mirror of
https://github.com/mudler/LocalAI.git
synced 2026-06-28 18:37:43 -04:00
* 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>
261 lines
12 KiB
Go
261 lines
12 KiB
Go
package routes
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import (
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"github.com/labstack/echo/v4"
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"github.com/mudler/LocalAI/core/application"
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"github.com/mudler/LocalAI/core/config"
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"github.com/mudler/LocalAI/core/http/endpoints/localai"
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mcpTools "github.com/mudler/LocalAI/core/http/endpoints/mcp"
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"github.com/mudler/LocalAI/core/http/endpoints/openai"
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"github.com/mudler/LocalAI/core/http/middleware"
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"github.com/mudler/LocalAI/core/schema"
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"github.com/mudler/LocalAI/core/services/routing/pii"
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"github.com/mudler/LocalAI/core/services/routing/piiadapter"
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"github.com/mudler/LocalAI/core/services/routing/router"
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)
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func RegisterOpenAIRoutes(app *echo.Echo,
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re *middleware.RequestExtractor,
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application *application.Application,
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) {
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// openAI compatible API endpoint
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traceMiddleware := middleware.TraceMiddleware(application)
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usageMiddleware := middleware.UsageMiddleware(application.StatsRecorder(), application.FallbackUser())
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// X-LocalAI-Node attribution middleware: wraps the response writer and
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// stamps the header on first write when --expose-node-header is on. No-op
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// otherwise. Applied to every inference path that routes through
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// ml.Load (chat, completion, embeddings, audio transcriptions/speech,
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// image generation/inpainting) so distributed-mode operators can observe
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// which worker served each request.
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nodeHeaderMiddleware := middleware.ExposeNodeHeader(application.ApplicationConfig())
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// realtime
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// TODO: Modify/disable the API key middleware for this endpoint to allow ephemeral keys created by sessions
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app.GET("/v1/realtime", openai.Realtime(application))
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app.POST("/v1/realtime/sessions", openai.RealtimeTranscriptionSession(application), traceMiddleware)
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app.POST("/v1/realtime/transcription_session", openai.RealtimeTranscriptionSession(application), traceMiddleware)
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app.POST("/v1/realtime/calls", openai.RealtimeCalls(application), traceMiddleware)
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// NATS client for distributed MCP tool routing (nil when not in distributed mode)
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var natsClient mcpTools.MCPNATSClient
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if d := application.Distributed(); d != nil {
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natsClient = d.Nats
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}
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// chat
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chatHandler := openai.ChatEndpoint(application.ModelConfigLoader(), application.ModelLoader(), application.TemplatesEvaluator(), application.ApplicationConfig(), natsClient, application.LocalAIAssistant())
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chatMiddleware := []echo.MiddlewareFunc{
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nodeHeaderMiddleware,
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usageMiddleware,
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traceMiddleware,
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re.BuildFilteredFirstAvailableDefaultModel(config.BuildUsecaseFilterFn(config.FLAG_CHAT)),
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re.SetModelAndConfig(func() schema.LocalAIRequest { return new(schema.OpenAIRequest) }),
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func(next echo.HandlerFunc) echo.HandlerFunc {
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return func(c echo.Context) error {
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if err := re.SetOpenAIRequest(c); err != nil {
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return err
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}
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return next(c)
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}
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},
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// RouteModel runs AFTER the schema-specific request parser so
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// the classifier sees a populated *schema.OpenAIRequest. When
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// the resolved model has a Router config, the middleware
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// rewrites input.Model to the chosen candidate, swaps
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// MODEL_CONFIG, and stamps RequestedModel/ServedModel for the
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// usage log. Models without a Router pass through.
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middleware.RouteModel(
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application.ModelConfigLoader(),
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application.ApplicationConfig(),
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application.RouterDecisions(),
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application.FallbackUser(),
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middleware.OpenAIProbe,
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router.SourceChat,
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middleware.ClassifierDeps{
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Scorer: application.Scorer,
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TokenCounter: application.TokenCounter,
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Embedder: application.Embedder,
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VectorStore: application.VectorStore,
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Reranker: application.Reranker,
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ModelLookup: application.ModelConfigLookup(),
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Registry: application.RouterClassifierRegistry(),
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Evaluator: application.TemplatesEvaluator(),
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},
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),
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// Admission control runs after RouteModel so the SERVED
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// model's limits apply — a router fanout that lands on a
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// saturated downstream gets rejected even when the requested
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// router-model has slack.
