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renovate/g
| Author | SHA1 | Date |
|---|---|---|
renovate[bot]
|
33835e0ccb | 1 year ago |
@ -1,42 +0,0 @@ |
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package backend |
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|
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import ( |
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"context" |
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"fmt" |
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|
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config "github.com/go-skynet/LocalAI/api/config" |
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|
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"github.com/go-skynet/LocalAI/api/options" |
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"github.com/go-skynet/LocalAI/pkg/grpc/proto" |
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"github.com/go-skynet/LocalAI/pkg/grpc/whisper/api" |
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model "github.com/go-skynet/LocalAI/pkg/model" |
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) |
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|
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func ModelTranscription(audio, language string, loader *model.ModelLoader, c config.Config, o *options.Option) (*api.Result, error) { |
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opts := []model.Option{ |
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model.WithBackendString(model.WhisperBackend), |
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model.WithModelFile(c.Model), |
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model.WithContext(o.Context), |
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model.WithThreads(uint32(c.Threads)), |
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model.WithAssetDir(o.AssetsDestination), |
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} |
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|
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for k, v := range o.ExternalGRPCBackends { |
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opts = append(opts, model.WithExternalBackend(k, v)) |
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} |
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|
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whisperModel, err := o.Loader.BackendLoader(opts...) |
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if err != nil { |
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return nil, err |
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} |
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|
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if whisperModel == nil { |
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return nil, fmt.Errorf("could not load whisper model") |
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} |
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|
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return whisperModel.AudioTranscription(context.Background(), &proto.TranscriptRequest{ |
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Dst: audio, |
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Language: language, |
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Threads: uint32(c.Threads), |
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}) |
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} |
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@ -1,72 +0,0 @@ |
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package backend |
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|
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import ( |
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"context" |
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"fmt" |
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"os" |
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"path/filepath" |
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|
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"github.com/go-skynet/LocalAI/api/options" |
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"github.com/go-skynet/LocalAI/pkg/grpc/proto" |
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model "github.com/go-skynet/LocalAI/pkg/model" |
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"github.com/go-skynet/LocalAI/pkg/utils" |
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) |
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|
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func generateUniqueFileName(dir, baseName, ext string) string { |
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counter := 1 |
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fileName := baseName + ext |
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|
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for { |
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filePath := filepath.Join(dir, fileName) |
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_, err := os.Stat(filePath) |
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if os.IsNotExist(err) { |
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return fileName |
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} |
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|
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counter++ |
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fileName = fmt.Sprintf("%s_%d%s", baseName, counter, ext) |
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} |
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} |
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|
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func ModelTTS(text, modelFile string, loader *model.ModelLoader, o *options.Option) (string, *proto.Result, error) { |
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opts := []model.Option{ |
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model.WithBackendString(model.PiperBackend), |
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model.WithModelFile(modelFile), |
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model.WithContext(o.Context), |
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model.WithAssetDir(o.AssetsDestination), |
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} |
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|
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for k, v := range o.ExternalGRPCBackends { |
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opts = append(opts, model.WithExternalBackend(k, v)) |
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} |
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|
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piperModel, err := o.Loader.BackendLoader(opts...) |
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if err != nil { |
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return "", nil, err |
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} |
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|
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if piperModel == nil { |
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return "", nil, fmt.Errorf("could not load piper model") |
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} |
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|
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if err := os.MkdirAll(o.AudioDir, 0755); err != nil { |
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return "", nil, fmt.Errorf("failed creating audio directory: %s", err) |
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} |
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|
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fileName := generateUniqueFileName(o.AudioDir, "piper", ".wav") |
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filePath := filepath.Join(o.AudioDir, fileName) |
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|
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modelPath := filepath.Join(o.Loader.ModelPath, modelFile) |
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|
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if err := utils.VerifyPath(modelPath, o.Loader.ModelPath); err != nil { |
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return "", nil, err |
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} |
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|
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res, err := piperModel.TTS(context.Background(), &proto.TTSRequest{ |
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Text: text, |
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Model: modelPath, |
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Dst: filePath, |
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}) |
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|
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return filePath, res, err |
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} |
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@ -1,25 +0,0 @@ |
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package main |
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|
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// GRPC Falcon server
|
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|
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// Note: this is started internally by LocalAI and a server is allocated for each model
|
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|
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import ( |
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"flag" |
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|
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llama "github.com/go-skynet/LocalAI/pkg/grpc/llm/llama-grammar" |
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|
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grpc "github.com/go-skynet/LocalAI/pkg/grpc" |
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) |
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|
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var ( |
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addr = flag.String("addr", "localhost:50051", "the address to connect to") |
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) |
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|
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func main() { |
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flag.Parse() |
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|
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if err := grpc.StartServer(*addr, &llama.LLM{}); err != nil { |
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panic(err) |
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} |
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} |
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@ -1,9 +0,0 @@ |
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OPENAI_API_KEY=sk---anystringhere |
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OPENAI_API_BASE=http://api:8080/v1 |
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# Models to preload at start |
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# Here we configure gpt4all as gpt-3.5-turbo and bert as embeddings |
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PRELOAD_MODELS=[{"url": "github:go-skynet/model-gallery/openllama-7b-open-instruct.yaml", "name": "gpt-3.5-turbo"}] |
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|
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## Change the default number of threads |
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#THREADS=14 |
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|
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@ -1,5 +0,0 @@ |
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FROM python:3.10-bullseye |
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COPY . /app |
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WORKDIR /app |
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RUN pip install --no-cache-dir -r requirements.txt |
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ENTRYPOINT [ "python", "./functions-openai.py" ]; |
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@ -1,18 +0,0 @@ |
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# LocalAI functions |
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|
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Example of using LocalAI functions, see the [OpenAI](https://openai.com/blog/function-calling-and-other-api-updates) blog post. |
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|
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## Run |
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|
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```bash |
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# Clone LocalAI |
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git clone https://github.com/go-skynet/LocalAI |
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|
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cd LocalAI/examples/functions |
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|
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docker-compose run --rm functions |
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``` |
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|
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Note: The example automatically downloads the `openllama` model as it is under a permissive license. |
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|
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See the `.env` configuration file to set a different model with the [model-gallery](https://github.com/go-skynet/model-gallery) by editing `PRELOAD_MODELS`. |
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@ -1,23 +0,0 @@ |
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version: "3.9" |
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services: |
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api: |
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image: quay.io/go-skynet/local-ai:master |
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ports: |
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- 8080:8080 |
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env_file: |
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- .env |
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environment: |
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- DEBUG=true |
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- MODELS_PATH=/models |
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volumes: |
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- ./models:/models:cached |
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command: ["/usr/bin/local-ai" ] |
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functions: |
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build: |
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context: . |
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dockerfile: Dockerfile |
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depends_on: |
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api: |
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condition: service_healthy |
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env_file: |
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- .env |
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@ -1,76 +0,0 @@ |
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import openai |
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import json |
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|
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# Example dummy function hard coded to return the same weather |
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# In production, this could be your backend API or an external API |
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def get_current_weather(location, unit="fahrenheit"): |
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"""Get the current weather in a given location""" |
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weather_info = { |
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"location": location, |
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"temperature": "72", |
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"unit": unit, |
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"forecast": ["sunny", "windy"], |
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} |
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return json.dumps(weather_info) |
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|
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|
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def run_conversation(): |
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# Step 1: send the conversation and available functions to GPT |
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messages = [{"role": "user", "content": "What's the weather like in Boston?"}] |
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functions = [ |
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{ |
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"name": "get_current_weather", |
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"description": "Get the current weather in a given location", |
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"parameters": { |
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"type": "object", |
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"properties": { |
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"location": { |
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"type": "string", |
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"description": "The city and state, e.g. San Francisco, CA", |
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}, |
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"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}, |
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}, |
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"required": ["location"], |
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}, |
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} |
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] |
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response = openai.ChatCompletion.create( |
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model="gpt-3.5-turbo", |
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messages=messages, |
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functions=functions, |
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function_call="auto", # auto is default, but we'll be explicit |
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) |
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response_message = response["choices"][0]["message"] |
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|
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# Step 2: check if GPT wanted to call a function |
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if response_message.get("function_call"): |
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# Step 3: call the function |
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# Note: the JSON response may not always be valid; be sure to handle errors |
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available_functions = { |
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"get_current_weather": get_current_weather, |
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} # only one function in this example, but you can have multiple |
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function_name = response_message["function_call"]["name"] |
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fuction_to_call = available_functions[function_name] |
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function_args = json.loads(response_message["function_call"]["arguments"]) |
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function_response = fuction_to_call( |
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location=function_args.get("location"), |
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unit=function_args.get("unit"), |
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) |
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|
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# Step 4: send the info on the function call and function response to GPT |
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messages.append(response_message) # extend conversation with assistant's reply |
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messages.append( |
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{ |
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"role": "function", |
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"name": function_name, |
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"content": function_response, |
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} |
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) # extend conversation with function response |
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second_response = openai.ChatCompletion.create( |
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model="gpt-3.5-turbo", |
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messages=messages, |
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) # get a new response from GPT where it can see the function response |
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return second_response |
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|
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|
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print(run_conversation()) |
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@ -1,2 +0,0 @@ |
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langchain==0.0.234 |
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openai==0.27.8 |
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@ -1,49 +0,0 @@ |
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# -*- coding: utf-8 -*- |
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# Generated by the protocol buffer compiler. DO NOT EDIT! |
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# source: backend.proto |
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"""Generated protocol buffer code.""" |
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from google.protobuf import descriptor as _descriptor |
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from google.protobuf import descriptor_pool as _descriptor_pool |
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from google.protobuf import symbol_database as _symbol_database |
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from google.protobuf.internal import builder as _builder |
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# @@protoc_insertion_point(imports) |
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|
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_sym_db = _symbol_database.Default() |
||||
|
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|
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|
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|
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DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\rbackend.proto\x12\x07\x62\x61\x63kend\"\x0f\n\rHealthMessage\"\xa4\x05\n\x0ePredictOptions\x12\x0e\n\x06Prompt\x18\x01 \x01(\t\x12\x0c\n\x04Seed\x18\x02 \x01(\x05\x12\x0f\n\x07Threads\x18\x03 \x01(\x05\x12\x0e\n\x06Tokens\x18\x04 \x01(\x05\x12\x0c\n\x04TopK\x18\x05 \x01(\x05\x12\x0e\n\x06Repeat\x18\x06 \x01(\x05\x12\r\n\x05\x42\x61tch\x18\x07 \x01(\x05\x12\r\n\x05NKeep\x18\x08 \x01(\x05\x12\x13\n\x0bTemperature\x18\t \x01(\x02\x12\x0f\n\x07Penalty\x18\n \x01(\x02\x12\r\n\x05\x46\x31\x36KV\x18\x0b \x01(\x08\x12\x11\n\tDebugMode\x18\x0c \x01(\x08\x12\x13\n\x0bStopPrompts\x18\r \x03(\t\x12\x11\n\tIgnoreEOS\x18\x0e \x01(\x08\x12\x19\n\x11TailFreeSamplingZ\x18\x0f \x01(\x02\x12\x10\n\x08TypicalP\x18\x10 \x01(\x02\x12\x18\n\x10\x46requencyPenalty\x18\x11 \x01(\x02\x12\x17\n\x0fPresencePenalty\x18\x12 \x01(\x02\x12\x10\n\x08Mirostat\x18\x13 \x01(\x05\x12\x13\n\x0bMirostatETA\x18\x14 \x01(\x02\x12\x13\n\x0bMirostatTAU\x18\x15 \x01(\x02\x12\x12\n\nPenalizeNL\x18\x16 \x01(\x08\x12\x11\n\tLogitBias\x18\x17 \x01(\t\x12\r\n\x05MLock\x18\x19 \x01(\x08\x12\x0c\n\x04MMap\x18\x1a \x01(\x08\x12\x16\n\x0ePromptCacheAll\x18\x1b \x01(\x08\x12\x15\n\rPromptCacheRO\x18\x1c \x01(\x08\x12\x0f\n\x07Grammar\x18\x1d \x01(\t\x12\x0f\n\x07MainGPU\x18\x1e \x01(\t\x12\x13\n\x0bTensorSplit\x18\x1f \x01(\t\x12\x0c\n\x04TopP\x18 \x01(\x02\x12\x17\n\x0fPromptCachePath\x18! \x01(\t\x12\r\n\x05\x44\x65\x62ug\x18\" \x01(\x08\x12\x17\n\x0f\x45mbeddingTokens\x18# \x03(\x05\x12\x12\n\nEmbeddings\x18$ \x01(\t\"\x18\n\x05Reply\x12\x0f\n\x07message\x18\x01 \x01(\t\"\xac\x02\n\x0cModelOptions\x12\r\n\x05Model\x18\x01 \x01(\t\x12\x13\n\x0b\x43ontextSize\x18\x02 \x01(\x05\x12\x0c\n\x04Seed\x18\x03 \x01(\x05\x12\x0e\n\x06NBatch\x18\x04 \x01(\x05\x12\x11\n\tF16Memory\x18\x05 \x01(\x08\x12\r\n\x05MLock\x18\x06 \x01(\x08\x12\x0c\n\x04MMap\x18\x07 \x01(\x08\x12\x11\n\tVocabOnly\x18\x08 \x01(\x08\x12\x0f\n\x07LowVRAM\x18\t \x01(\x08\x12\x12\n\nEmbeddings\x18\n \x01(\x08\x12\x0c\n\x04NUMA\x18\x0b \x01(\x08\x12\x12\n\nNGPULayers\x18\x0c \x01(\x05\x12\x0f\n\x07MainGPU\x18\r \x01(\t\x12\x13\n\x0bTensorSplit\x18\x0e \x01(\t\x12\x0f\n\x07Threads\x18\x0f \x01(\x05\x12\x19\n\x11LibrarySearchPath\x18\x10 \x01(\t\"*\n\x06Result\x12\x0f\n\x07message\x18\x01 \x01(\t\x12\x0f\n\x07success\x18\x02 \x01(\x08\"%\n\x0f\x45mbeddingResult\x12\x12\n\nembeddings\x18\x01 \x03(\x02\"C\n\x11TranscriptRequest\x12\x0b\n\x03\x64st\x18\x02 \x01(\t\x12\x10\n\x08language\x18\x03 \x01(\t\x12\x0f\n\x07threads\x18\x04 \x01(\r\"N\n\x10TranscriptResult\x12,\n\x08segments\x18\x01 \x03(\x0b\x32\x1a.backend.TranscriptSegment\x12\x0c\n\x04text\x18\x02 \x01(\t\"Y\n\x11TranscriptSegment\x12\n\n\x02id\x18\x01 \x01(\x05\x12\r\n\x05start\x18\x02 \x01(\x03\x12\x0b\n\x03\x65nd\x18\x03 \x01(\x03\x12\x0c\n\x04text\x18\x04 \x01(\t\x12\x0e\n\x06tokens\x18\x05 \x03(\x05\"\x9e\x01\n\x14GenerateImageRequest\x12\x0e\n\x06height\x18\x01 \x01(\x05\x12\r\n\x05width\x18\x02 \x01(\x05\x12\x0c\n\x04mode\x18\x03 \x01(\x05\x12\x0c\n\x04step\x18\x04 \x01(\x05\x12\x0c\n\x04seed\x18\x05 \x01(\x05\x12\x17\n\x0fpositive_prompt\x18\x06 \x01(\t\x12\x17\n\x0fnegative_prompt\x18\x07 \x01(\t\x12\x0b\n\x03\x64st\x18\x08 \x01(\t\"6\n\nTTSRequest\x12\x0c\n\x04text\x18\x01 \x01(\t\x12\r\n\x05model\x18\x02 \x01(\t\x12\x0b\n\x03\x64st\x18\x03 \x01(\t2\xeb\x03\n\x07\x42\x61\x63kend\x12\x32\n\x06Health\x12\x16.backend.HealthMessage\x1a\x0e.backend.Reply\"\x00\x12\x34\n\x07Predict\x12\x17.backend.PredictOptions\x1a\x0e.backend.Reply\"\x00\x12\x35\n\tLoadModel\x12\x15.backend.ModelOptions\x1a\x0f.backend.Result\"\x00\x12<\n\rPredictStream\x12\x17.backend.PredictOptions\x1a\x0e.backend.Reply\"\x00\x30\x01\x12@\n\tEmbedding\x12\x17.backend.PredictOptions\x1a\x18.backend.EmbeddingResult\"\x00\x12\x41\n\rGenerateImage\x12\x1d.backend.GenerateImageRequest\x1a\x0f.backend.Result\"\x00\x12M\n\x12\x41udioTranscription\x12\x1a.backend.TranscriptRequest\x1a\x19.backend.TranscriptResult\"\x00\x12-\n\x03TTS\x12\x13.backend.TTSRequest\x1a\x0f.backend.Result\"\x00\x42Z\n\x19io.skynet.localai.backendB\x0eLocalAIBackendP\x01Z+github.com/go-skynet/LocalAI/pkg/grpc/protob\x06proto3') |
||||
|
||||
_globals = globals() |
||||
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals) |
||||
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'backend_pb2', _globals) |
||||
if _descriptor._USE_C_DESCRIPTORS == False: |
||||
|
||||
DESCRIPTOR._options = None |
||||
DESCRIPTOR._serialized_options = b'\n\031io.skynet.localai.backendB\016LocalAIBackendP\001Z+github.com/go-skynet/LocalAI/pkg/grpc/proto' |
||||
_globals['_HEALTHMESSAGE']._serialized_start=26 |
||||
_globals['_HEALTHMESSAGE']._serialized_end=41 |
||||
_globals['_PREDICTOPTIONS']._serialized_start=44 |
||||
_globals['_PREDICTOPTIONS']._serialized_end=720 |
||||
_globals['_REPLY']._serialized_start=722 |
||||
_globals['_REPLY']._serialized_end=746 |
||||
_globals['_MODELOPTIONS']._serialized_start=749 |
||||
_globals['_MODELOPTIONS']._serialized_end=1049 |
||||
_globals['_RESULT']._serialized_start=1051 |
||||
_globals['_RESULT']._serialized_end=1093 |
||||
_globals['_EMBEDDINGRESULT']._serialized_start=1095 |
||||
_globals['_EMBEDDINGRESULT']._serialized_end=1132 |
||||
_globals['_TRANSCRIPTREQUEST']._serialized_start=1134 |
||||
_globals['_TRANSCRIPTREQUEST']._serialized_end=1201 |
||||
_globals['_TRANSCRIPTRESULT']._serialized_start=1203 |
||||
_globals['_TRANSCRIPTRESULT']._serialized_end=1281 |
||||
_globals['_TRANSCRIPTSEGMENT']._serialized_start=1283 |
||||
_globals['_TRANSCRIPTSEGMENT']._serialized_end=1372 |
||||
_globals['_GENERATEIMAGEREQUEST']._serialized_start=1375 |
||||
_globals['_GENERATEIMAGEREQUEST']._serialized_end=1533 |
||||
_globals['_TTSREQUEST']._serialized_start=1535 |
||||
_globals['_TTSREQUEST']._serialized_end=1589 |
||||
_globals['_BACKEND']._serialized_start=1592 |
||||
_globals['_BACKEND']._serialized_end=2083 |
||||
# @@protoc_insertion_point(module_scope) |
||||
@ -1,297 +0,0 @@ |
||||
# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT! |
||||
"""Client and server classes corresponding to protobuf-defined services.""" |
||||
import grpc |
||||
|
||||
import backend_pb2 as backend__pb2 |
||||
|
||||
|
||||
class BackendStub(object): |
||||
"""Missing associated documentation comment in .proto file.""" |
||||
|
||||
def __init__(self, channel): |
||||
"""Constructor. |
||||
|
||||
Args: |
||||
channel: A grpc.Channel. |
||||
""" |
||||
self.Health = channel.unary_unary( |
||||
'/backend.Backend/Health', |
||||
request_serializer=backend__pb2.HealthMessage.SerializeToString, |
||||
response_deserializer=backend__pb2.Reply.FromString, |
||||
) |
||||
self.Predict = channel.unary_unary( |
||||
'/backend.Backend/Predict', |
||||
request_serializer=backend__pb2.PredictOptions.SerializeToString, |
||||
response_deserializer=backend__pb2.Reply.FromString, |
||||
) |
||||
self.LoadModel = channel.unary_unary( |
||||
'/backend.Backend/LoadModel', |
||||
request_serializer=backend__pb2.ModelOptions.SerializeToString, |
||||
response_deserializer=backend__pb2.Result.FromString, |
||||
) |
||||
self.PredictStream = channel.unary_stream( |
||||
'/backend.Backend/PredictStream', |
||||
request_serializer=backend__pb2.PredictOptions.SerializeToString, |
||||
response_deserializer=backend__pb2.Reply.FromString, |
||||
) |
||||
self.Embedding = channel.unary_unary( |
||||
'/backend.Backend/Embedding', |
||||
request_serializer=backend__pb2.PredictOptions.SerializeToString, |
||||
response_deserializer=backend__pb2.EmbeddingResult.FromString, |
||||
) |
||||
self.GenerateImage = channel.unary_unary( |
||||
'/backend.Backend/GenerateImage', |
||||
request_serializer=backend__pb2.GenerateImageRequest.SerializeToString, |
||||
response_deserializer=backend__pb2.Result.FromString, |
||||
) |
||||
self.AudioTranscription = channel.unary_unary( |
||||
'/backend.Backend/AudioTranscription', |
||||
request_serializer=backend__pb2.TranscriptRequest.SerializeToString, |
||||
response_deserializer=backend__pb2.TranscriptResult.FromString, |
||||
) |
||||
self.TTS = channel.unary_unary( |
||||
'/backend.Backend/TTS', |
||||
request_serializer=backend__pb2.TTSRequest.SerializeToString, |
||||
response_deserializer=backend__pb2.Result.FromString, |
||||
) |
||||
|
||||
|
||||
class BackendServicer(object): |
||||
"""Missing associated documentation comment in .proto file.""" |
||||
|
||||
def Health(self, request, context): |
||||
"""Missing associated documentation comment in .proto file.""" |
||||
context.set_code(grpc.StatusCode.UNIMPLEMENTED) |
||||
context.set_details('Method not implemented!') |
||||
raise NotImplementedError('Method not implemented!') |
||||
|
||||
def Predict(self, request, context): |
||||
"""Missing associated documentation comment in .proto file.""" |
||||
context.set_code(grpc.StatusCode.UNIMPLEMENTED) |
||||
context.set_details('Method not implemented!') |
||||
raise NotImplementedError('Method not implemented!') |
||||
|
||||
def LoadModel(self, request, context): |
||||
"""Missing associated documentation comment in .proto file.""" |
||||
context.set_code(grpc.StatusCode.UNIMPLEMENTED) |
||||
context.set_details('Method not implemented!') |
||||
raise NotImplementedError('Method not implemented!') |
||||
|
||||
def PredictStream(self, request, context): |
||||
"""Missing associated documentation comment in .proto file.""" |
||||
context.set_code(grpc.StatusCode.UNIMPLEMENTED) |
||||
context.set_details('Method not implemented!') |
||||
raise NotImplementedError('Method not implemented!') |
||||
|
||||
def Embedding(self, request, context): |
||||
"""Missing associated documentation comment in .proto file.""" |
||||
context.set_code(grpc.StatusCode.UNIMPLEMENTED) |
||||
context.set_details('Method not implemented!') |
||||
raise NotImplementedError('Method not implemented!') |
||||
|
||||
def GenerateImage(self, request, context): |
||||
"""Missing associated documentation comment in .proto file.""" |
||||
context.set_code(grpc.StatusCode.UNIMPLEMENTED) |
||||
context.set_details('Method not implemented!') |
||||
raise NotImplementedError('Method not implemented!') |
||||
|
||||
def AudioTranscription(self, request, context): |
||||
"""Missing associated documentation comment in .proto file.""" |
||||
context.set_code(grpc.StatusCode.UNIMPLEMENTED) |
||||
context.set_details('Method not implemented!') |
||||
raise NotImplementedError('Method not implemented!') |
||||
|
||||
def TTS(self, request, context): |
||||
"""Missing associated documentation comment in .proto file.""" |
||||
context.set_code(grpc.StatusCode.UNIMPLEMENTED) |
||||
context.set_details('Method not implemented!') |
||||
raise NotImplementedError('Method not implemented!') |
||||
|
||||
|
||||
def add_BackendServicer_to_server(servicer, server): |
||||
rpc_method_handlers = { |
||||
'Health': grpc.unary_unary_rpc_method_handler( |
||||
servicer.Health, |
||||
request_deserializer=backend__pb2.HealthMessage.FromString, |
||||
response_serializer=backend__pb2.Reply.SerializeToString, |
||||
), |
||||
'Predict': grpc.unary_unary_rpc_method_handler( |
||||
servicer.Predict, |
||||
request_deserializer=backend__pb2.PredictOptions.FromString, |
||||
response_serializer=backend__pb2.Reply.SerializeToString, |
||||
), |
||||
'LoadModel': grpc.unary_unary_rpc_method_handler( |
||||
servicer.LoadModel, |
||||
request_deserializer=backend__pb2.ModelOptions.FromString, |
||||
response_serializer=backend__pb2.Result.SerializeToString, |
||||
), |
||||
'PredictStream': grpc.unary_stream_rpc_method_handler( |
||||
servicer.PredictStream, |
||||
request_deserializer=backend__pb2.PredictOptions.FromString, |
||||
response_serializer=backend__pb2.Reply.SerializeToString, |
||||
), |
||||
'Embedding': grpc.unary_unary_rpc_method_handler( |
||||
servicer.Embedding, |
||||
request_deserializer=backend__pb2.PredictOptions.FromString, |
||||
response_serializer=backend__pb2.EmbeddingResult.SerializeToString, |
||||
), |
||||
'GenerateImage': grpc.unary_unary_rpc_method_handler( |
||||
servicer.GenerateImage, |
||||
request_deserializer=backend__pb2.GenerateImageRequest.FromString, |
||||
response_serializer=backend__pb2.Result.SerializeToString, |
||||
), |
||||
'AudioTranscription': grpc.unary_unary_rpc_method_handler( |
||||
servicer.AudioTranscription, |
||||
request_deserializer=backend__pb2.TranscriptRequest.FromString, |
||||
response_serializer=backend__pb2.TranscriptResult.SerializeToString, |
||||
), |
||||
'TTS': grpc.unary_unary_rpc_method_handler( |
||||
servicer.TTS, |
||||
request_deserializer=backend__pb2.TTSRequest.FromString, |
||||
response_serializer=backend__pb2.Result.SerializeToString, |
||||
), |
||||
} |
||||
generic_handler = grpc.method_handlers_generic_handler( |
||||
'backend.Backend', rpc_method_handlers) |
||||
server.add_generic_rpc_handlers((generic_handler,)) |
||||
|
||||
|
||||
# This class is part of an EXPERIMENTAL API. |
||||
class Backend(object): |
||||
"""Missing associated documentation comment in .proto file.""" |
||||
|
||||
@staticmethod |
||||
def Health(request, |
||||
target, |
||||
options=(), |
||||
channel_credentials=None, |
||||
call_credentials=None, |
||||
insecure=False, |
||||
compression=None, |
||||
wait_for_ready=None, |
||||
timeout=None, |
||||
metadata=None): |
||||
return grpc.experimental.unary_unary(request, target, '/backend.Backend/Health', |
||||
backend__pb2.HealthMessage.SerializeToString, |
||||
backend__pb2.Reply.FromString, |
||||
options, channel_credentials, |
||||
insecure, call_credentials, compression, wait_for_ready, timeout, metadata) |
||||
|
||||
@staticmethod |
||||
def Predict(request, |
||||
target, |
||||
options=(), |
||||
channel_credentials=None, |
||||
call_credentials=None, |
||||
insecure=False, |
||||
compression=None, |
||||
wait_for_ready=None, |
||||
timeout=None, |
||||
metadata=None): |
||||
return grpc.experimental.unary_unary(request, target, '/backend.Backend/Predict', |
||||
backend__pb2.PredictOptions.SerializeToString, |
||||
backend__pb2.Reply.FromString, |
||||
options, channel_credentials, |
||||
insecure, call_credentials, compression, wait_for_ready, timeout, metadata) |
||||
|
||||
@staticmethod |
||||
def LoadModel(request, |
||||
target, |
||||
options=(), |
||||
channel_credentials=None, |
||||
call_credentials=None, |
||||
insecure=False, |
||||
compression=None, |
||||
wait_for_ready=None, |
||||
timeout=None, |
||||
metadata=None): |
||||
return grpc.experimental.unary_unary(request, target, '/backend.Backend/LoadModel', |
||||
backend__pb2.ModelOptions.SerializeToString, |
||||
backend__pb2.Result.FromString, |
||||
options, channel_credentials, |
||||
insecure, call_credentials, compression, wait_for_ready, timeout, metadata) |
||||
|
||||
@staticmethod |
||||
def PredictStream(request, |
||||
target, |
||||
options=(), |
||||
channel_credentials=None, |
||||
call_credentials=None, |
||||
insecure=False, |
||||
compression=None, |
||||
wait_for_ready=None, |
||||
timeout=None, |
||||
metadata=None): |
||||
return grpc.experimental.unary_stream(request, target, '/backend.Backend/PredictStream', |
||||
backend__pb2.PredictOptions.SerializeToString, |
||||
backend__pb2.Reply.FromString, |
||||
options, channel_credentials, |
||||
insecure, call_credentials, compression, wait_for_ready, timeout, metadata) |
||||
|
||||
@staticmethod |
||||
def Embedding(request, |
||||
target, |
||||
options=(), |
||||
channel_credentials=None, |
||||
call_credentials=None, |
||||
insecure=False, |
||||
compression=None, |
||||
wait_for_ready=None, |
||||
timeout=None, |
||||
metadata=None): |
||||
return grpc.experimental.unary_unary(request, target, '/backend.Backend/Embedding', |
||||
backend__pb2.PredictOptions.SerializeToString, |
||||
backend__pb2.EmbeddingResult.FromString, |
||||
options, channel_credentials, |
||||
insecure, call_credentials, compression, wait_for_ready, timeout, metadata) |
||||
|
||||
@staticmethod |
||||
def GenerateImage(request, |
||||
target, |
||||
options=(), |
||||
channel_credentials=None, |
||||
call_credentials=None, |
||||
insecure=False, |
||||
compression=None, |
||||
wait_for_ready=None, |
||||
timeout=None, |
||||
metadata=None): |
||||
return grpc.experimental.unary_unary(request, target, '/backend.Backend/GenerateImage', |
||||
backend__pb2.GenerateImageRequest.SerializeToString, |
||||
backend__pb2.Result.FromString, |
||||
options, channel_credentials, |
||||
insecure, call_credentials, compression, wait_for_ready, timeout, metadata) |
||||
|
||||
@staticmethod |
||||
def AudioTranscription(request, |
||||
target, |
||||
options=(), |
||||
channel_credentials=None, |
||||
call_credentials=None, |
||||
insecure=False, |
||||
compression=None, |
||||
wait_for_ready=None, |
||||
timeout=None, |
||||
metadata=None): |
||||
return grpc.experimental.unary_unary(request, target, '/backend.Backend/AudioTranscription', |
||||
backend__pb2.TranscriptRequest.SerializeToString, |
||||
backend__pb2.TranscriptResult.FromString, |
||||
options, channel_credentials, |
||||
insecure, call_credentials, compression, wait_for_ready, timeout, metadata) |
||||
|
||||
@staticmethod |
||||
def TTS(request, |
||||
target, |
||||
options=(), |
||||
channel_credentials=None, |
||||
call_credentials=None, |
||||
insecure=False, |
||||
compression=None, |
||||
wait_for_ready=None, |
||||
timeout=None, |
||||
metadata=None): |
||||
return grpc.experimental.unary_unary(request, target, '/backend.Backend/TTS', |
||||
backend__pb2.TTSRequest.SerializeToString, |
||||
backend__pb2.Result.FromString, |
||||
options, channel_credentials, |
||||
insecure, call_credentials, compression, wait_for_ready, timeout, metadata) |
||||
@ -1,67 +0,0 @@ |
||||
#!/usr/bin/env python3 |
||||
import grpc |
||||
from concurrent import futures |
||||
import time |
||||
import backend_pb2 |
||||
import backend_pb2_grpc |
||||
import argparse |
||||
import signal |
||||
import sys |
||||
import os |
||||
from sentence_transformers import SentenceTransformer |
||||
|
||||
_ONE_DAY_IN_SECONDS = 60 * 60 * 24 |
||||
|
||||
# Implement the BackendServicer class with the service methods |
||||
class BackendServicer(backend_pb2_grpc.BackendServicer): |
||||
def Health(self, request, context): |
||||
return backend_pb2.Reply(message="OK") |
||||
def LoadModel(self, request, context): |
||||
model_name = request.Model |
||||
model_name = os.path.basename(model_name) |
||||
try: |
||||
self.model = SentenceTransformer(model_name) |
||||
except Exception as err: |
||||
return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}") |
||||
# Implement your logic here for the LoadModel service |
||||
# Replace this with your desired response |
||||
return backend_pb2.Result(message="Model loaded successfully", success=True) |
||||
def Embedding(self, request, context): |
||||
# Implement your logic here for the Embedding service |
||||
# Replace this with your desired response |
||||
print("Calculated embeddings for: " + request.Embeddings, file=sys.stderr) |
||||
sentence_embeddings = self.model.encode(request.Embeddings) |
||||
return backend_pb2.EmbeddingResult(embeddings=sentence_embeddings) |
||||
|
||||
|
||||
def serve(address): |
||||
server = grpc.server(futures.ThreadPoolExecutor(max_workers=10)) |
||||
backend_pb2_grpc.add_BackendServicer_to_server(BackendServicer(), server) |
||||
server.add_insecure_port(address) |
||||
server.start() |
||||
print("Server started. Listening on: " + address, file=sys.stderr) |
||||
|
||||
# Define the signal handler function |
||||
def signal_handler(sig, frame): |
||||
print("Received termination signal. Shutting down...") |
||||
server.stop(0) |
||||
sys.exit(0) |
||||
|
||||
# Set the signal handlers for SIGINT and SIGTERM |
||||
signal.signal(signal.SIGINT, signal_handler) |
||||
signal.signal(signal.SIGTERM, signal_handler) |
||||
|
||||
try: |
||||
while True: |
||||
time.sleep(_ONE_DAY_IN_SECONDS) |
||||
except KeyboardInterrupt: |
||||
server.stop(0) |
||||
|
||||
if __name__ == "__main__": |
||||
parser = argparse.ArgumentParser(description="Run the gRPC server.") |
||||
parser.add_argument( |
||||
"--addr", default="localhost:50051", help="The address to bind the server to." |
||||
) |
||||
args = parser.parse_args() |
||||
|
||||
serve(args.addr) |
||||
@ -1,4 +0,0 @@ |
||||
sentence_transformers |
||||
grpcio |
||||
google |
||||
protobuf |
||||
@ -1,170 +0,0 @@ |
||||
package llama |
||||
|
||||
// This is a wrapper to statisfy the GRPC service interface
|
||||
// It is meant to be used by the main executable that is the server for the specific backend type (falcon, gpt3, etc)
|
||||
import ( |
||||
"fmt" |
||||
|
||||
"github.com/go-skynet/LocalAI/pkg/grpc/base" |
||||
pb "github.com/go-skynet/LocalAI/pkg/grpc/proto" |
||||
"github.com/go-skynet/go-llama.cpp-grammar" |
||||
) |
||||
|
||||
type LLM struct { |
||||
base.Base |
||||
|
||||
llama *llama.LLama |
||||
} |
||||
|
||||
func (llm *LLM) Load(opts *pb.ModelOptions) error { |
||||
llamaOpts := []llama.ModelOption{} |
||||
|
||||
if opts.ContextSize != 0 { |
||||
llamaOpts = append(llamaOpts, llama.SetContext(int(opts.ContextSize))) |
||||
} |
||||
if opts.F16Memory { |
||||
llamaOpts = append(llamaOpts, llama.EnableF16Memory) |
||||
} |
||||
if opts.Embeddings { |
||||
llamaOpts = append(llamaOpts, llama.EnableEmbeddings) |
||||
} |
||||
if opts.NGPULayers != 0 { |
||||
llamaOpts = append(llamaOpts, llama.SetGPULayers(int(opts.NGPULayers))) |
||||
} |
||||
|
||||
llamaOpts = append(llamaOpts, llama.SetMMap(opts.MMap)) |
||||
llamaOpts = append(llamaOpts, llama.SetMainGPU(opts.MainGPU)) |
||||
llamaOpts = append(llamaOpts, llama.SetTensorSplit(opts.TensorSplit)) |
||||
if opts.NBatch != 0 { |
||||
llamaOpts = append(llamaOpts, llama.SetNBatch(int(opts.NBatch))) |
||||
} else { |
||||
llamaOpts = append(llamaOpts, llama.SetNBatch(512)) |
||||
} |
||||
|
||||
if opts.NUMA { |
||||
llamaOpts = append(llamaOpts, llama.EnableNUMA) |
||||
} |
||||
|
||||
if opts.LowVRAM { |
||||
llamaOpts = append(llamaOpts, llama.EnabelLowVRAM) |
||||
} |
||||
|
||||
model, err := llama.New(opts.Model, llamaOpts...) |
||||
llm.llama = model |
||||
return err |
||||
} |
||||
|
||||
func buildPredictOptions(opts *pb.PredictOptions) []llama.PredictOption { |
||||
predictOptions := []llama.PredictOption{ |
||||
llama.SetTemperature(float64(opts.Temperature)), |
||||
llama.SetTopP(float64(opts.TopP)), |
||||
llama.SetTopK(int(opts.TopK)), |
||||
llama.SetTokens(int(opts.Tokens)), |
||||
llama.SetThreads(int(opts.Threads)), |
||||
} |
||||
|
||||
if opts.PromptCacheAll { |
||||
predictOptions = append(predictOptions, llama.EnablePromptCacheAll) |
||||
} |
||||
|
||||
if opts.PromptCacheRO { |
||||
predictOptions = append(predictOptions, llama.EnablePromptCacheRO) |
||||
} |
||||
|
||||
predictOptions = append(predictOptions, llama.WithGrammar(opts.Grammar)) |
||||
|
||||
// Expected absolute path
|
||||
if opts.PromptCachePath != "" { |
||||
predictOptions = append(predictOptions, llama.SetPathPromptCache(opts.PromptCachePath)) |
||||
} |
||||
|
||||
if opts.Mirostat != 0 { |
||||
predictOptions = append(predictOptions, llama.SetMirostat(int(opts.Mirostat))) |
||||
} |
||||
|
||||
if opts.MirostatETA != 0 { |
||||
predictOptions = append(predictOptions, llama.SetMirostatETA(float64(opts.MirostatETA))) |
||||
} |
||||
|
||||
if opts.MirostatTAU != 0 { |
||||
predictOptions = append(predictOptions, llama.SetMirostatTAU(float64(opts.MirostatTAU))) |
||||
} |
||||
|
||||
if opts.Debug { |
||||
predictOptions = append(predictOptions, llama.Debug) |
||||
} |
||||
|
||||
predictOptions = append(predictOptions, llama.SetStopWords(opts.StopPrompts...)) |
||||
|
||||
if opts.PresencePenalty != 0 { |
||||
predictOptions = append(predictOptions, llama.SetPenalty(float64(opts.PresencePenalty))) |
||||
} |
||||
|
||||
if opts.NKeep != 0 { |
||||
predictOptions = append(predictOptions, llama.SetNKeep(int(opts.NKeep))) |
||||
} |
||||
|
||||
if opts.Batch != 0 { |
||||
predictOptions = append(predictOptions, llama.SetBatch(int(opts.Batch))) |
||||
} |
||||
|
||||
if opts.F16KV { |
||||
predictOptions = append(predictOptions, llama.EnableF16KV) |
||||
} |
||||
|
||||
if opts.IgnoreEOS { |
||||
predictOptions = append(predictOptions, llama.IgnoreEOS) |
||||
} |
||||
|
||||
if opts.Seed != 0 { |
||||
predictOptions = append(predictOptions, llama.SetSeed(int(opts.Seed))) |
||||
} |
||||
|
||||
//predictOptions = append(predictOptions, llama.SetLogitBias(c.Seed))
|
||||
|
||||
predictOptions = append(predictOptions, llama.SetFrequencyPenalty(float64(opts.FrequencyPenalty))) |
||||
predictOptions = append(predictOptions, llama.SetMlock(opts.MLock)) |
||||
predictOptions = append(predictOptions, llama.SetMemoryMap(opts.MMap)) |
||||
predictOptions = append(predictOptions, llama.SetPredictionMainGPU(opts.MainGPU)) |
||||
predictOptions = append(predictOptions, llama.SetPredictionTensorSplit(opts.TensorSplit)) |
||||
predictOptions = append(predictOptions, llama.SetTailFreeSamplingZ(float64(opts.TailFreeSamplingZ))) |
||||
predictOptions = append(predictOptions, llama.SetTypicalP(float64(opts.TypicalP))) |
||||
return predictOptions |
||||
} |
||||
|
||||
func (llm *LLM) Predict(opts *pb.PredictOptions) (string, error) { |
||||
return llm.llama.Predict(opts.Prompt, buildPredictOptions(opts)...) |
||||
} |
||||
|
||||
func (llm *LLM) PredictStream(opts *pb.PredictOptions, results chan string) error { |
||||
predictOptions := buildPredictOptions(opts) |
||||
|
||||
predictOptions = append(predictOptions, llama.SetTokenCallback(func(token string) bool { |
||||
results <- token |
||||
return true |
||||
})) |
||||
|
||||
go func() { |
||||
_, err := llm.llama.Predict(opts.Prompt, predictOptions...) |
||||
if err != nil { |
||||
fmt.Println("err: ", err) |
||||
} |
||||
close(results) |
||||
}() |
||||
|
||||
return nil |
||||
} |
||||
|
||||
func (llm *LLM) Embeddings(opts *pb.PredictOptions) ([]float32, error) { |
||||
predictOptions := buildPredictOptions(opts) |
||||
|
||||
if len(opts.EmbeddingTokens) > 0 { |
||||
tokens := []int{} |
||||
for _, t := range opts.EmbeddingTokens { |
||||
tokens = append(tokens, int(t)) |
||||
} |
||||
return llm.llama.TokenEmbeddings(tokens, predictOptions...) |
||||
} |
||||
|
||||
return llm.llama.Embeddings(opts.Embeddings, predictOptions...) |
||||
} |
||||
@ -1,37 +0,0 @@ |
||||
package utils |
||||
|
||||
import ( |
||||
"time" |
||||
|
||||
"github.com/rs/zerolog/log" |
||||
) |
||||
|
||||
var lastProgress time.Time = time.Now() |
||||
var startTime time.Time = time.Now() |
||||
|
||||
func ResetDownloadTimers() { |
||||
lastProgress = time.Now() |
||||
startTime = time.Now() |
||||
} |
||||
|
||||
func DisplayDownloadFunction(fileName string, current string, total string, percentage float64) { |
||||
currentTime := time.Now() |
||||
|
||||
if currentTime.Sub(lastProgress) >= 5*time.Second { |
||||
|
||||
lastProgress = currentTime |
||||
|
||||
// calculate ETA based on percentage and elapsed time
|
||||
var eta time.Duration |
||||
if percentage > 0 { |
||||
elapsed := currentTime.Sub(startTime) |
||||
eta = time.Duration(float64(elapsed)*(100/percentage) - float64(elapsed)) |
||||
} |
||||
|
||||
if total != "" { |
||||
log.Debug().Msgf("Downloading %s: %s/%s (%.2f%%) ETA: %s", fileName, current, total, percentage, eta) |
||||
} else { |
||||
log.Debug().Msgf("Downloading: %s", current) |
||||
} |
||||
} |
||||
} |
||||
@ -1,5 +0,0 @@ |
||||
name: code-search-ada-code-001 |
||||
backend: huggingface |
||||
embeddings: true |
||||
parameters: |
||||
model: all-MiniLM-L6-v2 |
||||
Loading…
Reference in new issue