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937
Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 LocalAI [bot]andEttore Di Giacinto
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bbe018c1a0 |
feat(bonsai): PrismML llama.cpp fork backend + Bonsai/Ternary-Bonsai gallery models (#10834)
feat(bonsai): add PrismML llama.cpp fork backend + Bonsai gallery models Adds a new `bonsai` backend that runs the PrismML fork of llama.cpp (github.com/PrismML-Eng/llama.cpp, `prism` branch), which ships the Q1_0 (1-bit) and Q2_0 (ternary / 1.58-bit) weight-quantization kernels used by the Bonsai and Ternary-Bonsai models. Stock llama.cpp cannot decode these quants. Modeled on the turboquant backend: reuses backend/cpp/llama-cpp/grpc-server.cpp against the fork's libllama via a thin wrapper Makefile, so the sub-2-bit models are served with the same OpenAI-compatible API. No grpc-server allow-list patch is needed (bonsai adds weight quants, transparent to the server, not KV-cache types), and the reused server compiles cleanly against the fork with no skew patches (validated locally via a CPU docker build; patches/ is present but empty for any future re-pin skew). Backend wiring: backend/cpp/bonsai/, .docker/bonsai-compile.sh, backend/Dockerfile.bonsai, top-level Makefile targets, backend-matrix.yml build rows (CPU, CUDA 12/13, L4T, SYCL f32/f16, Vulkan, ROCm/hipblas), backend/index.yaml meta-backend + per-platform images, and a nightly bump_deps entry tracking the `prism` branch. Gallery: 8 entries across 4 families - bonsai-8b-1bit, ternary-bonsai-8b (+g64, +pq2), bonsai-27b-1bit (vision), ternary-bonsai-27b (+pq2, +g64, vision). The 27B models wire the mmproj vision tower; the DSpark speculative drafter GGUFs are not wired (custom semi-autoregressive drafter, not a standard llama.cpp draft model). 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> |
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Tai AnandAnai Guo
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808312b4b9 |
fix(watchdog): guard StopWatchdog with watchdogMutex to prevent double close (#10841) (#10859)
fix(watchdog): guard StopWatchdog with watchdogMutex to prevent double close StopWatchdog checked, closed and cleared a.watchdogStop without holding a.watchdogMutex, while startWatchdog and RestartWatchdog reassign and close the same channel under that lock. POST /api/settings dispatches to StopWatchdog or RestartWatchdog depending on ApplicationConfig.WatchdogShouldRun(), so both are reachable concurrently. Two callers can observe a non-nil watchdogStop and both close it, which panics with 'close of closed channel' and takes the server down. Take the mutex, matching the other two writers. StopWatchdog is only called from the settings handler, which holds no lock, so this cannot deadlock. Fixes #10841 Co-authored-by: Anai Guo <antai12232931@anaiguo.com> |
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Tai An
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5fe48e4910 |
fix(backend): don't crash the whole process on an invalid cutstrings/extract_regex (#10855)
Finetune() compiled every model cutstrings/extract_regex entry via regexp.Compile
and called xlog.Fatal on failure, which terminates the entire local-ai process.
A single model config with an invalid regex (e.g. cutstrings: ["("]) turns one
/v1/chat/completions request into a process-level denial of service.
Log the compile error and skip the offending pattern instead. The mutex is
released before continuing, and skipping avoids dereferencing the nil regexp
that removing the fatal would otherwise leave behind.
Fixes #10843
Signed-off-by: Tai An <antai12232931@outlook.com>
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Tai AnandAnai-Guo
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688f904a10 |
fix(runtime-settings): apply persisted threads/context_size/f16 at startup (#10853)
ApplyRuntimeSettings persists the performance settings (threads, context_size, f16) on the live /api/settings path, but the startup loader loadRuntimeSettingsFromFile never read them back, so a value saved via the Middleware UI was silently ignored on the next restart: the model booted with the CLI/physical-core default and GET /api/settings echoed that default instead of the saved value (#10845). Threads needs special handling: unlike context_size/f16, WithThreads eagerly resolves an unset (0) value to xsysinfo.CPUPhysicalCores() at option-apply time, so options.Threads is never 0 in the loader and the usual "== default" heuristic cannot tell an env/CLI value from the physical-core fallback. Detect LOCALAI_THREADS/THREADS explicitly so the env still wins over the persisted file value. Signed-off-by: Anai-Guo <Anai-Guo@users.noreply.github.com> Co-authored-by: Anai-Guo <Anai-Guo@users.noreply.github.com> |
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LocalAI [bot]andEttore Di Giacinto
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8cec22c3b7 |
feat(vram): per-node VRAM allocation budget (LOCALAI_VRAM_BUDGET) (#10833)
* feat(vram): add vrambudget primitive for per-node VRAM caps Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): apply default VRAM budget in xsysinfo aggregate getters Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): wire LOCALAI_VRAM_BUDGET flag to xsysinfo default budget Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): persist VRAM budget via runtime settings with live apply Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(vram): reset process-global VRAM budget after runtime-settings spec Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): add VRAM budget field to Settings page Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): store and enforce per-node VRAM budget in the node registry Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): apply per-node VRAM budget in router hardware defaults Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): report worker VRAM budget in node registration The distributed worker now reports its operator-set VRAM budget string (LOCALAI_VRAM_BUDGET) to the server on registration. The worker keeps reporting RAW total/available VRAM and never sets the xsysinfo process-global budget (that stays standalone-only); the server resolves and enforces the budget uniformly (Task 6). Also closes a Task 6 gap: on re-registration, a struct Updates zero-skips an empty budget, so a worker that dropped LOCALAI_VRAM_BUDGET left the stale cap in place. For non-admin-override nodes the budget columns are now force-written (map Updates) even when empty, so removing the env var clears the cap; admin overrides are preserved unchanged. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * style(vram): drop em dash from worker-clear comment Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): add node VRAM budget admin endpoints Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): add node VRAM budget control to the node UI Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): expose set_node_vram_budget MCP admin tool Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(vram): document LOCALAI_VRAM_BUDGET and node VRAM budget UI Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(vram): avoid double-applying VRAM budget in GetResourceAggregateInfo The GPU-branch aggregate returned by GetResourceInfo is sourced from GetGPUAggregateInfo, which already caps total/free/used against the process-wide VRAM budget. GetResourceAggregateInfo then applied the budget a second time. For an absolute budget this is idempotent, but for a percentage budget b.Apply resolves the ceiling as a fraction of its input total, so a second pass yields P*(P*T) instead of P*T and distorts UsagePercent (read by the memory reclaimer in pkg/model/watchdog.go). Remove the redundant second application so the budget is applied exactly once, against the raw physical totals, upstream in GetGPUAggregateInfo. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(vram): implement SetNodeVRAMBudget on mcp assistant test stub The LocalAIClient interface gained SetNodeVRAMBudget; the stubClient in core/http/endpoints/mcp used by the assistant tests is a separate implementer and needs the method too (broke golangci-lint typecheck and both test jobs). Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]andEttore Di Giacinto