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middleware.AdmissionControl(application.AdmissionLimiter(), application.PIIEvents()),
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// PII redaction runs INNERMOST, after RouteModel has resolved
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// the actual served model. This is what makes per-model PII
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// configs honour the routed target (e.g., a router fans out to
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// claude-strict; that model's pii block applies, not the
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// router model's).
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pii.RequestMiddleware(application.PIIRedactor(), application.PIIEvents(), piiadapter.OpenAI(), application.FallbackUser(), pii.WithNERResolver(application.PIINERResolver()), pii.WithPolicyResolver(application.PIIPolicyResolver())),
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}
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app.POST("/v1/chat/completions", chatHandler, chatMiddleware...)
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app.POST("/chat/completions", chatHandler, chatMiddleware...)
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// edit
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editHandler := openai.EditEndpoint(application.ModelConfigLoader(), application.ModelLoader(), application.TemplatesEvaluator(), application.ApplicationConfig())
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editMiddleware := []echo.MiddlewareFunc{
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usageMiddleware,
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traceMiddleware,
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re.BuildFilteredFirstAvailableDefaultModel(config.BuildUsecaseFilterFn(config.FLAG_EDIT)),
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re.BuildConstantDefaultModelNameMiddleware("gpt-4o"),
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re.SetModelAndConfig(func() schema.LocalAIRequest { return new(schema.OpenAIRequest) }),
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func(next echo.HandlerFunc) echo.HandlerFunc {
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return func(c echo.Context) error {
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if err := re.SetOpenAIRequest(c); err != nil {
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return err
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}
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return next(c)
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}
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},
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pii.RequestMiddleware(application.PIIRedactor(), application.PIIEvents(), piiadapter.OpenAICompletion(), application.FallbackUser(), pii.WithNERResolver(application.PIINERResolver()), pii.WithPolicyResolver(application.PIIPolicyResolver())),
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}
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app.POST("/v1/edits", editHandler, editMiddleware...)
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app.POST("/edits", editHandler, editMiddleware...)
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// completion
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completionHandler := openai.CompletionEndpoint(application.ModelConfigLoader(), application.ModelLoader(), application.TemplatesEvaluator(), application.ApplicationConfig())
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completionMiddleware := []echo.MiddlewareFunc{
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nodeHeaderMiddleware,
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usageMiddleware,
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traceMiddleware,
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re.BuildFilteredFirstAvailableDefaultModel(config.BuildUsecaseFilterFn(config.FLAG_COMPLETION)),
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re.BuildConstantDefaultModelNameMiddleware("gpt-4o"),
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re.SetModelAndConfig(func() schema.LocalAIRequest { return new(schema.OpenAIRequest) }),
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func(next echo.HandlerFunc) echo.HandlerFunc {
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return func(c echo.Context) error {
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if err := re.SetOpenAIRequest(c); err != nil {
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return err
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}
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return next(c)
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}
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},
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pii.RequestMiddleware(application.PIIRedactor(), application.PIIEvents(), piiadapter.OpenAICompletion(), application.FallbackUser(), pii.WithNERResolver(application.PIINERResolver()), pii.WithPolicyResolver(application.PIIPolicyResolver())),
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}
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app.POST("/v1/completions", completionHandler, completionMiddleware...)
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app.POST("/completions", completionHandler, completionMiddleware...)
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app.POST("/v1/engines/:model/completions", completionHandler, completionMiddleware...)
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// embeddings
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embeddingHandler := openai.EmbeddingsEndpoint(application.ModelConfigLoader(), application.ModelLoader(), application.ApplicationConfig())
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embeddingMiddleware := []echo.MiddlewareFunc{
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nodeHeaderMiddleware,
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usageMiddleware,
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traceMiddleware,
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re.BuildFilteredFirstAvailableDefaultModel(config.BuildUsecaseFilterFn(config.FLAG_EMBEDDINGS)),
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re.BuildConstantDefaultModelNameMiddleware("gpt-4o"),
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re.SetModelAndConfig(func() schema.LocalAIRequest { return new(schema.OpenAIRequest) }),
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func(next echo.HandlerFunc) echo.HandlerFunc {
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return func(c echo.Context) error {
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if err := re.SetOpenAIRequest(c); err != nil {
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return err
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}
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return next(c)
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}
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},
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pii.RequestMiddleware(application.PIIRedactor(), application.PIIEvents(), piiadapter.OpenAICompletion(), application.FallbackUser(), pii.WithNERResolver(application.PIINERResolver()), pii.WithPolicyResolver(application.PIIPolicyResolver())),
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}
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app.POST("/v1/embeddings", embeddingHandler, embeddingMiddleware...)
