mirror of
https://github.com/mudler/LocalAI.git
synced 2026-05-17 04:56:52 -04:00
0245b33eab
* feat(liquid-audio): add LFM2.5-Audio any-to-any backend + realtime_audio usecase
Wires LiquidAI's LFM2.5-Audio-1.5B as a self-contained Realtime API model:
single engine handles VAD, transcription, LLM, and TTS in one bidirectional
stream — drop-in alternative to a VAD+STT+LLM+TTS pipeline.
Backend
- backend/python/liquid-audio/ — new Python gRPC backend wrapping the
`liquid-audio` package. Modes: chat / asr / tts / s2s, voice presets,
Load/Predict/PredictStream/AudioTranscription/TTS/VAD/AudioToAudioStream/
Free and StartFineTune/FineTuneProgress/StopFineTune. Runtime monkey-patch
on `liquid_audio.utils.snapshot_download` so absolute local paths from
LocalAI's gallery resolve without a HF round-trip. soundfile in place of
torchaudio.load/save (torchcodec drags NVIDIA NPP we don't bundle).
- backend/backend.proto + pkg/grpc/{backend,client,server,base,embed,
interface}.go — new AudioToAudioStream RPC mirroring AudioTransformStream
(config/frame/control oneof in; typed event+pcm+meta out).
- core/services/nodes/{health_mock,inflight}_test.go — add stubs for the
new RPC to the test fakes.
Config + capabilities
- core/config/backend_capabilities.go — UsecaseRealtimeAudio, MethodAudio
ToAudioStream, UsecaseInfoMap entry, liquid-audio BackendCapability row.
- core/config/model_config.go — FLAG_REALTIME_AUDIO bitmask, ModalityGroups
membership in both speech-input and audio-output groups so a lone flag
still reads as multimodal, GetAllModelConfigUsecases entry, GuessUsecases
branch.
Realtime endpoint
- core/http/endpoints/openai/realtime.go — extract prepareRealtimeConfig()
so the gate is unit-testable; accept realtime_audio models and self-fill
empty pipeline slots with the model's own name (user-pinned slots win).
- core/http/endpoints/openai/realtime_gate_test.go — six specs covering nil
cfg, empty pipeline, legacy pipeline, self-contained realtime_audio,
user-pinned VAD slot, and partial legacy pipeline.
UI + endpoints
- core/http/routes/ui.go — /api/pipeline-models accepts either a legacy
VAD+STT+LLM+TTS pipeline or a realtime_audio model; surfaces a
self_contained flag so the Talk page can collapse the four cards.
- core/http/routes/ui_api.go — realtime_audio in usecaseFilters.
- core/http/routes/ui_pipeline_models_test.go — covers both code paths.
- core/http/react-ui/src/pages/Talk.jsx — self-contained badge instead of
the four-slot grid; rename Edit Pipeline → Edit Model Config; less
pipeline-specific wording.
- core/http/react-ui/src/pages/Models.jsx + locales/en/models.json — new
realtime_audio filter button + i18n.
- core/http/react-ui/src/utils/capabilities.js — CAP_REALTIME_AUDIO.
- core/http/react-ui/src/pages/FineTune.jsx — voice + validation-dataset
fields, surfaced when backend === liquid-audio, plumbed via
extra_options on submit/export/import.
Gallery + importer
- gallery/liquid-audio.yaml — config template with known_usecases:
[realtime_audio, chat, tts, transcript, vad].
- gallery/index.yaml — four model entries (realtime/chat/asr/tts) keyed by
mode option. Fixed pre-existing `transcribe` typo on the asr entry
(loader silently dropped the unknown string → entry never surfaced as a
transcript model).
- gallery/lfm.yaml — function block for the LFM2 Pythonic tool-call format
`<|tool_call_start|>[name(k="v")]<|tool_call_end|>` matching
common_chat_params_init_lfm2 in vendored llama.cpp.
- core/gallery/importers/{liquid-audio,liquid-audio_test}.go — detector
matches LFM2-Audio HF repos (excludes -gguf mirrors); mode/voice
preferences plumbed through to options.
- core/gallery/importers/importers.go — register LiquidAudioImporter
before LlamaCPPImporter.
- pkg/functions/parse_lfm2_test.go — seven specs for the response/argument
regex pair on the LFM2 pythonic format.
Build matrix
- .github/backend-matrix.yml — seven liquid-audio targets (cuda12, cuda13,
l4t-cuda-13, hipblas, intel, cpu amd64, cpu arm64). Jetpack r36 cuda-12
is skipped (Ubuntu 22.04 / Python 3.10 incompatible with liquid-audio's
3.12 floor).
- backend/index.yaml — anchor + 13 image entries.
- Makefile — .NOTPARALLEL, prepare-test-extra, test-extra,
docker-build-liquid-audio.
Docs
- .agents/plans/liquid-audio-integration.md — phased plan; PR-D (real
any-to-any wiring via AudioToAudioStream), PR-E (mid-audio tool-call
detector), PR-G (GGUF entries once upstream llama.cpp PR #18641 lands)
remain.
- .agents/api-endpoints-and-auth.md — expand the capability-surface
checklist with every place a new FLAG_* needs to be registered.
Assisted-by: claude-code:claude-opus-4-7-1m [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(realtime): function calling + history cap for any-to-any models
Three pieces, all on the realtime_audio path that just landed:
1. liquid-audio backend (backend/python/liquid-audio/backend.py):
- _build_chat_state grows a `tools_prelude` arg.
- new _render_tools_prelude parses request.Tools (the OpenAI Chat
Completions function array realtime.go already serialises) and
emits an LFM2 `<|tool_list_start|>…<|tool_list_end|>` system turn
ahead of the user history. Mirrors gallery/lfm.yaml's `function:`
template so the model sees the same prompt shape whether served
via llama-cpp or here. Without this the backend silently dropped
tools — function calling was wired end-to-end on the Go side but
the model never saw a tool list.
