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1 Commits

Author SHA1 Message Date
Ettore Di Giacinto
cc11323d1c fix(ci): install latest git
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2024-10-24 14:55:24 +02:00
230 changed files with 9524 additions and 1805 deletions

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@@ -1,11 +0,0 @@
meta {
name: model delete
type: http
seq: 7
}
post {
url: {{PROTOCOL}}{{HOST}}:{{PORT}}/models/galleries
body: none
auth: none
}

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@@ -1,16 +0,0 @@
meta {
name: transcribe
type: http
seq: 1
}
post {
url: {{PROTOCOL}}{{HOST}}:{{PORT}}/v1/audio/transcriptions
body: multipartForm
auth: none
}
body:multipart-form {
file: @file(transcription/gb1.ogg)
model: whisper-1
}

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@@ -105,6 +105,14 @@ jobs:
tests-parler-tts:
runs-on: ubuntu-latest
steps:
- name: Force Install GIT latest
run: |
sudo apt-get update \
&& sudo apt-get install -y software-properties-common \
&& sudo apt-get update \
&& sudo add-apt-repository -y ppa:git-core/ppa \
&& sudo apt-get update \
&& sudo apt-get install -y git
- name: Clone
uses: actions/checkout@v4
with:
@@ -123,13 +131,6 @@ jobs:
run: |
make --jobs=5 --output-sync=target -C backend/python/parler-tts
make --jobs=5 --output-sync=target -C backend/python/parler-tts test
- name: Setup tmate session if tests fail
if: ${{ failure() }}
uses: mxschmitt/action-tmate@v3.19
with:
detached: true
connect-timeout-seconds: 180
limit-access-to-actor: true
tests-openvoice:
runs-on: ubuntu-latest

View File

@@ -224,7 +224,7 @@ jobs:
- name: Dependencies
run: |
brew install protobuf grpc make protoc-gen-go protoc-gen-go-grpc libomp llvm
pip install --user --no-cache-dir grpcio-tools
pip install --user --no-cache-dir grpcio-tools==1.64.1
- name: Test
run: |
export C_INCLUDE_PATH=/usr/local/include

View File

@@ -85,8 +85,7 @@ WORKDIR /build
# The requirements-extras target is for any builds with IMAGE_TYPE=extras. It should not be placed in this target unless every IMAGE_TYPE=extras build will use it
FROM requirements-core AS requirements-extras
# Install uv as a system package
RUN curl -LsSf https://astral.sh/uv/install.sh | UV_INSTALL_DIR=/usr/bin sh
RUN curl -LsSf https://astral.sh/uv/install.sh | sh
ENV PATH="/root/.cargo/bin:${PATH}"
RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y

View File

@@ -8,7 +8,7 @@ DETECT_LIBS?=true
# llama.cpp versions
GOLLAMA_REPO?=https://github.com/go-skynet/go-llama.cpp
GOLLAMA_VERSION?=2b57a8ae43e4699d3dc5d1496a1ccd42922993be
CPPLLAMA_VERSION?=6423c65aa8be1b98f990cf207422505ac5a441a1
CPPLLAMA_VERSION?=0a1c750c80147687df267114c81956757cc14382
# go-rwkv version
RWKV_REPO?=https://github.com/donomii/go-rwkv.cpp
@@ -16,7 +16,7 @@ RWKV_VERSION?=661e7ae26d442f5cfebd2a0881b44e8c55949ec6
# whisper.cpp version
WHISPER_REPO?=https://github.com/ggerganov/whisper.cpp
WHISPER_CPP_VERSION?=31aea563a83803c710691fed3e8d700e06ae6788
WHISPER_CPP_VERSION?=0fbaac9c891055796456df7b9122a70c220f9ca1
# bert.cpp version
BERT_REPO?=https://github.com/go-skynet/go-bert.cpp
@@ -24,7 +24,7 @@ BERT_VERSION?=710044b124545415f555e4260d16b146c725a6e4
# go-piper version
PIPER_REPO?=https://github.com/mudler/go-piper
PIPER_VERSION?=e10ca041a885d4a8f3871d52924b47792d5e5aa0
PIPER_VERSION?=9d0100873a7dbb0824dfea40e8cec70a1b110759
# stablediffusion version
STABLEDIFFUSION_REPO?=https://github.com/mudler/go-stable-diffusion

View File

@@ -40,7 +40,7 @@
> :bulb: Get help - [❓FAQ](https://localai.io/faq/) [💭Discussions](https://github.com/go-skynet/LocalAI/discussions) [:speech_balloon: Discord](https://discord.gg/uJAeKSAGDy) [:book: Documentation website](https://localai.io/)
>
> [💻 Quickstart](https://localai.io/basics/getting_started/) [🖼️ Models](https://models.localai.io/) [🚀 Roadmap](https://github.com/mudler/LocalAI/issues?q=is%3Aissue+is%3Aopen+label%3Aroadmap) [🥽 Demo](https://demo.localai.io) [🌍 Explorer](https://explorer.localai.io) [🛫 Examples](https://github.com/mudler/LocalAI-examples)
> [💻 Quickstart](https://localai.io/basics/getting_started/) [🖼️ Models](https://models.localai.io/) [🚀 Roadmap](https://github.com/mudler/LocalAI/issues?q=is%3Aissue+is%3Aopen+label%3Aroadmap) [🥽 Demo](https://demo.localai.io) [🌍 Explorer](https://explorer.localai.io) [🛫 Examples](https://github.com/go-skynet/LocalAI/tree/master/examples/)
[![tests](https://github.com/go-skynet/LocalAI/actions/workflows/test.yml/badge.svg)](https://github.com/go-skynet/LocalAI/actions/workflows/test.yml)[![Build and Release](https://github.com/go-skynet/LocalAI/actions/workflows/release.yaml/badge.svg)](https://github.com/go-skynet/LocalAI/actions/workflows/release.yaml)[![build container images](https://github.com/go-skynet/LocalAI/actions/workflows/image.yml/badge.svg)](https://github.com/go-skynet/LocalAI/actions/workflows/image.yml)[![Bump dependencies](https://github.com/go-skynet/LocalAI/actions/workflows/bump_deps.yaml/badge.svg)](https://github.com/go-skynet/LocalAI/actions/workflows/bump_deps.yaml)[![Artifact Hub](https://img.shields.io/endpoint?url=https://artifacthub.io/badge/repository/localai)](https://artifacthub.io/packages/search?repo=localai)
@@ -85,7 +85,6 @@ local-ai run oci://localai/phi-2:latest
## 📰 Latest project news
- Oct 2024: examples moved to [LocalAI-examples](https://github.com/mudler/LocalAI-examples)
- Aug 2024: 🆕 FLUX-1, [P2P Explorer](https://explorer.localai.io)
- July 2024: 🔥🔥 🆕 P2P Dashboard, LocalAI Federated mode and AI Swarms: https://github.com/mudler/LocalAI/pull/2723
- June 2024: 🆕 You can browse now the model gallery without LocalAI! Check out https://models.localai.io

View File

@@ -233,11 +233,6 @@ message ModelOptions {
bool FlashAttention = 56;
bool NoKVOffload = 57;
string ModelPath = 59;
repeated string LoraAdapters = 60;
repeated float LoraScales = 61;
}
message Result {

View File

@@ -670,6 +670,7 @@ struct llama_server_context
slot->sparams.top_k = json_value(data, "top_k", default_sparams.top_k);
slot->sparams.top_p = json_value(data, "top_p", default_sparams.top_p);
slot->sparams.min_p = json_value(data, "min_p", default_sparams.min_p);
slot->sparams.tfs_z = json_value(data, "tfs_z", default_sparams.tfs_z);
slot->sparams.typ_p = json_value(data, "typical_p", default_sparams.typ_p);
slot->sparams.temp = json_value(data, "temperature", default_sparams.temp);
slot->sparams.dynatemp_range = json_value(data, "dynatemp_range", default_sparams.dynatemp_range);
@@ -1205,6 +1206,7 @@ struct llama_server_context
{"top_k", slot.sparams.top_k},
{"top_p", slot.sparams.top_p},
{"min_p", slot.sparams.min_p},
{"tfs_z", slot.sparams.tfs_z},
{"typical_p", slot.sparams.typ_p},
{"repeat_last_n", slot.sparams.penalty_last_n},
{"repeat_penalty", slot.sparams.penalty_repeat},
@@ -2103,6 +2105,7 @@ json parse_options(bool streaming, const backend::PredictOptions* predict, llama
// slot->params.n_predict = json_value(data, "n_predict", default_params.n_predict);
// slot->sparams.top_k = json_value(data, "top_k", default_sparams.top_k);
// slot->sparams.top_p = json_value(data, "top_p", default_sparams.top_p);
// slot->sparams.tfs_z = json_value(data, "tfs_z", default_sparams.tfs_z);
// slot->sparams.typical_p = json_value(data, "typical_p", default_sparams.typical_p);
// slot->sparams.temp = json_value(data, "temperature", default_sparams.temp);
// slot->sparams.penalty_last_n = json_value(data, "repeat_last_n", default_sparams.penalty_last_n);
@@ -2126,6 +2129,7 @@ json parse_options(bool streaming, const backend::PredictOptions* predict, llama
data["n_predict"] = predict->tokens() == 0 ? -1 : predict->tokens();
data["top_k"] = predict->topk();
data["top_p"] = predict->topp();
data["tfs_z"] = predict->tailfreesamplingz();
data["typical_p"] = predict->typicalp();
data["temperature"] = predict->temperature();
data["repeat_last_n"] = predict->repeat();
@@ -2172,6 +2176,7 @@ json parse_options(bool streaming, const backend::PredictOptions* predict, llama
// llama.params.n_predict = predict->tokens() == 0 ? -1 : predict->tokens();
// llama.params.sparams.top_k = predict->topk();
// llama.params.sparams.top_p = predict->topp();
// llama.params.sparams.tfs_z = predict->tailfreesamplingz();
// llama.params.sparams.typical_p = predict->typicalp();
// llama.params.sparams.penalty_last_n = predict->repeat();
// llama.params.sparams.temp = predict->temperature();

View File

@@ -1,6 +1,6 @@
accelerate
auto-gptq==0.7.1
grpcio==1.67.1
grpcio==1.67.0
protobuf
certifi
transformers

View File

@@ -1,4 +1,4 @@
bark==0.1.5
grpcio==1.67.1
grpcio==1.67.0
protobuf
certifi

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@@ -1,9 +1,8 @@
.DEFAULT_GOAL := install
.PHONY: install
install:
install: protogen
bash install.sh
$(MAKE) protogen
.PHONY: protogen
protogen: backend_pb2_grpc.py backend_pb2.py
@@ -13,7 +12,7 @@ protogen-clean:
$(RM) backend_pb2_grpc.py backend_pb2.py
backend_pb2_grpc.py backend_pb2.py:
bash protogen.sh
python3 -m grpc_tools.protoc -I../.. --python_out=. --grpc_python_out=. backend.proto
.PHONY: clean
clean: protogen-clean

View File

@@ -1,6 +0,0 @@
#!/bin/bash
set -e
source $(dirname $0)/../common/libbackend.sh
python3 -m grpc_tools.protoc -I../.. --python_out=. --grpc_python_out=. backend.proto

