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30 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
Richard Palethorpe
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969005b2a1 |
feat(gallery): Speed up load times and clean gallery entries (#9211)
* feat: Rework VRAM estimation and use known_usecases in gallery Signed-off-by: Richard Palethorpe <io@richiejp.com> Assisted-by: Claude:claude-opus-4-7[1m] [Claude Code] * chore(gallery): regenerate gallery index and add known_usecases to model entries Signed-off-by: Richard Palethorpe <io@richiejp.com> --------- Signed-off-by: Richard Palethorpe <io@richiejp.com> |
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Ettore Di Giacinto
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75fba9e03f |
fix(distributed): scope Upgrade All to nodes that have the backend installed (#9678)
In distributed mode the React UI's "Upgrade All" button fanned every detected outdated backend out to every healthy backend node, including nodes that never had that backend installed. On heterogeneous clusters this surfaced as platform errors (e.g. mac-mini-m4 asked to upgrade cpu-insightface-development, which has no darwin/arm64 variant) and left forever-retrying pending_backend_ops rows. DistributedBackendManager.UpgradeBackend now queries ListBackends() first, builds the target node-ID set from SystemBackend.Nodes, and only fans out to those nodes — every per-node primitive (adapter.InstallBackend, the pending-ops queue, BackendOpResult) is unchanged. enqueueAndDrainBackendOp gains an optional targetNodeIDs allowlist; Install/Delete keep their fan-to-everyone semantics by passing nil. If no node reports the backend installed, UpgradeBackend now returns a clear "not installed on any node" error instead of producing a stuck queue. Adds Ginkgo coverage for the smart fan-out: backend on a subset of nodes goes only to those nodes; backend on no node returns the new error and never sends a NATS install request. Assisted-by: Claude:claude-opus-4-7 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Ettore Di Giacinto
|
e86ade54a6 |
feat(api): add /v1/audio/diarization endpoint with sherpa-onnx + vibevoice.cpp (#9654)
* feat(api): add /v1/audio/diarization endpoint with sherpa-onnx + vibevoice.cpp
Closes #1648.
OpenAI-style multipart endpoint that returns "who spoke when". Single
endpoint instead of the issue's three-endpoint sketch (refactor /vad,
/vad/embedding, /diarization) — the typical client wants one call, and
embeddings can land later as a sibling without breaking this surface.
Response shape borrows from Pyannote/Deepgram: segments carry a
normalised SPEAKER_NN id (zero-padded, stable across the response) plus
the raw backend label, optional per-segment text when the backend bundles
ASR, and a speakers summary in verbose_json. response_format also accepts
rttm so consumers can pipe straight into pyannote.metrics / dscore.
Backends:
* vibevoice-cpp — Diarize() reuses the existing vv_capi_asr pass.
vibevoice's ASR prompt asks the model to emit
[{Start,End,Speaker,Content}] natively, so diarization is a by-product
of the same pass; include_text=true preserves the transcript per
segment, otherwise we drop it.
* sherpa-onnx — wraps the upstream SherpaOnnxOfflineSpeakerDiarization
C API (pyannote segmentation + speaker-embedding extractor + fast
clustering). libsherpa-shim grew config builders, a SetClustering
wrapper for per-call num_clusters/threshold overrides, and a
segment_at accessor (purego can't read field arrays out of
SherpaOnnxOfflineSpeakerDiarizationSegment[] directly).
Plumbing: new Diarize gRPC RPC + DiarizeRequest / DiarizeSegment /
DiarizeResponse messages, threaded through interface.go, base, server,
client, embed. Default Base impl returns unimplemented.
Capability surfaces all updated: FLAG_DIARIZATION usecase,
FeatureAudioDiarization permission (default-on), RouteFeatureRegistry
entries for /v1/audio/diarization and /audio/diarization, audio
instruction-def description widened, CAP_DIARIZATION JS symbol,
swagger regenerated, /api/instructions discovery map updated.
Tests:
* core/backend: speaker-label normalisation (first-seen → SPEAKER_NN,
per-speaker totals, nil-safety, fallback to backend NumSpeakers when
no segments).
* core/http/endpoints/openai: RTTM rendering (file-id basename, negative
duration clamping, fallback id).
* tests/e2e: mock-backend grew a deterministic Diarize that emits
raw labels "5","2","5" so the e2e suite verifies SPEAKER_NN
remapping, verbose_json speakers summary + transcript pass-through
(gated by include_text), RTTM bytes content-type, and rejection of
unknown response_format. mock-diarize model config registered with
known_usecases=[FLAG_DIARIZATION] to bypass the backend-name guard.
Docs: new features/audio-diarization.md (request/response, RTTM example,
sherpa-onnx + vibevoice setup), cross-link from audio-to-text.md, entry
in whats-new.md.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
* fix(diarization): correct sherpa-onnx symbol name + lint cleanup
CI failures on #9654:
* sherpa-onnx-grpc-{tts,transcription} and sherpa-onnx-realtime panicked
at backend startup with `undefined symbol: SherpaOnnxDestroyOfflineSpeakerDiarizationResult`.
Upstream's actual symbol is SherpaOnnxOfflineSpeakerDiarizationDestroyResult
(Destroy in the middle, not the prefix); the rest of the diarization
surface follows the same naming pattern. The mismatched name made
purego.RegisterLibFunc fail at dlopen time and crashed the gRPC server
before the BeforeAll could probe Health, taking down every sherpa-onnx
test job — not just the diarization-related ones.
* golangci-lint flagged 5 errcheck violations on new defer cleanups
(os.RemoveAll / Close / conn.Close); wrap each in a `defer func() { _ = X() }()`
closure (matches the pattern other LocalAI files use for new code, since
pre-existing bare defers are grandfathered in via new-from-merge-base).
* golangci-lint also flagged forbidigo violations: the new
diarization_test.go files used testing.T-style `t.Errorf` / `t.Fatalf`,
which are forbidden by the project's coding-style policy
(.agents/coding-style.md). Convert both files to Ginkgo/Gomega
Describe/It with Expect(...) — they get picked up by the existing
TestBackend / TestOpenAI suites, no new suite plumbing needed.
* modernize linter: tightened the diarization segment loop to
`for i := range int(numSegments)` (Go 1.22+ idiom).
Verified locally: golangci-lint with new-from-merge-base=origin/master
reports 0 issues across all touched packages, and the four mocked
diarization e2e specs in tests/e2e/mock_backend_test.go still pass.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
* fix(vibevoice-cpp): convert non-WAV input via ffmpeg + raise ASR token budget
Confirmed end-to-end against a real LocalAI instance with vibevoice-asr-q4_k
loaded and the multi-speaker MP3 sample at vibevoice.cpp/samples/2p_argument.mp3:
both /v1/audio/transcriptions and /v1/audio/diarization now succeed and
return correctly attributed speaker turns for the full clip.
