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LocalAI/Makefile
Ettore Di Giacinto 20baec77ab feat(face-recognition): add insightface/onnx backend for 1:1 verify, 1:N identify, embedding, detection, analysis (#9480) 
* feat(face-recognition): add insightface backend for 1:1 verify, 1:N identify, embedding, detection, analysis

Adds face recognition as a new first-class capability in LocalAI via the
`insightface` Python backend, with a pluggable two-engine design so
non-commercial (insightface model packs) and commercial-safe
(OpenCV Zoo YuNet + SFace) models share the same gRPC/HTTP surface.

New gRPC RPCs (backend/backend.proto):
  * FaceVerify(FaceVerifyRequest) returns FaceVerifyResponse
  * FaceAnalyze(FaceAnalyzeRequest) returns FaceAnalyzeResponse

Existing Embedding and Detect RPCs are reused (face image in
PredictOptions.Images / DetectOptions.src) for face embedding and
face detection respectively.

New HTTP endpoints under /v1/face/:
  * verify     — 1:1 image pair same-person decision
  * analyze    — per-face age + gender (emotion/race reserved)
  * register   — 1:N enrollment; stores embedding in vector store
  * identify   — 1:N recognition; detect → embed → StoresFind
  * forget     — remove a registered face by opaque ID

Service layer (core/services/facerecognition/) introduces a
`Registry` interface with one in-memory `storeRegistry` impl backed
by LocalAI's existing local-store gRPC vector backend. HTTP handlers
depend on the interface, not on StoresSet/StoresFind directly, so a
persistent PostgreSQL/pgvector implementation can be slotted in via a
single constructor change in core/application (TODO marker in the
package doc).

New usecase flag FLAG_FACE_RECOGNITION; insightface is also wired
into FLAG_DETECTION so /v1/detection works for face bounding boxes.

Gallery (backend/index.yaml) ships three entries:
  * insightface-buffalo-l   — SCRFD-10GF + ArcFace R50 + genderage
                              (~326MB pre-baked; non-commercial research use only)
  * insightface-opencv      — YuNet + SFace (~40MB pre-baked; Apache 2.0)
  * insightface-buffalo-s   — SCRFD-500MF + MBF (runtime download; non-commercial)

Python backend (backend/python/insightface/):
  * engines.py — FaceEngine protocol with InsightFaceEngine and
    OnnxDirectEngine; resolves model paths relative to the backend
    directory so the same gallery config works in docker-scratch and
    in the e2e-backends rootfs-extraction harness.
  * backend.py — gRPC servicer implementing Health, LoadModel, Status,
    Embedding, Detect, FaceVerify, FaceAnalyze.
  * install.sh — pre-bakes buffalo_l + OpenCV YuNet/SFace inside the
    backend directory so first-run is offline-clean (the final scratch
    image only preserves files under /<backend>/).
  * test.py — parametrized unit tests over both engines.

Tests:
  * Registry unit tests (go test -race ./core/services/facerecognition/...)
    — in-memory fake grpc.Backend, table-driven, covers register/
    identify/forget/error paths + concurrent access.
  * tests/e2e-backends/backend_test.go extended with face caps
    (face_detect, face_embed, face_verify, face_analyze); relative
    ordering + configurable verifyCeiling per engine.
  * Makefile targets: test-extra-backend-insightface-buffalo-l,
    -opencv, and the -all aggregate.
  * CI: .github/workflows/test-extra.yml gains tests-insightface-grpc,
    auto-triggered by changes under backend/python/insightface/.

Docs:
  * docs/content/features/face-recognition.md — feature page with
    license table, quickstart (defaults to the commercial-safe model),
    models matrix, API reference, 1:N workflow, storage caveats.
  * Cross-refs in object-detection.md, stores.md, embeddings.md, and
    whats-new.md.
  * Contributor README at backend/python/insightface/README.md.

Verified end-to-end:
  * buffalo_l: 6/6 specs (health, load, face_detect, face_embed,
    face_verify, face_analyze).
  * opencv: 5/5 specs (same minus face_analyze — SFace has no
    demographic head; correctly skipped via BACKEND_TEST_CAPS).

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

* fix(face-recognition): move engine selection to model gallery, collapse backend entries

The previous commit put engine/model_pack options on backend gallery
entries (`backend/index.yaml`). That was wrong — `GalleryBackend`
(core/gallery/backend_types.go:32) has no `options` field, so the
YAML decoder silently dropped those keys and all three "different
insightface-*" backend entries resolved to the same container image
with no distinguishing configuration.

Correct split:

  * `backend/index.yaml` now has ONE `insightface` backend entry
    shipping the CPU + CUDA 12 container images. The Python backend
    bundles both the non-commercial insightface model packs
    (buffalo_l / buffalo_s) and the commercial-safe OpenCV Zoo
    weights (YuNet + SFace); the active engine is selected at
    LoadModel time via `options: ["engine:..."]`.

  * `gallery/index.yaml` gains three model entries —
    `insightface-buffalo-l`, `insightface-opencv`,
    `insightface-buffalo-s` — each setting the appropriate
    `overrides.backend` + `overrides.options` so installing one
    actually gives the user the intended engine. This matches how
    `rfdetr-base` lives in the model gallery against the `rfdetr`
    backend.

The earlier e2e tests passed despite this bug because the Makefile
targets pass `BACKEND_TEST_OPTIONS` directly to LoadModel via gRPC,
bypassing any gallery resolution entirely. No code changes needed.

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

* feat(face-recognition): cover all supported models in the gallery + drop weight baking

Follows up on the model-gallery split: adds entries for every model
configuration either engine actually supports, and switches weight
delivery from image-baked to LocalAI's standard gallery mechanism.

Gallery now has seven `insightface-*` model entries (gallery/index.yaml):

  insightface (family)  — non-commercial research use
    • buffalo-l   (326MB)  — SCRFD-10GF + ResNet50 + genderage, default
    • buffalo-m   (313MB)  — SCRFD-2.5GF + ResNet50 + genderage
    • buffalo-s   (159MB)  — SCRFD-500MF + MBF + genderage
    • buffalo-sc  (16MB)   — SCRFD-500MF + MBF, recognition only
                             (no landmarks, no demographics — analyze
                             returns empty attributes)
    • antelopev2  (407MB)  — SCRFD-10GF + ResNet100@Glint360K + genderage

  OpenCV Zoo family — Apache 2.0 commercial-safe
    • opencv       — YuNet + SFace fp32 (~40MB)
    • opencv-int8  — YuNet + SFace int8 (~12MB, ~3x smaller, faster on CPU)

Model weights are no longer baked into the backend image. The image
now ships only the Python runtime + libraries (~275MB content size,
~1.18GB disk vs ~1.21GB when weights were baked). Weights flow through
LocalAI's gallery mechanism:

  * OpenCV variants list `files:` with ONNX URIs + SHA-256, so
    `local-ai models install insightface-opencv` pulls them into the
    models directory exactly like any other gallery-managed model.

  * insightface packs (upstream distributes .zip archives only, not
    individual ONNX files) auto-download on first LoadModel via
    FaceAnalysis' built-in machinery, rooted at the LocalAI models
    directory so they live alongside everything else — same pattern
    `rfdetr` uses with `inference.get_model()`.

Backend changes (backend/python/insightface/):

  * backend.py — LoadModel propagates `ModelOptions.ModelPath` (the
    LocalAI models directory) to engines via a `_model_dir` hint.
    This replaces the earlier ModelFile-dirname approach; ModelPath
    is the canonical "models directory" variable set by the Go loader
    (pkg/model/initializers.go:144) and is always populated.

  * engines.py::_resolve_model_path — picks up `model_dir` and searches
    it (plus basename-in-model-dir) before falling back to the dev
    script-dir. This is how OnnxDirectEngine finds gallery-downloaded
    YuNet/SFace files by filename only.

  * engines.py::_flatten_insightface_pack — new helper that works
    around an upstream packaging inconsistency: buffalo_l/s/sc zips
    expand flat, but buffalo_m and antelopev2 zips wrap their ONNX
    files in a redundant `<name>/` directory. insightface's own
    loader looks one level too shallow and fails. We call
    `ensure_available()` explicitly, flatten if nested, then hand to
    FaceAnalysis.

  * engines.py::InsightFaceEngine.prepare — root-resolution order now
    includes the `_model_dir` hint so packs download into the LocalAI
    models directory by default.

  * install.sh — no longer pre-downloads any weights. Everything is
    gallery-managed now.

  * smoke.py (new) — parametrized smoke test that iterates over every
    gallery configuration, simulating the LocalAI install flow
    (creates a models dir, fetches OpenCV files with checksum
    verification, lets insightface auto-download its packs), then
    runs detect + embed + verify (+ analyze where supported) through
    the in-process BackendServicer.

  * test.py — OnnxDirectEngineTest no longer hardcodes `/models/opencv/`
    paths; downloads ONNX files to a temp dir at setUpClass time and
    passes ModelPath accordingly.

Registry change (core/services/facerecognition/store_registry.go):

  * `dim=0` in NewStoreRegistry now means "accept whatever dimension
    arrives" — needed because the backend supports 512-d ArcFace/MBF
    and 128-d SFace via the same Registry. A non-zero dim still fails
    fast with ErrDimensionMismatch.

  * core/application plumbs `faceEmbeddingDim = 0`, explaining the
    rationale in the comment.

Backend gallery description updated to reflect that the image carries
no weights — it's just Python + engines.