2. Realtime history cap (core/http/endpoints/openai/realtime.go):
- Session grows MaxHistoryItems int; default picked by new
defaultMaxHistoryItems(cfg) — 6 for realtime_audio models (LFM2.5
1.5B degrades quickly past a handful of turns), 0/unlimited for
legacy pipelines composing larger LLMs.
- triggerResponse runs conv.Items through trimRealtimeItems before
building conversationHistory. Helper walks the cut left if it
would orphan a function_call_output, so tool result + call pairs
stay intact.
- realtime_gate_test.go: specs for defaultMaxHistoryItems and
trimRealtimeItems (zero cap, under cap, over cap, tool-call pair
preservation).
3. Talk page (core/http/react-ui/src/pages/Talk.jsx):
- Reuses the chat page's MCP plumbing — useMCPClient hook,
ClientMCPDropdown component, same auto-connect/disconnect effect
pattern. No bespoke tool registry, no new REST endpoints; tools
come from whichever MCP servers the user toggles on, exactly as
on the chat page.
- sendSessionUpdate now passes session.tools=getToolsForLLM(); the
update re-fires when the active server set changes mid-session.
- New response.function_call_arguments.done handler executes via
the hook's executeTool (which round-trips through the MCP client
SDK), then replies with conversation.item.create
{type:function_call_output} + response.create so the model
completes its turn with the tool output. Mirrors chat's
client-side agentic loop, translated to the realtime wire shape.
UI changes require a LocalAI image rebuild (Dockerfile:308-313 bakes
react-ui/dist into the runtime image). Backend.py changes can be
swapped live in /backends/<id>/backend.py + /backend/shutdown.
Assisted-by: claude-code:claude-opus-4-7-1m [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(realtime): LocalAI Assistant ("Manage Mode") for the Talk page
Mirrors the chat-page metadata.localai_assistant flow so users can ask the
realtime model what's loaded / installed / configured. Tools are run
server-side via the same in-process MCP holder that powers the chat
modality — no transport switch, no proxy, no new wire protocol.
Wire:
- core/http/endpoints/openai/realtime.go:
- RealtimeSessionOptions{LocalAIAssistant,IsAdmin}; isCurrentUserAdmin
helper mirrors chat.go's requireAssistantAccess (no-op when auth
disabled, else requires auth.RoleAdmin).
- Session grows AssistantExecutor mcpTools.ToolExecutor.
- runRealtimeSession, when opts.LocalAIAssistant is set: gate on admin,
fail closed if DisableLocalAIAssistant or the holder has no tools,
DiscoverTools and inject into session.Tools, prepend
holder.SystemPrompt() to instructions.
- Tool-call dispatch loop: when AssistantExecutor.IsTool(name), run
ExecuteTool inproc, append a FunctionCallOutput to conv.Items, skip
the function_call_arguments client emit (the client can't execute
these — it doesn't know about them). After the loop, if any
assistant tool ran, trigger another response so the model speaks the
result. Mirrors chat's agentic loop, driven server-side rather than
via client round-trip.
- core/http/endpoints/openai/realtime_webrtc.go: RealtimeCallRequest
gains `localai_assistant` (JSON omitempty). Handshake calls
isCurrentUserAdmin and builds RealtimeSessionOptions.
- core/http/react-ui/src/pages/Talk.jsx: admin-only "Manage Mode"
checkbox under the Tools dropdown; passes localai_assistant: true to
realtimeApi.call's body, captured in the connect callback's deps.
Mirroring chat's pattern means the in-process MCP tools surface "just
works" for the Talk page without exposing a Streamable-HTTP MCP endpoint
(which was the alternative). Clients with their own MCP servers can
still use the existing ClientMCPDropdown path in parallel; the realtime
handler distinguishes them by AssistantExecutor.IsTool() at dispatch
time.
Assisted-by: claude-code:claude-opus-4-7-1m [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(realtime): render Manage Mode tool calls in the Talk transcript
Previously the realtime endpoint only emitted response.output_item.added
for the FunctionCall item, and Talk.jsx's switch ignored the event — so
server-side tool runs were invisible in the UI. The model would speak
the result but the user had no way to see what tool was actually
called.
realtime.go: after executing an assistant tool inproc, emit a second
output_item.added/.done pair for the FunctionCallOutput item. Mirrors
the way the chat page displays tool_call + tool_result blocks.
Talk.jsx: handle both response.output_item.added and .done. Render
FunctionCall (with arguments) and FunctionCallOutput (pretty-printed
JSON when possible) as two transcript entries — `tool_call` with the
wrench icon, `tool_result` with the clipboard icon, both in mono-space
secondary-colour. Resets streamingRef after the result so the next
assistant text delta starts a fresh transcript entry instead of
appending to the previous turn.
Assisted-by: claude-code:claude-opus-4-7-1m [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* refactor(realtime): bound the Manage Mode tool-loop + preserve assistant tools
Fallout from a review pass on the Manage Mode patches:
- Bound the server-side agentic loop. triggerResponse used to recurse on
executedAssistantTool with no cap — a model that kept calling tools
would blow the goroutine stack. New maxAssistantToolTurns = 10 (mirrors
useChat.js's maxToolTurns). Public triggerResponse is now a thin shim
over triggerResponseAtTurn(toolTurn int); recursion increments the
counter and stops at the cap with an xlog.Warn.
- Preserve Manage Mode tools across client session.update. The handler
used to blindly overwrite session.Tools, so toggling a client MCP
server mid-session silently wiped the in-process admin tools. Session
now caches the original AssistantTools slice at session creation and
the session.update handler merges them back in (client names win on
collision — the client is explicit).
- strconv.ParseBool for the localai_assistant query param instead of
hand-rolled "1" || "true". Mirrors LocalAIAssistantFromMetadata.
