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Author SHA1 Message Date
Ettore Di Giacinto
ca4222e3e3 chore: bump RFDETR_VERSION to ecf64d7 (current rf-detr.cpp main)
The previous pin (5d5549e) was overwritten by an amend+force-push on
rf-detr.cpp main when the video example was added. Current HEAD on
rf-detr.cpp is ecf64d7 (still single-commit 'Initial release.').

Assisted-by: Claude:claude-opus-4-7 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-05-27 22:32:43 +00:00
mudler
36bf088f34 ⬆️ Update mudler/rf-detr.cpp
Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
2026-05-27 20:48:56 +00:00
LocalAI [bot]
373dc44992 fix(react-ui): force .check() on hidden Toggle input in fits-filter e2e (#10031)
* fix(react-ui): force .check() on hidden Toggle input in fits-filter e2e

The polish PR (#10030) swapped the raw <input type=checkbox> for the
shared <Toggle> component, which visually hides its native input via
opacity:0;width:0;height:0. Playwright's .check() waits for visibility
before clicking and times out after 30 s, breaking two UI E2E tests:

  - enabling fits filter hides models that exceed available VRAM
  - fits filter state persists after reload

Pass { force: true } to skip the visibility check; the input is still
the real focusable checkbox and toggles state on click. The companion
.toBeChecked() assertion only reads state and works unchanged.

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-7

* fix(react-ui): click visible Toggle track in fits-filter e2e

force:true skips the actionability checks but not the viewport check,
and the Toggle's hidden input has width:0;height:0 so Playwright still
reports "Element is outside of the viewport". Click the visible
.toggle__track inside the filter-bar-group__toggle wrapper instead —
that's what a real user clicks, and label-input association toggles
the wrapped checkbox naturally.

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-7

---------

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
2026-05-27 22:41:01 +02:00
LocalAI [bot]
02a0e70396 fix(react-ui): polish 'Fits in my GPU' filter to use design-system Toggle (#10030)
* fix(react-ui): polish 'Fits in my GPU' filter to use design-system Toggle

The recently added VRAM-fit filter in the Models page used a raw
<input type="checkbox"> next to the themed range slider, breaking the
visual language of the rest of the row. Swap it for the shared
<Toggle> component (already used by Backends, Settings, Traces,
AgentCreate), adopt the filter-bar-group__toggle class to drop the
duplicated inline styles, add a fa-microchip icon to mirror the
per-row fit indicator, and add a subtle left divider so the filter
reads as separate from the context-size slider on its left.

Assisted-by: Claude:claude-opus-4-7
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* fix(react-ui): move 'Fits in GPU' filter to filter row and unify copy

Two follow-ups on the previous polish pass:

1. Move the toggle from the context-slider row into the filter-button
   row above. The toggle is a filter on the result set, not a config
   for VRAM estimation, so it belongs with the type chips and backend
   select. The context slider stays its own thing.

2. Unify the label copy. The same locale file had "Fits in my GPU"
   for the filter and "Fits in GPU" for the per-row indicator; pick
   the shorter, possessive-free variant everywhere (en/de/es/it/zh-CN).
   Update e2e selectors to match.

Assisted-by: Claude:claude-opus-4-7
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

---------

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
2026-05-27 21:09:14 +02:00
LocalAI [bot]
7a4ca8f60d feat(backend): rfdetr-cpp native object detection + segmentation backend (#10028)
Adds a Go native gRPC backend that dlopens librfdetrcpp.so (built from
mudler/rf-detr.cpp at the pinned RFDETR_VERSION) via purego and exposes
the rfdetr.cpp inference pipeline through LocalAI's existing Detect RPC.

Supports all 5 RF-DETR detection variants (Nano/Small/Base/Medium/Large)
and 6 segmentation variants (SegNano/SegSmall/SegMedium/SegLarge/
SegXLarge/Seg2XLarge) with F32/F16/Q8_0/Q4_K quantizations. Pre-built
GGUFs ship at mudler/rfdetr-cpp-* on HuggingFace.

Detection returns Bbox + class_name + confidence; segmentation also
returns PNG-encoded per-detection masks via the rfdetr_capi accessor
functions (rfdetr_capi_get_detection_{class_id,box,score,class_name,
mask_png}).

End-to-end verified through POST /v1/detection: HTTP -> gRPC -> purego
dlopen -> rfdetr.cpp -> ggml -> response (9 detections on the detection
model, 21 detections + valid PNG masks on the seg-nano model against
the kitchen fixture).

Wiring:
  - backend/go/rfdetr-cpp/{main.go,gorfdetrcpp.go,CMakeLists.txt,
    Makefile,run.sh,package.sh,test.sh,.gitignore}
  - Top-level Makefile: BACKEND_RFDETR_CPP, docker-build target,
    .NOTPARALLEL, prepare-test-extra, test-extra
  - backend/go/rfdetr-cpp/Makefile: `test` target invoked by test-extra
  - .github/backend-matrix.yml: CPU + CUDA-12/13 + L4T CUDA-12/13
    (arm64) + HIP + Vulkan (amd64 + arm64) + SYCL f32/f16
  - backend/index.yaml: rfdetr-cpp meta anchor + latest/development
    image entries for every matrix tag-suffix
  - .github/workflows/bump_deps.yaml: RFDETR_VERSION pin tracking
    (mudler/rf-detr.cpp branch main)
  - gallery/index.yaml: 11 rfdetr-cpp-* entries (nano + 4 detection
    variants + 6 seg variants), all backed by mudler/rfdetr-cpp-*
    on HuggingFace with sha256 pinning on the F16 default
  - core/gallery/importers/rfdetr.go: GGUF auto-routing for HF imports
    (mudler/rfdetr-cpp-* repos route to rfdetr-cpp, Transformer-format
    repos stay on the Python rfdetr backend; explicit preferences.backend
    overrides both heuristics)
  - core/gallery/importers/rfdetr_test.go: table-driven coverage of the
    auto-routing + a live mudler/rfdetr-cpp-nano cross-check

scripts/changed-backends.js needs no change: the existing
Dockerfile.golang -> backend/go/${item.backend}/ branch already routes
the 9 rfdetr-cpp matrix entries to the correct backend path.

Assisted-by: Claude:claude-opus-4-7 [Claude Code]

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
2026-05-27 18:43:57 +02:00
LocalAI [bot]
893e69cbf8 fix(react-ui): share single /api/operations poller across consumers (#10029)
useOperations() spun up its own setInterval per hook instance, so on
pages like /app/models the OperationsBar in App.jsx plus the page's
own useOperations() call each polled /api/operations at 1 Hz - 2 RPS
sustained for the whole session, repeated on Backends and Chat.

Lift the poller into an OperationsProvider mounted under AuthProvider
so all consumers (OperationsBar, Models, Backends, Chat) share one
timer. The hook file re-exports from the context to keep call sites
unchanged.


Assisted-by: Claude:claude-opus-4-7 [Claude Code]

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
2026-05-27 16:39:09 +02:00
Siddharth More
c9a1a7e6a0 UI: add 'Fits in my GPU' filter on Install Models (#10017)
* feat(ui): add GPU fit filter on models install page

* Delete docs/vram-fits-filter-backend-optionals.md

Signed-off-by: Siddharth More <siddimore@gmail.com>

---------

Signed-off-by: Siddharth More <siddimore@gmail.com>
2026-05-27 15:17:44 +02:00
LocalAI [bot]
4d01298048 chore: ⬆️ Update antirez/ds4 to e8e8779b261c10f36ad6270ba732c8f0be5b62e3 (#10024)
⬆️ Update antirez/ds4

Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>
2026-05-27 15:16:43 +02:00
LocalAI [bot]
b6055e7ecf chore: ⬆️ Update leejet/stable-diffusion.cpp to 92dc7268fc4ffb0c0cc0bd52dfcefea91326e797 (#10023)
⬆️ Update leejet/stable-diffusion.cpp

Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>
2026-05-27 15:16:23 +02:00
LocalAI [bot]
51bad74bf8 chore: ⬆️ Update ggml-org/llama.cpp to 0d18aaa9d1a8af3df9abccd828e22eeaac7f840b (#10022)
⬆️ Update ggml-org/llama.cpp

Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>
2026-05-27 00:29:14 +02:00
LocalAI [bot]
f3236b74cf chore: ⬆️ Update ggml-org/whisper.cpp to 27101c01dcac1676e2b6422256233cd0f1f9ae28 (#10021)
⬆️ Update ggml-org/whisper.cpp

Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>
2026-05-27 00:28:55 +02:00
LocalAI [bot]
eed3ecff82 chore: ⬆️ Update ikawrakow/ik_llama.cpp to d2da6da05c73aeb658a3d1751f386c24e6963856 (#10020)
⬆️ Update ikawrakow/ik_llama.cpp

Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>
2026-05-27 00:28:32 +02:00
番茄摔成番茄酱
df7623fd87 fix(nemo): extract Hypothesis.text for TDT/RNNT ASR models (#10012)
* fix(nemo): extract Hypothesis.text for TDT/RNNT ASR models

CTC models (e.g. Whisper) return List[str] from transcribe(), but
TDT/RNNT models (e.g. parakeet-tdt-0.6b-v3) return List[Hypothesis]
where the decoded text lives in the Hypothesis.text attribute.

Previously, results[0] was assigned directly to the protobuf string
field, causing silent empty output for non-CTC models.

Now checks the return type and extracts .text from Hypothesis objects,
with a safe fallback via getattr().

* refactor: simplify Hypothesis text extraction per Copilot review

Use single getattr() call instead of hasattr() + double access,
and return empty string for unknown types instead of str(result)
to avoid leaking internal repr to clients.
2026-05-26 20:35:23 +00:00
番茄摔成番茄酱
4e5ec6f67b fix(qwen-asr): enable timestamp output when forced_aligner is configured (#10013)
* fix(qwen-asr): enable timestamp output when forced_aligner is configured

Two bugs prevented timestamps from working in the qwen-asr backend:

1. transcribe() was called without return_time_stamps=True, so the
   forced aligner was loaded but never invoked. Now we pass
   return_time_stamps=True when a forced_aligner is present.

2. The timestamp parsing code expected (list, tuple) items, but the
   qwen_asr library returns ForcedAlignItem dataclass instances with
   .text, .start_time, .end_time attributes. Added hasattr() check
   to handle this correctly, falling back to tuple parsing for
   backward compatibility.

* refactor: address Copilot review for qwen-asr timestamps

- Wrap return_time_stamps kwarg in try/except TypeError for safety
- Add defensive float() normalization for timestamp times
- Use str() for text extraction to ensure string type

* fix(qwen-asr): convert seconds to nanoseconds for Go time.Duration

The Go server reads TranscriptSegment.start/end via time.Duration,
which is in nanoseconds. Previously the backend sent milliseconds
(* 1000), causing timestamps to be 1000x too small (e.g. 8e-8
instead of 0.08). Convert seconds → nanoseconds (* 1e9) instead.

Also applies to the legacy tuple path for consistency.

* feat(qwen-asr): respect timestamp_granularities (segment vs word)

Read request.timestamp_granularities from the gRPC request.
- 'word': return one segment per aligned item (character / word)
- 'segment' (default): merge consecutive items at sentence boundaries

Sentence boundaries detected via CJK punctuation (。!?;…)
and Latin endings (. ! ? ;). This matches the OpenAI Whisper API
contract where omitting the parameter defaults to segment-level.

* fix(qwen-asr): escape smart quotes in punctuation set

Unicode curly quotes (U+2018/2019) were being interpreted as Python
string delimiters, causing SyntaxError. Use explicit unicode escapes.

* fix(qwen-asr): use time-gap threshold for segment boundaries

The forced aligner strips punctuation from its output, so text-based
sentence detection doesn't work. Instead, detect segment boundaries
by measuring time gaps between consecutive aligned items.

Threshold = max(median_gap * 4, 0.3s). This cleanly separates
intra-sentence gaps (< 0.24s) from inter-sentence gaps (> 0.3s)
across Chinese, English, and other languages.

* fix(qwen-asr): smart join with spaces for non-CJK tokens

The forced aligner strips whitespace from tokenized text, so English
words like ['hello', 'world'] were joined as 'helloworld'. Add
_smart_join() that inserts spaces between non-CJK tokens while
keeping CJK characters and punctuation unspaced. Works for Chinese,
English, Korean, Japanese, and mixed-language text.

---------

Co-authored-by: fqscfqj <fqsfqj@outlook.com>
2026-05-26 20:34:21 +00:00
Richard Palethorpe
8d70855ea6 test: add Go + React UI coverage gates and fill test gaps (#9989)
- Strict monotonic Go coverage gate (make test-coverage-check, 45% baseline)
  run in CI; fixes ginkgo dropping all-but-one coverprofile across multiple
  recursive roots, builds with -tags auth, and folds in the in-process
  tests/e2e suite via --coverpkg.
- React UI e2e coverage (make test-ui-coverage: vite-plugin-istanbul + nyc,
  nix-provided Chromium) plus e2e specs for 6 previously-untested pages, and a
  UI coverage gate (make test-ui-coverage-check) with a small tolerance since
  e2e line coverage jitters ~0.5pp run-to-run.
- pre-commit hook: lint + coverage on Go changes, Playwright e2e + UI coverage
  gate on react-ui changes; install with make install-hooks.
- New Go handler tests (settings, branding), hermetic base64 download test.
- fix(ui): model editor reads vram_display (snake_case), so the VRAM estimate
  renders again; covered by a regression test.

Assisted-by: Claude:claude-opus-4-7

Signed-off-by: Richard Palethorpe <io@richiejp.com>
2026-05-26 22:06:10 +02:00
dependabot[bot]
90c29f9258 chore(deps): bump protobuf from 6.33.5 to 7.35.0 in /backend/python/transformers (#10004)
chore(deps): bump protobuf in /backend/python/transformers

Bumps [protobuf](https://github.com/protocolbuffers/protobuf) from 6.33.5 to 7.35.0.
- [Release notes](https://github.com/protocolbuffers/protobuf/releases)
- [Commits](https://github.com/protocolbuffers/protobuf/commits)

---
updated-dependencies:
- dependency-name: protobuf
  dependency-version: 7.35.0
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
2026-05-26 22:03:59 +02:00
dependabot[bot]
66aaa525e5 chore(deps): update transformers requirement from >=5.8.1 to >=5.9.0 in /backend/python/transformers (#10005)
chore(deps): update transformers requirement

Updates the requirements on [transformers](https://github.com/huggingface/transformers) to permit the latest version.
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](https://github.com/huggingface/transformers/compare/v5.8.1...v5.9.0)

---
updated-dependencies:
- dependency-name: transformers
  dependency-version: 5.9.0
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
2026-05-26 22:03:38 +02:00
dependabot[bot]
a29a06f6b6 chore(deps): bump sentence-transformers from 5.5.0 to 5.5.1 in /backend/python/transformers (#10007)
chore(deps): bump sentence-transformers in /backend/python/transformers

Bumps [sentence-transformers](https://github.com/huggingface/sentence-transformers) from 5.5.0 to 5.5.1.
- [Release notes](https://github.com/huggingface/sentence-transformers/releases)
- [Commits](https://github.com/huggingface/sentence-transformers/compare/v5.5.0...v5.5.1)

---
updated-dependencies:
- dependency-name: sentence-transformers
  dependency-version: 5.5.1
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
2026-05-26 22:03:05 +02:00
LocalAI [bot]
80893a298b chore(model gallery): 🤖 add 1 new models via gallery agent (#10016)
chore(model gallery): 🤖 add new models via gallery agent

Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>
2026-05-26 22:02:12 +02:00
Richard Palethorpe
9d90e04418 fix(dockerignore): exclude local-only artifacts from build context (#10015)
The build context shipped to the daemon included several large
untracked directories the image never needs: saved image tarballs
(backend-images), locally-installed backends (local-backends), the
host-built binary (local-ai), the rust target/ build output, and
host node_modules/protoc/tests. This bloated the context to ~23GB.

Exclude them so only the sources the Dockerfile actually copies are
transferred. backend/rust sources stay tracked; only target/ is ignored.

Assisted-by: Claude:claude-opus-4-7 [Claude Code]

Signed-off-by: Richard Palethorpe <io@richiejp.com>
2026-05-26 22:01:50 +02:00
Ettore Di Giacinto
11f43ad8b0 docs(readme): refresh News section for 4.0-4.3 and split Past sponsors
Walk down the release history and add per-release one-liners for 4.3.0,
4.2.0, 4.1.0, and 4.0.0 in the Latest News section, leading with the
headline win for each release. Move Prem into a collapsible "Past
sponsors" block under the active sponsors row.

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:opus-4.7 [claude-code]
2026-05-26 16:40:40 +00:00
LocalAI [bot]
437f0fa193 chore(model gallery): 🤖 add 1 new models via gallery agent (#10011)
chore(model gallery): 🤖 add new models via gallery agent

Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>
2026-05-26 08:45:10 +02:00
dependabot[bot]
aa743f8824 chore(deps): bump actions/stale from 10.2.0 to 10.3.0 (#10002)
Bumps [actions/stale](https://github.com/actions/stale) from 10.2.0 to 10.3.0.
- [Release notes](https://github.com/actions/stale/releases)
- [Changelog](https://github.com/actions/stale/blob/main/CHANGELOG.md)
- [Commits](b5d41d4e1d...eb5cf3af3a)

---
updated-dependencies:
- dependency-name: actions/stale
  dependency-version: 10.3.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
2026-05-26 08:39:13 +02:00
dependabot[bot]
2162611dca chore(deps): bump github.com/aws/aws-sdk-go-v2/credentials from 1.19.15 to 1.19.17 (#10008)
chore(deps): bump github.com/aws/aws-sdk-go-v2/credentials

Bumps [github.com/aws/aws-sdk-go-v2/credentials](https://github.com/aws/aws-sdk-go-v2) from 1.19.15 to 1.19.17.
- [Release notes](https://github.com/aws/aws-sdk-go-v2/releases)
- [Commits](https://github.com/aws/aws-sdk-go-v2/compare/credentials/v1.19.15...credentials/v1.19.17)

---
updated-dependencies:
- dependency-name: github.com/aws/aws-sdk-go-v2/credentials
  dependency-version: 1.19.17
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
2026-05-26 08:34:56 +02:00
LocalAI [bot]
4aad97971c chore: ⬆️ Update ggml-org/llama.cpp to 35c9b1f39ebe5a7bb83986d64415a079218be78d (#9998)
* ⬆️ Update ggml-org/llama.cpp

Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

* fix(llama-cpp): track upstream rename checkpoint_every_nt -> checkpoint_min_step

Upstream llama.cpp renamed common_params::checkpoint_every_nt to
checkpoint_min_step and changed its default from 8192 to 256. The semantics
also shifted: it used to enforce a fixed checkpoint cadence during prefill,
now it sets a minimum spacing between context checkpoints. Track the new
field name in grpc-server.cpp and accept the old option names as backward-
compatible aliases for users with existing configs.

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: claude-code:claude-opus-4-7

---------

Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
2026-05-26 08:34:41 +02:00
LocalAI [bot]
e4c70fca7a fix(streaming/tools): don't leak prefill-misclassified content as trailing reasoning chunk (#10000)
When the C++ autoparser is in pure-content fallback mode (qwen3-4b after
model emits a tool-call JSON in non-thinking mode, the streaming worker
ended the SSE stream with a spurious

    data: {...,"delta":{"reasoning":"{\"name\":\"exec\",\"arguments\":...}"}}

chunk carrying the same JSON that was already in delta.tool_calls.

The Go-side ReasoningExtractor is configured from
DetectThinkingStartToken, which scans the model's jinja chat template
verbatim and finds <think> inside an {% if enable_thinking %} block
without evaluating the conditional. Every output chunk then runs through
PrependThinkingTokenIfNeeded, which synthesizes a <think> in front and
makes ExtractReasoning treat everything after as reasoning. The autoparser
correctly classifies zero reasoning (qwen3's tool format isn't on
llama.cpp's recognized-tool list, so all tokens land in
ChatDelta.Content), but processStreamWithTools then preferred
extractor.Reasoning() over functions.ReasoningFromChatDeltas at the
end-of-stream flush — handing the polluted Go-side state to
buildDeferredToolCallChunks, which emitted it as a trailing reasoning
chunk.

Two changes:

* Add a sticky preferAutoparser flag to processStreamWithTools, mirroring
  the analogous flag in processStream from #9985. Once any ChatDelta
  carries content or reasoning, the flag stays on for the rest of the
  stream and the worker stops falling back to the Go-side extractor for
  per-token deltas. This avoids the per-chunk leak path and the cumulative
  pollution.

* Extract chooseDeferredReasoning, a small helper that selects the
  end-of-stream reasoning source. When preferAutoparser is set, return
  functions.ReasoningFromChatDeltas(chatDeltas); otherwise fall back to
  extractor.Reasoning() (the correct source for vLLM and other backends
  with no autoparser).

The helper has a focused test suite covering both sides of the contract:
autoparser-active with empty reasoning (the qwen3 case — the fix's
purpose), autoparser-active with real reasoning_content
(jinja-with-recognized-format models), and autoparser-not-active with
genuine Go-side reasoning (vLLM-style backends).

E2E with combined #9988 and this fix on qwen3-4b post-#9985 gallery
shape: 18 content chunks of the tool-call JSON, 1 tool_call chunk with
name='exec' and the right arguments, finish_reason=tool_calls, and zero
reasoning chunks — down from one polluted reasoning chunk before this
fix.

Depends on #9999 (the streaming JSON tool-call gating bug for qwen3) to
make the trailing chunk observable end-to-end; the helper unit tests are
independent.

Assisted-by: Claude:opus-4-7 [Read] [Edit] [Bash] [Write]

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
2026-05-26 08:34:26 +02:00
dependabot[bot]
4b398c9798 chore(deps): bump github.com/nats-io/nats.go from 1.50.0 to 1.52.0 (#10003)
Bumps [github.com/nats-io/nats.go](https://github.com/nats-io/nats.go) from 1.50.0 to 1.52.0.
- [Release notes](https://github.com/nats-io/nats.go/releases)
- [Commits](https://github.com/nats-io/nats.go/compare/v1.50.0...v1.52.0)

---
updated-dependencies:
- dependency-name: github.com/nats-io/nats.go
  dependency-version: 1.52.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
2026-05-26 08:34:09 +02:00
LocalAI [bot]
4a5219fa9c chore: ⬆️ Update ggml-org/whisper.cpp to e0fd1f6787a5bd4a4957dd97c5b64df882ee7b0c (#9997)
⬆️ Update ggml-org/whisper.cpp

Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>
2026-05-26 08:33:53 +02:00
LocalAI [bot]
b5a620294e chore: ⬆️ Update leejet/stable-diffusion.cpp to 1ceb5bd9df7784bcdf67dd9ed8bf0198b542ebc9 (#9994)
⬆️ Update leejet/stable-diffusion.cpp

Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>
2026-05-26 08:33:37 +02:00
LocalAI [bot]
5d544a7868 chore: ⬆️ Update ikawrakow/ik_llama.cpp to b4e1d916c5ec7e75ea3c124dd090425a99fc613f (#9995)
⬆️ Update ikawrakow/ik_llama.cpp

Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>
2026-05-25 23:57:17 +02:00
LocalAI [bot]
87e01aa290 chore: ⬆️ Update antirez/ds4 to ad0209f6a4b067574d2b4afe896c08c177156b31 (#9996)
⬆️ Update antirez/ds4

Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>
2026-05-25 23:56:33 +02:00
LocalAI [bot]
f17d99f6e5 fix(streaming/tools): stop healing-marker stubs from gating off content (#9999)
* fix(streaming/tools): stop healing-marker stubs from gating off content

When the C++ autoparser is in pure-content fallback mode (e.g. qwen3
without --jinja) and the model emits a tool call as JSON, the streaming
worker calls ParseJSONIterative on each new chunk. parseJSONWithStack
heals partial input like `{` into `{"<marker>":1}` where <marker> is a
random integer. removeHealingMarkerFromJSON only stripped the marker
from values, so the synthetic key survived and downstream callers saw
a stub object with a random-looking key.

chat_stream_workers.go's JSON tool-call detector then bumped
lastEmittedCount past the stub even though no real tool call was
emitted, gating off ALL subsequent content chunks. The qwen3 + tools +
streaming case ended up dribbling only the first `{"` to clients and
then nothing, even when the model went on to call the noAction
`answer({"message": "…"})` pseudo-tool.

Three changes, each with its own regression test:

* removeHealingMarkerFromJSON now strips the marker suffix from keys
  too, dropping the entry when the truncated key is empty. Inputs like
  `{` no longer leak `{"<marker>":1}` to callers; partial keys like
  `{ "code` still preserve the model-typed prefix `code`.

* ParseJSONIterative skips empty-after-healing maps so a healed `{`
  doesn't surface as a stub result.

* The streaming JSON detector now breaks (not continues) on entries
  without a usable `name`, and only bumps lastEmittedCount past
  successfully-emitted entries. Defense-in-depth against any future
  partial-parse shape.

The parser tests cover eight partial-JSON-prefix shapes and verify no
marker characters leak into keys, plus the two early shapes (`{`,
`{"`) that should not surface a stub at all.

Fixes #9988

Assisted-by: Claude:opus-4-7 [Read] [Edit] [Bash]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

* test(streaming/tools): cover the autoparser-correctly-working path

Extract the JSON tool-call streaming emit loop into emitJSONToolCallDeltas
and unit-test it against every shape that can hit the streaming worker:

* the bug case — a healing-marker stub at index 0 must NOT bump
  lastEmittedCount, so subsequent content chunks keep flowing;
* the autoparser-correctly-working case — empty jsonResults (because
  the C++ autoparser cleared the raw text and delivers tool calls via
  TokenUsage.ChatDeltas) is a no-op, leaving the deferred end-of-stream
  emitter to ship the autoparser's tool calls;
* a single complete tool call — emit one chunk, advance to 1;
* arguments arriving as a JSON-string vs as a nested object — both
  serialize to the wire as JSON-string arguments;
* multiple parallel tool calls — one chunk each;
* a real tool call followed by a partial stub — emit the real one,
  stop at the stub, resume on a later chunk once the stub completes.

Locks down the no-regression guarantee the user asked for: this PR's
fix is scoped to the pure-content fallback path; when the autoparser
actually classifies tool calls (jinja-recognized chat format with tool
support), the helper is a no-op and nothing changes.

Assisted-by: Claude:opus-4-7 [Read] [Edit] [Bash] [Write]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

---------

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
2026-05-25 23:55:35 +02:00
LocalAI [bot]
597daa925b docs: ⬆️ update docs version mudler/LocalAI (#9993)
⬆️ Update docs version mudler/LocalAI

Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>
2026-05-25 22:40:52 +02:00
LocalAI [bot]
3e0612b8b4 feat(swagger): update swagger (#9992)
Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>
2026-05-25 22:40:32 +02:00
LocalAI [bot]
de2ce74bea fix(stablediffusion-ggml): mux LTX-2 audio into output MP4 (#9990)
feat(stablediffusion-ggml): mux LTX-2 audio into output MP4

sd.cpp's generate_video now returns a sd_audio_t* alongside the video
frames for models with an audio VAE (LTX-2.3). Our gosd wrapper was
already collecting that pointer but immediately freed it without ever
muxing it into the output, so LTX-2 generations landed as silent MP4s
even though the audio VAE decode succeeded.

Stage the planar float32 waveform to a temp WAV (IEEE float, header
hand-built; samples interleaved on the fly), then add it as a second
ffmpeg input with -c:a aac -map 0:v:0 -map 1:a:0 -shortest. The temp
WAV is cleaned up unconditionally after ffmpeg exits, including on
the write/waitpid error paths.

Non-LTX models (Wan i2v / FLF2V) keep their current behaviour: audio
arg is nullptr, the audio-related ffmpeg flags are not added, and no
temp file is created.

Assisted-by: Claude:claude-opus-4-7

Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
2026-05-25 22:40:16 +02:00
LocalAI [bot]
1c6c3adad6 fix(reasoning): stop <think> leaking into content when autoparser is in pure-content mode (#9991)
When LocalAI templates a thinking model outside of jinja (the default for
the qwen3 gallery family), llama.cpp's chat parser falls back to a
"pure content" PEG parser that dumps the entire raw response into
ChatDelta.Content with an empty ReasoningContent. The Go side then
trusted that content verbatim and overrode tokenCallback's
correctly-split reasoning, so <think>...</think> blocks ended up in the
OpenAI `content` field. Regression from v4.0.0 introduced when the
autoparser ChatDeltas path was added (#9224).

The override now runs Go-side reasoning extraction defensively when the
autoparser delivered content but no reasoning. The streaming worker
gains a sticky preferAutoparser flag that flips on the first chunk
where the autoparser classified reasoning_content; until then we use
the streaming Go-side extractor. Realtime mirrors the non-streaming
fallback. When the autoparser already populated ReasoningContent we
trust it untouched, so jinja-enabled installs are not regressed.

gallery/qwen3.yaml now enables use_jinja, letting the autoparser
classify <think> natively for all 20+ qwen3 family entries that share
this template.

Fixes #9985

Assisted-by: Claude:opus-4-7 [Read] [Edit] [Bash] [Write]

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
2026-05-25 22:39:50 +02:00
Ettore Di Giacinto
c2cd3b9ada fix(gallery/ltx-2.3): add vae_decode_only:false for i2v / flf2v (#9987)
LTX-2.3 i2v inference fails inside generate_video with:

  [ERROR] LTXAV image conditioning requires VAE encoder weights;
  create the context with vae_decode_only=false

Without vae_decode_only:false in the options block, gosd.cpp creates
the sd_ctx with VAE encoder weights freed, so latent encoding of the
init_image is impossible. Adding the option mirrors what we already
do for Wan i2v entries.

Affects all six LTX-2.3 entries (dev/distilled × UD-Q4_K_M, Q4_K_M,
Q8_0). T2V wasn't impacted by the missing option since it has no
init image to encode, which is why the T2V smoke earlier passed.

Assisted-by: Claude:claude-opus-4-7
2026-05-25 21:40:12 +02:00
Ettore Di Giacinto
9ff270eb65 fix(gallery/ltx-2.3): add diffusion_model flag to all variants (#9986)
LTX-2.3 entries (dev / distilled, UD-Q4_K_M / Q4_K_M / Q8_0) were
missing the `diffusion_model` option in their overrides. Without it,
gosd.cpp routes the main GGUF through the regular `model_path` code
path in sd.cpp, which doesn't apply the `model.diffusion_model.` tensor
prefix. sd.cpp's LTX-2.3 architecture detection (`VERSION_LTXAV`) in
get_sd_version checks for prefixed tensor names — without the prefix,
detection fails and load_model returns "could not load model".

This is the same bug we hit for Wan when the option was missing.
Adding `- diffusion_model` to all six LTX-2.3 entries' option blocks
makes load_model take the diffusion_model_path branch so detection
succeeds.

Assisted-by: Claude:claude-opus-4-7
2026-05-25 21:10:40 +02:00
112 changed files with 5357 additions and 515 deletions

View File

@@ -15,3 +15,32 @@ Let's say the user wants to build a particular backend for a given platform. For
- Unless the user specifies that they want you to run the command, then just print it because not all agent frontends handle long running jobs well and the output may overflow your context
- The user may say they want to build AMD or ROCM instead of hipblas, or Intel instead of SYCL or NVIDIA insted of l4t or cublas. Ask for confirmation if there is ambiguity.
- Sometimes the user may need extra parameters to be added to `docker build` (e.g. `--platform` for cross-platform builds or `--progress` to view the full logs), in which case you can generate the `docker build` command directly.
## Test coverage gate
The core Go suites (`./pkg`, `./core`, plus the in-process integration suite `./tests/e2e`) are covered by a **strict, monotonic coverage ratchet**:
- `make test-coverage` — runs the suites with `covermode=atomic` instrumentation and writes a merged profile to `coverage/coverage.out`. Uses the same prerequisites as `make test`.
- **`--coverpkg` (`COVERAGE_COVERPKG = core/...,pkg/...`):** coverage is attributed to the core+pkg packages, not just the package under test. This is what lets the in-process `tests/e2e` suite (which drives the real HTTP server over loopback via `application.New`) credit the `core/http/endpoints/...` handlers it exercises — folding it in roughly doubled endpoint coverage (e.g. `endpoints/openai` 13.6% → 52%). The denominator is therefore *all* of `core`+`pkg` (minus generated proto, dropped via `COVERAGE_EXCLUDE_RE`), so the number isn't comparable to a plain per-package figure.
- **Integration suites (`COVERAGE_E2E_ROOTS = ./tests/e2e`)** run non-recursively (excludes `tests/e2e/distributed`, which needs containers) with `--label-filter=!real-models` (those need a downloaded model) against the mock backend built by `prepare-test`. `tests/integration` is deliberately excluded — it needs `make backends/local-store`, which the coverage CI job doesn't build.
- **Flake note:** folding integration tests into a *strict* gate means a hard e2e failure (or a spec that silently stops running) can fail the coverage gate, not just the test. `--flake-attempts` absorbs transient retryable failures; covermode=atomic keeps line coverage deterministic otherwise.
- **Why one ginkgo run per root (`scripts/run-coverage.sh`):** passing several recursive roots to a *single* ginkgo invocation (e.g. `ginkgo -r ./pkg ./core`) only merges **one** root's coverprofile into `--output-dir`/`--coverprofile` — the others are silently dropped. Verified with ginkgo 2.29.0: `-r ./pkg ./core` yields only `./pkg` coverage, while `-r ./core` alone yields all 34 core packages. So the script runs each root separately and concatenates the (disjoint) profiles. Don't "simplify" it back to a single multi-root invocation — that's how `core/` (including all of `core/http`, ~7.4k statements) silently vanished from the number before.
- **Build tags (`COVERAGE_TAGS`, passed via `GINKGO_TAGS`):** defaults to `debug auth`. The `auth` tag is required to compile the real (sqlite-backed) auth implementation and its ~150 `//go:build auth` tests — without it those files aren't built, the tests don't run, and the gate scores auth against a stub (~3.7% instead of ~38%). If you add new tag-gated tests, extend `COVERAGE_TAGS` or they won't count (and likely won't run in CI at all).
- `make test-coverage-check` — runs `test-coverage`, then `scripts/coverage-check.sh` fails the build if total coverage is **below** the committed baseline in `coverage-baseline.txt`. The Linux job in `.github/workflows/test.yml` runs this instead of `make test`.
- `make test-coverage-baseline` — regenerates and overwrites `coverage-baseline.txt` from the current run.
- `make install-hooks` — sets `core.hooksPath` to the versioned `.githooks/`, whose `pre-commit` runs checks scoped to what's staged: Go changes → `make lint` + `make test-coverage-check`; `core/http/react-ui/` changes → `make test-ui-coverage-check` (Playwright e2e + UI coverage gate). A commit touching neither is skipped; bypass with `git commit --no-verify`. The hook resolves golangci-lint's new-from base to `upstream/master``origin/master``master`, so it works from a fork clone where `origin/master` is stale (passed to `make lint` via `LINT_NEW_FROM`).
### React UI coverage
The React UI (`core/http/react-ui/`) has **no component/unit tests** — its only tests are the Playwright e2e specs in `e2e/`, which run against the real app served by `tests/e2e-ui/ui-test-server` (the dist is `//go:embed`ed, so the server is rebuilt per coverage run). Those specs do genuinely exercise the UI (clicks, `fill`, `setInputFiles`, `getByRole`/`getByText`, visibility/value assertions).
- `make test-ui-coverage` — builds an istanbul-instrumented bundle (`COVERAGE=true`, via `vite-plugin-istanbul` with `forceBuildInstrument: true` — the plugin skips production builds otherwise), re-embeds it into `ui-test-server` (the dist is `//go:embed`ed), runs the Playwright specs, and writes an `nyc` report to `core/http/react-ui/coverage/`. The specs import `{ test, expect }` from `e2e/coverage-fixtures.js` (re-exports Playwright's, plus harvests `window.__coverage__` into `.nyc_output/` after each test). Instrumentation is off unless `COVERAGE=true`, so dev/prod builds and plain `make test-ui-e2e` are unaffected (the fixture no-ops when `window.__coverage__` is absent).
- **Browser:** the flake dev shell ships `chromium` and exports `PLAYWRIGHT_CHROMIUM_PATH`; `playwright.config.js` uses it via `launchOptions.executablePath`, and the Makefile skips `playwright install` when it's set. This avoids Playwright's downloaded browser, which can't resolve system libs (`libglib-2.0`, …) on NixOS. In CI (no `PLAYWRIGHT_CHROMIUM_PATH`) the Makefile falls back to `playwright install --with-deps chromium`.
- The app is a React SPA, so coverage accumulates across in-app navigation within a test; a full `page.goto`/reload resets it.
- `.nycrc.json` uses `all: true`, so **every `src/**` file is in the report**, including 0%-coverage ones — that's how you spot features with no test at all (sort the HTML report or `coverage-summary.json` by line% ascending).
- **UI coverage gate:** `make test-ui-coverage-check` runs the suite then `scripts/ui-coverage-check.sh`, failing if total line coverage drops more than `UI_COVERAGE_TOLERANCE` (default **1.0pp**) below `core/http/react-ui/coverage-baseline.txt`. `make test-ui-coverage-baseline` regenerates the baseline. **Why a tolerance (unlike the strict Go gate):** UI e2e line coverage is *non-deterministic* — async/debounced paths (e.g. the VRAM estimate's 500ms debounce) make identical specs vary ~0.5pp run-to-run, so a zero-tolerance gate would flake. Keep the tolerance just above the observed jitter. Run in CI (`tests-ui-e2e.yml`) and pre-commit on `core/http/react-ui/` changes.
Rules:
- The gate is **strict — there is no tolerance**. Any decrease fails, regardless of how many lines a PR adds or deletes. `covermode=atomic` makes line coverage deterministic, so there's no run-to-run jitter to excuse.
- When a change legitimately **raises** coverage, run `make test-coverage-baseline` and **commit** the updated `coverage-baseline.txt` so the ratchet moves up. Never lower the baseline by hand.
- If you can't get coverage back to baseline, the fix is to **add tests**, not to edit the baseline.