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3601174ce0 |
fix(distributed): make per-node backend upgrade actually upgrade (#10838)
* test(core/http): make the suite's HTTP port overridable app_test.go and openresponses_test.go hardcoded 127.0.0.1:9090. When another service already listens on 9090 the suite does not fail fast: the server goroutine logs the bind error and the specs then poll whatever is squatting the port until Eventually times out. On machines where 9090 is permanently taken this makes the pre-commit coverage gate impossible to pass. Introduce testHTTPAddr, defaulting to 127.0.0.1:9090 (what CI has always used) and overridable via LOCALAI_TEST_HTTP_PORT for local runs. Assisted-by: Claude:claude-fable-5 golangci-lint Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(distributed): make per-node backend upgrade actually upgrade The node detail page's Upgrade button reused the node-scoped install path (POST /api/nodes/:id/backends/install). That fires NATS backend.install with force=false, and the worker's install handler is deliberately "ensure installed": when the backend binary already exists on disk it short-circuits without touching the gallery. Since only an installed backend can be upgraded, the whole chain was a guaranteed successful no-op - the UI then toasted "backend upgraded" without even waiting for the async job. Route upgrades through the real force-reinstall path instead: - BackendManager.UpgradeBackend now receives the ManagementOp (like InstallBackend already did) so implementations can honor op.TargetNodeID. - DistributedBackendManager.UpgradeBackend scopes the backend.upgrade fan-out to op.TargetNodeID when set, and errors when the target node does not report the backend as installed. - New POST /api/nodes/:id/backends/upgrade endpoint enqueues an Upgrade=true node-scoped op (async 202 + jobID, mirroring install). - NodeDetail UI calls the new endpoint and reports the dispatch ("Upgrading ... on this node...") instead of claiming success; the Operations panel tracks the actual job. Verified against a live local cluster (NATS + Postgres + two workers): the target worker stops the running process, force-reinstalls from the gallery and re-downloads the OCI image; the second worker receives no backend.upgrade event; upgrading a backend missing from the target node fails the job with a clear error. Assisted-by: Claude:claude-fable-5 golangci-lint Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]andEttore Di Giacinto
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bcc41219f7 |
feat: materialize Hugging Face model artifacts (#10825)
* feat(config): add model artifact source contract Assisted-by: Codex:GPT-5 [Codex] * feat(downloader): add authenticated raw-byte progress Assisted-by: Codex:GPT-5 [Codex] * feat(huggingface): resolve immutable snapshot manifests Assisted-by: Codex:GPT-5 [Codex] * feat(models): add artifact storage primitives Assisted-by: Codex:GPT-5 [Codex] * feat(models): materialize pinned Hugging Face snapshots Assisted-by: Codex:GPT-5 [Codex] * feat(models): bind managed snapshots at runtime Assisted-by: Codex:GPT-5 [Codex] * feat(gallery): materialize model artifacts during install Assisted-by: Codex:GPT-5 [Codex] * feat(gallery): declare managed Hugging Face artifacts Assisted-by: Codex:GPT-5 [Codex] * feat(models): preload managed model artifacts Assisted-by: Codex:GPT-5 [Codex] * fix(gallery): retain shared artifact caches on delete Assisted-by: Codex:GPT-5 [Codex] * feat(models): report artifact acquisition progress Assisted-by: Codex:GPT-5 [Codex] * refactor(backends): load managed models from ModelFile Assisted-by: Codex:GPT-5 [Codex] * refactor(backends): load staged speech model snapshots Assisted-by: Codex:GPT-5 [Codex] * refactor(backends): use staged snapshots in engine backends Assisted-by: Codex:GPT-5 [Codex] * test(distributed): cover staged artifact snapshots Assisted-by: Codex:GPT-5 [Codex] * docs: explain managed model artifacts Assisted-by: Codex:GPT-5 [Codex] * docs: add product design context Assisted-by: Codex:GPT-5 [Codex] * feat(ui): show model artifact download progress Assisted-by: Codex:GPT-5 [Codex] * Eagerly materialize Hugging Face artifacts Materialize HF-backed model references as managed GGUF artifacts during load, with lazy download retained only as fallback. Assisted-by: Codex:GPT-5 [shell] * Refactor HF downloads through a shared executor Assisted-by: Codex:GPT-5 [shell] * drop Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]andEttore Di Giacinto
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b224c96db6 |
fix(config): only inject llama.cpp serving options on the llama.cpp path (#10822)
SetDefaults injected the llama.cpp server options cache_reuse (ApplyServingDefaults) and parallel (ApplyHardwareDefaults, re-applied per selected node by the distributed router) onto every model config regardless of backend. Every other backend ignores options it does not understand, so this was harmless until longcat-video, which strictly validates its options and fails LoadModel with "unknown model option(s): cache_reuse, parallel". Gate both injections behind a new UsesLlamaCppServingOptions allow-list (llama-cpp plus the empty/auto-detect case that resolves to llama.cpp from a GGUF file, mirroring how llamaCppDefaults is registered). This follows the existing UsesLlamaSamplerDefaults precedent for llama-only defaults. The typed NBatch field is deliberately left alone: it is a proto field every backend simply ignores, which is why batch never triggered the error. Also harden the longcat-video backend to warn-and-ignore unknown model options and request params through a testable select_known_options helper, matching the other LocalAI Python backends, so a future server-injected option cannot break loading again. 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> |
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Ijasandajuijas
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a5aa56db81 |
fix: preserve uploaded file content when regenerating a non-last answer (#10819)
handleRegenerate rebuilt the outbound message from the display-only
message.files metadata ({name, type: 'file'|'image'|..., content}),
which doesn't carry the base64/textContent payload sendMessage's
file-building loop expects. As a result, regenerating any answer whose
own question had an attachment silently dropped that attachment from
the resent message. This wasn't fork-specific, but forking a chat and
then regenerating an earlier (now non-last) answer is the natural way
to hit it.
Fix by reusing the original message's already-assembled `content`
verbatim (it already has the file text / image_url / audio_url /
video_url parts embedded from the first send) instead of trying to
reconstruct it from lossy display metadata.
