mirror of
https://github.com/mudler/LocalAI.git
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a6121e240e37d44b0cdde4dc6778dcf5df3e15b5
1204 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
Russell Sim
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18e039f305 |
fix(ci): fix AMDGPU_TARGETS empty-string bypass in hipblas builds (#9626)
* fix(ci): fix AMDGPU_TARGETS empty-string bypass in hipblas builds
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Ettore Di Giacinto
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7325046650 |
fix(diffusers): drop compel from requirements to unblock pip resolver (#9632)
compel 2.3.1 (latest, Nov 2025) declares transformers~=4.25 in its
metadata, i.e. >=4.25,<5.0. After transformers 5.0 (2026-01-26) and
huggingface-hub 1.0 (2025-10-27) shipped, the weekly DEPS_REFRESH
cache rotation in CI started seeing the new majors and pip's resolver
went into multi-hour backtracking storms walking every transformers
4.x candidate against every accelerate/hf-hub/tokenizers combination
to find a set compel would accept. The 2026-04-29 backend-build for
the diffusers backend (darwin-mps + l4t + cublas13-turboquant matrix
cells) hit the GitHub Actions 6h job timeout still inside pip
install — the build itself never started.
compel is the only hard upper bound on transformers in this stack
(diffusers, accelerate, peft, optimum-quanto are all flexible), and
upstream support for transformers 5 is still in flight: damian0815/
compel#129 ("Modernize Compel for Transformers 5") and #128 ("Bump
transformers version to >5.0") are both open as of today.
backend.py only constructs Compel() when COMPEL=1 is set in the env
(default off), so make compel a true optional extra:
- Wrap the top-level `from compel import ...` in try/except
ImportError, mirroring the existing sd_embed pattern.
- Auto-disable COMPEL with a warning when the module isn't
installed, instead of crashing on module load.
- Drop compel from all eight requirements-*.txt variants so the
resolver no longer has to satisfy its transformers cap.
- Leave a TODO in backend.py and in each requirements file
pointing at the upstream PR/issue, so the dependency can be
reinstated once compel supports transformers >= 5.
Users who rely on weighted-prompt embeddings can opt in with a
manual `pip install compel` alongside COMPEL=1; the warning emitted
on startup tells them how.
Assisted-by: Claude:claude-opus-4-7 [Bash Read Edit WebFetch]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
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Tai An
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80961d2da6 |
feat(backends/python): use tempfile.gettempdir() instead of hardcoded /tmp (#9629)
Closes #9601 Makes the temporary scratch paths in vllm, vllm-omni, tinygrad, and pocket-tts backends configurable via the standard TMPDIR env var, instead of always writing to /tmp. This is a one-line change per call site that calls tempfile.gettempdir() for the directory and keeps the same filename suffix. Users who run on systems with a small root partition (or want to relocate scratch files to a larger volume) can now redirect these by setting TMPDIR (e.g. TMPDIR=/data/tmp), without affecting the existing LOCALAI_GENERATED_CONTENT_PATH or LOCALAI_UPLOAD_PATH options that already cover other temp paths. Files touched: - backend/python/vllm/backend.py (1 site: video base64 scratch) - backend/python/tinygrad/backend.py (1 site: image fallback dst) - backend/python/pocket-tts/backend.py (1 site: tts wav fallback dst) - backend/python/vllm-omni/backend.py (2 sites: video + audio scratch) |
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LocalAI [bot]
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9c4c3f9d8f |
chore: ⬆️ Update ggml-org/llama.cpp to beb42fffa45eded44804a1fd4916146222371581 (#9624)
⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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LocalAI [bot]
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273416f54b |
chore: ⬆️ Update ikawrakow/ik_llama.cpp to a8aecbf15933295af96504f9a693998322185b5c (#9625)
⬆️ Update ikawrakow/ik_llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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Ettore Di Giacinto
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c02a50f2ab |
feat(llama-cpp): bump to d775992 and adapt to spec params refactor (#9618)
Bumps backend/cpp/llama-cpp/Makefile LLAMA_VERSION from 665abc6 to
d775992, picking up upstream PR ggml-org/llama.cpp#22397 which splits
common_params_speculative into nested draft / ngram_simple / ngram_mod
sub-structs. Renames every grpc-server.cpp reference to match:
speculative.mparams_dft.path -> speculative.draft.mparams.path
speculative.{n_max,n_min} -> speculative.draft.{n_max,n_min}
speculative.{p_min,p_split} -> speculative.draft.{p_min,p_split}
speculative.{n_gpu_layers,n_ctx} -> speculative.draft.{n_gpu_layers,n_ctx}
speculative.ngram_size_n -> speculative.ngram_simple.size_n
speculative.ngram_size_m -> speculative.ngram_simple.size_m
speculative.ngram_min_hits -> speculative.ngram_simple.min_hits
The "speculative.n_max" JSON key sent to the upstream server stays
unchanged — server-task.cpp still reads it and routes the value into
draft.n_max internally.
The turboquant fork (TheTom/llama-cpp-turboquant @ 11a241d) branched
before #22397 and still exposes the flat layout. Since turboquant
reuses the shared backend/cpp/llama-cpp/grpc-server.cpp, extend
patch-grpc-server.sh with an idempotent sed block that reverts the
ten field references back to the legacy flat names on the build copy
only — the original under backend/cpp/llama-cpp/ stays compiling
against vanilla upstream. Drop the block once the fork rebases.
ik-llama-cpp has its own grpc-server.cpp with no speculative refs
(0/2661 lines), so it is unaffected.
Validated locally with `make docker-build-llama-cpp` (avx, avx2,
avx512, fallback, grpc + rpc-server all built; image exported).
Assisted-by: Claude:claude-opus-4-7 [Bash Read Edit]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
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LocalAI [bot]
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76971fb2aa |
chore: ⬆️ Update leejet/stable-diffusion.cpp to 3d6064b37ef4607917f8acf2ca8c8906d5087413 (#9617)
⬆️ Update leejet/stable-diffusion.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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Ettore Di Giacinto
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fe6eb57082 |
feat(vibevoice-cpp): add purego TTS+ASR backend (#9610)
* feat(vibevoice-cpp): add purego TTS+ASR backend
Wire up Microsoft VibeVoice via the vibevoice.cpp C ABI as a new
purego-based Go backend that serves both Backend.TTS and
Backend.AudioTranscription from a single gRPC binary. Mirrors the
qwen3-tts-cpp / sherpa-onnx pattern so the variant matrix
(cpu/cuda12/cuda13/metal/rocm/sycl-f16/f32/vulkan/l4t) and the
e2e-backends gRPC harness reuse existing infrastructure.
- backend/go/vibevoice-cpp/ - Makefile, CMakeLists, purego shim, gRPC
Backend with model-dir auto-detection, closed-loop TTS->ASR smoke test
- backend/index.yaml - &vibevoicecpp meta + 18 image entries
- Makefile - .NOTPARALLEL, BACKEND_VIBEVOICE_CPP, docker-build wiring,
test-extra-backend-vibevoice-cpp-{tts,transcription} e2e wrappers
- .github/workflows/backend.yml - matrix entries for all variants
- .github/workflows/test-extra.yml - per-backend smoke + 2 gRPC e2e jobs
* feat(vibevoice-cpp): drop hardcoded glob detection, add gallery entries
Refactor backend Load() to follow the standard Options[] convention
used by sherpa-onnx and the rest of the multi-role backends:
ModelFile is the primary gguf, supplementary paths come through
opts.Options[] as key=value (or key:value for Make-target compat),
resolved against opts.ModelPath. type=asr/tts decides the role of
ModelFile when neither tts_model nor asr_model is set explicitly.
Add gallery/index.yaml entries:
- vibevoice-cpp - realtime 0.5B Q8_0 TTS + tokenizer + Carter voice
- vibevoice-cpp-asr - long-form ASR Q8_0 + tokenizer
Both pull from huggingface://mudler/vibevoice.cpp-models with sha256
verification. parameters.model + Options[] paths are siblings under
{models_dir} per the qwen3-tts-cpp convention.
Update Makefile e2e wrappers to pass BACKEND_TEST_OPTIONS comma+colon
style, and tighten the per-backend Go closed-loop test to use the
explicit Options API.
* fix(vibevoice-cpp): force whole-archive link so vv_capi_* exports survive
libvibevoice is a STATIC archive linked into the MODULE library.
Without --whole-archive (or -force_load on Apple, /WHOLEARCHIVE on
MSVC), the linker garbage-collects symbols not referenced from this
translation unit - which means dlopen+RegisterLibFunc panics with
'undefined symbol: vv_capi_load' at backend startup, since purego
looks them up by name and our cpp/govibevoicecpp.cpp doesn't call
them directly.
* test(vibevoice-cpp): rewrite suite with Ginkgo v2
Match the convention used by backend/go/sherpa-onnx/backend_test.go.
The suite now covers backend semantics that don't need purego (Locking,
empty-ModelFile rejection, TTS/ASR-without-loaded-model errors) on top
of the gRPC lifecycle specs (Health, Load, closed-loop TTS->ASR).
Model-dependent specs Skip() when VIBEVOICE_MODEL_DIR is unset, so
`go test ./backend/go/vibevoice-cpp/` is green on a clean checkout
and runs the heavyweight closed-loop spec when test.sh has staged
the bundle.