- Talk.jsx: render both tool_call and tool_result on
response.output_item.done instead of splitting them across .added and
.done. The server's event pairing (added → done) stays correct; the
UI just doesn't need to inspect both phases of the same item. One
switch case instead of two, no behavioural change.
Out of scope (noted for follow-ups): extract a shared assistant-tools
helper between chat.go and realtime.go (duplication is small enough
that two parallel implementations stay readable for now), and an i18n
key for the Manage Mode helper text (Talk.jsx doesn't use i18n
anywhere else yet).
Assisted-by: claude-code:claude-opus-4-7-1m [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* ci(test-extra): wire liquid-audio backend smoke test
The backend ships test.py + a `make test` target and is listed in
backend-matrix.yml, so scripts/changed-backends.js already writes a
`liquid-audio=true|false` output when files under backend/python/liquid-audio/
change. The workflow just wasn't reading it.
- Expose the `liquid-audio` output on the detect-changes job
- Add a tests-liquid-audio job that runs `make` + `make test` in
backend/python/liquid-audio, gated on the per-backend detect flag
The smoke covers Health() and LoadModel(mode:finetune); fine-tune mode
short-circuits before any HuggingFace download (backend.py:192), so the
job needs neither weights nor a GPU. The full-inference path remains
gated on LIQUID_AUDIO_MODEL_ID, which CI doesn't set.
The four new Go test files (core/gallery/importers/liquid-audio_test.go,
core/http/endpoints/openai/realtime_gate_test.go,
core/http/routes/ui_pipeline_models_test.go, pkg/functions/parse_lfm2_test.go)
are already picked up by the existing test.yml workflow via `make test` →
`ginkgo -r ./pkg/... ./core/...`; their packages all carry RunSpecs entries.
Assisted-by: Claude:claude-opus-4-7
Signed-off-by: Richard Palethorpe <io@richiejp.com>
---------
Signed-off-by: Richard Palethorpe <io@richiejp.com>
1279 lines
61 KiB
Makefile
1279 lines
61 KiB
Makefile
# Disable parallel execution for backend builds
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.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
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GOCMD=go
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GOTEST=$(GOCMD) test
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GOVET=$(GOCMD) vet
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BINARY_NAME=local-ai
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LAUNCHER_BINARY_NAME=local-ai-launcher
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UBUNTU_VERSION?=2404
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UBUNTU_CODENAME?=noble
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# Optional Ubuntu apt mirror overrides forwarded to docker builds.
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# Empty = use upstream archive.ubuntu.com / security.ubuntu.com / ports.ubuntu.com.
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# Set e.g. APT_MIRROR=http://azure.archive.ubuntu.com to route apt traffic
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# during outages of the default Ubuntu pool.
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APT_MIRROR?=
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APT_PORTS_MIRROR?=
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GORELEASER?=
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export BUILD_TYPE?=
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export CUDA_MAJOR_VERSION?=13
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export CUDA_MINOR_VERSION?=0
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GO_TAGS?=
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BUILD_ID?=
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NATIVE?=false
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TEST_DIR=/tmp/test
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TEST_FLAKES?=5
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RANDOM := $(shell bash -c 'echo $$RANDOM')
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VERSION?=$(shell git describe --always --tags || echo "dev" )
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# go tool nm ./local-ai | grep Commit
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LD_FLAGS?=-s -w
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override LD_FLAGS += -X "github.com/mudler/LocalAI/internal.Version=$(VERSION)"
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override LD_FLAGS += -X "github.com/mudler/LocalAI/internal.Commit=$(shell git rev-parse HEAD)"
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OPTIONAL_TARGETS?=
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export OS := $(shell uname -s)
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ARCH := $(shell uname -m)
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GREEN := $(shell tput -Txterm setaf 2)
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YELLOW := $(shell tput -Txterm setaf 3)
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WHITE := $(shell tput -Txterm setaf 7)
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CYAN := $(shell tput -Txterm setaf 6)
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RESET := $(shell tput -Txterm sgr0)
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# Default Docker bridge IP
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E2E_BRIDGE_IP?=172.17.0.1
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ifndef UNAME_S
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UNAME_S := $(shell uname -s)
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endif
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ifeq ($(OS),Darwin)
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ifeq ($(OSX_SIGNING_IDENTITY),)
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OSX_SIGNING_IDENTITY := $(shell security find-identity -v -p codesigning | grep '"' | head -n 1 | sed -E 's/.*"(.*)"/\1/')
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endif
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endif
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# check if goreleaser exists
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ifeq (, $(shell which goreleaser))
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GORELEASER=curl -sfL https://goreleaser.com/static/run | bash -s --
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else
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GORELEASER=$(shell which goreleaser)
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endif
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TEST_PATHS?=./api/... ./pkg/... ./core/...
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.PHONY: all test build vendor lint lint-all
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all: help
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## GENERIC
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rebuild: ## Rebuilds the project
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$(GOCMD) clean -cache
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$(MAKE) build
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clean: ## Remove build related file
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$(GOCMD) clean -cache
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rm -f prepare
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rm -rf $(BINARY_NAME)
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rm -rf release/
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$(MAKE) protogen-clean
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rmdir pkg/grpc/proto || true
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clean-tests:
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rm -rf test-models
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rm -rf test-dir
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rm -f tests/e2e/mock-backend/mock-backend
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## Install Go tools
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install-go-tools:
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go install google.golang.org/grpc/cmd/protoc-gen-go-grpc@1958fcbe2ca8bd93af633f11e97d44e567e945af
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go install google.golang.org/protobuf/cmd/protoc-gen-go@v1.34.2
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## React UI:
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react-ui:
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ifneq ($(wildcard core/http/react-ui/dist),)
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@echo "react-ui dist already exists, skipping build"
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else
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cd core/http/react-ui && npm install && npm run build
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endif
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react-ui-docker:
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docker run --entrypoint /bin/bash -v $(CURDIR):/app:z oven/bun:1 \
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-c "cd /app/core/http/react-ui && bun install && bun run build"
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core/http/react-ui/dist: react-ui
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## Build:
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build: protogen-go generate install-go-tools core/http/react-ui/dist ## Build the project
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$(info ${GREEN}I local-ai build info:${RESET})
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$(info ${GREEN}I BUILD_TYPE: ${YELLOW}$(BUILD_TYPE)${RESET})
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$(info ${GREEN}I GO_TAGS: ${YELLOW}$(GO_TAGS)${RESET})
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$(info ${GREEN}I LD_FLAGS: ${YELLOW}$(LD_FLAGS)${RESET})
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$(info ${GREEN}I UPX: ${YELLOW}$(UPX)${RESET})
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rm -rf $(BINARY_NAME) || true
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CGO_LDFLAGS="$(CGO_LDFLAGS)" $(GOCMD) build -ldflags "$(LD_FLAGS)" -tags "$(GO_TAGS)" -o $(BINARY_NAME) ./cmd/local-ai
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build-launcher: ## Build the launcher application
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$(info ${GREEN}I local-ai launcher build info:${RESET})
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$(info ${GREEN}I BUILD_TYPE: ${YELLOW}$(BUILD_TYPE)${RESET})
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$(info ${GREEN}I GO_TAGS: ${YELLOW}$(GO_TAGS)${RESET})
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$(info ${GREEN}I LD_FLAGS: ${YELLOW}$(LD_FLAGS)${RESET})
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rm -rf $(LAUNCHER_BINARY_NAME) || true
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CGO_LDFLAGS="$(CGO_LDFLAGS)" $(GOCMD) build -ldflags "$(LD_FLAGS)" -tags "$(GO_TAGS)" -o $(LAUNCHER_BINARY_NAME) ./cmd/launcher
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build-all: build build-launcher ## Build both server and launcher
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build-dev: ## Run LocalAI in dev mode with live reload
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@command -v air >/dev/null 2>&1 || go install github.com/air-verse/air@latest
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air -c .air.toml
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dev-dist:
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$(GORELEASER) build --snapshot --clean
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dist:
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$(GORELEASER) build --clean
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osx-signed: build
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codesign --deep --force --sign "$(OSX_SIGNING_IDENTITY)" --entitlements "./Entitlements.plist" "./$(BINARY_NAME)"
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## Run
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run: ## run local-ai
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CGO_LDFLAGS="$(CGO_LDFLAGS)" $(GOCMD) run ./
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prepare-test: protogen-go build-mock-backend
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########################################################
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## Tests
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########################################################
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## Test targets
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## After the test-suite reorg (see plans/test-reorg) the default `make test`
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## no longer downloads multi-GB GGUF/whisper fixtures or builds llama-cpp /
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## transformers / piper / whisper / stablediffusion-ggml. core/http/app_test.go
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## now drives the mock-backend binary built by build-mock-backend; real-backend
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## inference moved into tests/e2e-backends/ (per-backend, path-filtered) and
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## tests/e2e-aio/ (nightly).
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test: prepare-test
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@echo 'Running tests'
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export GO_TAGS="debug"
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OPUS_SHIM_LIBRARY=$(abspath ./pkg/opus/shim/libopusshim.so) \
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$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --flake-attempts $(TEST_FLAKES) --fail-fast -v -r $(TEST_PATHS)
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########################################################
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## Lint
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########################################################
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## Runs golangci-lint with config from .golangci.yml. Includes the standard
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## linter set plus forbidigo, which enforces the Ginkgo/Gomega-only test
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## convention documented in .agents/coding-style.md.
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##
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## LINT_EXCLUDE_DIRS_RE matches directories whose Go packages can't typecheck
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## without C/C++ headers we don't install in the lint runner (cgo wrappers
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## around llama.cpp, piper/spdlog, silero-vad/onnxruntime, and Fyne/OpenGL for
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## the launcher). Their compile-time correctness is enforced by their own
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## build pipelines. Keep this as a deny list — `go list ./...` discovers
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## everything else automatically, so new packages are scanned by default.
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LINT_EXCLUDE_DIRS_RE=/(backend/go/(piper|silero-vad|llm)|cmd/launcher)(/|$$)
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lint:
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@command -v golangci-lint >/dev/null 2>&1 || { \
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echo 'golangci-lint not installed. Install: go install github.com/golangci/golangci-lint/v2/cmd/golangci-lint@latest'; \
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exit 1; \
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}
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golangci-lint run $$(go list -e -f '{{.Dir}}' ./... | grep -vE '$(LINT_EXCLUDE_DIRS_RE)')
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## Like `lint` but reports every issue, including the pre-existing baseline
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## that `lint` ignores via .golangci.yml's new-from-merge-base. Use this to
|
|
## see what's available to clean up.
|
|
lint-all:
|
|
@command -v golangci-lint >/dev/null 2>&1 || { \
|
|
echo 'golangci-lint not installed. Install: go install github.com/golangci/golangci-lint/v2/cmd/golangci-lint@latest'; \
|
|
exit 1; \
|
|
}
|
|
golangci-lint run --new=false --new-from-merge-base= --new-from-rev= $$(go list -e -f '{{.Dir}}' ./... | grep -vE '$(LINT_EXCLUDE_DIRS_RE)')
|
|
|
|
########################################################
|
|
## E2E AIO tests (uses standard image with pre-configured models)
|
|
########################################################
|
|
|
|
docker-build-e2e:
|
|
docker build \
|
|
--build-arg MAKEFLAGS="--jobs=5 --output-sync=target" \
|
|
--build-arg BASE_IMAGE=$(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 APT_MIRROR=$(APT_MIRROR) \
|
|
--build-arg APT_PORTS_MIRROR=$(APT_PORTS_MIRROR) \
|
|
--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 $(TEST_FLAKES) -v -r ./tests/e2e-aio
|
|
|
|
# vLLM multi-node DP smoke (CPU). Builds local-ai:tests and the
|
|
# cpu-vllm backend from the current working tree, then drives a
|
|
# head + headless follower via testcontainers-go and asserts a chat
|
|
# completion. BuildKit caches both images, so re-runs only rebuild
|
|
# what changed. The test lives under tests/e2e/distributed and is
|
|
# selected by the VLLMMultinode label so it doesn't run alongside
|
|
# the other distributed-suite tests by default.
|
|
test-e2e-vllm-multinode: docker-build-e2e extract-backend-vllm protogen-go
|
|
@echo 'Running e2e vLLM multi-node DP test'
|
|
LOCALAI_IMAGE=local-ai \
|
|
LOCALAI_IMAGE_TAG=tests \
|
|
LOCALAI_VLLM_BACKEND_DIR=$(abspath ./local-backends/vllm) \
|
|
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --label-filter='VLLMMultinode' -v -r ./tests/e2e/distributed
|
|
|
|
########################################################
|
|
## E2E tests
|
|
########################################################
|
|
|
|
prepare-e2e:
|
|
docker build \
|
|
--build-arg IMAGE_TYPE=core \
|
|
--build-arg BUILD_TYPE=$(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=$(UBUNTU_CODENAME) \
|
|
--build-arg APT_MIRROR=$(APT_MIRROR) \
|
|
--build-arg APT_PORTS_MIRROR=$(APT_PORTS_MIRROR) \
|
|
--build-arg GO_TAGS="$(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
|
|
########################################################
|
|
|
|
## Storage / vector-store integration. Requires the local-store backend to
|
|
## be available — we build it on demand and pass its location via
|
|
## BACKENDS_PATH (the model loader looks there for the gRPC binary).
|
|
test-stores: backends/local-store
|
|
BACKENDS_PATH=$(abspath ./)/backends \
|
|
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --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 APT_MIRROR=$(APT_MIRROR) \
|
|
--build-arg APT_PORTS_MIRROR=$(APT_PORTS_MIRROR) \
|
|
--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
|
|
|
|
# Container-based real-model realtime testing. Build env vars / pipeline
|
|
# definition kept here so test-realtime-models-docker can drive a fully wired
|
|
# pipeline (VAD + STT + LLM + TTS) from inside a containerised runner.
|
|
REALTIME_VAD?=silero-vad-ggml
|
|
REALTIME_STT?=whisper-1
|
|
REALTIME_LLM?=qwen3-0.6b
|
|
REALTIME_TTS?=tts-1
|
|
|
|
# --- 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 --entrypoint=/run.sh 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 $(CUDA_MINOR_VERSION),0) \
|
|
--build-arg APT_MIRROR=$(APT_MIRROR) \
|
|
--build-arg APT_PORTS_MIRROR=$(APT_PORTS_MIRROR) \
|
|
-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
|
|
# install-go-tools writes protoc-gen-go and protoc-gen-go-grpc into
|
|
# $(shell go env GOPATH)/bin, which isn't on every dev's PATH. protoc
|
|
# resolves its code-gen plugins via PATH, so without this prefix the
|
|
# generate step fails with "protoc-gen-go: program not found". Prepend
|
|
# GOPATH/bin so the freshly-installed plugins win without requiring a
|
|
# shell-profile change.
|
|
PATH="$$(go env GOPATH)/bin:$$PATH" ./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/liquid-audio
|
|
$(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/python/speaker-recognition
|
|
$(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/liquid-audio 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/python/speaker-recognition 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 \
|
|
BACKEND_TEST_CAPS=health,load,predict,stream,logprobs,logit_bias \
|
|
$(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 \
|
|
BACKEND_TEST_CTX_SIZE=2048 \
|
|
$(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
|
|
|
|
## vllm multi-node data-parallel smoke test. Runs LocalAI head + a
|
|
## `local-ai p2p-worker vllm` follower in docker compose against
|
|
## Qwen2.5-0.5B with data_parallel_size=2. Requires 2 NVIDIA GPUs and
|
|
## nvidia-container-runtime on the host — vLLM v1's DP coordinator is
|
|
## not viable on CPU so this cannot run in CI without GPU.
|
|
test-extra-backend-vllm-multinode:
|
|
./tests/e2e/vllm-multinode/smoke.sh
|
|
|
|
## 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
|
|
## Known spoof fixture used by the face_antispoof e2e cap. This is
|
|
## upstream's own `image_F2.jpg` (Silent-Face repo, via yakhyo mirror)
|
|
## — verified to classify as is_real=false with score < 0.05 on the
|
|
## MiniFASNetV2 + MiniFASNetV1SE ensemble.
|
|
FACE_SPOOF_IMAGE_URL ?= https://github.com/yakhyo/face-anti-spoofing/raw/main/assets/image_F2.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
|
|
|
|
## Silent-Face antispoofing (MiniFASNetV2 + MiniFASNetV1SE) — shared
|
|
## between the buffalo_sc and opencv e2e targets. Both ONNX files are
|
|
## ~1.7MB, Apache 2.0. URLs + SHAs mirror the gallery entries.
|
|
INSIGHTFACE_ANTISPOOF_DIR := /tmp/localai-insightface-antispoof-cache
|
|
INSIGHTFACE_ANTISPOOF_V2_URL := https://github.com/yakhyo/face-anti-spoofing/releases/download/weights/MiniFASNetV2.onnx
|
|
INSIGHTFACE_ANTISPOOF_V2_SHA := b32929adc2d9c34b9486f8c4c7bc97c1b69bc0ea9befefc380e4faae4e463907
|
|
INSIGHTFACE_ANTISPOOF_V1SE_URL := https://github.com/yakhyo/face-anti-spoofing/releases/download/weights/MiniFASNetV1SE.onnx
|
|
INSIGHTFACE_ANTISPOOF_V1SE_SHA := ebab7f90c7833fbccd46d3a555410e78d969db5438e169b6524be444862b3676
|
|
|
|
.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 $$1}')" != "$(INSIGHTFACE_OPENCV_SFACE_SHA)" ]; then \
|
|
echo "Fetching SFace..."; \
|
|
curl -fsSL -o $(INSIGHTFACE_OPENCV_DIR)/sface.onnx $(INSIGHTFACE_OPENCV_SFACE_URL); \
|
|
echo "$(INSIGHTFACE_OPENCV_SFACE_SHA) $(INSIGHTFACE_OPENCV_DIR)/sface.onnx" | sha256sum -c; \
|
|
fi
|
|
|
|
.PHONY: insightface-antispoof-models
|
|
insightface-antispoof-models:
|
|
@mkdir -p $(INSIGHTFACE_ANTISPOOF_DIR)
|
|
@if [ "$$(sha256sum $(INSIGHTFACE_ANTISPOOF_DIR)/MiniFASNetV2.onnx 2>/dev/null | awk '{print $$1}')" != "$(INSIGHTFACE_ANTISPOOF_V2_SHA)" ]; then \
|
|
echo "Fetching MiniFASNetV2..."; \
|
|
curl -fsSL -o $(INSIGHTFACE_ANTISPOOF_DIR)/MiniFASNetV2.onnx $(INSIGHTFACE_ANTISPOOF_V2_URL); \
|
|
echo "$(INSIGHTFACE_ANTISPOOF_V2_SHA) $(INSIGHTFACE_ANTISPOOF_DIR)/MiniFASNetV2.onnx" | sha256sum -c; \
|
|
fi
|
|
@if [ "$$(sha256sum $(INSIGHTFACE_ANTISPOOF_DIR)/MiniFASNetV1SE.onnx 2>/dev/null | awk '{print $$1}')" != "$(INSIGHTFACE_ANTISPOOF_V1SE_SHA)" ]; then \
|
|
echo "Fetching MiniFASNetV1SE..."; \
|
|
curl -fsSL -o $(INSIGHTFACE_ANTISPOOF_DIR)/MiniFASNetV1SE.onnx $(INSIGHTFACE_ANTISPOOF_V1SE_URL); \
|
|
echo "$(INSIGHTFACE_ANTISPOOF_V1SE_SHA) $(INSIGHTFACE_ANTISPOOF_DIR)/MiniFASNetV1SE.onnx" | sha256sum -c; \
|
|
fi
|
|
|
|
.PHONY: insightface-buffalo-sc-models
|
|
insightface-buffalo-sc-models:
|
|
@mkdir -p $(INSIGHTFACE_BUFFALO_SC_DIR)
|
|
@if [ "$$(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 insightface-antispoof-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),antispoof_v2_onnx:$(INSIGHTFACE_ANTISPOOF_DIR)/MiniFASNetV2.onnx,antispoof_v1se_onnx:$(INSIGHTFACE_ANTISPOOF_DIR)/MiniFASNetV1SE.onnx \
|
|
BACKEND_TEST_CAPS=health,load,face_detect,face_embed,face_verify,face_antispoof \
|
|
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_FACE_SPOOF_IMAGE_URL=$(FACE_SPOOF_IMAGE_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 insightface-antispoof-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,antispoof_v2_onnx:$(INSIGHTFACE_ANTISPOOF_DIR)/MiniFASNetV2.onnx,antispoof_v1se_onnx:$(INSIGHTFACE_ANTISPOOF_DIR)/MiniFASNetV1SE.onnx \
|
|
BACKEND_TEST_CAPS=health,load,face_detect,face_embed,face_verify,face_antispoof \
|
|
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_FACE_SPOOF_IMAGE_URL=$(FACE_SPOOF_IMAGE_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
|
|
|
|
## speaker-recognition — voice (speaker) biometrics.
|
|
##
|
|
## Audio fixtures default to the speechbrain test samples served
|
|
## straight from their GitHub repo — public, no auth needed, and they
|
|
## ship as 16kHz mono WAV/FLAC which is exactly what the engine wants.
|
|
## example{1,2,5} are three different speakers; the suite treats
|
|
## example1 as the "same-image twin" probe (verify(clip, clip) must
|
|
## return distance≈0) and the other two as cross-speaker ceilings.
|
|
## Override with BACKEND_TEST_VOICE_AUDIO_{1,2,3}_FILE for offline runs.
|
|
VOICE_AUDIO_1_URL ?= https://github.com/speechbrain/speechbrain/raw/develop/tests/samples/single-mic/example1.wav
|
|
VOICE_AUDIO_2_URL ?= https://github.com/speechbrain/speechbrain/raw/develop/tests/samples/single-mic/example2.flac
|
|
VOICE_AUDIO_3_URL ?= https://github.com/speechbrain/speechbrain/raw/develop/tests/samples/single-mic/example5.wav
|
|
|
|
## ECAPA-TDNN via SpeechBrain — default CI configuration. Auto-downloads
|
|
## the checkpoint from HuggingFace on first LoadModel (bundled in the
|
|
## backend image pip install). 192-d embeddings, cosine-distance based.
|
|
## The e2e suite drives LoadModel directly so we don't rely on LocalAI's
|
|
## gallery flow here.
|
|
test-extra-backend-speaker-recognition-ecapa: docker-build-speaker-recognition
|
|
BACKEND_IMAGE=local-ai-backend:speaker-recognition \
|
|
BACKEND_TEST_MODEL_NAME=speechbrain/spkrec-ecapa-voxceleb \
|
|
BACKEND_TEST_OPTIONS=engine:speechbrain,source:speechbrain/spkrec-ecapa-voxceleb \
|
|
BACKEND_TEST_CAPS=health,load,voice_embed,voice_verify \
|
|
BACKEND_TEST_VOICE_AUDIO_1_URL=$(VOICE_AUDIO_1_URL) \
|
|
BACKEND_TEST_VOICE_AUDIO_2_URL=$(VOICE_AUDIO_2_URL) \
|
|
BACKEND_TEST_VOICE_AUDIO_3_URL=$(VOICE_AUDIO_3_URL) \
|
|
BACKEND_TEST_VOICE_VERIFY_DISTANCE_CEILING=0.4 \
|
|
$(MAKE) test-extra-backend
|
|
|
|
## Aggregate — today there's only one voice config; the target exists
|
|
## so the CI workflow matches the insightface-all naming convention and
|
|
## can grow to include WeSpeaker / 3D-Speaker later.
|
|
test-extra-backend-speaker-recognition-all: \
|
|
test-extra-backend-speaker-recognition-ecapa
|
|
|
|
## Realtime e2e with sherpa-onnx driving VAD + STT + TTS against a mocked
|
|
## LLM. Extracts the sherpa-onnx Docker image rootfs, downloads the three
|
|
## gallery-referenced model bundles (silero-vad, omnilingual-asr, vits-ljs),
|
|
## writes the corresponding model config YAMLs, and runs the realtime
|
|
## websocket spec in tests/e2e with REALTIME_* env vars wiring the sherpa
|
|
## slots into the pipeline. The LLM slot stays on the in-repo mock-backend
|
|
## registered unconditionally by tests/e2e/e2e_suite_test.go. See
|
|
## tests/e2e/run-realtime-sherpa.sh for the full orchestration.
|
|
test-extra-e2e-realtime-sherpa: build-mock-backend docker-build-sherpa-onnx protogen-go react-ui
|
|
bash tests/e2e/run-realtime-sherpa.sh
|
|
|
|
## Streaming ASR via the sherpa-onnx online recognizer. Uses the streaming
|
|
## zipformer English model (encoder/decoder/joiner int8 + tokens) from the
|
|
## sherpa-onnx gallery entry. Drives both AudioTranscription and
|
|
## AudioTranscriptionStream via the e2e-backends gRPC harness; streaming
|
|
## emits real partial deltas during decode. Each file is renamed on download
|
|
## to the shape sherpa-onnx's online loader expects (encoder.int8.onnx etc.).
|
|
test-extra-backend-sherpa-onnx-transcription: docker-build-sherpa-onnx
|
|
BACKEND_IMAGE=local-ai-backend:sherpa-onnx \
|
|
BACKEND_TEST_MODEL_URL='https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-en-2023-06-26/resolve/main/encoder-epoch-99-avg-1-chunk-16-left-128.int8.onnx#encoder.int8.onnx' \
|
|
BACKEND_TEST_EXTRA_FILES='https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-en-2023-06-26/resolve/main/decoder-epoch-99-avg-1-chunk-16-left-128.int8.onnx#decoder.int8.onnx|https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-en-2023-06-26/resolve/main/joiner-epoch-99-avg-1-chunk-16-left-128.int8.onnx#joiner.int8.onnx|https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-en-2023-06-26/resolve/main/tokens.txt' \
|
|
BACKEND_TEST_AUDIO_URL=https://github.com/ggml-org/whisper.cpp/raw/master/samples/jfk.wav \
|
|
BACKEND_TEST_CAPS=health,load,transcription \
|
|
BACKEND_TEST_OPTIONS=subtype=online \
|
|
$(MAKE) test-extra-backend
|
|
|
|
## VITS TTS via the sherpa-onnx backend. Pulls the individual files from
|
|
## HuggingFace (the vits-ljs release tarball lives on the k2-fsa github
|
|
## but is also mirrored as discrete files on HF). Exercises both
|
|
## TTS (write-to-file) and TTSStream (PCM chunks + WAV header) via the
|
|
## e2e-backends gRPC harness.
|
|
test-extra-backend-sherpa-onnx-tts: docker-build-sherpa-onnx
|
|
BACKEND_IMAGE=local-ai-backend:sherpa-onnx \
|
|
BACKEND_TEST_MODEL_URL='https://huggingface.co/csukuangfj/vits-ljs/resolve/main/vits-ljs.onnx#vits-ljs.onnx' \
|
|
BACKEND_TEST_EXTRA_FILES='https://huggingface.co/csukuangfj/vits-ljs/resolve/main/tokens.txt|https://huggingface.co/csukuangfj/vits-ljs/resolve/main/lexicon.txt' \
|
|
BACKEND_TEST_CAPS=health,load,tts \
|
|
$(MAKE) test-extra-backend
|
|
|
|
## VibeVoice TTS via the vibevoice-cpp backend. ModelFile is the
|
|
## realtime gguf; the supplementary tokenizer + voice prompt land
|
|
## alongside it under the harness's models dir and are wired through
|
|
## via the standard Options[] convention (tokenizer=, voice=).
|
|
test-extra-backend-vibevoice-cpp-tts: docker-build-vibevoice-cpp
|
|
BACKEND_IMAGE=local-ai-backend:vibevoice-cpp \
|
|
BACKEND_TEST_MODEL_URL='https://huggingface.co/mudler/vibevoice.cpp-models/resolve/main/vibevoice-realtime-0.5B-q8_0.gguf#vibevoice-realtime-0.5B-q8_0.gguf' \
|
|
BACKEND_TEST_EXTRA_FILES='https://huggingface.co/mudler/vibevoice.cpp-models/resolve/main/tokenizer.gguf#tokenizer.gguf|https://huggingface.co/mudler/vibevoice.cpp-models/resolve/main/voice-en-Carter_man.gguf#voice-en-Carter_man.gguf' \
|
|
BACKEND_TEST_OPTIONS=tokenizer:tokenizer.gguf,voice:voice-en-Carter_man.gguf \
|
|
BACKEND_TEST_CAPS=health,load,tts \
|
|
$(MAKE) test-extra-backend
|
|
|
|
## VibeVoice ASR (long-form, with diarization). type=asr tells the
|
|
## backend's Load() to slot ModelFile into the asr_model role; the
|
|
## tokenizer is supplied via Options[]. Uses the Q4_K quant (~10 GB)
|
|
## rather than Q8_0 (~14 GB) so the bundle fits inside ubuntu-latest's
|
|
## post-image disk budget.
|
|
test-extra-backend-vibevoice-cpp-transcription: docker-build-vibevoice-cpp
|
|
BACKEND_IMAGE=local-ai-backend:vibevoice-cpp \
|
|
BACKEND_TEST_MODEL_URL='https://huggingface.co/mudler/vibevoice.cpp-models/resolve/main/vibevoice-asr-q4_k.gguf#vibevoice-asr-q4_k.gguf' \
|
|
BACKEND_TEST_EXTRA_FILES='https://huggingface.co/mudler/vibevoice.cpp-models/resolve/main/tokenizer.gguf#tokenizer.gguf' \
|
|
BACKEND_TEST_AUDIO_URL=https://github.com/ggml-org/whisper.cpp/raw/master/samples/jfk.wav \
|
|
BACKEND_TEST_OPTIONS=type:asr,tokenizer:tokenizer.gguf \
|
|
BACKEND_TEST_CAPS=health,load,transcription \
|
|
$(MAKE) test-extra-backend
|
|
|
|
## Audio transcription wrapper for the whisper.cpp backend.
|
|
## Drives the AudioTranscription / AudioTranscriptionStream RPCs against
|
|
## ggml-base.en (~145 MB) using the JFK 11s clip. The streaming spec
|
|
## asserts len(deltas) >= 1 and concat(deltas) == final.Text - whisper-
|
|
## specific multi-segment assertions live in backend/go/whisper/gowhisper_test.go.
|
|
test-extra-backend-whisper-transcription: docker-build-whisper
|
|
BACKEND_IMAGE=local-ai-backend:whisper \
|
|
BACKEND_TEST_MODEL_URL=https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-base.en.bin \
|
|
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
|
|
|
|
## LocalVQE audio transform (joint AEC + noise suppression + dereverb).
|
|
## Exercises the audio_transform capability end-to-end: batch transform
|
|
## of a real WAV fixture and bidi streaming of synthetic silent frames.
|
|
test-extra-backend-localvqe-transform: docker-build-localvqe
|
|
BACKEND_IMAGE=local-ai-backend:localvqe \
|
|
BACKEND_TEST_MODEL_URL='https://huggingface.co/LocalAI-io/LocalVQE/resolve/main/localvqe-v1-1.3M-f32.gguf#localvqe-v1-1.3M-f32.gguf' \
|
|
BACKEND_TEST_AUDIO_URL=https://github.com/ggml-org/whisper.cpp/raw/master/samples/jfk.wav \
|
|
BACKEND_TEST_CAPS=health,load,audio_transform \
|
|
$(MAKE) test-extra-backend
|
|
|
|
## 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) \
|
|
--build-arg APT_MIRROR=$(APT_MIRROR) \
|
|
--build-arg APT_PORTS_MIRROR=$(APT_PORTS_MIRROR) \
|
|
-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) \
|
|
--build-arg APT_MIRROR=$(APT_MIRROR) \
|
|
--build-arg APT_PORTS_MIRROR=$(APT_PORTS_MIRROR) \
|
|
-t $(DOCKER_IMAGE)-cuda-12 .
|
|
|
|
docker-image-intel:
|
|
docker build \
|
|
--build-arg BASE_IMAGE=intel/oneapi-basekit:2025.3.2-0-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) \
|
|
--build-arg APT_MIRROR=$(APT_MIRROR) \
|
|
--build-arg APT_PORTS_MIRROR=$(APT_PORTS_MIRROR) \
|
|
-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)"
|
|
|
|
backends/ds4-darwin: build
|
|
bash ./scripts/build/ds4-darwin.sh
|
|
./local-ai backends install "ocifile://$(abspath ./backend-images/ds4.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
|
|
# ds4 is antirez/ds4, a DeepSeek V4 Flash-specific inference engine.
|
|
# Single-model; hardware-only validation lives at tests/e2e-backends/
|
|
# (BACKEND_BINARY mode); see docs/superpowers/plans/2026-05-11-ds4-backend.md.
|
|
BACKEND_DS4 = ds4|ds4|.|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_VIBEVOICE_CPP = vibevoice-cpp|golang|.|false|true
|
|
BACKEND_LOCALVQE = localvqe|golang|.|false|true
|
|
BACKEND_OPUS = opus|golang|.|false|true
|
|
BACKEND_SHERPA_ONNX = sherpa-onnx|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_SPEAKER_RECOGNITION = speaker-recognition|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_LIQUID_AUDIO = liquid-audio|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) \
|
|
--build-arg APT_MIRROR=$(APT_MIRROR) \
|
|
--build-arg APT_PORTS_MIRROR=$(APT_PORTS_MIRROR) \
|
|
$(if $(FROM_SOURCE),--build-arg FROM_SOURCE=$(FROM_SOURCE)) \
|
|
$(if $(AMDGPU_TARGETS),--build-arg AMDGPU_TARGETS=$(AMDGPU_TARGETS)) \
|
|
$(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_DS4)))
|
|
$(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_SPEAKER_RECOGNITION)))
|
|
$(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_LIQUID_AUDIO)))
|
|
$(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_VIBEVOICE_CPP)))
|
|
$(eval $(call generate-docker-build-target,$(BACKEND_LOCALVQE)))
|
|
$(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)))
|
|
$(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
|
|
|
|
########################################################
|
|
### 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
|