleware...)
app.POST("/images/inpainting", inpaintingHandler, imageMiddleware...)
// List models
app.GET("/v1/models", openai.ListModelsEndpoint(application.ModelConfigLoader(), application.ModelLoader(), application.ApplicationConfig(), application.AuthDB()))
app.GET("/models", openai.ListModelsEndpoint(application.ModelConfigLoader(), application.ModelLoader(), application.ApplicationConfig(), application.AuthDB()))
}