Walkthrough of using private assets

How would one go about using assets from their own private bucket (and not the public one in the case of generativeai-downloads/images/scones.jpg)?

Is your feature request related to a problem? Please describe.

I am facing errors (500 internal server) when attempting to access an image in my own GCS and there seems to be an access/permission issue even though I have tried to instantiate a Storage object with my application_default_credentials in order to access the files inside the bucket, and to then use the file to compose a fileUri (i.e. gs://bucket-name/filename.jpeg).

Describe the solution you'd like.

I would like to be able to provide private assets from GCS to VertexAI and get the same result as described here https://cloud.google.com/vertex-ai/docs/generative-ai/start/quickstarts/quickstart-multimodal#send-request

I created a bucket
Then placed an image inside the bucket

Populated the code below with above info


const { Storage } = require('@google-cloud/storage');
const { VertexAI } = require('@google-cloud/vertexai');

const PROJECT_ID = process.env.PROJECT_ID; const bucketName = 'arsia-toki-bucket-standard';

async function createNonStreamingMultipartContent( projectId = PROJECT_ID, location = 'us-central1', model = 'gemini-pro-vision', mimeType = 'image/jpeg' ) { const storage = new Storage({ projectId, keyFilename: './application_default_credentials.json' });

// Initialize Vertex with your Cloud project and location
const vertexAI = new VertexAI({ project: projectId, location: location });

// Instantiate the model
const generativeVisionModel = vertexAI.preview.getGenerativeModel({
    model: model,
});

const [files] = await storage.bucket(bucketName).getFiles();
if (files.length === 0) {
    throw new Error('No files found in the bucket.');
}

const fileUri = `gs://${bucketName}/${files[0].name}`;

// Create the request
// For images, the SDK supports both Google Cloud Storage URI and base64 strings
const filePart = {
    fileData: {
        fileUri: fileUri,
        mimeType: mimeType,
    },
};
const textPart = {
    text: 'what is shown in this image?',
};

const request = {
    contents: [{ role: 'user', parts: [filePart, textPart] }],
};

console.log('Prompt Text:');
console.log(request.contents[0].parts[1].text);

console.log('Non-Streaming Response Text:');
// Create the response stream
const responseStream = await generativeVisionModel.generateContentStream(request);

// Wait for the response stream to complete
const aggregatedResponse = await responseStream.response;

// Select the text from the response
const fullTextResponse = aggregatedResponse.candidates[0].content.parts[0].text;

console.log(fullTextResponse);

// [END aiplatform_gemini_get_started]

}

createNonStreamingMultipartContent(...process.argv.slice(2)).catch(err => { console.error(err.message); process.exitCode = 1; });

4. 
![Screenshot 2024-01-03 at 2 11 16 PM](https://github.com/GoogleCloudPlatform/nodejs-docs-samples/assets/28409758/a0e26aa9-155c-4345-bf06-76e08cc4b70e)

## Describe alternatives you've considered.
<!-- A clear and concise description of any alternative solutions or features you've considered. -->
So one solution that I did conjure up was to add to the acl rules before sending the asset to VertexAI and once the analysis is complete, to revoke the public acl rule. But I wonder if you have a more elegant solution.

```js
const { Storage } = require('@google-cloud/storage');
const { VertexAI } = require('@google-cloud/vertexai');

const PROJECT_ID = process.env.PROJECT_ID;
const bucketName = 'arsia-toki-bucket-standard';

async function createNonStreamingMultipartContent(
    projectId = PROJECT_ID,
    location = 'us-central1',
    model = 'gemini-pro-vision',
    mimeType = 'image/jpeg'
) {

    // Initialize Vertex with your Cloud project and location
    const vertexAI = new VertexAI({ project: projectId, location: location });

    // Instantiate the model
    const generativeVisionModel = vertexAI.preview.getGenerativeModel({
        model: model,
    });

    const storage = new Storage({ projectId: PROJECT_ID, keyFilename: './key.json' });

    // Get the file from the storage bucket
    const [files] = await storage.bucket(bucketName).getFiles();
    if (files.length === 0) {
        throw new Error('No files found in the bucket.');
    }

    // the second file is what I want the AI to analyze this time 
    const file = files[1];
    const fileUri = `gs://${bucketName}/${file.name}`;
    console.log('File URI:', fileUri);

    try {
        await file.acl.add({
            entity: 'allUsers',
            role: 'READER',
        });
        console.log('File is temporarily public.');

        // Create the request
        // For images, the SDK supports both Google Cloud Storage URI and base64 strings
        const filePart = {
            fileData: {
                fileUri: fileUri,
                mimeType: mimeType,
            },
        };
        const textPart = {
            text: 'what food items are shown in this image?',
        };
        const request = {
            contents: [{ role: 'user', parts: [filePart, textPart] }],
        };
        console.log('Prompt Text:');
        console.log(request.contents[0].parts[1].text);
        console.log('Non-Streaming Response Text:');

// Create the response stream
        const responseStream = await generativeVisionModel.generateContentStream(request);

        // Wait for the response stream to complete
        const aggregatedResponse = await responseStream.response;

        await file.acl.add({
            entity: 'allUsers',
            role: 'READER',
        });

        // Select the text from the response
        const fullTextResponse = aggregatedResponse.candidates[0].content.parts[0].text;

        console.log(fullTextResponse);

    } finally {
        try {
            await file.acl.remove({
                entity: 'allUsers',
                role: 'READER',
            });

            console.log('Public access revoked. File is private again.');
        } catch (error) {
            console.error('Error revoking public access:', error.message);
        }
    }
}

createNonStreamingMultipartContent(...process.argv.slice(2)).catch(err => {
    console.error(err.message);
    process.exitCode = 1;
});

Additional context.

Thanks!

GoogleCloudPlatform / nodejs-docs-samples