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176 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
LocalAI [bot]
|
b4fdb41dcc |
fix(distributed): cascade-clean stale node_models rows + filter routing by healthy status (#9754)
* fix(distributed): cascade-clean stale node_models on drain and filter routing by healthy status Stale node_models rows (state="loaded") were surviving past the healthy state of their owning node, causing /embeddings (and other inference paths) to dispatch to a backend whose process was gone or drained. The downstream symptom in a live cluster was pgvector rejecting inserts with "vector cannot have more than 16000 dimensions (SQLSTATE 54000)" because the misbehaving backend silently returned a malformed (oversized) tensor; the Models page showed the model as "running" without an associated node, like a stale entry, even though the node was no longer visible in the Nodes view. Two changes here, plus a third in a follow-up commit: - MarkDraining now cascade-deletes node_models rows for the affected node, mirroring MarkOffline. Drains are explicit operator actions — the box has been intentionally taken out of rotation — so clearing the rows stops the Models UI from misreporting and prevents the routing layer from picking those rows if scheduling logic is ever relaxed. In-flight requests already hold their gRPC client through Route() and finish normally; the only observable effect is a non-fatal IncrementInFlight warning, acceptable for a drain. MarkUnhealthy is deliberately left status-only: it fires from managers_distributed / reconciler on a single nats.ErrNoResponders with no retry, so a transient NATS hiccup must not nuke every loaded model and force a full reload on recovery. - FindAndLockNodeWithModel's inner JOIN now filters on backend_nodes.status = healthy in addition to node_models.state = loaded. The previous version relied on the second node-fetch step to reject non-healthy nodes, but a concurrent reader could still pick the same stale row in the same window. Belt-and-braces. - DistributedConfig.PerModelHealthCheck renamed to DisablePerModelHealthCheck and inverted at the call site so per-model gRPC probing is on by default. The probe (now made consecutive-miss aware in a follow-up commit) independently health- checks each model's gRPC address and removes stale node_models rows when the backend has crashed even though the worker's node-level heartbeat is still arriving. Migration: the field had no CLI flag, env var binding, or YAML key in tree (only the bare struct field), so there is no user-facing migration. Anything constructing DistributedConfig in code needs to drop the assignment (default now does the right thing) or invert it. Assisted-by: Claude:claude-opus-4-7 go-vet go-test golangci-lint Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(distributed): require consecutive misses before per-model probe removes a row The per-model gRPC probe used to remove a node_models row on a single failed health check. With the per-model probe now on by default, that made any 5-second gRPC blip (network jitter, a long-running request hogging the worker's gRPC server thread, brief GC pause) trigger a full reload of the affected model — too eager for production. Require perModelMissThreshold (3) consecutive failed probes before removal. At the default 15s tick a model must be unreachable for ~45s before reap; a single successful probe in between resets the streak. Per-(node, model, replica) state tracked under a mutex on the monitor. If the removal call itself fails, the miss counter is left in place so the next tick retries rather than starting the streak over. Tests: - removes stale model via per-model health check after consecutive failures (replaces the single-shot expectation) - preserves model row when an intermittent failure is followed by a success (covers the reset-on-success path and verifies the counter reset by failing twice more without crossing threshold) - newTestHealthMonitor initializes the misses map so direct-construct test helpers don't nil-map-panic in the probe path Assisted-by: Claude:claude-opus-4-7 go-vet go-test golangci-lint Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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|
Richard Palethorpe
|
0245b33eab |
feat(realtime): Add Liquid Audio s2s model and assistant mode on talk page (#9801)
* feat(liquid-audio): add LFM2.5-Audio any-to-any backend + realtime_audio usecase
Wires LiquidAI's LFM2.5-Audio-1.5B as a self-contained Realtime API model:
single engine handles VAD, transcription, LLM, and TTS in one bidirectional
stream — drop-in alternative to a VAD+STT+LLM+TTS pipeline.
Backend
- backend/python/liquid-audio/ — new Python gRPC backend wrapping the
`liquid-audio` package. Modes: chat / asr / tts / s2s, voice presets,
Load/Predict/PredictStream/AudioTranscription/TTS/VAD/AudioToAudioStream/
Free and StartFineTune/FineTuneProgress/StopFineTune. Runtime monkey-patch
on `liquid_audio.utils.snapshot_download` so absolute local paths from
LocalAI's gallery resolve without a HF round-trip. soundfile in place of
torchaudio.load/save (torchcodec drags NVIDIA NPP we don't bundle).
- backend/backend.proto + pkg/grpc/{backend,client,server,base,embed,
interface}.go — new AudioToAudioStream RPC mirroring AudioTransformStream
(config/frame/control oneof in; typed event+pcm+meta out).
- core/services/nodes/{health_mock,inflight}_test.go — add stubs for the
new RPC to the test fakes.
Config + capabilities
- core/config/backend_capabilities.go — UsecaseRealtimeAudio, MethodAudio
ToAudioStream, UsecaseInfoMap entry, liquid-audio BackendCapability row.
- core/config/model_config.go — FLAG_REALTIME_AUDIO bitmask, ModalityGroups
membership in both speech-input and audio-output groups so a lone flag
still reads as multimodal, GetAllModelConfigUsecases entry, GuessUsecases
branch.
Realtime endpoint
- core/http/endpoints/openai/realtime.go — extract prepareRealtimeConfig()
so the gate is unit-testable; accept realtime_audio models and self-fill
empty pipeline slots with the model's own name (user-pinned slots win).
- core/http/endpoints/openai/realtime_gate_test.go — six specs covering nil
cfg, empty pipeline, legacy pipeline, self-contained realtime_audio,
user-pinned VAD slot, and partial legacy pipeline.
UI + endpoints
- core/http/routes/ui.go — /api/pipeline-models accepts either a legacy
VAD+STT+LLM+TTS pipeline or a realtime_audio model; surfaces a
self_contained flag so the Talk page can collapse the four cards.
- core/http/routes/ui_api.go — realtime_audio in usecaseFilters.
- core/http/routes/ui_pipeline_models_test.go — covers both code paths.
- core/http/react-ui/src/pages/Talk.jsx — self-contained badge instead of
the four-slot grid; rename Edit Pipeline → Edit Model Config; less
pipeline-specific wording.
- core/http/react-ui/src/pages/Models.jsx + locales/en/models.json — new
realtime_audio filter button + i18n.
- core/http/react-ui/src/utils/capabilities.js — CAP_REALTIME_AUDIO.
- core/http/react-ui/src/pages/FineTune.jsx — voice + validation-dataset
fields, surfaced when backend === liquid-audio, plumbed via
extra_options on submit/export/import.
Gallery + importer
- gallery/liquid-audio.yaml — config template with known_usecases:
[realtime_audio, chat, tts, transcript, vad].
- gallery/index.yaml — four model entries (realtime/chat/asr/tts) keyed by
mode option. Fixed pre-existing `transcribe` typo on the asr entry
(loader silently dropped the unknown string → entry never surfaced as a
transcript model).
- gallery/lfm.yaml — function block for the LFM2 Pythonic tool-call format
`<|tool_call_start|>[name(k="v")]<|tool_call_end|>` matching
common_chat_params_init_lfm2 in vendored llama.cpp.
- core/gallery/importers/{liquid-audio,liquid-audio_test}.go — detector
matches LFM2-Audio HF repos (excludes -gguf mirrors); mode/voice
preferences plumbed through to options.
- core/gallery/importers/importers.go — register LiquidAudioImporter
before LlamaCPPImporter.
- pkg/functions/parse_lfm2_test.go — seven specs for the response/argument
regex pair on the LFM2 pythonic format.
Build matrix
- .github/backend-matrix.yml — seven liquid-audio targets (cuda12, cuda13,
l4t-cuda-13, hipblas, intel, cpu amd64, cpu arm64). Jetpack r36 cuda-12
is skipped (Ubuntu 22.04 / Python 3.10 incompatible with liquid-audio's
3.12 floor).
- backend/index.yaml — anchor + 13 image entries.
- Makefile — .NOTPARALLEL, prepare-test-extra, test-extra,
docker-build-liquid-audio.
Docs
- .agents/plans/liquid-audio-integration.md — phased plan; PR-D (real
any-to-any wiring via AudioToAudioStream), PR-E (mid-audio tool-call
detector), PR-G (GGUF entries once upstream llama.cpp PR #18641 lands)
remain.
- .agents/api-endpoints-and-auth.md — expand the capability-surface
checklist with every place a new FLAG_* needs to be registered.
Assisted-by: claude-code:claude-opus-4-7-1m [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(realtime): function calling + history cap for any-to-any models
Three pieces, all on the realtime_audio path that just landed:
1. liquid-audio backend (backend/python/liquid-audio/backend.py):
- _build_chat_state grows a `tools_prelude` arg.
- new _render_tools_prelude parses request.Tools (the OpenAI Chat
Completions function array realtime.go already serialises) and
emits an LFM2 `<|tool_list_start|>…<|tool_list_end|>` system turn
ahead of the user history. Mirrors gallery/lfm.yaml's `function:`
template so the model sees the same prompt shape whether served
via llama-cpp or here. Without this the backend silently dropped
tools — function calling was wired end-to-end on the Go side but
the model never saw a tool list.
2. Realtime history cap (core/http/endpoints/openai/realtime.go):
- Session grows MaxHistoryItems int; default picked by new
defaultMaxHistoryItems(cfg) — 6 for realtime_audio models (LFM2.5
1.5B degrades quickly past a handful of turns), 0/unlimited for
legacy pipelines composing larger LLMs.
- triggerResponse runs conv.Items through trimRealtimeItems before
building conversationHistory. Helper walks the cut left if it
would orphan a function_call_output, so tool result + call pairs
stay intact.
- realtime_gate_test.go: specs for defaultMaxHistoryItems and
trimRealtimeItems (zero cap, under cap, over cap, tool-call pair
preservation).
3. Talk page (core/http/react-ui/src/pages/Talk.jsx):
- Reuses the chat page's MCP plumbing — useMCPClient hook,
ClientMCPDropdown component, same auto-connect/disconnect effect
pattern. No bespoke tool registry, no new REST endpoints; tools
come from whichever MCP servers the user toggles on, exactly as
on the chat page.
- sendSessionUpdate now passes session.tools=getToolsForLLM(); the
update re-fires when the active server set changes mid-session.
- New response.function_call_arguments.done handler executes via
the hook's executeTool (which round-trips through the MCP client
SDK), then replies with conversation.item.create
{type:function_call_output} + response.create so the model
completes its turn with the tool output. Mirrors chat's
client-side agentic loop, translated to the realtime wire shape.
UI changes require a LocalAI image rebuild (Dockerfile:308-313 bakes
react-ui/dist into the runtime image). Backend.py changes can be
swapped live in /backends/<id>/backend.py + /backend/shutdown.
Assisted-by: claude-code:claude-opus-4-7-1m [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(realtime): LocalAI Assistant ("Manage Mode") for the Talk page
Mirrors the chat-page metadata.localai_assistant flow so users can ask the
realtime model what's loaded / installed / configured. Tools are run
server-side via the same in-process MCP holder that powers the chat
modality — no transport switch, no proxy, no new wire protocol.
Wire:
- core/http/endpoints/openai/realtime.go:
- RealtimeSessionOptions{LocalAIAssistant,IsAdmin}; isCurrentUserAdmin
helper mirrors chat.go's requireAssistantAccess (no-op when auth
disabled, else requires auth.RoleAdmin).
- Session grows AssistantExecutor mcpTools.ToolExecutor.
- runRealtimeSession, when opts.LocalAIAssistant is set: gate on admin,
fail closed if DisableLocalAIAssistant or the holder has no tools,
DiscoverTools and inject into session.Tools, prepend
holder.SystemPrompt() to instructions.
- Tool-call dispatch loop: when AssistantExecutor.IsTool(name), run
ExecuteTool inproc, append a FunctionCallOutput to conv.Items, skip
the function_call_arguments client emit (the client can't execute
these — it doesn't know about them). After the loop, if any
assistant tool ran, trigger another response so the model speaks the
result. Mirrors chat's agentic loop, driven server-side rather than
via client round-trip.
- core/http/endpoints/openai/realtime_webrtc.go: RealtimeCallRequest
gains `localai_assistant` (JSON omitempty). Handshake calls
isCurrentUserAdmin and builds RealtimeSessionOptions.
- core/http/react-ui/src/pages/Talk.jsx: admin-only "Manage Mode"
checkbox under the Tools dropdown; passes localai_assistant: true to
realtimeApi.call's body, captured in the connect callback's deps.
Mirroring chat's pattern means the in-process MCP tools surface "just
works" for the Talk page without exposing a Streamable-HTTP MCP endpoint
(which was the alternative). Clients with their own MCP servers can
still use the existing ClientMCPDropdown path in parallel; the realtime
handler distinguishes them by AssistantExecutor.IsTool() at dispatch
time.
Assisted-by: claude-code:claude-opus-4-7-1m [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(realtime): render Manage Mode tool calls in the Talk transcript
Previously the realtime endpoint only emitted response.output_item.added
for the FunctionCall item, and Talk.jsx's switch ignored the event — so
server-side tool runs were invisible in the UI. The model would speak
the result but the user had no way to see what tool was actually
called.
realtime.go: after executing an assistant tool inproc, emit a second
output_item.added/.done pair for the FunctionCallOutput item. Mirrors
the way the chat page displays tool_call + tool_result blocks.
Talk.jsx: handle both response.output_item.added and .done. Render
FunctionCall (with arguments) and FunctionCallOutput (pretty-printed
JSON when possible) as two transcript entries — `tool_call` with the
wrench icon, `tool_result` with the clipboard icon, both in mono-space
secondary-colour. Resets streamingRef after the result so the next
assistant text delta starts a fresh transcript entry instead of
appending to the previous turn.
Assisted-by: claude-code:claude-opus-4-7-1m [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* refactor(realtime): bound the Manage Mode tool-loop + preserve assistant tools
Fallout from a review pass on the Manage Mode patches:
- Bound the server-side agentic loop. triggerResponse used to recurse on
executedAssistantTool with no cap — a model that kept calling tools
would blow the goroutine stack. New maxAssistantToolTurns = 10 (mirrors
useChat.js's maxToolTurns). Public triggerResponse is now a thin shim
over triggerResponseAtTurn(toolTurn int); recursion increments the
counter and stops at the cap with an xlog.Warn.
- Preserve Manage Mode tools across client session.update. The handler
used to blindly overwrite session.Tools, so toggling a client MCP
server mid-session silently wiped the in-process admin tools. Session
now caches the original AssistantTools slice at session creation and
the session.update handler merges them back in (client names win on
collision — the client is explicit).
- strconv.ParseBool for the localai_assistant query param instead of
hand-rolled "1" || "true". Mirrors LocalAIAssistantFromMetadata.
- Talk.jsx: render both tool_call and tool_result on
response.output_item.done instead of splitting them across .added and
.done. The server's event pairing (added → done) stays correct; the
UI just doesn't need to inspect both phases of the same item. One
switch case instead of two, no behavioural change.
Out of scope (noted for follow-ups): extract a shared assistant-tools
helper between chat.go and realtime.go (duplication is small enough
that two parallel implementations stay readable for now), and an i18n
key for the Manage Mode helper text (Talk.jsx doesn't use i18n
anywhere else yet).
Assisted-by: claude-code:claude-opus-4-7-1m [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* ci(test-extra): wire liquid-audio backend smoke test
The backend ships test.py + a `make test` target and is listed in
backend-matrix.yml, so scripts/changed-backends.js already writes a
`liquid-audio=true|false` output when files under backend/python/liquid-audio/
change. The workflow just wasn't reading it.
- Expose the `liquid-audio` output on the detect-changes job
- Add a tests-liquid-audio job that runs `make` + `make test` in
backend/python/liquid-audio, gated on the per-backend detect flag
The smoke covers Health() and LoadModel(mode:finetune); fine-tune mode
short-circuits before any HuggingFace download (backend.py:192), so the
job needs neither weights nor a GPU. The full-inference path remains
gated on LIQUID_AUDIO_MODEL_ID, which CI doesn't set.
The four new Go test files (core/gallery/importers/liquid-audio_test.go,
core/http/endpoints/openai/realtime_gate_test.go,
core/http/routes/ui_pipeline_models_test.go, pkg/functions/parse_lfm2_test.go)
are already picked up by the existing test.yml workflow via `make test` →
`ginkgo -r ./pkg/... ./core/...`; their packages all carry RunSpecs entries.
Assisted-by: Claude:claude-opus-4-7
Signed-off-by: Richard Palethorpe <io@richiejp.com>
---------
Signed-off-by: Richard Palethorpe <io@richiejp.com>
|
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Richard Palethorpe
|
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
|
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
|
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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|
Ettore Di Giacinto
|
b1a99436c7 |
feat(branding): admin-configurable instance name, tagline, and assets (#9635)
Adds a whitelabeling feature so an operator can replace the LocalAI
instance name, tagline, square logo, horizontal logo, and favicon from
the admin Settings page. Defaults fall back to the bundled assets so
existing installs are unaffected.
The public GET /api/branding endpoint is reachable pre-auth so the
login screen can render the configured branding before sign-in.
Mutating routes (POST/DELETE /api/branding/asset/:kind) remain
admin-only. Text fields (instance_name, instance_tagline) ride the
existing /api/settings flow; binary assets get a dedicated multipart
upload route that persists files under DynamicConfigsDir/branding/.
To prevent the Settings page's stale local state from clobbering an
upload on save, UpdateSettingsEndpoint preserves whatever the on-disk
asset filename fields are regardless of the body — /api/branding/asset/*
are the sole writers for those fields.
The MCP catalog gains get_branding and set_branding tools (text fields
only; file upload stays UI-only) plus a configure_branding skill prompt.
While wiring this up, the same restart-loss class of bug surfaced for
several existing fields whose RuntimeSettings entries were never read
by the startup loader. Fix loadRuntimeSettingsFromFile() to load:
- branding (instance_name, instance_tagline, *_file basenames)
- auto_upgrade_backends, prefer_development_backends
- localai_assistant_enabled
- open_responses_store_ttl
- the 7 existing AgentPool fields (enabled, default/embedding model,
chunking sizes, enable_logs, collection_db_path)
Also exposes 3 new AgentPool runtime settings (vector_engine,
database_url, agent_hub_url) via /api/settings + the Settings UI, with
the same load-on-startup wiring. The file watcher's manual-edit path
is intentionally not changed — the in-process API endpoints already
update appConfig directly, so the watcher is redundant for supported
flows and a separate refactor for everything else.
15 TDD specs cover the loader behaviour (1 branding + 11 adjacent + 3
new agent-pool); 2 specs cover the persistence helpers and the
clobber-prevention contract.
Assisted-by: claude-code:claude-opus-4-7
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>
|
||
|
Ettore Di Giacinto
|
bcef72b9c1 |
feat: localai assistant chat modality (#9602)
* fix(tests): inline model_test fixtures after tests/models_fixtures removal The previous reorg removed tests/models_fixtures/ but core/config/model_test.go still read CONFIG_FILE/MODELS_PATH env vars pointing into that directory, so `make test` failed with "open : no such file or directory" on the readConfigFile spec (the suite ran with --fail-fast and bailed before openresponses_test). Inline the YAMLs (config/embeddings/grpc/rwkv/whisper) directly into the test file, materialise them into a per-test tmpdir via BeforeEach, and drop the env-var lookups. The test no longer depends on Makefile plumbing. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: claude-code:claude-opus-4-7 [Edit] [Write] [Bash] * refactor(modeladmin): extract model-admin helpers into a service package Lift the bodies of EditModelEndpoint, PatchConfigEndpoint, ToggleStateModelEndpoint, TogglePinnedModelEndpoint and VRAMEstimateEndpoint into core/services/modeladmin so the same logic can be called by non-HTTP clients (notably the in-process MCP server that backs the LocalAI Assistant chat modality, landing in a follow-up commit). The HTTP handlers shrink to thin shells that parse echo inputs, call the matching helper, map typed errors (ErrNotFound, ErrConflict, ErrPathNotTrusted, ErrBadAction, ...) to the existing HTTP status codes, and render the existing response shapes. No REST-surface behaviour change; the existing localai endpoint tests cover the regression net. Adds focused unit tests for each helper against tmp-dir-backed ModelConfigLoader fixtures (deep-merge patch, rename + conflict, path separator guard, toggle/pin enable/disable, sync callback). Assisted-by: Claude:claude-opus-4-7 [Read] [Edit] [Write] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(assistant): LocalAI Assistant chat modality with in-memory MCP server Adds a chat modality, admin-only, that wires the chat session to an in-memory MCP server exposing LocalAI's own admin/management surface as tools. An admin can install models, manage backends, edit configs and check status by chatting; the LLM calls tools like gallery_search, install_model, import_model_uri, list_installed_models, edit_model_config and surfaces the results. Same Go package powers two modes: pkg/mcp/localaitools/ NewServer(client, opts) builds an MCP server that registers the 19-tool admin catalog. The LocalAIClient interface has two impls: - inproc.Client — calls services directly (no HTTP loopback, no synthetic admin API key). Used in-process by the chat handler. - httpapi.Client — calls the LocalAI REST API. Used by the new `local-ai mcp-server --target=…` subcommand to control a remote LocalAI from a stdio MCP host. Tools and their embedded skill prompts are agnostic to which client backs them. Skill prompts are markdown files under prompts/, embedded via go:embed and assembled into the system prompt at server init. Wiring: - core/http/endpoints/mcp/localai_assistant.go — process-wide holder that spins up the in-memory MCP server once at Application start using paired net.Pipe transports, then reuses LocalToolExecutor (no fork) for every chat request that opts in. - core/http/endpoints/openai/chat.go — small branch ahead of the existing MCP block: when metadata.localai_assistant=true, defense-in-depth admin check + executor swap + system-prompt injection. All downstream tool dispatch is unchanged. - core/http/auth/{permissions,features}.go — adds FeatureLocalAIAssistant; gating happens at the chat handler entry plus admin-only `/api/settings`. - core/cli/{run.go,cli.go,mcp_server.go} — LOCALAI_DISABLE_ASSISTANT flag (runtime-toggleable via Settings, no restart), plus `local-ai mcp-server` stdio subcommand. - core/config/runtime_settings.go — `localai_assistant_enabled` runtime setting; the chat handler reads `DisableLocalAIAssistant` live at request entry. UI: - Home.jsx — prominent self-explanatory CTA card on first run ("Manage LocalAI by chatting"); collapses to a compact "Manage by chat" button in the quick-links row once used, persisted via localStorage. - Chat.jsx — admin-only "Manage" toggle in the chat header, "Manage mode" badge, dedicated empty-state copy, starter chips. - Settings.jsx — "LocalAI Assistant" section with the runtime enable toggle. - useChat.js — `localaiAssistant` flag on the chat schema; injects `metadata.localai_assistant=true` on requests when active. Distributed mode: the in-memory MCP server lives only on the head node; inproc.Client wraps already-distributed-aware services so installs propagate to workers via the existing GalleryService machinery. Documentation: `.agents/localai-assistant-mcp.md` is the contributor contract — when adding an admin REST endpoint, also add a LocalAIClient method, an inproc + httpapi impl, a tool registration, and a skill prompt update; the AGENTS.md index links to it. Out of scope (follow-ups): per-tool RBAC granularity for non-admin read-only access; streaming mcp_tool_progress for long installs; React Vitest rig for the UI changes. Assisted-by: Claude:claude-opus-4-7 [Read] [Edit] [Write] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(assistant): extract tool/capability/MiB/server-name constants The MCP tool surface, capability tag set, server-name default, and the chat-handler metadata key were repeated as bare string literals across seven files. Renaming any one required hand-editing every call site and risked code/test/prompt drift. This pulls them into typed constants: - pkg/mcp/localaitools/tools.go — Tool* constants for the 19 MCP tools, plus DefaultServerName. - pkg/mcp/localaitools/capability.go — typed Capability + constants for the capability tag set the LLM passes to list_installed_models. The type rides through LocalAIClient.ListInstalledModels and replaces the triplet of "embed"/"embedding"/"embeddings" with the single CapabilityEmbeddings. - pkg/mcp/localaitools/inproc/client.go — bytesPerMiB constant for the VRAMEstimate byte→MB conversion. - core/http/endpoints/mcp/tools.go — MetadataKeyLocalAIAssistant for the "localai_assistant" request-metadata key consumed by the chat handler. Tool registrations, the test catalog, the dispatch table, the validation fixtures, and the fake/stub clients all reference the constants. The embedded skill prompts under prompts/ keep their bare strings (go:embed markdown can't import Go constants); the existing TestPromptsContain SafetyAnchors guards the alignment. No behaviour change. All tests pass with -race. Assisted-by: Claude:claude-opus-4-7 [Read] [Edit] [Write] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(modeladmin): typed Action for ToggleState/TogglePinned The toggle/pin verbs were bare strings everywhere — handler signatures, service implementations, MCP tool args, the fake/stub clients, the inproc and httpapi LocalAIClient impls, plus 4 test files. A typo in any caller silently fell through to the runtime "must be 'enable' or 'disable'" check. Introduce core/services/modeladmin.Action (string alias) with ActionEnable, ActionDisable, ActionPin, ActionUnpin and a small Valid helper. The compiler now catches mismatches at every boundary; renames ripple through one source of truth. LocalAIClient.ToggleModelState/Pinned signatures change to take modeladmin.Action. The package is brand-new and unreleased so this is a free public-API tightening. Assisted-by: Claude:claude-opus-4-7 [Read] [Edit] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(assistant): respect ctx cancellation on gallery channel sends InstallModel, DeleteModel, ImportModelURI, InstallBackend and UpgradeBackend all pushed onto galleryop channels with bare sends. If the worker was paused or the buffer full, the chat-handler goroutine blocked forever — the LLM kept polling and the request leaked. Wrap the five sends in a sendModelOp/sendBackendOp helper that selects on ctx.Done() so a cancelled chat completion surfaces context.Canceled back to the LLM instead of hanging. Adds inproc/client_test.go with a pre-cancelled-ctx regression test on InstallModel; the helpers are shared so the same guarantee covers the other four call sites. Assisted-by: Claude:claude-opus-4-7 [Edit] [Write] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(assistant): graceful shutdown for in-memory holder and stdio CLI Two related leaks: - Application.start() built the LocalAIAssistantHolder but never wired Close() into the graceful-termination chain — the in-memory MCP transport pair stayed alive until process exit, and the goroutines behind net.Pipe() didn't drain. Hook into the existing signals.RegisterGracefulTerminationHandler chain (same pattern as core/http/endpoints/mcp/tools.go:770). - core/cli/mcp_server.go ran srv.Run with context.Background(); a Ctrl-C from the host (Claude Desktop, mcphost, npx inspector) or a SIGTERM from process supervision left the stdio loop reading from a closed pipe. Switch to signal.NotifyContext to surface the signal through ctx and let srv.Run drain. Assisted-by: Claude:claude-opus-4-7 [Read] [Edit] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(assistant): typed HTTPError + propagate prompt walk error The httpapi client detected "no such job" by substring-matching on the error string ("404", "could not find") — brittle to status-code formatting changes and to LocalAI fixing /models/jobs/:uuid to return a proper 404. Replace with a typed *HTTPError whose Is() method honours errors.Is(err, ErrHTTPNotFound). The 500-with-"could not find" branch stays as a transitional fallback documented in Is(). Same change covers ListNodes' 404 fallback for the /api/nodes endpoint. Adds httptest tests for both 404 and the legacy 500 path, plus a direct errors.Is exposure test so external callers (the standalone stdio CLI host) can match without re-string-parsing. Also tightens prompts.SystemPrompt: panic when fs.WalkDir on the embedded FS fails. The only realistic cause is a build-time //go:embed misconfiguration; serving an empty system prompt to the LLM is much worse than crashing init. TestSystemPromptIncludesAllEmbeddedFiles catches regressions in CI. Assisted-by: Claude:claude-opus-4-7 [Edit] [Write] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(modeladmin): atomic writes for model config files The five sites that wrote model YAML used os.WriteFile, which opens with O_TRUNC|O_WRONLY|O_CREATE. A crash mid-write left the destination truncated and the model unloadable until manual repair. Pre-existing behaviour inherited from the original endpoint handlers — fix once now that there's a single helper. Adds writeFileAtomic: writes to a sibling temp file, chmods, syncs via Close(), then os.Rename. Same-directory temp keeps the rename atomic on the same filesystem; cleanup runs on every error path so stray temps don't accumulate. No new dependency. Applied to: - ConfigService.PatchConfig - ConfigService.EditYAML (both rename and in-place branches) - mutateYAMLBoolFlag (drives ToggleState + TogglePinned) atomic_test.go covers the happy path plus a read-only-dir failure case that asserts the original file is preserved (skipped on Windows where the chmod trick is POSIX-specific). Assisted-by: Claude:claude-opus-4-7 [Edit] [Write] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore(assistant): prune dead code, mark stub, document conventions Three small cleanups landing together: - Drop the unused errNotImplemented sentinel from inproc/client.go. All five methods that used to return it are wired to modeladmin helpers since the Phase B commit; the package var is dead. - Annotate httpapi.Client.GetModelConfig as a known stub. LocalAI's /models/edit/:name returns rendered HTML, not JSON, so the standalone CLI's get_model_config tool surfaces a clear error to the LLM. A future JSON-only /api/models/config-yaml/:name endpoint is tracked in the agent contract; FIXME points at it. - Extend `.agents/localai-assistant-mcp.md` with a "Code conventions" section that documents the audit-driven rules: tool/Capability/Action constants, errors.Is over substring matching, ctx-aware channel sends, atomic writes, and graceful shutdown. Refresh the file map so it lists tools.go and capability.go and drops the removed tools_bootstrap.go. The tools_models.go diff is a comment-only change explaining why the ModelName empty-string check stays at the tool layer (consistency across LocalAIClient implementations, since the SDK schema validator only enforces presence, not non-empty). Assisted-by: Claude:claude-opus-4-7 [Read] [Edit] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(assistant): convert test files to ginkgo + gomega The repo convention (per core/http/endpoints/localai/*_test.go, core/gallery/**, etc.) is Ginkgo v2 with Gomega assertions. The tests I introduced for the assistant feature used vanilla testing.T, which made them stand out and stripped the BDD structure the rest of the suite relies on. Convert every test file in the assistant scope to Ginkgo: pkg/mcp/localaitools/ dto_test.go — Describe("DTOs round-trip through JSON") prompts_test.go — Describe("SystemPrompt assembler") server_test.go — Describe("Server tool catalog"), Describe("Tool dispatch"), Describe("Tool error surfacing"), Describe("Argument validation"), Describe("Concurrent tool calls") parity_test.go — Describe("LocalAIClient parity"), hosts the suite's single RunSpecs (the file is package localaitools_test so it can import httpapi without an import cycle; Ginkgo aggregates Describes from both the internal and external test packages into one run). httpapi/client_test.go — Describe("httpapi.Client against the LocalAI admin REST surface"), Describe("ErrHTTPNotFound"), Describe("Bearer token") inproc/client_test.go — Describe("inproc.Client cancellation") core/services/modeladmin/ config_test.go — Describe("ConfigService") with sub-Describes for GetConfig, PatchConfig, EditYAML state_test.go — Describe("ConfigService.ToggleState") pinned_test.go — Describe("ConfigService.TogglePinned") atomic_test.go — Describe("writeFileAtomic") core/http/endpoints/mcp/ localai_assistant_test.go — Describe("LocalAIAssistantHolder") Each package gets a `*_suite_test.go` with the standard `RegisterFailHandler(Fail) + RunSpecs(t, "...")` boilerplate. Helpers that previously took *testing.T (newTestService, writeModelYAML, readMap, sortedStrings, sortGalleries, etc.) drop the *T receiver and use Gomega Expectations directly. tmp dirs come from GinkgoT().TempDir(). No semantic change to test coverage — every original assertion has a direct Gomega counterpart. All suites pass with -race. Assisted-by: Claude:claude-opus-4-7 [Read] [Edit] [Write] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test+docs(assistant): drift detector for Tool ↔ REST route mapping Honest gap from the audit: the parity_test.go suite only checks four methods, and uses the same httpapi.Client for both sides — it asserts stability of the DTO shapes, not equivalence between in-process and HTTP. If a contributor adds an admin REST endpoint without an MCP tool, or a tool without a matching httpapi route, both surfaces silently diverge. Add a coverage test plus stronger docs: - pkg/mcp/localaitools/coverage_test.go introduces a hand-maintained toolToHTTPRoute map: every Tool* constant must list the REST endpoint the httpapi.Client hits (or "(none)" with a documented reason). Two Ginkgo specs assert the map and the published catalog stay in sync — one fails when a Tool is added without a route entry, the other fails when a route entry references a tool that no longer exists. Verified by removing the ToolDeleteModel entry locally; the test fired with a clear message pointing the contributor at the file. Deliberate non-test: we don't enumerate live admin REST routes from here. Walking the route registry requires booting Application; parsing core/http/routes/localai.go is brittle. The "new admin REST endpoint → MCP tool" direction stays a PR checklist item — see below. - AGENTS.md gets a new Quick Reference bullet that calls out the rule and points at the test by name. - .agents/api-endpoints-and-auth.md tightens the existing "Companion: MCP admin tool surface" subsection from "if useful, consider..." to "MUST be considered, with three concrete outcomes (tool added, deliberately skipped with documented reason, or forgot — which breaks the contract)". Adds a checklist item at the bottom of the file's authoritative checklist. Assisted-by: Claude:claude-opus-4-7 [Read] [Edit] [Write] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(assistant): drop duplicate DTOs, surface canonical types Audit feedback: localaitools/dto.go reinvented several types that already existed in the codebase. Replace the duplicates with the canonical types so the LLM-visible wire format stays aligned with the rest of LocalAI by construction (no parallel structs to keep in sync). Removed (and the canonical type now used by the LocalAIClient interface): localaitools.Gallery → config.Gallery localaitools.GalleryModelHit → gallery.Metadata localaitools.VRAMEstimate → vram.EstimateResult Tightened scope: localaitools.Backend → kept, but reduced to {Name, Installed}. ListKnownBackends now returns []schema.KnownBackend (the canonical type already used by REST /backends/known). Kept with documented rationale: localaitools.JobStatus — galleryop.OpStatus has Error error which marshals to "{}". JobStatus is the JSON-friendly mirror. localaitools.Node — nodes.BackendNode carries gorm internals + token hash; we expose only the LLM-relevant fields. ImportModelURIRequest/Response — schema.ImportModelRequest and GalleryResponse are wire-shaped, mine are LLM-shaped (BackendPreference flat, AmbiguousBackend exposed). Side wins: - Drop bytesPerMiB; vram.EstimateResult already carries human-readable display strings (size_display, vram_display) the LLM uses directly. - Drop the handler-private vramEstimateRequest in core/http/endpoints/localai/vram.go and bind directly into modeladmin.VRAMRequest (now JSON-tagged). Both clients pass through these types now where possible (e.g. ListGalleries in inproc.Client is a one-liner returning AppConfig.Galleries; httpapi.Client.GallerySearch decodes straight into []gallery.Metadata). All tests green with -race. Assisted-by: Claude:claude-opus-4-7 [Read] [Edit] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(assistant): extract REST route paths into named constants httpapi.Client had 18 bare-string path sites scattered across methods. Pull them into pkg/mcp/localaitools/httpapi/routes.go: static paths as package-private constants, dynamic paths as small builders that handle url.PathEscape on segment values. No behaviour change. Drops the now-unused net/url import from client.go since path escaping moved into routes.go alongside the path it applies to. Local-only by design: the server-side registrations in core/http/routes/localai.go remain bare strings. Sharing constants across the pkg/ ↔ core/ boundary would invert the layering today; the existing Tool↔REST drift-detector in coverage_test.go is the safety net for that direction. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-7 [Claude Code] * docs(assistant): align with shipped UI and dropped bootstrap env vars The LocalAI Assistant doc still described the older iteration: - The in-chat toggle was renamed from "Admin" to "Manage" (the badge is now "Manage mode" and the home page exposes a "Manage by chat" CTA). - LOCALAI_ASSISTANT_BOOTSTRAP_MODEL / --localai-assistant-bootstrap-model and the bootstrap_default_model tool were removed — admins pick a model from the existing selector instead, no env-var configuration required. - The shipped tool catalog includes import_model_uri but didn't appear in the doc; bootstrap_default_model appeared but no longer exists. - The Settings → LocalAI Assistant runtime toggle wasn't mentioned as the preferred way to disable without restart. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> 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
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142919fc79 |
fix(tests): inline model_test fixtures after tests/models_fixtures removal
The previous reorg removed tests/models_fixtures/ but core/config/model_test.go still read CONFIG_FILE/MODELS_PATH env vars pointing into that directory, so `make test` failed with "open : no such file or directory" on the readConfigFile spec (the suite ran with --fail-fast and bailed before openresponses_test). Inline the YAMLs (config/embeddings/grpc/rwkv/whisper) directly into the test file, materialise them into a per-test tmpdir via BeforeEach, and drop the env-var lookups. The test no longer depends on Makefile plumbing. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: claude-code:claude-opus-4-7 [Edit] [Write] [Bash] |
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Richard Palethorpe
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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
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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]
|
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leinasi2014
|
d18d434bb2 |
Respect explicit reasoning config during GGUF thinking probe (#9463)
Signed-off-by: leinasi2014 <leinasi2014@gmail.com> Co-authored-by: Ettore Di Giacinto <mudler@users.noreply.github.com> |
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LocalAI [bot]
|
315b634a91 |
feat: improve CLI error messages with actionable guidance (#8880)
- transcript.go: Model not found error now suggests available models commands - util.go: GGUF error explains format and how to get models - worker_p2p.go: Token error explains purpose and how to obtain one - run.go: Startup failure includes troubleshooting steps and docs link - model_config_loader.go: Config validation errors include file path and guidance Refs: H2 - UX Review Issue Signed-off-by: localai-bot <localai-bot@noreply.github.com> Co-authored-by: localai-bot <localai-bot@noreply.github.com> Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> |
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|
leinasi2014
|
486b5e25a3 |
fix(config): ignore yaml backup files in model loader (#9443)
Only load files whose real extension is .yaml or .yml so backup files like model.yaml.bak do not override active configs. Add a regression test covering plain and timestamped backup files. Assisted-by: Codex:gpt-5.4 docker Signed-off-by: leinasi2014 <leinasi2014@gmail.com> |
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|
Ettore Di Giacinto
|
7809c5f5d0 |
fix(vision): propagate mtmd media marker from backend via ModelMetadata (#9412)
Upstream llama.cpp (PR #21962) switched the server-side mtmd media marker to a random per-server string and removed the legacy "<__media__>" backward-compat replacement in mtmd_tokenizer. The Go layer still emitted the hardcoded "<__media__>", so on the non-tokenizer-template path the prompt arrived with a marker mtmd did not recognize and tokenization failed with "number of bitmaps (1) does not match number of markers (0)". Report the active media marker via ModelMetadataResponse.media_marker and substitute the sentinel "<__media__>" with it right before the gRPC call, after the backend has been loaded and probed. Also skip the Go-side multimodal templating entirely when UseTokenizerTemplate is true — llama.cpp's oaicompat_chat_params_parse already injects its own marker and StringContent is unused in that path. Backends that do not expose the field keep the legacy "<__media__>" behavior. |
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|
github-actions[bot]
|
48e87db400 |
chore: bump inference defaults from unsloth (#9396)
Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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|
Ettore Di Giacinto
|
d67623230f |
feat(vllm): parity with llama.cpp backend (#9328)
* fix(schema): serialize ToolCallID and Reasoning in Messages.ToProto
The ToProto conversion was dropping tool_call_id and reasoning_content
even though both proto and Go fields existed, breaking multi-turn tool
calling and reasoning passthrough to backends.
* refactor(config): introduce backend hook system and migrate llama-cpp defaults
Adds RegisterBackendHook/runBackendHooks so each backend can register
default-filling functions that run during ModelConfig.SetDefaults().
Migrates the existing GGUF guessing logic into hooks_llamacpp.go,
registered for both 'llama-cpp' and the empty backend (auto-detect).
Removes the old guesser.go shim.
* feat(config): add vLLM parser defaults hook and importer auto-detection
Introduces parser_defaults.json mapping model families to vLLM
tool_parser/reasoning_parser names, with longest-pattern-first matching.
The vllmDefaults hook auto-fills tool_parser and reasoning_parser
options at load time for known families, while the VLLMImporter writes
the same values into generated YAML so users can review and edit them.
Adds tests covering MatchParserDefaults, hook registration via
SetDefaults, and the user-override behavior.
* feat(vllm): wire native tool/reasoning parsers + chat deltas + logprobs
- Use vLLM's ToolParserManager/ReasoningParserManager to extract structured
output (tool calls, reasoning content) instead of reimplementing parsing
- Convert proto Messages to dicts and pass tools to apply_chat_template
- Emit ChatDelta with content/reasoning_content/tool_calls in Reply
- Extract prompt_tokens, completion_tokens, and logprobs from output
- Replace boolean GuidedDecoding with proper GuidedDecodingParams from Grammar
- Add TokenizeString and Free RPC methods
- Fix missing `time` import used by load_video()
* feat(vllm): CPU support + shared utils + vllm-omni feature parity
- Split vllm install per acceleration: move generic `vllm` out of
requirements-after.txt into per-profile after files (cublas12, hipblas,
intel) and add CPU wheel URL for cpu-after.txt
- requirements-cpu.txt now pulls torch==2.7.0+cpu from PyTorch CPU index
- backend/index.yaml: register cpu-vllm / cpu-vllm-development variants
- New backend/python/common/vllm_utils.py: shared parse_options,
messages_to_dicts, setup_parsers helpers (used by both vllm backends)
- vllm-omni: replace hardcoded chat template with tokenizer.apply_chat_template,
wire native parsers via shared utils, emit ChatDelta with token counts,
add TokenizeString and Free RPCs, detect CPU and set VLLM_TARGET_DEVICE
- Add test_cpu_inference.py: standalone script to validate CPU build with
a small model (Qwen2.5-0.5B-Instruct)
* fix(vllm): CPU build compatibility with vllm 0.14.1
Validated end-to-end on CPU with Qwen2.5-0.5B-Instruct (LoadModel, Predict,
TokenizeString, Free all working).
- requirements-cpu-after.txt: pin vllm to 0.14.1+cpu (pre-built wheel from
GitHub releases) for x86_64 and aarch64. vllm 0.14.1 is the newest CPU
wheel whose torch dependency resolves against published PyTorch builds
(torch==2.9.1+cpu). Later vllm CPU wheels currently require
torch==2.10.0+cpu which is only available on the PyTorch test channel
with incompatible torchvision.
- requirements-cpu.txt: bump torch to 2.9.1+cpu, add torchvision/torchaudio
so uv resolves them consistently from the PyTorch CPU index.
- install.sh: add --index-strategy=unsafe-best-match for CPU builds so uv
can mix the PyTorch index and PyPI for transitive deps (matches the
existing intel profile behaviour).
- backend.py LoadModel: vllm >= 0.14 removed AsyncLLMEngine.get_model_config
so the old code path errored out with AttributeError on model load.
Switch to the new get_tokenizer()/tokenizer accessor with a fallback
to building the tokenizer directly from request.Model.
* fix(vllm): tool parser constructor compat + e2e tool calling test
Concrete vLLM tool parsers override the abstract base's __init__ and
drop the tools kwarg (e.g. Hermes2ProToolParser only takes tokenizer).
Instantiating with tools= raised TypeError which was silently caught,
leaving chat_deltas.tool_calls empty.
Retry the constructor without the tools kwarg on TypeError — tools
aren't required by these parsers since extract_tool_calls finds tool
syntax in the raw model output directly.
Validated with Qwen/Qwen2.5-0.5B-Instruct + hermes parser on CPU:
the backend correctly returns ToolCallDelta{name='get_weather',
arguments='{"location": "Paris, France"}'} in ChatDelta.
test_tool_calls.py is a standalone smoke test that spawns the gRPC
backend, sends a chat completion with tools, and asserts the response
contains a structured tool call.
* ci(backend): build cpu-vllm container image
Add the cpu-vllm variant to the backend container build matrix so the
image registered in backend/index.yaml (cpu-vllm / cpu-vllm-development)
is actually produced by CI.
Follows the same pattern as the other CPU python backends
(cpu-diffusers, cpu-chatterbox, etc.) with build-type='' and no CUDA.
backend_pr.yml auto-picks this up via its matrix filter from backend.yml.
* test(e2e-backends): add tools capability + HF model name support
Extends tests/e2e-backends to cover backends that:
- Resolve HuggingFace model ids natively (vllm, vllm-omni) instead of
loading a local file: BACKEND_TEST_MODEL_NAME is passed verbatim as
ModelOptions.Model with no download/ModelFile.
- Parse tool calls into ChatDelta.tool_calls: new "tools" capability
sends a Predict with a get_weather function definition and asserts
the Reply contains a matching ToolCallDelta. Uses UseTokenizerTemplate
with OpenAI-style Messages so the backend can wire tools into the
model's chat template.
- Need backend-specific Options[]: BACKEND_TEST_OPTIONS lets a test set
e.g. "tool_parser:hermes,reasoning_parser:qwen3" at LoadModel time.
Adds make target test-extra-backend-vllm that:
- docker-build-vllm
- loads Qwen/Qwen2.5-0.5B-Instruct
- runs health,load,predict,stream,tools with tool_parser:hermes
Drops backend/python/vllm/test_{cpu_inference,tool_calls}.py — those
standalone scripts were scaffolding used while bringing up the Python
backend; the e2e-backends harness now covers the same ground uniformly
alongside llama-cpp and ik-llama-cpp.
* ci(test-extra): run vllm e2e tests on CPU
Adds tests-vllm-grpc to the test-extra workflow, mirroring the
llama-cpp and ik-llama-cpp gRPC jobs. Triggers when files under
backend/python/vllm/ change (or on run-all), builds the local-ai
vllm container image, and runs the tests/e2e-backends harness with
BACKEND_TEST_MODEL_NAME=Qwen/Qwen2.5-0.5B-Instruct, tool_parser:hermes,
and the tools capability enabled.
Uses ubuntu-latest (no GPU) — vllm runs on CPU via the cpu-vllm
wheel we pinned in requirements-cpu-after.txt. Frees disk space
before the build since the docker image + torch + vllm wheel is
sizeable.
* fix(vllm): build from source on CI to avoid SIGILL on prebuilt wheel
The prebuilt vllm 0.14.1+cpu wheel from GitHub releases is compiled with
SIMD instructions (AVX-512 VNNI/BF16 or AMX-BF16) that not every CPU
supports. GitHub Actions ubuntu-latest runners SIGILL when vllm spawns
the model_executor.models.registry subprocess for introspection, so
LoadModel never reaches the actual inference path.
- install.sh: when FROM_SOURCE=true on a CPU build, temporarily hide
requirements-cpu-after.txt so installRequirements installs the base
deps + torch CPU without pulling the prebuilt wheel, then clone vllm
and compile it with VLLM_TARGET_DEVICE=cpu. The resulting binaries
target the host's actual CPU.
- backend/Dockerfile.python: accept a FROM_SOURCE build-arg and expose
it as an ENV so install.sh sees it during `make`.
- Makefile docker-build-backend: forward FROM_SOURCE as --build-arg
when set, so backends that need source builds can opt in.
- Makefile test-extra-backend-vllm: call docker-build-vllm via a
recursive $(MAKE) invocation so FROM_SOURCE flows through.
- .github/workflows/test-extra.yml: set FROM_SOURCE=true on the
tests-vllm-grpc job. Slower but reliable — the prebuilt wheel only
works on hosts that share the build-time SIMD baseline.
Answers 'did you test locally?': yes, end-to-end on my local machine
with the prebuilt wheel (CPU supports AVX-512 VNNI). The CI runner CPU
gap was not covered locally — this commit plugs that gap.
* ci(vllm): use bigger-runner instead of source build
The prebuilt vllm 0.14.1+cpu wheel requires SIMD instructions (AVX-512
VNNI/BF16) that stock ubuntu-latest GitHub runners don't support —
vllm.model_executor.models.registry SIGILLs on import during LoadModel.
Source compilation works but takes 30-40 minutes per CI run, which is
too slow for an e2e smoke test. Instead, switch tests-vllm-grpc to the
bigger-runner self-hosted label (already used by backend.yml for the
llama-cpp CUDA build) — that hardware has the required SIMD baseline
and the prebuilt wheel runs cleanly.
FROM_SOURCE=true is kept as an opt-in escape hatch:
- install.sh still has the CPU source-build path for hosts that need it
- backend/Dockerfile.python still declares the ARG + ENV
- Makefile docker-build-backend still forwards the build-arg when set
Default CI path uses the fast prebuilt wheel; source build can be
re-enabled by exporting FROM_SOURCE=true in the environment.
* ci(vllm): install make + build deps on bigger-runner
bigger-runner is a bare self-hosted runner used by backend.yml for
docker image builds — it has docker but not the usual ubuntu-latest
toolchain. The make-based test target needs make, build-essential
(cgo in 'go test'), and curl/unzip (the Makefile protoc target
downloads protoc from github releases).
protoc-gen-go and protoc-gen-go-grpc come via 'go install' in the
install-go-tools target, which setup-go makes possible.
* ci(vllm): install libnuma1 + libgomp1 on bigger-runner
The vllm 0.14.1+cpu wheel ships a _C C++ extension that dlopens
libnuma.so.1 at import time. When the runner host doesn't have it,
the extension silently fails to register its torch ops, so
EngineCore crashes on init_device with:
AttributeError: '_OpNamespace' '_C_utils' object has no attribute
'init_cpu_threads_env'
Also add libgomp1 (OpenMP runtime, used by torch CPU kernels) to be
safe on stripped-down runners.
* feat(vllm): bundle libnuma/libgomp via package.sh
The vllm CPU wheel ships a _C extension that dlopens libnuma.so.1 at
import time; torch's CPU kernels in turn use libgomp.so.1 (OpenMP).
Without these on the host, vllm._C silently fails to register its
torch ops and EngineCore crashes with:
AttributeError: '_OpNamespace' '_C_utils' object has no attribute
'init_cpu_threads_env'
Rather than asking every user to install libnuma1/libgomp1 on their
host (or every LocalAI base image to ship them), bundle them into
the backend image itself — same pattern fish-speech and the GPU libs
already use. libbackend.sh adds ${EDIR}/lib to LD_LIBRARY_PATH at
run time so the bundled copies are picked up automatically.
- backend/python/vllm/package.sh (new): copies libnuma.so.1 and
libgomp.so.1 from the builder's multilib paths into ${BACKEND}/lib,
preserving soname symlinks. Runs during Dockerfile.python's
'Run backend-specific packaging' step (which already invokes
package.sh if present).
- backend/Dockerfile.python: install libnuma1 + libgomp1 in the
builder stage so package.sh has something to copy (the Ubuntu
base image otherwise only has libgomp in the gcc dep chain).
- test-extra.yml: drop the workaround that installed these libs on
the runner host — with the backend image self-contained, the
runner no longer needs them, and the test now exercises the
packaging path end-to-end the way a production host would.
* ci(vllm): disable tests-vllm-grpc job (heterogeneous runners)
Both ubuntu-latest and bigger-runner have inconsistent CPU baselines:
some instances support the AVX-512 VNNI/BF16 instructions the prebuilt
vllm 0.14.1+cpu wheel was compiled with, others SIGILL on import of
vllm.model_executor.models.registry. The libnuma packaging fix doesn't
help when the wheel itself can't be loaded.
FROM_SOURCE=true compiles vllm against the actual host CPU and works
everywhere, but takes 30-50 minutes per run — too slow for a smoke
test on every PR.
Comment out the job for now. The test itself is intact and passes
locally; run it via 'make test-extra-backend-vllm' on a host with the
required SIMD baseline. Re-enable when:
- we have a self-hosted runner label with guaranteed AVX-512 VNNI/BF16, or
- vllm publishes a CPU wheel with a wider baseline, or
- we set up a docker layer cache that makes FROM_SOURCE acceptable
The detect-changes vllm output, the test harness changes (tests/
e2e-backends + tools cap), the make target (test-extra-backend-vllm),
the package.sh and the Dockerfile/install.sh plumbing all stay in
place.
|
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|
Ettore Di Giacinto
|
2865f0f8d3 |
feat(ux): backend management enhancement (#9325)
* feat: add PreferDevelopmentBackends setting, expose isMeta/isDevelopment in API - Add PreferDevelopmentBackends config field, CLI flag, runtime setting - Add IsDevelopment() method to GalleryBackend - Use AvailableBackendsUnfiltered in UI API to show all backends - Expose isMeta, isDevelopment, preferDevelopmentBackends in backend API response * feat: upgrade banner with Upgrade All button, detect pre-existing backends - Add upgrade banner on Backends page showing count and Upgrade All button - Fix upgrade detection for backends installed before version tracking: flag as upgradeable when gallery has a version but installed has none - Fix OCI digest check to flag backends with no stored digest as upgradeable |
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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 |
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|
Ettore Di Giacinto
|
5c35e85fe2 |
feat: allow to pin models and skip from reaping (#9309)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Leigh Phillips
|
062e0d0d00 |
feat: Add toggle mechanism to enable/disable models from loading on demand (#9304)
* feat: add toggle mechanism to enable/disable models from loading on demand Implements #9303 - Adds ability to disable models from being auto-loaded while keeping them in the collection. Backend changes: - Add Disabled field to ModelConfig struct with IsDisabled() getter - New ToggleModelEndpoint handler (PUT /models/toggle/:name/:action) - Request middleware returns 403 when disabled model is requested - Capabilities endpoint exposes disabled status Frontend changes: - Toggle switch in System > Models table Actions column - Visual indicators: dimmed row, red Disabled badge, muted icons - Tooltip describes toggle function on hover - Loading state while API call is in progress * fix: remove extra closing brace causing syntax error in request middleware * refactor: reorder Actions column - Stop button before toggle switch * refactor: migrate from toggle to toggle-state per PR review feedback |
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|
Ettore Di Giacinto
|
706cf5d43c |
feat(sam.cpp): add sam.cpp detection backend (#9288)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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|
Ettore Di Giacinto
|
85be4ff03c |
feat(api): add ollama compatibility (#9284)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Richard Palethorpe
|
9ac1bdc587 |
feat(ui): Interactive model config editor with autocomplete (#9149)
* feat(ui): Add dynamic model editor with autocomplete Signed-off-by: Richard Palethorpe <io@richiejp.com> * chore(docs): Add link to longformat installation video Signed-off-by: Richard Palethorpe <io@richiejp.com> --------- Signed-off-by: Richard Palethorpe <io@richiejp.com> |
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|
Richard Palethorpe
|
557d0f0f04 |
feat(api): Allow coding agents to interactively discover how to control and configure LocalAI (#9084)
Signed-off-by: Richard Palethorpe <io@richiejp.com> |
||
|
github-actions[bot]
|
57c0026715 |
chore: bump inference defaults from unsloth (#9219)
Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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|
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> |
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|
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> |
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|
Ettore Di Giacinto
|
031a36c995 |
feat: inferencing default, automatic tool parsing fallback and wire min_p (#9092)
* feat: wire min_p Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat: inferencing defaults Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore(refactor): re-use iterative parser Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore: generate automatically inference defaults from unsloth Instead of trying to re-invent the wheel and maintain here the inference defaults, prefer to consume unsloth ones, and contribute there as necessary. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore: apply defaults also to models installed via gallery Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore: be consistent and apply fallback to all endpoint 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
|
f7e8d9e791 |
feat(quantization): add quantization backend (#9096)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Ettore Di Giacinto
|
d9c1db2b87 |
feat: add (experimental) fine-tuning support with TRL (#9088)
* feat: add fine-tuning endpoint Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(experimental): add fine-tuning endpoint and TRL support This changeset defines new GRPC signatues for Fine tuning backends, and add TRL backend as initial fine-tuning engine. This implementation also supports exporting to GGUF and automatically importing it to LocalAI after fine-tuning. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * commit TRL backend, stop by killing process Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * move fine-tune to generic features Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * add evals, reorder menu Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Fix tests 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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cb63bdb9e4 |
feat(ui): Add model pipeline editor (#9070)
This creates a new model config page. Presently just allows configuring pipelines, but can be extending the future to other types of models. However pipelines are quite easy to create a form for and require editing to create. Signed-off-by: Richard Palethorpe <io@richiejp.com> |
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Ettore Di Giacinto
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c3174f9543 |
chore(deps): bump llama-cpp to 'a0bbcdd9b6b83eeeda6f1216088f42c33d464e38' (#9079)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Ettore Di Giacinto
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aea21951a2 |
feat: add users and authentication support (#9061)
* feat(ui): add users and authentication support Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat: allow the admin user to impersonificate users Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore: ui improvements, disable 'Users' button in navbar when no auth is configured Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat: add OIDC support Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix: gate models Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore: cache requests to optimize speed Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * small UI enhancements Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore(ui): style improvements Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix: cover other paths by auth Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore: separate local auth, refactor Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * security hardening, approval mode Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix: fix tests and expectations Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore: update localagi/localrecall 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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35d509d8e7 |
feat(ui): Per model backend logs and various fixes (#9028)
* feat(gallery): Switch to expandable box instead of pop-over and display model files Signed-off-by: Richard Palethorpe <io@richiejp.com> * feat(ui, backends): Add individual backend logging Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(ui): Set the context settings from the model config 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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a738f8b0e4 |
feat(backends): add ace-step.cpp (#8965)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Ettore Di Giacinto
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a026277ab9 |
feat(mlx-distributed): add new MLX-distributed backend (#8801)
* feat(mlx-distributed): add new MLX-distributed backend Add new MLX distributed backend with support for both TCP and RDMA for model sharding. This implementation ties in the discovery implementation already in place, and re-uses the same P2P mechanism for the TCP MLX-distributed inferencing. The Auto-parallel implementation is inspired by Exo's ones (who have been added to acknowledgement for the great work!) Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * expose a CLI to facilitate backend starting Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat: make manual rank0 configurable via model configs Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Add missing features from mlx backend Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Apply suggestion from @mudler Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com> |
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LocalAI [bot]
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d200401e86 |
feat: Add --data-path CLI flag for persistent data separation (#8888)
feat: add --data-path CLI flag for persistent data separation - Add LOCALAI_DATA_PATH environment variable and --data-path CLI flag - Default data path: /data (separate from configuration directory) - Automatic migration on startup: moves agent_tasks.json, agent_jobs.json, collections/, and assets/ from old config dir to new data path - Backward compatible: preserves old behavior if LOCALAI_DATA_PATH is not set - Agent state and job directories now use DataPath with proper fallback chain - Update documentation with new flag and docker-compose example This separates mutable persistent data (collectiondb, agents, assets, skills) from configuration files, enabling better volume mounting and data persistence in containerized deployments. Signed-off-by: localai-bot <localai-bot@noreply.github.com> Co-authored-by: localai-bot <localai-bot@noreply.github.com> |
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Ettore Di Giacinto
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b2f81bfa2e |
feat(functions): add peg-based parsing and allow backends to return tool calls directly (#8838)
* feat(functions): add peg-based parsing Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat: support returning toolcalls directly from backends Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore: do run PEG only if backend didn't send deltas 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
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ac48867b7d |
feat: add agentic management (#8820)
* feat: add standalone and agentic functionalities Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * expose agents via responses api Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
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ab315f2725 |
feat: Add LOCALAI_DISABLE_MCP environment variable to disable MCP support (#8816)
* feat: Add LOCALAI_DISABLE_MCP environment variable to disable MCP support - Added DisableMCP field to RunCMD struct in core/cli/run.go - Added LOCALAI_DISABLE_MCP environment variable support - Added DisableMCP field to ApplicationConfig struct - Added DisableMCP AppOption function - Updated MCP endpoint routing to check appConfig.DisableMCP - When LOCALAI_DISABLE_MCP is set to true/1/yes, MCP endpoints are not registered When set, all MCP functionality is disabled and appropriate error messages are returned to users. Use Cases: - Security-conscious deployments where MCP is not needed - Reducing attack surface - Compliance requirements that prohibit certain protocol support Environment variable: LOCALAI_DISABLE_MCP=true Signed-off-by: localai-bot <localai-bot@users.noreply.github.com> * docs: Add documentation for LOCALAI_DISABLE_MCP environment variable - Add section explaining how to disable MCP support using environment variable - Document use cases for disabling MCP - Provide examples for CLI and Docker usage Signed-off-by: localai-bot <localai-bot@users.noreply.github.com> --------- Signed-off-by: localai-bot <localai-bot@users.noreply.github.com> Co-authored-by: localai-bot <localai-bot@users.noreply.github.com> |
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LocalAI [bot]
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9fc77909e0 |
fix: Add vllm-omni backend to video generation model detection (#8659) (#8781)
fix: Add vllm-omni backend to video generation model detection - Include vllm-omni in the list of backends that support FLAG_VIDEO - This allows models like vllm-omni-wan2.2-t2v to appear in the video model selector UI - Fixes issue #8659 where video generation models using vllm-omni backend were not showing in the dropdown Co-authored-by: team-coding-agent-1 <team-coding-agent-1@localai.dev> |
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LocalAI [bot]
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6d182281cf |
fix: allow reranking models configured with known_usecases (#8681)
When a model is configured with 'known_usecases: [rerank]' in the YAML config, the reranking endpoint was not being matched because: 1. The GuessUsecases function only checked for backend == 'rerankers' 2. The syncKnownUsecasesFromString() was not being called when loading configs via yaml.Unmarshal in readModelConfigsFromFile This fix: 1. Updates GuessUsecases to also check if Reranking is explicitly set to true in the model config (in addition to checking backend type) 2. Adds syncKnownUsecasesFromString() calls after yaml.Unmarshal in readModelConfigsFromFile to ensure known_usecases are properly parsed Fixes #8658 Signed-off-by: localai-bot <localai-bot@users.noreply.github.com> Co-authored-by: localai-bot <localai-bot@users.noreply.github.com> |
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Andres
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e45d63c86e |
fix(cli): Fix watchdog running constantly and spamming logs (#8624)
* Fix watchdog running constantly and spamming logs Signed-off-by: Andres Smith <andressmithdev@pm.me> * Update docs Signed-off-by: Andres Smith <andressmithdev@pm.me> --------- Signed-off-by: Andres Smith <andressmithdev@pm.me> |
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Ettore Di Giacinto
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b471619ad9 |
chore(deps): bump cogito and add new options to the agent config (#8601)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Richard Palethorpe
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7270a98ce5 |
fix(realtime): Use user provided voice and allow pipeline models to have no backend (#8415)
* fix(realtime): Use the voice provided by the user or none at all Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(ui,config): Allow pipeline models to have no backend and use same validation in frontend 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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Ettore Di Giacinto
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53276d28e7 |
feat(musicgen): add ace-step and UI interface (#8396)
* feat(musicgen): add ace-step and UI interface Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Correctly handle model dir Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Drop auto-download Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Fixups Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Add to models, fixup UIs icons Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fixups Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Update docs Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * l4t13 is incompatbile Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * avoid pinning version for cuda12 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Drop l4t12 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
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800f749c7b |
fix: drop gguf VRAM estimation (now redundant) (#8325)
fix: drop gguf VRAM estimation Cleanup. This is now handled directly in llama.cpp, no need to estimate from Go. VRAM estimation in general is tricky, but llama.cpp ( |