View File

@@ -1,3 +1,2 @@
grpcio==1.67.1
protobuf
grpcio-tools
grpcio==1.67.0
protobuf

View File

@@ -1,4 +1,4 @@
grpcio==1.67.1
grpcio==1.67.0
protobuf
certifi
packaging==24.1

View File

@@ -247,16 +247,11 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
use_safetensors=True,
variant=variant)
elif request.PipelineType == "FluxPipeline":
if fromSingleFile:
self.pipe = FluxPipeline.from_single_file(modelFile,
torch_dtype=torchType,
use_safetensors=True)
else:
self.pipe = FluxPipeline.from_pretrained(
request.Model,
torch_dtype=torch.bfloat16)
if request.LowVRAM:
self.pipe.enable_model_cpu_offload()
if request.LowVRAM:
self.pipe.enable_model_cpu_offload()
elif request.PipelineType == "FluxTransformer2DModel":
dtype = torch.bfloat16
# specify from environment or default to "ChuckMcSneed/FLUX.1-dev"
@@ -301,34 +296,22 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
self.pipe.controlnet = self.controlnet
else:
self.controlnet = None
if request.LoraAdapter and not os.path.isabs(request.LoraAdapter):
# Assume directory from request.ModelFile.
# Only if request.LoraAdapter it's not an absolute path
if request.LoraAdapter and request.ModelFile != "" and not os.path.isabs(request.LoraAdapter) and request.LoraAdapter:
# get base path of modelFile
modelFileBase = os.path.dirname(request.ModelFile)
# modify LoraAdapter to be relative to modelFileBase
request.LoraAdapter = os.path.join(request.ModelPath, request.LoraAdapter)
request.LoraAdapter = os.path.join(modelFileBase, request.LoraAdapter)
device = "cpu" if not request.CUDA else "cuda"
self.device = device
if request.LoraAdapter:
# Check if its a local file and not a directory ( we load lora differently for a safetensor file )
if os.path.exists(request.LoraAdapter) and not os.path.isdir(request.LoraAdapter):
# self.load_lora_weights(request.LoraAdapter, 1, device, torchType)
self.pipe.load_lora_weights(request.LoraAdapter)
else:
self.pipe.unet.load_attn_procs(request.LoraAdapter)
if len(request.LoraAdapters) > 0:
i = 0
adapters_name = []
adapters_weights = []
for adapter in request.LoraAdapters:
if not os.path.isabs(adapter):
adapter = os.path.join(request.ModelPath, adapter)
self.pipe.load_lora_weights(adapter, adapter_name=f"adapter_{i}")
adapters_name.append(f"adapter_{i}")
i += 1
for adapters_weight in request.LoraScales:
adapters_weights.append(adapters_weight)
self.pipe.set_adapters(adapters_name, adapter_weights=adapters_weights)
if request.CUDA:
self.pipe.to('cuda')
@@ -409,6 +392,8 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
# create a dictionary of values for the parameters
options = {
"negative_prompt": request.negative_prompt,
"width": request.width,
"height": request.height,
"num_inference_steps": steps,
}
@@ -426,13 +411,13 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
keys = options.keys()
if request.EnableParameters != "":
keys = [key.strip() for key in request.EnableParameters.split(",")]
keys = request.EnableParameters.split(",")
if request.EnableParameters == "none":
keys = []
# create a dictionary of parameters by using the keys from EnableParameters and the values from defaults
kwargs = {key: options.get(key) for key in keys if key in options}
kwargs = {key: options[key] for key in keys}
# Set seed
if request.seed > 0:
@@ -443,12 +428,6 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
if self.PipelineType == "FluxPipeline":
kwargs["max_sequence_length"] = 256
if request.width:
kwargs["width"] = request.width
if request.height:
kwargs["height"] = request.height
if self.PipelineType == "FluxTransformer2DModel":
kwargs["output_type"] = "pil"
kwargs["generator"] = torch.Generator("cpu").manual_seed(0)
@@ -468,7 +447,6 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
export_to_video(video_frames, request.dst)
return backend_pb2.Result(message="Media generated successfully", success=True)
print(f"Generating image with {kwargs=}", file=sys.stderr)
image = {}
if COMPEL:
conditioning, pooled = self.compel.build_conditioning_tensor(prompt)

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@@ -1,5 +1,5 @@
setuptools
grpcio==1.67.1
grpcio==1.67.0
pillow
protobuf
certifi

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@@ -1,4 +1,4 @@
grpcio==1.67.1
grpcio==1.67.0
protobuf
certifi
wheel

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@@ -1,3 +1,3 @@
grpcio==1.67.1
grpcio==1.67.0
protobuf
certifi

View File

@@ -2,7 +2,7 @@
intel-extension-for-pytorch
torch
optimum[openvino]
grpcio==1.67.1
grpcio==1.67.0
protobuf
librosa==0.9.1
faster-whisper==0.9.0

View File

@@ -1,4 +1,4 @@
grpcio==1.67.1
grpcio==1.67.0
protobuf
librosa
faster-whisper

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@@ -12,10 +12,9 @@ export SKIP_CONDA=1
endif
.PHONY: parler-tts
parler-tts:
parler-tts: protogen
@echo "Installing $(CONDA_ENV_PATH)..."
bash install.sh $(CONDA_ENV_PATH)
$(MAKE) protogen
.PHONY: run
run: protogen
@@ -37,7 +36,7 @@ protogen-clean:
$(RM) backend_pb2_grpc.py backend_pb2.py
backend_pb2_grpc.py backend_pb2.py:
bash protogen.sh
python3 -m grpc_tools.protoc -I../.. --python_out=. --grpc_python_out=. backend.proto
.PHONY: clean
clean: protogen-clean

View File

@@ -11,18 +11,16 @@ if [ "x${BUILD_PROFILE}" == "xintel" ]; then
EXTRA_PIP_INSTALL_FLAGS+=" --upgrade --index-strategy=unsafe-first-match"
fi
installRequirements
# https://github.com/descriptinc/audiotools/issues/101
# incompatible protobuf versions.
PYDIR=python3.10
pyenv="${MY_DIR}/venv/lib/${PYDIR}/site-packages/google/protobuf/internal/"
# PYDIR=python3.10
# pyenv="${MY_DIR}/venv/lib/${PYDIR}/site-packages/google/protobuf/internal/"
if [ ! -d ${pyenv} ]; then
echo "(parler-tts/install.sh): Error: ${pyenv} does not exist"
exit 1
fi
# if [ ! -d ${pyenv} ]; then
# echo "(parler-tts/install.sh): Error: ${pyenv} does not exist"
# exit 1
# fi
curl -L https://raw.githubusercontent.com/protocolbuffers/protobuf/main/python/google/protobuf/internal/builder.py -o ${pyenv}/builder.py
# curl -L https://raw.githubusercontent.com/protocolbuffers/protobuf/main/python/google/protobuf/internal/builder.py -o ${pyenv}/builder.py

View File

@@ -1,6 +0,0 @@
#!/bin/bash
set -e
source $(dirname $0)/../common/libbackend.sh
python3 -m grpc_tools.protoc -I../.. --python_out=. --grpc_python_out=. backend.proto

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@@ -1,4 +1,4 @@
git+https://github.com/huggingface/parler-tts.git@8e465f1b5fcd223478e07175cb40494d19ffbe17
llvmlite==0.43.0
numba==0.60.0
grpcio-tools==1.42.0
git+https://github.com/descriptinc/audiotools

View File

@@ -1,3 +1,4 @@
grpcio==1.67.1
grpcio==1.67.0
protobuf
certifi
llvmlite==0.43.0
llvmlite==0.43.0

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@@ -1,3 +1,3 @@
grpcio==1.67.1
grpcio==1.67.0
protobuf
certifi

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@@ -1,4 +1,4 @@
grpcio==1.67.1
grpcio==1.67.0
protobuf
certifi
datasets

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@@ -1,4 +1,4 @@
grpcio==1.67.1
grpcio==1.67.0
protobuf
scipy==1.14.0
certifi

View File

@@ -1,4 +1,4 @@
grpcio==1.67.1
grpcio==1.67.0
protobuf
certifi
setuptools==69.5.1 # https://github.com/mudler/LocalAI/issues/2406

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@@ -1,3 +1,3 @@
grpcio==1.67.1
grpcio==1.67.0
protobuf
certifi

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@@ -22,7 +22,7 @@ if [ "x${BUILD_TYPE}" == "x" ] && [ "x${FROM_SOURCE}" == "xtrue" ]; then
git clone https://github.com/vllm-project/vllm
fi
pushd vllm
uv pip install wheel packaging ninja "setuptools>=49.4.0" numpy typing-extensions pillow setuptools-scm grpcio==1.67.1 protobuf bitsandbytes
uv pip install wheel packaging ninja "setuptools>=49.4.0" numpy typing-extensions pillow setuptools-scm grpcio==1.67.0 protobuf bitsandbytes
uv pip install -v -r requirements-cpu.txt --extra-index-url https://download.pytorch.org/whl/cpu
VLLM_TARGET_DEVICE=cpu python setup.py install
popd

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@@ -1,4 +1,4 @@
grpcio==1.67.1
grpcio==1.67.0
protobuf
certifi
setuptools

View File

@@ -11,9 +11,17 @@ import (
func ModelEmbedding(s string, tokens []int, loader *model.ModelLoader, backendConfig config.BackendConfig, appConfig *config.ApplicationConfig) (func() ([]float32, error), error) {
opts := ModelOptions(backendConfig, appConfig)
var inferenceModel interface{}
var err error
inferenceModel, err := loader.Load(opts...)
opts := ModelOptions(backendConfig, appConfig, []model.Option{})
if backendConfig.Backend == "" {
inferenceModel, err = loader.GreedyLoader(opts...)
} else {
opts = append(opts, model.WithBackendString(backendConfig.Backend))
inferenceModel, err = loader.BackendLoader(opts...)
}
if err != nil {
return nil, err
}

View File

@@ -9,8 +9,9 @@ import (
func ImageGeneration(height, width, mode, step, seed int, positive_prompt, negative_prompt, src, dst string, loader *model.ModelLoader, backendConfig config.BackendConfig, appConfig *config.ApplicationConfig) (func() error, error) {
opts := ModelOptions(backendConfig, appConfig)
inferenceModel, err := loader.Load(
opts := ModelOptions(backendConfig, appConfig, []model.Option{})
inferenceModel, err := loader.BackendLoader(
opts...,
)
if err != nil {

View File

@@ -16,6 +16,7 @@ import (
"github.com/mudler/LocalAI/core/schema"
"github.com/mudler/LocalAI/core/gallery"
"github.com/mudler/LocalAI/pkg/grpc"
"github.com/mudler/LocalAI/pkg/grpc/proto"
model "github.com/mudler/LocalAI/pkg/model"
"github.com/mudler/LocalAI/pkg/utils"
@@ -34,6 +35,15 @@ type TokenUsage struct {
func ModelInference(ctx context.Context, s string, messages []schema.Message, images, videos, audios []string, loader *model.ModelLoader, c config.BackendConfig, o *config.ApplicationConfig, tokenCallback func(string, TokenUsage) bool) (func() (LLMResponse, error), error) {
modelFile := c.Model
var inferenceModel grpc.Backend
var err error
opts := ModelOptions(c, o, []model.Option{})
if c.Backend != "" {
opts = append(opts, model.WithBackendString(c.Backend))
}
// Check if the modelFile exists, if it doesn't try to load it from the gallery
if o.AutoloadGalleries { // experimental
if _, err := os.Stat(modelFile); os.IsNotExist(err) {
@@ -46,8 +56,12 @@ func ModelInference(ctx context.Context, s string, messages []schema.Message, im
}
}
opts := ModelOptions(c, o)
inferenceModel, err := loader.Load(opts...)
if c.Backend == "" {
inferenceModel, err = loader.GreedyLoader(opts...)
} else {
inferenceModel, err = loader.BackendLoader(opts...)
}
if err != nil {
return nil, err
}

View File

@@ -11,7 +11,7 @@ import (
"github.com/rs/zerolog/log"
)
func ModelOptions(c config.BackendConfig, so *config.ApplicationConfig, opts ...model.Option) []model.Option {
func ModelOptions(c config.BackendConfig, so *config.ApplicationConfig, opts []model.Option) []model.Option {
name := c.Name
if name == "" {
name = c.Model
@@ -125,8 +125,6 @@ func grpcModelOpts(c config.BackendConfig) *pb.ModelOptions {
CFGScale: c.Diffusers.CFGScale,
LoraAdapter: c.LoraAdapter,
LoraScale: c.LoraScale,
LoraAdapters: c.LoraAdapters,
LoraScales: c.LoraScales,
F16Memory: f16,
LoraBase: c.LoraBase,
IMG2IMG: c.Diffusers.IMG2IMG,

