mirror of
https://github.com/mudler/LocalAI.git
synced 2026-06-28 18:37:43 -04:00
ea72a56e2c8f423e40c839d18833eed669c18e03
898 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
Ettore Di Giacinto
|
ea72a56e2c |
Merge origin/master + pin-sync paged backend to 0ed235ea
master auto-bumped the stock llama-cpp pin 9d5d882d -> 0ed235ea and updated the shared grpc-server.cpp. The paged backend's pin must track the stock pin (the grpc-server.cpp is shared), so bump its LLAMA_VERSION to match. All 28 paged patches apply clean on 0ed235ea (verified against a fresh upstream clone). The bf16-tau state-serialization fix (patch 0026) is included. Bit-exact gate + full grpc-server build verify on GPU/CI to follow. Assisted-by: Claude:opus-4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
LocalAI [bot]
|
de2ec2f136 |
feat(backends): add voice-detect + face-detect ggml backends (replace Python insightface/speaker-recognition) (#10441)
* feat(voice-detect): add Go purego backend for voice-detect.cpp Add backend/go/voice-detect implementing the Backend gRPC voice subset (VoiceEmbed/VoiceVerify/VoiceAnalyze) over libvoicedetect.so via purego, mirroring the parakeet-cpp / omnivoice-cpp backends. The flat voicedetect_capi C ABI is dlopen'd cgo-less; malloc'd string and float-vector returns are owned by Go and released through the matching capi free functions, with the per-ctx last error surfaced into Go errors. Calls are serialized via base.SingleThread since the C context is not reentrant. Proto field mapping: - VoiceEmbed: VoiceEmbedRequest.audio (path) -> embed_path -> Embedding+Model. - VoiceVerify: audio1/audio2 + threshold (<=0 falls back to the verify_threshold option, default 0.25) -> verify_paths -> verified/distance/ threshold/confidence/model/processing_time_ms. - VoiceAnalyze: audio (path) -> analyze_path_json; the JSON age/gender/emotion document maps to a single VoiceAnalysis segment (start/end 0; gender "label" -> dominant_gender with the remaining float scores as the gender map; emotion label/scores -> dominant_emotion/emotion). The Makefile pins voice-detect.cpp to 47546430, clones+builds libvoicedetect.so with ggml static-linked (PIC, GGML_NATIVE off) so dlopen needs no external libggml/libvoicedetect; ldd on the artifact shows only system libs. Ginkgo tests cover option parsing and analyze-JSON mapping; embed/verify smoke specs gate on VOICEDETECT_BACKEND_TEST_MODEL + VOICEDETECT_BACKEND_TEST_WAV. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(voice-detect): wire backend into index, gallery and build Register the voice-detect.cpp speaker-recognition + voice-analysis backend (added in Voice-INT-A) into LocalAI's distribution surfaces, mirroring the ced backend (the closest mudler C++/ggml audio analogue): - backend/index.yaml: add the &voicedetect meta-backend (capabilities platform map, no top-level uri) plus the full set of concrete per-arch image entries (cpu/cuda12/cuda13/metal/rocm/sycl/vulkan/l4t and the -development variants). Referential integrity audited - every alias target resolves. - gallery/index.yaml: add 5 model entries on backend voice-detect - ECAPA-TDNN, WeSpeaker ResNet34, 3D-Speaker ERes2Net, CAM++ and the wav2vec2 age/gender/emotion analyze model. The engine architecture is read from GGUF metadata (voicedetect.arch) at load. GGUF artifacts are not yet published: each files: entry points at the intended mudler/voice-detect-gguf location with a TODO to fill sha256 after upload (no fabricated hashes). - .github/backend-matrix.yml: add the linux build matrix block + the darwin metal entry mirroring ced. - .github/workflows/bump_deps.yaml: track mudler/voice-detect.cpp via VOICEDETECT_VERSION (pin 47546430, = 4754643). - core/config/backend_capabilities.go: register voice-detect in the backend capability map (VoiceVerify/VoiceEmbed/VoiceAnalyze -> speaker_recognition), mirroring speaker-recognition. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(face-detect): add purego Go backend for face-detect.cpp Add the LocalAI Go backend that dlopens libfacedetect.so (the flat facedetect_capi_* C-ABI) via purego, mirroring the sibling voice-detect backend. Implements the Face subset of the Backend gRPC service: - Embeddings(PredictOptions): Images[0] base64 -> temp file -> embed_path -> L2-normalized ArcFace embedding. - Detect(DetectOptions): src -> detect_path_json -> Detection boxes (class_name "face", [x1,y1,x2,y2] -> x/y/w/h). - FaceVerify(FaceVerifyRequest): two images + threshold + anti_spoof -> verify_paths; best-effort img areas via detect. - FaceAnalyze(FaceAnalyzeRequest): img -> analyze_path_json -> per-face age + gender ("M"/"F" normalized to "Man"/"Woman"). The Makefile pins face-detect.cpp to 636a1963 and builds the shared lib with ggml + vendored libjpeg-turbo static (PIC), so the .so is ldd-clean (no libggml) and exports only facedetect_capi_* (no jpeg_ symbols). Gated Ginkgo e2e mirrors voice-detect. Note for the gallery-wiring task: backend registration (index.yaml, gallery, core/config/backend_capabilities.go) is intentionally not touched here. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * fix(voice-detect): replace em dashes in net-new descriptions Project style forbids em/en dashes. Replace the three U+2014 chars introduced by the voice-detect gallery/index wiring with `-`/`:`. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(face-detect): wire backend into index, gallery and build Register the face-detect.cpp face detection / embedding / verification / analysis backend (added in Face-INT-A) into LocalAI's distribution surfaces, mirroring the voice-detect wiring (the closest mudler C++/ggml recognition analogue): - backend/index.yaml: add the &facedetect meta-backend (capabilities platform map, no top-level uri to avoid the meta-backend gotcha) plus the full set of concrete per-arch image entries (cpu/cuda12/cuda13/ metal/rocm/sycl-f16/sycl-f32/vulkan/l4t and the -development variants), 22 entries. Referential integrity audited: every alias target resolves. - gallery/index.yaml: add 4 model entries on backend face-detect - face-detect-buffalo-l/m/s (insightface SCRFD + ArcFace/MBF, NON-COMMERCIAL) and face-detect-yunet-sface (OpenCV-Zoo YuNet + SFace, APACHE-2.0, the commercial-friendly alternative). The detector/embedder architecture is read from GGUF metadata (facedetect.arch) at load; only the real verify_threshold option is set (0.35 buffalo, 0.363 sface). GGUF artifacts are not yet published: each files: entry points at the intended mudler/face-detect-gguf location with a TODO to fill sha256 after upload (no fabricated hashes). - core/config/backend_capabilities.go: register face-detect in the backend capability map (Embedding/Detect/FaceVerify/FaceAnalyze -> face_recognition), mirroring insightface. - .github/backend-matrix.yml: add the linux build matrix block + the darwin metal entry mirroring voice-detect. - .github/workflows/bump_deps.yaml: track mudler/face-detect.cpp via FACEDETECT_VERSION (pin 636a1963). Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * fix(recon): voice-detect metal build branch + face-detect gallery usecases Add the missing metal BUILD_TYPE branch to the voice-detect Makefile forwarding -DVOICEDETECT_GGML_METAL=ON, mirroring face-detect, so the darwin metal CI artifact is built with the Metal backend instead of CPU-only. Expand the 4 face-detect gallery models' known_usecases to [face_recognition, detection, embeddings] to match the backend capabilities map and the mirrored insightface-buffalo entries, so auto-selection for /v1/detect and /embeddings works. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * docs(recon): document voice-detect and face-detect ggml backends Document the new standalone C++/ggml biometric backends as the recommended/default option for face and voice recognition, keeping the existing Python insightface / speaker-recognition backends framed as the legacy path. - features/face-recognition.md: add a face-detect (ggml) backend section with the gallery entries (buffalo-l/m/s non-commercial, yunet-sface Apache-2.0), licensing, and verify/detect/analyze quickstart. - features/voice-recognition.md: add a voice-detect (ggml) backend section with the gallery entries (ecapa-tdnn, wespeaker-resnet34, eres2net, campplus speaker recognizers; emotion-wav2vec2 non-commercial analyze head) and quickstart. - reference/compatibility-table.md: add face-detect.cpp and voice-detect.cpp rows to the Vision, Detection & Recognition table. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * chore(gallery): publish recon backend GGUF uris + sha256 Fill in the published HuggingFace GGUF uris and verified sha256 for the 9 recon gallery entries (voice-detect-* and face-detect-*), and remove the TODO publish markers. Correct the eres2net, campplus, and emotion-wav2vec2 uris to the actual published filenames. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(gallery): re-embed buffalo anti-spoof + add audeering age/gender voice model Update the 3 buffalo face-detect GGUF sha256 (anti-spoof ensemble now embedded and re-uploaded under the same filenames/uris) and note the FaceVerify anti_spoof request flag in each description. Add a new voice-detect-age-gender-wav2vec2 gallery entry mirroring the emotion model. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(gallery): add face-detect-buffalo-sc and antelopev2 packs Add gallery entries for two newly-published insightface face packs on the face-detect backend: buffalo_sc (smallest pack, SCRFD-500M + small ArcFace) and antelopev2 (higher-accuracy, SCRFD-10G + ArcFace glint360k R100, 512-d). Both are non-commercial research-only. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(recon): honor LocalAI per-model threads in voice/face-detect backends LocalAI spawns one backend process per model and serves requests concurrently, so the engines' own min(hardware_concurrency, 8) default can oversubscribe cores. Forward the per-model Threads value from the gRPC LoadModel options into the engine via VOICEDETECT_THREADS / FACEDETECT_THREADS (read at backend construction) before the capi load. A non-positive Threads is treated as unset, leaving the engine default. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * chore(recon): bump backend pins to CPU-optimized engine commits voice-detect.cpp -> 0d9c1b3 (radix-2 FFT FBank, threads, flash attn + cached pos-conv); face-detect.cpp -> 523aee1 (thread-gated direct conv, threads). Brings the CPU optimizations into the LocalAI backend builds. GGUF format and parity unchanged, so the published HF GGUFs remain valid. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * chore(recon): bump backend pins to round-2 CPU-optimized engines voice-detect.cpp -> fe7e6a3 (ERes2Net 1x1->mul_mat, CAM++ layout+context, wav2vec2 conv-LN, ECAPA capture-drop, AVX512 dispatch opt-in); face-detect.cpp -> 9c8adb7 (AVX2 Winograd F(2x2,3x3) for SCRFD/ArcFace 3x3 convs, ArcFace BN-fold). Parity unchanged (cosine=1.0); GGUF format unchanged, HF GGUFs valid. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * chore(recon): bump backend pins to round-3 Winograd engines voice-detect.cpp -> 45122ec (Winograd F(2x2,3x3) for WeSpeaker/ERes2Net 3x3 convs, -22%/-20% @8t); face-detect.cpp -> cd5c962 (Winograd F(4x4,3x3) for SCRFD large maps, -22% @1t on top of F(2x2), more load-stable). Parity held (cosine=1.0); GGUF format unchanged, HF GGUFs valid. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * chore(recon): bump backend pins to round-4 Winograd engines (CPU opt complete) voice-detect.cpp -> d2839ca (CAM++ FCM 2D convs through Winograd, -15.5%/-10.3%); face-detect.cpp -> c1db23d (AVX2-vectorized Winograd tile transforms, SCRFD detect -14%/-9.6%). Final CPU optimization round; the conv-kernel lever class is now exhausted (parity held cosine=1.0; GGUF/parity unchanged, HF GGUFs valid). Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * chore(recon): bump face-detect pin to deep-kernel engine (7ae5c4d) face-detect.cpp -> 7ae5c4d: register-blocked winograd-domain GEMM microkernel (2.8x isolated GFLOP/s), AVX-512 zmm evolution behind runtime CPUID dispatch (ship-safe, AVX2 fallback bit-identical), bias/relu fused into the winograd output transform, and SFace Conv+BN fold + bias/PReLU fusion. SCRFD detect ~1.4x faster end-to-end vs the round-4 baseline; parity bit-exact; portable single binary (function-multiversioned, no global -mavx512f). Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * chore(recon): bump voice-detect pin to ECAPA operand-order win (e9c56ae) voice-detect.cpp -> e9c56ae: weight-as-src0 mul_mat order in ECAPA's F32 conv1d_same (routes through tinyBLAS sgemm); ECAPA embed 1.67x @1t / ~1.3x @8t, parity cosine=1.0. Isolated to encoder.cpp (ECAPA-only); ERes2Net/CAM++/WeSpeaker do not call conv1d_same so are provably unaffected. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * chore(recon): bump pins to FMA-throughput engines (voice f7b9f89, face 2d2d5f0) face -> 2d2d5f0: route ArcFace 3x3 body convs through the AVX-512 winograd microkernel (kWinoMinSize 80->14); ArcFace 1.62x @1t, SCRFD detect to 0.966 of MLAS @1t, no regression. voice -> f7b9f89: runtime-CPUID-dispatched AVX-512 winograd-GEMM microkernel (ship-safe, AVX2 fallback bit-identical); WeSpeaker 1.90x @1t. Parity cosine=1.0 throughout; portable single binaries. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * chore(recon): bump pins to MLAS-class direct-conv engines (voice 7ecfd07, face be22d67) Hand-tuned nChw16c AVX-512 register-tiled direct-conv microkernel (~263 GFLOP/s, within 6-7% of MLAS per-op efficiency), runtime-CPUID-dispatched + AVX2 fallback, fused bias/relu. voice 7ecfd07: default 3x3-s1 kernel for WeSpeaker (+37%/+32%) + ERes2Net, CAM++ pinned to Winograd. face be22d67: shape-gated to the ArcFace recognizer body (+25-27% @8t); SCRFD detector stays on Winograd (no regression). Parity cosine=1.0 / detect <=1px on AVX-512 + AVX2 paths. Portable single binaries. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * chore(recon): bump voice pin to Phase-A blocked backbone (f4e7eef) WeSpeaker ResNet34 runs as one nChw16c blocked island (2 reorders/forward vs ~60) on AVX-512, default; per-conv directconv fallback on AVX2. +2.9% @1t / +17-19% @8t vs per-conv directconv, parity cosine=1.0. The conv microkernel is already FMA-bound near peak (~0.86-0.98x MLAS-implied); residual to MLAS is sub-peak edge + non-conv tail, documented in docs/cpu-optimization.md. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * chore(recon): bump pins to breadth blocked-backbone (voice 7f66871, face d80092b) voice 7f66871: AVX2-vectorized (ymm) blocked island - AVX2-only hosts now run the blocked backbone for WeSpeaker (2.3x over per-conv-AVX2, cosine=1.0); ERes2Net stays per-conv (blocked regresses, opt-in only); CAM++ Winograd-pinned. face d80092b: ArcFace recognizer blocked island, AVX-512 default (-13% @8t, ~0.90x MLAS, the closest conv result), auto per-conv on AVX2; SCRFD untouched on Winograd (0 island invocations during detect). Parity cosine=1.0 / detect <=1px throughout. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * chore(recon): bump pins to small-spatial + stem conv kernels (voice 99b1804, face 47fdab6) Measured-gap-driven conv kernels: small-spatial (fill the register tile when output width <= tile width) + small-IC stem + strided-1x1/downsample recovery. ArcFace recognizer 0.57 -> 0.70x MLAS @1t (the closest conv model), WeSpeaker 0.65 -> 0.79x @1t. Parity cosine=1.0 / detect <=1px. The OC-block-sharing lever was a measured dead-end (deep stride-1 is L3-weight-bandwidth bound, not read-port bound) and was NOT shipped. Kernel ceiling reached; further gap needs an algorithm-class change (cache-blocked weight-stationary GEMM, or q8 weights). Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * chore(recon): bump pins to GPU persistent-graph + multi-model-safe cache (voice 45d2e6b, face 0a4799a) GPU wins (CUDA/ggml backend, no CPU-path change): persistent per-shape graph+context cache in Backend::compute() eliminates the per-call cudaGraph re-instantiation churn -> wav2vec2 emotion+age-gender now AT GPU parity with torch-cuDNN on GB10 (0.97-0.98x), CAM++ -5.7ms; bit-identical parity. Cache hardened multi-model-safe (invalidate-on-free keyed by the ModelLoader weights buffer) so LocalAI multi-model hosting cannot stale-hit. Conv models still trail cuDNN (im2col-materialization-bound) - cuDNN implicit-GEMM lever next. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * chore(recon): bump pins to cuDNN-conv-capable engines (voice b6e4356, face 6107a24) Adds the opt-in cuDNN implicit-GEMM conv path (VOICEDETECT_GGML_CUDNN / FACEDETECT_GGML_CUDNN, DEFAULT OFF -> zero build/runtime dep until enabled). On GPU it kills the im2col-materialization bottleneck and reaches torch-cuDNN parity on the spill-bound convs: SCRFD detect 14.8->6.4ms (2.3x, ~parity), WeSpeaker ~parity, ERes2Net beats torch (1.10x); ArcFace/CAM++ neutral (no spill). Parity exact (SCRFD <=1px, cosine=1.0). To USE it in LocalAI, the CUDA backend build must enable the flag AND bundle libcudnn - deferred until a cuDNN-bundled GPU image; flag stays OFF here. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(recon): enable cuDNN conv path on arm64+CUDA13 recon backends The voice-detect.cpp / face-detect.cpp engines have an opt-in cuDNN implicit-GEMM conv path behind VOICEDETECT_GGML_CUDNN / FACEDETECT_GGML_CUDNN (default OFF) that kills im2col on the GPU and reaches torch-cuDNN parity (SCRFD 2.3x, WeSpeaker/ERes2Net parity), measured on the GB10 (arm64, CUDA 13, sm_121a). Enable it for the CUDA build, but only where cuDNN actually ships: the arm64 + CUDA 13 image (GB10/Jetson/L4T). x86 CUDA images carry no cuDNN, so flipping it on globally for BUILD_TYPE=cublas would be a link failure. The Makefiles gate on CUDA_MAJOR_VERSION=13 + arch (TARGETARCH from the matrix/Docker build, uname -m fallback for local builds). backend/Dockerfile.golang already installs the runtime libcudnn9-cuda-13 in the arm64+CUDA13 apt block; add the matching libcudnn9-dev-cuda-13 so the build-time link resolves. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * chore(recon): bump voice-detect pin to ERes2Net blocked-default (30beecd) Defaults VD_ERES2NET_BLOCKED ON: routes the ERes2Net Res2Net body through the blocked nChw16c AVX-512 directconv island instead of the 1x1 mul_mat fast path (CONT-transpose + skinny low-K GEMM). On the shipped GGML_NATIVE=OFF build (ggml mul_mat is AVX2-only) this wins ~2x at every thread count (2.07x@1t, 2.2x@4t, 2.05x@8t); pure-AVX2 fallback still 1.3-1.62x. Parity exact (cosine=1.000000 vs golden), so registered voices + verify/identify thresholds are unaffected. The prior default-OFF rested on a stale comment whose 23pct regression only held on the non-shipping GGML_NATIVE=ON build. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * docs(readme): announce native voice-detect + face-detect backends in Latest News Add a Latest News entry for the new from-scratch C++/ggml biometric backends (voice-detect.cpp + face-detect.cpp) that replace the Python insightface and speaker-recognition backends: no Python/onnxruntime at inference, self-contained GGUF, bit-exact parity, GPU cuDNN parity. Mirrors the parakeet.cpp / locate-anything.cpp native-backend news entries. Refs PR #10441. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * chore(recon): re-pin to the squashed engine release commits The voice-detect.cpp and face-detect.cpp histories were squashed to a single release commit, which orphaned the previous pins (voice 30beecd, face 6107a24). Re-pin to the new single-commit SHAs (voice 3d51077, face 06914b0); the tree is identical, so the backend build is unchanged. 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> |
||
|
LocalAI [bot]
|
ade9cc9e37 |
fix(openresponses): bound resume-stream buffer and enforce response ownership (#10569)
The background=true resumable-stream path had two latent issues. 1. Unbounded resume buffer. AppendEvent grew StreamEvents without limit, so a long-running or abandoned background generation could consume process memory without bound. The store now caps the buffer (event count and total bytes, mirroring llama.cpp's byte-capped slot ring), evicting oldest events from the front and advancing a droppedThrough watermark. GetEventsAfter returns ErrOffsetLost when the requested starting_after is below the watermark, and handleStreamResume surfaces that as HTTP 409 before committing to the SSE response, so a resuming client gets a clear error instead of a silently truncated stream. 2. Missing ownership check (IDOR). GET /responses/:id, its stream resume, and /cancel looked up responses purely by ID, letting any caller who knows or guesses an ID read or cancel another caller's response. Responses now carry the creating caller's identity (auth.GetUser), stamped at creation and compared on read/cancel/resume; a mismatch returns 404 (not 403) so existence is not leaked. Backward compatible: responses with no owner (single-key / no-auth deployments) remain accessible. 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> |
||
|
LocalAI [bot]
|
f3d829e2ef |
feat(distributed): add LOCALAI_DISTRIBUTED_SHARED_MODELS to skip staging on shared volumes (#10556) (#10566)
In distributed mode, even when the frontend and workers share the same models directory via a shared volume mount, starting a model on a worker re-staged (re-downloaded) it: stageModelFiles always uploads model files into a tracking-key-namespaced subdir on the worker, and the staging probe only checks that staged location, so a file already present on the shared volume at the canonical path was never reused. Add a config switch LOCALAI_DISTRIBUTED_SHARED_MODELS (default false). When enabled, the operator asserts that all nodes mount the SAME models directory at the SAME path, so staging is unnecessary: the frontend's absolute model paths are already valid on the worker. In that mode stageModelFiles returns the cloned opts unchanged without uploading, leaving the path fields pointing at their canonical absolute paths so the worker loads them directly from the shared volume. The value is plumbed from DistributedConfig through SmartRouterOptions into the SmartRouter. Docs and docker-compose.distributed.yaml updated. 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> |
||
|
LocalAI [bot]
|
91885c2c7e |
fix(distributed): return empty backend list for agent nodes instead of failing backend.list (#10545) (#10565)
Opening an AGENT-type worker node's detail page errored with "failed to list backends on node" / NATS "nodes.<id>.backend.list: no responders available". Agent workers only subscribe to agent.*, jobs.*, mcp.* and <prefix>.backend.stop; they never subscribe to backend.list, so the per-node ListBackendsOnNodeEndpoint request had no responder and timed out. The aggregate cluster-wide list already guards this in managers_distributed.go (skip nodes whose NodeType is set and not "backend"). The single-node endpoint lacked the same guard. Thread the NodeRegistry into ListBackendsOnNodeEndpoint and short-circuit to an empty (non-nil) list for non-backend node types before issuing the doomed NATS request, mirroring the aggregate-list gate so both views stay consistent. 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> |
||
|
LocalAI [bot]
|
f1fcafb888 |
fix(gallery): match mmproj/model quant as a whole token so F16 no longer selects BF16 (#10559) (#10564)
pickPreferredGroup matched a quant preference against the shard base filename with strings.Contains. Because `f16` is a substring of `bf16`, asking for the `F16` mmproj quant would wrongly satisfy a `BF16` file and select it when its group came first. Match the preference as a whole token instead: it must be delimited by a non-alphanumeric character (or the string start/end) on both outer edges. Separators inside the preference itself (e.g. `ud-q4_k_xl`) are left untouched, and all occurrences are scanned before rejecting. 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> |
||
|
LocalAI [bot]
|
1154be5eea |
fix(config): fall back to DefaultContextSize for unparseable GGUFs; pin NVFP4 gallery context_size (#10563)
The GGUF metadata parser (gpustack/gguf-parser-go) cannot read NVFP4-quantized GGUFs at all: it errors with "read tensor info 0: This quantized type is currently unsupported" because NVFP4 is a ggml tensor type it does not know. When ParseGGUFFile errors, the llama-cpp defaults hook skips guessGGUFFromFile entirely and the deferred fallback sets the context window to the conservative GGUFFallbackContextSize (1024). The result: a model that trains to 262144 tokens runs with n_ctx=1024, and every prompt over ~1k tokens fails with "request (N tokens) exceeds the available context size (1024 tokens)". Two changes: - Drop GGUFFallbackContextSize (1024) and fall back to DefaultContextSize (4096) in both the GGUF run-estimate path (gguf.go) and the deferred hook fallback (hooks_llamacpp.go). 1024 is a sensible floor for a tiny CPU GGUF but a footgun for a large, long-context model whose header simply cannot be parsed. Strengthen the existing "GGUF unreadable" test to assert the value. - Set context_size explicitly on the four NVFP4 gallery entries (qwen3.6-35b-a3b-nvfp4-mtp, qwopus3.6-27b-v2-mtp-nvfp4, qwopus3.6-27b-coder-mtp-nvfp4, qwen3.6-27b-nvfp4-mtp) so the parser failure is irrelevant for them. 32768 matches sibling Qwen entries and is safe on memory; operators can raise it toward the 262144 train length. 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> |
||
|
LocalAI [bot]
|
d7d7721eae |
feat(distributed): SyncedMap component + migrate finetune/quant/agent-tasks to cross-replica state (#10542)
