* feat(llama-cpp): add main-model cpu_moe/n_cpu_moe options
Mirror the existing draft_cpu_moe/draft_n_cpu_moe siblings for the main
model, matching upstream --cpu-moe / --n-cpu-moe (common/arg.cpp). Lets
users keep MoE expert weights on CPU to manage VRAM on large MoE models.
Closes part of #10483
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(llama-cpp): forward unknown '-' options to upstream arg parser
Any options: entry starting with '-' is collected and passed verbatim to
llama.cpp's own common_params_parse (LLAMA_EXAMPLE_SERVER) at the end of
params_parse, so every upstream llama-server flag works without a new
hand-wired branch. Passthrough runs last and wins on overlap; n_parallel is
snapshotted to survive parser_init's SERVER reset, and help/usage/completion
flags are skipped to avoid exiting the backend.
Closes#10483
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* docs(llama-cpp): document cpu_moe/n_cpu_moe and option passthrough
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(llama-cpp): terminate tensor/kv override vectors after passthrough
The tensor_buft_overrides padding and the kv/draft override terminators
ran before the generic option passthrough, so a passthrough flag
(--cpu-moe, --override-tensor, --override-kv, ...) appended a real entry
after the null sentinel - tripping the model loader's
back().pattern == nullptr assertion (crash) or being silently dropped.
Move all three termination/padding blocks to the end of params_parse,
after both the named-option loop and common_params_parse have pushed
their real entries. Also widen the exit()-flag skip list so --version,
--license, --list-devices and --cache-list cannot terminate the backend.
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>
* feat(backends): add darwin/metal (MPS) build for trl
Authors backend/python/trl/requirements-mps.txt and wires trl into the
darwin CI matrix and gallery so the MPS training path can be built and
validated on Apple Silicon. The MPS variant installs plain PyPI torch
wheels (MPS-capable on macOS arm64) and the trl training stack; bitsandbytes
is omitted as it is a CUDA-only dependency with poor Apple Silicon support.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:opus-4.8 [Claude Code]
* fix(trl): guard uv-only --index-strategy for the pip/darwin path
The darwin/MPS build installs with pip (USE_PIP=true), which rejects the
uv-only --index-strategy flag and failed the darwin backend build. Add it
only on the uv path; Linux/CUDA resolution is unchanged.
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>
* fix(parakeet-cpp): darwin/metal support (libparakeet.dylib + DYLD path)
The parakeet-cpp backend had no macOS support and panicked at startup on
Apple/Metal nodes when purego.Dlopen could not find "libparakeet.so".
Fix it across the same four layers the sibling voxtral backend already
handles correctly:
- main.go: default the dlopen target to libparakeet.dylib on darwin
(runtime.GOOS), libparakeet.so elsewhere; PARAKEET_LIBRARY still wins.
- Makefile: also stage the built libparakeet.dylib next to the Go sources.
- package.sh: accept either the Linux .so[.X.Y] or the macOS .dylib when
bundling instead of hard-failing when no .so is present (the macOS case);
note that on Darwin only system frameworks are linked.
- run.sh: on Darwin set DYLD_LIBRARY_PATH and PARAKEET_LIBRARY to the
packaged .dylib; keep LD_LIBRARY_PATH + .so on Linux.
Mirrors backend/go/voxtral.
Assisted-by: Claude:claude-opus-4-8
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(backends): darwin/metal support across purego Go backends
The parakeet-cpp fix in the previous commit was an instance of a bug
shared by nearly every purego/dlopen Go backend: the dlopen target was
hardcoded to a .so name and run.sh exported only LD_LIBRARY_PATH, so the
backend panicked at startup on macOS/Apple-Metal nodes (dyld needs the
.dylib name and DYLD_LIBRARY_PATH). voxtral was the only backend handling
this correctly.
Apply the same four-layer fix (mirroring backend/go/voxtral) to the
remaining affected backends:
whisper, sherpa-onnx, ced, stablediffusion-ggml, vibevoice-cpp,
qwen3-tts-cpp, omnivoice-cpp, crispasr, acestep-cpp, locate-anything-cpp,
depth-anything-cpp, rfdetr-cpp, sam3-cpp, localvqe
Per backend:
- main.go (sherpa-onnx: backend.go, two libraries): default the dlopen
target to the .dylib on darwin (runtime.GOOS), .so elsewhere; the
existing <BACKEND>_LIBRARY env override still wins.
- run.sh: on Darwin set DYLD_LIBRARY_PATH and point <BACKEND>_LIBRARY at
the packaged .dylib; keep LD_LIBRARY_PATH + the Linux CPU-variant
(avx/avx2/avx512) selection unchanged in the else branch.
- package.sh: also bundle the .dylib and stop hard-failing when no .so is
present (the macOS case).
- Makefile: also stage the built .dylib.
Notes:
- stablediffusion-ggml and acestep-cpp build their lib as a CMake MODULE,
which emits .so (not .dylib) on macOS; run.sh prefers .dylib and falls
back to .so so both layouts work.
- sherpa-onnx was already partly darwin-aware (Makefile/package.sh); only
run.sh and the two dlopen defaults needed fixing.
Linux behavior is unchanged. Verified gofmt-clean and
`CGO_ENABLED=0 go build` for every backend.
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>
* feat(ui): make hardware starter models data-driven
The empty-state starter widget recommended from a hardcoded list, which
drifts as the gallery evolves. Add useRecommendedModels: it queries the
live gallery for chat-capable models (their natural curated order, since
the gallery exposes no popularity signal), estimates size/VRAM for the top
candidates via the existing estimate endpoint, and ranks by hardware fit -
smallest on CPU-only boxes, largest-that-fits on GPUs.
StarterModels now renders those live picks and keeps the curated static
list only as an offline/trimmed-gallery fallback.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* feat(ui): recommend models for your hardware in the gallery
Hardware-aware recommendations were only shown on the first-run empty
state. Surface them on the main Models gallery too: a dismissible
"Recommended for your hardware" strip at the top, sharing the
useRecommendedModels fit-ranking with the starter widget. CPU-only boxes
get small models; GPUs get the largest picks that fit VRAM, with size and
VRAM shown per card. One-click install; dismissal persists per browser.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* feat(ui): gpu-mid tier + NVIDIA NVFP4 model recommendations
Refine the hardware recommendation tiers and curated picks:
- Add a gpu-mid tier (8-24GB VRAM) between gpu-small and gpu-large, so
~27B-class models are suggested separately from the 30B+ large tier.
- Detect NVIDIA GPUs (resources.gpus[].vendor) and, on NVIDIA only, prefer
NVFP4 + MTP variants (Blackwell-optimised); NVFP4 models are filtered out
of recommendations on non-NVIDIA hardware where they can't run. This
applies to both the live ranking and the static fallback, with an NVFP4
badge shown on those picks.
- Refresh the curated fallback to current models: Gemma-4 QAT Q4 builds at
every tier, low qwen3.5 (4B distilled / 9B) on CPU/small, qwen3.6-27b
and MTP variants at mid, qwen3.6/qwen3.5 35B-A3B apex/distilled at large.
All names verified against gallery/index.yaml.
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>
The hardware-tuned defaults from #10411 were measured on a GB10 / DGX Spark
(128 GiB unified memory) and over-provisioned multi-GPU consumer Blackwell
(e.g. 2x16 GiB RTX 50-series) into CUDA OOM during model init:
- The Blackwell physical batch (512 -> 2048) sets both n_batch and n_ubatch.
The compute buffer scales ~n_ubatch * n_ctx and is allocated PER DEVICE
(it can't be split across GPUs), so a large context turns ub2048 into
multi-GiB of scratch that must fit one 16 GiB card.
- The VRAM-scaled parallel-slot default tiered off TotalAvailableVRAM(),
which SUMS all GPUs (2x16 -> "32 GiB" -> 8 slots), but the allocations
are per-device.
Make both decisions per-device and context-aware:
- xsysinfo.MinPerGPUVRAM() reports the smallest device's VRAM; localGPU()
uses it so the parallel tier and batch guard reason about one card.
- PhysicalBatchForContext(gpu, ctx) raises the batch only when the extra
compute buffer fits VRAM/4 at this model's context (16 GiB crosses over
~174k ctx, 32 GiB ~349k; GB10 reports system RAM so it still clears it).
- Apply hardware defaults AFTER runBackendHooks in SetDefaults so the
GGUF-guessed context is resolved before the batch decision.
- The distributed router gates the node batch the same way.
Unified-memory devices (GB10, Apple) report system RAM as their single
device's VRAM, so they keep the prefill win.
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>
* feat(ui): remember last-used model per capability
ModelSelector auto-selected the first option whenever the bound value was
empty or stale, so every visit to the Home chat box, Image, TTS or Talk
pages reset the choice to whatever sorted first. Persist the user's pick
in localStorage keyed by capability and prefer it on auto-select when the
model is still available, falling back to the first option otherwise.
Because every modality picker funnels through ModelSelector, this fixes
the friction everywhere at once. External-options callers pass no
capability and keep the previous first-item behaviour.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* feat(ui): add visibility-aware polling hook
The app had 26 hand-rolled setInterval polls, none of which paused when
the browser tab was hidden, so backgrounded dashboards kept hitting the
server every few seconds for data nobody was looking at.
Add usePolling: runs immediately, polls on a fixed interval, pauses while
document.hidden, fires a catch-up poll on return, and guards against
overlapping slow requests. Route useResources (the highest-frequency
shared poll) through it. Further callers can be migrated incrementally.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* feat(ui): hardware-aware starter models on empty home
A fresh install dropped admins straight into a 1000+ model gallery with
no guidance. Add a StarterModels widget to the empty-state wizard that
recommends a small, curated set tuned to the detected hardware:
- CPU-only machines (no GPU VRAM) are steered to genuinely small models
(1-4B, Q4) that stay responsive without a GPU.
- GPU machines get suggestions scaled to available VRAM.
Curated names are real gallery entries, intersected against the live
gallery at render time so a trimmed/custom gallery degrades gracefully.
Install is one click via the existing model-install API.
Also routes Home's cluster and system-info polls through usePolling so a
backgrounded home page stops fetching.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* feat(ui): optional token-cost estimates on usage dashboard
The usage dashboard tracked tokens but had no monetary view. Multi-user
deployments that bill back or budget compute had to export and compute
cost elsewhere.
Add an opt-in pricing control: admins set $ per 1M prompt/completion
tokens (stored per-browser). When set, an estimated-cost summary card and
per-model / per-user cost columns appear, computed from recorded token
counts. The entire cost surface stays hidden until a price is entered, so
the default view is unchanged. Cost is clearly labelled an estimate -
LocalAI itself has no notion of price.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* fix(ui): label icon-only send buttons for screen readers
The chat and agent-chat send buttons were a bare paper-plane icon with
no accessible name, so screen readers announced only "button". Add an
aria-label/title ("Send message") and mark the icon aria-hidden. An audit
of all icon-only buttons found these were the only two unlabeled controls;
the rest already carry visible text.
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>
* feat(backends): add darwin/metal build for liquid-audio
Wire the already-MPS-ready liquid-audio backend (it ships
requirements-mps.txt) into the darwin CI matrix and the gallery so
metal-darwin-arm64 images are built and selectable.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:opus-4.8 [Claude Code]
* ci(liquid-audio): trigger darwin build via requirements-mps note
The changed-backends path filter only builds a backend when a file under
its directory changes. The metal wiring lived in index.yaml + the matrix,
so the darwin job was skipped. Add a documenting comment to the MPS
requirements so CI actually exercises the darwin build.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:opus-4.8 [Claude Code]
* fix(liquid-audio): guard uv-only --index-strategy for the pip/darwin path
Same fix as trl: the darwin/MPS build installs with pip (USE_PIP=true), which
rejects the uv-only --index-strategy flag and failed the darwin backend build.
