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* feat(3d): add Generate3D RPC, FLAG_3D capability, and /v1/3d/generations endpoint Adds the plumbing for image-conditioned 3D asset generation (binary glTF / GLB output), modeled on the video generation path: - backend.proto: Generate3D RPC + Generate3DRequest (staged image src, glb dst, seed/step/cfg_scale/texture_steps, quality and background enums, params map for backend-specific extras) - pkg/grpc: thread Generate3D through client, server, embed, base and the backend interfaces; connection-evicting and distributed-node wrappers (in-flight tracking + file staging) included - core/config: FLAG_3D usecase (guessed only for the trellis2cpp backend), '3d' canonical usecase string mapped to the Generate3D method, and a '3d' output modality - REST: POST /v1/3d/generations (+ unversioned alias) returning OpenAIResponse with a /generated-3d URL or b64_json; conditioning image accepted as URL, base64, or data URI; quality/background validated at the edge; .glb served as model/gltf-binary - auth: '3d' route feature (default ON); /api/instructions entry Assisted-by: Claude:claude-fable-5 [Claude Code] Signed-off-by: Richard Palethorpe <io@richiejp.com> * feat(trellis2cpp): add the trellis2.cpp image-to-3D backend Wraps localai-org/trellis2cpp (C++/GGML port of Microsoft TRELLIS.2, pbr-textures branch) as a Go+purego backend, following the stablediffusion-ggml pattern: - backend/go/trellis2cpp: purego bindings to the flat C ABI (v9, asserted at startup), eager pipeline load with model-set validation (refuses non-trellis GGUFs; degrades coarse/geometry-only/textured exactly like the upstream demo), Generate3D via t2_generate + t2_bake_glb writing a binary glTF to dst. Weight-free unit tests cover resolution/validation/param mapping — CI never downloads the multi-GB GGUF set or runs inference. - CPU SIMD variants build into per-variant directories (the shared libggml sonames collide across variants, unlike sd-ggml's flat renamed-.so scheme); run.sh picks one via /proc/cpuinfo. - CI wiring: backend-matrix entries (cpu, cuda12/13, vulkan amd64+arm64, l4t, l4t-cuda13, darwin metal), index.yaml meta + latest/master image entries, bump_deps tracking of the pbr-textures branch, changed-backends.js mapping, top-level Makefile targets. - Importer: auto-detects trellis GGUF repos/URIs (registered before llama-cpp so the .gguf match isn't stolen) and expands any trellis URI to the full 10-file component set spanning the three LocalAI-io HF repos. - Gallery: trellis2-4b (full PBR + 1024 cascade) and trellis2-4b-geometry (512 untextured) with verified sha256s. Assisted-by: Claude:claude-fable-5 [Claude Code] Signed-off-by: Richard Palethorpe <io@richiejp.com> * feat(ui): 3D generation page with native GLB viewer and IndexedDB history Adds a Studio tab + /app/3d page for the new image-to-3D endpoint: - GlbViewer ports the trellis2cpp demo's dependency-free WebGL2 renderer (quaternion trackball, metallic-roughness PBR, ACES, hidden-line wireframe with a bounded index budget) and pairs it with a minimal GLB parser for the two forms t2_bake_glb emits — dense vertex-PBR (linear COLOR_0 + _METALLIC_ROUGHNESS, uploaded as normalized integers) and the opt-in UV-atlas textured form. Parsing happens before any GL so stats and errors render without WebGL2. - use3DHistory stores past generations (params, input thumbnail, and the GLB blob itself) in IndexedDB with keep-newest-20 eviction — GLBs are multi-MB binaries localStorage can't hold — and the page offers a download button for the active GLB. - Wiring: CAP_3D capability constant (FLAG_3D — the exact string /api/models/capabilities serves), threeDApi, router entries, Studio tab, vite dev proxy, en locale keys. - e2e: render-smoke entry plus a focused spec that feeds a real one-triangle vertex-PBR GLB through the parser/viewer and exercises IndexedDB persistence, selection, deletion, and API errors. Assisted-by: Claude:claude-fable-5 [Claude Code] Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(3d): address API correctness and UX issues Keep 3D generation on the LocalAI-specific /3d/generations route and ensure authentication and permissions cover it. Propagate distributed transfer failures, publish a portable ARM64 backend image, honor importer overrides, and align discovery, upload validation, and touch controls. Assisted-by: Codex:gpt-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * feat(3d): add previewable print remeshing Add a single-detail CGAL Alpha Wrap workflow for existing Trellis GLBs, including PBR reprojection, API documentation, tracing, and an in-browser preview before download. Allow the remesh route to enforce its 512 MiB upload cap independently of the smaller global default so generated high-resolution meshes can be processed. Assisted-by: Codex:gpt-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * build(trellis2cpp): centralize remesh dependency pins Assisted-by: Codex:GPT-5 [apply_patch] [exec_command] Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(kokoros): implement Generate3D stub for new proto RPC The Generate3D RPC added to backend.proto for the trellis2cpp backend made tonic's generated Backend trait require generate3_d, breaking the kokoros-grpc build. Return unimplemented like the other unsupported modalities. Assisted-by: Claude Code:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> --------- Signed-off-by: Richard Palethorpe <io@richiejp.com> Co-authored-by: localai-org-maint-bot <bot-opensource@localaisrl.com>
150 lines
6.4 KiB
Go
150 lines
6.4 KiB
Go
package config
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import (
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. "github.com/onsi/ginkgo/v2"
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. "github.com/onsi/gomega"
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)
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func usecaseBits(flags ModelConfigUsecase) *ModelConfigUsecase {
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return &flags
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}
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var _ = Describe("Model capabilities derivation", func() {
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Describe("VisionSupported", func() {
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It("is false for a plain text chat model", func() {
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cfg := &ModelConfig{KnownUsecases: usecaseBits(FLAG_CHAT), Backend: "llama.cpp"}