* fix(vibevoice-cpp): implement TTSStream + AudioTranscriptionStream
The gRPC server's stream handlers (pkg/grpc/server.go) spawn a
goroutine that ranges over a chan; the only thing closing that chan
is the backend's own *Stream method. With the default Base stub
returning 'unimplemented' and never touching the chan, the server
goroutine hangs forever and the client hits DeadlineExceeded - which
is exactly what the e2e harness saw in the test-extra-backend-vibevoice-cpp-tts
matrix run.
TTSStream synthesizes via vv_capi_tts to a tempfile, then emits a
streaming WAV header (chunk sizes 0xFFFFFFFF so HTTP clients can
start playback before the full PCM lands) followed by the PCM body
in 64 KB slices. The header + >=2 PCM frames satisfy the harness's
'expected >=2 chunks' assertion and give a real progressive stream.
AudioTranscriptionStream runs the offline transcription, emits each
segment as a delta, and closes with a final_result whose Text equals
the concatenated deltas (the harness asserts those match).
Two new Ginkgo specs guard the close-channel-on-error path so the
deadline-exceeded regression can't come back silently.
* fix(vibevoice-cpp): silence errcheck on cleanup paths
Lint flagged six unchecked Close()/Remove()/RemoveAll() calls along
purely-cleanup deferred paths. Wrap each in '_ = ...' (or a closure
for defers that take args) - matches what the rest of the LocalAI
backend/go/* tree already does for these callsites.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(vibevoice-cpp): closed-loop slot fill + modelRoot-relative path resolution
Two bugs the test-extra-backend-vibevoice-cpp-* CI matrix surfaced:
1. Closed-loop Load with ModelFile=tts.gguf + Options[asr_model=...] left
v.ttsModel empty, because the default-fill block only ran when BOTH
slots were empty. vv_capi_load then got tts="" + a voice and the
C side rejected it with rc=-3 'TTS model required to load a voice'.
Fix: ModelFile fills the *primary* role-slot (decided by 'type=' in
Options, defaulting to tts) independently of the secondary, so
ModelFile + asr_model resolves to both.
2. resolvePath stat'd CWD before falling back to relTo. With LocalAI
launched from a directory that happens to contain a same-named
file, supplementary Options[] paths could leak away from the
models dir. Drop the CWD probe entirely - relative paths now
*always* join onto opts.ModelPath (the gallery convention).
New Ginkgo coverage:
* 'ModelFile slot resolution' (4 specs) - asr_model+ModelFile, type=asr,
explicit tts_model override, key:value variant.
* 'resolvePath (relative-to-modelRoot)' (5 specs) - join, abs passthrough,
empty input, empty relTo, and the CWD-trap regression test.
* 'Load resolves relative Options paths against opts.ModelPath' - end-
to-end gallery layout round-trip.
Verified locally: 19/19 specs pass (with model bundle, including the
closed-loop TTS->ASR; without bundle, 17 pass + 2 model-dependent skip).
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* test(vibevoice-cpp): use gallery convention in closed-loop spec
The 'loads the realtime TTS model' / closed-loop specs were passing
already-prefixed paths into Options[]:
Options: ['tokenizer=' + filepath.Join(modelDir, 'tokenizer.gguf')]
Combined with no ModelPath set on the request, the backend's
modelRoot fell back to filepath.Dir(ModelFile) = modelDir, then
resolvePath joined the prefixed Options path on top of it -
producing 'vibevoice-models/vibevoice-models/tokenizer.gguf' when
the CI's VIBEVOICE_MODEL_DIR is the relative './vibevoice-models'.
The fix is to mirror the gallery contract LocalAI core actually
sends in production: ModelPath is the models root (absolute),
ModelFile is a name *under* it, every Options[] path is relative
to ModelPath. Uses filepath.Base() to get bare filenames.
Verified locally with both VIBEVOICE_MODEL_DIR=/tmp/vv-bundle (abs)
and VIBEVOICE_MODEL_DIR=vibevoice-models (the relative shape that
broke CI). Both: 19/19 specs pass, ~55-60s.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* ci(vibevoice-cpp): switch ASR to Q4_K + bump transcription timeout
The Q8_0 ASR gguf is ~14 GB - too big to fit alongside the runner
image, the docker build cache, and the test artifacts on a free
ubuntu-latest GHA runner; 'test-extra-backend-vibevoice-cpp-transcription'
was getting SIGTERM'd at 90 min before the model could finish loading.
Switch to Q4_K (~10 GB on disk, slightly faster CPU decode) for:
* the e2e harness Make target
* the gallery 'vibevoice-cpp-asr' entry (parameters + files block)
* the per-backend test.sh auto-download list
Bump tests-vibevoice-cpp-grpc-transcription's timeout-minutes from
90 to 150 - even with Q4_K, the 30 s JFK clip on a CPU runner needs
runway above the previous 90 min cap.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* ci(vibevoice-cpp): drop transcription gRPC e2e job - too heavy for free runners
The vibevoice ASR is a 7B-parameter model. Even on Q4_K (~10 GB on
disk) a single 30 s transcription saturates the per-test 30 min
timeout in the e2e-backends harness on a 4-core ubuntu-latest, and
the 10 GB download + Docker layer + working space leaves no headroom
on the runner's free disk. Two attempts in CI got SIGTERM'd at the
LoadModel boundary - the bottleneck isn't tunable from the workflow
side without a paid-tier runner.
The per-backend tests-vibevoice-cpp job already runs the same
AudioTranscription path via a closed-loop TTS->ASR Ginkgo spec - same
gRPC contract, same model, single process - so the standalone
tests-vibevoice-cpp-grpc-transcription job was redundant on top of
the disk/CPU pressure.
The Makefile target test-extra-backend-vibevoice-cpp-transcription
stays for local invocation on workstations that can afford it -
useful when developing the streaming codepaths.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* ci(vibevoice-cpp): restore transcription gRPC e2e on bigger-runner
Switch tests-vibevoice-cpp-grpc-transcription from ubuntu-latest to
the self-hosted 'bigger-runner' label that GPU image builds in
backend.yml use, plus the documented Free-disk-space prep step (purge
dotnet / ghc / android / CodeQL caches) the disabled vllm/sglang
entries in this file describe. That gives the 7B-param Q4_K ASR
model the disk + CPU runway it needs.
Keep timeout-minutes: 150 - even on a beefier runner the 30 s JFK
decode plus 10 GB download has to fit comfortably.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* ci(vibevoice-cpp): apt-get install make on bigger-runner before transcription e2e
bigger-runner is a self-hosted bare runner without the standard
ubuntu image's preinstalled build tools, so the previous job died at
the very first command with 'make: command not found' (exit 127).
Add the Dependencies step that the disabled vllm/sglang entries in
this file already document - apt-get installs make + build-essential
+ curl + unzip + ca-certificates + git + tar before the make target
runs. Mirrors how every other 'runs-on: bigger-runner' entry in
backend.yml prepares the runner.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
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Richard Palethorpe
|
4916f8c880 |
feat(vllm): expose AsyncEngineArgs via generic engine_args YAML map (#9563)
* feat(vllm): expose AsyncEngineArgs via generic engine_args YAML map
LocalAI's vLLM backend wraps a small typed subset of vLLM's
AsyncEngineArgs (quantization, tensor_parallel_size, dtype, etc.).
Anything outside that subset -- pipeline/data/expert parallelism,
speculative_config, kv_transfer_config, all2all_backend, prefix
caching, chunked prefill, etc. -- requires a new protobuf field, a
Go struct field, an options.go line, and a backend.py mapping per
feature. That cadence is the bottleneck on shipping vLLM's
production feature set.
Add a generic `engine_args:` map on the model YAML that is
JSON-serialised into a new ModelOptions.EngineArgs proto field and
applied verbatim to AsyncEngineArgs at LoadModel time. Validation
is done by the Python backend via dataclasses.fields(); unknown
keys fail with the closest valid name as a hint.
dataclasses.replace() is used so vLLM's __post_init__ re-runs and
auto-converts dict values into nested config dataclasses
(CompilationConfig, AttentionConfig, ...). speculative_config and
kv_transfer_config flow through as dicts; vLLM converts them at
engine init.
Operators can now write:
engine_args:
data_parallel_size: 8
enable_expert_parallel: true
all2all_backend: deepep_low_latency
speculative_config:
method: deepseek_mtp
num_speculative_tokens: 3
kv_cache_dtype: fp8
without further proto/Go/Python plumbing per field.
Production defaults seeded by hooks_vllm.go: enable_prefix_caching
and enable_chunked_prefill default to true unless explicitly set.
Existing typed YAML fields (gpu_memory_utilization,
tensor_parallel_size, etc.) remain for back-compat; engine_args
overrides them when both are set.
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* chore(vllm): pin cublas13 to vLLM 0.20.0 cu130 wheel
vLLM's PyPI wheel is built against CUDA 12 (libcudart.so.12) and won't
load on a cu130 host. Switch the cublas13 build to vLLM's per-tag cu130
simple-index (https://wheels.vllm.ai/0.20.0/cu130/) and pin
vllm==0.20.0. The cu130-flavoured wheel ships libcudart.so.13 and
includes the DFlash speculative-decoding method that landed in 0.20.0.
cublas13 install gets --index-strategy=unsafe-best-match so uv consults
both the cu130 index and PyPI when resolving — PyPI also publishes
vllm==0.20.0, but with cu12 binaries that error at import time.
