from__future__importannotationsfromvertexai.language_modelsimportTextEmbeddingInput,TextEmbeddingModeldefembed_text()->list[list[float]]:"""Embeds texts with a pre-trained, foundational model.    Returns:        A list of lists containing the embedding vectors for each input text    """# A list of texts to be embedded.texts=["banana muffins? ","banana bread? banana muffins?"]# The dimensionality of the output embeddings.dimensionality=3072# The task type for embedding. Check the available tasks in the model's documentation.task="RETRIEVAL_DOCUMENT"model=TextEmbeddingModel.from_pretrained("gemini-embedding-001")kwargs=dict(output_dimensionality=dimensionality)ifdimensionalityelse{}embeddings=[]# gemini-embedding-001 takes one input at a timefortextintexts:text_input=TextEmbeddingInput(text,task)embedding=model.get_embeddings([text_input],**kwargs)print(embedding)# Example response:# [[0.006135190837085247, -0.01462465338408947, 0.004978656303137541, ...]]embeddings.append(embedding[0].values)returnembeddings

import("context""fmt""io"aiplatform"cloud.google.com/go/aiplatform/apiv1""cloud.google.com/go/aiplatform/apiv1/aiplatformpb""google.golang.org/api/option""google.golang.org/protobuf/types/known/structpb")// embedTexts shows how embeddings are set for gemini-embedding-001 modelfuncembedTexts(wio.Writer,project,locationstring)error{// location := "us-central1"ctx:=context.Background()apiEndpoint:=fmt.Sprintf("%s-aiplatform.googleapis.com:443",location)dimensionality:=3072model:="gemini-embedding-001"texts:=[]string{"banana muffins? ","banana bread? banana muffins?"}client,err:=aiplatform.NewPredictionClient(ctx,option.WithEndpoint(apiEndpoint))iferr!=nil{returnerr}deferclient.Close()endpoint:=fmt.Sprintf("projects/%s/locations/%s/publishers/google/models/%s",project,location,model)allEmbeddings:=make([][]float32,0,len(texts))// gemini-embedding-001 takes 1 input at a timefor_,text:=rangetexts{instances:=make([]*structpb.Value,1)instances[0]=structpb.NewStructValue(&structpb.Struct{Fields:map[string]*structpb.Value{"content":structpb.NewStringValue(text),"task_type":structpb.NewStringValue("QUESTION_ANSWERING"),},})params:=structpb.NewStructValue(&structpb.Struct{Fields:map[string]*structpb.Value{"outputDimensionality":structpb.NewNumberValue(float64(dimensionality)),},})req:=&aiplatformpb.PredictRequest{Endpoint:endpoint,Instances:instances,Parameters:params,}resp,err:=client.Predict(ctx,req)iferr!=nil{returnerr}// Process the prediction for the single text// The response will contain one prediction because we sent one instance.iflen(resp.Predictions)==0{returnfmt.Errorf("no predictions returned for text \"%s\"",text)}prediction:=resp.Predictions[0]embeddingValues:=prediction.GetStructValue().Fields["embeddings"].GetStructValue().Fields["values"].GetListValue().ValuescurrentEmbedding:=make([]float32,len(embeddingValues))forj,value:=rangeembeddingValues{currentEmbedding[j]=float32(value.GetNumberValue())}allEmbeddings=append(allEmbeddings,currentEmbedding)}iflen(allEmbeddings) >0{fmt.Fprintf(w,"Dimensionality: %d. Embeddings length: %d",len(allEmbeddings[0]),len(allEmbeddings))}else{fmt.Fprintln(w,"No texts were processed.")}returnnil}

import staticjava.util.stream.Collectors.toList;importcom.google.cloud.aiplatform.v1.EndpointName;importcom.google.cloud.aiplatform.v1.PredictRequest;importcom.google.cloud.aiplatform.v1.PredictResponse;importcom.google.cloud.aiplatform.v1.PredictionServiceClient;importcom.google.cloud.aiplatform.v1.PredictionServiceSettings;importcom.google.protobuf.Struct;importcom.google.protobuf.Value;importjava.io.IOException;importjava.util.ArrayList;importjava.util.List;importjava.util.OptionalInt;importjava.util.regex.Matcher;importjava.util.regex.Pattern;publicclassPredictTextEmbeddingsSample{publicstaticvoidmain(String[]args)throwsIOException{// TODO(developer): Replace these variables before running the sample.// Details about text embedding request structure and supported models are available in:// https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddingsStringendpoint="us-central1-aiplatform.googleapis.com:443";Stringproject="YOUR_PROJECT_ID";Stringmodel="gemini-embedding-001";predictTextEmbeddings(endpoint,project,model,List.of("banana bread?","banana muffins?"),"QUESTION_ANSWERING",OptionalInt.of(3072));}// Gets text embeddings from a pretrained, foundational model.publicstaticList<List<Float>>predictTextEmbeddings(Stringendpoint,Stringproject,Stringmodel,List<String>texts,Stringtask,OptionalIntoutputDimensionality)throwsIOException{PredictionServiceSettingssettings=PredictionServiceSettings.newBuilder().setEndpoint(endpoint).build();Matchermatcher=Pattern.compile("^(?<Location>\\w+-\\w+)").matcher(endpoint);Stringlocation=matcher.matches()?matcher.group("Location"):"us-central1";EndpointNameendpointName=EndpointName.ofProjectLocationPublisherModelName(project,location,"google",model);List<List<Float>>floats=newArrayList<>();// You can use this prediction service client for multiple requests.try(PredictionServiceClientclient=PredictionServiceClient.create(settings)){// gemini-embedding-001 takes one input at a time.for(inti=0;i <texts.size();i++){PredictRequest.Builderrequest=PredictRequest.newBuilder().setEndpoint(endpointName.toString());if(outputDimensionality.isPresent()){request.setParameters(Value.newBuilder().setStructValue(Struct.newBuilder().putFields("outputDimensionality",valueOf(outputDimensionality.getAsInt())).build()));}request.addInstances(Value.newBuilder().setStructValue(Struct.newBuilder().putFields("content",valueOf(texts.get(i))).putFields("task_type",valueOf(task)).build()));PredictResponseresponse=client.predict(request.build());for(Valueprediction:response.getPredictionsList()){Valueembeddings=prediction.getStructValue().getFieldsOrThrow("embeddings");Valuevalues=embeddings.getStructValue().getFieldsOrThrow("values");floats.add(values.getListValue().getValuesList().stream().map(Value::getNumberValue).map(Double::floatValue).collect(toList()));}}returnfloats;}}privatestaticValuevalueOf(Strings){returnValue.newBuilder().setStringValue(s).build();}privatestaticValuevalueOf(intn){returnValue.newBuilder().setNumberValue(n).build();}}

asyncfunctionmain(project,model='gemini-embedding-001',texts='banana bread?;banana muffins?',task='QUESTION_ANSWERING',dimensionality=0,apiEndpoint='us-central1-aiplatform.googleapis.com'){constaiplatform=require('@google-cloud/aiplatform');const{PredictionServiceClient}=aiplatform.v1;const{helpers}=aiplatform;// helps construct protobuf.Value objects.constclientOptions={apiEndpoint:apiEndpoint};constlocation='us-central1';constendpoint=`projects/${project}/locations/${location}/publishers/google/models/${model}`;asyncfunctioncallPredict(){constinstances=texts.split(';').map(e=>helpers.toValue({content:e,task_type:task}));constclient=newPredictionServiceClient(clientOptions);constparameters=helpers.toValue(dimensionality >0?{outputDimensionality:parseInt(dimensionality)}:{});constallEmbeddings=[]// gemini-embedding-001 takes one input at a time.for(constinstanceofinstances){constrequest={endpoint,instances:[instance],parameters};const[response]=awaitclient.predict(request);constpredictions=response.predictions;constembeddings=predictions.map(p=>{constembeddingsProto=p.structValue.fields.embeddings;constvaluesProto=embeddingsProto.structValue.fields.values;returnvaluesProto.listValue.values.map(v=>v.numberValue);});allEmbeddings.push(embeddings[0])}console.log('Got embeddings: \n'+JSON.stringify(allEmbeddings));}callPredict();}