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feat: sentiment analysis sample for Vertex LLMs #3250

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127 changes: 127 additions & 0 deletions ai-platform/snippets/predict-text-sentiment.js
Original file line number Diff line number Diff line change
@@ -0,0 +1,127 @@
/*
* Copyright 2023 Google LLC
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/

'use strict';

async function main(project, location = 'us-central1') {
// [START aiplatform_sdk_sentiment_analysis]
/**
* TODO(developer): Uncomment these variables before running the sample.\
* (Not necessary if passing values as arguments)
*/
// const project = 'YOUR_PROJECT_ID';
// const location = 'YOUR_PROJECT_LOCATION';

const aiplatform = require('@google-cloud/aiplatform');

// Imports the Google Cloud Prediction service client
const {PredictionServiceClient} = aiplatform.v1;

// Import the helper module for converting arbitrary protobuf.Value objects.
const {helpers} = aiplatform;

// Specifies the location of the api endpoint
const clientOptions = {
apiEndpoint: 'us-central1-aiplatform.googleapis.com',
};

const publisher = 'google';
const model = 'text-bison@001';

// Instantiates a client
const predictionServiceClient = new PredictionServiceClient(clientOptions);

async function callPredict() {
// Configure the parent resource
const endpoint = `projects/${project}/locations/${location}/publishers/${publisher}/models/${model}`;

const instance = {
content: `I had to compare two versions of Hamlet for my Shakespeare class and \
unfortunately I picked this version. Everything from the acting (the actors \
deliver most of their lines directly to the camera) to the camera shots (all \
medium or close up shots...no scenery shots and very little back ground in the \
shots) were absolutely terrible. I watched this over my spring break and it is \
very safe to say that I feel that I was gypped out of 114 minutes of my \
vacation. Not recommended by any stretch of the imagination.
Classify the sentiment of the message: negative

Something surprised me about this movie - it was actually original. It was not \
the same old recycled crap that comes out of Hollywood every month. I saw this \
movie on video because I did not even know about it before I saw it at my \
local video store. If you see this movie available - rent it - you will not \
regret it.
Classify the sentiment of the message: positive

My family has watched Arthur Bach stumble and stammer since the movie first \
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I would consider cutting this paragraph and the next since it references actors by name.

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Agreed. Removed content.

came out. We have most lines memorized. I watched it two weeks ago and still \
get tickled at the simple humor and view-at-life that Dudley Moore portrays. \
Liza Minelli did a wonderful job as the side kick - though I'm not her \
biggest fan. This movie makes me just enjoy watching movies. My favorite scene \
is when Arthur is visiting his fiancée's house. His conversation with the \
butler and Susan's father is side-spitting. The line from the butler, \
"Would you care to wait in the Library" followed by Arthur's reply, \
"Yes I would, the bathroom is out of the question", is my NEWMAIL \
notification on my computer.
Classify the sentiment of the message: positive

This Charles outing is decent but this is a pretty low-key performance. Marlon \
Brando stands out. There's a subplot with Mira Sorvino and Donald Sutherland \
that forgets to develop and it hurts the film a little. I'm still trying to \
figure out why Charlie want to change his name.
Classify the sentiment of the message: negative

Tweet: The Pixel 7 Pro, is too big to fit in my jeans pocket, so I bought \
new jeans.
Classify the sentiment of the message:
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It looks like this is missing a sentiment value.

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I see. The sentiment is supposed to be missing here. This is the prompt for the model to predict.

`,
};
const instanceValue = helpers.toValue(instance);
const instances = [instanceValue];

const parameter = {
temperature: 0.2,
maxOutputTokens: 5,
topP: 0,
topK: 1,
};
const parameters = helpers.toValue(parameter);

const request = {
endpoint,
instances,
parameters,
};

// Predict request
const [response] = await predictionServiceClient.predict(request);
console.log('Get text sentiment response');
const predictions = response.predictions;
console.log('\tPredictions :');
for (const prediction of predictions) {
console.log(`\t\tPrediction : ${JSON.stringify(prediction)}`);
}
}

callPredict();
// [END aiplatform_sdk_sentiment_analysis]
}

process.on('unhandledRejection', err => {
console.error(err.message);
process.exitCode = 1;
});

main(...process.argv.slice(2));
40 changes: 40 additions & 0 deletions ai-platform/snippets/test/predict-text-sentiment.test.js
Original file line number Diff line number Diff line change
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/*
* Copyright 2023 Google LLC
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/

'use strict';

const path = require('path');
const {assert} = require('chai');
const {describe, it} = require('mocha');

const cp = require('child_process');
const execSync = cmd => cp.execSync(cmd, {encoding: 'utf-8'});
const cwd = path.join(__dirname, '..');

const project = process.env.CAIP_PROJECT_ID;
const location = 'us-central1';

describe('AI platform predict text sentiment', () => {
it('should make predictions using a large language model', async () => {
const stdout = execSync(
`node ./predict-text-sentiment.js ${project} ${location}`,
{
cwd,
}
);
assert.match(stdout, /Get text sentiment response/);
});
});