fix(distributed): track in-flight for non-LLM inference methods
InFlightTrackingClient only wrapped a subset of the grpc.Backend
inference methods (Predict, Embeddings, TTS, AudioTranscription, Detect,
Rerank, ...). Methods like VAD were left as embedded passthrough, so
track() never ran for them.
In distributed mode every model is loaded with in_flight=1 as a
reservation; that reservation is only released by the OnFirstComplete
callback, which fires after the first *tracked* inference call completes.
A VAD-only model (e.g. silero-vad) never calls a tracked method, so the
reservation is never released and in-flight stays pinned at 1 forever -
which also blocks the router's idle-eviction logic.
Wrap the remaining unary inference methods (VAD, Diarize, Face*, Voice*,
TokenClassify, Score, AudioEncode, AudioDecode, AudioTransform) with the
same track()/reconcile() pattern. The three bidi-stream constructors
(AudioTransformStream, AudioToAudioStream, Forward) are deliberately left
as passthrough - their inference spans the stream lifetime, not the
constructor call, so track() there would fire onFirstComplete before any
data flows.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
The /v1/realtime WebRTC handler created the peer connection with a bare
webrtc.Configuration and no SettingEngine, so pion gathered a host ICE
candidate for every local interface. Under Docker host networking that
includes bridge addresses (docker0/veth, 172.x) a remote browser cannot
route to; the call establishes on a good pair and then drops once ICE
consent freshness checks fail on the unreachable candidates.
Add two opt-in knobs, applied via a pion SettingEngine:
- LOCALAI_WEBRTC_NAT_1TO1_IPS: advertise these IPs as the host candidates
(e.g. the host LAN IP)
- LOCALAI_WEBRTC_ICE_INTERFACES: restrict ICE gathering to these interfaces
Defaults are unchanged (empty => current all-interface behavior).
Assisted-by: Claude:claude-opus-4-8
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
Propagate terminal write errors from the chat prompt and explicitly ignore stream close errors during cleanup.
Update chat tests to assert response writer errors so errcheck passes without hiding failed writes.
Tests:
- go test -count=1 ./core/cli/chat
- go test -count=1 ./core/cli
Assisted-by: Codex:GPT-5
Signed-off-by: Ching Kao <0980124jim@gmail.com>
Add an opt-in `local-ai chat` command for testing chat models directly from the terminal without manually sending curl requests.
The command connects to a running LocalAI server, lists available models through the existing OpenAI-compatible API, streams chat completions, and supports interactive commands such as `/models`, `/model`, `/clear`, and `/exit`.
Keep `local-ai run` focused on the server lifecycle so the web UI, API clients, and multiple chat terminals can coexist against the same server.
Document the new command and terminal workflow in the README and CLI docs.
Tests:
- go test -count=1 ./core/cli/chat
- go test -count=1 ./core/cli
Assisted-by: Codex:GPT-5
Signed-off-by: Ching Kao <0980124jim@gmail.com>
* dev knowledge.go structure
Signed-off-by: Pete Chen <petechentw@gmail.com>
* feat(agents): append KB source citations to responses
Render structured KB citations as a Sources block after agent responses, linking each source to the existing raw collection entry endpoint.
Keep long-term memory writes on the original model response so citation blocks do not get stored back into the knowledge base.
Tested with: go test ./core/services/agents
Assisted-by: Codex:gpt-5
Signed-off-by: Pete Chen <petechentw@gmail.com>
* Collect KB citations from tool searches
Signed-off-by: Pete Chen <petechentw@gmail.com>
* fix(agents): append KB sources in local chats
Apply the shared KB citation post-processing to standalone LocalAGI chat responses so the React agent chat receives the same clickable Sources block as the native executor path. Also fix the run target to use the current cmd/local-ai entrypoint.
Assisted-by: Codex:gpt-5
Signed-off-by: Pete Chen <petechentw@gmail.com>
---------
Signed-off-by: Pete Chen <petechentw@gmail.com>
Co-authored-by: shihyunhuang <shihyunhuang88@gmail.com>
Co-authored-by: TLoE419 <tloemizuchizu@gmail.com>
Co-authored-by: Ching Kao <0980124jim@gmail.com>
* fix(reasoning): stop prefilled <think> from swallowing tag-less answers
When a chat template injects the thinking start token into the prompt (so
DetectThinkingStartToken returns e.g. "<think>"), the model's output begins
inside a reasoning block and carries only the closing tag. The non-jinja
autoparser fallback (peg-native "pure content" mode, issue #9985) prepends the
start token so the extractor can pair it with the model's </think>.