Smoke-tested all 7 configurations against the rebuilt image (with the
flatten fix applied), exit 0:

    PASS: insightface-buffalo-l    faces=6 dim=512 same-dist=0.000
    PASS: insightface-buffalo-sc   faces=6 dim=512 same-dist=0.000
    PASS: insightface-buffalo-s    faces=6 dim=512 same-dist=0.000
    PASS: insightface-buffalo-m    faces=6 dim=512 same-dist=0.000
    PASS: insightface-antelopev2   faces=6 dim=512 same-dist=0.000
    PASS: insightface-opencv       faces=6 dim=128 same-dist=0.000
    PASS: insightface-opencv-int8  faces=6 dim=128 same-dist=0.000
    7/7 passed

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

* fix(face-recognition): pre-fetch OpenCV ONNX for e2e target; drop stale pre-baked claim

CI regression from the previous commit: I moved OpenCV Zoo weight
delivery to LocalAI's gallery `files:` mechanism, but the
test-extra-backend-insightface-opencv target was still passing
relative paths `detector_onnx:models/opencv/yunet.onnx` in
BACKEND_TEST_OPTIONS. The e2e suite drives LoadModel directly over
gRPC without going through the gallery, so those relative paths
resolved to nothing and OpenCV's ONNXImporter failed:

    LoadModel failed: Failed to load face engine:
    OpenCV(4.13.0) ... Can't read ONNX file: models/opencv/yunet.onnx

Fix: add an `insightface-opencv-models` prerequisite target that
fetches the two ONNX files (YuNet + SFace) to a deterministic host
cache at /tmp/localai-insightface-opencv-cache/, verifies SHA-256,
and skips the download on re-runs. The opencv test target depends on
it and passes absolute paths in BACKEND_TEST_OPTIONS, so the backend
finds the files via its normal absolute-path resolution branch.

Also refresh the buffalo_l comment: it no longer says "pre-baked"
(nothing is — the pack auto-downloads from upstream's GitHub release
on first LoadModel, same as in CI).

Locally verified: `make test-extra-backend-insightface-opencv` passes
5/5 specs (health, load, face_detect, face_embed, face_verify).

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

* feat(face-recognition): add POST /v1/face/embed + correct /v1/embeddings docs

The docs promised that /v1/embeddings returns face vectors when you
send an image data-URI. That was never true: /v1/embeddings is
OpenAI-compatible and text-only by contract — its handler goes
through `core/backend/embeddings.go::ModelEmbedding`, which sets
`predictOptions.Embeddings = s` (a string of TEXT to embed) and never
populates `predictOptions.Images[]`. The Python backend's Embedding
gRPC method does handle Images[] (that's how /v1/face/register reaches
it internally via `backend.FaceEmbed`), but the HTTP embeddings
endpoint wasn't wired to populate it.

Rather than overload /v1/embeddings with image-vs-text detection —
messy, and the endpoint is OpenAI-compatible by design — add a
dedicated /v1/face/embed endpoint that wraps `backend.FaceEmbed`
(already used internally by /v1/face/register and /v1/face/identify).

Matches LocalAI's convention of a dedicated path per non-standard flow
(/v1/rerank, /v1/detection, /v1/face/verify etc.).

Response:

    {
      "embedding": [<dim> floats, L2-normed],
      "dim": int,           // 512 for ArcFace R50 / MBF, 128 for SFace
      "model": "<name>"
    }

Live-tested on the opencv engine: returns a 128-d L2-normalized vector
(sum(x^2) = 1.0000). Sentinel in docs updated to note /v1/embeddings
is text-only and point image users at /v1/face/embed instead.

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

* fix(http): map malformed image input + gRPC status codes to proper 4xx

Image-input failures on LocalAI's single-image endpoints (/v1/detection,
/v1/face/{verify,analyze,embed,register,identify}) have historically
returned 500 — even when the client was the one who sent garbage.
Classic example: you POST an "image" that isn't a URL, isn't a
data-URI, and isn't a valid JPEG/PNG — the server shouldn't claim
that's its fault.

Two helpers land in core/http/endpoints/localai/images.go and every
single-image handler is switched over:

  * decodeImageInput(s)
      Wraps utils.GetContentURIAsBase64 and turns any failure
      (invalid URL, not a data-URI, download error, etc.) into
      echo.NewHTTPError(400, "invalid image input: ...").

  * mapBackendError(err)
      Inspects the gRPC status on a backend call error and maps:
        INVALID_ARGUMENT     → 400 Bad Request
        NOT_FOUND            → 404 Not Found
        FAILED_PRECONDITION  → 412 Precondition Failed
        Unimplemented        → 501 Not Implemented
      All other codes fall through unchanged (still 500).

Before, my 1×1 PNG error-path test returned:
    HTTP 500 "rpc error: code = InvalidArgument desc = failed to decode one or both images"
After:
    HTTP 400 "failed to decode one or both images"

Scope-limited to the LocalAI single-image endpoints. The multi-modal
paths (middleware/request.go, openresponses/responses.go,
openai/realtime.go) intentionally log-and-skip individual media parts
when decoding fails — different design intent (graceful degradation
of a multi-part message), not a 400-worthy failure. Left untouched.

Live-verified: every error case in /tmp/face_errors.py now returns
4xx with a meaningful message; the "image with no face (1x1 PNG)"
case specifically went from 500 → 400.

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

* refactor(face-recognition): insightface packs go through gallery files:, drop FaceAnalysis

Follows up on the discovery that LocalAI's gallery `files:` mechanism
handles archives (zip, tar.gz, …) via mholt/archiver/v3 — the rhasspy
piper voices use exactly this pattern. Insightface packs are zip
archives, so we can now deliver them the same way every other
gallery-managed model gets delivered: declaratively, checksum-verified,
through LocalAI's standard download+extract pipeline.

Two changes:

1. Gallery (gallery/index.yaml) — every insightface-* entry gains a
   `files:` list with the pack zip's URI + SHA-256. `local-ai models
   install insightface-buffalo-l` now fetches the zip, verifies the
   hash, and extracts it into the models directory. No more reliance
   on insightface's library-internal `ensure_available()` auto-download
   or its hardcoded `BASE_REPO_URL`.

2. InsightFaceEngine (backend/python/insightface/engines.py) — drops
   the FaceAnalysis wrapper and drives insightface's `model_zoo`
   directly. The ~50 lines FaceAnalysis provides — glob ONNX files,
   route each through `model_zoo.get_model()`, build a
   `{taskname: model}` dict, loop per-face at inference — are
   reimplemented in `InsightFaceEngine`. The actual inference classes
   (RetinaFace, ArcFaceONNX, Attribute, Landmark) are still
   insightface's — we only replicate the glue, so drift risk against
   upstream is minimal.

   Why drop FaceAnalysis: it hard-codes a `<root>/models/<name>/*.onnx`
   layout that doesn't match what LocalAI's zip extraction produces.
   LocalAI unpacks archives flat into `<models_dir>`. Upstream packs
   are inconsistent — buffalo_l/s/sc ship ONNX at the zip root (lands
   at `<models_dir>/*.onnx`), buffalo_m/antelopev2 wrap in a redundant
   `<name>/` dir (lands at `<models_dir>/<name>/*.onnx`). The new
   `_locate_insightface_pack` helper searches both locations plus
   legacy paths and returns whichever has ONNX files. Replaces the
   earlier `_flatten_insightface_pack` helper (which tried to fight
   FaceAnalysis's layout expectations; now we just find the files
   wherever they are).

Net effect for users: install once via LocalAI's managed flow,
weights live alongside every other model, progress shows in the
jobs endpoint, no first-load network call. Same API surface,
cleaner plumbing.

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

* fix(face-recognition): CI's insightface e2e path needs the pack pre-fetched

The e2e suite drives LoadModel over gRPC without going through LocalAI's
gallery flow, so the engine's `_model_dir` option (normally populated
from ModelPath) is empty. Previously the insightface target relied on
FaceAnalysis auto-download to paper over this, but we dropped
FaceAnalysis in favor of direct model_zoo calls — so the buffalo_l
target started failing at LoadModel with "no insightface pack found".

Mirror the opencv target's pre-fetch pattern: download buffalo_sc.zip
(same SHA as the gallery entry), extract it on the host, and pass
`root:<dir>` so the engine locates the pack without needing
ModelPath. Switched to buffalo_sc (smallest pack, ~16MB) to keep CI
fast; it covers the same insightface engine code path as buffalo_l.

Face analyze cap dropped since buffalo_sc has no age/gender head.

Assisted-by: Claude:claude-opus-4-7[1m]

* feat(face-recognition): surface face-recognition in advertised feature maps

The six /v1/face/* endpoints were missing from every place LocalAI
advertises its feature surface to clients:

  * api_instructions — the machine-readable capability index at
    GET /api/instructions. Added `face-recognition` as a dedicated
    instruction area with an intro that calls out the in-memory
    registry caveat and the /v1/face/embed vs /v1/embeddings split.
  * auth/permissions — added FeatureFaceRecognition constant, routed
    all six face endpoints through it so admins can gate them per-user
    like any other API feature. Default ON (matches the other API
    features).
  * React UI capabilities — CAP_FACE_RECOGNITION symbol mapped to
    FLAG_FACE_RECOGNITION. Declared only for now; the Face page is a
    follow-up (noted in the plan).

Instruction count bumped 9 → 10; test updated.

Assisted-by: Claude:claude-opus-4-7[1m]

* docs(agents): capture advertising-surface steps in the endpoint guide

Before this change, adding a new /v1/* endpoint reliably missed one or
more of: the swagger @Tags annotation, the /api/instructions registry,
the auth RouteFeatureRegistry, and the React UI CAP_* symbol. The
endpoint would work but be invisible to API consumers, admins, and the
UI — and nothing in the existing docs said to look in those places.

Extend .agents/api-endpoints-and-auth.md with a new "Advertising
surfaces" section covering all four surfaces (swagger tags, /api/
instructions, capabilities.js, docs/), and expand the closing checklist
so it's impossible to ship a feature without visiting each one. Hoist a
one-liner reminder into AGENTS.md's Quick Reference so agents skim it
before diving in.

