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87 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
LocalAI [bot]
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7a4ca8f60d |
feat(backend): rfdetr-cpp native object detection + segmentation backend (#10028)
Adds a Go native gRPC backend that dlopens librfdetrcpp.so (built from
mudler/rf-detr.cpp at the pinned RFDETR_VERSION) via purego and exposes
the rfdetr.cpp inference pipeline through LocalAI's existing Detect RPC.
Supports all 5 RF-DETR detection variants (Nano/Small/Base/Medium/Large)
and 6 segmentation variants (SegNano/SegSmall/SegMedium/SegLarge/
SegXLarge/Seg2XLarge) with F32/F16/Q8_0/Q4_K quantizations. Pre-built
GGUFs ship at mudler/rfdetr-cpp-* on HuggingFace.
Detection returns Bbox + class_name + confidence; segmentation also
returns PNG-encoded per-detection masks via the rfdetr_capi accessor
functions (rfdetr_capi_get_detection_{class_id,box,score,class_name,
mask_png}).
End-to-end verified through POST /v1/detection: HTTP -> gRPC -> purego
dlopen -> rfdetr.cpp -> ggml -> response (9 detections on the detection
model, 21 detections + valid PNG masks on the seg-nano model against
the kitchen fixture).
Wiring:
- backend/go/rfdetr-cpp/{main.go,gorfdetrcpp.go,CMakeLists.txt,
Makefile,run.sh,package.sh,test.sh,.gitignore}
- Top-level Makefile: BACKEND_RFDETR_CPP, docker-build target,
.NOTPARALLEL, prepare-test-extra, test-extra
- backend/go/rfdetr-cpp/Makefile: `test` target invoked by test-extra
- .github/backend-matrix.yml: CPU + CUDA-12/13 + L4T CUDA-12/13
(arm64) + HIP + Vulkan (amd64 + arm64) + SYCL f32/f16
- backend/index.yaml: rfdetr-cpp meta anchor + latest/development
image entries for every matrix tag-suffix
- .github/workflows/bump_deps.yaml: RFDETR_VERSION pin tracking
(mudler/rf-detr.cpp branch main)
- gallery/index.yaml: 11 rfdetr-cpp-* entries (nano + 4 detection
variants + 6 seg variants), all backed by mudler/rfdetr-cpp-*
on HuggingFace with sha256 pinning on the F16 default
- core/gallery/importers/rfdetr.go: GGUF auto-routing for HF imports
(mudler/rfdetr-cpp-* repos route to rfdetr-cpp, Transformer-format
repos stay on the Python rfdetr backend; explicit preferences.backend
overrides both heuristics)
- core/gallery/importers/rfdetr_test.go: table-driven coverage of the
auto-routing + a live mudler/rfdetr-cpp-nano cross-check
scripts/changed-backends.js needs no change: the existing
Dockerfile.golang -> backend/go/${item.backend}/ branch already routes
the 9 rfdetr-cpp matrix entries to the correct backend path.
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
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LocalAI [bot]
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4aad97971c |
chore: ⬆️ Update ggml-org/llama.cpp to 35c9b1f39ebe5a7bb83986d64415a079218be78d (#9998)
* ⬆️ Update ggml-org/llama.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> * fix(llama-cpp): track upstream rename checkpoint_every_nt -> checkpoint_min_step Upstream llama.cpp renamed common_params::checkpoint_every_nt to checkpoint_min_step and changed its default from 8192 to 256. The semantics also shifted: it used to enforce a fixed checkpoint cadence during prefill, now it sets a minimum spacing between context checkpoints. Track the new field name in grpc-server.cpp and accept the old option names as backward- compatible aliases for users with existing configs. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: claude-code:claude-opus-4-7 --------- Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
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a891eedd08 |
fix(distributed): persist per-model load info so reconciler survives frontend restart (#9981)
* feat(distributed): add per-model ModelLoadInfo persistence
Adds a dedicated ModelLoadInfo table keyed by model name, decoupled from
the per-replica NodeModel rows. The reconciler can now recover model load
metadata after every NodeModel row has been removed (worker death,
eviction, MarkOffline reaping, frontend restart with stale heartbeats),
which is the read side of Bug-1 from the distributed mode bug hunt.
Registry exposes:
- UpsertModelLoadInfo: ON CONFLICT (model_name) update; last-write-wins,
matching the existing per-replica blob semantics under concurrent
multi-frontend dispatch.
- GetModelLoadInfo: read from the new table first; fall back to the
legacy NodeModel-blob scan for rows written before any frontend in
the cluster ran an UpsertModelLoadInfo (rolling-upgrade transition).
SetNodeModelLoadInfo (per-replica blob) is preserved for backward
compatibility and per-replica diagnostics; the dispatch-path hook in the
next commit calls both.
The new table joins the existing nodes AutoMigrate set under the same
schema-migration advisory lock.
Refs: Bug-1, docs/superpowers/specs/2026-05-24-distributed-mode-bug-hunt-findings.md
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-7[1m]
* fix(distributed): persist per-model load info on dispatch
scheduleAndLoad now writes the (backendType, ModelOptions blob) pair to
the new ModelLoadInfo table in addition to the existing per-replica
NodeModel.model_opts_blob field. The per-replica blob still works for
the hot path; the per-model row outlives every NodeModel row going away,
which is what unblocks the reconciler on the read side.
Both writes are best-effort with warn-level logging on failure: a write
miss here just means the reconciler may need a fresh inference request
to repopulate, which is the pre-fix behavior.
Concurrency: two frontends loading the same model at the same time both
fire UpsertModelLoadInfo; ON CONFLICT (model_name) makes the row
converge to whichever commits last. Matches the existing per-replica
blob semantics.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-7[1m]
* test(distributed): cover load info persistence and Bug-1 recovery
Adds Ginkgo specs that prove the persistence layer behaves correctly and
that the reconciler actually recovers from the frontend-restart scenario
that was failing in production:
registry_test.go:
- per-model row survives RemoveAllNodeModelReplicas (the bug repro)
- ON CONFLICT (model_name) updates backend type + blob, last-write-wins
- legacy NodeModel-blob fallback still works (rolling-upgrade transition)
- GetModelLoadInfo returns ErrRecordNotFound when both sources are empty
- UpsertModelLoadInfo rejects empty model names
reconciler_test.go:
- Bug-1 end-to-end: with min_replicas=2, no NodeModel rows, but a
ModelLoadInfo row present, one reconcile tick fires two scheduler
calls. Pre-fix this returned "no load info" and the scheduler never
got called until a fresh inference request arrived.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-7[1m]
* docs(distributed): note restart-safe reconciler behavior
Adds a bullet to the Replica Reconciler section explaining that per-model
load metadata is persisted across frontend restarts via the new
model_load_infos PostgreSQL table, so a rolling upgrade no longer needs a
fresh inference request per model before the reconciler can replace dead
replicas.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-7[1m]
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
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LocalAI [bot]
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06e777b75e |
feat(distributed): gated X-LocalAI-Node response header (middleware + wrapper) (#9976)
* feat(distributed): add per-request node ID context holder Introduce pkg/distributedhdr, a leaf package carrying a per-request *atomic.Value holder for the picked worker node ID from the SmartRouter (core/services/nodes) up to the HTTP response writer wrapper (core/http/middleware). Avoids the import cycle that a shared key in either consumer would create. Exposes NewHolder, WithHolder, Holder, Stamp, Load, Inherit. The holder is atomic.Value so cross-goroutine publish from the router to the response writer wrapper is race-clean. Assisted-by: Claude:claude-opus-4-7[1m] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(distributed): add ExposeNodeHeader middleware + response writer wrapper New ApplicationConfig.ExposeNodeHeader bool + --expose-node-header CLI flag / LOCALAI_EXPOSE_NODE_HEADER env var (default off; the node ID reveals internal topology and is opt-in). The middleware creates a per-request *atomic.Value holder, attaches it to c.Request().Context() via distributedhdr.WithHolder, and wraps c.Response().Writer with a custom http.ResponseWriter that sets the X-LocalAI-Node header on first Write / WriteHeader / Flush by reading the holder. Implements http.Flusher, http.Hijacker, Unwrap so it composes cleanly with Echo and http.NewResponseController. request.go propagates the holder onto derived contexts via distributedhdr.Inherit so the holder survives the correlation-ID context replacement. Unit + race-clean concurrency + integration specs. Assisted-by: Claude:claude-opus-4-7[1m] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(distributed): stamp node ID in router and wire middleware to inference routes ModelRouterAdapter.Route stamps the picked node ID into the per-request holder via distributedhdr.Stamp(ctx, result.Node.ID) right after replica selection. Wire ExposeNodeHeader middleware to: - OpenAI chat/completion/embeddings + audio transcriptions/speech + image generations/inpainting - Anthropic /v1/messages - Ollama /api/chat, /api/generate, /api/embed, /api/embeddings - Jina /v1/rerank - LocalAI /v1/vad The middleware's wrapper reads the holder on first byte and sets the X-LocalAI-Node response header before delegating to the underlying writer. Per-request scope means no race under concurrent multi-replica routing. Assisted-by: Claude:claude-opus-4-7[1m] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(distributed): thread request context through backend Load + cover ctx propagation Five non-OpenAI backend helpers were silently using app.Context instead of the request context for the gRPC backend call: transcription, TTS, image generation, rerank, VAD. Effect: distributedhdr.Stamp in the router callback was a silent no-op for these paths, AND client cancellation didn't propagate to in-flight inference. Thread c.Request().Context() (or the equivalent input.Context after the request middleware has installed the correlation-ID derived context) through each helper and into ModelOptions via model.WithContext(ctx). ImageGeneration's signature gains a leading ctx parameter; in-tree callers (openai image, openai inpainting, openai inpainting_test) are updated to match. ModelEmbedding gains a leading ctx parameter for the same reason; the openai and ollama embedding handlers pass the request context through. chat_stream_workers.go defers the initial role=assistant chunk emission until the first token callback so the wrapper's lazy X-LocalAI-Node lookup against the loader runs AFTER ml.Load has stamped the per-modelID node ID; semantically identical for clients (role still arrives before any text). Regression test core/backend/ctx_propagation_test.go pins ctx propagation for all five helpers. Docs updated to enumerate the full endpoint coverage of the --expose-node-header flag. Assisted-by: Claude:claude-opus-4-7[1m] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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Richard Palethorpe
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6a80e23733 |
feat(middleware): Model routing, PII filtering, Cloud model proxies (#9802)
Add a routing middleware stack and a cloud-proxy backend. * cloud-proxy: a Go gRPC backend that forwards OpenAI- and Anthropic-shaped chat requests to upstream providers, with an optional translate mode (OpenAI request -> Anthropic /v1/messages -> OpenAI response) and full tool-calling support. * routing: admission control, content-aware model routing (embedding cache + classifier + rerank + Arch-Router score), PII detection/redaction (regex + NER) with streaming filter and OpenAI/Anthropic adapters, and a per-user/per-key billing recorder backed by GORM or in-memory storage. * middleware: UsageMiddleware records usage via the billing recorder, plus admission, route-model, usage-stamp and trace middlewares. * observability: BackendTrace ring buffer stores full request bodies (capped), MITM proxy emits structured trace events, and router classifier decisions surface at /api/router/decide. * gallery: Arch-Router-1.5B (Q4_K_M and Q8_0). * UI: cloud-proxy model-editor fields, classifier system-prompt and score-normalization config, and a Traces page rendering request bodies. Assisted-by: claude-code:claude-opus-4-7 [Read] [Edit] [Bash] Signed-off-by: Richard Palethorpe <io@richiejp.com> |
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LocalAI [bot]
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a0f3e26245 |
fix(distributed): make admin backend installs resilient and observable (#9958)
* feat(distributed): add configurable NATS backend install/upgrade timeouts Adds BackendInstallTimeout and BackendUpgradeTimeout to DistributedConfig with 15m defaults, following the existing MCPToolTimeout / WorkerWaitTimeout pattern. These will replace the hardcoded literals in RemoteUnloaderAdapter so admin-driven backend installs across the cluster survive long OCI image pulls that previously timed out at 3m. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * style(distributed): gofmt alignment after timeout fields Re-aligns the Validate() negative-duration map and the Default* const block so the new BackendInstall/UpgradeTimeout entries do not leave the surrounding columns mis-padded. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(cli): surface LOCALAI_NATS_BACKEND_INSTALL_TIMEOUT and _UPGRADE_TIMEOUT Parses the two new env vars on the run CLI and threads them through the existing AppOption builder so DistributedConfig picks them up. Invalid duration strings now fail loudly at startup rather than silently falling back to the default. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(distributed): inject NATS install/upgrade timeouts into RemoteUnloaderAdapter Removes the hardcoded 3m / 15m literals from RemoteUnloaderAdapter and threads in DistributedConfig.BackendInstallTimeoutOrDefault() and BackendUpgradeTimeoutOrDefault() at construction. Install now defaults to 15m (was 3m); cold OCI image pulls on Jetson Wi-Fi routinely blew past the old ceiling. Scripted messaging client captures the timeout so tests can assert the configured value actually reaches the NATS request. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(distributed): introduce galleryop.ErrWorkerStillInstalling sentinel When the NATS request-reply for backend.install (or .upgrade) times out the worker is almost always still pulling the OCI image. Wrap the timeout in a typed sentinel so the manager above can distinguish "worker hung" from "worker still working" and leave the pending_backend_ops row in place for the reconciler to confirm via backend.list. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(distributed): treat NATS install timeout as in-progress, not failure When a worker times out replying to backend.install but the install is still running on the worker, enqueueAndDrainBackendOp now reports a running_on_worker status and pushes NextRetryAt out by the install timeout so the reconciler does not immediately re-fire another install while the worker is still pulling the image. The pending_backend_ops row stays in place for the next reconciler pass to confirm via backend.list. InstallBackend wraps the result in galleryop.ErrWorkerStillInstalling so callers can branch (galleryop renders yellow in-progress instead of red error). UpgradeBackend uses the same wrap. Adds RemoteUnloaderAdapter.InstallTimeout() so the manager can push NextRetryAt by the configured timeout without reaching into a private field, and NodeRegistry.RecordPendingBackendOpInFlight as the soft cousin of RecordPendingBackendOpFailure. Also includes incidental gofmt-driven struct-field alignment in registry.go on lines unrelated to the change (touched files are re-formatted to canonical form per project policy). Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(distributed): don't increment Attempts on in-flight install timeout An in-flight timeout (worker still pulling the OCI image) is not a failed attempt, it's a delayed one. Incrementing Attempts let genuinely-progressing slow installs (e.g. 30 GB CUDA images on Wi-Fi) trip the reconciler's maxPendingBackendOpAttempts cap and dead-letter the queue row while the worker was still legitimately working. RecordPendingBackendOpInFlight now only updates LastError and NextRetryAt. Also documents "running_on_worker" in the NodeOpStatus.Status enum comment so Task 6 implementers see the full surface. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(galleryop): surface ErrWorkerStillInstalling as non-error OpStatus When the distributed backend manager returns an error that wraps ErrWorkerStillInstalling, backendHandler now completes the op with a "still installing in background" message rather than marking it as a red failure. Admin UI sees a yellow in-progress state; reconciler confirms completion on its next pass. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(distributed): end-to-end install-timeout-then-reconcile Wires Task 1-6 end-to-end so any seam mismatch surfaces in CI rather than during a real cluster install. NATS times out, the queue row stays alive with running_on_worker status, the worker eventually reports the backend installed via backend.list, the manager surfaces it via ListBackends. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(distributed): document LOCALAI_NATS_BACKEND_INSTALL_TIMEOUT / _UPGRADE_TIMEOUT Add the two new operator-tunable env vars to the Frontend Configuration table in the distributed-mode docs. Explains the 15m default, when to raise it (slow links pulling multi-GB OCI images), and the new "still installing in background" admin-UI state when the round-trip times out but the worker is still working. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(distributed): clear pending install rows when backend.list confirms DistributedBackendManager.ListBackends now proactively clears pending_backend_ops install rows whose (nodeID, backend) is reported installed by backend.list. Operator UI updates immediately instead of waiting up to installTimeout (default 15m) for the next reconciler tick after NextRetryAt. Only install rows are cleared; upgrade and delete intents are not satisfied by presence in backend.list and continue to drain through their normal reconciler paths. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(messaging): add BackendInstallProgressEvent wire type and subject New NATS subject nodes.<nodeID>.backend.install.<opID>.progress lets the worker publish transient progress events (file, current/total bytes, percentage, phase) while a long-running install pulls its OCI image. BackendInstallRequest gains an optional OpID field so the worker knows which subject to publish on. Transient pub/sub (not JetStream): the install reply remains ground truth for success/failure; dropped progress events are tolerable. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * style(messaging): drop em-dash from BackendInstallProgress test comment Per project convention (no em-dashes anywhere). Comment substance is unchanged. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(distributed): worker publishes debounced install progress over NATS When BackendInstallRequest.OpID is set, the worker's backend.install handler wires a debounced publisher (250ms window) into the gallery download callback. Each tick becomes a BackendInstallProgressEvent on nodes.<nodeID>.backend.install.<opID>.progress; the publisher always emits a final event on Flush so the UI sees the terminal percentage. Old masters that do not set OpID continue to run silent installs: no behavior change for them. Lock ordering: the publisher releases its mutex before calling messaging.Publish so a slow network never stalls the install loop. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(distributed): RemoteUnloaderAdapter subscribes to install progress InstallBackend gains opID + onProgress parameters. When both are set, the adapter subscribes to nodes.<nodeID>.backend.install.<opID>.progress BEFORE publishing the install request, decodes each message into the caller's onProgress callback in a goroutine (so a slow callback never stalls the NATS reader thread), and unsubscribes after RequestJSON returns. When onProgress is nil OR opID is empty (the reconciler retry path), subscription is skipped entirely - silent installs cost nothing extra. Subscribe failure is logged at Warn and the install proceeds without progress streaming; the NATS round-trip still owns terminal status. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(distributed): forward backend install progress into galleryop OpStatus DistributedBackendManager.InstallBackend now passes the gallery op ID and a progress bridge into the adapter call. Each BackendInstallProgressEvent from the worker becomes a galleryop.ProgressCallback tick - which the existing backendHandler already turns into OpStatus.UpdateStatus, so the admin UI/SSE polling sees per-byte progress for distributed installs without any UI-side change. UpgradeBackend is intentionally left silent for now: its wire request (BackendUpgradeRequest) does not carry OpID, and rolling-update fallback is the rarer path. Will be picked up in a follow-up if the worker upgrade path also gets a progress channel. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(distributed): InstallBackend tolerates silent (pre-Phase-2) workers A worker on pre-Phase-2 code never publishes progress events. The new master subscribes optimistically; this spec pins that a silent worker still produces a green install with no progressCb ticks. The install reply is the source of truth for terminal state; the progress stream is a best-effort UX enrichment. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(distributed): document install progress streaming Note the new nodes.<nodeID>.backend.install.<opID>.progress subject and the silent-worker compatibility behavior so operators know to expect real-time progress and what happens on a mixed-version cluster. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(distributed): note progress-event ordering trade-off in InstallBackend Document near the goroutine dispatch why ordering at the consumer is best-effort, why it rarely matters in practice (worker debounce >> goroutine jitter), and what a future hardening pass would look like (Seq field + stale-by-seq drop). Stops the next reader from accidentally "fixing" the goroutine pool away. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(galleryop): add NodeProgress + OpStatus.Nodes for per-node breakdown Adds the data model the UI needs to render an expandable per-node breakdown of a fanned-out backend install. NodeProgress carries node identity (ID + name), per-node status (queued / running_on_worker / success / error / downloading), the current file + bytes + percentage from the Phase 2 progress stream, and any per-node error. OpStatus.Nodes is the slice the /api/operations handler will surface in a follow-up. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(galleryop): UpdateNodeProgress merges per-node ticks by NodeID GalleryService.UpdateNodeProgress(opID, nodeID, np) merges a NodeProgress into OpStatus.Nodes (keyed by NodeID, no duplicates) and mirrors the latest tick into the aggregate Progress / FileName / DownloadedFileSize / TotalFileSize fields so the legacy single-bar OperationsBar view keeps working unchanged alongside the new per-node breakdown. Concurrent-safe via the existing g.Mutex. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(distributed): write per-node OpStatus entries during install fan-out DistributedBackendManager now accepts a nodeProgressSink and feeds it two streams: 1. enqueueAndDrainBackendOp emits a per-node terminal entry on each status it appends to BackendOpResult (queued, success, error, running_on_worker). The opID is threaded through the function so the sink gets the right gallery op identity. 2. The install apply closure fans each BackendInstallProgressEvent into the sink as a downloading entry, alongside the legacy progressCb path so the aggregate single-bar view stays correct. Production wiring passes the GalleryService (which implements UpdateNodeProgress via Task 2) as the sink. Single-node tests pass nil. DeleteBackend and UpgradeBackend pass an empty opID so the sink path no-ops for ops that aren't gallery-tracked the same way as Install. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(operations): expose per-node breakdown on /api/operations When an operation's OpStatus has Nodes entries (populated by the Phase 4 progress sink wiring), surface them as a "nodes" array on the /api/operations response, sorted by node_name for stable rendering. Backward compatible: legacy clients ignore the field; ops without any node entries (single-node mode, model installs) omit the array entirely thanks to the empty-slice guard. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ui): per-node breakdown in OperationsBar When an install op fans out to more than one worker, the operations bar now shows a "N nodes" chevron that expands into a per-node list. Each row carries the node's status (color-coded pill), the current file being downloaded, byte counts, percentage, and a thin per-node progress bar. Yellow "Worker busy" pill marks running_on_worker status with a tooltip explaining the NATS round-trip timed out but the worker is still installing in the background. Backward compatible: ops without a nodes field (legacy or single-node mode) render as before. State for expand/collapse is local to the component, keyed by jobID/id - reload starts collapsed. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(distributed): document per-node breakdown in the operations bar Adds a short subsection covering the expandable "N nodes" chevron in the OperationsBar admin UI, the meaning of each status pill, and how it relates to the /api/operations nodes array. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(galleryop): UpdateStatus preserves Nodes when caller sends none Real-world bug surfaced by the Phase 4 multi-worker smoke test: the nodes[] array in /api/operations flickered between a single node at a time on a 2-worker install. Root cause: the Phase 2 progress bridge also calls the legacy progressCb -> UpdateStatus(&OpStatus{...}) on every tick. UpdateStatus then overwrote the entire status pointer, wiping the Nodes slice that UpdateNodeProgress had just merged in. Fix: in UpdateStatus, if the incoming op has an empty Nodes slice, carry forward the previous status's Nodes before storing. Callers that explicitly populate Nodes still win (their slice replaces the prior one, no merge across the two code paths). Two regression specs added pinning both directions of the contract. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(distributed): strip implementation details from user-facing docs Trim the new install/upgrade timeout rows and the install-progress sections to focus on what the operator sees and tunes. Drops: - the NATS subject names and pub/sub mechanics - "round-trip" / reconciler / backend.list jargon - /api/operations polling cadence - "pre-2026-05-22" version references Reframes the breakdown text around the admin UI (Operations Bar, chevron, status pills, "Worker busy" tooltip). Implementation context lives in the agent notes and code comments. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(config): move DistributedConfig.Validate flag names to constants The negative-duration check map was a wall of literal kebab-case strings that had to stay in sync with the kong-derived CLI flag names manually. Move them to a Flag* const block alongside the existing Default* block so a rename of either the Go field or the CLI naming convention forces a compile error rather than silent drift. Sole consumer today is Validate; the constants are exported so future operator-facing surfaces (e.g. error messages on other validation paths) can reference them by name instead of repeating the literals. Tests pin both the literal values (so a future "let's just rename this" doesn't accidentally regress the CLI flag) and the negative- duration error message for the new BackendInstall / BackendUpgrade fields. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(distributed): extract NodeStatus and Phase enums to constants Sweep for the same literal-string-as-identifier pattern called out on the Validate flag names: the per-node install status enum ("queued" | "downloading" | "running_on_worker" | "success" | "error") appeared as raw literals across managers_distributed.go (10+ sites, including 3 separate `n.Status == "running_on_worker"` checks), operation.go, and the test suite. Same shape for the Phase enum ("resolving" | "downloading" | "extracting" | "starting") in the worker-side progress publisher. Promote both to exported const blocks: - galleryop.NodeStatus{Queued,Downloading,RunningOnWorker,Success,Error} shared between galleryop.NodeProgress.Status (the wire field) and nodes.NodeOpStatus.Status (the in-process per-node summary) - messaging.Phase{Resolving,Downloading,Extracting,Starting} shared between the worker publisher and any future consumer that needs to switch on phase Tests pin both the literal values (so a future "let's just rename" doesn't silently change the JSON wire) and use the constants in setup (so the producer side stays drift-protected). Wire-format assertions on the /api/operations JSON output keep their literals deliberately, so the constant value can never silently diverge from what the UI receives. Out of scope for this PR (separate cleanup): the finetune and quantization job-status enums have the same anti-pattern with 14+ literal sites each, but predate this PR's work. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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|
LocalAI [bot]
|
f15b9178ec |
feat(usage): track and visualise usage per API key (#9920)
* feat(usage): add Source, APIKeyID, APIKeyName columns to UsageRecord Adds three additive columns plus UsageSource* constants. The columns are auto-migrated by InitDB. APIKeyID is a nullable foreign reference to UserAPIKey.ID; APIKeyName is snapshotted on each row so revoked keys keep showing their name in history. Refs: #9862 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(usage): backfill Source on pre-feature usage rows InitDB now classifies any pre-existing usage_record with an empty source: 'legacy-api-key' user -> legacy, everything else -> web. The backfill is idempotent (only touches NULL/empty rows). Refs: #9862 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(usage): add GetUserUsageBySource aggregator Groups by (bucket, source, api_key_id, api_key_name). Filters out legacy by default. Returns both per-bucket detail and roll-ups (by_source, by_key sorted desc and capped at 200, grand_total). The MAX(created_at) projection is iterated via Rows().Scan into a string column and parsed manually because the SQLite driver surfaces the aggregated timestamp as a string, which database/sql refuses to scan directly into time.Time. Postgres returns a real timestamp; the same string path handles its RFC3339 form too. Refs: #9862 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(usage): log Rows() errors and assert LastUsed in tests Adds rows.Err() and Rows() open-failure logging in computeSourceTotals so silent data drops surface in logs. Logs on parseLastUsedString format misses for the same reason. Strengthens the snapshot-survival test to assert LastUsed is a recent timestamp, locking the SQLite time-string parser behaviour. Refs: #9862 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(usage): add admin GetAllUsageBySource with filters and truncation Optional user_id and api_key_id filters (composed with AND). Legacy bucket is included for admin callers. truncated=true when more than 200 distinct keys would be in the by_key roll-up. Refs: #9862 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(auth): plumb auth_source and auth_apikey through Echo context tryAuthenticate now sets auth_source on every successful branch (web for session/Bearer-session, apikey for Bearer-key/x-api-key/ token-cookie, legacy for legacy env key match). For named-key branches it also stores the resolved *UserAPIKey under auth_apikey so downstream middlewares can snapshot id+name without re-validating. Refs: #9862 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(auth): expand tryAuthenticate godoc and cover Bearer-session branch Documents all three context-keys side effects (auth_source, auth_apikey, _auth_session) plus the split of responsibilities with the parent Middleware. Adds a test for the Bearer-as-session-token classification so future regressions there fail loudly. Refs: #9862 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(usage): UsageMiddleware records source + snapshots key name Reads auth_source and auth_apikey from the Echo context (set by auth.Middleware in the previous task). Snapshots UserAPIKey.ID and Name onto each row so revoked keys remain readable in history. Falls back to source=web when no auth_source is set (auth disabled or unrecognised path). Refs: #9862 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(usage): add /api/auth/usage/sources and admin variant Self endpoint filters legacy server-side; admin endpoint includes legacy and accepts user_id + api_key_id filters. Response includes buckets, totals.{by_source, by_key, grand_total}, and a truncated flag set when the per-key roll-up was capped at 200. Refs: #9862 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(routes): mark test mirror handlers as keep-in-sync with production The newTestAuthApp helper duplicates production route handlers inline because it cannot use RegisterAuthRoutes (which requires a *application.Application). Naming the source path on each mirror makes the drift contract explicit for future maintainers. Refs: #9862 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ui): add usageApi.getMySources/getAdminSources + i18n strings Refs: #9862 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ui): add Sources tab skeleton with data fetch Adds Usage page tab that fetches /api/auth/usage/sources (or the admin variant). Renders raw totals plus a placeholder key list; real visualisations land in subsequent commits. Restructures the existing tab button block so Models and Sources are visible to non-admins (Users remains admin-only). Refs: #9862 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ui): source mix ribbon + searchable/sortable sources table Replaces the SourcesTab placeholder rendering with two reusable components: SourceMixRibbon (one segmented bar per source class) and SourcesTable (search + sort + revoked-key dim). Pulls the current API key list to detect revoked keys. Refs: #9862 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(ui): skip revoked-key detection until the key list is known existingKeyIds defaulted to an empty Set, which made every live api_key row render as (revoked) during the brief window before apiKeysApi.list() resolved, and permanently after a fetch failure. Use null as the unknown state and suppress the revoked badge until the parent provides a real Set. Refs: #9862 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ui): top-N stacked time chart and drill-in chip for Sources tab Top 7 sources by total tokens get distinct colours; the rest roll up into 'Other'. Clicking a row in the SourcesTable dims everything except that series in the chart; the chip is the canonical clear. Refs: #9862 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(usage): document per-API-key Sources tab and endpoints Extends features/authentication.md Usage Tracking section with: - A 'Sources' tab description and source-class taxonomy - Endpoint documentation for /api/auth/usage/sources and the admin variant - Response shape example with by_source / by_key / grand_total - Migration note about pre-feature row backfill Refs: #9862 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(usage): silence errcheck on deferred rows.Close CI errcheck flagged the bare 'defer rows.Close()' in computeSourceTotals. Wrap in a closure that discards the close error explicitly; an error here is non-actionable since we have already drained the rows and logged any iteration failure. Refs: #9862 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(usage): bound batcher intake and add Shutdown/FlushNow hooks The pre-existing usage batcher had no cap on its add() path; the usageMaxPending=5000 constant only guarded the re-queue path after a failed write, leaving memory growth unbounded if the DB fell behind. This commit: - Adds the cap to add() so saturation drops new records (rate-limited warn at 1/1024) instead of growing unbounded. - Raises usageMaxPending to 50000 to absorb realistic inference bursts. - Replaces the package-level batcher global with a mutex-guarded pair plus a currentBatcher() accessor so Init / Shutdown cycles are race-free. - Adds ShutdownUsageRecorder() for graceful drain on process exit (not yet wired into app shutdown, just published). - Adds FlushNow() for deterministic tests; the middleware suite no longer needs 6s sleeps per spec and now runs in ~50ms instead of 18s. - Re-queue on failed flush is now cap-aware: prepends as much of the failed batch as fits alongside concurrent arrivals, instead of dropping the whole batch when full. Refs: #9862 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(usage): drain usage batcher on graceful shutdown Registers ShutdownUsageRecorder with the existing signals.RegisterGracefulTerminationHandler so SIGINT/SIGTERM synchronously flushes any in-memory usage records before the process exits. Without this, up to one flush interval (5s) of recorded usage was lost when LocalAI restarted. Refs: #9862 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]
|
959de86761 |
feat(llama-cpp): make server-side prompt cache work by default (#9925)
Aligns LocalAI's llama-cpp gRPC backend with upstream's auto-on prompt cache path so repeated system prompts (agents, OpenAI/Anthropic-compatible CLIs, coding assistants) skip prefill on subsequent calls without any YAML changes. Reported in #9921. Upstream's server enables `kv_unified=true` (and bumps `n_parallel` to 4) when slot count is auto, which unlocks `cache_idle_slots`. LocalAI hardcodes `n_parallel=1` and so far also hardcoded `kv_unified=false`, which silently force-disables idle-slot saving at server init. The host prompt cache was allocated but never written across requests. Changes in backend/cpp/llama-cpp/grpc-server.cpp: - params.kv_unified: false -> true (single-slot path now benefits from the prompt cache; users can opt out with `kv_unified:false`) - params.n_ctx_checkpoints: 8 -> 32 (match upstream default) - params.cache_idle_slots = true initialized explicitly (upstream default) - params.checkpoint_every_nt = 8192 initialized explicitly (upstream default) - New option parsers: cache_idle_slots / idle_slots_cache, checkpoint_every_nt / checkpoint_every_n_tokens Docs: - features/text-generation.md: fix misleading `cache_ram` description (it's the host-side prompt cache, not the KV cache), document the kv_unified + cache_ram + cache_idle_slots interaction, add rows for the two newly-exposed options, and add a worked example for the agent/CLI workload from the issue. - advanced/model-configuration.md: mark the legacy `prompt_cache_path` / `prompt_cache_all` / `prompt_cache_ro` YAML fields as unused by the llama-cpp gRPC backend (they target upstream's CLI completion tool and are not consumed by grpc-server.cpp) and point readers at the new prompt-cache explainer. Closes #9921 Assisted-by: claude:opus-4.7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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Richard Palethorpe
|
c894d9c826 |
feat(sglang): wire engine_args, add cuda13 build, ship MTP gallery demos (#9686)
Bring the sglang Python backend up to feature parity with vllm by adding
the same engine_args:-map plumbing the vLLM backend already has. Any
ServerArgs field (~380 in sglang 0.5.11) becomes settable from a model
YAML, including the speculative-decoding flags needed for Multi-Token
Prediction. Validation matches the vllm backend's: keys are checked
against dataclasses.fields(ServerArgs), unknown keys raise ValueError
with a difflib close-match suggestion at LoadModel time, and the typed
ModelOptions fields keep their existing meaning with engine_args
overriding them.
Backend code:
* backend/python/sglang/backend.py: add _apply_engine_args, import
dataclasses/difflib/ServerArgs, call from LoadModel; rename Seed ->
sampling_seed (sglang 0.5.11 renamed the SamplingParams field).
* backend/python/sglang/test.py + test.sh + Makefile: six unit tests
exercising the helper directly (no engine load required).
Build / CI / backend gallery (cuda13 + l4t13 paths are now first-class):
* backend/python/sglang/install.sh: add --prerelease=allow because
sglang 0.5.11 hard-pins flash-attn-4 which only ships beta wheels;
add --index-strategy=unsafe-best-match for cublas12 so the cu128
torch index wins over default-PyPI's cu130; new pyproject.toml-driven
l4t13 install path so [tool.uv.sources] can pin torch/torchvision/
torchaudio/sglang to the jetson-ai-lab index without forcing every
transitive PyPI dep through the L4T mirror's flaky proxy (mirrors the
equivalent fix in backend/python/vllm/install.sh).
* backend/python/sglang/pyproject.toml (new): L4T project spec with
explicit-source jetson-ai-lab index. Replaces requirements-l4t13.txt
for the l4t13 BUILD_PROFILE; other profiles still go through the
requirements-*.txt pipeline via libbackend.sh's installRequirements.
* backend/python/sglang/requirements-l4t13.txt: removed; superseded
by pyproject.toml.
* backend/python/sglang/requirements-cublas{12,13}{,-after}.txt: pin
sglang>=0.5.11 (Gemma 4 floor); add cu130 torch index for cublas13
(new files) and cu128 torch index for cublas12 (default PyPI now
ships cu130 torch wheels by default and breaks cu12 hosts).
* backend/index.yaml: add cuda13-sglang and cuda13-sglang-development
capability mappings + image entries pointing at
quay.io/.../-gpu-nvidia-cuda-13-sglang.
* .github/workflows/backend.yml: new cublas13 sglang matrix entry,
mirroring vllm's cuda13 build.
Model gallery + docs:
* gallery/sglang.yaml: base sglang config template, mirrors vllm.yaml.
* gallery/sglang-gemma-4-{e2b,e4b}-mtp.yaml: Gemma 4 MTP demos
transcribed verbatim from the SGLang Gemma 4 cookbook MTP commands.
* gallery/sglang-mimo-7b-mtp.yaml: MiMo-7B-RL with built-in MTP heads
+ online fp8 weight quantization, verified end-to-end on a 16 GB
RTX 5070 Ti at ~88 tok/s. Uses mem_fraction_static: 0.7 because the
MTP draft worker's vocab embedding is loaded unquantised and OOMs
the static reservation at sglang's 0.85 default.
* gallery/index.yaml: three new entries (gemma-4-e2b-it:sglang-mtp,
gemma-4-e4b-it:sglang-mtp, mimo-7b-mtp:sglang).
* docs/content/features/text-generation.md: new SGLang section with
setup, engine_args reference, MTP demos, version requirements.
* .agents/sglang-backend.md (new): agent one-pager covering the flat
ServerArgs structure, the typed-vs-engine_args precedence, the
speculative-decoding cheatsheet, and the mem_fraction_static gotcha
documented above.
* AGENTS.md: index entry for the new agent doc.
Known limitation: the two Gemma 4 MTP gallery entries ship a recipe
that doesn't yet run on stock libraries. The drafter checkpoints
(google/gemma-4-{E2B,E4B}-it-assistant) declare
model_type: gemma4_assistant / Gemma4AssistantForCausalLM, which
neither transformers (<=5.6.0, including the SGLang cookbook's pinned
commit 91b1ab1f... and main HEAD) nor sglang's own model registry
(<=0.5.11) registers as of 2026-05-06. They will start working when
HF or sglang upstream registers the architecture -- no LocalAI
changes needed. The MiMo MTP demo and the non-MTP Gemma 4 paths work
today on this build (verified on RTX 5070 Ti, 16 GB).
Assisted-by: Claude:claude-opus-4-7 [Read] [Edit] [Bash] [WebFetch] [WebSearch]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
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LocalAI [bot]
|
a8d7d37a3c |
fix: unbreak master CI (docs, kokoros, vibevoice-cpp ABI) (#9682)
* fix(docs): correct broken Hugo relrefs The Hugo build has been failing on master since the relevant pages landed: - text-generation.md:720 referenced `/docs/features/distributed-mode`, but Hugo `relref` paths are relative to the content root, not the rendered URL. Drop the `/docs/` prefix so the lookup matches the existing `features/...` form used elsewhere in the file. - audio-transform.md:144 referenced `tts.md`; the actual page is `text-to-audio.md`. Assisted-by: Claude:claude-opus-4-7[1m] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(kokoros): stub Diarize and AudioTransform Backend trait methods The recent backend.proto additions (Diarize, AudioTransform, AudioTransformStream) extended the gRPC Backend trait, breaking kokoros-grpc compilation with E0046 because the Rust implementation hadn't picked up the new methods. Add Unimplemented stubs matching the existing pattern for non-applicable RPCs in this TTS-only backend. Assisted-by: Claude:claude-opus-4-7[1m] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(vibevoice-cpp): track upstream ABI + wire 1.5B voice cloning Two recent commits in mudler/vibevoice.cpp reshaped the vv_capi_tts signature without a corresponding bump on the LocalAI side: 3bd759c "1.5b: unify into a single tts entry point" inserted a ref_audio_path parameter between voice_path and dst_wav_path. ad856bd "1.5b: multi-speaker dialog support" promoted that to a (const char* const* ref_audio_paths, int n_ref_audio_paths) pair for per-speaker conditioning. Because purego resolves symbols by name and not by signature, the build kept linking; at runtime the misaligned arguments turned the TTS->ASR closed-loop test into a SIGSEGV inside cgo. Track HEAD explicitly and bring the bridge in line with it: * Update the CppTTS purego binding to the 9-arg form. purego marshals []*byte as a **char by handing the C side the underlying array address; nil/empty maps to NULL, which matches the C contract for "no reference audio" on the realtime-0.5B path. * Add a `ref_audio` gallery option (comma-separated, repeatable) that the 1.5B path consumes for runtime voice cloning. Multiple entries are interpreted as one WAV per speaker (Speaker 0..n-1). * TTSRequest.Voice now routes by extension/shape: `.wav` or a comma-separated list goes to ref_audio_paths; anything else stays on voice_path (realtime-0.5B's pre-baked voice gguf). * Pin VIBEVOICE_CPP_VERSION to ad856bd and wire the Makefile into the existing bump_deps matrix so future upstream rolls land as reviewable PRs instead of a silent CI break. Assisted-by: Claude:claude-opus-4-7[1m] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(vibevoice-cpp): use ModelOptions.AudioPath for 1.5B ref audio Use the existing audio_path field from ModelOptions (already plumbed through config_file's `audio_path:` YAML and consumed by other audio backends like kokoros) instead of inventing a custom `ref_audio:` Options[] string. Multi-speaker setups stay on a single comma- separated value. No behavior change beyond the gallery key name; per-call routing via TTSRequest.Voice is unchanged. Assisted-by: Claude:claude-opus-4-7[1m] 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
|
8e43842175 |
feat(vllm, distributed): tensor parallel distributed workers (#9612)
* feat(vllm): build vllm from source for Intel XPU
Upstream publishes no XPU wheels for vllm. The Intel profile was
silently picking up a non-XPU wheel that imported but errored at
engine init, and several runtime deps (pillow, charset-normalizer,
chardet) were missing on Intel -- backend.py crashed at import time
before the gRPC server came up.
Switch the Intel profile to upstream's documented from-source
procedure (docs/getting_started/installation/gpu.xpu.inc.md in
vllm-project/vllm):
- Bump portable Python to 3.12 -- vllm-xpu-kernels ships only a
cp312 wheel.
- Source /opt/intel/oneapi/setvars.sh so vllm's CMake build sees
the dpcpp/sycl compiler from the oneapi-basekit base image.
- Hide requirements-intel-after.txt during installRequirements
(it used to 'pip install vllm'); install vllm's deps from a
fresh git clone of vllm via 'uv pip install -r
requirements/xpu.txt', swap stock triton for
triton-xpu==3.7.0, then 'VLLM_TARGET_DEVICE=xpu uv pip install
--no-deps .'.
- requirements-intel.txt trimmed to LocalAI's direct deps
(accelerate / transformers / bitsandbytes); torch-xpu, vllm,
vllm_xpu_kernels and the rest come from upstream's xpu.txt
during the source build.
- requirements.txt: add pillow + charset-normalizer + chardet --
used by backend.py and missing on the Intel install profile.
- run.sh: 'set -x' so backend startup is visible in container
logs (the gRPC startup error path was previously opaque).
Also adds a one-line docs example for engine_args.attention_backend
under the vLLM section, since older XE-HPG GPUs (e.g. Arc A770)
need TRITON_ATTN to bypass the cutlass path in vllm_xpu_kernels.
Tested end-to-end on an Intel Arc A770 with Qwen2.5-0.5B-Instruct
via LocalAI's /v1/chat/completions.
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(vllm): add multi-node data-parallel follower worker
vLLM v1's multi-node story is one process per node sharing a DP
coordinator over ZMQ -- the head runs the API server with
data_parallel_size > 1 and followers run `vllm serve --headless ...`
with matching topology. Today LocalAI can already configure DP on the
head via the engine_args YAML map, but there's no way to bring up the
follower nodes -- so the head sits waiting for ranks that never
handshake.
Add `local-ai p2p-worker vllm`, mirroring MLXDistributed's structural
precedent (operator-launched, static config, no NATS placement). The
worker:
- Optionally self-registers with the frontend as an agent-type node
tagged `node.role=vllm-follower` so it's visible in the admin UI
and operators can scope ordinary models away via inverse
selectors.
- Resolves the platform-specific vllm backend via the gallery's
"vllm" meta-entry (cuda*, intel-vllm, rocm-vllm, ...).
- Runs vLLM as a child process so the heartbeat goroutine survives
until vLLM exits; forwards SIGINT/SIGTERM so vLLM can clean up its
ZMQ sockets before we tear down.
- Validates --headless + --start-rank 0 is rejected (rank 0 is the
head and must serve the API).
Backend run.sh dispatches `serve` as the first arg to vllm's own CLI
instead of LocalAI's backend.py gRPC server -- the follower speaks
ZMQ directly to the head, there is no LocalAI gRPC on the follower
side. Single-node usage is unchanged.
Generalises the gallery resolution helper into findBackendPath()
shared by MLX and vLLM workers; extracts ParseNodeLabels for the
comma-separated label parsing both use.
Ships with two compose recipes (`docker-compose.vllm-multinode.yaml`
for NVIDIA, `docker-compose.vllm-multinode.intel.yaml` for Intel
XPU/xccl) plus `tests/e2e/vllm-multinode/smoke.sh`. Both vendors are
supported (NCCL for CUDA/ROCm, xccl for XPU) but mixed-vendor DP is
not -- PyTorch's process group requires every rank to use the same
collective backend, and NCCL/xccl/gloo don't interoperate.
Out of scope (deferred): SmartRouter-driven placement of follower
ranks via NATS backend.install events, follower log streaming through
/api/backend-logs, tensor-parallel across nodes, disaggregated
prefill via KVTransferConfig.
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* test(vllm): CPU-only end-to-end test for multi-node DP
Adds tests/e2e/vllm-multinode/, a Ginkgo + testcontainers-go suite
that brings up a head + headless follower from the locally-built
local-ai:tests image, bind-mounts the cpu-vllm backend extracted by
make extract-backend-vllm so it's seen as a system backend (no gallery
fetch, no registry server), and asserts a chat completion across both
DP ranks. New `make test-e2e-vllm-multinode` target wires the docker
build, backend extract, and ginkgo run together; BuildKit caches both
images so re-runs only rebuild what changed. Tagged Label("VLLMMultinode")
so the existing distributed suite isn't pulled along.
Two pre-existing bugs surfaced by the test:
1. extract-backend-% (Makefile) failed for every backend, because all
backend images end with `FROM scratch` and `docker create` rejects
an image with no CMD/ENTRYPOINT. Fixed by passing
--entrypoint=/run.sh -- the container is never started, only
docker-cp'd, so the path doesn't have to exist; we just need
anything that satisfies the daemon's create-time validation.
2. backend/python/vllm/run.sh's `serve` shortcut for the multi-node DP
follower exec'd ${EDIR}/venv/bin/vllm directly, but uv bakes an
absolute build-time shebang (`#!/vllm/venv/bin/python3`) that no
longer resolves once the backend is relocated to BackendsPath.
_makeVenvPortable's shebang rewriter only matches paths that
already point at ${EDIR}, so the original shebang slips through
unchanged. Fixed by exec-ing ${EDIR}/venv/bin/python with the script
as an argument -- Python ignores the script's shebang in that case.
The test fixture caps memory aggressively (max_model_len=512,
VLLM_CPU_KVCACHE_SPACE=1, TORCH_COMPILE_DISABLE=1) so two CPU engines
fit on a 32 GB box. TORCH_COMPILE_DISABLE is currently mandatory for
cpu-vllm: torch._inductor's CPU-ISA probe runs even with
enforce_eager=True and needs g++ on PATH, which the LocalAI runtime
image doesn't ship -- to be addressed in a follow-up that bundles a
toolchain in the cpu-vllm backend.
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(vllm): bundle a g++ toolchain in the cpu-vllm backend image
torch._inductor's CPU-ISA probe (`cpu_model_runner.py:65 "Warming up
model for the compilation"`) shells out to `g++` at vllm engine
startup, regardless of `enforce_eager=True` -- the eager flag only
disables CUDA graphs, not inductor's first-batch warmup. The LocalAI
CPU runtime image (Dockerfile, unconditional apt list) does not ship
build-essential, and the cpu-vllm backend image is `FROM scratch`,
so any non-trivial inference on cpu-vllm crashes with:
torch._inductor.exc.InductorError:
InvalidCxxCompiler: No working C++ compiler found in
torch._inductor.config.cpp.cxx: (None, 'g++')
Bundling the toolchain in the CPU runtime image would bloat every
non-vllm-CPU deployment and force a single GCC version on backends
that may want clang or a different version. So this lives in the
backend, gated to BUILD_TYPE=='' (the CPU profile).
`package.sh` snapshots g++ + binutils + cc1plus + libstdc++ + libc6
(runtime + dev) + the math libs cc1plus links (libisl/libmpc/libmpfr/
libjansson) into ${BACKEND}/toolchain/, mirroring /usr/... layout. The
unversioned binaries on Debian/Ubuntu are symlink chains pointing into
multiarch packages (`g++` -> `g++-13` -> `x86_64-linux-gnu-g++-13`,
the latter in `g++-13-x86-64-linux-gnu`), so the package list resolves
both the version and the arch-triplet variant. Symlinks /lib ->
usr/lib and /lib64 -> usr/lib64 are recreated under the toolchain
root because Ubuntu's UsrMerge keeps them at /, and ld scripts
(`libc.so`, `libm.so`) hardcode `/lib/...` paths that --sysroot
re-roots into the toolchain.
The unversioned `g++`/`gcc`/`cpp` symlinks are replaced with wrapper
shell scripts that resolve their own location at runtime and pass
`--sysroot=<toolchain>` and `-B <toolchain>/usr/lib/gcc/<triplet>/<ver>/`
to the underlying versioned binary. That's how torch's bare `g++ foo.cpp
-o foo` invocation finds cc1plus (-B), system headers (--sysroot), and
the bundled libstdc++ (--sysroot, --sysroot is recursive into linker).
`run.sh` adds the toolchain bin dir to PATH and the toolchain's
shared-lib dir to LD_LIBRARY_PATH -- everything else (header search,
linker search, executable search) is encapsulated in the wrappers.
No-op for non-CPU builds, the dir doesn't exist there.
The cpu-vllm image grows by ~217 MB. Tradeoff is acceptable -- cpu-vllm
is already a niche profile (few users compared to GPU vllm) and the
alternative is a backend that crashes at first inference unless the
operator manually sets TORCH_COMPILE_DISABLE=1, which silently disables
all torch.compile optimizations.
Drops `TORCH_COMPILE_DISABLE=1` from tests/e2e/vllm-multinode -- the
smoke now exercises the real compile path through the bundled toolchain.
Test runtime is +20s for the warmup compile, still <90s end to end.
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* fix(vllm): scope jetson-ai-lab index to L4T-specific wheels via pyproject.toml
The L4T arm64 build resolves dependencies through pypi.jetson-ai-lab.io,
which hosts the L4T-specific torch / vllm / flash-attn wheels but also
transparently proxies the rest of PyPI through `/+f/<sha>/<filename>`
URLs. With `--extra-index-url` + `--index-strategy=unsafe-best-match`
uv would pick those proxy URLs for ordinary PyPI packages —
anthropic/openai/propcache/annotated-types — and fail when the proxy
503s. Master is hitting the same bug on its own l4t-vllm matrix entry.
Switch the l4t13 install path to a pyproject.toml that marks the
jetson-ai-lab index `explicit = true` and pins only torch, torchvision,
torchaudio, flash-attn, and vllm to it via [tool.uv.sources]. uv won't
consult the L4T mirror for anything else, so transitive deps fall back
to PyPI as the default index — no exposure to the proxy 503s.
`uv pip install -r requirements.txt` ignores [tool.uv.sources], so the
l4t13 branch in install.sh now invokes `uv pip install --requirement
pyproject.toml` directly, replacing the old requirements-l4t13*.txt
files. Other BUILD_PROFILEs continue using libbackend.sh's
installRequirements and never read pyproject.toml.
Local resolution test (x86_64, dry-run) confirms uv hits the L4T
index for torch and falls through to PyPI for everything else.
Assisted-by: claude-code:claude-opus-4-7-1m [Read] [Edit] [Bash] [Write]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
---------
Signed-off-by: Richard Palethorpe <io@richiejp.com>
|
||
|
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 (
|
||
|
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>
|
||
|
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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fbe4f0a99b |
fix(docs): replace Docsy alert shortcode with Relearn notice
The docs site uses the hugo-theme-relearn theme, which provides
`notice` instead of Docsy's `alert`. The face-recognition,
voice-recognition, and stores feature pages used `{{% alert %}}`,
breaking `hugo build` with "template for shortcode \"alert\" not
found".
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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21eace40ec |
feat(llama-cpp): expose split_mode option for multi-GPU placement (#9560)
Adds split_mode (alias sm) to the llama.cpp backend options allowlist, accepting none|layer|row|tensor. The tensor value targets the experimental backend-agnostic tensor parallelism from ggml-org/llama.cpp#19378 and requires a llama.cpp build that includes that PR, FlashAttention enabled, KV-cache quantization disabled, and a manually set context size. Assisted-by: Claude:claude-opus-4-7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Ettore Di Giacinto
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551ebdb57a |
fix(distributed): correct VRAM/RAM reporting on NVIDIA unified-memory hosts (#9545)
Workers on NVIDIA unified-memory hardware (DGX Spark / GB10, Jetson AGX Thor, Jetson Orin/Xavier/Nano) were reporting `available_vram=0` back to the frontend, so the Nodes UI showed the node as fully used even when most of the unified memory was actually free. Three causes addressed: * `isTegraDevice` only matched `/sys/devices/soc0/family == "Tegra"`. DGX Spark (SBSA) reports JEDEC codes there instead — `jep106:0426` for the NVIDIA manufacturer — so the Tegra/unified-memory fallback never ran. Renamed to `isNVIDIAIntegratedGPU` and extended to also match `jep106:0426[:*]` via `/sys/devices/soc0/soc_id`. * The unified-iGPU code defaulted the device name to `"NVIDIA Jetson"` when `/proc/device-tree/model` was missing. That's what happens for Thor inside a docker container, and always on DGX Spark. New `nvidiaIntegratedGPUName` resolves via dt-model → `/sys/devices/soc0/machine` → `soc_id` lookup (`jep106:0426:8901` → `"NVIDIA GB10"`) so the Nodes UI labels the box correctly. * Worker heartbeat sent `available_vram=0` (or total-as-available) when VRAM usage was momentarily unknown — e.g. when `nvidia-smi` intermittently failed with `waitid: no child processes` under containers without `--init`. Each such heartbeat overwrote the DB and made the UI flip to "fully used". `heartbeatBody` now omits `available_vram` in that case so the DB keeps its last good value. Also updates the commented GPU blocks in both compose files with `NVIDIA_DRIVER_CAPABILITIES=compute,utility`, `capabilities: [gpu, utility]`, and `init: true`, and documents the requirement in the distributed-mode and nvidia-l4t pages. Without `utility`, NVML/`nvidia-smi` are absent inside the container, which is what put the DGX Spark worker into the buggy fallback in the first place. Detection verified on live hardware (dgx.casa / GB10 and 192.168.68.23 / Thor) by running a cross-compiled probe of the new helpers on both host and inside the worker container. Assisted-by: Claude:opus-4.7 [Claude Code] |
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Ettore Di Giacinto
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f5eb13d3c2 |
feat(insightface): add antispoofing (liveness) detection (#9515)
* feat(insightface): add antispoofing (liveness) detection
Light up the anti_spoofing flag that was parked during the first pass.
Both FaceVerify and FaceAnalyze now run the Silent-Face MiniFASNetV2 +
MiniFASNetV1SE ensemble (~4 MB, Apache 2.0, CPU <10ms) when the flag is
set. Failed liveness on either image vetoes FaceVerify regardless of
embedding similarity. Every insightface* gallery entry now ships the
MiniFASNet ONNX weights so existing packs light up after reinstall.
Setting the flag against a model without the MiniFASNet files returns
FAILED_PRECONDITION (HTTP 412) with a clear install message — no
silent is_real=false.
FaceVerifyResponse gained per-image img{1,2}_is_real and
img{1,2}_antispoof_score (proto 9-12); FaceAnalysis's existing
is_real/antispoof_score fields are now populated. Schema fields are
pointers so they are fully absent from the JSON response when
anti_spoofing was not requested — avoids collapsing "not checked" with
"checked and fake" under Go's omitempty on bool.
Validated end-to-end over HTTP against a local install:
- verify + anti_spoofing, both real -> verified=true, score ~0.76
- verify + anti_spoofing, img2 spoof -> verified=false, img2_is_real=false
- analyze + anti_spoofing -> is_real and score per face
- flag against model without MiniFASNet -> HTTP 412 fail-loud
Assisted-by: Claude:claude-opus-4-7 go vet
* test(insightface): wire test target into test-extra
The root Makefile's `test-extra` already runs
`$(MAKE) -C backend/python/insightface test`, but the backend's
Makefile never defined the target — so the command silently errored
and the suite was never executed in CI. Adding the two-line target
(matching ace-step/Makefile) hooks `test.sh` → `runUnittests` →
`python -m unittest test.py`, which discovers both the pre-existing
engine classes (InsightFaceEngineTest, OnnxDirectEngineTest) and the
new AntispoofingTest. Each class skips gracefully when its weights
can't be downloaded from a network-restricted runner.
Assisted-by: Claude:claude-opus-4-7
* test(insightface): exercise antispoofing in e2e-backends (both paths)
Add a `face_antispoof` capability to the Ginkgo e2e suite and extend
the existing FaceVerify + FaceAnalyze specs with liveness assertions
covering BOTH paths:
real fixture -> is_real=true, score>0, verified stays true
spoof fixture -> is_real=false, verified vetoed to false
The spoof fixture is upstream's own `image_F2.jpg` (via the yakhyo
mirror) — verified locally against the MiniFASNetV2+V1SE ensemble to
classify as is_real=false with score ~0.013. That makes the assertion
deterministic across CI runs; synthetic/derived spoofs fool the model
unpredictably and would be flaky.
Makefile wires it up end-to-end:
- New INSIGHTFACE_ANTISPOOF_* cache dir + two ONNX downloads with
pinned SHAs, matching the gallery entries.
- insightface-antispoof-models target shared by both backend configs.
- FACE_SPOOF_IMAGE_URL passed via BACKEND_TEST_FACE_SPOOF_IMAGE_URL.
- Both e2e targets (buffalo-sc + opencv) now:
* depend on insightface-antispoof-models
* pass antispoof_v2_onnx / antispoof_v1se_onnx in BACKEND_TEST_OPTIONS
* include face_antispoof in BACKEND_TEST_CAPS
backend_test.go adds the new capability constant and a faceSpoofFile
fixture resolved the same way as faceFile1/2/3. Spoof assertions are
gated on both capFaceAntispoof AND faceSpoofFile being set, so a test
config that omits the spoof fixture degrades gracefully to "real path
only" instead of failing.
Assisted-by: Claude:claude-opus-4-7 go vet
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Ettore Di Giacinto
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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]
|
||
|
Adira
|
607efe5a4c |
fix(backend-monitor): accept model as a query parameter (#9411)
The /backend/monitor endpoint is routed as GET but its handler bound the
model name from a request body, which is invalid per REST and breaks
Swagger UI and OpenAPI codegen tools that refuse to send bodies with GET.
Switch to reading ?model=<name> as a query parameter and update the
Swagger annotation, regenerated spec files, and documentation. The
handler still falls back to body binding when the query parameter is
absent, so existing clients sending {"model": "..."} continue to work.
Fixes #9207
Signed-off-by: Adira Denis Muhando <dennisadira@gmail.com>
|
||
|
Ettore Di Giacinto
|
95efb8a562 |
feat(backend): add turboquant llama.cpp-fork backend (#9355)
* feat(backend): add turboquant llama.cpp-fork backend
turboquant is a llama.cpp fork (TheTom/llama-cpp-turboquant, branch
feature/turboquant-kv-cache) that adds a TurboQuant KV-cache scheme.
It ships as a first-class backend reusing backend/cpp/llama-cpp sources
via a thin wrapper Makefile: each variant target copies ../llama-cpp
into a sibling build dir and invokes llama-cpp's build-llama-cpp-grpc-server
with LLAMA_REPO/LLAMA_VERSION overridden to point at the fork. No
duplication of grpc-server.cpp — upstream fixes flow through automatically.
Wires up the full matrix (CPU, CUDA 12/13, L4T, L4T-CUDA13, ROCm, SYCL
f32/f16, Vulkan) in backend.yml and the gallery entries in index.yaml,
adds a tests-turboquant-grpc e2e job driven by BACKEND_TEST_CACHE_TYPE_K/V=q8_0
to exercise the KV-cache config path (backend_test.go gains dedicated env
vars wired into ModelOptions.CacheTypeKey/Value — a generic improvement
usable by any llama.cpp-family backend), and registers a nightly auto-bump
PR in bump_deps.yaml tracking feature/turboquant-kv-cache.
scripts/changed-backends.js gets a special-case so edits to
backend/cpp/llama-cpp/ also retrigger the turboquant CI pipeline, since
the wrapper reuses those sources.
* feat(turboquant): carry upstream patches against fork API drift
turboquant branched from llama.cpp before upstream commit 66060008
("server: respect the ignore eos flag", #21203) which added the
`logit_bias_eog` field to `server_context_meta` and a matching
parameter to `server_task::params_from_json_cmpl`. The shared
backend/cpp/llama-cpp/grpc-server.cpp depends on that field, so
building it against the fork unmodified fails.
Cherry-pick that commit as a patch file under
backend/cpp/turboquant/patches/ and apply it to the cloned fork
sources via a new apply-patches.sh hook called from the wrapper
Makefile. Simplifies the build flow too: instead of hopping through
llama-cpp's build-llama-cpp-grpc-server indirection, the wrapper now
drives the copied Makefile directly (clone -> patch -> build).
Drop the corresponding patch whenever the fork catches up with
upstream — the build fails fast if a patch stops applying, which
is the signal to retire it.
* docs: add turboquant backend section + clarify cache_type_k/v
Document the new turboquant (llama.cpp fork with TurboQuant KV-cache)
backend alongside the existing llama-cpp / ik-llama-cpp sections in
features/text-generation.md: when to pick it, how to install it from
the gallery, and a YAML example showing backend: turboquant together
with cache_type_k / cache_type_v.
Also expand the cache_type_k / cache_type_v table rows in
advanced/model-configuration.md to spell out the accepted llama.cpp
quantization values and note that these fields apply to all
llama.cpp-family backends, not just vLLM.
* feat(turboquant): patch ggml-rpc GGML_OP_COUNT assertion
The fork adds new GGML ops bringing GGML_OP_COUNT to 97, but
ggml/include/ggml-rpc.h static-asserts it equals 96, breaking
the GGML_RPC=ON build paths (turboquant-grpc / turboquant-rpc-server).
Carry a one-line patch that updates the expected count so the
assertion holds. Drop this patch whenever the fork fixes it upstream.
* feat(turboquant): allow turbo* KV-cache types and exercise them in e2e
The shared backend/cpp/llama-cpp/grpc-server.cpp carries its own
allow-list of accepted KV-cache types (kv_cache_types[]) and rejects
anything outside it before the value reaches llama.cpp's parser. That
list only contains the standard llama.cpp types — turbo2/turbo3/turbo4
would throw "Unsupported cache type" at LoadModel time, meaning
nothing the LocalAI gRPC layer accepted was actually fork-specific.
Add a build-time augmentation step (patch-grpc-server.sh, called from
the turboquant wrapper Makefile) that inserts GGML_TYPE_TURBO2_0/3_0/4_0
into the allow-list of the *copied* grpc-server.cpp under
turboquant-<flavor>-build/. The original file under backend/cpp/llama-cpp/
is never touched, so the stock llama-cpp build keeps compiling against
vanilla upstream which has no notion of those enum values.
Switch test-extra-backend-turboquant to set
BACKEND_TEST_CACHE_TYPE_K=turbo3 / _V=turbo3 so the e2e gRPC suite
actually runs the fork's TurboQuant KV-cache code paths (turbo3 also
auto-enables flash_attention in the fork). Picking q8_0 here would
only re-test the standard llama.cpp path that the upstream llama-cpp
backend already covers.
Refresh the docs (text-generation.md + model-configuration.md) to
list turbo2/turbo3/turbo4 explicitly and call out that you only get
the TurboQuant code path with this backend + a turbo* cache type.
* fix(turboquant): rewrite patch-grpc-server.sh in awk, not python3
The builder image (ubuntu:24.04 stage-2 in Dockerfile.turboquant)
does not install python3, so the python-based augmentation step
errored with `python3: command not found` at make time. Switch to
awk, which ships in coreutils and is already available everywhere
the rest of the wrapper Makefile runs.
* Apply suggestion from @mudler
Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com>
---------
Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com>
|
||
|
Ettore Di Giacinto
|
833b7e8557 |
chore(docs): update transcription endpoint
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Ettore Di Giacinto
|
0e7c0adee4 |
docs: document tool calling on vLLM and MLX backends
openai-functions.md used to claim LocalAI tool calling worked only on llama.cpp-compatible models. That was true when it was written; it's not true now — vLLM (since PR #9328) and MLX/MLX-VLM both extract structured tool calls from model output. - openai-functions.md: new 'Supported backends' matrix covering llama.cpp, vllm, vllm-omni, mlx, mlx-vlm, with the key distinction that vllm needs an explicit tool_parser: option while mlx auto- detects from the chat template. Reasoning content (think tags) is extracted on the same set of backends. Added setup snippets for both the vllm and mlx paths, and noted the gallery importer pre-fills tool_parser:/reasoning_parser: for known families. - compatibility-table.md: fix the stale 'Streaming: no' for vllm, vllm-omni, mlx, mlx-vlm (all four support streaming now). Add 'Functions' to their capabilities. Also widen the MLX Acceleration column to reflect the CPU/CUDA/Jetson L4T backends that already exist in backend/index.yaml — 'Metal' on its own was misleading. |
||
|
Ettore Di Giacinto
|
9ca03cf9cc |
feat(backends): add ik-llama-cpp (#9326)
* feat(backends): add ik-llama-cpp Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore: add grpc e2e suite, hook to CI, update README Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Apply suggestion from @mudler Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com> * 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> |
||
|
Ettore Di Giacinto
|
151ad271f2 |
feat(rocm): bump to 7.x (#9323)
feat(rocm): bump to 7.2.1 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Ettore Di Giacinto
|
706cf5d43c |
feat(sam.cpp): add sam.cpp detection backend (#9288)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Ettore Di Giacinto
|
b0d9ce4905 |
Remove header from OpenAI Realtime API documentation
Removed the header from the Realtime API documentation. Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com> |
||
|
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> |
||
|
Ettore Di Giacinto
|
11dc54bda9 |
fix(docs): commit distribution.md
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Ettore Di Giacinto
|
7e0b73deaa |
fix(docs): fix broken references to distributed mode
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Ettore Di Giacinto
|
8862e3ce60 |
feat: add node reconciler, allow to schedule to group of nodes, min/max autoscaler (#9186)
* always enable parallel requests Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat: add node reconciler, allow to schedule to group of nodes, min/max autoscaler Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore: move tests to ginkgo Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore(smart router): order by available vram Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Ettore Di Giacinto
|
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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Richard Palethorpe
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26384c5c70 |
fix(docs): Use notice instead of alert (#9134)
Signed-off-by: Richard Palethorpe <io@richiejp.com> |
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Ettore Di Giacinto
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f7e8d9e791 |
feat(quantization): add quantization backend (#9096)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Ettore Di Giacinto
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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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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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LocalAI [bot]
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bbe9067227 |
docs: Add troubleshooting guide for embedding models (fixes #9064) (#9065)
docs: Add troubleshooting guide for embedding models (#9064) - Add section on using gallery models for embeddings - Document common issues with embedding model configuration - Add troubleshooting guide for Qwen3 embedding models - Include correct configuration examples for Qwen3-Embedding-4B - Document context size limits and dimension parameters - Add table of Qwen3 embedding model specifications Fixes #9064 Signed-off-by: localai-bot <localai-bot@localai.io> Co-authored-by: localai-bot <localai-bot@localai.io> |
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Ettore Di Giacinto
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5affb747a9 |
chore: drop AIO images (#9004)
AIO images are behind, and takes effort to maintain these. Wizard and installation of models have been semplified massively, so AIO images lost their purpose. This allows us to be more laser focused on main images and reliefes stress from CI. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Richard Palethorpe
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f9a850c02a |
feat(realtime): WebRTC support (#8790)
* feat(realtime): WebRTC support Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(tracing): Show full LLM opts and deltas 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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8818452d85 |
feat(ui): MCP Apps, mcp streaming and client-side support (#8947)
* Revert "fix: Add timeout-based wait for model deletion completion (#8756)"
This reverts commit
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Ettore Di Giacinto
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85f3558d22 |
feat(ui): add canvas mode, support history in agent chat (#8927)
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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LocalAI [bot]
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9297074caa |
docs: expand GPU acceleration guide with L4T, multi-GPU, monitoring, and troubleshooting (#8858)
- Expand multi-GPU section to cover llama.cpp (CUDA_VISIBLE_DEVICES, HIP_VISIBLE_DEVICES) in addition to diffusers - Add NVIDIA L4T/Jetson section with quick start commands and cross-reference to the dedicated ARM64 page - Add GPU monitoring section with vendor-specific tools (nvidia-smi, rocm-smi, intel_gpu_top) - Add troubleshooting section covering common issues: GPU not detected, CPU fallback, OOM errors, unsupported ROCm targets, SYCL mmap hang - Replace "under construction" warning with useful cross-references to related docs (container images, VRAM management) Signed-off-by: localai-bot <localai-bot@users.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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LocalAI [bot]
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9090bca920 |
feat: Add documentation for undocumented API endpoints (#8852)
* feat: add documentation for undocumented API endpoints Creates comprehensive documentation for 8 previously undocumented endpoints: - Voice Activity Detection (/v1/vad) - Video Generation (/video) - Sound Generation (/v1/sound-generation) - Backend Monitor (/backend/monitor, /backend/shutdown) - Token Metrics (/tokenMetrics) - P2P endpoints (/api/p2p/* - 5 sub-endpoints) - System Info (/system, /version) Each documentation file includes HTTP method, request/response schemas, curl examples, sample JSON responses, and error codes. * docs: remove token-metrics endpoint documentation per review feedback The token-metrics endpoint is not wired into the HTTP router and should not be documented per reviewer request. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * docs: move system-info documentation to reference section Per review feedback, system-info endpoint docs are better suited for the reference section rather than features. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.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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Ettore Di Giacinto
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d21369ad7b |
Update shell completion documentation URL
Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com> |
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LocalAI [bot]
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efd402207c |
feat: Add shell completion support for bash, zsh, and fish (#8851)
feat: add shell completion support for bash, zsh, and fish - Add core/cli/completion.go with dynamic completion script generation - Add core/cli/completion_test.go with unit tests - Modify cmd/local-ai/main.go to support completion command - Modify core/cli/cli.go to add Completion subcommand - Add docs/content/features/shell-completion.md with installation instructions The completion scripts are generated dynamically from the Kong CLI model, so they automatically include all commands, subcommands, and flags. Co-authored-by: localai-bot <localai-bot@noreply.github.com> |
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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> |