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74 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
Richard Palethorpe
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4916f8c880 |
feat(vllm): expose AsyncEngineArgs via generic engine_args YAML map (#9563)
* feat(vllm): expose AsyncEngineArgs via generic engine_args YAML map
LocalAI's vLLM backend wraps a small typed subset of vLLM's
AsyncEngineArgs (quantization, tensor_parallel_size, dtype, etc.).
Anything outside that subset -- pipeline/data/expert parallelism,
speculative_config, kv_transfer_config, all2all_backend, prefix
caching, chunked prefill, etc. -- requires a new protobuf field, a
Go struct field, an options.go line, and a backend.py mapping per
feature. That cadence is the bottleneck on shipping vLLM's
production feature set.
Add a generic `engine_args:` map on the model YAML that is
JSON-serialised into a new ModelOptions.EngineArgs proto field and
applied verbatim to AsyncEngineArgs at LoadModel time. Validation
is done by the Python backend via dataclasses.fields(); unknown
keys fail with the closest valid name as a hint.
dataclasses.replace() is used so vLLM's __post_init__ re-runs and
auto-converts dict values into nested config dataclasses
(CompilationConfig, AttentionConfig, ...). speculative_config and
kv_transfer_config flow through as dicts; vLLM converts them at
engine init.
Operators can now write:
engine_args:
data_parallel_size: 8
enable_expert_parallel: true
all2all_backend: deepep_low_latency
speculative_config:
method: deepseek_mtp
num_speculative_tokens: 3
kv_cache_dtype: fp8
without further proto/Go/Python plumbing per field.
Production defaults seeded by hooks_vllm.go: enable_prefix_caching
and enable_chunked_prefill default to true unless explicitly set.
Existing typed YAML fields (gpu_memory_utilization,
tensor_parallel_size, etc.) remain for back-compat; engine_args
overrides them when both are set.
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* chore(vllm): pin cublas13 to vLLM 0.20.0 cu130 wheel
vLLM's PyPI wheel is built against CUDA 12 (libcudart.so.12) and won't
load on a cu130 host. Switch the cublas13 build to vLLM's per-tag cu130
simple-index (https://wheels.vllm.ai/0.20.0/cu130/) and pin
vllm==0.20.0. The cu130-flavoured wheel ships libcudart.so.13 and
includes the DFlash speculative-decoding method that landed in 0.20.0.
cublas13 install gets --index-strategy=unsafe-best-match so uv consults
both the cu130 index and PyPI when resolving — PyPI also publishes
vllm==0.20.0, but with cu12 binaries that error at import time.
Verified: Qwen3.5-4B + z-lab/Qwen3.5-4B-DFlash loads and serves chat
completions on RTX 5070 Ti (sm_120, cu130).
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* ci(vllm): bot job to bump cublas13 vLLM wheel pin
vLLM's cu130 wheel index URL is itself version-locked
(wheels.vllm.ai/<TAG>/cu130/, no /latest/ alias upstream), so a vLLM
bump means rewriting two values atomically — the URL segment and the
version constraint. bump_deps.sh handles git-sha-in-Makefile only;
add a sibling bump_vllm_wheel.sh and a matching workflow job that
mirrors the existing matrix's PR-creation pattern.
The bumper queries /releases/latest (which excludes prereleases),
strips the leading 'v', and seds both lines unconditionally. When the
file is already on the latest tag the rewrite is a no-op and
peter-evans/create-pull-request opens no PR.
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* docs(vllm): document engine_args and speculative decoding
The new engine_args: map plumbs arbitrary AsyncEngineArgs through to
vLLM, but the public docs only covered the basic typed fields. Add a
short subsection in the vLLM section explaining the typed/generic
split and showing a worked DFlash speculative-decoding config, with
pointers to vLLM's SpeculativeConfig reference and z-lab's drafter
collection.
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
---------
Signed-off-by: Richard Palethorpe <io@richiejp.com>
Co-authored-by: Ettore Di Giacinto <mudler@users.noreply.github.com>
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Ettore Di Giacinto
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bcef72b9c1 |
feat: localai assistant chat modality (#9602)
* fix(tests): inline model_test fixtures after tests/models_fixtures removal The previous reorg removed tests/models_fixtures/ but core/config/model_test.go still read CONFIG_FILE/MODELS_PATH env vars pointing into that directory, so `make test` failed with "open : no such file or directory" on the readConfigFile spec (the suite ran with --fail-fast and bailed before openresponses_test). Inline the YAMLs (config/embeddings/grpc/rwkv/whisper) directly into the test file, materialise them into a per-test tmpdir via BeforeEach, and drop the env-var lookups. The test no longer depends on Makefile plumbing. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: claude-code:claude-opus-4-7 [Edit] [Write] [Bash] * refactor(modeladmin): extract model-admin helpers into a service package Lift the bodies of EditModelEndpoint, PatchConfigEndpoint, ToggleStateModelEndpoint, TogglePinnedModelEndpoint and VRAMEstimateEndpoint into core/services/modeladmin so the same logic can be called by non-HTTP clients (notably the in-process MCP server that backs the LocalAI Assistant chat modality, landing in a follow-up commit). The HTTP handlers shrink to thin shells that parse echo inputs, call the matching helper, map typed errors (ErrNotFound, ErrConflict, ErrPathNotTrusted, ErrBadAction, ...) to the existing HTTP status codes, and render the existing response shapes. No REST-surface behaviour change; the existing localai endpoint tests cover the regression net. Adds focused unit tests for each helper against tmp-dir-backed ModelConfigLoader fixtures (deep-merge patch, rename + conflict, path separator guard, toggle/pin enable/disable, sync callback). Assisted-by: Claude:claude-opus-4-7 [Read] [Edit] [Write] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(assistant): LocalAI Assistant chat modality with in-memory MCP server Adds a chat modality, admin-only, that wires the chat session to an in-memory MCP server exposing LocalAI's own admin/management surface as tools. An admin can install models, manage backends, edit configs and check status by chatting; the LLM calls tools like gallery_search, install_model, import_model_uri, list_installed_models, edit_model_config and surfaces the results. Same Go package powers two modes: pkg/mcp/localaitools/ NewServer(client, opts) builds an MCP server that registers the 19-tool admin catalog. The LocalAIClient interface has two impls: - inproc.Client — calls services directly (no HTTP loopback, no synthetic admin API key). Used in-process by the chat handler. - httpapi.Client — calls the LocalAI REST API. Used by the new `local-ai mcp-server --target=…` subcommand to control a remote LocalAI from a stdio MCP host. Tools and their embedded skill prompts are agnostic to which client backs them. Skill prompts are markdown files under prompts/, embedded via go:embed and assembled into the system prompt at server init. Wiring: - core/http/endpoints/mcp/localai_assistant.go — process-wide holder that spins up the in-memory MCP server once at Application start using paired net.Pipe transports, then reuses LocalToolExecutor (no fork) for every chat request that opts in. - core/http/endpoints/openai/chat.go — small branch ahead of the existing MCP block: when metadata.localai_assistant=true, defense-in-depth admin check + executor swap + system-prompt injection. All downstream tool dispatch is unchanged. - core/http/auth/{permissions,features}.go — adds FeatureLocalAIAssistant; gating happens at the chat handler entry plus admin-only `/api/settings`. - core/cli/{run.go,cli.go,mcp_server.go} — LOCALAI_DISABLE_ASSISTANT flag (runtime-toggleable via Settings, no restart), plus `local-ai mcp-server` stdio subcommand. - core/config/runtime_settings.go — `localai_assistant_enabled` runtime setting; the chat handler reads `DisableLocalAIAssistant` live at request entry. UI: - Home.jsx — prominent self-explanatory CTA card on first run ("Manage LocalAI by chatting"); collapses to a compact "Manage by chat" button in the quick-links row once used, persisted via localStorage. - Chat.jsx — admin-only "Manage" toggle in the chat header, "Manage mode" badge, dedicated empty-state copy, starter chips. - Settings.jsx — "LocalAI Assistant" section with the runtime enable toggle. - useChat.js — `localaiAssistant` flag on the chat schema; injects `metadata.localai_assistant=true` on requests when active. Distributed mode: the in-memory MCP server lives only on the head node; inproc.Client wraps already-distributed-aware services so installs propagate to workers via the existing GalleryService machinery. Documentation: `.agents/localai-assistant-mcp.md` is the contributor contract — when adding an admin REST endpoint, also add a LocalAIClient method, an inproc + httpapi impl, a tool registration, and a skill prompt update; the AGENTS.md index links to it. Out of scope (follow-ups): per-tool RBAC granularity for non-admin read-only access; streaming mcp_tool_progress for long installs; React Vitest rig for the UI changes. Assisted-by: Claude:claude-opus-4-7 [Read] [Edit] [Write] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(assistant): extract tool/capability/MiB/server-name constants The MCP tool surface, capability tag set, server-name default, and the chat-handler metadata key were repeated as bare string literals across seven files. Renaming any one required hand-editing every call site and risked code/test/prompt drift. This pulls them into typed constants: - pkg/mcp/localaitools/tools.go — Tool* constants for the 19 MCP tools, plus DefaultServerName. - pkg/mcp/localaitools/capability.go — typed Capability + constants for the capability tag set the LLM passes to list_installed_models. The type rides through LocalAIClient.ListInstalledModels and replaces the triplet of "embed"/"embedding"/"embeddings" with the single CapabilityEmbeddings. - pkg/mcp/localaitools/inproc/client.go — bytesPerMiB constant for the VRAMEstimate byte→MB conversion. - core/http/endpoints/mcp/tools.go — MetadataKeyLocalAIAssistant for the "localai_assistant" request-metadata key consumed by the chat handler. Tool registrations, the test catalog, the dispatch table, the validation fixtures, and the fake/stub clients all reference the constants. The embedded skill prompts under prompts/ keep their bare strings (go:embed markdown can't import Go constants); the existing TestPromptsContain SafetyAnchors guards the alignment. No behaviour change. All tests pass with -race. Assisted-by: Claude:claude-opus-4-7 [Read] [Edit] [Write] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(modeladmin): typed Action for ToggleState/TogglePinned The toggle/pin verbs were bare strings everywhere — handler signatures, service implementations, MCP tool args, the fake/stub clients, the inproc and httpapi LocalAIClient impls, plus 4 test files. A typo in any caller silently fell through to the runtime "must be 'enable' or 'disable'" check. Introduce core/services/modeladmin.Action (string alias) with ActionEnable, ActionDisable, ActionPin, ActionUnpin and a small Valid helper. The compiler now catches mismatches at every boundary; renames ripple through one source of truth. LocalAIClient.ToggleModelState/Pinned signatures change to take modeladmin.Action. The package is brand-new and unreleased so this is a free public-API tightening. Assisted-by: Claude:claude-opus-4-7 [Read] [Edit] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(assistant): respect ctx cancellation on gallery channel sends InstallModel, DeleteModel, ImportModelURI, InstallBackend and UpgradeBackend all pushed onto galleryop channels with bare sends. If the worker was paused or the buffer full, the chat-handler goroutine blocked forever — the LLM kept polling and the request leaked. Wrap the five sends in a sendModelOp/sendBackendOp helper that selects on ctx.Done() so a cancelled chat completion surfaces context.Canceled back to the LLM instead of hanging. Adds inproc/client_test.go with a pre-cancelled-ctx regression test on InstallModel; the helpers are shared so the same guarantee covers the other four call sites. Assisted-by: Claude:claude-opus-4-7 [Edit] [Write] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(assistant): graceful shutdown for in-memory holder and stdio CLI Two related leaks: - Application.start() built the LocalAIAssistantHolder but never wired Close() into the graceful-termination chain — the in-memory MCP transport pair stayed alive until process exit, and the goroutines behind net.Pipe() didn't drain. Hook into the existing signals.RegisterGracefulTerminationHandler chain (same pattern as core/http/endpoints/mcp/tools.go:770). - core/cli/mcp_server.go ran srv.Run with context.Background(); a Ctrl-C from the host (Claude Desktop, mcphost, npx inspector) or a SIGTERM from process supervision left the stdio loop reading from a closed pipe. Switch to signal.NotifyContext to surface the signal through ctx and let srv.Run drain. Assisted-by: Claude:claude-opus-4-7 [Read] [Edit] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(assistant): typed HTTPError + propagate prompt walk error The httpapi client detected "no such job" by substring-matching on the error string ("404", "could not find") — brittle to status-code formatting changes and to LocalAI fixing /models/jobs/:uuid to return a proper 404. Replace with a typed *HTTPError whose Is() method honours errors.Is(err, ErrHTTPNotFound). The 500-with-"could not find" branch stays as a transitional fallback documented in Is(). Same change covers ListNodes' 404 fallback for the /api/nodes endpoint. Adds httptest tests for both 404 and the legacy 500 path, plus a direct errors.Is exposure test so external callers (the standalone stdio CLI host) can match without re-string-parsing. Also tightens prompts.SystemPrompt: panic when fs.WalkDir on the embedded FS fails. The only realistic cause is a build-time //go:embed misconfiguration; serving an empty system prompt to the LLM is much worse than crashing init. TestSystemPromptIncludesAllEmbeddedFiles catches regressions in CI. Assisted-by: Claude:claude-opus-4-7 [Edit] [Write] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(modeladmin): atomic writes for model config files The five sites that wrote model YAML used os.WriteFile, which opens with O_TRUNC|O_WRONLY|O_CREATE. A crash mid-write left the destination truncated and the model unloadable until manual repair. Pre-existing behaviour inherited from the original endpoint handlers — fix once now that there's a single helper. Adds writeFileAtomic: writes to a sibling temp file, chmods, syncs via Close(), then os.Rename. Same-directory temp keeps the rename atomic on the same filesystem; cleanup runs on every error path so stray temps don't accumulate. No new dependency. Applied to: - ConfigService.PatchConfig - ConfigService.EditYAML (both rename and in-place branches) - mutateYAMLBoolFlag (drives ToggleState + TogglePinned) atomic_test.go covers the happy path plus a read-only-dir failure case that asserts the original file is preserved (skipped on Windows where the chmod trick is POSIX-specific). Assisted-by: Claude:claude-opus-4-7 [Edit] [Write] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore(assistant): prune dead code, mark stub, document conventions Three small cleanups landing together: - Drop the unused errNotImplemented sentinel from inproc/client.go. All five methods that used to return it are wired to modeladmin helpers since the Phase B commit; the package var is dead. - Annotate httpapi.Client.GetModelConfig as a known stub. LocalAI's /models/edit/:name returns rendered HTML, not JSON, so the standalone CLI's get_model_config tool surfaces a clear error to the LLM. A future JSON-only /api/models/config-yaml/:name endpoint is tracked in the agent contract; FIXME points at it. - Extend `.agents/localai-assistant-mcp.md` with a "Code conventions" section that documents the audit-driven rules: tool/Capability/Action constants, errors.Is over substring matching, ctx-aware channel sends, atomic writes, and graceful shutdown. Refresh the file map so it lists tools.go and capability.go and drops the removed tools_bootstrap.go. The tools_models.go diff is a comment-only change explaining why the ModelName empty-string check stays at the tool layer (consistency across LocalAIClient implementations, since the SDK schema validator only enforces presence, not non-empty). Assisted-by: Claude:claude-opus-4-7 [Read] [Edit] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(assistant): convert test files to ginkgo + gomega The repo convention (per core/http/endpoints/localai/*_test.go, core/gallery/**, etc.) is Ginkgo v2 with Gomega assertions. The tests I introduced for the assistant feature used vanilla testing.T, which made them stand out and stripped the BDD structure the rest of the suite relies on. Convert every test file in the assistant scope to Ginkgo: pkg/mcp/localaitools/ dto_test.go — Describe("DTOs round-trip through JSON") prompts_test.go — Describe("SystemPrompt assembler") server_test.go — Describe("Server tool catalog"), Describe("Tool dispatch"), Describe("Tool error surfacing"), Describe("Argument validation"), Describe("Concurrent tool calls") parity_test.go — Describe("LocalAIClient parity"), hosts the suite's single RunSpecs (the file is package localaitools_test so it can import httpapi without an import cycle; Ginkgo aggregates Describes from both the internal and external test packages into one run). httpapi/client_test.go — Describe("httpapi.Client against the LocalAI admin REST surface"), Describe("ErrHTTPNotFound"), Describe("Bearer token") inproc/client_test.go — Describe("inproc.Client cancellation") core/services/modeladmin/ config_test.go — Describe("ConfigService") with sub-Describes for GetConfig, PatchConfig, EditYAML state_test.go — Describe("ConfigService.ToggleState") pinned_test.go — Describe("ConfigService.TogglePinned") atomic_test.go — Describe("writeFileAtomic") core/http/endpoints/mcp/ localai_assistant_test.go — Describe("LocalAIAssistantHolder") Each package gets a `*_suite_test.go` with the standard `RegisterFailHandler(Fail) + RunSpecs(t, "...")` boilerplate. Helpers that previously took *testing.T (newTestService, writeModelYAML, readMap, sortedStrings, sortGalleries, etc.) drop the *T receiver and use Gomega Expectations directly. tmp dirs come from GinkgoT().TempDir(). No semantic change to test coverage — every original assertion has a direct Gomega counterpart. All suites pass with -race. Assisted-by: Claude:claude-opus-4-7 [Read] [Edit] [Write] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test+docs(assistant): drift detector for Tool ↔ REST route mapping Honest gap from the audit: the parity_test.go suite only checks four methods, and uses the same httpapi.Client for both sides — it asserts stability of the DTO shapes, not equivalence between in-process and HTTP. If a contributor adds an admin REST endpoint without an MCP tool, or a tool without a matching httpapi route, both surfaces silently diverge. Add a coverage test plus stronger docs: - pkg/mcp/localaitools/coverage_test.go introduces a hand-maintained toolToHTTPRoute map: every Tool* constant must list the REST endpoint the httpapi.Client hits (or "(none)" with a documented reason). Two Ginkgo specs assert the map and the published catalog stay in sync — one fails when a Tool is added without a route entry, the other fails when a route entry references a tool that no longer exists. Verified by removing the ToolDeleteModel entry locally; the test fired with a clear message pointing the contributor at the file. Deliberate non-test: we don't enumerate live admin REST routes from here. Walking the route registry requires booting Application; parsing core/http/routes/localai.go is brittle. The "new admin REST endpoint → MCP tool" direction stays a PR checklist item — see below. - AGENTS.md gets a new Quick Reference bullet that calls out the rule and points at the test by name. - .agents/api-endpoints-and-auth.md tightens the existing "Companion: MCP admin tool surface" subsection from "if useful, consider..." to "MUST be considered, with three concrete outcomes (tool added, deliberately skipped with documented reason, or forgot — which breaks the contract)". Adds a checklist item at the bottom of the file's authoritative checklist. Assisted-by: Claude:claude-opus-4-7 [Read] [Edit] [Write] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(assistant): drop duplicate DTOs, surface canonical types Audit feedback: localaitools/dto.go reinvented several types that already existed in the codebase. Replace the duplicates with the canonical types so the LLM-visible wire format stays aligned with the rest of LocalAI by construction (no parallel structs to keep in sync). Removed (and the canonical type now used by the LocalAIClient interface): localaitools.Gallery → config.Gallery localaitools.GalleryModelHit → gallery.Metadata localaitools.VRAMEstimate → vram.EstimateResult Tightened scope: localaitools.Backend → kept, but reduced to {Name, Installed}. ListKnownBackends now returns []schema.KnownBackend (the canonical type already used by REST /backends/known). Kept with documented rationale: localaitools.JobStatus — galleryop.OpStatus has Error error which marshals to "{}". JobStatus is the JSON-friendly mirror. localaitools.Node — nodes.BackendNode carries gorm internals + token hash; we expose only the LLM-relevant fields. ImportModelURIRequest/Response — schema.ImportModelRequest and GalleryResponse are wire-shaped, mine are LLM-shaped (BackendPreference flat, AmbiguousBackend exposed). Side wins: - Drop bytesPerMiB; vram.EstimateResult already carries human-readable display strings (size_display, vram_display) the LLM uses directly. - Drop the handler-private vramEstimateRequest in core/http/endpoints/localai/vram.go and bind directly into modeladmin.VRAMRequest (now JSON-tagged). Both clients pass through these types now where possible (e.g. ListGalleries in inproc.Client is a one-liner returning AppConfig.Galleries; httpapi.Client.GallerySearch decodes straight into []gallery.Metadata). All tests green with -race. Assisted-by: Claude:claude-opus-4-7 [Read] [Edit] [Bash] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactor(assistant): extract REST route paths into named constants httpapi.Client had 18 bare-string path sites scattered across methods. Pull them into pkg/mcp/localaitools/httpapi/routes.go: static paths as package-private constants, dynamic paths as small builders that handle url.PathEscape on segment values. No behaviour change. Drops the now-unused net/url import from client.go since path escaping moved into routes.go alongside the path it applies to. Local-only by design: the server-side registrations in core/http/routes/localai.go remain bare strings. Sharing constants across the pkg/ ↔ core/ boundary would invert the layering today; the existing Tool↔REST drift-detector in coverage_test.go is the safety net for that direction. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-7 [Claude Code] * docs(assistant): align with shipped UI and dropped bootstrap env vars The LocalAI Assistant doc still described the older iteration: - The in-chat toggle was renamed from "Admin" to "Manage" (the badge is now "Manage mode" and the home page exposes a "Manage by chat" CTA). - LOCALAI_ASSISTANT_BOOTSTRAP_MODEL / --localai-assistant-bootstrap-model and the bootstrap_default_model tool were removed — admins pick a model from the existing selector instead, no env-var configuration required. - The shipped tool catalog includes import_model_uri but didn't appear in the doc; bootstrap_default_model appeared but no longer exists. - The Settings → LocalAI Assistant runtime toggle wasn't mentioned as the preferred way to disable without restart. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-7 [Claude Code] --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Ettore Di Giacinto
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fbe4f0a99b |
fix(docs): replace Docsy alert shortcode with Relearn notice
The docs site uses the hugo-theme-relearn theme, which provides
`notice` instead of Docsy's `alert`. The face-recognition,
voice-recognition, and stores feature pages used `{{% alert %}}`,
breaking `hugo build` with "template for shortcode \"alert\" not
found".
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
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Ettore Di Giacinto
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21eace40ec |
feat(llama-cpp): expose split_mode option for multi-GPU placement (#9560)
Adds split_mode (alias sm) to the llama.cpp backend options allowlist, accepting none|layer|row|tensor. The tensor value targets the experimental backend-agnostic tensor parallelism from ggml-org/llama.cpp#19378 and requires a llama.cpp build that includes that PR, FlashAttention enabled, KV-cache quantization disabled, and a manually set context size. Assisted-by: Claude:claude-opus-4-7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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Ettore Di Giacinto
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551ebdb57a |
fix(distributed): correct VRAM/RAM reporting on NVIDIA unified-memory hosts (#9545)
Workers on NVIDIA unified-memory hardware (DGX Spark / GB10, Jetson AGX Thor, Jetson Orin/Xavier/Nano) were reporting `available_vram=0` back to the frontend, so the Nodes UI showed the node as fully used even when most of the unified memory was actually free. Three causes addressed: * `isTegraDevice` only matched `/sys/devices/soc0/family == "Tegra"`. DGX Spark (SBSA) reports JEDEC codes there instead — `jep106:0426` for the NVIDIA manufacturer — so the Tegra/unified-memory fallback never ran. Renamed to `isNVIDIAIntegratedGPU` and extended to also match `jep106:0426[:*]` via `/sys/devices/soc0/soc_id`. * The unified-iGPU code defaulted the device name to `"NVIDIA Jetson"` when `/proc/device-tree/model` was missing. That's what happens for Thor inside a docker container, and always on DGX Spark. New `nvidiaIntegratedGPUName` resolves via dt-model → `/sys/devices/soc0/machine` → `soc_id` lookup (`jep106:0426:8901` → `"NVIDIA GB10"`) so the Nodes UI labels the box correctly. * Worker heartbeat sent `available_vram=0` (or total-as-available) when VRAM usage was momentarily unknown — e.g. when `nvidia-smi` intermittently failed with `waitid: no child processes` under containers without `--init`. Each such heartbeat overwrote the DB and made the UI flip to "fully used". `heartbeatBody` now omits `available_vram` in that case so the DB keeps its last good value. Also updates the commented GPU blocks in both compose files with `NVIDIA_DRIVER_CAPABILITIES=compute,utility`, `capabilities: [gpu, utility]`, and `init: true`, and documents the requirement in the distributed-mode and nvidia-l4t pages. Without `utility`, NVML/`nvidia-smi` are absent inside the container, which is what put the DGX Spark worker into the buggy fallback in the first place. Detection verified on live hardware (dgx.casa / GB10 and 192.168.68.23 / Thor) by running a cross-compiled probe of the new helpers on both host and inside the worker container. Assisted-by: Claude:opus-4.7 [Claude Code] |
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Ettore Di Giacinto
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f5eb13d3c2 |
feat(insightface): add antispoofing (liveness) detection (#9515)
* feat(insightface): add antispoofing (liveness) detection
Light up the anti_spoofing flag that was parked during the first pass.
Both FaceVerify and FaceAnalyze now run the Silent-Face MiniFASNetV2 +
MiniFASNetV1SE ensemble (~4 MB, Apache 2.0, CPU <10ms) when the flag is
set. Failed liveness on either image vetoes FaceVerify regardless of
embedding similarity. Every insightface* gallery entry now ships the
MiniFASNet ONNX weights so existing packs light up after reinstall.
Setting the flag against a model without the MiniFASNet files returns
FAILED_PRECONDITION (HTTP 412) with a clear install message — no
silent is_real=false.
FaceVerifyResponse gained per-image img{1,2}_is_real and
img{1,2}_antispoof_score (proto 9-12); FaceAnalysis's existing
is_real/antispoof_score fields are now populated. Schema fields are
pointers so they are fully absent from the JSON response when
anti_spoofing was not requested — avoids collapsing "not checked" with
"checked and fake" under Go's omitempty on bool.