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Expect(cfg.VisionSupported()).To(BeFalse())
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})
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It("is true when the FLAG_VISION bit is declared", func() {
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cfg := &ModelConfig{KnownUsecases: usecaseBits(FLAG_CHAT | FLAG_VISION), Backend: "llama.cpp"}
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Expect(cfg.VisionSupported()).To(BeTrue())
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})
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It("is true when image input is declared explicitly", func() {
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cfg := &ModelConfig{
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KnownUsecases: usecaseBits(FLAG_CHAT),
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KnownInputModalities: []string{ModalityText, ModalityImage},
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}
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Expect(cfg.VisionSupported()).To(BeTrue())
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})
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It("is true when an mmproj projector is set", func() {
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cfg := &ModelConfig{KnownUsecases: usecaseBits(FLAG_CHAT), Backend: "llama.cpp"}
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cfg.MMProj = "mmproj.gguf" // promoted field from the embedded options struct
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Expect(cfg.VisionSupported()).To(BeTrue())
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})
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It("does not fall for the GuessUsecases FLAG_VISION false positive", func() {
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// A chat model with a chat template would make HasUsecases(FLAG_VISION)
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// return true via the guess heuristic; VisionSupported must not.
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cfg := &ModelConfig{Backend: "llama.cpp"}
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cfg.TemplateConfig.Chat = "{{.Input}}"
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Expect(cfg.VisionSupported()).To(BeFalse())
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})
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})
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Describe("AudioInputSupported / VideoInputSupported", func() {
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It("honors explicit model modality declarations", func() {
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cfg := &ModelConfig{
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KnownInputModalities: []string{ModalityAudio, ModalityVideo},
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}
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Expect(cfg.AudioInputSupported()).To(BeTrue())
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Expect(cfg.VideoInputSupported()).To(BeTrue())
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})
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It("detects vLLM omni audio input via limit_mm_per_prompt", func() {
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cfg := &ModelConfig{KnownUsecases: usecaseBits(FLAG_CHAT), Backend: "vllm"}
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cfg.LimitMMPerPrompt.LimitAudioPerPrompt = 1
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Expect(cfg.AudioInputSupported()).To(BeTrue())
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Expect(cfg.VideoInputSupported()).To(BeFalse())
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})
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It("detects vLLM omni video input via limit_mm_per_prompt", func() {
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cfg := &ModelConfig{KnownUsecases: usecaseBits(FLAG_CHAT), Backend: "vllm"}
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cfg.LimitMMPerPrompt.LimitVideoPerPrompt = 2
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Expect(cfg.VideoInputSupported()).To(BeTrue())
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})
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})
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Describe("Capabilities + modalities", func() {
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It("a text-only chat model exposes chat and text-only modalities", func() {
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cfg := &ModelConfig{KnownUsecases: usecaseBits(FLAG_CHAT), Backend: "llama.cpp"}
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Expect(cfg.Capabilities()).To(ContainElement(UsecaseChat))
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Expect(cfg.Capabilities()).NotTo(ContainElement(UsecaseVision))
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Expect(cfg.Capabilities()).NotTo(ContainElement(UsecaseTranscript))
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Expect(cfg.InputModalities()).To(Equal([]string{"text"}))
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Expect(cfg.OutputModalities()).To(Equal([]string{"text"}))
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})
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It("a vision chat model accepts text+image input", func() {
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cfg := &ModelConfig{KnownUsecases: usecaseBits(FLAG_CHAT | FLAG_VISION), Backend: "llama.cpp"}
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Expect(cfg.Capabilities()).To(ContainElements(UsecaseChat, UsecaseVision))
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Expect(cfg.InputModalities()).To(Equal([]string{"text", "image"}))
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Expect(cfg.OutputModalities()).To(Equal([]string{"text"}))
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})
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It("an omni chat model accepts text+audio input without an audio capability flag", func() {
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cfg := &ModelConfig{KnownUsecases: usecaseBits(FLAG_CHAT), Backend: "vllm"}
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cfg.LimitMMPerPrompt.LimitAudioPerPrompt = 1
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// audio-in is a modality, not a usecase string — this is exactly the
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// case a plain capability list cannot express.