* feat(distributed): add SyncedMap cross-replica in-memory state component Introduce core/services/syncstate.SyncedMap[K,V]: a thread-safe in-memory map that keeps itself consistent across frontend replicas via NATS, with an optional pluggable durable Store and hydrate-from-source convergence. Several features keep process-local state surfaced to the API (finetune/quant jobs, agent tasks, model configs) and each hand-wired the same in-memory + NATS broadcast + read-through-store legs - or forgot to, reintroducing cross-replica staleness. SyncedMap makes that consistency a configuration choice: - local writes mutate the map, write through the Store, then broadcast a delta; - the apply path is memory-only and never re-publishes or re-writes the Store (structural echo-loop guard, mirroring galleryop.mergeStatus); - on Start and on NATS reconnect the map re-hydrates from the source (Store, else Loader); an optional periodic Reconcile repairs silent drift; - standalone mode (nil NATS client) is a strict in-memory no-op. Reconnect re-hydrate is wired via a new *messaging.Client.OnReconnect callback, consumed through an optional type-assertion so MessagingClient stays minimal. Adds messaging.SubjectSyncStateDelta and a reusable testutil.FakeBus (synchronous in-process MessagingClient with wildcard matching) for adopter tests. Component only; service migrations follow in subsequent commits. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * refactor(finetune): back jobs with SyncedMap for cross-replica consistency FineTuneService kept jobs in a process-local map and, although it wrote them to Postgres, ListJobs/GetJob never read the store back and the wired natsClient was never used - so in distributed mode a job created on one replica was invisible to the others. Replace the map and the dead client with a syncstate.SyncedMap keyed by job ID, value *schema.FineTuneJob (the exact REST shape, so responses are unchanged). - Add a Store adapter (core/services/finetune/syncstore.go) over FineTuneStore, plus FineTuneStore.ListAll (global hydrate; per-user List kept) and an idempotent Upsert (create-or-update; Create alone fails on dup key). - Writes go through SyncedMap.Set/Delete (write-through + broadcast); reads use List/Get. The on-disk state.json path becomes the standalone Loader, keeping single-node restart recovery (stale->stopped / exporting->failed fixups). - Fold SetNATSClient/SetFineTuneStore into NewFineTuneService; app.go passes the distributed NATS client + store when distributed, nil otherwise. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * refactor(agentpool): back agent tasks with SyncedMap for cross-replica consistency AgentJobService.ListTasks read the process-local tasks map only, while ListJobs already read through the DB persister + dispatcher NATS - so in distributed mode a task created on one replica was invisible to the others. Back tasks with a syncstate.SyncedMap keyed by task ID (value schema.Task, the exact REST shape); jobs are left untouched. - Store adapter (task_syncstore.go) over the existing JobPersister (LoadTasks/SaveTask/DeleteTask); reads svc.persister/userID live so a persister swap needs no rebuild. No new persister methods required. - Task reads -> SyncedMap.List/Get; create/update -> Set (write-through + broadcast); delete -> Delete. The file persister now owns its own task set so the write-through path does not re-enter the SyncedMap lock (deadlock guard). - The distributed NATS client is not available at construction (start() precedes initDistributed), so it is injected via SetTaskSyncNATS, which rebuilds the still-empty map before Start/hydrate. Wired at the main, restart, and per-user (UserServicesManager) distributed sites. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * refactor(quantization): back jobs with SyncedMap + durable QuantStore QuantizationService kept jobs in a process-local map persisted only to a local state.json, so in distributed mode jobs were neither visible across replicas nor durable cluster-wide. Back jobs with a syncstate.SyncedMap keyed by job ID (value *schema.QuantizationJob, the exact REST shape). - New distributed.QuantStore (GORM, table quantization_jobs) mirroring FineTuneStore: Create/Get/ListAll/Upsert(idempotent)/Delete, registered for AutoMigrate via distributed.InitStores (Stores.Quant). - New adapter (quantization/syncstore.go) over QuantStore implementing syncstate.Store, with record<->schema conversion. - Reads go through List/Get, writes through Set/Delete (write-through + broadcast); state.json is kept as the standalone Loader for single-node restart recovery (stale-job fixups preserved). - app.go passes the distributed NATS client + QuantStore when distributed, nil otherwise; Start/Close lifecycle mirrors finetune. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * fix(syncstate): annotate gosec G118 false positive on lifeCtx gosec flagged the WithCancel in Start as "cancellation function not called" because the returned cancel is stored on the struct rather than called/deferred in scope. It is invoked in Close (covered by tests), and lifeCtx must outlive Start to drive the reconnect/reconcile goroutines. Suppress the verified false positive with a justified #nosec G118. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * test(distributed): e2e two-replica SyncedMap sync over real NATS + Postgres Adds the real-infrastructure counterpart to the fake-bus unit tests, in the existing distributed e2e suite (testcontainers NATS + PostgreSQL). Two SyncedMap instances stand in for two frontend replicas - each with its OWN NATS connection to a shared server and a SHARED Postgres store (the distributed-mode invariant) - and assert, over the wire: - a create on replica A is observed by replica B; - an update and a delete propagate A -> B (delete prunes, which a reload cannot); - a late-joining replica recovers a job it never received a delta for, via store hydrate on Start (the at-most-once gap a fake bus cannot exercise); - a local Set is written through to the shared Postgres store. 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> |
||
|
Ettore Di Giacinto
|
400930db19 |
Merge remote-tracking branch 'origin/master' into worktree-feat+paged-attention
# Conflicts: # gallery/index.yaml |
||
|
LocalAI [bot]
|
f0d0bff232 |
fix(llama-cpp): stop reinterpreting plain-string message content as JSON (#10524) (#10538)
The llama-cpp gRPC backend reconstructs OpenAI messages from proto for the tokenizer-template path and blindly json::parse'd each message's content string. LocalAI's Go layer always flattens content to a plain string, so a user prompt that merely looks like JSON (e.g. mealie's ingredient array ["1/4 cup brown sugar", ...]) was reinterpreted as structured content parts and rejected by oaicompat_chat_params_parse with "unsupported content[].type". Normalize content per role instead: user/system/developer content is opaque text and is never JSON-sniffed; assistant/tool content still collapses a literal JSON null/object (tool-call bookkeeping) to a string, but a plain string is never turned into an array/scalar. The array defense is role-independent, so the role gate only governs the benign null/object case. While here, extract the duplicated per-message reconstruction and the pre-template content sanitization into shared, unit-tested helpers (message_content.h) so the streaming (PredictStream) and non-streaming (Predict) paths cannot drift. This removes ~490 lines of copy-pasted defensive code, the dead tool-role parse branches, and the redundant Predict-only tool_calls branch, while preserving the prior #7324 (null content -> "") and #7528 (tool array content -> string) fixes. Tests: - backend/cpp/llama-cpp/message_content_test.cpp: standalone C++ unit tests for all three helpers (#10524, #7324, #7528, multimodal), discovered and run by `make test-backend-cpp` and a new generic tests-backend-cpp CI job. Also wired as an opt-in CMake/ctest target (-DLLAMA_GRPC_BUILD_TESTS=ON). - core/schema/message_test.go: Go regression pinning that ToProto flattens a JSON-array-looking text part to the verbatim string. - prepare.sh now copies message_content.h into the build tree. 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> |
||
|
LocalAI [bot]
|
64150ca7ab |
fix(distributed): broadcast admin model-config changes across replicas (#10540)
In distributed mode the admin model endpoints (/models/edit, /models/import, /models/toggle-state and the PATCH config-json endpoint) wrote the YAML to the shared models dir but reloaded only the local replica's in-memory ModelConfigLoader. With multiple frontend replicas behind one service, a save landed on whichever replica handled the request; peers kept serving their stale in-memory view, so a load-balanced request was a coin-flip between old and new config (a created alias visible on one replica and missing on the other, an edited alias target diverging, etc.). The NATS cache-invalidation channel (SubjectCacheInvalidateModels + OnModelsChanged) already existed for the gallery install/delete path; these admin endpoints simply never published on it. Wire them up via a new GalleryService.BroadcastModelsChanged helper (no-op in standalone mode). Also fix delete propagation: LoadModelConfigsFromPath is additive and never drops an entry whose file is gone, so the subscriber hook (which only reloaded from disk) could not propagate a removal. ApplyRemoteChange now honors the event op - pruning the element on "delete" and reloading otherwise - and shuts down any running instance of the affected model so the new config takes effect. This closes the same latent gap on the gallery delete path. 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> |
||
|
Ettore Di Giacinto
|
c1f1d1e8ea |
Merge remote-tracking branch 'origin/master' into worktree-feat+paged-attention
# Conflicts: # gallery/index.yaml |
||
|
LocalAI [bot]
|
56600eec3e |
fix(nodes): show a node's existing labels on the detail view (#10529)
fix(nodes): return labels in single-node GET so the detail view shows them The node detail view (/app/nodes/:id) reads `node.labels` to render a node's existing labels, but the single-node GET endpoint returned a bare BackendNode whose Labels live in a separate table - so the list was always empty and operators could only add labels, never see what was already set (#10527). The same response also lacked in_flight_count and model_count. Add NodeRegistry.GetWithExtras, mirroring the existing List vs ListWithExtras split: bare Get stays cheap for the routing hot paths and existence checks, while the detail endpoint uses the enriched variant to attach the labels map and live counts. No frontend change is needed - the UI already renders existing labels once the data is present. Closes #10527 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> |
||
|
Ettore Di Giacinto
|
30a2b590d9 |
Merge branch 'master' into worktree-feat+paged-attention (llama.cpp pin -> 9d5d882d)
Sync to master (12 commits) + the llama.cpp pin bump 8be759e6 -> 9d5d882d. Conflicts resolved: - Makefile .NOTPARALLEL: union (keep both backends/llama-cpp-localai-paged and master's backends/privacy-filter-darwin). - gallery/index.yaml: our 2 base NVFP4 entries (qwen3.6-27b-nvfp4, qwen3.6-35b-a3b-nvfp4) for the paged backend prepended to master's full list; master keeps its own *-nvfp4-mtp variants (distinct entries). Go build + YAML validated; the 8 duplicate gallery names are pre-existing in master, not introduced here. The patchset still needs re-verification against the new tip (pin-sync, next step). Assisted-by: Claude:opus-4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Ettore Di Giacinto
|
167768cac3 |
feat(backend): llama-cpp-localai-paged variant + NVFP4 Qwen3.6 gallery
New backend = stock llama-cpp grpc-server + the paged patchset (forces LLAMA_PAGED=on), shipped as its own meta-backend (mirrors turboquant, simpler: no fork pin, no grpc-server patching - the paged runtime hooks already exist in grpc-server.cpp). Stock llama-cpp untouched (LLAMA_PAGED?=on retained; the de-risk flip deferred for sign-off). Gallery: qwen3.6-27b-nvfp4 (dense) + qwen3.6-35b-a3b-nvfp4 (MoE) with the benchmark run config (paged_kv, max_batch_tokens, parallel, flash_attention, f16), mudler/ GGUF uris (sha256 TODO until publish). Importer dropdown entry + tests. Assisted-by: Claude:opus-4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
LocalAI [bot]
|
114eeaae81 |
feat(backends): make PreferDevelopmentBackends install the development image as primary (#10520)
When LOCALAI_PREFER_DEV_BACKENDS is set, install the -development image as the primary backend URI (keeping the released image reachable as the first fallback), instead of only reaching development as a download fallback when the released image is missing. This lets an operator force backends built from the development branch — e.g. to pick up a fix already on master before a release. Threads PreferDevelopmentBackends through SystemState so InstallBackend can see it, and reuses the same development-URI convention as the existing failure-path fallback (released tag -> branch tag + dev suffix). The unexported developmentURI helper is covered by a Ginkgo spec. 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> |
||
|
Ettore Di Giacinto
|
64766ecc85 |
Merge branch 'master' into worktree-feat+paged-attention