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app.POST("/embeddings", embeddingHandler, embeddingMiddleware...)
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app.POST("/v1/engines/:model/embeddings", embeddingHandler, embeddingMiddleware...)
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audioHandler := openai.TranscriptEndpoint(application.ModelConfigLoader(), application.ModelLoader(), application.ApplicationConfig())
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audioMiddleware := []echo.MiddlewareFunc{
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nodeHeaderMiddleware,
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traceMiddleware,
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re.BuildFilteredFirstAvailableDefaultModel(config.BuildUsecaseFilterFn(config.FLAG_TRANSCRIPT)),
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re.SetModelAndConfig(func() schema.LocalAIRequest { return new(schema.OpenAIRequest) }),
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func(next echo.HandlerFunc) echo.HandlerFunc {
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return func(c echo.Context) error {
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if err := re.SetOpenAIRequest(c); err != nil {
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return err
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}
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return next(c)
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}
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},
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}
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// audio
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app.POST("/v1/audio/transcriptions", audioHandler, audioMiddleware...)
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app.POST("/audio/transcriptions", audioHandler, audioMiddleware...)
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diarizationHandler := openai.DiarizationEndpoint(application.ModelConfigLoader(), application.ModelLoader(), application.ApplicationConfig())
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diarizationMiddleware := []echo.MiddlewareFunc{
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traceMiddleware,
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re.BuildFilteredFirstAvailableDefaultModel(config.BuildUsecaseFilterFn(config.FLAG_DIARIZATION)),
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re.SetModelAndConfig(func() schema.LocalAIRequest { return new(schema.OpenAIRequest) }),
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func(next echo.HandlerFunc) echo.HandlerFunc {
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return func(c echo.Context) error {
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if err := re.SetOpenAIRequest(c); err != nil {
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return err
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}
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return next(c)
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}
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},
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}
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app.POST("/v1/audio/diarization", diarizationHandler, diarizationMiddleware...)
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app.POST("/audio/diarization", diarizationHandler, diarizationMiddleware...)
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soundClassificationHandler := openai.SoundClassificationEndpoint(application.ModelConfigLoader(), application.ModelLoader(), application.ApplicationConfig())
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soundClassificationMiddleware := []echo.MiddlewareFunc{
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traceMiddleware,
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re.BuildFilteredFirstAvailableDefaultModel(config.BuildUsecaseFilterFn(config.FLAG_SOUND_CLASSIFICATION)),
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re.SetModelAndConfig(func() schema.LocalAIRequest { return new(schema.OpenAIRequest) }),
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func(next echo.HandlerFunc) echo.HandlerFunc {
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return func(c echo.Context) error {
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if err := re.SetOpenAIRequest(c); err != nil {
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return err
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}
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return next(c)
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}
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},
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}
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app.POST("/v1/audio/classification", soundClassificationHandler, soundClassificationMiddleware...)
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app.POST("/audio/classification", soundClassificationHandler, soundClassificationMiddleware...)
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audioSpeechHandler := localai.TTSEndpoint(application.ModelConfigLoader(), application.ModelLoader(), application.ApplicationConfig())
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audioSpeechMiddleware := []echo.MiddlewareFunc{
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nodeHeaderMiddleware,
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traceMiddleware,
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re.BuildFilteredFirstAvailableDefaultModel(config.BuildUsecaseFilterFn(config.FLAG_TTS)),
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re.SetModelAndConfig(func() schema.LocalAIRequest { return new(schema.TTSRequest) }),
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}
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app.POST("/v1/audio/speech", audioSpeechHandler, audioSpeechMiddleware...)
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app.POST("/audio/speech", audioSpeechHandler, audioSpeechMiddleware...)
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// images
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imageHandler := openai.ImageEndpoint(application.ModelConfigLoader(), application.ModelLoader(), application.ApplicationConfig())
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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()))
|
|
}
|