Assisted-by: Claude:claude-opus-4-7[1m]
2026-04-22 21:55:41 +02:00

1043 lines
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# 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/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/tinygrad
GOCMD=go
GOTEST=$(GOCMD) test
GOVET=$(GOCMD) vet
BINARY_NAME=local-ai
LAUNCHER_BINARY_NAME=local-ai-launcher
UBUNTU_VERSION?=2404
UBUNTU_CODENAME?=noble
GORELEASER?=
export BUILD_TYPE?=
export CUDA_MAJOR_VERSION?=13
export CUDA_MINOR_VERSION?=0
GO_TAGS?=
BUILD_ID?=
NATIVE?=false
TEST_DIR=/tmp/test
TEST_FLAKES?=5
RANDOM := $(shell bash -c 'echo $$RANDOM')
VERSION?=$(shell git describe --always --tags || echo "dev" )
# go tool nm ./local-ai | grep Commit
LD_FLAGS?=-s -w
override LD_FLAGS += -X "github.com/mudler/LocalAI/internal.Version=$(VERSION)"
override LD_FLAGS += -X "github.com/mudler/LocalAI/internal.Commit=$(shell git rev-parse HEAD)"
OPTIONAL_TARGETS?=
export OS := $(shell uname -s)
ARCH := $(shell uname -m)
GREEN := $(shell tput -Txterm setaf 2)
YELLOW := $(shell tput -Txterm setaf 3)
WHITE := $(shell tput -Txterm setaf 7)
CYAN := $(shell tput -Txterm setaf 6)
RESET := $(shell tput -Txterm sgr0)
# Default Docker bridge IP
E2E_BRIDGE_IP?=172.17.0.1
ifndef UNAME_S
UNAME_S := $(shell uname -s)
endif
ifeq ($(OS),Darwin)
ifeq ($(OSX_SIGNING_IDENTITY),)
OSX_SIGNING_IDENTITY := $(shell security find-identity -v -p codesigning | grep '"' | head -n 1 | sed -E 's/.*"(.*)"/\1/')
endif
endif
# check if goreleaser exists
ifeq (, $(shell which goreleaser))
GORELEASER=curl -sfL https://goreleaser.com/static/run | bash -s --
else
GORELEASER=$(shell which goreleaser)
endif
TEST_PATHS?=./api/... ./pkg/... ./core/...
.PHONY: all test build vendor
all: help
## GENERIC
rebuild: ## Rebuilds the project
$(GOCMD) clean -cache
$(MAKE) build
clean: ## Remove build related file
$(GOCMD) clean -cache
rm -f prepare
rm -rf $(BINARY_NAME)
rm -rf release/
$(MAKE) protogen-clean
rmdir pkg/grpc/proto || true
clean-tests:
rm -rf test-models
rm -rf test-dir
## Install Go tools
install-go-tools:
go install google.golang.org/grpc/cmd/protoc-gen-go-grpc@1958fcbe2ca8bd93af633f11e97d44e567e945af
go install google.golang.org/protobuf/cmd/protoc-gen-go@v1.34.2
## React UI:
react-ui:
ifneq ($(wildcard core/http/react-ui/dist),)
@echo "react-ui dist already exists, skipping build"
else
cd core/http/react-ui && npm install && npm run build
endif
react-ui-docker:
docker run --entrypoint /bin/bash -v $(CURDIR):/app:z oven/bun:1 \
-c "cd /app/core/http/react-ui && bun install && bun run build"
core/http/react-ui/dist: react-ui
## Build:
build: protogen-go generate install-go-tools core/http/react-ui/dist ## Build the project
$(info ${GREEN}I local-ai build info:${RESET})
$(info ${GREEN}I BUILD_TYPE: ${YELLOW}$(BUILD_TYPE)${RESET})
$(info ${GREEN}I GO_TAGS: ${YELLOW}$(GO_TAGS)${RESET})
$(info ${GREEN}I LD_FLAGS: ${YELLOW}$(LD_FLAGS)${RESET})
$(info ${GREEN}I UPX: ${YELLOW}$(UPX)${RESET})
rm -rf $(BINARY_NAME) || true
CGO_LDFLAGS="$(CGO_LDFLAGS)" $(GOCMD) build -ldflags "$(LD_FLAGS)" -tags "$(GO_TAGS)" -o $(BINARY_NAME) ./cmd/local-ai
build-launcher: ## Build the launcher application
$(info ${GREEN}I local-ai launcher build info:${RESET})
$(info ${GREEN}I BUILD_TYPE: ${YELLOW}$(BUILD_TYPE)${RESET})
$(info ${GREEN}I GO_TAGS: ${YELLOW}$(GO_TAGS)${RESET})
$(info ${GREEN}I LD_FLAGS: ${YELLOW}$(LD_FLAGS)${RESET})
rm -rf $(LAUNCHER_BINARY_NAME) || true
CGO_LDFLAGS="$(CGO_LDFLAGS)" $(GOCMD) build -ldflags "$(LD_FLAGS)" -tags "$(GO_TAGS)" -o $(LAUNCHER_BINARY_NAME) ./cmd/launcher
build-all: build build-launcher ## Build both server and launcher
build-dev: ## Run LocalAI in dev mode with live reload
@command -v air >/dev/null 2>&1 || go install github.com/air-verse/air@latest
air -c .air.toml
dev-dist:
$(GORELEASER) build --snapshot --clean
dist:
$(GORELEASER) build --clean
osx-signed: build
codesign --deep --force --sign "$(OSX_SIGNING_IDENTITY)" --entitlements "./Entitlements.plist" "./$(BINARY_NAME)"
## Run
run: ## run local-ai
CGO_LDFLAGS="$(CGO_LDFLAGS)" $(GOCMD) run ./
test-models/testmodel.ggml:
mkdir -p test-models
mkdir -p test-dir
wget -q https://huggingface.co/mradermacher/gpt2-alpaca-gpt4-GGUF/resolve/main/gpt2-alpaca-gpt4.Q4_K_M.gguf -O test-models/testmodel.ggml
wget -q https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-base.en.bin -O test-models/whisper-en
wget -q https://cdn.openai.com/whisper/draft-20220913a/micro-machines.wav -O test-dir/audio.wav
cp tests/models_fixtures/* test-models
prepare-test: protogen-go
cp tests/models_fixtures/* test-models
########################################################
## Tests
########################################################
## Test targets
test: test-models/testmodel.ggml protogen-go
@echo 'Running tests'
export GO_TAGS="debug"
$(MAKE) prepare-test
OPUS_SHIM_LIBRARY=$(abspath ./pkg/opus/shim/libopusshim.so) \
HUGGINGFACE_GRPC=$(abspath ./)/backend/python/transformers/run.sh TEST_DIR=$(abspath ./)/test-dir/ FIXTURES=$(abspath ./)/tests/fixtures CONFIG_FILE=$(abspath ./)/test-models/config.yaml MODELS_PATH=$(abspath ./)/test-models BACKENDS_PATH=$(abspath ./)/backends \
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --label-filter="!llama-gguf" --flake-attempts $(TEST_FLAKES) --fail-fast -v -r $(TEST_PATHS)
$(MAKE) test-llama-gguf
$(MAKE) test-tts
$(MAKE) test-stablediffusion
########################################################
## E2E AIO tests (uses standard image with pre-configured models)
########################################################
docker-build-e2e:
docker build \
--build-arg MAKEFLAGS="--jobs=5 --output-sync=target" \
--build-arg BASE_IMAGE=$(BASE_IMAGE) \
--build-arg IMAGE_TYPE=$(IMAGE_TYPE) \
--build-arg BUILD_TYPE=$(BUILD_TYPE) \
--build-arg CUDA_MAJOR_VERSION=$(CUDA_MAJOR_VERSION) \
--build-arg CUDA_MINOR_VERSION=$(CUDA_MINOR_VERSION) \
--build-arg UBUNTU_VERSION=$(UBUNTU_VERSION) \
--build-arg UBUNTU_CODENAME=$(UBUNTU_CODENAME) \
--build-arg GO_TAGS="$(GO_TAGS)" \
-t local-ai:tests -f Dockerfile .
e2e-aio:
LOCALAI_BACKEND_DIR=$(abspath ./backends) \
LOCALAI_MODELS_DIR=$(abspath ./tests/e2e-aio/models) \
LOCALAI_IMAGE_TAG=tests \
LOCALAI_IMAGE=local-ai \
$(MAKE) run-e2e-aio
run-e2e-aio: protogen-go
@echo 'Running e2e AIO tests'
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --flake-attempts $(TEST_FLAKES) -v -r ./tests/e2e-aio
########################################################
## E2E tests
########################################################
prepare-e2e:
docker build \
--build-arg IMAGE_TYPE=core \
--build-arg BUILD_TYPE=$(BUILD_TYPE) \
--build-arg BASE_IMAGE=$(BASE_IMAGE) \
--build-arg CUDA_MAJOR_VERSION=$(CUDA_MAJOR_VERSION) \
--build-arg CUDA_MINOR_VERSION=$(CUDA_MINOR_VERSION) \
--build-arg UBUNTU_VERSION=$(UBUNTU_VERSION) \
--build-arg UBUNTU_CODENAME=$(UBUNTU_CODENAME) \
--build-arg GO_TAGS="$(GO_TAGS)" \
--build-arg MAKEFLAGS="$(DOCKER_MAKEFLAGS)" \
-t localai-tests .
run-e2e-image:
docker run -p 5390:8080 -e MODELS_PATH=/models -e THREADS=1 -e DEBUG=true -d --rm -v $(TEST_DIR):/models --name e2e-tests-$(RANDOM) localai-tests
test-e2e: build-mock-backend prepare-e2e run-e2e-image
@echo 'Running e2e tests'
BUILD_TYPE=$(BUILD_TYPE) \
LOCALAI_API=http://$(E2E_BRIDGE_IP):5390 \
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --flake-attempts $(TEST_FLAKES) -v -r ./tests/e2e
$(MAKE) clean-mock-backend
$(MAKE) teardown-e2e
docker rmi localai-tests
teardown-e2e:
rm -rf $(TEST_DIR) || true
docker stop $$(docker ps -q --filter ancestor=localai-tests)
########################################################
## Integration and unit tests
########################################################
test-llama-gguf: prepare-test
TEST_DIR=$(abspath ./)/test-dir/ FIXTURES=$(abspath ./)/tests/fixtures CONFIG_FILE=$(abspath ./)/test-models/config.yaml MODELS_PATH=$(abspath ./)/test-models BACKENDS_PATH=$(abspath ./)/backends \
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --label-filter="llama-gguf" --flake-attempts $(TEST_FLAKES) -v -r $(TEST_PATHS)
test-tts: prepare-test
TEST_DIR=$(abspath ./)/test-dir/ FIXTURES=$(abspath ./)/tests/fixtures CONFIG_FILE=$(abspath ./)/test-models/config.yaml MODELS_PATH=$(abspath ./)/test-models BACKENDS_PATH=$(abspath ./)/backends \
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --label-filter="tts" --flake-attempts $(TEST_FLAKES) -v -r $(TEST_PATHS)
test-stablediffusion: prepare-test
TEST_DIR=$(abspath ./)/test-dir/ FIXTURES=$(abspath ./)/tests/fixtures CONFIG_FILE=$(abspath ./)/test-models/config.yaml MODELS_PATH=$(abspath ./)/test-models BACKENDS_PATH=$(abspath ./)/backends \
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --label-filter="stablediffusion" --flake-attempts $(TEST_FLAKES) -v -r $(TEST_PATHS)
test-stores:
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --label-filter="stores" --flake-attempts $(TEST_FLAKES) -v -r tests/integration
test-opus:
@echo 'Running opus backend tests'
$(MAKE) -C backend/go/opus libopusshim.so
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --flake-attempts $(TEST_FLAKES) -v -r ./backend/go/opus/...
test-opus-docker:
@echo 'Running opus backend tests in Docker'
docker build --target builder \
--build-arg BUILD_TYPE=$(or $(BUILD_TYPE),) \
--build-arg BASE_IMAGE=$(or $(BASE_IMAGE),ubuntu:24.04) \
--build-arg BACKEND=opus \
-t localai-opus-test -f backend/Dockerfile.golang .
docker run --rm localai-opus-test \
bash -c 'cd /LocalAI && go run github.com/onsi/ginkgo/v2/ginkgo --flake-attempts $(TEST_FLAKES) -v -r ./backend/go/opus/...'
test-realtime: build-mock-backend
@echo 'Running realtime e2e tests (mock backend)'
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --label-filter="Realtime && !real-models" --flake-attempts $(TEST_FLAKES) -v -r ./tests/e2e
# Real-model realtime tests. Set REALTIME_TEST_MODEL to use your own pipeline,
# or leave unset to auto-build one from the component env vars below.
REALTIME_VAD?=silero-vad-ggml
REALTIME_STT?=whisper-1
REALTIME_LLM?=qwen3-0.6b
REALTIME_TTS?=tts-1
REALTIME_BACKENDS_PATH?=$(abspath ./)/backends
test-realtime-models: build-mock-backend
@echo 'Running realtime e2e tests (real models)'
REALTIME_TEST_MODEL=$${REALTIME_TEST_MODEL:-realtime-test-pipeline} \
REALTIME_VAD=$(REALTIME_VAD) \
REALTIME_STT=$(REALTIME_STT) \
REALTIME_LLM=$(REALTIME_LLM) \
REALTIME_TTS=$(REALTIME_TTS) \
REALTIME_BACKENDS_PATH=$(REALTIME_BACKENDS_PATH) \
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --label-filter="Realtime" --flake-attempts $(TEST_FLAKES) -v -r ./tests/e2e
# --- Container-based real-model testing ---
REALTIME_BACKEND_NAMES ?= silero-vad whisper llama-cpp kokoro
REALTIME_MODELS_DIR ?= $(abspath ./models)
REALTIME_BACKENDS_DIR ?= $(abspath ./local-backends)
REALTIME_DOCKER_FLAGS ?= --gpus all
local-backends:
mkdir -p local-backends
extract-backend-%: docker-build-% local-backends
@echo "Extracting backend $*..."
@CID=$$(docker create local-ai-backend:$*) && \
rm -rf local-backends/$* && mkdir -p local-backends/$* && \
docker cp $$CID:/ - | tar -xf - -C local-backends/$* && \
docker rm $$CID > /dev/null
extract-realtime-backends: $(addprefix extract-backend-,$(REALTIME_BACKEND_NAMES))
test-realtime-models-docker: build-mock-backend
docker build --target build-requirements \
--build-arg BUILD_TYPE=$(or $(BUILD_TYPE),cublas) \
--build-arg CUDA_MAJOR_VERSION=$(or $(CUDA_MAJOR_VERSION),13) \
--build-arg CUDA_MINOR_VERSION=$(or $(CUDA_MINOR_VERSION),0) \
-t localai-test-runner .
docker run --rm \
$(REALTIME_DOCKER_FLAGS) \
-v $(abspath ./):/build \
-v $(REALTIME_MODELS_DIR):/models:ro \
-v $(REALTIME_BACKENDS_DIR):/backends \
-v localai-go-cache:/root/go/pkg/mod \
-v localai-go-build-cache:/root/.cache/go-build \
-e REALTIME_TEST_MODEL=$${REALTIME_TEST_MODEL:-realtime-test-pipeline} \
-e REALTIME_VAD=$(REALTIME_VAD) \
-e REALTIME_STT=$(REALTIME_STT) \
-e REALTIME_LLM=$(REALTIME_LLM) \
-e REALTIME_TTS=$(REALTIME_TTS) \
-e REALTIME_BACKENDS_PATH=/backends \
-e REALTIME_MODELS_PATH=/models \
-w /build \
localai-test-runner \
bash -c 'git config --global --add safe.directory /build && \
make protogen-go && make build-mock-backend && \
go run github.com/onsi/ginkgo/v2/ginkgo --label-filter="Realtime" --flake-attempts $(TEST_FLAKES) -v -r ./tests/e2e'
test-container:
docker build --target requirements -t local-ai-test-container .
docker run -ti --rm --entrypoint /bin/bash -ti -v $(abspath ./):/build local-ai-test-container
########################################################
## Help
########################################################
## Help:
help: ## Show this help.
@echo ''
@echo 'Usage:'
@echo ' ${YELLOW}make${RESET} ${GREEN}<target>${RESET}'
@echo ''
@echo 'Targets:'
@awk 'BEGIN {FS = ":.*?## "} { \
if (/^[a-zA-Z_-]+:.*?##.*$$/) {printf " ${YELLOW}%-20s${GREEN}%s${RESET}\n", $$1, $$2} \
else if (/^## .*$$/) {printf " ${CYAN}%s${RESET}\n", substr($$1,4)} \
}' $(MAKEFILE_LIST)
########################################################
## Backends
########################################################
.PHONY: protogen
protogen: protogen-go
protoc:
@OS_NAME=$$(uname -s | tr '[:upper:]' '[:lower:]'); \
ARCH_NAME=$$(uname -m); \
if [ "$$OS_NAME" = "darwin" ]; then \
if [ "$$ARCH_NAME" = "arm64" ]; then \
FILE=protoc-31.1-osx-aarch_64.zip; \
elif [ "$$ARCH_NAME" = "x86_64" ]; then \
FILE=protoc-31.1-osx-x86_64.zip; \
else \
echo "Unsupported macOS architecture: $$ARCH_NAME"; exit 1; \
fi; \
elif [ "$$OS_NAME" = "linux" ]; then \
if [ "$$ARCH_NAME" = "x86_64" ]; then \
FILE=protoc-31.1-linux-x86_64.zip; \
elif [ "$$ARCH_NAME" = "aarch64" ] || [ "$$ARCH_NAME" = "arm64" ]; then \
FILE=protoc-31.1-linux-aarch_64.zip; \
elif [ "$$ARCH_NAME" = "ppc64le" ]; then \
FILE=protoc-31.1-linux-ppcle_64.zip; \
elif [ "$$ARCH_NAME" = "s390x" ]; then \
FILE=protoc-31.1-linux-s390_64.zip; \
elif [ "$$ARCH_NAME" = "i386" ] || [ "$$ARCH_NAME" = "x86" ]; then \
FILE=protoc-31.1-linux-x86_32.zip; \
else \
echo "Unsupported Linux architecture: $$ARCH_NAME"; exit 1; \
fi; \
else \
echo "Unsupported OS: $$OS_NAME"; exit 1; \
fi; \
URL=https://github.com/protocolbuffers/protobuf/releases/download/v31.1/$$FILE; \
curl -L $$URL -o protoc.zip && \
unzip -j -d $(CURDIR) protoc.zip bin/protoc && rm protoc.zip
.PHONY: protogen-go
protogen-go: protoc install-go-tools
mkdir -p pkg/grpc/proto
./protoc --experimental_allow_proto3_optional -Ibackend/ --go_out=pkg/grpc/proto/ --go_opt=paths=source_relative --go-grpc_out=pkg/grpc/proto/ --go-grpc_opt=paths=source_relative \
backend/backend.proto
core/config/inference_defaults.json: ## Fetch inference defaults from unsloth (only if missing)
$(GOCMD) generate ./core/config/...
.PHONY: generate
generate: core/config/inference_defaults.json ## Ensure inference defaults exist
.PHONY: generate-force
generate-force: ## Re-fetch inference defaults from unsloth (always)
$(GOCMD) generate ./core/config/...
.PHONY: protogen-go-clean
protogen-go-clean:
$(RM) pkg/grpc/proto/backend.pb.go pkg/grpc/proto/backend_grpc.pb.go
$(RM) bin/*
prepare-test-extra: protogen-python
$(MAKE) -C backend/python/transformers
$(MAKE) -C backend/python/outetts
$(MAKE) -C backend/python/diffusers
$(MAKE) -C backend/python/chatterbox
$(MAKE) -C backend/python/vllm
$(MAKE) -C backend/python/vllm-omni
$(MAKE) -C backend/python/sglang
$(MAKE) -C backend/python/vibevoice
$(MAKE) -C backend/python/moonshine
$(MAKE) -C backend/python/pocket-tts
$(MAKE) -C backend/python/qwen-tts
$(MAKE) -C backend/python/fish-speech
$(MAKE) -C backend/python/faster-qwen3-tts
$(MAKE) -C backend/python/qwen-asr
$(MAKE) -C backend/python/nemo
$(MAKE) -C backend/python/voxcpm
$(MAKE) -C backend/python/faster-whisper
$(MAKE) -C backend/python/whisperx
$(MAKE) -C backend/python/ace-step
$(MAKE) -C backend/python/trl
$(MAKE) -C backend/python/tinygrad
$(MAKE) -C backend/python/insightface
$(MAKE) -C backend/rust/kokoros kokoros-grpc
test-extra: prepare-test-extra
$(MAKE) -C backend/python/transformers test
$(MAKE) -C backend/python/outetts test
$(MAKE) -C backend/python/diffusers test
$(MAKE) -C backend/python/chatterbox test
$(MAKE) -C backend/python/vllm test
$(MAKE) -C backend/python/vllm-omni test
$(MAKE) -C backend/python/vibevoice test
$(MAKE) -C backend/python/moonshine test
$(MAKE) -C backend/python/pocket-tts test
$(MAKE) -C backend/python/qwen-tts test
$(MAKE) -C backend/python/fish-speech test
$(MAKE) -C backend/python/faster-qwen3-tts test
$(MAKE) -C backend/python/qwen-asr test
$(MAKE) -C backend/python/nemo test
$(MAKE) -C backend/python/voxcpm test
$(MAKE) -C backend/python/faster-whisper test
$(MAKE) -C backend/python/whisperx test
$(MAKE) -C backend/python/ace-step test
$(MAKE) -C backend/python/trl test
$(MAKE) -C backend/python/tinygrad test
$(MAKE) -C backend/python/insightface test
$(MAKE) -C backend/rust/kokoros test
##
## End-to-end gRPC tests that exercise a built backend container image.
##
## The test suite in tests/e2e-backends is backend-agnostic. You drive it via env
## vars (see tests/e2e-backends/backend_test.go for the full list) and the
## capability-driven harness picks which gRPC RPCs to exercise:
##
## BACKEND_IMAGE Required. Docker image to test, e.g. local-ai-backend:llama-cpp.
## BACKEND_TEST_MODEL_URL URL of a model file to download and load.
## BACKEND_TEST_MODEL_FILE Path to an already-downloaded model (skips download).
## BACKEND_TEST_MODEL_NAME HuggingFace repo id (e.g. Qwen/Qwen2.5-0.5B-Instruct).
## Use this instead of MODEL_URL for backends that
## resolve HF model ids natively (vllm, vllm-omni).
## BACKEND_TEST_CAPS Comma-separated capabilities, default "health,load,predict,stream".
## Adds "tools" to exercise ChatDelta tool call extraction.
## BACKEND_TEST_PROMPT Override the prompt used in predict/stream specs.
## BACKEND_TEST_OPTIONS Comma-separated Options[] entries forwarded to LoadModel,
## e.g. "tool_parser:hermes,reasoning_parser:qwen3".
##
## Direct usage (image already built, no docker-build-* dependency):
##
## make test-extra-backend BACKEND_IMAGE=local-ai-backend:llama-cpp \
## BACKEND_TEST_MODEL_URL=https://.../model.gguf
##
## Convenience wrappers below build a specific backend image first, then run the
## suite against it.
##
BACKEND_TEST_MODEL_URL?=https://huggingface.co/Qwen/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B-Q8_0.gguf
## Generic target — runs the suite against whatever BACKEND_IMAGE points at.
## Depends on protogen-go so pkg/grpc/proto is generated before `go test`.
test-extra-backend: protogen-go
@test -n "$$BACKEND_IMAGE" || { echo "BACKEND_IMAGE must be set" >&2; exit 1; }
BACKEND_IMAGE="$$BACKEND_IMAGE" \
BACKEND_TEST_MODEL_URL="$${BACKEND_TEST_MODEL_URL:-$(BACKEND_TEST_MODEL_URL)}" \
BACKEND_TEST_MODEL_FILE="$$BACKEND_TEST_MODEL_FILE" \
BACKEND_TEST_MODEL_NAME="$$BACKEND_TEST_MODEL_NAME" \
BACKEND_TEST_MMPROJ_URL="$$BACKEND_TEST_MMPROJ_URL" \
BACKEND_TEST_MMPROJ_FILE="$$BACKEND_TEST_MMPROJ_FILE" \
BACKEND_TEST_AUDIO_URL="$$BACKEND_TEST_AUDIO_URL" \
BACKEND_TEST_AUDIO_FILE="$$BACKEND_TEST_AUDIO_FILE" \
BACKEND_TEST_CAPS="$$BACKEND_TEST_CAPS" \
BACKEND_TEST_PROMPT="$$BACKEND_TEST_PROMPT" \
BACKEND_TEST_OPTIONS="$$BACKEND_TEST_OPTIONS" \
BACKEND_TEST_TOOL_PROMPT="$$BACKEND_TEST_TOOL_PROMPT" \
BACKEND_TEST_TOOL_NAME="$$BACKEND_TEST_TOOL_NAME" \
BACKEND_TEST_CACHE_TYPE_K="$$BACKEND_TEST_CACHE_TYPE_K" \
BACKEND_TEST_CACHE_TYPE_V="$$BACKEND_TEST_CACHE_TYPE_V" \
BACKEND_TEST_FACE_IMAGE_1_URL="$$BACKEND_TEST_FACE_IMAGE_1_URL" \
BACKEND_TEST_FACE_IMAGE_1_FILE="$$BACKEND_TEST_FACE_IMAGE_1_FILE" \
BACKEND_TEST_FACE_IMAGE_2_URL="$$BACKEND_TEST_FACE_IMAGE_2_URL" \
BACKEND_TEST_FACE_IMAGE_2_FILE="$$BACKEND_TEST_FACE_IMAGE_2_FILE" \
BACKEND_TEST_FACE_IMAGE_3_URL="$$BACKEND_TEST_FACE_IMAGE_3_URL" \
BACKEND_TEST_FACE_IMAGE_3_FILE="$$BACKEND_TEST_FACE_IMAGE_3_FILE" \
BACKEND_TEST_VERIFY_DISTANCE_CEILING="$$BACKEND_TEST_VERIFY_DISTANCE_CEILING" \
go test -v -timeout 30m ./tests/e2e-backends/...
## Convenience wrappers: build the image, then exercise it.
test-extra-backend-llama-cpp: docker-build-llama-cpp
BACKEND_IMAGE=local-ai-backend:llama-cpp $(MAKE) test-extra-backend
test-extra-backend-ik-llama-cpp: docker-build-ik-llama-cpp
BACKEND_IMAGE=local-ai-backend:ik-llama-cpp $(MAKE) test-extra-backend
## turboquant: exercises the llama.cpp-fork backend with the fork's
## *TurboQuant-specific* KV-cache types (turbo3 for both K and V). turbo3
## is what makes this backend distinct from stock llama-cpp — picking q8_0
## here would only test the standard llama.cpp code path that the upstream
## llama-cpp backend already covers. The fork auto-enables flash_attention
## when turbo3/turbo4 are active, so we don't need to set it explicitly.
test-extra-backend-turboquant: docker-build-turboquant
BACKEND_IMAGE=local-ai-backend:turboquant \
BACKEND_TEST_CACHE_TYPE_K=q8_0 \
BACKEND_TEST_CACHE_TYPE_V=turbo3 \
$(MAKE) test-extra-backend
## Audio transcription wrapper for the llama-cpp backend.
## Drives the new AudioTranscription / AudioTranscriptionStream RPCs against
## ggml-org/Qwen3-ASR-0.6B-GGUF (a small ASR model that requires its mmproj
## audio encoder companion). The audio fixture is a short public-domain
## "jfk.wav" clip ggml-org bundles with whisper.cpp's CI assets.
test-extra-backend-llama-cpp-transcription: docker-build-llama-cpp
BACKEND_IMAGE=local-ai-backend:llama-cpp \
BACKEND_TEST_MODEL_URL=https://huggingface.co/ggml-org/Qwen3-ASR-0.6B-GGUF/resolve/main/Qwen3-ASR-0.6B-Q8_0.gguf \
BACKEND_TEST_MMPROJ_URL=https://huggingface.co/ggml-org/Qwen3-ASR-0.6B-GGUF/resolve/main/mmproj-Qwen3-ASR-0.6B-Q8_0.gguf \
BACKEND_TEST_AUDIO_URL=https://github.com/ggml-org/whisper.cpp/raw/master/samples/jfk.wav \
BACKEND_TEST_CAPS=health,load,transcription \
$(MAKE) test-extra-backend
## vllm is resolved from a HuggingFace model id (no file download) and
## exercises Predict + streaming + tool-call extraction via the hermes parser.
## Requires a host CPU with the SIMD instructions the prebuilt vllm CPU
## wheel was compiled against (AVX-512 VNNI/BF16); older CPUs will SIGILL
## on import — on CI this means using the bigger-runner label.
test-extra-backend-vllm: docker-build-vllm
BACKEND_IMAGE=local-ai-backend:vllm \
BACKEND_TEST_MODEL_NAME=Qwen/Qwen2.5-0.5B-Instruct \
BACKEND_TEST_CAPS=health,load,predict,stream,tools \
BACKEND_TEST_OPTIONS=tool_parser:hermes \
$(MAKE) test-extra-backend
## tinygrad mirrors the vllm target (same model, same caps, same parser) so
## the two backends are directly comparable. The LLM path covers Predict,
## streaming and native tool-call extraction. Companion targets below cover
## embeddings, Stable Diffusion and Whisper — run them individually or via
## the `test-extra-backend-tinygrad-all` aggregate.
test-extra-backend-tinygrad: docker-build-tinygrad
BACKEND_IMAGE=local-ai-backend:tinygrad \
BACKEND_TEST_MODEL_NAME=Qwen/Qwen3-0.6B \
BACKEND_TEST_CAPS=health,load,predict,stream,tools \
BACKEND_TEST_OPTIONS=tool_parser:hermes \
$(MAKE) test-extra-backend
## tinygrad — embeddings via LLM last-hidden-state pooling. Reuses the same
## Qwen3-0.6B as the chat target so we don't need a separate BERT vendor;
## the Embedding RPC mean-pools and L2-normalizes the last-layer hidden
## state.
test-extra-backend-tinygrad-embeddings: docker-build-tinygrad
BACKEND_IMAGE=local-ai-backend:tinygrad \
BACKEND_TEST_MODEL_NAME=Qwen/Qwen3-0.6B \
BACKEND_TEST_CAPS=health,load,embeddings \
$(MAKE) test-extra-backend
## tinygrad — Stable Diffusion 1.5. The original CompVis/runwayml repos have
## been gated, so we use the community-maintained mirror at
## stable-diffusion-v1-5/stable-diffusion-v1-5 with the EMA-only pruned
## checkpoint (~4.3GB). Step count is kept low (4) so a CPU-only run finishes
## in a few minutes; bump BACKEND_TEST_IMAGE_STEPS for higher quality.
test-extra-backend-tinygrad-sd: docker-build-tinygrad
BACKEND_IMAGE=local-ai-backend:tinygrad \
BACKEND_TEST_MODEL_NAME=stable-diffusion-v1-5/stable-diffusion-v1-5 \
BACKEND_TEST_CAPS=health,load,image \
$(MAKE) test-extra-backend
## tinygrad — Whisper. Loads OpenAI's tiny.en checkpoint (smallest at ~75MB)
## from the original azure CDN through tinygrad's `fetch` helper, and
## transcribes the canonical jfk.wav fixture from whisper.cpp's CI samples.
## Exercises both AudioTranscription and AudioTranscriptionStream.
test-extra-backend-tinygrad-whisper: docker-build-tinygrad
BACKEND_IMAGE=local-ai-backend:tinygrad \
BACKEND_TEST_MODEL_NAME=openai/whisper-tiny.en \
BACKEND_TEST_AUDIO_URL=https://github.com/ggml-org/whisper.cpp/raw/master/samples/jfk.wav \
BACKEND_TEST_CAPS=health,load,transcription \
$(MAKE) test-extra-backend
test-extra-backend-tinygrad-all: \
test-extra-backend-tinygrad \
test-extra-backend-tinygrad-embeddings \
test-extra-backend-tinygrad-sd \
test-extra-backend-tinygrad-whisper
## insightface — face recognition.
##
## Face fixtures default to the sample images shipped in the
## deepinsight/insightface repository (MIT-licensed). For offline/local
## runs override with BACKEND_TEST_FACE_IMAGE_{1,2,3}_FILE pointing at
## local paths.
FACE_IMAGE_1_URL ?= https://github.com/deepinsight/insightface/raw/master/python-package/insightface/data/images/t1.jpg
FACE_IMAGE_2_URL ?= https://github.com/deepinsight/insightface/raw/master/python-package/insightface/data/images/t1.jpg
FACE_IMAGE_3_URL ?= https://github.com/deepinsight/insightface/raw/master/python-package/insightface/data/images/mask_white.jpg
## Host-side cache for the OpenCV Zoo face ONNX files used by the
## opencv e2e target. The backend image no longer bakes model weights —
## gallery installs bring them via `files:` — but the e2e suite drives
## LoadModel over gRPC directly without going through the gallery. We
## pre-download the ONNX files to a stable host path and pass absolute
## paths in BACKEND_TEST_OPTIONS; `make` skips the downloads when the
## SHA-256 already matches.
INSIGHTFACE_OPENCV_DIR := /tmp/localai-insightface-opencv-cache
INSIGHTFACE_OPENCV_YUNET_URL := https://github.com/opencv/opencv_zoo/raw/main/models/face_detection_yunet/face_detection_yunet_2023mar.onnx
INSIGHTFACE_OPENCV_SFACE_URL := https://github.com/opencv/opencv_zoo/raw/main/models/face_recognition_sface/face_recognition_sface_2021dec.onnx
INSIGHTFACE_OPENCV_YUNET_SHA := 8f2383e4dd3cfbb4553ea8718107fc0423210dc964f9f4280604804ed2552fa4
INSIGHTFACE_OPENCV_SFACE_SHA := 0ba9fbfa01b5270c96627c4ef784da859931e02f04419c829e83484087c34e79
## buffalo_sc (insightface) — pack zip + SHA-256 mirrors the gallery
## entry so the e2e target matches exactly what `local-ai models install
## insightface-buffalo-sc` would have fetched. Smallest insightface pack
## (~16MB) — keeps CI fast while still covering the insightface engine
## code path end-to-end.
INSIGHTFACE_BUFFALO_SC_DIR := /tmp/localai-insightface-buffalo-sc-cache
INSIGHTFACE_BUFFALO_SC_URL := https://github.com/deepinsight/insightface/releases/download/v0.7/buffalo_sc.zip
INSIGHTFACE_BUFFALO_SC_SHA := 57d31b56b6ffa911c8a73cfc1707c73cab76efe7f13b675a05223bf42de47c72
.PHONY: insightface-opencv-models
insightface-opencv-models:
@mkdir -p $(INSIGHTFACE_OPENCV_DIR)
@if [ "$$(sha256sum $(INSIGHTFACE_OPENCV_DIR)/yunet.onnx 2>/dev/null | awk '{print $$1}')" != "$(INSIGHTFACE_OPENCV_YUNET_SHA)" ]; then \
echo "Fetching YuNet..."; \
curl -fsSL -o $(INSIGHTFACE_OPENCV_DIR)/yunet.onnx $(INSIGHTFACE_OPENCV_YUNET_URL); \
echo "$(INSIGHTFACE_OPENCV_YUNET_SHA) $(INSIGHTFACE_OPENCV_DIR)/yunet.onnx" | sha256sum -c; \
fi
@if [ "$$(sha256sum $(INSIGHTFACE_OPENCV_DIR)/sface.onnx 2>/dev/null | awk '{print $$1}')" != "$(INSIGHTFACE_OPENCV_SFACE_SHA)" ]; then \
echo "Fetching SFace..."; \
curl -fsSL -o $(INSIGHTFACE_OPENCV_DIR)/sface.onnx $(INSIGHTFACE_OPENCV_SFACE_URL); \
echo "$(INSIGHTFACE_OPENCV_SFACE_SHA) $(INSIGHTFACE_OPENCV_DIR)/sface.onnx" | sha256sum -c; \
fi
.PHONY: insightface-buffalo-sc-models
insightface-buffalo-sc-models:
@mkdir -p $(INSIGHTFACE_BUFFALO_SC_DIR)
@if [ "$$(sha256sum $(INSIGHTFACE_BUFFALO_SC_DIR)/buffalo_sc.zip 2>/dev/null | awk '{print $$1}')" != "$(INSIGHTFACE_BUFFALO_SC_SHA)" ]; then \
echo "Fetching buffalo_sc..."; \
curl -fsSL -o $(INSIGHTFACE_BUFFALO_SC_DIR)/buffalo_sc.zip $(INSIGHTFACE_BUFFALO_SC_URL); \
echo "$(INSIGHTFACE_BUFFALO_SC_SHA) $(INSIGHTFACE_BUFFALO_SC_DIR)/buffalo_sc.zip" | sha256sum -c; \
rm -f $(INSIGHTFACE_BUFFALO_SC_DIR)/*.onnx; \
fi
@if [ ! -f "$(INSIGHTFACE_BUFFALO_SC_DIR)/det_500m.onnx" ]; then \
echo "Extracting buffalo_sc..."; \
unzip -o -q $(INSIGHTFACE_BUFFALO_SC_DIR)/buffalo_sc.zip -d $(INSIGHTFACE_BUFFALO_SC_DIR); \
fi
## buffalo_sc — smallest insightface pack (SCRFD-500MF detector + MBF
## recognizer, ~16MB). Exercises the insightface engine code path
## (model_zoo-backed inference) without the ~326MB buffalo_l download.
## No age/gender/landmark heads — face_analyze is dropped from caps.
## The pack is pre-fetched on the host and passed as `root:<dir>` since
## the e2e suite drives LoadModel directly without going through
## LocalAI's gallery flow (which is what would normally populate
## ModelPath and in turn the engine's `_model_dir` option).
test-extra-backend-insightface-buffalo-sc: docker-build-insightface insightface-buffalo-sc-models
BACKEND_IMAGE=local-ai-backend:insightface \
BACKEND_TEST_MODEL_NAME=insightface-buffalo-sc \
BACKEND_TEST_OPTIONS=engine:insightface,model_pack:buffalo_sc,root:$(INSIGHTFACE_BUFFALO_SC_DIR) \
BACKEND_TEST_CAPS=health,load,face_detect,face_embed,face_verify \
BACKEND_TEST_FACE_IMAGE_1_URL=$(FACE_IMAGE_1_URL) \
BACKEND_TEST_FACE_IMAGE_2_URL=$(FACE_IMAGE_2_URL) \
BACKEND_TEST_FACE_IMAGE_3_URL=$(FACE_IMAGE_3_URL) \
BACKEND_TEST_VERIFY_DISTANCE_CEILING=0.55 \
$(MAKE) test-extra-backend
## OpenCV Zoo YuNet + SFace — Apache 2.0, commercial-safe. face_analyze
## cap is dropped (SFace has no demographic head). The ONNX files are
## pre-fetched on the host via the insightface-opencv-models target and
## passed as absolute paths, since the e2e suite drives LoadModel
## directly without going through LocalAI's gallery flow.
test-extra-backend-insightface-opencv: docker-build-insightface insightface-opencv-models
BACKEND_IMAGE=local-ai-backend:insightface \
BACKEND_TEST_MODEL_NAME=insightface-opencv \
BACKEND_TEST_OPTIONS=engine:onnx_direct,detector_onnx:$(INSIGHTFACE_OPENCV_DIR)/yunet.onnx,recognizer_onnx:$(INSIGHTFACE_OPENCV_DIR)/sface.onnx \
BACKEND_TEST_CAPS=health,load,face_detect,face_embed,face_verify \
BACKEND_TEST_FACE_IMAGE_1_URL=$(FACE_IMAGE_1_URL) \
BACKEND_TEST_FACE_IMAGE_2_URL=$(FACE_IMAGE_2_URL) \
BACKEND_TEST_FACE_IMAGE_3_URL=$(FACE_IMAGE_3_URL) \
BACKEND_TEST_VERIFY_DISTANCE_CEILING=0.55 \
$(MAKE) test-extra-backend
## Aggregate — runs both face-recognition model configurations so CI
## catches regressions across engines together.
test-extra-backend-insightface-all: \
test-extra-backend-insightface-buffalo-sc \
test-extra-backend-insightface-opencv
## sglang mirrors the vllm setup: HuggingFace model id, same tiny Qwen,
## tool-call extraction via sglang's native qwen parser. CPU builds use
## sglang's upstream pyproject_cpu.toml recipe (see backend/python/sglang/install.sh).
test-extra-backend-sglang: docker-build-sglang
BACKEND_IMAGE=local-ai-backend:sglang \
BACKEND_TEST_MODEL_NAME=Qwen/Qwen2.5-0.5B-Instruct \
BACKEND_TEST_CAPS=health,load,predict,stream,tools \
BACKEND_TEST_OPTIONS=tool_parser:qwen \
$(MAKE) test-extra-backend
## mlx is Apple-Silicon-first — the MLX backend auto-detects the right tool
## parser from the chat template, so no tool_parser: option is needed (it
## would be ignored at runtime). Run this on macOS / arm64 with Metal; the
## Linux/CPU mlx variant is untested in CI.
test-extra-backend-mlx: docker-build-mlx
BACKEND_IMAGE=local-ai-backend:mlx \
BACKEND_TEST_MODEL_NAME=mlx-community/Qwen2.5-0.5B-Instruct-4bit \
BACKEND_TEST_CAPS=health,load,predict,stream,tools \
$(MAKE) test-extra-backend
test-extra-backend-mlx-vlm: docker-build-mlx-vlm
BACKEND_IMAGE=local-ai-backend:mlx-vlm \
BACKEND_TEST_MODEL_NAME=mlx-community/Qwen2.5-0.5B-Instruct-4bit \
BACKEND_TEST_CAPS=health,load,predict,stream,tools \
$(MAKE) test-extra-backend
DOCKER_IMAGE?=local-ai
IMAGE_TYPE?=core
BASE_IMAGE?=ubuntu:24.04
docker:
docker build \
--build-arg BASE_IMAGE=$(BASE_IMAGE) \
--build-arg IMAGE_TYPE=$(IMAGE_TYPE) \
--build-arg GO_TAGS="$(GO_TAGS)" \
--build-arg MAKEFLAGS="$(DOCKER_MAKEFLAGS)" \
--build-arg BUILD_TYPE=$(BUILD_TYPE) \
--build-arg CUDA_MAJOR_VERSION=$(CUDA_MAJOR_VERSION) \
--build-arg CUDA_MINOR_VERSION=$(CUDA_MINOR_VERSION) \
--build-arg UBUNTU_VERSION=$(UBUNTU_VERSION) \
--build-arg UBUNTU_CODENAME=$(UBUNTU_CODENAME) \
-t $(DOCKER_IMAGE) .
docker-cuda12:
docker build \
--build-arg CUDA_MAJOR_VERSION=${CUDA_MAJOR_VERSION} \
--build-arg CUDA_MINOR_VERSION=${CUDA_MINOR_VERSION} \
--build-arg BASE_IMAGE=$(BASE_IMAGE) \
--build-arg IMAGE_TYPE=$(IMAGE_TYPE) \
--build-arg GO_TAGS="$(GO_TAGS)" \
--build-arg MAKEFLAGS="$(DOCKER_MAKEFLAGS)" \
--build-arg BUILD_TYPE=$(BUILD_TYPE) \
--build-arg UBUNTU_VERSION=$(UBUNTU_VERSION) \
--build-arg UBUNTU_CODENAME=$(UBUNTU_CODENAME) \
-t $(DOCKER_IMAGE)-cuda-12 .
docker-image-intel:
docker build \
--build-arg BASE_IMAGE=intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04 \
--build-arg IMAGE_TYPE=$(IMAGE_TYPE) \
--build-arg GO_TAGS="$(GO_TAGS)" \
--build-arg MAKEFLAGS="$(DOCKER_MAKEFLAGS)" \
--build-arg BUILD_TYPE=intel \
--build-arg CUDA_MAJOR_VERSION=$(CUDA_MAJOR_VERSION) \
--build-arg CUDA_MINOR_VERSION=$(CUDA_MINOR_VERSION) \
--build-arg UBUNTU_VERSION=$(UBUNTU_VERSION) \
--build-arg UBUNTU_CODENAME=$(UBUNTU_CODENAME) \
-t $(DOCKER_IMAGE) .
########################################################
## Backends
########################################################
# Pattern rule for standard backends (docker-based)
# This matches all backends that use docker-build-* and docker-save-*
backends/%: docker-build-% docker-save-% build
./local-ai backends install "ocifile://$(abspath ./backend-images/$*.tar)"
# Darwin-specific backends (keep as explicit targets since they have special build logic)
backends/llama-cpp-darwin: build
bash ./scripts/build/llama-cpp-darwin.sh
./local-ai backends install "ocifile://$(abspath ./backend-images/llama-cpp.tar)"
build-darwin-python-backend: build
bash ./scripts/build/python-darwin.sh
build-darwin-go-backend: build
bash ./scripts/build/golang-darwin.sh
backends/mlx:
BACKEND=mlx $(MAKE) build-darwin-python-backend
./local-ai backends install "ocifile://$(abspath ./backend-images/mlx.tar)"
backends/diffuser-darwin:
BACKEND=diffusers $(MAKE) build-darwin-python-backend
./local-ai backends install "ocifile://$(abspath ./backend-images/diffusers.tar)"
backends/mlx-vlm:
BACKEND=mlx-vlm $(MAKE) build-darwin-python-backend
./local-ai backends install "ocifile://$(abspath ./backend-images/mlx-vlm.tar)"
backends/mlx-audio:
BACKEND=mlx-audio $(MAKE) build-darwin-python-backend
./local-ai backends install "ocifile://$(abspath ./backend-images/mlx-audio.tar)"
backends/mlx-distributed:
BACKEND=mlx-distributed $(MAKE) build-darwin-python-backend
./local-ai backends install "ocifile://$(abspath ./backend-images/mlx-distributed.tar)"
backends/stablediffusion-ggml-darwin:
BACKEND=stablediffusion-ggml BUILD_TYPE=metal $(MAKE) build-darwin-go-backend
./local-ai backends install "ocifile://$(abspath ./backend-images/stablediffusion-ggml.tar)"
backend-images:
mkdir -p backend-images
# Backend metadata: BACKEND_NAME | DOCKERFILE_TYPE | BUILD_CONTEXT | PROGRESS_FLAG | NEEDS_BACKEND_ARG
# llama-cpp is special - uses llama-cpp Dockerfile and doesn't need BACKEND arg
BACKEND_LLAMA_CPP = llama-cpp|llama-cpp|.|false|false
# ik-llama-cpp is a fork of llama.cpp with superior CPU performance
BACKEND_IK_LLAMA_CPP = ik-llama-cpp|ik-llama-cpp|.|false|false
# turboquant is a llama.cpp fork with TurboQuant KV-cache quantization.
# Reuses backend/cpp/llama-cpp grpc-server sources via a thin wrapper Makefile.
BACKEND_TURBOQUANT = turboquant|turboquant|.|false|false
# Golang backends
BACKEND_PIPER = piper|golang|.|false|true
BACKEND_LOCAL_STORE = local-store|golang|.|false|true
BACKEND_HUGGINGFACE = huggingface|golang|.|false|true
BACKEND_SILERO_VAD = silero-vad|golang|.|false|true
BACKEND_STABLEDIFFUSION_GGML = stablediffusion-ggml|golang|.|--progress=plain|true
BACKEND_WHISPER = whisper|golang|.|false|true
BACKEND_VOXTRAL = voxtral|golang|.|false|true
BACKEND_ACESTEP_CPP = acestep-cpp|golang|.|false|true
BACKEND_QWEN3_TTS_CPP = qwen3-tts-cpp|golang|.|false|true
BACKEND_OPUS = opus|golang|.|false|true
# Python backends with root context
BACKEND_RERANKERS = rerankers|python|.|false|true
BACKEND_TRANSFORMERS = transformers|python|.|false|true
BACKEND_OUTETTS = outetts|python|.|false|true
BACKEND_FASTER_WHISPER = faster-whisper|python|.|false|true
BACKEND_COQUI = coqui|python|.|false|true
BACKEND_RFDETR = rfdetr|python|.|false|true
BACKEND_INSIGHTFACE = insightface|python|.|false|true
BACKEND_KITTEN_TTS = kitten-tts|python|.|false|true
BACKEND_NEUTTS = neutts|python|.|false|true
BACKEND_KOKORO = kokoro|python|.|false|true
BACKEND_VLLM = vllm|python|.|false|true
BACKEND_VLLM_OMNI = vllm-omni|python|.|false|true
BACKEND_SGLANG = sglang|python|.|false|true
BACKEND_DIFFUSERS = diffusers|python|.|--progress=plain|true
BACKEND_CHATTERBOX = chatterbox|python|.|false|true
BACKEND_VIBEVOICE = vibevoice|python|.|--progress=plain|true
BACKEND_MOONSHINE = moonshine|python|.|false|true
BACKEND_POCKET_TTS = pocket-tts|python|.|false|true
BACKEND_QWEN_TTS = qwen-tts|python|.|false|true
BACKEND_FISH_SPEECH = fish-speech|python|.|false|true
BACKEND_FASTER_QWEN3_TTS = faster-qwen3-tts|python|.|false|true
BACKEND_QWEN_ASR = qwen-asr|python|.|false|true
BACKEND_NEMO = nemo|python|.|false|true
BACKEND_VOXCPM = voxcpm|python|.|false|true
BACKEND_WHISPERX = whisperx|python|.|false|true
BACKEND_ACE_STEP = ace-step|python|.|false|true
BACKEND_MLX = mlx|python|.|false|true
BACKEND_MLX_VLM = mlx-vlm|python|.|false|true
BACKEND_MLX_DISTRIBUTED = mlx-distributed|python|./|false|true
BACKEND_TRL = trl|python|.|false|true
BACKEND_LLAMA_CPP_QUANTIZATION = llama-cpp-quantization|python|.|false|true
BACKEND_TINYGRAD = tinygrad|python|.|false|true
# Rust backends
BACKEND_KOKOROS = kokoros|rust|.|false|true
# C++ backends (Go wrapper with purego)
BACKEND_SAM3_CPP = sam3-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)
define docker-build-backend
docker build $(if $(filter-out false,$(4)),$(4)) \
--build-arg BUILD_TYPE=$(BUILD_TYPE) \
--build-arg BASE_IMAGE=$(BASE_IMAGE) \
--build-arg CUDA_MAJOR_VERSION=$(CUDA_MAJOR_VERSION) \
--build-arg CUDA_MINOR_VERSION=$(CUDA_MINOR_VERSION) \
--build-arg UBUNTU_VERSION=$(UBUNTU_VERSION) \
--build-arg UBUNTU_CODENAME=$(UBUNTU_CODENAME) \
$(if $(FROM_SOURCE),--build-arg FROM_SOURCE=$(FROM_SOURCE)) \
$(if $(filter true,$(5)),--build-arg BACKEND=$(1)) \
-t local-ai-backend:$(1) -f backend/Dockerfile.$(2) $(3)
endef
# Generate docker-build targets from backend definitions
define generate-docker-build-target
docker-build-$(word 1,$(subst |, ,$(1))):
$$(call docker-build-backend,$(word 1,$(subst |, ,$(1))),$(word 2,$(subst |, ,$(1))),$(word 3,$(subst |, ,$(1))),$(word 4,$(subst |, ,$(1))),$(word 5,$(subst |, ,$(1))))
endef
# Generate all docker-build targets
$(eval $(call generate-docker-build-target,$(BACKEND_LLAMA_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_IK_LLAMA_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_TURBOQUANT)))
$(eval $(call generate-docker-build-target,$(BACKEND_PIPER)))
$(eval $(call generate-docker-build-target,$(BACKEND_LOCAL_STORE)))
$(eval $(call generate-docker-build-target,$(BACKEND_HUGGINGFACE)))
$(eval $(call generate-docker-build-target,$(BACKEND_SILERO_VAD)))
$(eval $(call generate-docker-build-target,$(BACKEND_STABLEDIFFUSION_GGML)))
$(eval $(call generate-docker-build-target,$(BACKEND_WHISPER)))
$(eval $(call generate-docker-build-target,$(BACKEND_VOXTRAL)))
$(eval $(call generate-docker-build-target,$(BACKEND_OPUS)))
$(eval $(call generate-docker-build-target,$(BACKEND_RERANKERS)))
$(eval $(call generate-docker-build-target,$(BACKEND_TRANSFORMERS)))
$(eval $(call generate-docker-build-target,$(BACKEND_OUTETTS)))
$(eval $(call generate-docker-build-target,$(BACKEND_FASTER_WHISPER)))
$(eval $(call generate-docker-build-target,$(BACKEND_COQUI)))
$(eval $(call generate-docker-build-target,$(BACKEND_RFDETR)))
$(eval $(call generate-docker-build-target,$(BACKEND_INSIGHTFACE)))
$(eval $(call generate-docker-build-target,$(BACKEND_KITTEN_TTS)))
$(eval $(call generate-docker-build-target,$(BACKEND_NEUTTS)))
$(eval $(call generate-docker-build-target,$(BACKEND_KOKORO)))
$(eval $(call generate-docker-build-target,$(BACKEND_VLLM)))
$(eval $(call generate-docker-build-target,$(BACKEND_VLLM_OMNI)))
$(eval $(call generate-docker-build-target,$(BACKEND_SGLANG)))
$(eval $(call generate-docker-build-target,$(BACKEND_DIFFUSERS)))
$(eval $(call generate-docker-build-target,$(BACKEND_CHATTERBOX)))
$(eval $(call generate-docker-build-target,$(BACKEND_VIBEVOICE)))
$(eval $(call generate-docker-build-target,$(BACKEND_MOONSHINE)))
$(eval $(call generate-docker-build-target,$(BACKEND_POCKET_TTS)))
$(eval $(call generate-docker-build-target,$(BACKEND_QWEN_TTS)))
$(eval $(call generate-docker-build-target,$(BACKEND_FISH_SPEECH)))
$(eval $(call generate-docker-build-target,$(BACKEND_FASTER_QWEN3_TTS)))
$(eval $(call generate-docker-build-target,$(BACKEND_QWEN_ASR)))
$(eval $(call generate-docker-build-target,$(BACKEND_NEMO)))
$(eval $(call generate-docker-build-target,$(BACKEND_VOXCPM)))
$(eval $(call generate-docker-build-target,$(BACKEND_WHISPERX)))
$(eval $(call generate-docker-build-target,$(BACKEND_ACE_STEP)))
$(eval $(call generate-docker-build-target,$(BACKEND_ACESTEP_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_QWEN3_TTS_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_MLX)))
$(eval $(call generate-docker-build-target,$(BACKEND_MLX_VLM)))
$(eval $(call generate-docker-build-target,$(BACKEND_MLX_DISTRIBUTED)))
$(eval $(call generate-docker-build-target,$(BACKEND_TRL)))
$(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)))
# 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-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-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-insightface
########################################################
### Mock Backend for E2E Tests
########################################################
build-mock-backend: protogen-go
$(GOCMD) build -o tests/e2e/mock-backend/mock-backend ./tests/e2e/mock-backend
clean-mock-backend:
rm -f tests/e2e/mock-backend/mock-backend
########################################################
### UI E2E Test Server
########################################################
build-ui-test-server: build-mock-backend react-ui protogen-go
$(GOCMD) build -o tests/e2e-ui/ui-test-server ./tests/e2e-ui
test-ui-e2e: build-ui-test-server
cd core/http/react-ui && npm install && npx playwright install --with-deps chromium && npx playwright test
test-ui-e2e-docker:
docker build -t localai-ui-e2e -f tests/e2e-ui/Dockerfile .
docker run --rm localai-ui-e2e
clean-ui-test-server:
rm -f tests/e2e-ui/ui-test-server
########################################################
### END Backends
########################################################
.PHONY: swagger
swagger:
swag init -g core/http/app.go --output swagger
# DEPRECATED: gen-assets is for the legacy Alpine.js UI. Remove when legacy UI is removed.
.PHONY: gen-assets
gen-assets:
$(GOCMD) run core/dependencies_manager/manager.go webui_static.yaml core/http/static/assets
## Documentation
docs/layouts/_default:
mkdir -p docs/layouts/_default
docs/static/gallery.html: docs/layouts/_default
$(GOCMD) run ./.github/ci/modelslist.go ./gallery/index.yaml > docs/static/gallery.html
docs/public: docs/layouts/_default docs/static/gallery.html
cd docs && hugo --minify
docs-clean:
rm -rf docs/public
rm -rf docs/static/gallery.html
.PHONY: docs
docs: docs/static/gallery.html
cd docs && hugo serve
########################################################
## Platform-specific builds
########################################################
## fyne cross-platform build
build-launcher-darwin: build-launcher
go run github.com/tiagomelo/macos-dmg-creator/cmd/createdmg@latest \
--appName "LocalAI" \
--appBinaryPath "$(LAUNCHER_BINARY_NAME)" \
--bundleIdentifier "com.localai.launcher" \
--iconPath "core/http/static/logo.png" \
--outputDir "dist/"
build-launcher-linux:
cd cmd/launcher && go run fyne.io/tools/cmd/fyne@latest package -os linux -icon ../../core/http/static/logo.png --executable $(LAUNCHER_BINARY_NAME)-linux && mv launcher.tar.xz ../../$(LAUNCHER_BINARY_NAME)-linux.tar.xz
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