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

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/*
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* Copyright 2023 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(project, location = 'us-central1') {
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// [START aiplatform_sdk_extraction]
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/**
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* TODO(developer): Uncomment these variables before running the sample.\
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* (Not necessary if passing values as arguments)
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*/
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// const project = 'YOUR_PROJECT_ID';
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// const location = 'YOUR_PROJECT_LOCATION';
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const aiplatform = require('@google-cloud/aiplatform');
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// Imports the Google Cloud Prediction service client
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const {PredictionServiceClient} = aiplatform.v1;
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// Import the helper module for converting arbitrary protobuf.Value objects.
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const {helpers} = aiplatform;
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// Specifies the location of the api endpoint
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const clientOptions = {
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apiEndpoint: 'us-central1-aiplatform.googleapis.com',
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};
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const publisher = 'google';
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const model = 'text-bison@001';
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// Instantiates a client
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const predictionServiceClient = new PredictionServiceClient(clientOptions);
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async function callPredict() {
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// Configure the parent resource
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const endpoint = `projects/${project}/locations/${location}/publishers/${publisher}/models/${model}`;
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const instance = {
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content: `Background: There is evidence that there have been significant changes \
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in Amazon rainforest vegetation over the last 21,000 years through the Last \
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Glacial Maximum (LGM) and subsequent deglaciation. Analyses of sediment \
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deposits from Amazon basin paleo lakes and from the Amazon Fan indicate that \
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rainfall in the basin during the LGM was lower than for the present, and this \
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was almost certainly associated with reduced moist tropical vegetation cover \
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in the basin. There is debate, however, over how extensive this reduction \
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was. Some scientists argue that the rainforest was reduced to small, isolated \
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refugia separated by open forest and grassland; other scientists argue that \
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the rainforest remained largely intact but extended less far to the north, \
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south, and east than is seen today. This debate has proved difficult to \
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resolve because the practical limitations of working in the rainforest mean \
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that data sampling is biased away from the center of the Amazon basin, and \
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both explanations are reasonably well supported by the available data.
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Q: What does LGM stands for?
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A: Last Glacial Maximum.
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Q: What did the analysis from the sediment deposits indicate?
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A: Rainfall in the basin during the LGM was lower than for the present.
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Q: What are some of scientists arguments?
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A: The rainforest was reduced to small, isolated refugia separated by open forest and grassland.
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Q: There have been major changes in Amazon rainforest vegetation over the last how many years?
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A: 21,000.
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Q: What caused changes in the Amazon rainforest vegetation?
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A: The Last Glacial Maximum (LGM) and subsequent deglaciation
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Q: What has been analyzed to compare Amazon rainfall in the past and present?
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A: Sediment deposits.
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Q: What has the lower rainfall in the Amazon during the LGM been attributed to?
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A:
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`,
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};
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const instanceValue = helpers.toValue(instance);
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const instances = [instanceValue];
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const parameter = {
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temperature: 0.2,
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maxOutputTokens: 256,
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topP: 0,
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topK: 1,
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};
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const parameters = helpers.toValue(parameter);
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const request = {
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endpoint,
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instances,
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parameters,
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};
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// Predict request
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const [response] = await predictionServiceClient.predict(request);
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console.log('Get text extraction response');
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const predictions = response.predictions;
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console.log('\tPredictions :');
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for (const prediction of predictions) {
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console.log(`\t\tPrediction : ${JSON.stringify(prediction)}`);
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}
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}
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callPredict();
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// [END aiplatform_sdk_extraction]
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}
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process.on('unhandledRejection', err => {
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console.error(err.message);
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process.exitCode = 1;
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});
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main(...process.argv.slice(2));

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

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/*
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* Copyright 2023 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(project, location = 'us-central1') {
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// [START aiplatform_sdk_summarization]
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/**
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* TODO(developer): Uncomment these variables before running the sample.\
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* (Not necessary if passing values as arguments)
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*/
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// const project = 'YOUR_PROJECT_ID';
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// const location = 'YOUR_PROJECT_LOCATION';
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const aiplatform = require('@google-cloud/aiplatform');
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// Imports the Google Cloud Prediction service client
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const {PredictionServiceClient} = aiplatform.v1;
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// Import the helper module for converting arbitrary protobuf.Value objects.
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const {helpers} = aiplatform;
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// Specifies the location of the api endpoint
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const clientOptions = {
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apiEndpoint: 'us-central1-aiplatform.googleapis.com',
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};
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const publisher = 'google';
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const model = 'text-bison@001';
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// Instantiates a client
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const predictionServiceClient = new PredictionServiceClient(clientOptions);
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async function callPredict() {
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// Configure the parent resource
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const endpoint = `projects/${project}/locations/${location}/publishers/${publisher}/models/${model}`;
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const instance = {
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content: `Provide a summary with about two sentences for the following article:
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The efficient-market hypothesis (EMH) is a hypothesis in financial \
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economics that states that asset prices reflect all available \
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information. A direct implication is that it is impossible to \
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"beat the market" consistently on a risk-adjusted basis since market \
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prices should only react to new information. Because the EMH is \
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formulated in terms of risk adjustment, it only makes testable \
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predictions when coupled with a particular model of risk. As a \
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result, research in financial economics since at least the 1990s has \
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focused on market anomalies, that is, deviations from specific \
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models of risk. The idea that financial market returns are difficult \
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to predict goes back to Bachelier, Mandelbrot, and Samuelson, but \
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is closely associated with Eugene Fama, in part due to his \
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influential 1970 review of the theoretical and empirical research. \
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The EMH provides the basic logic for modern risk-based theories of \
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asset prices, and frameworks such as consumption-based asset pricing \
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and intermediary asset pricing can be thought of as the combination \
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of a model of risk with the EMH. Many decades of empirical research \
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on return predictability has found mixed evidence. Research in the \
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1950s and 1960s often found a lack of predictability (e.g. Ball and \
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Brown 1968; Fama, Fisher, Jensen, and Roll 1969), yet the \
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1980s-2000s saw an explosion of discovered return predictors (e.g. \
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Rosenberg, Reid, and Lanstein 1985; Campbell and Shiller 1988; \
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Jegadeesh and Titman 1993). Since the 2010s, studies have often \
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found that return predictability has become more elusive, as \
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predictability fails to work out-of-sample (Goyal and Welch 2008), \
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or has been weakened by advances in trading technology and investor \
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learning (Chordia, Subrahmanyam, and Tong 2014; McLean and Pontiff \
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2016; Martineau 2021).
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Summary:
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`,
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};
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const instanceValue = helpers.toValue(instance);
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const instances = [instanceValue];
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const parameter = {
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temperature: 0.2,
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maxOutputTokens: 256,
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topP: 0.95,
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topK: 40,
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};
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const parameters = helpers.toValue(parameter);
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const request = {
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endpoint,
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instances,
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parameters,
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};
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// Predict request
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const [response] = await predictionServiceClient.predict(request);
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console.log('Get text summarization response');
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const predictions = response.predictions;
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console.log('\tPredictions :');
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for (const prediction of predictions) {
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console.log(`\t\tPrediction : ${JSON.stringify(prediction)}`);
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}
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}
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callPredict();
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// [END aiplatform_sdk_summarization]
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}
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process.on('unhandledRejection', err => {
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console.error(err.message);
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process.exitCode = 1;
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});
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main(...process.argv.slice(2));

‎ai-platform/snippets/test/predict-text-extraction.test.js‎

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/*
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* Copyright 2023 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 path = require('path');
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const {assert} = require('chai');
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const {describe, it} = require('mocha');
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const cp = require('child_process');
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const execSync = cmd => cp.execSync(cmd, {encoding: 'utf-8'});
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const cwd = path.join(__dirname, '..');
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const project = process.env.CAIP_PROJECT_ID;
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const location = 'us-central1';
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describe('AI platform predict text extraction', () => {
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it('should make predictions using a large language model', async () => {
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const stdout = execSync(
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`node ./predict-text-extraction.js ${project} ${location}`,
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{
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cwd,
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}
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);
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assert.match(stdout, /Get text extraction response/);
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});
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});

‎ai-platform/snippets/test/predict-text-summarization.test.js‎

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/*
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* Copyright 2023 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 path = require('path');
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const {assert} = require('chai');
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const {describe, it} = require('mocha');
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const cp = require('child_process');
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const execSync = cmd => cp.execSync(cmd, {encoding: 'utf-8'});
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const cwd = path.join(__dirname, '..');
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const project = process.env.CAIP_PROJECT_ID;
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const location = 'us-central1';
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describe('AI platform predict text summarization', () => {
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it('should make predictions using a large language model', async () => {
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const stdout = execSync(
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`node ./predict-text-summarization.js ${project} ${location}`,
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{
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cwd,
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}
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);
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assert.match(stdout, /Get text summarization response/);
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});
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});

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