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‎ai-platform/snippets/batch-prediction/batch-predict-bq.js‎

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/*
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* Copyright 2024 Google LLC
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* https://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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'use strict';
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async function main(projectId, outputUri) {
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// [START generativeaionvertexai_batch_predict_gemini_createjob_bigquery]
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// Import the aiplatform library
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const aiplatformLib = require('@google-cloud/aiplatform');
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const aiplatform = aiplatformLib.protos.google.cloud.aiplatform.v1;
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/**
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* TODO(developer): Uncomment/update these variables before running the sample.
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*/
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// projectId = 'YOUR_PROJECT_ID';
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// URI of the output BigQuery table.
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// E.g. "bq://[PROJECT].[DATASET].[TABLE]"
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// outputUri = 'bq://projectid.dataset.table';
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// URI of the multimodal input BigQuery table.
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// E.g. "bq://[PROJECT].[DATASET].[TABLE]"
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const inputUri =
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'bq://storage-samples.generative_ai.batch_requests_for_multimodal_input';
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const location = 'us-central1';
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const parent = `projects/${projectId}/locations/${location}`;
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const modelName = `${parent}/publishers/google/models/gemini-1.5-flash-002`;
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// Specify the location of the api endpoint.
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const clientOptions = {
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apiEndpoint: `${location}-aiplatform.googleapis.com`,
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};
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// Instantiate the client.
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const jobServiceClient = new aiplatformLib.JobServiceClient(clientOptions);
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// Create a Gemini batch prediction job using BigQuery input and output datasets.
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async function create_batch_prediction_gemini_bq() {
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const bqSource = new aiplatform.BigQuerySource({
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inputUri: inputUri,
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});
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const inputConfig = new aiplatform.BatchPredictionJob.InputConfig({
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bigquerySource: bqSource,
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instancesFormat: 'bigquery',
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});
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const bqDestination = new aiplatform.BigQueryDestination({
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outputUri: outputUri,
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});
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const outputConfig = new aiplatform.BatchPredictionJob.OutputConfig({
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bigqueryDestination: bqDestination,
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predictionsFormat: 'bigquery',
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});
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const batchPredictionJob = new aiplatform.BatchPredictionJob({
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displayName: 'Batch predict with Gemini - BigQuery',
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model: modelName, // Add model parameters per request in the input BigQuery table.
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inputConfig: inputConfig,
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outputConfig: outputConfig,
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});
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const request = {
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parent: parent,
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batchPredictionJob,
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};
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// Create batch prediction job request
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const [response] = await jobServiceClient.createBatchPredictionJob(request);
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console.log('Response name: ', response.name);
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// Example response:
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// Response name: projects/<project>/locations/us-central1/batchPredictionJobs/<job-id>
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}
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await create_batch_prediction_gemini_bq();
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// [END generativeaionvertexai_batch_predict_gemini_createjob_bigquery]
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}
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main(...process.argv.slice(2)).catch(err => {
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console.error(err.message);
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process.exitCode = 1;
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});

‎ai-platform/snippets/batch-prediction/batch-predict-gcs.js‎

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/*
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* Copyright 2024 Google LLC
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* https://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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'use strict';
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async function main(projectId, outputUri) {
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// [START generativeaionvertexai_batch_predict_gemini_createjob_gcs]
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// Import the aiplatform library
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const aiplatformLib = require('@google-cloud/aiplatform');
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const aiplatform = aiplatformLib.protos.google.cloud.aiplatform.v1;
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/**
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* TODO(developer): Uncomment/update these variables before running the sample.
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*/
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// projectId = 'YOUR_PROJECT_ID';
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// URI of the output folder in Google Cloud Storage.
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// E.g. "gs://[BUCKET]/[OUTPUT]"
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// outputUri = 'gs://my-bucket';
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// URI of the input file in Google Cloud Storage.
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// E.g. "gs://[BUCKET]/[DATASET].jsonl"
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// Or try:
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// "gs://cloud-samples-data/generative-ai/batch/gemini_multimodal_batch_predict.jsonl"
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// for a batch prediction that uses audio, video, and an image.
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const inputUri =
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'gs://cloud-samples-data/generative-ai/batch/batch_requests_for_multimodal_input.jsonl';
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const location = 'us-central1';
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const parent = `projects/${projectId}/locations/${location}`;
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const modelName = `${parent}/publishers/google/models/gemini-1.5-flash-002`;
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// Specify the location of the api endpoint.
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const clientOptions = {
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apiEndpoint: `${location}-aiplatform.googleapis.com`,
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};
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// Instantiate the client.
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const jobServiceClient = new aiplatformLib.JobServiceClient(clientOptions);
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// Create a Gemini batch prediction job using Google Cloud Storage input and output buckets.
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async function create_batch_prediction_gemini_gcs() {
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const gcsSource = new aiplatform.GcsSource({
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uris: [inputUri],
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});
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const inputConfig = new aiplatform.BatchPredictionJob.InputConfig({
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gcsSource: gcsSource,
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instancesFormat: 'jsonl',
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});
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const gcsDestination = new aiplatform.GcsDestination({
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outputUriPrefix: outputUri,
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});
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const outputConfig = new aiplatform.BatchPredictionJob.OutputConfig({
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gcsDestination: gcsDestination,
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predictionsFormat: 'jsonl',
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});
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const batchPredictionJob = new aiplatform.BatchPredictionJob({
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displayName: 'Batch predict with Gemini - GCS',
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model: modelName,
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inputConfig: inputConfig,
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outputConfig: outputConfig,
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});
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const request = {
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parent: parent,
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batchPredictionJob,
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};
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// Create batch prediction job request
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const [response] = await jobServiceClient.createBatchPredictionJob(request);
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console.log('Response name: ', response.name);
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// Example response:
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// Response name: projects/<project>/locations/us-central1/batchPredictionJobs/<job-id>
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}
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await create_batch_prediction_gemini_gcs();
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// [END generativeaionvertexai_batch_predict_gemini_createjob_gcs]
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}
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main(...process.argv.slice(2)).catch(err => {
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console.error(err.message);
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process.exitCode = 1;
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});

‎ai-platform/snippets/embedding-model-tuning.js‎

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project,
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outputDir,
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pipelineJobDisplayName = 'embedding-customization-pipeline-sample',
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baseModelVersionId = 'text-embedding-004',
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baseModelVersionId = 'text-embedding-005',
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taskType = 'DEFAULT',
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corpusPath = 'gs://cloud-samples-data/ai-platform/embedding/goog-10k-2024/r11/corpus.jsonl',
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queriesPath = 'gs://cloud-samples-data/ai-platform/embedding/goog-10k-2024/r11/queries.jsonl',
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};
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const pipelineJob = {
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templateUri:
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'https://us-kfp.pkg.dev/ml-pipeline/llm-text-embedding/tune-text-embedding-model/v1.1.3',
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'https://us-kfp.pkg.dev/ml-pipeline/llm-text-embedding/tune-text-embedding-model/v1.1.4',
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displayName: pipelineJobDisplayName,
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runtimeConfig,
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};

‎ai-platform/snippets/predict-text-embeddings-preview.js‎

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// TODO(developer): Update the following for your own use case.
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const project = 'long-door-651';
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const model = 'text-embedding-preview-0815';
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const model = 'text-embedding-005';
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const location = 'us-central1';
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// Calculate the embedding for code blocks. Using 'RETRIEVAL_DOCUMENT' for corpus.
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// Specify the task type as 'CODE_RETRIEVAL_QUERY' for query, e.g. 'Retrieve a function that adds two numbers'.

‎ai-platform/snippets/predict-text-embeddings.js‎

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// [START generativeaionvertexai_sdk_embedding]
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async function main(
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project,
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model = 'text-embedding-004',
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model = 'text-embedding-005',
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texts = 'banana bread?;banana muffins?',
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task = 'QUESTION_ANSWERING',
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dimensionality = 0,

‎ai-platform/snippets/test/batch-prediction-gemini.test.js‎

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/*
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* Copyright 2024 Google LLC
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* https://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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'use strict';
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const {assert} = require('chai');
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const {after, describe, it} = require('mocha');
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const cp = require('child_process');
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const {JobServiceClient} = require('@google-cloud/aiplatform');
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const execSync = cmd => cp.execSync(cmd, {encoding: 'utf-8'});
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describe('Batch predict with Gemini', async () => {
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const projectId = process.env.CAIP_PROJECT_ID;
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const outputGCSUri = 'gs://ucaip-samples-test-output/';
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const outputBqUri = `bq://${process.env.CAIP_PROJECT_ID}.gen_ai_batch_prediction.predictions_${Date.now()}`;
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const location = 'us-central1';
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const jobServiceClient = new JobServiceClient({
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apiEndpoint: `${location}-aiplatform.googleapis.com`,
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});
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let batchPredictionGcsJobId;
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let batchPredictionBqJobId;
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after(async () => {
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let name = jobServiceClient.batchPredictionJobPath(
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projectId,
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location,
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batchPredictionGcsJobId
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);
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cancelAndDeleteJob(name);
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name = jobServiceClient.batchPredictionJobPath(
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projectId,
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location,
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batchPredictionBqJobId
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);
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cancelAndDeleteJob(name);
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function cancelAndDeleteJob(name) {
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const cancelRequest = {
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name,
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};
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jobServiceClient.cancelBatchPredictionJob(cancelRequest).then(() => {
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const deleteRequest = {
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name,
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};
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return jobServiceClient.deleteBatchPredictionJob(deleteRequest);
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});
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}
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});
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it('should create Batch prediction Gemini job with GCS ', async () => {
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const response = execSync(
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`node ./batch-prediction/batch-predict-gcs.js ${projectId} ${outputGCSUri}`
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);
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assert.match(response, new RegExp('/batchPredictionJobs/'));
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batchPredictionGcsJobId = response
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.split('/locations/us-central1/batchPredictionJobs/')[1]
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.split('\n')[0];
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}).timeout(10000);
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it('should create Batch prediction Gemini job with BigQuery', async () => {
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const response = execSync(
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`node ./batch-prediction/batch-predict-bq.js ${projectId} ${outputBqUri}`
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);
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assert.match(response, new RegExp('/batchPredictionJobs/'));
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batchPredictionBqJobId = response
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.split('/locations/us-central1/batchPredictionJobs/')[1]
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.split('\n')[0];
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}).timeout(10000);
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});

‎functions/pubsub/publish/index.js‎

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/**
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* Publishes a message to a Cloud Pub/Sub Topic.
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*
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* @example
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* gcloud functions call publish --data '{"topic":"[YOUR_TOPIC_NAME]","message":"Hello, world!"}'
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*
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* - Replace `[YOUR_TOPIC_NAME]` with your Cloud Pub/Sub topic name.
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*
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* @param {object} req Cloud Function request context.
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* @param {object} req.body The request body.
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* @param {string} req.body.topic Topic name on which to publish.

‎generative-ai/snippets/function-calling/functionCallingStreamContent.js‎

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contents: [
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{role: 'user', parts: [{text: 'What is the weather in Boston?'}]},
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{
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role: 'model',
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role: 'ASSISTANT',
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parts: [
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{
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functionCall: {
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},
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],
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},
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{role: 'user', parts: functionResponseParts},
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{role: 'USER', parts: functionResponseParts},
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],
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tools: functionDeclarations,
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};

‎renovate.json‎

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{
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"extends": [
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"config:base",
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"config:semverAllMonthly"
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],
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"extends": ["config:recommended", "config:semverAllMonthly"],
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"baseBranches": ["main"],
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"ignorePaths": [
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"**/flexible_nodejs16_and_earlier/**"
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],
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"ignorePaths": ["**/flexible_nodejs16_and_earlier/**"],
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"packageRules": [
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{
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"matchUpdateTypes": ["major"],
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"enabled": false
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},
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{
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"groupName": "GitHub Actions",
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"matchManagers": ["github-actions"],
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"pinDigests": true,
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"groupName": "GitHub Actions",
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"matchManagers": ["github-actions"],
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"pinDigests": true
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}
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],
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"force": {
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"constraints": {
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"node": "< 15.0.0"
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}
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},
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"dependencyDashboardAutoclose": true,
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"dependencyDashboardAutoclose": true,
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"schedule": "after 11am every 3 weeks on Monday",
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"stabilityDays": 15,
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"pinVersions": false,
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"rebaseStalePrs": true
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"minimumReleaseAge": "15 days",
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"rangeStrategy": "replace",
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"rebaseWhen": "behind-base-branch"
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}

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