Fixes #10806
Assisted-by: Claude:claude-sonnet-5
Signed-off-by: ajuijas <189517297+ajuijas@users.noreply.github.com>
Co-authored-by: ajuijas <189517297+ajuijas@users.noreply.github.com>
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Dedy F. Setyawan
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b10e330590 |
feat(react-ui): localize MediaHistory, AudioTransform, and Sound components (#10802)
- Replace hardcoded text with useTranslation hook in UI components - Add localization support for both English (en) and Indonesian (id) locales Signed-off-by: Dedy F. Setyawan <dedyfajars@gmail.com> |
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LocalAI [bot]andEttore Di Giacinto
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4056283aa4 |
[voice] feat: add managed voice cloning profiles (#10799)
* feat(ui): add voice library workflow Give administrators a production-ready flow to record or upload consented reference audio, manage reusable profiles, inspect API usage, discover compatible models, and hand a saved voice directly to text-to-speech. Assisted-by: Codex:gpt-5 * feat(voice): add managed voice cloning profiles Make reusable reference voices manageable through the admin API instead of requiring model-directory and YAML edits. Discover compatible installed and gallery models from server-side backend capabilities, retain explicit model configuration controls, and stage saved references for supported backends. Expose profile management through REST and MCP, document backend-specific behavior, and cover the workflow from profile creation through real Qwen3-TTS synthesis. Harden the agent-job HTTP test against completion racing cancellation. Assisted-by: Codex:gpt-5 --------- Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]andEttore Di Giacinto
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b00422e45f |
feat(backends): add LongCat video and avatar generation (#10792)
* feat(backends): add LongCat video and avatar generation Assisted-by: Codex:GPT-5 [apply_patch] [exec_command] [web] * refactor(config): declare model I/O modalities Make model configs declare input and output modalities so capability discovery no longer branches on backend or checkpoint names. Complete the LongCat gallery and user documentation, make the SDPA patch apply to the pinned upstream revision, and stabilize the Agent Jobs race exposed by the required hook. Assisted-by: Codex:GPT-5 [web] --------- Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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Richard Palethorpe
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1f9fda7138 |
fix(backends): refuse foreign model loads in opus and local-store (#10769)
When a model config has no explicit backend, the model loader greedily probes every installed backend and binds to the first Load that succeeds. opus and local-store were the only in-tree backends with no model artefact to validate, so they accepted anything — an LLM installed after them could silently bind to the audio codec or the vector store and then fail at inference with "unimplemented" (see #9287). opus now accepts only its own name (what the realtime WebRTC path sends) or none. local-store namespaces are arbitrary (router caches, biometrics, user-named stores), so core's StoreBackend now marks genuine store loads with a store:// prefix on the gRPC model name and the backend refuses names without it; core and backend ship from the same release, so the convention upgrades in lockstep. Also repair the bit-rotted 'make test-stores' bootstrap (the suite never registered external backends, so BACKENDS_PATH was dead weight) and add the Load-validation rule to the adding-backends checklist. Related: #9287 Assisted-by: Claude:claude-fable-5 golangci-lint Signed-off-by: Richard Palethorpe <io@richiejp.com> |
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LocalAI [bot]andEttore Di Giacinto
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94bdc825dc |
feat(backend): add moss-transcribe-cpp backend (MOSS-Transcribe-Diarize) (#10756)
C++/ggml transcription + speaker diarization + timestamps backend. Purego dlopens libmoss-transcribe.so (ggml statically linked) from moss-transcribe.cpp and serves offline AudioTranscription, parsing the [start][Sxx]text[end] output into segments with nanosecond timestamps. Adds the importer (surfaces in GET /backends/known), backend-matrix (Linux + Darwin/metal), backend/index.yaml, and a gallery entry (default q5_k GGUF from mudler/moss-transcribe.cpp-gguf). Local L0 smoke (go build + go test ./... = 16 pass, golangci-lint 0 issues) passed against the real libmoss-transcribe.so. The pre-commit coverage gate (full pkg/core + tests/e2e) could not run in the authoring sandbox (no live models, port 9090 held); CI must enforce it before merge. Assisted-by: Claude:claude-opus-4-8 golangci-lint Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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Dedy F. Setyawan
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294487eb61 |
fix(ui): prevent table container from breaking flexbox layout (#10754)
Signed-off-by: Dedy F. Setyawan <dedyfajars@gmail.com> |
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LocalAI [bot]andEttore Di Giacinto
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5569b2de56 |
feat(config): context_size: -1 to auto-use model's full trained context (#10752)
* feat(config): clamp negative context_size to default in EffectiveContextSize Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(config): resolve context_size=-1 to model trained max with VRAM warn Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * fix(config): treat negative context_size as unset when GGUF is unparseable Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * docs(config): document context_size=-1 auto-max Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * docs(backend): drop em dashes from EffectiveContextSize comment 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> |
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LocalAI [bot]andEttore Di Giacinto
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40dae953f4 |
feat: interleaved thinking with tool calls (reasoning_content alias + Anthropic thinking blocks) (#10744)
* feat(schema): accept reasoning_content as inbound alias for reasoning Interleaved-thinking clients (cogito, vLLM/DeepSeek-style) emit reasoning_content on assistant turns. Accept it as an inbound alias so reasoning survives the tool-result loop; canonical reasoning wins when both are present. Emission is unchanged (still reasoning). Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(schema): pin interleaved reasoning+tool_calls round-trip Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(openai): pin reachedTokenBudget truncation detection Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(anthropic): add thinking and signature fields to content blocks Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(anthropic): parse inbound thinking blocks into reasoning Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(anthropic): emit thinking blocks with synthetic signature on tool turns Extract buildAnthropicContentBlocks so non-streaming content assembly is unit-testable, and prepend a thinking block (with an opaque synthetic signature) before text/tool_use blocks when the request opts into thinking. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(anthropic): stream thinking_delta and signature_delta before tool_use Extract anthropicStreamSequence so the streaming block order is unit-testable, and emit content_block_start(thinking) -> thinking_delta -> signature_delta -> content_block_stop before the tool_use block sequence when thinking is enabled. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: add interleaved thinking with tool calls guide Add a features guide describing interleaved thinking: an assistant turn carrying reasoning and tool_calls together, the reasoning-round-trip contract (including the reasoning_content inbound alias and Anthropic thinking blocks with a synthetic signature), per-backend enablement (reasoning_format for llama.cpp, reasoning_parser/tool_call_parser for vLLM/SGLang plus the vLLM auto-config hook), a worked request/response example, and known limitations. Cross-link from model-configuration, text-generation, and openai-functions. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]andEttore Di Giacinto
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a0ed395cf9 |
fix(auth): accept EC/PS/EdDSA-signed OIDC ID tokens, not just RS256 (#10736)
The OIDC verifier was built with a bare oidc.Config{ClientID: ...}, so
go-oidc applied its default of accepting RS256-signed ID tokens only. An
identity provider configured with an EC signing key (e.g. Authentik) issues
ES256-signed tokens, and the callback failed verification with:
failed to verify ID token: oidc: malformed jwt: unexpected signature
algorithm "HS256"; expected ["RS256"]
surfacing to the user as HTTP 500 "failed to fetch user info" (#10677; the
underlying cause became visible after the logging fix in #10679).
Set SupportedSigningAlgs to the standard asymmetric algorithms
(RS256/384/512, ES256/384/512, PS256/384/512, EdDSA). All are verified
against the provider's published JWKS. HS256 is intentionally excluded: it
is symmetric and would validate against the client secret, a different and
security-sensitive trust model.
Tested with a functional spec that signs an ES256 ID token and confirms it
verifies with the configured algorithms and is rejected under go-oidc's
RS256-only default (using oidc.StaticKeySet, no network).
Closes #10677
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>
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LocalAI [bot]andEttore Di Giacinto
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40c29db8c4 |
fix(logs): capture backend logs by default in single mode (#10742)
Backend log capture into the per-model BackendLogStore (which feeds the UI "Backend Logs" page and /api/backend-logs) was opt-in and off by default in single mode, while worker/distributed mode force-enables it via SetBackendLoggingEnabled(true). There was no CLI flag either, so the only way to populate the store was the Settings UI toggle - and the page was silently empty out of the box. Distributed "just worked"; single mode looked broken. Default EnableBackendLogging to true in NewApplicationConfig so single mode matches worker mode. The store is a small in-memory ring buffer, so the cost is negligible. Now that the default is on, loadRuntimeSettingsFromFile's usual "only flip false->true" merge would ignore a persisted false and revert the UI toggle-off on every restart. There is no env var/CLI flag for this setting, so an explicit persisted value is now authoritative in both directions, letting the toggle-off survive a restart. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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github-actions[bot]andmudler
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0ae84be362 |
chore: bump inference defaults from unsloth (#10741)
Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> |
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LocalAI [bot]andEttore Di Giacinto
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cd65a1f645 |
fix(transcription): honor model-config language/translate + OpenAI language form field (#10731)
fix(transcription): honor model-config language/translate, add language form field The /v1/audio/transcriptions endpoint read only input.Language / input.Translate from the parsed request, and the request middleware never populates those from a multipart upload -- nor did it read a `language` form field. As a result the model config's parameters.language / parameters.translate (a valid PredictionOptions field under `parameters:`) were silently ignored, and multilingual models like canary defaulted to translating into English even when the YAML set language: ru, translate: false (#10655). Resolve both with clear precedence: the request form field wins, then any language on the parsed request, then the model config default. This also makes the endpoint honor OpenAI's `language` form parameter, which was not read before. Applies to both the streaming and non-streaming paths (the resolved values are built into the shared TranscriptionRequest). Note this ensures the language/translate flags reach the backend; whether a given engine acts on them is up to the backend. Closes #10655 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> |
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LocalAI [bot]andEttore Di Giacinto
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97175f4b5a |
feat(model): debounce model loads after a failure to stop retry-storms (#10728)
A client that keeps polling a model whose load fails (e.g. a backend that crashes deterministically on init) triggered a fresh backend start on every request: request -> load -> crash in ~10s -> 500, repeat on the next poll. Each attempt could leak GPU/CUDA state, and under LOCALAI_SINGLE_ACTIVE_BACKEND it kept stealing the active slot from healthy models. The existing loading-coalesce map only dedups *concurrent* loads, so sequential polls were never covered. Track load failures per modelID in ModelLoader. After a load fails, refuse fresh load triggers for that model until a cooldown elapses, returning a typed ModelLoadCooldownError that the HTTP layer maps to 503 with a Retry-After header. The cooldown grows exponentially per consecutive failure (base, doubling, capped at 5m) and resets on a successful load. The coalesced follower-retry of an in-flight burst bypasses the gate, so a genuinely concurrent burst still gets its one retry -- only new, independent triggers are refused, matching the report's "refuse new load-triggers" wording. Configurable via --model-load-failure-cooldown / LOCALAI_MODEL_LOAD_FAILURE_COOLDOWN (default 10s, 0 disables), plumbed through ApplicationConfig and applied unconditionally at startup. Closes #10719 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> |
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LocalAI [bot]andEttore Di Giacinto
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1d5139f0a0 |
fix(gallery): make backend (re)install a clean replace instead of an overlay (#10726)
InstallBackend extracted the artifact directly into the target directory with no pre-clean, so a reinstall overlaid the new files onto the old ones. Files present in a previous version but absent in the new artifact (a stale .so, an orphaned package dir) survived and could shadow the new build at import time -- e.g. an old vllm shared object lingering next to a freshly pulled one. Only a failed download cleaned the directory. Stage the download/extraction into a `<name>.install-tmp` dir, validate run.sh is present, write metadata, then atomically swap it into place (rename current -> .install-backup, staging -> current, drop backup), rolling back on failure. This mirrors the atomic swap UpgradeBackend already performs, so install and upgrade now leave identical on-disk state with no orphaned files. Reported as part of #10720. 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> |
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weifanglab
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a6cf67cc6b |
refactor: use slices.Contains to simplify code (#10702)
Signed-off-by: weifanglab <weifanglab@outlook.com> |
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LocalAI [bot]andEttore Di Giacinto
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85f5267ed2 |
fix(llama-cpp): cap single-pass embedding batch to fit VRAM (#10695)
* fix(llama-cpp): cap single-pass embedding batch to fit VRAM Embedding/score/rerank all decode or pool the whole input in one physical batch, so EffectiveBatchSize sized the batch to the full context window. For a large context that makes n_ubatch huge, and the per-device CUDA compute buffer (forward-graph scratch, ~n_ubatch * n_ctx, NOT split across GPUs) balloons into multi-GiB: a large-context embedding model then aborts on load (exitCode=-1) even with plenty of free VRAM. Reproduced with qwen3-embedding-4b (context 40960 -> n_batch 40960 -> abort) and qwen3-embedding-0.6b (n_batch 8192); pinning batch:512 avoided it. This is the same root cause as issue #10485 (a large context turns the batch into multi-GiB of scratch that must fit on a SINGLE card), but the single-pass path bypassed the VRAM headroom guard the config layer already had — it returned the unbounded context as the batch with no GPU awareness. Make the single-pass batch VRAM-aware: cap it to the largest batch whose compute buffer fits the per-device VRAM headroom, clamped to [DefaultPhysicalBatch, ctx], reusing the existing computeBufferBytesPerCell and headroom-divisor math (no duplication). Unknown per-device VRAM (0) stays conservative (DefaultPhysicalBatch, not the context) so a detection gap can't OOM. The GPU is resolved through an injectable package var (config.LocalGPU, backed by sync.Once-cached xsysinfo detection) so the per-request router call stays cheap and tests inject a deterministic device. Explicit batch: still wins. An input longer than the cap can no longer be pooled in one pass — the accepted tradeoff, since a batch that OOMs the device processes nothing. Assisted-by: Claude:claude-opus-4-8 golangci-lint go-test Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(config): single-pass batch follows context on unknown VRAM The single-pass (embedding/score/rerank) batch cap must only shrink the batch when the per-device VRAM ceiling is KNOWN. On unknown VRAM (CPU-only or a GPU detection gap) SinglePassBatchForContext returned DefaultPhysicalBatch, which under-sized the batch below the context — over-trimming score/embed/rerank inputs (the modelTokenTrim middleware regression) with no OOM benefit on CPU where the compute buffer lives in system RAM. Return the full context instead, preserving the original single-pass behavior; the VRAM cap stays a downward safety that only engages when VRAM is known. Assisted-by: Claude:claude-opus-4-8 [go-test go-vet] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]andEttore Di Giacinto
|
ed3b59baf1 |
fix(config): cap auto-derived context to fit VRAM (#10696)
When a model is imported without an explicit context_size, the GGUF importer defaulted the model's context to its full trained window (n_ctx_train). For long-context models (128k / 256k / 1M) that KV cache cannot fit a consumer GPU, so the backend aborts on load (exitCode=-1) even though the model file is perfectly fine. Reproduced live: gemma-4-26b-a4b-it-qat-q4_0 defaulted to context=262144 and qwythos-9b-claude-mythos-5-1m to 1048576, both aborting on a 20 GB card. Instead of chasing the trained max, auto-derive a conservative default: min(trainedMax, DefaultAutoContextSize=8192). A small model keeps its trained window; a long-context model caps at 8k and users opt into more via context_size. This cap applies always, including CPU / unknown-VRAM hosts, so it never regresses those paths. Per-device VRAM is used only as a DOWNWARD safety: when a per-device ceiling is detected (xsysinfo.MinPerGPUVRAM) and even the 8k cap would not fit it with headroom, step down through candidate contexts to the largest that fits, floored at DefaultContextSize. When VRAM is unknown (0) or no GPU is detected we do NOT clamp — the bug is GPU OOM and the 8k cap is already safe, so detection gaps must not shrink the window. The footprint estimate reuses gpustack/gguf-parser-go's EstimateLLaMACppRun at a given context with all layers offloaded, taking the per-device NonUMA VRAM figure. The estimate and VRAM detection are package vars so tests inject deterministic values. Explicit context_size always wins (guessGGUFFromFile only acts when it is nil). Assisted-by: Claude:claude-opus-4-8 [golangci-lint go-test] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]andEttore Di Giacinto
|
461ae84732 |
fix(startup): scope generated-content and upload dirs to the current user (#10698)
The `--generated-content-path` and `--upload-path` defaults were the fixed
shared locations `/tmp/generated/content` and `/tmp/localai/upload`. On any
multi-user host these collide across accounts: macOS routes `/tmp` to the
shared `/private/tmp` for every user, so whichever account starts LocalAI
first creates the parent with 0750 perms and every other account then fails
startup with:
unable to create ImageDir: "mkdir /tmp/generated/content: permission denied"
unable to create UploadDir: "mkdir /tmp/localai/upload: permission denied"
The same happens on Linux once a stale root-owned `/tmp/generated` (e.g. from
a prior `sudo` run) is left behind. This bites the desktop launcher and any
app embedding the raw binary (Wingman, nib-desktop), which start `local-ai
run` with no path flags.
Default both paths under the OS temp dir (`os.TempDir()`, honoring `$TMPDIR`;
already per-user on macOS) namespaced by the current UID
(`TMPDIR/localai-<uid>/...`), so accounts never collide while the paths stay
ephemeral. Wired via new kong vars in main.go so every consumer of the raw
binary inherits the fix. All content subdirs (audio, images) derive from
`GeneratedContentDir`, so they are fixed transitively.
As defense in depth, the launcher also anchors these two paths under its own
per-user data directory (mirroring the #10610 fix for data/config), extracted
into a testable `BuildRunArgs`.
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>
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2a4426c5ec |
fix(reasoning): don't persist request-scoped reasoning_effort as an operator disable (#10622) (#10623)
* fix(reasoning): don't persist request-scoped reasoning_effort into model config When a model sets `reasoning_effort: none` (or any default) in its YAML without an explicit `reasoning.disable`, ApplyReasoningEffort resolves that default at request time and sets ReasoningConfig.DisableReasoning on the request-scoped config copy. The post-load thinking/marker probe then wrote that request-scoped value back into the loader's persistent config via UpdateModelConfig, making it look as though the operator had explicitly set reasoning.disable=true. From then on, per-request `reasoning_effort` overrides were silently ignored (an explicit operator disable wins over a request asking to think). DetectThinkingSupportFromBackend only fills reasoning slots that are still nil, so a slot already set here came from ApplyReasoningEffort, not the probe. Snapshot which slots were nil before the probe and only persist those, so the probe's genuine backend detection is still saved while request-time reasoning effort never leaks into the persistent config. Fixes #10622 Signed-off-by: Tai An <antai12232931@outlook.com> * test(reasoning): cover persist-guard added in this PR, extract for testability ModelInference's post-probe persistence of ReasoningConfig.DisableReasoning / DisableReasoningTagPrefill had no test: the guard logic lived inline in a closure only reachable through a live gRPC backend. Extract it into persistProbedReasoning (pure refactor, no behavior change) so it can be exercised directly against a ModelConfigLoader, then add specs covering: - a probe-filled slot (nil beforehand) gets persisted - a slot that already carried a request-scoped value (e.g. from reasoning_effort: none) is left alone, i.e. the #10622 regression stays fixed - an operator's explicit persisted disable is preserved when the guard is false - the media marker still persists unconditionally Verified red/green: reverting persistProbedReasoning to the old unconditional copy fails exactly the two guard specs. Assisted-by: Claude:claude-sonnet-5 go vet Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(reasoning): ignore os.Remove error in temp file cleanup (errcheck) Signed-off-by: Tai An <antai12232931@outlook.com> * chore: empty commit to re-trigger flaky Agent Jobs CI test Signed-off-by: Tai An <antai12232931@outlook.com> --------- Signed-off-by: Tai An <antai12232931@outlook.com> 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> |
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walcz-de
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2ccc67bc7f |
feat(agents): native Prometheus metrics for agent chat runs (#10689)
Operators need a scrape-friendly signal for agent-turn health (completing,
erroring, cancelled, duration) — log-derived counters proved brittle (ANSI/
timezone parsing, restart gaps). Adds localai_agent_runs_total{agent,outcome}
and localai_agent_run_seconds histogram, recorded at the Chat() response
handoff (single choke point of the local execution path). Lazy meter init,
same pattern as the PII events counter (#10641).
Signed-off-by: Stefan Walcz <stefan.walcz@walcz.de>
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LocalAI [bot]andEttore Di Giacinto
|
bd3f2588fd |
fix(ui): center the home empty-state wizard (#10691)
The no-models getting-started wizard (`.home-wizard`) rendered left-aligned instead of centered. `.home-page` is a column flexbox with the default `align-items: stretch`; a child with `max-width: 48rem` cannot be stretched past its max-width, so it falls back to the cross-start (left) edge. The populated home branch never exposed this because its children are full-width. Add `margin: 0 auto` to `.home-wizard` so the max-width block centers horizontally, for both the admin getting-started wizard and the non-admin no-models hero. 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> |
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LocalAI [bot]andEttore Di Giacinto
|
b0959d4756 |
feat(api): add GET /v1/models/capabilities endpoint (#10687)
Additive superset of /v1/models that enriches each model entry with the capabilities it supports plus its input/output modalities (text / image / audio / video). Clients that only understand /v1/models are unaffected -- they simply never call the new route. Audio and video *input* are derived from the model's multimodal limits (vLLM limit_mm_per_prompt), which no single usecase FLAG expresses. That gap is exactly why a plain capability list is insufficient and this enriched endpoint exists: an attachment router can now decide whether an image/audio/video file can go to the active model directly, or must be converted/transcribed first. Capability derivation lives in core/config as the single source of truth (ModelConfig.Capabilities / InputModalities / OutputModalities / VisionSupported / ...); the Ollama capability surface now delegates to it instead of keeping a parallel copy. Vision is gated on chat/completion capability so a MediaMarker hydrated onto a non-chat model (e.g. a pure ASR/TTS backend) no longer reports a false vision capability. Read-only listing: no new FLAG_* flag, reuses the existing `models` swagger tag, and intentionally exposes no MCP admin tool (there is nothing to manage conversationally). 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> |
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LocalAI [bot]andEttore Di Giacinto
|
9e41be4bfb |
fix(auth): log the real cause of OIDC/OAuth user-info failures (#10679)
The OAuth callback discarded the error returned by user-info resolution before sending the generic 500, so real failures were completely opaque in the logs: ID-token verification errors (e.g. issuer/audience mismatch behind a reverse proxy), a missing id_token, claim-parse errors, or a rejecting GitHub userinfo endpoint all collapsed into "failed to fetch user info" with nothing logged. Log the wrapped cause with xlog.Error (provider + error), matching the code-exchange step just above it. The client-facing message is unchanged, so no internal detail leaks to the browser. Refs #10677 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> |
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LocalAI [bot]andEttore Di Giacinto
|
2cbb3c96b3 |
fix(gallery): block SSRF in gallery config URL fetch (#10665) (#10673)
POST /models/apply with an empty "id" fetches the attacker-supplied "url" gallery config directly via http.Client, with no check that the URL resolves to a public IP. In the default Docker deployment no API key is configured, so any network-reachable client can coerce LocalAI into issuing requests to internal services or cloud-metadata endpoints (and exfiltrate a small slice of the response through the job error message). Guard the config fetch chokepoints (GetGalleryConfigFromURL and GetGalleryConfigFromURLWithContext, which back both the /models/apply worker and gallery installs) with utils.ValidateExternalURL, matching the protection already applied to the CORS proxy and image/video/audio download paths. Only plain http(s) URLs are validated; non-network schemes (huggingface://, github:, oci://, ollama://, file://) resolve to fixed public services or local files and are left untouched. 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> |
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Ettore Di Giacinto
|
1152acc167 |
Revert "feat(config): default swa_full:true for sliding-window-attention models" (#10674)
Revert "feat(config): default swa_full:true for sliding-window-attention mode…"
This reverts commit
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walcz-de
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cc8ee62db0 |
feat(pii): export PII/audit events as a Prometheus counter (#10641)
The PII EventStore ring buffer is capacity-bound and meant for
recent-audit browsing via /api/pii/events; operators also want a
monotonic, scrape-friendly signal on /metrics — how many
detections/masks/blocks per hour, per origin, and whether the filter
stopped firing after a deploy (silent-failure class).