Resolve pkg/xsysinfo/gpu.go: keep master's NVIDIAComputeCapability + parseComputeCap (the #10485 multi-GPU work); re-express our IsNVIDIABlackwell as a thin wrapper over NVIDIAComputeCapability instead of a duplicate nvidia-smi probe. Assisted-by: Claude:opus-4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
LocalAI [bot]
|
f72046b5b5 |
fix(auth): make advisory locks dialect-aware and harden SQLite DSN (#10509)
* fix(auth): make advisory locks dialect-aware and harden SQLite DSN Fixes #10506. Two failures hit deployments that use the default SQLite auth database: 1. advisorylock executed PostgreSQL-only SQL (pg_advisory_lock / pg_try_advisory_lock) unconditionally. On a SQLite auth DB the job store, agent store and node registry migrations failed with "no such function: pg_advisory_lock". WithLockCtx/TryWithLockCtx now branch on the gorm dialect: PostgreSQL keeps the cross-process advisory lock, every other dialect uses a context-aware, per-key in-process lock (a SQLite auth DB is effectively single-process, so serializing within the process is sufficient). 2. The SQLite auth DSN set no busy timeout, so transient SQLITE_BUSY over network-backed storage (SMB/CIFS/NFS, e.g. Azure Files) failed the auth migration immediately with "database is locked". The DSN now sets _busy_timeout=5000 and _txlock=immediate (caller-supplied values are preserved). WAL is intentionally not enabled since its shared-memory mmap does not work over network filesystems. Docs note that PostgreSQL should be used when the data directory lives on shared storage. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * test(jobs): regression test for #10506 SQLite job store migration Exercises the exact caller chain that failed in the issue: auth.InitDB(sqlite) -> jobs.NewJobStore -> advisorylock.WithLockCtx -> AutoMigrate. Before the dialect-aware advisory lock fix this failed with "no such function: pg_advisory_lock"; the test now asserts it migrates cleanly on a SQLite auth DB. 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> |
||
|
LocalAI [bot]
|
79783120dd |
fix(config): gate parallel-slot default on per-device VRAM too (#10485) (#10507)
The first #10485 fix (#10494) made the Blackwell physical-batch boost per-device/context-aware, which neutralized the big compute-buffer OOM, but the reporter's 2x16 GiB consumer Blackwell still OOM'd. Tracing the post-fix log: the model now loads its weights, builds the main context and warms up fine, and dies only on the *last* allocation — the MTP draft context's 800 MiB KV cache on the tighter device. #10411 changed only two defaults: the physical batch (now gated) and a VRAM-scaled parallel-slot count. The KV cache is unified (n_ctx_seq == full context proves slots share the budget, so parallel doesn't multiply KV), but n_seq_max=4 still adds per-slot compute-graph / context-checkpoint / output scratch. On a device packed ~99% by a 27B model spanning both cards, that overhead is the few-hundred-MiB straw — which is why reverting #10411 (and only #10411) restores a working load. Gate the parallel-slot default on the same per-device headroom predicate as the batch boost: when a large context already fills a single card (largeContextForDevice), keep n_parallel=1. A user running one big-context model that barely fits across two consumer GPUs is not serving four concurrent tenants. Small contexts and large unified-memory devices (GB10) keep full concurrency. Applied on both the single-host path and the distributed router. Also make the auto-tuning visible and reversible (the debugging here needed DEBUG logs and a git bisect): - Log the effective performance-relevant runtime options at INFO once per model load ("effective runtime tuning …": context, n_batch, n_gpu_layers, parallel, flash_attention, f16) so an admin can see what will run and pin or override any value in the model YAML. - LOCALAI_DISABLE_HARDWARE_DEFAULTS=true skips the hardware auto-tuning entirely (mirrors LOCALAI_DISABLE_GUESSING) for stock llama.cpp behavior. Assisted-by: Claude:opus-4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
LocalAI [bot]
|
fe4f425fb5 |
fix: correct scheme/host on self-referential URLs behind an HTTPS reverse proxy (#10482) (#10504)
* fix(http): harden BaseURL proxy scheme/host detection Split comma-separated X-Forwarded-Proto and honor the RFC 7239 Forwarded header so generated links use https behind common reverse-proxy setups. Refs #10482 Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(http): honor explicit external base URL in BaseURL When _external_base_url is set in the request context it dictates the origin (scheme+host+port); the proxy path prefix is still appended. Refs #10482 Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(config): generalize LOCALAI_BASE_URL to ExternalBaseURL LOCALAI_BASE_URL now sets a single instance-wide external base URL used for OAuth callbacks and all self-referential links. A Pre middleware stamps it into the request context for middleware.BaseURL. Refs #10482 Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: document LOCALAI_BASE_URL and reverse-proxy headers Refs #10482 Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(http): cover parseForwarded edge cases; clarify base-url flag group Adds direct unit coverage for quoted/malformed/multi-element Forwarded headers and regroups the external base URL flag away from auth-only. Refs #10482 Assisted-by: Claude:claude-opus-4-8 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> |
||
|
LocalAI [bot]
|
f88981cdce |
feat(ui): data-driven hardware model recommendations + gallery surfacing (#10500)
* feat(ui): make hardware starter models data-driven The empty-state starter widget recommended from a hardcoded list, which drifts as the gallery evolves. Add useRecommendedModels: it queries the live gallery for chat-capable models (their natural curated order, since the gallery exposes no popularity signal), estimates size/VRAM for the top candidates via the existing estimate endpoint, and ranks by hardware fit - smallest on CPU-only boxes, largest-that-fits on GPUs. StarterModels now renders those live picks and keeps the curated static list only as an offline/trimmed-gallery fallback. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(ui): recommend models for your hardware in the gallery Hardware-aware recommendations were only shown on the first-run empty state. Surface them on the main Models gallery too: a dismissible "Recommended for your hardware" strip at the top, sharing the useRecommendedModels fit-ranking with the starter widget. CPU-only boxes get small models; GPUs get the largest picks that fit VRAM, with size and VRAM shown per card. One-click install; dismissal persists per browser. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(ui): gpu-mid tier + NVIDIA NVFP4 model recommendations Refine the hardware recommendation tiers and curated picks: - Add a gpu-mid tier (8-24GB VRAM) between gpu-small and gpu-large, so ~27B-class models are suggested separately from the 30B+ large tier. - Detect NVIDIA GPUs (resources.gpus[].vendor) and, on NVIDIA only, prefer NVFP4 + MTP variants (Blackwell-optimised); NVFP4 models are filtered out of recommendations on non-NVIDIA hardware where they can't run. This applies to both the live ranking and the static fallback, with an NVFP4 badge shown on those picks. - Refresh the curated fallback to current models: Gemma-4 QAT Q4 builds at every tier, low qwen3.5 (4B distilled / 9B) on CPU/small, qwen3.6-27b and MTP variants at mid, qwen3.6/qwen3.5 35B-A3B apex/distilled at large. All names verified against gallery/index.yaml. 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> |
||
|
LocalAI [bot]
|
0d6de15ae9 |
fix(config): per-device VRAM headroom for Blackwell defaults (#10485) (#10494)
The hardware-tuned defaults from #10411 were measured on a GB10 / DGX Spark (128 GiB unified memory) and over-provisioned multi-GPU consumer Blackwell (e.g. 2x16 GiB RTX 50-series) into CUDA OOM during model init: - The Blackwell physical batch (512 -> 2048) sets both n_batch and n_ubatch. The compute buffer scales ~n_ubatch * n_ctx and is allocated PER DEVICE (it can't be split across GPUs), so a large context turns ub2048 into multi-GiB of scratch that must fit one 16 GiB card. - The VRAM-scaled parallel-slot default tiered off TotalAvailableVRAM(), which SUMS all GPUs (2x16 -> "32 GiB" -> 8 slots), but the allocations are per-device. Make both decisions per-device and context-aware: - xsysinfo.MinPerGPUVRAM() reports the smallest device's VRAM; localGPU() uses it so the parallel tier and batch guard reason about one card. - PhysicalBatchForContext(gpu, ctx) raises the batch only when the extra compute buffer fits VRAM/4 at this model's context (16 GiB crosses over ~174k ctx, 32 GiB ~349k; GB10 reports system RAM so it still clears it). - Apply hardware defaults AFTER runBackendHooks in SetDefaults so the GGUF-guessed context is resolved before the batch decision. - The distributed router gates the node batch the same way. Unified-memory devices (GB10, Apple) report system RAM as their single device's VRAM, so they keep the prefill win. Assisted-by: Claude:opus-4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
LocalAI [bot]
|
5c3d48ab50 |
feat(ui): usage & UX enhancements (last-used model, polling, starter models, usage cost, a11y) (#10496)
* feat(ui): remember last-used model per capability ModelSelector auto-selected the first option whenever the bound value was empty or stale, so every visit to the Home chat box, Image, TTS or Talk pages reset the choice to whatever sorted first. Persist the user's pick in localStorage keyed by capability and prefer it on auto-select when the model is still available, falling back to the first option otherwise. Because every modality picker funnels through ModelSelector, this fixes the friction everywhere at once. External-options callers pass no capability and keep the previous first-item behaviour. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(ui): add visibility-aware polling hook The app had 26 hand-rolled setInterval polls, none of which paused when the browser tab was hidden, so backgrounded dashboards kept hitting the server every few seconds for data nobody was looking at. Add usePolling: runs immediately, polls on a fixed interval, pauses while document.hidden, fires a catch-up poll on return, and guards against overlapping slow requests. Route useResources (the highest-frequency shared poll) through it. Further callers can be migrated incrementally. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(ui): hardware-aware starter models on empty home A fresh install dropped admins straight into a 1000+ model gallery with no guidance. Add a StarterModels widget to the empty-state wizard that recommends a small, curated set tuned to the detected hardware: - CPU-only machines (no GPU VRAM) are steered to genuinely small models (1-4B, Q4) that stay responsive without a GPU. - GPU machines get suggestions scaled to available VRAM. Curated names are real gallery entries, intersected against the live gallery at render time so a trimmed/custom gallery degrades gracefully. Install is one click via the existing model-install API. Also routes Home's cluster and system-info polls through usePolling so a backgrounded home page stops fetching. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(ui): optional token-cost estimates on usage dashboard The usage dashboard tracked tokens but had no monetary view. Multi-user deployments that bill back or budget compute had to export and compute cost elsewhere. Add an opt-in pricing control: admins set $ per 1M prompt/completion tokens (stored per-browser). When set, an estimated-cost summary card and per-model / per-user cost columns appear, computed from recorded token counts. The entire cost surface stays hidden until a price is entered, so the default view is unchanged. Cost is clearly labelled an estimate - LocalAI itself has no notion of price. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * fix(ui): label icon-only send buttons for screen readers The chat and agent-chat send buttons were a bare paper-plane icon with no accessible name, so screen readers announced only "button". Add an aria-label/title ("Send message") and mark the icon aria-hidden. An audit of all icon-only buttons found these were the only two unlabeled controls; the rest already carry visible text. 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> |
||
|
LocalAI [bot]
|
482314c623 |
fix(realtime): resolve model aliases for pipeline sub-models (#10484)
Realtime pipeline sub-models (llm/transcription/tts/vad/sound-detection)
were loaded via cl.LoadModelConfigFileByName without alias resolution,
unlike top-level API requests which resolve aliases in
core/http/middleware/request.go. So a pipeline that references an alias
(e.g. `pipeline.llm: default`, where `default` is an alias for a real
LLM) reached model loading as the alias stub with an empty Backend.