Two latent issues surfaced once the diarization endpoint actually exercised
the backend with a non-trivial input:
1. vv_capi_asr only accepts WAV via load_wav_24k_mono. The previous code
passed the uploaded path straight through, so anything that wasn't
already a 24 kHz mono s16le WAV failed at the C side with rc=-8 and
the very unhelpful "vv_capi_asr failed". prepareWavInput shells out
to ffmpeg ("-ar 24000 -ac 1 -acodec pcm_s16le") in a per-call temp
dir, matching the rate the model was trained on; both AudioTranscription
and Diarize now route through it. This is the same shape sherpa-onnx
uses (utils.AudioToWav), but vibevoice needs 24 kHz rather than 16 kHz
so we don't reuse that helper.
2. The C ABI's max_new_tokens defaults to 256 when 0 is passed. That's
fine for a five-second clip but not for anything past ~10 s — vibevoice
stops mid-JSON, the parse fails, and the caller sees a hard error.
Pass a much larger budget (16 384 ≈ ~9 minutes of speech at the
model's ~30 tok/s rate); generation stops at EOS so this is a cap
rather than a target.
3. As a defensive belt-and-braces, mirror AudioTranscription's existing
"fall back to a single segment if the model emits non-JSON text"
pattern in Diarize, so partial / unusual model output never produces
a 500. This kept the endpoint usable while diagnosing (1) and (2),
and is the right behaviour to keep.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
* fix(vibevoice-cpp): pass valid WAVs through directly so ffmpeg is not required at runtime
Spotted by tests-e2e-backend (1.25.x): the previous fix forced every
incoming audio file through `ffmpeg -ar 24000 ...`, which meant the
backend container — which does not ship ffmpeg — failed even for the
existing happy path where the caller already uploads a WAV. The
container-side error was:
rpc error: code = Unknown desc = vibevoice-cpp: ffmpeg convert to
24k mono wav: exec: "ffmpeg": executable file not found in $PATH
Reading vibevoice.cpp's audio_io.cpp, `load_wav_24k_mono` uses drwav and
already accepts any PCM/IEEE-float WAV at any sample rate, downmixes
multi-channel input to mono, and resamples to 24 kHz internally. So the
only inputs that genuinely need an external converter are non-WAV
formats (MP3, OGG, FLAC, ...).
Detect WAVs by RIFF/WAVE magic at bytes 0..3 / 8..11 and pass them
straight through with a no-op cleanup; everything else still goes
through ffmpeg with the same 24 kHz mono s16le target. The result:
* Container builds without ffmpeg keep working for WAV uploads
(the e2e-backends fixture is jfk.wav at 16 kHz mono s16le).
* MP3 and other non-WAV inputs still get the new ffmpeg conversion
path so the diarization endpoint stays useful.
* If the caller uploads a non-WAV but ffmpeg isn't on PATH, the
surfaced error is still descriptive enough to act on.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
* fix(ci): make gcc-14 install in Dockerfile.golang best-effort for jammy bases
The LocalVQE PR (
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Ettore Di Giacinto
|
bbcaebc1ef |
feat(concurrency-groups): per-model exclusive groups for backend loading (#9662)
* feat(concurrency-groups): per-model exclusive groups for backend loading Adds `concurrency_groups: [...]` to model YAML configs. Two models that share a group cannot be loaded concurrently on the same node — loading one evicts the others, reusing the existing pinned/busy/retry policy from LRU eviction. Layered design: - Watchdog (pkg/model): per-node correctness floor — on every Load(), evict any loaded model that shares a group with the requested one. Pinned skips surface NeedMore so the loader retries (and ultimately logs a clear warning), instead of silently allowing the rule to be violated. - Distributed scheduler (core/services/nodes): soft anti-affinity hint — scheduleNewModel prefers nodes that don't already host a same-group model, falling back to eviction only if every candidate has a conflict. Composes with NodeSelector at the same point in the candidate pipeline. Per-node, not cluster-wide: VRAM is a node-local resource, and two heavy models running on different nodes is fine. The ConfigLoader is wired into SmartRouter via a small ConcurrencyConflictResolver interface so the nodes package keeps a narrow surface on core/config. Refactors the inner LRU eviction body into a shared collectEvictionsLocked helper and the loader retry loop into retryEnforce(fn, maxRetries, interval), so both LRU and group enforcement share busy/pinned/retry semantics. Closes #9659. Assisted-by: Claude:claude-opus-4-7 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(watchdog): sync pinned + concurrency_groups at startup The startup-time watchdog setup lives in initializeWatchdog (startup.go), not in startWatchdog (watchdog.go). The latter is only invoked from the runtime-settings RestartWatchdog path. As a result, neither SyncPinnedModelsToWatchdog nor SyncModelGroupsToWatchdog ran at boot, so `pinned: true` and `concurrency_groups: [...]` only became effective after a settings-driven watchdog restart. Fix by adding both sync calls to initializeWatchdog. Confirmed end-to-end: loading model A in group "heavy", then C with no group (coexists), then B in group "heavy" now correctly evicts A and leaves [B, C]. Assisted-by: Claude:claude-opus-4-7 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(test): satisfy errcheck on new os.Remove in concurrency_groups spec CI lint runs new-from-merge-base, so the existing pre-existing `defer os.Remove(tmp.Name())` lines are baseline-grandfathered but the one introduced by the concurrency_groups YAML round-trip test is held to errcheck. Wrap the remove in a closure that discards the error. 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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Richard Palethorpe
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bb033b16a9 |
feat: add LocalVQE backend and audio transformations UI (#9640)
feat(audio-transform): add LocalVQE backend, bidi gRPC RPC, Studio UI
Introduce a generic "audio transform" capability for any audio-in / audio-out
operation (echo cancellation, noise suppression, dereverberation, voice
conversion, etc.) and ship LocalVQE as the first backend implementation.
Backend protocol:
- Two new gRPC RPCs in backend.proto: unary AudioTransform for batch and
bidirectional AudioTransformStream for low-latency frame-by-frame use.
This is the first bidi stream in the proto; per-frame unary at LocalVQE's
16 ms hop would be RTT-bound. Wire it through pkg/grpc/{client,server,
embed,interface,base} with paired-channel ergonomics.
LocalVQE backend (backend/go/localvqe/):
- Go-Purego wrapper around upstream liblocalvqe.so. CMake builds the upstream
shared lib + its libggml-cpu-*.so runtime variants directly — no MODULE
wrapper needed because LocalVQE handles CPU feature selection internally
via GGML_BACKEND_DL.
- Sets GGML_NTHREADS from opts.Threads (or runtime.NumCPU()-1) — without it
LocalVQE runs single-threaded at ~1× realtime instead of the documented
~9.6×.
- Reference-length policy: zero-pad short refs, truncate long ones (the
trailing portion can't have leaked into a mic that wasn't recording).
- Ginkgo test suite (9 always-on specs + 2 model-gated).
HTTP layer:
- POST /audio/transformations (alias /audio/transform): multipart batch
endpoint, accepts audio + optional reference + params[*]=v form fields.
Persists inputs alongside the output in GeneratedContentDir/audio so the
React UI history can replay past (audio, reference, output) triples.
- GET /audio/transformations/stream: WebSocket bidi, 16 ms PCM frames
(interleaved stereo mic+ref in, mono out). JSON session.update envelope
for config; constants hoisted in core/schema/audio_transform.go.
