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88726f2da44254e24afb2c244eba492dbca2a3e4
844 Commits
| Author | SHA1 | Message | Date | |
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LocalAI [bot]
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88726f2da4 |
fix(react-ui): restore sidebar collapse in dev + stop Talk page auto-scroll (#10383)
The sidebar collapse toggle silently no-op'd in dev builds. toggleCollapse ran its side effects (localStorage write + sidebar-collapse dispatch) inside the setCollapsed updater. StrictMode double-invokes updaters in dev to surface impurity, and the synchronous dispatch re-entered setState from the App/Sidebar listeners mid-update, so the toggle never committed. Production builds don't double-invoke, which is why only the dev server was affected. Compute next from current state and move the persist + broadcast into the handler body so the updater is pure. Also fix the Talk page anchoring to the transcript box on load. The transcript is its own overflow container, but scrollIntoView bubbles to every scrollable ancestor including the window, yanking the whole page down on mount. Scroll the transcript container directly instead. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
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5ac864dbed |
feat(ui): console-based navigation + drop-in API endpoint section (#10377)
* feat(ui): restructure sidebar into Create/Recognition/Build tiers Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(ui): preserve exact sidebar gating for agent items and fine-tune/quantize Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * i18n(ui): add nav tier + console keys to all locales Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ui): add grouped admin console via pathless layout route Wrap the existing admin pages in a pathless AdminConsoleLayout route so they keep their exact flat URLs while gaining a grouped left rail (Inference / Cluster / Observability / Access / System). Rail item gating mirrors the sidebar (adminOnly / authOnly / feature + /api/features). The layout forwards the App-level outlet context (addToast) to the wrapped pages, which read it via useOutletContext(). Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ui): fold Audio Transform into Studio as a tab Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(ui): update e2e specs for tiered nav + admin console Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(ui): gate embedded Studio transform view on audio_transform feature Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ui): visual polish + console-ize Build/Recognition tiers Generalize the one-off admin console into a reusable ConsoleLayout driven by a shared consoleConfig (single source of truth for the rail, its gating, and the sidebar entry that opens it — removes the prior rail/sidebar drift). - Promote Install Models to the top menu next to Home. - Build and Operate are now console tiers (secondary rail); Create stays inline. - Fold Recognition (Faces/Voices) into the Build console as a group alongside Automation and Training so it no longer feels split off. - Style the console rail as a panel (header, grouped dividers, rounded active pills) with a hover nudge; sidebar items become inset rounded pills. The rail slide-in plays only when entering a console, not on item-to-item sub-nav (which remounts the layout), so switching no longer flashes the menu. All token-based (light + dark), respects reduced-motion. - Add a delayed RouteFallback loader so lazy routes no longer flash blank; scoped inside ConsoleLayout so the rail stays put while the body loads. - Update e2e specs for the new structure (.console-* classes, console entries). Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ui): persist console layout across sub-nav + add drop-in endpoint section - Keep the page-transition key stable within a console (derived from the shared console config) so the ConsoleLayout and its rail persist across item-to-item navigation instead of remounting — fixes the submenu flash. Cache /api/features across mounts and play the rail entrance animation only when actually entering a console. - Add a "One endpoint, every API" section to Home: leads with LocalAI's own native API (images, video, realtime voice over WebRTC/WS, depth, object detection, rerank, audio/TTS, face & voice recognition) plus a Full API reference link, then the drop-in compatibility layer (OpenAI, Anthropic, Ollama, OpenAI Responses) with the live copyable base URL. All 7 locales. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(ui): revert Middleware nav label rename (keep Middleware in all locales) 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> |
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LocalAI [bot]
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294170d3ed |
feat(backend): add depth-anything (Depth Anything 3) C++/ggml backend + gallery (#10352)
* feat(backend): add depth-anything (Depth Anything 3) C++/ggml backend + gallery Mirrors the locate-anything-cpp backend to register a new depth-anything backend that wraps the Depth Anything 3 ggml port (depth-anything.cpp) via purego (cgo-less, no Python at inference). - backend/go/depth-anything-cpp/: gRPC backend (Load + Predict + GenerateImage), purego binding to the da_capi_* C ABI, CMake/Makefile/run/package/test scripts building depth-anything.cpp's DA_SHARED static .so per CPU variant. - backend/index.yaml: depth-anything backend meta + all hardware-variant capability entries (cpu/cuda12/cuda13/intel-sycl-f32+f16/vulkan/nvidia-l4t). - gallery/index.yaml: 8 Depth Anything 3 GGUF models (base q4_k/q8_0/f16/f32, small, large, giant, mono-large). - .github/backend-matrix.yml: one build entry per hardware variant. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(depth): typed Depth RPC + REST endpoint exposing full DA3 data Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(depth): pin depth-anything.cpp to e0b6814 (ABI 3 dense C-API) The Depth RPC handler calls da_capi_depth_dense / da_capi_points (C-API ABI 3); pin the native build to the commit that exports them. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(depth): pin depth-anything.cpp to v0.1.0 release (b515c31) Repoint the native version from the now-orphaned e0b6814 to the b515c31 release commit, kept alive by the upstream v0.1.0 tag. C-API is unchanged (da_capi_abi_version == 3). Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(depth): wire depth-anything-cpp into build, CI bump, and importer The backend dir, gallery index, and CI build-matrix were present but the backend was never wired into the integration points that adding-backends.md requires: - root Makefile: add to .NOTPARALLEL, the test-extra chain, a BACKEND_* definition, the docker-build target eval, and docker-build-backends (mirrors parakeet-cpp; the backend's own Makefile already documented that its `test` target is driven by test-extra). - bump_deps.yaml: register the DEPTHANYTHING_VERSION pin so the daily auto-bump bot tracks mudler/depth-anything.cpp master (it cannot see an unregistered Makefile pin). - import form: add a preference-only KnownBackend entry so depth-anything is selectable at /import-model (mirrors sam3-cpp; no reliable GGUF auto-detect signal, so pref-only per the doc's default). changed-backends.js needs no entry: the generic golang suffix branch already resolves backend/go/depth-anything-cpp/. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(depth): auto-detect importer for depth-anything GGUFs Replace the preference-only entry with a real auto-detect importer (mirrors parakeet-cpp / locate-anything): - DepthAnythingImporter matches a .gguf whose name carries a depth-anything token (depth-anything-<size>-<quant>.gguf), so /import-model recognises mudler/depth-anything.cpp-gguf repos and direct GGUF URLs without an explicit backend preference. preferences.backend= "depth-anything" still forces it. - Registered before LlamaCPPImporter so its GGUF bundles aren't claimed by the generic .gguf importer; the narrow name match means it cannot claim arbitrary llama GGUFs or the upstream safetensors PyTorch repos. - Multi-quant repos pick the smallest quant by default (q4_k -> ... -> f32, depth stays >0.998 corr even at q4_k); quantizations preference overrides. - Drops the now-redundant knownPrefOnlyBackends entry (importer-backed backends are not listed there, matching parakeet-cpp). - Table-driven Ginkgo test covers detection, negative cases (llama GGUF, upstream safetensors), default/override/fallback quant pick, and direct URL import. 10/10 specs pass. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(depth): check conn.Close error in grpc Depth client (errcheck) The new Depth() client method used a bare `defer conn.Close()`. golangci-lint runs with new-from-merge-base, so although the 39 sibling methods use the same bare form (grandfathered), the newly added line trips errcheck. Drop the result explicitly to satisfy the linter. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 * fix(depth): bump depth-anything.cpp to v0.1.1 (embeddable CMake) v0.1.0 (b515c31) used ${CMAKE_SOURCE_DIR} for its include dirs, which points at the parent project when built via add_subdirectory() as this backend does, so the container build failed with missing stb_image.h / da_gguf_keys.h. v0.1.1 (2d42897) switches to project-relative paths. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 * fix(depth): resolve gosec findings in the backend wrapper The code-scanning gate flagged three new failure-level alerts in godepthanythingcpp.go (gosec runs with -no-fail; GitHub gates on new alerts): - G301: export dirs were created with 0o755. Tighten to 0o750 (no world access needed for backend-written export output). - G304: writeDepthPNG creates req.GetDst(). That path is chosen by the LocalAI core as the intended output destination (same pattern every image backend uses), not attacker input, so annotate with #nosec G304 and document why. The remaining G103 "audit unsafe" notes on the unsafe.Slice C-buffer copies are warning-level (the same purego interop whisper/parakeet use) and do not gate the check, per the supertonic exclusion precedent in secscan.yaml. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 * fix(depth): bump depth-anything.cpp to v0.1.2 (CUDA cross-build arch) v0.1.1 forced CMAKE_CUDA_ARCHITECTURES=native, which breaks the GPU-less l4t/cublas CI builds (nvcc "Unsupported gpu architecture 'compute_'" on CMake 3.22). v0.1.2 (442eea4) drops the override and lets ggml pick its default cross-build arch list. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
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1ab61a0875 |
feat: generic chat_template_kwargs (model config + per-request metadata) (#10359)
* feat(config): add chat_template_kwargs model field + resolver Adds the ChatTemplateKwargs model-config map and RequestMetadata carrier, plus ResolveChatTemplateKwargs which layers the config map under coerced request metadata. Foundation for generic jinja chat-template kwargs (issue #10329). Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(backend): forward resolved chat_template_kwargs blob to backends gRPCPredictOpts now merges per-request client metadata over the server-derived enable_thinking/reasoning_effort (reaching all backends via the standalone keys) and serialises the resolved chat_template_kwargs map into a JSON blob for llama.cpp, written last so a client cannot clobber it. Issue #10329. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(http): wire request metadata to config.RequestMetadata The OpenAI request metadata field was parsed but unused; stamp it onto the per-request ModelConfig so gRPCPredictOpts forwards it as chat_template_kwargs overrides. Issue #10329. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(llama-cpp): generic chat_template_kwargs merge (drop per-key blocks) Replace the per-key enable_thinking/reasoning_effort handling in both the streaming and non-streaming chat paths with a single block that parses the chat_template_kwargs JSON blob resolved by the Go layer and merges every key into body_json. New jinja template levers (e.g. preserve_thinking) now need no C++ change. Issue #10329. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: document custom chat_template_kwargs (model + per-request) Issue #10329. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(backend): pin reasoning_effort as a string in the chat_template_kwargs blob Issue #10329. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(http): e2e guard pinning chat_template_kwargs forwarded to gRPC Adds an ECHO_PREDICT_METADATA marker to the mock-backend that echoes the received PredictOptions.Metadata, and an app_test.go spec that drives a real /v1/chat/completions request (model chat_template_kwargs + per-request metadata override) and asserts the exact metadata + chat_template_kwargs blob the REST layer forwards to gRPC. Locks the REST->gRPC contract against regressions. Issue #10329. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(config): grandfather chat_template_kwargs in registry coverage chat_template_kwargs is a free-form map[string]any (like engine_args, already on the list), not a scalar the config UI registry can surface, so it is exempt from the registry-entry requirement. Fixes the TestAllFieldsHaveRegistryEntries failure introduced by the new field. Issue #10329. Assisted-by: Claude:claude-opus-4-8 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> |
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github-actions[bot]
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416f871bea |
chore: bump inference defaults from unsloth (#10358)
Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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Dedy F. Setyawan
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9ba8521e7e |
feat(react-ui): localize models and fix 'Import' typo (#10341)
* feat(react-ui): localize SearchableSelect component Signed-off-by: Dedy F. Setyawan <dedyfajars@gmail.com> * feat(react-ui): localize ModelSelector component Signed-off-by: Dedy F. Setyawan <dedyfajars@gmail.com> * fix(react-ui): dynamically localize back navigation caption to match page title Signed-off-by: Dedy F. Setyawan <dedyfajars@gmail.com> * feat(react-ui): localize back navigation state on Models page Signed-off-by: Dedy F. Setyawan <dedyfajars@gmail.com> * feat(react-ui): localize ModelEditor page Signed-off-by: Dedy F. Setyawan <dedyfajars@gmail.com> * fix(react-ui): fix Indonesian typo 'Import' to 'Impor' in importModel locale Signed-off-by: Dedy F. Setyawan <dedyfajars@gmail.com> --------- Signed-off-by: Dedy F. Setyawan <dedyfajars@gmail.com> Co-authored-by: Ettore Di Giacinto <mudler@users.noreply.github.com> |
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LocalAI [bot]
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2df2876db2 |
feat(supertonic): add Supertonic ONNX TTS backend (CPU) (#10342)
* feat(supertonic): vendor upstream Go TTS pipeline (helper.go) Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(supertonic): add gRPC backend (Load/TTS/TTSStream, CPU) Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(supertonic): satisfy unused linter (use onnxProvider; exclude vendored helper.go) Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(supertonic): unit tests for resolvers + gated end-to-end synthesis Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * style(supertonic): gofmt backend.go comment block Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(supertonic): add Makefile, run.sh, package.sh (CPU build) Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * build(supertonic): wire backend into root Makefile Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(supertonic): check ort.DestroyEnvironment return (errcheck) Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(supertonic): resolve voice_styles as sibling of onnx dir; guard trim; test voice Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(supertonic): add CPU build matrix + gallery index entries Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(supertonic): expose as pref-only importable backend Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(supertonic): add Supertonic/supertonic-3 TTS model to the gallery 16 files (4 onnx + tts.json + unicode_indexer.json + 10 voice styles) from HF Supertone/supertonic-3, served via the supertonic backend. Defaults to voice F1; onnx/ + sibling voice_styles/ layout matches the backend's resolveVoicesDir. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(meta): register pipeline.max_history_items config field Pre-existing on master: the field was added without a registry entry, failing TestAllFieldsHaveRegistryEntries (core/config/meta). Add the entry so it renders properly in the model-config UI. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci(secscan): exclude vendored supertonic backend from gosec helper.go is vendored from supertone-inc/supertonic; its G304/G404/G104 findings are inherent to upstream and the math/rand use is correct for flow-matching noise (crypto/rand would be wrong). Assisted-by: Claude:claude-opus-4-8 [Claude Code] 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> |
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LocalAI [bot]
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7d2a762b53 |
feat(realtime): configurable pipeline.max_history_items (#10331)
Composed realtime pipelines (VAD+STT+LLM+TTS) defaulted to unlimited history, so a long-running session grew every turn and fed the whole conversation to the LLM until its context window filled. Add an optional pipeline.max_history_items to cap the trailing items per turn; explicit value (including 0=unlimited) wins over the per-model-type default. Self-contained any-to-any models keep their 6-item default. Co-authored-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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LocalAI [bot]
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fdc352a618 |
fix(settings): start watchdog on cold-enable from the React UI (#9125) (#10287)
fix(watchdog): start the live watchdog on a cold enable from Settings (#9125) The React Settings "Enable Watchdog" master toggle only ever writes the idle/busy flags; watchdog_enabled is vestigial in that UI. The live start/stop decision in UpdateSettingsEndpoint keyed off the raw, stale watchdog_enabled request field, so a cold enable (idle/busy=true, watchdog_enabled=false) called StopWatchdog() and the watchdog stayed stopped until the next restart - at which point startup re-derived it from the idle flag. Net: enabling the watchdog appeared to do nothing. Derive the run-state from idle||busy as the single source of truth, mirroring the startup invariant: - ApplyRuntimeSettings now sets WatchDog = idle||busy whenever either field is present (so a full disable also brings it down), while an API client posting only watchdog_enabled keeps its explicit value. - Add ApplicationConfig.WatchdogShouldRun() mirroring startWatchdog's gating (idle/busy, LRU eviction, memory reclaimer); the /api/settings handler uses it to decide start vs stop. - Belt-and-suspenders: the Settings.jsx master toggle also writes watchdog_enabled = idle||busy. Assisted-by: claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
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e5c95e0449 |
fix(distributed): stage backend companion assets to remote nodes (#10330)
A model whose ModelFile is a single file (e.g. sherpa-onnx VITS/piper: the .onnx) failed to load on remote worker nodes because the sibling assets the backend resolves from the model dir — tokens.txt, lexicon.txt, the espeak-ng-data / dict directories, Kokoro's voices.bin — were never staged. Only the declared ModelFile was shipped, so the worker hit "failed to create sherpa-onnx TTS engine" and TTS produced no audio. Lean on the existing option-path staging instead of hardcoding filenames: - stageGenericOptions now also resolves an option value relative to the model's own directory (not just the frontend models dir), so a shared config can declare companions with bare names regardless of whether Model includes a subdirectory; and it expands directory-valued options (e.g. espeak-ng-data) file-by-file rather than handing a directory fd to the stager. - gallery/sherpa-onnx-tts.yaml declares the companion assets as option paths (tokens, lexicon, espeak-ng-data, voices.bin, dict, per-lang lexicons). The backend ignores these keys and keeps resolving siblings from the model dir; they exist only so distributed staging ships them. Absent files are skipped. Adds router_optionstage_test.go covering file + directory companion staging via the model-dir fallback. Co-authored-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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LocalAI [bot]
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4ec6e3221e |
feat(realtime): gate realtime pipeline voice models behind voice recognition (#10319)
* feat(realtime): add pipeline voice_recognition gate config schema Add the PipelineVoiceRecognition config block that gates a realtime pipeline behind speaker verification (identify against the voice registry, or verify against reference audios), with Normalize defaults and Validate enum/shape checks. Register the new fields in the config meta registry so the UI renders them with proper labels/components (required by the registry-coverage gate). Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:opus-4.8 [Claude Code] * fix(realtime): range-check voice gate threshold and floor UI min Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:opus-4.8 [Claude Code] * feat(realtime): add cosineDistance helper for voice gate Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:opus-4.8 [Claude Code] * feat(realtime): add voiceGate identify-mode authorization Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:opus-4.8 [Claude Code] * test(realtime): cover voice gate fail-closed error paths Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:opus-4.8 [Claude Code] * feat(realtime): add voiceGate verify-mode authorization Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:opus-4.8 [Claude Code] * feat(realtime): add voiceGate decide policy helper Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:opus-4.8 [Claude Code] * feat(realtime): add newVoiceGate constructor Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:opus-4.8 [Claude Code] * feat(realtime): gate pipeline responses behind voice recognition Run speaker verification concurrently with transcription and join on a hard barrier before generateResponse, so unauthorized utterances never reach the LLM, tools, or TTS. Supports identify (registry) and verify (reference) modes with multiple authorized speakers, per-utterance or first-utterance checking, and drop-with-event or silent-drop on reject. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:opus-4.8 [Claude Code] * fix(realtime): harden voice gate goroutine lifecycle Only launch the verification goroutine on the transcription path and drain it before the temp WAV is removed on the transcription-error return, so an in-flight backend read never races the deferred cleanup. Drop the write-only voiceMatched field; log the matched speaker instead. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:opus-4.8 [Claude Code] * docs(realtime): document the voice_recognition pipeline gate Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:opus-4.8 [Claude Code] * fix(realtime): fail closed on an incomplete voice_recognition block A present voice_recognition block with no model previously disabled the gate silently, authorizing every speaker. Treat block presence as the intent signal and reject an empty model in Validate, so the session is refused instead of running unprotected. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:opus-4.8 [Claude Code] * test(realtime): integration-test the voice gate through commitUtterance Drive the real commitUtterance path (gate goroutine, hard join before the LLM, reject event, when:first session trust) with the existing transport/model doubles: authorized speakers reach a full response, unauthorized ones are dropped before the LLM with a speaker_not_authorized event, backend errors fail closed, drop_silent stays quiet, and when:first trusts the session after one match. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:opus-4.8 [Claude Code] --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
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4bb592cf91 |
feat(qwen3-tts-cpp): migrate to ServeurpersoCom/qwentts.cpp (streaming, speakers, voice design) (#10316)
* feat(qwen3-tts-cpp): repoint upstream to ServeurpersoCom/qwentts.cpp Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(qwen3-tts-cpp): flatten qt_* ABI into qt3_* purego shim Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(qwen3-tts-cpp): build shim against upstream qwen-core static lib Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(qwen3-tts-cpp): add option/language/voice/sampling parsing Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(qwen3-tts-cpp): add 24kHz WAV encode/decode/stream-header helpers Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(qwen3-tts-cpp): purego backend with streaming, speakers, voice design Map TTSRequest onto qwentts.cpp: instructions->instruct, voice->named speaker or clone-reference path, params map->ref_text + sampling. Add TTSStream over the qt chunk callback. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * test(qwen3-tts-cpp): unit specs + build-gated TTS/TTSStream e2e Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * fix(qwen3-tts-cpp): close defensive PCM-free gap on zero-sample result Register CppPCMFree before the n<=0 guard so a non-null buffer with zero samples cannot leak (the C contract returns NULL on failure, so this is defensive). Raised in code review. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(qwen3-tts-cpp): advertise TTSStream capability Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * chore(qwen3-tts-cpp): update backend index metadata for qwentts.cpp Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(gallery): qwentts.cpp models - base/customvoice/voicedesign, Q8_0 & Q4_K_M Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * docs(qwen3-tts-cpp): release note for qwentts.cpp migration Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * test(qwen3-tts-cpp): cover audio_path voice-cloning fallback Add resolveRequest unit specs (config audio_path used as the clone reference when Voice is empty; per-request audio Voice overrides it; a named-speaker Voice does not trigger cloning) plus a real-inference e2e that clones from audio_path (confirmed ref_spk_emb=yes in the pipeline). Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * chore(qwen3-tts-cpp): drop the release-note doc Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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moduvoice
|
36b4a81d1e |
feat(i18n): add Korean (ko) translation (#10312)
Add a full Korean locale (core/http/react-ui/public/locales/ko/, 13 namespaces,
840 keys, full parity with en/) and register ko in SUPPORTED_LANGUAGES
(core/http/react-ui/src/i18n/index.js). All i18next {{interpolation}} and
_one/_other plural keys preserved; brand/model names kept untranslated.
Assisted-by: Claude:claude-opus-4-8
Signed-off-by: moduvoice <moduvoicr77@gmail.com>
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LocalAI [bot]
|
0854932a25 |
feat(omnivoice-cpp): add OmniVoice TTS backend (file + streaming, voice cloning + voice design) (#10310)
* feat(omnivoice-cpp): add C wrapper + CMake/Makefile build over OmniVoice ov_* ABI Assisted-by: claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(omnivoice-cpp): add option/language parsing + WAV framing helpers with tests Assisted-by: claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(omnivoice-cpp): wire purego binding with TTS + streaming TTSStream Assisted-by: claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * build(omnivoice-cpp): wire backend into root Makefile Assisted-by: claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci(omnivoice-cpp): add build matrix entries + dep-bump registration Assisted-by: claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(omnivoice-cpp): register backend meta + image entries Assisted-by: claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(omnivoice-cpp): expose as preference-only importable backend Assisted-by: claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(gallery): add omnivoice-cpp TTS models (Q8_0 default + BF16 HQ) Assisted-by: claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(omnivoice-cpp): document the OmniVoice TTS backend Assisted-by: claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(omnivoice-cpp): add env-gated e2e for TTS + streaming Assisted-by: claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(omnivoice-cpp): honor tts.audio_path/tts.voice config as default cloning reference The model config tts.audio_path (ModelOptions.AudioPath) and tts.voice now provide a default voice-cloning reference used when a request omits Voice, so a cloned voice can be pinned in the model YAML instead of passed per request. A per-request voice still overrides. Paths resolve relative to the model dir. Assisted-by: claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(omnivoice-cpp): add missing omnivoice-cpp-development backend meta Mirrors the whisper/vibevoice convention: a -development meta aggregating the master-tagged image variants (the production meta and per-variant prod+dev image entries already existed; only the development meta aggregator was missing). Assisted-by: claude:claude-opus-4-8 [Claude Code] 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> |
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LocalAI [bot]
|
7637f8cf1b |
feat(distributed): declarative per-model scheduling via env/args (#10308)
* feat(distributed): add SpreadAll column and authoritative scheduling seeding Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(distributed): parse declarative model scheduling config (env/file) Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(distributed): reconcile spread_all to one replica per matching node Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(distributed): wire LOCALAI_MODEL_SCHEDULING env/args and startup seeding Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(distributed): expose spread_all on the scheduling API endpoint Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(distributed): add spread-to-all-nodes mode to the scheduling UI Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(distributed): document LOCALAI_MODEL_SCHEDULING env/args Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(distributed): clarify replica modes and all-nodes spread in scheduling config 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> |
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LocalAI [bot]
|
e1556aa1dc |
fix(react-ui): make agent chat timestamps format-agnostic (#9867) (#10290)
fix(agents): make React agent chat timestamps format-agnostic The agent SSE bridge emits the json_message timestamp in three different encodings depending on deploy mode: an RFC3339 string (standalone agent pool), Unix milliseconds (local dispatcher), and Unix nanoseconds (the older NATS path). The React AgentChat handler passed data.timestamp straight through, so the standalone string and any numeric value outside the millisecond range rendered as "Invalid Timestamp" or a constant epoch-ish time. Add a small pure helper, normalizeTimestampMs, that accepts an RFC3339 string or a numeric epoch in s/ms/us/ns and returns JS milliseconds, falling back to Date.now() on null/empty/unparseable input. Use it in the json_message handler so the rendered time is correct regardless of which backend path produced it. Fixes #9867 Assisted-by: claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
|
99c8205740 |
fix(react-ui): stop Talk pipeline overflow and center collapsed-rail avatar (#10305)
Two small visual fixes in the React UI:
- Talk page pipeline summary: the four-column grid used
`repeat(4, 1fr)`, which resolves to `minmax(auto, 1fr)` so each track
refuses to shrink below the min-content width of its `nowrap` model
name. Long names (e.g. a verbose GGUF LLM id) blew the grid out past
the container despite the per-cell ellipsis styling. Switching to
`minmax(0, 1fr)` lets the tracks shrink and the ellipsis take effect.
- Sidebar user avatar: the desktop collapsed look centers the avatar via
`.sidebar.collapsed .sidebar-user{-link}` rules, but the tablet
icon-rail (640-1023px) collapses visually through `.sidebar:not(.open)`
without necessarily carrying the `.collapsed` class, so the avatar kept
its left-aligned negative margins and looked misaligned. Mirror the
centering rules under `.sidebar:not(.open)`.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
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LocalAI [bot]
|
a906438a69 |
fix(config): backend-gate the top_k=40 sampler default (#6632) (#10285)
fix(config): gate top_k=40 default on backend family (#6632) SetDefaults injected top_k=40 (llama.cpp's sampling default) for every model config regardless of backend. That value is wrong for backends whose native default differs: mlx_lm's intended default is top_k=0 (disabled) and mlx does not remap 0->40, so a client that omits top_k silently got 40 shipped to mlx, changing sampling. The mlx backend's own getattr(request,'TopK',0) fallback is dead because proto3 int32 is always present. Gate the injection on backend family via UsesLlamaSamplerDefaults: keep top_k=40 for the llama.cpp family and for the empty/auto backend (the GGUF auto-detect path resolves to llama.cpp, so existing behavior is preserved), but leave TopK nil for the known non-llama backends (mlx, mlx-vlm, mlx-distributed). gRPCPredictOpts now sends 0 when TopK is nil, which is the value mlx actually wants. Only TopK is gated - the confirmed bug. The sibling sampler defaults (top_p, temperature, min_p) are left global to avoid widening scope and introducing nil-deref risk; revisit per-backend if needed. Assisted-by: claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
|
edeacf22c4 |
fix(realtime): keep transcription model on a language-only session.update (#10295)
A transcription session.update that carries only a language (no model) — e.g. a client forcing the STT input language — has an empty Transcription.Model. updateSession unconditionally copied that into session.ModelConfig.Pipeline.Transcription, blanking the pipeline's configured transcription backend. The next utterance then transcribed against an empty model and the backend RPC failed with "unimplemented" (surfaced to the client as transcription_failed), so transcription silently stopped whenever a language was selected. Only adopt the incoming transcription model when it is non-empty, and preserve the existing model otherwise (mirroring updateTransSession). Signed-off-by: mudler <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com> |
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|
Aniruddh Jha
|
51f4f67c47 |
fix(agents): emit chat event timestamps in milliseconds (#9867) (#10243)
Agent chat replies rendered a broken timestamp in the web UI
("Invalid Timestamp" / "12:00 AM", identical for every reply) because
the SSE timestamp unit was inconsistent across producers.
EventBridge.PublishEvent emitted Unix nanoseconds while the local
dispatcher (dispatcher.go) already emitted Unix milliseconds, and the
React UI fed the value straight into `new Date(ts)` after dividing by
1e6. Nanoseconds also overflow JS's safe-integer range (~1.7e18).
Standardize on Unix milliseconds: switch PublishEvent to UnixMilli and
drop the /1e6 conversion in AgentChat.jsx so both SSE paths agree and
match the React UI's expectation. Add a regression test asserting the
published timestamp is in milliseconds.
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LocalAI [bot]
|
a7a7bd646b |
fix(mlx): route vision-language models to the mlx-vlm backend (#10274)
Vision-language checkpoints such as mlx-community/gemma-4-E4B-it-qat-4bit declare the "image-text-to-text" pipeline tag on HuggingFace. The mlx importer hardcoded backend "mlx" for every mlx-community model, so these VLMs were served by the text-only mlx-lm backend whose tokenizer does not carry the processor chat template. The template was never applied and the model produced degenerate, looping output that echoed the prompt. Detect the "image-text-to-text" pipeline tag in the importer and route those models to mlx-vlm, which applies the processor-aware chat template. An explicit backend preference still wins. As a defensive backstop, the mlx backend now warns loudly when the loaded model has no chat template, so a misrouted VLM surfaces the problem instead of silently looping. Fixes #10269 Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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Richard Palethorpe
|
085fc53bbc |
fix(router): production-ready request router + auto-size batch for embedding/rerank (#10104)
* fix(router): score classifier production-readiness Conversation trimming runs through the classifier model's chat template and trims by exact token count, sized to the model's n_batch which is now scaled to context so long probes can't crash the backend. Missing chat_message templates are a hard error at router build time. Router- facing factories (Embedder/Scorer/Reranker/TokenCounter) re-resolve ModelConfig per call so a model installed post-startup doesn't bind a stub Backend="" config and silently fall into the loader's auto- iterate path. New 'vector_store' backend trace recorded inside localVectorStore on every Search/Insert — including the backend-load-failure path that previously vanished into an xlog.Warn — with outcome tagging (hit/miss/empty_store/backend_load_error/find_error/insert_error/ok). Companion cleanup drops misleading similarity:0 and input_tokens_count:0 from non-hit and text-mode traces. Gallery local-store-development aliases to 'local-store' so the master image satisfies pkg/model.LocalStoreBackend lookups from the embedding cache. Misc: llama-cpp TokenizeString reads the correct 'prompt' JSON key (the original bug); ModelTokenize nil-guard; non-fatal mitm proxy startup; PII 'route_local' renamed to 'allow' with docs/UI in sync; model-editor footer no longer eats the edit area on small screens; several config-editor template/dropdown/section fixes. Tests: e2e router specs (casual/code-hint + long-conversation trim), vector_store trace specs, lazy-factory specs, gallery dev-alias resolution, Playwright trace badge + scroll regression. Assisted-by: Claude:claude-opus-4-7 [Claude Code] Signed-off-by: Richard Palethorpe <io@richiejp.com> * feat(backend): auto-size batch to context for embedding and rerank models Embedding and rerank models pool over the whole input in a single physical batch (n_ubatch). With batch left at the 512 default, the backend rejects longer inputs with "input is too large to process", silently capping a large-context embedder (e.g. 8k/32k) at 512 tokens. Size n_batch to the context for these single-pass usecases, mirroring the existing FLAG_SCORE behaviour; an explicit batch: still wins. Extracts EffectiveContextSize/EffectiveBatchSize from grpcModelOpts so the effective decode window has one home for other callers to reuse. Adds an e2e-aio regression test that embeds a >512-token input. The AIO embedding model is switched to nomic-embed-text-v1.5 (2048 context) because the previous granite model was capped at 512 tokens and could not exercise the larger batch. Assisted-by: claude-code:claude-opus-4-8 [Claude Code] Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(gallery): raise arch-router scoring output cap via parallel:64 Scoring decodes the whole prompt+candidate in a single llama_decode and reads one logit row per candidate token. The vendored llama.cpp server caps causal output rows at n_parallel, so the default of 1 aborts with GGML_ASSERT(n_outputs_max <= cparams.n_outputs_max) on multi-token route labels. Set options: [parallel:64] on both arch-router quant entries to lift the cap; kv_unified (the grpc-server default) keeps the full context per sequence, so this does not split the KV cache. Assisted-by: claude-code:claude-opus-4-8 [Claude Code] Signed-off-by: Richard Palethorpe <io@richiejp.com> --------- Signed-off-by: Richard Palethorpe <io@richiejp.com> |
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LocalAI [bot]
|
56cc4f63fc |
feat(backend): locate-anything-cpp (open-vocabulary object detection via ggml) (#10264)
* feat(backend): add locate-anything-cpp backend (open-vocab detection via la_capi) A Go/purego backend wrapping locate-anything.cpp's la_capi C ABI, implementing the gRPC Detect RPC: image + open-vocabulary text prompt -> labeled boxes. Mirrors backend/go/rfdetr-cpp; static-links ggml into a per-CPU-variant .so. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci(backend): register locate-anything-cpp in build matrix Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(gallery): locate-anything gallery entry + model importer Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(backend): locate-anything-cpp Load+Detect wire test Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(gallery): add locate-anything-3b model to the gallery index Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci(backend): register locate-anything.cpp in bump_deps auto-bump Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: mudler <mudler@localai.io> * ci(test): e2e smoke for locate-anything-cpp in test-extra (loads the 3B + image, runs Detect) Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: mudler <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Signed-off-by: mudler <mudler@localai.io> Co-authored-by: mudler <mudler@localai.io> |
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|
Dedy F. Setyawan
|
1cea96f09f |
feat(react-ui): add Indonesian language support (#10266)
Signed-off-by: Dedy F. Setyawan <dedyfajars@gmail.com> |
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LocalAI [bot]
|
892fc49949 |
feat(realtime): stream the LLM / TTS / transcription pipeline stages (#10176)
* feat(realtime): pipeline streaming + disable_thinking config
Add a nested pipeline.streaming.{llm,tts,transcription} block plus
pipeline.disable_thinking, with StreamLLM/StreamTTS/StreamTranscription/
ThinkingDisabled helpers. Pointer-bools so unset keeps the unary path;
existing configs are unaffected. Wiring into the realtime handler follows.
Assisted-by: Claude:claude-opus-4-8 go vet
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(realtime): sentence segmenter for streamed LLM->TTS pipelining
streamSegmenter accumulates streamed LLM tokens and emits complete
sentence/clause segments (terminator+whitespace, or newline) so TTS can
synthesize each segment as it completes instead of waiting for the whole
reply. Pure helper; the streaming handler wiring consumes it next.
Assisted-by: Claude:claude-opus-4-8 go vet
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(realtime): streaming TTS/transcription methods on Model interface
Add TTSStream and TranscribeStream to the realtime Model interface and
implement them on wrappedModel (delegating to backend.ModelTTSStream /
ModelTranscriptionStream) and transcriptOnlyModel. ttsStream adapts the
backend's WAV-framed stream (44-byte header carrying the sample rate, then
PCM) into raw PCM + sample rate for the realtime transports. Handler wiring
that consumes these (flag-gated) follows.
Assisted-by: Claude:claude-opus-4-8 go vet
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(realtime): emitSpeech with flag-gated streaming TTS
emitSpeech synthesizes a piece of text and forwards audio to the client,
streaming one output_audio.delta per backend PCM chunk when the pipeline
sets streaming.tts, or one delta for the whole utterance otherwise. WebRTC
gets raw PCM (it resamples internally); WebSocket gets base64 PCM at the
session rate. It emits no transcript/audio-done events so a streamed reply
can be split into multiple spoken segments sharing one response.
Adds fakeModel/fakeTransport test doubles for the realtime Model/Transport
interfaces, driving streaming assertions deterministically.
Assisted-by: Claude:claude-opus-4-8 go vet
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(realtime): route response audio through emitSpeech (streaming TTS)
Replace the inline unary TTS block in the response handler with emitSpeech,
which streams a response.output_audio.delta per backend PCM chunk when
pipeline.streaming.tts is set and otherwise preserves the single-delta unary
behaviour. emitSpeech returns the accumulated base64 audio, stored on the
conversation item as before. Transcript and audio-done events stay in the
handler so later per-segment streaming can reuse emitSpeech.
Assisted-by: Claude:claude-opus-4-8 go vet
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(realtime): streaming transcription text deltas
Add emitTranscription and route commitUtterance through it. With
pipeline.streaming.transcription set it streams each transcript fragment as
a conversation.item.input_audio_transcription.delta via TranscribeStream
then a completed event; otherwise it preserves the single completed-event
unary behaviour. Returns the final transcript for response generation.
Assisted-by: Claude:claude-opus-4-8 go vet
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(realtime): pipeline disable_thinking maps to enable_thinking off
applyPipelineThinking forces the LLM's ReasoningConfig.DisableReasoning when
pipeline.disable_thinking is set, which gRPCPredictOpts turns into the
enable_thinking=false backend metadata. Applied at newModel construction on
the per-session LLM config copy, so it doesn't leak to other model users and
needs no realtime-specific request plumbing.
Assisted-by: Claude:claude-opus-4-8 go vet
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(realtime): speechStreamer for token-streamed LLM->TTS
emitSpeech now returns raw PCM (caller base64-encodes) so streamed segments
accumulate correctly. speechStreamer consumes streamed LLM tokens: it strips
reasoning via the streaming ReasoningExtractor, emits a transcript delta per
content fragment, and sentence-pipes content into emitSpeech so each sentence
is synthesized as soon as it's ready. Handler wiring (plain-content turns)
follows.
Assisted-by: Claude:claude-opus-4-8 go vet
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(realtime): wire streamLLMResponse for token-streamed replies
triggerResponseAtTurn takes a streamed path when pipeline.streaming.llm is
set, the turn has no tools, and audio is requested: streamLLMResponse
announces the assistant item, drives the LLM token callback through a
speechStreamer (reasoning-stripped transcript deltas + sentence-piped TTS),
and emits the terminal events. Tool turns and non-streaming pipelines keep
the existing buffered path unchanged, so this is strictly opt-in.
Assisted-by: Claude:claude-opus-4-8 go vet
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* docs(realtime): document pipeline streaming + disable_thinking
Assisted-by: Claude:claude-opus-4-8
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(realtime): register pipeline streaming/thinking config fields
TestAllFieldsHaveRegistryEntries (core/config/meta) requires every config
field to have a meta registry entry. The four new pipeline fields
(disable_thinking, streaming.{llm,tts,transcription}) had none, failing
tests-linux/tests-apple. Add toggle entries for them.
Also handle the os.Remove return in realtime_speech_test.go to satisfy
errcheck (golangci-lint).
Assisted-by: Claude:claude-opus-4-8 go test, golangci-lint
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(realtime): always strip reasoning from spoken output
disable_thinking maps to ReasoningConfig.DisableReasoning=true on the LLM
config, which the backend reads as enable_thinking=false. But the realtime
handler reads that SAME config to drive reasoning extraction, and there
DisableReasoning=true means "skip stripping". PredictConfig() returns this
LLM config, so both the streamed (speechStreamer) and buffered realtime
paths stopped stripping <think>…</think> exactly when disable_thinking was
on — leaking raw reasoning to the client whenever the model ignored the
enable_thinking hint (e.g. lfm2.5).
Add spokenReasoningConfig() which clears DisableReasoning for extraction
(keeping custom tokens/tag pairs) and route both realtime paths through it.
Spoken output now always strips reasoning, independent of the backend
suppression hint.
Assisted-by: Claude:claude-opus-4-8 go test, golangci-lint
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(realtime): clean TTS temp path before read (gosec G304)
emitSpeech reads the WAV file the TTS backend wrote. The read moved here
from realtime.go, so code-scanning flagged it as a new G304 alert even
though the path is backend-controlled (a temp file), not user input.
Wrap it in filepath.Clean — a real path normalization that also clears
the alert, keeping with the repo's no-#nosec convention.
Assisted-by: Claude:claude-opus-4-8 gosec, golangci-lint
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactor(realtime): buffer whole message for TTS, drop sentence segmenter
Per review (richiejp): the sentence segmenter pipelined unary TTS by
splitting on ASCII .!?/newline, which does nothing for languages without
those boundaries (CJK/Thai) — there it already degraded to buffering the
whole message anyway.
Replace it with a uniform model: stream the LLM transcript live, buffer the
full message, then synthesize it once. emitSpeech already streams the audio
chunks when the backend implements TTSStream and falls back to a single
unary delta otherwise, so this is real streaming TTS where supported and a
clean whole-message synthesis elsewhere — no per-sentence emulation, no
language assumptions. speechStreamer becomes transcriptStreamer (transcript
deltas only); the whole-message synthesis moves into streamLLMResponse.
Assisted-by: Claude:claude-opus-4-8 go test, golangci-lint
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(realtime): stream tool-call turns via tokenizer-template autoparser
Per review (richiejp): tool-call deltas exist, so streaming should work with
tools too. It does — for models that use their tokenizer template. The C++
autoparser then clears reply.Message and delivers content + tool calls via
ChatDeltas, so the streamed transcript carries only spoken content (no
tool-call JSON leak) and the tool calls are parsed from the final response.
- Drop the len(tools)==0 gate; stream when no tools OR use_tokenizer_template
(grammar-based function calling still buffers, since its call is emitted as
JSON in the token stream and would leak into the transcript).
- streamLLMResponse takes tools/toolChoice/toolTurn, reads ChatDelta content
in the token callback, parses tool calls from the final ChatDeltas, and
creates the assistant content item lazily so a content-less tool turn emits
only the tool calls.
- Extract emitToolCallItems from the buffered path so both paths finalize tool
calls, response.done, and server-side assistant-tool follow-ups identically.
Assisted-by: Claude:claude-opus-4-8 go test, golangci-lint
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(realtime): script-aware clause chunking + streamed-reply fixes
Opt-in pipeline.streaming.clause_chunking splits the streamed LLM reply
into speakable clauses and synthesizes each as soon as it completes,
lowering time-to-first-audio instead of buffering the whole message. The
splitter is script-aware (rivo/uniseg, pure Go): UAX#29 sentence
segmentation handles CJK 。！？ with no whitespace, CJK clause
punctuation (，、；：) and Thai/Lao spaces give finer cuts, and a UAX#14
line-break cap bounds an over-long punctuation-less run. Unlike the old
ASCII .!?/newline segmenter (dropped in
|
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LocalAI [bot]
|
fba8c9c498 |
fix(distributed): track in-flight for non-LLM inference methods (VAD, diarize, voice, ...) (#10238)
fix(distributed): track in-flight for non-LLM inference methods InFlightTrackingClient only wrapped a subset of the grpc.Backend inference methods (Predict, Embeddings, TTS, AudioTranscription, Detect, Rerank, ...). Methods like VAD were left as embedded passthrough, so track() never ran for them. In distributed mode every model is loaded with in_flight=1 as a reservation; that reservation is only released by the OnFirstComplete callback, which fires after the first *tracked* inference call completes. A VAD-only model (e.g. silero-vad) never calls a tracked method, so the reservation is never released and in-flight stays pinned at 1 forever - which also blocks the router's idle-eviction logic. Wrap the remaining unary inference methods (VAD, Diarize, Face*, Voice*, TokenClassify, Score, AudioEncode, AudioDecode, AudioTransform) with the same track()/reconcile() pattern. The three bidi-stream constructors (AudioTransformStream, AudioToAudioStream, Forward) are deliberately left as passthrough - their inference spans the stream lifetime, not the constructor call, so track() there would fire onFirstComplete before any data flows. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
|
b203b32e57 |
feat(realtime): make WebRTC ICE candidates configurable (#10231)
The /v1/realtime WebRTC handler created the peer connection with a bare webrtc.Configuration and no SettingEngine, so pion gathered a host ICE candidate for every local interface. Under Docker host networking that includes bridge addresses (docker0/veth, 172.x) a remote browser cannot route to; the call establishes on a good pair and then drops once ICE consent freshness checks fail on the unreachable candidates. Add two opt-in knobs, applied via a pion SettingEngine: - LOCALAI_WEBRTC_NAT_1TO1_IPS: advertise these IPs as the host candidates (e.g. the host LAN IP) - LOCALAI_WEBRTC_ICE_INTERFACES: restrict ICE gathering to these interfaces Defaults are unchanged (empty => current all-interface behavior). Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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|
Ching
|
48a8ce98aa |
fix(cli): handle chat output errors (#10229)
Propagate terminal write errors from the chat prompt and explicitly ignore stream close errors during cleanup. Update chat tests to assert response writer errors so errcheck passes without hiding failed writes. Tests: - go test -count=1 ./core/cli/chat - go test -count=1 ./core/cli Assisted-by: Codex:GPT-5 Signed-off-by: Ching Kao <0980124jim@gmail.com> |
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|
Ching
|
8344d1c865 |
feat(cli): add interactive chat mode (#10226)
Add an opt-in `local-ai chat` command for testing chat models directly from the terminal without manually sending curl requests. The command connects to a running LocalAI server, lists available models through the existing OpenAI-compatible API, streams chat completions, and supports interactive commands such as `/models`, `/model`, `/clear`, and `/exit`. Keep `local-ai run` focused on the server lifecycle so the web UI, API clients, and multiple chat terminals can coexist against the same server. Document the new command and terminal workflow in the README and CLI docs. Tests: - go test -count=1 ./core/cli/chat - go test -count=1 ./core/cli Assisted-by: Codex:GPT-5 Signed-off-by: Ching Kao <0980124jim@gmail.com> |
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|
Pete
|
d2e6b93369 |
feat(agents): surface KB source citations in RAG responses (#10228)
* dev knowledge.go structure Signed-off-by: Pete Chen <petechentw@gmail.com> * feat(agents): append KB source citations to responses Render structured KB citations as a Sources block after agent responses, linking each source to the existing raw collection entry endpoint. Keep long-term memory writes on the original model response so citation blocks do not get stored back into the knowledge base. Tested with: go test ./core/services/agents Assisted-by: Codex:gpt-5 Signed-off-by: Pete Chen <petechentw@gmail.com> * Collect KB citations from tool searches Signed-off-by: Pete Chen <petechentw@gmail.com> * fix(agents): append KB sources in local chats Apply the shared KB citation post-processing to standalone LocalAGI chat responses so the React agent chat receives the same clickable Sources block as the native executor path. Also fix the run target to use the current cmd/local-ai entrypoint. Assisted-by: Codex:gpt-5 Signed-off-by: Pete Chen <petechentw@gmail.com> --------- Signed-off-by: Pete Chen <petechentw@gmail.com> Co-authored-by: shihyunhuang <shihyunhuang88@gmail.com> Co-authored-by: TLoE419 <tloemizuchizu@gmail.com> Co-authored-by: Ching Kao <0980124jim@gmail.com> |
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|
LocalAI [bot]
|
e1ec03d33f |
fix(reasoning): stop prefilled <think> from swallowing tag-less answers (#10225)
* fix(reasoning): stop prefilled <think> from swallowing tag-less answers When a chat template injects the thinking start token into the prompt (so DetectThinkingStartToken returns e.g. "<think>"), the model's output begins inside a reasoning block and carries only the closing tag. The non-jinja autoparser fallback (peg-native "pure content" mode, issue #9985) prepends the start token so the extractor can pair it with the model's </think>. But on a COMPLETE response that contains no closing tag, the model answered directly with no reasoning at all. Prepending the start token there manufactures an unclosed block that swallows the entire answer into reasoning, leaving the OpenAI `content` field empty. This breaks short/direct answers — session names, JSON summaries, any terse completion where the model skips the think block — which come back with empty content. Regression surfaced by #9991, which added the defensive prefill extraction to the complete-response paths. Add reasoning.ExtractReasoningComplete: it only honors a prefilled start token when the response actually contains the matching closing tag (proof a reasoning block exists). Genuine reasoning tags already in the content still extract; tag-less content stays content. Apply it at every complete-response site (applyAutoparserOverride, realtime, openresponses). The streaming per-token extractor is intentionally left on ExtractReasoningWithConfig — mid-stream an as-yet-unclosed block is legitimate and must surface as reasoning deltas. Also adds reasoning.ClosingTokenForStart and hoists the default reasoning tag pairs to package scope so both helpers share one source of truth. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * test(reasoning): cover the enable_thinking=false non-thinking-mode regression Adds the end-to-end case that actually broke session summaries / auto-titles and was not covered before: a request with enable_thinking=false against a <think>-capable model. In non-thinking mode the model emits no reasoning block, so llama.cpp's autoparser returns ChatDeltas with content set and reasoning_content empty (verified against stock llama-server: same model with chat_template_kwargs.enable_thinking=false returns reasoning_content=null, content="hello"). thinkingStartToken is still "<think>" because it is detected per-model from the enable_thinking=true render, so the old code prepended it and swallowed the answer. The test fails without the ExtractReasoningComplete gate. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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|
LocalAI [bot]
|
9323f4b5ca |
feat(llama-cpp): video input support (mtmd #24269) (#10216)
* chore(llama-cpp): bump to 8f83d6c for mtmd video input support Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(llama-cpp): forward video input to mtmd (template + non-template paths) Wire request->videos() into grpc-server.cpp mirroring the existing image and audio handling: a video_data build + non-template files extraction, and input_video chat chunks on the tokenizer-template path. allow_video is auto-set at model load by the vendored upstream chat_params. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ui): add video attachment support to the chat UI Mirror the image/audio attachment path for video: emit video_url content parts, accept video/* in the picker, keep video files as base64, show a film icon badge, and render attached video inline with a <video> player. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(llama-cpp): patch mtmd video stdin double-close (heap crash) Upstream mtmd video input (ggml-org/llama.cpp#24269) double-fcloses the ffmpeg/ffprobe stdin FILE: feed_stdin() fclose()s the FILE returned by subprocess_stdin() (which is sp->stdin_file), then subprocess_destroy() fclose()s the same pointer again -> heap corruption that aborts the backend on any base64 input_video request (the CLI --video file path is unaffected). Vendor a one-line fix (null sp->stdin_file after fclose) via prepare.sh's patches/ until upstream merges it. Verified e2e with gemma-4-e2b-it-qat-q4_0: video frames decode via ffmpeg and the model answers correctly (red clip -> 'Red', blue -> 'Blue'). Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore(llama-cpp): re-pin to upstream #24316, drop vendored stdin patch Upstream replaced the ad-hoc video stdin handling with a proper RAII refactor (ggml-org/llama.cpp#24316, "mtmd: refactor video subproc handling"), which includes the same `sp->stdin_file = nullptr` guard our patch added (plus join-before-destroy ordering). Re-pin LLAMA_VERSION to that branch head and drop patches/0001 - it's now redundant. Verified e2e with gemma-4-e2b-it-qat-q4_0: no crash, video frames decode and the model answers correctly (red clip -> "Red", blue -> "Blue"). NOTE: #24316 is not yet merged, so this pins to its branch-head commit (28ca1e60). Re-pin to the squash-merge commit on master once it lands, otherwise `git fetch` may lose the commit after the branch is deleted. 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> |
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|
LocalAI [bot]
|
92dea961c2 |
fix: distributed backend reinstall/upgrade UI stuck on 'reinstalling' (#10214)
* fix(galleryop): self-evict terminal ops from OpCache.GetStatus The processingBackends map (the UI 'reinstalling' spinner source) only cleared an op when a client polled /api/backends/job/:uid. The Manage-page Reinstall and Upgrade buttons never poll, so completed installs leaked into processingBackends forever and the backend card spun 'reinstalling' even though the install had finished. Evict terminal ops on the list read instead; DeleteUUID already broadcasts the eviction so peer replicas converge. Reproduced on a live 5-node distributed cluster: 5 backends sat in processingBackends with underlying jobs reporting completed:true,progress:100. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(nodes): clear pending backend ops behind offline/draining nodes ListDuePendingBackendOps filters status=healthy, so a backend op queued against a node that went offline (stale heartbeat) or draining (admin action) was never retried, aged out, or deleted - it leaked forever and kept the UI operation spinning. Add DeleteStalePendingBackendOps and run it each reconcile pass: draining nodes are cleared immediately (model rows already purged), offline nodes once their heartbeat is older than a grace window (blip protection). Reproduced on a live cluster: orphaned llama-cpp install rows targeting an offline (nvidia-thor) and a draining (mac-mini-m4) node sat at attempts=0 indefinitely. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(nodes): stream per-node progress during backend upgrade The install dispatch subscribed to a per-op progress subject and streamed per-node download ticks; the upgrade dispatch did a bare 15-minute blocking NATS round-trip with no subscription, so the UI showed progress:0 the whole time (the 'reinstalling but nothing happens' report on a slow node). Thread the op ID through BackendManager.UpgradeBackend -> the distributed manager -> the adapter, and have the adapter subscribe to the per-op progress subject before the request (extracted into a shared subscribeProgress helper reused by install/upgrade/force-fallback). The worker's upgradeBackend now creates the same DebouncedInstallProgressPublisher installBackend uses. An upgrade is a force-reinstall, so it reuses SubjectNodeBackendInstallProgress rather than minting a new subject - no new NATS permission, no new rolling-update compat surface. Reconciler-driven retries pass empty opID/onProgress and stay on the silent path. Reproduced on a live cluster: upgrade of llama-cpp-development on agx-orin-slow sat at progress:0 for 4+ minutes with no per-node feedback. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(galleryop): persist cancellation + periodically reap orphaned ops Two distributed gaps surfaced when a replica was killed mid-upgrade on a live cluster, leaving the backend stuck 'processing' in the UI forever: 1. CancelOperation flipped the in-memory status to cancelled and broadcast a NATS event but never persisted the terminal status. On the next replica restart the still-active row re-hydrated straight back into processingBackends and the UI spun again. It now calls store.Cancel(id) so the cancel survives a restart. 2. CleanStale (which marks abandoned active ops failed) only ran once on startup, so an op orphaned AFTER startup - its owning replica's foreground handler goroutine gone - was never reaped until the next restart. Add GalleryService.ReapStaleOperations and run it on a 15m ticker (CleanStale now returns the reaped count for observability). Neither is covered by the OpCache self-evict fix: an orphaned op never reaches Processed, so it would never self-evict. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(review): address self-review findings on the distributed install fixes Three findings from an adversarial review of this branch: 1. CRITICAL - OpCache.GetStatus crashed under concurrent load. m.Map() returns the live internal map by reference, so deleting from it on the read path was an unsynchronized write to a map four HTTP handlers poll every ~1s -> a 'concurrent map writes' fatal. Rewritten to iterate a Keys() snapshot, build a fresh result map, and apply evictions via the locked DeleteUUID after the loop. Added a -race concurrency regression guard. 2. HIGH - GetStatus evicted failed ops too, hiding them from /api/operations and breaking the dismiss-failed-op flow (the panel keeps Error != nil ops so the admin can read the error and click Dismiss). Eviction now fires only for terminal ops with Error == nil (success/cancelled); failures are retained. 3. MEDIUM - DeleteStalePendingBackendOps missed StatusUnhealthy nodes. A node marked unhealthy on a NATS ErrNoResponders never transitions to offline (health.go skips re-marking it), so its pending ops leaked exactly like the offline case. Unhealthy is now reaped via the same stale-heartbeat grace path (a fresh-heartbeat node is recovering and keeps its op). Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(review-2): don't evict the still-installing soft-path; don't spin on failed ops Second review pass found two issues: 1. MEDIUM (Go) - OpCache.GetStatus evicted the ErrWorkerStillInstalling soft-path op. That op is deliberately Processed=true with no error to show a yellow in-progress state when a worker timed out the NATS round-trip but is still installing in the background; the reconciler confirms the real outcome later. Evicting it (and broadcasting OpEnd + marking the DB completed) hid an install that may still fail. Eviction is now scoped to a clean success (progress 100 + 'completed', matching the job-poll's historical condition) or a cancellation - the soft-path (progress != 100) and failures are kept. 2. MEDIUM (React) - the Backends gallery card rendered ANY operation as an 'Installing...' spinner, so a failed op (now intentionally kept in the list for the OperationsBar error + Dismiss) spun forever. Exclude errored ops from the card spinner, mirroring Models.jsx (isInstalling already excludes op.error). The error + Dismiss still surface in the global OperationsBar. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(ui): refresh Manage backends table when an operation settles The Manage backends table fetched installed backends only on mount/after delete and checked upgrades only on tab activation. After a reinstall/upgrade completed neither re-ran, so the installed-version cell and the 'update available' badge stayed stale until the user switched tabs - the op looked like it 'did nothing'. Watch the operations list (via useOperations) and re-fetch installed backends + available upgrades whenever the count settles, mirroring the operations.length watch Backends.jsx already uses. Consolidates the prior tab-activation upgrades check into the same effect. Assisted-by: Claude:claude-opus-4-8 [Claude Code] 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> |
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|
Adira
|
2c804bef5a |
fix(config): skip vocab arrays and mmap GGUF headers to speed up startup (#10213)
When the models directory holds many GGUF files, startup parsed every model's full GGUF — including the tokenizer vocab arrays (tokenizer.ggml.tokens/scores/merges, often >100k entries) — once per model while guessing defaults. On slow storage (e.g. a models directory on a Docker volume) those hundreds of thousands of tiny reads dominate boot time before the HTTP server comes up. The default-guessing path and the VRAM metadata reader only consume scalar metadata and array lengths, never the array contents. Parse with SkipLargeMetadata (seek past large arrays) and UseMMap (fault in a few header pages instead of issuing per-element read() syscalls). For a 256k-token vocab this cuts the parse from ~524k read() syscalls to 8. The mapping is released when ParseGGUFFile returns. Fixes #9790 Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Adira Denis Muhando <dennisadira@gmail.com> |
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LocalAI [bot]
|
67f80a152b |
fix(mtp): don't auto-enable self-spec MTP for draft-only assistant GGUFs (#10208)
Gemma4 MTP (ggml-org/llama.cpp#23398) registers the prediction head as a separate `gemma4-assistant` architecture. That assistant GGUF still carries `<arch>.nextn_predict_layers`, so the architecture-agnostic detection in HasEmbeddedMTPHead matched it and appended the `spec_type:draft-mtp` defaults. Unlike the DeepSeek/Qwen embedded-head models, an assistant checkpoint cannot self-speculate: it is a draft model that requires a paired target context (`ctx_other`) and throws if loaded alone. Auto-applying the self-spec defaults to a standalone assistant import therefore produces a broken config. Guard the detection against draft-only assistant architectures (the `-assistant` suffix is upstream's naming convention) so importing one no longer yields a self-speculation config. Two-model target+draft pairing remains expressible manually via `draft_model:` and is left to a follow-up. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
|
e837921c2c |
feat: forward reasoning_effort to the backend so jinja models honor it (#10184)
* feat: forward reasoning_effort to the backend so jinja models honor it reasoning_effort was only mapped to the binary enable_thinking toggle and otherwise reached Go-side templates — it was never sent to the backend. So jinja-templated models whose chat template keys on reasoning_effort (gpt-oss Harmony, LFM2.5) could not be driven by it: LFM2.5 ignores enable_thinking and kept emitting <think>. Forward the effective reasoning_effort to the backend as a chat_template_kwarg (mirroring enable_thinking) in grpc-server.cpp, and put it in PredictOptions metadata (gRPCPredictOpts). Add a config-level default: ModelConfig.reasoning_effort and Pipeline.reasoning_effort, resolved by ModelConfig.ApplyReasoningEffort (request value overrides config default, none->disable / level->enable, an operator's reasoning.disable wins). request.go now uses that helper. Assisted-by: Claude:claude-opus-4-8 go test, golangci-lint Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(realtime): set the pipeline LLM's reasoning_effort Apply Pipeline.ReasoningEffort to the pipeline's LLM config when the realtime model is built (per-session copy, overrides the LLM's own reasoning_effort), and surface the resolved effort on the template input so Go-templated models get it too. jinja models receive it via the backend metadata. This lets a realtime pipeline disable thinking on models that only honor reasoning_effort (e.g. LFM2.5), which enable_thinking can't. Assisted-by: Claude:claude-opus-4-8 go test, golangci-lint 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> |
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|
Richard Palethorpe
|
73385713ca |
feat(distributed): enforce registration token for worker file transfer (#10183)
The worker HTTP file-transfer server is authenticated by the registration token via checkBearerToken, which fails open on an empty token: every /v1/files, /v1/files-list and /v1/backend-logs request is then served unauthenticated, granting read/write to the worker's models/staging/data directories. The fail-open was also silent (the only auth log sat on the unreachable reject branch), and the worker process never runs DistributedConfig.Validate(), so the existing frontend warning did not cover the component that exposes the server. Mirror the NatsRequireAuth pattern: keep anonymous as the default but make it loud and opt-in enforceable. - Log a prominent warning when the file-transfer server starts tokenless. - Add LOCALAI_REGISTRATION_REQUIRE_AUTH: DistributedConfig.Validate() errors on an empty token (frontend) and the worker refuses to start (fail-fast, before registration), so production can fail closed. Also satisfies the F-003 suggestion to fail Validate() on distributed + empty token. - Add LOCALAI_DISTRIBUTED_REQUIRE_AUTH umbrella switch implying both RegistrationRequireAuth and NatsRequireAuth — one production knob locking down the registration/file-transfer layer and the NATS bus together; the granular flags remain available as single-layer overrides. Wired into the frontend, supervisor worker, and agent worker (vLLM worker has neither a NATS connection nor a file-transfer server, so it is left untouched). - Document in distributed-mode.md (warning callout + flag tables). Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Richard Palethorpe <io@richiejp.com> |
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LocalAI [bot]
|
858257eaf0 |
fix(distributed): self-heal stale 'model not loaded' routing (#10181)
* fix(distributed): self-heal stale 'model not loaded' routing In distributed mode the registry can list a model as loaded on a node while the worker has evicted it (autonomous LRU eviction, an out-of-band unload, etc.) yet the backend process survives. The router's cached-node check only verifies the process is alive (probeHealth), so it routes there and inference fails with "<backend>: model not loaded" — and stays broken until the controller restarts and rebuilds its registry. InFlightTrackingClient now reconciles this: when a tracked inference call returns a model-not-loaded error, it drops the stale replica row (RemoveNodeModel) so the next request reloads the model on a healthy node instead of routing back to the evicted one. The original error is returned unchanged; only the registry is corrected. Assisted-by: Claude:claude-opus-4-8 go vet Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(distributed): typed model-not-loaded error via gRPC status code Replace the controller-side error-string match with a shared, code-aware helper. Go error types don't survive the gRPC boundary, so the signal is carried as a status code (FailedPrecondition): - pkg/grpc/grpcerrors: ModelNotLoaded(backend) constructor + IsModelNotLoaded(err) checker (status-code first, message fallback for backends not yet migrated). - InFlightTrackingClient.reconcile now uses grpcerrors.IsModelNotLoaded. - Migrate the Go backends that emit this error (parakeet-cpp, cloud-proxy, rfdetr-cpp) to the typed constructor. Acting on a false positive is harmless (the model is just reloaded). Assisted-by: Claude:claude-opus-4-8 go vet 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> |
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Adira
|
ef80a0e825 |
fix(config): add face/speaker recognition constants and register insightface + speaker-recognition (#10110)
FLAG_FACE_RECOGNITION and FLAG_SPEAKER_RECOGNITION already existed as
ModelConfigUsecase bitmask flags, and GuessUsecases already gate-checks
both backends by name — but BackendCapabilities had no entries for
either, so the UI could not classify them.
Also missing were the Method* constants for the five proto-defined RPCs
these backends implement (FaceVerify, FaceAnalyze, VoiceVerify,
VoiceEmbed, VoiceAnalyze) and the corresponding Usecase* strings
and UsecaseInfoMap entries needed to wire them into the rest of the
capability system.
Changes:
- Add MethodFaceVerify, MethodFaceAnalyze, MethodVoiceVerify,
MethodVoiceEmbed, MethodVoiceAnalyze GRPCMethod constants
- Add UsecaseFaceRecognition ("face_recognition") and
UsecaseSpeakerRecognition ("speaker_recognition") Usecase constants
- Add UsecaseInfoMap entries for both new usecases, referencing the
existing FLAG_FACE_RECOGNITION and FLAG_SPEAKER_RECOGNITION flags
- Register insightface: Embedding + Detect + FaceVerify + FaceAnalyze
- Register speaker-recognition: VoiceVerify + VoiceEmbed + VoiceAnalyze
Follows up on #10107 which left these two out because they needed new
constants first.
Assisted-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Signed-off-by: Adira Denis Muhando <dennisadira@gmail.com>
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LocalAI [bot]
|
92726f7631 |
fix(distributed): stage directory-based models to remote nodes (#10175)
Distributed file-staging treated every model path field (ModelFile, etc.) as a single regular file: it os.Open'd the path and streamed its fd as the HTTP PUT body. For directory-based models — e.g. qwen3-tts-cpp, whose weights and tokenizer ggufs live under one directory referenced by parameters.model — opening the directory succeeds but reading its fd returns EISDIR, so routing the model to a remote NATS worker failed with "read /models/<model>: is a directory". Single-file models were unaffected, so only multi-file pipelines (e.g. the realtime TTS stage) broke. stageModelFiles now detects a directory path field and stages each contained file individually (via the new stageDirectory helper), preserving structure with the existing StagingKeyMapper and rewriting the field to the remote directory (deriving ModelPath as before). countStageableFiles makes the progress total count a directory's files so the staging tracker stays accurate. Assisted-by: Claude:claude-opus-4-8 go vet Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
|
27e63b9a78 |
feat(tts): support per-request instructions and params (#10172)
The OpenAI-compatible TTS endpoint accepts an `instructions` field, but it was silently dropped at the HTTP->gRPC boundary: neither schema.TTSRequest nor the gRPC TTSRequest proto carried it, so backends could only read such a value from static YAML options (identical for every request). This blocked per-line emotion/style and, for Qwen3-TTS VoiceDesign, limited a model config to a single designed voice. Plumb a generic per-request instruction string end to end, plus an optional backend-specific params map: - proto: add `optional string instructions` and `map<string,string> params` to TTSRequest. - schema: add Instructions (maps OpenAI `instructions`) and Params (LocalAI extension) to schema.TTSRequest. - core: thread both through ModelTTS/ModelTTSStream via a newTTSRequest helper that attaches instructions only when non-empty (so backends can fall back to YAML when unset); forward them from the /v1/audio/speech handler. - qwen-tts: prefer the per-request instruction over the YAML `instruct` option (used by both mode detection and generation) and merge per-request params. - chatterbox: merge per-request params (coerced to float/int/bool) over YAML options into generate() kwargs. Fully backward compatible: empty instructions fall back to the YAML option and backends that don't support style/voice instructions ignore the field. Closes #10164 Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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Richard Palethorpe
|
3a932a9803 |
feat(distributed): Add NATS JWT authentication and TLS/mTLS options (#10159)
* feat(distributed): NATS JWT auth, TLS/mTLS options, and e2e coverage Mint per-node NATS user JWTs at registration when LOCALAI_NATS_ACCOUNT_SEED is set, and connect workers with scoped credentials from the register response. Add optional LOCALAI_NATS_TLS_CA/CERT/KEY for private CA and mTLS alongside tls:// URLs, plus test-e2e-distributed and NatsJWT container e2e specs. Document JWT setup (nats-auth-setup.sh) and TLS env vars in distributed-mode. Assisted-by: Grok:grok grok-build Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(distributed): correct NATS JWT scoping and harden client auth The JWT-auth path added in 46467cc7 had several gaps that fail silently under LOCALAI_NATS_REQUIRE_AUTH: - Agent-worker minted JWTs did not allow the subjects the agent worker actually subscribes to (jobs.mcp-ci.new and nodes.<id>.backend.stop), so MCP-CI jobs and backend-stop session cleanup were silently dropped. Scope the agent permission set to those subjects. - NATS subscription permission violations were swallowed (Subscribe returned a live-but-dead subscription). Confirm subscriptions with a server round-trip so a denial surfaces synchronously, and log async permission errors. - The backend worker connected anonymously when given a JWT without its paired seed; reject the unpaired credential instead. - The documented service-user permissions in nats-auth-setup.sh omitted prefixcache.>, which the frontend publishes and subscribes; add it. Also: add a credential-provider hook to the messaging client (consumed by the follow-up credential-lifecycle change), drop the always-nil error from NatsMessagingOptions, run go mod tidy (jwt/v2 and nkeys are now direct), and gofmt the feature's files. Tests: an agent-JWT e2e spec that connects to the enforcing NATS server and exercises every subscription the agent worker makes, plus permission allow-list coverage unit tests. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Richard Palethorpe <io@richiejp.com> * feat(distributed): acquire and auto-refresh worker NATS credentials Workers fetched NATS credentials once at startup, which broke two cases under JWT auth: a worker that registered while still pending admin approval never received a minted JWT (it connected unauthenticated and gave up), and a long-running worker's 24h JWT expired with no way to renew it. Introduce workerregistry.NATSCredentialManager, built on idempotent re-registration (the frontend preserves the node row and mints a fresh JWT each call): - Acquire re-registers through admin approval until the node is approved and credentials are minted (or returns the first success when auth is not required, preserving anonymous-NATS behavior). - RefreshLoop re-registers before the JWT expires (~75% of its lifetime), updating the credentials served to the connection. - Both are bounded (default 100 attempts / consecutive failures) and return an error on exhaustion, so an unapprovable or unrenewable worker exits non-zero and surfaces the problem instead of hanging or drifting toward an expired credential. The messaging client gains WithUserJWTProvider, fetching credentials on each (re)connect so the connection transparently adopts a refreshed JWT when the server expires the old one. RegisterFull exposes the approval status and full response; Register delegates to it. Both the backend worker and the agent worker are wired to this: explicit env credentials are used as-is, minted credentials are acquired-with-wait and refreshed, and a permanent refresh failure shuts the worker down so it restarts and re-acquires. Tests cover Acquire (wait-through-pending, bounded give-up, context cancel), RefreshLoop (refresh-before-expiry, bounded failure, no-expiry exit) and jwtExpiry decoding. Docs updated in distributed-mode.md. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Richard Palethorpe <io@richiejp.com> --------- Signed-off-by: Richard Palethorpe <io@richiejp.com> |
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Richard Palethorpe
|
5a0013defe |
test(react-ui): add page render-smoke specs, reset the coverage gate (#10122)
The UI coverage gate was tightened to 0.1pp against a fast-local measurement (39.86% baseline); CI's slower runners measure ~0.9pp lower, so tests-ui-e2e failed there. UI e2e coverage is diffusely non-deterministic and tracks machine speed — a 0.1pp band can't hold across environments. Rather than loosen the gate, raise the floor under it: a render-smoke spec mounts each lazy page (navigate + assert the header renders), covering a dozen previously-untested pages and lifting coverage from ~39% to ~42.7% locally. Restore the tolerance to 0.8pp and set the baseline conservatively (40.0), below the slow-CI floor, so the ratchet holds without flapping. Document the coverage policy — install the git hooks and don't bypass them (no --no-verify, no hand-lowering the baseline or widening the tolerance); raise coverage by adding tests instead; set the UI baseline below the slow-CI floor — in AGENTS.md, CONTRIBUTING.md and .agents/building-and-testing.md. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Richard Palethorpe <io@richiejp.com> |
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LocalAI [bot]
|
c01ed631d6 |
refactor(routing): extract replica picker into pkg/clusterrouting (#10123)
Move ReplicaCandidate and PickBestReplica out of core/services/nodes (which depends on gorm) into a new dependency-light leaf package pkg/clusterrouting, so the p2p federation server can later share the same replica-selection policy without pulling in a database driver. core/services/nodes keeps a type alias and a thin delegator, so every existing reference (the LoadedReplicaStats interface method, the ReplicaCandidate row conversion in registry.go, and the SQL policy-mirror test) compiles and behaves unchanged. This is a pure, behavior-preserving refactor: the full nodes suite, including the policy-mirror spec that pins the SQL ORDER BY to PickBestReplica, stays green. Assisted-by: Claude Code:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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|
Richard Palethorpe
|
718223f33b |
feat(localvqe/audio): v1.3 release and add spectrograms to audio transform UI (#10113)
* chore(localvqe): update backend to v1.3, add v1.2/v1.3 gallery models Bump the LocalVQE backend pin 72bfb4c6 -> b0f0378a, which adds the v1.2 (1.3 M) and v1.3 (4.8 M) GGUF SHA-256s to the upstream released-models allowlist (and the arch_version=3 loader) so both load without LOCALVQE_ALLOW_UNHASHED. Add gallery entries for localvqe-v1.2-1.3m and localvqe-v1.3-4.8m (SHA-256 verified against the downloaded weights) and update the audio-transform docs to make v1.3 the current default while noting the compact v1.1/v1.2 alternatives. Assisted-by: Claude:claude-opus-4-8 Claude-Code Signed-off-by: Richard Palethorpe <io@richiejp.com> * chore(flake): add ffmpeg-headless to the dev shell pkg/utils/ffmpeg_test.go shells out to the `ffmpeg` CLI, and the pre-commit gate runs those tests via `make test-coverage`. Without ffmpeg in the dev shell the gate fails with "executable file not found in $PATH". The headless build provides the CLI without GUI/X deps. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(localvqe): parse WAV by walking RIFF sub-chunks Walk the RIFF chunk list instead of assuming the canonical 44-byte header layout. Real inputs (browser-recorded clips, ffmpeg output with an 18/40-byte extensible `fmt ` chunk or trailing LIST/INFO metadata) would otherwise splice header/metadata bytes into the PCM stream as an audible impulse. Honour the `data` chunk size and validate that both `fmt ` and `data` chunks are present. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(security-headers): allow blob: in connect-src for waveform fetch The waveform renderer XHRs/fetches a freshly-created blob: object URL (e.g. an uploaded or enhanced clip before it has a server URL). XHR/fetch of blob: is governed by connect-src, not media-src, so it was blocked by the CSP. Add blob: to connect-src. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Richard Palethorpe <io@richiejp.com> * feat(react-ui): add input/output spectrogram view to AudioTransform The transform page only showed time-domain amplitude waveforms, so you could see how loud a clip was but not which frequencies the model touched. Add a time x frequency spectrogram heatmap and render the input and output spectrums side by side, so it's visible which bands the enhancement attenuates (bright input bands that go dark in the output). Computed client-side via a Hann-windowed STFT over both clips (a small dependency-free radix-2 FFT), defaulting to the LocalVQE 512/256 frame geometry. This shows the net input->output spectral change; the model's internal gain mask is not exposed by the backend. - src/utils/fft.js radix-2 FFT - src/hooks/useSpectrogram.js decode + STFT -> normalised dB magnitude grid - src/components/audio/Spectrogram.jsx canvas heatmap (magma colormap) - AudioTransform.jsx dual-spectrogram panel + CSS - e2e spec + UI coverage baseline bump (38.29 -> 39.0; measured ~39.4-40.2) Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Richard Palethorpe <io@richiejp.com> * test(react-ui): make UI coverage deterministic, tighten the gate UI e2e line coverage swung ~1pp run-to-run (39.1% <-> 40.2%), which forced a loose 0.8pp tolerance on the monotonic gate — a band wide enough to let a real ~300-line regression through silently. The swing was a bug, not inherent jitter: the 'Create Agent navigates' spec ended on the URL assertion, so AgentCreate.jsx's ~400 lines were collected only when its render happened to beat the coverage teardown. Wait for the page to actually render (assert its heading) so those lines are covered every run. With the race gone, repeated runs land within ~0.013pp of each other, so: - tighten UI_COVERAGE_TOLERANCE 0.8 -> 0.1 (noise floor, not a drift band) - set the baseline to the real, reliably-achieved value (39.0 -> 39.86) Localised by running the V8-coverage suite repeatedly and diffing per-file line coverage; AgentCreate.jsx was the sole ~1pp flipper. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Richard Palethorpe <io@richiejp.com> --------- Signed-off-by: Richard Palethorpe <io@richiejp.com> |
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LocalAI [bot]
|
c222161291 |
feat(distributed): resumable file uploads via HTTP Content-Range (#10109)
Large model GGUFs (multi-GB) transferred between master and worker over
flaky / bandwidth-throttled paths (e.g. libp2p relays with byte caps) used
to restart from byte 0 on every transport error. This change adds standard
HTTP Range/resume semantics to the worker's PUT /v1/files/<key> endpoint
and teaches the master-side HTTPFileStager to consult the worker for the
last accepted offset and resume from there.
Server side (file_transfer_server.go):
- PUT now honors Content-Range: bytes <start>-<end>/<total>. The handler
validates that <start> matches the current on-disk size; mismatches
return 416 with the actual size in X-File-Size.
- Mid-upload chunks return 308 Permanent Redirect ("Resume Incomplete")
with the new size, so the client can keep going.
- An optional X-Content-SHA256 request header binds an upload to a target
hash; cross-attempt drift returns 409. On the final chunk the server
re-computes SHA-256 and returns 400 if it doesn't match.
- HEAD now advertises Accept-Ranges: bytes and Content-Length, and exposes
X-Target-SHA256 for in-progress files (so clients can resume only when
the partial bytes belong to the file they want to upload).
- Legacy PUTs with no Content-Range keep the original truncate-create
semantics — zero behavior change on the happy path.
Client side (file_stager_http.go):
- Pre-PUT HEAD probe reads X-File-Size + X-Target-SHA256 to determine the
resume offset.
- doUpload seeks to that offset and sends Content-Range + X-Content-SHA256.
- Retry loop switches from fixed 3 attempts / 5s-10s-20s backoff to an
outer time budget
with exponential backoff (1s -> 30s cap), so a 5GB upload over a flaky
link can outlast many short disconnects.
- 308 and 416 responses are treated as transient: the next iteration
re-HEADs to learn the correct offset.
Tests:
- Two-chunk Content-Range round-trip produces the correct file + sidecar.
- 416 on a Content-Range/file-size mismatch.
- 409 on X-Content-SHA256 drift between chunks.
- 400 on final-hash mismatch.
- HEAD on a partial upload exposes X-Target-SHA256 (not a misleading
hash-of-partial-bytes via X-Content-SHA256).
- Pre-existing finished file with a different hash is transparently
overwritten when a new PUT starts at byte 0.
- End-to-end resume: EnsureRemote against a worker that already holds a
partial file transfers only the remainder.
- Mid-stream connection drop on attempt #1 is recovered by attempt #2
resuming from the partial offset.
Assisted-by: Claude:claude-opus-4-7
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
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LocalAI [bot]
|
0d57957ebb |
feat(worker): add LOCALAI_PREFETCH_MODELS for boot-time gallery prefetch (#10108)
In LocalAI distributed mode the master streams a model GGUF to a worker on first inference. On bandwidth-constrained cluster networks (libp2p circuit-v2 relays under NAT, double-NAT residential, slow overlays) that transfer can be slow or unreliable — meanwhile each worker's outbound internet is usually fine. LOCALAI_PREFETCH_MODELS lets the operator name gallery model IDs to download at worker boot, BEFORE the worker subscribes to backend.install events. Reuses gallery.InstallModelFromGallery so the on-disk /models layout matches what the master would have pushed, and the master can still push files on demand if the gallery is unreachable at boot (prefetch is non-fatal on every error path). The installer is wrapped in a function-value indirection so tests can swap a fake without touching the real gallery; production never reassigns the binding. Assisted-by: Claude:claude-opus-4-7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
|
76fe0bb929 |
feat(crispasr): add CrispASR backend — multi-architecture ASR + TTS (#10099)
* feat(crispasr): backend source files (Go gRPC server, C-ABI shim, build files) Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * polish(crispasr): brand error strings + fix stale shim comment Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * build(crispasr): register backend in root Makefile Mirror the whisper Go backend registration for the new crispasr backend: NOTPARALLEL entry, prepare-test-extra/test-extra hooks, BACKEND_CRISPASR definition, docker-build target generation, and the docker-build-backends aggregate target. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci(crispasr): add backend build matrix entries Mirror the 11 whisper golang Dockerfile matrix entries (CPU amd64/arm64, CUDA 12/13, L4T CUDA 13, Intel SYCL f32/f16, Vulkan amd64/arm64, L4T arm64, ROCm hipblas) with backend and tag-suffix substituted to crispasr. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(gallery): add crispasr backend gallery entries Add the crispasr meta anchor and its full set of image gallery entries (cpu, metal, cuda12/13, rocm, intel-sycl f32/f16, vulkan, L4T arm64, L4T cuda13 arm64, plus -development variants), mirroring the whisper backend gallery block. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci(crispasr): bump CRISPASR_VERSION via bump_deps workflow Track CrispStrobe/CrispASR main branch and bump CRISPASR_VERSION in backend/go/crispasr/Makefile. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * build(crispasr): don't wire fixture-gated test into test-extra Mirror the whisper Go backend: its AudioTranscription test is gated on model/audio fixtures and skips in CI, so building crispasr (the heaviest ggml compile in the tree) inside the unit-test lane adds a long compile for zero coverage. The backend image build in backend-matrix.yml remains the authoritative compile check. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci(crispasr): add darwin metal build entry (mirror whisper) The metal-crispasr gallery entries and capabilities.metal mapping reference -metal-darwin-arm64-crispasr, which is only produced by an includeDarwin entry. Mirror whisper's darwin metal entry so the tag actually gets built. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci(crispasr): place hipblas matrix entry next to whisper twin Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(crispasr): register crispasr as pref-only ASR backend + test Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(crispasr): port whisper behavioral suite (cancellation + streaming) Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(crispasr): fix skip message env var names to CRISPASR_* Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(crispasr): switch shim to crispasr_session_* multi-architecture API The shim used whisper_full(), which in CrispASR is the whisper-only path: libcrispasr only transcribes Whisper GGUFs through it. Multi-architecture transcription (Parakeet, Voxtral, Qwen3-ASR, Canary, Granite, FunASR, Paraformer, SenseVoice, ...) goes through the crispasr_session_* C-ABI, which auto-detects the architecture from the GGUF and dispatches to the matching backend. Rewrite the C shim around crispasr_session_open / _transcribe_lang / _result_* and add get_backend() so the selected backend is logged. load_model now takes a threads param (session_open binds n_threads at open). The session result is segment+word based with no token IDs and no per-decode callback, so drop n_tokens / get_token_id / get_segment_speaker_turn_next / set_new_segment_callback. set_abort is kept for API parity but is best-effort: the session transcribe is blocking with no abort hook. Update the purego bindings and gocrispasr.go to match: tokens are left empty, speaker-turn handling is removed, and AudioTranscriptionStream emits one delta per non-empty segment after the blocking decode returns (no progressive streaming via the session API), preserving the concat(deltas) == final.Text invariant. crispasr_session_set_translate is exported by libcrispasr but not declared in crispasr.h, so it is forward-declared in the shim alongside the open/transcribe/result functions. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * build(crispasr): link full CrispASR backend set for multi-arch support The shim's crispasr_session_* dispatch calls into the per-architecture backend libs (parakeet, voxtral, qwen3_asr, canary, funasr, paraformer, sensevoice, ...), which CrispASR builds as static archives. Linking only crispasr + ggml dead-stripped every backend object from the final module (nm backend-symbol count: 0), leaving a whisper-only .so. Link the same backend set as crispasr-cli so the static archives are pulled in. After this the module carries the backend symbols (nm count 407, .so grows from ~2.1MB to ~6.7MB) and the session API can dispatch to every compiled-in architecture. Also rewrite ${CMAKE_SOURCE_DIR}/examples/talk-llama to ${PROJECT_SOURCE_DIR}/... in the vendored src/CMakeLists.txt: CrispASR locates its vendored llama.cpp via ${CMAKE_SOURCE_DIR}, which is wrong when CrispASR is add_subdirectory'd (CMAKE_SOURCE_DIR points at this backend dir, not the CrispASR root). PROJECT_SOURCE_DIR is correct both standalone and as a subproject; the sed is idempotent. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(crispasr): adapt suite to session API (blocking, no decode callback) Register the new symbol set (drop the removed token/speaker/callback funcs, add get_backend; load_model now takes 2 args). The session transcribe is blocking with no abort hook, so a mid-decode cancel can't interrupt it: change the cancellation spec to cancel the context before the call and assert codes.Canceled from the pre-call ctx.Err() check, dropping the <5s mid-decode timing assertion. The streaming spec still holds with per-segment post-decode emission (>=2 deltas, concat(deltas) == final.Text). Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(gallery): add CrispASR ASR model entries (-crispasr) Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(gallery): keep only session-auto-detectable CrispASR ASR models The crispasr backend loads models via crispasr_session_open, which auto-detects the backend from the GGUF general.architecture using crispasr_detect_backend_from_gguf. Architectures not in that detect map cannot be opened, so those gallery entries fail to load. Removed entries whose architecture is not wired into CrispASR v0.6.11's session auto-detect router (they can be re-added when upstream maps them): - Not in the detect map: data2vec, firered-asr, funasr, fun-asr-mlt-nano, glm-asr, hubert, kyutai-stt, mega-asr, mimo-asr, moonshine{,-de,-streaming,-tiny-de}, omniasr{,-llm,-llm-1b}, paraformer, sensevoice. - Pending verification (filename-heuristic routed, not arch-detected): parakeet-ctc-0.6b, parakeet-ctc-1.1b. Their GGUFs are routed to the fastconformer-ctc backend by a filename heuristic in the model registry, which implies general.architecture is not a mapped string. Kept the parakeet rnnt/tdt_ctc variants: convert-parakeet-to-gguf.py writes general.architecture="parakeet" unconditionally and encodes the rnnt/ctc distinction in metadata fields, so they session-auto-detect. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(crispasr): TTS synthesis via crispasr_session_synthesize (24kHz) Add tts_synthesize/tts_free/tts_set_voice to the C-ABI shim. They reuse the already-open g_session (crispasr_session_open auto-detects a TTS model) and dispatch to the upstream synthesis call, which returns malloc'd 24 kHz mono float PCM. Orpheus needs a SNAC codec path that we do not set, so it returns NULL here and surfaces as an error Go-side. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(crispasr): implement TTS/TTSStream gRPC methods Bind the new shim functions via purego and implement TTS, TTSStream and a writeWAV24k helper. synthesize copies the C-owned PCM out before freeing it; TTS writes a 24 kHz mono 16-bit WAV to req.Dst via go-audio/wav. CrispASR has no progressive synth, so TTSStream synthesizes fully, encodes to WAV, and emits the bytes as a single chunk; it owns the results-channel close (the gRPC server wrapper ranges until close), mirroring vibevoice-cpp's TTSStream. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(crispasr): log when a TTS voice override is not honored Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(gallery): add CrispASR vibevoice-tts model entry Only vibevoice-tts works through the current shim: qwen3-tts, chatterbox, and orpheus require companion codec/s3gen/SNAC paths (set_codec_path / set_s3gen_path) that the shim doesn't wire yet, and kokoro/indextts/voxcpm2 aren't in the session auto-detect map. Those are follow-ups. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(crispasr): gated TTS synthesis spec Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(crispasr): satisfy golangci-lint (errcheck defers + unsafeptr nolint) The crispasr Go file is entirely new, so new-from-merge-base lints every line (unlike the grandfathered whisper backend it was forked from): - handle os.RemoveAll / fh.Close return values in AudioTranscription - annotate the two intentional C-pointer unsafe.Slice sites with //nolint:govet Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(crispasr): backend: and codec: model options (explicit arch + companion files) Add two model-config options to the CrispASR backend via opts.Options: - backend:<name> selects an explicit CrispASR backend (bypassing auto-detect) by routing load_model through crispasr_session_open_explicit, unlocking architectures the detector won't pick on its own (qwen3, cohere, granite, voxtral, moonshine, mimo-asr, orpheus, kokoro, chatterbox, etc.). - codec:<path> loads a companion file (qwen3-tts codec, orpheus SNAC, chatterbox s3gen, or mimo-asr tokenizer) via the universal crispasr_session_set_codec_path setter after the session opens. A relative path resolves against the model directory. rc==0 means success or not-applicable; only a negative rc is fatal. The C shim load_model gains a backend_name argument and a new set_codec_path entry point; the Go bridge parses the prefix:value options and registers the new symbol. The vad_only path is unchanged. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(gallery): expand CrispASR models via backend:/codec: options (explicit arch + companions) Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(gallery): use virtual.yaml base for crispasr models The crispasr entries are just backend + model + a couple options, fully expressed inline via overrides:/files: in gallery/index.yaml. Point each url: at the shared gallery/virtual.yaml (the established 'virtual' model trick) and drop the 36 redundant per-model gallery/*-crispasr.yaml files. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(gallery): drop voice-requiring TTS entries (keep vibevoice-tts) Real e2e showed qwen3-tts/orpheus/chatterbox don't synthesize through the current shim: the codec: companion loads fine, but these engines additionally need a voice pack / voice prompt / reference clip (qwen3-tts base errors 'no voice'; chatterbox is zero-shot cloning; orpheus uses named voices) that the backend doesn't wire. (qwen3-tts also can't auto-detect: its GGUF arch is 'qwen3tts', unmapped by the detector — would need backend:qwen3-tts.) Removed to avoid shipping non-working gallery entries; vibevoice-tts (built-in voice, e2e-verified) remains the working TTS. Voice-pack wiring is a follow-up. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(crispasr): speaker: and voice: TTS options (baked speakers + voice packs/prompts) speaker:<name> -> crispasr_session_set_speaker_name (baked speakers: qwen3-tts CustomVoice, orpheus). voice:<path>(+voice_text:<ref>) -> crispasr_session_set_voice (voice-pack GGUF, or WAV zero-shot clone with ref text). Applied at Load as the default voice; req.Voice still overrides the speaker per request. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(gallery): re-add e2e-verified TTS engines (chatterbox, qwen3-tts-customvoice, orpheus) 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> |
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Adira
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baa11133f1 |
fix(config): register parakeet-cpp as a transcript backend (#9718) (#10106)
parakeet-cpp was added in #10084 but not registered in BackendCapabilities, so GuessUsecases only allowed "whisper" for FLAG_TRANSCRIPT and the UI could not classify parakeet-cpp models as speech-to-text. The result was that parakeet models appeared only in the LLM selector in the speech-to-speech pipeline, making them unusable for transcription through the UI. Closes #9718 Assisted-by: Claude Sonnet 4.6 <noreply@anthropic.com> |
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Adira
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1bdd3338a6 |
fix(config): register 5 backends missing from BackendCapabilities (#10107)
Cross-referencing backend/ directories against BackendCapabilities found
five backends that exist and work but have no entry in the map, so
GuessUsecases falls back to heuristics that mis-classify them (e.g.
a TTS backend appears as an LLM in the UI).
Added entries, each modelled on the corresponding Python twin or the
nearest equivalent already in the map:
sglang — LLM (Predict/PredictStream/TokenizeString, vision)
vibevoice-cpp — ASR + TTS/TTSStream (mirrors vibevoice Python)
sherpa-onnx — ASR + TTS/TTSStream + VAD (multi-model toolkit)
qwen3-tts-cpp — TTS (mirrors qwen-tts Python)
rfdetr-cpp — object detection (mirrors rfdetr Python)
Found by diffing `ls backend/{go,python}/` against the keys in
BackendCapabilities. Remaining gaps (insightface, speaker-recognition,
sam3-cpp) use custom gRPC methods not yet in the Method* constants —
left for a follow-up.
Assisted-by: Claude Sonnet 4.6 <noreply@anthropic.com>
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