But on a COMPLETE response that contains no closing tag, the model answered
directly with no reasoning at all. Prepending the start token there manufactures
an unclosed block that swallows the entire answer into reasoning, leaving the
OpenAI `content` field empty. This breaks short/direct answers — session names,
JSON summaries, any terse completion where the model skips the think block —
which come back with empty content. Regression surfaced by #9991, which added
the defensive prefill extraction to the complete-response paths.
Add reasoning.ExtractReasoningComplete: it only honors a prefilled start token
when the response actually contains the matching closing tag (proof a reasoning
block exists). Genuine reasoning tags already in the content still extract;
tag-less content stays content. Apply it at every complete-response site
(applyAutoparserOverride, realtime, openresponses). The streaming per-token
extractor is intentionally left on ExtractReasoningWithConfig — mid-stream an
as-yet-unclosed block is legitimate and must surface as reasoning deltas.
Also adds reasoning.ClosingTokenForStart and hoists the default reasoning tag
pairs to package scope so both helpers share one source of truth.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* test(reasoning): cover the enable_thinking=false non-thinking-mode regression
Adds the end-to-end case that actually broke session summaries / auto-titles
and was not covered before: a request with enable_thinking=false against a
<think>-capable model. In non-thinking mode the model emits no reasoning block,
so llama.cpp's autoparser returns ChatDeltas with content set and
reasoning_content empty (verified against stock llama-server: same model with
chat_template_kwargs.enable_thinking=false returns reasoning_content=null,
content="hello"). thinkingStartToken is still "<think>" because it is detected
per-model from the enable_thinking=true render, so the old code prepended it and
swallowed the answer. The test fails without the ExtractReasoningComplete gate.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* chore(llama-cpp): bump to 8f83d6c for mtmd video input support
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(llama-cpp): forward video input to mtmd (template + non-template paths)
Wire request->videos() into grpc-server.cpp mirroring the existing image
and audio handling: a video_data build + non-template files extraction, and
input_video chat chunks on the tokenizer-template path. allow_video is
auto-set at model load by the vendored upstream chat_params.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(ui): add video attachment support to the chat UI
Mirror the image/audio attachment path for video: emit video_url content
parts, accept video/* in the picker, keep video files as base64, show a
film icon badge, and render attached video inline with a <video> player.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(llama-cpp): patch mtmd video stdin double-close (heap crash)
Upstream mtmd video input (ggml-org/llama.cpp#24269) double-fcloses the
ffmpeg/ffprobe stdin FILE: feed_stdin() fclose()s the FILE returned by
subprocess_stdin() (which is sp->stdin_file), then subprocess_destroy()
fclose()s the same pointer again -> heap corruption that aborts the
backend on any base64 input_video request (the CLI --video file path is
unaffected). Vendor a one-line fix (null sp->stdin_file after fclose)
via prepare.sh's patches/ until upstream merges it.
Verified e2e with gemma-4-e2b-it-qat-q4_0: video frames decode via
ffmpeg and the model answers correctly (red clip -> 'Red', blue -> 'Blue').
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore(llama-cpp): re-pin to upstream #24316, drop vendored stdin patch
Upstream replaced the ad-hoc video stdin handling with a proper RAII
refactor (ggml-org/llama.cpp#24316, "mtmd: refactor video subproc
handling"), which includes the same `sp->stdin_file = nullptr` guard our
patch added (plus join-before-destroy ordering). Re-pin LLAMA_VERSION to
that branch head and drop patches/0001 - it's now redundant.
Verified e2e with gemma-4-e2b-it-qat-q4_0: no crash, video frames decode
and the model answers correctly (red clip -> "Red", blue -> "Blue").
NOTE: #24316 is not yet merged, so this pins to its branch-head commit
(28ca1e60). Re-pin to the squash-merge commit on master once it lands,
otherwise `git fetch` may lose the commit after the branch is deleted.
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>
* ⬆️ Update CrispStrobe/CrispASR
Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
* fix(crispasr): link crispasr-lib CMake target instead of crispasr
The dependency-bump regeneration of this branch reset CMakeLists.txt to
master and dropped the prior link-target fix, reintroducing the
`cannot find -lcrispasr` failure. Upstream CrispASR (f7838a3) defines the
library as the CMake target `crispasr-lib` (with OUTPUT_NAME crispasr);
there is no target named `crispasr`, so target_link_libraries falls back
to a bare `-lcrispasr` linker flag that cannot be resolved. Point the link
at the real target name.
Verified locally: CPU cmake-configure of the bumped source generates a
gocrispasr link line referencing sources/CrispASR/src/libcrispasr.a with no
dangling -lcrispasr.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:opus-4.8 [Claude Code]
---------
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>
* ⬆️ Update antirez/ds4
Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
* fix(ds4): link ds4_ssd.o into the backend build
Upstream antirez/ds4 splits the SSD expert-cache into its own ds4_ssd.c
translation unit, whose symbols (ds4_ssd_memory_lock_acquire/release,
ds4_ssd_cache_experts_for_byte_budget, ds4_ssd_auto_cache_plan) are
referenced by ds4.c/ds4_cpu.o. The dependency-bump automation regenerated
this branch from clean master and dropped the prior linkage fix, so the
cpu-ds4 / cublas-ds4 backend builds fail again with undefined references.
Re-apply the ds4_ssd.o linkage GPU-agnostically (mirroring ds4_distributed.o)
in both the backend Makefile (DS4_OBJ_TARGET + the engine-object build rule
for every GPU mode) and CMakeLists.txt (list(APPEND DS4_OBJS ds4_ssd.o)).
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:opus-4.8 [Claude Code]
---------
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>
Add the remaining official Google Gemma 4 QAT Q4_0 GGUFs (E2B, E4B,
26B-A4B, 31B) next to the existing 12B entry, each shipping its
multimodal mmproj.
Also add three MTP (Multi-Token Prediction) speculative-decoding bundles
that pair each QAT target with a QAT-matched assistant/drafter head:
- 12B <- Janvitos/gemma-4-12B-it-qat-assistant-MTP-Q8_0-GGUF
- 26B-A4B <- boxwrench/gemma-4-qat-mtp-assistant-heads
- 31B <- boxwrench/gemma-4-qat-mtp-assistant-heads
The assistant heads use the gemma4_assistant architecture and are not
standalone chat models, so each entry bundles the target + draft and
sets draft_model together with the draft-mtp spec options
(spec_type:draft-mtp / spec_n_max:6 / spec_p_min:0.75), matching
MTPSpecOptions() in core/config/mtp.go. QAT-matched heads raise draft
acceptance substantially over generic non-QAT heads.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
* fix(galleryop): self-evict terminal ops from OpCache.GetStatus
The processingBackends map (the UI 'reinstalling' spinner source) only cleared
an op when a client polled /api/backends/job/:uid. The Manage-page Reinstall and
Upgrade buttons never poll, so completed installs leaked into processingBackends
forever and the backend card spun 'reinstalling' even though the install had
finished. Evict terminal ops on the list read instead; DeleteUUID already
broadcasts the eviction so peer replicas converge.
Reproduced on a live 5-node distributed cluster: 5 backends sat in
processingBackends with underlying jobs reporting completed:true,progress:100.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(nodes): clear pending backend ops behind offline/draining nodes
ListDuePendingBackendOps filters status=healthy, so a backend op queued against
a node that went offline (stale heartbeat) or draining (admin action) was never
retried, aged out, or deleted - it leaked forever and kept the UI operation
spinning. Add DeleteStalePendingBackendOps and run it each reconcile pass:
draining nodes are cleared immediately (model rows already purged), offline
nodes once their heartbeat is older than a grace window (blip protection).
Reproduced on a live cluster: orphaned llama-cpp install rows targeting an
offline (nvidia-thor) and a draining (mac-mini-m4) node sat at attempts=0
indefinitely.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(nodes): stream per-node progress during backend upgrade
The install dispatch subscribed to a per-op progress subject and streamed
per-node download ticks; the upgrade dispatch did a bare 15-minute blocking
NATS round-trip with no subscription, so the UI showed progress:0 the whole
time (the 'reinstalling but nothing happens' report on a slow node).
Thread the op ID through BackendManager.UpgradeBackend -> the distributed
manager -> the adapter, and have the adapter subscribe to the per-op progress
subject before the request (extracted into a shared subscribeProgress helper
reused by install/upgrade/force-fallback). The worker's upgradeBackend now
creates the same DebouncedInstallProgressPublisher installBackend uses. An
upgrade is a force-reinstall, so it reuses SubjectNodeBackendInstallProgress
rather than minting a new subject - no new NATS permission, no new
rolling-update compat surface. Reconciler-driven retries pass empty
opID/onProgress and stay on the silent path.
Reproduced on a live cluster: upgrade of llama-cpp-development on agx-orin-slow
sat at progress:0 for 4+ minutes with no per-node feedback.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(galleryop): persist cancellation + periodically reap orphaned ops
Two distributed gaps surfaced when a replica was killed mid-upgrade on a live
cluster, leaving the backend stuck 'processing' in the UI forever:
1. CancelOperation flipped the in-memory status to cancelled and broadcast a
NATS event but never persisted the terminal status. On the next replica
restart the still-active row re-hydrated straight back into
processingBackends and the UI spun again. It now calls store.Cancel(id) so
the cancel survives a restart.
2. CleanStale (which marks abandoned active ops failed) only ran once on
startup, so an op orphaned AFTER startup - its owning replica's foreground
handler goroutine gone - was never reaped until the next restart. Add
GalleryService.ReapStaleOperations and run it on a 15m ticker (CleanStale
now returns the reaped count for observability).
Neither is covered by the OpCache self-evict fix: an orphaned op never reaches
Processed, so it would never self-evict.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(review): address self-review findings on the distributed install fixes
Three findings from an adversarial review of this branch:
1. CRITICAL - OpCache.GetStatus crashed under concurrent load. m.Map() returns
the live internal map by reference, so deleting from it on the read path was
an unsynchronized write to a map four HTTP handlers poll every ~1s -> a
'concurrent map writes' fatal. Rewritten to iterate a Keys() snapshot, build
a fresh result map, and apply evictions via the locked DeleteUUID after the
loop. Added a -race concurrency regression guard.
2. HIGH - GetStatus evicted failed ops too, hiding them from /api/operations
and breaking the dismiss-failed-op flow (the panel keeps Error != nil ops so
the admin can read the error and click Dismiss). Eviction now fires only for
terminal ops with Error == nil (success/cancelled); failures are retained.
3. MEDIUM - DeleteStalePendingBackendOps missed StatusUnhealthy nodes. A node
marked unhealthy on a NATS ErrNoResponders never transitions to offline
(health.go skips re-marking it), so its pending ops leaked exactly like the
offline case. Unhealthy is now reaped via the same stale-heartbeat grace path
(a fresh-heartbeat node is recovering and keeps its op).
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(review-2): don't evict the still-installing soft-path; don't spin on failed ops
Second review pass found two issues:
1. MEDIUM (Go) - OpCache.GetStatus evicted the ErrWorkerStillInstalling
soft-path op. That op is deliberately Processed=true with no error to show a
yellow in-progress state when a worker timed out the NATS round-trip but is
still installing in the background; the reconciler confirms the real outcome
later. Evicting it (and broadcasting OpEnd + marking the DB completed) hid an
install that may still fail. Eviction is now scoped to a clean success
(progress 100 + 'completed', matching the job-poll's historical condition) or
a cancellation - the soft-path (progress != 100) and failures are kept.
2. MEDIUM (React) - the Backends gallery card rendered ANY operation as an
'Installing...' spinner, so a failed op (now intentionally kept in the list
for the OperationsBar error + Dismiss) spun forever. Exclude errored ops from
the card spinner, mirroring Models.jsx (isInstalling already excludes
op.error). The error + Dismiss still surface in the global OperationsBar.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(ui): refresh Manage backends table when an operation settles
The Manage backends table fetched installed backends only on mount/after delete
and checked upgrades only on tab activation. After a reinstall/upgrade completed
neither re-ran, so the installed-version cell and the 'update available' badge
stayed stale until the user switched tabs - the op looked like it 'did nothing'.
Watch the operations list (via useOperations) and re-fetch installed backends +
available upgrades whenever the count settles, mirroring the operations.length
watch Backends.jsx already uses. Consolidates the prior tab-activation upgrades
check into the same effect.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
When the models directory holds many GGUF files, startup parsed every
model's full GGUF — including the tokenizer vocab arrays
(tokenizer.ggml.tokens/scores/merges, often >100k entries) — once per
model while guessing defaults. On slow storage (e.g. a models directory
on a Docker volume) those hundreds of thousands of tiny reads dominate
boot time before the HTTP server comes up.
The default-guessing path and the VRAM metadata reader only consume
scalar metadata and array lengths, never the array contents. Parse with
SkipLargeMetadata (seek past large arrays) and UseMMap (fault in a few
header pages instead of issuing per-element read() syscalls). For a
256k-token vocab this cuts the parse from ~524k read() syscalls to 8.
The mapping is released when ParseGGUFFile returns.
Fixes#9790
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Adira Denis Muhando <dennisadira@gmail.com>
Gemma4 MTP (ggml-org/llama.cpp#23398) registers the prediction head as a
separate `gemma4-assistant` architecture. That assistant GGUF still carries
`<arch>.nextn_predict_layers`, so the architecture-agnostic detection in
HasEmbeddedMTPHead matched it and appended the `spec_type:draft-mtp` defaults.
Unlike the DeepSeek/Qwen embedded-head models, an assistant checkpoint cannot
self-speculate: it is a draft model that requires a paired target context
(`ctx_other`) and throws if loaded alone. Auto-applying the self-spec defaults
to a standalone assistant import therefore produces a broken config.
Guard the detection against draft-only assistant architectures (the `-assistant`
suffix is upstream's naming convention) so importing one no longer yields a
self-speculation config. Two-model target+draft pairing remains expressible
manually via `draft_model:` and is left to a follow-up.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
* feat(parakeet-cpp): real segment timestamps (NeMo-faithful)
Offline: replace the single synthetic whole-clip segment with multiple
segments grouped exactly like NeMo's get_segment_offsets - a new segment
after sentence-ending punctuation ('. ? !'), each carrying start/end and
its time-window token ids. The optional model option segment_gap_threshold
(NeMo's unit: encoder FRAMES, default 0=off) adds NeMo's silence-gap split,
converted to seconds via the JSON frame_sec the engine now reports.
Per-segment words are still gated behind timestamp_granularities=["word"];
a zero-word document falls back to a single text segment.
Streaming: when libparakeet.so exposes the ABI v4 JSON entry points
(probed), drive parakeet_capi_stream_feed_json / _finalize_json and
accumulate the streamed per-word timestamps into per-utterance segments
(EOU stays the boundary), so streaming FinalResult segments now carry
start/end. Falls back to the text-only feed against an older library.
Pure-Go specs cover splitWordsIntoSegments (punctuation + gap rules, NeMo
elif order, fallback), transcriptResultFromDoc (multi-segment, token
windows, word-granularity gate), and the streaming segmenter.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* docs(audio): document parakeet-cpp segment timestamps + segment_gap_threshold
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* test(parakeet-cpp): update model-gated specs for multi-segment output
The offline AudioTranscription specs asserted the old single synthetic
segment (Segments HaveLen(1), Segments[0].Text == res.Text). With
NeMo-faithful segmentation a multi-sentence clip now yields multiple
punctuation-delimited segments, so assert the new contract instead:
one-or-more time-ordered segments, each with text and (under word
granularity) per-segment words whose span tracks the segment start/end.
Caught by running the model-gated suite on the dgx (GB10) against the
real tdt_ctc-110m + realtime_eou models.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
* chore(turboquant): bump TheTom/llama-cpp-turboquant to 7d9715f1
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* fix(turboquant): drop obsolete legacy-spec shim after fork rebased
The TheTom/llama-cpp-turboquant fork (pin c9aa86a) rebased past the
upstream common_params_speculative refactor (ggml-org/llama.cpp
#22397/#22838/#22964), the model_tgt rename (#22838) and get_media_marker
(#21962). The old fork-compat shim forced now-wrong legacy code paths,
breaking the build with errors like 'struct common_params_speculative has
no member named mparams_dft / type' and 'server_context_impl has no member
named model'.
Remove the obsolete LOCALAI_LEGACY_LLAMA_CPP_SPEC branches from the shared
grpc-server.cpp (stock llama-cpp and the modern fork both take the modern
path now), and narrow the one remaining gap (the fork still lacks
common_params::checkpoint_min_step) to a dedicated
LOCALAI_TURBOQUANT_NO_CHECKPOINT_MIN_STEP guard injected by
patch-grpc-server.sh. The patch script now only adds the turbo2/3/4
KV-cache types and injects that one macro.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* fix(turboquant): HIP-port the fork's CUDA additions (copy2d 3D-peer + cudaEventCreate)
The turboquant fork adds/modifies a few ggml-cuda.cu spots with CUDA APIs that
ggml's HIP/MUSA shim does not provide, breaking the -gpu-rocm-hipblas-turboquant
build. patches/0001-hip-guard-copy2d-peer-fastpath.patch (applied by
apply-patches.sh) ports them:
- Guard ggml_cuda_copy2d_across_devices's 3D-peer copy fast path with
#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) so HIP/MUSA fall through
to the existing cudaMemcpyAsync staging fallback (HIP genuinely lacks
cudaMemcpy3DPeerAsync, per the fork's own comment).
- Create the device event in ggml_backend_cuda_device_event_new with the
HIP-aliased cudaEventCreateWithFlags(.., cudaEventDisableTiming) instead of the
un-aliased plain cudaEventCreate, matching this file's own usage elsewhere.
CUDA builds are unaffected.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* ci(turboquant): drop the ROCm/hipblas build flavor
The TheTom/llama-cpp-turboquant fork is not ROCm-clean at the current pin:
beyond the CUDA-API gaps already patched (3D-peer copy, cudaEventCreate),
its llama.cpp base fails to compile the flash-attention MMA f16 kernels for
head-dim 640 under HIP (cols_per_warp evaluates to 0 -> division-by-zero /
non-constant static asserts in fattn-mma-f16.cuh). That is a deep
ggml-on-ROCm kernel issue, not something a small fork patch can paper over.
Drop -gpu-rocm-hipblas-turboquant from the build matrix so turboquant still
ships for cpu / cublas / vulkan / sycl. Re-add it once the fork's HIP path
compiles (or upstream ggml fixes the large-head-dim MMA kernels for ROCm).
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
The Hugo relearn theme does not provide an "alert" shortcode, so the
docs deploy failed at the Build site step:
failed to extract shortcode: template for shortcode "alert" not found
docs/content/features/distributed-mode.md:136
Convert the warning block to the theme-supported notice shortcode used
everywhere else in the docs.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
* feat(stablediffusion-ggml): support Ideogram4 unconditional diffusion model
Bump stable-diffusion.cpp from 1f9ee88 to b9254dd, the upstream commit that
adds Ideogram4 support (leejet/stable-diffusion.cpp#1609). Ideogram4 derives
its classifier-free guidance from a separate unconditional diffusion model,
exposed upstream through the new sd_ctx_params_t.uncond_diffusion_model_path
field.
Wire that field into the gosd wrapper via a new uncond_diffusion_model_path
option. The _path suffix is deliberate: the Go loader only resolves options
whose name contains "path" to an absolute path under the model directory, so
this keeps the option consistent with diffusion_model_path and
high_noise_diffusion_model_path.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* feat(gallery): add Ideogram4 stablediffusion-ggml models
Single-file GGUF weights for Ideogram4 are now published
(stduhpf/ideogram-4-gguf), so add the model to the gallery. Ideogram4 is a
text-to-image model with strong, accurate in-image text rendering, driven by
a Qwen3-VL-8B text encoder and real classifier-free guidance from a separate
unconditional diffusion model (the uncond_diffusion_model_path support added
in the preceding commit).
Two index entries, both built on gallery/virtual.yaml with the full config
inlined in overrides (same pattern as the other models, no dedicated template
file):
- ideogram-4-iq4nl-ggml (4-bit, ~11.6GB diffusion)
- ideogram-4-q8_0-ggml (8-bit, ~20GB diffusion)
Each bundles the diffusion + unconditional GGUF (stduhpf), the
Qwen3-VL-8B-Instruct text encoder (unsloth), and the FLUX.2 VAE (Comfy-Org
mirror, non-gated). cfg_scale is 7 to match the upstream Ideogram4 default,
since it performs real CFG unlike the guidance-distilled Flux/Z-Image models.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
* feat(parakeet-cpp): honor request language (multilingual nemotron) on batched + streaming paths
Reads opts.GetLanguage() and threads it through to the new
parakeet_capi_transcribe_pcm_batch_json_lang and parakeet_capi_stream_begin_lang
C-API entry points, both probed with Dlsym so the backend still loads against an
older libparakeet.so (falling back to the non-lang paths, i.e. model default).
parakeet.cpp's batched C-API takes a single target_lang for the whole batch, so
the dispatcher only coalesces same-language requests: a request whose language
differs from the batch leader is held as a single carry-over and becomes the
leader of the next batch, never dropped and never left waiting (including on
shutdown). A new batcher test asserts no dispatched batch is ever mixed-language
and that every submitted request still receives a reply.