- ffmpeg-based input normalisation to 16 kHz mono s16 WAV via the existing
utils.AudioToWav (with passthrough fast-path), so the user can upload any
format / rate without seeing the model's strict 16 kHz constraint.
- BackendTraceAudioTransform integration so /api/backend-traces and the
Traces UI light up with audio_snippet base64 and timing.
- Routes registered under routes/localai.go (LocalAI extension; OpenAI has
no /audio/transformations endpoint), traced via TraceMiddleware.
Auth + capability + importer:
- FLAG_AUDIO_TRANSFORM (model_config.go), FeatureAudioTransform (default-on,
in APIFeatures), three RouteFeatureRegistry rows.
- localvqe added to knownPrefOnlyBackends with modality "audio-transform".
- Gallery entry localvqe-v1-1.3m (sha256-pinned, hosted on
huggingface.co/LocalAI-io/LocalVQE).
React UI:
- New /app/transform page surfaced via a dedicated "Enhance" sidebar
section (sibling of Tools / Biometrics) — the page is enhancement, not
generation, so it lives outside Studio. Two AudioInput components
(Upload + Record tabs, drag-drop, mic capture).
- Echo-test button: records mic while playing the loaded reference through
the speakers — the mic naturally picks up speaker bleed, giving a real
(mic, ref) pair for AEC testing without leaving the UI.
- Reusable WaveformPlayer (canvas peaks + click-to-seek + audio controls)
and useAudioPeaks hook (shared module-scoped AudioContext to avoid
hitting browser context limits with three players on one page); migrated
TTS, Sound, Traces audio blocks to use it.
- Past runs saved in localStorage via useMediaHistory('audio-transform') —
the history entry stores all three URLs so clicking re-renders the full
triple, not just the output.
Build + e2e:
- 11 matrix entries removed from .github/workflows/backend.yml (CUDA, ROCm,
SYCL, Metal, L4T): upstream supports only CPU + Vulkan, so we ship those
two and let GPU-class hardware route through Vulkan in the gallery
capabilities map.
- tests-localvqe-grpc-transform job in test-extra.yml (gated on
detect-changes.outputs.localvqe).
- New audio_transform capability + 4 specs in tests/e2e-backends.
- Playwright spec suite in core/http/react-ui/e2e/audio-transform.spec.js
(8 specs covering tabs, file upload, multipart shape, history, errors).
Docs:
- New docs/content/features/audio-transform.md covering the (audio,
reference) mental model, batch + WebSocket wire formats, LocalVQE param
keys, and a YAML config example. Cross-links from text-to-audio and
audio-to-text feature pages.
Assisted-by: Claude:claude-opus-4-7 [Bash Read Edit Write Agent TaskCreate]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
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LocalAI [bot]
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de83b72bb7 |
fix(distributed): orchestrator resilience — auto-upgrade routing, worker bind-wait, RAG-init crash, log spam (#9657)
* fix(nodes/health): skip stale-marking already-offline nodes The health monitor re-emitted "Node heartbeat stale" + "Marking stale node offline" + MarkOffline on every cycle for nodes that were already in the offline (or unhealthy) state. For an operator-stopped node this flooded the logs with the same WARN+INFO pair every check interval. Skip the staleness branch when the node is already StatusOffline / StatusUnhealthy — the state is already what we'd write, so neither the log lines nor the DB update carry information. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(worker): wait for backend gRPC bind before replying to backend.install The backend supervisor used to wait up to 4s (20 × 200ms) for the backend's gRPC server to answer a HealthCheck, then log a warning and reply Success with the bind address anyway. On slower nodes (a Jetson Orin doing first-boot CUDA init, large CGO library load) the gRPC listener wasn't up yet, so the frontend's first LoadModel dial returned "connect: connection refused" and the operator chased a phantom network issue instead of a startup-timing one. Two changes: - Bump the readiness window to 30s. CUDA init on Orin/Thor first boot measures in seconds, not milliseconds. - On deadline-exceeded, stop the half-started process, recycle the port, and return an error with the backend's stderr tail. The frontend now gets a real failure with diagnostic context instead of a misleading ECONNREFUSED on a downstream dial. Process death during the wait window keeps its existing fast-fail path. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(distributed): route auto-upgrade through BackendManager + bump LocalAGI/LocalRecall Two distributed-mode bugs that surfaced together in the orchestrator logs: 1. Auto-upgrade always failed with "backend not found". UpgradeChecker correctly routed CheckUpgrades through the active BackendManager (so the frontend aggregates worker state), but the auto-upgrade branch right below called gallery.UpgradeBackend directly with the frontend's SystemState. In distributed mode the frontend has no backends installed locally, so ListSystemBackends returned empty and Get(name) failed for every reported upgrade. Auto-upgrade now also goes through BackendManager.UpgradeBackend, which fans out to workers via NATS. 2. Embedding-load failure on a remote node crashed the orchestrator. When RAG init lazily called NewPersistentPostgresCollection and the remote embedding worker was unreachable, LocalRecall called os.Exit(1) inside the constructor, killing the orchestrator pod. LocalRecall now returns errors instead, LocalAGI surfaces them as a nil collection, and the existing RAGProviderFromState path returns (nil, nil, false) — the same code path the agent pool already takes when no RAG is configured. The orchestrator stays up; chat requests degrade to "no RAG available" until the embedding worker recovers. Bumps: github.com/mudler/LocalAGI → e83bf515d010 github.com/mudler/localrecall → 6138c1f535ab Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
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170d55c67d |
fix(distributed): honor NodeSelector in cached-replica lookup, stop empty-backend reconciler scaleups (#9652)
* fix(distributed): honor NodeSelector in cached-replica lookup, stop empty-backend reconciler scaleups
Two distinct bugs were causing tight retry loops in the distributed scheduler:
1. FindAndLockNodeWithModel ignored the model's NodeSelector. When a model
was loaded on multiple nodes and only some matched the current selector,
the function returned the lowest-in_flight node — even one the selector
excluded. Route()'s post-check then fell through to scheduleNewModel,
which targeted the matching node where the model was already at
MaxReplicasPerModel capacity. Eviction couldn't help (the only loaded
model on that node was the one being requested, and it was busy), so
every request looped through "evicting LRU" → "all models busy".
Fix: thread an optional candidateNodeIDs filter through
FindAndLockNodeWithModel. Route() resolves the selector once via a new
resolveSelectorCandidates helper and passes the matching IDs to both
the cached-replica lookup and scheduleNewModel. The same helper
replaces the inline selector block in scheduleNewModel.
2. ScheduleAndLoadModel (reconciler scale-up path) fell back to
scheduleNewModel with backendType="" when no replica had ever been
loaded for a model. The worker rejected the resulting backend.install
("backend name is empty") on every reconciler tick (~30s).
Fix: remove the broken fallback. When GetModelLoadInfo has nothing
stored, return a clear error instead of firing a doomed NATS install.
The reconciler's existing scale-up failure log surfaces it once per
tick; the model auto-replicates as soon as Route() serves it once and
stores load info.
Also downgrade the post-LoadModel-failure StopGRPC error to Debug — that
cleanup attempt usually hits "model not found" because LoadModel failed
before registering the process, and the outer "Failed to load model"
error already carries the real reason.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: claude-code:claude-opus-4-7 [Read] [Edit] [Bash]
* test(distributed): cover selector-aware FindAndLockNodeWithModel and reconciler scaleup guard
Two regression tests for the bugs fixed in the previous commit:
1. FindAndLockNodeWithModel — registry-level integration tests verify the
candidateNodeIDs filter:
- Returns the included node even when an excluded node has lower
in_flight (the original selector-mismatch loop scenario).
- Returns not-found when the model is loaded only on excluded nodes,
forcing Route() to fall through to a fresh schedule instead of
reusing the excluded replica.
2. ScheduleAndLoadModel — mock-based test verifies the reconciler scale-up
path returns an error and does NOT fire backend.install when no replica
has been loaded yet. fakeUnloader gains an installCalls slice so this
negative assertion is direct.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: claude-code:claude-opus-4-7 [Read] [Edit] [Bash]
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
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Ettore Di Giacinto
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6b63b47f61 |
feat(distributed): support multiple replicas of one model on the same node (#9583)
* feat(distributed): support multiple replicas of one model on the same node The distributed scheduler implicitly assumed `(node_id, model_name)` was unique, but the schema didn't enforce it and the worker keyed all gRPC processes by model name alone. With `MinReplicas=2` against a single worker, the reconciler "scaled up" every 30s but the registry never advanced past 1 row — the worker re-loaded the model in-place every tick until VRAM fragmented and the gRPC process died. This change introduces multi-replica-per-node as a first-class concept, with capacity-aware scheduling, a circuit breaker, and VRAM soft-reservation. Operators can declare per-node capacity via the worker flag `--max-replicas-per-model` (mirrored as auto-label `node.replica-slots=N`) or override per-node from the UI. * Schema: BackendNode gains MaxReplicasPerModel (default 1) and ReservedVRAM. NodeModel gains ReplicaIndex (composite with node_id + model_name). ModelSchedulingConfig gains UnsatisfiableUntil/Ticks for the reconciler circuit breaker. * Registry: replica_index threaded through SetNodeModel, RemoveNodeModel, IncrementInFlight, DecrementInFlight, TouchNodeModel, GetNodeModel, SetNodeModelLoadInfo and the InFlightTrackingClient. New helpers: CountReplicasOnNode, NextFreeReplicaIndex (with ErrNoFreeSlot), RemoveAllNodeModelReplicas, FindNodesWithFreeSlot, ClusterCapacityForModel, ReserveVRAM/ReleaseVRAM (atomic UPDATE with ErrInsufficientVRAM), and the unsatisfiable-flag CRUD. * Worker: processKey now `<modelID>#<replicaIndex>` so concurrent loads of the same model land on distinct ports. Adds CLI flag --max-replicas-per-model (env LOCALAI_MAX_REPLICAS_PER_MODEL, default 1) and emits the auto-label. * Router: scheduleNewModel filters candidates by free slot, allocates the replica index, and soft-reserves VRAM before installing the backend. evictLRUAndFreeNode now deletes the targeted row by ID instead of all replicas of the model on the node — fixes a latent bug where evicting one replica orphaned its siblings. * Reconciler: caps scale-up at ClusterCapacityForModel so a misconfig (MinReplicas > capacity) doesn't loop forever. After 3 consecutive ticks of capacity==0 it sets UnsatisfiableUntil for a 5m cooldown and emits a warning. ClearAllUnsatisfiable fires from Register, ApproveNode, SetNodeLabel(s), RemoveNodeLabel and UpdateMaxReplicasPerModel so a new node joining or label changes wake the reconciler immediately. scaleDownIdle removes highest-replica-index first to keep slots compact. * Heartbeat resets reserved_vram to 0 — worker is the source of truth for actual free VRAM; the reservation is only for the in-tick race window between two scheduling decisions. * Probe path (reconciler.probeLoadedModels and health.doCheckAll) now pass the row's replica_index to RemoveNodeModel so an unreachable replica doesn't orphan healthy siblings. * Admin override: PUT /api/nodes/:id/max-replicas-per-model sets a sticky override (preserved across worker re-registration). DELETE clears the override so the worker's flag applies again on next register. Required because Kong defaults the worker flag to 1, so every worker restart would have silently reverted the UI value. * React UI: always-visible slot badge on the node row (muted at default 1, accented when >1); inline editor in the expanded drawer with pencil-to-edit, Save/Cancel, Esc/Enter, "(override)" indicator when the value is admin-set, and a "Reset" button to hand control back to the worker. Soft confirm when shrinking the cap below the count of loaded replicas. Scheduling rules table gets an "Unsatisfiable until HH:MM" status badge surfacing the cooldown. * node.replica-slots filtered out of the labels strip on the row to avoid duplicating the slot badge. 23 new Ginkgo specs (registry, reconciler, inflight, health) cover: multi-replica row independence, RemoveNodeModel of one replica preserving siblings, NextFreeReplicaIndex slot allocation including ErrNoFreeSlot, capacity-gated scale-up with circuit breaker tripping and recovery on Register, scheduleDownIdle ordering, ClusterCapacity math, ReserveVRAM admission gating, Heartbeat reset, override survival across worker re-registration, and ResetMaxReplicasPerModel handing control back. Plus 8 stdlib tests for the worker processKey / CLI / auto-label. Closes the flap reproduced on Qwen3.6-35B against the nvidia-thor worker (single 128 GiB node, MinReplicas=2): the reconciler now caps the scale-up at the cluster's actual capacity instead of looping. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: claude-code:opus-4-7 [Read] [Edit] [Bash] [Skill:critique] [Skill:audit] [Skill:polish] [Skill:golang-testing] * refactor(react-ui/nodes): tighten capacity editor copy + adopt ActionMenu for row actions * Capacity editor hint trimmed from operator-doc-style ("Sourced from the worker's `--max-replicas-per-model` flag. Changing it here makes it a sticky admin override that survives worker restarts." → "Saved values stick across worker restarts.") and the override-state copy similarly compressed. The full mechanic is no longer needed in the UI — the override pill carries the meaning and the docs cover the rest. * Node row actions migrated from an inline cluster of icon buttons (Drain / Resume / Trash) to the kebab ActionMenu used by /manage for per-row model actions, so dense Nodes tables stay clean. Approve stays as a prominent primary button — it's a stateful admission gate, not a routine action, and elevating it matches how /manage surfaces install-time decisions outside the menu. * The expanded drawer's Labels section now filters node.replica-slots out of the editable label list. The label is owned by the Capacity editor above; surfacing it again as an editable label invited confusion (the Capacity save would clobber any direct edit). Both backend and agent workers benefit — they share the row rendering path, so the action menu and label filter apply to both. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: claude-code:opus-4-7 [Edit] [chrome-devtools-mcp] [Skill:critique] [Skill:audit] [Skill:polish] * fix(react-ui/nodes): suppress slot badge on agent workers Agent workers don't load models, so the per-node replica capacity is inapplicable to them. Showing "1× slots" on agent rows was a tiny inconsistency from the unified rendering path — gate the badge on node_type !== 'agent' so it only appears on backend workers. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: claude-code:opus-4-7 [Edit] [chrome-devtools-mcp] * refactor(react-ui/nodes): distill expanded drawer + restyle scheduling form The expanded node drawer used to stack five panels — slot badge, filled capacity box, Loaded Models h4+empty-state, Installed Backends h4+empty-state, Labels h4+chips+form — making routine inspections feel like a control panel. The scheduling rule form wrapped its mode toggle as two 50%-width filled buttons that competed visually with the actual primary action. * Drawer: collapse three rarely-touched config zones (Capacity, Backends, Labels) into one `<details>` "Manage" disclosure (closed by default) with small uppercase eyebrow labels for each zone instead of parallel h4 sub-headings. Loaded Models stays as the at-a-glance headline with a single-line empty hint instead of a boxed empty state. CapacityEditor renders flat (no filled background) — the Manage disclosure provides framing. * Scheduling form: replace the chunky 50%-width button-tabs with the project's existing `.segmented` control (icon + label, sized to content). Mode hint becomes a single tied line below. Fields stack vertically with helper text under inputs and a hairline divider above the right-aligned Save / Cancel. The empty drawer collapses from ~5 stacked sections (~280px tall) to two lines (~80px). The scheduling form now reads as a designed dialog instead of raw building blocks. Both surfaces now match the typographic density and weight of the rest of the admin pages. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: claude-code:opus-4-7 [Edit] [chrome-devtools-mcp] [Skill:distill] [Skill:audit] [Skill:polish] * feat(react-ui/nodes): replace scheduling form's model picker with searchable combobox The native <select> made operators scroll through every gallery entry to find a model name. The project already has SearchableModelSelect (used in Studio/Talk/etc.) which combines free-text search with the gallery list and accepts typed model names that aren't installed yet — useful for pre-staging a scheduling rule before the node it'll run on has finished bootstrapping. Also drops the now-unused useModels import (the combobox manages the gallery hook internally). Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: claude-code:opus-4-7 [Edit] * refactor(react-ui/nodes): consolidate key/value chip editor + add replica preset chips The Nodes page was rendering the same key=value chip pattern in two places with subtly different markup: the Labels editor in the expanded drawer and (post-distill) the Node Selector input in the scheduling form. The form's input was also a comma-separated string that operators were getting wrong. * Extract <KeyValueChips> as a fully controlled chip-builder. Parent owns the map and decides what onAdd/onRemove does — form state for the scheduling form, API calls for the live drawer Labels editor. Same visuals everywhere; one component to change when polish needs apply. * Replace the comma-separated Node Selector text input with KeyValueChips. Operators were copying syntax from docs and missing commas; the chip vocabulary makes the key=value structure self-documenting. * Add <ReplicaInput>: numeric input + quick-pick preset chips for Min/Max replicas. Picked over a slider because replica counts are exact specs derived from VRAM math (operator decision, not a fuzzy estimate). The chips give one-click access to common values (1/2/3/4 for Min, 0=no-limit/2/4/8 for Max) without the slider's special-value problem (MaxReplicas=0 is categorical, not a position on a continuum). * Drop the now-unused labelInputs state in the Nodes page (the inline label editor's per-node draft state lived there and is now owned by KeyValueChips). Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: claude-code:opus-4-7 [Edit] [Skill:distill] * test: fix CI fallout from multi-replica refactor (e2e/distributed + playwright) Two breakages caught by CI that didn't surface in the local run: * tests/e2e/distributed/*.go — multiple files used the pre-PR2 registry signatures for SetNodeModel / IncrementInFlight / DecrementInFlight / RemoveNodeModel / TouchNodeModel / GetNodeModel / SetNodeModelLoadInfo and one stale adapter.InstallBackend call in node_lifecycle_test.go. All updated to pass replicaIndex=0 — these tests don't exercise multi-replica behavior, they just need to compile against the new signatures. The chip-builder tests in core/services/nodes/ already cover the multi-replica logic. * core/http/react-ui/e2e/nodes-per-node-backend-actions.spec.js — the drawer's distill refactor moved Backends inside a "Manage" <details> disclosure that's collapsed by default. The test helper expanded the node row but never opened Manage, so the per-node backend table was never in the DOM. Helper now clicks `.node-manage > summary` after expanding the row. All 100 playwright tests pass locally; tests/e2e/distributed compiles clean. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: claude-code:opus-4-7 [Edit] [Bash] --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Ettore Di Giacinto
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3810fe1a1e |
fix(distributed): worker container healthcheck always unhealthy
The Dockerfile's HEALTHCHECK probes http://localhost:8080/readyz, which is the OpenAI API server port. When the same image runs as a worker, it listens on the gRPC base port (50051) and an HTTP file transfer server on port-1 (50050) — nothing on 8080 — so docker always reports the container as unhealthy. Add unauthenticated /readyz and /healthz endpoints to the worker's HTTP file transfer server, and override HEALTHCHECK_ENDPOINT for worker-1 in the distributed compose file. Disable the healthcheck for agent-worker since it is NATS-only and exposes no HTTP server. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: claude-code:claude-opus-4-7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Ettore Di Giacinto
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2da1a4d230 |
feat(distributed): per-node backend installation from the gallery
In distributed mode the Backends gallery used to fan every install out to every worker — fine for auto-resolving (meta) backends like llama-cpp where each node picks its own variant, but wrong for hardware-specific builds like cpu-llama-cpp that would silently land on every GPU node. Adds a node-targeted install path through the existing POST /api/nodes/:id/backends/install plumbing, with two entry points: - Backends gallery row gets a split-button in distributed mode. Auto- resolving keeps "Install on all nodes" as the primary; chevron menu opens the picker. Hardware-specific routes the primary directly to the picker — no fan-out path on the row. - Nodes-page drawer gets a "+ Add backend" button that navigates to /app/backends?target=<node-id>; the gallery scopes itself to that node (banner, single per-row install button, Reinstall/Remove for already- installed). One gallery, two scopes — no second UI to maintain. The picker (new NodeInstallPicker) shows a 3-state suitability column (Compatible / Override / Installed), an auto-expanding variant override disclosure that fires when selected nodes have no working GPU, parallel per-node installs with inline status and Retry-failed-nodes, and a mismatch confirm that names the consequence on the button itself. A 409 fan-out guard on /api/backends/apply protects CLI/Terraform/script users from the same footgun: hardware-specific installs in distributed mode now return code "concrete_backend_requires_target" with a human- readable error and a meta_alternative pointer. The gallery list payload now surfaces capabilities, metaBackendFor and per-row nodes (NodeBackendRef) so the picker and the new Nodes column have everything they need without re-walking the gallery client-side. GODEBUG=netdns=go is set on the compose services because the cgo DNS resolver follows the container's nsswitch.conf to host systemd-resolved (127.0.0.53), unreachable from inside the container; the pure-Go resolver reads /etc/resolv.conf directly and uses Docker's embedded DNS. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude Code:claude-opus-4-7[1m] [Edit] [Bash] [Read] [Write] |
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Ettore Di Giacinto
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83b384de97 |
feat: surface distributed backend management errors (#9552)
* fix(distributed): surface per-node backend op errors to OpStatus
DistributedBackendManager.{Install,Upgrade,Delete}Backend discarded the
per-node BackendOpResult from enqueueAndDrainBackendOp with `_, err :=`.
When workers replied Success=false (e.g. an OCI image with no arm64
variant on a Jetson host), the per-node Error string was recorded in
result.Nodes[].Error but never reached the toplevel return value, so
OpStatus.Error stayed empty and the UI reported the install as
"completed" while the backend was nowhere on the cluster.
Add BackendOpResult.Err() that aggregates per-node Status=="error"
entries into a single error. Queued nodes (waiting for reconciler retry)
are deliberately not treated as failures. Wire the three callers and
DeleteBackendDetailed to call result.Err() so reply.Success=false
finally reaches OpStatus.Error → /api/backends/job/:uid → the UI.
The Delete closures had a related bug: they discarded the reply with
`_` and only checked the NATS round-trip error, so reply.Success=false
was a silent success even with the new aggregation. Check both.
Standalone mode (LocalBackendManager) already surfaces gallery errors
correctly through the same OpStatus.Error path; no change needed there.
Tests: 9 new Ginkgo specs covering all-success / all-fail with distinct
errors / mixed / all-queued / no-nodes for Install, Upgrade, Delete.
Assisted-by: Claude:claude-opus-4-7 [Bash] [Edit] [Read] [Write]
* feat(react-ui): per-node backend delete + clearer upgrade affordance
The Nodes page exposed a per-node "reinstall" button (fa-sync-alt,
tooltip "Reinstall backend") but no per-node delete, even though the
Go side has had POST /api/nodes/:id/backends/delete →
RemoteUnloaderAdapter.DeleteBackend → NATS-to-specific-node wired up
for a while. Sync icons read as "refresh data" — the action is
functionally an upgrade (re-pulls the gallery image), so the affordance
was misleading.
Per-node backend row now renders two icon buttons:
- Upgrade: btn-secondary btn-sm + fa-arrow-up, tooltip "Upgrade backend
on this node". Names both action and scope to differentiate from the
cluster-wide upgrade on the Backends page.
- Delete: btn-danger-ghost btn-sm + fa-trash, tooltip "Delete backend
from this node". Matches the node-level destructive style at the row
action column rather than the solid btn-danger of primary destructive
pages, since this is a secondary action inside a busy row.
Delete goes through the existing ConfirmDialog (danger=true) with copy
that names the backend and the node explicitly — it's a non-recoverable
op on a specific scope. Reuses nodesApi.deleteBackend(id, backend) which
already existed in the API client.
Tests: 4 new Playwright specs covering upgrade clarity (icon + tooltip),
delete button presence, confirm dialog flow with POST body assertion,
and cancel-doesn't-POST.
Assisted-by: Claude:claude-opus-4-7 [Bash] [Edit] [Read] [Write]
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Ettore Di Giacinto
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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
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Ettore Di Giacinto
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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]
|
||
|
Russell Sim
|
02bb715c0a |
fix(distributed): pass ExternalURI through NATS backend install (#9446)
When installing a backend with a custom OCI URI in distributed mode, the URI was captured in ManagementOp.ExternalURI by the HTTP handler but never forwarded to workers. BackendInstallRequest had no URI field, so workers fell through to the gallery lookup and failed with "no backend found with name <custom-name>". Add URI/Name/Alias fields to BackendInstallRequest and thread them from ManagementOp through DistributedBackendManager.InstallBackend() and the RemoteUnloaderAdapter. On the worker side, route to InstallExternalBackend when URI is set instead of InstallBackendFromGallery. Update all remaining InstallBackend call sites (UpgradeBackend, reconciler pending-op drain, router auto-install) to pass empty strings for the new params. Assisted-by: Claude Code:claude-sonnet-4-6 Signed-off-by: Russell Sim <rsl@simopolis.xyz> |
||
|
Ettore Di Giacinto
|
fda1c553a1 |
fix(distributed): stop queue loops on agent nodes + dead-letter cap (#9433)
pending_backend_ops rows targeting agent-type workers looped forever: the reconciler fan-out hit a NATS subject the worker doesn't subscribe to, returned ErrNoResponders, we marked the node unhealthy, and the health monitor flipped it back to healthy on the next heartbeat. Next tick, same row, same failure. Three related fixes: 1. enqueueAndDrainBackendOp skips nodes whose NodeType != backend. Agent workers handle agent NATS subjects, not backend.install / delete / list, so enqueueing for them guarantees an infinite retry loop. Silent skip is correct — they aren't consumers of these ops. 2. Reconciler drain mirrors enqueueAndDrainBackendOp's behavior on nats.ErrNoResponders: mark the node unhealthy before recording the failure, so subsequent ListDuePendingBackendOps (filters by status=healthy) stops picking the row until the node actually recovers. Matches the synchronous fan-out path. 3. Dead-letter cap at maxPendingBackendOpAttempts (10). After ~1h of exponential backoff the row is a poison message; further retries just thrash NATS. Row is deleted and logged at ERROR so it stays visible without staying infinite. Plus a one-shot startup cleanup in NewNodeRegistry: drop queue rows that target agent-type nodes, non-existent nodes, or carry an empty backend name. Guarded by the same schema-migration advisory lock so only one instance performs it. The guards above prevent new rows of this shape; this closes the migration gap for existing ones. Tests: the prune migration (valid row stays, agent + empty-name rows drop) on top of existing upsert / backoff coverage. |
||
|
Ettore Di Giacinto
|
75a63f87d8 |
feat(distributed): sync state with frontends, better backend management reporting (#9426)
* fix(distributed): detect backend upgrades across worker nodes
Before this change `DistributedBackendManager.CheckUpgrades` delegated to the
local manager, which read backends from the frontend filesystem. In
distributed deployments the frontend has no backends installed locally —
they live on workers — so the upgrade-detection loop never ran and the UI
silently never surfaced upgrades even when the gallery advertised newer
versions or digests.
Worker-side: NATS backend.list reply now carries Version, URI and Digest
for each installed backend (read from metadata.json).
Frontend-side: DistributedBackendManager.ListBackends aggregates per-node
refs (name, status, version, digest) instead of deduping, and CheckUpgrades
feeds that aggregation into gallery.CheckUpgradesAgainst — a new entrypoint
factored out of CheckBackendUpgrades so both paths share the same core
logic.
Cluster drift policy: when per-node version/digest tuples disagree, the
backend is flagged upgradeable regardless of whether any single node
matches the gallery, and UpgradeInfo.NodeDrift enumerates the outliers so
operators can see *why* it is out of sync. The next upgrade-all realigns
the cluster.
Tests cover: drift detection, unanimous-match (no upgrade), and the
empty-installed-version path that the old distributed code silently
missed.
* feat(ui): surface backend upgrades in the System page
The System page (Manage.jsx) only showed updates as a tiny inline arrow,
so operators routinely missed them. Port the Backend Gallery's upgrade UX
so System speaks the same visual language:
- Yellow banner at the top of the Backends tab when upgrades are pending,
with an "Upgrade all" button (serial fan-out, matches the gallery) and a
"Updates only" filter toggle.
- Warning pill (↑ N) next to the tab label so the count is glanceable even
when the banner is scrolled out of view.
- Per-row labeled "Upgrade to vX.Y" button (replaces the icon-only button
that silently flipped semantics between Reinstall and Upgrade), plus an
"Update available" badge in the new Version column.
- New columns: Version (with upgrade + drift chips), Nodes (per-node
attribution badges for distributed mode, degrading to a compact
"on N nodes · M offline" chip above three nodes), Installed (relative
time).
- System backends render a "Protected" chip instead of a bare "—" so rows
still align and the reason is obvious.
- Delete uses the softer btn-danger-ghost so rows don't scream red; the
ConfirmDialog still owns the "are you sure".
The upgrade checker also needed the same per-worker fix as the previous
commit: NewUpgradeChecker now takes a BackendManager getter so its
periodic runs call the distributed CheckUpgrades (which asks workers)
instead of the empty frontend filesystem. Without this the /api/backends/
upgrades endpoint stayed empty in distributed mode even with the protocol
change in place.
New CSS primitives — .upgrade-banner, .tab-pill, .badge-row, .cell-stack,
.cell-mono, .cell-muted, .row-actions, .btn-danger-ghost — all live in
App.css so other pages can adopt them without duplicating styles.
* feat(ui): polish the Nodes page so it reads like a product
The Nodes page was the biggest visual liability in distributed mode.
Rework the main dashboard surfaces in place without changing behavior:
StatCards: uniform height (96px min), left accent bar colored by the
metric's semantic (success/warning/error/primary), icon lives in a
36x36 soft-tinted chip top-right, value is left-aligned and large.
Grid auto-fills so the row doesn't collapse on narrow viewports. This
replaces the previous thin-bordered boxes with inconsistent heights.
Table rows: expandable rows now show a chevron cue on the left (rotates
on expand) so users know rows open. Status cell became a dedicated chip
with an LED-style halo dot instead of a bare bullet. Action buttons gained
labels — "Approve", "Resume", "Drain" — so the icons aren't doing all
the semantic work; the destructive remove action uses the softer
btn-danger-ghost variant so rows don't scream red, with the ConfirmDialog
still owning the real "are you sure". Applied cell-mono/cell-muted
utility classes so label chips and addresses share one spacing/font
grammar instead of re-declaring inline styles everywhere.
Expanded drawer: empty states for Loaded Models and Installed Backends
now render as a proper drawer-empty card (dashed border, icon, one-line
hint) instead of a plain muted string that read like broken formatting.
Tabs: three inline-styled buttons became the shared .tab class so they
inherit focus ring, hover state, and the rest of the design system —
matches the System page.
"Add more workers" toggle turned into a .nodes-add-worker dashed-border
button labelled "Register a new worker" (action voice) instead of a
chevron + muted link that operators kept mistaking for broken text.
New shared CSS primitives carry over to other pages:
.stat-grid + .stat-card, .row-chevron, .node-status, .drawer-empty,
.nodes-add-worker.
* feat(distributed): durable backend fan-out + state reconciliation
Two connected problems handled together:
1) Backend delete/install/upgrade used to silently skip non-healthy nodes,
so a delete during an outage left a zombie on the offline node once it
returned. The fan-out now records intent in a new pending_backend_ops
table before attempting the NATS round-trip. Currently-healthy nodes
get an immediate attempt; everyone else is queued. Unique index on
(node_id, backend, op) means reissuing the same operation refreshes
next_retry_at instead of stacking duplicates.
2) Loaded-model state could drift from reality: a worker OOM'd, got
killed, or restarted a backend process would leave a node_models row
claiming the model was still loaded, feeding ghost entries into the
/api/nodes/models listing and the router's scheduling decisions.
The existing ReplicaReconciler gains two new passes that run under a
fresh KeyStateReconciler advisory lock (non-blocking, so one wedged
frontend doesn't freeze the cluster):
- drainPendingBackendOps: retries queued ops whose next_retry_at has
passed on currently-healthy nodes. Success deletes the row; failure
bumps attempts and pushes next_retry_at out with exponential backoff
(30s → 15m cap). ErrNoResponders also marks the node unhealthy.
- probeLoadedModels: gRPC-HealthChecks addresses the DB thinks are
loaded but hasn't seen touched in the last probeStaleAfter (2m).
Unreachable addresses are removed from the registry. A pluggable
ModelProber lets tests substitute a fake without standing up gRPC.
DistributedBackendManager exposes DeleteBackendDetailed so the HTTP
handler can surface per-node outcomes ("2 succeeded, 1 queued") to the
UI in a follow-up commit; the existing DeleteBackend still returns
error-only for callers that don't care about node breakdown.
Multi-frontend safety: the state pass uses advisorylock.TryWithLockCtx
on a new key so N frontends coordinate — the same pattern the health
monitor and replica reconciler already rely on. Single-node mode runs
both passes inline (adapter is nil, state drain is a no-op).
Tests cover the upsert semantics, backoff math, the probe removing an
unreachable model but keeping a reachable one, and filtering by
probeStaleAfter.
* feat(ui): show cluster distribution of models in the System page
When a frontend restarted in distributed mode, models that workers had
already loaded weren't visible until the operator clicked into each node
manually — the /api/models/capabilities endpoint only knew about
configs on the frontend's filesystem, not the registry-backed truth.
/api/models/capabilities now joins in ListAllLoadedModels() when the
registry is active, returning loaded_on[] with node id/name/state/status
for each model. Models that live in the registry but lack a local config
(the actual ghosts, not recovered from the frontend's file cache) still
surface with source="registry-only" so operators can see and persist
them; without that emission they'd be invisible to this frontend.
Manage → Models replaces the old Running/Idle pill with a distribution
cell that lists the first three nodes the model is loaded on as chips
colored by state (green loaded, blue loading, amber anything else). On
wider clusters the remaining count collapses into a +N chip with a
title-attribute breakdown. Disabled / single-node behavior unchanged.
Adopted models get an extra "Adopted" ghost-icon chip with hover copy
explaining what it means and how to make it permanent.
Distributed mode also enables a 10s auto-refresh and a "Last synced Xs
ago" indicator next to the Update button so ghost rows drop off within
one reconcile tick after their owning process dies. Non-distributed
mode is untouched — no polling, no cell-stack, same old Running/Idle.
* feat(ui): NodeDistributionChip — shared per-node attribution component
Large clusters were going to break the Manage → Backends Nodes column:
the old inline logic rendered every node as a badge and would shred the
layout at >10 workers, plus the Manage → Models distribution cell had
copy-pasted its own slightly-different version.
NodeDistributionChip handles any cluster size with two render modes:
- small (≤3 nodes): inline chips of node names, colored by health.
- large: a single "on N nodes · M offline · K drift" summary chip;
clicking opens a Popover with a per-node table (name, status,
version, digest for backends; name, status, state for models).
Drift counting mirrors the backend's summarizeNodeDrift so the UI
number matches UpgradeInfo.NodeDrift. Digests are truncated to the
docker-style 12-char form with the full value preserved in the title.
Popover is a new general-purpose primitive: fixed positioning anchored
to the trigger, flips above when there's no room below, closes on
outside-click or Escape, returns focus to the trigger. Uses .card as
its surface so theming is inherited. Also useful for a future
labels-editor popup and the user menu.
Manage.jsx drops its duplicated inline Nodes-column + loaded_on cell
and uses the shared chip with context="backends" / "models"
respectively. Delete code removes ~40 lines of ad-hoc logic.
* feat(ui): shared FilterBar across the System page tabs
The Backends gallery had a nice search + chip + toggle strip; the System
page had nothing, so the two surfaces felt like different apps. Lift the
pattern into a reusable FilterBar and wire both System tabs through it.
New component core/http/react-ui/src/components/FilterBar.jsx renders a
search input, a role="tablist" chip row (aria-selected for a11y), and
optional toggles / right slot. Chips support an optional `count` which
the System page uses to show "User 3", "Updates 1" etc.
System Models tab: search by id or backend; chips for
All/Running/Idle/Disabled/Pinned plus a conditional Distributed chip in
distributed mode. "Last synced" + Update button live in the right slot.
System Backends tab: search by name/alias/meta-backend-for; chips for
All/User/System/Meta plus conditional Updates / Offline-nodes chips
when relevant. The old ad-hoc "Updates only" toggle from the upgrade
banner folded into the Updates chip — one source of truth for that
filter. Offline chip only appears in distributed mode when at least
one backend has an unhealthy node, so the chip row stays quiet on
healthy clusters.
Filter state persists in URL query params (mq/mf/bq/bf) so deep links
and tab switches keep the operator's filter context instead of
resetting every time.
Also adds an "Adopted" distribution path: when a model in
/api/models/capabilities carries source="registry-only" (discovered on
a worker but not configured locally), the Models tab shows a ghost chip
labelled "Adopted" with hover copy explaining how to persist it — this
is what closes the loop on the ghost-model story end-to-end.
|
||
|
Ettore Di Giacinto
|
87e6de1989 |
feat: wire transcription for llama.cpp, add streaming support (#9353)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Ettore Di Giacinto
|
8ab0744458 |
feat: backend versioning, upgrade detection and auto-upgrade (#9315)
* feat: add backend versioning data model foundation Add Version, URI, and Digest fields to BackendMetadata for tracking installed backend versions and enabling upgrade detection. Add Version field to GalleryBackend. Add UpgradeAvailable/AvailableVersion fields to SystemBackend. Implement GetImageDigest() for lightweight OCI digest lookups via remote.Head. Record version, URI, and digest at install time in InstallBackend() and propagate version through meta backends. * feat: add backend upgrade detection and execution logic Add CheckBackendUpgrades() to compare installed backend versions/digests against gallery entries, and UpgradeBackend() to perform atomic upgrades with backup-based rollback on failure. Includes Agent A's data model changes (Version/URI/Digest fields, GetImageDigest). * feat: add AutoUpgradeBackends config and runtime settings Add configuration and runtime settings for backend auto-upgrade: - RuntimeSettings field for dynamic config via API/JSON - ApplicationConfig field, option func, and roundtrip conversion - CLI flag with LOCALAI_AUTO_UPGRADE_BACKENDS env var - Config file watcher support for runtime_settings.json - Tests for ToRuntimeSettings, ApplyRuntimeSettings, and roundtrip * feat(ui): add backend version display and upgrade support - Add upgrade check/trigger API endpoints to config and api module - Backends page: version badge, upgrade indicator, upgrade button - Manage page: version in metadata, context-aware upgrade/reinstall button - Settings page: auto-upgrade backends toggle * feat: add upgrade checker service, API endpoints, and CLI command - UpgradeChecker background service: checks every 6h, auto-upgrades when enabled - API endpoints: GET /backends/upgrades, POST /backends/upgrades/check, POST /backends/upgrade/:name - CLI: `localai backends upgrade` command, version display in `backends list` - BackendManager interface: add UpgradeBackend and CheckUpgrades methods - Wire upgrade op through GalleryService backend handler - Distributed mode: fan-out upgrade to worker nodes via NATS * fix: use advisory lock for upgrade checker in distributed mode In distributed mode with multiple frontend instances, use PostgreSQL advisory lock (KeyBackendUpgradeCheck) so only one instance runs periodic upgrade checks and auto-upgrades. Prevents duplicate upgrade operations across replicas. Standalone mode is unchanged (simple ticker loop). * test: add e2e tests for backend upgrade API - Test GET /api/backends/upgrades returns 200 (even with no upgrade checker) - Test POST /api/backends/upgrade/:name accepts request and returns job ID - Test full upgrade flow: trigger upgrade via API, wait for job completion, verify run.sh updated to v2 and metadata.json has version 2.0.0 - Test POST /api/backends/upgrades/check returns 200 - Fix nil check for applicationInstance in upgrade API routes |
||
|
Ettore Di Giacinto
|
39c6b3ed66 |
feat: track files being staged (#9275)
This changeset makes visible when files are being staged, so users are aware that the model "isn't ready yet" for requests. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Ettore Di Giacinto
|
154fa000d3 |
fix(autoscaling): extract load model from Route() and use as well when doing autoscale (#9270)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Ettore Di Giacinto
|
6f304d1201 |
chore(refactor): use interface (#9226)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Ettore Di Giacinto
|
223deb908d |
fix(nats): improve error handling (#9222)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Ettore Di Giacinto
|
6c635e8353 |
feat: add resume endpoint to undrain nodes (#9197)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Ettore Di Giacinto
|
6b6c136210 |
fix(inflight): count inflight from load model, but release afterwards (#9194)
This should fix the count of 1 in flight always showing in the node list Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Richard Palethorpe
|
952635fba6 |
feat(distributed): Avoid resending models to backend nodes (#9193)
Signed-off-by: Richard Palethorpe <io@richiejp.com> Co-authored-by: Ettore Di Giacinto <mudler@users.noreply.github.com> |
||
|
Ettore Di Giacinto
|
3cc05af2e5 |
chore(nodes): restore offline nodes too
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Ettore Di Giacinto
|
b4fff9293d |
chore: small ui improvements in the node page
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Ettore Di Giacinto
|
8862e3ce60 |
feat: add node reconciler, allow to schedule to group of nodes, min/max autoscaler (#9186)
* always enable parallel requests Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat: add node reconciler, allow to schedule to group of nodes, min/max autoscaler Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore: move tests to ginkgo Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore(smart router): order by available vram Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Ettore Di Giacinto
|
dd3376e0a9 |
chore(workers): improve logging, set header timeouts (#9171)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Ettore Di Giacinto
|
59108fbe32 |
feat: add distributed mode (#9124)
* feat: add distributed mode (experimental) Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix data races, mutexes, transactions Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactorings Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fixups Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix events and tool stream in agent chat Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * use ginkgo Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactoring and consolidation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactoring and consolidation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactoring and consolidation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactoring and consolidation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactoring and consolidation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactoring and consolidation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactoring and consolidation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactoring and consolidation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(cron): compute correctly time boundaries avoiding re-triggering Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * enhancements, refactorings Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * do not flood of healthy checks Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * do not list obvious backends as text backends Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * tests fixups Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactoring and consolidation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Drop redundant healthcheck Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * enhancements, refactorings Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |