* feat(api): add /v1/audio/diarization endpoint with sherpa-onnx + vibevoice.cpp
Closes#1648.
OpenAI-style multipart endpoint that returns "who spoke when". Single
endpoint instead of the issue's three-endpoint sketch (refactor /vad,
/vad/embedding, /diarization) — the typical client wants one call, and
embeddings can land later as a sibling without breaking this surface.
Response shape borrows from Pyannote/Deepgram: segments carry a
normalised SPEAKER_NN id (zero-padded, stable across the response) plus
the raw backend label, optional per-segment text when the backend bundles
ASR, and a speakers summary in verbose_json. response_format also accepts
rttm so consumers can pipe straight into pyannote.metrics / dscore.
Backends:
* vibevoice-cpp — Diarize() reuses the existing vv_capi_asr pass.
vibevoice's ASR prompt asks the model to emit
[{Start,End,Speaker,Content}] natively, so diarization is a by-product
of the same pass; include_text=true preserves the transcript per
segment, otherwise we drop it.
* sherpa-onnx — wraps the upstream SherpaOnnxOfflineSpeakerDiarization
C API (pyannote segmentation + speaker-embedding extractor + fast
clustering). libsherpa-shim grew config builders, a SetClustering
wrapper for per-call num_clusters/threshold overrides, and a
segment_at accessor (purego can't read field arrays out of
SherpaOnnxOfflineSpeakerDiarizationSegment[] directly).
Plumbing: new Diarize gRPC RPC + DiarizeRequest / DiarizeSegment /
DiarizeResponse messages, threaded through interface.go, base, server,
client, embed. Default Base impl returns unimplemented.
Capability surfaces all updated: FLAG_DIARIZATION usecase,
FeatureAudioDiarization permission (default-on), RouteFeatureRegistry
entries for /v1/audio/diarization and /audio/diarization, audio
instruction-def description widened, CAP_DIARIZATION JS symbol,
swagger regenerated, /api/instructions discovery map updated.
Tests:
* core/backend: speaker-label normalisation (first-seen → SPEAKER_NN,
per-speaker totals, nil-safety, fallback to backend NumSpeakers when
no segments).
* core/http/endpoints/openai: RTTM rendering (file-id basename, negative
duration clamping, fallback id).
* tests/e2e: mock-backend grew a deterministic Diarize that emits
raw labels "5","2","5" so the e2e suite verifies SPEAKER_NN
remapping, verbose_json speakers summary + transcript pass-through
(gated by include_text), RTTM bytes content-type, and rejection of
unknown response_format. mock-diarize model config registered with
known_usecases=[FLAG_DIARIZATION] to bypass the backend-name guard.
Docs: new features/audio-diarization.md (request/response, RTTM example,
sherpa-onnx + vibevoice setup), cross-link from audio-to-text.md, entry
in whats-new.md.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
* fix(diarization): correct sherpa-onnx symbol name + lint cleanup
CI failures on #9654:
* sherpa-onnx-grpc-{tts,transcription} and sherpa-onnx-realtime panicked
at backend startup with `undefined symbol: SherpaOnnxDestroyOfflineSpeakerDiarizationResult`.
Upstream's actual symbol is SherpaOnnxOfflineSpeakerDiarizationDestroyResult
(Destroy in the middle, not the prefix); the rest of the diarization
surface follows the same naming pattern. The mismatched name made
purego.RegisterLibFunc fail at dlopen time and crashed the gRPC server
before the BeforeAll could probe Health, taking down every sherpa-onnx
test job — not just the diarization-related ones.
* golangci-lint flagged 5 errcheck violations on new defer cleanups
(os.RemoveAll / Close / conn.Close); wrap each in a `defer func() { _ = X() }()`
closure (matches the pattern other LocalAI files use for new code, since
pre-existing bare defers are grandfathered in via new-from-merge-base).
* golangci-lint also flagged forbidigo violations: the new
diarization_test.go files used testing.T-style `t.Errorf` / `t.Fatalf`,
which are forbidden by the project's coding-style policy
(.agents/coding-style.md). Convert both files to Ginkgo/Gomega
Describe/It with Expect(...) — they get picked up by the existing
TestBackend / TestOpenAI suites, no new suite plumbing needed.
