Retrieves vectors from the encrypted index by their IDs, with options to specify which fields to include in the results. 
async get({
    ids: string[],     // required
    include?: string[]  // optional, default: ["vector", "contents", "metadata"]
}): Promise<Array<object>>

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Parameters

ParameterTypeDefaultDescription
idsstring[]-Array of vector IDs to retrieve from the index
includestring[]["vector", "contents", "metadata"](Optional) Fields to include in the response. Valid options: "vector", "contents", "metadata"

​
Returns

Promise<Array<object>>: A Promise that resolves to an array of retrieved vector items, each containing the requested fields based on the include parameter.

​
Exceptions

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Example Usage

import { Client } from 'cyborgdb';

const client = new Client({ baseUrl: 'http://localhost:8000', apiKey: 'your-api-key' });

// Load an existing index
const indexKey = new Uint8Array(Buffer.from('your-stored-hex-key', 'hex'));
const index = await client.loadIndex({ indexName: 'my-vector-index', indexKey });

// Add some vectors first
await index.upsert({
    items: [
        { 
            id: 'doc1', 
            vector: [0.1, 0.2, 0.3, 0.4], 
            contents: 'This is the first document content',
                metadata: { title: 'Document 1', category: 'research', date: '2024-01-15' }
        },
        { 
            id: 'doc2', 
            vector: [0.4, 0.5, 0.6, 0.7], 
            contents: 'This is the second document content',
            metadata: { title: 'Document 2', category: 'tutorial', date: '2024-01-16' }
        }
    ]
});

// Retrieve vectors with all fields (default behavior)
try {
    const vectors = await index.get({ ids: ['doc1', 'doc2'] });

    vectors.forEach(item => {
        console.log(`ID: ${item.id}`);
        console.log(`Vector dimensions: ${item.vector?.length}`);
        console.log(`Contents: ${item.contents}`);
        console.log(`Metadata: ${JSON.stringify(item.metadata)}`);
        console.log('---');
    });
    
} catch (error: any) {
    console.error('Failed to retrieve vectors:', error.message);
}

​
Response Format

The method returns an array of objects with the following structure: 
// Example response with all fields
[
    {
        "id": "doc1",
        "vector": [0.1, 0.2, 0.3, 0.4],
        "contents": "Document content as string or Buffer",
        "metadata": {
            "title": "Document 1",
            "category": "research",
            "date": "2024-01-15"
        }
    },
    {
        "id": "doc2",
        "vector": [0.4, 0.5, 0.6, 0.7],
        "contents": "Another document content",
        "metadata": {
            "title": "Document 2", 
            "category": "tutorial",
            "date": "2024-01-16"
        }
    }
]

​
Response Item Fields

FieldTypeDescription
idstringUnique identifier of the vector (always included)
vectornumber[]The vector data (included if "vector" in include array)
contentsBuffer | stringThe content data, automatically decoded from base64 if needed (included if "contents" in include array)
metadataanyAssociated metadata object (included if "metadata" in include array)