View File

@@ -11,8 +11,8 @@ import (
func Rerank(modelFile string, request *proto.RerankRequest, loader *model.ModelLoader, appConfig *config.ApplicationConfig, backendConfig config.BackendConfig) (*proto.RerankResult, error) {
opts := ModelOptions(backendConfig, appConfig, model.WithModel(modelFile))
rerankModel, err := loader.Load(opts...)
opts := ModelOptions(backendConfig, appConfig, []model.Option{model.WithModel(modelFile)})
rerankModel, err := loader.BackendLoader(opts...)
if err != nil {
return nil, err
}

View File

@@ -25,8 +25,9 @@ func SoundGeneration(
backendConfig config.BackendConfig,
) (string, *proto.Result, error) {
opts := ModelOptions(backendConfig, appConfig, model.WithModel(modelFile))
soundGenModel, err := loader.Load(opts...)
opts := ModelOptions(backendConfig, appConfig, []model.Option{model.WithModel(modelFile)})
soundGenModel, err := loader.BackendLoader(opts...)
if err != nil {
return "", nil, err
}

View File

@@ -8,15 +8,16 @@ import (
)
func StoreBackend(sl *model.ModelLoader, appConfig *config.ApplicationConfig, storeName string) (grpc.Backend, error) {
if storeName == "" {
storeName = "default"
}
if storeName == "" {
storeName = "default"
}
sc := []model.Option{
model.WithBackendString(model.LocalStoreBackend),
model.WithAssetDir(appConfig.AssetsDestination),
model.WithModel(storeName),
}
sc := []model.Option{
model.WithBackendString(model.LocalStoreBackend),
model.WithAssetDir(appConfig.AssetsDestination),
model.WithModel(storeName),
}
return sl.Load(sc...)
return sl.BackendLoader(sc...)
}

View File

@@ -15,8 +15,10 @@ func TokenMetrics(
appConfig *config.ApplicationConfig,
backendConfig config.BackendConfig) (*proto.MetricsResponse, error) {
opts := ModelOptions(backendConfig, appConfig, model.WithModel(modelFile))
model, err := loader.Load(opts...)
opts := ModelOptions(backendConfig, appConfig, []model.Option{
model.WithModel(modelFile),
})
model, err := loader.BackendLoader(opts...)
if err != nil {
return nil, err
}

View File

@@ -14,13 +14,15 @@ func ModelTokenize(s string, loader *model.ModelLoader, backendConfig config.Bac
var inferenceModel grpc.Backend
var err error
opts := ModelOptions(backendConfig, appConfig, model.WithModel(modelFile))
opts := ModelOptions(backendConfig, appConfig, []model.Option{
model.WithModel(modelFile),
})
if backendConfig.Backend == "" {
inferenceModel, err = loader.Load(opts...)
inferenceModel, err = loader.GreedyLoader(opts...)
} else {
opts = append(opts, model.WithBackendString(backendConfig.Backend))
inferenceModel, err = loader.Load(opts...)
inferenceModel, err = loader.BackendLoader(opts...)
}
if err != nil {
return schema.TokenizeResponse{}, err

View File

@@ -18,9 +18,9 @@ func ModelTranscription(audio, language string, translate bool, ml *model.ModelL
backendConfig.Backend = model.WhisperBackend
}
opts := ModelOptions(backendConfig, appConfig)
opts := ModelOptions(backendConfig, appConfig, []model.Option{})
transcriptionModel, err := ml.Load(opts...)
transcriptionModel, err := ml.BackendLoader(opts...)
if err != nil {
return nil, err
}

View File

@@ -28,8 +28,11 @@ func ModelTTS(
bb = model.PiperBackend
}
opts := ModelOptions(backendConfig, appConfig, model.WithBackendString(bb), model.WithModel(modelFile))
ttsModel, err := loader.Load(opts...)
opts := ModelOptions(config.BackendConfig{}, appConfig, []model.Option{
model.WithBackendString(bb),
model.WithModel(modelFile),
})
ttsModel, err := loader.BackendLoader(opts...)
if err != nil {
return "", nil, err
}

View File

@@ -134,25 +134,23 @@ type LLMConfig struct {
TrimSpace []string `yaml:"trimspace"`
TrimSuffix []string `yaml:"trimsuffix"`
ContextSize *int `yaml:"context_size"`
NUMA bool `yaml:"numa"`
LoraAdapter string `yaml:"lora_adapter"`
LoraBase string `yaml:"lora_base"`
LoraAdapters []string `yaml:"lora_adapters"`
LoraScales []float32 `yaml:"lora_scales"`
LoraScale float32 `yaml:"lora_scale"`
NoMulMatQ bool `yaml:"no_mulmatq"`
DraftModel string `yaml:"draft_model"`
NDraft int32 `yaml:"n_draft"`
Quantization string `yaml:"quantization"`
LoadFormat string `yaml:"load_format"`
GPUMemoryUtilization float32 `yaml:"gpu_memory_utilization"` // vLLM
TrustRemoteCode bool `yaml:"trust_remote_code"` // vLLM
EnforceEager bool `yaml:"enforce_eager"` // vLLM
SwapSpace int `yaml:"swap_space"` // vLLM
MaxModelLen int `yaml:"max_model_len"` // vLLM
TensorParallelSize int `yaml:"tensor_parallel_size"` // vLLM
MMProj string `yaml:"mmproj"`
ContextSize *int `yaml:"context_size"`
NUMA bool `yaml:"numa"`
LoraAdapter string `yaml:"lora_adapter"`
LoraBase string `yaml:"lora_base"`
LoraScale float32 `yaml:"lora_scale"`
NoMulMatQ bool `yaml:"no_mulmatq"`
DraftModel string `yaml:"draft_model"`
NDraft int32 `yaml:"n_draft"`
Quantization string `yaml:"quantization"`
LoadFormat string `yaml:"load_format"`
GPUMemoryUtilization float32 `yaml:"gpu_memory_utilization"` // vLLM
TrustRemoteCode bool `yaml:"trust_remote_code"` // vLLM
EnforceEager bool `yaml:"enforce_eager"` // vLLM
SwapSpace int `yaml:"swap_space"` // vLLM
MaxModelLen int `yaml:"max_model_len"` // vLLM
TensorParallelSize int `yaml:"tensor_parallel_size"` // vLLM
MMProj string `yaml:"mmproj"`
FlashAttention bool `yaml:"flash_attention"`
NoKVOffloading bool `yaml:"no_kv_offloading"`

View File

@@ -438,7 +438,7 @@ var _ = Describe("API test", func() {
Eventually(func() bool {
response := getModelStatus("http://127.0.0.1:9090/models/jobs/" + uuid)
return response["processed"].(bool)
}, "900s", "10s").Should(Equal(true))
}, "360s", "10s").Should(Equal(true))
Eventually(func() []string {
models, _ := client.ListModels(context.TODO())
@@ -562,7 +562,7 @@ var _ = Describe("API test", func() {
Eventually(func() bool {
response := getModelStatus("http://127.0.0.1:9090/models/jobs/" + uuid)
return response["processed"].(bool)
}, "900s", "10s").Should(Equal(true))
}, "360s", "10s").Should(Equal(true))
By("testing chat")
resp, err := client.CreateChatCompletion(context.TODO(), openai.ChatCompletionRequest{Model: modelName, Messages: []openai.ChatCompletionMessage{

View File

@@ -1,97 +0,0 @@
package elements
import (
"strings"
"github.com/chasefleming/elem-go"
"github.com/chasefleming/elem-go/attrs"
"github.com/mudler/LocalAI/core/gallery"
)
func installButton(galleryName string) elem.Node {
return elem.Button(
attrs.Props{
"data-twe-ripple-init": "",
"data-twe-ripple-color": "light",
"class": "float-right inline-block rounded bg-primary px-6 pb-2.5 mb-3 pt-2.5 text-xs font-medium uppercase leading-normal text-white shadow-primary-3 transition duration-150 ease-in-out hover:bg-primary-accent-300 hover:shadow-primary-2 focus:bg-primary-accent-300 focus:shadow-primary-2 focus:outline-none focus:ring-0 active:bg-primary-600 active:shadow-primary-2 dark:shadow-black/30 dark:hover:shadow-dark-strong dark:focus:shadow-dark-strong dark:active:shadow-dark-strong",
"hx-swap": "outerHTML",
// post the Model ID as param
"hx-post": "/browse/install/model/" + galleryName,
},
elem.I(
attrs.Props{
"class": "fa-solid fa-download pr-2",
},
),
elem.Text("Install"),
)
}
func reInstallButton(galleryName string) elem.Node {
return elem.Button(
attrs.Props{
"data-twe-ripple-init": "",
"data-twe-ripple-color": "light",
"class": "float-right inline-block rounded bg-primary ml-2 px-6 pb-2.5 mb-3 pt-2.5 text-xs font-medium uppercase leading-normal text-white shadow-primary-3 transition duration-150 ease-in-out hover:bg-primary-accent-300 hover:shadow-primary-2 focus:bg-primary-accent-300 focus:shadow-primary-2 focus:outline-none focus:ring-0 active:bg-primary-600 active:shadow-primary-2 dark:shadow-black/30 dark:hover:shadow-dark-strong dark:focus:shadow-dark-strong dark:active:shadow-dark-strong",
"hx-target": "#action-div-" + dropBadChars(galleryName),
"hx-swap": "outerHTML",
// post the Model ID as param
"hx-post": "/browse/install/model/" + galleryName,
},
elem.I(
attrs.Props{
"class": "fa-solid fa-arrow-rotate-right pr-2",
},
),
elem.Text("Reinstall"),
)
}
func infoButton(m *gallery.GalleryModel) elem.Node {
return elem.Button(
attrs.Props{
"data-twe-ripple-init": "",
"data-twe-ripple-color": "light",
"class": "float-left inline-block rounded bg-primary px-6 pb-2.5 mb-3 pt-2.5 text-xs font-medium uppercase leading-normal text-white shadow-primary-3 transition duration-150 ease-in-out hover:bg-primary-accent-300 hover:shadow-primary-2 focus:bg-primary-accent-300 focus:shadow-primary-2 focus:outline-none focus:ring-0 active:bg-primary-600 active:shadow-primary-2 dark:shadow-black/30 dark:hover:shadow-dark-strong dark:focus:shadow-dark-strong dark:active:shadow-dark-strong",
"data-modal-target": modalName(m),
"data-modal-toggle": modalName(m),
},
elem.P(
attrs.Props{
"class": "flex items-center",
},
elem.I(
attrs.Props{
"class": "fas fa-info-circle pr-2",
},
),
elem.Text("Info"),
),
)
}
func deleteButton(galleryID string) elem.Node {
return elem.Button(
attrs.Props{
"data-twe-ripple-init": "",
"data-twe-ripple-color": "light",
"hx-confirm": "Are you sure you wish to delete the model?",
"class": "float-right inline-block rounded bg-red-800 px-6 pb-2.5 mb-3 pt-2.5 text-xs font-medium uppercase leading-normal text-white shadow-primary-3 transition duration-150 ease-in-out hover:bg-red-accent-300 hover:shadow-red-2 focus:bg-red-accent-300 focus:shadow-primary-2 focus:outline-none focus:ring-0 active:bg-red-600 active:shadow-primary-2 dark:shadow-black/30 dark:hover:shadow-dark-strong dark:focus:shadow-dark-strong dark:active:shadow-dark-strong",
"hx-target": "#action-div-" + dropBadChars(galleryID),
"hx-swap": "outerHTML",
// post the Model ID as param
"hx-post": "/browse/delete/model/" + galleryID,
},
elem.I(
attrs.Props{
"class": "fa-solid fa-cancel pr-2",
},
),
elem.Text("Delete"),
)
}
// Javascript/HTMX doesn't like weird IDs
func dropBadChars(s string) string {
return strings.ReplaceAll(s, "@", "__")
}

View File

@@ -2,11 +2,13 @@ package elements
import (
"fmt"
"strings"
"github.com/chasefleming/elem-go"
"github.com/chasefleming/elem-go/attrs"
"github.com/microcosm-cc/bluemonday"
"github.com/mudler/LocalAI/core/gallery"
"github.com/mudler/LocalAI/core/p2p"
"github.com/mudler/LocalAI/core/services"
)
@@ -14,6 +16,231 @@ const (
noImage = "https://upload.wikimedia.org/wikipedia/commons/6/65/No-Image-Placeholder.svg"
)
func renderElements(n []elem.Node) string {
render := ""
for _, r := range n {
render += r.Render()
}
return render
}
func DoneProgress(galleryID, text string, showDelete bool) string {
var modelName = galleryID
// Split by @ and grab the name
if strings.Contains(galleryID, "@") {
modelName = strings.Split(galleryID, "@")[1]
}
return elem.Div(
attrs.Props{
"id": "action-div-" + dropBadChars(galleryID),
},
elem.H3(
attrs.Props{
"role": "status",
"id": "pblabel",
"tabindex": "-1",
"autofocus": "",
},
elem.Text(bluemonday.StrictPolicy().Sanitize(text)),
),
elem.If(showDelete, deleteButton(galleryID, modelName), reInstallButton(galleryID)),
).Render()
}
func ErrorProgress(err, galleryName string) string {
return elem.Div(
attrs.Props{},
elem.H3(
attrs.Props{
"role": "status",
"id": "pblabel",
"tabindex": "-1",
"autofocus": "",
},
elem.Text("Error "+bluemonday.StrictPolicy().Sanitize(err)),
),
installButton(galleryName),
).Render()
}
func ProgressBar(progress string) string {
return elem.Div(attrs.Props{
"class": "progress",
"role": "progressbar",
"aria-valuemin": "0",
"aria-valuemax": "100",
"aria-valuenow": "0",
"aria-labelledby": "pblabel",
},
elem.Div(attrs.Props{
"id": "pb",
"class": "progress-bar",
"style": "width:" + progress + "%",
}),
).Render()
}
func P2PNodeStats(nodes []p2p.NodeData) string {
/*
<div class="bg-gray-800 p-6 rounded-lg shadow-lg text-left">
<p class="text-xl font-semibold text-gray-200">Total Workers Detected: {{ len .Nodes }}</p>
{{ $online := 0 }}
{{ range .Nodes }}
{{ if .IsOnline }}
{{ $online = add $online 1 }}
{{ end }}
{{ end }}
<p class="text-xl font-semibold text-gray-200">Total Online Workers: {{$online}}</p>
</div>
*/
online := 0
for _, n := range nodes {
if n.IsOnline() {
online++
}
}
class := "text-green-500"
if online == 0 {
class = "text-red-500"
}
/*
<i class="fas fa-circle animate-pulse text-green-500 ml-2 mr-1"></i>
*/
circle := elem.I(attrs.Props{
"class": "fas fa-circle animate-pulse " + class + " ml-2 mr-1",
})
nodesElements := []elem.Node{
elem.Span(
attrs.Props{
"class": class,
},
circle,
elem.Text(fmt.Sprintf("%d", online)),
),
elem.Span(
attrs.Props{
"class": "text-gray-200",
},
elem.Text(fmt.Sprintf("/%d", len(nodes))),
),
}
return renderElements(nodesElements)
}
func P2PNodeBoxes(nodes []p2p.NodeData) string {
/*
<div class="bg-gray-800 p-4 rounded-lg shadow-lg text-left">
<div class="flex items-center mb-2">
<i class="fas fa-desktop text-gray-400 mr-2"></i>
<span class="text-gray-200 font-semibold">{{.ID}}</span>
</div>
<p class="text-sm text-gray-400 mt-2 flex items-center">
Status:
<i class="fas fa-circle {{ if .IsOnline }}text-green-500{{ else }}text-red-500{{ end }} ml-2 mr-1"></i>
<span class="{{ if .IsOnline }}text-green-400{{ else }}text-red-400{{ end }}">
{{ if .IsOnline }}Online{{ else }}Offline{{ end }}
</span>
</p>
</div>
*/
nodesElements := []elem.Node{}
for _, n := range nodes {
nodesElements = append(nodesElements,
elem.Div(
attrs.Props{
"class": "bg-gray-700 p-6 rounded-lg shadow-lg text-left",
},
elem.P(
attrs.Props{
"class": "text-sm text-gray-400 mt-2 flex",
},
elem.I(
attrs.Props{
"class": "fas fa-desktop text-gray-400 mr-2",
},
),
elem.Text("Name: "),
elem.Span(
attrs.Props{
"class": "text-gray-200 font-semibold ml-2 mr-1",
},
elem.Text(bluemonday.StrictPolicy().Sanitize(n.ID)),
),
elem.Text("Status: "),
elem.If(
n.IsOnline(),
elem.I(
attrs.Props{
"class": "fas fa-circle animate-pulse text-green-500 ml-2 mr-1",
},
),
elem.I(
attrs.Props{
"class": "fas fa-circle animate-pulse text-red-500 ml-2 mr-1",
},
),
),
elem.If(
n.IsOnline(),
elem.Span(
attrs.Props{
"class": "text-green-400",
},
elem.Text("Online"),
),
elem.Span(
attrs.Props{
"class": "text-red-400",
},
elem.Text("Offline"),
),
),
),
))
}
return renderElements(nodesElements)
}
func StartProgressBar(uid, progress, text string) string {
if progress == "" {
progress = "0"
}
return elem.Div(
attrs.Props{
"hx-trigger": "done",
"hx-get": "/browse/job/" + uid,
"hx-swap": "outerHTML",
"hx-target": "this",
},
elem.H3(
attrs.Props{
"role": "status",
"id": "pblabel",
"tabindex": "-1",
"autofocus": "",
},
elem.Text(bluemonday.StrictPolicy().Sanitize(text)), //Perhaps overly defensive
elem.Div(attrs.Props{
"hx-get": "/browse/job/progress/" + uid,
"hx-trigger": "every 600ms",
"hx-target": "this",
"hx-swap": "innerHTML",
},
elem.Raw(ProgressBar(progress)),
),
),
).Render()
}
func cardSpan(text, icon string) elem.Node {
return elem.Span(
attrs.Props{
@@ -41,6 +268,7 @@ func searchableElement(text, icon string) elem.Node {
attrs.Props{
"class": "inline-block bg-gray-200 rounded-full px-3 py-1 text-sm font-semibold text-gray-700 mr-2 mb-2 hover:bg-gray-300 hover:shadow-gray-2",
},
elem.A(
attrs.Props{
// "name": "search",
@@ -62,8 +290,7 @@ func searchableElement(text, icon string) elem.Node {
)
}
/*
func buttonLink(text, url string) elem.Node {
func link(text, url string) elem.Node {
return elem.A(
attrs.Props{
"class": "inline-block bg-gray-200 rounded-full px-3 py-1 text-sm font-semibold text-gray-700 mr-2 mb-2 hover:bg-gray-300 hover:shadow-gray-2",
@@ -76,255 +303,163 @@ func buttonLink(text, url string) elem.Node {
elem.Text(bluemonday.StrictPolicy().Sanitize(text)),
)
}
*/
func link(text, url string) elem.Node {
return elem.A(
func installButton(galleryName string) elem.Node {
return elem.Button(
attrs.Props{
"class": "text-base leading-relaxed text-gray-500 dark:text-gray-400",
"href": url,
"target": "_blank",
"data-twe-ripple-init": "",
"data-twe-ripple-color": "light",
"class": "float-right inline-block rounded bg-primary px-6 pb-2.5 mb-3 pt-2.5 text-xs font-medium uppercase leading-normal text-white shadow-primary-3 transition duration-150 ease-in-out hover:bg-primary-accent-300 hover:shadow-primary-2 focus:bg-primary-accent-300 focus:shadow-primary-2 focus:outline-none focus:ring-0 active:bg-primary-600 active:shadow-primary-2 dark:shadow-black/30 dark:hover:shadow-dark-strong dark:focus:shadow-dark-strong dark:active:shadow-dark-strong",
"hx-swap": "outerHTML",
// post the Model ID as param
"hx-post": "/browse/install/model/" + galleryName,
},
elem.I(attrs.Props{
"class": "fas fa-link pr-2",
}),
elem.Text(bluemonday.StrictPolicy().Sanitize(text)),
elem.I(
attrs.Props{
"class": "fa-solid fa-download pr-2",
},
),
elem.Text("Install"),
)
}
func reInstallButton(galleryName string) elem.Node {
return elem.Button(
attrs.Props{
"data-twe-ripple-init": "",
"data-twe-ripple-color": "light",
"class": "float-right inline-block rounded bg-primary ml-2 px-6 pb-2.5 mb-3 pt-2.5 text-xs font-medium uppercase leading-normal text-white shadow-primary-3 transition duration-150 ease-in-out hover:bg-primary-accent-300 hover:shadow-primary-2 focus:bg-primary-accent-300 focus:shadow-primary-2 focus:outline-none focus:ring-0 active:bg-primary-600 active:shadow-primary-2 dark:shadow-black/30 dark:hover:shadow-dark-strong dark:focus:shadow-dark-strong dark:active:shadow-dark-strong",
"hx-target": "#action-div-" + dropBadChars(galleryName),
"hx-swap": "outerHTML",
// post the Model ID as param
"hx-post": "/browse/install/model/" + galleryName,
},
elem.I(
attrs.Props{
"class": "fa-solid fa-arrow-rotate-right pr-2",
},
),
elem.Text("Reinstall"),
)
}
func deleteButton(galleryID, modelName string) elem.Node {
return elem.Button(
attrs.Props{
"data-twe-ripple-init": "",
"data-twe-ripple-color": "light",
"hx-confirm": "Are you sure you wish to delete the model?",
"class": "float-right inline-block rounded bg-red-800 px-6 pb-2.5 mb-3 pt-2.5 text-xs font-medium uppercase leading-normal text-white shadow-primary-3 transition duration-150 ease-in-out hover:bg-red-accent-300 hover:shadow-red-2 focus:bg-red-accent-300 focus:shadow-primary-2 focus:outline-none focus:ring-0 active:bg-red-600 active:shadow-primary-2 dark:shadow-black/30 dark:hover:shadow-dark-strong dark:focus:shadow-dark-strong dark:active:shadow-dark-strong",
"hx-target": "#action-div-" + dropBadChars(galleryID),
"hx-swap": "outerHTML",
// post the Model ID as param
"hx-post": "/browse/delete/model/" + galleryID,
},
elem.I(
attrs.Props{
"class": "fa-solid fa-cancel pr-2",
},
),
elem.Text("Delete"),
)
}
// Javascript/HTMX doesn't like weird IDs
func dropBadChars(s string) string {
return strings.ReplaceAll(s, "@", "__")
}
type ProcessTracker interface {
Exists(string) bool
Get(string) string
}
func modalName(m *gallery.GalleryModel) string {
return m.Name + "-modal"
}
func modelDescription(m *gallery.GalleryModel) elem.Node {
urls := []elem.Node{}
for _, url := range m.URLs {
urls = append(urls,
elem.Li(attrs.Props{}, link(url, url)),
)
}
tagsNodes := []elem.Node{}
for _, tag := range m.Tags {
tagsNodes = append(tagsNodes,
searchableElement(tag, "fas fa-tag"),
)
}
return elem.Div(
attrs.Props{
"class": "p-6 text-surface dark:text-white",
},
elem.H5(
func ListModels(models []*gallery.GalleryModel, processTracker ProcessTracker, galleryService *services.GalleryService) string {
modelsElements := []elem.Node{}
descriptionDiv := func(m *gallery.GalleryModel) elem.Node {
return elem.Div(
attrs.Props{
"class": "mb-2 text-xl font-bold leading-tight",
"class": "p-6 text-surface dark:text-white",
},
elem.Text(bluemonday.StrictPolicy().Sanitize(m.Name)),
),
elem.Div( // small description
attrs.Props{
"class": "mb-4 text-sm truncate text-base",
},
elem.Text(bluemonday.StrictPolicy().Sanitize(m.Description)),
),
elem.Div(
attrs.Props{
"id": modalName(m),
"tabindex": "-1",
"aria-hidden": "true",
"class": "hidden overflow-y-auto overflow-x-hidden fixed top-0 right-0 left-0 z-50 justify-center items-center w-full md:inset-0 h-[calc(100%-1rem)] max-h-full",
},
elem.Div(
elem.H5(
attrs.Props{
"class": "relative p-4 w-full max-w-2xl max-h-full",
"class": "mb-2 text-xl font-bold leading-tight",
},
elem.Div(
attrs.Props{
"class": "relative p-4 w-full max-w-2xl max-h-full bg-white rounded-lg shadow dark:bg-gray-700",
},
// header
elem.Div(
attrs.Props{
"class": "flex items-center justify-between p-4 md:p-5 border-b rounded-t dark:border-gray-600",
},
elem.H3(
attrs.Props{
"class": "text-xl font-semibold text-gray-900 dark:text-white",
},
elem.Text(bluemonday.StrictPolicy().Sanitize(m.Name)),
),
elem.Button( // close button
attrs.Props{
"class": "text-gray-400 bg-transparent hover:bg-gray-200 hover:text-gray-900 rounded-lg text-sm w-8 h-8 ms-auto inline-flex justify-center items-center dark:hover:bg-gray-600 dark:hover:text-white",
"data-modal-hide": modalName(m),
},
elem.Raw(
`<svg class="w-3 h-3" aria-hidden="true" xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 14 14">
<path stroke="currentColor" stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="m1 1 6 6m0 0 6 6M7 7l6-6M7 7l-6 6"/>
</svg>`,
),
elem.Span(
attrs.Props{
"class": "sr-only",
},
elem.Text("Close modal"),
),
),
),
// body
elem.Div(
attrs.Props{
"class": "p-4 md:p-5 space-y-4",
},
elem.Div(
attrs.Props{
"class": "flex justify-center items-center",
},
elem.Img(attrs.Props{
// "class": "rounded-t-lg object-fit object-center h-96",
"class": "lazy rounded-t-lg max-h-48 max-w-96 object-cover mt-3 entered loaded",
"src": m.Icon,
"loading": "lazy",
}),
),
elem.P(
attrs.Props{
"class": "text-base leading-relaxed text-gray-500 dark:text-gray-400",
},
elem.Text(bluemonday.StrictPolicy().Sanitize(m.Description)),
),
elem.Hr(
attrs.Props{},
),
elem.P(
attrs.Props{
"class": "text-sm font-semibold text-gray-900 dark:text-white",
},
elem.Text("Links"),
),
elem.Ul(
attrs.Props{},
urls...,
),
elem.If(
len(m.Tags) > 0,
elem.Div(
attrs.Props{},
elem.P(
attrs.Props{
"class": "text-sm mb-5 font-semibold text-gray-900 dark:text-white",
},
elem.Text("Tags"),
),
elem.Div(
attrs.Props{
"class": "flex flex-row flex-wrap content-center",
},
tagsNodes...,
),
),
elem.Div(attrs.Props{}),
),
),
// Footer
elem.Div(
attrs.Props{
"class": "flex items-center p-4 md:p-5 border-t border-gray-200 rounded-b dark:border-gray-600",
},
elem.Button(
attrs.Props{
"data-modal-hide": modalName(m),
"class": "py-2.5 px-5 ms-3 text-sm font-medium text-gray-900 focus:outline-none bg-white rounded-lg border border-gray-200 hover:bg-gray-100 hover:text-blue-700 focus:z-10 focus:ring-4 focus:ring-gray-100 dark:focus:ring-gray-700 dark:bg-gray-800 dark:text-gray-400 dark:border-gray-600 dark:hover:text-white dark:hover:bg-gray-700",
},
elem.Text("Close"),
),
),
),
elem.Text(bluemonday.StrictPolicy().Sanitize(m.Name)),
),
elem.P(
attrs.Props{
"class": "mb-4 text-sm [&:not(:hover)]:truncate text-base",
},
elem.Text(bluemonday.StrictPolicy().Sanitize(m.Description)),
),
),
)
}
func modelActionItems(m *gallery.GalleryModel, processTracker ProcessTracker, galleryService *services.GalleryService) elem.Node {
galleryID := fmt.Sprintf("%s@%s", m.Gallery.Name, m.Name)
currentlyProcessing := processTracker.Exists(galleryID)
jobID := ""
isDeletionOp := false
if currentlyProcessing {
status := galleryService.GetStatus(galleryID)
if status != nil && status.Deletion {
isDeletionOp = true
}
jobID = processTracker.Get(galleryID)
// TODO:
// case not handled, if status == nil : "Waiting"
}
nodes := []elem.Node{
cardSpan("Repository: "+m.Gallery.Name, "fa-brands fa-git-alt"),
}
if m.License != "" {
nodes = append(nodes,
cardSpan("License: "+m.License, "fas fa-book"),
)
}
/*
tagsNodes := []elem.Node{}
for _, tag := range m.Tags {
tagsNodes = append(tagsNodes,
searchableElement(tag, "fas fa-tag"),
)
actionDiv := func(m *gallery.GalleryModel) elem.Node {
galleryID := fmt.Sprintf("%s@%s", m.Gallery.Name, m.Name)
currentlyProcessing := processTracker.Exists(galleryID)
jobID := ""
isDeletionOp := false
if currentlyProcessing {
status := galleryService.GetStatus(galleryID)
if status != nil && status.Deletion {
isDeletionOp = true
}
jobID = processTracker.Get(galleryID)
// TODO:
// case not handled, if status == nil : "Waiting"
}
nodes := []elem.Node{
cardSpan("Repository: "+m.Gallery.Name, "fa-brands fa-git-alt"),
}
nodes = append(nodes,
elem.Div(
attrs.Props{
"class": "flex flex-row flex-wrap content-center",
},
tagsNodes...,
),
)
if m.License != "" {
nodes = append(nodes,
cardSpan("License: "+m.License, "fas fa-book"),
)
}
for i, url := range m.URLs {
nodes = append(nodes,
buttonLink("Link #"+fmt.Sprintf("%d", i+1), url),
)
}
*/
tagsNodes := []elem.Node{}
for _, tag := range m.Tags {
tagsNodes = append(tagsNodes,
searchableElement(tag, "fas fa-tag"),
)
}
progressMessage := "Installation"
if isDeletionOp {
progressMessage = "Deletion"
}
return elem.Div(
attrs.Props{
"class": "px-6 pt-4 pb-2",
},
elem.P(
attrs.Props{
"class": "mb-4 text-base",
},
nodes...,
),
elem.Div(
attrs.Props{
"id": "action-div-" + dropBadChars(galleryID),
"class": "flow-root", // To order buttons left and right
},
infoButton(m),
nodes = append(nodes,
elem.Div(
attrs.Props{
"class": "float-right",
"class": "flex flex-row flex-wrap content-center",
},
tagsNodes...,
),
)
for i, url := range m.URLs {
nodes = append(nodes,
link("Link #"+fmt.Sprintf("%d", i+1), url),
)
}
progressMessage := "Installation"
if isDeletionOp {
progressMessage = "Deletion"
}
return elem.Div(
attrs.Props{
"class": "px-6 pt-4 pb-2",
},
elem.P(
attrs.Props{
"class": "mb-4 text-base",
},
nodes...,
),
elem.Div(
attrs.Props{
"id": "action-div-" + dropBadChars(galleryID),
},
elem.If(
currentlyProcessing,
@@ -335,18 +470,14 @@ func modelActionItems(m *gallery.GalleryModel, processTracker ProcessTracker, ga
elem.Node(elem.Div(
attrs.Props{},
reInstallButton(m.ID()),
deleteButton(m.ID()),
deleteButton(m.ID(), m.Name),
)),
installButton(m.ID()),
),
),
),
),
)
}
func ListModels(models []*gallery.GalleryModel, processTracker ProcessTracker, galleryService *services.GalleryService) string {
modelsElements := []elem.Node{}
)
}
for _, m := range models {
elems := []elem.Node{}
@@ -390,10 +521,7 @@ func ListModels(models []*gallery.GalleryModel, processTracker ProcessTracker, g
))
}
elems = append(elems,
modelDescription(m),
modelActionItems(m, processTracker, galleryService),
)
elems = append(elems, descriptionDiv(m), actionDiv(m))
modelsElements = append(modelsElements,
elem.Div(
attrs.Props{

View File

@@ -1,147 +0,0 @@
package elements
import (
"fmt"
"github.com/chasefleming/elem-go"
"github.com/chasefleming/elem-go/attrs"
"github.com/microcosm-cc/bluemonday"
"github.com/mudler/LocalAI/core/p2p"
)
func renderElements(n []elem.Node) string {
render := ""
for _, r := range n {
render += r.Render()
}
return render
}
func P2PNodeStats(nodes []p2p.NodeData) string {
/*
<div class="bg-gray-800 p-6 rounded-lg shadow-lg text-left">
<p class="text-xl font-semibold text-gray-200">Total Workers Detected: {{ len .Nodes }}</p>
{{ $online := 0 }}
{{ range .Nodes }}
{{ if .IsOnline }}
{{ $online = add $online 1 }}
{{ end }}
{{ end }}
<p class="text-xl font-semibold text-gray-200">Total Online Workers: {{$online}}</p>
</div>
*/
online := 0
for _, n := range nodes {
if n.IsOnline() {
online++
}
}
class := "text-green-500"
if online == 0 {
class = "text-red-500"
}
/*
<i class="fas fa-circle animate-pulse text-green-500 ml-2 mr-1"></i>
*/
circle := elem.I(attrs.Props{
"class": "fas fa-circle animate-pulse " + class + " ml-2 mr-1",
})
nodesElements := []elem.Node{
elem.Span(
attrs.Props{
"class": class,
},
circle,
elem.Text(fmt.Sprintf("%d", online)),
),
elem.Span(
attrs.Props{
"class": "text-gray-200",
},
elem.Text(fmt.Sprintf("/%d", len(nodes))),
),
}
return renderElements(nodesElements)
}
func P2PNodeBoxes(nodes []p2p.NodeData) string {
/*
<div class="bg-gray-800 p-4 rounded-lg shadow-lg text-left">
<div class="flex items-center mb-2">
<i class="fas fa-desktop text-gray-400 mr-2"></i>
<span class="text-gray-200 font-semibold">{{.ID}}</span>
</div>
<p class="text-sm text-gray-400 mt-2 flex items-center">
Status:
<i class="fas fa-circle {{ if .IsOnline }}text-green-500{{ else }}text-red-500{{ end }} ml-2 mr-1"></i>
<span class="{{ if .IsOnline }}text-green-400{{ else }}text-red-400{{ end }}">
{{ if .IsOnline }}Online{{ else }}Offline{{ end }}
</span>
</p>
</div>
*/
nodesElements := []elem.Node{}
for _, n := range nodes {
nodesElements = append(nodesElements,
elem.Div(
attrs.Props{
"class": "bg-gray-700 p-6 rounded-lg shadow-lg text-left",
},
elem.P(
attrs.Props{
"class": "text-sm text-gray-400 mt-2 flex",
},
elem.I(
attrs.Props{
"class": "fas fa-desktop text-gray-400 mr-2",
},
),
elem.Text("Name: "),
elem.Span(
attrs.Props{
"class": "text-gray-200 font-semibold ml-2 mr-1",
},
elem.Text(bluemonday.StrictPolicy().Sanitize(n.ID)),
),
elem.Text("Status: "),
elem.If(
n.IsOnline(),
elem.I(
attrs.Props{
"class": "fas fa-circle animate-pulse text-green-500 ml-2 mr-1",
},
),
elem.I(
attrs.Props{
"class": "fas fa-circle animate-pulse text-red-500 ml-2 mr-1",
},
),
),
elem.If(
n.IsOnline(),
elem.Span(
attrs.Props{
"class": "text-green-400",
},
elem.Text("Online"),
),
elem.Span(
attrs.Props{
"class": "text-red-400",
},
elem.Text("Offline"),
),
),
),
))
}
return renderElements(nodesElements)
}

View File

@@ -1,89 +0,0 @@
package elements
import (
"github.com/chasefleming/elem-go"
"github.com/chasefleming/elem-go/attrs"
"github.com/microcosm-cc/bluemonday"
)
func DoneProgress(galleryID, text string, showDelete bool) string {
return elem.Div(
attrs.Props{
"id": "action-div-" + dropBadChars(galleryID),
},
elem.H3(
attrs.Props{
"role": "status",
"id": "pblabel",
"tabindex": "-1",
"autofocus": "",
},
elem.Text(bluemonday.StrictPolicy().Sanitize(text)),
),
elem.If(showDelete, deleteButton(galleryID), reInstallButton(galleryID)),
).Render()
}
func ErrorProgress(err, galleryName string) string {
return elem.Div(
attrs.Props{},
elem.H3(
attrs.Props{
"role": "status",
"id": "pblabel",
"tabindex": "-1",
"autofocus": "",
},
elem.Text("Error "+bluemonday.StrictPolicy().Sanitize(err)),
),
installButton(galleryName),
).Render()
}
func ProgressBar(progress string) string {
return elem.Div(attrs.Props{
"class": "progress",
"role": "progressbar",
"aria-valuemin": "0",
"aria-valuemax": "100",
"aria-valuenow": "0",
"aria-labelledby": "pblabel",
},
elem.Div(attrs.Props{
"id": "pb",
"class": "progress-bar",
"style": "width:" + progress + "%",
}),
).Render()
}
func StartProgressBar(uid, progress, text string) string {
if progress == "" {
progress = "0"
}
return elem.Div(
attrs.Props{
"hx-trigger": "done",
"hx-get": "/browse/job/" + uid,
"hx-swap": "outerHTML",
"hx-target": "this",
},
elem.H3(
attrs.Props{
"role": "status",
"id": "pblabel",
"tabindex": "-1",
"autofocus": "",
},
elem.Text(bluemonday.StrictPolicy().Sanitize(text)), //Perhaps overly defensive
elem.Div(attrs.Props{
"hx-get": "/browse/job/progress/" + uid,
"hx-trigger": "every 600ms",
"hx-target": "this",
"hx-swap": "innerHTML",
},
elem.Raw(ProgressBar(progress)),
),
),
).Render()
}

View File

@@ -9,19 +9,16 @@ import (
"github.com/gofiber/fiber/v2"
"github.com/mudler/LocalAI/core/schema"
"github.com/rs/zerolog/log"
"github.com/mudler/LocalAI/pkg/utils"
)
// TTSEndpoint is the OpenAI Speech API endpoint https://platform.openai.com/docs/api-reference/audio/createSpeech
//
// @Summary Generates audio from the input text.
// @Accept json
// @Produce audio/x-wav
// @Param request body schema.TTSRequest true "query params"
// @Success 200 {string} binary "generated audio/wav file"
// @Router /v1/audio/speech [post]
// @Router /tts [post]
// @Summary Generates audio from the input text.
// @Accept json
// @Produce audio/x-wav
// @Param request body schema.TTSRequest true "query params"
// @Success 200 {string} binary "generated audio/wav file"
// @Router /v1/audio/speech [post]
// @Router /tts [post]
func TTSEndpoint(cl *config.BackendConfigLoader, ml *model.ModelLoader, appConfig *config.ApplicationConfig) func(c *fiber.Ctx) error {
return func(c *fiber.Ctx) error {
@@ -70,13 +67,6 @@ func TTSEndpoint(cl *config.BackendConfigLoader, ml *model.ModelLoader, appConfi
if err != nil {
return err
}
// Convert generated file to target format
filePath, err = utils.AudioConvert(filePath, input.Format)
if err != nil {
return err
}
return c.Download(filePath)
}
}

View File

@@ -136,11 +136,6 @@ func ImageEndpoint(cl *config.BackendConfigLoader, ml *model.ModelLoader, appCon
config.Backend = model.StableDiffusionBackend
}
if !strings.Contains(input.Size, "x") {
input.Size = "512x512"
log.Warn().Msgf("Invalid size, using default 512x512")
}
sizeParts := strings.Split(input.Size, "x")
if len(sizeParts) != 2 {
return fmt.Errorf("invalid value for 'size'")

View File

File diff suppressed because one or more lines are too long

View File

@@ -1,5 +1,5 @@
<footer class="text-center py-8">
LocalAI Version {{.Version}}<br>
<a href='https://github.com/mudler/LocalAI' class="text-blue-400 hover:text-blue-600" target="_blank">LocalAI</a> © 2023-2024 <a href='https://mudler.pm' class="text-blue-400 hover:text-blue-600" target="_blank">Ettore Di Giacinto</a>
<a href='https://localai.io' class="text-blue-400 hover:text-blue-600" target="_blank">LocalAI</a> © 2023-2024 <a href='https://mudler.pm' class="text-blue-400 hover:text-blue-600" target="_blank">Ettore Di Giacinto</a>
</footer>
<script src="/static/assets/tw-elements.js"></script>

View File

@@ -57,7 +57,6 @@
<link href="/static/assets/fontawesome/css/fontawesome.css" rel="stylesheet" />
<link href="/static/assets/fontawesome/css/brands.css" rel="stylesheet" />
<link href="/static/assets/fontawesome/css/solid.css" rel="stylesheet" />
<script src="/static/assets/flowbite.min.js"></script>
<script src="/static/assets/htmx.js" crossorigin="anonymous"></script>
<!-- P2P Animation START -->
<style>
@@ -119,11 +118,4 @@
100% { transform: rotate(0deg); } /* Return to center */
}
</style>
<!-- https://stackoverflow.com/questions/76051980/flowbite-component-not-working-when-loaded-via-htmx-django-project -->
<script>
htmx.onLoad(function(content) {
initFlowbite();
})
</script>
</head>

View File

@@ -231,14 +231,10 @@ func discoveryTunnels(ctx context.Context, n *node.Node, token, servicesID strin
data := ledger.LastBlock().Storage[servicesID]
if logLevel == logLevelDebug {
// We want to surface this debugging data only if p2p logging is set to debug
// (and not generally the whole application, as this can be really noisy)
zlog.Debug().Any("data", ledger.LastBlock().Storage).Msg("Ledger data")
}
zlog.Debug().Any("data", ledger.LastBlock().Storage).Msg("Ledger data")
for k, v := range data {
// New worker found in the ledger data as k (worker id)
zlog.Debug().Msgf("New worker found in the ledger data '%s'", k)
nd := &NodeData{}
if err := v.Unmarshal(nd); err != nil {
zlog.Error().Msg("cannot unmarshal node data")
@@ -273,7 +269,7 @@ func ensureService(ctx context.Context, n *node.Node, nd *NodeData, sserv string
if ndService, found := service[nd.Name]; !found {
if !nd.IsOnline() {
// if node is offline and not present, do nothing
// Node nd.ID is offline
zlog.Debug().Msgf("Node %s is offline", nd.ID)
return
}
@@ -385,6 +381,10 @@ func newNodeOpts(token string) ([]node.Option, error) {
noDHT := os.Getenv("LOCALAI_P2P_DISABLE_DHT") == "true"
noLimits := os.Getenv("LOCALAI_P2P_ENABLE_LIMITS") == "true"
loglevel := os.Getenv("LOCALAI_P2P_LOGLEVEL")
if loglevel == "" {
loglevel = "info"
}
libp2ploglevel := os.Getenv("LOCALAI_LIBP2P_LOGLEVEL")
if libp2ploglevel == "" {
libp2ploglevel = "fatal"
@@ -396,7 +396,7 @@ func newNodeOpts(token string) ([]node.Option, error) {
},
NetworkToken: token,
LowProfile: false,
LogLevel: logLevel,
LogLevel: loglevel,
Libp2pLogLevel: libp2ploglevel,
Ledger: config.Ledger{
SyncInterval: defaultInterval,

View File

@@ -1,19 +0,0 @@
package p2p
import (
"os"
"strings"
)
var logLevel = strings.ToLower(os.Getenv("LOCALAI_P2P_LOGLEVEL"))
const (
logLevelDebug = "debug"
logLevelInfo = "info"
)
func init() {
if logLevel == "" {
logLevel = logLevelInfo
}
}

View File

@@ -32,7 +32,6 @@ type TTSRequest struct {
Voice string `json:"voice" yaml:"voice"` // voice audio file or speaker id
Backend string `json:"backend" yaml:"backend"`
Language string `json:"language,omitempty" yaml:"language,omitempty"` // (optional) language to use with TTS model
Format string `json:"response_format,omitempty" yaml:"response_format,omitempty"` // (optional) output format
}
type StoresSet struct {

View File

@@ -160,10 +160,15 @@ func Startup(opts ...config.AppOption) (*config.BackendConfigLoader, *model.Mode
log.Debug().Msgf("Auto loading model %s into memory from file: %s", m, cfg.Model)
o := backend.ModelOptions(*cfg, options)
o := backend.ModelOptions(*cfg, options, []model.Option{})
var backendErr error
_, backendErr = ml.Load(o...)
if cfg.Backend != "" {
o = append(o, model.WithBackendString(cfg.Backend))
_, backendErr = ml.BackendLoader(o...)
} else {
_, backendErr = ml.GreedyLoader(o...)
}
if backendErr != nil {
return nil, nil, nil, err
}

View File

@@ -201,21 +201,3 @@ curl -L http://localhost:8080/tts \
"input": "Bonjour, je suis Ana Florence. Comment puis-je vous aider?"
}' | aplay
```
## Response format
To provide some compatibility with OpenAI API regarding `response_format`, ffmpeg must be installed (or a docker image including ffmpeg used) to leverage converting the generated wav file before the api provide its response.
Warning regarding a change in behaviour. Before this addition, the parameter was ignored and a wav file was always returned, with potential codec errors later in the integration (like trying to decode a mp3 file from a wav, which is the default format used by OpenAI)
Supported format thanks to ffmpeg are `wav`, `mp3`, `aac`, `flac`, `opus`, defaulting to `wav` if an unknown or no format is provided.
```bash
curl http://localhost:8080/tts -H "Content-Type: application/json" -d '{
"input": "Hello world",
"model": "tts",
"response_format": "mp3"
}'
```
If a `response_format` is added in the query (other than `wav`) and ffmpeg is not available, the call will fail.

View File

@@ -1,133 +0,0 @@
<!-- Top Header -->
<div id="top-header" class="top-header d-print-none">
<div class="header-bar d-flex justify-content-between">
<div class="d-flex align-items-center">
<a href='{{ with .Site.Params.docs.logoLinkURL }}{{ . }}{{ else }}{{ relLangURL "" }}{{ end }}' class="logo-icon me-3" aria-label="HomePage" alt="HomePage">
<div class="small">
{{ with resources.Get "images/logos/mark.svg" }}
{{ .Content | safeHTML }}
{{ end }}
</div>
<div class="big">
{{ with resources.Get "images/logos/logo.svg" }}
{{ .Content | safeHTML }}
{{ end }}
</div>
</a>
<button id="close-sidebar" class="btn btn-icon btn-soft">
<span class="material-icons size-20 menu-icon align-middle">menu</span>
</button>
{{ if and (.Site.Params.docsearch.appID) (.Site.Params.docsearch.apiKey) -}}
<span><div id="docsearch"></div></span>
{{ end }}
{{ if or (not (isset .Site.Params.flexsearch "enabled")) (eq .Site.Params.flexsearch.enabled true) -}}
{{ if and (.Site.Params.docsearch.appID) (.Site.Params.docsearch.apiKey) -}}
{{ else }}
<!-- <form class="flexsearch position-relative flex-grow-1 ms-2 me-lg-2 d-none">
<input id="flexsearch" class="form-control is-search" type="search" placeholder="{{ i18n "search_title" }}" aria-label="{{ i18n "search_title" }}" autocomplete="off">
<div id="suggestions" class="shadow bg-white rounded d-none"></div>
</form> -->
<button id="flexsearch-button" class="ms-3 btn btn-soft" data-bs-toggle="collapse" data-bs-target="#FlexSearchCollapse" aria-expanded="false" aria-controls="FlexSearchCollapse">
<span class="material-icons size-20 menu-icon align-middle">search</span>
<span class="flexsearch-button-placeholder ms-1 me-2 d-none d-sm-block">{{ i18n "search_title" }}</span>
<div class="d-none d-sm-block">
<span class="flexsearch-button-keys">
<kbd class="flexsearch-button-cmd-key">
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View File

@@ -6,4 +6,4 @@
### For models with an entire YAML file to be embededd, put the file inside the `models`
### directory, it will be automatically available with the file name as key (without the .yaml extension)
phi-2: "github://mudler/LocalAI-examples/configurations/phi-2.yaml@main"
phi-2: "github://mudler/LocalAI/examples/configurations/phi-2.yaml@master"

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@@ -56,7 +56,4 @@
sha: "8a9a74f4455f392ec3e7499cfda6097b536bb4b7f1e529a079c3d953c08b54ca"
- filename: "KFOlCnqEu92Fr1MmYUtfBBc9.ttf"
url: "https://fonts.gstatic.com/s/roboto/v30/KFOlCnqEu92Fr1MmYUtfBBc9.ttf"
sha: "361a50f8a6c816ba4306c5290b7e487a726e1b4dcc3d8d7e4acf1fc2dae9f551"
- filename: "flowbite.min.js"
url: "https://cdnjs.cloudflare.com/ajax/libs/flowbite/2.3.0/flowbite.min.js"
sha: "d2a1a72a4c2399e43c01412b86b9957c4df1845f2e0586607c7e55b9ae949cf8"
sha: "361a50f8a6c816ba4306c5290b7e487a726e1b4dcc3d8d7e4acf1fc2dae9f551"

View File

@@ -1,3 +1,190 @@
# LocalAI Examples
# Examples
LocalAI examples were moved to a dedicated repository: https://github.com/mudler/LocalAI-examples
| [ChatGPT OSS alternative](https://github.com/go-skynet/LocalAI/tree/master/examples/chatbot-ui) | [Image generation](https://localai.io/api-endpoints/index.html#image-generation) |
|------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------|
| ![Screenshot from 2023-04-26 23-59-55](https://user-images.githubusercontent.com/2420543/234715439-98d12e03-d3ce-4f94-ab54-2b256808e05e.png) | ![b6441997879](https://github.com/go-skynet/LocalAI/assets/2420543/d50af51c-51b7-4f39-b6c2-bf04c403894c) |
| [Telegram bot](https://github.com/go-skynet/LocalAI/tree/master/examples/telegram-bot) | [Flowise](https://github.com/go-skynet/LocalAI/tree/master/examples/flowise) |
|------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------|
![Screenshot from 2023-06-09 00-36-26](https://github.com/go-skynet/LocalAI/assets/2420543/e98b4305-fa2d-41cf-9d2f-1bb2d75ca902) | ![Screenshot from 2023-05-30 18-01-03](https://github.com/go-skynet/LocalAI/assets/2420543/02458782-0549-4131-971c-95ee56ec1af8)| |
Here is a list of projects that can easily be integrated with the LocalAI backend.
### Projects
### AutoGPT
_by [@mudler](https://github.com/mudler)_
This example shows how to use AutoGPT with LocalAI.
[Check it out here](https://github.com/go-skynet/LocalAI/tree/master/examples/autoGPT/)
### Chatbot-UI
_by [@mkellerman](https://github.com/mkellerman)_
![Screenshot from 2023-04-26 23-59-55](https://user-images.githubusercontent.com/2420543/234715439-98d12e03-d3ce-4f94-ab54-2b256808e05e.png)
This integration shows how to use LocalAI with [mckaywrigley/chatbot-ui](https://github.com/mckaywrigley/chatbot-ui).
[Check it out here](https://github.com/go-skynet/LocalAI/tree/master/examples/chatbot-ui/)
There is also a separate example to show how to manually setup a model: [example](https://github.com/go-skynet/LocalAI/tree/master/examples/chatbot-ui-manual/)
### K8sGPT
_by [@mudler](https://github.com/mudler)_
This example show how to use LocalAI inside Kubernetes with [k8sgpt](https://k8sgpt.ai).
![Screenshot from 2023-06-19 23-58-47](https://github.com/go-skynet/go-ggml-transformers.cpp/assets/2420543/cab87409-ee68-44ae-8d53-41627fb49509)
### Fine-tuning a model and convert it to gguf to use it with LocalAI
_by [@mudler](https://github.com/mudler)_
This example is an e2e example on how to fine-tune a model with [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) and convert it to gguf to use it with LocalAI.
[Check it out here](https://github.com/mudler/LocalAI/tree/master/examples/e2e-fine-tuning/)
### Flowise
_by [@mudler](https://github.com/mudler)_
This example shows how to use [FlowiseAI/Flowise](https://github.com/FlowiseAI/Flowise) with LocalAI.
[Check it out here](https://github.com/go-skynet/LocalAI/tree/master/examples/flowise/)
### Discord bot
_by [@mudler](https://github.com/mudler)_
Run a discord bot which lets you talk directly with a model
[Check it out here](https://github.com/go-skynet/LocalAI/tree/master/examples/discord-bot/), or for a live demo you can talk with our bot in #random-bot in our discord server.
### Langchain
_by [@dave-gray101](https://github.com/dave-gray101)_
A ready to use example to show e2e how to integrate LocalAI with langchain
[Check it out here](https://github.com/go-skynet/LocalAI/tree/master/examples/langchain/)
### Langchain Python
_by [@mudler](https://github.com/mudler)_
A ready to use example to show e2e how to integrate LocalAI with langchain
[Check it out here](https://github.com/go-skynet/LocalAI/tree/master/examples/langchain-python/)
### LocalAI functions
_by [@mudler](https://github.com/mudler)_
A ready to use example to show how to use OpenAI functions with LocalAI
[Check it out here](https://github.com/go-skynet/LocalAI/tree/master/examples/functions/)
### LocalAI WebUI
_by [@dhruvgera](https://github.com/dhruvgera)_
![image](https://user-images.githubusercontent.com/42107491/235344183-44b5967d-ba22-4331-804c-8da7004a5d35.png)
A light, community-maintained web interface for LocalAI
[Check it out here](https://github.com/go-skynet/LocalAI/tree/master/examples/localai-webui/)
### How to run rwkv models
_by [@mudler](https://github.com/mudler)_
A full example on how to run RWKV models with LocalAI
[Check it out here](https://github.com/go-skynet/LocalAI/tree/master/examples/rwkv/)
### PrivateGPT
_by [@mudler](https://github.com/mudler)_
A full example on how to run PrivateGPT with LocalAI
[Check it out here](https://github.com/go-skynet/LocalAI/tree/master/examples/privateGPT/)
### Slack bot
_by [@mudler](https://github.com/mudler)_
Run a slack bot which lets you talk directly with a model
[Check it out here](https://github.com/go-skynet/LocalAI/tree/master/examples/slack-bot/)
### Slack bot (Question answering)
_by [@mudler](https://github.com/mudler)_
Run a slack bot, ideally for teams, which lets you ask questions on a documentation website, or a github repository.
[Check it out here](https://github.com/go-skynet/LocalAI/tree/master/examples/slack-qa-bot/)
### Question answering on documents with llama-index
_by [@mudler](https://github.com/mudler)_
Shows how to integrate with [Llama-Index](https://gpt-index.readthedocs.io/en/stable/getting_started/installation.html) to enable question answering on a set of documents.
[Check it out here](https://github.com/go-skynet/LocalAI/tree/master/examples/query_data/)
### Question answering on documents with langchain and chroma
_by [@mudler](https://github.com/mudler)_
Shows how to integrate with `Langchain` and `Chroma` to enable question answering on a set of documents.
[Check it out here](https://github.com/go-skynet/LocalAI/tree/master/examples/langchain-chroma/)
### Telegram bot
_by [@mudler](https://github.com/mudler)
![Screenshot from 2023-06-09 00-36-26](https://github.com/go-skynet/LocalAI/assets/2420543/e98b4305-fa2d-41cf-9d2f-1bb2d75ca902)
Use LocalAI to power a Telegram bot assistant, with Image generation and audio support!
[Check it out here](https://github.com/go-skynet/LocalAI/tree/master/examples/telegram-bot/)
### Template for Runpod.io
_by [@fHachenberg](https://github.com/fHachenberg)_
Allows to run any LocalAI-compatible model as a backend on the servers of https://runpod.io
[Check it out here](https://runpod.io/gsc?template=uv9mtqnrd0&ref=984wlcra)
### Continue
_by [@gruberdev](https://github.com/gruberdev)_
<img src="continue/img/screen.png" width="600" height="200" alt="Screenshot">
Demonstrates how to integrate an open-source copilot alternative that enhances code analysis, completion, and improvements. This approach seamlessly integrates with any LocalAI model, offering a more user-friendly experience.
[Check it out here](https://github.com/go-skynet/LocalAI/tree/master/examples/continue/)
### Streamlit bot
_by [@majoshi1](https://github.com/majoshi1)_
![Screenshot](streamlit-bot/streamlit-bot.png)
A chat bot made using `Streamlit` & LocalAI.
[Check it out here](https://github.com/go-skynet/LocalAI/tree/master/examples/streamlit-bot/)
## Want to contribute?
Create an issue, and put `Example: <description>` in the title! We will post your examples here.

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@@ -0,0 +1,9 @@
# CPU .env docs: https://localai.io/howtos/easy-setup-docker-cpu/
# GPU .env docs: https://localai.io/howtos/easy-setup-docker-gpu/
OPENAI_API_KEY=sk---anystringhere
OPENAI_API_BASE=http://api:8080/v1
# Models to preload at start
# Here we configure gpt4all as gpt-3.5-turbo and bert as embeddings,
# see other options in the model gallery at https://github.com/go-skynet/model-gallery
PRELOAD_MODELS=[{"url": "github:go-skynet/model-gallery/gpt4all-j.yaml", "name": "gpt-3.5-turbo"}, { "url": "github:go-skynet/model-gallery/bert-embeddings.yaml", "name": "text-embedding-ada-002"}]

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@@ -0,0 +1,36 @@
# AutoGPT
Example of integration with [AutoGPT](https://github.com/Significant-Gravitas/Auto-GPT).
## Run
```bash
# Clone LocalAI
git clone https://github.com/go-skynet/LocalAI
cd LocalAI/examples/autoGPT
cp -rfv .env.example .env
# Edit the .env file to set a different model by editing `PRELOAD_MODELS`.
vim .env
docker-compose run --rm auto-gpt
```
Note: The example automatically downloads the `gpt4all` model as it is under a permissive license. The GPT4All model does not seem to be enough to run AutoGPT. WizardLM-7b-uncensored seems to perform better (with `f16: true`).
## Without docker
Run AutoGPT with `OPENAI_API_BASE` pointing to the LocalAI endpoint. If you run it locally for instance:
```
OPENAI_API_BASE=http://localhost:8080 python ...
```
Note: you need a model named `gpt-3.5-turbo` and `text-embedding-ada-002`. You can preload those in LocalAI at start by setting in the env:
```
PRELOAD_MODELS=[{"url": "github:go-skynet/model-gallery/gpt4all-j.yaml", "name": "gpt-3.5-turbo"}, { "url": "github:go-skynet/model-gallery/bert-embeddings.yaml", "name": "text-embedding-ada-002"}]
```

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@@ -0,0 +1,42 @@
version: "3.9"
services:
api:
image: quay.io/go-skynet/local-ai:latest
ports:
- 8080:8080
env_file:
- .env
environment:
- DEBUG=true
- MODELS_PATH=/models
volumes:
- ./models:/models:cached
command: ["/usr/bin/local-ai" ]
auto-gpt:
image: significantgravitas/auto-gpt
depends_on:
api:
condition: service_healthy
redis:
condition: service_started
env_file:
- .env
environment:
MEMORY_BACKEND: ${MEMORY_BACKEND:-redis}
REDIS_HOST: ${REDIS_HOST:-redis}
profiles: ["exclude-from-up"]
volumes:
- ./auto_gpt_workspace:/app/autogpt/auto_gpt_workspace
- ./data:/app/data
## allow auto-gpt to write logs to disk
- ./logs:/app/logs
## uncomment following lines if you want to make use of these files
## you must have them existing in the same folder as this docker-compose.yml
#- type: bind
# source: ./azure.yaml
# target: /app/azure.yaml
#- type: bind
# source: ./ai_settings.yaml
# target: /app/ai_settings.yaml
redis:
image: "redis/redis-stack-server:latest"

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# Use an official Python runtime as a parent image
FROM python:3.12-slim
# Set the working directory in the container
WORKDIR /app
# Copy the current directory contents into the container at /app
COPY requirements.txt /app
# Install c++ compiler
RUN apt-get update \
&& DEBIAN_FRONTEND=noninteractive apt-get install -y build-essential \
&& apt-get clean \
&& rm -rf /var/lib/apt/lists/*
# Install any needed packages specified in requirements.txt
RUN pip install --no-cache-dir -r requirements.txt \
&& DEBIAN_FRONTEND=noninteractive apt-get remove -y build-essential \
&& apt-get clean \
&& rm -rf /var/lib/apt/lists/*
COPY . /app
# Run app.py when the container launches
CMD ["chainlit", "run", "-h", "--host", "0.0.0.0", "main.py" ]

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# LocalAI Demonstration with Embeddings and Chainlit
This demonstration shows you how to use embeddings with existing data in `LocalAI`, and how to integrate it with Chainlit for an interactive querying experience. We are using the `llama_index` library to facilitate the embedding and querying processes, and `chainlit` to provide an interactive interface. The `Weaviate` client is used as the embedding source.
## Prerequisites
Before proceeding, make sure you have the following installed:
- Weaviate client
- LocalAI and its dependencies
- Chainlit and its dependencies
## Getting Started
1. Clone this repository:
2. Navigate to the project directory:
3. Run the example: `chainlit run main.py`
# Highlight on `llama_index` and `chainlit`
`llama_index` is the key library that facilitates the process of embedding and querying data in LocalAI. It provides a seamless interface to integrate various components, such as `WeaviateVectorStore`, `LocalAI`, `ServiceContext`, and more, for a smooth querying experience.
`chainlit` is used to provide an interactive interface for users to query the data and see the results in real-time. It integrates with llama_index to handle the querying process and display the results to the user.
In this example, `llama_index` is used to set up the `VectorStoreIndex` and `QueryEngine`, and `chainlit` is used to handle the user interactions with `LocalAI` and display the results.

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localAI:
temperature: 0
modelName: gpt-3.5-turbo
apiBase: http://local-ai.default
apiKey: stub
streaming: True
weviate:
url: http://weviate.local
index: AIChroma
query:
mode: hybrid
topK: 1
alpha: 0.0
chunkSize: 1024
embedding:
model: BAAI/bge-small-en-v1.5

82
examples/chainlit/main.py Normal file
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import os
import weaviate
from llama_index.storage.storage_context import StorageContext
from llama_index.vector_stores import WeaviateVectorStore
from llama_index.query_engine.retriever_query_engine import RetrieverQueryEngine
from llama_index.callbacks.base import CallbackManager
from llama_index import (
LLMPredictor,
ServiceContext,
StorageContext,
VectorStoreIndex,
)
import chainlit as cl
from llama_index.llms import LocalAI
from llama_index.embeddings import HuggingFaceEmbedding
import yaml
# Load the configuration file
with open("config.yaml", "r") as ymlfile:
cfg = yaml.safe_load(ymlfile)
# Get the values from the configuration file or set the default values
temperature = cfg['localAI'].get('temperature', 0)
model_name = cfg['localAI'].get('modelName', "gpt-3.5-turbo")
api_base = cfg['localAI'].get('apiBase', "http://local-ai.default")
api_key = cfg['localAI'].get('apiKey', "stub")
streaming = cfg['localAI'].get('streaming', True)
weaviate_url = cfg['weviate'].get('url', "http://weviate.default")
index_name = cfg['weviate'].get('index', "AIChroma")
query_mode = cfg['query'].get('mode', "hybrid")
topK = cfg['query'].get('topK', 1)
alpha = cfg['query'].get('alpha', 0.0)
embed_model_name = cfg['embedding'].get('model', "BAAI/bge-small-en-v1.5")
chunk_size = cfg['query'].get('chunkSize', 1024)
embed_model = HuggingFaceEmbedding(model_name=embed_model_name)
llm = LocalAI(temperature=temperature, model_name=model_name, api_base=api_base, api_key=api_key, streaming=streaming)
llm.globally_use_chat_completions = True;
client = weaviate.Client(weaviate_url)
vector_store = WeaviateVectorStore(weaviate_client=client, index_name=index_name)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
@cl.on_chat_start
async def factory():
llm_predictor = LLMPredictor(
llm=llm
)
service_context = ServiceContext.from_defaults(embed_model=embed_model, callback_manager=CallbackManager([cl.LlamaIndexCallbackHandler()]), llm_predictor=llm_predictor, chunk_size=chunk_size)
index = VectorStoreIndex.from_vector_store(
vector_store,
storage_context=storage_context,
service_context=service_context
)
query_engine = index.as_query_engine(vector_store_query_mode=query_mode, similarity_top_k=topK, alpha=alpha, streaming=True)
cl.user_session.set("query_engine", query_engine)
@cl.on_message
async def main(message: cl.Message):
query_engine = cl.user_session.get("query_engine")
response = await cl.make_async(query_engine.query)(message.content)
response_message = cl.Message(content="")
for token in response.response_gen:
await response_message.stream_token(token=token)
if response.response_txt:
response_message.content = response.response_txt
await response_message.send()

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llama_index==0.11.19
requests==2.32.3
weaviate_client==4.9.0
transformers
torch
chainlit

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# chatbot-ui
Example of integration with [mckaywrigley/chatbot-ui](https://github.com/mckaywrigley/chatbot-ui).
![Screenshot from 2023-04-26 23-59-55](https://user-images.githubusercontent.com/2420543/234715439-98d12e03-d3ce-4f94-ab54-2b256808e05e.png)
## Setup
```bash
# Clone LocalAI
git clone https://github.com/go-skynet/LocalAI
cd LocalAI/examples/chatbot-ui
# (optional) Checkout a specific LocalAI tag
# git checkout -b build <TAG>
# Download gpt4all-j to models/
wget https://gpt4all.io/models/ggml-gpt4all-j.bin -O models/ggml-gpt4all-j
# start with docker-compose
docker-compose up -d --pull always
# or you can build the images with:
# docker-compose up -d --build
```
Then browse to `http://localhost:3000` to view the Web UI.
## Pointing chatbot-ui to a separately managed LocalAI service
If you want to use the [chatbot-ui example](https://github.com/go-skynet/LocalAI/tree/master/examples/chatbot-ui) with an externally managed LocalAI service, you can alter the `docker-compose.yaml` file so that it looks like the below. You will notice the file is smaller, because we have removed the section that would normally start the LocalAI service. Take care to update the IP address (or FQDN) that the chatbot-ui service tries to access (marked `<<LOCALAI_IP>>` below):
```yaml
version: '3.6'
services:
chatgpt:
image: ghcr.io/mckaywrigley/chatbot-ui:main
ports:
- 3000:3000
environment:
- 'OPENAI_API_KEY=sk-XXXXXXXXXXXXXXXXXXXX'
- 'OPENAI_API_HOST=http://<<LOCALAI_IP>>:8080'
```
Once you've edited the `docker-compose.yaml`, you can start it with `docker compose up`, then browse to `http://localhost:3000` to view the Web UI.
## Accessing chatbot-ui
Open http://localhost:3000 for the Web UI.

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version: '3.6'
services:
api:
image: quay.io/go-skynet/local-ai:latest
build:
context: ../../
dockerfile: Dockerfile
ports:
- 8080:8080
environment:
- DEBUG=true
- MODELS_PATH=/models
volumes:
- ./models:/models:cached
command: ["/usr/bin/local-ai" ]
chatgpt:
image: ghcr.io/mckaywrigley/chatbot-ui:main
ports:
- 3000:3000
environment:
- 'OPENAI_API_KEY=sk-XXXXXXXXXXXXXXXXXXXX'
- 'OPENAI_API_HOST=http://api:8080'

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../models

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# chatbot-ui
Example of integration with [mckaywrigley/chatbot-ui](https://github.com/mckaywrigley/chatbot-ui).
![Screenshot from 2023-04-26 23-59-55](https://user-images.githubusercontent.com/2420543/234715439-98d12e03-d3ce-4f94-ab54-2b256808e05e.png)
## Run
In this example LocalAI will download the gpt4all model and set it up as "gpt-3.5-turbo". See the `docker-compose.yaml`
```bash
# Clone LocalAI
git clone https://github.com/go-skynet/LocalAI
cd LocalAI/examples/chatbot-ui
# start with docker-compose
docker-compose up --pull always
# or you can build the images with:
# docker-compose up -d --build
```
Then browse to `http://localhost:3000` to view the Web UI.
## Pointing chatbot-ui to a separately managed LocalAI service
If you want to use the [chatbot-ui example](https://github.com/go-skynet/LocalAI/tree/master/examples/chatbot-ui) with an externally managed LocalAI service, you can alter the `docker-compose.yaml` file so that it looks like the below. You will notice the file is smaller, because we have removed the section that would normally start the LocalAI service. Take care to update the IP address (or FQDN) that the chatbot-ui service tries to access (marked `<<LOCALAI_IP>>` below):
```yaml
version: '3.6'
services:
chatgpt:
image: ghcr.io/mckaywrigley/chatbot-ui:main
ports:
- 3000:3000
environment:
- 'OPENAI_API_KEY=sk-XXXXXXXXXXXXXXXXXXXX'
- 'OPENAI_API_HOST=http://<<LOCALAI_IP>>:8080'
```
Once you've edited the `docker-compose.yaml`, you can start it with `docker compose up`, then browse to `http://localhost:3000` to view the Web UI.
## Accessing chatbot-ui
Open http://localhost:3000 for the Web UI.

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version: '3.6'
services:
api:
image: quay.io/go-skynet/local-ai:latest
# As initially LocalAI will download the models defined in PRELOAD_MODELS
# you might need to tweak the healthcheck values here according to your network connection.
# Here we give a timespan of 20m to download all the required files.
healthcheck:
test: ["CMD", "curl", "-f", "http://localhost:8080/readyz"]
interval: 1m
timeout: 20m
retries: 20
build:
context: ../../
dockerfile: Dockerfile
ports:
- 8080:8080
environment:
- DEBUG=true
- MODELS_PATH=/models
# You can preload different models here as well.
# See: https://github.com/go-skynet/model-gallery
- 'PRELOAD_MODELS=[{"url": "github:go-skynet/model-gallery/gpt4all-j.yaml", "name": "gpt-3.5-turbo"}]'
volumes:
- ./models:/models:cached
command: ["/usr/bin/local-ai" ]
chatgpt:
depends_on:
api:
condition: service_healthy
image: ghcr.io/mckaywrigley/chatbot-ui:main
ports:
- 3000:3000
environment:
- 'OPENAI_API_KEY=sk-XXXXXXXXXXXXXXXXXXXX'
- 'OPENAI_API_HOST=http://api:8080'

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