* modernize linter: tightened the diarization segment loop to
`for i := range int(numSegments)` (Go 1.22+ idiom).
Verified locally: golangci-lint with new-from-merge-base=origin/master
reports 0 issues across all touched packages, and the four mocked
diarization e2e specs in tests/e2e/mock_backend_test.go still pass.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
* fix(vibevoice-cpp): convert non-WAV input via ffmpeg + raise ASR token budget
Confirmed end-to-end against a real LocalAI instance with vibevoice-asr-q4_k
loaded and the multi-speaker MP3 sample at vibevoice.cpp/samples/2p_argument.mp3:
both /v1/audio/transcriptions and /v1/audio/diarization now succeed and
return correctly attributed speaker turns for the full clip.
Two latent issues surfaced once the diarization endpoint actually exercised
the backend with a non-trivial input:
1. vv_capi_asr only accepts WAV via load_wav_24k_mono. The previous code
passed the uploaded path straight through, so anything that wasn't
already a 24 kHz mono s16le WAV failed at the C side with rc=-8 and
the very unhelpful "vv_capi_asr failed". prepareWavInput shells out
to ffmpeg ("-ar 24000 -ac 1 -acodec pcm_s16le") in a per-call temp
dir, matching the rate the model was trained on; both AudioTranscription
and Diarize now route through it. This is the same shape sherpa-onnx
uses (utils.AudioToWav), but vibevoice needs 24 kHz rather than 16 kHz
so we don't reuse that helper.
2. The C ABI's max_new_tokens defaults to 256 when 0 is passed. That's
fine for a five-second clip but not for anything past ~10 s — vibevoice
stops mid-JSON, the parse fails, and the caller sees a hard error.
Pass a much larger budget (16 384 ≈ ~9 minutes of speech at the
model's ~30 tok/s rate); generation stops at EOS so this is a cap
rather than a target.
3. As a defensive belt-and-braces, mirror AudioTranscription's existing
"fall back to a single segment if the model emits non-JSON text"
pattern in Diarize, so partial / unusual model output never produces
a 500. This kept the endpoint usable while diagnosing (1) and (2),
and is the right behaviour to keep.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
* fix(vibevoice-cpp): pass valid WAVs through directly so ffmpeg is not required at runtime
Spotted by tests-e2e-backend (1.25.x): the previous fix forced every
incoming audio file through `ffmpeg -ar 24000 ...`, which meant the
backend container — which does not ship ffmpeg — failed even for the
existing happy path where the caller already uploads a WAV. The
container-side error was:
rpc error: code = Unknown desc = vibevoice-cpp: ffmpeg convert to
24k mono wav: exec: "ffmpeg": executable file not found in $PATH
Reading vibevoice.cpp's audio_io.cpp, `load_wav_24k_mono` uses drwav and
already accepts any PCM/IEEE-float WAV at any sample rate, downmixes
multi-channel input to mono, and resamples to 24 kHz internally. So the
only inputs that genuinely need an external converter are non-WAV
formats (MP3, OGG, FLAC, ...).
Detect WAVs by RIFF/WAVE magic at bytes 0..3 / 8..11 and pass them
straight through with a no-op cleanup; everything else still goes
through ffmpeg with the same 24 kHz mono s16le target. The result:
* Container builds without ffmpeg keep working for WAV uploads
(the e2e-backends fixture is jfk.wav at 16 kHz mono s16le).
* MP3 and other non-WAV inputs still get the new ffmpeg conversion
path so the diarization endpoint stays useful.
* If the caller uploads a non-WAV but ffmpeg isn't on PATH, the
surfaced error is still descriptive enough to act on.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
* fix(ci): make gcc-14 install in Dockerfile.golang best-effort for jammy bases
The LocalVQE PR (bb033b16) made `gcc-14 g++-14` an unconditional apt
install in backend/Dockerfile.golang and pointed update-alternatives at
them. That works on the default `BASE_IMAGE=ubuntu:24.04` (noble has
gcc-14 in main), but every Go backend that builds on
`nvcr.io/nvidia/l4t-jetpack:r36.4.0` — jammy under the hood — now fails
at the apt step:
E: Unable to locate package gcc-14
This blocked unrelated jobs:
backend-jobs(*-nvidia-l4t-arm64-{stablediffusion-ggml, sam3-cpp, whisper,
acestep-cpp, qwen3-tts-cpp, vibevoice-cpp}). LocalVQE itself is only
matrix-built on ubuntu:24.04 (CPU + Vulkan), so it doesn't actually
need gcc-14 anywhere else.
Make the gcc-14 install conditional on the package being available in
the configured apt repos. On noble: identical behaviour to today (gcc-14
installed, update-alternatives points at it). On jammy: skip the
gcc-14 stanza entirely and let build-essential's default gcc take over,
which is what the other Go backends compile with anyway.
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>
* 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>
The llama.cpp C++-side chat autoparser clears Reply.Message and delivers
parsed content/reasoning/tool-calls via Reply.chat_deltas. chat.go handles
this (non-SSE path uses ToolCallsFromChatDeltas/ContentFromChatDeltas/
ReasoningFromChatDeltas), but realtime.go only read pred.Response, so any
model routed through the autoparser (Qwen2.5/3 and friends) produced a
silent reply: backend emitted N tokens, the session surface saw zero.
Mirror the non-SSE chat path in realtime's triggerResponse: when deltas
carry tool calls or content, use them directly; otherwise fall back to
the existing raw-text parsing.
Assisted-by: claude-opus-4-7-1M [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* fix(anthropic): use SetFunctionCallNameString for specific tool forcing
* fix(openai/realtime): use SetFunctionCallNameString for specific tool forcing
* fix(openresponses): use SetFunctionCallNameString for specific tool forcing
* fix(streaming): dedupe content, recover reasoning, unique tool IDs
When tool calls are discovered only during final parsing (after the
streaming token callback returns), processTools' default switch branch
used to emit the full accumulated content alongside the tool_call args
chunk. Clients that accumulate delta.content per the OpenAI streaming
contract end up showing every narration line twice. Three related bugs
in the same flush path:
1. Content duplication: the args chunk carried Content:textContentToReturn
even though the text had already been streamed token-by-token via
the token callback, so delta.content was both the running total and
bundled with tool_calls in one delta (two spec violations).
2. Reasoning drop: when the C++ autoparser surfaces reasoning only as
a final aggregate (no incremental tokens), the callback never emits
it and the flush branch didn't either, silently losing it.
3. tool_call ID collision: empty ss.ID fell back to the request id, so
multiple empty-ID calls in the same turn all shared the same id,
breaking tool_result matching by tool_call_id.
Extracted the block into buildDeferredToolCallChunks (pure function,
unit-testable) and added 19 Ginkgo specs covering streamed vs.
not-streamed content/reasoning, single vs. multi call, and
incremental-vs-deferred emission. Every case asserts the invariant
that no delta carries both non-empty Content/Reasoning and non-empty
ToolCalls.
Fix summary:
- emit reasoning in its own leading chunk when !reasoningAlreadyStreamed
- emit role+content in their own chunks when !contentAlreadyStreamed
- drop Content from the tool_call args chunk
- fallback to fmt.Sprintf("%s-%d", id, i) for empty ss.ID so calls stay
uniquely addressable
Reproduced live against qwen3.6-35b-a3b-apex served by LocalAI with
the C++ autoparser; the full-content replay chunk that preceded each
tool_calls block is gone after the fix.
Assisted-by: Claude:claude-opus-4-7 go vet
* fix(streaming): dedupe reasoning in the noActionToRun final chunk
extractor.Reasoning() returns only the Go-side extractor's lastReasoning
accumulator (pkg/reasoning/extractor.go:129). ChatDelta reasoning
coming through ProcessChatDeltaReasoning lives in a separate
accumulator (cdLastStrippedReasoning) that Reasoning() does not
expose. The "reasoning != \"\" && extractor.Reasoning() == \"\"" guard
therefore fires exactly when the autoparser streamed reasoning
incrementally via the callback — producing a duplicate final delivery.
Replace both guard sites in the noActionToRun branch with the
sentReasoning flag introduced in the previous commit. Extract the
closing-chunk logic into buildNoActionFinalChunks so the refactor is
testable; the helper mirrors buildDeferredToolCallChunks.
Add Ginkgo coverage for both the content-streamed and
content-not-streamed paths: reasoning is dropped when it was streamed,
delivered once when it arrived only as a final aggregate, and omitted
when empty. Metadata invariants carried over from the sibling helper.
Assisted-by: Claude:claude-opus-4-7 go vet
* fix(streaming): detect noActionToRun anywhere in functionResults
The previous condition only looked at functionResults[0].Name, which
misbehaved when a real tool call followed a noAction sentinel — the
noAction shadowed the real call and the whole turn was treated as a
question to answer, silently dropping the tool call. The mirror case,
[realCall, noActionCall], fell into the default branch and emitted the
noAction entry as if it were a real tool_call.
Replace with hasRealCall, which scans the slice and returns true as
soon as it finds a non-noAction entry. noActionToRun now matches the
semantic intent: "every entry is the noAction sentinel (or the slice
is empty)".
Note: this does not change incremental emission, where noAction
entries may still be forwarded as tool_call chunks by the XML/JSON
iterative parsers. That is a separate layer (functions.Parse*) and
addressing it requires threading noAction through the parser APIs —
out of scope for this change.
Assisted-by: Claude:claude-opus-4-7 go vet
Upstream llama.cpp (PR #21962) switched the server-side mtmd media
marker to a random per-server string and removed the legacy
"<__media__>" backward-compat replacement in mtmd_tokenizer. The
Go layer still emitted the hardcoded "<__media__>", so on the
non-tokenizer-template path the prompt arrived with a marker mtmd
did not recognize and tokenization failed with "number of bitmaps
(1) does not match number of markers (0)".
Report the active media marker via ModelMetadataResponse.media_marker
and substitute the sentinel "<__media__>" with it right before the
gRPC call, after the backend has been loaded and probed. Also skip
the Go-side multimodal templating entirely when UseTokenizerTemplate
is true — llama.cpp's oaicompat_chat_params_parse already injects its
own marker and StringContent is unused in that path. Backends that do
not expose the field keep the legacy "<__media__>" behavior.
The Go-side incremental JSON parser was emitting the same tool call on
every streaming token because it lacked the len > lastEmittedCount guard
that the XML parser had. On top of that, the post-streaming default:
case re-emitted all tool calls from index 0, duplicating everything.
This produced duplicate delta.tool_calls events causing clients to
accumulate arguments as "{args}{args}" — invalid JSON.
Fixes:
- JSON incremental parser: add len(jsonResults) > lastEmittedCount guard
and loop from lastEmittedCount (matching the XML parser pattern)
- Post-streaming default: case: skip i < lastEmittedCount entries that
were already emitted during streaming
- JSON parser: use blocking channel send (matching XML parser behavior)
When clients like Nextcloud or Home Assistant send requests with tools
to thinking models (e.g. Gemma 4 with <|channel>thought tags), the
response was empty despite the backend producing valid content.
Root cause: the C++ autoparser puts clean content in both the raw
Response and ChatDeltas. The Go-side PrependThinkingTokenIfNeeded
then prepends the thinking start token to the already-clean content,
causing ExtractReasoning to classify the entire response as unclosed
reasoning. This made cbRawResult empty, triggering a retry loop that
never succeeds.
Two fixes:
- inference.go: check ChatDeltas for content/tool_calls regardless of
whether Response is empty, so skipCallerRetry fires correctly
- chat.go: when ChatDeltas have content but no tool calls, use that
content directly instead of falling back to the empty cbRawResult
* fix(chat): do not retry if we had chatdeltas or tooldeltas from backend
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix: use oai compat for llama.cpp
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix: apply to non-streaming path too
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* map also other fields
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
The C++ PEG parser needs a few tokens to identify the reasoning format
(e.g. "<|channel>thought\n" for Gemma 4). During this warm-up, the gRPC
layer was sending raw partial tag tokens to Go, which leaked into the
reasoning field.
- Clear reply.message in gRPC when autoparser is active but has no diffs
yet, matching llama.cpp server behavior of only emitting classified output
- Prefer C++ autoparser chat deltas for reasoning/content in all streaming
paths, falling back to Go-side extraction for backends without autoparser
(e.g. vLLM)
- Override non-streaming no-tools result with chat delta content when available
- Guard PrependThinkingTokenIfNeeded against partial tag prefixes during
streaming accumulation
- Reorder default thinking tokens so <|channel>thought is checked before
<|think|> (Gemma 4 templates contain both)
* 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>
The OpenAI Node.js SDK v4+ sends encoding_format=base64 by default.
LocalAI previously ignored this parameter and always returned a float
JSON array, causing a silent data corruption bug in any Node.js client
(AnythingLLM Desktop, LangChain.js, LlamaIndex.TS, …):
// What the client does when it expects base64 but receives a float array:
Buffer.from(floatArray, 'base64')
Node.js treats a non-string first argument as a byte array — each
float32 value is truncated to a single byte — and Float32Array then
reads those bytes as floats, yielding dims/4 values. Vector databases
(Qdrant, pgvector, …) then create collections with the wrong dimension,
causing all similarity searches to fail silently.
e.g. granite-embedding-107m (384 dims) → 96 stored in Qdrant
jina-embeddings-v3 (1024 dims) → 256 stored in Qdrant
Changes:
- core/schema/prediction.go: add EncodingFormat string field to
PredictionOptions so the request parameter is parsed and available
throughout the request pipeline
- core/schema/openai.go: add EmbeddingBase64 string field to Item;
add MarshalJSON so the "embedding" JSON key emits either []float32
or a base64 string depending on which field is populated — all other
Item consumers (image, video endpoints) are unaffected
- core/http/endpoints/openai/embeddings.go: add floatsToBase64()
which packs a float32 slice as little-endian bytes and base64-encodes
it; add embeddingItem() helper; both InputToken and InputStrings loops
now honour encoding_format=base64
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
* feat: wire min_p
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat: inferencing defaults
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore(refactor): re-use iterative parser
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore: generate automatically inference defaults from unsloth
Instead of trying to re-invent the wheel and maintain here the inference
defaults, prefer to consume unsloth ones, and contribute there as
necessary.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore: apply defaults also to models installed via gallery
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore: be consistent and apply fallback to all endpoint
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
First when sending errors over SSE we now clearly identify them as such
instead of just sending the error string as a chat completion message.
We use this in the UI to identify errors and link to them to the traces.
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* 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>
* 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>
* fix: add missing bufio.Flush in processImageFile
The processImageFile function writes decoded image data (from base64
or URL download) through a bufio.NewWriter but never calls Flush()
before closing the underlying file. Since bufio's default buffer is
4096 bytes, small images produce 0-byte files and large images are
truncated — causing PIL to fail with "cannot identify image file".
This breaks all image input paths: file, files, and ref_images
parameters in /v1/images/generations, making img2img, inpainting,
and reference image features non-functional.
Signed-off-by: Attila Györffy <attila+git@attilagyorffy.com>
* fix: merge options into kwargs in diffusers GenerateImage
The GenerateImage method builds a local `options` dict containing the
source image (PIL), negative_prompt, and num_inference_steps, but
never merges it into `kwargs` before calling self.pipe(**kwargs).
This causes img2img to fail with "Input is in incorrect format"
because the pipeline never receives the image parameter.
Signed-off-by: Attila Györffy <attila+git@attilagyorffy.com>
* test: add unit test for processImageFile base64 decoding
Verifies that a base64-encoded PNG survives the write path
(encode → decode → bufio.Write → Flush → file on disk) with
byte-for-byte fidelity. The test image is small enough to fit
entirely in bufio's 4096-byte buffer, which is the exact scenario
where the missing Flush() produced a 0-byte file.
Also tests that invalid base64 input is handled gracefully.
Signed-off-by: Attila Györffy <attila+git@attilagyorffy.com>
* test: verify GenerateImage merges options into pipeline kwargs
Mocks the diffusers pipeline and calls GenerateImage with a source
image and negative prompt. Asserts that the pipeline receives the
image, negative_prompt, and num_inference_steps via kwargs — the
exact parameters that were silently dropped before the fix.
Signed-off-by: Attila Györffy <attila+git@attilagyorffy.com>
* fix: move kwargs.update(options) earlier in GenerateImage
Move the options merge right after self.options merge (L742) so that
image, negative_prompt, and num_inference_steps are available to all
downstream code paths including img2vid and txt2vid.
Signed-off-by: Attila Györffy <attila+git@attilagyorffy.com>
* test: convert processImageFile tests to ginkgo
Replace standard testing with ginkgo/gomega to be consistent with
the rest of the test suites in the project.
Signed-off-by: Attila Györffy <attila+git@attilagyorffy.com>
---------
Signed-off-by: Attila Györffy <attila+git@attilagyorffy.com>
Co-authored-by: Ettore Di Giacinto <mudler@users.noreply.github.com>
* feat(functions): add peg-based parsing
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat: support returning toolcalls directly from backends
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore: do run PEG only if backend didn't send deltas
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* debug
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* retry instead of re-computing a response
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(realtime): Wrap functions in OpenAI chat completions format
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(realtime): Set max tokens from session object
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* fix(realtime): Find thinking start tag for thinking extraction
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* fix(realtime): Don't send buffer cleared message when we automatically drop it
Signed-off-by: Richard Palethorpe <io@richiejp.com>
---------
Signed-off-by: Richard Palethorpe <io@richiejp.com>
feat(realtime): Allow sending text and image conversation items
Signed-off-by: Richard Palethorpe <io@richiejp.com>
Co-authored-by: Ettore Di Giacinto <mudler@users.noreply.github.com>
* fix(realtime): Use locked websocket for concurrent access
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* fix(realtime): Use sample rate set in session
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* fix(config): Allow pipelines to have no model parameters
Signed-off-by: Richard Palethorpe <io@richiejp.com>
---------
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* fix(realtime): Use the voice provided by the user or none at all
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* fix(ui,config): Allow pipeline models to have no backend and use same validation in frontend
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>
User-supplied URLs passed to GetContentURIAsBase64() and downloadFile()
were fetched without validation, allowing SSRF attacks against internal
services. Added URL validation that blocks private IPs, loopback,
link-local, and cloud metadata endpoints before fetching.
Co-authored-by: kolega.dev <faizan@kolega.ai>
The realtime endpoint was not passing the noAction "answer" function to the
model in the prompt template, causing the model to always call user-provided
tools even when a direct response was appropriate.
Root cause:
- User tools were added to the funcs list
- TemplateMessages() was called to generate the prompt
- noAction function was only added AFTER templating
- This meant the prompt didn't include the "answer" function, even though
the grammar did
Fix:
- Move noAction function creation before TemplateMessages() call so it's
included in both the prompt and grammar
- Add proper tool_choice parameter handling to support "auto", "required",
"none", and specific function selection
- Match behavior of the standard chat endpoint
💘 Generated with Crush
Assisted-by: Claude Sonnet 4.5 via Crush <crush@charm.land>
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* 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>
* feat(realtime): Add audio conversations
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* chore(realtime): Vendor the updated API and modify for server side
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(realtime): Update to the GA realtime API
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* chore: Document realtime API and add docs to AGENTS.md
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat: Filter reasoning from spoken output
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* fix(realtime): Send delta and done events for tool calls and audio transcripts
Ensure that content is sent in both deltas and done events for function call arguments and audio transcripts. This fixes compatibility with clients that rely on delta events for parsing.
💘 Generated with Crush
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* fix(realtime): Improve tool call handling and error reporting
- Refactor Model interface to accept []types.ToolUnion and *types.ToolChoiceUnion
instead of JSON strings, eliminating unnecessary marshal/unmarshal cycles
- Fix Parameters field handling: support both map[string]any and JSON string formats
- Add PredictConfig() method to Model interface for accessing model configuration
- Add comprehensive debug logging for tool call parsing and function config
- Add missing return statement after prediction error (critical bug fix)
- Add warning logs for NoAction function argument parsing failures
- Improve error visibility throughout generateResponse function
💘 Generated with Crush
Assisted-by: Claude Sonnet 4.5 via Crush <crush@charm.land>
Signed-off-by: Richard Palethorpe <io@richiejp.com>
---------
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* Debug
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Drop openai video endpoint (is not complete)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Add download button
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(openresponses): support reasoning blocks
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* allow to disable reasoning, refactor common logic
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Add option to only strip reasoning
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Add configurations for custom reasoning tokens
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore: extract reasoning to its own package
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* make sure we detect thinking tokens from template
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Allow to override via config, add tests
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
This PR adds support to support the 'reasoning' API field of the OpenAI
spec.
LocalAI now will extract automatically thinking tags in both SSE and
non-SSE mode. The changes are adapted as well to the Chat UI now that
will use the reasoning field to extract the thinking process and display
it in the chat.
This fixes https://github.com/mudler/LocalAI/issues/7944
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(function): Add XML Tool Call Parsing Support
Extend the function parsing system in LocalAI to support XML-style tool calls, similar to how JSON tool calls are currently parsed. This will allow models that return XML format (like <tool_call><function=name><parameter=key>value</parameter></function></tool_call>) to be properly parsed alongside text content.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* thinking before tool calls, more strict support for corner cases with no tools
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Support streaming tools
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Iterative JSON
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Iterative parsing
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Consume JSON marker
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Fixup
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* add tests
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Fix pending TODOs
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Don't run other parsing with ParseRegex
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix: resolve duplicate MCP route registration causing 50% failure rate
Fixes#7772
The issue was caused by duplicate registration of the MCP endpoint
/mcp/v1/chat/completions in both openai.go and localai.go, leading
to a race condition where requests would randomly hit different
handlers with incompatible behaviors.
Changes:
- Removed duplicate MCP route registration from openai.go
- Kept the localai.MCPStreamEndpoint as the canonical handler
- Added all three MCP route patterns for backward compatibility:
* /v1/mcp/chat/completions
* /mcp/v1/chat/completions
* /mcp/chat/completions
- Added comments to clarify route ownership and prevent future conflicts
- Fixed formatting in ui_api.go
The localai.MCPStreamEndpoint handler is more feature-complete as it
supports both streaming and non-streaming modes, while the removed
openai.MCPCompletionEndpoint only supported synchronous requests.
This eliminates the ~50% failure rate where the cogito library would
receive "Invalid http method" errors when internal HTTP requests were
routed to the wrong handler.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Signed-off-by: majiayu000 <1835304752@qq.com>
* Address feedback from review
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: majiayu000 <1835304752@qq.com>
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Claude Sonnet 4.5 <noreply@anthropic.com>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
* chore: drop mode from image generation(unused)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(UI): improve image generation front-end
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(UI): only ref images. files is to be deprecated
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* do not override default steps
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix: Add usage fields to image generation response for OpenAI API compatibility
Fixes#7354
Added input_tokens, output_tokens, and input_tokens_details fields to the
image generation API response to comply with OpenAI's image generation API
specification. This resolves validation errors in LiteLLM and the OpenAI SDK.
Changes:
- Added InputTokensDetails struct with text_tokens and image_tokens fields
- Extended OpenAIUsage struct with input_tokens, output_tokens, and input_tokens_details
- Updated ImageEndpoint to populate usage object with required fields
- Updated InpaintingEndpoint to populate usage object with required fields
- All fields initialized to 0 as per current behavior
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Signed-off-by: majiayu000 <1835304752@qq.com>
* fix: Correct usage field types for image generation API compatibility
Changed InputTokens and OutputTokens from pointer types (*int) to
regular int types to match OpenAI API specification. This fixes
validation errors with LiteLLM and OpenAI SDK when parsing image
generation responses.
Fixes#7354🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Signed-off-by: majiayu000 <1835304752@qq.com>
---------
Signed-off-by: majiayu000 <1835304752@qq.com>
Co-authored-by: Claude Sonnet 4.5 <noreply@anthropic.com>