View File

@@ -30,3 +30,19 @@ backend/python/**/source
# up compiled against whatever (likely older) commit the host had.
backend/cpp/llama-cpp/llama.cpp
backend/cpp/llama-cpp-*-build
# Rust backend build output (sources are tracked; target/ is generated)
backend/rust/*/target
# Local-only artifacts that bloat the build context but the image never needs.
# Saved image tarballs, locally-installed backends, the host-built binary, and
# assorted tool/scratch dirs. None of these are git-tracked.
backend-images
local-backends
local-ai
.crush
protoc
tests
# Installed via npm inside the build stage; no need to ship the host copy.
**/node_modules

60
.githooks/pre-commit Executable file
View File

@@ -0,0 +1,60 @@
#!/usr/bin/env sh
#
# LocalAI pre-commit hook. Install it (once per clone) with:
#
# make install-hooks
#
# Runs only the checks relevant to what's staged:
# - Go files -> make lint + make test-coverage-check
# - core/http/react-ui -> make test-ui-coverage-check (Playwright e2e + gate)
# A commit touching neither is skipped entirely (docs/YAML/etc. can't change
# lint findings, Go coverage, or the UI).
#
# To bypass for a single commit (e.g. a WIP checkpoint): git commit --no-verify
set -eu
repo_root="$(git rev-parse --show-toplevel)"
cd "$repo_root"
staged="$(git diff --cached --name-only --diff-filter=ACMRD)"
go_changed=0
ui_changed=0
if echo "$staged" | grep -qE '\.go$'; then go_changed=1; fi
if echo "$staged" | grep -qE '^core/http/react-ui/'; then ui_changed=1; fi
if [ "$go_changed" -eq 0 ] && [ "$ui_changed" -eq 0 ]; then
echo "pre-commit: no Go or React UI changes staged — skipping."
exit 0
fi
if [ "$go_changed" -eq 1 ]; then
# Resolve the ref golangci-lint's new-from-merge-base should compare
# against. .golangci.yml pins origin/master, which is correct in CI
# (origin == the canonical repo) but wrong from a fork clone, where
# origin/master lags behind and lint would report the whole upstream
# backlog. Prefer upstream/master, then origin/master, then master.
lint_base=""
for ref in upstream/master origin/master master; do
if git rev-parse --verify --quiet "${ref}^{commit}" >/dev/null 2>&1; then
lint_base="$ref"
break
fi
done
echo "pre-commit ▶ golangci-lint (make lint${lint_base:+, new-from $lint_base})"
make lint LINT_NEW_FROM="$lint_base"
echo "pre-commit ▶ coverage gate (make test-coverage-check) — builds and runs the"
echo " pkg/core suites plus tests/e2e; can take a few minutes."
make test-coverage-check
fi
if [ "$ui_changed" -eq 1 ]; then
echo "pre-commit ▶ React UI e2e + coverage gate (make test-ui-coverage-check) —"
echo " rebuilds the UI + ui-test-server, runs the Playwright specs, and"
echo " fails if line coverage regressed; can take a couple of minutes."
make test-ui-coverage-check
fi
echo "pre-commit ✓ all relevant checks passed"

View File

@@ -690,6 +690,19 @@ include:
dockerfile: "./backend/Dockerfile.golang"
context: "./"
ubuntu-version: '2404'
- build-type: 'cublas'
cuda-major-version: "12"
cuda-minor-version: "8"
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-nvidia-cuda-12-rfdetr-cpp'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
backend: "rfdetr-cpp"
dockerfile: "./backend/Dockerfile.golang"
context: "./"
ubuntu-version: '2404'
- build-type: 'cublas'
cuda-major-version: "12"
cuda-minor-version: "8"
@@ -1491,6 +1504,19 @@ include:
dockerfile: "./backend/Dockerfile.golang"
context: "./"
ubuntu-version: '2404'
- build-type: 'cublas'
cuda-major-version: "13"
cuda-minor-version: "0"
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-nvidia-cuda-13-rfdetr-cpp'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
backend: "rfdetr-cpp"
dockerfile: "./backend/Dockerfile.golang"
context: "./"
ubuntu-version: '2404'
- build-type: 'cublas'
cuda-major-version: "13"
cuda-minor-version: "0"
@@ -1504,6 +1530,19 @@ include:
backend: "sam3-cpp"
dockerfile: "./backend/Dockerfile.golang"
context: "./"
- build-type: 'cublas'
cuda-major-version: "13"
cuda-minor-version: "0"
platforms: 'linux/arm64'
skip-drivers: 'false'
tag-latest: 'auto'
tag-suffix: '-nvidia-l4t-cuda-13-arm64-rfdetr-cpp'
base-image: "ubuntu:24.04"
ubuntu-version: '2404'
runs-on: 'ubuntu-24.04-arm'
backend: "rfdetr-cpp"
dockerfile: "./backend/Dockerfile.golang"
context: "./"
- build-type: 'cublas'
cuda-major-version: "13"
cuda-minor-version: "0"
@@ -2635,6 +2674,74 @@ include:
dockerfile: "./backend/Dockerfile.golang"
context: "./"
ubuntu-version: '2404'
# rfdetr-cpp
- build-type: ''
cuda-major-version: ""
cuda-minor-version: ""
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-cpu-rfdetr-cpp'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
backend: "rfdetr-cpp"
dockerfile: "./backend/Dockerfile.golang"
context: "./"
ubuntu-version: '2404'
- build-type: 'sycl_f32'
cuda-major-version: ""
cuda-minor-version: ""
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-intel-sycl-f32-rfdetr-cpp'
runs-on: 'ubuntu-latest'
base-image: "intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04"
skip-drivers: 'false'
backend: "rfdetr-cpp"
dockerfile: "./backend/Dockerfile.golang"
context: "./"
ubuntu-version: '2404'
- build-type: 'sycl_f16'
cuda-major-version: ""
cuda-minor-version: ""
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-intel-sycl-f16-rfdetr-cpp'
runs-on: 'ubuntu-latest'
base-image: "intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04"
skip-drivers: 'false'
backend: "rfdetr-cpp"
dockerfile: "./backend/Dockerfile.golang"
context: "./"
ubuntu-version: '2404'
- build-type: 'vulkan'
cuda-major-version: ""
cuda-minor-version: ""
platforms: 'linux/amd64'
platform-tag: 'amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-vulkan-rfdetr-cpp'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
backend: "rfdetr-cpp"
dockerfile: "./backend/Dockerfile.golang"
context: "./"
ubuntu-version: '2404'
- build-type: 'vulkan'
cuda-major-version: ""
cuda-minor-version: ""
platforms: 'linux/arm64'
platform-tag: 'arm64'
tag-latest: 'auto'
tag-suffix: '-gpu-vulkan-rfdetr-cpp'
runs-on: 'ubuntu-24.04-arm'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
backend: "rfdetr-cpp"
dockerfile: "./backend/Dockerfile.golang"
context: "./"
ubuntu-version: '2404'
- build-type: 'sycl_f32'
cuda-major-version: ""
cuda-minor-version: ""
@@ -2715,6 +2822,19 @@ include:
dockerfile: "./backend/Dockerfile.golang"
context: "./"
ubuntu-version: '2204'
- build-type: 'cublas'
cuda-major-version: "12"
cuda-minor-version: "0"
platforms: 'linux/arm64'
skip-drivers: 'false'
tag-latest: 'auto'
tag-suffix: '-nvidia-l4t-arm64-rfdetr-cpp'
base-image: "nvcr.io/nvidia/l4t-jetpack:r36.4.0"
runs-on: 'ubuntu-24.04-arm'
backend: "rfdetr-cpp"
dockerfile: "./backend/Dockerfile.golang"
context: "./"
ubuntu-version: '2204'
# whisper
- build-type: ''
cuda-major-version: ""

View File

@@ -50,6 +50,10 @@ jobs:
variable: "SAM3_VERSION"
branch: "main"
file: "backend/go/sam3-cpp/Makefile"
- repository: "mudler/rf-detr.cpp"
variable: "RFDETR_VERSION"
branch: "main"
file: "backend/go/rfdetr-cpp/Makefile"
- repository: "predict-woo/qwen3-tts.cpp"
variable: "QWEN3TTS_CPP_VERSION"
branch: "main"

View File

@@ -11,7 +11,7 @@ jobs:
if: github.repository == 'mudler/LocalAI'
runs-on: ubuntu-latest
steps:
- uses: actions/stale@b5d41d4e1d5dceea10e7104786b73624c18a190f # v9
- uses: actions/stale@eb5cf3af3ac0a1aa4c9c45633dd1ae542a27a899 # v9
with:
stale-issue-message: 'This issue is stale because it has been open 90 days with no activity. Remove stale label or comment or this will be closed in 5 days.'
stale-pr-message: 'This PR is stale because it has been open 90 days with no activity. Remove stale label or comment or this will be closed in 10 days.'

View File

@@ -37,6 +37,7 @@ jobs:
sglang: ${{ steps.detect.outputs.sglang }}
acestep-cpp: ${{ steps.detect.outputs.acestep-cpp }}
qwen3-tts-cpp: ${{ steps.detect.outputs.qwen3-tts-cpp }}
rfdetr-cpp: ${{ steps.detect.outputs.rfdetr-cpp }}
vibevoice-cpp: ${{ steps.detect.outputs.vibevoice-cpp }}
localvqe: ${{ steps.detect.outputs.localvqe }}
voxtral: ${{ steps.detect.outputs.voxtral }}
@@ -843,6 +844,42 @@ jobs:
- name: Test qwen3-tts-cpp
run: |
make --jobs=5 --output-sync=target -C backend/go/qwen3-tts-cpp test
# Per-backend smoke for rfdetr-cpp: builds the .so + Go binary and runs
# `make -C backend/go/rfdetr-cpp test`. test.sh fetches the small (~20 MB)
# rfdetr-nano-q8_0 GGUF from the published mudler/rfdetr-cpp-nano HF repo
# via curl and synthesises a tiny PNG to exercise the wire protocol.
tests-rfdetr-cpp:
needs: detect-changes
if: needs.detect-changes.outputs.rfdetr-cpp == 'true' || needs.detect-changes.outputs.run-all == 'true'
runs-on: ubuntu-latest
steps:
- name: Clone
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
run: |
sudo apt-get update
sudo apt-get install -y build-essential cmake curl libopenblas-dev
- name: Setup Go
uses: actions/setup-go@v5
- name: Display Go version
run: go version
- name: Proto Dependencies
run: |
# Install protoc
curl -L -s https://github.com/protocolbuffers/protobuf/releases/download/v26.1/protoc-26.1-linux-x86_64.zip -o protoc.zip && \
unzip -j -d /usr/local/bin protoc.zip bin/protoc && \
rm protoc.zip
go install google.golang.org/protobuf/cmd/protoc-gen-go@v1.34.2
go install google.golang.org/grpc/cmd/protoc-gen-go-grpc@1958fcbe2ca8bd93af633f11e97d44e567e945af
PATH="$PATH:$HOME/go/bin" make protogen-go
- name: Build rfdetr-cpp
run: |
make --jobs=5 --output-sync=target -C backend/go/rfdetr-cpp
- name: Test rfdetr-cpp
run: |
make --jobs=5 --output-sync=target -C backend/go/rfdetr-cpp test
# Per-backend smoke for vibevoice-cpp: builds the .so + Go binary and
# runs `make -C backend/go/vibevoice-cpp test`. test.sh auto-downloads
# the published mudler/vibevoice.cpp-models bundle (TTS Q8_0 + ASR Q4_K

View File

@@ -53,9 +53,22 @@ jobs:
node-version: '22'
- name: Build React UI
run: make react-ui
- name: Test
# Runs the core suite with coverage and fails if total coverage dropped
# below the committed baseline (coverage-baseline.txt). The gate is
# strict — any decrease fails. Raise the baseline with
# `make test-coverage-baseline` and commit it when coverage rises.
- name: Test (with coverage gate)
run: |
PATH="$PATH:/root/go/bin" make --jobs 5 --output-sync=target test
PATH="$PATH:/root/go/bin" make --jobs 5 --output-sync=target test-coverage-check
- name: Upload coverage report
if: ${{ always() }}
uses: actions/upload-artifact@v4
with:
name: coverage-linux
path: |
coverage/coverage.out
coverage/coverage.html
if-no-files-found: ignore
- name: Setup tmate session if tests fail
if: ${{ failure() }}
uses: mxschmitt/action-tmate@v3.23

View File

@@ -37,6 +37,10 @@ jobs:
uses: actions/setup-node@v6
with:
node-version: '22'
- name: Setup Bun
uses: oven-sh/setup-bun@v2
with:
bun-version: '1.3.11'
- name: Proto Dependencies
run: |
curl -L -s https://github.com/protocolbuffers/protobuf/releases/download/v26.1/protoc-26.1-linux-x86_64.zip -o protoc.zip && \
@@ -48,16 +52,12 @@ jobs:
run: |
sudo apt-get update
sudo apt-get install -y build-essential libopus-dev
- name: Build UI test server
run: PATH="$PATH:$HOME/go/bin" make build-ui-test-server
- name: Install Playwright
working-directory: core/http/react-ui
run: |
npm install
npx playwright install --with-deps chromium
- name: Run Playwright tests
working-directory: core/http/react-ui
run: npx playwright test
# Builds an instrumented UI bundle, runs the Playwright specs, and fails
# if line coverage regressed beyond the jitter tolerance (the gate is
# in `make test-ui-coverage-check`). PLAYWRIGHT_CHROMIUM_PATH is unset
# here, so scripts/ensure-playwright-browser.sh installs Chromium via apt.
- name: Run UI e2e + coverage gate
run: PATH="$PATH:$HOME/go/bin" make test-ui-coverage-check
- name: Upload Playwright report
if: ${{ failure() }}
uses: actions/upload-artifact@v7
@@ -65,6 +65,14 @@ jobs:
name: playwright-report
path: core/http/react-ui/playwright-report/
retention-days: 7
- name: Upload UI coverage report
if: ${{ always() }}
uses: actions/upload-artifact@v7
with:
name: ui-coverage
path: core/http/react-ui/coverage/
if-no-files-found: ignore
retention-days: 7
- name: Setup tmate session if tests fail
if: ${{ failure() }}
uses: mxschmitt/action-tmate@v3.23

7
.gitignore vendored
View File

@@ -70,10 +70,17 @@ docs/static/gallery.html
# per-developer customization files for the development container
.devcontainer/customization/*
# Coverage profiles (the committed baseline is coverage-baseline.txt)
/coverage/
# React UI build artifacts (keep placeholder dist/index.html)
core/http/react-ui/node_modules/
core/http/react-ui/dist
# React UI coverage (vite-plugin-istanbul + nyc, via `make test-ui-coverage`)
core/http/react-ui/.nyc_output/
core/http/react-ui/coverage/
# Extracted backend binaries for container-based testing
local-backends/

View File

@@ -198,6 +198,7 @@ For AI-assisted development, see [`AGENTS.md`](AGENTS.md) (or the equivalent [`C
- Prefer modern Go idioms — for example, use `any` instead of `interface{}`.
- Use [`golangci-lint`](https://golangci-lint.run) to catch common issues before submitting a PR.
- Run `make install-hooks` once per clone to enable the pre-commit hook: Go changes run `make lint` + the coverage gate (`make test-coverage-check`); `core/http/react-ui/` changes run the Playwright e2e suite (`make test-ui`). Bypass a single commit with `git commit --no-verify`.
- Use [`github.com/mudler/xlog`](https://github.com/mudler/xlog) for logging (same API as `slog`). Do not use `fmt.Println` or the standard `log` package for operational logging.
- Use tab indentation for Go files (as defined in `.editorconfig`).

124
Makefile
View File

@@ -1,5 +1,5 @@
# Disable parallel execution for backend builds
.NOTPARALLEL: backends/diffusers backends/llama-cpp backends/turboquant backends/outetts backends/piper backends/stablediffusion-ggml backends/whisper backends/faster-whisper backends/silero-vad backends/local-store backends/huggingface backends/rfdetr backends/insightface backends/speaker-recognition backends/kitten-tts backends/kokoro backends/chatterbox backends/llama-cpp-darwin backends/neutts build-darwin-python-backend build-darwin-go-backend backends/mlx backends/diffuser-darwin backends/mlx-vlm backends/mlx-audio backends/mlx-distributed backends/stablediffusion-ggml-darwin backends/vllm backends/vllm-omni backends/sglang backends/moonshine backends/pocket-tts backends/qwen-tts backends/faster-qwen3-tts backends/qwen-asr backends/nemo backends/voxcpm backends/whisperx backends/ace-step backends/acestep-cpp backends/fish-speech backends/voxtral backends/opus backends/trl backends/llama-cpp-quantization backends/kokoros backends/sam3-cpp backends/qwen3-tts-cpp backends/vibevoice-cpp backends/localvqe backends/tinygrad backends/sherpa-onnx backends/ds4 backends/ds4-darwin backends/liquid-audio
.NOTPARALLEL: backends/diffusers backends/llama-cpp backends/turboquant backends/outetts backends/piper backends/stablediffusion-ggml backends/whisper backends/faster-whisper backends/silero-vad backends/local-store backends/huggingface backends/rfdetr backends/rfdetr-cpp backends/insightface backends/speaker-recognition backends/kitten-tts backends/kokoro backends/chatterbox backends/llama-cpp-darwin backends/neutts build-darwin-python-backend build-darwin-go-backend backends/mlx backends/diffuser-darwin backends/mlx-vlm backends/mlx-audio backends/mlx-distributed backends/stablediffusion-ggml-darwin backends/vllm backends/vllm-omni backends/sglang backends/moonshine backends/pocket-tts backends/qwen-tts backends/faster-qwen3-tts backends/qwen-asr backends/nemo backends/voxcpm backends/whisperx backends/ace-step backends/acestep-cpp backends/fish-speech backends/voxtral backends/opus backends/trl backends/llama-cpp-quantization backends/kokoros backends/sam3-cpp backends/qwen3-tts-cpp backends/vibevoice-cpp backends/localvqe backends/tinygrad backends/sherpa-onnx backends/ds4 backends/ds4-darwin backends/liquid-audio
GOCMD=go
GOTEST=$(GOCMD) test
@@ -71,8 +71,39 @@ endif
TEST_PATHS?=./api/... ./pkg/... ./core/... ./backend/go/cloud-proxy/... ./backend/go/local-store/...
## Coverage output and the committed baseline that CI compares against.
## The gate is strict: total coverage must never decrease (no tolerance).
## covermode=atomic makes line coverage deterministic regardless of test
## ordering or flake retries, so there is no run-to-run jitter to absorb.
COVERAGE_DIR?=$(abspath ./coverage)
COVERAGE_PROFILE?=$(COVERAGE_DIR)/coverage.out
COVERAGE_BASELINE?=coverage-baseline.txt
## Coverage is collected one recursive root at a time and merged (see
## scripts/run-coverage.sh): passing several recursive roots to a single
## ginkgo invocation only keeps one root's coverprofile. Mirrors TEST_PATHS
## minus ./api (which doesn't exist).
COVERAGE_ROOTS?=./pkg ./core
## Build tags for the coverage build. `auth` is required to compile the real
## auth implementation and its ~150 `//go:build auth` tests (otherwise they're
## invisible and the gate scores auth against a stub). `debug` matches `test`.
COVERAGE_TAGS?=debug auth
## Coverage is attributed to these packages via --coverpkg, so the in-process
## integration suites (COVERAGE_E2E_ROOTS) credit the core/http handlers they
## drive over HTTP — not just their own test package.
COVERAGE_COVERPKG?=github.com/mudler/LocalAI/core/...,github.com/mudler/LocalAI/pkg/...
## In-process integration suites folded into coverage. Run non-recursively
## (excludes tests/e2e/distributed, which needs containers) with the mock
## backend built by prepare-test. real-models specs need a downloaded model,
## so they're filtered out. NOTE: tests/integration is intentionally NOT here —
## it needs the local-store backend built (`make backends/local-store`), which
## the coverage CI job doesn't do.
COVERAGE_E2E_ROOTS?=./tests/e2e
COVERAGE_E2E_LABELS?=!real-models
## Drop generated protobuf from the denominator (it has no tests by design).
COVERAGE_EXCLUDE_RE?=grpc/proto/.*[.]pb[.]go
.PHONY: all test build vendor lint lint-all
.PHONY: all test test-coverage test-coverage-baseline test-coverage-check test-ui test-ui-coverage-baseline test-ui-coverage-check install-hooks build vendor lint lint-all
all: help
@@ -170,6 +201,36 @@ test: prepare-test
OPUS_SHIM_LIBRARY=$(abspath ./pkg/opus/shim/libopusshim.so) \
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --flake-attempts $(TEST_FLAKES) --fail-fast -v -r $(TEST_PATHS)
## Runs the core suite ($(TEST_PATHS)) with statement-coverage instrumentation
## and writes a merged profile to $(COVERAGE_PROFILE). Deliberately omits
## --fail-fast so a single failure doesn't truncate the coverage number, and
## uses covermode=atomic so the result is deterministic. Prints the total.
test-coverage: prepare-test
@echo 'Running tests with coverage'
GINKGO_TAGS="$(COVERAGE_TAGS)" \
COVERAGE_COVERPKG="$(COVERAGE_COVERPKG)" \
COVERAGE_E2E_ROOTS="$(COVERAGE_E2E_ROOTS)" \
COVERAGE_E2E_LABELS="$(COVERAGE_E2E_LABELS)" \
COVERAGE_EXCLUDE_RE='$(COVERAGE_EXCLUDE_RE)' \
OPUS_SHIM_LIBRARY=$(abspath ./pkg/opus/shim/libopusshim.so) \
scripts/run-coverage.sh $(COVERAGE_DIR) $(COVERAGE_PROFILE) $(TEST_FLAKES) $(COVERAGE_ROOTS)
@$(GOCMD) tool cover -html=$(COVERAGE_PROFILE) -o $(COVERAGE_DIR)/coverage.html
@$(GOCMD) tool cover -func=$(COVERAGE_PROFILE) | tail -n1
## Writes the current total coverage to $(COVERAGE_BASELINE). Run this (and
## commit the result) whenever a change legitimately raises coverage so the
## ratchet moves up. Never lower it by hand.
test-coverage-baseline: test-coverage
@$(GOCMD) tool cover -func=$(COVERAGE_PROFILE) | awk '/^total:/{gsub(/%/,"",$$NF); print $$NF}' > $(COVERAGE_BASELINE)
@echo "Saved coverage baseline: $$(cat $(COVERAGE_BASELINE))%"
## CI gate: fails if total coverage dropped more than COVERAGE_TOLERANCE
## (default 0.5pp) below the committed baseline. A small tolerance absorbs the
## run-to-run jitter from the in-process tests/e2e suite folded in via
## --coverpkg (timing-dependent which handler lines execute).
test-coverage-check: test-coverage
@scripts/coverage-check.sh $(COVERAGE_PROFILE) $(COVERAGE_BASELINE)
########################################################
## Lint
########################################################
@@ -185,12 +246,17 @@ test: prepare-test
## everything else automatically, so new packages are scanned by default.
LINT_EXCLUDE_DIRS_RE=/(backend/go/(piper|silero-vad|llm)|cmd/launcher)(/|$$)
## Set LINT_NEW_FROM to a git ref to override .golangci.yml's
## new-from-merge-base (origin/master). Useful from a fork clone where
## origin/master is stale relative to the canonical repo — the pre-commit
## hook passes the resolved upstream ref here so local lint matches CI.
LINT_NEW_FROM?=
lint:
@command -v golangci-lint >/dev/null 2>&1 || { \
echo 'golangci-lint not installed. Install: go install github.com/golangci/golangci-lint/v2/cmd/golangci-lint@latest'; \
exit 1; \
}
golangci-lint run $$(go list -e -f '{{.Dir}}' ./... | grep -vE '$(LINT_EXCLUDE_DIRS_RE)')
golangci-lint run $(if $(LINT_NEW_FROM),--new-from-merge-base=$(LINT_NEW_FROM),) $$(go list -e -f '{{.Dir}}' ./... | grep -vE '$(LINT_EXCLUDE_DIRS_RE)')
## Like `lint` but reports every issue, including the pre-existing baseline
## that `lint` ignores via .golangci.yml's new-from-merge-base. Use this to
@@ -202,6 +268,17 @@ lint-all:
}
golangci-lint run --new=false --new-from-merge-base= --new-from-rev= $$(go list -e -f '{{.Dir}}' ./... | grep -vE '$(LINT_EXCLUDE_DIRS_RE)')
########################################################
## Git hooks
########################################################
## Points git at the versioned .githooks/ directory so the pre-commit hook
## (lint + coverage gate) runs locally. Run once per clone. Undo with:
## `git config --unset core.hooksPath`. Skip a single commit with
## `git commit --no-verify`.
install-hooks:
git config core.hooksPath .githooks
@echo 'Installed git hooks: core.hooksPath -> .githooks (pre-commit runs lint + test-coverage-check on Go changes)'
########################################################
## E2E AIO tests (uses standard image with pre-configured models)
########################################################
@@ -481,6 +558,7 @@ prepare-test-extra: protogen-python
$(MAKE) -C backend/python/insightface
$(MAKE) -C backend/python/speaker-recognition
$(MAKE) -C backend/rust/kokoros kokoros-grpc
$(MAKE) -C backend/go/rfdetr-cpp
test-extra: prepare-test-extra
$(MAKE) -C backend/python/transformers test
@@ -507,6 +585,7 @@ test-extra: prepare-test-extra
$(MAKE) -C backend/python/insightface test
$(MAKE) -C backend/python/speaker-recognition test
$(MAKE) -C backend/rust/kokoros test
$(MAKE) -C backend/go/rfdetr-cpp test
##
## End-to-end gRPC tests that exercise a built backend container image.
@@ -1119,6 +1198,7 @@ BACKEND_KOKOROS = kokoros|rust|.|false|true
# C++ backends (Go wrapper with purego)
BACKEND_SAM3_CPP = sam3-cpp|golang|.|false|true
BACKEND_RFDETR_CPP = rfdetr-cpp|golang|.|false|true
# Helper function to build docker image for a backend
# Usage: $(call docker-build-backend,BACKEND_NAME,DOCKERFILE_TYPE,BUILD_CONTEXT,PROGRESS_FLAG,NEEDS_BACKEND_ARG)
@@ -1198,13 +1278,14 @@ $(eval $(call generate-docker-build-target,$(BACKEND_LLAMA_CPP_QUANTIZATION)))
$(eval $(call generate-docker-build-target,$(BACKEND_TINYGRAD)))
$(eval $(call generate-docker-build-target,$(BACKEND_KOKOROS)))
$(eval $(call generate-docker-build-target,$(BACKEND_SAM3_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_RFDETR_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_SHERPA_ONNX)))
# Pattern rule for docker-save targets
docker-save-%: backend-images
docker save local-ai-backend:$* -o backend-images/$*.tar
docker-build-backends: docker-build-llama-cpp docker-build-ik-llama-cpp docker-build-turboquant docker-build-ds4 docker-build-rerankers docker-build-vllm docker-build-vllm-omni docker-build-sglang docker-build-transformers docker-build-outetts docker-build-diffusers docker-build-kokoro docker-build-faster-whisper docker-build-coqui docker-build-chatterbox docker-build-vibevoice docker-build-liquid-audio docker-build-moonshine docker-build-pocket-tts docker-build-qwen-tts docker-build-fish-speech docker-build-faster-qwen3-tts docker-build-qwen-asr docker-build-nemo docker-build-voxcpm docker-build-whisperx docker-build-ace-step docker-build-acestep-cpp docker-build-voxtral docker-build-mlx-distributed docker-build-trl docker-build-llama-cpp-quantization docker-build-tinygrad docker-build-kokoros docker-build-sam3-cpp docker-build-qwen3-tts-cpp docker-build-vibevoice-cpp docker-build-localvqe docker-build-insightface docker-build-speaker-recognition docker-build-sherpa-onnx docker-build-cloud-proxy
docker-build-backends: docker-build-llama-cpp docker-build-ik-llama-cpp docker-build-turboquant docker-build-ds4 docker-build-rerankers docker-build-vllm docker-build-vllm-omni docker-build-sglang docker-build-transformers docker-build-outetts docker-build-diffusers docker-build-kokoro docker-build-faster-whisper docker-build-coqui docker-build-chatterbox docker-build-vibevoice docker-build-liquid-audio docker-build-moonshine docker-build-pocket-tts docker-build-qwen-tts docker-build-fish-speech docker-build-faster-qwen3-tts docker-build-qwen-asr docker-build-nemo docker-build-voxcpm docker-build-whisperx docker-build-ace-step docker-build-acestep-cpp docker-build-voxtral docker-build-mlx-distributed docker-build-trl docker-build-llama-cpp-quantization docker-build-tinygrad docker-build-kokoros docker-build-sam3-cpp docker-build-rfdetr-cpp docker-build-qwen3-tts-cpp docker-build-vibevoice-cpp docker-build-localvqe docker-build-insightface docker-build-speaker-recognition docker-build-sherpa-onnx docker-build-cloud-proxy
########################################################
### Mock Backend for E2E Tests
@@ -1232,6 +1313,41 @@ build-ui-test-server: build-mock-backend react-ui protogen-go
test-ui-e2e: build-ui-test-server
cd core/http/react-ui && npm install && npx playwright install --with-deps chromium && npx playwright test
## Fast Playwright e2e run used by the pre-commit hook on React UI changes.
## Force-rebuilds the (non-instrumented) dist so the suite tests the working
## tree — not a stale dist the `react-ui` skip-guard would leave — re-embeds
## it into ui-test-server, and runs the specs. Uses the nix-provided browser
## when PLAYWRIGHT_CHROMIUM_PATH is set (flake dev shell), else falls back to
## downloading it as `test-ui-e2e` does.
test-ui: build-mock-backend protogen-go
cd core/http/react-ui && bun install && bun run build
$(GOCMD) build -o tests/e2e-ui/ui-test-server ./tests/e2e-ui
cd core/http/react-ui && sh $(CURDIR)/scripts/ensure-playwright-browser.sh && bunx playwright test
## React UI code coverage from the Playwright e2e suite. Builds an
## istanbul-instrumented bundle (COVERAGE=true), re-embeds it into the
## ui-test-server (the dist is //go:embed'ed at compile time), runs the
## Playwright specs — which harvest window.__coverage__ via the coverage
## fixture — and writes an nyc report to core/http/react-ui/coverage/.
## Removes the instrumented dist afterwards so normal builds aren't served
## instrumented assets.
test-ui-coverage: build-mock-backend protogen-go
trap 'rm -rf "$(CURDIR)/core/http/react-ui/dist"' EXIT; \
( cd core/http/react-ui && bun install && bun run build:coverage ) && \
$(GOCMD) build -o tests/e2e-ui/ui-test-server ./tests/e2e-ui && \
( cd core/http/react-ui && rm -rf .nyc_output coverage && \
sh $(CURDIR)/scripts/ensure-playwright-browser.sh && \
bunx playwright test && bun run coverage:report )
## UI coverage baseline (committed) and the strict gate that compares against
## it — the React mirror of test-coverage-baseline / test-coverage-check.
test-ui-coverage-baseline: test-ui-coverage
@node -e 'const fs=require("fs");process.stdout.write(String(JSON.parse(fs.readFileSync("core/http/react-ui/coverage/coverage-summary.json")).total.lines.pct))' > core/http/react-ui/coverage-baseline.txt
@echo "Saved UI coverage baseline: $$(cat core/http/react-ui/coverage-baseline.txt)% lines"
test-ui-coverage-check: test-ui-coverage
sh $(CURDIR)/scripts/ui-coverage-check.sh core/http/react-ui/coverage/coverage-summary.json core/http/react-ui/coverage-baseline.txt
test-ui-e2e-docker:
docker build -t localai-ui-e2e -f tests/e2e-ui/Dockerfile .
docker run --rm localai-ui-e2e

View File

@@ -149,8 +149,10 @@ For more details, see the [Getting Started guide](https://localai.io/basics/gett
## Latest News
- **April 2026**: [Voice recognition](https://github.com/mudler/LocalAI/pull/9500), [Face recognition, identification & liveness detection](https://github.com/mudler/LocalAI/pull/9480), [Ollama API compatibility](https://github.com/mudler/LocalAI/pull/9284), [Video generation in stable-diffusion.ggml](https://github.com/mudler/LocalAI/pull/9420), [Backend versioning with auto-upgrade](https://github.com/mudler/LocalAI/pull/9315), [Pin models & load-on-demand toggle](https://github.com/mudler/LocalAI/pull/9309), [Universal model importer](https://github.com/mudler/LocalAI/pull/9466), new backends: [sglang](https://github.com/mudler/LocalAI/pull/9359), [ik-llama-cpp](https://github.com/mudler/LocalAI/pull/9326), [TurboQuant](https://github.com/mudler/LocalAI/pull/9355), [sam.cpp](https://github.com/mudler/LocalAI/pull/9288), [Kokoros](https://github.com/mudler/LocalAI/pull/9212), [qwen3tts.cpp](https://github.com/mudler/LocalAI/pull/9316), [tinygrad multimodal](https://github.com/mudler/LocalAI/pull/9364)
- **March 2026**: [Agent management](https://github.com/mudler/LocalAI/pull/8820), [New React UI](https://github.com/mudler/LocalAI/pull/8772), [WebRTC](https://github.com/mudler/LocalAI/pull/8790), [MLX-distributed via P2P and RDMA](https://github.com/mudler/LocalAI/pull/8801), [MCP Apps, MCP Client-side](https://github.com/mudler/LocalAI/pull/8947)
- **May 2026**: **LocalAI 4.3.0** - `llama.cpp` [prompt cache on by default](https://github.com/mudler/LocalAI/pull/9925) (repeated system prompts collapse from minutes to seconds), [keyless cosign signing of backend OCI images](https://github.com/mudler/LocalAI/pull/9823), [per-API-key + per-user usage attribution](https://github.com/mudler/LocalAI/pull/9920), Distributed v3 with [per-request replica routing](https://github.com/mudler/LocalAI/pull/9968). [Release notes](https://github.com/mudler/LocalAI/releases/tag/v4.3.0)
- **May 2026**: **LocalAI 4.2.0** - LocalAI sees and hears: [voice recognition](https://github.com/mudler/LocalAI/pull/9500), [face recognition + antispoofing liveness](https://github.com/mudler/LocalAI/pull/9480), speaker diarization. Plus [drop-in Ollama API](https://github.com/mudler/LocalAI/pull/9284), [video generation](https://github.com/mudler/LocalAI/pull/9420), redesigned UI with i18n + admin-configurable branding, vLLM at feature parity with llama.cpp, and 11 new backends. [Release notes](https://github.com/mudler/LocalAI/releases/tag/v4.2.0)
- **April 2026**: **LocalAI 4.1.0** - LocalAI becomes a control tower: distributed cluster mode with VRAM-aware smart routing + autoscaling, multi-user platform with OIDC and API keys, per-user quotas with predictive analytics, in-UI fine-tuning with TRL (auto-export to GGUF), on-the-fly quantization backend, visual pipeline editor. [Release notes](https://github.com/mudler/LocalAI/releases/tag/v4.1.0)
- **March 2026**: **LocalAI 4.0.0** - native agentic orchestration with the new [Agenthub](https://agenthub.localai.io) community hub, full React UI rewrite with Canvas mode, [MCP Apps + client-side](https://github.com/mudler/LocalAI/pull/8947) with tool streaming, [WebRTC realtime audio](https://github.com/mudler/LocalAI/pull/8790), [MLX-distributed](https://github.com/mudler/LocalAI/pull/8801). [Release notes](https://github.com/mudler/LocalAI/releases/tag/v4.0.0)
- **February 2026**: [Realtime API for audio-to-audio with tool calling](https://github.com/mudler/LocalAI/pull/6245), [ACE-Step 1.5 support](https://github.com/mudler/LocalAI/pull/8396)
- **January 2026**: **LocalAI 3.10.0** — Anthropic API support, Open Responses API, video & image generation (LTX-2), unified GPU backends, tool streaming, Moonshine, Pocket-TTS. [Release notes](https://github.com/mudler/LocalAI/releases/tag/v3.10.0)
- **December 2025**: [Dynamic Memory Resource reclaimer](https://github.com/mudler/LocalAI/pull/7583), [Automatic multi-GPU model fitting (llama.cpp)](https://github.com/mudler/LocalAI/pull/7584), [Vibevoice backend](https://github.com/mudler/LocalAI/pull/7494)
@@ -236,11 +238,22 @@ A huge thank you to our generous sponsors who support this project covering CI e
<a href="https://www.spectrocloud.com/" target="blank">
<img height="200" src="https://github.com/user-attachments/assets/72eab1dd-8b93-4fc0-9ade-84db49f24962">
</a>
</p>
<details>
<summary>
Past sponsors
</summary>
<p align="center">
<a href="https://www.premai.io/" target="blank">
<img height="200" src="https://github.com/mudler/LocalAI/assets/2420543/42e4ca83-661e-4f79-8e46-ae43689683d6"> <br>
</a>
</p>
</details>
### Individual sponsors
A special thanks to individual sponsors, a full list is on [GitHub](https://github.com/sponsors/mudler) and [buymeacoffee](https://buymeacoffee.com/mudler). Special shout out to [drikster80](https://github.com/drikster80) for being generous. Thank you everyone!

View File

@@ -1,10 +1,10 @@
# ds4 backend Makefile.
#
# Upstream pin lives below as DS4_VERSION?=f91c12b50a1448527c435c028bfc70d1b00f6c33
# Upstream pin lives below as DS4_VERSION?=e8e8779b261c10f36ad6270ba732c8f0be5b62e3
# (.github/bump_deps.sh) can find and update it - matches the
# llama-cpp / ik-llama-cpp / turboquant convention.
DS4_VERSION?=f91c12b50a1448527c435c028bfc70d1b00f6c33
DS4_VERSION?=e8e8779b261c10f36ad6270ba732c8f0be5b62e3
DS4_REPO?=https://github.com/antirez/ds4
CURRENT_MAKEFILE_DIR := $(dir $(abspath $(lastword $(MAKEFILE_LIST))))

View File

@@ -1,5 +1,5 @@
IK_LLAMA_VERSION?=9f7ba245ab41e118f03aa8dd5134d18a81159d02
IK_LLAMA_VERSION?=d2da6da05c73aeb658a3d1751f386c24e6963856
LLAMA_REPO?=https://github.com/ikawrakow/ik_llama.cpp
CMAKE_ARGS?=

View File

@@ -1,5 +1,5 @@
LLAMA_VERSION?=549b9d84330c327e6791fa812a7d60c0cf63572e
LLAMA_VERSION?=0d18aaa9d1a8af3df9abccd828e22eeaac7f840b
LLAMA_REPO?=https://github.com/ggerganov/llama.cpp
CMAKE_ARGS?=

View File

@@ -570,9 +570,11 @@ static void params_parse(server_context& /*ctx_server*/, const backend::ModelOpt
// kv_unified=false or cache_ram_mib=0, so flipping kv_unified above is
// what actually unlocks it.
params.cache_idle_slots = true;
// checkpoint_every_nt: create a context checkpoint every N tokens during
// prefill (-1 disables). Match upstream's default (8192).
params.checkpoint_every_nt = 8192;
// checkpoint_min_step: minimum spacing between context checkpoints in
// tokens (0 disables the minimum). Match upstream's default (256). This
// field was renamed from `checkpoint_every_nt` in llama.cpp; the semantics
// also shifted from a fixed cadence to a minimum spacing.
params.checkpoint_min_step = 256;
// decode options. Options are in form optname:optvale, or if booleans only optname.
for (int i = 0; i < request->options_size(); i++) {
@@ -746,14 +748,18 @@ static void params_parse(server_context& /*ctx_server*/, const backend::ModelOpt
params.cache_idle_slots = false;
}
// --- prefill checkpoint cadence (upstream -cpent / --checkpoint-every-n-tokens) ---
// -1 disables checkpointing during prefill.
} else if (!strcmp(optname, "checkpoint_every_nt") || !strcmp(optname, "checkpoint_every_n_tokens")) {
// --- minimum context-checkpoint spacing (upstream -cms / --checkpoint-min-step) ---
// 0 disables the minimum-spacing gate. Old option names (`checkpoint_every_nt`,
// `checkpoint_every_n_tokens`) are kept as aliases for backward compatibility
// with existing user configs: upstream renamed the field and shifted its
// semantics from a fixed cadence to a minimum spacing.
} else if (!strcmp(optname, "checkpoint_min_step") || !strcmp(optname, "checkpoint_min_spacing") ||
!strcmp(optname, "checkpoint_every_nt") || !strcmp(optname, "checkpoint_every_n_tokens")) {
if (optval != NULL) {
try {
params.checkpoint_every_nt = std::stoi(optval_str);
params.checkpoint_min_step = std::stoi(optval_str);
} catch (const std::exception& e) {
// If conversion fails, keep default value (8192)
// If conversion fails, keep default value (256)
}
}

7
backend/go/rfdetr-cpp/.gitignore vendored Normal file
View File

@@ -0,0 +1,7 @@
sources/
build*/
package/
librfdetrcpp*.so
rfdetr-cpp
test-models/
test-data/

View File

@@ -0,0 +1,79 @@
cmake_minimum_required(VERSION 3.18)
project(librfdetrcpp LANGUAGES C CXX)
set(CMAKE_POSITION_INDEPENDENT_CODE ON)
set(CMAKE_CXX_STANDARD 17)
set(CMAKE_CXX_STANDARD_REQUIRED ON)
# Static-link ggml + rfdetr so the resulting .so has no runtime dependency on
# extra ggml/rfdetr shared libraries — only on libc/libstdc++/libgomp, which
# the LocalAI package step bundles into the docker image.
set(BUILD_SHARED_LIBS OFF CACHE BOOL "Build static libraries" FORCE)
# rfdetr.cpp build switches: skip CLI/tests, keep static lib.
set(RFDETR_BUILD_CLI OFF CACHE BOOL "Disable rfdetr CLI" FORCE)
set(RFDETR_BUILD_TESTS OFF CACHE BOOL "Disable rfdetr tests" FORCE)
set(RFDETR_SHARED OFF CACHE BOOL "Build rfdetr as static lib" FORCE)
# rt-detr.cpp's top-level CMakeLists invokes
# `bash ${CMAKE_SOURCE_DIR}/scripts/apply_ggml_patches.sh` to apply its
# in-tree ggml patches before descending into the submodule. When we
# `add_subdirectory` it from a parent project, `CMAKE_SOURCE_DIR` points
# at *our* directory, not theirs, so the script path resolves wrong.
#
# Run the patches script ourselves up front (it's idempotent — re-running
# is a no-op once patches are applied) so the rt-detr.cpp configure step
# is essentially a no-op for the patch hook.
set(RFDETR_CPP_SRC ${CMAKE_CURRENT_SOURCE_DIR}/sources/rt-detr.cpp)
if(EXISTS ${RFDETR_CPP_SRC}/scripts/apply_ggml_patches.sh)
execute_process(
COMMAND bash ${RFDETR_CPP_SRC}/scripts/apply_ggml_patches.sh
RESULT_VARIABLE _rfdetr_patch_result
OUTPUT_VARIABLE _rfdetr_patch_output
ERROR_VARIABLE _rfdetr_patch_error
OUTPUT_STRIP_TRAILING_WHITESPACE
ERROR_STRIP_TRAILING_WHITESPACE)
if(NOT _rfdetr_patch_result EQUAL 0)
message(FATAL_ERROR
"Failed to apply ggml patches (exit ${_rfdetr_patch_result}):\n"
"stdout:\n${_rfdetr_patch_output}\n"
"stderr:\n${_rfdetr_patch_error}")
endif()
message(STATUS "${_rfdetr_patch_output}")
endif()
# Stage a shim 'scripts/apply_ggml_patches.sh' under our source dir so that
# rt-detr.cpp's CMakeLists — which calls
# bash ${CMAKE_SOURCE_DIR}/scripts/apply_ggml_patches.sh
# — finds an idempotent no-op there. The real patches have already been
# applied above; this just satisfies the path lookup.
file(MAKE_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/scripts)
file(WRITE ${CMAKE_CURRENT_SOURCE_DIR}/scripts/apply_ggml_patches.sh
"#!/usr/bin/env bash
# Shim - patches were already applied by the parent CMakeLists.
exit 0
")
execute_process(COMMAND chmod +x ${CMAKE_CURRENT_SOURCE_DIR}/scripts/apply_ggml_patches.sh)
add_subdirectory(./sources/rt-detr.cpp)
# rfdetr.cpp's C-API symbols already live inside librfdetr (src/rfdetr_capi.cpp
# is compiled into the lib). We re-export them via a MODULE library that
# whole-archive-links rfdetr so the symbols are visible at dlopen time.
add_library(rfdetrcpp MODULE
sources/rt-detr.cpp/src/rfdetr_capi.cpp)
target_include_directories(rfdetrcpp PRIVATE
sources/rt-detr.cpp/include
sources/rt-detr.cpp/src
sources/rt-detr.cpp/third_party/stb
)
target_link_libraries(rfdetrcpp PRIVATE rfdetr ggml)
if(CMAKE_CXX_COMPILER_ID MATCHES "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_LESS 9.0)
target_link_libraries(rfdetrcpp PRIVATE stdc++fs)
endif()
set_property(TARGET rfdetrcpp PROPERTY CXX_STANDARD 17)
set_target_properties(rfdetrcpp PROPERTIES LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR})

View File

@@ -0,0 +1,135 @@
CMAKE_ARGS?=
BUILD_TYPE?=
NATIVE?=false
GOCMD?=go
GO_TAGS?=
JOBS?=$(shell nproc --ignore=1)
# rt-detr.cpp (GitHub redirects the historical mudler/rt-detr.cpp to the new
# mudler/rf-detr.cpp slug). Pin to a specific commit if you need a stable
# build; leaving this on `master` always picks up the latest C-API surface
# (incl. the per-detection accessor functions used by gorfdetrcpp.go).
RFDETR_REPO?=https://github.com/mudler/rf-detr.cpp.git
RFDETR_VERSION?=ecf64d77b09013e7e90af6a17b9ce884e7daa86c
ifeq ($(NATIVE),false)
CMAKE_ARGS+=-DGGML_NATIVE=OFF
endif
# Forward LocalAI's BUILD_TYPE to the matching ggml backend switch.
ifeq ($(BUILD_TYPE),cublas)
CMAKE_ARGS+=-DGGML_CUDA=ON -DRFDETR_GGML_CUDA=ON
else ifeq ($(BUILD_TYPE),openblas)
CMAKE_ARGS+=-DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS
else ifeq ($(BUILD_TYPE),clblas)
CMAKE_ARGS+=-DGGML_CLBLAST=ON
else ifeq ($(BUILD_TYPE),hipblas)
ROCM_HOME ?= /opt/rocm
ROCM_PATH ?= /opt/rocm
export CXX=$(ROCM_HOME)/llvm/bin/clang++
export CC=$(ROCM_HOME)/llvm/bin/clang
AMDGPU_TARGETS?=gfx908,gfx90a,gfx942,gfx950,gfx1030,gfx1100,gfx1101,gfx1102,gfx1200,gfx1201
CMAKE_ARGS+=-DGGML_HIPBLAS=ON -DRFDETR_GGML_HIPBLAS=ON -DAMDGPU_TARGETS=$(AMDGPU_TARGETS)
else ifeq ($(BUILD_TYPE),vulkan)
CMAKE_ARGS+=-DGGML_VULKAN=ON -DRFDETR_GGML_VULKAN=ON
else ifeq ($(OS),Darwin)
ifneq ($(BUILD_TYPE),metal)
CMAKE_ARGS+=-DGGML_METAL=OFF
else
CMAKE_ARGS+=-DGGML_METAL=ON
CMAKE_ARGS+=-DGGML_METAL_EMBED_LIBRARY=ON
CMAKE_ARGS+=-DRFDETR_GGML_METAL=ON
endif
endif
ifeq ($(BUILD_TYPE),sycl_f16)
CMAKE_ARGS+=-DGGML_SYCL=ON \
-DCMAKE_C_COMPILER=icx \
-DCMAKE_CXX_COMPILER=icpx \
-DGGML_SYCL_F16=ON
endif
ifeq ($(BUILD_TYPE),sycl_f32)
CMAKE_ARGS+=-DGGML_SYCL=ON \
-DCMAKE_C_COMPILER=icx \
-DCMAKE_CXX_COMPILER=icpx
endif
sources/rt-detr.cpp:
mkdir -p sources && \
git clone --recursive $(RFDETR_REPO) sources/rt-detr.cpp && \
cd sources/rt-detr.cpp && \
git checkout $(RFDETR_VERSION) && \
git submodule update --init --recursive --depth 1 --single-branch
# Detect OS
UNAME_S := $(shell uname -s)
# Only build CPU variants on Linux
ifeq ($(UNAME_S),Linux)
VARIANT_TARGETS = librfdetrcpp-avx.so librfdetrcpp-avx2.so librfdetrcpp-avx512.so librfdetrcpp-fallback.so
else
# On non-Linux (e.g., Darwin), build only fallback variant
VARIANT_TARGETS = librfdetrcpp-fallback.so
endif
rfdetr-cpp: main.go gorfdetrcpp.go $(VARIANT_TARGETS)
CGO_ENABLED=0 $(GOCMD) build -tags "$(GO_TAGS)" -o rfdetr-cpp ./
package: rfdetr-cpp
bash package.sh
build: package
clean: purge
rm -rf librfdetrcpp*.so rfdetr-cpp package sources
purge:
rm -rf build*
# Build all variants (Linux only)
ifeq ($(UNAME_S),Linux)
librfdetrcpp-avx.so: sources/rt-detr.cpp
rm -rfv build-$@
$(info ${GREEN}I rfdetr-cpp build info:avx${RESET})
SO_TARGET=$@ CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_AVX=on -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_BMI2=off" $(MAKE) librfdetrcpp-custom
rm -rfv build-$@
librfdetrcpp-avx2.so: sources/rt-detr.cpp
rm -rfv build-$@
$(info ${GREEN}I rfdetr-cpp build info:avx2${RESET})
SO_TARGET=$@ CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_AVX=on -DGGML_AVX2=on -DGGML_AVX512=off -DGGML_FMA=on -DGGML_F16C=on -DGGML_BMI2=on" $(MAKE) librfdetrcpp-custom
rm -rfv build-$@
librfdetrcpp-avx512.so: sources/rt-detr.cpp
rm -rfv build-$@
$(info ${GREEN}I rfdetr-cpp build info:avx512${RESET})
SO_TARGET=$@ CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_AVX=on -DGGML_AVX2=on -DGGML_AVX512=on -DGGML_FMA=on -DGGML_F16C=on -DGGML_BMI2=on" $(MAKE) librfdetrcpp-custom
rm -rfv build-$@
endif
# Build fallback variant (all platforms)
librfdetrcpp-fallback.so: sources/rt-detr.cpp
rm -rfv build-$@
$(info ${GREEN}I rfdetr-cpp build info:fallback${RESET})
SO_TARGET=$@ CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_AVX=off -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_BMI2=off" $(MAKE) librfdetrcpp-custom
rm -rfv build-$@
librfdetrcpp-custom: CMakeLists.txt
mkdir -p build-$(SO_TARGET) && \
cd build-$(SO_TARGET) && \
cmake .. $(CMAKE_ARGS) && \
cmake --build . --config Release -j$(JOBS) && \
cd .. && \
mv build-$(SO_TARGET)/librfdetrcpp.so ./$(SO_TARGET)
all: rfdetr-cpp package
# `test` is invoked by the top-level Makefile's `test-extra` target. It builds
# the backend binary + the fallback shared library (needed for dlopen at
# runtime), then runs test.sh which downloads the test models + COCO image
# and exercises the gRPC Load/Detect wire path via the Go smoke test in
# main_test.go for both the detection and segmentation models.
test: rfdetr-cpp librfdetrcpp-fallback.so
bash test.sh

View File

@@ -0,0 +1,195 @@
package main
// gorfdetrcpp.go - gRPC handlers (Load, Detect) for the rfdetr-cpp backend.
//
// Embeds base.SingleThread to default unimplemented RPCs to "not supported"
// while we only implement object detection.
import (
"encoding/base64"
"fmt"
"os"
"path/filepath"
"strconv"
"unsafe"
"github.com/mudler/LocalAI/pkg/grpc/base"
pb "github.com/mudler/LocalAI/pkg/grpc/proto"
)
// Default upper bound on detections returned per image. RF-DETR's decoder
// queries are limited to a few hundred; 300 is a safe ceiling.
const defaultTopK = 300
// rfdetr_handle_t is a uintptr-typed opaque handle (see include/rfdetr_capi.h).
var (
// rfdetr_capi_load(const char* model_path, int n_threads, rfdetr_handle_t* out_handle) -> int
CapiLoad func(modelPath string, nThreads int32, outHandle *uintptr) int32
// rfdetr_capi_unload(rfdetr_handle_t handle) -> int
CapiUnload func(handle uintptr) int32
// rfdetr_capi_detect_path(handle, image_path, threshold, top_k, out_json) -> int
CapiDetectPath func(handle uintptr, imagePath string, threshold float32, topK uint32, outJSON *uintptr) int32
// rfdetr_capi_detect_buffer(handle, bytes, len, threshold, top_k, out_json) -> int
CapiDetectBuffer func(handle uintptr, bytes uintptr, length uintptr, threshold float32, topK uint32, outJSON *uintptr) int32
// rfdetr_capi_free_string(char* s)
CapiFreeString func(s uintptr)
// rfdetr_capi_get_n_detections(handle) -> int
CapiGetNDetections func(handle uintptr) int32
// rfdetr_capi_get_detection_class_id(handle, i) -> int
CapiGetDetectionClassID func(handle uintptr, i int32) int32
// rfdetr_capi_get_detection_box(handle, i, out_xyxy[4]) -> int (0 on success)
CapiGetDetectionBox func(handle uintptr, i int32, outXYXY uintptr) int32
// rfdetr_capi_get_detection_score(handle, i) -> float
CapiGetDetectionScore func(handle uintptr, i int32) float32
// rfdetr_capi_get_detection_class_name(handle, i, buf, buf_size) -> int (needed/written; two-call sizing)
CapiGetDetectionClassName func(handle uintptr, i int32, buf uintptr, bufSize int32) int32
// rfdetr_capi_get_detection_mask_png(handle, i, buf, buf_size) -> int (needed/written; 0 means no mask)
CapiGetDetectionMaskPNG func(handle uintptr, i int32, buf uintptr, bufSize int32) int32
)
type RFDetrCpp struct {
base.SingleThread
handle uintptr
}
// Load loads the GGUF model at opts.ModelFile (joined with opts.ModelPath if relative)
// and stores the handle for later Detect calls.
func (r *RFDetrCpp) Load(opts *pb.ModelOptions) error {
modelFile := opts.ModelFile
if modelFile == "" {
modelFile = opts.Model
}
if modelFile == "" {
return fmt.Errorf("rfdetr-cpp: ModelFile is empty")
}
var modelPath string
if filepath.IsAbs(modelFile) {
modelPath = modelFile
} else {
modelPath = filepath.Join(opts.ModelPath, modelFile)
}
if _, err := os.Stat(modelPath); err != nil {
return fmt.Errorf("rfdetr-cpp: model file not found: %s: %w", modelPath, err)
}
threads := opts.Threads
if threads <= 0 {
threads = 4
}
// Release previous model if any (re-Load).
if r.handle != 0 {
CapiUnload(r.handle)
r.handle = 0
}
var h uintptr
rc := CapiLoad(modelPath, threads, &h)
if rc != 0 || h == 0 {
return fmt.Errorf("rfdetr-cpp: rfdetr_capi_load failed with rc=%d for %s", rc, modelPath)
}
r.handle = h
return nil
}
// Detect runs object detection on the base64-encoded image in opts.Src at
// opts.Threshold, returning one pb.Detection per result. Seg models also
// populate Detection.Mask with PNG-encoded mask bytes.
func (r *RFDetrCpp) Detect(opts *pb.DetectOptions) (pb.DetectResponse, error) {
if r.handle == 0 {
return pb.DetectResponse{}, fmt.Errorf("rfdetr-cpp: model not loaded")
}
// Decode base64 image and write to temp file.
imgData, err := base64.StdEncoding.DecodeString(opts.Src)
if err != nil {
return pb.DetectResponse{}, fmt.Errorf("rfdetr-cpp: failed to decode base64 image: %w", err)
}
tmpFile, err := os.CreateTemp("", "rfdetr-*.img")
if err != nil {
return pb.DetectResponse{}, fmt.Errorf("rfdetr-cpp: failed to create temp file: %w", err)
}
defer func() { _ = os.Remove(tmpFile.Name()) }()
if _, err := tmpFile.Write(imgData); err != nil {
_ = tmpFile.Close()
return pb.DetectResponse{}, fmt.Errorf("rfdetr-cpp: failed to write temp file: %w", err)
}
if err := tmpFile.Close(); err != nil {
return pb.DetectResponse{}, fmt.Errorf("rfdetr-cpp: failed to close temp file: %w", err)
}
threshold := opts.Threshold
if threshold <= 0 {
threshold = 0.5
}
// JSON output from detect_path is unused: we read structured detections via
// the accessor functions. Still must free the returned string.
var jsonPtr uintptr
rc := CapiDetectPath(r.handle, tmpFile.Name(), threshold, uint32(defaultTopK), &jsonPtr)
if jsonPtr != 0 {
CapiFreeString(jsonPtr)
}
if rc != 0 {
return pb.DetectResponse{}, fmt.Errorf("rfdetr-cpp: detect failed with rc=%d", rc)
}
n := CapiGetNDetections(r.handle)
if n < 0 {
return pb.DetectResponse{}, fmt.Errorf("rfdetr-cpp: invalid n_detections=%d", n)
}
detections := make([]*pb.Detection, 0, n)
for i := int32(0); i < n; i++ {
var bbox [4]float32 // x1, y1, x2, y2
if rc := CapiGetDetectionBox(r.handle, i, uintptr(unsafe.Pointer(&bbox[0]))); rc != 0 {
continue
}
cid := CapiGetDetectionClassID(r.handle, i)
score := CapiGetDetectionScore(r.handle, i)
// Two-call sizing for class_name.
var className string
nameSize := CapiGetDetectionClassName(r.handle, i, 0, 0)
if nameSize > 1 {
buf := make([]byte, nameSize)
written := CapiGetDetectionClassName(r.handle, i, uintptr(unsafe.Pointer(&buf[0])), nameSize)
// `written` is the same number (needed bytes including NUL); strip NUL.
if written > 0 && int(written) <= len(buf) {
className = string(buf[:written-1])
} else {
className = string(buf[:len(buf)-1])
}
}
if className == "" {
className = strconv.Itoa(int(cid))
}
// Two-call sizing for mask PNG (returns 0 when no mask).
var mask []byte
maskSize := CapiGetDetectionMaskPNG(r.handle, i, 0, 0)
if maskSize > 0 {
maskBuf := make([]byte, maskSize)
CapiGetDetectionMaskPNG(r.handle, i, uintptr(unsafe.Pointer(&maskBuf[0])), maskSize)
mask = maskBuf
}
detections = append(detections, &pb.Detection{
X: bbox[0],
Y: bbox[1],
Width: bbox[2] - bbox[0],
Height: bbox[3] - bbox[1],
Confidence: score,
ClassName: className,
Mask: mask,
})
}
return pb.DetectResponse{
Detections: detections,
}, nil
}

View File

@@ -0,0 +1,61 @@
package main
// main.go - entry point for the rfdetr-cpp gRPC backend.
//
// Dlopens librfdetrcpp-<variant>.so via purego at the path in
// RFDETR_LIBRARY (set by run.sh based on /proc/cpuinfo), registers the
// rfdetr_capi_* C ABI symbols, then starts the gRPC server.
import (
"flag"
"os"
"github.com/ebitengine/purego"
grpc "github.com/mudler/LocalAI/pkg/grpc"
)
var (
addr = flag.String("addr", "localhost:50051", "the address to connect to")
)
type LibFuncs struct {
FuncPtr any
Name string
}
func main() {
// Get library name from environment variable, default to fallback
libName := os.Getenv("RFDETR_LIBRARY")
if libName == "" {
libName = "./librfdetrcpp-fallback.so"
}
rfdetrLib, err := purego.Dlopen(libName, purego.RTLD_NOW|purego.RTLD_GLOBAL)
if err != nil {
panic(err)
}
libFuncs := []LibFuncs{
{&CapiLoad, "rfdetr_capi_load"},
{&CapiUnload, "rfdetr_capi_unload"},
{&CapiDetectPath, "rfdetr_capi_detect_path"},
{&CapiDetectBuffer, "rfdetr_capi_detect_buffer"},
{&CapiFreeString, "rfdetr_capi_free_string"},
{&CapiGetNDetections, "rfdetr_capi_get_n_detections"},
{&CapiGetDetectionClassID, "rfdetr_capi_get_detection_class_id"},
{&CapiGetDetectionBox, "rfdetr_capi_get_detection_box"},
{&CapiGetDetectionScore, "rfdetr_capi_get_detection_score"},
{&CapiGetDetectionClassName, "rfdetr_capi_get_detection_class_name"},
{&CapiGetDetectionMaskPNG, "rfdetr_capi_get_detection_mask_png"},
}
for _, lf := range libFuncs {
purego.RegisterLibFunc(lf.FuncPtr, rfdetrLib, lf.Name)
}
flag.Parse()
if err := grpc.StartServer(*addr, &RFDetrCpp{}); err != nil {
panic(err)
}
}

View File

@@ -0,0 +1,220 @@
package main
// main_test.go - end-to-end smoke test for the rfdetr-cpp gRPC backend.
//
// Spawns the compiled rfdetr-cpp binary on a free local port, dials it via
// gRPC, and exercises LoadModel + Detect against the test fixtures
// downloaded by test.sh. Two scenarios:
//
// 1. detection — loads rfdetr-nano-q8_0.gguf and asserts at least one
// detection comes back with a non-empty class name and a bounding box
// of non-zero size.
// 2. segmentation — loads rfdetr-seg-nano-q8_0.gguf and additionally
// asserts that at least one detection carries a PNG-encoded mask blob
// (verified by PNG magic bytes).
//
// Both specs Skip cleanly if their fixtures are missing so the test target
// stays usable on a fresh checkout where models haven't been downloaded.
import (
"context"
"encoding/base64"
"fmt"
"net"
"os"
"os/exec"
"path/filepath"
"testing"
"time"
pb "github.com/mudler/LocalAI/pkg/grpc/proto"
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
"google.golang.org/grpc"
"google.golang.org/grpc/credentials/insecure"
)
func TestRFDetrCpp(t *testing.T) {
RegisterFailHandler(Fail)
RunSpecs(t, "rfdetr-cpp backend smoke suite")
}
// freePort grabs an ephemeral TCP port and immediately releases it so the
// spawned backend can bind to it. There is a tiny TOCTOU window here but in
// practice it's adequate for a smoke test on a quiet runner.
func freePort() int {
l, err := net.Listen("tcp", "127.0.0.1:0")
Expect(err).ToNot(HaveOccurred(), "freePort listen")
port := l.Addr().(*net.TCPAddr).Port
Expect(l.Close()).To(Succeed())
return port
}
// startBackend spawns the rfdetr-cpp binary on the given port and waits
// until it accepts TCP connections (up to 10s). The returned cleanup func
// kills the process and reaps it.
func startBackend(port int) func() {
binary, err := filepath.Abs("./rfdetr-cpp")
Expect(err).ToNot(HaveOccurred())
if _, err := os.Stat(binary); err != nil {
Skip(fmt.Sprintf("backend binary not built: %s (run `make rfdetr-cpp` first)", binary))
}
libPath, err := filepath.Abs("./librfdetrcpp-fallback.so")
Expect(err).ToNot(HaveOccurred())
if _, err := os.Stat(libPath); err != nil {
Skip(fmt.Sprintf("fallback library not built: %s (run `make librfdetrcpp-fallback.so` first)", libPath))
}
addr := fmt.Sprintf("127.0.0.1:%d", port)
cmd := exec.Command(binary, "--addr", addr)
cmd.Env = append(os.Environ(), "RFDETR_LIBRARY="+libPath)
cmd.Stdout = os.Stderr
cmd.Stderr = os.Stderr
Expect(cmd.Start()).To(Succeed())
cleanup := func() {
if cmd.Process != nil {
_ = cmd.Process.Kill()
_, _ = cmd.Process.Wait()
}
}
deadline := time.Now().Add(10 * time.Second)
for time.Now().Before(deadline) {
c, err := net.DialTimeout("tcp", addr, 200*time.Millisecond)
if err == nil {
_ = c.Close()
return cleanup
}
time.Sleep(200 * time.Millisecond)
}
cleanup()
Fail(fmt.Sprintf("backend did not become ready on %s within 10s", addr))
return func() {}
}
// loadTestImage reads the COCO test image downloaded by test.sh and returns
// its base64-encoded content (the wire format accepted by the Detect RPC).
func loadTestImage() string {
imgPath, err := filepath.Abs("test-data/test.jpg")
Expect(err).ToNot(HaveOccurred())
imgBytes, err := os.ReadFile(imgPath)
if err != nil {
Skip(fmt.Sprintf("test image not present: %s (run test.sh first)", imgPath))
}
return base64.StdEncoding.EncodeToString(imgBytes)
}
// dialBackend opens a gRPC client connection to the spawned backend.
func dialBackend(port int) (pb.BackendClient, func()) {
addr := fmt.Sprintf("127.0.0.1:%d", port)
conn, err := grpc.NewClient(addr, grpc.WithTransportCredentials(insecure.NewCredentials()))
Expect(err).ToNot(HaveOccurred())
return pb.NewBackendClient(conn), func() { _ = conn.Close() }
}
// modelPathOrSkip resolves a model file under ./test-models/ and Skip()s
// the current spec if it's missing.
func modelPathOrSkip(name string) string {
modelDir, err := filepath.Abs("test-models")
Expect(err).ToNot(HaveOccurred())
modelPath := filepath.Join(modelDir, name)
if _, err := os.Stat(modelPath); err != nil {
Skip(fmt.Sprintf("model not present: %s (run test.sh first)", modelPath))
}
return modelPath
}
var _ = Describe("rfdetr-cpp backend", func() {
It("runs object detection against a known-good COCO image", func() {
modelPath := modelPathOrSkip("rfdetr-nano-q8_0.gguf")
imgB64 := loadTestImage()
port := freePort()
cleanup := startBackend(port)
defer cleanup()
client, closeConn := dialBackend(port)
defer closeConn()
ctx, cancel := context.WithTimeout(context.Background(), 60*time.Second)
defer cancel()
loadResp, err := client.LoadModel(ctx, &pb.ModelOptions{
Model: "rfdetr-nano-q8_0.gguf",
ModelFile: modelPath,
Threads: 2,
})
Expect(err).ToNot(HaveOccurred(), "LoadModel")
Expect(loadResp.GetSuccess()).To(BeTrue(), "LoadModel reported failure: %s", loadResp.GetMessage())
detResp, err := client.Detect(ctx, &pb.DetectOptions{
Src: imgB64,
Threshold: 0.5,
})
Expect(err).ToNot(HaveOccurred(), "Detect")
Expect(detResp.GetDetections()).ToNot(BeEmpty(), "no detections returned on a known-good COCO image")
_, _ = fmt.Fprintf(GinkgoWriter, "detection OK: %d detections\n", len(detResp.GetDetections()))
for i, d := range detResp.GetDetections() {
Expect(d.GetClassName()).ToNot(BeEmpty(), "detection %d has empty class_name", i)
Expect(d.GetConfidence()).To(BeNumerically(">=", float32(0.5)),
"detection %d below threshold", i)
Expect(d.GetWidth()).To(BeNumerically(">", float32(0)),
"detection %d has non-positive width", i)
Expect(d.GetHeight()).To(BeNumerically(">", float32(0)),
"detection %d has non-positive height", i)
}
})
It("runs segmentation and returns PNG-encoded masks", func() {
modelPath := modelPathOrSkip("rfdetr-seg-nano-q8_0.gguf")
imgB64 := loadTestImage()
port := freePort()
cleanup := startBackend(port)
defer cleanup()
client, closeConn := dialBackend(port)
defer closeConn()
ctx, cancel := context.WithTimeout(context.Background(), 60*time.Second)
defer cancel()
loadResp, err := client.LoadModel(ctx, &pb.ModelOptions{
Model: "rfdetr-seg-nano-q8_0.gguf",
ModelFile: modelPath,
Threads: 2,
})
Expect(err).ToNot(HaveOccurred(), "LoadModel")
Expect(loadResp.GetSuccess()).To(BeTrue(), "LoadModel reported failure: %s", loadResp.GetMessage())
detResp, err := client.Detect(ctx, &pb.DetectOptions{
Src: imgB64,
Threshold: 0.5,
})
Expect(err).ToNot(HaveOccurred(), "Detect")
Expect(detResp.GetDetections()).ToNot(BeEmpty(), "no detections returned from segmentation model")
haveMask := false
for i, d := range detResp.GetDetections() {
m := d.GetMask()
if len(m) == 0 {
continue
}
haveMask = true
// Verify PNG magic: 89 50 4E 47 ("\x89PNG").
Expect(len(m)).To(BeNumerically(">=", 4), "detection %d mask too short", i)
Expect([]byte{m[0], m[1], m[2], m[3]}).To(Equal([]byte{0x89, 'P', 'N', 'G'}),
"detection %d mask is not a PNG", i)
}
Expect(haveMask).To(BeTrue(),
"segmentation model returned %d detections but none carried a mask",
len(detResp.GetDetections()))
_, _ = fmt.Fprintf(GinkgoWriter, "segmentation OK: %d detections, at least one with PNG mask\n",
len(detResp.GetDetections()))
})
})

View File

@@ -0,0 +1,59 @@
#!/bin/bash
# Script to copy the appropriate libraries based on architecture
set -e
CURDIR=$(dirname "$(realpath $0)")
REPO_ROOT="${CURDIR}/../../.."
# Create lib directory
mkdir -p $CURDIR/package/lib
cp -avf $CURDIR/librfdetrcpp-*.so $CURDIR/package/
cp -avf $CURDIR/rfdetr-cpp $CURDIR/package/
cp -fv $CURDIR/run.sh $CURDIR/package/
# Detect architecture and copy appropriate libraries
if [ -f "/lib64/ld-linux-x86-64.so.2" ]; then
# x86_64 architecture
echo "Detected x86_64 architecture, copying x86_64 libraries..."
cp -arfLv /lib64/ld-linux-x86-64.so.2 $CURDIR/package/lib/ld.so
cp -arfLv /lib/x86_64-linux-gnu/libc.so.6 $CURDIR/package/lib/libc.so.6
cp -arfLv /lib/x86_64-linux-gnu/libgcc_s.so.1 $CURDIR/package/lib/libgcc_s.so.1
cp -arfLv /lib/x86_64-linux-gnu/libstdc++.so.6 $CURDIR/package/lib/libstdc++.so.6
cp -arfLv /lib/x86_64-linux-gnu/libm.so.6 $CURDIR/package/lib/libm.so.6
cp -arfLv /lib/x86_64-linux-gnu/libgomp.so.1 $CURDIR/package/lib/libgomp.so.1
cp -arfLv /lib/x86_64-linux-gnu/libdl.so.2 $CURDIR/package/lib/libdl.so.2
cp -arfLv /lib/x86_64-linux-gnu/librt.so.1 $CURDIR/package/lib/librt.so.1
cp -arfLv /lib/x86_64-linux-gnu/libpthread.so.0 $CURDIR/package/lib/libpthread.so.0
elif [ -f "/lib/ld-linux-aarch64.so.1" ]; then
# ARM64 architecture
echo "Detected ARM64 architecture, copying ARM64 libraries..."
cp -arfLv /lib/ld-linux-aarch64.so.1 $CURDIR/package/lib/ld.so
cp -arfLv /lib/aarch64-linux-gnu/libc.so.6 $CURDIR/package/lib/libc.so.6
cp -arfLv /lib/aarch64-linux-gnu/libgcc_s.so.1 $CURDIR/package/lib/libgcc_s.so.1
cp -arfLv /lib/aarch64-linux-gnu/libstdc++.so.6 $CURDIR/package/lib/libstdc++.so.6
cp -arfLv /lib/aarch64-linux-gnu/libm.so.6 $CURDIR/package/lib/libm.so.6
cp -arfLv /lib/aarch64-linux-gnu/libgomp.so.1 $CURDIR/package/lib/libgomp.so.1
cp -arfLv /lib/aarch64-linux-gnu/libdl.so.2 $CURDIR/package/lib/libdl.so.2
cp -arfLv /lib/aarch64-linux-gnu/librt.so.1 $CURDIR/package/lib/librt.so.1
cp -arfLv /lib/aarch64-linux-gnu/libpthread.so.0 $CURDIR/package/lib/libpthread.so.0
elif [ $(uname -s) = "Darwin" ]; then
echo "Detected Darwin"
else
echo "Error: Could not detect architecture"
exit 1
fi
# Package GPU libraries based on BUILD_TYPE
GPU_LIB_SCRIPT="${REPO_ROOT}/scripts/build/package-gpu-libs.sh"
if [ -f "$GPU_LIB_SCRIPT" ]; then
echo "Packaging GPU libraries for BUILD_TYPE=${BUILD_TYPE:-cpu}..."
source "$GPU_LIB_SCRIPT" "$CURDIR/package/lib"
package_gpu_libs
fi
echo "Packaging completed successfully"
ls -liah $CURDIR/package/
ls -liah $CURDIR/package/lib/

52
backend/go/rfdetr-cpp/run.sh Executable file
View File

@@ -0,0 +1,52 @@
#!/bin/bash
set -ex
# Get the absolute current dir where the script is located
CURDIR=$(dirname "$(realpath $0)")
cd /
echo "CPU info:"
if [ "$(uname)" != "Darwin" ]; then
grep -e "model\sname" /proc/cpuinfo | head -1
grep -e "flags" /proc/cpuinfo | head -1
fi
LIBRARY="$CURDIR/librfdetrcpp-fallback.so"
if [ "$(uname)" != "Darwin" ]; then
if grep -q -e "\savx\s" /proc/cpuinfo ; then
echo "CPU: AVX found OK"
if [ -e $CURDIR/librfdetrcpp-avx.so ]; then
LIBRARY="$CURDIR/librfdetrcpp-avx.so"
fi
fi
if grep -q -e "\savx2\s" /proc/cpuinfo ; then
echo "CPU: AVX2 found OK"
if [ -e $CURDIR/librfdetrcpp-avx2.so ]; then
LIBRARY="$CURDIR/librfdetrcpp-avx2.so"
fi
fi
# Check avx 512
if grep -q -e "\savx512f\s" /proc/cpuinfo ; then
echo "CPU: AVX512F found OK"
if [ -e $CURDIR/librfdetrcpp-avx512.so ]; then
LIBRARY="$CURDIR/librfdetrcpp-avx512.so"
fi
fi
fi
export LD_LIBRARY_PATH=$CURDIR/lib:$LD_LIBRARY_PATH
export RFDETR_LIBRARY=$LIBRARY
# If there is a lib/ld.so, use it
if [ -f $CURDIR/lib/ld.so ]; then
echo "Using lib/ld.so"
echo "Using library: $LIBRARY"
exec $CURDIR/lib/ld.so $CURDIR/rfdetr-cpp "$@"
fi
echo "Using library: $LIBRARY"
exec $CURDIR/rfdetr-cpp "$@"

55
backend/go/rfdetr-cpp/test.sh Executable file
View File

@@ -0,0 +1,55 @@
#!/bin/bash
set -e
CURDIR=$(dirname "$(realpath $0)")
echo "Running rfdetr-cpp backend tests..."
# Test models from the mudler/rfdetr-cpp-* HuggingFace repos. Both the
# detection (nano-q8_0, ~36 MB) and segmentation (seg-nano-q8_0, ~40 MB)
# variants are downloaded so the Go smoke test exercises both code paths.
RFDETR_MODEL_DIR="${RFDETR_MODEL_DIR:-$CURDIR/test-models}"
RFDETR_DET_FILE="${RFDETR_DET_FILE:-rfdetr-nano-q8_0.gguf}"
RFDETR_DET_URL="${RFDETR_DET_URL:-https://huggingface.co/mudler/rfdetr-cpp-nano/resolve/main/rfdetr-nano-q8_0.gguf}"
RFDETR_SEG_FILE="${RFDETR_SEG_FILE:-rfdetr-seg-nano-q8_0.gguf}"
RFDETR_SEG_URL="${RFDETR_SEG_URL:-https://huggingface.co/mudler/rfdetr-cpp-seg-nano/resolve/main/rfdetr-seg-nano-q8_0.gguf}"
mkdir -p "$RFDETR_MODEL_DIR"
if [ ! -f "$RFDETR_MODEL_DIR/$RFDETR_DET_FILE" ]; then
echo "Downloading rfdetr nano-q8_0 detection model..."
curl -L -o "$RFDETR_MODEL_DIR/$RFDETR_DET_FILE" "$RFDETR_DET_URL" --progress-bar
fi
if [ ! -f "$RFDETR_MODEL_DIR/$RFDETR_SEG_FILE" ]; then
echo "Downloading rfdetr seg-nano-q8_0 segmentation model..."
curl -L -o "$RFDETR_MODEL_DIR/$RFDETR_SEG_FILE" "$RFDETR_SEG_URL" --progress-bar
fi
# Use a real COCO test image from the upstream rf-detr.cpp repo (~46 KB).
# A synthetic 64x64 red PNG was too synthetic to elicit detections from a
# real model — the smoke test would always trivially pass with zero
# detections.
TEST_IMAGE_DIR="$CURDIR/test-data"
TEST_IMAGE_FILE="$TEST_IMAGE_DIR/test.jpg"
TEST_IMAGE_URL="${TEST_IMAGE_URL:-https://raw.githubusercontent.com/mudler/rf-detr.cpp/main/tests/fixtures/ci/test_image.jpg}"
mkdir -p "$TEST_IMAGE_DIR"
if [ ! -f "$TEST_IMAGE_FILE" ]; then
echo "Downloading COCO test image..."
curl -L -o "$TEST_IMAGE_FILE" "$TEST_IMAGE_URL" --progress-bar
fi
echo "rfdetr-cpp test setup complete."
echo " detection model: $RFDETR_MODEL_DIR/$RFDETR_DET_FILE"
echo " segmentation model: $RFDETR_MODEL_DIR/$RFDETR_SEG_FILE"
echo " test image: $TEST_IMAGE_FILE"
# Run the Go smoke test: spawns the backend binary on a free port, calls
# LoadModel + Detect via gRPC for both detection and segmentation models.
echo ""
echo "Running Go smoke test..."
cd "$CURDIR"
go test -v -timeout 5m ./...

View File

@@ -8,7 +8,7 @@ JOBS?=$(shell nproc --ignore=1)
# stablediffusion.cpp (ggml)
STABLEDIFFUSION_GGML_REPO?=https://github.com/leejet/stable-diffusion.cpp
STABLEDIFFUSION_GGML_VERSION?=a397e03488cc27e1a42da646b82dfce9f50741c0
STABLEDIFFUSION_GGML_VERSION?=92dc7268fc4ffb0c0cc0bd52dfcefea91326e797
CMAKE_ARGS+=-DGGML_MAX_NAME=128

View File

@@ -27,6 +27,7 @@
#include <stdlib.h>
#include <regex>
#include <errno.h>
#include <inttypes.h>
#include <signal.h>
#include <unistd.h>
#include <sys/wait.h>
@@ -1075,9 +1076,71 @@ static uint8_t* load_and_resize_image(const char* path, int target_width, int ta
return buf;
}
// Write sd.cpp's audio buffer to a temp WAV file (IEEE float, interleaved).
// sd_audio_t.data is planar (all channel 0 samples, then channel 1, etc.) — we
// interleave on the fly so ffmpeg's standard wav demuxer can read it directly.
// Returns 0 on success and fills wav_path (must be at least 64 bytes).
static int write_planar_float_wav(const sd_audio_t* a, char* wav_path, size_t wav_path_sz) {
if (!a || !a->data || a->sample_count == 0 || a->channels == 0 || a->sample_rate == 0) {
return -1;
}
snprintf(wav_path, wav_path_sz, "/tmp/gosd-audio-XXXXXX.wav");
int fd = mkstemps(wav_path, 4);
if (fd < 0) { perror("mkstemps wav"); return -1; }
FILE* f = fdopen(fd, "wb");
if (!f) { perror("fdopen wav"); close(fd); return -1; }
uint64_t frames = a->sample_count;
uint32_t channels = a->channels;
uint32_t sample_rate = a->sample_rate;
uint64_t total_samples64 = frames * (uint64_t)channels;
uint64_t data_bytes64 = total_samples64 * sizeof(float);
if (data_bytes64 > 0xFFFFFFFFull - 44) {
fprintf(stderr, "audio too large for 32-bit WAV (%" PRIu64 " bytes)\n", data_bytes64);
fclose(f);
unlink(wav_path);
return -1;
}
uint32_t data_bytes = (uint32_t)data_bytes64;
uint32_t riff_size = 36 + data_bytes;
uint16_t fmt_code = 3; // WAVE_FORMAT_IEEE_FLOAT
uint16_t bits_per_sample = 32;
uint16_t block_align = (uint16_t)(channels * sizeof(float));
uint32_t byte_rate = sample_rate * block_align;
uint16_t ch16 = (uint16_t)channels;
uint32_t fmt_size = 16;
fwrite("RIFF", 1, 4, f);
fwrite(&riff_size, 4, 1, f);
fwrite("WAVEfmt ", 1, 8, f);
fwrite(&fmt_size, 4, 1, f);
fwrite(&fmt_code, 2, 1, f);
fwrite(&ch16, 2, 1, f);
fwrite(&sample_rate, 4, 1, f);
fwrite(&byte_rate, 4, 1, f);
fwrite(&block_align, 2, 1, f);
fwrite(&bits_per_sample, 2, 1, f);
fwrite("data", 1, 4, f);
fwrite(&data_bytes, 4, 1, f);
// Interleave planar [ch0_samples..., ch1_samples...] → [ch0_s0, ch1_s0, ...]
for (uint64_t s = 0; s < frames; s++) {
for (uint32_t c = 0; c < channels; c++) {
float v = a->data[(size_t)c * frames + s];
fwrite(&v, sizeof(float), 1, f);
}
}
fclose(f);
return 0;
}
// Pipe raw RGB/RGBA frames to ffmpeg stdin and let it produce an MP4 at dst.
// Uses fork+execvp to avoid shell interpretation of dst.
static int ffmpeg_mux_raw_to_mp4(sd_image_t* frames, int num_frames, int fps, const char* dst) {
// Uses fork+execvp to avoid shell interpretation of dst. When `audio` is
// non-null, the audio waveform is staged to a temp WAV and added as a second
// ffmpeg input so the final MP4 contains both video and AAC audio.
static int ffmpeg_mux_raw_to_mp4(sd_image_t* frames, int num_frames, int fps,
const sd_audio_t* audio, const char* dst) {
if (num_frames <= 0 || !frames || !frames[0].data) {
fprintf(stderr, "ffmpeg_mux: empty frames\n");
return 1;
@@ -1092,38 +1155,87 @@ static int ffmpeg_mux_raw_to_mp4(sd_image_t* frames, int num_frames, int fps, co
snprintf(size_str, sizeof(size_str), "%dx%d", width, height);
snprintf(fps_str, sizeof(fps_str), "%d", fps);
// Optional audio: write a temp WAV file if the model produced audio.
char wav_path[64] = {0};
bool have_audio = false;
if (audio && audio->data && audio->sample_count > 0 && audio->channels > 0 && audio->sample_rate > 0) {
if (write_planar_float_wav(audio, wav_path, sizeof(wav_path)) == 0) {
have_audio = true;
fprintf(stderr, "ffmpeg_mux: audio %u Hz × %u ch × %" PRIu64 " frames → %s\n",
audio->sample_rate, audio->channels, audio->sample_count, wav_path);
} else {
fprintf(stderr, "ffmpeg_mux: failed to stage audio; producing silent video\n");
}
}
int pipefd[2];
if (pipe(pipefd) != 0) { perror("pipe"); return 1; }
if (pipe(pipefd) != 0) {
perror("pipe");
if (have_audio) unlink(wav_path);
return 1;
}
pid_t pid = fork();
if (pid < 0) { perror("fork"); close(pipefd[0]); close(pipefd[1]); return 1; }
if (pid < 0) {
perror("fork");
close(pipefd[0]); close(pipefd[1]);
if (have_audio) unlink(wav_path);
return 1;
}
if (pid == 0) {
// child
close(pipefd[1]);
if (dup2(pipefd[0], STDIN_FILENO) < 0) { perror("dup2"); _exit(127); }
close(pipefd[0]);
std::vector<char*> argv = {
const_cast<char*>("ffmpeg"),
const_cast<char*>("-y"),
const_cast<char*>("-hide_banner"),
const_cast<char*>("-loglevel"), const_cast<char*>("warning"),
const_cast<char*>("-f"), const_cast<char*>("rawvideo"),
const_cast<char*>("-pix_fmt"), const_cast<char*>(pix_fmt_in),
const_cast<char*>("-s"), size_str,
const_cast<char*>("-framerate"), fps_str,
const_cast<char*>("-i"), const_cast<char*>("-"),
const_cast<char*>("-c:v"), const_cast<char*>("libx264"),
const_cast<char*>("-pix_fmt"), const_cast<char*>("yuv420p"),
const_cast<char*>("-movflags"), const_cast<char*>("+faststart"),
// Force MP4 container. Distributed LocalAI hands us a staging
// path (e.g. /staging/localai-output-NNN.tmp) with a non-standard
// extension; relying on filename suffix makes ffmpeg bail with
// "Unable to choose an output format".
const_cast<char*>("-f"), const_cast<char*>("mp4"),
const_cast<char*>(dst),
nullptr
};
std::vector<char*> argv;
argv.push_back(const_cast<char*>("ffmpeg"));
argv.push_back(const_cast<char*>("-y"));
argv.push_back(const_cast<char*>("-hide_banner"));
argv.push_back(const_cast<char*>("-loglevel"));
argv.push_back(const_cast<char*>("warning"));
// Input 0: raw video from stdin
argv.push_back(const_cast<char*>("-f"));
argv.push_back(const_cast<char*>("rawvideo"));
argv.push_back(const_cast<char*>("-pix_fmt"));
argv.push_back(const_cast<char*>(pix_fmt_in));
argv.push_back(const_cast<char*>("-s"));
argv.push_back(size_str);
argv.push_back(const_cast<char*>("-framerate"));
argv.push_back(fps_str);
argv.push_back(const_cast<char*>("-i"));
argv.push_back(const_cast<char*>("-"));
// Input 1: optional audio WAV
if (have_audio) {
argv.push_back(const_cast<char*>("-i"));
argv.push_back(wav_path);
argv.push_back(const_cast<char*>("-map"));
argv.push_back(const_cast<char*>("0:v:0"));
argv.push_back(const_cast<char*>("-map"));
argv.push_back(const_cast<char*>("1:a:0"));
argv.push_back(const_cast<char*>("-c:a"));
argv.push_back(const_cast<char*>("aac"));
argv.push_back(const_cast<char*>("-b:a"));
argv.push_back(const_cast<char*>("192k"));
// -shortest so the final clip ends with the shorter of the two
// streams — guards against an audio buffer that overshoots the
// video duration (or vice versa) on certain LTX variants.
argv.push_back(const_cast<char*>("-shortest"));
}
argv.push_back(const_cast<char*>("-c:v"));
argv.push_back(const_cast<char*>("libx264"));
argv.push_back(const_cast<char*>("-pix_fmt"));
argv.push_back(const_cast<char*>("yuv420p"));
argv.push_back(const_cast<char*>("-movflags"));
argv.push_back(const_cast<char*>("+faststart"));
// Force MP4 container. Distributed LocalAI hands us a staging
// path (e.g. /staging/localai-output-NNN.tmp) with a non-standard
// extension; relying on filename suffix makes ffmpeg bail with
// "Unable to choose an output format".
argv.push_back(const_cast<char*>("-f"));
argv.push_back(const_cast<char*>("mp4"));
argv.push_back(const_cast<char*>(dst));
argv.push_back(nullptr);
execvp(argv[0], argv.data());
perror("execvp ffmpeg");
_exit(127);
@@ -1148,6 +1260,7 @@ static int ffmpeg_mux_raw_to_mp4(sd_image_t* frames, int num_frames, int fps, co
close(pipefd[1]);
int status;
waitpid(pid, &status, 0);
if (have_audio) unlink(wav_path);
return 1;
}
p += n;
@@ -1158,8 +1271,13 @@ static int ffmpeg_mux_raw_to_mp4(sd_image_t* frames, int num_frames, int fps, co
int status = 0;
while (waitpid(pid, &status, 0) < 0) {
if (errno != EINTR) { perror("waitpid"); return 1; }
if (errno != EINTR) {
perror("waitpid");
if (have_audio) unlink(wav_path);
return 1;
}
}
if (have_audio) unlink(wav_path);
if (!WIFEXITED(status) || WEXITSTATUS(status) != 0) {
fprintf(stderr, "ffmpeg exited with status %d\n", status);
return 1;
@@ -1234,7 +1352,7 @@ int gen_video(sd_vid_gen_params_t *p, int steps, char *dst, float cfg_scale, int
fprintf(stderr, "Generated %d frames, muxing to %s via ffmpeg\n", num_frames_out, dst);
int rc = ffmpeg_mux_raw_to_mp4(frames, num_frames_out, fps, dst);
int rc = ffmpeg_mux_raw_to_mp4(frames, num_frames_out, fps, audio, dst);
for (int i = 0; i < num_frames_out; i++) {
if (frames[i].data) free(frames[i].data);

View File

@@ -8,7 +8,7 @@ JOBS?=$(shell nproc --ignore=1)
# whisper.cpp version
WHISPER_REPO?=https://github.com/ggml-org/whisper.cpp
WHISPER_CPP_VERSION?=0ccd896f5b882628e1c077f9769735ef4ce52860
WHISPER_CPP_VERSION?=27101c01dcac1676e2b6422256233cd0f1f9ae28
SO_TARGET?=libgowhisper.so
CMAKE_ARGS+=-DBUILD_SHARED_LIBS=OFF

View File

@@ -253,6 +253,34 @@
nvidia-l4t-cuda-13: "cuda13-nvidia-l4t-arm64-sam3-cpp"
intel: "intel-sycl-f32-sam3-cpp"
vulkan: "vulkan-sam3-cpp"
- &rfdetrcpp
name: "rfdetr-cpp"
alias: "rfdetr-cpp"
license: apache-2.0
description: |
Native RF-DETR object detection and instance segmentation in C/C++
using GGML. Loads pre-built GGUF weights from the mudler/rfdetr-cpp-*
family (Nano/Small/Base/Medium/Large + SegNano/SegSmall/SegMedium)
and returns bounding boxes, class labels, confidence scores, and
(for segmentation variants) PNG-encoded per-detection masks.
urls:
- https://github.com/mudler/rf-detr.cpp
tags:
- object-detection
- image-segmentation
- rfdetr
- gpu
- cpu
capabilities:
default: "cpu-rfdetr-cpp"
nvidia: "cuda12-rfdetr-cpp"
nvidia-cuda-12: "cuda12-rfdetr-cpp"
nvidia-cuda-13: "cuda13-rfdetr-cpp"
nvidia-l4t: "nvidia-l4t-arm64-rfdetr-cpp"
nvidia-l4t-cuda-12: "nvidia-l4t-arm64-rfdetr-cpp"
nvidia-l4t-cuda-13: "cuda13-nvidia-l4t-arm64-rfdetr-cpp"
intel: "intel-sycl-f32-rfdetr-cpp"
vulkan: "vulkan-rfdetr-cpp"
- &vllm
name: "vllm"
license: apache-2.0
@@ -2349,6 +2377,99 @@
uri: "quay.io/go-skynet/local-ai-backends:master-gpu-vulkan-sam3-cpp"
mirrors:
- localai/localai-backends:master-gpu-vulkan-sam3-cpp
## rfdetr-cpp
- !!merge <<: *rfdetrcpp
name: "rfdetr-cpp-development"
capabilities:
default: "cpu-rfdetr-cpp-development"
nvidia: "cuda12-rfdetr-cpp-development"
nvidia-cuda-12: "cuda12-rfdetr-cpp-development"
nvidia-cuda-13: "cuda13-rfdetr-cpp-development"
nvidia-l4t: "nvidia-l4t-arm64-rfdetr-cpp-development"
nvidia-l4t-cuda-12: "nvidia-l4t-arm64-rfdetr-cpp-development"
nvidia-l4t-cuda-13: "cuda13-nvidia-l4t-arm64-rfdetr-cpp-development"
intel: "intel-sycl-f32-rfdetr-cpp-development"
vulkan: "vulkan-rfdetr-cpp-development"
- !!merge <<: *rfdetrcpp
name: "cpu-rfdetr-cpp"
uri: "quay.io/go-skynet/local-ai-backends:latest-cpu-rfdetr-cpp"
mirrors:
- localai/localai-backends:latest-cpu-rfdetr-cpp
- !!merge <<: *rfdetrcpp
name: "cpu-rfdetr-cpp-development"
uri: "quay.io/go-skynet/local-ai-backends:master-cpu-rfdetr-cpp"
mirrors:
- localai/localai-backends:master-cpu-rfdetr-cpp
- !!merge <<: *rfdetrcpp
name: "cuda12-rfdetr-cpp"
uri: "quay.io/go-skynet/local-ai-backends:latest-gpu-nvidia-cuda-12-rfdetr-cpp"
mirrors:
- localai/localai-backends:latest-gpu-nvidia-cuda-12-rfdetr-cpp
- !!merge <<: *rfdetrcpp
name: "cuda12-rfdetr-cpp-development"
uri: "quay.io/go-skynet/local-ai-backends:master-gpu-nvidia-cuda-12-rfdetr-cpp"
mirrors:
- localai/localai-backends:master-gpu-nvidia-cuda-12-rfdetr-cpp
- !!merge <<: *rfdetrcpp
name: "cuda13-rfdetr-cpp"
uri: "quay.io/go-skynet/local-ai-backends:latest-gpu-nvidia-cuda-13-rfdetr-cpp"
mirrors:
- localai/localai-backends:latest-gpu-nvidia-cuda-13-rfdetr-cpp
- !!merge <<: *rfdetrcpp
name: "cuda13-rfdetr-cpp-development"
uri: "quay.io/go-skynet/local-ai-backends:master-gpu-nvidia-cuda-13-rfdetr-cpp"
mirrors:
- localai/localai-backends:master-gpu-nvidia-cuda-13-rfdetr-cpp
- !!merge <<: *rfdetrcpp
name: "nvidia-l4t-arm64-rfdetr-cpp"
uri: "quay.io/go-skynet/local-ai-backends:latest-nvidia-l4t-arm64-rfdetr-cpp"
mirrors:
- localai/localai-backends:latest-nvidia-l4t-arm64-rfdetr-cpp
- !!merge <<: *rfdetrcpp
name: "nvidia-l4t-arm64-rfdetr-cpp-development"
uri: "quay.io/go-skynet/local-ai-backends:master-nvidia-l4t-arm64-rfdetr-cpp"
mirrors:
- localai/localai-backends:master-nvidia-l4t-arm64-rfdetr-cpp
- !!merge <<: *rfdetrcpp
name: "cuda13-nvidia-l4t-arm64-rfdetr-cpp"
uri: "quay.io/go-skynet/local-ai-backends:latest-nvidia-l4t-cuda-13-arm64-rfdetr-cpp"
mirrors:
- localai/localai-backends:latest-nvidia-l4t-cuda-13-arm64-rfdetr-cpp
- !!merge <<: *rfdetrcpp
name: "cuda13-nvidia-l4t-arm64-rfdetr-cpp-development"
uri: "quay.io/go-skynet/local-ai-backends:master-nvidia-l4t-cuda-13-arm64-rfdetr-cpp"
mirrors:
- localai/localai-backends:master-nvidia-l4t-cuda-13-arm64-rfdetr-cpp
- !!merge <<: *rfdetrcpp
name: "intel-sycl-f32-rfdetr-cpp"
uri: "quay.io/go-skynet/local-ai-backends:latest-gpu-intel-sycl-f32-rfdetr-cpp"
mirrors:
- localai/localai-backends:latest-gpu-intel-sycl-f32-rfdetr-cpp
- !!merge <<: *rfdetrcpp
name: "intel-sycl-f32-rfdetr-cpp-development"
uri: "quay.io/go-skynet/local-ai-backends:master-gpu-intel-sycl-f32-rfdetr-cpp"
mirrors:
- localai/localai-backends:master-gpu-intel-sycl-f32-rfdetr-cpp
- !!merge <<: *rfdetrcpp
name: "intel-sycl-f16-rfdetr-cpp"
uri: "quay.io/go-skynet/local-ai-backends:latest-gpu-intel-sycl-f16-rfdetr-cpp"
mirrors:
- localai/localai-backends:latest-gpu-intel-sycl-f16-rfdetr-cpp
- !!merge <<: *rfdetrcpp
name: "intel-sycl-f16-rfdetr-cpp-development"
uri: "quay.io/go-skynet/local-ai-backends:master-gpu-intel-sycl-f16-rfdetr-cpp"
mirrors:
- localai/localai-backends:master-gpu-intel-sycl-f16-rfdetr-cpp
- !!merge <<: *rfdetrcpp
name: "vulkan-rfdetr-cpp"
uri: "quay.io/go-skynet/local-ai-backends:latest-gpu-vulkan-rfdetr-cpp"
mirrors:
- localai/localai-backends:latest-gpu-vulkan-rfdetr-cpp
- !!merge <<: *rfdetrcpp
name: "vulkan-rfdetr-cpp-development"
uri: "quay.io/go-skynet/local-ai-backends:master-gpu-vulkan-rfdetr-cpp"
mirrors:
- localai/localai-backends:master-gpu-vulkan-rfdetr-cpp
## Rerankers
- !!merge <<: *rerankers
name: "rerankers-development"

View File

@@ -99,8 +99,15 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
if not results or len(results) == 0:
return backend_pb2.TranscriptResult(segments=[], text="")
# Get the transcript text from the first result
text = results[0]
# Get the transcript text from the first result.
# CTC models return List[str], TDT/RNNT models return List[Hypothesis]
# where the actual text lives in Hypothesis.text.
result = results[0]
if isinstance(result, str):
text = result
else:
text = getattr(result, 'text', None) or ""
if text:
# Create a single segment with the full transcription
result_segments.append(backend_pb2.TranscriptSegment(

View File

@@ -134,6 +134,156 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
return backend_pb2.Result(message="Model loaded successfully", success=True)
@staticmethod
def _is_cjk(ch):
"""Check if a character is CJK (Chinese/Japanese/Korean)."""
cp = ord(ch)
return (
0x4E00 <= cp <= 0x9FFF # CJK Unified Ideographs
or 0x3400 <= cp <= 0x4DBF # Extension A
or 0x20000 <= cp <= 0x2A6DF # Extension B
or 0xF900 <= cp <= 0xFAFF # Compatibility Ideographs
or 0x3040 <= cp <= 0x309F # Hiragana
or 0x30A0 <= cp <= 0x30FF # Katakana
or 0xAC00 <= cp <= 0xD7AF # Hangul Syllables
)
@staticmethod
def _is_punct(ch):
"""Check if a character is punctuation (no space before it)."""
import unicodedata
cat = unicodedata.category(ch)
return cat.startswith('P')
@staticmethod
def _smart_join(tokens):
"""Join tokens with spaces for non-CJK text, without spaces for CJK.
Rules:
- Between two CJK chars: no space
- Between two non-CJK tokens: space
- Before punctuation: no space
- CJK adjacent to non-CJK: no space (smooth mixed-text transition)
"""
if not tokens:
return ""
result = [tokens[0]]
for token in tokens[1:]:
if not token:
continue
prev_ch = result[-1][-1] if result[-1] else ''
curr_ch = token[0]
# Punctuation never gets a space before it
if BackendServicer._is_punct(curr_ch):
result.append(token)
# CJK to CJK: no space
elif prev_ch and BackendServicer._is_cjk(prev_ch) and BackendServicer._is_cjk(curr_ch):
result.append(token)
# CJK adjacent to non-CJK or vice versa: no space
elif prev_ch and (BackendServicer._is_cjk(prev_ch) or BackendServicer._is_cjk(curr_ch)):
result.append(token)
# Both non-CJK (Latin, Cyrillic, etc.): add space
else:
result.append(' ' + token)
return "".join(result)
@staticmethod
def _extract_word_info(ts):
"""Return (start_sec, end_sec, text) from a ForcedAlignItem or tuple."""
if hasattr(ts, 'start_time') and hasattr(ts, 'end_time') and hasattr(ts, 'text'):
return (
float(ts.start_time) if ts.start_time is not None else 0.0,
float(ts.end_time) if ts.end_time is not None else 0.0,
str(ts.text) if ts.text else "",
)
elif isinstance(ts, (list, tuple)) and len(ts) >= 3:
return (
float(ts[0]) if ts[0] is not None else 0.0,
float(ts[1]) if ts[1] is not None else 0.0,
ts[2] if len(ts) > 2 and ts[2] is not None else "",
)
return (0.0, 0.0, "")
@staticmethod
def _compute_gap_threshold(time_stamps):
"""Compute a gap threshold for sentence boundary detection.
Uses the median inter-item gap multiplied by a factor, with a
minimum floor of 0.3s. Returns 0 if there are too few items.
"""
if len(time_stamps) < 2:
return 0.0
gaps = []
for i in range(1, len(time_stamps)):
prev_s, prev_e, _ = BackendServicer._extract_word_info(time_stamps[i - 1])
curr_s, _, _ = BackendServicer._extract_word_info(time_stamps[i])
gaps.append(curr_s - prev_e)
if not gaps:
return 0.0
gaps.sort()
median = gaps[len(gaps) // 2]
# threshold = max(median * 4, 0.3s)
return max(median * 4, 0.3)
def _build_segments(self, time_stamps, granularity):
"""Build TranscriptSegment list from forced-aligner output.
granularity:
- "word": one segment per aligned item (character / word)
- "segment" (default): merge consecutive items, splitting at
time gaps that exceed a dynamic threshold (sentence boundaries).
"""
if granularity == "word":
result = []
for idx, ts in enumerate(time_stamps):
s, e, t = self._extract_word_info(ts)
result.append(backend_pb2.TranscriptSegment(
id=idx,
start=int(s * 1_000_000_000),
end=int(e * 1_000_000_000),
text=t,
))
return result
# segment mode — merge at time-gap boundaries
threshold = self._compute_gap_threshold(time_stamps)
result = []
buf_text = []
buf_start = None
buf_end = 0.0
prev_end = None
for ts in time_stamps:
s, e, t = self._extract_word_info(ts)
# Detect sentence boundary via time gap
if prev_end is not None and (s - prev_end) >= threshold and buf_text:
result.append(backend_pb2.TranscriptSegment(
id=len(result),
start=int(buf_start * 1_000_000_000),
end=int(buf_end * 1_000_000_000),
text=self._smart_join(buf_text),
))
buf_text = []
buf_start = None
if buf_start is None:
buf_start = s
buf_text.append(t)
buf_end = e
prev_end = e
# flush remaining
if buf_text and buf_start is not None:
result.append(backend_pb2.TranscriptSegment(
id=len(result),
start=int(buf_start * 1_000_000_000),
end=int(buf_end * 1_000_000_000),
text=self._smart_join(buf_text),
))
return result
def AudioTranscription(self, request, context):
result_segments = []
text = ""
@@ -147,11 +297,22 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
if request.language and request.language.strip():
language = request.language.strip()
context = ""
ctx = ""
if request.prompt and request.prompt.strip():
context = request.prompt.strip()
ctx = request.prompt.strip()
results = self.model.transcribe(audio=audio_path, language=language, context=context)
# Determine requested granularity (default: segment)
granularities = list(request.timestamp_granularities) if request.timestamp_granularities else []
granularity = "word" if "word" in granularities else "segment"
has_aligner = getattr(self.model, 'forced_aligner', None) is not None
try:
results = self.model.transcribe(
audio=audio_path, language=language, context=ctx,
return_time_stamps=has_aligner,
)
except TypeError:
results = self.model.transcribe(audio=audio_path, language=language, context=ctx)
if not results:
return backend_pb2.TranscriptResult(segments=[], text="")
@@ -160,17 +321,7 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
text = r.text or ""
if getattr(r, 'time_stamps', None) and len(r.time_stamps) > 0:
for idx, ts in enumerate(r.time_stamps):
start_ms = 0
end_ms = 0
seg_text = text
if isinstance(ts, (list, tuple)) and len(ts) >= 3:
start_ms = int(float(ts[0]) * 1000) if ts[0] is not None else 0
end_ms = int(float(ts[1]) * 1000) if ts[1] is not None else 0
seg_text = ts[2] if len(ts) > 2 and ts[2] is not None else ""
result_segments.append(backend_pb2.TranscriptSegment(
id=idx, start=start_ms, end=end_ms, text=seg_text
))
result_segments = self._build_segments(r.time_stamps, granularity)
else:
if text:
result_segments.append(backend_pb2.TranscriptSegment(

View File

@@ -2,9 +2,9 @@ torch==2.7.1
llvmlite==0.43.0
numba==0.60.0
accelerate
transformers>=5.8.1
transformers>=5.9.0
bitsandbytes
sentence-transformers==5.5.0
sentence-transformers==5.5.1
diffusers
soundfile
protobuf==6.33.5
protobuf==7.35.0

View File

@@ -2,9 +2,9 @@ torch==2.7.1
accelerate
llvmlite==0.43.0
numba==0.60.0
transformers>=5.8.1
transformers>=5.9.0
bitsandbytes
sentence-transformers==5.5.0
sentence-transformers==5.5.1
diffusers
soundfile
protobuf==6.33.5
protobuf==7.35.0

View File

@@ -2,9 +2,9 @@
torch==2.9.0
llvmlite==0.43.0
numba==0.60.0
transformers>=5.8.1
transformers>=5.9.0
bitsandbytes
sentence-transformers==5.5.0
sentence-transformers==5.5.1
diffusers
soundfile
protobuf==6.33.5
protobuf==7.35.0

View File

@@ -1,11 +1,11 @@
--extra-index-url https://download.pytorch.org/whl/rocm7.0
torch==2.10.0+rocm7.0
accelerate
transformers>=5.8.1
transformers>=5.9.0
llvmlite==0.43.0
numba==0.60.0
bitsandbytes
sentence-transformers==5.5.0
sentence-transformers==5.5.1
diffusers
soundfile
protobuf==6.33.5
protobuf==7.35.0

View File

@@ -3,9 +3,9 @@ torch
optimum[openvino]
llvmlite==0.43.0
numba==0.60.0
transformers>=5.8.1
transformers>=5.9.0
bitsandbytes
sentence-transformers==5.5.0
sentence-transformers==5.5.1
diffusers
soundfile
protobuf==6.33.5
protobuf==7.35.0

View File

@@ -2,9 +2,9 @@ torch==2.7.1
llvmlite==0.43.0
numba==0.60.0
accelerate
transformers>=5.8.1
transformers>=5.9.0
bitsandbytes
sentence-transformers==5.5.0
sentence-transformers==5.5.1
diffusers
soundfile
protobuf==6.33.5
protobuf==7.35.0

View File

@@ -1,5 +1,5 @@
grpcio==1.80.0
protobuf==6.33.5
protobuf==7.35.0
certifi
setuptools
scipy==1.15.1

View File

@@ -31,6 +31,29 @@ func repoLooksLikeRFDetr(repo string) bool {
return strings.Contains(lower, "rf-detr") || strings.Contains(lower, "rfdetr")
}
// repoHasGGUF inspects the HuggingFace file list (when available) to decide
// whether the repo ships RF-DETR weights in ggml/GGUF form — the native
// rfdetr-cpp backend's input format. Mudler's rfdetr-cpp-* repos
// (mudler/rfdetr-cpp-nano, mudler/rfdetr-cpp-base, ...) match.
func repoHasGGUF(details Details) bool {
if details.HuggingFace == nil {
return false
}
for _, f := range details.HuggingFace.Files {
if strings.HasSuffix(strings.ToLower(f.Path), ".gguf") {
return true
}
}
return false
}
func repoLooksLikeRFDetrCpp(repo string) bool {
lower := strings.ToLower(repo)
return strings.Contains(lower, "rfdetr-cpp") || strings.Contains(lower, "rf-detr-cpp") ||
strings.Contains(lower, "rfdetr.cpp") || strings.Contains(lower, "rt-detr.cpp") ||
strings.Contains(lower, "rf-detr.cpp")
}
func (i *RFDetrImporter) Match(details Details) bool {
preferences, err := details.Preferences.MarshalJSON()
if err != nil {
@@ -43,7 +66,7 @@ func (i *RFDetrImporter) Match(details Details) bool {
}
}
if b, ok := preferencesMap["backend"].(string); ok && b == "rfdetr" {
if b, ok := preferencesMap["backend"].(string); ok && (b == "rfdetr" || b == "rfdetr-cpp") {
return true
}
@@ -99,10 +122,28 @@ func (i *RFDetrImporter) Import(details Details) (gallery.ModelConfig, error) {
model = owner + "/" + repo
}
// Route GGUF-bearing repos (mudler/rfdetr-cpp-*) to the native
// rfdetr-cpp backend; HF transformer repos keep the Python rfdetr
// backend. Explicit preferences.backend overrides the heuristic.
backend := "rfdetr"
if b, ok := preferencesMap["backend"].(string); ok && b != "" {
backend = b
} else if repoHasGGUF(details) {
backend = "rfdetr-cpp"
} else if details.HuggingFace != nil {
repoName := details.HuggingFace.ModelID
if idx := strings.Index(repoName, "/"); idx >= 0 {
repoName = repoName[idx+1:]
}
if repoLooksLikeRFDetrCpp(repoName) {
backend = "rfdetr-cpp"
}
}
modelConfig := config.ModelConfig{
Name: name,
Description: description,
Backend: "rfdetr",
Backend: backend,
KnownUsecaseStrings: []string{"detection"},
PredictionOptions: schema.PredictionOptions{
BasicModelRequest: schema.BasicModelRequest{Model: model},

View File

@@ -129,4 +129,125 @@ var _ = Describe("RFDetrImporter", func() {
Expect(modelConfig.Description).To(Equal("Custom"))
})
})
// Table-driven coverage of the GGUF auto-routing path between the
// Python rfdetr backend (HF transformer repos) and the native
// rfdetr-cpp backend (GGUF repos like mudler/rfdetr-cpp-*).
//
// Cases are kept offline-deterministic by injecting Details directly
// rather than going through DiscoverModelConfig (which would hit live HF).
// The live HF cross-check lives in its own Context below.
Context("GGUF auto-routing (offline)", func() {
hfFile := func(path string) hfapi.ModelFile {
return hfapi.ModelFile{Path: path}
}
type tc struct {
name string
uri string
modelID string
files []hfapi.ModelFile
prefs string
expectBackend string // expected `backend:` line content
rejectBackends []string
}
entries := []tc{
{
name: "GGUF repo with rfdetr-cpp prefix routes to rfdetr-cpp",
uri: "https://huggingface.co/mudler/rfdetr-cpp-nano",
modelID: "mudler/rfdetr-cpp-nano",
files: []hfapi.ModelFile{hfFile("rfdetr-nano-q8_0.gguf"), hfFile("README.md")},
prefs: "",
expectBackend: "backend: rfdetr-cpp",
},
{
name: "GGUF presence alone routes to rfdetr-cpp even when repo name lacks -cpp",
uri: "https://huggingface.co/some/rf-detr-ggml",
modelID: "some/rf-detr-ggml",
files: []hfapi.ModelFile{hfFile("rfdetr-base-f16.gguf")},
prefs: "",
expectBackend: "backend: rfdetr-cpp",
},
{
name: "transformer repo without GGUF stays on the Python rfdetr backend",
uri: "https://huggingface.co/roboflow/rf-detr-base",
modelID: "roboflow/rf-detr-base",
files: []hfapi.ModelFile{hfFile("config.json"), hfFile("pytorch_model.bin")},
prefs: "",
expectBackend: "backend: rfdetr\n",
rejectBackends: []string{"backend: rfdetr-cpp"},
},
{
name: "explicit preferences.backend=rfdetr overrides GGUF auto-detect",
uri: "https://huggingface.co/mudler/rfdetr-cpp-nano",
modelID: "mudler/rfdetr-cpp-nano",
files: []hfapi.ModelFile{hfFile("rfdetr-nano-q8_0.gguf")},
prefs: `{"backend": "rfdetr"}`,
expectBackend: "backend: rfdetr\n",
rejectBackends: []string{"backend: rfdetr-cpp"},
},
{
name: "explicit preferences.backend=rfdetr-cpp wins on non-GGUF transformer repo",
uri: "https://huggingface.co/roboflow/rf-detr-base",
modelID: "roboflow/rf-detr-base",
files: []hfapi.ModelFile{hfFile("config.json")},
prefs: `{"backend": "rfdetr-cpp"}`,
expectBackend: "backend: rfdetr-cpp",
},
{
name: "repo name with rfdetr.cpp pattern routes to rfdetr-cpp even without HF file list",
uri: "https://huggingface.co/some/rfdetr.cpp-bundle",
modelID: "some/rfdetr.cpp-bundle",
files: nil,
prefs: "",
expectBackend: "backend: rfdetr-cpp",
},
}
for _, e := range entries {
e := e // capture for closure
It(e.name, func() {
imp := &importers.RFDetrImporter{}
details := importers.Details{
URI: e.uri,
HuggingFace: &hfapi.ModelDetails{
ModelID: e.modelID,
Files: e.files,
},
}
if e.prefs != "" {
details.Preferences = json.RawMessage(e.prefs)
}
// Match must always be true for these fixtures — they're
// either preference-driven or have an rfdetr/rf-detr token.
Expect(imp.Match(details)).To(BeTrue(), fmt.Sprintf("Match should fire for %+v", details))
modelConfig, err := imp.Import(details)
Expect(err).ToNot(HaveOccurred(), fmt.Sprintf("Import error: %v", err))
Expect(modelConfig.ConfigFile).To(ContainSubstring(e.expectBackend),
fmt.Sprintf("Model config: %+v", modelConfig))
for _, rej := range e.rejectBackends {
Expect(modelConfig.ConfigFile).ToNot(ContainSubstring(rej),
fmt.Sprintf("did not expect %q in: %+v", rej, modelConfig))
}
})
}
})
// Live HF cross-check: the canonical native GGUF repo for the
// rfdetr-cpp backend. Marked broad — we only assert the routing
// decision, not file lists (upstream may add quants over time).
Context("detection from HuggingFace: mudler/rfdetr-cpp-nano", func() {
It("auto-routes to the native rfdetr-cpp backend without preferences", func() {
uri := "https://huggingface.co/mudler/rfdetr-cpp-nano"
modelConfig, err := importers.DiscoverModelConfig(uri, json.RawMessage(`{}`))
Expect(err).ToNot(HaveOccurred(), fmt.Sprintf("Error: %v", err))
Expect(modelConfig.ConfigFile).To(ContainSubstring("backend: rfdetr-cpp"),
fmt.Sprintf("Model config: %+v", modelConfig))
Expect(modelConfig.ConfigFile).To(ContainSubstring("mudler/rfdetr-cpp-nano"))
})
})
})

View File

@@ -0,0 +1,233 @@
package localai_test
import (
"bytes"
"encoding/json"
"fmt"
"mime/multipart"
"net/http"
"net/http/httptest"
"net/textproto"
"os"
"path/filepath"
"github.com/labstack/echo/v4"
"github.com/mudler/LocalAI/core/config"
. "github.com/mudler/LocalAI/core/http/endpoints/localai"
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
)
// multipartFile builds a multipart/form-data body with a single "file" part,
// letting the caller control the part's filename and Content-Type so the
// upload handler's MIME allow-list and extension-fallback paths can be tested.
func multipartFile(filename, contentType string, data []byte) (*bytes.Buffer, string) {
body := &bytes.Buffer{}
w := multipart.NewWriter(body)
h := make(textproto.MIMEHeader)
h.Set("Content-Disposition", fmt.Sprintf(`form-data; name="file"; filename="%s"`, filename))
if contentType != "" {
h.Set("Content-Type", contentType)
}
part, err := w.CreatePart(h)
Expect(err).ToNot(HaveOccurred())
_, err = part.Write(data)
Expect(err).ToNot(HaveOccurred())
Expect(w.Close()).To(Succeed())
return body, w.FormDataContentType()
}
var _ = Describe("Branding endpoints", func() {
var (
dir string
e *echo.Echo
appCfg *config.ApplicationConfig
)
BeforeEach(func() {
var err error
dir, err = os.MkdirTemp("", "branding-endpoints-*")
Expect(err).ToNot(HaveOccurred())
appCfg = config.NewApplicationConfig()
appCfg.DynamicConfigsDir = dir
appCfg.Branding.InstanceName = "Acme AI"
appCfg.Branding.InstanceTagline = "do things"
e = echo.New()
e.GET("/api/branding", GetBrandingEndpoint(appCfg))
e.POST("/api/branding/asset/:kind", UploadBrandingAssetEndpoint(appCfg))
e.DELETE("/api/branding/asset/:kind", DeleteBrandingAssetEndpoint(appCfg))
e.GET("/branding/asset/:kind", ServeBrandingAssetEndpoint(appCfg))
})
AfterEach(func() {
Expect(os.RemoveAll(dir)).To(Succeed())
})
decode := func(rec *httptest.ResponseRecorder) BrandingResponse {
var b BrandingResponse
Expect(json.Unmarshal(rec.Body.Bytes(), &b)).To(Succeed())
return b
}
Describe("GET /api/branding", func() {
It("returns instance text and bundled default asset URLs when nothing is uploaded", func() {
req := httptest.NewRequest(http.MethodGet, "/api/branding", nil)
rec := httptest.NewRecorder()
e.ServeHTTP(rec, req)
Expect(rec.Code).To(Equal(http.StatusOK))
b := decode(rec)
Expect(b.InstanceName).To(Equal("Acme AI"))
Expect(b.InstanceTagline).To(Equal("do things"))
Expect(b.LogoURL).To(Equal("/static/logo.png"))
Expect(b.LogoHorizontalURL).To(Equal("/static/logo_horizontal.png"))
Expect(b.FaviconURL).To(Equal("/favicon.svg"))
})
It("points an uploaded asset at the dynamic serve route", func() {
appCfg.Branding.LogoFile = "logo.png"
req := httptest.NewRequest(http.MethodGet, "/api/branding", nil)
rec := httptest.NewRecorder()
e.ServeHTTP(rec, req)
Expect(decode(rec).LogoURL).To(Equal("/branding/asset/logo"))
})
})
Describe("POST /api/branding/asset/:kind", func() {
It("stores an uploaded PNG and persists the setting", func() {
body, ct := multipartFile("mylogo.png", "image/png", []byte("\x89PNG\r\n\x1a\n"))
req := httptest.NewRequest(http.MethodPost, "/api/branding/asset/logo", body)
req.Header.Set("Content-Type", ct)
rec := httptest.NewRecorder()
e.ServeHTTP(rec, req)
Expect(rec.Code).To(Equal(http.StatusOK))
Expect(decode(rec).LogoURL).To(Equal("/branding/asset/logo"))
// File landed under <dir>/branding/logo.png ...
_, err := os.Stat(filepath.Join(dir, "branding", "logo.png"))
Expect(err).ToNot(HaveOccurred())
// ... the in-memory config was updated ...
Expect(appCfg.Branding.LogoFile).To(Equal("logo.png"))
// ... and it was persisted to runtime_settings.json.
_, err = os.Stat(filepath.Join(dir, "runtime_settings.json"))
Expect(err).ToNot(HaveOccurred())
})
It("accepts a generic content-type when the filename extension is allowed", func() {
body, ct := multipartFile("favicon.svg", "application/octet-stream", []byte("<svg/>"))
req := httptest.NewRequest(http.MethodPost, "/api/branding/asset/favicon", body)
req.Header.Set("Content-Type", ct)
rec := httptest.NewRecorder()
e.ServeHTTP(rec, req)
Expect(rec.Code).To(Equal(http.StatusOK))
Expect(appCfg.Branding.FaviconFile).To(Equal("favicon.svg"))
})
It("replaces a prior asset of a different extension", func() {
brandingDir := filepath.Join(dir, "branding")
Expect(os.MkdirAll(brandingDir, 0o750)).To(Succeed())
Expect(os.WriteFile(filepath.Join(brandingDir, "logo.svg"), []byte("<svg/>"), 0o644)).To(Succeed())
body, ct := multipartFile("new.png", "image/png", []byte("\x89PNG\r\n\x1a\n"))
req := httptest.NewRequest(http.MethodPost, "/api/branding/asset/logo", body)
req.Header.Set("Content-Type", ct)
rec := httptest.NewRecorder()
e.ServeHTTP(rec, req)
Expect(rec.Code).To(Equal(http.StatusOK))
// Old companion removed, new one present.
_, err := os.Stat(filepath.Join(brandingDir, "logo.svg"))
Expect(os.IsNotExist(err)).To(BeTrue())
_, err = os.Stat(filepath.Join(brandingDir, "logo.png"))
Expect(err).ToNot(HaveOccurred())
})
It("rejects an unknown asset kind", func() {
body, ct := multipartFile("x.png", "image/png", []byte("x"))
req := httptest.NewRequest(http.MethodPost, "/api/branding/asset/banner", body)
req.Header.Set("Content-Type", ct)
rec := httptest.NewRecorder()
e.ServeHTTP(rec, req)
Expect(rec.Code).To(Equal(http.StatusBadRequest))
Expect(rec.Body.String()).To(ContainSubstring("invalid asset kind"))
})
It("rejects a request with no file", func() {
body := &bytes.Buffer{}
w := multipart.NewWriter(body)
Expect(w.Close()).To(Succeed())
req := httptest.NewRequest(http.MethodPost, "/api/branding/asset/logo", body)
req.Header.Set("Content-Type", w.FormDataContentType())
rec := httptest.NewRecorder()
e.ServeHTTP(rec, req)
Expect(rec.Code).To(Equal(http.StatusBadRequest))
Expect(rec.Body.String()).To(ContainSubstring("file is required"))
})
It("rejects an unsupported file type", func() {
body, ct := multipartFile("evil.txt", "text/html", []byte("<script>"))
req := httptest.NewRequest(http.MethodPost, "/api/branding/asset/logo", body)
req.Header.Set("Content-Type", ct)
rec := httptest.NewRecorder()
e.ServeHTTP(rec, req)
Expect(rec.Code).To(Equal(http.StatusBadRequest))
Expect(rec.Body.String()).To(ContainSubstring("unsupported file type"))
})
})
Describe("DELETE /api/branding/asset/:kind", func() {
It("removes the asset file and clears the setting", func() {
// Seed an uploaded asset first.
body, ct := multipartFile("logo.png", "image/png", []byte("\x89PNG\r\n\x1a\n"))
req := httptest.NewRequest(http.MethodPost, "/api/branding/asset/logo", body)
req.Header.Set("Content-Type", ct)
e.ServeHTTP(httptest.NewRecorder(), req)
Expect(appCfg.Branding.LogoFile).To(Equal("logo.png"))
req = httptest.NewRequest(http.MethodDelete, "/api/branding/asset/logo", nil)
rec := httptest.NewRecorder()
e.ServeHTTP(rec, req)
Expect(rec.Code).To(Equal(http.StatusOK))
Expect(appCfg.Branding.LogoFile).To(Equal(""))
Expect(decode(rec).LogoURL).To(Equal("/static/logo.png"))
_, err := os.Stat(filepath.Join(dir, "branding", "logo.png"))
Expect(os.IsNotExist(err)).To(BeTrue())
})
It("rejects an unknown asset kind", func() {
req := httptest.NewRequest(http.MethodDelete, "/api/branding/asset/banner", nil)
rec := httptest.NewRecorder()
e.ServeHTTP(rec, req)
Expect(rec.Code).To(Equal(http.StatusBadRequest))
})
})
Describe("GET /branding/asset/:kind (serve)", func() {
It("404s for an unknown kind", func() {
req := httptest.NewRequest(http.MethodGet, "/branding/asset/banner", nil)
rec := httptest.NewRecorder()
e.ServeHTTP(rec, req)
Expect(rec.Code).To(Equal(http.StatusNotFound))
})
It("404s when no override is configured", func() {
req := httptest.NewRequest(http.MethodGet, "/branding/asset/logo", nil)
rec := httptest.NewRecorder()
e.ServeHTTP(rec, req)
Expect(rec.Code).To(Equal(http.StatusNotFound))
})
It("404s on a path-traversal basename", func() {
appCfg.Branding.LogoFile = "../runtime_settings.json"
req := httptest.NewRequest(http.MethodGet, "/branding/asset/logo", nil)
rec := httptest.NewRecorder()
e.ServeHTTP(rec, req)
Expect(rec.Code).To(Equal(http.StatusNotFound))
})
})
})

View File

@@ -0,0 +1,111 @@
package localai_test
import (
"bytes"
"context"
"encoding/json"
"net/http"
"net/http/httptest"
"os"
"path/filepath"
"github.com/labstack/echo/v4"
"github.com/mudler/LocalAI/core/application"
"github.com/mudler/LocalAI/core/config"
. "github.com/mudler/LocalAI/core/http/endpoints/localai"
"github.com/mudler/LocalAI/pkg/system"
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
)
// The settings handlers take the concrete *application.Application, so the
// suite builds a minimal real app (no watchdog/p2p — both default off — so
// start() doesn't spawn background services) the same way the in-process HTTP
// suite does, then drives the handlers via httptest.
var _ = Describe("Settings endpoints", func() {
var (
tmp string
app *application.Application
e *echo.Echo
cancel context.CancelFunc
)
BeforeEach(func() {
var err error
tmp, err = os.MkdirTemp("", "settings-test-*")
Expect(err).ToNot(HaveOccurred())
var ctx context.Context
ctx, cancel = context.WithCancel(context.Background())
st, err := system.GetSystemState(
system.WithModelPath(filepath.Join(tmp, "models")),
system.WithBackendPath(filepath.Join(tmp, "backends")),
)
Expect(err).ToNot(HaveOccurred())
app, err = application.New(
config.WithContext(ctx),
config.WithSystemState(st),
)
Expect(err).ToNot(HaveOccurred())
// Settings are persisted here; set after construction since there's no
// dedicated AppOption for it.
app.ApplicationConfig().DynamicConfigsDir = tmp
e = echo.New()
e.GET("/api/settings", GetSettingsEndpoint(app))
e.POST("/api/settings", UpdateSettingsEndpoint(app))
})
AfterEach(func() {
cancel()
Expect(os.RemoveAll(tmp)).To(Succeed())
})
post := func(body string) *httptest.ResponseRecorder {
req := httptest.NewRequest(http.MethodPost, "/api/settings", bytes.NewBufferString(body))
req.Header.Set("Content-Type", "application/json")
rec := httptest.NewRecorder()
e.ServeHTTP(rec, req)
return rec
}
It("GET returns the current runtime settings", func() {
req := httptest.NewRequest(http.MethodGet, "/api/settings", nil)
rec := httptest.NewRecorder()
e.ServeHTTP(rec, req)
Expect(rec.Code).To(Equal(http.StatusOK))
var settings map[string]any
Expect(json.Unmarshal(rec.Body.Bytes(), &settings)).To(Succeed())
Expect(settings).ToNot(BeEmpty())
})
It("rejects malformed JSON", func() {
rec := post("{not json")
Expect(rec.Code).To(Equal(http.StatusBadRequest))
Expect(rec.Body.String()).To(ContainSubstring("Failed to parse JSON"))
})
It("rejects an invalid watchdog timeout duration", func() {
rec := post(`{"watchdog_idle_timeout":"notaduration"}`)
Expect(rec.Code).To(Equal(http.StatusBadRequest))
Expect(rec.Body.String()).To(ContainSubstring("Invalid watchdog_idle_timeout"))
})
It("errors when DynamicConfigsDir is unset", func() {
app.ApplicationConfig().DynamicConfigsDir = ""
rec := post(`{}`)
Expect(rec.Code).To(Equal(http.StatusBadRequest))
Expect(rec.Body.String()).To(ContainSubstring("DynamicConfigsDir is not set"))
})
It("saves valid settings to runtime_settings.json", func() {
rec := post(`{}`)
Expect(rec.Code).To(Equal(http.StatusOK))
Expect(rec.Body.String()).To(ContainSubstring("Settings updated successfully"))
_, err := os.Stat(filepath.Join(tmp, "runtime_settings.json"))
Expect(err).ToNot(HaveOccurred())
})
})

View File

@@ -68,6 +68,57 @@ func mergeToolCallDeltas(existing []schema.ToolCall, deltas []schema.ToolCall) [
return existing
}
// applyAutoparserOverride replaces the Go-side reasoning-extraction result with
// the C++ autoparser's classified ChatDeltas when those deltas contain
// actionable content or reasoning. It preserves the original logprobs.
//
// When the autoparser did not classify any reasoning (deltaReasoning == "") but
// deltaContent still carries an unparsed reasoning tag pair (e.g. the
// non-jinja "pure content" fallback path on a <think> model — issue #9985),
// the Go-side reasoning extractor is run on deltaContent as a defensive
// fallback so <think>…</think> blocks do not leak into the OpenAI `content`
// field.
func applyAutoparserOverride(
chatDeltas []*pb.ChatDelta,
thinkingStartToken string,
reasoningConfig reason.Config,
existing []schema.Choice,
) []schema.Choice {
if len(chatDeltas) == 0 {
return existing
}
deltaContent := functions.ContentFromChatDeltas(chatDeltas)
deltaReasoning := functions.ReasoningFromChatDeltas(chatDeltas)
if deltaContent == "" && deltaReasoning == "" {
return existing
}
// Fallback for non-jinja models (issue #9985): when the C++ autoparser
// did not classify reasoning but the raw content still contains a known
// reasoning tag pair, run Go-side extraction on the content so that the
// <think>…</think> block does not leak into the OpenAI `content` field.
// When the autoparser DID populate ReasoningContent, leave its
// content/reasoning split alone — trust the parser. We replace
// deltaContent unconditionally because ExtractReasoningWithConfig is a
// no-op when no tag pair matches; this also strips empty thinking
// blocks like "<think></think>" that some models emit when reasoning
// is disabled.
if deltaReasoning == "" && deltaContent != "" {
deltaReasoning, deltaContent = reason.ExtractReasoningWithConfig(deltaContent, thinkingStartToken, reasoningConfig)
}
xlog.Debug("[ChatDeltas] non-SSE no-tools: overriding result with C++ autoparser deltas",
"content_len", len(deltaContent), "reasoning_len", len(deltaReasoning))
stopReason := FinishReasonStop
message := &schema.Message{Role: "assistant", Content: &deltaContent}
if deltaReasoning != "" {
message.Reasoning = &deltaReasoning
}
newChoice := schema.Choice{FinishReason: &stopReason, Index: 0, Message: message}
if len(existing) > 0 && existing[0].Logprobs != nil {
newChoice.Logprobs = existing[0].Logprobs
}
return []schema.Choice{newChoice}
}
// ChatEndpoint is the OpenAI Completion API endpoint https://platform.openai.com/docs/api-reference/chat/create
// @Summary Generate a chat completions for a given prompt and model.
// @Tags inference
@@ -757,24 +808,8 @@ func ChatEndpoint(cl *config.ModelConfigLoader, ml *model.ModelLoader, evaluator
// For non-tool requests: prefer C++ autoparser chat deltas over
// Go-side tag extraction (which can mangle output when thinkingStartToken
// differs from the model's actual reasoning tags, e.g. Gemma 4).
if !shouldUseFn && len(chatDeltas) > 0 {
deltaContent := functions.ContentFromChatDeltas(chatDeltas)
deltaReasoning := functions.ReasoningFromChatDeltas(chatDeltas)
if deltaContent != "" || deltaReasoning != "" {
xlog.Debug("[ChatDeltas] non-SSE no-tools: overriding result with C++ autoparser deltas",
"content_len", len(deltaContent), "reasoning_len", len(deltaReasoning))
stopReason := FinishReasonStop
message := &schema.Message{Role: "assistant", Content: &deltaContent}
if deltaReasoning != "" {
message.Reasoning = &deltaReasoning
}
newChoice := schema.Choice{FinishReason: &stopReason, Index: 0, Message: message}
// Preserve logprobs from the original result
if len(result) > 0 && result[0].Logprobs != nil {
newChoice.Logprobs = result[0].Logprobs
}
result = []schema.Choice{newChoice}
}
if !shouldUseFn {
result = applyAutoparserOverride(chatDeltas, thinkingStartToken, config.ReasoningConfig, result)
}
// Tool parsing is deferred here (only when shouldUseFn) so chat deltas are available

View File

@@ -0,0 +1,99 @@
package openai
import (
pb "github.com/mudler/LocalAI/pkg/grpc/proto"
reason "github.com/mudler/LocalAI/pkg/reasoning"
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
)
// Regression test for the prefill-misclassification artifact surfaced in
// the review of #9991: when LocalAI templates qwen3 with
// use_tokenizer_template (the post-#9985 gallery shape),
// DetectThinkingStartToken finds <think> in the model's jinja chat
// template — without evaluating the surrounding {% if enable_thinking %}
// guard — and the Go-side extractor's PrependThinkingTokenIfNeeded then
// treats every non-thinking output token as reasoning. The autoparser does
// not classify qwen3's tool calls into ChatDelta.ToolCalls (qwen3's tool
// format isn't on llama.cpp's recognized-tool list), so all tokens land in
// ChatDelta.Content while the Go-side extractor silently accumulates a
// "reasoning" string equal to the raw tool-call JSON. End-of-stream this
// is flushed as a trailing `delta.reasoning` chunk to the client.
//
// chooseDeferredReasoning is the gate: when the autoparser was active for
// any chunk (preferAutoparser sticky), we trust its reasoning_content
// classification (usually empty) instead of the polluted Go-side state.
var _ = Describe("chooseDeferredReasoning", func() {
// Simulate the qwen3-after-#9985 misclassification: build a real
// extractor with a <think> thinking-start token, then feed it
// non-thinking content. The extractor will (correctly per its own
// contract) treat the content as reasoning because
// PrependThinkingTokenIfNeeded synthesizes a leading <think>.
pollutedExtractor := func(content string) *reason.ReasoningExtractor {
e := reason.NewReasoningExtractor("<think>", reason.Config{})
e.ProcessToken(content)
Expect(e.Reasoning()).To(Equal(content),
"sanity: when the thinking-start token is set and content has no real <think>...</think>, "+
"the extractor classifies all content as reasoning — this is exactly the prefill pollution "+
"we want chooseDeferredReasoning to guard against")
return e
}
Context("autoparser was active (preferAutoparser=true)", func() {
It("returns the autoparser's reasoning classification, ignoring the polluted Go-side state", func() {
toolCallJSON := `{"arguments": {"cmd": "echo hello"}, "name": "exec"}`
extractor := pollutedExtractor(toolCallJSON)
// What the C++ autoparser sent: content chunks but no
// reasoning_content (qwen3 tool calls aren't classified by
// the upstream PEG parser).
chatDeltas := []*pb.ChatDelta{
{Content: toolCallJSON, ReasoningContent: ""},
}
got := chooseDeferredReasoning(true, chatDeltas, extractor)
Expect(got).To(BeEmpty(),
"chooseDeferredReasoning must NOT return the polluted extractor state "+
"when the autoparser was active — the autoparser correctly classified zero reasoning")
})
It("returns the autoparser's reasoning when it actually did classify reasoning", func() {
// The other side of the contract: when the autoparser was
// in jinja-with-recognized-format mode and DID classify
// reasoning, pass that through verbatim.
actualReasoning := "Okay, the user asked X. I should call exec."
extractor := pollutedExtractor("ignored polluted state")
chatDeltas := []*pb.ChatDelta{
{Content: "", ReasoningContent: actualReasoning},
}
got := chooseDeferredReasoning(true, chatDeltas, extractor)
Expect(got).To(Equal(actualReasoning))
})
})
Context("autoparser was NOT active (preferAutoparser=false)", func() {
It("falls back to the Go-side extractor — the right source for vLLM and other autoparser-less backends", func() {
realReasoning := "Genuine reasoning from a backend without an autoparser"
extractor := reason.NewReasoningExtractor("<think>", reason.Config{})
extractor.ProcessToken("<think>" + realReasoning + "</think>final answer")
got := chooseDeferredReasoning(false, nil, extractor)
Expect(got).To(Equal(realReasoning))
})
It("falls back even when ChatDeltas are present but the autoparser never classified anything", func() {
// Defensive: chatDeltas could carry vestigial data; if
// preferAutoparser wasn't flipped, we still use the
// extractor.
extractor := reason.NewReasoningExtractor("", reason.Config{})
extractor.ProcessToken("<think>some thoughts</think>answer")
got := chooseDeferredReasoning(false, []*pb.ChatDelta{{Content: "answer"}}, extractor)
Expect(got).To(Equal("some thoughts"))
})
})
})

View File

@@ -7,11 +7,111 @@ import (
"github.com/mudler/LocalAI/core/config"
"github.com/mudler/LocalAI/core/schema"
"github.com/mudler/LocalAI/pkg/functions"
pb "github.com/mudler/LocalAI/pkg/grpc/proto"
"github.com/mudler/LocalAI/pkg/model"
reason "github.com/mudler/LocalAI/pkg/reasoning"
"github.com/mudler/xlog"
)
// emitJSONToolCallDeltas iterates the JSON tool-call objects produced by the
// streaming tool-call detector and emits SSE chunks for the ones the caller
// hasn't already emitted. It returns the new lastEmittedCount.
//
// Semantics:
// - Skips entries before lastEmittedCount (already emitted).
// - Emits one tool_call chunk per consecutive entry that has a usable
// `name` string.
// - Stops at the first entry without a name (typically the partial-JSON
// tail or a healing-marker stub — see issue #9988) so the caller doesn't
// advance past it. Bumping lastEmittedCount past an unparsed stub
// permanently gates off content emission for the rest of the stream.
// - When jsonResults is empty (the autoparser-working case, where the raw
// text result is cleared and only ChatDeltas carry tool calls), this is
// a no-op and lastEmittedCount is returned unchanged.
//
// The autoparser-correctly-classifying-tool-calls path is unaffected: it
// delivers tool calls via TokenUsage.ChatDeltas, and the deferred
// end-of-stream block (ToolCallsFromChatDeltas → buildDeferredToolCallChunks)
// emits them; this helper sees an empty jsonResults and emits nothing.
func emitJSONToolCallDeltas(
jsonResults []map[string]any,
lastEmittedCount int,
id, model string,
created int,
responses chan<- schema.OpenAIResponse,
) int {
for i := lastEmittedCount; i < len(jsonResults); i++ {
jsonObj := jsonResults[i]
name, ok := jsonObj["name"].(string)
if !ok || name == "" {
break
}
args := "{}"
if argsVal, ok := jsonObj["arguments"]; ok {
if argsStr, ok := argsVal.(string); ok {
args = argsStr
} else {
argsBytes, _ := json.Marshal(argsVal)
args = string(argsBytes)
}
}
responses <- schema.OpenAIResponse{
ID: id,
Created: created,
Model: model,
Choices: []schema.Choice{{
Delta: &schema.Message{
Role: "assistant",
ToolCalls: []schema.ToolCall{
{
Index: i,
ID: id,
Type: "function",
FunctionCall: schema.FunctionCall{
Name: name,
Arguments: args,
},
},
},
},
Index: 0,
FinishReason: nil,
}},
Object: "chat.completion.chunk",
}
lastEmittedCount = i + 1
}
return lastEmittedCount
}
// chooseDeferredReasoning picks the source of truth for the end-of-stream
// reasoning flush in processStreamWithTools. When the C++ autoparser was
// active during the stream (preferAutoparser), it returns the autoparser's
// own classified reasoning_content from ChatDeltas — usually empty when the
// autoparser is in pure-content fallback mode. Otherwise it falls back to
// the Go-side streaming extractor, which is the right source for backends
// without an autoparser (vLLM, etc.).
//
// Why: the Go-side extractor's accumulated Reasoning() can be polluted by
// PrependThinkingTokenIfNeeded — when the tokenizer template contains a
// thinking start token (qwen3's jinja template has <think> inside an
// {% if enable_thinking %} block, and DetectThinkingStartToken does not
// evaluate jinja conditionals), prefill detection treats every chunk's
// content as reasoning, even when the model emitted a raw tool-call JSON
// in non-thinking mode. Without this guard, qwen3-4b with streaming + tools
// (after #9985 flipped the gallery to use_tokenizer_template) emits a
// trailing SSE chunk where `reasoning` carries the tool-call JSON.
func chooseDeferredReasoning(
preferAutoparser bool,
chatDeltas []*pb.ChatDelta,
extractor *reason.ReasoningExtractor,
) string {
if preferAutoparser {
return functions.ReasoningFromChatDeltas(chatDeltas)
}
return extractor.Reasoning()
}
// processStream is the streaming worker for chat completions with no
// tool/function calling involved. It pushes SSE-shaped chunks onto
// `responses` and returns the authoritative cumulative TokenUsage from
@@ -52,6 +152,13 @@ func processStream(
thinkingStartToken := reason.DetectThinkingStartToken(template, &cfg.ReasoningConfig)
extractor := reason.NewReasoningExtractor(thinkingStartToken, cfg.ReasoningConfig)
// preferAutoparser is sticky: once the C++ autoparser has ever classified
// reasoning_content, we trust it for the rest of the stream. Until then we
// fall back to Go-side extraction so that a "pure content" autoparser
// (non-jinja path, issue #9985) does not leak <think>…</think> tokens
// straight into the OpenAI `content` field.
preferAutoparser := false
_, finalUsage, _, err := ComputeChoices(req, s, cfg, cl, startupOptions, loader, func(s string, c *[]schema.Choice) {}, func(s string, tokenUsage backend.TokenUsage) bool {
var reasoningDelta, contentDelta string
@@ -64,8 +171,16 @@ func processStream(
// Otherwise fall back to Go-side extraction.
if tokenUsage.HasChatDeltaContent() {
rawReasoning, cd := tokenUsage.ChatDeltaReasoningAndContent()
contentDelta = cd
reasoningDelta = extractor.ProcessChatDeltaReasoning(rawReasoning)
if rawReasoning != "" {
preferAutoparser = true
}
if preferAutoparser {
contentDelta = cd
reasoningDelta = extractor.ProcessChatDeltaReasoning(rawReasoning)
} else {
reasoningDelta = goReasoning
contentDelta = goContent
}
} else {
reasoningDelta = goReasoning
contentDelta = goContent
@@ -142,6 +257,17 @@ func processStreamWithTools(
hasChatDeltaToolCalls := false
hasChatDeltaContent := false
// preferAutoparser is sticky: once the C++ autoparser has ever delivered
// content or reasoning via ChatDeltas, we trust its classification for the
// rest of the stream — including for the end-of-stream reasoning flush in
// buildDeferredToolCallChunks. Otherwise the Go-side extractor's
// accumulated Reasoning() can be polluted by prefill detection
// misclassifying content as reasoning (this happens when <think> appears
// in the tokenizer template and the model emits non-reasoning content
// like a raw tool-call JSON — qwen3-4b after #9985 enabled
// use_tokenizer_template). Mirrors the analogous flag in processStream.
preferAutoparser := false
// X-LocalAI-Node attribution is handled by middleware.ExposeNodeHeader
// at the wrapper layer; no in-band signalling from this worker.
@@ -165,12 +291,17 @@ func processStreamWithTools(
if usage.HasChatDeltaContent() {
rawReasoning, cd := usage.ChatDeltaReasoningAndContent()
preferAutoparser = true
contentDelta = cd
reasoningDelta = extractor.ProcessChatDeltaReasoning(rawReasoning)
} else {
} else if !preferAutoparser {
reasoningDelta = goReasoning
contentDelta = goContent
}
// If preferAutoparser is already true but this chunk carried no
// autoparser data, leave both deltas empty — the next autoparser
// chunk will pick things up. Falling back to Go-side here would
// re-introduce the prefill-misclassification leak.
// Emit reasoning deltas in their own SSE chunks before any tool-call chunks
// (OpenAI spec: reasoning and tool_calls never share a delta)
@@ -264,49 +395,10 @@ func processStreamWithTools(
// Try JSON tool call parsing for streaming.
// Only emit NEW tool calls (same guard as XML parser above).
jsonResults, jsonErr := functions.ParseJSONIterative(cleanedResult, true)
if jsonErr == nil && len(jsonResults) > lastEmittedCount {
for i := lastEmittedCount; i < len(jsonResults); i++ {
jsonObj := jsonResults[i]
name, ok := jsonObj["name"].(string)
if !ok || name == "" {
continue
}
args := "{}"
if argsVal, ok := jsonObj["arguments"]; ok {
if argsStr, ok := argsVal.(string); ok {
args = argsStr
} else {
argsBytes, _ := json.Marshal(argsVal)
args = string(argsBytes)
}
}
initialMessage := schema.OpenAIResponse{
ID: id,
Created: created,
Model: req.Model,
Choices: []schema.Choice{{
Delta: &schema.Message{
Role: "assistant",
ToolCalls: []schema.ToolCall{
{
Index: i,
ID: id,
Type: "function",
FunctionCall: schema.FunctionCall{
Name: name,
Arguments: args,
},
},
},
},
Index: 0,
FinishReason: nil,
}},
Object: "chat.completion.chunk",
}
responses <- initialMessage
}
lastEmittedCount = len(jsonResults)
if jsonErr == nil {
lastEmittedCount = emitJSONToolCallDeltas(
jsonResults, lastEmittedCount, id, req.Model, created, responses,
)
}
}
return true
@@ -352,7 +444,14 @@ func processStreamWithTools(
} else {
// Fallback: parse tool calls from raw text (no chat deltas from backend)
xlog.Debug("[ChatDeltas] no pre-parsed tool calls, falling back to Go-side text parsing")
reasoning = extractor.Reasoning()
// When the autoparser was active during streaming (preferAutoparser),
// trust its reasoning classification rather than the Go-side
// extractor's accumulated state — the latter may have misclassified
// content as reasoning due to prefill detection on a tokenizer
// template that contains <think>. This was visible on qwen3-4b after
// #9985 enabled use_tokenizer_template: a streaming tool-call JSON
// would leak as a trailing reasoning chunk via the deferred flush.
reasoning = chooseDeferredReasoning(preferAutoparser, chatDeltas, extractor)
cleanedResult := extractor.CleanedContent()
*textContentToReturn = functions.ParseTextContent(cleanedResult, cfg.FunctionsConfig)
cleanedResult = functions.CleanupLLMResult(cleanedResult, cfg.FunctionsConfig)

View File

@@ -0,0 +1,197 @@
package openai
import (
"github.com/mudler/LocalAI/core/schema"
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
)
// drainChannel reads everything currently buffered on a channel without
// blocking on close. The helper test channels are sized for the assertions.
func drainChannel(ch <-chan schema.OpenAIResponse) []schema.OpenAIResponse {
var out []schema.OpenAIResponse
for {
select {
case r, ok := <-ch:
if !ok {
return out
}
out = append(out, r)
default:
return out
}
}
}
// nameOf returns the name of the first tool call carried on the choice's
// delta, or "" if none.
func nameOf(r schema.OpenAIResponse) string {
if len(r.Choices) == 0 || r.Choices[0].Delta == nil {
return ""
}
if len(r.Choices[0].Delta.ToolCalls) == 0 {
return ""
}
return r.Choices[0].Delta.ToolCalls[0].FunctionCall.Name
}
var _ = Describe("emitJSONToolCallDeltas", func() {
const (
id = "test-stream"
model = "test-model"
created = 1700000000
)
// The case that motivated this helper. With the previous version of
// the streaming worker, ParseJSONIterative would hand back a stub
// object like `{"4310046988783340008":1}` after the model had only
// emitted `{`. The worker bumped lastEmittedCount unconditionally,
// which permanently gated off content emission for the rest of the
// stream (qwen3-4b with stream:true + tools dribbled only `{"` to
// the client and then nothing). See issue #9988.
Context("partial stub without a usable name", func() {
It("does NOT bump lastEmittedCount and emits nothing", func() {
responses := make(chan schema.OpenAIResponse, 4)
// What ParseJSONIterative used to return for `{`:
stubResults := []map[string]any{
{"4310046988783340008": float64(1)},
}
next := emitJSONToolCallDeltas(stubResults, 0, id, model, created, responses)
Expect(next).To(Equal(0),
"lastEmittedCount must NOT advance past a stub without a name "+
"— otherwise content emission gets permanently gated off")
Expect(drainChannel(responses)).To(BeEmpty(),
"no tool_call chunk should be emitted for a stub without a name")
})
})
// No-regression #1: the autoparser-correctly-working path. When the
// C++ autoparser classifies tool calls itself, the raw text result is
// cleared and ParseJSONIterative on it returns no results — this
// helper must be a no-op so the deferred end-of-stream code can emit
// the tool calls from TokenUsage.ChatDeltas.
Context("empty jsonResults (autoparser-correctly-working path)", func() {
It("is a no-op and leaves lastEmittedCount unchanged", func() {
responses := make(chan schema.OpenAIResponse, 4)
next := emitJSONToolCallDeltas(nil, 0, id, model, created, responses)
Expect(next).To(Equal(0))
Expect(drainChannel(responses)).To(BeEmpty())
})
It("leaves a non-zero lastEmittedCount unchanged when later called with the same length", func() {
responses := make(chan schema.OpenAIResponse, 4)
results := []map[string]any{
{"name": "search", "arguments": map[string]any{"q": "hi"}},
}
// First call emits the one available tool call.
next := emitJSONToolCallDeltas(results, 0, id, model, created, responses)
Expect(next).To(Equal(1))
Expect(drainChannel(responses)).To(HaveLen(1))
// Subsequent chunks haven't grown the slice — must be a no-op.
next = emitJSONToolCallDeltas(results, next, id, model, created, responses)
Expect(next).To(Equal(1))
Expect(drainChannel(responses)).To(BeEmpty())
})
})
// No-regression #2: the normal completed-JSON path. When the model
// emits a real, complete tool call as JSON in raw content (e.g. qwen3
// without jinja but with tools), we should emit exactly one tool_call
// SSE chunk on the first call and become a no-op on later calls.
Context("single complete tool call", func() {
It("emits one tool_call chunk and bumps lastEmittedCount to 1", func() {
responses := make(chan schema.OpenAIResponse, 4)
results := []map[string]any{
{
"name": "search",
"arguments": map[string]any{
"q": "hello",
},
},
}
next := emitJSONToolCallDeltas(results, 0, id, model, created, responses)
Expect(next).To(Equal(1))
out := drainChannel(responses)
Expect(out).To(HaveLen(1))
Expect(nameOf(out[0])).To(Equal("search"))
Expect(out[0].Choices[0].Delta.ToolCalls[0].FunctionCall.Arguments).
To(ContainSubstring(`"q":"hello"`))
})
It("accepts arguments already serialized as a string", func() {
responses := make(chan schema.OpenAIResponse, 4)
results := []map[string]any{
{
"name": "search",
"arguments": `{"q":"hello"}`,
},
}
emitJSONToolCallDeltas(results, 0, id, model, created, responses)
out := drainChannel(responses)
Expect(out).To(HaveLen(1))
Expect(out[0].Choices[0].Delta.ToolCalls[0].FunctionCall.Arguments).
To(Equal(`{"q":"hello"}`))
})
})
// No-regression #3: multiple tool calls (parallel tool calling).
// Both must be emitted, lastEmittedCount must end at 2.
Context("multiple complete tool calls", func() {
It("emits one chunk per tool call and bumps lastEmittedCount to len(results)", func() {
responses := make(chan schema.OpenAIResponse, 8)
results := []map[string]any{
{"name": "search", "arguments": map[string]any{"q": "a"}},
{"name": "browse", "arguments": map[string]any{"url": "b"}},
}
next := emitJSONToolCallDeltas(results, 0, id, model, created, responses)
Expect(next).To(Equal(2))
out := drainChannel(responses)
Expect(out).To(HaveLen(2))
Expect(nameOf(out[0])).To(Equal("search"))
Expect(nameOf(out[1])).To(Equal("browse"))
})
})
// The streaming-tail case: incremental chunks. First parse returns
// one complete tool call followed by a partial stub; later chunks
// complete the second tool call. We must emit the first immediately
// and the second on the later call — without ever bumping past the
// stub mid-stream.
Context("partial tail behind a real tool call", func() {
It("emits the complete entry, stops at the stub, and resumes once the tail completes", func() {
responses := make(chan schema.OpenAIResponse, 8)
// Chunk 1: one real call + a partial stub for the next.
chunk1 := []map[string]any{
{"name": "search", "arguments": map[string]any{"q": "a"}},
{"4310046988783340008": float64(1)},
}
next := emitJSONToolCallDeltas(chunk1, 0, id, model, created, responses)
Expect(next).To(Equal(1),
"must NOT advance to 2 — the stub at index 1 has no usable name")
out := drainChannel(responses)
Expect(out).To(HaveLen(1))
Expect(nameOf(out[0])).To(Equal("search"))
// Chunk 2: the stub completes into a real call.
chunk2 := []map[string]any{
{"name": "search", "arguments": map[string]any{"q": "a"}},
{"name": "browse", "arguments": map[string]any{"url": "b"}},
}
next = emitJSONToolCallDeltas(chunk2, next, id, model, created, responses)
Expect(next).To(Equal(2))
out = drainChannel(responses)
Expect(out).To(HaveLen(1))
Expect(nameOf(out[0])).To(Equal("browse"))
})
})
})

View File

@@ -3,6 +3,8 @@ package openai
import (
"github.com/mudler/LocalAI/core/config"
"github.com/mudler/LocalAI/pkg/functions"
pb "github.com/mudler/LocalAI/pkg/grpc/proto"
reason "github.com/mudler/LocalAI/pkg/reasoning"
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
@@ -94,6 +96,98 @@ var _ = Describe("handleQuestion", func() {
})
})
var _ = Describe("applyAutoparserOverride", func() {
// Regression test for https://github.com/mudler/LocalAI/issues/9985.
// When LocalAI templates a <think>-style reasoning model outside of jinja
// (e.g. the gallery qwen3 entry), the llama.cpp autoparser falls back to
// the "pure content" PEG parser which dumps the entire raw response,
// including <think>…</think>, into ChatDelta.Content and leaves
// ChatDelta.ReasoningContent empty. The Go side previously trusted that
// content verbatim and clobbered the tokenCallback's correctly-split
// reasoning, so <think> blocks leaked into the OpenAI `content` field.
Context("autoparser delivered content with embedded <think> tags and empty reasoning (issue #9985)", func() {
It("splits <think>…</think> out of content into the reasoning field", func() {
raw := "<think>\nOkay, the user said \"Hello\". I should reply warmly.\n</think>\n\nHello! How can I assist you today? 😊"
chatDeltas := []*pb.ChatDelta{
{Content: raw, ReasoningContent: ""},
}
result := applyAutoparserOverride(chatDeltas, "", reason.Config{}, nil)
Expect(result).To(HaveLen(1))
Expect(result[0].Message).ToNot(BeNil())
Expect(result[0].Message.Content).ToNot(BeNil())
content := *(result[0].Message.Content.(*string))
Expect(content).ToNot(ContainSubstring("<think>"),
"raw <think> tag must not leak into OpenAI content field")
Expect(content).ToNot(ContainSubstring("</think>"),
"raw </think> tag must not leak into OpenAI content field")
Expect(content).To(ContainSubstring("Hello! How can I assist you today?"),
"the model's actual answer must still be in content")
Expect(result[0].Message.Reasoning).ToNot(BeNil(),
"reasoning extracted from <think>…</think> must populate Reasoning")
Expect(*result[0].Message.Reasoning).To(ContainSubstring("Okay, the user said"))
})
It("does not run extraction when the autoparser already populated reasoning", func() {
// When the autoparser actually classified reasoning, leave its
// content/reasoning split untouched.
content := "Hello! How can I assist you today?"
reasoning := "Already split by the C++ autoparser."
chatDeltas := []*pb.ChatDelta{
{Content: content, ReasoningContent: reasoning},
}
result := applyAutoparserOverride(chatDeltas, "", reason.Config{}, nil)
Expect(result).To(HaveLen(1))
Expect(*(result[0].Message.Content.(*string))).To(Equal(content))
Expect(result[0].Message.Reasoning).ToNot(BeNil())
Expect(*result[0].Message.Reasoning).To(Equal(reasoning))
})
It("passes plain content through unchanged when no reasoning tags are present", func() {
content := "Just a normal answer with no reasoning at all."
chatDeltas := []*pb.ChatDelta{
{Content: content, ReasoningContent: ""},
}
result := applyAutoparserOverride(chatDeltas, "", reason.Config{}, nil)
Expect(result).To(HaveLen(1))
Expect(*(result[0].Message.Content.(*string))).To(Equal(content))
Expect(result[0].Message.Reasoning).To(BeNil())
})
It("strips an empty <think></think> block (qwen3 /no_think mode)", func() {
// qwen3 with the /no_think directive still emits an empty thinking
// block. The Go-side fallback must strip it from content rather than
// pass <think></think> through verbatim. No reasoning is set because
// the block has no body.
raw := "<think>\n\n</think>\n\nHello! How can I assist you today?"
chatDeltas := []*pb.ChatDelta{
{Content: raw, ReasoningContent: ""},
}
result := applyAutoparserOverride(chatDeltas, "", reason.Config{}, nil)
Expect(result).To(HaveLen(1))
content := *(result[0].Message.Content.(*string))
Expect(content).ToNot(ContainSubstring("<think>"))
Expect(content).ToNot(ContainSubstring("</think>"))
Expect(content).To(ContainSubstring("Hello! How can I assist you today?"))
})
It("returns the existing result when chatDeltas is empty", func() {
existing := []schema.Choice{{Index: 7}}
result := applyAutoparserOverride(nil, "", reason.Config{}, existing)
Expect(result).To(Equal(existing))
})
})
})
var _ = Describe("mergeToolCallDeltas", func() {
Context("with new tool calls", func() {
It("should append new tool calls", func() {

View File

@@ -1572,6 +1572,15 @@ func triggerResponseAtTurn(ctx context.Context, session *Session, conv *Conversa
"tool_calls", len(deltaToolCalls),
"content_len", len(deltaContent),
"reasoning_len", len(deltaReasoning))
// Issue #9985: when the autoparser only delivered content (no
// reasoning_content), it may be running in the "pure content"
// PEG fallback (non-jinja path) which leaves <think>…</think>
// embedded in the content. Run Go-side extraction defensively.
// ExtractReasoningWithConfig is a no-op when no tag pair matches,
// so it's safe to apply unconditionally in the no-reasoning branch.
if deltaReasoning == "" && deltaContent != "" {
deltaReasoning, deltaContent = reasoning.ExtractReasoningWithConfig(deltaContent, thinkingStartToken, config.ReasoningConfig)
}
reasoningText = deltaReasoning
responseWithoutReasoning = deltaContent
textContent = deltaContent

View File

@@ -1971,6 +1971,10 @@ func handleOpenResponsesStream(c echo.Context, responseID string, createdAt int6
// Source reasoning from: (1) ChatDeltas from C++ autoparser, (2) extractor's
// streaming state, (3) final extraction from the finetuned result.
// Issue #9985: when the autoparser delivered Content but no
// ReasoningContent, it was running in the "pure content" PEG fallback
// (non-jinja path) which leaves reasoning tags embedded in content.
// Fall back to the streaming Go-side extractor's split in that case.
if chatDeltaReasoning := functions.ReasoningFromChatDeltas(chatDeltas); chatDeltaReasoning != "" {
finalReasoning = chatDeltaReasoning
finalCleanedResult = functions.ContentFromChatDeltas(chatDeltas)

View File

@@ -0,0 +1,8 @@
{
"all": true,
"include": ["src/**"],
"extension": [".js", ".jsx"],
"reporter": ["text-summary", "json-summary", "html"],
"report-dir": "coverage",
"temp-dir": ".nyc_output"
}

View File

@@ -34,13 +34,15 @@
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"nyc": "^18.0.0",
"vite": "^8.0.14",
"vite-plugin-istanbul": "^9.0.0",
},
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@@ -61,8 +63,6 @@
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@@ -73,10 +73,6 @@
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@@ -101,6 +97,12 @@
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@@ -191,6 +193,10 @@
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@@ -215,70 +221,50 @@
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"wrap-ansi-cjs/string-width/emoji-regex": ["emoji-regex@8.0.0", "", {}, "sha512-MSjYzcWNOA0ewAHpz0MxpYFvwg6yjy1NG3xteoqz644VCo/RPgnr1/GGt+ic3iJTzQ8Eu3TdM14SawnVUmGE6A=="],
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"wrap-ansi-cjs/strip-ansi/ansi-regex": ["ansi-regex@5.0.1", "", {}, "sha512-quJQXlTSUGL2LH9SUXo8VwsY4soanhgo6LNSm84E1LBcE8s3O0wpdiRzyR9z/ZZJMlMWv37qOOb9pdJlMUEKFQ=="],
"yargs/find-up/locate-path": ["locate-path@5.0.0", "", { "dependencies": { "p-locate": "^4.1.0" } }, "sha512-t7hw9pI+WvuwNJXwk5zVHpyhIqzg2qTlklJOf0mVxGSbe3Fp2VieZcduNYjaLDoy6p9uGpQEGWG87WpMKlNq8g=="],
"@istanbuljs/load-nyc-config/find-up/locate-path/p-locate": ["p-locate@4.1.0", "", { "dependencies": { "p-limit": "^2.2.0" } }, "sha512-R79ZZ/0wAxKGu3oYMlz8jy/kbhsNrS7SKZ7PxEHBgJ5+F2mtFW2fK2cOtBh1cHYkQsbzFV7I+EoRKe6Yt0oK7A=="],
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"glob/minimatch/brace-expansion/balanced-match": ["balanced-match@4.0.4", "", {}, "sha512-BLrgEcRTwX2o6gGxGOCNyMvGSp35YofuYzw9h1IMTRmKqttAZZVU67bdb9Pr2vUHA8+j3i2tJfjO6C6+4myGTA=="],
"nyc/find-up/locate-path/p-locate": ["p-locate@4.1.0", "", { "dependencies": { "p-limit": "^2.2.0" } }, "sha512-R79ZZ/0wAxKGu3oYMlz8jy/kbhsNrS7SKZ7PxEHBgJ5+F2mtFW2fK2cOtBh1cHYkQsbzFV7I+EoRKe6Yt0oK7A=="],
"pkg-dir/find-up/locate-path/p-locate": ["p-locate@4.1.0", "", { "dependencies": { "p-limit": "^2.2.0" } }, "sha512-R79ZZ/0wAxKGu3oYMlz8jy/kbhsNrS7SKZ7PxEHBgJ5+F2mtFW2fK2cOtBh1cHYkQsbzFV7I+EoRKe6Yt0oK7A=="],
"quick-temp/rimraf/glob/minimatch": ["minimatch@9.0.9", "", { "dependencies": { "brace-expansion": "^2.0.2" } }, "sha512-OBwBN9AL4dqmETlpS2zasx+vTeWclWzkblfZk7KTA5j3jeOONz/tRCnZomUyvNg83wL5Zv9Ss6HMJXAgL8R2Yg=="],
"quick-temp/rimraf/glob/path-scurry": ["path-scurry@1.11.1", "", { "dependencies": { "lru-cache": "^10.2.0", "minipass": "^5.0.0 || ^6.0.2 || ^7.0.0" } }, "sha512-Xa4Nw17FS9ApQFJ9umLiJS4orGjm7ZzwUrwamcGQuHSzDyth9boKDaycYdDcZDuqYATXw4HFXgaqWTctW/v1HA=="],
"test-exclude/minimatch/brace-expansion/balanced-match": ["balanced-match@4.0.4", "", {}, "sha512-BLrgEcRTwX2o6gGxGOCNyMvGSp35YofuYzw9h1IMTRmKqttAZZVU67bdb9Pr2vUHA8+j3i2tJfjO6C6+4myGTA=="],
"yargs/find-up/locate-path/p-locate": ["p-locate@4.1.0", "", { "dependencies": { "p-limit": "^2.2.0" } }, "sha512-R79ZZ/0wAxKGu3oYMlz8jy/kbhsNrS7SKZ7PxEHBgJ5+F2mtFW2fK2cOtBh1cHYkQsbzFV7I+EoRKe6Yt0oK7A=="],
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"nyc/find-up/locate-path/p-locate/p-limit": ["p-limit@2.3.0", "", { "dependencies": { "p-try": "^2.0.0" } }, "sha512-//88mFWSJx8lxCzwdAABTJL2MyWB12+eIY7MDL2SqLmAkeKU9qxRvWuSyTjm3FUmpBEMuFfckAIqEaVGUDxb6w=="],
"pkg-dir/find-up/locate-path/p-locate/p-limit": ["p-limit@2.3.0", "", { "dependencies": { "p-try": "^2.0.0" } }, "sha512-//88mFWSJx8lxCzwdAABTJL2MyWB12+eIY7MDL2SqLmAkeKU9qxRvWuSyTjm3FUmpBEMuFfckAIqEaVGUDxb6w=="],
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"quick-temp/rimraf/glob/path-scurry/lru-cache": ["lru-cache@10.4.3", "", {}, "sha512-JNAzZcXrCt42VGLuYz0zfAzDfAvJWW6AfYlDBQyDV5DClI2m5sAmK+OIO7s59XfsRsWHp02jAJrRadPRGTt6SQ=="],
"yargs/find-up/locate-path/p-locate/p-limit": ["p-limit@2.3.0", "", { "dependencies": { "p-try": "^2.0.0" } }, "sha512-//88mFWSJx8lxCzwdAABTJL2MyWB12+eIY7MDL2SqLmAkeKU9qxRvWuSyTjm3FUmpBEMuFfckAIqEaVGUDxb6w=="],
}
}

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@@ -0,0 +1 @@
30.66

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@@ -0,0 +1,24 @@
import { test, expect } from './coverage-fixtures.js'
// Agents feature page (src/pages/Agents.jsx).
test.describe('Agents page', () => {
test.beforeEach(async ({ page }) => {
await page.goto('/app/agents')
})
test('renders the agents list and empty state', async ({ page }) => {
await expect(page).toHaveURL(/\/app\/agents$/)
await expect(page.getByRole('heading', { name: 'Agents', exact: true })).toBeVisible()
await expect(page.getByText(/No agents configured/i)).toBeVisible()
await expect(page.getByRole('button', { name: 'Create Agent' }).first()).toBeVisible()
})
test('Create Agent navigates to the agent creation form', async ({ page }) => {
const create = page.getByRole('button', { name: 'Create Agent' }).last()
await create.scrollIntoViewIfNeeded()
await Promise.all([
page.waitForURL(/\/app\/agents\/new$/),
create.click(),
])
})
})

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@@ -1,4 +1,4 @@
import { test, expect } from '@playwright/test'
import { test, expect } from './coverage-fixtures.js'
function mockCapabilities(page, capabilities) {
return page.route('**/api/models/capabilities', (route) => {

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@@ -1,4 +1,4 @@
import { test, expect } from '@playwright/test'
import { test, expect } from './coverage-fixtures.js'
test.describe('Backend Logs', () => {
test('model detail page shows title', async ({ page }) => {

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@@ -0,0 +1,28 @@
import { test, expect } from './coverage-fixtures.js'
// Backends admin page (src/pages/Backends.jsx).
test.describe('Backends management page', () => {
test.beforeEach(async ({ page }) => {
await page.goto('/app/backends')
})
test('renders the management header and gallery tabs', async ({ page }) => {
await expect(page).toHaveURL(/\/app\/backends$/)
await expect(page.getByRole('heading', { name: 'Backend Management' })).toBeVisible()
await expect(page.getByRole('button', { name: 'Manual Install' })).toBeVisible()
await expect(page.getByRole('button').filter({ hasText: /^All$/ })).toBeVisible()
await expect(page.getByRole('button').filter({ hasText: /^Image$/ })).toBeVisible()
})
test('search field accepts input', async ({ page }) => {
const search = page.getByPlaceholder(/search backends/i)
await expect(search).toBeVisible()
await search.fill('whisper')
await expect(search).toHaveValue('whisper')
})
test('Manual Install reveals the OCI install form', async ({ page }) => {
await page.getByRole('button', { name: 'Manual Install' }).click()
await expect(page.getByPlaceholder('oci://quay.io/example/backend:latest')).toBeVisible()
})
})

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@@ -1,4 +1,4 @@
import { test, expect } from '@playwright/test'
import { test, expect } from './coverage-fixtures.js'
async function setupChatPage(page) {
// Mock capabilities endpoint so ModelSelector auto-selects a model

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@@ -1,4 +1,4 @@
import { test, expect } from '@playwright/test'
import { test, expect } from './coverage-fixtures.js'
// Regression coverage for issue #9904:
// - /api/operations was polled every 1s and *always* re-rendered the Chat

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@@ -0,0 +1,22 @@
import { test, expect } from './coverage-fixtures.js'
// Collections (Knowledge Base) feature page (src/pages/Collections.jsx).
test.describe('Collections page', () => {
test.beforeEach(async ({ page }) => {
await page.goto('/app/collections')
})
test('renders the knowledge base with an empty state and create control', async ({ page }) => {
await expect(page).toHaveURL(/\/app\/collections$/)
await expect(page.getByRole('heading', { name: 'Knowledge Base' })).toBeVisible()
await expect(page.getByText(/No collections yet/i)).toBeVisible()
await expect(page.getByRole('button').filter({ hasText: 'Create' })).toBeVisible()
})
test('new-collection name field accepts input', async ({ page }) => {
const input = page.locator('input, textarea').first()
await expect(input).toBeVisible()
await input.fill('my-kb')
await expect(input).toHaveValue('my-kb')
})
})

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@@ -0,0 +1,40 @@
// Playwright test fixture that harvests istanbul code coverage.
//
// When the app is built with COVERAGE=true (see vite.config.js), every source
// module is instrumented and exposes its counters on window.__coverage__. This
// fixture writes that object to .nyc_output/ after each test so `nyc report`
// can merge the runs into a per-file coverage report.
//
// The app is a React SPA (client-side routing), so window.__coverage__
// accumulates across in-app navigation within a single test; only a full page
// reload / fresh page.goto resets it. Specs import { test, expect } from this
// module instead of '@playwright/test' so collection is automatic.
import { test as base, expect } from '@playwright/test'
import { mkdirSync, writeFileSync } from 'node:fs'
import { randomUUID } from 'node:crypto'
import path from 'node:path'
const COVERAGE_DIR = path.resolve(process.cwd(), '.nyc_output')
export const test = base.extend({
page: async ({ page }, use) => {
await use(page)
let coverage
try {
coverage = await page.evaluate(() => window.__coverage__)
} catch {
// Page was already closed by the test — nothing to collect.
return
}
if (!coverage) return // build wasn't instrumented (COVERAGE unset)
mkdirSync(COVERAGE_DIR, { recursive: true })
writeFileSync(
path.join(COVERAGE_DIR, `playwright-${randomUUID()}.json`),
JSON.stringify(coverage),
)
},
})
export { expect }

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@@ -1,4 +1,4 @@
import { test, expect } from '@playwright/test'
import { test, expect } from './coverage-fixtures.js'
// Mock /backends/known shape mirrors schema.KnownBackend. The suite covers
// three concerns from Batch A: manual-pick badge (A1), inline ambiguity

View File

@@ -1,4 +1,4 @@
import { test, expect } from '@playwright/test'
import { test, expect } from './coverage-fixtures.js'
// Batch B — Simple / Power mode with quick switch. This suite exercises the
// mode switch itself, the collapsible Options disclosure in Simple mode,

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@@ -1,4 +1,4 @@
import { test, expect } from '@playwright/test'
import { test, expect } from './coverage-fixtures.js'
// Batch D — progressive disclosure of preference fields. Power > Preferences
// tab gates Quantizations, MMProj Quantizations, and Model Type so they only

View File

@@ -1,4 +1,4 @@
import { test, expect } from '@playwright/test'
import { test, expect } from './coverage-fixtures.js'
// Batch E — modality chip row. A horizontal chip row sits above the Backend
// dropdown (inside the Simple-mode Options disclosure and in Power >

View File

@@ -1,4 +1,4 @@
import { test, expect } from '@playwright/test'
import { test, expect } from './coverage-fixtures.js'
// Batch F3 — Enter-to-submit on the URI input in Simple mode. Wrapping the
// URI input + ambiguity alert + Options disclosure in a <form> means pressing

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@@ -0,0 +1,38 @@
import { test, expect } from './coverage-fixtures.js'
// Login page (src/pages/Login.jsx). /login redirects to /app when auth is
// disabled, so the harness mocks /api/auth/status to enable auth with no
// signed-in user. With no password/OAuth providers configured, the page
// offers API-token login — the path exercised here.
test.describe('Login page', () => {
test.beforeEach(async ({ page }) => {
await page.route('**/api/auth/status', (route) =>
route.fulfill({
contentType: 'application/json',
body: JSON.stringify({
authEnabled: true,
staticApiKeyRequired: false,
user: null,
hasUsers: true,
providers: [],
registrationMode: 'open',
permissions: {},
}),
}),
)
await page.goto('/login')
})
test('renders the API token login option', async ({ page }) => {
await expect(page).toHaveURL(/\/login$/)
await expect(page.getByRole('button', { name: /Login with API Token/i })).toBeVisible()
})
test('reveals and accepts an API token', async ({ page }) => {
await page.getByRole('button', { name: /Login with API Token/i }).click()
const tokenInput = page.locator('input').first()
await expect(tokenInput).toBeVisible()
await tokenInput.fill('sk-test-token')
await expect(tokenInput).toHaveValue('sk-test-token')
})
})

View File

@@ -1,4 +1,4 @@
import { test, expect } from '@playwright/test'
import { test, expect } from './coverage-fixtures.js'
test.describe('Manage Page - Backend Logs Link', () => {
test('row action menu exposes Backend logs entry with terminal icon', async ({ page }) => {

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@@ -1,4 +1,4 @@
import { test, expect } from '@playwright/test'
import { test, expect } from './coverage-fixtures.js'
function mockCapabilities(page, capabilities) {
return page.route('**/api/models/capabilities', (route) => {

View File

@@ -1,4 +1,4 @@
import { test, expect } from '@playwright/test'
import { test, expect } from './coverage-fixtures.js'
const MOCK_METADATA = {
sections: [
@@ -188,4 +188,40 @@ test.describe('Model Editor - Interactive Tab', () => {
await expect(page.locator('input[placeholder="Search fields to add..."]')).toBeVisible()
await expect(page.locator('text=Model Name')).toBeVisible()
})
test('shows the estimated VRAM annotation when the model has a context size', async ({ page }) => {
// Regression: the editor reads the /api/models/vram-estimate response,
// whose shape is snake_case (vram_display). The hook previously read
// camelCase (vramDisplay) and silently showed nothing.
await page.route('**/api/models/edit/mock-model', (route) => {
route.fulfill({
contentType: 'application/json',
body: JSON.stringify({
config: 'name: mock-model\nbackend: mock-backend\ncontext_size: 4096\nparameters:\n model: mock-model.bin\n',
name: 'mock-model',
}),
})
})
let estimateCalled = false
await page.route('**/api/models/vram-estimate', (route) => {
estimateCalled = true
route.fulfill({
contentType: 'application/json',
body: JSON.stringify({
size_bytes: 4294967296,
size_display: '4 GiB',
vram_bytes: 5583457484,
vram_display: '5.2 GiB',
context_length: 4096,
}),
})
})
await page.goto('/app/model-editor/mock-model')
await expect(page.locator('h1', { hasText: 'Model Editor' })).toBeVisible({ timeout: 10_000 })
await expect(page.getByText(/~\s*5\.2 GiB VRAM/)).toBeVisible({ timeout: 10_000 })
expect(estimateCalled).toBe(true)
})
})

View File

@@ -1,4 +1,4 @@
import { test, expect } from '@playwright/test'
import { test, expect } from './coverage-fixtures.js'
const MOCK_MODELS_RESPONSE = {
models: [
@@ -15,6 +15,62 @@ const MOCK_MODELS_RESPONSE = {
currentPage: 1,
}
const MOCK_GPU_RESOURCES_RESPONSE = {
type: 'gpu',
available: true,
gpus: [
{
index: 0,
name: 'Mock GPU',
vendor: 'nvidia',
total_vram: 12 * 1024 * 1024 * 1024,
used_vram: 2 * 1024 * 1024 * 1024,
free_vram: 10 * 1024 * 1024 * 1024,
usage_percent: 16.7,
},
],
aggregate: {
total_memory: 12 * 1024 * 1024 * 1024,
used_memory: 2 * 1024 * 1024 * 1024,
free_memory: 10 * 1024 * 1024 * 1024,
usage_percent: 16.7,
gpu_count: 1,
},
}
const MOCK_ESTIMATES = {
'llama-model': {
sizeBytes: 4 * 1024 * 1024 * 1024,
sizeDisplay: '4.00 GB',
estimates: {
'8192': {
vramBytes: 8 * 1024 * 1024 * 1024,
vramDisplay: '8.00 GB',
},
},
},
'whisper-model': {
sizeBytes: 1 * 1024 * 1024 * 1024,
sizeDisplay: '1.00 GB',
estimates: {
'8192': {
vramBytes: 2 * 1024 * 1024 * 1024,
vramDisplay: '2.00 GB',
},
},
},
'stablediffusion-model': {
sizeBytes: 8 * 1024 * 1024 * 1024,
sizeDisplay: '8.00 GB',
estimates: {
'8192': {
vramBytes: 16 * 1024 * 1024 * 1024,
vramDisplay: '16.00 GB',
},
},
},
}
test.describe('Models Gallery - Backend Features', () => {
test.beforeEach(async ({ page }) => {
await page.route('**/api/models*', (route) => {
@@ -196,3 +252,56 @@ test.describe('Models Gallery - Multi-select Filters', () => {
await expect(ttsBtn).not.toHaveClass(/active/)
})
})
test.describe('Models Gallery - Fits In GPU Filter', () => {
test.beforeEach(async ({ page }) => {
await page.route('**/api/models*', (route) => {
route.fulfill({
contentType: 'application/json',
body: JSON.stringify(MOCK_MODELS_RESPONSE),
})
})
await page.route('**/api/resources', (route) => {
route.fulfill({
contentType: 'application/json',
body: JSON.stringify(MOCK_GPU_RESOURCES_RESPONSE),
})
})
await page.route('**/api/models/estimate/*', (route) => {
const url = new URL(route.request().url())
const id = decodeURIComponent(url.pathname.split('/').pop() || '')
route.fulfill({
contentType: 'application/json',
body: JSON.stringify(MOCK_ESTIMATES[id] || {}),
})
})
await page.goto('/app/models')
await expect(page.locator('th', { hasText: 'Backend' })).toBeVisible({ timeout: 10_000 })
})
test('fits toggle is visible when GPU resources are available', async ({ page }) => {
await expect(page.getByText('Fits in GPU')).toBeVisible()
})
test('enabling fits filter hides models that exceed available VRAM', async ({ page }) => {
await expect(page.locator('tr', { hasText: 'stablediffusion-model' })).toBeVisible()
// The shared <Toggle> visually hides its native input (opacity:0;w:0;h:0),
// so .check() can't interact with it directly — click the visible track.
await page.locator('label.filter-bar-group__toggle', { hasText: 'Fits in GPU' }).locator('.toggle__track').click()
await expect(page.locator('tr', { hasText: 'stablediffusion-model' })).toHaveCount(0)
await expect(page.locator('tr', { hasText: 'llama-model' })).toBeVisible()
// Unknown estimate stays visible until an explicit non-fit verdict exists.
await expect(page.locator('tr', { hasText: 'unknown-model' })).toBeVisible()
})
test('fits filter state persists after reload', async ({ page }) => {
await page.locator('label.filter-bar-group__toggle', { hasText: 'Fits in GPU' }).locator('.toggle__track').click()
await page.reload()
await expect(page.getByLabel('Fits in GPU')).toBeChecked()
})
})

View File

@@ -1,4 +1,4 @@
import { test, expect } from '@playwright/test'
import { test, expect } from './coverage-fixtures.js'
test.describe('Navigation', () => {
test('/ redirects to /app', async ({ page }) => {

View File

@@ -1,4 +1,4 @@
import { test, expect } from '@playwright/test'
import { test, expect } from './coverage-fixtures.js'
// These specs cover the per-node backend row in the Nodes page:
// - the upgrade affordance is self-explanatory (icon + tooltip)

View File

@@ -0,0 +1,26 @@
import { test, expect } from './coverage-fixtures.js'
// P2P (Swarm) admin page — renders in the no-auth test harness (isAdmin).
test.describe('P2P page', () => {
test.beforeEach(async ({ page }) => {
await page.goto('/app/p2p')
})
test('renders the P2P distribution overview and capability cards', async ({ page }) => {
await expect(page).toHaveURL(/\/app\/p2p$/)
await expect(page.getByRole('heading', { name: /P2P Distribution Not Enabled/i })).toBeVisible()
await expect(page.getByRole('heading', { name: 'Instance Federation' })).toBeVisible()
await expect(page.getByRole('heading', { name: 'Model Sharding' })).toBeVisible()
await expect(page.getByRole('heading', { name: 'Resource Sharing' })).toBeVisible()
await expect(page.getByRole('heading', { name: /How to Enable P2P/i })).toBeVisible()
})
test('hardware selector offers build targets and responds to selection', async ({ page }) => {
const cpu = page.getByRole('button').filter({ hasText: /^CPU$/ })
const cuda = page.getByRole('button').filter({ hasText: /^CUDA 12$/ })
await expect(cpu).toBeVisible()
await expect(cuda).toBeVisible()
await cuda.click() // selecting a build target must not break the page
await expect(page.getByRole('heading', { name: /How to Enable P2P/i })).toBeVisible()
})
})

View File

@@ -1,4 +1,4 @@
import { test, expect } from '@playwright/test'
import { test, expect } from './coverage-fixtures.js'
test.describe('Settings - Backend Logging', () => {
test.beforeEach(async ({ page }) => {

View File

@@ -0,0 +1,20 @@
import { test, expect } from './coverage-fixtures.js'
// Skills feature page (src/pages/Skills.jsx).
test.describe('Skills page', () => {
test.beforeEach(async ({ page }) => {
await page.goto('/app/skills')
})
test('renders the skills list with create affordances', async ({ page }) => {
await expect(page).toHaveURL(/\/app\/skills$/)
await expect(page.getByRole('heading', { name: 'Skills', exact: true })).toBeVisible()
await expect(page.getByRole('button', { name: 'New skill' })).toBeVisible()
await expect(page.getByRole('button', { name: 'Git Repos' })).toBeVisible()
})
test('New skill navigates to the skill editor', async ({ page }) => {
await page.getByRole('button', { name: 'New skill' }).click()
await expect(page).toHaveURL(/\/app\/skills\/new$/)
})
})

View File

@@ -1,4 +1,4 @@
import { test, expect } from '@playwright/test'
import { test, expect } from './coverage-fixtures.js'
test.describe('Traces - Error Display', () => {
test.beforeEach(async ({ page }) => {

View File

@@ -1,4 +1,4 @@
import { test, expect } from '@playwright/test'
import { test, expect } from './coverage-fixtures.js'
test.describe('Traces Settings', () => {
test.beforeEach(async ({ page }) => {

View File

@@ -10,7 +10,9 @@
"lint": "eslint .",
"i18n:extract": "i18next-parser",
"test:e2e": "playwright test",
"test:e2e:ui": "playwright test --ui"
"test:e2e:ui": "playwright test --ui",
"build:coverage": "COVERAGE=true vite build",
"coverage:report": "nyc report"
},
"dependencies": {
"@codemirror/autocomplete": "^6.18.6",
@@ -42,12 +44,14 @@
"devDependencies": {
"@eslint/js": "^9.27.0",
"@playwright/test": "1.58.2",
"@vitejs/plugin-react": "^4.5.2",
"@vitejs/plugin-react": "^6.0.2",
"eslint": "^9.27.0",
"eslint-plugin-react-hooks": "^5.2.0",
"eslint-plugin-react-refresh": "^0.4.20",
"globals": "^16.1.0",
"i18next-parser": "^9.4.0",
"vite": "^6.4.2"
"nyc": "^18.0.0",
"vite": "^8.0.14",
"vite-plugin-istanbul": "^9.0.0"
}
}

View File

@@ -12,7 +12,16 @@ export default defineConfig({
projects: [
{
name: 'chromium',
use: { browserName: 'chromium' },
use: {
browserName: 'chromium',
// Use a nix-provided Chromium when PLAYWRIGHT_CHROMIUM_PATH is set
// (the flake dev shell exports it). Avoids Playwright's downloaded
// browser, which can't resolve system libs (libglib-2.0, …) on NixOS.
// Unset in CI, where `playwright install --with-deps` is used instead.
...(process.env.PLAYWRIGHT_CHROMIUM_PATH
? { launchOptions: { executablePath: process.env.PLAYWRIGHT_CHROMIUM_PATH } }
: {}),
},
},
],
webServer: process.env.PLAYWRIGHT_EXTERNAL_SERVER ? undefined : {

View File

@@ -23,6 +23,7 @@
"diarization": "Diarisierung",
"embedding": "Embedding",
"rerank": "Rerank",
"fitsGpu": "Passt in die GPU",
"allBackends": "Alle Backends",
"searchBackends": "Backends suchen..."
},

View File

@@ -29,6 +29,7 @@
"rerank": "Rerank",
"detection": "Detection",
"vad": "VAD",
"fitsGpu": "Fits in GPU",
"allBackends": "All Backends",
"searchBackends": "Search backends..."
},

View File

@@ -23,6 +23,7 @@
"diarization": "Diarización",
"embedding": "Embedding",
"rerank": "Rerank",
"fitsGpu": "Cabe en la GPU",
"allBackends": "Todos los backends",
"searchBackends": "Buscar backends..."
},

View File

@@ -23,6 +23,7 @@
"diarization": "Diarizzazione",
"embedding": "Embedding",
"rerank": "Rerank",
"fitsGpu": "Entra nella GPU",
"allBackends": "Tutti i backend",
"searchBackends": "Cerca backend..."
},

View File

@@ -23,6 +23,7 @@
"diarization": "说话人分离",
"embedding": "嵌入",
"rerank": "重排",
"fitsGpu": "适合 GPU",
"allBackends": "所有后端",
"searchBackends": "搜索后端..."
},

View File

@@ -0,0 +1,100 @@
import { createContext, useContext, useState, useEffect, useCallback, useRef } from 'react'
import { operationsApi } from '../utils/api'
import { useAuth } from '../context/AuthContext'
// Serialize ops into a stable comparison key. Each op is a flat map of
// primitives, so JSON.stringify is good enough and stable as long as the
// server emits keys in the same order (Go's map iteration into JSON happens
// to be stable here because we build an explicit map[string]any).
function serializeOps(ops) {
return JSON.stringify(ops)
}
const OperationsContext = createContext(null)
// Single shared poller for /api/operations. Before this provider existed,
// each useOperations() call ran its own setInterval; with OperationsBar
// always mounted plus the per-page consumers (Models, Backends, Chat), the
// browser was firing 2-3 polls per second against the API for the lifetime
// of the session.
export function OperationsProvider({ children, pollInterval = 1000 }) {
const [operations, setOperations] = useState([])
const [loading, setLoading] = useState(true)
const [error, setError] = useState(null)
const { isAdmin } = useAuth()
const intervalRef = useRef(null)
const lastSerializedRef = useRef('[]')
const fetchOperations = useCallback(async () => {
if (!isAdmin) {
setLoading((prev) => (prev ? false : prev))
return
}
try {
const data = await operationsApi.list()
const ops = data?.operations || (Array.isArray(data) ? data : [])
const serialized = serializeOps(ops)
if (serialized !== lastSerializedRef.current) {
lastSerializedRef.current = serialized
setOperations(ops)
}
setError((prev) => (prev === null ? prev : null))
} catch (err) {
setError((prev) => (prev === err.message ? prev : err.message))
} finally {
setLoading((prev) => (prev ? false : prev))
}
}, [isAdmin])
useEffect(() => {
if (!isAdmin) return
fetchOperations()
intervalRef.current = setInterval(fetchOperations, pollInterval)
return () => {
if (intervalRef.current) {
clearInterval(intervalRef.current)
intervalRef.current = null
}
}
}, [fetchOperations, pollInterval, isAdmin])
const cancelOperation = useCallback(async (jobID) => {
try {
await operationsApi.cancel(jobID)
await fetchOperations()
} catch (err) {
setError(err.message)
}
}, [fetchOperations])
const dismissFailedOp = useCallback(async (opId) => {
try {
const op = operations.find((o) => o.id === opId)
if (op?.jobID) {
await operationsApi.dismiss(op.jobID)
await fetchOperations()
}
} catch {
// Ignore dismiss errors
}
}, [operations, fetchOperations])
const value = {
operations,
loading,
error,
cancelOperation,
dismissFailedOp,
refetch: fetchOperations,
}
return <OperationsContext.Provider value={value}>{children}</OperationsContext.Provider>
}
export function useOperations() {
const ctx = useContext(OperationsContext)
if (!ctx) throw new Error('useOperations must be used within OperationsProvider')
return ctx
}

View File

@@ -1,98 +1,4 @@
import { useState, useEffect, useCallback, useRef } from 'react'
import { operationsApi } from '../utils/api'
import { useAuth } from '../context/AuthContext'
// Serialize ops into a stable comparison key. Each op is a flat map of
// primitives, so JSON.stringify is good enough and stable as long as the
// server emits keys in the same order (Go's map iteration into JSON happens
// to be stable here because we build an explicit map[string]any).
function serializeOps(ops) {
return JSON.stringify(ops)
}
export function useOperations(pollInterval = 1000) {
const [operations, setOperations] = useState([])
const [loading, setLoading] = useState(true)
const [error, setError] = useState(null)
const intervalRef = useRef(null)
const { isAdmin } = useAuth()
const previousCountRef = useRef(0)
const onAllCompleteRef = useRef(null)
// Track the last payload we wrote into state. Each poll otherwise produces
// a fresh array reference even when nothing changed, and that re-render
// ripples into the Chat page — wiping the user's text selection mid-read
// (#9904).
const lastSerializedRef = useRef('[]')
const fetchOperations = useCallback(async () => {
if (!isAdmin) {
setLoading((prev) => (prev ? false : prev))
return
}
try {
const data = await operationsApi.list()
const ops = data?.operations || (Array.isArray(data) ? data : [])
const serialized = serializeOps(ops)
if (serialized !== lastSerializedRef.current) {
lastSerializedRef.current = serialized
setOperations(ops)
}
// Separate active (non-failed) operations from failed ones
const activeOps = ops.filter(op => !op.error)
const failedOps = ops.filter(op => op.error)
// Notify when all operations complete (no active or failed remaining)
if (previousCountRef.current > 0 && activeOps.length === 0 && failedOps.length === 0) {
onAllCompleteRef.current?.()
}
previousCountRef.current = activeOps.length
setError((prev) => (prev === null ? prev : null))
} catch (err) {
setError((prev) => (prev === err.message ? prev : err.message))
} finally {
setLoading((prev) => (prev ? false : prev))
}
}, [isAdmin])
const cancelOperation = useCallback(async (jobID) => {
try {
await operationsApi.cancel(jobID)
await fetchOperations()
} catch (err) {
setError(err.message)
}
}, [fetchOperations])
// Dismiss a failed operation (acknowledge the error and remove it)
const dismissFailedOp = useCallback(async (opId) => {
try {
const op = operations.find(o => o.id === opId)
if (op?.jobID) {
await operationsApi.dismiss(op.jobID)
await fetchOperations()
}
} catch {
// Ignore dismiss errors
}
}, [operations, fetchOperations])
useEffect(() => {
if (!isAdmin) return
fetchOperations()
intervalRef.current = setInterval(fetchOperations, pollInterval)
return () => {
if (intervalRef.current) clearInterval(intervalRef.current)
}
}, [fetchOperations, pollInterval, isAdmin])
// Allow callers to register a callback for when all operations finish
const onAllComplete = useCallback((cb) => {
onAllCompleteRef.current = cb
}, [])
return { operations, loading, error, cancelOperation, dismissFailedOp, refetch: fetchOperations, onAllComplete }
}
// useOperations now lives in OperationsContext so all consumers
// (OperationsBar, Models, Backends, Chat) share a single poller instead
// of each spinning up its own setInterval against /api/operations.
export { useOperations } from '../contexts/OperationsContext'

View File

@@ -32,7 +32,11 @@ export function useVramEstimate({ model, contextSize, gpuLayers }) {
const data = await modelsApi.estimateVram(body, { signal: controller.signal })
if (!controller.signal.aborted) {
setVramDisplay(data?.vramDisplay || null)
// The /api/models/vram-estimate response is the legacy EstimateResult
// shape (snake_case: size_bytes / vram_bytes / vram_display), not
// camelCase. Reading data.vramDisplay silently yielded undefined, so
// the estimate never rendered.
setVramDisplay(data?.vram_display || null)
setLoading(false)
}
} catch {

View File

@@ -4,6 +4,7 @@ import { RouterProvider } from 'react-router-dom'
import { ThemeProvider } from './contexts/ThemeContext'
import { BrandingProvider } from './contexts/BrandingContext'
import { AuthProvider } from './context/AuthContext'
import { OperationsProvider } from './contexts/OperationsContext'
import { router } from './router'
import './i18n'
import '@fortawesome/fontawesome-free/css/all.min.css'
@@ -30,7 +31,9 @@ createRoot(document.getElementById('root')).render(
<ThemeProvider>
<BrandingProvider>
<AuthProvider>
<RouterProvider router={router} />
<OperationsProvider>
<RouterProvider router={router} />
</OperationsProvider>
</AuthProvider>
</BrandingProvider>
</ThemeProvider>

View File

@@ -9,11 +9,13 @@ import { useResources } from '../hooks/useResources'
import SearchableSelect from '../components/SearchableSelect'
import ConfirmDialog from '../components/ConfirmDialog'
import GalleryLoader from '../components/GalleryLoader'
import Toggle from '../components/Toggle'
import React from 'react'
const CONTEXT_SIZES = [8192, 16384, 32768, 65536, 131072, 262144]
const CONTEXT_LABELS = ['8K', '16K', '32K', '64K', '128K', '256K']
const FITS_FILTER_STORAGE_KEY = 'localai-models-fits-filter'
const FILTERS = [
@@ -59,6 +61,13 @@ export default function Models() {
const [estimates, setEstimates] = useState({})
const [contextSize, setContextSize] = useState(CONTEXT_SIZES[0])
const [confirmDialog, setConfirmDialog] = useState(null)
const [fitsFilter, setFitsFilter] = useState(() => {
try {
return localStorage.getItem(FITS_FILTER_STORAGE_KEY) === '1'
} catch {
return false
}
})
// Total GPU memory for "fits" check
const totalGpuMemory = resources?.aggregate?.total_memory || 0
@@ -240,6 +249,23 @@ export default function Models() {
return vramBytes <= totalGpuMemory * 0.95
}
useEffect(() => {
try {
localStorage.setItem(FITS_FILTER_STORAGE_KEY, fitsFilter ? '1' : '0')
} catch {
// Ignore storage errors (e.g., private browsing restrictions).
}
}, [fitsFilter])
const visibleModels = models.filter((model) => {
if (!fitsFilter) return true
const name = model.name || model.id
const vramBytes = estimates[name]?.estimates?.[String(contextSize)]?.vramBytes
const fit = fitsGpu(vramBytes)
// Keep models visible while estimate is still loading; hide only explicit non-fits.
return fit !== false
})
return (
<div className="page page--wide">
<div className="page-header" style={{ display: 'flex', justifyContent: 'space-between', alignItems: 'flex-start' }}>
@@ -300,6 +326,13 @@ export default function Models() {
</button>
)
})}
{totalGpuMemory > 0 && (
<label className="filter-bar-group__toggle" style={{ marginLeft: 'auto' }}>
<Toggle checked={fitsFilter} onChange={setFitsFilter} />
<i className="fas fa-microchip" />
<span>{t('filters.fitsGpu')}</span>
</label>
)}
{allBackends.length > 0 && (
<SearchableSelect
value={backendFilter}
@@ -308,7 +341,7 @@ export default function Models() {
placeholder={t('filters.allBackends')}
allOption={t('filters.allBackends')}
searchPlaceholder={t('filters.searchBackends')}
style={{ marginLeft: 'auto' }}
style={totalGpuMemory > 0 ? undefined : { marginLeft: 'auto' }}
/>
)}
</div>
@@ -335,17 +368,17 @@ export default function Models() {
{/* Table */}
{loading ? (
<GalleryLoader />
) : models.length === 0 ? (
) : visibleModels.length === 0 ? (
<div className="empty-state">
<div className="empty-state-icon"><i className="fas fa-search" /></div>
<h2 className="empty-state-title">{t('empty.title')}</h2>
<p className="empty-state-text">
{search || filters.length > 0 || backendFilter ? t('empty.withFilters') : t('empty.noFilters')}
{search || filters.length > 0 || backendFilter || fitsFilter ? t('empty.withFilters') : t('empty.noFilters')}
</p>
{(search || filters.length > 0 || backendFilter) && (
{(search || filters.length > 0 || backendFilter || fitsFilter) && (
<button
className="btn btn-secondary btn-sm"
onClick={() => { handleSearch(''); setFilters([]); setBackendFilter(''); setPage(1) }}
onClick={() => { handleSearch(''); setFilters([]); setBackendFilter(''); setFitsFilter(false); setPage(1) }}
>
<i className="fas fa-times" /> {t('search.clearFilters')}
</button>
@@ -372,7 +405,7 @@ export default function Models() {
</tr>
</thead>
<tbody>
{models.map((model, idx) => {
{visibleModels.map((model, idx) => {
const name = model.name || model.id
const estData = estimates[name]
const sizeDisplay = estData?.sizeDisplay

View File

@@ -1,10 +1,32 @@
import { defineConfig } from 'vite'
import react from '@vitejs/plugin-react'
import istanbul from 'vite-plugin-istanbul'
const backendUrl = process.env.LOCALAI_URL || 'http://localhost:8080'
// COVERAGE=true produces an instrumented build whose modules report istanbul
// counters on window.__coverage__, harvested by the Playwright coverage
// fixture (e2e/coverage-fixtures.js). Off by default so normal/dev/prod builds
// carry no instrumentation overhead.
const coverage = process.env.COVERAGE === 'true'
export default defineConfig({
plugins: [react()],
plugins: [
react(),
...(coverage
? [
istanbul({
include: 'src/**/*',
extension: ['.js', '.jsx', '.ts', '.tsx'],
requireEnv: false,
// The e2e suite runs against `vite build` output, not the dev
// server, so instrumentation must be applied to the production
// build too (the plugin only instruments dev mode otherwise).
forceBuildInstrument: true,
}),
]
: []),
],
base: '/',
server: {
port: 3000,

1
coverage-baseline.txt Normal file
View File

@@ -0,0 +1 @@
45.0

View File

@@ -5,7 +5,7 @@ weight = 13
url = "/features/object-detection/"
+++
LocalAI supports object detection and image segmentation through various backends. This feature allows you to identify and locate objects within images with high accuracy and real-time performance. Available backends include [RF-DETR](https://github.com/roboflow/rf-detr) for object detection and [sam3.cpp](https://github.com/PABannier/sam3.cpp) for image segmentation (SAM 3/2/EdgeTAM).
LocalAI supports object detection and image segmentation through various backends. This feature allows you to identify and locate objects within images with high accuracy and real-time performance. Available backends include [RF-DETR](https://github.com/roboflow/rf-detr) (Python) and [rf-detr.cpp](https://github.com/mudler/rf-detr.cpp) (native C++/ggml) for object detection and segmentation, and [sam3.cpp](https://github.com/PABannier/sam3.cpp) for image segmentation (SAM 3/2/EdgeTAM).
For detecting **faces** specifically, see the dedicated
[Face Recognition](/features/face-recognition/) feature — its
@@ -135,6 +135,74 @@ Currently, the following model is available in the [Model Gallery]({{%relref "fe
You can browse and install this model through the LocalAI web interface or using the command line.
### RF-DETR Native Backend (rfdetr-cpp)
The `rfdetr-cpp` backend is a native C++/ggml implementation of RF-DETR
inference based on [rf-detr.cpp](https://github.com/mudler/rf-detr.cpp). It
runs as a Go gRPC service that dlopens a per-CPU-variant shared library, so
there is no Python runtime on the inference path — startup is fast and the
binary is self-contained.
Compared to the Python `rfdetr` backend, the native backend:
- Has no Python or PyTorch dependency at inference time
- Loads quantized GGUF models (F32, F16, Q8_0, Q4_K) for smaller footprint
- Supports both detection and segmentation variants of RF-DETR
- Returns segmentation masks as PNG bytes in `Detection.mask`
#### Setup
1. **Install the backend**
```bash
local-ai backends install rfdetr-cpp
```
2. **Using the Model Gallery (Recommended)**
The gallery ships ready-to-run entries for every published variant:
```bash
# Detection variants
local-ai run rfdetr-cpp-nano
local-ai run rfdetr-cpp-small
local-ai run rfdetr-cpp-base
local-ai run rfdetr-cpp-medium
local-ai run rfdetr-cpp-large
# Segmentation variants (return per-instance PNG masks)
local-ai run rfdetr-cpp-seg-nano
local-ai run rfdetr-cpp-seg-small
local-ai run rfdetr-cpp-seg-medium
local-ai run rfdetr-cpp-seg-large
local-ai run rfdetr-cpp-seg-xlarge
local-ai run rfdetr-cpp-seg-2xlarge
```
3. **Manual Configuration**
```yaml
name: rfdetr-cpp-seg-nano
backend: rfdetr-cpp
parameters:
model: rfdetr-seg-nano-f16.gguf
threads: 4
known_usecases:
- detection
```
Pre-quantized GGUFs are published under
[`mudler/rfdetr-cpp-*`](https://huggingface.co/mudler?search_models=rfdetr-cpp)
on Hugging Face. Each repo carries the F32/F16/Q8_0/Q4_K quants — F16 is
the recommended default (matches F32 accuracy, ~1.86x smaller).
#### Segmentation Output
When running a segmentation model (any `rfdetr-cpp-seg-*` variant), each
`Detection` in the response carries a `mask` field with a base64-encoded
PNG of the per-instance binary mask. The mask is sized to the original
image resolution and aligns with the corresponding bounding box.
### SAM3 Backend (sam3-cpp)
The sam3-cpp backend provides image segmentation using [sam3.cpp](https://github.com/PABannier/sam3.cpp), a portable C++ implementation of Meta's Segment Anything Model. It supports multiple model architectures:
@@ -261,7 +329,8 @@ local-ai run --debug rfdetr-base
LocalAI includes a dedicated **object-detection** category for models and backends that specialize in identifying and locating objects within images. This category currently includes:
- **RF-DETR**: Real-time transformer-based object detection
- **RF-DETR**: Real-time transformer-based object detection (Python backend)
- **rfdetr-cpp**: Native C++/ggml RF-DETR for detection + segmentation
- **sam3-cpp**: SAM 3/2/EdgeTAM image segmentation
Additional object detection models and backends will be added to this category in the future. You can filter models by the `object-detection` tag in the model gallery to find all available object detection models.

View File

@@ -515,7 +515,7 @@ The `llama.cpp` backend supports additional configuration options that can be sp
| `kv_unified` or `unified_kv` | boolean | Use a single unified KV buffer shared across all sequences. Default: `true` (LocalAI override; upstream defaults to `false` but auto-enables it when slot count is auto). **Required for `cache_idle_slots` to work**: without it the server force-disables idle-slot saving at init, and the prompt cache is never written across requests. | `kv_unified:false` |
| `cache_idle_slots` or `idle_slots_cache` | boolean | On a new task, save the previous slot's KV state into the prompt cache (and clear the slot) so a later request with the same prefix can warm-load it. Default: `true`. Auto-disabled by the server if `kv_unified=false` or `cache_ram=0`. | `cache_idle_slots:false` |
| `n_ctx_checkpoints` or `ctx_checkpoints` | integer | Maximum number of context checkpoints per slot (used for partial-prefix recovery, e.g. SWA). Default: `32`. | `ctx_checkpoints:16` |
| `checkpoint_every_nt` or `checkpoint_every_n_tokens` | integer | Create a context checkpoint every N tokens during prefill. `-1` disables checkpointing. Default: `8192`. | `checkpoint_every_nt:4096` |
| `checkpoint_min_step` or `checkpoint_min_spacing` (aliases: `checkpoint_every_nt`, `checkpoint_every_n_tokens`) | integer | Minimum spacing in tokens between context checkpoints. `0` disables the minimum-spacing gate. Default: `256`. (Renamed upstream from `checkpoint_every_nt`; semantics shifted from a fixed cadence to a minimum spacing.) | `checkpoint_min_step:1024` |
| `split_mode` or `sm` | string | How to split the model across multiple GPUs: `none` (single GPU only), `layer` (default — split layers and KV across GPUs), `row` (split rows across GPUs), `tensor` (experimental tensor parallelism — requires `flash_attention: true`, no KV-cache quantization, manually set `context_size`, and a llama.cpp build that includes [#19378](https://github.com/ggml-org/llama.cpp/pull/19378)). | `split_mode:tensor` |
**Example configuration with options:**

View File

@@ -1,3 +1,3 @@
{
"version": "v4.2.6"
"version": "v4.3.1"
}

View File

@@ -73,6 +73,7 @@
# React UI build (core/http/react-ui — `make react-ui`)
nodejs
bun # alternative to npm, used by `make react-ui-docker`
chromium # Playwright e2e / UI coverage browser (see PLAYWRIGHT_CHROMIUM_PATH below)
# Linting / static analysis (see `make lint`)
golangci-lint
@@ -86,6 +87,13 @@
];
shellHook = ''
# Point Playwright at the nix-provided Chromium instead of its own
# downloaded build, which can't resolve system libs (libglib-2.0, )
# on NixOS. playwright.config.js reads PLAYWRIGHT_CHROMIUM_PATH and
# the Makefile skips `playwright install` when it's set.
export PLAYWRIGHT_CHROMIUM_PATH="${pkgs.chromium}/bin/chromium"
export PLAYWRIGHT_SKIP_BROWSER_DOWNLOAD=1
echo "LocalAI dev shell: $(go version), node $(node --version)"
echo "Build: make build (Go binary + React UI)"
echo "React UI: make react-ui (npm install && vite build)"

View File

@@ -1,4 +1,118 @@
---
- name: "qwopus3.5-9b-coder-mtp"
url: "github:mudler/LocalAI/gallery/virtual.yaml@master"
urls:
- https://huggingface.co/Jackrong/Qwopus3.5-9B-Coder-MTP-GGUF
description: |
# 🌟 Qwopus3.5-9B-v3.5
## 💡 Model Overview & v3.5 Design
Qwopus3.5-9B-v3.5 is a **data-scaled continuation** of the Qwopus3.5-9B-v3 model.
The training data in v3.5 is expanded to cover a broader range of domains, including mathematics, programming, puzzle-solving, multilingual dialogue, instruction-following, multi-turn interactions, and STEM-related tasks.
Qwopus3.5-9B-v3.5 is a reasoning-enhanced model based on **Qwen3.5-9B**, designed for:
- 🧩 Structured reasoning
- 🔧 Tool-augmented workflows
- 🔁 Multi-step agentic tasks
- ⚡ Token-efficient inference
Compared with Qwopus3.5-9B-v3, **3.5 version does not introduce a new architecture, RL stage, or template redesign**.
This version is trained with approximately **2× more SFT data**.
## 🎯 Motivation & Generalization Insight
The motivation behind v3.5 comes from a simple observation:
> This work is motivated by the hypothesis that scaling high-quality SFT data may further enhance the generalization ability of large language models.
In earlier Qwopus3.5 experiments, structured reasoning was observed to improve both **accuracy and efficiency**:
...
license: "apache-2.0"
tags:
- llm
- gguf
- reasoning
icon: https://cdn-uploads.huggingface.co/production/uploads/66309bd090589b7c65950665/9EnS13MSxNU3snpAgEiLq.jpeg
overrides:
backend: llama-cpp
function:
automatic_tool_parsing_fallback: true
grammar:
disable: true
known_usecases:
- chat
mmproj: llama-cpp/mmproj/Qwopus3.5-9B-Coder-MTP-GGUF/Qwopus3.5-9B-Coder-MTP-mmproj.gguf
options:
- use_jinja:true
- spec_type:draft-mtp
- spec_n_max:6
- spec_p_min:0.75
parameters:
model: llama-cpp/models/Qwopus3.5-9B-Coder-MTP-GGUF/Qwopus3.5-9B-Coder-MTP-Q4_K_M.gguf
template:
use_tokenizer_template: true
files:
- filename: llama-cpp/models/Qwopus3.5-9B-Coder-MTP-GGUF/Qwopus3.5-9B-Coder-MTP-Q4_K_M.gguf
sha256: f6fc5d193045796d9e1870cbc40f827fe55f53f70593c3f5c1968b82b9331991
uri: https://huggingface.co/Jackrong/Qwopus3.5-9B-Coder-MTP-GGUF/resolve/main/Qwopus3.5-9B-Coder-MTP-Q4_K_M.gguf
- filename: llama-cpp/mmproj/Qwopus3.5-9B-Coder-MTP-GGUF/Qwopus3.5-9B-Coder-MTP-mmproj.gguf
sha256: f48daca405a1c768a9514e392c3955dcc4a9d66a5cf64cf45e064092b5f20ee4
uri: https://huggingface.co/Jackrong/Qwopus3.5-9B-Coder-MTP-GGUF/resolve/main/Qwopus3.5-9B-Coder-MTP-mmproj.gguf
- name: "qwopus3.6-27b-v2-mtp"
url: "github:mudler/LocalAI/gallery/virtual.yaml@master"
urls:
- https://huggingface.co/Jackrong/Qwopus3.6-27B-v2-MTP-GGUF
description: |
🪐 Qwopus3.6-27B-v2-MTP
MTP Release
Multi-Token Prediction reasoning model fine-tuned from Qwen3.6-27B
🧬 Trace Inversion & Negentropy
🧠 27B Parameters
⚡ Speculative Decoding
🛠️ Coding / DevOps / Math
💡 What is Qwopus3.6-27B-v2-MTP?
🪐 Qwopus3.6-27B-v2-MTP is a speed-oriented reasoning release built on top of Qwen3.6-27B. It keeps the Qwopus line's focus on reconstructed reasoning traces, coding discipline, DevOps procedures, and mathematical derivations, while adding Multi-Token Prediction for faster generation. The goal is simple: preserve the depth and structure of a 27B reasoning model while making real interactive use noticeably faster.
⚡ MTP DecodingAuxiliary future-token prediction improves throughput on long reasoning, code, math, and strict-format prompts.
🧩 Structured ReasoningInherits the Qwopus training recipe built around reconstructed step-by-step reasoning trajectories.
🧪 GB10 TestedValidated on a 30-question local benchmark across Logic, Coding, DevOps, Math, and Edge tasks.
🚀 Practical SpeedDesigned for workflows where strong answers matter, but waiting several extra minutes per task does not.
...
license: "apache-2.0"
tags:
- llm
- gguf
- reasoning
overrides:
backend: llama-cpp
function:
automatic_tool_parsing_fallback: true
grammar:
disable: true
known_usecases:
- chat
options:
- use_jinja:true
- spec_type:draft-mtp
- spec_n_max:6
- spec_p_min:0.75
parameters:
model: llama-cpp/models/Qwopus3.6-27B-v2-MTP-GGUF/Qwopus3.6-27B-v2-MTP-Q4_K_M.gguf
template:
use_tokenizer_template: true
files:
- filename: llama-cpp/models/Qwopus3.6-27B-v2-MTP-GGUF/Qwopus3.6-27B-v2-MTP-Q4_K_M.gguf
sha256: 818d68223be4d8518dac0b3b5604dde633cbbcbae1f491d842a3e26711c6606d
uri: https://huggingface.co/Jackrong/Qwopus3.6-27B-v2-MTP-GGUF/resolve/main/Qwopus3.6-27B-v2-MTP-Q4_K_M.gguf
- name: "qwen3.6-40b-claude-4.6-opus-deckard-heretic-uncensored-thinking-neo-code-di-imatrix-max"
url: "github:mudler/LocalAI/gallery/virtual.yaml@master"
urls:
@@ -6068,6 +6182,317 @@
- detection
parameters:
model: rfdetr-base
- name: rfdetr-cpp-nano
url: github:mudler/LocalAI/gallery/virtual.yaml@master
urls:
- https://github.com/mudler/rf-detr.cpp
- https://huggingface.co/mudler/rfdetr-cpp-nano
description: |
RF-DETR Nano object detection model, served via the native rfdetr.cpp backend (ggml + purego, no Python).
Q8_0 quantization is the recommended default for CPU: same accuracy as F16/F32, ~20MB on disk, fastest CPU latency.
Pure C++/ggml runtime; no Python dependencies. Drop-in for the /v1/detection endpoint.
license: apache-2.0
icon: https://avatars.githubusercontent.com/u/53104118?s=200&v=4
tags:
- object-detection
- rfdetr
- native
- cpp
- cpu
overrides:
backend: rfdetr-cpp
known_usecases:
- detection
parameters:
model: rfdetr-nano-q8_0.gguf
files:
- filename: rfdetr-nano-q8_0.gguf
uri: huggingface://mudler/rfdetr-cpp-nano/rfdetr-nano-q8_0.gguf
- name: rfdetr-cpp-base
url: github:mudler/LocalAI/gallery/virtual.yaml@master
urls:
- https://github.com/mudler/rf-detr.cpp
- https://huggingface.co/mudler/rfdetr-cpp-base
description: |
RF-DETR Base object detection model, served via the native rfdetr.cpp backend.
F16 quantization is recommended on CPU: identical accuracy to F32, half the size, fastest.
license: apache-2.0
icon: https://avatars.githubusercontent.com/u/53104118?s=200&v=4
tags:
- object-detection
- rfdetr
- native
- cpp
- cpu
overrides:
backend: rfdetr-cpp
known_usecases:
- detection
parameters:
model: rfdetr-base-f16.gguf
files:
- filename: rfdetr-base-f16.gguf
uri: huggingface://mudler/rfdetr-cpp-base/rfdetr-base-f16.gguf
- name: rfdetr-cpp-small
url: github:mudler/LocalAI/gallery/virtual.yaml@master
urls:
- https://github.com/mudler/rf-detr.cpp
- https://huggingface.co/mudler/rfdetr-cpp-small
description: |
RF-DETR Small object detection model (DINOv2-small backbone, 512px input, 3 decoder layers), served
via the native rfdetr.cpp backend (ggml + purego, no Python). A step up from Nano in accuracy while
staying lightweight on CPU. F16 quantization is the recommended default: identical accuracy to F32
at roughly half the size. Drop-in for the /v1/detection endpoint.
license: apache-2.0
icon: https://avatars.githubusercontent.com/u/53104118?s=200&v=4
tags:
- object-detection
- rfdetr
- native
- cpp
- cpu
overrides:
backend: rfdetr-cpp
known_usecases:
- detection
parameters:
model: rfdetr-small-f16.gguf
files:
- filename: rfdetr-small-f16.gguf
sha256: 5365264a976bb99ab31f735f43326e50b0804a60cd1709abe8c1c95114c4d79d
uri: huggingface://mudler/rfdetr-cpp-small/rfdetr-small-f16.gguf
- name: rfdetr-cpp-medium
url: github:mudler/LocalAI/gallery/virtual.yaml@master
urls:
- https://github.com/mudler/rf-detr.cpp
- https://huggingface.co/mudler/rfdetr-cpp-medium
description: |
RF-DETR Medium object detection model (DINOv2-small backbone, 576px input, 4 decoder layers), served
via the native rfdetr.cpp backend. Balanced detection quality vs. CPU latency — recommended when
Base is not accurate enough but Large is too slow. F16 quantization is the recommended default:
identical accuracy to F32, half the size. Drop-in for the /v1/detection endpoint.
license: apache-2.0
icon: https://avatars.githubusercontent.com/u/53104118?s=200&v=4
tags:
- object-detection
- rfdetr
- native
- cpp
- cpu
overrides:
backend: rfdetr-cpp
known_usecases:
- detection
parameters:
model: rfdetr-medium-f16.gguf
files:
- filename: rfdetr-medium-f16.gguf
sha256: 685b8f50890f099bbc603454309b2d5f1d471541420b95c20c6ed296aec1e7ae
uri: huggingface://mudler/rfdetr-cpp-medium/rfdetr-medium-f16.gguf
- name: rfdetr-cpp-large
url: github:mudler/LocalAI/gallery/virtual.yaml@master
urls:
- https://github.com/mudler/rf-detr.cpp
- https://huggingface.co/mudler/rfdetr-cpp-large
description: |
RF-DETR Large object detection model (DINOv2-small backbone, 704px input, 4 decoder layers), served
via the native rfdetr.cpp backend. Highest-accuracy detection variant — best for offline workflows
and high-resolution inputs where CPU latency is secondary to recall. F16 quantization is the
recommended default: identical accuracy to F32, half the size. Drop-in for the /v1/detection endpoint.
license: apache-2.0
icon: https://avatars.githubusercontent.com/u/53104118?s=200&v=4
tags:
- object-detection
- rfdetr
- native
- cpp
- cpu
overrides:
backend: rfdetr-cpp
known_usecases:
- detection
parameters:
model: rfdetr-large-f16.gguf
files:
- filename: rfdetr-large-f16.gguf
sha256: 819f1abc72f746a686722eacc9c4db992b7ca853b26e390ab0a66ca6ea70060a
uri: huggingface://mudler/rfdetr-cpp-large/rfdetr-large-f16.gguf
- name: rfdetr-cpp-seg-nano
url: github:mudler/LocalAI/gallery/virtual.yaml@master
urls:
- https://github.com/mudler/rf-detr.cpp
- https://huggingface.co/mudler/rfdetr-cpp-seg-nano
description: |
RF-DETR Seg-Nano instance segmentation model (DINOv2-small backbone, 312px input, 4 decoder layers,
100 queries), served via the native rfdetr.cpp backend. Smallest segmentation variant — fastest CPU
latency, ideal for edge deployment. Returns both bounding boxes and per-instance masks via the
/v1/detection endpoint. F16 quantization is the recommended default: identical accuracy to F32,
half the size.
license: apache-2.0
icon: https://avatars.githubusercontent.com/u/53104118?s=200&v=4
tags:
- object-detection
- image-segmentation
- rfdetr
- native
- cpp
- cpu
overrides:
backend: rfdetr-cpp
known_usecases:
- detection
parameters:
model: rfdetr-seg-nano-f16.gguf
files:
- filename: rfdetr-seg-nano-f16.gguf
sha256: 9f9a0ab547743992b6c664d41ee1a6afcd66b21b04609a68f76c0eec88648c2b
uri: huggingface://mudler/rfdetr-cpp-seg-nano/rfdetr-seg-nano-f16.gguf
- name: rfdetr-cpp-seg-small
url: github:mudler/LocalAI/gallery/virtual.yaml@master
urls:
- https://github.com/mudler/rf-detr.cpp
- https://huggingface.co/mudler/rfdetr-cpp-seg-small
description: |
RF-DETR Seg-Small instance segmentation model (DINOv2-small backbone, 384px input, 4 decoder layers,
100 queries), served via the native rfdetr.cpp backend. Step up from Seg-Nano in mask quality while
staying CPU-friendly. Returns both bounding boxes and per-instance masks via the /v1/detection
endpoint. F16 quantization is the recommended default: identical accuracy to F32, half the size.
license: apache-2.0
icon: https://avatars.githubusercontent.com/u/53104118?s=200&v=4
tags:
- object-detection
- image-segmentation
- rfdetr
- native
- cpp
- cpu
overrides:
backend: rfdetr-cpp
known_usecases:
- detection
parameters:
model: rfdetr-seg-small-f16.gguf
files:
- filename: rfdetr-seg-small-f16.gguf
sha256: 1b569a182aea941ec645a1923c1e8ad9db05e006db36136da9f148d1ec066670
uri: huggingface://mudler/rfdetr-cpp-seg-small/rfdetr-seg-small-f16.gguf
- name: rfdetr-cpp-seg-medium
url: github:mudler/LocalAI/gallery/virtual.yaml@master
urls:
- https://github.com/mudler/rf-detr.cpp
- https://huggingface.co/mudler/rfdetr-cpp-seg-medium
description: |
RF-DETR Seg-Medium instance segmentation model (DINOv2-small backbone, 432px input, 5 decoder layers,
200 queries), served via the native rfdetr.cpp backend. Balanced segmentation quality vs. CPU latency
— recommended for everyday segmentation workloads. Returns both bounding boxes and per-instance masks
via the /v1/detection endpoint. F16 quantization is the recommended default.
license: apache-2.0
icon: https://avatars.githubusercontent.com/u/53104118?s=200&v=4
tags:
- object-detection
- image-segmentation
- rfdetr
- native
- cpp
- cpu
overrides:
backend: rfdetr-cpp
known_usecases:
- detection
parameters:
model: rfdetr-seg-medium-f16.gguf
files:
- filename: rfdetr-seg-medium-f16.gguf
sha256: 885d85ed6935495fc50ff464e06b6ea3bd8e8386865852d68a8be0f649d65afe
uri: huggingface://mudler/rfdetr-cpp-seg-medium/rfdetr-seg-medium-f16.gguf
- name: rfdetr-cpp-seg-large
url: github:mudler/LocalAI/gallery/virtual.yaml@master
urls:
- https://github.com/mudler/rf-detr.cpp
- https://huggingface.co/mudler/rfdetr-cpp-seg-large
description: |
RF-DETR Seg-Large instance segmentation model (DINOv2-small backbone, 504px input, 5 decoder layers,
200 queries), served via the native rfdetr.cpp backend. Higher-resolution input than Seg-Medium for
sharper mask boundaries. Returns both bounding boxes and per-instance masks via the /v1/detection
endpoint. F16 quantization is the recommended default: identical accuracy to F32, half the size.
license: apache-2.0
icon: https://avatars.githubusercontent.com/u/53104118?s=200&v=4
tags:
- object-detection
- image-segmentation
- rfdetr
- native
- cpp
- cpu
overrides:
backend: rfdetr-cpp
known_usecases:
- detection
parameters:
model: rfdetr-seg-large-f16.gguf
files:
- filename: rfdetr-seg-large-f16.gguf
sha256: 90423066d0791b4ae249f3986cce1f095a1e4090bf46800bf7f9e371ea80d559
uri: huggingface://mudler/rfdetr-cpp-seg-large/rfdetr-seg-large-f16.gguf
- name: rfdetr-cpp-seg-xlarge
url: github:mudler/LocalAI/gallery/virtual.yaml@master
urls:
- https://github.com/mudler/rf-detr.cpp
- https://huggingface.co/mudler/rfdetr-cpp-seg-xlarge
description: |
RF-DETR Seg-XLarge instance segmentation model (DINOv2-small backbone, 624px input, 6 decoder layers,
300 queries), served via the native rfdetr.cpp backend. High-capacity segmentation variant with more
queries and deeper decoder — best for dense scenes with many instances. Returns both bounding boxes
and per-instance masks via the /v1/detection endpoint. F16 quantization is the recommended default.
license: apache-2.0
icon: https://avatars.githubusercontent.com/u/53104118?s=200&v=4
tags:
- object-detection
- image-segmentation
- rfdetr
- native
- cpp
- cpu
overrides:
backend: rfdetr-cpp
known_usecases:
- detection
parameters:
model: rfdetr-seg-xlarge-f16.gguf
files:
- filename: rfdetr-seg-xlarge-f16.gguf
sha256: 0b82de4a6e65a40bc930979a1a4281cb24de35203d30eeefd797c858101a7bec
uri: huggingface://mudler/rfdetr-cpp-seg-xlarge/rfdetr-seg-xlarge-f16.gguf
- name: rfdetr-cpp-seg-2xlarge
url: github:mudler/LocalAI/gallery/virtual.yaml@master
urls:
- https://github.com/mudler/rf-detr.cpp
- https://huggingface.co/mudler/rfdetr-cpp-seg-2xlarge
description: |
RF-DETR Seg-2XLarge instance segmentation model (DINOv2-small backbone, 768px input, 6 decoder layers,
300 queries), served via the native rfdetr.cpp backend. Highest-accuracy segmentation variant — best
for offline workflows and high-resolution inputs where CPU latency is secondary to mask quality.
Returns both bounding boxes and per-instance masks via the /v1/detection endpoint. F16 quantization
is the recommended default: identical accuracy to F32, half the size.
license: apache-2.0
icon: https://avatars.githubusercontent.com/u/53104118?s=200&v=4
tags:
- object-detection
- image-segmentation
- rfdetr
- native
- cpp
- cpu
overrides:
backend: rfdetr-cpp
known_usecases:
- detection
parameters:
model: rfdetr-seg-2xlarge-f16.gguf
files:
- filename: rfdetr-seg-2xlarge-f16.gguf
sha256: 7f957997db23e844194ea8266a95b4adc3deb6d0b71c0924922b20fbdeafa299
uri: huggingface://mudler/rfdetr-cpp-seg-2xlarge/rfdetr-seg-2xlarge-f16.gguf
- name: edgetam
url: github:mudler/LocalAI/gallery/virtual.yaml@master
urls:

View File

@@ -11,36 +11,12 @@ config_file: |
- <dummy32000>
- </s>
- <|endoftext|>
# Delegate templating to llama.cpp's jinja runtime so the C++ autoparser
# can classify <think>…</think> blocks into reasoning_content natively
# (issue #9985). Without use_jinja the autoparser falls back to a
# "pure content" PEG parser that leaks reasoning tags into content.
options:
- use_jinja:true
template:
chat: |
{{.Input -}}
<|im_start|>assistant
chat_message: |
<|im_start|>{{if eq .RoleName "tool" }}user{{else}}{{ .RoleName }}{{end}}
{{ if eq .RoleName "tool" -}}
<tool_response>
{{ end -}}
{{ if .Content -}}
{{.Content }}
{{ end -}}
{{ if eq .RoleName "tool" -}}
</tool_response>
{{ end -}}
{{ if .FunctionCall -}}
<tool_call>
{{toJson .FunctionCall}}
</tool_call>
{{ end -}}<|im_end|>
completion: |
{{.Input}}
function: |
<|im_start|>system
You are a function calling AI model. You are provided with functions to execute. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools:
{{range .Functions}}
{"type": "function", "function": {"name": "{{.Name}}", "description": "{{.Description}}", "parameters": {{toJson .Parameters}} }}
{{end}}
For each function call return a json object with function name and arguments: {"name": <function-name>, "arguments": <json-arguments-object>}
<|im_end|>
{{.Input -}}
<|im_start|>assistant
use_tokenizer_template: true
name: qwen3

26
go.mod
View File

@@ -10,7 +10,7 @@ require (
github.com/anthropics/anthropic-sdk-go v1.42.0
github.com/aws/aws-sdk-go-v2 v1.41.7
github.com/aws/aws-sdk-go-v2/config v1.32.16
github.com/aws/aws-sdk-go-v2/credentials v1.19.15
github.com/aws/aws-sdk-go-v2/credentials v1.19.17
github.com/aws/aws-sdk-go-v2/service/s3 v1.99.1
github.com/charmbracelet/glamour v1.0.0
github.com/containerd/containerd v1.7.31
@@ -41,7 +41,7 @@ require (
github.com/mudler/go-processmanager v0.1.1
github.com/mudler/memory v0.0.0-20260406210934-424c1ecf2cf8
github.com/mudler/xlog v0.0.6
github.com/nats-io/nats.go v1.50.0
github.com/nats-io/nats.go v1.52.0
github.com/ollama/ollama v0.20.4
github.com/onsi/ginkgo/v2 v2.29.0
github.com/onsi/gomega v1.40.0
@@ -81,18 +81,18 @@ require (
filippo.io/keygen v0.0.0-20260114151900-8e2790ea4c5b // indirect
github.com/asaskevich/govalidator v0.0.0-20230301143203-a9d515a09cc2 // indirect
github.com/aws/aws-sdk-go-v2/aws/protocol/eventstream v1.7.9 // indirect
github.com/aws/aws-sdk-go-v2/feature/ec2/imds v1.18.22 // indirect
github.com/aws/aws-sdk-go-v2/internal/configsources v1.4.22 // indirect
github.com/aws/aws-sdk-go-v2/internal/endpoints/v2 v2.7.22 // indirect
github.com/aws/aws-sdk-go-v2/internal/v4a v1.4.23 // indirect
github.com/aws/aws-sdk-go-v2/service/internal/accept-encoding v1.13.8 // indirect
github.com/aws/aws-sdk-go-v2/feature/ec2/imds v1.18.23 // indirect
github.com/aws/aws-sdk-go-v2/internal/configsources v1.4.23 // indirect
github.com/aws/aws-sdk-go-v2/internal/endpoints/v2 v2.7.23 // indirect
github.com/aws/aws-sdk-go-v2/internal/v4a v1.4.24 // indirect
github.com/aws/aws-sdk-go-v2/service/internal/accept-encoding v1.13.9 // indirect
github.com/aws/aws-sdk-go-v2/service/internal/checksum v1.9.14 // indirect
github.com/aws/aws-sdk-go-v2/service/internal/presigned-url v1.13.22 // indirect
github.com/aws/aws-sdk-go-v2/service/internal/presigned-url v1.13.23 // indirect
github.com/aws/aws-sdk-go-v2/service/internal/s3shared v1.19.22 // indirect
github.com/aws/aws-sdk-go-v2/service/signin v1.0.10 // indirect
github.com/aws/aws-sdk-go-v2/service/sso v1.30.16 // indirect
github.com/aws/aws-sdk-go-v2/service/ssooidc v1.35.20 // indirect
github.com/aws/aws-sdk-go-v2/service/sts v1.42.0 // indirect
github.com/aws/aws-sdk-go-v2/service/signin v1.0.11 // indirect
github.com/aws/aws-sdk-go-v2/service/sso v1.30.17 // indirect
github.com/aws/aws-sdk-go-v2/service/ssooidc v1.36.0 // indirect
github.com/aws/aws-sdk-go-v2/service/sts v1.42.1 // indirect
github.com/aws/smithy-go v1.25.1 // indirect
github.com/bahlo/generic-list-go v0.2.0 // indirect
github.com/blang/semver v3.5.1+incompatible // indirect
@@ -498,7 +498,7 @@ require (
golang.org/x/mod v0.35.0 // indirect
golang.org/x/sync v0.20.0
golang.org/x/sys v0.44.0 // indirect
golang.org/x/term v0.43.0 // indirect
golang.org/x/term v0.43.0
golang.org/x/text v0.37.0 // indirect
golang.org/x/tools v0.44.0 // indirect
golang.zx2c4.com/wintun v0.0.0-20230126152724-0fa3db229ce2 // indirect

52
go.sum
View File

@@ -150,36 +150,36 @@ github.com/aws/aws-sdk-go-v2/aws/protocol/eventstream v1.7.9 h1:adBsCIIpLbLmYnkQ
github.com/aws/aws-sdk-go-v2/aws/protocol/eventstream v1.7.9/go.mod h1:uOYhgfgThm/ZyAuJGNQ5YgNyOlYfqnGpTHXvk3cpykg=
github.com/aws/aws-sdk-go-v2/config v1.32.16 h1:Q0iQ7quUgJP0F/SCRTieScnaMdXr9h/2+wze1u3cNeM=
github.com/aws/aws-sdk-go-v2/config v1.32.16/go.mod h1:duCCnJEFqpt2RC6no1iK6q+8HpwOAkiUua0pY507dQc=
github.com/aws/aws-sdk-go-v2/credentials v1.19.15 h1:fyvgWTszojq8hEnMi8PPBTvZdTtEVmAVyo+NFLHBhH4=
github.com/aws/aws-sdk-go-v2/credentials v1.19.15/go.mod h1:gJiYyMOjNg8OEdRWOf3CrFQxM2a98qmrtjx1zuiQfB8=
github.com/aws/aws-sdk-go-v2/feature/ec2/imds v1.18.22 h1:IOGsJ1xVWhsi+ZO7/NW8OuZZBtMJLZbk4P5HDjJO0jQ=
github.com/aws/aws-sdk-go-v2/feature/ec2/imds v1.18.22/go.mod h1:b+hYdbU+jGKfXE8kKM6g1+h+L/Go3vMvzlxBsiuGsxg=
github.com/aws/aws-sdk-go-v2/internal/configsources v1.4.22 h1:GmLa5Kw1ESqtFpXsx5MmC84QWa/ZrLZvlJGa2y+4kcQ=
github.com/aws/aws-sdk-go-v2/internal/configsources v1.4.22/go.mod h1:6sW9iWm9DK9YRpRGga/qzrzNLgKpT2cIxb7Vo2eNOp0=
github.com/aws/aws-sdk-go-v2/internal/endpoints/v2 v2.7.22 h1:dY4kWZiSaXIzxnKlj17nHnBcXXBfac6UlsAx2qL6XrU=
github.com/aws/aws-sdk-go-v2/internal/endpoints/v2 v2.7.22/go.mod h1:KIpEUx0JuRZLO7U6cbV204cWAEco2iC3l061IxlwLtI=
github.com/aws/aws-sdk-go-v2/internal/v4a v1.4.23 h1:FPXsW9+gMuIeKmz7j6ENWcWtBGTe1kH8r9thNt5Uxx4=
github.com/aws/aws-sdk-go-v2/internal/v4a v1.4.23/go.mod h1:7J8iGMdRKk6lw2C+cMIphgAnT8uTwBwNOsGkyOCm80U=
github.com/aws/aws-sdk-go-v2/service/internal/accept-encoding v1.13.8 h1:HtOTYcbVcGABLOVuPYaIihj6IlkqubBwFj10K5fxRek=
github.com/aws/aws-sdk-go-v2/service/internal/accept-encoding v1.13.8/go.mod h1:VsK9abqQeGlzPgUr+isNWzPlK2vKe9INMLWnY65f5Xs=
github.com/aws/aws-sdk-go-v2/credentials v1.19.17 h1:gP2nkGsS+KMvF/jfFz2Vv2qiiOqWKyPACSzPsqHgoW8=
github.com/aws/aws-sdk-go-v2/credentials v1.19.17/go.mod h1:Bsew3S/moG5iT77giPj1q8wb/s0RE5/QfH+ASjYtuQc=
github.com/aws/aws-sdk-go-v2/feature/ec2/imds v1.18.23 h1:UuSfcORqNSz/ey3VPRS8TcVH2Ikf0/sC+Hdj400QI6U=
github.com/aws/aws-sdk-go-v2/feature/ec2/imds v1.18.23/go.mod h1:+G/OSGiOFnSOkYloKj/9M35s74LgVAdJBSD5lsFfqKg=
github.com/aws/aws-sdk-go-v2/internal/configsources v1.4.23 h1:GpT/TrnBYuE5gan2cZbTtvP+JlHsutdmlV2YfEyNde0=
github.com/aws/aws-sdk-go-v2/internal/configsources v1.4.23/go.mod h1:xYWD6BS9ywC5bS3sz9Xh04whO/hzK2plt2Zkyrp4JuA=
github.com/aws/aws-sdk-go-v2/internal/endpoints/v2 v2.7.23 h1:bpd8vxhlQi2r1hiueOw02f/duEPTMK59Q4QMAoTTtTo=
github.com/aws/aws-sdk-go-v2/internal/endpoints/v2 v2.7.23/go.mod h1:15DfR2nw+CRHIk0tqNyifu3G1YdAOy68RftkhMDDwYk=
github.com/aws/aws-sdk-go-v2/internal/v4a v1.4.24 h1:OQqn11BtaYv1WLUowvcA30MpzIu8Ti4pcLPIIyoKZrA=
github.com/aws/aws-sdk-go-v2/internal/v4a v1.4.24/go.mod h1:X5ZJyfwVrWA96GzPmUCWFQaEARPR7gCrpq2E92PJwAE=
github.com/aws/aws-sdk-go-v2/service/internal/accept-encoding v1.13.9 h1:FLudkZLt5ci0ozzgkVo8BJGwvqNaZbTWb3UcucAateA=
github.com/aws/aws-sdk-go-v2/service/internal/accept-encoding v1.13.9/go.mod h1:w7wZ/s9qK7c8g4al+UyoF1Sp/Z45UwMGcqIzLWVQHWk=
github.com/aws/aws-sdk-go-v2/service/internal/checksum v1.9.14 h1:xnvDEnw+pnj5mctWiYuFbigrEzSm35x7k4KS/ZkCANg=
github.com/aws/aws-sdk-go-v2/service/internal/checksum v1.9.14/go.mod h1:yS5rNogD8e0Wu9+l3MUwr6eENBzEeGejvINpN5PAYfY=
github.com/aws/aws-sdk-go-v2/service/internal/presigned-url v1.13.22 h1:PUmZeJU6Y1Lbvt9WFuJ0ugUK2xn6hIWUBBbKuOWF30s=
github.com/aws/aws-sdk-go-v2/service/internal/presigned-url v1.13.22/go.mod h1:nO6egFBoAaoXze24a2C0NjQCvdpk8OueRoYimvEB9jo=
github.com/aws/aws-sdk-go-v2/service/internal/presigned-url v1.13.23 h1:pbrxO/kuIwgEsOPLkaHu0O+m4fNgLU8B3vxQ+72jTPw=
github.com/aws/aws-sdk-go-v2/service/internal/presigned-url v1.13.23/go.mod h1:/CMNUqoj46HpS3MNRDEDIwcgEnrtZlKRaHNaHxIFpNA=
github.com/aws/aws-sdk-go-v2/service/internal/s3shared v1.19.22 h1:SE+aQ4DEqG53RRCAIHlCf//B2ycxGH7jFkpnAh/kKPM=
github.com/aws/aws-sdk-go-v2/service/internal/s3shared v1.19.22/go.mod h1:ES3ynECd7fYeJIL6+oax+uIEljmfps0S70BaQzbMd/o=
github.com/aws/aws-sdk-go-v2/service/kms v1.48.2 h1:aL8Y/AbB6I+uw0MjLbdo68NQ8t5lNs3CY3S848HpETk=
github.com/aws/aws-sdk-go-v2/service/kms v1.48.2/go.mod h1:VJcNH6BLr+3VJwinRKdotLOMglHO8mIKlD3ea5c7hbw=
github.com/aws/aws-sdk-go-v2/service/s3 v1.99.1 h1:kU/eBN5+MWNo/LcbNa4hWDdN76hdcd7hocU5kvu7IsU=
github.com/aws/aws-sdk-go-v2/service/s3 v1.99.1/go.mod h1:Fw9aqhJicIVee1VytBBjH+l+5ov6/PhbtIK/u3rt/ls=
github.com/aws/aws-sdk-go-v2/service/signin v1.0.10 h1:a1Fq/KXn75wSzoJaPQTgZO0wHGqE9mjFnylnqEPTchA=
github.com/aws/aws-sdk-go-v2/service/signin v1.0.10/go.mod h1:p6+MXNxW7IA6dMgHfTAzljuwSKD0NCm/4lbS4t6+7vI=
github.com/aws/aws-sdk-go-v2/service/sso v1.30.16 h1:x6bKbmDhsgSZwv6q19wY/u3rLk/3FGjJWyqKcIRufpE=
github.com/aws/aws-sdk-go-v2/service/sso v1.30.16/go.mod h1:CudnEVKRtLn0+3uMV0yEXZ+YZOKnAtUJ5DmDhilVnIw=
github.com/aws/aws-sdk-go-v2/service/ssooidc v1.35.20 h1:oK/njaL8GtyEihkWMD4k3VgHCT64RQKkZwh0DG5j8ak=
github.com/aws/aws-sdk-go-v2/service/ssooidc v1.35.20/go.mod h1:JHs8/y1f3zY7U5WcuzoJ/yAYGYtNIVPKLIbp61euvmg=
github.com/aws/aws-sdk-go-v2/service/sts v1.42.0 h1:ks8KBcZPh3PYISr5dAiXCM5/Thcuxk8l+PG4+A0exds=
github.com/aws/aws-sdk-go-v2/service/sts v1.42.0/go.mod h1:pFw33T0WLvXU3rw1WBkpMlkgIn54eCB5FYLhjDc9Foo=
github.com/aws/aws-sdk-go-v2/service/signin v1.0.11 h1:TdJ+HdzOBhU8+iVAOGUTU63VXopcumCOF1paFulHWZc=
github.com/aws/aws-sdk-go-v2/service/signin v1.0.11/go.mod h1:R82ZRExE/nheo0N+T8zHPcLRTcH8MGsnR3BiVGX0TwI=
github.com/aws/aws-sdk-go-v2/service/sso v1.30.17 h1:7byT8HUWrgoRp6sXjxtZwgOKfhss5fW6SkLBtqzgRoE=
github.com/aws/aws-sdk-go-v2/service/sso v1.30.17/go.mod h1:xNWknVi4Ezm1vg1QsB/5EWpAJURq22uqd38U8qKvOJc=
github.com/aws/aws-sdk-go-v2/service/ssooidc v1.36.0 h1:nDARhv/oF55bcxF7rCI/4PDxOKnVXVWwDuDwCs2I2SQ=
github.com/aws/aws-sdk-go-v2/service/ssooidc v1.36.0/go.mod h1:4vIRDq+CJB2xFAXZ+YgGUTiEft7oAQlhIs71xcSeuVg=
github.com/aws/aws-sdk-go-v2/service/sts v1.42.1 h1:F/M5Y9I3nwr2IEpshZgh1GeHpOItExNM9L1euNuh/fk=
github.com/aws/aws-sdk-go-v2/service/sts v1.42.1/go.mod h1:mTNxImtovCOEEuD65mKW7DCsL+2gjEH+RPEAexAzAio=
github.com/aws/smithy-go v1.25.1 h1:J8ERsGSU7d+aCmdQur5Txg6bVoYelvQJgtZehD12GkI=
github.com/aws/smithy-go v1.25.1/go.mod h1:YE2RhdIuDbA5E5bTdciG9KrW3+TiEONeUWCqxX9i1Fc=
github.com/aymanbagabas/go-osc52/v2 v2.0.1 h1:HwpRHbFMcZLEVr42D4p7XBqjyuxQH5SMiErDT4WkJ2k=
@@ -982,10 +982,6 @@ github.com/mudler/localrecall v0.6.1-0.20260507074622-a7724fef6f81 h1:8D9NJ/ikhs
github.com/mudler/localrecall v0.6.1-0.20260507074622-a7724fef6f81/go.mod h1:28k5n19raUrkuwXkacdNsBlj8yuSnGhpT16tu+2+4dU=
github.com/mudler/memory v0.0.0-20260406210934-424c1ecf2cf8 h1:Ry8RiWy8fZ6Ff4E7dPmjRsBrnHOnPeOOj2LhCgyjQu0=
github.com/mudler/memory v0.0.0-20260406210934-424c1ecf2cf8/go.mod h1:EA8Ashhd56o32qN7ouPKFSRUs/Z+LrRCF4v6R2Oarm8=
github.com/mudler/skillserver v0.0.6 h1:ixz6wUekLdTmbnpAavCkTydDF6UdXAG3ncYufSPK9G0=
github.com/mudler/skillserver v0.0.6/go.mod h1:z3yFhcL9bSykmmh6xgGu0hyoItd4CnxgtWMEWw8uFJU=
github.com/mudler/skillserver v0.0.7-0.20260520212528-3dae7f041b1e h1:ryXE1UEzGhLkDFYuaxJ0fZ6fg4l++TWfMCTJ1E7bYS8=
github.com/mudler/skillserver v0.0.7-0.20260520212528-3dae7f041b1e/go.mod h1:z3yFhcL9bSykmmh6xgGu0hyoItd4CnxgtWMEWw8uFJU=
github.com/mudler/skillserver v0.0.7-0.20260520220837-a7317cbf9145 h1:z59tA3IDYPt71nzH1jpxeaA1LuDw8aZfpTQFNU43Zb8=
github.com/mudler/skillserver v0.0.7-0.20260520220837-a7317cbf9145/go.mod h1:z3yFhcL9bSykmmh6xgGu0hyoItd4CnxgtWMEWw8uFJU=
github.com/mudler/water v0.0.0-20250808092830-dd90dcf09025 h1:WFLP5FHInarYGXi6B/Ze204x7Xy6q/I4nCZnWEyPHK0=
@@ -1022,8 +1018,8 @@ github.com/munnerz/goautoneg v0.0.0-20191010083416-a7dc8b61c822 h1:C3w9PqII01/Oq
github.com/munnerz/goautoneg v0.0.0-20191010083416-a7dc8b61c822/go.mod h1:+n7T8mK8HuQTcFwEeznm/DIxMOiR9yIdICNftLE1DvQ=
github.com/natefinch/atomic v1.0.1 h1:ZPYKxkqQOx3KZ+RsbnP/YsgvxWQPGxjC0oBt2AhwV0A=
github.com/natefinch/atomic v1.0.1/go.mod h1:N/D/ELrljoqDyT3rZrsUmtsuzvHkeB/wWjHV22AZRbM=
github.com/nats-io/nats.go v1.50.0 h1:5zAeQrTvyrKrWLJ0fu02W3br8ym57qf7csDzgLOpcds=
github.com/nats-io/nats.go v1.50.0/go.mod h1:26HypzazeOkyO3/mqd1zZd53STJN0EjCYF9Uy2ZOBno=
github.com/nats-io/nats.go v1.52.0 h1:n3avV4VBsCgsdwh71TppsTwtv+QdPs7ntSKM8qJLGsc=
github.com/nats-io/nats.go v1.52.0/go.mod h1:26HypzazeOkyO3/mqd1zZd53STJN0EjCYF9Uy2ZOBno=
github.com/nats-io/nkeys v0.4.15 h1:JACV5jRVO9V856KOapQ7x+EY8Jo3qw1vJt/9Jpwzkk4=
github.com/nats-io/nkeys v0.4.15/go.mod h1:CpMchTXC9fxA5zrMo4KpySxNjiDVvr8ANOSZdiNfUrs=
github.com/nats-io/nuid v1.0.1 h1:5iA8DT8V7q8WK2EScv2padNa/rTESc1KdnPw4TC2paw=

Some files were not shown because too many files have changed in this diff Show More