EventStore.Record is the single choke point every producer already goes
through (request middleware, response scrubbing, MITM proxy
connects/intercepts), so one lazily-initialised counter there covers all
paths without touching any producer:
localai_pii_events_total{kind, origin, action, direction}
Same lazy otel.Meter pattern as core/services/routing/billing, so the
counter lands on the Prometheus-backed global MeterProvider installed by
the monitoring service. No behaviour change; label cardinality is
bounded (enum-like fields only, no pattern IDs or user IDs).
Assisted-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Signed-off-by: stefanwalcz <stefan.walcz@walcz.de>
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Richard Palethorpe
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eb32cd9073 |
feat(realtime): eager blocking pipeline warm-up + /backend/load API (#10662)
Realtime sessions previously lazy-loaded each pipeline sub-model (VAD,
transcription, LLM, TTS) on first use, so every cold session paid a
per-request model-load stall and load errors only surfaced mid-stream.
Warm the whole pipeline eagerly and blockingly at session start
(including the voice-gate speaker-recognition model, which an enforced
gate blocks each utterance on; compaction's summary_model stays lazy
since it only runs off the response path):
- Add backend.PreloadModel / PreloadModelByName as the single load path
for every modality (no transcription special-case; backend-omitted
configs are deprecated).
- The realtime session blocks on Model.Warmup and returns a
model_load_error to the client if any stage fails to load;
updateSession warms in the background. Opt out per pipeline with
pipeline.disable_warmup, exposed as a UI toggle via the
config-metadata registry.
Add a LocalAI-native POST /backend/load (and /v1/backend/load) that
pre-loads a model -- expanding realtime pipelines into their sub-models
-- as the inverse of /backend/shutdown. There is one preload engine
(backend.PreloadStages): the realtime Warmup methods, /backend/load and
the --load-to-memory startup flag all use it, so --load-to-memory now
also expands pipeline models and records load-failure traces. Pipeline
sub-model alias resolution is likewise shared
(ModelConfigLoader.LoadResolvedModelConfig). Surface the endpoint
everywhere an admin manages models:
- MCP admin tool load_model (httpapi + inproc clients, safety/catalog
prompts, catalog/dispatch tests).
- "Load into memory" action in the React models UI.
- Swagger regenerated; docs moved to the general backend-monitor page
since it is not realtime-specific.
Fix a Traces UI crash ("json: unsupported value: -Inf"): audio-snippet
RMS/peak now floor at a finite dBFS, and backend-trace data is sanitized
to drop non-finite floats before marshaling. The sanitizer is
copy-on-write -- it runs on every RecordBackendTrace, so containers are
only re-allocated on the paths that actually changed.
Migrate core/http/openresponses_test.go onto the prebuilt mock-backend
the rest of the http suite already uses -- it was the last spec still
pointing at a real HuggingFace model, so it 404'd wherever no vision
backend was built -- and fix its item_reference specs to send the
spec's "id" field instead of "item_id", which the handler never
accepted.
Assisted-by: Claude:claude-opus-4-8 Claude Code
Signed-off-by: Richard Palethorpe <io@richiejp.com>
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LocalAI [bot]andEttore Di Giacinto
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237bce48e8 |
feat(ui): forking chat - retry any answer, copy, duplicate, branch (#10645) (#10654)
* feat(ui): clone a chat into a new conversation (#10645) Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ui): retry any assistant answer, not just the last (#10645) Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ui): copy an entire chat to the clipboard (#10645) Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ui): branch a new chat from any assistant answer (#10645) Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(ui): send truncated history on mid-conversation retry (#10645) Mid-conversation retry regenerated an answer with the downstream turns still in the model's context. handleRegenerate truncated the DOM history via updateChatSettings (a scheduled state update), but the synchronous sendMessage that followed read the stale, pre-truncation history from its closure to build the outbound API payload. Thread the intended base history explicitly through sendMessage's options.baseHistory so the request body matches the truncated view. Backward compatible: the normal send path (no baseHistory) is unchanged. Also guard two minor issues in Chat.jsx: the "Branch from here" button now renders under !isStreaming to match the retry button, and the duplicate toast only fires when forkChat returns a chat (not on a null result). 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> |
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LocalAI [bot]andEttore Di Giacinto
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6eea3ef2ac |
fix(backends): make backend install ops idempotent unless forced (#10643)
* fix(backends): make backend install ops idempotent unless forced POST /backends/apply hardcoded force=true through LocalBackendManager.InstallBackend, so applying an already-installed backend re-downloaded and re-extracted the whole artifact every time. API clients that ensure a backend exists at startup paid a full OCI image pull on every boot. Backend install ops now default to non-forced — an installed, runnable backend short-circuits (the orphaned-meta reinstall path in InstallBackendFromGallery is preserved) — and reinstall stays available: - ManagementOp gains a Force field; the local manager passes it through instead of hardcoding true. - /backends/apply accepts an optional "force" boolean in the body. - The React UI install route keeps forcing, since its button doubles as the explicit "Reinstall backend" action. Distributed installs already behaved this way (workers skip when the binary exists unless force is set); this aligns single-node behavior. Assisted-by: Claude:claude-fable-5 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(backends): don't force-reinstall LOCALAI_EXTERNAL_BACKENDS on boot The startup loop for LOCALAI_EXTERNAL_BACKENDS runs InstallExternalBackend for each listed backend on every boot, and its gallery-name path hardcoded force=true — so every start re-downloaded and re-extracted each listed backend's OCI image even when it was installed and runnable. Supervising apps that list several backends paid several full OCI pulls per launch. Give InstallExternalBackend an explicit force parameter (it only affects the gallery-name fallback; URI installs always write) and pass: - false from the boot loop and `local-ai backends install` (idempotent ensure — `backends upgrade` is the refresh path), - op.Force from the local manager's external-URI op, - the request's force on the worker install path and true on its upgrade path (behavior unchanged). Assisted-by: Claude:claude-fable-5 [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> |
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29001a88c1 |
fix(distributed): don't let a dead worker pin the model-load advisory lock (#10600)
* fix(distributed): don't let a dead worker pin the model-load advisory lock In distributed mode a chat request could fail with: failed to route model with internal loader: routing model ...: loading model ...: advisorylock: acquiring lock <id>: ERROR: canceling statement due to lock timeout (SQLSTATE 55P03) Root cause is two independent defects in the cross-replica model-load path: 1. SmartRouter.Route holds a per-model PostgreSQL advisory lock for the whole cold-load sequence, which includes installBackendOnNode -> InstallBackend, a NATS request-reply with a 15m deadline (DefaultBackendInstallTimeout) that ignored ctx. When the chosen worker died mid-install, the holder sat on the lock for up to 15m. The detached loadCtx (WithoutCancel) had no deadline, so nothing capped the hold. 2. The acquiring statement, pg_advisory_lock(), is subject to any deployment global lock_timeout. A common operator setting (e.g. 10s) aborts the wait with SQLSTATE 55P03, so every other replica's request for that model hard -errored instead of waiting for the in-progress load and reusing it. For the ~15m window the model was effectively unroutable. Fixes: - advisorylock.WithLockCtx (postgres): SET lock_timeout = 0 on its dedicated connection (RESET before it returns to the pool) so the Go context, not a deployment-wide GUC, governs how long we wait. Waiters now block and then re-check, reusing the model another replica just loaded. - SmartRouter: bound the detached loadCtx with a single ModelLoadCeiling so the lock is always released in bounded time even if a sub-step wedges. Default is the configured backend.install deadline + 10m (staging + LoadModel margin), so a legitimately slow load is never cut. - installBackendOnNode: use singleflight.DoChan + select on ctx.Done() so the install wait honors cancellation; the ceiling can then actually free a caller pinned behind a dead worker. The shared install still coalesces via singleflight. Reproduced both defects as failing tests first (a real 55P03 against a testcontainer with a short lock_timeout; a wedged install that blocks Route) and confirmed green. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * fix(distributed): bound advisory-lock wait instead of disabling lock_timeout Setting lock_timeout = 0 to override a deployment's short global lock_timeout meant "wait forever" server-side. Safe for SmartRouter.Route (its loadCtx now carries the model-load ceiling) but unsafe for the schema-migration callers that pass context.Background(): a holder whose session never releases would hang them indefinitely. Derive the server-side lock_timeout from the caller's context instead: its remaining budget plus a margin (so the Go context's cancellation still wins with a clean error and the server bound is only a backstop), or a finite 30m backstop when the context has no deadline. Never zero - "wait forever" is no longer possible, while a deployment's hostile short lock_timeout is still overridden so legitimate cross-replica waits don't fail with 55P03. Added a spec proving a deadline-less waiter gives up at the (shrunk) backstop rather than hanging. 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> Co-authored-by: Ettore Di Giacinto <mudler@users.noreply.github.com> |
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LocalAI [bot]andEttore Di Giacinto
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02b007a31e |
feat(config): default swa_full:true for sliding-window-attention models (#10611)
LocalAI enables a cross-request prompt-prefix cache (cache_reuse, see core/config/serving_defaults.go) so repeated prefixes — system prompts, RAG context, agent scaffolds, multi-turn chat — are not reprocessed every turn. For sliding-window-attention (SWA) models (Gemma 2/3, Cohere2, Llama 4, ...) this silently does nothing: llama.cpp defaults to a reduced SWA KV cache sized to the sliding window, and that reduced cache cannot preserve a prompt prefix across requests, so every turn reprocesses the whole prompt anyway. llama.cpp's --swa-full (params.swa_full, already wired through the LocalAI llama.cpp backend's `swa_full` option) keeps the full KV cache so the shared prefix is reused. Enable it automatically, but only for models that are actually SWA: detection reads the gguf-parser-normalized `<arch>.attention.sliding_window` metadata (which also applies llama.cpp's family rules, e.g. Phi-3 → not SWA), right where the GGUF is already parsed for defaults. It is never applied to dense models (pure memory waste) and never overrides an explicit user `swa_full`/`n_swa` choice. Tradeoff: the full SWA cache scales with context_size, so it costs more memory at large contexts — hence the SWA gating and the documented `swa_full:false` opt-out. Assisted-by: Claude:claude-opus-4-8 [Claude Code] golangci-lint Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
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LocalAI [bot]andEttore Di Giacinto
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fd8cebd0b3 |
fix(watchdog): persist UI-saved Check Interval across restarts (#10601) (#10605)
fix(watchdog): persist a UI-saved Check Interval across restarts (#10601) The watchdog Check Interval saved via /api/settings reverted to 500ms on every restart, while the idle/busy timeouts persisted correctly. Root cause: NewApplicationConfig baseline-defaulted WatchDogInterval to 500ms, whereas the idle/busy timeouts default to 0. The startup loader (loadRuntimeSettingsFromFile) applies a persisted runtime_settings.json value only when the field is still at its zero default - its heuristic for "this wasn't set by an env var". Because the interval was always 500ms at that point, the loader never read the persisted value back, so the saved interval was silently discarded on each boot. Fix: drop the non-zero baseline default so the interval behaves like the sibling timeouts (0 = unset). The effective 500ms default is now supplied at the watchdog layer: WithWatchdogInterval ignores a non-positive value so DefaultWatchDogOptions' 500ms is preserved (and a 0 interval can never turn the watchdog loop into a busy spin). Also mirror the interval in the live config file watcher alongside idle/busy, and report the real 500ms default (not the stale "2s") from ToRuntimeSettings. 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> |
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LocalAI [bot]andEttore Di Giacinto
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d74f88357e |
fix(tests): align openresponses test model name with GGUF-derived naming (#10589) (#10609)
PR #10589 changed repo-root HuggingFace URI imports to name the model after
the selected GGUF file rather than the repository. The Open Responses API
integration test still requested the old repo-derived name
("Qwen3-VL-2B-Instruct-GGUF"), so every request 404'd on an unknown model and
the suite has failed on master since
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Adira
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dfaec3bd51 |
fix(import): strip file:// scheme from model path for local imports (#10599)
Importing a model from a local directory (e.g. a HuggingFace checkout or an LM Studio store) via a file:// URI produced a config whose model field kept the scheme verbatim, e.g. model: file:///Users/u/.../Qwen3-4bit. The mlx and vllm backends treat that field as a HuggingFace repo id or local path and reject the file:// form with "Repo id must be in the form 'repo_name' or 'namespace/repo_name'", so the model imported fine but failed to load (issue #7461). Add a shared LocalModelPath helper that reduces a file:// URI to the bare filesystem path it points at and leaves HuggingFace/HTTP URIs untouched, and route the mlx, vllm, transformers and diffusers importers (all of which pass details.URI straight into the model field for from_pretrained-style loading) through it. Cover the helper directly plus end-to-end file:// import specs for the mlx and vllm importers. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Adira Denis Muhando <dennisadira@gmail.com> |
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Adira
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1a4f68ed4a |
fix(import): derive model name from selected GGUF for repo-root URIs (#10589)
When importing a HuggingFace GGUF model from a repository-root URI (no file component, e.g. hf://owner/repo) with the Model Name field left blank, the importer named the model after the repository (filepath.Base(details.URI)) instead of the GGUF file it actually selected from the repo listing (issue #10587). Track whether the user supplied an explicit name; the URI base is now only a fallback. In the HuggingFace branch, once the model group is picked, re-derive the name from the selected GGUF via a new modelNameFromShardGroup helper that uses ShardGroup.Base minus the .gguf extension. For sharded models this yields a clean logical name (e.g. Qwen3-30B-A3B-Q4_K_M) rather than a shard filename like ...-00001-of-00002. An explicit name preference still always wins, and the .gguf/URL/OCI paths are unchanged. Add network-free unit specs covering name-from-GGUF, clean-name-from-shard-base, and explicit-name precedence, and update the live integration specs that had encoded the previous repo-name behaviour. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Adira Denis Muhando <dennisadira@gmail.com> |
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Adira
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28d7397743 |
fix(openai): stop max_tokens streaming retry loop on reasoning models (#9716) (#10448)
fix(openai): stop max_tokens streaming retry loop on reasoning models When a thinking model spends its entire max_tokens budget on the reasoning block, the C++ autoparser clears the raw Response and delivers reasoning-only ChatDeltas (no content, no tool calls). ComputeChoices' empty-response retry then fires and regenerates from scratch up to maxRetries times, each re-consuming the whole budget, instead of terminating with finish_reason "length" (issue #9716). Add a reachedTokenBudget helper and suppress both the built-in and caller-driven retries when the completion count has reached the configured max_tokens ceiling. Report finish_reason "length" instead of "stop" in the streaming and non-streaming chat paths when the budget was exhausted. Adds a deterministic regression test that counts backend invocations (previously 6, now 1) plus boundary tests for the helper. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Dennisadira <dennisadira@gmail.com> |
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Richard Palethorpe
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5d0c43ec6e |
feat(realtime): Semantic VAD EOU token (#10444)
* feat(realtime): EOU-driven semantic_vad turn detection Add a `semantic_vad` turn-detection mode to the realtime API that feeds the transcription model live and decides "the user finished speaking" from the `<EOU>` end-of-utterance token rather than from silence alone. When EOU fires the turn commits immediately (~0.3s); otherwise it falls back to an eagerness-scaled silence threshold (low/med/high = 8/4/2s). Plumbing, bottom to top: - proto: `AudioTranscriptionLive` bidirectional RPC (config-first oneof, mono float PCM @16k, ready-ack / Unimplemented degrade signal) plus `TranscriptResult.eou` for the unary retranscribe gate. - pkg/grpc: client/server/base/embed scaffolding for the bidi stream, modeled on AudioTransformStream; release stream conns on terminal Recv. - parakeet-cpp: live transcription RPC with per-C-call engine locking (one live stream per turn, finalize+free at commit); bump parakeet.cpp to ABI v5 — incremental StreamingMel (no more quadratic per-feed mel recompute that delayed EOU on long turns) and the <EOU>/<EOB> split; strip the literal <EOU>/<EOB> from offline text and set Eou. - core/backend: LiveTranscriptionSession wrapper + pipeline `turn_detection:` config block (type/eagerness/retranscribe). - realtime: semantic_vad integration — live input captions streamed as transcription deltas while the user speaks, EOU-immediate commit with eagerness fallback, optional retranscribe gate (batch re-decode must also end in <EOU> to confirm), clause synthesis off the LLM token callback, and per-turn live-transcription / model_load telemetry. - UI: show the realtime pipeline components as a vertical list. Docs and tests included; opt-in via the pipeline YAML or per-session `session.update`. Non-streaming STT backends degrade to silence-only. Assisted-by: Claude Code:claude-opus-4-8 [Read] [Edit] [Write] [Bash] Assisted-by: Claude Code:claude-fable-5 [Read] [Edit] [Bash] Signed-off-by: Richard Palethorpe <io@richiejp.com> * feat(realtime): explicit formally-verified state machines + parakeet streaming driver The realtime API had several implicit state machines whose state was inferred from scattered booleans, channels, and five separate mutexes, leaving illegal/inconsistent states reachable. Make them explicit and keep the implementation in step with a formal design; rework the parakeet streaming backend along the same lines. Realtime state machines (M1-M5). Each is a sealed sum-type State/Event/Effect with a total, pure Next(state,event)->(state,[]effect) behind a single-writer Coordinator: M1 conncoord connection lifecycle: VAD toggle + once-only teardown (replaces vadServerStarted + a `done` channel closed from two sites). M2 turncoord turn detection: collapses speechStarted and the live-stream "turn open" flag into one state, so discardTurn can no longer desync them and suppress the next onset. M3 respcoord response coordination: serializes the dual-writer start/cancel so at most one response is live; one response.done per response.create. M4 compactcoord conversation compaction: single-flight (replaces the `compacting atomic.Bool` CAS). M5 ttscoord TTS pipeline: open->closing->closed, idempotent wait(), rejects enqueue-after-close (was a silent drop). The Coordinator/Sink/Next plumbing — only the sealed types and Next differed per machine — is extracted once into core/http/endpoints/openai/coordinator as a generic Coordinator[S,E,F]; each machine keeps its public API via type aliases, so no sink, call-site, or test moved. Hierarchy. session_lifecycle.fizz models M1 as the parent region with its children (M2/M3/M4) as one statechart and asserts ChildrenDieWithParent (conn torn => all children terminal, none start after teardown). respcoord and compactcoord gain an absorbing Terminated state + Shutdown event; conncoord's teardown drives the children terminal. This closes a compaction teardown gap: a fire-and-forget compaction could outlive a torn session — compactionSink now takes a session-scoped cancellable context + WaitGroup and joins the in-flight summarize+evict on shutdown. Formal verification. formal-verification/ holds one authoritative FizzBee spec per machine plus the composition spec, each with an always-assertion and a documented one-line edit that makes the checker fail (verified non-vacuous). scripts/realtime-conformance.sh is fail-closed: all Go conformance suites under -race AND a model-check of every .fizz spec; a missing FizzBee is a hard error (only the loud REALTIME_CONFORMANCE_SKIP_FIZZBEE=1 bypasses it, never in CI). FizzBee is pinned by sha256 and installed via scripts/install-fizzbee.sh into .tools/ (gitignored). Wired as make test-realtime-conformance, a CI workflow, and a pre-commit path filter. Go conformance tests are Ginkgo/Gomega (per the repo's forbidigo lint): transition tables + fixed-seed property walks + concurrent/-race specs, no rapid dependency. Design map: docs/design/realtime-state-machines.md. Parakeet streaming backend. The same treatment applied to the parakeet-cpp streaming paths: - AudioTranscriptionStream returns codes.Unimplemented for non-streaming models instead of decoding offline and emitting it as one delta + final. A client that asked for streaming learns the model cannot stream rather than receiving a batch result shaped like a stream. New grpcerrors.StreamTranscriptionUnsupported carries that signal; the HTTP /v1/audio/transcriptions stream path surfaces it as an SSE error event. Mirrors AudioTranscriptionLive, which already did this. - utteranceBoundary (boundary.go): a single definition of the end-of-utterance latch, replacing three open-coded finalEou toggles. Modelled as a two-valued type so illegal states are unrepresentable. - Shared decode driver (driver.go): streamFeedResult (one per-feed event) + feedChunk (hides the ABI v4 JSON vs text-only split) + feedSlices + flushTail. The feed loop is written once. - AudioTranscriptionLive becomes a bidi adapter: it streams the per-feed {delta,eou,eob,words} the realtime turn detector consumes and a terminal FinalResult carrying only Text. Segments/duration/eou are offline-only and no longer produced (nor read) on the live path; liveTraceState drops the terminal eou and keeps the per-feed eou_events count. - AudioTranscriptionStream + streamJSON merge into one driver-based function; streamSegmenter is generalized to the unified event with a text-only fallback that preserves the legacy (no-words) library's per-utterance segmentation. Verified: build/vet/gofumpt clean, golangci-lint 0 issues, all coordinator and parakeet packages under -race, the fail-closed conformance gate green, and make test-realtime (12 e2e WS+WebRTC). 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> |
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LocalAI [bot]andEttore Di Giacinto
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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> |
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LocalAI [bot]andEttore Di Giacinto
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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> |
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LocalAI [bot]andEttore Di Giacinto
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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> |
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LocalAI [bot]andEttore Di Giacinto
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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> |