Verified: Qwen3.5-4B + z-lab/Qwen3.5-4B-DFlash loads and serves chat
completions on RTX 5070 Ti (sm_120, cu130).
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* ci(vllm): bot job to bump cublas13 vLLM wheel pin
vLLM's cu130 wheel index URL is itself version-locked
(wheels.vllm.ai/<TAG>/cu130/, no /latest/ alias upstream), so a vLLM
bump means rewriting two values atomically — the URL segment and the
version constraint. bump_deps.sh handles git-sha-in-Makefile only;
add a sibling bump_vllm_wheel.sh and a matching workflow job that
mirrors the existing matrix's PR-creation pattern.
The bumper queries /releases/latest (which excludes prereleases),
strips the leading 'v', and seds both lines unconditionally. When the
file is already on the latest tag the rewrite is a no-op and
peter-evans/create-pull-request opens no PR.
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* docs(vllm): document engine_args and speculative decoding
The new engine_args: map plumbs arbitrary AsyncEngineArgs through to
vLLM, but the public docs only covered the basic typed fields. Add a
short subsection in the vLLM section explaining the typed/generic
split and showing a worked DFlash speculative-decoding config, with
pointers to vLLM's SpeculativeConfig reference and z-lab's drafter
collection.
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
---------
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]
|
55afda22e3 |
chore: ⬆️ Update ikawrakow/ik_llama.cpp to 453a027c17e4d63a7f16b871197a396240a65138 (#9608)
⬆️ Update ikawrakow/ik_llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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Ettore Di Giacinto
|
e370318bd7 |
fix(vllm): seed pybind11 for fastsafetensors build under --no-build-isolation
fastsafetensors==0.3 (transitive dep of vllm) imports pybind11 in setup.py without declaring it in build-system.requires. With --no-build-isolation it has to already exist in the venv, otherwise the wheel build fails with ModuleNotFoundError on arm64 L4T CUDA 13 (and any other profile that picks up vllm 0.20.0). Assisted-by: Claude:claude-opus-4-7 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Richard Palethorpe
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4443250756 |
chore: add golangci-lint with new-from-merge-base baseline (#9603)
* chore: add golangci-lint with new-from-merge-base baseline
Configure golangci-lint v2 with the standard linter set (errcheck, govet,
ineffassign, unused) plus forbidigo, which enforces the Ginkgo/Gomega-only
test convention from .agents/coding-style.md by rejecting stdlib testing
calls (t.Errorf, t.Fatalf, t.Run, ...). staticcheck is disabled — the
codebase has many pre-existing QF-style suggestions not worth gating on.
issues.new-from-merge-base = master makes the lint job a gate for new
issues only; the ~1300 pre-existing baseline stays visible via
'make lint-all' for incremental cleanup. CI runs 'make lint'.
Backends needing C/C++ headers we don't install in the lint runner are
excluded via a deny list in the Makefile (backend/go/{piper,silero-vad,
llm}, cmd/launcher). Discovery still flows through 'go list ./...', so
new packages are scanned automatically.
To make backend/go/{sam3-cpp,stablediffusion-ggml,whisper} typecheckable,
move their .cpp/.h sources into cpp/ subdirs (matching qwen3-tts-cpp /
acestep-cpp). Without this 'go list' rejects the package because Go does
not allow .cpp alongside .go without cgo.
Fix two real bugs found by lint in tests/integration/ (run only via
'make test-stores', not default CI): a stale zerolog reference left over
from the slog migration (
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dependabot[bot]
|
439471baec |
chore(deps): bump packaging from 24.1 to 26.2 in /backend/python/coqui (#9594)
Bumps [packaging](https://github.com/pypa/packaging) from 24.1 to 26.2. - [Release notes](https://github.com/pypa/packaging/releases) - [Changelog](https://github.com/pypa/packaging/blob/main/CHANGELOG.rst) - [Commits](https://github.com/pypa/packaging/compare/24.1...26.2) --- updated-dependencies: - dependency-name: packaging dependency-version: '26.2' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> |
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LocalAI [bot]
|
f3500223d7 |
chore: ⬆️ Update leejet/stable-diffusion.cpp to a81677f59c92d90343aebca51dfed7decf0a0cb0 (#9586)
⬆️ Update leejet/stable-diffusion.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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LocalAI [bot]
|
b69bacfcdc |
chore: ⬆️ Update ikawrakow/ik_llama.cpp to d6f3e4e28fbf75e6181e6ea32e734de9ce9304fd (#9585)
⬆️ Update ikawrakow/ik_llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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LocalAI [bot]
|
8e50066fa2 |
chore: ⬆️ Update ggml-org/llama.cpp to 665abc609740d397d30c0d8ef4157dbf900bd1a3 (#9584)
⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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Ettore Di Giacinto
|
f4036fa83f |
ci(python-backends): add weekly DEPS_REFRESH cache-buster
The shared backend/Dockerfile.python ends in:
RUN cd /${BACKEND} && PORTABLE_PYTHON=true make
which `pip install`s each backend's requirements*.txt. A scan of all 34
Python backends shows every single one ships at least some unpinned deps
(torch, transformers, vllm, diffusers, ...). With the registry cache now
enabled, that `make` layer's BuildKit hash depends only on Dockerfile
instructions + COPYed source — not on what pip resolves at runtime — so
a warm cache would freeze upstream versions indefinitely.
DEPS_REFRESH is an ARG declared right before that RUN. backend_build.yml
computes `date -u +%Y-W%V` (ISO week, e.g. `2026-W17`) and passes it as
a build-arg, so the install layer invalidates at most once per week and
re-resolves PyPI / nightly indexes. Within a week, builds stay warm.
Only Dockerfile.python is affected: Go (go.sum) and Rust (Cargo.lock)
already lock their deps, and the C++ backends pull gRPC at a pinned tag
and llama.cpp at a pinned commit.
Add .agents/ci-caching.md documenting the cache layout
(quay.io/go-skynet/ci-cache:cache<tag-suffix>), read/write semantics
(master writes, PRs read-only), DEPS_REFRESH semantics, and how to
manually evict tags. Index it from AGENTS.md (CLAUDE.md is a symlink).
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: claude-code:claude-opus-4-7-1m
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LocalAI [bot]
|
05e94bd9e7 |
chore: ⬆️ Update ggml-org/llama.cpp to f53577432541bb9edc1588c4ef45c66bf07e4468 (#9577)
⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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Ettore Di Giacinto
|
8d124d080f |
feat(gallery): add whisper-development umbrella stanza
Mirrors the whisper capabilities map with -development variants so clients can pull the master-tagged whisper.cpp backend via a single platform-resolved name, matching the existing faster-whisper-development and whisperx-development entries. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Ettore Di Giacinto
|
5b0196c7d0 |
fix(whisper): scrub invalid UTF-8 from segment text before protobuf marshal
whisper.cpp can emit bytes that are not valid UTF-8 — typically a multibyte codepoint split across token boundaries. protobuf string fields reject those at marshal time, which would surface as a transcribe failure. Run strings.ToValidUTF8 on the segment text before it leaves the cgo boundary so the bad byte gets replaced with U+FFFD. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-7 [Claude Code] |
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LocalAI [bot]
|
d9cb0d6133 |
chore: ⬆️ Update ggml-org/llama.cpp to dcad77cc3b0865153f486327064fb0320a57a476 (#9572)
⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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LocalAI [bot]
|
f5c268deac |
chore: ⬆️ Update TheTom/llama-cpp-turboquant to 11a241d0db78a68e0a5b99fe6f36de6683100f6a (#9571)
⬆️ Update TheTom/llama-cpp-turboquant Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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LocalAI [bot]
|
1c45227346 |
chore: ⬆️ Update ikawrakow/ik_llama.cpp to 3a945af45d45936341a45bbf7deda56776a4af26 (#9570)
⬆️ Update ikawrakow/ik_llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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Ettore Di Giacinto
|
d733c9cd13 |
fix(mlx-vlm): pin upstream to v0.4.4 to unblock CUDA builds (#9568)
Blaizzy/mlx-vlm git HEAD bumped its constraint to mlx>=0.31.2, but mlx-cuda-12 and mlx-cuda-13 are only published up to 0.31.1 on PyPI. Since mlx[cudaXX]==0.31.2 forces a sibling wheel that doesn't exist, pip backtracks through every older mlx[cudaXX], none of which satisfy mlx>=0.31.2, producing ResolutionImpossible. Pin all variants to the v0.4.4 tag (mlx>=0.30.0), which resolves cleanly against mlx[cuda13]==0.31.1. cpu/mps weren't broken yet but are pinned for consistency. Assisted-by: Claude:claude-opus-4-7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Richard Palethorpe
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73aacad2f9 |
fix(vllm): drop flash-attn wheel to avoid torch 2.10 ABI mismatch (#9557)
The pinned flash-attn 2.8.3+cu12torch2.7 wheel breaks at import time
once vllm 0.19.1 upgrades torch to its hard-pinned 2.10.0:
ImportError: .../flash_attn_2_cuda...so: undefined symbol:
_ZN3c104cuda29c10_cuda_check_implementationEiPKcS2_ib
That C10 CUDA symbol is libtorch-version-specific. Dao-AILab has not yet
published flash-attn wheels for torch 2.10 -- the latest release (2.8.3)
tops out at torch 2.8 -- so any wheel pinned here is silently ABI-broken
the moment vllm completes its install.
vllm 0.19.1 lists flashinfer-python==0.6.6 as a hard dep, which already
covers the attention path. The only other use of flash-attn in vllm is
the rotary apply_rotary import in
vllm/model_executor/layers/rotary_embedding/common.py, which is guarded
by find_spec("flash_attn") and falls back cleanly when absent.