Add it only on the uv path; Linux/CUDA resolution is unchanged.
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>
The supertonic Go TTS backend dlopens ONNX Runtime, but its runtime and
packaging scripts were Linux-only: run.sh exported LD_LIBRARY_PATH, pointed
ONNXRUNTIME_LIB_PATH at libonnxruntime.so, and always tried the ld.so exec
path, while package.sh hard-failed on any non-Linux host. On macOS dyld has
no ld.so loader, uses DYLD_LIBRARY_PATH, and ONNX Runtime ships as a .dylib.
This applies the same purego .dylib/DYLD_LIBRARY_PATH fix that PR #10481
landed for 15 other ONNX/purego backends (sherpa-onnx, silero-vad, etc.) but
which omitted supertonic:
- run.sh: on darwin export DYLD_LIBRARY_PATH and point ONNXRUNTIME_LIB_PATH
at libonnxruntime.dylib; guard the ld.so exec path to Linux only.
- package.sh: recognize Darwin instead of erroring out; the bundled .dylib is
resolved via DYLD_LIBRARY_PATH, no glibc/ld.so to bundle.
- helper.go: platform-native default library extension (dylib on darwin) for
the last-resort dlopen fallback.
It also wires the darwin CI build and gallery entries, resolving the
inconsistency where backend/index.yaml advertised metal for supertonic but no
includeDarwin matrix entry built the image:
- .github/backend-matrix.yml: add the -metal-darwin-arm64-supertonic Go entry.
- backend/index.yaml: declare metal capabilities and add the concrete
metal-supertonic / metal-supertonic-development child entries.
The Makefile already detects Darwin/osx/arm64 and stages the per-OS ONNX
Runtime tarball, mirroring sherpa-onnx, so no Makefile change is required.
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>
Realtime pipeline sub-models (llm/transcription/tts/vad/sound-detection)
were loaded via cl.LoadModelConfigFileByName without alias resolution,
unlike top-level API requests which resolve aliases in
core/http/middleware/request.go. So a pipeline that references an alias
(e.g. `pipeline.llm: default`, where `default` is an alias for a real
LLM) reached model loading as the alias stub with an empty Backend.
This was silently broken on a single host (it failed downstream) and a
hard error in distributed/p2p mode:
routing model : loading model default: ... installing backend on
node X: backend name is empty
Fix by routing every pipeline sub-model load through a small helper that
follows a single alias hop (mirroring the top-level resolution), so
non-alias sub-models behave identically and aliased ones get the
target's full config (Backend, Model, ...).
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>
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>
PR #10475 fixed SoundDetection in-flight tracking, but the underlying trap
remains: InFlightTrackingClient embedded the whole grpc.Backend interface
"for passthrough of untracked methods", so any newly added inference method
is silently satisfied by the embedded passthrough and never wrapped with
track(). That leaves onFirstComplete unfired and in-flight stuck at 1 - the
exact SoundDetection bug, waiting to recur for the next backend method.
Close the gap at the type level instead of relying on reviewers to remember:
- Split grpc.Backend into two composed sub-interfaces: InferenceBackend
(methods that are one discrete inference call and must be tracked) and
ControlBackend (control-plane calls plus the streaming constructors whose
work spans the returned stream, safe to pass through). The classification
now lives next to the interface it documents.
- InFlightTrackingClient embeds only grpc.ControlBackend and implements every
InferenceBackend method explicitly, delegating to an inner InferenceBackend.
A `var _ grpc.Backend = (*InFlightTrackingClient)(nil)` assertion makes the
package fail to compile if any inference method is left unwrapped.
Now adding a method to InferenceBackend is a build error (at the assertion and
every call site: "does not implement grpc.Backend (missing method X)"), not a
silent runtime leak - and the obvious fix is to copy a neighbouring wrapper,
which calls track(). No runtime guard or reviewer vigilance required.
Pure refactor: the composed Backend interface is identical to the old flat
one, so all implementers and consumers are unaffected (verified with a full
`go build ./...`). Behaviour is unchanged; the existing nodes suite passes.
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>
The distributed router wraps backend clients in InFlightTrackingClient so
the eviction logic knows which replicas are actively serving. Every
inference method must be wrapped: track() increments in-flight on entry
and decrements (plus fires onFirstComplete, which releases the load-time
reservation) on return.
SoundDetection was added after the tracking client and never got a
wrapper, so its calls fell through to the embedded passthrough Backend.
The increment/decrement never ran and, critically, onFirstComplete never
fired, so the reservation set at model load was never released - leaving
in-flight stuck at 1 and the replica permanently ineligible for eviction.
Wrap SoundDetection like the other non-LLM methods and cover it in the
"non-LLM inference methods track in-flight" table test.
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>
fix(agents): URL-decode collection/agent name path params
Collection and agent names carry a "legacy-api-key:" prefix, so the ':'
arrives percent-encoded as %3A in the request path. Echo routes such
paths via URL.RawPath and stores the matched path-param value still
escaped, so c.Param("name") returned "legacy-api-key%3ALiteraryResearch"
and the store lookup 404'd ("collection not found").
This was second-order fallout of #10375/#10387: once colons became valid
in names, the URL-decode gap surfaced on every name-bearing endpoint.
Add a decodedParam helper that url.PathUnescape's the param (falling back
to the raw value on invalid encoding) and wire it into all collection
endpoints and the agent :name endpoints, which share the identical
prefix. The entry endpoints already unescaped c.Param("*"); this closes
the same gap for :name.
Fixes#10443
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>
POST /api/settings rebuilt runtime_settings.json from only the request
body, so a focused admin page that submits a single field wiped every
other persisted setting. The Middleware proxy tab (mitm_listen) and
detector table (pii_default_detectors), plus the MCP SetBranding tool
(instance_name/instance_tagline), all POST partial bodies; the
no-omitempty api_keys and pii_default_detectors fields even round-tripped
as JSON null.
Read the persisted settings and overlay only the fields the request set
(RuntimeSettings.MergeNonNil) before writing. Every field is a pointer, so
the reflection-based merge is total over the struct and any field added
later is preserved automatically. Absent or null fields are now kept;
clearing a setting is done by sending its explicit empty/zero value
(api_keys [], mitm_listen "", etc.), unchanged from before. The full
Settings page sends every field, so its Save behaves identically.
Assisted-by: Claude:claude-opus-4-8 Claude-Code
Signed-off-by: Richard Palethorpe <io@richiejp.com>
PR #10454 added a `cancellable bool` parameter to GalleryStore.UpdateProgress
but missed two callers under tests/e2e/distributed, breaking the build on
master (golangci-lint and tests-e2e-backend both failed to compile with
"not enough arguments in call to ... UpdateProgress").
Pass cancellable=true (both ops are downloading installs, which are
cancellable) and assert the flag is persisted, exercising the new behavior.
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>
In distributed mode a model/backend install marks OpStatus.Cancellable=true
while downloading, but the gallery_operations row never recorded it:
UpdateStatus persisted only progress/status and Create left the cancellable
column at its zero value. After a replica restart Hydrate rebuilt the op with
cancellable=false, /api/operations reported false, and the UI hid the cancel
button - the orphaned op then lingered until the 30-minute stale reaper
expired it ("stays there on restart, can't cancel, after a bit it expires").
Persist the flag on every progress tick and at row creation (installs are
cancellable, deletes are not), and clear it on terminal transitions. A
rehydrated in-flight op is now cancellable, so an admin can dismiss the
orphaned op immediately instead of waiting out the reaper. The functional
cancel path already survived restart (CancelOperation persists store.Cancel
even with no live CancelFunc); this restores the UI affordance that drives it.
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>
* 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>
* feat(ui): add shared DeploymentContext (features + p2p signal)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactor(ui): extract launchAssistantChat shared helper
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(ui): role/mode-aware landing redirect at /app
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(ui): pin Cluster group and collapse Create for cluster admins
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(ui): desktop top navbar with mode pill and admin-via-chat jump
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(ui): admin token-usage meter in the top navbar
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(ui): top-navbar breakpoint handoff + assistant jump from chat page
M1: the desktop .top-navbar was hidden at max-width 768px while the
.mobile-header only appears at max-width 639px, leaving 640-768px with
neither bar so admins lost the mode pill, token meter and admin-via-chat
jump. Hide the top bar at 639px instead so it covers every width the rail
sidebar is shown and hands off to the mobile-header exactly at 639px.
M2: the navbar 'Admin via chat' button wrote localStorage and called
navigate('/app/chat'), but when already on the chat page Chat does not
remount so its mount-time payload reader never fired and the click was a
no-op until reload. The payload consume logic is factored into a shared
callback; the launcher now dispatches a localai-open-assistant event that
the mounted Chat listens for to re-consume the payload. Mount behavior is
unchanged.
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>
* fix(pii): post-merge review fixes + live NER e2e for the privacy-filter tier
Follow-up to the NER tier engine (#10360), already on master. This carries
only the incremental review fixes and tests that postdate that merge — the
feature itself is not re-introduced.
Review fixes:
- openai_completion.go: remove the dead `elem >= 0` conjunct in applyAnyText
(the `elem < 0` guard above already returns).
- application.go: collapse ResolvePIIPolicy's inline re-implementation of
PIIIsEnabled to a single cfg.PIIIsEnabled() call (sole source of the
"explicit pii.enabled wins, else cloud-proxy default" rule) and return true
past the !enabled guard where it is provable.
- pattern.go: hoist the triple `appConfig != nil && EnableTracing` check in
patternDetector.Detect into one local.
- grammar.go: MaxQuantifier was 4096, but Go's regexp/syntax rejects repeat
bounds above 1000 at Parse time, so walk()'s {n,m} guard could never fire —
dead code shadowed by the parser. Lower it to 512 so a bound in (512,1000]
is rejected here with an actionable error; >1000 still fails closed via
Parse. Specs pin the relationship so the guard can't silently revert.
- PatternListEditor.jsx: clamp a directly-typed negative min_len to >=0 and
force the DOM value back when clamping (min={0} only constrained the spinner,
so a negative reached saved config and silently disabled the length filter).
Tests:
- piipattern_test.go: MaxQuantifier guard specs (must stay live, not dead).
- model-config.spec.js: assert the min_len clamp, and that entity_actions
collapses a duplicate group to a single row (map semantics; regression guard
against emitting an array that drops a row on save).
- tests/e2e-backends: token_classify capability driving the TokenClassify gRPC
RPC against the backend image, asserting byte-correct, UTF-8 rune-aligned
spans (entity.Text == text[start:end]) at threshold 0. Verified on CPU via
`make test-extra-backend-privacy-filter` (3/3 specs).
- Makefile: test-extra-backend-privacy-filter wrapper.
- tests/e2e: e2e_pii_ner_test.go drives /api/pii/analyze + /api/pii/redact
(mask + block) through the full HTTP -> detector -> redactor path; gated on
PII_NER_MODEL_GGUF so the default suite is unaffected.
- .github/workflows/tests-pii-ner-e2e.yml: path-filtered / nightly CI job
running the container harness on CPU.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(gallery): add privacy-filter-nemotron (f16 + q8)
GGUF conversions of OpenMed/privacy-filter-nemotron — a fine-grained English
PII token-classifier (55 categories / 221 BIOES classes), fine-tuned from
openai/privacy-filter on NVIDIA's Nemotron-PII dataset. Sibling to the existing
privacy-filter-multilingual entry, trading language breadth for category depth.
- privacy-filter-nemotron: F16 reference artifact (~2.8 GB).
- privacy-filter-nemotron-q8: Q8_0 quant (~1.64 GB) for RAM-constrained / edge
use; description notes the size/speed tradeoff and to validate on your own
data (a single dropped span is a PII leak).
Both run on the privacy-filter backend with known_usecases [token_classify] and
a default mask policy (min_score 0.5); operators add per-category entity_actions
as needed. sha256s taken from the HF repo's LFS object ids.
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>
fix(diffusers): pin diffusers and transformers to a known-good pair
The diffusers backend tracked git+https://github.com/huggingface/diffusers
(main) with an unpinned transformers. transformers v5 restructured
CLIPTextModel and removed the .text_model attribute that diffusers' single
-file loader reads, so loading any single-file Stable Diffusion checkpoint
fails:
create_diffusers_clip_model_from_ldm (single_file_utils.py)
position_embedding_dim = model.text_model.embeddings.position_embedding...
AttributeError: 'CLIPTextModel' object has no attribute 'text_model'
No released diffusers (<=0.38.0) supports transformers v5 - only unreleased
diffusers main does. Because the requirements tracked main plus an unpinned
transformers, every backend image froze whichever pair existed at build
time, and images built once transformers v5 shipped but before diffusers
main caught up are permanently broken.
Pin the last known-good released pair across all requirements files:
diffusers==0.38.0 and transformers==4.57.6. 0.38.0 still exposes every
pipeline backend.py imports (Flux, Wan, Sana, LTX2, Qwen, GGUF), so no
functionality is lost, and builds become reproducible instead of drifting
into the broken window.
Fixes#9979
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Adira Denis Muhando <dennisadira@gmail.com>
File-staging progress lived only in the SmartRouter's in-memory
StagingTracker on the replica performing the transfer. In a multi-replica
deployment behind a round-robin load balancer, a /api/operations poll
that lands on any other replica saw no staging row, so the progress
("processing file ... Total ... Current ...") flickered in and out as
polls rotated between frontends.
Mirror the pattern already used for gallery-install progress: the origin
replica broadcasts staging ticks over NATS (SubjectStagingProgress, a
new staging.<model>.progress subject), and peers merge them via
ApplyRemote (SubscribeBroadcasts on the wildcard). Byte-level ticks are
leading-edge debounced (~1/s); Start/FileComplete/Complete always
publish. A locally-owned op stays authoritative so the origin's own echo
and stray peer events can't clobber it, and mirrored remote ops expire
after a TTL so a missed Done event can't leave a phantom row. The UI read
path (StagingTracker.GetAll) is unchanged.
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>
A model not yet loaded on a worker is staged lazily on the inference
request path. Staging a multi-GB model takes minutes - far longer than
any client keeps its HTTP request open - so a browser refresh, an
ingress/LB idle-timeout, or a round-robined retry landing on another
frontend replica cancels the request context and aborts the upload with
"context canceled" mid-transfer. Large models then never finish staging,
so they never load (observed in a 2-replica deployment: both frontends
repeatedly failed to stage a 15.7 GB GGUF, each attempt dying at a
different offset).
Bind the cold load (staging + LoadModel + the per-model advisory lock) to
context.WithoutCancel(ctx): it keeps the request's values (prefix chain)
but drops cancellation/deadline. Each long step keeps its own bound (the
file stager's resume budget, LoadModel's 5m timeout), and the advisory
lock still de-dupes concurrent loaders across replicas.
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>
LocalAI pulls models from OCI registries (via go-containerregistry), the
Ollama registry, and OCI blob stores (via oras), but every request went
out with the underlying library's generic User-Agent, so registry
operators had no way to attribute traffic to LocalAI.
Add an oci.UserAgent() helper that returns "LocalAI" (or
"LocalAI/<version>" when the binary is built with a version stamp via
internal.Version) and wire it into all three pull paths:
- pkg/oci/image.go: remote.WithUserAgent on the go-containerregistry
image and digest requests
- pkg/oci/ollama.go: a User-Agent header on the Ollama manifest request
- pkg/oci/blob.go: a LocalAI User-Agent on the oras blob client. This
mirrors oras' auth.DefaultClient (same retry.DefaultClient policy);
only the advertised User-Agent changes.
Implements #6258.
Assisted-by: Claude:claude-opus-4-8 golangci-lint
Signed-off-by: Vijay Sai <vijaysaijnv@gmail.com>
* 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>