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Expect(cfg.Capabilities()).To(ContainElement(UsecaseChat))
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Expect(cfg.InputModalities()).To(Equal([]string{"text", "audio"}))
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})
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It("a transcription model reads audio and writes text", func() {
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cfg := &ModelConfig{KnownUsecases: usecaseBits(FLAG_TRANSCRIPT), Backend: "parakeet-cpp"}
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Expect(cfg.Capabilities()).To(Equal([]string{UsecaseTranscript}))
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Expect(cfg.InputModalities()).To(Equal([]string{"audio"}))
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Expect(cfg.OutputModalities()).To(Equal([]string{"text"}))
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})
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It("an image-generation model reads text and writes an image", func() {
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// stablediffusion-ggml is image-only; plain "stablediffusion" is also
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// in GuessUsecases' video-backend list, so it would report video too.
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cfg := &ModelConfig{KnownUsecases: usecaseBits(FLAG_IMAGE), Backend: "stablediffusion-ggml"}
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Expect(cfg.Capabilities()).To(Equal([]string{UsecaseImage}))
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Expect(cfg.InputModalities()).To(Equal([]string{"text"}))
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Expect(cfg.OutputModalities()).To(Equal([]string{"image"}))
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})
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It("guesses the 3d usecase from the trellis2cpp backend and only that backend", func() {
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cfg := &ModelConfig{Backend: "trellis2cpp"}
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Expect(cfg.HasUsecases(FLAG_3D)).To(BeTrue())
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Expect(cfg.Capabilities()).To(ContainElement(Usecase3D))
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other := &ModelConfig{Backend: "llama-cpp"}
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Expect(other.HasUsecases(FLAG_3D)).To(BeFalse())
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})
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It("a 3D-generation model reads an image and writes a 3D asset", func() {
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// Pins the wire strings the UI depends on: capability "3d",
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// input modality "image" (no text prompt — TRELLIS.2 is
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// image-conditioned only), output modality "3d".
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cfg := &ModelConfig{KnownUsecases: usecaseBits(FLAG_3D), Backend: "trellis2cpp"}
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Expect(cfg.Capabilities()).To(Equal([]string{Usecase3D}))
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Expect(cfg.InputModalities()).To(Equal([]string{ModalityImage}))
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Expect(cfg.OutputModalities()).To(Equal([]string{Modality3D}))
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})
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It("conditioned video uses declared modalities without backend-specific inference", func() {
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cfg := &ModelConfig{
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KnownUsecases: usecaseBits(FLAG_VIDEO),
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KnownInputModalities: []string{ModalityAudio, ModalityImage, ModalityText, ModalityAudio, "unknown"},
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KnownOutputModalities: []string{ModalityVideo},
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}
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Expect(cfg.Capabilities()).To(Equal([]string{UsecaseVideo}))
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Expect(cfg.InputModalities()).To(Equal([]string{ModalityText, ModalityImage, ModalityAudio}))
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Expect(cfg.OutputModalities()).To(Equal([]string{ModalityVideo}))
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})
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It("a TTS model reads text and writes audio", func() {
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cfg := &ModelConfig{KnownUsecases: usecaseBits(FLAG_TTS), Backend: "piper"}
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Expect(cfg.Capabilities()).To(ContainElement(UsecaseTTS))
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Expect(cfg.InputModalities()).To(Equal([]string{"text"}))
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Expect(cfg.OutputModalities()).To(Equal([]string{"audio"}))
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})
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})
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})
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