Also unpin torch in requirements-cublas12.txt: the 2.7.0 pin only
existed to give the flash-attn wheel a matching torch to link against.
With flash-attn gone, vllm's own torch==2.10.0 dep is the binding
constraint regardless of what we put here.
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
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LocalAI [bot]
|
806ea24ff4 |
chore: ⬆️ Update TheTom/llama-cpp-turboquant to 67559e580b10e4e47e9a6fd6218873997976886d (#9497)
⬆️ Update TheTom/llama-cpp-turboquant Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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Ettore Di Giacinto
|
21eace40ec |
feat(llama-cpp): expose split_mode option for multi-GPU placement (#9560)
Adds split_mode (alias sm) to the llama.cpp backend options allowlist, accepting none|layer|row|tensor. The tensor value targets the experimental backend-agnostic tensor parallelism from ggml-org/llama.cpp#19378 and requires a llama.cpp build that includes that PR, FlashAttention enabled, KV-cache quantization disabled, and a manually set context size. Assisted-by: Claude:claude-opus-4-7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Ettore Di Giacinto
|
24505e57f5 |
feat(backends): add CUDA 13 + L4T arm64 CUDA 13 variants for vllm/vllm-omni/sglang (#9553)
* feat(backends): add CUDA 13 + L4T arm64 CUDA 13 variants for vllm/vllm-omni/sglang
Adds new build profiles mirroring the diffusers/ace-step pattern so vLLM
serving (and SGLang on arm64) can be deployed on CUDA 13 hosts and
JetPack 7 boards:
- vllm: cublas13 (PyPI cu130 channel) + l4t13 (jetson-ai-lab SBSA cu130
prebuilt vllm + flash-attn).
- vllm-omni: cublas13 + l4t13. Floats vllm version on cu13 since vllm
0.19+ ships cu130 wheels by default and vllm-omni tracks vllm master;
cu12 path keeps the 0.14.0 pin to avoid disturbing existing images.
- sglang: l4t13 arm64 only — uses the prebuilt sglang wheel from the
jetson-ai-lab SBSA cu130 index, so no source build is needed.
Cublas13 sglang on x86_64 is intentionally deferred.
CI matrix gains five new images (-gpu-nvidia-cuda-13-vllm{,-omni},
-nvidia-l4t-cuda-13-arm64-{vllm,vllm-omni,sglang}); backend/index.yaml
gains the matching capability keys (nvidia-cuda-13, nvidia-l4t-cuda-13)
and latest/development merge entries.
Assisted-by: Claude:claude-opus-4-7 [Read] [Edit] [Write] [Bash]
* fix(backends): use unsafe-best-match index strategy on l4t13 builds
The jetson-ai-lab SBSA cu130 index lists transitive deps (decord, etc.)
at limited versions / older Python ABIs. uv defaults to the first index
that contains a package and refuses to fall through to PyPI, so sglang
l4t13 build fails resolving decord. Mirror the existing cpu sglang
profile by setting --index-strategy=unsafe-best-match on l4t13 across
the three backends, and apply it to the explicit vllm install line in
vllm-omni's install.sh (which doesn't honor EXTRA_PIP_INSTALL_FLAGS).
Assisted-by: Claude:claude-opus-4-7 [Read] [Edit] [Bash]
* fix(sglang): drop [all] extras on l4t13, floor version at 0.5.0
The [all] extra brings in outlines→decord, and decord has no aarch64
cp312 wheel on PyPI nor the jetson-ai-lab index (only legacy cp35-cp37
tags). With unsafe-best-match enabled, uv backtracked through sglang
versions trying to satisfy decord and silently landed on
sglang==0.1.16, an ancient version with an entirely different dep
tree (cloudpickle/outlines 0.0.44, etc.).
Drop [all] so decord is no longer required, and floor sglang at 0.5.0
to prevent any future resolver misfire from degrading the version
again.
Assisted-by: Claude:claude-opus-4-7 [Read] [Edit] [Bash]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
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LocalAI [bot]
|
08e393f7db |
chore: ⬆️ Update ikawrakow/ik_llama.cpp to cb58a561f0c49f68b6d125cdfda037ed80433821 (#9549)
⬆️ Update ikawrakow/ik_llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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LocalAI [bot]
|
47cc3dc8d7 |
chore: ⬆️ Update ggml-org/llama.cpp to 361fe72acb7b9bd79059cc177cbeda99b35b5db9 (#9548)
⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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dependabot[bot]
|
9ab3496de2 |
chore(deps): bump rustls-webpki from 0.103.10 to 0.103.13 in /backend/rust/kokoros in the cargo group across 1 directory (#9546)
chore(deps): bump rustls-webpki Bumps the cargo group with 1 update in the /backend/rust/kokoros directory: [rustls-webpki](https://github.com/rustls/webpki). Updates `rustls-webpki` from 0.103.10 to 0.103.13 - [Release notes](https://github.com/rustls/webpki/releases) - [Commits](https://github.com/rustls/webpki/compare/v/0.103.10...v/0.103.13) --- updated-dependencies: - dependency-name: rustls-webpki dependency-version: 0.103.13 dependency-type: indirect dependency-group: cargo ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> |
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Andreas Egli
|
1d0de757c3 |
fix: add hipblaslt library (#9541)
Signed-off-by: Andreas Egli <github@kharan.ch> |
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LocalAI [bot]
|
1c9592c77f |
chore: ⬆️ Update leejet/stable-diffusion.cpp to b8bdffc19962be7e5a84bfefeb2e31bd885b571a (#9521)
⬆️ Update leejet/stable-diffusion.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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Richard Palethorpe
|
13734ae9fa |
feat: Add Sherpa ONNX backend for ASR and TTS (#8523)
feat(backend): Add Sherpa ONNX backend and Omnilingual ASR Adds a new Go backend wrapping sherpa-onnx via purego (no cgo). Same approach as opus/stablediffusion-ggml/whisper — a thin C shim (csrc/shim.c + shim.h → libsherpa-shim.so) wraps the bits purego can't reach directly: nested struct config writes, result-struct field reads, and the streaming TTS callback trampoline. The Go side uses opaque uintptr handles and purego.NewCallback for the TTS callback. Supports: - VAD via sherpa-onnx's Silero VAD - Offline ASR: Whisper, Paraformer, SenseVoice, Omnilingual CTC - Online/streaming ASR: zipformer transducer with endpoint detection (AudioTranscriptionStream emits delta events during decode) - Offline TTS: VITS (LJS, etc.) - Streaming TTS: sherpa-onnx's callback API → PCM chunks on a channel, prefixed by a streaming WAV header Gallery entries: omnilingual-0.3b-ctc-q8-sherpa (1600-language offline ASR), streaming-zipformer-en-sherpa (low-latency streaming ASR), silero-vad-sherpa, vits-ljs-sherpa. E2E coverage: tests/e2e-backends for offline + streaming ASR, tests/e2e for the full realtime pipeline (VAD + STT + TTS). Assisted-by: claude-opus-4-7-1M [Claude Code] Signed-off-by: Richard Palethorpe <io@richiejp.com> |
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Ettore Di Giacinto
|
c0920f3273 |
fix(ik-llama-cpp): patch clip.cpp for new ggml_quantize_chunk signature (#9531)
Bumps ik_llama.cpp pin to 16996aeab7. Upstream 286ce32...16996ae adds a trailing `const struct quantize_user_data *` parameter to `ggml_quantize_chunk` (PR ikawrakow/ik_llama.cpp#1677) but leaves `examples/llava/clip.cpp` unchanged because their build has moved to `examples/mtmd/`. LocalAI's prepare.sh still copies from `examples/llava/`, so the dead 7-arg call reaches the grpc-server compile and fails. Patch the call site to pass `nullptr` for the new param. Assisted-by: Claude:Opus-4.7 [Read] [Edit] [Bash] |
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LocalAI [bot]
|
7c1934b183 |
chore: ⬆️ Update ggml-org/llama.cpp to 187a45637054881ecacf17f8e2f6f8f2ba7df1c7 (#9520)
⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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Ettore Di Giacinto
|
4906cbad04 |
feat: add biometrics UI (#9524)
* feat(react-ui): add Face & Voice Recognition pages
Expose the face and voice biometrics endpoints
(/v1/face/*, /v1/voice/*) through the React UI. Each page has four
tabs driving the six endpoints per modality: Analyze (demographics
with bounding boxes / waveform segments), Compare (verify with a
match gauge and live threshold slider), Enrollment (register /
identify / forget with a top-K matches view), Embedding (raw
vector inspector with sparkline + copy).
MediaInput supports file upload plus live capture: webcam
snap-to-canvas for face, MediaRecorder -> AudioContext ->
16-bit PCM mono WAV transcode for voice (libsndfile on the
backend only handles WAV/FLAC/OGG natively).
Sidebar gets a new Biometrics section feature-gated on
face_recognition / voice_recognition; routes are wrapped in
<RequireFeature>. No new dependencies -- Font Awesome icons
picked from the Free set.
Assisted-by: Claude:Opus 4.7
* fix(localai): accept data URI prefixes with codec/charset params
Browser MediaRecorder produces data URIs like
data:audio/webm;codecs=opus;base64,...
so the pre-';base64,' section can carry multiple parameter
segments. The `^data:([^;]+);base64,` regex in pkg/utils/base64.go
and core/http/endpoints/localai/audio.go only matched exactly one
segment, so recordings straight from the React UI's live-capture
tab failed the strip and then tripped the base64 decoder on the
leading 'data:' literal, surfacing as
"invalid audio base64: illegal base64 data at input byte 4"
Widened both regexes to `^data:[^,]+?;base64,` so any number of
';param=value' segments between the mime type and ';base64,' are
tolerated. Added a regression test covering the MediaRecorder
shape.
Assisted-by: Claude:Opus 4.7
* fix(insightface): scope pack ONNX loading to known manifests
LocalAI's gallery extracts buffalo_* zips flat into the models
directory, which inevitably mixes with ONNX files from other
backends (opencv face engine, MiniFASNet antispoof, WeSpeaker
voice embedding) and older buffalo pack installs. Feeding those
foreign files into insightface's model_zoo.get_model() blows up
inside the router -- it assumes a 4-D NCHW input and indexes
`input_shape[2]` on tensors that aren't shaped like a face model,
raising IndexError mid-load and leaving the backend unusable.
The router's dispatch isn't amenable to per-file try/except alone
(first-file-wins picks det_10g.onnx from buffalo_l even when the
user asked for buffalo_sc -- alphabetical order happens to favour
the wrong pack). Instead, ship an explicit manifest of the
upstream v0.7 pack contents and scope the glob to that when the
requested pack is known. The manifest is small and stable; future
packs can be added alongside or fall through to the tolerance
loop, which also swallows any remaining IndexError / ValueError
from foreign files with a clear `[insightface] skipped` stderr
line for diagnostics.
Assisted-by: Claude:Opus 4.7
* fix(speaker-recognition): extract FBank features for rank-3 ONNX encoders
Pre-exported speaker-encoder ONNX graphs come in two shapes:
rank-2 [batch, samples] -- some 3D-Speaker exports,
take raw waveform directly.
rank-3 [batch, frames, n_mels] -- WeSpeaker and most Kaldi-
lineage encoders, expect
pre-computed Kaldi FBank.
OnnxDirectEngine unconditionally fed `audio.reshape(1, -1)` --
correct for rank-2, IndexError-on-input_shape[3] on rank-3, which
surfaced to the UI as
"Invalid rank for input: feats Got: 2 Expected: 3"
Detect the input rank at session init and run Kaldi FBank
(80-dim, 25ms/10ms frames, dither=0.0, per-utterance CMN) before
the forward pass when rank>=3. All knobs are configurable via
backend options for encoders that deviate from defaults.
torchaudio.compliance.kaldi is already in the backend's
requirements (SpeechBrain pulls torchaudio in), so no new
dependency.
Assisted-by: Claude:Opus 4.7
* fix(biometrics): isolate face and voice vector stores
Face (ArcFace, 512-D) and voice (ECAPA-TDNN 192-D / WeSpeaker
256-D) biometric embeddings were colliding inside a single
in-memory local-store instance. Enrolling one after the other
failed with
"Try to add key with length N when existing length is M"
because local-store correctly refuses to mix dimensions in one
keyspace.
The registries were constructed with `storeName=""`, which in
StoreBackend() is just a WithModel() call. But ModelLoader's
cache is keyed on `modelID`, not `model` -- so both registries
collapsed to the same `modelID=""` slot and reused the same
backend process despite looking isolated on paper.
Three complementary fixes:
1. application.go -- give each registry a distinct default
namespace ("localai-face-biometrics" /
"localai-voice-biometrics"). The comment claimed
isolation, now it's actually enforced.
2. stores.go -- pass the storeName as both WithModelID and
WithModel so the ModelLoader cache key separates
namespaces and the loader spawns distinct processes.
3. local-store/store.go -- drop the Load() `opts.Model != ""`
guard. It was there to prevent generic model-loading loops
from picking up local-store by accident, but that auto-load
path is being retired; the guard now just blocks legitimate
namespace isolation. opts.Model is treated as a tag; the
per-tuple process isolation upstream handles discrimination.
Assisted-by: Claude:Opus 4.7
* fix(gallery): stale-file cleanup and upgrade-tmp directory safety
Two related robustness fixes for backend install/upgrade:
pkg/downloader/uri.go
OCI downloads passed through
if filepath.Ext(filePath) != "" ...
filePath = filepath.Dir(filePath)
which was intended to redirect file-shaped download targets
into their parent directory for OCI extraction. The heuristic
misfires on directory-shaped paths with a dot-suffix --
gallery.UpgradeBackend uses
tmpPath = "<backendsPath>/<name>.upgrade-tmp"
and Go's filepath.Ext treats ".upgrade-tmp" as an extension.
The rewrite landed the extraction at "<backendsPath>/", which
then **overwrote the real install** (backends/<name>/) with a
flat-layout file and left a stray run.sh at the top level. The
tmp dir itself stayed empty, so the validation step that
checked "<tmpPath>/run.sh" predictably failed with
"upgrade validation failed: run.sh not found in new backend"
Every manual upgrade silently corrupted the backends tree this
way. Guard the rewrite behind "target isn't already an existing
directory" -- InstallBackend / UpgradeBackend both pre-create
the target as a directory, so they get the correct behaviour;
existing file-path callers with a genuine dot-extension still
get the parent redirect.
core/gallery/backends.go
InstallBackend's MkdirAll returned ENOTDIR when something at
the target path was already a file (legacy dev builds dropped
golang backend binaries directly at `<backendsPath>/<name>`
instead of nesting them under their own subdir). That
permanently blocked reinstall and upgrade for anyone carrying
that state, since every retry hit the same error. Detect a
pre-existing non-directory, warn, and remove it before the
MkdirAll so the fresh install can write the correct nested
layout with metadata.json + run.sh.
Assisted-by: Claude:Opus 4.7
* fix(galleryop): refresh upgrade cache after backend ops
UpgradeChecker caches the last upgrade-check result and only
refreshes on the 6-hour tick or after an auto-upgrade cycle.
Manual upgrades (POST /api/backends/upgrade/:name) go through
the async galleryop worker, which completes the upgrade
correctly but never tells UpgradeChecker to re-check -- so
/api/backends/upgrades continued to list a just-upgraded backend
as upgradeable, indistinguishable from a failed upgrade, for up
to six hours.
Add an optional `OnBackendOpCompleted func()` hook on
GalleryService that fires after every successful install /
upgrade / delete on the backend channel (async, so a slow
callback doesn't stall the queue). startup.go wires it to
UpgradeChecker.TriggerCheck after both services exist. Result:
the upgrade banner clears within milliseconds of the worker
finishing.
Assisted-by: Claude:Opus 4.7
* build: prepend GOPATH/bin to PATH for protogen-go
install-go-tools runs `go install` for protoc-gen-go and
protoc-gen-go-grpc, which writes them into `go env GOPATH`/bin.
That directory isn't on every dev's PATH, and protoc resolves
its code-gen plugins via PATH, so the immediately-following
protoc invocation fails with
"protoc-gen-go: program not found"
which in turn blocks `make build` and any
`make backends/%` target that depends on build.
Prepend `go env GOPATH`/bin to PATH for the protoc invocation
so the freshly-installed plugins are found without requiring a
shell-profile change.
Assisted-by: Claude:Opus 4.7
* refactor(ui-api): non-blocking backend upgrade handler with opcache
POST /api/backends/upgrade/:name used to send the ManagementOp
directly onto the unbuffered BackendGalleryChannel, which blocked
the HTTP request whenever the galleryop worker was busy with a
prior operation. The op also didn't show up in /api/operations,
so the Backends UI couldn't reflect upgrade progress on the
affected row.
Register the op in opcache immediately, wrap it in a cancellable
context, store the cancellation function on the GalleryService,
and push onto the channel from a goroutine so the handler
returns right away. Response gains a `jobID` field and a
`message` string so clients have a consistent handle regardless
of whether the op is queued or running.
Pairs with the OnBackendOpCompleted hook added in the galleryop
commit — together the UI sees the upgrade start, watches
progress via /api/operations, and drops the "upgradeable" flag
the moment the worker finishes.
Assisted-by: Claude:Opus 4.7
|
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Ettore Di Giacinto
|
f5eb13d3c2 |
feat(insightface): add antispoofing (liveness) detection (#9515)
* feat(insightface): add antispoofing (liveness) detection
Light up the anti_spoofing flag that was parked during the first pass.
Both FaceVerify and FaceAnalyze now run the Silent-Face MiniFASNetV2 +
MiniFASNetV1SE ensemble (~4 MB, Apache 2.0, CPU <10ms) when the flag is
set. Failed liveness on either image vetoes FaceVerify regardless of
embedding similarity. Every insightface* gallery entry now ships the
MiniFASNet ONNX weights so existing packs light up after reinstall.
Setting the flag against a model without the MiniFASNet files returns
FAILED_PRECONDITION (HTTP 412) with a clear install message — no
silent is_real=false.
FaceVerifyResponse gained per-image img{1,2}_is_real and
img{1,2}_antispoof_score (proto 9-12); FaceAnalysis's existing
is_real/antispoof_score fields are now populated. Schema fields are
pointers so they are fully absent from the JSON response when
anti_spoofing was not requested — avoids collapsing "not checked" with
"checked and fake" under Go's omitempty on bool.
Validated end-to-end over HTTP against a local install:
- verify + anti_spoofing, both real -> verified=true, score ~0.76
- verify + anti_spoofing, img2 spoof -> verified=false, img2_is_real=false
- analyze + anti_spoofing -> is_real and score per face
- flag against model without MiniFASNet -> HTTP 412 fail-loud
Assisted-by: Claude:claude-opus-4-7 go vet
* test(insightface): wire test target into test-extra
The root Makefile's `test-extra` already runs
`$(MAKE) -C backend/python/insightface test`, but the backend's
Makefile never defined the target — so the command silently errored
and the suite was never executed in CI. Adding the two-line target
(matching ace-step/Makefile) hooks `test.sh` → `runUnittests` →
`python -m unittest test.py`, which discovers both the pre-existing
engine classes (InsightFaceEngineTest, OnnxDirectEngineTest) and the
new AntispoofingTest. Each class skips gracefully when its weights
can't be downloaded from a network-restricted runner.
Assisted-by: Claude:claude-opus-4-7
* test(insightface): exercise antispoofing in e2e-backends (both paths)
Add a `face_antispoof` capability to the Ginkgo e2e suite and extend
the existing FaceVerify + FaceAnalyze specs with liveness assertions
covering BOTH paths:
real fixture -> is_real=true, score>0, verified stays true
spoof fixture -> is_real=false, verified vetoed to false
The spoof fixture is upstream's own `image_F2.jpg` (via the yakhyo
mirror) — verified locally against the MiniFASNetV2+V1SE ensemble to
classify as is_real=false with score ~0.013. That makes the assertion
deterministic across CI runs; synthetic/derived spoofs fool the model
unpredictably and would be flaky.
Makefile wires it up end-to-end:
- New INSIGHTFACE_ANTISPOOF_* cache dir + two ONNX downloads with
pinned SHAs, matching the gallery entries.
- insightface-antispoof-models target shared by both backend configs.
- FACE_SPOOF_IMAGE_URL passed via BACKEND_TEST_FACE_SPOOF_IMAGE_URL.
- Both e2e targets (buffalo-sc + opencv) now:
* depend on insightface-antispoof-models
* pass antispoof_v2_onnx / antispoof_v1se_onnx in BACKEND_TEST_OPTIONS
* include face_antispoof in BACKEND_TEST_CAPS
backend_test.go adds the new capability constant and a faceSpoofFile
fixture resolved the same way as faceFile1/2/3. Spoof assertions are
gated on both capFaceAntispoof AND faceSpoofFile being set, so a test
config that omits the spoof fixture degrades gracefully to "real path
only" instead of failing.
Assisted-by: Claude:claude-opus-4-7 go vet
|
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|
Ettore Di Giacinto
|
ed648b3b4e |
fix(llama-cpp): include server-chat.cpp in grpc-server translation unit (#9511)
* fix(llama-cpp): include server-chat.cpp in grpc-server translation unit Upstream llama.cpp refactor (ggml-org/llama.cpp#20690) moved the OAI/Anthropic/Responses and transcription conversion helpers out of server-common.cpp into a new server-chat.cpp, and server-task.cpp and server-context.cpp now call those symbols (convert_transcriptions_to_chatcmpl, server_chat_convert_responses_to_chatcmpl, server_chat_convert_anthropic_to_oai, server_chat_msg_diff_to_json_oaicompat) via server-chat.h. grpc-server.cpp builds as a single translation unit by #include-ing the upstream .cpp files directly. Without including server-chat.cpp, the declarations are satisfied at compile time via server-chat.h but the link step fails with undefined references once LLAMA_VERSION crosses the refactor commit (134d6e54). Guard the include with __has_include so the same source stays buildable on older LLAMA_VERSION pins that predate the refactor (where prepare.sh won't copy server-chat.cpp into tools/grpc-server/). Assisted-by: Claude:claude-opus-4-7 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore(llama-cpp): bump LLAMA_VERSION to 0d0764dfd Bump to ggml-org/llama.cpp@0d0764dfd2. Paired with the preceding grpc-server server-chat.cpp include so the refactor at 134d6e54 links cleanly. Supersedes PR #9494. Assisted-by: Claude:claude-opus-4-7 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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|
Ettore Di Giacinto
|
04f1a0285d |
fix(ik-llama-cpp): adapt to common_grammar struct in sampling.h (#9512)
Upstream ik_llama.cpp commit e0596bf6 ("Autoparser") changed
common_params_sampling::grammar from std::string to a common_grammar
struct (type + grammar), which broke our two direct accesses:
- JSON ingest fed the field through json_value<common_grammar>(...),
for which nlohmann has no from_json adapter.
- JSON export emitted the struct directly, for which nlohmann has no
to_json adapter.
Wrap the incoming JSON string in common_grammar{COMMON_GRAMMAR_TYPE_USER, ...}
and serialize via the inner .grammar member, mirroring upstream's
examples/server/server-context.cpp.
Also bump IK_LLAMA_VERSION to 286ce324baed17c95faec77792eaa6bdb1c7a5f5
so the local-ai side lines up with the dependency bump in #9496.
Assisted-by: Claude-Code:claude-opus-4-7
|
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|
Ettore Di Giacinto
|
181ebb6df4 |
feat: voice recognition (#9500)
* feat(voice-recognition): add /v1/voice/{verify,analyze,embed} + speaker-recognition backend
Audio analog to face recognition. Adds three gRPC RPCs
(VoiceVerify / VoiceAnalyze / VoiceEmbed), their Go service and HTTP
layers, a new FLAG_SPEAKER_RECOGNITION capability flag, and a Python
backend scaffold under backend/python/speaker-recognition/ wrapping
SpeechBrain ECAPA-TDNN with a parallel OnnxDirectEngine for
WeSpeaker / 3D-Speaker ONNX exports.
The kokoros Rust backend gets matching unimplemented trait stubs —
tonic's async_trait has no defaults, so adding an RPC without Rust
stubs breaks the build (same regression fixed by
|
||
|
LocalAI [bot]
|
eb00d9b178 |
chore: ⬆️ Update leejet/stable-diffusion.cpp to c97702e1057c2fe13a7074cd9069cb9dd6edc1bf (#9495)
⬆️ Update leejet/stable-diffusion.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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|
Ettore Di Giacinto
|
eb01c77214 |
fix(kokoros): implement face_verify and face_analyze trait stubs (#9499)
The backend.proto was updated to add FaceVerify and FaceAnalyze RPCs
(face detection support), but the Rust KokorosService was never updated
to match the regenerated tonic trait, breaking compilation with E0046:
not all trait items implemented, missing: `face_verify`, `face_analyze`
Stubs both methods as unimplemented, matching the pattern used for the
other RPCs Kokoros does not support.
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
|
||
|
orbisai0security
|
bbeacf140d |
fix: remove unsafe sprintf() in grpc-server.cpp (#9486)
fix: V-001 security vulnerability Automated security fix generated by Orbis Security AI |
||
|
Ettore Di Giacinto
|
20baec77ab |
feat(face-recognition): add insightface/onnx backend for 1:1 verify, 1:N identify, embedding, detection, analysis (#9480)
* feat(face-recognition): add insightface backend for 1:1 verify, 1:N identify, embedding, detection, analysis
Adds face recognition as a new first-class capability in LocalAI via the
`insightface` Python backend, with a pluggable two-engine design so
non-commercial (insightface model packs) and commercial-safe
(OpenCV Zoo YuNet + SFace) models share the same gRPC/HTTP surface.
New gRPC RPCs (backend/backend.proto):
* FaceVerify(FaceVerifyRequest) returns FaceVerifyResponse
* FaceAnalyze(FaceAnalyzeRequest) returns FaceAnalyzeResponse
Existing Embedding and Detect RPCs are reused (face image in
PredictOptions.Images / DetectOptions.src) for face embedding and
face detection respectively.
New HTTP endpoints under /v1/face/:
* verify — 1:1 image pair same-person decision
* analyze — per-face age + gender (emotion/race reserved)
* register — 1:N enrollment; stores embedding in vector store
* identify — 1:N recognition; detect → embed → StoresFind
* forget — remove a registered face by opaque ID
Service layer (core/services/facerecognition/) introduces a
`Registry` interface with one in-memory `storeRegistry` impl backed
by LocalAI's existing local-store gRPC vector backend. HTTP handlers
depend on the interface, not on StoresSet/StoresFind directly, so a
persistent PostgreSQL/pgvector implementation can be slotted in via a
single constructor change in core/application (TODO marker in the
package doc).
New usecase flag FLAG_FACE_RECOGNITION; insightface is also wired
into FLAG_DETECTION so /v1/detection works for face bounding boxes.
Gallery (backend/index.yaml) ships three entries:
* insightface-buffalo-l — SCRFD-10GF + ArcFace R50 + genderage
(~326MB pre-baked; non-commercial research use only)
* insightface-opencv — YuNet + SFace (~40MB pre-baked; Apache 2.0)
* insightface-buffalo-s — SCRFD-500MF + MBF (runtime download; non-commercial)
Python backend (backend/python/insightface/):
* engines.py — FaceEngine protocol with InsightFaceEngine and
OnnxDirectEngine; resolves model paths relative to the backend
directory so the same gallery config works in docker-scratch and
in the e2e-backends rootfs-extraction harness.
* backend.py — gRPC servicer implementing Health, LoadModel, Status,
Embedding, Detect, FaceVerify, FaceAnalyze.
* install.sh — pre-bakes buffalo_l + OpenCV YuNet/SFace inside the
backend directory so first-run is offline-clean (the final scratch
image only preserves files under /<backend>/).
* test.py — parametrized unit tests over both engines.
Tests:
* Registry unit tests (go test -race ./core/services/facerecognition/...)
— in-memory fake grpc.Backend, table-driven, covers register/
identify/forget/error paths + concurrent access.
* tests/e2e-backends/backend_test.go extended with face caps
(face_detect, face_embed, face_verify, face_analyze); relative
ordering + configurable verifyCeiling per engine.
* Makefile targets: test-extra-backend-insightface-buffalo-l,
-opencv, and the -all aggregate.
* CI: .github/workflows/test-extra.yml gains tests-insightface-grpc,
auto-triggered by changes under backend/python/insightface/.
Docs:
* docs/content/features/face-recognition.md — feature page with
license table, quickstart (defaults to the commercial-safe model),
models matrix, API reference, 1:N workflow, storage caveats.
* Cross-refs in object-detection.md, stores.md, embeddings.md, and
whats-new.md.
* Contributor README at backend/python/insightface/README.md.
Verified end-to-end:
* buffalo_l: 6/6 specs (health, load, face_detect, face_embed,
face_verify, face_analyze).
* opencv: 5/5 specs (same minus face_analyze — SFace has no
demographic head; correctly skipped via BACKEND_TEST_CAPS).
Assisted-by: Claude:claude-opus-4-7
* fix(face-recognition): move engine selection to model gallery, collapse backend entries
The previous commit put engine/model_pack options on backend gallery
entries (`backend/index.yaml`). That was wrong — `GalleryBackend`
(core/gallery/backend_types.go:32) has no `options` field, so the
YAML decoder silently dropped those keys and all three "different
insightface-*" backend entries resolved to the same container image
with no distinguishing configuration.
Correct split:
* `backend/index.yaml` now has ONE `insightface` backend entry
shipping the CPU + CUDA 12 container images. The Python backend
bundles both the non-commercial insightface model packs
(buffalo_l / buffalo_s) and the commercial-safe OpenCV Zoo
weights (YuNet + SFace); the active engine is selected at
LoadModel time via `options: ["engine:..."]`.
* `gallery/index.yaml` gains three model entries —
`insightface-buffalo-l`, `insightface-opencv`,
`insightface-buffalo-s` — each setting the appropriate
`overrides.backend` + `overrides.options` so installing one
actually gives the user the intended engine. This matches how
`rfdetr-base` lives in the model gallery against the `rfdetr`
backend.
The earlier e2e tests passed despite this bug because the Makefile
targets pass `BACKEND_TEST_OPTIONS` directly to LoadModel via gRPC,
bypassing any gallery resolution entirely. No code changes needed.
Assisted-by: Claude:claude-opus-4-7
* feat(face-recognition): cover all supported models in the gallery + drop weight baking
Follows up on the model-gallery split: adds entries for every model
configuration either engine actually supports, and switches weight
delivery from image-baked to LocalAI's standard gallery mechanism.
Gallery now has seven `insightface-*` model entries (gallery/index.yaml):
insightface (family) — non-commercial research use
• buffalo-l (326MB) — SCRFD-10GF + ResNet50 + genderage, default
• buffalo-m (313MB) — SCRFD-2.5GF + ResNet50 + genderage
• buffalo-s (159MB) — SCRFD-500MF + MBF + genderage
• buffalo-sc (16MB) — SCRFD-500MF + MBF, recognition only
(no landmarks, no demographics — analyze
returns empty attributes)
• antelopev2 (407MB) — SCRFD-10GF + ResNet100@Glint360K + genderage
OpenCV Zoo family — Apache 2.0 commercial-safe
• opencv — YuNet + SFace fp32 (~40MB)
• opencv-int8 — YuNet + SFace int8 (~12MB, ~3x smaller, faster on CPU)
Model weights are no longer baked into the backend image. The image
now ships only the Python runtime + libraries (~275MB content size,
~1.18GB disk vs ~1.21GB when weights were baked). Weights flow through
LocalAI's gallery mechanism:
* OpenCV variants list `files:` with ONNX URIs + SHA-256, so
`local-ai models install insightface-opencv` pulls them into the
models directory exactly like any other gallery-managed model.
* insightface packs (upstream distributes .zip archives only, not
individual ONNX files) auto-download on first LoadModel via
FaceAnalysis' built-in machinery, rooted at the LocalAI models
directory so they live alongside everything else — same pattern
`rfdetr` uses with `inference.get_model()`.
Backend changes (backend/python/insightface/):
* backend.py — LoadModel propagates `ModelOptions.ModelPath` (the
LocalAI models directory) to engines via a `_model_dir` hint.
This replaces the earlier ModelFile-dirname approach; ModelPath
is the canonical "models directory" variable set by the Go loader
(pkg/model/initializers.go:144) and is always populated.
* engines.py::_resolve_model_path — picks up `model_dir` and searches
it (plus basename-in-model-dir) before falling back to the dev
script-dir. This is how OnnxDirectEngine finds gallery-downloaded
YuNet/SFace files by filename only.
* engines.py::_flatten_insightface_pack — new helper that works
around an upstream packaging inconsistency: buffalo_l/s/sc zips
expand flat, but buffalo_m and antelopev2 zips wrap their ONNX
files in a redundant `<name>/` directory. insightface's own
loader looks one level too shallow and fails. We call
`ensure_available()` explicitly, flatten if nested, then hand to
FaceAnalysis.
* engines.py::InsightFaceEngine.prepare — root-resolution order now
includes the `_model_dir` hint so packs download into the LocalAI
models directory by default.
* install.sh — no longer pre-downloads any weights. Everything is
gallery-managed now.
* smoke.py (new) — parametrized smoke test that iterates over every
gallery configuration, simulating the LocalAI install flow
(creates a models dir, fetches OpenCV files with checksum
verification, lets insightface auto-download its packs), then
runs detect + embed + verify (+ analyze where supported) through
the in-process BackendServicer.
* test.py — OnnxDirectEngineTest no longer hardcodes `/models/opencv/`
paths; downloads ONNX files to a temp dir at setUpClass time and
passes ModelPath accordingly.
Registry change (core/services/facerecognition/store_registry.go):
* `dim=0` in NewStoreRegistry now means "accept whatever dimension
arrives" — needed because the backend supports 512-d ArcFace/MBF
and 128-d SFace via the same Registry. A non-zero dim still fails
fast with ErrDimensionMismatch.
* core/application plumbs `faceEmbeddingDim = 0`, explaining the
rationale in the comment.
Backend gallery description updated to reflect that the image carries
no weights — it's just Python + engines.
Smoke-tested all 7 configurations against the rebuilt image (with the
flatten fix applied), exit 0:
PASS: insightface-buffalo-l faces=6 dim=512 same-dist=0.000
PASS: insightface-buffalo-sc faces=6 dim=512 same-dist=0.000
PASS: insightface-buffalo-s faces=6 dim=512 same-dist=0.000
PASS: insightface-buffalo-m faces=6 dim=512 same-dist=0.000
PASS: insightface-antelopev2 faces=6 dim=512 same-dist=0.000
PASS: insightface-opencv faces=6 dim=128 same-dist=0.000
PASS: insightface-opencv-int8 faces=6 dim=128 same-dist=0.000
7/7 passed
Assisted-by: Claude:claude-opus-4-7
* fix(face-recognition): pre-fetch OpenCV ONNX for e2e target; drop stale pre-baked claim
CI regression from the previous commit: I moved OpenCV Zoo weight
delivery to LocalAI's gallery `files:` mechanism, but the
test-extra-backend-insightface-opencv target was still passing
relative paths `detector_onnx:models/opencv/yunet.onnx` in
BACKEND_TEST_OPTIONS. The e2e suite drives LoadModel directly over
gRPC without going through the gallery, so those relative paths
resolved to nothing and OpenCV's ONNXImporter failed:
LoadModel failed: Failed to load face engine:
OpenCV(4.13.0) ... Can't read ONNX file: models/opencv/yunet.onnx
Fix: add an `insightface-opencv-models` prerequisite target that
fetches the two ONNX files (YuNet + SFace) to a deterministic host
cache at /tmp/localai-insightface-opencv-cache/, verifies SHA-256,
and skips the download on re-runs. The opencv test target depends on
it and passes absolute paths in BACKEND_TEST_OPTIONS, so the backend
finds the files via its normal absolute-path resolution branch.
Also refresh the buffalo_l comment: it no longer says "pre-baked"
(nothing is — the pack auto-downloads from upstream's GitHub release
on first LoadModel, same as in CI).
Locally verified: `make test-extra-backend-insightface-opencv` passes
5/5 specs (health, load, face_detect, face_embed, face_verify).
Assisted-by: Claude:claude-opus-4-7
* feat(face-recognition): add POST /v1/face/embed + correct /v1/embeddings docs
The docs promised that /v1/embeddings returns face vectors when you
send an image data-URI. That was never true: /v1/embeddings is
OpenAI-compatible and text-only by contract — its handler goes
through `core/backend/embeddings.go::ModelEmbedding`, which sets
`predictOptions.Embeddings = s` (a string of TEXT to embed) and never
populates `predictOptions.Images[]`. The Python backend's Embedding
gRPC method does handle Images[] (that's how /v1/face/register reaches
it internally via `backend.FaceEmbed`), but the HTTP embeddings
endpoint wasn't wired to populate it.
Rather than overload /v1/embeddings with image-vs-text detection —
messy, and the endpoint is OpenAI-compatible by design — add a
dedicated /v1/face/embed endpoint that wraps `backend.FaceEmbed`
(already used internally by /v1/face/register and /v1/face/identify).
Matches LocalAI's convention of a dedicated path per non-standard flow
(/v1/rerank, /v1/detection, /v1/face/verify etc.).
Response:
{
"embedding": [<dim> floats, L2-normed],
"dim": int, // 512 for ArcFace R50 / MBF, 128 for SFace
"model": "<name>"
}
Live-tested on the opencv engine: returns a 128-d L2-normalized vector
(sum(x^2) = 1.0000). Sentinel in docs updated to note /v1/embeddings
is text-only and point image users at /v1/face/embed instead.
Assisted-by: Claude:claude-opus-4-7
* fix(http): map malformed image input + gRPC status codes to proper 4xx
Image-input failures on LocalAI's single-image endpoints (/v1/detection,
/v1/face/{verify,analyze,embed,register,identify}) have historically
returned 500 — even when the client was the one who sent garbage.
Classic example: you POST an "image" that isn't a URL, isn't a
data-URI, and isn't a valid JPEG/PNG — the server shouldn't claim
that's its fault.
Two helpers land in core/http/endpoints/localai/images.go and every
single-image handler is switched over:
* decodeImageInput(s)
Wraps utils.GetContentURIAsBase64 and turns any failure
(invalid URL, not a data-URI, download error, etc.) into
echo.NewHTTPError(400, "invalid image input: ...").
* mapBackendError(err)
Inspects the gRPC status on a backend call error and maps:
INVALID_ARGUMENT → 400 Bad Request
NOT_FOUND → 404 Not Found
FAILED_PRECONDITION → 412 Precondition Failed
Unimplemented → 501 Not Implemented
All other codes fall through unchanged (still 500).
Before, my 1×1 PNG error-path test returned:
HTTP 500 "rpc error: code = InvalidArgument desc = failed to decode one or both images"
After:
HTTP 400 "failed to decode one or both images"
Scope-limited to the LocalAI single-image endpoints. The multi-modal
paths (middleware/request.go, openresponses/responses.go,
openai/realtime.go) intentionally log-and-skip individual media parts
when decoding fails — different design intent (graceful degradation
of a multi-part message), not a 400-worthy failure. Left untouched.
Live-verified: every error case in /tmp/face_errors.py now returns
4xx with a meaningful message; the "image with no face (1x1 PNG)"
case specifically went from 500 → 400.
Assisted-by: Claude:claude-opus-4-7
* refactor(face-recognition): insightface packs go through gallery files:, drop FaceAnalysis
Follows up on the discovery that LocalAI's gallery `files:` mechanism
handles archives (zip, tar.gz, …) via mholt/archiver/v3 — the rhasspy
piper voices use exactly this pattern. Insightface packs are zip
archives, so we can now deliver them the same way every other
gallery-managed model gets delivered: declaratively, checksum-verified,
through LocalAI's standard download+extract pipeline.
Two changes:
1. Gallery (gallery/index.yaml) — every insightface-* entry gains a
`files:` list with the pack zip's URI + SHA-256. `local-ai models
install insightface-buffalo-l` now fetches the zip, verifies the
hash, and extracts it into the models directory. No more reliance
on insightface's library-internal `ensure_available()` auto-download
or its hardcoded `BASE_REPO_URL`.
2. InsightFaceEngine (backend/python/insightface/engines.py) — drops
the FaceAnalysis wrapper and drives insightface's `model_zoo`
directly. The ~50 lines FaceAnalysis provides — glob ONNX files,
route each through `model_zoo.get_model()`, build a
`{taskname: model}` dict, loop per-face at inference — are
reimplemented in `InsightFaceEngine`. The actual inference classes
(RetinaFace, ArcFaceONNX, Attribute, Landmark) are still
insightface's — we only replicate the glue, so drift risk against
upstream is minimal.
Why drop FaceAnalysis: it hard-codes a `<root>/models/<name>/*.onnx`
layout that doesn't match what LocalAI's zip extraction produces.
LocalAI unpacks archives flat into `<models_dir>`. Upstream packs
are inconsistent — buffalo_l/s/sc ship ONNX at the zip root (lands
at `<models_dir>/*.onnx`), buffalo_m/antelopev2 wrap in a redundant
`<name>/` dir (lands at `<models_dir>/<name>/*.onnx`). The new
`_locate_insightface_pack` helper searches both locations plus
legacy paths and returns whichever has ONNX files. Replaces the
earlier `_flatten_insightface_pack` helper (which tried to fight
FaceAnalysis's layout expectations; now we just find the files
wherever they are).
Net effect for users: install once via LocalAI's managed flow,
weights live alongside every other model, progress shows in the
jobs endpoint, no first-load network call. Same API surface,
cleaner plumbing.
Assisted-by: Claude:claude-opus-4-7
* fix(face-recognition): CI's insightface e2e path needs the pack pre-fetched
The e2e suite drives LoadModel over gRPC without going through LocalAI's
gallery flow, so the engine's `_model_dir` option (normally populated
from ModelPath) is empty. Previously the insightface target relied on
FaceAnalysis auto-download to paper over this, but we dropped
FaceAnalysis in favor of direct model_zoo calls — so the buffalo_l
target started failing at LoadModel with "no insightface pack found".
Mirror the opencv target's pre-fetch pattern: download buffalo_sc.zip
(same SHA as the gallery entry), extract it on the host, and pass
`root:<dir>` so the engine locates the pack without needing
ModelPath. Switched to buffalo_sc (smallest pack, ~16MB) to keep CI
fast; it covers the same insightface engine code path as buffalo_l.
Face analyze cap dropped since buffalo_sc has no age/gender head.
Assisted-by: Claude:claude-opus-4-7[1m]
* feat(face-recognition): surface face-recognition in advertised feature maps
The six /v1/face/* endpoints were missing from every place LocalAI
advertises its feature surface to clients:
* api_instructions — the machine-readable capability index at
GET /api/instructions. Added `face-recognition` as a dedicated
instruction area with an intro that calls out the in-memory
registry caveat and the /v1/face/embed vs /v1/embeddings split.
* auth/permissions — added FeatureFaceRecognition constant, routed
all six face endpoints through it so admins can gate them per-user
like any other API feature. Default ON (matches the other API
features).
* React UI capabilities — CAP_FACE_RECOGNITION symbol mapped to
FLAG_FACE_RECOGNITION. Declared only for now; the Face page is a
follow-up (noted in the plan).
Instruction count bumped 9 → 10; test updated.
Assisted-by: Claude:claude-opus-4-7[1m]
* docs(agents): capture advertising-surface steps in the endpoint guide
Before this change, adding a new /v1/* endpoint reliably missed one or
more of: the swagger @Tags annotation, the /api/instructions registry,
the auth RouteFeatureRegistry, and the React UI CAP_* symbol. The
endpoint would work but be invisible to API consumers, admins, and the
UI — and nothing in the existing docs said to look in those places.
Extend .agents/api-endpoints-and-auth.md with a new "Advertising
surfaces" section covering all four surfaces (swagger tags, /api/
instructions, capabilities.js, docs/), and expand the closing checklist
so it's impossible to ship a feature without visiting each one. Hoist a
one-liner reminder into AGENTS.md's Quick Reference so agents skim it
before diving in.
Assisted-by: Claude:claude-opus-4-7[1m]
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LocalAI [bot]
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cd7b035716 |
chore: ⬆️ Update ggml-org/llama.cpp to 5a4cd6741fc33227cdacb329f355ab21f8481de2 (#9479)
⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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Ettore Di Giacinto
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39573ecd2a |
chore(whisperx): drop ROCm/hipblas build target (#9474)
whisperx has no upstream AMD GPU support and its core transcription path (faster-whisper -> ctranslate2) falls back to CPU on AMD since the PyPI ctranslate2 is CUDA-only. The torch rocm wheels would accelerate only the alignment/diarization stages, producing a misleadingly half-working image. Drop the hipblas variant rather than shipping a partially accelerated build users can't distinguish from the real thing. AMD hosts now fall through the capability map to cpu-whisperx / cpu-whisperx-development. Also removes the now-dangling rocm-whisperx assertion from pkg/system/capabilities_test.go and the ROCm mention from the whisperx row in docs/content/reference/compatibility-table.md. Assisted-by: Claude Code:claude-opus-4-7 |
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LocalAI [bot]
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8bb1e8f21f |
chore: ⬆️ Update ggml-org/llama.cpp to cf8b0dbda9ac0eac30ee33f87bc6702ead1c4664 (#9448)
⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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LocalAI [bot]
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cd94a0b61a |
chore: ⬆️ Update ggml-org/whisper.cpp to fc674574ca27cac59a15e5b22a09b9d9ad62aafe (#9450)
⬆️ Update ggml-org/whisper.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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LocalAI [bot]
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5973c0a9df |
chore: ⬆️ Update ikawrakow/ik_llama.cpp to d4824131580b94ffa7b0e91c955e2b237c2fe16e (#9447)
⬆️ Update ikawrakow/ik_llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |