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Commits
| Author | SHA1 | Message | Date | |
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 mudler's LocalAI [bot]andEttore Di Giacinto
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83a0f16a21 |
feat(gallery): let one gallery entry offer several builds of the same model (#10943)
* feat(system): expose raw detected capability for model meta resolution Model meta gallery entries express hardware fallback through candidate ordering rather than a capability map, so they need the undecorated detected capability string without Capability's default/cpu fallback chain. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(system): drop duplicate capability accessor, cover DetectedCapability ReportedCapability was added with a body identical to the existing DetectedCapability. Keep one accessor and move the specs onto it, since DetectedCapability had no direct coverage of its no-fallback behavior. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): parse IEC binary size suffixes (KiB..PiB) ParseSizeString accepted only SI suffixes, so a "20GiB" floor was rejected outright. Model and VRAM sizes are conventionally quoted in IEC units, and silently reading GiB as GB would understate a floor by about 7%. Purely additive: these inputs previously returned an unknown-suffix error. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(gallery): add Candidate type for meta model entries Candidate is one option in a meta entry's ordered variant list. It names a concrete gallery entry and declares when that entry suits the host. EffectiveMinVRAM resolves the VRAM floor, letting an authored min_vram win over a nightly-inferred one. An unparseable floor errors instead of being treated as absent: swallowing a typo would turn a constrained candidate into an unconstrained one and select a too-large variant rather than fail loudly. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(gallery): add hardware-aware model variant resolver Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(gallery): allow gallery model entries to declare variant candidates A gallery entry with a non-empty candidates list is a meta entry: it names an ordered list of concrete entries and resolves to the first one the host can satisfy, instead of describing model files directly. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(gallery): resolve meta model entries to hardware-appropriate variants at install Meta gallery entries carry an ordered candidate list; at install time the first candidate the host satisfies is resolved and its payload installed under the meta's name, so the model keeps a stable name regardless of which variant backs it. The resolution is recorded in the installed gallery config so a reinstall honors a prior pin and operators can see the backing variant. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(gallery): key meta pin recall on the installed name and detach resolved entries Six review findings on the meta-entry install path. Pin recall was keyed on the gallery entry name while applyModel writes the record under the install name (req.Name when supplied), so a meta installed under a custom name with a pin lost that pin on reinstall and was silently re-resolved onto a different variant, possibly swapping its backend. Compute the install name with applyModel's own precedence before the recall. ResolveMetaModel returned a shallow struct copy, so the resolved entry's Overrides aliased the gallery entry's map and the install path's in-place mergo merge wrote the caller's request into the shared catalog. Detach Overrides, ConfigFile, AdditionalFiles, URLs and Tags. Not exploitable today only because this path re-unmarshals the gallery per call, which is a property nobody should have to rely on. Also: overlay the meta's name onto the persisted config for meta installs so the gallery file no longer records the variant's name; move the pinned-VRAM warning below the variant validation so a pin naming a nonexistent entry does not warn about VRAM before failing for an unrelated reason; and stop seeding config.URLs in the config_file branch, which duplicated every declared URL. Add seven network-free specs driving InstallModelFromGallery with a meta entry: variant payload wins over the meta's legacy url fallback, the resolution record round-trips to disk, a pin is recorded and honored on reinstall including under a custom install name, and the resolved entry does not alias the gallery's maps. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(gallery): deep-copy meta overrides and make two specs functional ResolveMetaModel detached the resolved entry's Overrides and ConfigFile with maps.Clone, which only copies the top level. Gallery overrides are nested in practice (parameters.model is near-universal) and the install path merges the caller's request with mergo.WithOverride, which recurses into nested maps and overwrites them in place, so the gallery entry's own inner maps were still reachable and still got rewritten by the last caller to install. Copy both maps all the way down instead, recursing through the container shapes a YAML decoder produces. ConfigFile is not mutated on the install path today, but it carries the same kind of nested payload and leaving it shallowly cloned would invite the bug back. Also fix two specs that passed whether or not their target fix was present: - "does not write the caller's overrides back into the gallery entry" re-read the catalog from disk, which re-unmarshals fresh structs and so cannot observe in-memory aliasing. It now asserts against the in-memory gallery entry and drives the real mergo merge. - "round-trips the resolution record to disk under the meta's name" asserted a name that is already correct in the config_file branch. It now drives the url branch via a file:// fixture, where the meta-name overlay actually applies. Both were verified red by reverting their fix. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(gallery): lint meta model entry invariants in index.yaml Adds Ginkgo specs that parse the shipped gallery/index.yaml and enforce the invariants that keep meta entries safe: a legacy url fallback equal to the final candidate's url, references only to existing non-meta entries, a min_vram floor on every candidate but the last-resort one, a capability drawn only from the vocabulary the system can report, and descending VRAM floors within a capability group. The capability check is the only compensating control for a typo there. Candidate matching is a case-sensitive exact comparison against SystemState.DetectedCapability(), so an unknown value never matches and falls through silently instead of erroring. The vocabulary therefore mirrors the raw return set of getSystemCapabilities(), which notably excludes "cpu": that is a fallback key inside Capability(capMap) on the meta backend path, never a reported capability. A CPU-only host reports "default". These pass vacuously until the pilot meta entry lands; the guard is intentionally in place before the thing it guards. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(gallery): close coverage gaps in the meta entry lint The ordering invariant grouped candidates by capability and asserted floors descend within a group. A candidate with an EMPTY capability matches every host, so it does not belong in its own group: it dominates every later candidate whose floor is at or above its own, across capability groups. Track a running minimum floor over the unconditional candidates instead, which subsumes the old same-group check for the empty capability. Every spec skipped non-meta entries, so with zero meta entries in the index all five bodies were no-ops. Aligning GalleryModel.IsMeta() with GalleryBackend.IsMeta(), whose semantics are deliberately opposite, would have made all of them pass while checking nothing. Extract each invariant into a helper over a slice of entries returning the violations it finds, and cover those helpers with synthetic fixtures so the logic stays tested at zero meta entries. The index-driven specs are now a thin application of already proven logic. Also assert the index parses non-empty, report every violation in one run rather than aborting on the first, and parse the index once for the suite. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci(gallery): add nightly denormalization of meta model candidates Fills the read-only backend, quantization and inferred_min_vram fields on meta gallery candidates and opens a PR, modeled on the existing checksum_checker job. Computing these needs network access, so it happens nightly rather than at install time. An authored min_vram is never modified: a human who measured a real load knows more than a pre-download estimate does. The index is rewritten via yaml.Node rather than a document round-trip. A full round-trip reflows all ~26k lines of gallery/index.yaml, which would bury the computed values and make the nightly PR unreviewable. The rewrite touches only the three derived keys, so authored styling survives and a run that computes nothing leaves the file untouched. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(ci): keep the gallery denormalize diff reviewable and self-healing The nightly denormalization job edits YAML nodes instead of round-tripping structs so its PR stays small enough for a human to review, but the write path undid that: yaml.Marshal re-encoded the node tree at yaml.v3's default 4-space indent and dropped the leading document marker, reflowing roughly 6000 lines around the handful of real changes. Encode through yaml.NewEncoder at the index's authored 2-space indent and restore the header. A write that changes three fields now changes three lines. Stale inferred_min_vram values were also never cleared. Both skip paths (an authored min_vram is present, or the candidate is the last resort) returned before touching the field, so a candidate that gained a floor or became the last resort after a reorder kept an inferred value that EffectiveMinVRAM reported as a real constraint, failing the meta lint with no way for the job to self-heal. Clear the field before both skips. The workflow discarded a whole night's work on any single failure: the program exits 1 when a candidate cannot be estimated, which aborted the job before the PR step, so one unreachable candidate blocked every other refresh indefinitely. Capture the status, open the PR with what was computed, mark the PR body as partial, and fail the run afterwards so the problem still surfaces. Also preserve the index's existing file mode instead of forcing 0644, and drop the redundant //go:build ignore tag, since Go already skips dot directories and the sibling modelslist.go carries no tag. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(gallery): add nanbeige4.1-3b meta entry with hardware-resolved variants Adds the first real meta entry to the gallery index. It resolves to the Q8_0 build on hosts with at least 6GiB of VRAM and to the Q4_K_M build everywhere else, installing either payload under the stable name nanbeige4.1-3b. The entry carries a url equal to its final candidate's url. LocalAI releases that predate candidates support parse the index non-strictly and drop the key silently, so without that url they would list the entry and install nothing. A regression spec parses the index the way those releases do and asserts every meta entry stays installable for them. Also teaches core/schema/gallery-model.schema.json about candidates. The schema sets additionalProperties: false at the top level, so an author following CONTRIBUTING.md and adding the yaml-language-server comment would otherwise get a validation error on this entry. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(gallery): make candidate entries complete, installable entries Reworks hardware-resolved gallery variants after a design pivot. There is no longer a separate "meta" entry kind. A gallery entry is a normal, complete entry that may additionally carry candidates:, a list of hardware-gated upgrades over itself, and the entry is itself the last-resort candidate. The previous design relied on a bare url: as the fallback for LocalAI releases that predate candidates support. That fallback is empty in practice: none of the 80 gallery/*.yaml files carry a top-level files:, and 1216 of 1281 index entries carry their payload in the index entry itself, so a url alone yields a config template with nothing to download. Since every released LocalAI reads gallery/index.yaml live from master, merging a payload-less entry would have shown every existing user a model that installs to a broken state. Making the entry its own base candidate removes the problem at the root: old clients drop the candidates key and install the entry exactly as they do today. Resolution order is now explicit pin, then capability plus VRAM over the declared upgrades, then the entry itself. The entry ALWAYS installs: when its own min_vram or capability is unmet the installer warns and installs it anyway, because there is nothing below it and refusing would make the gallery behave worse the newer the client is. A pin naming the entry's own name is valid and is how an operator declines an upgrade. IsMeta() becomes HasCandidates(), ResolveMetaModel becomes ResolveVariant, and the persisted meta_name record key becomes entry_name. GalleryBackend.IsMeta() is a separate concept and is untouched. The lint drops the three rules the pivot makes wrong (url equality with the final candidate, no inline payload, unconstrained final candidate) and gains one: the entry's own floor must sit strictly below every candidate's, since a base that outranks a candidate makes that candidate unreachable. The pilot entry is now the existing nanbeige4.1-3b-q4, which gains a 2GiB floor of its own and a single 6GiB upgrade to nanbeige4.1-3b-q8, replacing the separate nanbeige4.1-3b entry added in |
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mudler's LocalAI [bot]andEttore Di Giacinto
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e55cc3e2a7 |
fix(worker): bound the gRPC port allocator and stop leaking dead backends' ports (#10968)
The worker's gRPC port allocator grew monotonically with no upper bound: nextPort started at the base port and incremented whenever freePorts was empty, and nothing checked 65535. Past that it handed out integers that cannot be bound, surfacing as an opaque "backend won't start". #10961 estimated this needed ~15,000 concurrent-peak allocations, i.e. effectively unreachable. It is not, because of a second defect: the "process died unexpectedly" branch in startBackend deleted the process map entry without releasing its port at all. That port was leaked, never quarantined and never reused. A crash-looping backend leaks one port per restart, so a backend dying every 30s walks 50051 to 65535 in about five days. The leak, not concurrent peak, is the realistic route to exhaustion. Fixing the leak alone would have been wrong. Releasing that port makes it re-bindable, and the death path is the one teardown path with no request/reply to carry StoppedProcessKeys back to the controller (#10952's eager row removal), so a stale NodeModel row could then resolve to a live listener belonging to a different backend. probeHealth verifies liveness, not identity, so the request is silently misrouted. The 15s port quarantine does not cover this: the only reaper is the per-model health check at ~45s, and it can be disabled outright. The residual was masked only because the port was never rebound. So both are fixed together: - The allocator takes an explicit [basePort, LOCALAI_GRPC_MAX_PORT] range and returns ErrNoFreePort naming the range, the live backend count, the quarantined count, and the knob to raise. Exhaustion is now diagnosable instead of surfacing as an unbindable port. - Released ports carry per-key affinity: a port is offered back to the process key that last held it before any other key. Process keys (modelID#replica) and NodeModel rows (nodeID, modelName, replicaIndex) are isomorphic, so a port that can only be re-bound by its previous owner can only ever be named by that owner's row, which that key's re-registration overwrites. Misrouting to a different model becomes impossible by construction rather than by racing the quarantine timer. Affinity is a preference, not a reservation: under range pressure an owned port is stolen with a warning, because a guaranteed outage is worse than a rare misroute window on a port long out of quarantine. Claiming a port evicts its previous owner's entry, keeping ownership injective over ports so the affinity map can never exceed the range width regardless of how many distinct model keys the worker sees. Ownership also expires. It is only load-bearing while a controller row could still name the port, which the per-model reaper bounds at roughly 45s, so it lapses after five minutes and the port becomes ordinary free space again. Holding it indefinitely would have made every distinct model the worker ever served consume a port permanently: every release path is keyed, so nothing would ever be unowned, the allocator would climb to the end of its range on distinct-key count rather than concurrency, stealing would become routine, and the steal warning would tell operators to widen a range that was not the constraint. With expiry, reaching the steal branch means the worker is genuinely out of concurrent capacity, so that advice is correct when it appears. Closes #10961 Closes #10952 Assisted-by: Claude Code:claude-opus-4-8[1m] [Read] [Edit] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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mudler's LocalAI [bot]andEttore Di Giacinto
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fb4c61d1c9 |
fix(distributed): configurable remote model-load timeout, and reap the load when it times out (#10948)
* fix(distributed): make the remote LoadModel deadline configurable
The router hardcoded a 5 minute gRPC deadline for the remote LoadModel
call. Staging finishes before the timer starts, so those five minutes
cover only the worker backend's own checkpoint load and pipeline init.
A cold load of meituan-longcat/LongCat-Video-Avatar-1.5 (~83 GB) on an
ARM64 Thor worker fails at exactly 302s with DeadlineExceeded while the
backend process is still making progress (CPU time accumulating, RSS
moving as weights are mapped), so the load was cut short rather than
wedged.
Add LOCALAI_NATS_MODEL_LOAD_TIMEOUT / --model-load-timeout mirroring the
existing backend-install timeout knob, defaulting to 5m so unset
clusters keep today's behaviour.
The cold-load hold ceiling (which bounds how long one load may hold the
per-model advisory lock) was derived from the install timeout alone, so
raising the load deadline past it would have been silently clipped.
Derive it from both budgets via ModelLoadCeilingFor:
max(install + load + 5m staging margin, 25m)
With the defaults that is 15m + 5m + 5m = 25m, identical to the previous
constant, and the 25m floor means shrinking either budget can never
tighten the ceiling below what clusters relied on before.
Assisted-by: Claude:claude-opus-4-8 golangci-lint
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(distributed): reap the abandoned replica when a remote load times out
The gRPC deadline on the remote LoadModel call only cancels the client
side. A backend blocked in a synchronous weight load never observes its
cancelled handler context, so when scheduleAndLoad gave up it left the
worker loading with nobody waiting for the result.
Observed on an ARM64 Thor worker loading LongCat-Video-Avatar-1.5: the
client returned DeadlineExceeded at 302s, and the backend process was
still alive 30 minutes later having pulled ~57GB from HuggingFace. Every
retry stacked another multi-GB loader on the worker; they had to be
reaped by hand via POST /api/nodes/:id/models/unload.
Send backend.stop for the exact `modelID#replicaIndex` process key we
just abandoned. The exact key matters: a bare model ID stops every
replica on that node, including healthy ones serving traffic.
Only a deadline or cancellation triggers the reap. Any other LoadModel
failure is the backend answering, which means its handler returned and
the process is idle - stopping it there would discard a warm process and
its downloaded weights. The reap is best-effort and never replaces the
load error the caller is waiting on.
The `modelID#replicaIndex` format was already hand-rolled in two places
(the worker's buildProcessKey and pkg/model's log store). Rather than add
a third, export model.BackendProcessKey from pkg/model, the lowest common
dependency of both sides.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 golangci-lint
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
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mudler's LocalAI [bot]andEttore Di Giacinto
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40d35c0385 |
docs: onboarding overhaul, dedup, and error docs (#7711) (#10895)
* docs: fix CPU image tag (latest, not latest-cpu) Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: use canonical localai/localai registry in models guide Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: replace dead llama-stable backend with llama-cpp Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: correct mitm-proxy intercept config and redaction tier Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: fix text-to-audio endpoint and broken notice block Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: fix VAD example, stale FAQ, broken link, CLI list, whats-new dump Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: render advanced/reference section indexes (consolidate _index) Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: remove duplicate getting-started build/kubernetes pages Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: fold container image reference into installation/containers Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: remove stale advanced fine-tuning page (superseded by features/fine-tuning) Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: fold distribution/longcat/sound pages into their parents Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: make getting-started index accurate and complete Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: carry one concrete model through the getting-started path Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: add end-to-end 'build your first agent' walkthrough Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: add runtime errors reference; consolidate troubleshooting from FAQ Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: add agent actions catalog Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: agent-scoped MCP, skills walkthrough, agentic disambiguation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: add concrete gallery install lines to media feature pages Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: merge installation into getting-started (URLs preserved via aliases) Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: add Operations section; move operator pages and P2P API reference Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: journey-ordered top nav and grouped feature sections Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: add docs-with-code process gate (PR template + agent instructions) Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: remove em/en dashes from documentation prose 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]andEttore Di Giacinto
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3bb0d1cb49 |
feat(backend): add moss-tts-cpp text-to-speech backend (#10860)
* feat(backend): add moss-tts-cpp text-to-speech backend Add a Go + purego backend wrapping the moss-tts.cpp ggml port of the OpenMOSS MOSS-TTS-Local v1.5 text-to-speech model (GPT-J local transformer decoded through MOSS-Audio-Tokenizer-v2), producing 48 kHz stereo audio with optional reference-audio voice cloning. Mirrors the qwen3-tts-cpp backend: dlopen the static-ggml shared library, bind the moss-tts.cpp C-API via purego, and serve the gRPC TTS method. A thin C shim holds the pipeline handle and copies engine PCM into a Go-freeable buffer. Wires the CI registration: backend-matrix.yml (CPU, CUDA 12/13, Intel SYCL f16/f32, Vulkan, ROCm, NVIDIA L4T, plus Darwin metal), backend/index.yaml metas and image entries pointing at mudler/MOSS-TTS-Local-Transformer-v1.5-GGUF, the root Makefile build targets, and the changed-backends.js path mapping. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: list the moss-tts-cpp backend among the LocalAI-maintained engines Add moss-tts.cpp to the README "Backends built by us" table, the Text-to-Speech compatibility table, and the reference-audio voice-cloning backend list, so the new backend is documented alongside its peers. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * backend(moss-tts-cpp): pin moss-tts.cpp to the squashed single-commit release moss-tts.cpp history was collapsed to a single commit; repoint MOSSTTS_CPP_VERSION to ee722b8e9205ee9b1b1c398a4e87e4e393e9be41. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * backend(moss-tts-cpp): add the moss-tts-cpp-development gallery meta The gallery had the -development image entries but no matching -development meta anchor (as locate-anything-cpp and depth-anything-cpp have), so the master build was not installable as a gallery backend. Add moss-tts-cpp-development mirroring the production meta with the -development capability image names. 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]andEttore Di Giacinto
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c1a891662c |
refactor(settings): single declarative registry for runtime settings (fixes the #10845 bug class) (#10864)
* feat(settings): add declarative runtime-settings field registry One fieldSpec row per RuntimeSettings field, with a reflection completeness spec so a field added without a registry row is a red test instead of a silently-dropped setting (the #10845 bug class). Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude Code:claude-fable-5 * refactor(settings): drive ToRuntimeSettings/ApplyRuntimeSettings from the field registry Behavior-preserving: ~350 hand-written per-field lines become two loops over runtimeSettingsFields, gated by a To->Apply->To round-trip spec. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude Code:claude-fable-5 * feat(settings): baseline-driven startup merge for persisted runtime settings ApplyRuntimeSettingsAtStartup compares the live config against DefaultRuntimeBaseline (option-less-run defaults incl. kong-injected flag defaults) instead of per-field == 0 guards. Fixes persisted lru_eviction_max_retries, tracing_max_items, agent_job_retention_days, memory_reclaimer_threshold, galleries and autoload flags being silently ignored at boot. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude Code:claude-fable-5 * fix(settings): registry-driven startup merge, applied before consumers loadRuntimeSettingsFromFile becomes a thin wrapper over ApplyRuntimeSettingsAtStartup and runs at the top of New(), before model configs capture app-level defaults. WithThreads stops eagerly resolving 0 so a persisted thread count survives restart while LOCALAI_THREADS still wins (#10845); the physical-core fallback moves after the merge. Also: run.go now injects the memory-reclaimer threshold unconditionally so the option-less boot matches DefaultRuntimeBaseline and a UI-saved threshold survives restart. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude Code:claude-fable-5 * refactor(settings): file watcher delegates to the registry merge; shared API-key merge Manual edits to runtime_settings.json now behave like a boot-time load (env still wins) instead of the inverted diverged-from-startup guard that ignored most manual edits. MergeAPIKeys dedups env keys in one place for the endpoint and the watcher. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude Code:claude-fable-5 * docs(settings): document unified runtime-settings precedence Document the single env/CLI > runtime_settings.json > defaults rule, applied identically at boot, on POST /api/settings, and on manual file edits, plus the two known limitations (default-valued env vars are indistinguishable from unset; API-changed fields hot-apply on the next restart only). Also add a completion debug log when the watcher applies runtime_settings.json. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude Code:claude-fable-5 * test(settings): reset the global VRAM cap leaked by the round-trip spec The round-trip spec applies vram_budget=12GiB, whose post-loop hook installs a process-global default cap; without a reset every spec ordered after it runs under that phantom budget. Also drop a stale enumeration in the ApplyRuntimeSettings doc comment. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude Code:claude-fable-5 --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]andEttore Di Giacinto
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8cec22c3b7 |
feat(vram): per-node VRAM allocation budget (LOCALAI_VRAM_BUDGET) (#10833)
* feat(vram): add vrambudget primitive for per-node VRAM caps Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): apply default VRAM budget in xsysinfo aggregate getters Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): wire LOCALAI_VRAM_BUDGET flag to xsysinfo default budget Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): persist VRAM budget via runtime settings with live apply Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(vram): reset process-global VRAM budget after runtime-settings spec Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): add VRAM budget field to Settings page Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): store and enforce per-node VRAM budget in the node registry Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): apply per-node VRAM budget in router hardware defaults Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): report worker VRAM budget in node registration The distributed worker now reports its operator-set VRAM budget string (LOCALAI_VRAM_BUDGET) to the server on registration. The worker keeps reporting RAW total/available VRAM and never sets the xsysinfo process-global budget (that stays standalone-only); the server resolves and enforces the budget uniformly (Task 6). Also closes a Task 6 gap: on re-registration, a struct Updates zero-skips an empty budget, so a worker that dropped LOCALAI_VRAM_BUDGET left the stale cap in place. For non-admin-override nodes the budget columns are now force-written (map Updates) even when empty, so removing the env var clears the cap; admin overrides are preserved unchanged. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * style(vram): drop em dash from worker-clear comment Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): add node VRAM budget admin endpoints Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): add node VRAM budget control to the node UI Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): expose set_node_vram_budget MCP admin tool Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(vram): document LOCALAI_VRAM_BUDGET and node VRAM budget UI Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(vram): avoid double-applying VRAM budget in GetResourceAggregateInfo The GPU-branch aggregate returned by GetResourceInfo is sourced from GetGPUAggregateInfo, which already caps total/free/used against the process-wide VRAM budget. GetResourceAggregateInfo then applied the budget a second time. For an absolute budget this is idempotent, but for a percentage budget b.Apply resolves the ceiling as a fraction of its input total, so a second pass yields P*(P*T) instead of P*T and distorts UsagePercent (read by the memory reclaimer in pkg/model/watchdog.go). Remove the redundant second application so the budget is applied exactly once, against the raw physical totals, upstream in GetGPUAggregateInfo. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(vram): implement SetNodeVRAMBudget on mcp assistant test stub The LocalAIClient interface gained SetNodeVRAMBudget; the stubClient in core/http/endpoints/mcp used by the assistant tests is a separate implementer and needs the method too (broke golangci-lint typecheck and both test jobs). 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]andEttore Di Giacinto
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3601174ce0 |
fix(distributed): make per-node backend upgrade actually upgrade (#10838)
* test(core/http): make the suite's HTTP port overridable app_test.go and openresponses_test.go hardcoded 127.0.0.1:9090. When another service already listens on 9090 the suite does not fail fast: the server goroutine logs the bind error and the specs then poll whatever is squatting the port until Eventually times out. On machines where 9090 is permanently taken this makes the pre-commit coverage gate impossible to pass. Introduce testHTTPAddr, defaulting to 127.0.0.1:9090 (what CI has always used) and overridable via LOCALAI_TEST_HTTP_PORT for local runs. Assisted-by: Claude:claude-fable-5 golangci-lint Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(distributed): make per-node backend upgrade actually upgrade The node detail page's Upgrade button reused the node-scoped install path (POST /api/nodes/:id/backends/install). That fires NATS backend.install with force=false, and the worker's install handler is deliberately "ensure installed": when the backend binary already exists on disk it short-circuits without touching the gallery. Since only an installed backend can be upgraded, the whole chain was a guaranteed successful no-op - the UI then toasted "backend upgraded" without even waiting for the async job. Route upgrades through the real force-reinstall path instead: - BackendManager.UpgradeBackend now receives the ManagementOp (like InstallBackend already did) so implementations can honor op.TargetNodeID. - DistributedBackendManager.UpgradeBackend scopes the backend.upgrade fan-out to op.TargetNodeID when set, and errors when the target node does not report the backend as installed. - New POST /api/nodes/:id/backends/upgrade endpoint enqueues an Upgrade=true node-scoped op (async 202 + jobID, mirroring install). - NodeDetail UI calls the new endpoint and reports the dispatch ("Upgrading ... on this node...") instead of claiming success; the Operations panel tracks the actual job. Verified against a live local cluster (NATS + Postgres + two workers): the target worker stops the running process, force-reinstalls from the gallery and re-downloads the OCI image; the second worker receives no backend.upgrade event; upgrading a backend missing from the target node fails the job with a clear error. Assisted-by: Claude:claude-fable-5 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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LocalAI [bot]andEttore Di Giacinto
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bcc41219f7 |
feat: materialize Hugging Face model artifacts (#10825)
* feat(config): add model artifact source contract Assisted-by: Codex:GPT-5 [Codex] * feat(downloader): add authenticated raw-byte progress Assisted-by: Codex:GPT-5 [Codex] * feat(huggingface): resolve immutable snapshot manifests Assisted-by: Codex:GPT-5 [Codex] * feat(models): add artifact storage primitives Assisted-by: Codex:GPT-5 [Codex] * feat(models): materialize pinned Hugging Face snapshots Assisted-by: Codex:GPT-5 [Codex] * feat(models): bind managed snapshots at runtime Assisted-by: Codex:GPT-5 [Codex] * feat(gallery): materialize model artifacts during install Assisted-by: Codex:GPT-5 [Codex] * feat(gallery): declare managed Hugging Face artifacts Assisted-by: Codex:GPT-5 [Codex] * feat(models): preload managed model artifacts Assisted-by: Codex:GPT-5 [Codex] * fix(gallery): retain shared artifact caches on delete Assisted-by: Codex:GPT-5 [Codex] * feat(models): report artifact acquisition progress Assisted-by: Codex:GPT-5 [Codex] * refactor(backends): load managed models from ModelFile Assisted-by: Codex:GPT-5 [Codex] * refactor(backends): load staged speech model snapshots Assisted-by: Codex:GPT-5 [Codex] * refactor(backends): use staged snapshots in engine backends Assisted-by: Codex:GPT-5 [Codex] * test(distributed): cover staged artifact snapshots Assisted-by: Codex:GPT-5 [Codex] * docs: explain managed model artifacts Assisted-by: Codex:GPT-5 [Codex] * docs: add product design context Assisted-by: Codex:GPT-5 [Codex] * feat(ui): show model artifact download progress Assisted-by: Codex:GPT-5 [Codex] * Eagerly materialize Hugging Face artifacts Materialize HF-backed model references as managed GGUF artifacts during load, with lazy download retained only as fallback. Assisted-by: Codex:GPT-5 [shell] * Refactor HF downloads through a shared executor Assisted-by: Codex:GPT-5 [shell] * drop 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]andEttore Di Giacinto
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4056283aa4 |
[voice] feat: add managed voice cloning profiles (#10799)
* feat(ui): add voice library workflow Give administrators a production-ready flow to record or upload consented reference audio, manage reusable profiles, inspect API usage, discover compatible models, and hand a saved voice directly to text-to-speech. Assisted-by: Codex:gpt-5 * feat(voice): add managed voice cloning profiles Make reusable reference voices manageable through the admin API instead of requiring model-directory and YAML edits. Discover compatible installed and gallery models from server-side backend capabilities, retain explicit model configuration controls, and stage saved references for supported backends. Expose profile management through REST and MCP, document backend-specific behavior, and cover the workflow from profile creation through real Qwen3-TTS synthesis. Harden the agent-job HTTP test against completion racing cancellation. Assisted-by: Codex:gpt-5 --------- Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]andEttore Di Giacinto
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b00422e45f |
feat(backends): add LongCat video and avatar generation (#10792)
* feat(backends): add LongCat video and avatar generation Assisted-by: Codex:GPT-5 [apply_patch] [exec_command] [web] * refactor(config): declare model I/O modalities Make model configs declare input and output modalities so capability discovery no longer branches on backend or checkpoint names. Complete the LongCat gallery and user documentation, make the SDPA patch apply to the pinned upstream revision, and stabilize the Agent Jobs race exposed by the required hook. Assisted-by: Codex:GPT-5 [web] --------- Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]andEttore Di Giacinto
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94bdc825dc |
feat(backend): add moss-transcribe-cpp backend (MOSS-Transcribe-Diarize) (#10756)
C++/ggml transcription + speaker diarization + timestamps backend. Purego dlopens libmoss-transcribe.so (ggml statically linked) from moss-transcribe.cpp and serves offline AudioTranscription, parsing the [start][Sxx]text[end] output into segments with nanosecond timestamps. Adds the importer (surfaces in GET /backends/known), backend-matrix (Linux + Darwin/metal), backend/index.yaml, and a gallery entry (default q5_k GGUF from mudler/moss-transcribe.cpp-gguf). Local L0 smoke (go build + go test ./... = 16 pass, golangci-lint 0 issues) passed against the real libmoss-transcribe.so. The pre-commit coverage gate (full pkg/core + tests/e2e) could not run in the authoring sandbox (no live models, port 9090 held); CI must enforce it before merge. Assisted-by: Claude:claude-opus-4-8 golangci-lint Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]andEttore Di Giacinto
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40dae953f4 |
feat: interleaved thinking with tool calls (reasoning_content alias + Anthropic thinking blocks) (#10744)
* feat(schema): accept reasoning_content as inbound alias for reasoning Interleaved-thinking clients (cogito, vLLM/DeepSeek-style) emit reasoning_content on assistant turns. Accept it as an inbound alias so reasoning survives the tool-result loop; canonical reasoning wins when both are present. Emission is unchanged (still reasoning). Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(schema): pin interleaved reasoning+tool_calls round-trip Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(openai): pin reachedTokenBudget truncation detection Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(anthropic): add thinking and signature fields to content blocks Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(anthropic): parse inbound thinking blocks into reasoning Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(anthropic): emit thinking blocks with synthetic signature on tool turns Extract buildAnthropicContentBlocks so non-streaming content assembly is unit-testable, and prepend a thinking block (with an opaque synthetic signature) before text/tool_use blocks when the request opts into thinking. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(anthropic): stream thinking_delta and signature_delta before tool_use Extract anthropicStreamSequence so the streaming block order is unit-testable, and emit content_block_start(thinking) -> thinking_delta -> signature_delta -> content_block_stop before the tool_use block sequence when thinking is enabled. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: add interleaved thinking with tool calls guide Add a features guide describing interleaved thinking: an assistant turn carrying reasoning and tool_calls together, the reasoning-round-trip contract (including the reasoning_content inbound alias and Anthropic thinking blocks with a synthetic signature), per-backend enablement (reasoning_format for llama.cpp, reasoning_parser/tool_call_parser for vLLM/SGLang plus the vLLM auto-config hook), a worked request/response example, and known limitations. Cross-link from model-configuration, text-generation, and openai-functions. 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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Roman Mazurenkoandrvmzes
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a3fdfbc0d1 |
feat(llama-cpp): add device selection option (#10724)
Allow llama.cpp model configs to select the backend devices used for offload, matching upstream --device behavior so users can exclude a display or debug GPU. Signed-off-by: rvmzes <rvmzes@rvmzess-MacBook-Pro.local> Co-authored-by: rvmzes <rvmzes@rvmzess-MacBook-Pro.local> |
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LocalAI [bot]andEttore Di Giacinto
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b0959d4756 |
feat(api): add GET /v1/models/capabilities endpoint (#10687)
Additive superset of /v1/models that enriches each model entry with the capabilities it supports plus its input/output modalities (text / image / audio / video). Clients that only understand /v1/models are unaffected -- they simply never call the new route. Audio and video *input* are derived from the model's multimodal limits (vLLM limit_mm_per_prompt), which no single usecase FLAG expresses. That gap is exactly why a plain capability list is insufficient and this enriched endpoint exists: an attachment router can now decide whether an image/audio/video file can go to the active model directly, or must be converted/transcribed first. Capability derivation lives in core/config as the single source of truth (ModelConfig.Capabilities / InputModalities / OutputModalities / VisionSupported / ...); the Ollama capability surface now delegates to it instead of keeping a parallel copy. Vision is gated on chat/completion capability so a MediaMarker hydrated onto a non-chat model (e.g. a pure ASR/TTS backend) no longer reports a false vision capability. Read-only listing: no new FLAG_* flag, reuses the existing `models` swagger tag, and intentionally exposes no MCP admin tool (there is nothing to manage conversationally). 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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Ettore Di Giacinto
|
1152acc167 |
Revert "feat(config): default swa_full:true for sliding-window-attention models" (#10674)
Revert "feat(config): default swa_full:true for sliding-window-attention mode…"
This reverts commit
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Richard Palethorpe
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eb32cd9073 |
feat(realtime): eager blocking pipeline warm-up + /backend/load API (#10662)
Realtime sessions previously lazy-loaded each pipeline sub-model (VAD,
transcription, LLM, TTS) on first use, so every cold session paid a
per-request model-load stall and load errors only surfaced mid-stream.
Warm the whole pipeline eagerly and blockingly at session start
(including the voice-gate speaker-recognition model, which an enforced
gate blocks each utterance on; compaction's summary_model stays lazy
since it only runs off the response path):
- Add backend.PreloadModel / PreloadModelByName as the single load path
for every modality (no transcription special-case; backend-omitted
configs are deprecated).
- The realtime session blocks on Model.Warmup and returns a
model_load_error to the client if any stage fails to load;
updateSession warms in the background. Opt out per pipeline with
pipeline.disable_warmup, exposed as a UI toggle via the
config-metadata registry.
Add a LocalAI-native POST /backend/load (and /v1/backend/load) that
pre-loads a model -- expanding realtime pipelines into their sub-models
-- as the inverse of /backend/shutdown. There is one preload engine
(backend.PreloadStages): the realtime Warmup methods, /backend/load and
the --load-to-memory startup flag all use it, so --load-to-memory now
also expands pipeline models and records load-failure traces. Pipeline
sub-model alias resolution is likewise shared
(ModelConfigLoader.LoadResolvedModelConfig). Surface the endpoint
everywhere an admin manages models:
- MCP admin tool load_model (httpapi + inproc clients, safety/catalog
prompts, catalog/dispatch tests).
- "Load into memory" action in the React models UI.
- Swagger regenerated; docs moved to the general backend-monitor page
since it is not realtime-specific.
Fix a Traces UI crash ("json: unsupported value: -Inf"): audio-snippet
RMS/peak now floor at a finite dBFS, and backend-trace data is sanitized
to drop non-finite floats before marshaling. The sanitizer is
copy-on-write -- it runs on every RecordBackendTrace, so containers are
only re-allocated on the paths that actually changed.
Migrate core/http/openresponses_test.go onto the prebuilt mock-backend
the rest of the http suite already uses -- it was the last spec still
pointing at a real HuggingFace model, so it 404'd wherever no vision
backend was built -- and fix its item_reference specs to send the
spec's "id" field instead of "item_id", which the handler never
accepted.
Assisted-by: Claude:claude-opus-4-8 Claude Code
Signed-off-by: Richard Palethorpe <io@richiejp.com>
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LocalAI [bot]andEttore Di Giacinto
|
02b007a31e |
feat(config): default swa_full:true for sliding-window-attention models (#10611)
LocalAI enables a cross-request prompt-prefix cache (cache_reuse, see core/config/serving_defaults.go) so repeated prefixes — system prompts, RAG context, agent scaffolds, multi-turn chat — are not reprocessed every turn. For sliding-window-attention (SWA) models (Gemma 2/3, Cohere2, Llama 4, ...) this silently does nothing: llama.cpp defaults to a reduced SWA KV cache sized to the sliding window, and that reduced cache cannot preserve a prompt prefix across requests, so every turn reprocesses the whole prompt anyway. llama.cpp's --swa-full (params.swa_full, already wired through the LocalAI llama.cpp backend's `swa_full` option) keeps the full KV cache so the shared prefix is reused. Enable it automatically, but only for models that are actually SWA: detection reads the gguf-parser-normalized `<arch>.attention.sliding_window` metadata (which also applies llama.cpp's family rules, e.g. Phi-3 → not SWA), right where the GGUF is already parsed for defaults. It is never applied to dense models (pure memory waste) and never overrides an explicit user `swa_full`/`n_swa` choice. Tradeoff: the full SWA cache scales with context_size, so it costs more memory at large contexts — hence the SWA gating and the documented `swa_full:false` opt-out. Assisted-by: Claude:claude-opus-4-8 [Claude Code] golangci-lint Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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Richard Palethorpe
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5d0c43ec6e |
feat(realtime): Semantic VAD EOU token (#10444)
* feat(realtime): EOU-driven semantic_vad turn detection Add a `semantic_vad` turn-detection mode to the realtime API that feeds the transcription model live and decides "the user finished speaking" from the `<EOU>` end-of-utterance token rather than from silence alone. When EOU fires the turn commits immediately (~0.3s); otherwise it falls back to an eagerness-scaled silence threshold (low/med/high = 8/4/2s). Plumbing, bottom to top: - proto: `AudioTranscriptionLive` bidirectional RPC (config-first oneof, mono float PCM @16k, ready-ack / Unimplemented degrade signal) plus `TranscriptResult.eou` for the unary retranscribe gate. - pkg/grpc: client/server/base/embed scaffolding for the bidi stream, modeled on AudioTransformStream; release stream conns on terminal Recv. - parakeet-cpp: live transcription RPC with per-C-call engine locking (one live stream per turn, finalize+free at commit); bump parakeet.cpp to ABI v5 — incremental StreamingMel (no more quadratic per-feed mel recompute that delayed EOU on long turns) and the <EOU>/<EOB> split; strip the literal <EOU>/<EOB> from offline text and set Eou. - core/backend: LiveTranscriptionSession wrapper + pipeline `turn_detection:` config block (type/eagerness/retranscribe). - realtime: semantic_vad integration — live input captions streamed as transcription deltas while the user speaks, EOU-immediate commit with eagerness fallback, optional retranscribe gate (batch re-decode must also end in <EOU> to confirm), clause synthesis off the LLM token callback, and per-turn live-transcription / model_load telemetry. - UI: show the realtime pipeline components as a vertical list. Docs and tests included; opt-in via the pipeline YAML or per-session `session.update`. Non-streaming STT backends degrade to silence-only. Assisted-by: Claude Code:claude-opus-4-8 [Read] [Edit] [Write] [Bash] Assisted-by: Claude Code:claude-fable-5 [Read] [Edit] [Bash] Signed-off-by: Richard Palethorpe <io@richiejp.com> * feat(realtime): explicit formally-verified state machines + parakeet streaming driver The realtime API had several implicit state machines whose state was inferred from scattered booleans, channels, and five separate mutexes, leaving illegal/inconsistent states reachable. Make them explicit and keep the implementation in step with a formal design; rework the parakeet streaming backend along the same lines. Realtime state machines (M1-M5). Each is a sealed sum-type State/Event/Effect with a total, pure Next(state,event)->(state,[]effect) behind a single-writer Coordinator: M1 conncoord connection lifecycle: VAD toggle + once-only teardown (replaces vadServerStarted + a `done` channel closed from two sites). M2 turncoord turn detection: collapses speechStarted and the live-stream "turn open" flag into one state, so discardTurn can no longer desync them and suppress the next onset. M3 respcoord response coordination: serializes the dual-writer start/cancel so at most one response is live; one response.done per response.create. M4 compactcoord conversation compaction: single-flight (replaces the `compacting atomic.Bool` CAS). M5 ttscoord TTS pipeline: open->closing->closed, idempotent wait(), rejects enqueue-after-close (was a silent drop). The Coordinator/Sink/Next plumbing — only the sealed types and Next differed per machine — is extracted once into core/http/endpoints/openai/coordinator as a generic Coordinator[S,E,F]; each machine keeps its public API via type aliases, so no sink, call-site, or test moved. Hierarchy. session_lifecycle.fizz models M1 as the parent region with its children (M2/M3/M4) as one statechart and asserts ChildrenDieWithParent (conn torn => all children terminal, none start after teardown). respcoord and compactcoord gain an absorbing Terminated state + Shutdown event; conncoord's teardown drives the children terminal. This closes a compaction teardown gap: a fire-and-forget compaction could outlive a torn session — compactionSink now takes a session-scoped cancellable context + WaitGroup and joins the in-flight summarize+evict on shutdown. Formal verification. formal-verification/ holds one authoritative FizzBee spec per machine plus the composition spec, each with an always-assertion and a documented one-line edit that makes the checker fail (verified non-vacuous). scripts/realtime-conformance.sh is fail-closed: all Go conformance suites under -race AND a model-check of every .fizz spec; a missing FizzBee is a hard error (only the loud REALTIME_CONFORMANCE_SKIP_FIZZBEE=1 bypasses it, never in CI). FizzBee is pinned by sha256 and installed via scripts/install-fizzbee.sh into .tools/ (gitignored). Wired as make test-realtime-conformance, a CI workflow, and a pre-commit path filter. Go conformance tests are Ginkgo/Gomega (per the repo's forbidigo lint): transition tables + fixed-seed property walks + concurrent/-race specs, no rapid dependency. Design map: docs/design/realtime-state-machines.md. Parakeet streaming backend. The same treatment applied to the parakeet-cpp streaming paths: - AudioTranscriptionStream returns codes.Unimplemented for non-streaming models instead of decoding offline and emitting it as one delta + final. A client that asked for streaming learns the model cannot stream rather than receiving a batch result shaped like a stream. New grpcerrors.StreamTranscriptionUnsupported carries that signal; the HTTP /v1/audio/transcriptions stream path surfaces it as an SSE error event. Mirrors AudioTranscriptionLive, which already did this. - utteranceBoundary (boundary.go): a single definition of the end-of-utterance latch, replacing three open-coded finalEou toggles. Modelled as a two-valued type so illegal states are unrepresentable. - Shared decode driver (driver.go): streamFeedResult (one per-feed event) + feedChunk (hides the ABI v4 JSON vs text-only split) + feedSlices + flushTail. The feed loop is written once. - AudioTranscriptionLive becomes a bidi adapter: it streams the per-feed {delta,eou,eob,words} the realtime turn detector consumes and a terminal FinalResult carrying only Text. Segments/duration/eou are offline-only and no longer produced (nor read) on the live path; liveTraceState drops the terminal eou and keeps the per-feed eou_events count. - AudioTranscriptionStream + streamJSON merge into one driver-based function; streamSegmenter is generalized to the unified event with a text-only fallback that preserves the legacy (no-words) library's per-utterance segmentation. Verified: build/vet/gofumpt clean, golangci-lint 0 issues, all coordinator and parakeet packages under -race, the fail-closed conformance gate green, and make test-realtime (12 e2e WS+WebRTC). 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]andEttore Di Giacinto
|
de2ec2f136 |
feat(backends): add voice-detect + face-detect ggml backends (replace Python insightface/speaker-recognition) (#10441)
* feat(voice-detect): add Go purego backend for voice-detect.cpp Add backend/go/voice-detect implementing the Backend gRPC voice subset (VoiceEmbed/VoiceVerify/VoiceAnalyze) over libvoicedetect.so via purego, mirroring the parakeet-cpp / omnivoice-cpp backends. The flat voicedetect_capi C ABI is dlopen'd cgo-less; malloc'd string and float-vector returns are owned by Go and released through the matching capi free functions, with the per-ctx last error surfaced into Go errors. Calls are serialized via base.SingleThread since the C context is not reentrant. Proto field mapping: - VoiceEmbed: VoiceEmbedRequest.audio (path) -> embed_path -> Embedding+Model. - VoiceVerify: audio1/audio2 + threshold (<=0 falls back to the verify_threshold option, default 0.25) -> verify_paths -> verified/distance/ threshold/confidence/model/processing_time_ms. - VoiceAnalyze: audio (path) -> analyze_path_json; the JSON age/gender/emotion document maps to a single VoiceAnalysis segment (start/end 0; gender "label" -> dominant_gender with the remaining float scores as the gender map; emotion label/scores -> dominant_emotion/emotion). The Makefile pins voice-detect.cpp to 47546430, clones+builds libvoicedetect.so with ggml static-linked (PIC, GGML_NATIVE off) so dlopen needs no external libggml/libvoicedetect; ldd on the artifact shows only system libs. Ginkgo tests cover option parsing and analyze-JSON mapping; embed/verify smoke specs gate on VOICEDETECT_BACKEND_TEST_MODEL + VOICEDETECT_BACKEND_TEST_WAV. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(voice-detect): wire backend into index, gallery and build Register the voice-detect.cpp speaker-recognition + voice-analysis backend (added in Voice-INT-A) into LocalAI's distribution surfaces, mirroring the ced backend (the closest mudler C++/ggml audio analogue): - backend/index.yaml: add the &voicedetect meta-backend (capabilities platform map, no top-level uri) plus the full set of concrete per-arch image entries (cpu/cuda12/cuda13/metal/rocm/sycl/vulkan/l4t and the -development variants). Referential integrity audited - every alias target resolves. - gallery/index.yaml: add 5 model entries on backend voice-detect - ECAPA-TDNN, WeSpeaker ResNet34, 3D-Speaker ERes2Net, CAM++ and the wav2vec2 age/gender/emotion analyze model. The engine architecture is read from GGUF metadata (voicedetect.arch) at load. GGUF artifacts are not yet published: each files: entry points at the intended mudler/voice-detect-gguf location with a TODO to fill sha256 after upload (no fabricated hashes). - .github/backend-matrix.yml: add the linux build matrix block + the darwin metal entry mirroring ced. - .github/workflows/bump_deps.yaml: track mudler/voice-detect.cpp via VOICEDETECT_VERSION (pin 47546430, = 4754643). - core/config/backend_capabilities.go: register voice-detect in the backend capability map (VoiceVerify/VoiceEmbed/VoiceAnalyze -> speaker_recognition), mirroring speaker-recognition. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(face-detect): add purego Go backend for face-detect.cpp Add the LocalAI Go backend that dlopens libfacedetect.so (the flat facedetect_capi_* C-ABI) via purego, mirroring the sibling voice-detect backend. Implements the Face subset of the Backend gRPC service: - Embeddings(PredictOptions): Images[0] base64 -> temp file -> embed_path -> L2-normalized ArcFace embedding. - Detect(DetectOptions): src -> detect_path_json -> Detection boxes (class_name "face", [x1,y1,x2,y2] -> x/y/w/h). - FaceVerify(FaceVerifyRequest): two images + threshold + anti_spoof -> verify_paths; best-effort img areas via detect. - FaceAnalyze(FaceAnalyzeRequest): img -> analyze_path_json -> per-face age + gender ("M"/"F" normalized to "Man"/"Woman"). The Makefile pins face-detect.cpp to 636a1963 and builds the shared lib with ggml + vendored libjpeg-turbo static (PIC), so the .so is ldd-clean (no libggml) and exports only facedetect_capi_* (no jpeg_ symbols). Gated Ginkgo e2e mirrors voice-detect. Note for the gallery-wiring task: backend registration (index.yaml, gallery, core/config/backend_capabilities.go) is intentionally not touched here. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * fix(voice-detect): replace em dashes in net-new descriptions Project style forbids em/en dashes. Replace the three U+2014 chars introduced by the voice-detect gallery/index wiring with `-`/`:`. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(face-detect): wire backend into index, gallery and build Register the face-detect.cpp face detection / embedding / verification / analysis backend (added in Face-INT-A) into LocalAI's distribution surfaces, mirroring the voice-detect wiring (the closest mudler C++/ggml recognition analogue): - backend/index.yaml: add the &facedetect meta-backend (capabilities platform map, no top-level uri to avoid the meta-backend gotcha) plus the full set of concrete per-arch image entries (cpu/cuda12/cuda13/ metal/rocm/sycl-f16/sycl-f32/vulkan/l4t and the -development variants), 22 entries. Referential integrity audited: every alias target resolves. - gallery/index.yaml: add 4 model entries on backend face-detect - face-detect-buffalo-l/m/s (insightface SCRFD + ArcFace/MBF, NON-COMMERCIAL) and face-detect-yunet-sface (OpenCV-Zoo YuNet + SFace, APACHE-2.0, the commercial-friendly alternative). The detector/embedder architecture is read from GGUF metadata (facedetect.arch) at load; only the real verify_threshold option is set (0.35 buffalo, 0.363 sface). GGUF artifacts are not yet published: each files: entry points at the intended mudler/face-detect-gguf location with a TODO to fill sha256 after upload (no fabricated hashes). - core/config/backend_capabilities.go: register face-detect in the backend capability map (Embedding/Detect/FaceVerify/FaceAnalyze -> face_recognition), mirroring insightface. - .github/backend-matrix.yml: add the linux build matrix block + the darwin metal entry mirroring voice-detect. - .github/workflows/bump_deps.yaml: track mudler/face-detect.cpp via FACEDETECT_VERSION (pin 636a1963). Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * fix(recon): voice-detect metal build branch + face-detect gallery usecases Add the missing metal BUILD_TYPE branch to the voice-detect Makefile forwarding -DVOICEDETECT_GGML_METAL=ON, mirroring face-detect, so the darwin metal CI artifact is built with the Metal backend instead of CPU-only. Expand the 4 face-detect gallery models' known_usecases to [face_recognition, detection, embeddings] to match the backend capabilities map and the mirrored insightface-buffalo entries, so auto-selection for /v1/detect and /embeddings works. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * docs(recon): document voice-detect and face-detect ggml backends Document the new standalone C++/ggml biometric backends as the recommended/default option for face and voice recognition, keeping the existing Python insightface / speaker-recognition backends framed as the legacy path. - features/face-recognition.md: add a face-detect (ggml) backend section with the gallery entries (buffalo-l/m/s non-commercial, yunet-sface Apache-2.0), licensing, and verify/detect/analyze quickstart. - features/voice-recognition.md: add a voice-detect (ggml) backend section with the gallery entries (ecapa-tdnn, wespeaker-resnet34, eres2net, campplus speaker recognizers; emotion-wav2vec2 non-commercial analyze head) and quickstart. - reference/compatibility-table.md: add face-detect.cpp and voice-detect.cpp rows to the Vision, Detection & Recognition table. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * chore(gallery): publish recon backend GGUF uris + sha256 Fill in the published HuggingFace GGUF uris and verified sha256 for the 9 recon gallery entries (voice-detect-* and face-detect-*), and remove the TODO publish markers. Correct the eres2net, campplus, and emotion-wav2vec2 uris to the actual published filenames. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(gallery): re-embed buffalo anti-spoof + add audeering age/gender voice model Update the 3 buffalo face-detect GGUF sha256 (anti-spoof ensemble now embedded and re-uploaded under the same filenames/uris) and note the FaceVerify anti_spoof request flag in each description. Add a new voice-detect-age-gender-wav2vec2 gallery entry mirroring the emotion model. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(gallery): add face-detect-buffalo-sc and antelopev2 packs Add gallery entries for two newly-published insightface face packs on the face-detect backend: buffalo_sc (smallest pack, SCRFD-500M + small ArcFace) and antelopev2 (higher-accuracy, SCRFD-10G + ArcFace glint360k R100, 512-d). Both are non-commercial research-only. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(recon): honor LocalAI per-model threads in voice/face-detect backends LocalAI spawns one backend process per model and serves requests concurrently, so the engines' own min(hardware_concurrency, 8) default can oversubscribe cores. Forward the per-model Threads value from the gRPC LoadModel options into the engine via VOICEDETECT_THREADS / FACEDETECT_THREADS (read at backend construction) before the capi load. A non-positive Threads is treated as unset, leaving the engine default. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * chore(recon): bump backend pins to CPU-optimized engine commits voice-detect.cpp -> 0d9c1b3 (radix-2 FFT FBank, threads, flash attn + cached pos-conv); face-detect.cpp -> 523aee1 (thread-gated direct conv, threads). Brings the CPU optimizations into the LocalAI backend builds. GGUF format and parity unchanged, so the published HF GGUFs remain valid. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * chore(recon): bump backend pins to round-2 CPU-optimized engines voice-detect.cpp -> fe7e6a3 (ERes2Net 1x1->mul_mat, CAM++ layout+context, wav2vec2 conv-LN, ECAPA capture-drop, AVX512 dispatch opt-in); face-detect.cpp -> 9c8adb7 (AVX2 Winograd F(2x2,3x3) for SCRFD/ArcFace 3x3 convs, ArcFace BN-fold). Parity unchanged (cosine=1.0); GGUF format unchanged, HF GGUFs valid. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * chore(recon): bump backend pins to round-3 Winograd engines voice-detect.cpp -> 45122ec (Winograd F(2x2,3x3) for WeSpeaker/ERes2Net 3x3 convs, -22%/-20% @8t); face-detect.cpp -> cd5c962 (Winograd F(4x4,3x3) for SCRFD large maps, -22% @1t on top of F(2x2), more load-stable). Parity held (cosine=1.0); GGUF format unchanged, HF GGUFs valid. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * chore(recon): bump backend pins to round-4 Winograd engines (CPU opt complete) voice-detect.cpp -> d2839ca (CAM++ FCM 2D convs through Winograd, -15.5%/-10.3%); face-detect.cpp -> c1db23d (AVX2-vectorized Winograd tile transforms, SCRFD detect -14%/-9.6%). Final CPU optimization round; the conv-kernel lever class is now exhausted (parity held cosine=1.0; GGUF/parity unchanged, HF GGUFs valid). Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * chore(recon): bump face-detect pin to deep-kernel engine (7ae5c4d) face-detect.cpp -> 7ae5c4d: register-blocked winograd-domain GEMM microkernel (2.8x isolated GFLOP/s), AVX-512 zmm evolution behind runtime CPUID dispatch (ship-safe, AVX2 fallback bit-identical), bias/relu fused into the winograd output transform, and SFace Conv+BN fold + bias/PReLU fusion. SCRFD detect ~1.4x faster end-to-end vs the round-4 baseline; parity bit-exact; portable single binary (function-multiversioned, no global -mavx512f). Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * chore(recon): bump voice-detect pin to ECAPA operand-order win (e9c56ae) voice-detect.cpp -> e9c56ae: weight-as-src0 mul_mat order in ECAPA's F32 conv1d_same (routes through tinyBLAS sgemm); ECAPA embed 1.67x @1t / ~1.3x @8t, parity cosine=1.0. Isolated to encoder.cpp (ECAPA-only); ERes2Net/CAM++/WeSpeaker do not call conv1d_same so are provably unaffected. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * chore(recon): bump pins to FMA-throughput engines (voice f7b9f89, face 2d2d5f0) face -> 2d2d5f0: route ArcFace 3x3 body convs through the AVX-512 winograd microkernel (kWinoMinSize 80->14); ArcFace 1.62x @1t, SCRFD detect to 0.966 of MLAS @1t, no regression. voice -> f7b9f89: runtime-CPUID-dispatched AVX-512 winograd-GEMM microkernel (ship-safe, AVX2 fallback bit-identical); WeSpeaker 1.90x @1t. Parity cosine=1.0 throughout; portable single binaries. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * chore(recon): bump pins to MLAS-class direct-conv engines (voice 7ecfd07, face be22d67) Hand-tuned nChw16c AVX-512 register-tiled direct-conv microkernel (~263 GFLOP/s, within 6-7% of MLAS per-op efficiency), runtime-CPUID-dispatched + AVX2 fallback, fused bias/relu. voice 7ecfd07: default 3x3-s1 kernel for WeSpeaker (+37%/+32%) + ERes2Net, CAM++ pinned to Winograd. face be22d67: shape-gated to the ArcFace recognizer body (+25-27% @8t); SCRFD detector stays on Winograd (no regression). Parity cosine=1.0 / detect <=1px on AVX-512 + AVX2 paths. Portable single binaries. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * chore(recon): bump voice pin to Phase-A blocked backbone (f4e7eef) WeSpeaker ResNet34 runs as one nChw16c blocked island (2 reorders/forward vs ~60) on AVX-512, default; per-conv directconv fallback on AVX2. +2.9% @1t / +17-19% @8t vs per-conv directconv, parity cosine=1.0. The conv microkernel is already FMA-bound near peak (~0.86-0.98x MLAS-implied); residual to MLAS is sub-peak edge + non-conv tail, documented in docs/cpu-optimization.md. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * chore(recon): bump pins to breadth blocked-backbone (voice 7f66871, face d80092b) voice 7f66871: AVX2-vectorized (ymm) blocked island - AVX2-only hosts now run the blocked backbone for WeSpeaker (2.3x over per-conv-AVX2, cosine=1.0); ERes2Net stays per-conv (blocked regresses, opt-in only); CAM++ Winograd-pinned. face d80092b: ArcFace recognizer blocked island, AVX-512 default (-13% @8t, ~0.90x MLAS, the closest conv result), auto per-conv on AVX2; SCRFD untouched on Winograd (0 island invocations during detect). Parity cosine=1.0 / detect <=1px throughout. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * chore(recon): bump pins to small-spatial + stem conv kernels (voice 99b1804, face 47fdab6) Measured-gap-driven conv kernels: small-spatial (fill the register tile when output width <= tile width) + small-IC stem + strided-1x1/downsample recovery. ArcFace recognizer 0.57 -> 0.70x MLAS @1t (the closest conv model), WeSpeaker 0.65 -> 0.79x @1t. Parity cosine=1.0 / detect <=1px. The OC-block-sharing lever was a measured dead-end (deep stride-1 is L3-weight-bandwidth bound, not read-port bound) and was NOT shipped. Kernel ceiling reached; further gap needs an algorithm-class change (cache-blocked weight-stationary GEMM, or q8 weights). Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * chore(recon): bump pins to GPU persistent-graph + multi-model-safe cache (voice 45d2e6b, face 0a4799a) GPU wins (CUDA/ggml backend, no CPU-path change): persistent per-shape graph+context cache in Backend::compute() eliminates the per-call cudaGraph re-instantiation churn -> wav2vec2 emotion+age-gender now AT GPU parity with torch-cuDNN on GB10 (0.97-0.98x), CAM++ -5.7ms; bit-identical parity. Cache hardened multi-model-safe (invalidate-on-free keyed by the ModelLoader weights buffer) so LocalAI multi-model hosting cannot stale-hit. Conv models still trail cuDNN (im2col-materialization-bound) - cuDNN implicit-GEMM lever next. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * chore(recon): bump pins to cuDNN-conv-capable engines (voice b6e4356, face 6107a24) Adds the opt-in cuDNN implicit-GEMM conv path (VOICEDETECT_GGML_CUDNN / FACEDETECT_GGML_CUDNN, DEFAULT OFF -> zero build/runtime dep until enabled). On GPU it kills the im2col-materialization bottleneck and reaches torch-cuDNN parity on the spill-bound convs: SCRFD detect 14.8->6.4ms (2.3x, ~parity), WeSpeaker ~parity, ERes2Net beats torch (1.10x); ArcFace/CAM++ neutral (no spill). Parity exact (SCRFD <=1px, cosine=1.0). To USE it in LocalAI, the CUDA backend build must enable the flag AND bundle libcudnn - deferred until a cuDNN-bundled GPU image; flag stays OFF here. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(recon): enable cuDNN conv path on arm64+CUDA13 recon backends The voice-detect.cpp / face-detect.cpp engines have an opt-in cuDNN implicit-GEMM conv path behind VOICEDETECT_GGML_CUDNN / FACEDETECT_GGML_CUDNN (default OFF) that kills im2col on the GPU and reaches torch-cuDNN parity (SCRFD 2.3x, WeSpeaker/ERes2Net parity), measured on the GB10 (arm64, CUDA 13, sm_121a). Enable it for the CUDA build, but only where cuDNN actually ships: the arm64 + CUDA 13 image (GB10/Jetson/L4T). x86 CUDA images carry no cuDNN, so flipping it on globally for BUILD_TYPE=cublas would be a link failure. The Makefiles gate on CUDA_MAJOR_VERSION=13 + arch (TARGETARCH from the matrix/Docker build, uname -m fallback for local builds). backend/Dockerfile.golang already installs the runtime libcudnn9-cuda-13 in the arm64+CUDA13 apt block; add the matching libcudnn9-dev-cuda-13 so the build-time link resolves. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * chore(recon): bump voice-detect pin to ERes2Net blocked-default (30beecd) Defaults VD_ERES2NET_BLOCKED ON: routes the ERes2Net Res2Net body through the blocked nChw16c AVX-512 directconv island instead of the 1x1 mul_mat fast path (CONT-transpose + skinny low-K GEMM). On the shipped GGML_NATIVE=OFF build (ggml mul_mat is AVX2-only) this wins ~2x at every thread count (2.07x@1t, 2.2x@4t, 2.05x@8t); pure-AVX2 fallback still 1.3-1.62x. Parity exact (cosine=1.000000 vs golden), so registered voices + verify/identify thresholds are unaffected. The prior default-OFF rested on a stale comment whose 23pct regression only held on the non-shipping GGML_NATIVE=ON build. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * docs(readme): announce native voice-detect + face-detect backends in Latest News Add a Latest News entry for the new from-scratch C++/ggml biometric backends (voice-detect.cpp + face-detect.cpp) that replace the Python insightface and speaker-recognition backends: no Python/onnxruntime at inference, self-contained GGUF, bit-exact parity, GPU cuDNN parity. Mirrors the parakeet.cpp / locate-anything.cpp native-backend news entries. Refs PR #10441. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * chore(recon): re-pin to the squashed engine release commits The voice-detect.cpp and face-detect.cpp histories were squashed to a single release commit, which orphaned the previous pins (voice 30beecd, face 6107a24). Re-pin to the new single-commit SHAs (voice 3d51077, face 06914b0); the tree is identical, so the backend build is unchanged. 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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LocalAI [bot]andEttore Di Giacinto
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f3d829e2ef |
feat(distributed): add LOCALAI_DISTRIBUTED_SHARED_MODELS to skip staging on shared volumes (#10556) (#10566)
In distributed mode, even when the frontend and workers share the same models directory via a shared volume mount, starting a model on a worker re-staged (re-downloaded) it: stageModelFiles always uploads model files into a tracking-key-namespaced subdir on the worker, and the staging probe only checks that staged location, so a file already present on the shared volume at the canonical path was never reused. Add a config switch LOCALAI_DISTRIBUTED_SHARED_MODELS (default false). When enabled, the operator asserts that all nodes mount the SAME models directory at the SAME path, so staging is unnecessary: the frontend's absolute model paths are already valid on the worker. In that mode stageModelFiles returns the cloned opts unchanged without uploading, leaving the path fields pointing at their canonical absolute paths so the worker loads them directly from the shared volume. The value is plumbed from DistributedConfig through SmartRouterOptions into the SmartRouter. Docs and docker-compose.distributed.yaml updated. 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]andEttore Di Giacinto
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179210b970 |
chore: bump localrecall for postgres per-connection timeouts (#10517)
* chore: bump localrecall for postgres per-connection timeouts Pulls mudler/LocalRecall#49: sets lock_timeout / idle_in_transaction (default on) + opt-in statement_timeout on every pooled connection, so a corrupt/wedged index (e.g. a BM25 insert spinning on a buffer-content lock) can no longer hold its relation lock forever and head-of-line block the whole vector store. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * docs(agents): document PostgreSQL connection safety timeouts Note the POSTGRES_LOCK_TIMEOUT / POSTGRES_IDLE_IN_TRANSACTION_TIMEOUT / POSTGRES_STATEMENT_TIMEOUT env vars read by the embedded vector store, and that safe defaults are on automatically. 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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LocalAI [bot]andEttore Di Giacinto
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f72046b5b5 |
fix(auth): make advisory locks dialect-aware and harden SQLite DSN (#10509)
* fix(auth): make advisory locks dialect-aware and harden SQLite DSN Fixes #10506. Two failures hit deployments that use the default SQLite auth database: 1. advisorylock executed PostgreSQL-only SQL (pg_advisory_lock / pg_try_advisory_lock) unconditionally. On a SQLite auth DB the job store, agent store and node registry migrations failed with "no such function: pg_advisory_lock". WithLockCtx/TryWithLockCtx now branch on the gorm dialect: PostgreSQL keeps the cross-process advisory lock, every other dialect uses a context-aware, per-key in-process lock (a SQLite auth DB is effectively single-process, so serializing within the process is sufficient). 2. The SQLite auth DSN set no busy timeout, so transient SQLITE_BUSY over network-backed storage (SMB/CIFS/NFS, e.g. Azure Files) failed the auth migration immediately with "database is locked". The DSN now sets _busy_timeout=5000 and _txlock=immediate (caller-supplied values are preserved). WAL is intentionally not enabled since its shared-memory mmap does not work over network filesystems. Docs note that PostgreSQL should be used when the data directory lives on shared storage. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * test(jobs): regression test for #10506 SQLite job store migration Exercises the exact caller chain that failed in the issue: auth.InitDB(sqlite) -> jobs.NewJobStore -> advisorylock.WithLockCtx -> AutoMigrate. Before the dialect-aware advisory lock fix this failed with "no such function: pg_advisory_lock"; the test now asserts it migrates cleanly on a SQLite auth DB. 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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LocalAI [bot]andEttore Di Giacinto
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79783120dd |
fix(config): gate parallel-slot default on per-device VRAM too (#10485) (#10507)
The first #10485 fix (#10494) made the Blackwell physical-batch boost per-device/context-aware, which neutralized the big compute-buffer OOM, but the reporter's 2x16 GiB consumer Blackwell still OOM'd. Tracing the post-fix log: the model now loads its weights, builds the main context and warms up fine, and dies only on the *last* allocation — the MTP draft context's 800 MiB KV cache on the tighter device. #10411 changed only two defaults: the physical batch (now gated) and a VRAM-scaled parallel-slot count. The KV cache is unified (n_ctx_seq == full context proves slots share the budget, so parallel doesn't multiply KV), but n_seq_max=4 still adds per-slot compute-graph / context-checkpoint / output scratch. On a device packed ~99% by a 27B model spanning both cards, that overhead is the few-hundred-MiB straw — which is why reverting #10411 (and only #10411) restores a working load. Gate the parallel-slot default on the same per-device headroom predicate as the batch boost: when a large context already fills a single card (largeContextForDevice), keep n_parallel=1. A user running one big-context model that barely fits across two consumer GPUs is not serving four concurrent tenants. Small contexts and large unified-memory devices (GB10) keep full concurrency. Applied on both the single-host path and the distributed router. Also make the auto-tuning visible and reversible (the debugging here needed DEBUG logs and a git bisect): - Log the effective performance-relevant runtime options at INFO once per model load ("effective runtime tuning …": context, n_batch, n_gpu_layers, parallel, flash_attention, f16) so an admin can see what will run and pin or override any value in the model YAML. - LOCALAI_DISABLE_HARDWARE_DEFAULTS=true skips the hardware auto-tuning entirely (mirrors LOCALAI_DISABLE_GUESSING) for stock llama.cpp behavior. 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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Richard PalethorpeandEttore Di Giacinto
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e1994579f8 |
fix(pii): load default detectors at startup + add LOCALAI_PII_DEFAULT_DETECTORS (#10474)
pii_default_detectors was applied to the live config only by a live POST /api/settings (ApplyRuntimeSettings) — neither the startup loader nor the config file watcher read it back. So after a restart the persisted default detectors were dropped, and the cloud-proxy MITM listener (which resolves each intercept host's detectors once at start via ResolvePIIPolicy) came up with an empty set and forwarded intercepted traffic unredacted, even though the MITM model had pii.enabled:true and the defaults were on disk. Request-side default redaction broke the same way. - startup.go: loadRuntimeSettingsFromFile now applies pii_default_detectors, before startMITMIfConfigured, with env > file precedence. - config_file_watcher.go: apply pii_default_detectors on live file edits, matching the existing env-guard pattern used for the other fields. - settings endpoint: rebuild the MITM listener when pii_default_detectors changes (its per-host detector map is frozen at listener start), not only on a mitm_listen change — so toggling a default detector takes effect on cloud-proxy traffic immediately. - new LOCALAI_PII_DEFAULT_DETECTORS env var / CLI flag (WithPIIDefaultDetectors) so the default detector set can be pinned at boot for immutable deployments. Assisted-by: Claude:claude-opus-4-8 Claude-Code Signed-off-by: Richard Palethorpe <io@richiejp.com> Co-authored-by: Ettore Di Giacinto <mudler@users.noreply.github.com> |
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LocalAI [bot]andEttore Di Giacinto
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fdf475ec5f |
feat(realtime): conversation compaction (summarize-then-drop) + OpenAI item.delete/truncate/clear (#10446)
* feat(realtime): add pipeline.compaction config + resolution Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(realtime): extract itemID helper, reuse in item.retrieve Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(realtime): drop duplicate Ginkgo bootstrap, fold specs into openai suite Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(realtime): implement conversation.item.delete Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(realtime): implement input_audio_buffer.clear Add a handler for the input_audio_buffer.clear client event that discards a partially-captured utterance (raw PCM + buffered Opus frames) via a unit-tested clearInputAudio helper, then acks with input_audio_buffer.cleared. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(realtime): implement conversation.item.truncate (text) Clears both .Text and .Transcript of the assistant content part at contentIndex so barge-in truncation also works for audio turns whose spoken words live in .Transcript. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(realtime): add Conversation.Memory + pair-safe compactionCut Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(realtime): compactionCut returns 0 for keep<=0 (no-cap sentinel, avoids panic) Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * style(realtime): gofmt compaction test helper closures Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(realtime): inject rolling memory into the prompt + summary builders Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(realtime): server-side summarize-then-drop compactor Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(realtime): unit-test prefixMatches eviction-safety predicate Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(realtime): resolve summarizer model + schedule compaction per turn Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(realtime): document conversation compaction + new item events Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(realtime): resolve summary model inside compaction goroutine (lazy, off-path) Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(realtime): reuse reasoning.ExtractReasoningComplete for summary stripping Replace the bespoke <think> regex in the compactor with the shared pkg/reasoning extractor (via spokenReasoningConfig), matching the rest of the realtime path and covering all reasoning tag families, not just <think>. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(config): register pipeline.compaction fields in meta registry TestAllFieldsHaveRegistryEntries requires every ModelConfig field to have a UI/meta registry entry; add the four pipeline.compaction.* leaves so they render with proper labels/descriptions instead of the reflection fallback. 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]andEttore Di Giacinto
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95b058e1c5 |
feat(ui): restructure Cluster Nodes view (pulse + panel roster + detail page) (#10447)
* chore: gitignore SDD scratch directory Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(nodes): add GET /api/nodes/models cluster-wide loaded-models endpoint Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(ui): add nodesApi.allModels() for cluster-wide model roster Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(ui): move Scheduling to its own page and nav item Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(ui): replace nodes stat-card strip with cluster pulse + attention callout Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(ui): node-panel roster with inline model chips and segmented filter Replace the Nodes table with a full-width node-panel roster that shows each backend node's running-model chips without an expand click, plus an All/Backend/Agent segmented filter. Per-node detail (models, backends, labels, capacity) moves to the node detail page. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(ui): add deep-linkable node detail page at /app/nodes/:id Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * fix(ui): remove em-dash from CapacityEditor comment; align detail spec backend mock Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * chore(ui): nodes page cleanup, hover/chip polish, docs for restructured cluster view Nodes.jsx dead-code sweep confirmed clean (no StatCard/table/expand state/scheduling-form leftovers). Two App.css polish fixes: move the node-panel hover border-color onto the bordered element so hover gives real feedback, and add the missing .model-chip__state rule the ModelChip component already emits. Update distributed-mode docs prose to describe the restructured cluster view (cluster pulse, attention callout, node-panel roster with inline model chips, All/Backend/Agent filter, node detail page at /app/nodes/:id, Scheduling as its own page). Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * chore(ui): drop unused gpuVendorLabel export from nodeStatus 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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LocalAI [bot]andEttore Di Giacinto
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600dafd20b |
feat(ced): sound-event classification backend (CED audio tagger) (#10425)
* feat(ced): sketch sound-classification backend (CED audio tagger) Wires ced.cpp (CED, 527-class AudioSet sound-event tagger; baby cry, footsteps, glass, alarms, dog bark) into LocalAI as a Go/purego backend. SKETCH (backend skeleton real; core REST wiring + CI/gallery is a checklist in DESIGN.md): - backend/backend.proto: new SoundDetection rpc + SoundClass messages (run `make protogen-go` to regenerate pkg/grpc/proto). - backend/go/ced: main.go (purego dlopen libced.so + ced_capi.h), goced.go (Ced gRPC backend: Load + SoundDetection), Makefile (clone-at-pin CED_VERSION, ggml static-PIC shared build), run.sh, package.sh, .gitignore. - DESIGN.md: REST /v1/audio/classification wiring (handler/route/capability registration checklist), gallery/index + CI registration, and a scoping note for the realtime/websocket live-recognition path (sliding-window classify over the existing ws transport + voicegate; the ced C-API per-PCM entry point is already window-friendly). Backend code does not compile until protogen-go regenerates the pb types and a libced.so is built (Makefile clones+builds it). Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ced): REST /v1/audio/classification endpoint + capability registration Wires the ced sound-event classification backend (AudioSet audio tagger) end to end through the REST surface, mirroring the transcription path. - Handler: core/http/endpoints/openai/sound_classification.go parses the multipart audio upload, temp-files it, resolves the model config and calls the SoundDetection RPC; returns {model, detections[]} JSON. - Backend wrapper: core/backend/sound_classification.go (ModelSoundDetection) loads the model and normalizes the proto response into schema types. - Schema: core/schema/sound_classification.go (SoundClassificationResult). - gRPC layer: SoundDetection wired through the LocalAI wrapper (interface, Backend client, Client, embed, server, base default) so the loader-typed client exposes the RPC; proto regenerated via make protogen-go. - Route: POST /v1/audio/classification (+ /audio/classification alias) with the audio/multipart default-model middleware in routes/openai.go. - Capability surfaces: swagger @Tags/@Router on the handler; FLAG_SOUND_ CLASSIFICATION usecase flag + UsecaseSoundClassification + UsecaseInfoMap + GuessUsecases + ModalityGroups + GetAllModelConfigUsecases; meta usecase option; /api/instructions audio area updated; auth RouteFeatureRegistry + FeatureAudioClassification (APIFeatures, default ON) + FeatureMetas; UI usecaseFilters, capabilities.js CAP_SOUND_CLASSIFICATION, Models.jsx filter + i18n; docs page features/audio-classification.md + whats-new + crosslink. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ced): realtime sound-event detection over the websocket API When a realtime pipeline configures a sound-classification model, each VAD-committed utterance (the same window the transcription path produces) is also run through the CED sound-event classifier and the scored AudioSet tags are emitted as a new server event. No new backend rpc is needed: the SoundDetection gRPC method already exists on this branch. - config: add Pipeline.SoundDetection (yaml/json sound_detection,omitempty) beside Transcription/VAD. - realtime: add Model.SoundDetection(ctx, audio, topK, threshold) to the ModelInterface; implement it on wrappedModel and transcriptOnlyModel by calling backend.ModelSoundDetection with the session's sound-classification model config (mirrors how Transcribe dispatches). Load the optional config in newModel / newTranscriptionOnlyModel; nil config keeps it additive. - types: add ConversationItemSoundDetectionEvent (item_id, content_index, detections[]{label,score,index}) with type conversation.item.sound_detection, its ServerEventType constant and MarshalJSON, mirroring the transcription completed event. - realtime: add emitSoundDetection (unary path: classify the committed window, build the event, t.SendEvent) and wire it at the utterance-commit hook right after emitTranscription; gated on session.SoundDetectionEnabled (resolved from Pipeline.SoundDetection at session setup, defaults top_k=5, threshold=0). Its error is logged via xlog but never aborts the turn. - test: Ginkgo specs for emitSoundDetection (tags emitted, empty detections, classifier error) plus a SoundDetection method on the fakeModel double. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(ced): implement SoundDetection in nodes backend test doubles The SoundDetection method added to the grpc backend interface left two test doubles (fakeBackendClient, fakeGRPCBackend) incomplete, so core/services/nodes failed to compile under `go vet`/`go test` (go build missed it: the doubles live in _test.go). Add the method to both, mirroring their existing Detect mock. Repairs CI for the nodes package. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ced): decouple realtime sound detection from VAD (sound-only sessions) Sound-event detection must activate on sounds, not speech, so it no longer runs through the voice VAD/transcription path. A sound-detection-only pipeline (sound_detection set, no transcription/LLM) now: - is accepted by prepareRealtimeConfig (sound_detection counts as a pipeline stage), - builds a lightweight model via newSoundDetectionOnlyModel (no VAD/STT/LLM/TTS loaded), and - defaults the session to turn_detection none (no VAD) with no transcription stage, so the client drives windowing via input_audio_buffer.commit (option A: client-side sliding window). The per-PCM C-API already supports arbitrary windows. commitUtterance gains a sound-only branch: it emits the conversation.item.sound_detection event (scored AudioSet tags) and stops - no transcription, no LLM response. generateResponse is now guarded on a transcription stage being present, so a sound-only turn never invokes the LLM. Existing transcription/VAD sessions are unchanged (additive). Added a commitUtterance sound-only Ginkgo spec asserting it emits the sound event and neither transcribes nor generates a response. go vet + golangci-lint (new-from-merge-base) clean; openai suite green. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ced): register sound-classification backend in gallery + CI Mechanical backend-image registration for the ced sound-event classifier, mirroring the parakeet-cpp Go/purego backend everywhere it is wired up. - .github/backend-matrix.yml: add the ced build matrix, field-for-field copies of the parakeet-cpp entries (cpu amd64/arm64, cublas cuda 12/13 amd64, l4t cuda-13 arm64, l4t-jetpack cuda-12 arm64, sycl f32/f16, vulkan amd64/arm64, rocm hipblas, and the metal darwin entry), changing only backend and tag-suffix. dockerfile stays ./backend/Dockerfile.golang. - backend/index.yaml: add the &ced meta anchor (capabilities map per platform) plus ced-development and the per-arch image entries, each uri/mirror tag-suffix matching the matrix exactly. The model gallery (GGUF) entry is intentionally deferred pending the HuggingFace publish (TODO note inline). - scripts/changed-backends.js: add an explicit item.backend === "ced" branch in inferBackendPath mapping to backend/go/ced/, same mechanism and ordering as the parakeet-cpp branch (before the generic golang fallthrough). - .github/workflows/bump_deps.yaml: register mudler/ced.cpp -> CED_VERSION in backend/go/ced/Makefile so the daily bot bumps the pin. - swagger/{docs.go,swagger.json,swagger.yaml}: regenerated via make swagger so the existing /v1/audio/classification annotations land in the generated spec. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ced): server-side windowing for realtime sound detection (option B) Adds an optional server-driven sliding-window classifier so a sound-only realtime client only has to stream audio (no input_audio_buffer.commit): - Pipeline.sound_detection_window_ms / sound_detection_hop_ms config knobs. When both > 0 on a sound-only session, the server classifies the last window of streamed audio every hop and emits a conversation.item.sound_ detection event; the input buffer is trimmed to one window so a long stream stays bounded. When unset, the session stays client-driven (option A). Runs independent of VAD (sound events are not speech). - handleSoundWindow (ticker) + classifySoundWindow (one tick, extracted so it is unit-testable) + writeWindowWAV, which declares the true InputSampleRate (NewWAVHeaderWithRate) so the classifier resamples correctly. Goroutine is started after toggleVAD and torn down with the session (close + wg.Wait). - Register pipeline.sound_detection (+window_ms/hop_ms) in the config meta registry; the earlier realtime commit added pipeline.sound_detection without a registry entry, failing TestAllFieldsHaveRegistryEntries. This fixes that and covers the two new knobs. Tests: classifySoundWindow emits an event + trims the buffer to one window, no-ops on too-little audio; writeWindowWAV declares the given sample rate. go build/vet + golangci-lint (new-from-merge-base) clean; config + openai suites green. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ced): add ced-base GGUF model gallery entries (f16 + q8_0) The ced-base weights are now published at mudler/ced-base-gguf (Apache-2.0, converted from mispeech/ced-base). Adds gallery/ced.yaml (backend: ced + known_usecases: sound_classification) and two gallery/index.yaml entries (ced-base-f16 default, ced-base-q8 smallest) with sha256-pinned files, and removes the now-resolved TODO from backend/index.yaml. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ced): add tiny/mini/small GGUF model gallery entries Publishes the rest of the CED family (same architecture, metadata-driven port verified end-to-end on ced-tiny) to mudler/ced-{tiny,mini,small}-gguf and adds their f16 + q8_0 gallery entries: ced-tiny (5.5M, edge/Pi-class) f16 11MB / q8_0 6MB ced-mini (9.6M) f16 19MB / q8_0 11MB ced-small (22M) f16 42MB / q8_0 23MB All sha256-pinned. ced-base remains the accuracy default. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore(ced): point gallery entries at the consolidated mudler/ced-gguf repo All CED quantizations (tiny/mini/small/base, f16/q8_0) now live in a single HuggingFace repo, mudler/ced-gguf, instead of per-model repos. Repoint the 8 gallery model entries' urls + file uris accordingly. sha256 and filenames are unchanged. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore(ced): bump CED_VERSION to the short-clip fix Pin the ced backend to ced.cpp 99c6ed3, which fixes a crash on any clip shorter than target_length (~10.11s): time_pos_embed was added at its full 63-frame grid instead of being sliced to the clip's actual time grid, tripping ggml_can_repeat in ggml_add. Surfaced by the live realtime e2e (sub-10s windows) and gated with a short-clip parity test upstream. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(ced): list ced.cpp as a LocalAI-team engine + backend-guide directive - README.md: add ced.cpp to the "native C/C++/GGML engines developed and maintained by the LocalAI project" table. - docs/content/features/backends.md: add a Sound Classification backend category (sound-event classification / audio tagging) listing ced.cpp. - .agents/adding-backends.md: add a "Documenting the backend" section and two verification-checklist items requiring new backends to be documented in the backends.md category list, and in-house native engines to be added to the README maintained-engines table. This directive was missing. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore(ced): repin CED_VERSION to the v0.1.0 release commit ced.cpp history was squashed into a single release commit (tagged v0.1.0), so the previous pin (99c6ed3) no longer exists upstream. Pin to c04ac14, the v0.1.0 release commit, so the backend builds against a commit that exists. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(ced): silence gosec G304/G103 + govet unsafeptr on audited paths - sound_classification.go: os.Create(dst) where dst = temp dir + path.Base of the upload (no traversal). #nosec G304, matching the depth-anything-cpp handler. - goced.go: reading a NUL-terminated C string from a libced-owned buffer. #nosec G103 (gosec) + //nolint:govet (golangci-lint's unsafeptr check), since the uintptr is a C-owned malloc'd buffer, not Go-GC memory. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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32c47706ae |
feat(realtime): speaker-aware conversations - surface identity to client and LLM (#10424)
* feat(realtime): add voice_recognition enforce + identity config Add Enforce *bool and Identity *VoiceIdentityConfig to PipelineVoiceRecognition, plus EnforceGate/IdentityEnabled/ AnnounceEnabled/PersonalizeEnabled helpers. Enforce nil defaults to gating (backward compatible); identity surfacing is independent of the gate. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(realtime): add Speaker type and conversation.item.speaker event Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(realtime): split voiceGate into Resolve + authorize Split the speaker authorization into a Resolve step (embed once, produce a types.Speaker identity) and a pure authorize policy step, with a 0..100 confidence score mirroring /v1/voice/identify. The legacy Authorize wrapper is kept so existing specs stay green. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(realtime): resolve speaker per turn and emit conversation.item.speaker Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(realtime): personalize LLM turns with recognized speaker Set the per-message name field on each recognized user turn and append a current-speaker note to the system message, both gated by the voice recognition identity config. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(realtime): document speaker identity surfacing and personalization Document the new voice_recognition keys (enforce, identity.*) and the LocalAI-extension conversation.item.speaker server event in the realtime feature docs. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(realtime): cover when:first+identity re-resolution and multi-speaker history Add two integration specs to harden the speaker-aware realtime path: - when:first with an Identity block re-resolves the speaker every turn even though re-authorization is skipped after the first match: a later resolve error now fails closed, while a clean later resolve still surfaces and names the speaker. - multi-speaker history attribution: each user turn carries its own per-message name and the injected system note reflects the latest speaker. Test-only change; no production behavior was modified. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(realtime): surface speaker labels in conversation.item.speaker Carry the registered speaker's labels (identify mode) on types.Speaker so they flow into the conversation.item.speaker event and the stored item. Verify mode has no labels, so the field is omitted there. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(e2e): cover conversation.item.speaker over a real websocket Add a realtime-pipeline-identity config (verify mode, enforce:false, identity announce+announce_unknown+personalize) and two e2e specs driving the real server over a real WebSocket with the mock VoiceEmbed backend: an authorized speaker yields a conversation.item.speaker event naming e2e-speaker (matched true) and reaches response.done; an unauthorized speaker yields an unknown (matched false, no name) event and still responds, proving enforce:false never drops a turn. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(config): register voice_recognition enforce + identity fields The meta registry coverage test (TestAllFieldsHaveRegistryEntries) requires every config field to have an entry in core/config/meta/registry.go. The new voice_recognition.enforce and voice_recognition.identity.* fields were missing, failing tests-linux and tests-apple. Add registry entries (toggles) so the fields are surfaced in the model-config editor and the coverage test passes. 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> Co-authored-by: Ettore Di Giacinto <mudler@users.noreply.github.com> |
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LocalAI [bot]andEttore Di Giacinto
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9565db5f94 |
feat(models): model aliases - redirect a model name to another configured model (#10414)
* feat(config): add model alias field and self-validation Add ModelConfig.Alias (yaml: alias), IsAlias(), and an alias short-circuit at the top of Validate() that rejects self-reference and forbids setting backend/parameters.model on a pure-redirect alias. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(config): resolve and validate model alias targets in the loader Assisted-by: Claude:opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(middleware): resolve model aliases and stamp requested/served identity Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(modeladmin): reject alias configs with invalid targets on create/edit Validate alias targets at create/swap entry points (ImportModelEndpoint, EditYAML, PatchConfig) so a dangling, chained, or disabled alias target is rejected at save time rather than surfacing as a runtime error. Assisted-by: Claude:opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(api): add GET /api/aliases to list model aliases Adds an admin-gated read-only endpoint that lists every model alias config as {name, target} pairs, backed by the loader's existing GetAllModelsConfigs(). Assisted-by: Claude:opus-4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(mcp): add set_alias and list_aliases tools Expose model-alias management over the LocalAI Assistant MCP surface: list_aliases (read-only, GET /api/aliases) and set_alias (mutating). SetAlias is swap-first: PATCH /api/models/config-json/:name swaps an existing alias's target (validated, non-destructive) and a 404 falls back to POST /models/import to create a fresh {name, alias} config. The inproc client mirrors this via ConfigService.PatchConfig + a create path modeled on ImportModelEndpoint. Deletion reuses delete_model. Assisted-by: Claude:claude-opus-4 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * style(mcp): replace em dashes in alias tool comments Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(config-meta): expose alias as a model-select field Add an 'alias' section to DefaultSections() and an 'alias' field override in DefaultRegistry() so the schema-driven React editor renders the new top-level ModelConfig.Alias field as a model picker in its own section. Assisted-by: Claude:opus-4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ui): add alias template card and Manage alias badge Add an 'Alias / Routing' template to the create-flow gallery that seeds a minimal name + alias config, and a read-only 'alias -> target' badge on the Manage Models tab. The capabilities row payload does not carry the alias field, so the badge resolves targets from GET /api/aliases looked up by name. Assisted-by: Claude:claude-opus-4 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: document model aliases Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(swagger): regenerate for GET /api/aliases Adds the /api/aliases path and AliasInfo schema generated from the ListAliasesEndpoint annotation. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(localai): check os.RemoveAll error in aliases_test Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix: correct alias conversion docs and advertise /api/aliases in instructions Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(mcp): write alias config 0600 to satisfy gosec G306 The inproc createAlias path wrote the alias YAML with 0644, which gosec flags as a new G306 finding on the PR. The LocalAI process is the sole reader/writer of model configs, so 0600 is correct and keeps the scan clean. 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]andEttore Di Giacinto
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13f59f0822 |
docs: document the privacy-filter.cpp backend (#10386)
docs: document the privacy-filter.cpp backend in README and compatibility table The privacy-filter.cpp backend (#10360) was registered in backend/index.yaml and referenced from the PII feature docs, but was missing from the backend catalog surfaces. Add it to the README "Backends built by us" table, the compatibility table (Utilities & Other, CPU/CUDA 13/Vulkan), and the backend type list in the backends feature doc. 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
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3fa7b2955c |
feat(pii): NER tier engine — privacy-filter.cpp backend + NER-centric PII filter (#10360)
Squashed feat/pii-ner-tier-engine rebased onto master (was 45 commits; see backup/pii-ner-tier-engine-prerebase). Net change: - privacy-filter.cpp: standalone GGML engine for the openai-privacy-filter PII/NER token classifier, wired as a LocalAI gRPC backend (CPU/CUDA/Vulkan). TokenClassify moves off the patched llama.cpp path onto this backend. - PII filter reworked to be NER-centric (encoder/NER detection tier scanning whole conversations as one document), with a recreated bounded restricted- regex secret-matching pattern detector tier alongside it (per-model pii_detection.builtins / .patterns + core/services/routing/piipattern). - Detection labelled by source (ner vs pattern); backend trace / confidence / debug observability; analyze/redact exposed as a synchronous API. - Instance-wide default detector policy + per-usecase default-on; request filtering extended to completions, embeddings, edits & Ollama. - React UI: NER-centric PII editor, detector-models table, pattern/builtins editor, middleware default-policy UI. - Gallery: privacy-filter-multilingual token-classify model + NER install filter; token_classify known_usecase; batch sized to context for NER models. privacy-filter backend registered in the backend gallery (cpu/vulkan/cuda-13 meta + image entries with a capabilities map) matching its CI matrix jobs, and an /import-model auto-detect importer (PrivacyFilterImporter, narrow privacy-filter GGUF detection) replacing the prior pref-only registration. Reconciled against master's independent evolution: - Dropped master's PIIPatternOverrides feature (global-pattern runtime overrides + /api/pii/patterns API + runtime_settings.json persistence). The per-model NER + pattern-detector design supersedes it; it was built on the global redactor pattern set this branch replaced. - Reverted the llama.cpp Score carry-patch (0006-server-task-type-score): removed the patch and restored master's grpc-server.cpp Score RPC (direct llama_decode, slot-loop bypass) and LLAMA_VERSION pin, plus master's model_config validation forbidding score + chat/completion/embeddings on llama-cpp. token_classify is unaffected (it runs on the privacy-filter backend, not llama-cpp). Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Richard Palethorpe <io@richiejp.com> |
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LocalAI [bot]andEttore Di Giacinto
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9b57dcb721 |
docs: document all available backends and add "built by us" list (#10376)
Bring the Backend & Model Compatibility Table up to the full set of backends published in backend/index.yaml (60+), organized by modality with per-backend acceleration targets. Add an "Available Backends" pointer and expand the backend-type list in the backends feature doc. Update the README backend count to 60+ and add a "Backends built by us" section listing the native C/C++/GGML engines maintained by the LocalAI project (parakeet.cpp, voxtral.c, vibevoice.cpp, rf-detr.cpp, locate-anything.cpp, depth-anything.cpp, LocalVQE, local-store). 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]andEttore Di Giacinto
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a6e1c6d0b3 |
fix(docs): use relearn notice shortcode instead of unsupported alert (#10364)
The Hugo relearn theme does not provide an "alert" shortcode, so the docs deploy failed at the Build site step: failed to extract shortcode: template for shortcode "alert" not found docs/content/features/image-generation.md:106 Convert the vae_decode_only note to the theme-supported notice shortcode used everywhere else in the docs. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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f44034021e |
chore: ⬆️ Update leejet/stable-diffusion.cpp to 5a34bc7f6e0621dd2f899daa64476eac667d7ed3 (#10335)
* ⬆️ Update leejet/stable-diffusion.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> * fix(stablediffusion-ggml): adapt gosd.cpp to upstream sd_ctx_params_t API The bump to 5a34bc7 restructured sd_ctx_params_t: the boolean CPU-offload knobs (offload_params_to_cpu, keep_clip_on_cpu, keep_vae_on_cpu, keep_control_net_on_cpu) were replaced by backend assignment specs (backend/params_backend), and vae_decode_only / free_params_immediately were dropped entirely. The build broke with "no member named ..." on every arch. Translate the legacy options we still accept from gallery configs into the new backend assignment specs, mirroring prepare_backend_assignments() in the upstream CLI, so offload_params_to_cpu / keep_*_on_cpu keep working. vae_decode_only is parsed and ignored for config compatibility. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(stablediffusion-ggml): expose backend/params placement options The upstream bump introduced new sd_ctx_params_t fields for device and memory placement (backend, params_backend, rpc_servers, max_vram, stream_layers) plus PuLID-Flux weights (pulid_weights_path). Wire them up as backend options so models can be split across CPU/GPU/disk/RPC: - backend: per-component compute placement (e.g. clip=cpu,vae=cuda0) - params_backend: per-component weight storage incl. disk mmap - max_vram / stream_layers: graph-cut segmented parameter offload budget - rpc_servers: offload compute to remote RPC servers - pulid_weights_path: PuLID-Flux identity injection The legacy keep_*_on_cpu / offload_params_to_cpu booleans now seed and compose with the explicit backend/params_backend specs, matching upstream prepare_backend_assignments(). Option values are taken as everything after the first ':' so colon-bearing values (rpc_servers host:port) survive parsing. Documented the new options in the image-generation guide. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(stablediffusion-ggml): distributed RPC across ggml workers Enable the ggml RPC backend (-DSD_RPC=ON) so image generation can be sharded across remote rpc-server workers. The ggml rpc-server is backend-agnostic, so this reuses the exact same worker pool as the llama.cpp backend - one set of `local-ai worker llama-cpp-rpc` / `p2p-llama-cpp-rpc` workers accelerates both text and image generation. RPC servers are selected by precedence: - the explicit `rpc_servers` option, else - the LLAMACPP_GRPC_SERVERS env var, which LocalAI's p2p worker mode populates automatically with discovered workers (the backend inherits it from the parent process env), so distributed image generation needs no per-model configuration. Documented manual and p2p setup in the image-generation guide. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] --------- Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]andEttore Di Giacinto
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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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Ettore Di Giacinto
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3e838c0cff |
docs: add realtime voice demo example and refresh README news
Add the localai-org/localai-realtime-demo Go client to the README Examples list and to the realtime docs (integrations + realtime feature page). Refresh the Latest News section with June 2026 highlights pulled from history since v4.3.0: realtime pipeline streaming, the parakeet.cpp and CrispASR speech work, new backends (locate-anything.cpp, Ideogram4, llama.cpp video input), and distributed-mode hardening. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] |
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LocalAI [bot]andEttore Di Giacinto
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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]andEttore Di Giacinto
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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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Richard Palethorpe
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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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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 076dcdbe) it does not degrade to
whole-message buffering for CJK/Thai; scripts needing a dictionary
(Khmer/Burmese) stay buffered until a space or end-of-message. Clauses
are synthesized synchronously in the token callback (the LLM keeps
generating into the gRPC stream meanwhile), so audio still starts
mid-generation. Off by default — the whole-message path is unchanged.
Also fix the streamed-reply path and the Talk page:
- Don't swallow streamed autoparser content as reasoning: the
tokenizer-template path already delivers reasoning-free content via
ChatDeltas, so prefilling the thinking start token re-tagged it as an
unclosed reasoning block, leaving no spoken reply. Disable the prefill
on that path; closed tag pairs are still stripped (#9985).
- Generate collision-free realtime IDs (16 random bytes) instead of a
constant, so per-item bookkeeping (cancel, conversation.item.retrieve)
works.
- Key the Talk transcript by the server item_id and upsert entries.
Realtime events arrive over a WebRTC data channel — outside React's
event system — so React defers the setTranscript updaters while
synchronous ref writes in handler bodies run first; the old
index-tracking ref rendered a duplicate assistant bubble on
completion. Upserts by item_id are idempotent and order-independent.
- Drop the partial assistant bubble on a cancelled response (barge-in):
the server discards the interrupted item and sends response.done with
status "cancelled"; mirror that in the UI so the regenerated reply
isn't rendered as a second assistant message.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Assisted-by: Claude:claude-fable-5 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Signed-off-by: Richard Palethorpe <io@richiejp.com>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Richard Palethorpe <io@richiejp.com>
|
||
|
LocalAI [bot]andEttore Di Giacinto
|
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> |
||
|
LocalAI [bot]andEttore Di Giacinto
|
a7cb587d96 |
feat(parakeet-cpp): real segment timestamps (NeMo-faithful) (#10207)
* feat(parakeet-cpp): real segment timestamps (NeMo-faithful)
Offline: replace the single synthetic whole-clip segment with multiple
segments grouped exactly like NeMo's get_segment_offsets - a new segment
after sentence-ending punctuation ('. ? !'), each carrying start/end and
its time-window token ids. The optional model option segment_gap_threshold
(NeMo's unit: encoder FRAMES, default 0=off) adds NeMo's silence-gap split,
converted to seconds via the JSON frame_sec the engine now reports.
Per-segment words are still gated behind timestamp_granularities=["word"];
a zero-word document falls back to a single text segment.
Streaming: when libparakeet.so exposes the ABI v4 JSON entry points
(probed), drive parakeet_capi_stream_feed_json / _finalize_json and
accumulate the streamed per-word timestamps into per-utterance segments
(EOU stays the boundary), so streaming FinalResult segments now carry
start/end. Falls back to the text-only feed against an older library.
Pure-Go specs cover splitWordsIntoSegments (punctuation + gap rules, NeMo
elif order, fallback), transcriptResultFromDoc (multi-segment, token
windows, word-granularity gate), and the streaming segmenter.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* docs(audio): document parakeet-cpp segment timestamps + segment_gap_threshold
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* test(parakeet-cpp): update model-gated specs for multi-segment output
The offline AudioTranscription specs asserted the old single synthetic
segment (Segments HaveLen(1), Segments[0].Text == res.Text). With
NeMo-faithful segmentation a multi-sentence clip now yields multiple
punctuation-delimited segments, so assert the new contract instead:
one-or-more time-ordered segments, each with text and (under word
granularity) per-segment words whose span tracks the segment start/end.
Caught by running the model-gated suite on the dgx (GB10) against the
real tdt_ctc-110m + realtime_eou models.
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]andEttore Di Giacinto
|
cd2bf95862 |
fix(docs): use relearn notice shortcode instead of unsupported alert (#10206)
The Hugo relearn theme does not provide an "alert" shortcode, so the docs deploy failed at the Build site step: failed to extract shortcode: template for shortcode "alert" not found docs/content/features/distributed-mode.md:136 Convert the warning block to the theme-supported notice shortcode used everywhere else in the docs. 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]andEttore Di Giacinto
|
994063ba9a |
feat(qwen3-tts-cpp): normalize request language for flexible matching (#10174)
The qwen3-tts.cpp backend honored the request `language` field only via exact lowercase two-letter codes in the C++ language_to_id table, silently defaulting to English for anything else (en-US, EN, english, ...). Add normalizeLanguage() in the Go handler: lowercase + trim, strip the region/locale suffix (en-US, pt_BR, zh-Hans -> en/pt/zh), and resolve common English full names (english -> en). The canonical codes match the existing C++ table, so no C++ change is needed. Covered by a pure-Go Ginkgo spec. Also document the language field and accepted forms under the Qwen3-TTS docs. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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|
LocalAI [bot]andEttore Di Giacinto
|
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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|
Ettore Di Giacinto
|
e6a0d4c375 |
Remove diagram from distributed mode documentation
Removed ASCII diagram of distributed mode architecture from the documentation. Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com> |
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|
LocalAI [bot]andEttore Di Giacinto
|
7e59a5c7c5 |
docs: architecture & feature diagrams (blueprint style) (#10137)
* docs: add 'how LocalAI works' architecture diagram Add a blueprint-style architecture diagram: clients -> small core (API, router, WebUI, agents) -> gRPC -> backend processes pulled on demand as OCI images. Place it on the overview page and replace the stale external architecture image on the reference page. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: add blueprint diagrams across feature, distributed & getting-started docs Add 24 architecture/flow/comparison diagrams (PNG + HTML source) under docs/static/images/diagrams/, wired into their docs pages, from an impact-vs-effort audit of the docs. Broaden the API surface on the overview architecture diagram (OpenAI, Anthropic, ElevenLabs, Ollama, and LocalAI's own API) and move the gRPC boundary label clear of the arrows. Pages: distributed mode (architecture, scheduling, ds4 layer-split), distributed inferencing, MLX, realtime, quantization, MCP, agents, mitm & cloud proxy, middleware, reverse-proxy TLS, VRAM, voice & face recognition, reranker, function calling, fine-tuning (recipe + jobs), diarization, audio transform, quickstart, model resolution. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: add composable-core diagram to README hero Commit the composable-core card (small core + on-demand backend tiles) alongside the other diagrams and reference it from the README hero via a repo-relative path, so it renders on GitHub. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: fix composable-core connectors/badge and federated-vs-worker layout - composable-core: thicken the plug-in connectors so they read clearly, and widen the SEPARATE IMAGE badge so its text no longer overflows the box. - federated-vs-worker: shorten the WHOLE/SPLIT REQUEST pills to fit, and replace the tangled node-to-node activation arrows with a clean fan-out (request split across all sharded nodes), mirroring the federated panel. 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> |