Assisted-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* feat(gallery): add parakeet-cpp-nemotron-3.5-asr-streaming-0.6b; bump parakeet.cpp pin
Adds the multilingual prompt-conditioned streaming model to the gallery (q8_0
default, OpenMDW-1.1) and bumps the parakeet-cpp backend pin to the parakeet.cpp
commit that ships nemotron support plus batched causal subsampling and the
batched target_lang C-API.
Assisted-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
chore(parakeet-cpp): bump pin to banded long-audio attention (843600590)
Update PARAKEET_VERSION to mudler/parakeet.cpp@843600590f
(merge of parakeet.cpp#9). Brings NeMo rel_pos_local_attn banded/Longformer
attention with the chunk-matmul construction: long audio now uses O(T*window)
attention instead of global O(T^2), fixing the encoder OOM on long clips
(~16.6-min clip: 54GB->9.4GB peak, ~4x faster) at NeMo's full [128,128] window.
Short clips are unchanged (global path). No C-ABI change.
Assisted-by: Claude:claude-opus-4-8
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
chore: bump LocalAGI and localrecall (index-backed RRF hybrid search)
Bumps the agent stack to pull in the PostgreSQL hybrid-search fix:
- mudler/localrecall -> v0.6.3-...-9a3b3321a9cd (mudler/LocalRecall#46, merged)
- mudler/LocalAGI -> ...-14aed1ae4336 (mudler/LocalAGI#477, merged)
localrecall's hybrid search previously sorted on a wrapped scalar
similarity expression, which blinded the planner into a full sequential
scan over every row and exceeded the statement timeout on large
collections, returning an empty result set. It now uses the canonical
Reciprocal Rank Fusion pattern (index-backed candidate retrieval + FULL
OUTER JOIN + weighted RRF).
Fixes#10186
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
* feat: forward reasoning_effort to the backend so jinja models honor it
reasoning_effort was only mapped to the binary enable_thinking toggle and
otherwise reached Go-side templates — it was never sent to the backend. So
jinja-templated models whose chat template keys on reasoning_effort (gpt-oss
Harmony, LFM2.5) could not be driven by it: LFM2.5 ignores enable_thinking and
kept emitting <think>.
Forward the effective reasoning_effort to the backend as a chat_template_kwarg
(mirroring enable_thinking) in grpc-server.cpp, and put it in PredictOptions
metadata (gRPCPredictOpts). Add a config-level default: ModelConfig.reasoning_effort
and Pipeline.reasoning_effort, resolved by ModelConfig.ApplyReasoningEffort
(request value overrides config default, none->disable / level->enable, an
operator's reasoning.disable wins). request.go now uses that helper.
Assisted-by: Claude:claude-opus-4-8 go test, golangci-lint
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(realtime): set the pipeline LLM's reasoning_effort
Apply Pipeline.ReasoningEffort to the pipeline's LLM config when the realtime
model is built (per-session copy, overrides the LLM's own reasoning_effort),
and surface the resolved effort on the template input so Go-templated models
get it too. jinja models receive it via the backend metadata. This lets a
realtime pipeline disable thinking on models that only honor reasoning_effort
(e.g. LFM2.5), which enable_thinking can't.
Assisted-by: Claude:claude-opus-4-8 go test, golangci-lint
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
The worker HTTP file-transfer server is authenticated by the registration
token via checkBearerToken, which fails open on an empty token: every
/v1/files, /v1/files-list and /v1/backend-logs request is then served
unauthenticated, granting read/write to the worker's models/staging/data
directories. The fail-open was also silent (the only auth log sat on the
unreachable reject branch), and the worker process never runs
DistributedConfig.Validate(), so the existing frontend warning did not
cover the component that exposes the server.
Mirror the NatsRequireAuth pattern: keep anonymous as the default but make
it loud and opt-in enforceable.
- Log a prominent warning when the file-transfer server starts tokenless.
- Add LOCALAI_REGISTRATION_REQUIRE_AUTH: DistributedConfig.Validate() errors
on an empty token (frontend) and the worker refuses to start (fail-fast,
before registration), so production can fail closed. Also satisfies the
F-003 suggestion to fail Validate() on distributed + empty token.
- Add LOCALAI_DISTRIBUTED_REQUIRE_AUTH umbrella switch implying both
RegistrationRequireAuth and NatsRequireAuth — one production knob locking
down the registration/file-transfer layer and the NATS bus together; the
granular flags remain available as single-layer overrides. Wired into the
frontend, supervisor worker, and agent worker (vLLM worker has neither a
NATS connection nor a file-transfer server, so it is left untouched).
- Document in distributed-mode.md (warning callout + flag tables).
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* fix(distributed): self-heal stale 'model not loaded' routing
In distributed mode the registry can list a model as loaded on a node
while the worker has evicted it (autonomous LRU eviction, an out-of-band
unload, etc.) yet the backend process survives. The router's cached-node
check only verifies the process is alive (probeHealth), so it routes there
and inference fails with "<backend>: model not loaded" — and stays broken
until the controller restarts and rebuilds its registry.
InFlightTrackingClient now reconciles this: when a tracked inference call
returns a model-not-loaded error, it drops the stale replica row
(RemoveNodeModel) so the next request reloads the model on a healthy node
instead of routing back to the evicted one. The original error is returned
unchanged; only the registry is corrected.
Assisted-by: Claude:claude-opus-4-8 go vet
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactor(distributed): typed model-not-loaded error via gRPC status code
Replace the controller-side error-string match with a shared, code-aware
helper. Go error types don't survive the gRPC boundary, so the signal is
carried as a status code (FailedPrecondition):
- pkg/grpc/grpcerrors: ModelNotLoaded(backend) constructor +
IsModelNotLoaded(err) checker (status-code first, message fallback for
backends not yet migrated).
- InFlightTrackingClient.reconcile now uses grpcerrors.IsModelNotLoaded.
- Migrate the Go backends that emit this error (parakeet-cpp, cloud-proxy,
rfdetr-cpp) to the typed constructor.
Acting on a false positive is harmless (the model is just reloaded).
Assisted-by: Claude:claude-opus-4-8 go vet
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>
FLAG_FACE_RECOGNITION and FLAG_SPEAKER_RECOGNITION already existed as
ModelConfigUsecase bitmask flags, and GuessUsecases already gate-checks
both backends by name — but BackendCapabilities had no entries for
either, so the UI could not classify them.
Also missing were the Method* constants for the five proto-defined RPCs
these backends implement (FaceVerify, FaceAnalyze, VoiceVerify,
VoiceEmbed, VoiceAnalyze) and the corresponding Usecase* strings
and UsecaseInfoMap entries needed to wire them into the rest of the
capability system.
Changes:
- Add MethodFaceVerify, MethodFaceAnalyze, MethodVoiceVerify,
MethodVoiceEmbed, MethodVoiceAnalyze GRPCMethod constants
- Add UsecaseFaceRecognition ("face_recognition") and
UsecaseSpeakerRecognition ("speaker_recognition") Usecase constants
- Add UsecaseInfoMap entries for both new usecases, referencing the
existing FLAG_FACE_RECOGNITION and FLAG_SPEAKER_RECOGNITION flags
- Register insightface: Embedding + Detect + FaceVerify + FaceAnalyze
- Register speaker-recognition: VoiceVerify + VoiceEmbed + VoiceAnalyze
Follows up on #10107 which left these two out because they needed new
constants first.
Assisted-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Signed-off-by: Adira Denis Muhando <dennisadira@gmail.com>
Distributed file-staging treated every model path field (ModelFile, etc.)
as a single regular file: it os.Open'd the path and streamed its fd as the
HTTP PUT body. For directory-based models — e.g. qwen3-tts-cpp, whose
weights and tokenizer ggufs live under one directory referenced by
parameters.model — opening the directory succeeds but reading its fd
returns EISDIR, so routing the model to a remote NATS worker failed with
"read /models/<model>: is a directory". Single-file models were unaffected,
so only multi-file pipelines (e.g. the realtime TTS stage) broke.
stageModelFiles now detects a directory path field and stages each
contained file individually (via the new stageDirectory helper), preserving
structure with the existing StagingKeyMapper and rewriting the field to the
remote directory (deriving ModelPath as before). countStageableFiles makes
the progress total count a directory's files so the staging tracker stays
accurate.
Assisted-by: Claude:claude-opus-4-8 go vet
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
The qwen3-tts.cpp backend honored the request `language` field only via exact lowercase two-letter codes in the C++ language_to_id table, silently defaulting to English for anything else (en-US, EN, english, ...).
Add normalizeLanguage() in the Go handler: lowercase + trim, strip the region/locale suffix (en-US, pt_BR, zh-Hans -> en/pt/zh), and resolve common English full names (english -> en). The canonical codes match the existing C++ table, so no C++ change is needed. Covered by a pure-Go Ginkgo spec. Also document the language field and accepted forms under the Qwen3-TTS docs.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