This was silently broken on a single host (it failed downstream) and a
hard error in distributed/p2p mode:
routing model : loading model default: ... installing backend on
node X: backend name is empty
Fix by routing every pipeline sub-model load through a small helper that
follows a single alias hop (mirroring the top-level resolution), so
non-alias sub-models behave identically and aliased ones get the
target's full config (Backend, Model, ...).
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>
|
||
|
Dedy F. Setyawan
|
e8ae88a2a0 |
i18n(id): update and complete Indonesian translations (#10480)
- translate remaining English strings in chat, common, home, and media locales. - fix typo and improve wording consistency (e.g., klaster -> kluster, otomasi -> automasi). Signed-off-by: Dedy F. Setyawan <dedyfajars@gmail.com> |
||
|
Richard Palethorpe
|
e1994579f8 |
fix(pii): load default detectors at startup + add LOCALAI_PII_DEFAULT_DETECTORS (#10474)
pii_default_detectors was applied to the live config only by a live POST /api/settings (ApplyRuntimeSettings) — neither the startup loader nor the config file watcher read it back. So after a restart the persisted default detectors were dropped, and the cloud-proxy MITM listener (which resolves each intercept host's detectors once at start via ResolvePIIPolicy) came up with an empty set and forwarded intercepted traffic unredacted, even though the MITM model had pii.enabled:true and the defaults were on disk. Request-side default redaction broke the same way. - startup.go: loadRuntimeSettingsFromFile now applies pii_default_detectors, before startMITMIfConfigured, with env > file precedence. - config_file_watcher.go: apply pii_default_detectors on live file edits, matching the existing env-guard pattern used for the other fields. - settings endpoint: rebuild the MITM listener when pii_default_detectors changes (its per-host detector map is frozen at listener start), not only on a mitm_listen change — so toggling a default detector takes effect on cloud-proxy traffic immediately. - new LOCALAI_PII_DEFAULT_DETECTORS env var / CLI flag (WithPIIDefaultDetectors) so the default detector set can be pinned at boot for immutable deployments. Assisted-by: Claude:claude-opus-4-8 Claude-Code Signed-off-by: Richard Palethorpe <io@richiejp.com> Co-authored-by: Ettore Di Giacinto <mudler@users.noreply.github.com> |
||
|
LocalAI [bot]
|
e5620989dd |
refactor(distributed): make in-flight tracking coverage a compile-time contract (#10476)
PR #10475 fixed SoundDetection in-flight tracking, but the underlying trap remains: InFlightTrackingClient embedded the whole grpc.Backend interface "for passthrough of untracked methods", so any newly added inference method is silently satisfied by the embedded passthrough and never wrapped with track(). That leaves onFirstComplete unfired and in-flight stuck at 1 - the exact SoundDetection bug, waiting to recur for the next backend method. Close the gap at the type level instead of relying on reviewers to remember: - Split grpc.Backend into two composed sub-interfaces: InferenceBackend (methods that are one discrete inference call and must be tracked) and ControlBackend (control-plane calls plus the streaming constructors whose work spans the returned stream, safe to pass through). The classification now lives next to the interface it documents. - InFlightTrackingClient embeds only grpc.ControlBackend and implements every InferenceBackend method explicitly, delegating to an inner InferenceBackend. A `var _ grpc.Backend = (*InFlightTrackingClient)(nil)` assertion makes the package fail to compile if any inference method is left unwrapped. Now adding a method to InferenceBackend is a build error (at the assertion and every call site: "does not implement grpc.Backend (missing method X)"), not a silent runtime leak - and the obvious fix is to copy a neighbouring wrapper, which calls track(). No runtime guard or reviewer vigilance required. Pure refactor: the composed Backend interface is identical to the old flat one, so all implementers and consumers are unaffected (verified with a full `go build ./...`). Behaviour is unchanged; the existing nodes suite passes. 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> |
||
|
LocalAI [bot]
|
fc618dcee6 |
fix(distributed): track in-flight for SoundDetection requests (#10475)
The distributed router wraps backend clients in InFlightTrackingClient so the eviction logic knows which replicas are actively serving. Every inference method must be wrapped: track() increments in-flight on entry and decrements (plus fires onFirstComplete, which releases the load-time reservation) on return. SoundDetection was added after the tracking client and never got a wrapper, so its calls fell through to the embedded passthrough Backend. The increment/decrement never ran and, critically, onFirstComplete never fired, so the reservation set at model load was never released - leaving in-flight stuck at 1 and the replica permanently ineligible for eviction. Wrap SoundDetection like the other non-LLM methods and cover it in the "non-LLM inference methods track in-flight" table test. 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> |
||
|
LocalAI [bot]
|
e6042080c0 |
fix(agents): URL-decode collection/agent name path params (#10443) (#10471)
fix(agents): URL-decode collection/agent name path params
Collection and agent names carry a "legacy-api-key:" prefix, so the ':'
arrives percent-encoded as %3A in the request path. Echo routes such
paths via URL.RawPath and stores the matched path-param value still
escaped, so c.Param("name") returned "legacy-api-key%3ALiteraryResearch"
and the store lookup 404'd ("collection not found").
This was second-order fallout of #10375/#10387: once colons became valid
in names, the URL-decode gap surfaced on every name-bearing endpoint.
Add a decodedParam helper that url.PathUnescape's the param (falling back
to the raw value on invalid encoding) and wire it into all collection
endpoints and the agent :name endpoints, which share the identical
prefix. The entry endpoints already unescaped c.Param("*"); this closes
the same gap for :name.
Fixes #10443
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>
|
||
|
Richard Palethorpe
|
7888067914 |
fix(settings): merge partial /api/settings updates instead of overwriting (#10463)
POST /api/settings rebuilt runtime_settings.json from only the request body, so a focused admin page that submits a single field wiped every other persisted setting. The Middleware proxy tab (mitm_listen) and detector table (pii_default_detectors), plus the MCP SetBranding tool (instance_name/instance_tagline), all POST partial bodies; the no-omitempty api_keys and pii_default_detectors fields even round-tripped as JSON null. Read the persisted settings and overlay only the fields the request set (RuntimeSettings.MergeNonNil) before writing. Every field is a pointer, so the reflection-based merge is total over the struct and any field added later is preserved automatically. Absent or null fields are now kept; clearing a setting is done by sending its explicit empty/zero value (api_keys [], mitm_listen "", etc.), unchanged from before. The full Settings page sends every field, so its Save behaves identically. Assisted-by: Claude:claude-opus-4-8 Claude-Code Signed-off-by: Richard Palethorpe <io@richiejp.com> |
||
|
LocalAI [bot]
|
56f8a6623f |
fix(galleryop): persist cancellable so restarted in-flight ops stay cancellable (#10454)
In distributed mode a model/backend install marks OpStatus.Cancellable=true
while downloading, but the gallery_operations row never recorded it:
UpdateStatus persisted only progress/status and Create left the cancellable
column at its zero value. After a replica restart Hydrate rebuilt the op with
cancellable=false, /api/operations reported false, and the UI hid the cancel
button - the orphaned op then lingered until the 30-minute stale reaper
expired it ("stays there on restart, can't cancel, after a bit it expires").
Persist the flag on every progress tick and at row creation (installs are
cancellable, deletes are not), and clear it on terminal transitions. A
rehydrated in-flight op is now cancellable, so an admin can dismiss the
orphaned op immediately instead of waiting out the reaper. The functional
cancel path already survived restart (CancelOperation persists store.Cancel
even with no live CancelFunc); this restores the UI affordance that drives it.
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>
|
||
|
Ettore Di Giacinto
|
4755d676a3 |
Revert "feat(ui): role and deployment-mode adaptive UI (landing, sidebar, top navbar)" (#10453)
Revert "feat(ui): role and deployment-mode adaptive UI (landing, sidebar, top…"
This reverts commit
|
||
|
LocalAI [bot]
|
fdf475ec5f |
feat(realtime): conversation compaction (summarize-then-drop) + OpenAI item.delete/truncate/clear (#10446)
* feat(realtime): add pipeline.compaction config + resolution Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(realtime): extract itemID helper, reuse in item.retrieve Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(realtime): drop duplicate Ginkgo bootstrap, fold specs into openai suite Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(realtime): implement conversation.item.delete Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(realtime): implement input_audio_buffer.clear Add a handler for the input_audio_buffer.clear client event that discards a partially-captured utterance (raw PCM + buffered Opus frames) via a unit-tested clearInputAudio helper, then acks with input_audio_buffer.cleared. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(realtime): implement conversation.item.truncate (text) Clears both .Text and .Transcript of the assistant content part at contentIndex so barge-in truncation also works for audio turns whose spoken words live in .Transcript. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(realtime): add Conversation.Memory + pair-safe compactionCut Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(realtime): compactionCut returns 0 for keep<=0 (no-cap sentinel, avoids panic) Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * style(realtime): gofmt compaction test helper closures Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(realtime): inject rolling memory into the prompt + summary builders Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(realtime): server-side summarize-then-drop compactor Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(realtime): unit-test prefixMatches eviction-safety predicate Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(realtime): resolve summarizer model + schedule compaction per turn Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(realtime): document conversation compaction + new item events Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(realtime): resolve summary model inside compaction goroutine (lazy, off-path) Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(realtime): reuse reasoning.ExtractReasoningComplete for summary stripping Replace the bespoke <think> regex in the compactor with the shared pkg/reasoning extractor (via spokenReasoningConfig), matching the rest of the realtime path and covering all reasoning tag families, not just <think>. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(config): register pipeline.compaction fields in meta registry TestAllFieldsHaveRegistryEntries requires every ModelConfig field to have a UI/meta registry entry; add the four pipeline.compaction.* leaves so they render with proper labels/descriptions instead of the reflection fallback. 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> |
||
|
LocalAI [bot]
|
9d54a599b0 |
feat(ui): role and deployment-mode adaptive UI (landing, sidebar, top navbar) (#10449)
* feat(ui): add shared DeploymentContext (features + p2p signal) Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(ui): extract launchAssistantChat shared helper Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ui): role/mode-aware landing redirect at /app Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ui): pin Cluster group and collapse Create for cluster admins Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ui): desktop top navbar with mode pill and admin-via-chat jump Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ui): admin token-usage meter in the top navbar Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(ui): top-navbar breakpoint handoff + assistant jump from chat page M1: the desktop .top-navbar was hidden at max-width 768px while the .mobile-header only appears at max-width 639px, leaving 640-768px with neither bar so admins lost the mode pill, token meter and admin-via-chat jump. Hide the top bar at 639px instead so it covers every width the rail sidebar is shown and hands off to the mobile-header exactly at 639px. M2: the navbar 'Admin via chat' button wrote localStorage and called navigate('/app/chat'), but when already on the chat page Chat does not remount so its mount-time payload reader never fired and the click was a no-op until reload. The payload consume logic is factored into a shared callback; the launcher now dispatches a localai-open-assistant event that the mounted Chat listens for to re-consume the payload. Mount behavior is unchanged. 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> |
||
|
Richard Palethorpe
|
63bcbf6c12 |
fix(pii): post-merge review fixes + live NER e2e for the privacy-filter tier (#10401)
* fix(pii): post-merge review fixes + live NER e2e for the privacy-filter tier Follow-up to the NER tier engine (#10360), already on master. This carries only the incremental review fixes and tests that postdate that merge — the feature itself is not re-introduced. Review fixes: - openai_completion.go: remove the dead `elem >= 0` conjunct in applyAnyText (the `elem < 0` guard above already returns). - application.go: collapse ResolvePIIPolicy's inline re-implementation of PIIIsEnabled to a single cfg.PIIIsEnabled() call (sole source of the "explicit pii.enabled wins, else cloud-proxy default" rule) and return true past the !enabled guard where it is provable. - pattern.go: hoist the triple `appConfig != nil && EnableTracing` check in patternDetector.Detect into one local. - grammar.go: MaxQuantifier was 4096, but Go's regexp/syntax rejects repeat bounds above 1000 at Parse time, so walk()'s {n,m} guard could never fire — dead code shadowed by the parser. Lower it to 512 so a bound in (512,1000] is rejected here with an actionable error; >1000 still fails closed via Parse. Specs pin the relationship so the guard can't silently revert. - PatternListEditor.jsx: clamp a directly-typed negative min_len to >=0 and force the DOM value back when clamping (min={0} only constrained the spinner, so a negative reached saved config and silently disabled the length filter). Tests: - piipattern_test.go: MaxQuantifier guard specs (must stay live, not dead). - model-config.spec.js: assert the min_len clamp, and that entity_actions collapses a duplicate group to a single row (map semantics; regression guard against emitting an array that drops a row on save). - tests/e2e-backends: token_classify capability driving the TokenClassify gRPC RPC against the backend image, asserting byte-correct, UTF-8 rune-aligned spans (entity.Text == text[start:end]) at threshold 0. Verified on CPU via `make test-extra-backend-privacy-filter` (3/3 specs). - Makefile: test-extra-backend-privacy-filter wrapper. - tests/e2e: e2e_pii_ner_test.go drives /api/pii/analyze + /api/pii/redact (mask + block) through the full HTTP -> detector -> redactor path; gated on PII_NER_MODEL_GGUF so the default suite is unaffected. - .github/workflows/tests-pii-ner-e2e.yml: path-filtered / nightly CI job running the container harness on CPU. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Richard Palethorpe <io@richiejp.com> * feat(gallery): add privacy-filter-nemotron (f16 + q8) GGUF conversions of OpenMed/privacy-filter-nemotron — a fine-grained English PII token-classifier (55 categories / 221 BIOES classes), fine-tuned from openai/privacy-filter on NVIDIA's Nemotron-PII dataset. Sibling to the existing privacy-filter-multilingual entry, trading language breadth for category depth. - privacy-filter-nemotron: F16 reference artifact (~2.8 GB). - privacy-filter-nemotron-q8: Q8_0 quant (~1.64 GB) for RAM-constrained / edge use; description notes the size/speed tradeoff and to validate on your own data (a single dropped span is a PII leak). Both run on the privacy-filter backend with known_usecases [token_classify] and a default mask policy (min_score 0.5); operators add per-category entity_actions as needed. sha256s taken from the HF repo's LFS object ids. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Richard Palethorpe <io@richiejp.com> --------- Signed-off-by: Richard Palethorpe <io@richiejp.com> |
||
|
LocalAI [bot]
|
95b058e1c5 |
feat(ui): restructure Cluster Nodes view (pulse + panel roster + detail page) (#10447)
* chore: gitignore SDD scratch directory Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(nodes): add GET /api/nodes/models cluster-wide loaded-models endpoint Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(ui): add nodesApi.allModels() for cluster-wide model roster Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(ui): move Scheduling to its own page and nav item Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(ui): replace nodes stat-card strip with cluster pulse + attention callout Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(ui): node-panel roster with inline model chips and segmented filter Replace the Nodes table with a full-width node-panel roster that shows each backend node's running-model chips without an expand click, plus an All/Backend/Agent segmented filter. Per-node detail (models, backends, labels, capacity) moves to the node detail page. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(ui): add deep-linkable node detail page at /app/nodes/:id Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * fix(ui): remove em-dash from CapacityEditor comment; align detail spec backend mock Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * chore(ui): nodes page cleanup, hover/chip polish, docs for restructured cluster view Nodes.jsx dead-code sweep confirmed clean (no StatCard/table/expand state/scheduling-form leftovers). Two App.css polish fixes: move the node-panel hover border-color onto the bordered element so hover gives real feedback, and add the missing .model-chip__state rule the ModelChip component already emits. Update distributed-mode docs prose to describe the restructured cluster view (cluster pulse, attention callout, node-panel roster with inline model chips, All/Backend/Agent filter, node detail page at /app/nodes/:id, Scheduling as its own page). Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * chore(ui): drop unused gpuVendorLabel export from nodeStatus 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> |
||
|
LocalAI [bot]
|
569d9bbd9e |
fix(distributed): broadcast file-staging progress across replicas (#10440)
File-staging progress lived only in the SmartRouter's in-memory
StagingTracker on the replica performing the transfer. In a multi-replica
deployment behind a round-robin load balancer, a /api/operations poll
that lands on any other replica saw no staging row, so the progress
("processing file ... Total ... Current ...") flickered in and out as
polls rotated between frontends.
Mirror the pattern already used for gallery-install progress: the origin
replica broadcasts staging ticks over NATS (SubjectStagingProgress, a
new staging.<model>.progress subject), and peers merge them via
ApplyRemote (SubscribeBroadcasts on the wildcard). Byte-level ticks are
leading-edge debounced (~1/s); Start/FileComplete/Complete always
publish. A locally-owned op stays authoritative so the origin's own echo
and stray peer events can't clobber it, and mirrored remote ops expire
after a TTL so a missed Done event can't leave a phantom row. The UI read
path (StagingTracker.GetAll) is unchanged.
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>
|
||
|
LocalAI [bot]
|
682fb2718c |
fix(distributed): detach cold-load staging from the request context (#10438)
A model not yet loaded on a worker is staged lazily on the inference request path. Staging a multi-GB model takes minutes - far longer than any client keeps its HTTP request open - so a browser refresh, an ingress/LB idle-timeout, or a round-robined retry landing on another frontend replica cancels the request context and aborts the upload with "context canceled" mid-transfer. Large models then never finish staging, so they never load (observed in a 2-replica deployment: both frontends repeatedly failed to stage a 15.7 GB GGUF, each attempt dying at a different offset). Bind the cold load (staging + LoadModel + the per-model advisory lock) to context.WithoutCancel(ctx): it keeps the request's values (prefix chain) but drops cancellation/deadline. Each long step keeps its own bound (the file stager's resume budget, LoadModel's 5m timeout), and the advisory lock still de-dupes concurrent loaders across replicas. 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> |
||
|
LocalAI [bot]
|
600dafd20b |
feat(ced): sound-event classification backend (CED audio tagger) (#10425)
* feat(ced): sketch sound-classification backend (CED audio tagger) Wires ced.cpp (CED, 527-class AudioSet sound-event tagger; baby cry, footsteps, glass, alarms, dog bark) into LocalAI as a Go/purego backend. SKETCH (backend skeleton real; core REST wiring + CI/gallery is a checklist in DESIGN.md): - backend/backend.proto: new SoundDetection rpc + SoundClass messages (run `make protogen-go` to regenerate pkg/grpc/proto). - backend/go/ced: main.go (purego dlopen libced.so + ced_capi.h), goced.go (Ced gRPC backend: Load + SoundDetection), Makefile (clone-at-pin CED_VERSION, ggml static-PIC shared build), run.sh, package.sh, .gitignore. - DESIGN.md: REST /v1/audio/classification wiring (handler/route/capability registration checklist), gallery/index + CI registration, and a scoping note for the realtime/websocket live-recognition path (sliding-window classify over the existing ws transport + voicegate; the ced C-API per-PCM entry point is already window-friendly). Backend code does not compile until protogen-go regenerates the pb types and a libced.so is built (Makefile clones+builds it). Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ced): REST /v1/audio/classification endpoint + capability registration Wires the ced sound-event classification backend (AudioSet audio tagger) end to end through the REST surface, mirroring the transcription path. - Handler: core/http/endpoints/openai/sound_classification.go parses the multipart audio upload, temp-files it, resolves the model config and calls the SoundDetection RPC; returns {model, detections[]} JSON. - Backend wrapper: core/backend/sound_classification.go (ModelSoundDetection) loads the model and normalizes the proto response into schema types. - Schema: core/schema/sound_classification.go (SoundClassificationResult). - gRPC layer: SoundDetection wired through the LocalAI wrapper (interface, Backend client, Client, embed, server, base default) so the loader-typed client exposes the RPC; proto regenerated via make protogen-go. - Route: POST /v1/audio/classification (+ /audio/classification alias) with the audio/multipart default-model middleware in routes/openai.go. - Capability surfaces: swagger @Tags/@Router on the handler; FLAG_SOUND_ CLASSIFICATION usecase flag + UsecaseSoundClassification + UsecaseInfoMap + GuessUsecases + ModalityGroups + GetAllModelConfigUsecases; meta usecase option; /api/instructions audio area updated; auth RouteFeatureRegistry + FeatureAudioClassification (APIFeatures, default ON) + FeatureMetas; UI usecaseFilters, capabilities.js CAP_SOUND_CLASSIFICATION, Models.jsx filter + i18n; docs page features/audio-classification.md + whats-new + crosslink. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ced): realtime sound-event detection over the websocket API When a realtime pipeline configures a sound-classification model, each VAD-committed utterance (the same window the transcription path produces) is also run through the CED sound-event classifier and the scored AudioSet tags are emitted as a new server event. No new backend rpc is needed: the SoundDetection gRPC method already exists on this branch. - config: add Pipeline.SoundDetection (yaml/json sound_detection,omitempty) beside Transcription/VAD. - realtime: add Model.SoundDetection(ctx, audio, topK, threshold) to the ModelInterface; implement it on wrappedModel and transcriptOnlyModel by calling backend.ModelSoundDetection with the session's sound-classification model config (mirrors how Transcribe dispatches). Load the optional config in newModel / newTranscriptionOnlyModel; nil config keeps it additive. - types: add ConversationItemSoundDetectionEvent (item_id, content_index, detections[]{label,score,index}) with type conversation.item.sound_detection, its ServerEventType constant and MarshalJSON, mirroring the transcription completed event. - realtime: add emitSoundDetection (unary path: classify the committed window, build the event, t.SendEvent) and wire it at the utterance-commit hook right after emitTranscription; gated on session.SoundDetectionEnabled (resolved from Pipeline.SoundDetection at session setup, defaults top_k=5, threshold=0). Its error is logged via xlog but never aborts the turn. - test: Ginkgo specs for emitSoundDetection (tags emitted, empty detections, classifier error) plus a SoundDetection method on the fakeModel double. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(ced): implement SoundDetection in nodes backend test doubles The SoundDetection method added to the grpc backend interface left two test doubles (fakeBackendClient, fakeGRPCBackend) incomplete, so core/services/nodes failed to compile under `go vet`/`go test` (go build missed it: the doubles live in _test.go). Add the method to both, mirroring their existing Detect mock. Repairs CI for the nodes package. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ced): decouple realtime sound detection from VAD (sound-only sessions) Sound-event detection must activate on sounds, not speech, so it no longer runs through the voice VAD/transcription path. A sound-detection-only pipeline (sound_detection set, no transcription/LLM) now: - is accepted by prepareRealtimeConfig (sound_detection counts as a pipeline stage), - builds a lightweight model via newSoundDetectionOnlyModel (no VAD/STT/LLM/TTS loaded), and - defaults the session to turn_detection none (no VAD) with no transcription stage, so the client drives windowing via input_audio_buffer.commit (option A: client-side sliding window). The per-PCM C-API already supports arbitrary windows. commitUtterance gains a sound-only branch: it emits the conversation.item.sound_detection event (scored AudioSet tags) and stops - no transcription, no LLM response. generateResponse is now guarded on a transcription stage being present, so a sound-only turn never invokes the LLM. Existing transcription/VAD sessions are unchanged (additive). Added a commitUtterance sound-only Ginkgo spec asserting it emits the sound event and neither transcribes nor generates a response. go vet + golangci-lint (new-from-merge-base) clean; openai suite green. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ced): register sound-classification backend in gallery + CI Mechanical backend-image registration for the ced sound-event classifier, mirroring the parakeet-cpp Go/purego backend everywhere it is wired up. - .github/backend-matrix.yml: add the ced build matrix, field-for-field copies of the parakeet-cpp entries (cpu amd64/arm64, cublas cuda 12/13 amd64, l4t cuda-13 arm64, l4t-jetpack cuda-12 arm64, sycl f32/f16, vulkan amd64/arm64, rocm hipblas, and the metal darwin entry), changing only backend and tag-suffix. dockerfile stays ./backend/Dockerfile.golang. - backend/index.yaml: add the &ced meta anchor (capabilities map per platform) plus ced-development and the per-arch image entries, each uri/mirror tag-suffix matching the matrix exactly. The model gallery (GGUF) entry is intentionally deferred pending the HuggingFace publish (TODO note inline). - scripts/changed-backends.js: add an explicit item.backend === "ced" branch in inferBackendPath mapping to backend/go/ced/, same mechanism and ordering as the parakeet-cpp branch (before the generic golang fallthrough). - .github/workflows/bump_deps.yaml: register mudler/ced.cpp -> CED_VERSION in backend/go/ced/Makefile so the daily bot bumps the pin. - swagger/{docs.go,swagger.json,swagger.yaml}: regenerated via make swagger so the existing /v1/audio/classification annotations land in the generated spec. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ced): server-side windowing for realtime sound detection (option B) Adds an optional server-driven sliding-window classifier so a sound-only realtime client only has to stream audio (no input_audio_buffer.commit): - Pipeline.sound_detection_window_ms / sound_detection_hop_ms config knobs. When both > 0 on a sound-only session, the server classifies the last window of streamed audio every hop and emits a conversation.item.sound_ detection event; the input buffer is trimmed to one window so a long stream stays bounded. When unset, the session stays client-driven (option A). Runs independent of VAD (sound events are not speech). - handleSoundWindow (ticker) + classifySoundWindow (one tick, extracted so it is unit-testable) + writeWindowWAV, which declares the true InputSampleRate (NewWAVHeaderWithRate) so the classifier resamples correctly. Goroutine is started after toggleVAD and torn down with the session (close + wg.Wait). - Register pipeline.sound_detection (+window_ms/hop_ms) in the config meta registry; the earlier realtime commit added pipeline.sound_detection without a registry entry, failing TestAllFieldsHaveRegistryEntries. This fixes that and covers the two new knobs. Tests: classifySoundWindow emits an event + trims the buffer to one window, no-ops on too-little audio; writeWindowWAV declares the given sample rate. go build/vet + golangci-lint (new-from-merge-base) clean; config + openai suites green. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ced): add ced-base GGUF model gallery entries (f16 + q8_0) The ced-base weights are now published at mudler/ced-base-gguf (Apache-2.0, converted from mispeech/ced-base). Adds gallery/ced.yaml (backend: ced + known_usecases: sound_classification) and two gallery/index.yaml entries (ced-base-f16 default, ced-base-q8 smallest) with sha256-pinned files, and removes the now-resolved TODO from backend/index.yaml. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ced): add tiny/mini/small GGUF model gallery entries Publishes the rest of the CED family (same architecture, metadata-driven port verified end-to-end on ced-tiny) to mudler/ced-{tiny,mini,small}-gguf and adds their f16 + q8_0 gallery entries: ced-tiny (5.5M, edge/Pi-class) f16 11MB / q8_0 6MB ced-mini (9.6M) f16 19MB / q8_0 11MB ced-small (22M) f16 42MB / q8_0 23MB All sha256-pinned. ced-base remains the accuracy default. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore(ced): point gallery entries at the consolidated mudler/ced-gguf repo All CED quantizations (tiny/mini/small/base, f16/q8_0) now live in a single HuggingFace repo, mudler/ced-gguf, instead of per-model repos. Repoint the 8 gallery model entries' urls + file uris accordingly. sha256 and filenames are unchanged. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore(ced): bump CED_VERSION to the short-clip fix Pin the ced backend to ced.cpp 99c6ed3, which fixes a crash on any clip shorter than target_length (~10.11s): time_pos_embed was added at its full 63-frame grid instead of being sliced to the clip's actual time grid, tripping ggml_can_repeat in ggml_add. Surfaced by the live realtime e2e (sub-10s windows) and gated with a short-clip parity test upstream. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(ced): list ced.cpp as a LocalAI-team engine + backend-guide directive - README.md: add ced.cpp to the "native C/C++/GGML engines developed and maintained by the LocalAI project" table. - docs/content/features/backends.md: add a Sound Classification backend category (sound-event classification / audio tagging) listing ced.cpp. - .agents/adding-backends.md: add a "Documenting the backend" section and two verification-checklist items requiring new backends to be documented in the backends.md category list, and in-house native engines to be added to the README maintained-engines table. This directive was missing. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore(ced): repin CED_VERSION to the v0.1.0 release commit ced.cpp history was squashed into a single release commit (tagged v0.1.0), so the previous pin (99c6ed3) no longer exists upstream. Pin to c04ac14, the v0.1.0 release commit, so the backend builds against a commit that exists. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(ced): silence gosec G304/G103 + govet unsafeptr on audited paths - sound_classification.go: os.Create(dst) where dst = temp dir + path.Base of the upload (no traversal). #nosec G304, matching the depth-anything-cpp handler. - goced.go: reading a NUL-terminated C string from a libced-owned buffer. #nosec G103 (gosec) + //nolint:govet (golangci-lint's unsafeptr check), since the uintptr is a C-owned malloc'd buffer, not Go-GC memory. 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> |
||
|
LocalAI [bot]
|
32c47706ae |
feat(realtime): speaker-aware conversations - surface identity to client and LLM (#10424)
* feat(realtime): add voice_recognition enforce + identity config Add Enforce *bool and Identity *VoiceIdentityConfig to PipelineVoiceRecognition, plus EnforceGate/IdentityEnabled/ AnnounceEnabled/PersonalizeEnabled helpers. Enforce nil defaults to gating (backward compatible); identity surfacing is independent of the gate. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(realtime): add Speaker type and conversation.item.speaker event Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(realtime): split voiceGate into Resolve + authorize Split the speaker authorization into a Resolve step (embed once, produce a types.Speaker identity) and a pure authorize policy step, with a 0..100 confidence score mirroring /v1/voice/identify. The legacy Authorize wrapper is kept so existing specs stay green. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(realtime): resolve speaker per turn and emit conversation.item.speaker Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(realtime): personalize LLM turns with recognized speaker Set the per-message name field on each recognized user turn and append a current-speaker note to the system message, both gated by the voice recognition identity config. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(realtime): document speaker identity surfacing and personalization Document the new voice_recognition keys (enforce, identity.*) and the LocalAI-extension conversation.item.speaker server event in the realtime feature docs. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(realtime): cover when:first+identity re-resolution and multi-speaker history Add two integration specs to harden the speaker-aware realtime path: - when:first with an Identity block re-resolves the speaker every turn even though re-authorization is skipped after the first match: a later resolve error now fails closed, while a clean later resolve still surfaces and names the speaker. - multi-speaker history attribution: each user turn carries its own per-message name and the injected system note reflects the latest speaker. Test-only change; no production behavior was modified. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(realtime): surface speaker labels in conversation.item.speaker Carry the registered speaker's labels (identify mode) on types.Speaker so they flow into the conversation.item.speaker event and the stored item. Verify mode has no labels, so the field is omitted there. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(e2e): cover conversation.item.speaker over a real websocket Add a realtime-pipeline-identity config (verify mode, enforce:false, identity announce+announce_unknown+personalize) and two e2e specs driving the real server over a real WebSocket with the mock VoiceEmbed backend: an authorized speaker yields a conversation.item.speaker event naming e2e-speaker (matched true) and reaches response.done; an unauthorized speaker yields an unknown (matched false, no name) event and still responds, proving enforce:false never drops a turn. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(config): register voice_recognition enforce + identity fields The meta registry coverage test (TestAllFieldsHaveRegistryEntries) requires every config field to have an entry in core/config/meta/registry.go. The new voice_recognition.enforce and voice_recognition.identity.* fields were missing, failing tests-linux and tests-apple. Add registry entries (toggles) so the fields are surfaced in the model-config editor and the coverage test passes. Assisted-by: Claude:claude-opus-4-8 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> Co-authored-by: Ettore Di Giacinto <mudler@users.noreply.github.com> |
||
|
Tai An
|
e58870a573 |
feat(react-ui/chat): paste images from clipboard into chat input (#10428)
The chat input only accepted attachments via the file picker, so users who copied an image from a webpage or a screen region had to first save it to a file before attaching it (#10361). Add an onPaste handler on the input textarea that pulls image items out of the clipboard and routes them through the same staging path as the file picker. The per-file processing in handleFileChange is extracted into a shared processFiles helper so both entry points stay in sync. Clipboard images, which arrive unnamed or as a generic "image.png", are given unique typed names so multiple pastes don't collide, and the default paste is suppressed only when an image is actually attached so normal text paste is unaffected. Closes #10361 Signed-off-by: Anai-Guo <antai12232931@outlook.com> |
||
|
LocalAI [bot]
|
7b462a0d51 |
fix(backend): call vram.EstimateModelMultiContext (master build broken: undefined vram.EstimateModel) (#10426)
fix(backend): call vram.EstimateModelMultiContext for model size estimate core/backend/options.go called vram.EstimateModel, which does not exist in the vram package (it exposes EstimateModelMultiContext). This broke the build on master (undefined: vram.EstimateModel). Use EstimateModelMultiContext with a nil context-size slice (defaults to a single 8192 estimate); the returned MultiContextEstimate.SizeBytes is exactly what the caller consumes, so size estimation behavior is unchanged. 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> |
||
|
Leoy
|
b50b1fe418 |
feat(watchdog): add size-aware LRU eviction mode (#9527)
* feat(watchdog): add size-aware LRU eviction mode When the model count hits the LRU limit or the memory reclaimer fires, evict the largest model by on-disk file size first rather than the least-recently-used one. For GGUF models the file size is a reliable proxy for GPU/RAM footprint, so evicting the largest candidate maximises freed memory per eviction round while keeping small utility models (embeddings, classifiers, rerankers) resident. Changes: - `pkg/model/watchdog.go`: add `sizeAwareEviction` flag and `modelSizes map[string]int64` to `WatchDog`; sort candidates by `sizeBytes` desc (LRU time as tiebreaker) when the flag is set; add `RegisterModelSize`, `SetSizeAwareEviction`, `GetSizeAwareEviction` - `pkg/model/watchdog_options.go`: add `WithSizeAwareEviction` option - `pkg/model/initializers.go`: stat model file after load and call `RegisterModelSize` so size data is available before the first eviction - `core/config/application_config.go`, `runtime_settings.go`: add `SizeAwareEviction` field and `WithSizeAwareEviction` app option; expose via `ToRuntimeSettings` / `ApplyRuntimeSettings` for the `POST /api/settings` live-reload path - `core/cli/run.go`: add `--size-aware-eviction` flag / `LOCALAI_SIZE_AWARE_EVICTION` env var - `core/application/startup.go`, `watchdog.go`: wire the new option through to `NewWatchDog` - `pkg/model/watchdog_test.go`: 5 new specs — option enable, dynamic toggle, largest-first ordering, equal-size LRU tiebreaker, no-size fallback to LRU, and size-map cleanup on eviction Closes #9375 Signed-off-by: supermario_leo <leo.stack@outlook.com> * refactor(watchdog): use vram estimation scaffolding for model size Replace the brittle os.Stat(modelFile) approach with a proper call to pkg/vram, which handles multi-file models (DownloadFiles, MMProj) and all weight file types, not just single GGUF files. - Add estimateModelSizeBytes() in core/backend/options.go that collects all weight file URIs from the model config, resolves them to file:// URIs, and calls vram.Estimate() with the shared DefaultCachedSizeResolver (15-min TTL cache avoids redundant stat calls on repeated loads) - Thread the result through via a new WithModelSizeBytes() loader option - In initializers.go, consume the pre-computed size instead of calling os.Stat; if no size was supplied (e.g. for external/router-dispatched models) the registration is simply skipped Signed-off-by: supermario_leo <leo.stack@outlook.com> * refactor(watchdog): use EstimateModel with HF fallback for size estimation Switch estimateModelSizeBytes from calling vram.Estimate directly to the unified vram.EstimateModel entry point, which adds automatic fallbacks: file-based GGUF metadata → HF API → size string. Also extract the HuggingFace repo ID from model URIs (huggingface://, hf://, https://huggingface.co/ and org/model short-form) and pass it as ModelEstimateInput.HFRepo, so models not yet downloaded locally can still get a size estimate via the HF API. Addresses @mudler's review feedback: "better to rely on EstimateModel and pass by the HF URL of the model extracted from the URI". Signed-off-by: supermario_leo <leo.stack@outlook.com> * feat(webui): add Size-Aware Eviction toggle to settings page The size-aware eviction setting was wired through the CLI flag and the RuntimeSettings live-reload path (POST /api/settings) but had no handle on the React settings page, so it could not be toggled from the UI. Add a Size-Aware Eviction toggle to the Watchdog section, next to the existing Force Eviction When Busy / LRU eviction handles. The settings page loads and saves the whole RuntimeSettings object, so the new size_aware_eviction key is picked up with no extra plumbing. Addresses @mudler's review feedback: the application config setting should land on the same UI settings page as the other handles. Signed-off-by: supermario_leo <leo.stack@outlook.com> --------- Signed-off-by: supermario_leo <leo.stack@outlook.com> |
||
|
LocalAI [bot]
|
3e96d811b7 |
fix(ui): keep row action menu anchored and stop scroll snap on /app/manage (#10419)
Opening a model row's kebab (ActionMenu) on the Manage dashboard snapped the
page scroll to the top and rendered the menu detached from its trigger, making
it impossible to operate.
Two compounding causes:
- The menu auto-focus called el.focus() without preventScroll, so the browser
scrolled the focused element into view, yanking the page to the top.
- The position:fixed Popover was rendered inline inside the table row. The
editorial UI overhaul added hover transforms to rows/cards, and a transformed
ancestor re-anchors position:fixed to itself instead of the viewport, so the
menu (positioned from the trigger's viewport rect) landed in the wrong place.
Fix: portal the Popover to document.body so position:fixed always resolves
against the viewport, position it before paint with useLayoutEffect (no {0,0}
flash), and pass preventScroll:true to both focus calls.
Adds an e2e regression test that reproduces the symptom (scroll jumped from 564
to 0 on the old code) and asserts the menu tracks its trigger.
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>
|
||
|
LocalAI [bot]
|
23f225260c |
refactor(config): single source of truth for default values (#10418)
refactor(config): single source of truth for default values across config + backend Defaults were decided in two areas with duplicated/drifted literals: the config SetDefaults tiers vs core/backend/options.go's grpcModelOpts (which translates a ModelConfig to the backend wire format and supplied its own fallbacks). They had drifted - n_gpu_layers 9999999 (options.go) vs 99999999 (gguf.go), two 512 batch constants, context 1024 (gguf) vs 4096 (backend) scattered as bare literals. Introduce core/config/defaults.go as the canonical home (DefaultContextSize=4096, GGUFFallbackContextSize=1024, DefaultNGPULayers=99999999, DefaultFlashAttention= auto). gguf.go / hooks_llamacpp.go use them directly; core/backend references them (backend imports config, never the reverse) so DefaultContextSize/DefaultBatchSize and the flash-attn / n_gpu_layers fallbacks resolve to one place. The two context values (1024 GGUF-no-estimate vs 4096 general) are kept distinct but now named + documented, not blind literals. Behavior-preserving; config + backend suites green. Assisted-by: Claude:opus-4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
LocalAI [bot]
|
aef10723c9 |
feat(config): prefix caching default + consolidate scattered defaults (#10415)
* feat(config): enable cross-request prefix caching for serving (Phase 2) The llama.cpp backend ships n_cache_reuse=0 (cross-request KV prefix reuse via shifting disabled). Enable it by default (256) so repeated prefixes - system prompts, RAG context, agent scaffolds, multi-turn chat - aren't recomputed. This is the universally-useful part of 'paged attention' (shared-prefix reuse, which the upstream maintainers themselves identify as where paged attn actually helps) and needs none of the block-KV machinery. Lives in a serving_defaults.go sibling to hardware_defaults.go (device-driven vs serving-policy defaults); both run from SetDefaults and only fill unset values. Explicit cache_reuse/n_cache_reuse always wins. Device-independent, so it propagates to distributed nodes via the model options with no router change. Shares the backendOptionSet helper with the Phase-1 parallel default. Assisted-by: Claude:opus-4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(config): extract generic fallback defaults into ApplyGenericDefaults Behavior-preserving: move the inline sampling-param + runtime-flag fallbacks out of SetDefaults into ApplyGenericDefaults, completing the domain-grouped tiers (ApplyInferenceDefaults=family, ApplyHardwareDefaults=device, ApplyServingDefaults =serving, ApplyGenericDefaults=generic fallbacks). SetDefaults is now a clean orchestrator. Same order (runs after the family/hardware/serving tiers so those win) and same conditions (TopK gated on UsesLlamaSamplerDefaults, MMap on XPU). No behavior change; full config suite green. (NGPULayers stays in the GGUF-read path for now - it's device-driven but coupled to model-size detection; a separate follow-up.) Assisted-by: 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> |
||
|
LocalAI [bot]
|
9565db5f94 |
feat(models): model aliases - redirect a model name to another configured model (#10414)
* feat(config): add model alias field and self-validation Add ModelConfig.Alias (yaml: alias), IsAlias(), and an alias short-circuit at the top of Validate() that rejects self-reference and forbids setting backend/parameters.model on a pure-redirect alias. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(config): resolve and validate model alias targets in the loader Assisted-by: Claude:opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(middleware): resolve model aliases and stamp requested/served identity Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(modeladmin): reject alias configs with invalid targets on create/edit Validate alias targets at create/swap entry points (ImportModelEndpoint, EditYAML, PatchConfig) so a dangling, chained, or disabled alias target is rejected at save time rather than surfacing as a runtime error. Assisted-by: Claude:opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(api): add GET /api/aliases to list model aliases Adds an admin-gated read-only endpoint that lists every model alias config as {name, target} pairs, backed by the loader's existing GetAllModelsConfigs(). Assisted-by: Claude:opus-4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(mcp): add set_alias and list_aliases tools Expose model-alias management over the LocalAI Assistant MCP surface: list_aliases (read-only, GET /api/aliases) and set_alias (mutating). SetAlias is swap-first: PATCH /api/models/config-json/:name swaps an existing alias's target (validated, non-destructive) and a 404 falls back to POST /models/import to create a fresh {name, alias} config. The inproc client mirrors this via ConfigService.PatchConfig + a create path modeled on ImportModelEndpoint. Deletion reuses delete_model. Assisted-by: Claude:claude-opus-4 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * style(mcp): replace em dashes in alias tool comments Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(config-meta): expose alias as a model-select field Add an 'alias' section to DefaultSections() and an 'alias' field override in DefaultRegistry() so the schema-driven React editor renders the new top-level ModelConfig.Alias field as a model picker in its own section. Assisted-by: Claude:opus-4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ui): add alias template card and Manage alias badge Add an 'Alias / Routing' template to the create-flow gallery that seeds a minimal name + alias config, and a read-only 'alias -> target' badge on the Manage Models tab. The capabilities row payload does not carry the alias field, so the badge resolves targets from GET /api/aliases looked up by name. Assisted-by: Claude:claude-opus-4 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: document model aliases Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(swagger): regenerate for GET /api/aliases Adds the /api/aliases path and AliasInfo schema generated from the ListAliasesEndpoint annotation. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(localai): check os.RemoveAll error in aliases_test Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix: correct alias conversion docs and advertise /api/aliases in instructions Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(mcp): write alias config 0600 to satisfy gosec G306 The inproc createAlias path wrote the alias YAML with 0644, which gosec flags as a new G306 finding on the PR. The LocalAI process is the sole reader/writer of model configs, so 0600 is correct and keeps the scan clean. 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> |
||
|
LocalAI [bot]
|
b081247d95 |
feat(config): hardware-tuned defaults — Blackwell batch + VRAM-scaled concurrency (#10411)
* feat(config): node-aware hardware defaults — larger physical batch on Blackwell A larger physical batch (n_batch/n_ubatch) materially lifts MoE prefill on NVIDIA Blackwell consumer GPUs (sm_120/121, incl. GB10 / DGX Spark) — measured on a GB10 with Qwen3-Coder-30B-A3B, the prefill ceiling rises (ub512 ~2994 -> ub2048 ~3316 t/s) and saturates around 2048. The heuristic lives in core/config alongside the other config overriders (ApplyInferenceDefaults, guessDefaultsFromFile/NGPULayers) — they all fill the ModelConfig from heuristics, so hardware tuning is the same domain and stays in one place. It is parameterized on a GPU descriptor (not direct detection) so it works in both deployment shapes: - Single host: SetDefaults applies it with the LocalGPU. - Distributed: only the worker sees the GPU, so the worker reports its compute capability on registration (gpu_compute_capability -> BackendNode), and the router re-applies the SAME core/config heuristic for the SELECTED node before loading — fixing the case where the frontend has no GPU at all. Explicit `batch:` always wins (only managed default values are touched). xsysinfo gains NVIDIAComputeCapability() (detection only); all interpretation lives in core/config. Tests: core/config, pkg/xsysinfo, core/services/nodes. Assisted-by: Claude:opus-4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(config): injectable local-GPU seam + single-instance coverage Make local GPU detection an injectable package var (localGPU) so the single-instance path (SetDefaults -> ApplyHardwareDefaults) is deterministically testable without a real GPU, mirroring the distributed override's coverage. Adds specs asserting SetDefaults sets the Blackwell physical batch, leaves it unset on non-Blackwell, and never overrides an explicit batch. Assisted-by: Claude:opus-4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(config): default concurrent serving (n_parallel) by GPU VRAM The llama.cpp backend defaults n_parallel=1, which serializes multi-user requests and leaves continuous batching off (it auto-enables only at n_parallel>1). Fold a VRAM-scaled parallel-slot default into the hardware-config path so multi-user serving works out of the box: >=32GiB->8, >=8GiB->4, >=4GiB->2, else unchanged. With the backend's unified KV the slots SHARE the context budget, so this adds concurrency without multiplying KV memory. Explicit parallel/n_parallel always wins. EnsureParallelOption is shared by the single-host path (ApplyHardwareDefaults with the local GPU) and the distributed router (per selected node's reported VRAM, since the frontend may have no GPU). LocalGPU now also reports VRAM. Assisted-by: 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> |
||
|
Ettore Di Giacinto
|
aba0bfd24f |
feat(backend): auto-default physical batch to 2048 on Blackwell GPUs
On NVIDIA Blackwell consumer GPUs (sm_120/121, incl. GB10/DGX Spark) a larger physical batch (n_ubatch) materially lifts MoE prefill throughput - measured on a GB10 with Qwen3-30B-A3B to lift the prefill ceiling and saturate at ~2048. When a model config leaves `batch:` unset, EffectiveBatchSize now picks 2048 on Blackwell instead of 512; explicit `batch:` always overrides. Detection is a shared, cached Go helper (xsysinfo.IsNVIDIABlackwell, nvidia-smi compute_cap >= 12). Logic is isolated in core/backend/hardware_defaults.go and applied at the common ModelOptions builder, so it covers the C++ llama.cpp backend too. Measured (GB10, Qwen3-Coder-30B-A3B MXFP4): prefill ub512 2994 -> ub2048 3316 t/s; saturates past 2048. Also recorded in the DGX gap plan: 4-bit quant alone captures the decode win (Q4_K_M 93.5 >= MXFP4 86.4 t/s), MXFP4's only edge is prefill via Blackwell FP4 tensor cores. Tests: hardware_defaults_internal_test.go; existing NBatch specs pinned to the no-Blackwell branch for determinism. Assisted-by: Claude:opus-4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
LocalAI [bot]
|
079ac0e15a |
fix(realtime): raise WebRTC data-channel max-message-size + keep sendLoop alive (#10407)
* fix(realtime): raise WebRTC data-channel max-message-size for large events Browsers advertise a conservative SCTP max-message-size in their SDP offer (Chrome uses 256 KiB). pion enforces the remote's advertised value on send, so a single realtime event larger than it cannot be sent over the "oai-events" data channel: SendText fails, the event is dropped, and the turn silently yields no response. Some turns legitimately produce a >256 KiB JSON event — notably tool calls with sizeable schemas or results. Browsers advertise the value conservatively but their SCTP stacks reassemble much larger messages, so raise the max-message-size honored for our own server-generated events by rewriting the attribute in the offer before SetRemoteDescription. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(realtime): keep the WebRTC sendLoop alive when one event send fails A failed SendText on the oai-events data channel exited the sender goroutine, so a single dropped event (e.g. one over the negotiated SCTP max-message-size) tore down the session and silently dropped every subsequent event. Log and skip the offending event instead and keep draining; a genuinely dead transport is still handled by the closed / connection-state path. 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> |