Validated end-to-end over HTTP against a local install:
- verify + anti_spoofing, both real -> verified=true, score ~0.76
- verify + anti_spoofing, img2 spoof -> verified=false, img2_is_real=false
- analyze + anti_spoofing -> is_real and score per face
- flag against model without MiniFASNet -> HTTP 412 fail-loud
Assisted-by: Claude:claude-opus-4-7 go vet
* test(insightface): wire test target into test-extra
The root Makefile's `test-extra` already runs
`$(MAKE) -C backend/python/insightface test`, but the backend's
Makefile never defined the target — so the command silently errored
and the suite was never executed in CI. Adding the two-line target
(matching ace-step/Makefile) hooks `test.sh` → `runUnittests` →
`python -m unittest test.py`, which discovers both the pre-existing
engine classes (InsightFaceEngineTest, OnnxDirectEngineTest) and the
new AntispoofingTest. Each class skips gracefully when its weights
can't be downloaded from a network-restricted runner.
Assisted-by: Claude:claude-opus-4-7
* test(insightface): exercise antispoofing in e2e-backends (both paths)
Add a `face_antispoof` capability to the Ginkgo e2e suite and extend
the existing FaceVerify + FaceAnalyze specs with liveness assertions
covering BOTH paths:
real fixture -> is_real=true, score>0, verified stays true
spoof fixture -> is_real=false, verified vetoed to false
The spoof fixture is upstream's own `image_F2.jpg` (via the yakhyo
mirror) — verified locally against the MiniFASNetV2+V1SE ensemble to
classify as is_real=false with score ~0.013. That makes the assertion
deterministic across CI runs; synthetic/derived spoofs fool the model
unpredictably and would be flaky.
Makefile wires it up end-to-end:
- New INSIGHTFACE_ANTISPOOF_* cache dir + two ONNX downloads with
pinned SHAs, matching the gallery entries.
- insightface-antispoof-models target shared by both backend configs.
- FACE_SPOOF_IMAGE_URL passed via BACKEND_TEST_FACE_SPOOF_IMAGE_URL.
- Both e2e targets (buffalo-sc + opencv) now:
* depend on insightface-antispoof-models
* pass antispoof_v2_onnx / antispoof_v1se_onnx in BACKEND_TEST_OPTIONS
* include face_antispoof in BACKEND_TEST_CAPS
backend_test.go adds the new capability constant and a faceSpoofFile
fixture resolved the same way as faceFile1/2/3. Spoof assertions are
gated on both capFaceAntispoof AND faceSpoofFile being set, so a test
config that omits the spoof fixture degrades gracefully to "real path
only" instead of failing.
Assisted-by: Claude:claude-opus-4-7 go vet
|
||
|
Ettore Di Giacinto
|
181ebb6df4 |
feat: voice recognition (#9500)
* feat(voice-recognition): add /v1/voice/{verify,analyze,embed} + speaker-recognition backend
Audio analog to face recognition. Adds three gRPC RPCs
(VoiceVerify / VoiceAnalyze / VoiceEmbed), their Go service and HTTP
layers, a new FLAG_SPEAKER_RECOGNITION capability flag, and a Python
backend scaffold under backend/python/speaker-recognition/ wrapping
SpeechBrain ECAPA-TDNN with a parallel OnnxDirectEngine for
WeSpeaker / 3D-Speaker ONNX exports.
The kokoros Rust backend gets matching unimplemented trait stubs —
tonic's async_trait has no defaults, so adding an RPC without Rust
stubs breaks the build (same regression fixed by
|
||
|
Ettore Di Giacinto
|
20baec77ab |
feat(face-recognition): add insightface/onnx backend for 1:1 verify, 1:N identify, embedding, detection, analysis (#9480)
* feat(face-recognition): add insightface backend for 1:1 verify, 1:N identify, embedding, detection, analysis
Adds face recognition as a new first-class capability in LocalAI via the
`insightface` Python backend, with a pluggable two-engine design so
non-commercial (insightface model packs) and commercial-safe
(OpenCV Zoo YuNet + SFace) models share the same gRPC/HTTP surface.
New gRPC RPCs (backend/backend.proto):
* FaceVerify(FaceVerifyRequest) returns FaceVerifyResponse
* FaceAnalyze(FaceAnalyzeRequest) returns FaceAnalyzeResponse
Existing Embedding and Detect RPCs are reused (face image in
PredictOptions.Images / DetectOptions.src) for face embedding and
face detection respectively.
New HTTP endpoints under /v1/face/:
* verify — 1:1 image pair same-person decision
* analyze — per-face age + gender (emotion/race reserved)
* register — 1:N enrollment; stores embedding in vector store
* identify — 1:N recognition; detect → embed → StoresFind
* forget — remove a registered face by opaque ID
Service layer (core/services/facerecognition/) introduces a
`Registry` interface with one in-memory `storeRegistry` impl backed
by LocalAI's existing local-store gRPC vector backend. HTTP handlers
depend on the interface, not on StoresSet/StoresFind directly, so a
persistent PostgreSQL/pgvector implementation can be slotted in via a
single constructor change in core/application (TODO marker in the
package doc).
New usecase flag FLAG_FACE_RECOGNITION; insightface is also wired
into FLAG_DETECTION so /v1/detection works for face bounding boxes.
Gallery (backend/index.yaml) ships three entries:
* insightface-buffalo-l — SCRFD-10GF + ArcFace R50 + genderage
(~326MB pre-baked; non-commercial research use only)
* insightface-opencv — YuNet + SFace (~40MB pre-baked; Apache 2.0)
* insightface-buffalo-s — SCRFD-500MF + MBF (runtime download; non-commercial)
Python backend (backend/python/insightface/):
* engines.py — FaceEngine protocol with InsightFaceEngine and
OnnxDirectEngine; resolves model paths relative to the backend
directory so the same gallery config works in docker-scratch and
in the e2e-backends rootfs-extraction harness.
* backend.py — gRPC servicer implementing Health, LoadModel, Status,
Embedding, Detect, FaceVerify, FaceAnalyze.
* install.sh — pre-bakes buffalo_l + OpenCV YuNet/SFace inside the
backend directory so first-run is offline-clean (the final scratch
image only preserves files under /<backend>/).
* test.py — parametrized unit tests over both engines.
Tests:
* Registry unit tests (go test -race ./core/services/facerecognition/...)
— in-memory fake grpc.Backend, table-driven, covers register/
identify/forget/error paths + concurrent access.
* tests/e2e-backends/backend_test.go extended with face caps
(face_detect, face_embed, face_verify, face_analyze); relative
ordering + configurable verifyCeiling per engine.
* Makefile targets: test-extra-backend-insightface-buffalo-l,
-opencv, and the -all aggregate.
* CI: .github/workflows/test-extra.yml gains tests-insightface-grpc,
auto-triggered by changes under backend/python/insightface/.
Docs:
* docs/content/features/face-recognition.md — feature page with
license table, quickstart (defaults to the commercial-safe model),
models matrix, API reference, 1:N workflow, storage caveats.
* Cross-refs in object-detection.md, stores.md, embeddings.md, and
whats-new.md.
* Contributor README at backend/python/insightface/README.md.
Verified end-to-end:
* buffalo_l: 6/6 specs (health, load, face_detect, face_embed,
face_verify, face_analyze).
* opencv: 5/5 specs (same minus face_analyze — SFace has no
demographic head; correctly skipped via BACKEND_TEST_CAPS).
Assisted-by: Claude:claude-opus-4-7
* fix(face-recognition): move engine selection to model gallery, collapse backend entries
The previous commit put engine/model_pack options on backend gallery
entries (`backend/index.yaml`). That was wrong — `GalleryBackend`
(core/gallery/backend_types.go:32) has no `options` field, so the
YAML decoder silently dropped those keys and all three "different
insightface-*" backend entries resolved to the same container image
with no distinguishing configuration.
Correct split:
* `backend/index.yaml` now has ONE `insightface` backend entry
shipping the CPU + CUDA 12 container images. The Python backend
bundles both the non-commercial insightface model packs
(buffalo_l / buffalo_s) and the commercial-safe OpenCV Zoo
weights (YuNet + SFace); the active engine is selected at
LoadModel time via `options: ["engine:..."]`.
* `gallery/index.yaml` gains three model entries —
`insightface-buffalo-l`, `insightface-opencv`,
`insightface-buffalo-s` — each setting the appropriate
`overrides.backend` + `overrides.options` so installing one
actually gives the user the intended engine. This matches how
`rfdetr-base` lives in the model gallery against the `rfdetr`
backend.
The earlier e2e tests passed despite this bug because the Makefile
targets pass `BACKEND_TEST_OPTIONS` directly to LoadModel via gRPC,
bypassing any gallery resolution entirely. No code changes needed.
Assisted-by: Claude:claude-opus-4-7
* feat(face-recognition): cover all supported models in the gallery + drop weight baking
Follows up on the model-gallery split: adds entries for every model
configuration either engine actually supports, and switches weight
delivery from image-baked to LocalAI's standard gallery mechanism.
Gallery now has seven `insightface-*` model entries (gallery/index.yaml):
insightface (family) — non-commercial research use
• buffalo-l (326MB) — SCRFD-10GF + ResNet50 + genderage, default
• buffalo-m (313MB) — SCRFD-2.5GF + ResNet50 + genderage
• buffalo-s (159MB) — SCRFD-500MF + MBF + genderage
• buffalo-sc (16MB) — SCRFD-500MF + MBF, recognition only
(no landmarks, no demographics — analyze
returns empty attributes)
• antelopev2 (407MB) — SCRFD-10GF + ResNet100@Glint360K + genderage
OpenCV Zoo family — Apache 2.0 commercial-safe
• opencv — YuNet + SFace fp32 (~40MB)
• opencv-int8 — YuNet + SFace int8 (~12MB, ~3x smaller, faster on CPU)
Model weights are no longer baked into the backend image. The image
now ships only the Python runtime + libraries (~275MB content size,
~1.18GB disk vs ~1.21GB when weights were baked). Weights flow through
LocalAI's gallery mechanism:
* OpenCV variants list `files:` with ONNX URIs + SHA-256, so
`local-ai models install insightface-opencv` pulls them into the
models directory exactly like any other gallery-managed model.
* insightface packs (upstream distributes .zip archives only, not
individual ONNX files) auto-download on first LoadModel via
FaceAnalysis' built-in machinery, rooted at the LocalAI models
directory so they live alongside everything else — same pattern
`rfdetr` uses with `inference.get_model()`.
Backend changes (backend/python/insightface/):
* backend.py — LoadModel propagates `ModelOptions.ModelPath` (the
LocalAI models directory) to engines via a `_model_dir` hint.
This replaces the earlier ModelFile-dirname approach; ModelPath
is the canonical "models directory" variable set by the Go loader
(pkg/model/initializers.go:144) and is always populated.
* engines.py::_resolve_model_path — picks up `model_dir` and searches
it (plus basename-in-model-dir) before falling back to the dev
script-dir. This is how OnnxDirectEngine finds gallery-downloaded
YuNet/SFace files by filename only.
* engines.py::_flatten_insightface_pack — new helper that works
around an upstream packaging inconsistency: buffalo_l/s/sc zips
expand flat, but buffalo_m and antelopev2 zips wrap their ONNX
files in a redundant `<name>/` directory. insightface's own
loader looks one level too shallow and fails. We call
`ensure_available()` explicitly, flatten if nested, then hand to
FaceAnalysis.
* engines.py::InsightFaceEngine.prepare — root-resolution order now
includes the `_model_dir` hint so packs download into the LocalAI
models directory by default.
* install.sh — no longer pre-downloads any weights. Everything is
gallery-managed now.
* smoke.py (new) — parametrized smoke test that iterates over every
gallery configuration, simulating the LocalAI install flow
(creates a models dir, fetches OpenCV files with checksum
verification, lets insightface auto-download its packs), then
runs detect + embed + verify (+ analyze where supported) through
the in-process BackendServicer.
* test.py — OnnxDirectEngineTest no longer hardcodes `/models/opencv/`
paths; downloads ONNX files to a temp dir at setUpClass time and
passes ModelPath accordingly.
Registry change (core/services/facerecognition/store_registry.go):
* `dim=0` in NewStoreRegistry now means "accept whatever dimension
arrives" — needed because the backend supports 512-d ArcFace/MBF
and 128-d SFace via the same Registry. A non-zero dim still fails
fast with ErrDimensionMismatch.
* core/application plumbs `faceEmbeddingDim = 0`, explaining the
rationale in the comment.
Backend gallery description updated to reflect that the image carries
no weights — it's just Python + engines.
Smoke-tested all 7 configurations against the rebuilt image (with the
flatten fix applied), exit 0:
PASS: insightface-buffalo-l faces=6 dim=512 same-dist=0.000
PASS: insightface-buffalo-sc faces=6 dim=512 same-dist=0.000
PASS: insightface-buffalo-s faces=6 dim=512 same-dist=0.000
PASS: insightface-buffalo-m faces=6 dim=512 same-dist=0.000
PASS: insightface-antelopev2 faces=6 dim=512 same-dist=0.000
PASS: insightface-opencv faces=6 dim=128 same-dist=0.000
PASS: insightface-opencv-int8 faces=6 dim=128 same-dist=0.000
7/7 passed
Assisted-by: Claude:claude-opus-4-7
* fix(face-recognition): pre-fetch OpenCV ONNX for e2e target; drop stale pre-baked claim
CI regression from the previous commit: I moved OpenCV Zoo weight
delivery to LocalAI's gallery `files:` mechanism, but the
test-extra-backend-insightface-opencv target was still passing
relative paths `detector_onnx:models/opencv/yunet.onnx` in
BACKEND_TEST_OPTIONS. The e2e suite drives LoadModel directly over
gRPC without going through the gallery, so those relative paths
resolved to nothing and OpenCV's ONNXImporter failed:
LoadModel failed: Failed to load face engine:
OpenCV(4.13.0) ... Can't read ONNX file: models/opencv/yunet.onnx
Fix: add an `insightface-opencv-models` prerequisite target that
fetches the two ONNX files (YuNet + SFace) to a deterministic host
cache at /tmp/localai-insightface-opencv-cache/, verifies SHA-256,
and skips the download on re-runs. The opencv test target depends on
it and passes absolute paths in BACKEND_TEST_OPTIONS, so the backend
finds the files via its normal absolute-path resolution branch.
Also refresh the buffalo_l comment: it no longer says "pre-baked"
(nothing is — the pack auto-downloads from upstream's GitHub release
on first LoadModel, same as in CI).
Locally verified: `make test-extra-backend-insightface-opencv` passes
5/5 specs (health, load, face_detect, face_embed, face_verify).
Assisted-by: Claude:claude-opus-4-7
* feat(face-recognition): add POST /v1/face/embed + correct /v1/embeddings docs
The docs promised that /v1/embeddings returns face vectors when you
send an image data-URI. That was never true: /v1/embeddings is
OpenAI-compatible and text-only by contract — its handler goes
through `core/backend/embeddings.go::ModelEmbedding`, which sets
`predictOptions.Embeddings = s` (a string of TEXT to embed) and never
populates `predictOptions.Images[]`. The Python backend's Embedding
gRPC method does handle Images[] (that's how /v1/face/register reaches
it internally via `backend.FaceEmbed`), but the HTTP embeddings
endpoint wasn't wired to populate it.
Rather than overload /v1/embeddings with image-vs-text detection —
messy, and the endpoint is OpenAI-compatible by design — add a
dedicated /v1/face/embed endpoint that wraps `backend.FaceEmbed`
(already used internally by /v1/face/register and /v1/face/identify).
Matches LocalAI's convention of a dedicated path per non-standard flow
(/v1/rerank, /v1/detection, /v1/face/verify etc.).
Response:
{
"embedding": [<dim> floats, L2-normed],
"dim": int, // 512 for ArcFace R50 / MBF, 128 for SFace
"model": "<name>"
}
Live-tested on the opencv engine: returns a 128-d L2-normalized vector
(sum(x^2) = 1.0000). Sentinel in docs updated to note /v1/embeddings
is text-only and point image users at /v1/face/embed instead.
Assisted-by: Claude:claude-opus-4-7
* fix(http): map malformed image input + gRPC status codes to proper 4xx
Image-input failures on LocalAI's single-image endpoints (/v1/detection,
/v1/face/{verify,analyze,embed,register,identify}) have historically
returned 500 — even when the client was the one who sent garbage.
Classic example: you POST an "image" that isn't a URL, isn't a
data-URI, and isn't a valid JPEG/PNG — the server shouldn't claim
that's its fault.
Two helpers land in core/http/endpoints/localai/images.go and every
single-image handler is switched over:
* decodeImageInput(s)
Wraps utils.GetContentURIAsBase64 and turns any failure
(invalid URL, not a data-URI, download error, etc.) into
echo.NewHTTPError(400, "invalid image input: ...").
* mapBackendError(err)
Inspects the gRPC status on a backend call error and maps:
INVALID_ARGUMENT → 400 Bad Request
NOT_FOUND → 404 Not Found
FAILED_PRECONDITION → 412 Precondition Failed
Unimplemented → 501 Not Implemented
All other codes fall through unchanged (still 500).
Before, my 1×1 PNG error-path test returned:
HTTP 500 "rpc error: code = InvalidArgument desc = failed to decode one or both images"
After:
HTTP 400 "failed to decode one or both images"
Scope-limited to the LocalAI single-image endpoints. The multi-modal
paths (middleware/request.go, openresponses/responses.go,
openai/realtime.go) intentionally log-and-skip individual media parts
when decoding fails — different design intent (graceful degradation
of a multi-part message), not a 400-worthy failure. Left untouched.
Live-verified: every error case in /tmp/face_errors.py now returns
4xx with a meaningful message; the "image with no face (1x1 PNG)"
case specifically went from 500 → 400.
Assisted-by: Claude:claude-opus-4-7
* refactor(face-recognition): insightface packs go through gallery files:, drop FaceAnalysis
Follows up on the discovery that LocalAI's gallery `files:` mechanism
handles archives (zip, tar.gz, …) via mholt/archiver/v3 — the rhasspy
piper voices use exactly this pattern. Insightface packs are zip
archives, so we can now deliver them the same way every other
gallery-managed model gets delivered: declaratively, checksum-verified,
through LocalAI's standard download+extract pipeline.
Two changes:
1. Gallery (gallery/index.yaml) — every insightface-* entry gains a
`files:` list with the pack zip's URI + SHA-256. `local-ai models
install insightface-buffalo-l` now fetches the zip, verifies the
hash, and extracts it into the models directory. No more reliance
on insightface's library-internal `ensure_available()` auto-download
or its hardcoded `BASE_REPO_URL`.
2. InsightFaceEngine (backend/python/insightface/engines.py) — drops
the FaceAnalysis wrapper and drives insightface's `model_zoo`
directly. The ~50 lines FaceAnalysis provides — glob ONNX files,
route each through `model_zoo.get_model()`, build a
`{taskname: model}` dict, loop per-face at inference — are
reimplemented in `InsightFaceEngine`. The actual inference classes
(RetinaFace, ArcFaceONNX, Attribute, Landmark) are still
insightface's — we only replicate the glue, so drift risk against
upstream is minimal.
Why drop FaceAnalysis: it hard-codes a `<root>/models/<name>/*.onnx`
layout that doesn't match what LocalAI's zip extraction produces.
LocalAI unpacks archives flat into `<models_dir>`. Upstream packs
are inconsistent — buffalo_l/s/sc ship ONNX at the zip root (lands
at `<models_dir>/*.onnx`), buffalo_m/antelopev2 wrap in a redundant
`<name>/` dir (lands at `<models_dir>/<name>/*.onnx`). The new
`_locate_insightface_pack` helper searches both locations plus
legacy paths and returns whichever has ONNX files. Replaces the
earlier `_flatten_insightface_pack` helper (which tried to fight
FaceAnalysis's layout expectations; now we just find the files
wherever they are).
Net effect for users: install once via LocalAI's managed flow,
weights live alongside every other model, progress shows in the
jobs endpoint, no first-load network call. Same API surface,
cleaner plumbing.
Assisted-by: Claude:claude-opus-4-7
* fix(face-recognition): CI's insightface e2e path needs the pack pre-fetched
The e2e suite drives LoadModel over gRPC without going through LocalAI's
gallery flow, so the engine's `_model_dir` option (normally populated
from ModelPath) is empty. Previously the insightface target relied on
FaceAnalysis auto-download to paper over this, but we dropped
FaceAnalysis in favor of direct model_zoo calls — so the buffalo_l
target started failing at LoadModel with "no insightface pack found".
Mirror the opencv target's pre-fetch pattern: download buffalo_sc.zip
(same SHA as the gallery entry), extract it on the host, and pass
`root:<dir>` so the engine locates the pack without needing
ModelPath. Switched to buffalo_sc (smallest pack, ~16MB) to keep CI
fast; it covers the same insightface engine code path as buffalo_l.
Face analyze cap dropped since buffalo_sc has no age/gender head.
Assisted-by: Claude:claude-opus-4-7[1m]
* feat(face-recognition): surface face-recognition in advertised feature maps
The six /v1/face/* endpoints were missing from every place LocalAI
advertises its feature surface to clients:
* api_instructions — the machine-readable capability index at
GET /api/instructions. Added `face-recognition` as a dedicated
instruction area with an intro that calls out the in-memory
registry caveat and the /v1/face/embed vs /v1/embeddings split.
* auth/permissions — added FeatureFaceRecognition constant, routed
all six face endpoints through it so admins can gate them per-user
like any other API feature. Default ON (matches the other API
features).
* React UI capabilities — CAP_FACE_RECOGNITION symbol mapped to
FLAG_FACE_RECOGNITION. Declared only for now; the Face page is a
follow-up (noted in the plan).
Instruction count bumped 9 → 10; test updated.
Assisted-by: Claude:claude-opus-4-7[1m]
* docs(agents): capture advertising-surface steps in the endpoint guide
Before this change, adding a new /v1/* endpoint reliably missed one or
more of: the swagger @Tags annotation, the /api/instructions registry,
the auth RouteFeatureRegistry, and the React UI CAP_* symbol. The
endpoint would work but be invisible to API consumers, admins, and the
UI — and nothing in the existing docs said to look in those places.
Extend .agents/api-endpoints-and-auth.md with a new "Advertising
surfaces" section covering all four surfaces (swagger tags, /api/
instructions, capabilities.js, docs/), and expand the closing checklist
so it's impossible to ship a feature without visiting each one. Hoist a
one-liner reminder into AGENTS.md's Quick Reference so agents skim it
before diving in.
Assisted-by: Claude:claude-opus-4-7[1m]
|
||
|
Adira
|
607efe5a4c |
fix(backend-monitor): accept model as a query parameter (#9411)
The /backend/monitor endpoint is routed as GET but its handler bound the
model name from a request body, which is invalid per REST and breaks
Swagger UI and OpenAPI codegen tools that refuse to send bodies with GET.
Switch to reading ?model=<name> as a query parameter and update the
Swagger annotation, regenerated spec files, and documentation. The
handler still falls back to body binding when the query parameter is
absent, so existing clients sending {"model": "..."} continue to work.
Fixes #9207
Signed-off-by: Adira Denis Muhando <dennisadira@gmail.com>
|
||
|
Ettore Di Giacinto
|
95efb8a562 |
feat(backend): add turboquant llama.cpp-fork backend (#9355)
* feat(backend): add turboquant llama.cpp-fork backend
turboquant is a llama.cpp fork (TheTom/llama-cpp-turboquant, branch
feature/turboquant-kv-cache) that adds a TurboQuant KV-cache scheme.
It ships as a first-class backend reusing backend/cpp/llama-cpp sources
via a thin wrapper Makefile: each variant target copies ../llama-cpp
into a sibling build dir and invokes llama-cpp's build-llama-cpp-grpc-server
with LLAMA_REPO/LLAMA_VERSION overridden to point at the fork. No
duplication of grpc-server.cpp — upstream fixes flow through automatically.
Wires up the full matrix (CPU, CUDA 12/13, L4T, L4T-CUDA13, ROCm, SYCL
f32/f16, Vulkan) in backend.yml and the gallery entries in index.yaml,
adds a tests-turboquant-grpc e2e job driven by BACKEND_TEST_CACHE_TYPE_K/V=q8_0
to exercise the KV-cache config path (backend_test.go gains dedicated env
vars wired into ModelOptions.CacheTypeKey/Value — a generic improvement
usable by any llama.cpp-family backend), and registers a nightly auto-bump
PR in bump_deps.yaml tracking feature/turboquant-kv-cache.
scripts/changed-backends.js gets a special-case so edits to
backend/cpp/llama-cpp/ also retrigger the turboquant CI pipeline, since
the wrapper reuses those sources.
* feat(turboquant): carry upstream patches against fork API drift
turboquant branched from llama.cpp before upstream commit 66060008
("server: respect the ignore eos flag", #21203) which added the
`logit_bias_eog` field to `server_context_meta` and a matching
parameter to `server_task::params_from_json_cmpl`. The shared
backend/cpp/llama-cpp/grpc-server.cpp depends on that field, so
building it against the fork unmodified fails.
Cherry-pick that commit as a patch file under
backend/cpp/turboquant/patches/ and apply it to the cloned fork
sources via a new apply-patches.sh hook called from the wrapper
Makefile. Simplifies the build flow too: instead of hopping through
llama-cpp's build-llama-cpp-grpc-server indirection, the wrapper now
drives the copied Makefile directly (clone -> patch -> build).
Drop the corresponding patch whenever the fork catches up with
upstream — the build fails fast if a patch stops applying, which
is the signal to retire it.
* docs: add turboquant backend section + clarify cache_type_k/v
Document the new turboquant (llama.cpp fork with TurboQuant KV-cache)
backend alongside the existing llama-cpp / ik-llama-cpp sections in
features/text-generation.md: when to pick it, how to install it from
the gallery, and a YAML example showing backend: turboquant together
with cache_type_k / cache_type_v.
Also expand the cache_type_k / cache_type_v table rows in
advanced/model-configuration.md to spell out the accepted llama.cpp
quantization values and note that these fields apply to all
llama.cpp-family backends, not just vLLM.
* feat(turboquant): patch ggml-rpc GGML_OP_COUNT assertion
The fork adds new GGML ops bringing GGML_OP_COUNT to 97, but
ggml/include/ggml-rpc.h static-asserts it equals 96, breaking
the GGML_RPC=ON build paths (turboquant-grpc / turboquant-rpc-server).
Carry a one-line patch that updates the expected count so the
assertion holds. Drop this patch whenever the fork fixes it upstream.
* feat(turboquant): allow turbo* KV-cache types and exercise them in e2e
The shared backend/cpp/llama-cpp/grpc-server.cpp carries its own
allow-list of accepted KV-cache types (kv_cache_types[]) and rejects
anything outside it before the value reaches llama.cpp's parser. That
list only contains the standard llama.cpp types — turbo2/turbo3/turbo4
would throw "Unsupported cache type" at LoadModel time, meaning
nothing the LocalAI gRPC layer accepted was actually fork-specific.
Add a build-time augmentation step (patch-grpc-server.sh, called from
the turboquant wrapper Makefile) that inserts GGML_TYPE_TURBO2_0/3_0/4_0
into the allow-list of the *copied* grpc-server.cpp under
turboquant-<flavor>-build/. The original file under backend/cpp/llama-cpp/
is never touched, so the stock llama-cpp build keeps compiling against
vanilla upstream which has no notion of those enum values.
Switch test-extra-backend-turboquant to set
BACKEND_TEST_CACHE_TYPE_K=turbo3 / _V=turbo3 so the e2e gRPC suite
actually runs the fork's TurboQuant KV-cache code paths (turbo3 also
auto-enables flash_attention in the fork). Picking q8_0 here would
only re-test the standard llama.cpp path that the upstream llama-cpp
backend already covers.
Refresh the docs (text-generation.md + model-configuration.md) to
list turbo2/turbo3/turbo4 explicitly and call out that you only get
the TurboQuant code path with this backend + a turbo* cache type.
* fix(turboquant): rewrite patch-grpc-server.sh in awk, not python3
The builder image (ubuntu:24.04 stage-2 in Dockerfile.turboquant)
does not install python3, so the python-based augmentation step
errored with `python3: command not found` at make time. Switch to
awk, which ships in coreutils and is already available everywhere
the rest of the wrapper Makefile runs.
* Apply suggestion from @mudler
Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com>
---------
Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com>
|
||
|
Ettore Di Giacinto
|
833b7e8557 |
chore(docs): update transcription endpoint
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Ettore Di Giacinto
|
0e7c0adee4 |
docs: document tool calling on vLLM and MLX backends
openai-functions.md used to claim LocalAI tool calling worked only on llama.cpp-compatible models. That was true when it was written; it's not true now — vLLM (since PR #9328) and MLX/MLX-VLM both extract structured tool calls from model output. - openai-functions.md: new 'Supported backends' matrix covering llama.cpp, vllm, vllm-omni, mlx, mlx-vlm, with the key distinction that vllm needs an explicit tool_parser: option while mlx auto- detects from the chat template. Reasoning content (think tags) is extracted on the same set of backends. Added setup snippets for both the vllm and mlx paths, and noted the gallery importer pre-fills tool_parser:/reasoning_parser: for known families. - compatibility-table.md: fix the stale 'Streaming: no' for vllm, vllm-omni, mlx, mlx-vlm (all four support streaming now). Add 'Functions' to their capabilities. Also widen the MLX Acceleration column to reflect the CPU/CUDA/Jetson L4T backends that already exist in backend/index.yaml — 'Metal' on its own was misleading. |
||
|
Ettore Di Giacinto
|
9ca03cf9cc |
feat(backends): add ik-llama-cpp (#9326)
* feat(backends): add ik-llama-cpp Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore: add grpc e2e suite, hook to CI, update README Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Apply suggestion from @mudler Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com> * Apply suggestion from @mudler Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com> |
||
|
Ettore Di Giacinto
|
151ad271f2 |
feat(rocm): bump to 7.x (#9323)
feat(rocm): bump to 7.2.1 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Ettore Di Giacinto
|
706cf5d43c |
feat(sam.cpp): add sam.cpp detection backend (#9288)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Ettore Di Giacinto
|
b0d9ce4905 |
Remove header from OpenAI Realtime API documentation
Removed the header from the Realtime API documentation. Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com> |
||
|
Richard Palethorpe
|
557d0f0f04 |
feat(api): Allow coding agents to interactively discover how to control and configure LocalAI (#9084)
Signed-off-by: Richard Palethorpe <io@richiejp.com> |
||
|
Ettore Di Giacinto
|
11dc54bda9 |
fix(docs): commit distribution.md
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Ettore Di Giacinto
|
7e0b73deaa |
fix(docs): fix broken references to distributed mode
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Ettore Di Giacinto
|
8862e3ce60 |
feat: add node reconciler, allow to schedule to group of nodes, min/max autoscaler (#9186)
* always enable parallel requests Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat: add node reconciler, allow to schedule to group of nodes, min/max autoscaler Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore: move tests to ginkgo Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore(smart router): order by available vram Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Ettore Di Giacinto
|
59108fbe32 |
feat: add distributed mode (#9124)
* feat: add distributed mode (experimental) Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix data races, mutexes, transactions Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactorings Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fixups Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix events and tool stream in agent chat Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * use ginkgo Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactoring and consolidation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactoring and consolidation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactoring and consolidation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactoring and consolidation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactoring and consolidation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactoring and consolidation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactoring and consolidation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactoring and consolidation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(cron): compute correctly time boundaries avoiding re-triggering Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * enhancements, refactorings Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * do not flood of healthy checks Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * do not list obvious backends as text backends Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * tests fixups Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * refactoring and consolidation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Drop redundant healthcheck Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * enhancements, refactorings Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Richard Palethorpe
|
26384c5c70 |
fix(docs): Use notice instead of alert (#9134)
Signed-off-by: Richard Palethorpe <io@richiejp.com> |
||
|
Ettore Di Giacinto
|
f7e8d9e791 |
feat(quantization): add quantization backend (#9096)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Ettore Di Giacinto
|
d9c1db2b87 |
feat: add (experimental) fine-tuning support with TRL (#9088)
* feat: add fine-tuning endpoint Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(experimental): add fine-tuning endpoint and TRL support This changeset defines new GRPC signatues for Fine tuning backends, and add TRL backend as initial fine-tuning engine. This implementation also supports exporting to GGUF and automatically importing it to LocalAI after fine-tuning. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * commit TRL backend, stop by killing process Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * move fine-tune to generic features Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * add evals, reorder menu Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Fix tests Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Ettore Di Giacinto
|
aea21951a2 |
feat: add users and authentication support (#9061)
* feat(ui): add users and authentication support Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat: allow the admin user to impersonificate users Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore: ui improvements, disable 'Users' button in navbar when no auth is configured Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat: add OIDC support Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix: gate models Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore: cache requests to optimize speed Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * small UI enhancements Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore(ui): style improvements Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix: cover other paths by auth Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore: separate local auth, refactor Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * security hardening, approval mode Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix: fix tests and expectations Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore: update localagi/localrecall Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
LocalAI [bot]
|
bbe9067227 |
docs: Add troubleshooting guide for embedding models (fixes #9064) (#9065)
docs: Add troubleshooting guide for embedding models (#9064) - Add section on using gallery models for embeddings - Document common issues with embedding model configuration - Add troubleshooting guide for Qwen3 embedding models - Include correct configuration examples for Qwen3-Embedding-4B - Document context size limits and dimension parameters - Add table of Qwen3 embedding model specifications Fixes #9064 Signed-off-by: localai-bot <localai-bot@localai.io> Co-authored-by: localai-bot <localai-bot@localai.io> |
||
|
Ettore Di Giacinto
|
5affb747a9 |
chore: drop AIO images (#9004)
AIO images are behind, and takes effort to maintain these. Wizard and installation of models have been semplified massively, so AIO images lost their purpose. This allows us to be more laser focused on main images and reliefes stress from CI. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Richard Palethorpe
|
f9a850c02a |
feat(realtime): WebRTC support (#8790)
* feat(realtime): WebRTC support Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(tracing): Show full LLM opts and deltas Signed-off-by: Richard Palethorpe <io@richiejp.com> --------- Signed-off-by: Richard Palethorpe <io@richiejp.com> |
||
|
Ettore Di Giacinto
|
8818452d85 |
feat(ui): MCP Apps, mcp streaming and client-side support (#8947)
* Revert "fix: Add timeout-based wait for model deletion completion (#8756)"
This reverts commit
|
||
|
Ettore Di Giacinto
|
85f3558d22 |
feat(ui): add canvas mode, support history in agent chat (#8927)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Ettore Di Giacinto
|
a026277ab9 |
feat(mlx-distributed): add new MLX-distributed backend (#8801)
* feat(mlx-distributed): add new MLX-distributed backend Add new MLX distributed backend with support for both TCP and RDMA for model sharding. This implementation ties in the discovery implementation already in place, and re-uses the same P2P mechanism for the TCP MLX-distributed inferencing. The Auto-parallel implementation is inspired by Exo's ones (who have been added to acknowledgement for the great work!) Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * expose a CLI to facilitate backend starting Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat: make manual rank0 configurable via model configs Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Add missing features from mlx backend Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Apply suggestion from @mudler Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com> |
||
|
LocalAI [bot]
|
d200401e86 |
feat: Add --data-path CLI flag for persistent data separation (#8888)
feat: add --data-path CLI flag for persistent data separation - Add LOCALAI_DATA_PATH environment variable and --data-path CLI flag - Default data path: /data (separate from configuration directory) - Automatic migration on startup: moves agent_tasks.json, agent_jobs.json, collections/, and assets/ from old config dir to new data path - Backward compatible: preserves old behavior if LOCALAI_DATA_PATH is not set - Agent state and job directories now use DataPath with proper fallback chain - Update documentation with new flag and docker-compose example This separates mutable persistent data (collectiondb, agents, assets, skills) from configuration files, enabling better volume mounting and data persistence in containerized deployments. Signed-off-by: localai-bot <localai-bot@noreply.github.com> Co-authored-by: localai-bot <localai-bot@noreply.github.com> |
||
|
LocalAI [bot]
|
9297074caa |
docs: expand GPU acceleration guide with L4T, multi-GPU, monitoring, and troubleshooting (#8858)
- Expand multi-GPU section to cover llama.cpp (CUDA_VISIBLE_DEVICES, HIP_VISIBLE_DEVICES) in addition to diffusers - Add NVIDIA L4T/Jetson section with quick start commands and cross-reference to the dedicated ARM64 page - Add GPU monitoring section with vendor-specific tools (nvidia-smi, rocm-smi, intel_gpu_top) - Add troubleshooting section covering common issues: GPU not detected, CPU fallback, OOM errors, unsupported ROCm targets, SYCL mmap hang - Replace "under construction" warning with useful cross-references to related docs (container images, VRAM management) Signed-off-by: localai-bot <localai-bot@users.noreply.github.com> Co-authored-by: localai-bot <localai-bot@noreply.github.com> Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> |
||
|
LocalAI [bot]
|
9090bca920 |
feat: Add documentation for undocumented API endpoints (#8852)
* feat: add documentation for undocumented API endpoints Creates comprehensive documentation for 8 previously undocumented endpoints: - Voice Activity Detection (/v1/vad) - Video Generation (/video) - Sound Generation (/v1/sound-generation) - Backend Monitor (/backend/monitor, /backend/shutdown) - Token Metrics (/tokenMetrics) - P2P endpoints (/api/p2p/* - 5 sub-endpoints) - System Info (/system, /version) Each documentation file includes HTTP method, request/response schemas, curl examples, sample JSON responses, and error codes. * docs: remove token-metrics endpoint documentation per review feedback The token-metrics endpoint is not wired into the HTTP router and should not be documented per reviewer request. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * docs: move system-info documentation to reference section Per review feedback, system-info endpoint docs are better suited for the reference section rather than features. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> --------- Co-authored-by: localai-bot <localai-bot@noreply.github.com> Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> |
||
|
Ettore Di Giacinto
|
d21369ad7b |
Update shell completion documentation URL
Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com> |
||
|
LocalAI [bot]
|
efd402207c |
feat: Add shell completion support for bash, zsh, and fish (#8851)
feat: add shell completion support for bash, zsh, and fish - Add core/cli/completion.go with dynamic completion script generation - Add core/cli/completion_test.go with unit tests - Modify cmd/local-ai/main.go to support completion command - Modify core/cli/cli.go to add Completion subcommand - Add docs/content/features/shell-completion.md with installation instructions The completion scripts are generated dynamically from the Kong CLI model, so they automatically include all commands, subcommands, and flags. Co-authored-by: localai-bot <localai-bot@noreply.github.com> |
||
|
Ettore Di Giacinto
|
ac48867b7d |
feat: add agentic management (#8820)
* feat: add standalone and agentic functionalities Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * expose agents via responses api Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
LocalAI [bot]
|
ab315f2725 |
feat: Add LOCALAI_DISABLE_MCP environment variable to disable MCP support (#8816)
* feat: Add LOCALAI_DISABLE_MCP environment variable to disable MCP support - Added DisableMCP field to RunCMD struct in core/cli/run.go - Added LOCALAI_DISABLE_MCP environment variable support - Added DisableMCP field to ApplicationConfig struct - Added DisableMCP AppOption function - Updated MCP endpoint routing to check appConfig.DisableMCP - When LOCALAI_DISABLE_MCP is set to true/1/yes, MCP endpoints are not registered When set, all MCP functionality is disabled and appropriate error messages are returned to users. Use Cases: - Security-conscious deployments where MCP is not needed - Reducing attack surface - Compliance requirements that prohibit certain protocol support Environment variable: LOCALAI_DISABLE_MCP=true Signed-off-by: localai-bot <localai-bot@users.noreply.github.com> * docs: Add documentation for LOCALAI_DISABLE_MCP environment variable - Add section explaining how to disable MCP support using environment variable - Document use cases for disabling MCP - Provide examples for CLI and Docker usage Signed-off-by: localai-bot <localai-bot@users.noreply.github.com> --------- Signed-off-by: localai-bot <localai-bot@users.noreply.github.com> Co-authored-by: localai-bot <localai-bot@users.noreply.github.com> |
||
|
Ettore Di Giacinto
|
580517f9db |
feat: pass-by metadata to predict options (#8795)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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|
Andres
|
454d8adc76 |
feat(qwen-tts): Support using multiple voices (#8757)
* Add support for multiple voice clones in Qwen TTS Signed-off-by: Andres Smith <andressmithdev@pm.me> * Add voice prompt caching and generation logs to see generation time --------- Signed-off-by: Andres Smith <andressmithdev@pm.me> Co-authored-by: Ettore Di Giacinto <mudler@users.noreply.github.com> |
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|
Ettore Di Giacinto
|
983db7bedc |
feat(ui): add model size estimation (#8684)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Lukas Schaefer
|
ed0bfb8732 |
fix: rename json_verbose to verbose_json (#8627)
Signed-off-by: Lukas Schaefer <lukas@lschaefer.xyz> |
||
|
LocalAI [bot]
|
559ab99890 |
docs: update diffusers multi-GPU documentation to mention tensor_parallel_size configuration (#8621)
* docs: update diffusers multi-GPU documentation to mention tensor_parallel_size configuration * chore: revert backend/python/diffusers/README.md to original content --------- Co-authored-by: Your Name <you@example.com> |
||
|
Ettore Di Giacinto
|
b471619ad9 |
chore(deps): bump cogito and add new options to the agent config (#8601)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Ettore Di Giacinto
|
bf5a1dd840 |
feat(voxtral): add voxtral backend (#8451)
* feat(voxtral): add voxtral backend Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * simplify Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
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|
Varun Chawla
|
aa9ca401fa |
docs: update model gallery documentation to reference main repository (#8452)
Fixes #8212 - Updated the note about reporting broken models to reference the main LocalAI repository instead of the outdated separate gallery repository reference. |
||
|
Richard Palethorpe
|
c1d0b10b14 |
chore(docs): Document using a local model gallery (#8426)
Signed-off-by: Richard Palethorpe <io@richiejp.com> |
||
|
Ettore Di Giacinto
|
53276d28e7 |
feat(musicgen): add ace-step and UI interface (#8396)
* feat(musicgen): add ace-step and UI interface Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Correctly handle model dir Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Drop auto-download Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Fixups Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Add to models, fixup UIs icons Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fixups Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Update docs Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * l4t13 is incompatbile Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * avoid pinning version for cuda12 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Drop l4t12 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Andres
|
b6459ddd57 |
feat(api): Add transcribe response format request parameter & adjust STT backends (#8318)
* WIP response format implementation for audio transcriptions (cherry picked from commit e271dd764bbc13846accf3beb8b6522153aa276f) Signed-off-by: Andres Smith <andressmithdev@pm.me> * Rework transcript response_format and add more formats (cherry picked from commit 6a93a8f63e2ee5726bca2980b0c9cf4ef8b7aeb8) Signed-off-by: Andres Smith <andressmithdev@pm.me> * Add test and replace go-openai package with official openai go client (cherry picked from commit f25d1a04e46526429c89db4c739e1e65942ca893) Signed-off-by: Andres Smith <andressmithdev@pm.me> * Fix faster-whisper backend and refactor transcription formatting to also work on CLI Signed-off-by: Andres Smith <andressmithdev@pm.me> (cherry picked from commit 69a93977d5e113eb7172bd85a0f918592d3d2168) Signed-off-by: Andres Smith <andressmithdev@pm.me> --------- Signed-off-by: Andres Smith <andressmithdev@pm.me> Co-authored-by: nanoandrew4 <nanoandrew4@gmail.com> Co-authored-by: Ettore Di Giacinto <mudler@users.noreply.github.com> |
||
|
Ettore Di Giacinto
|
68dd9765a0 |
feat(tts): add support for streaming mode (#8291)
* feat(tts): add support for streaming mode Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Send first audio, make sure it's 16 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |