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Retrieves vectors from the encrypted index by their IDs, with options to specify which fields to include in the results. 
async get(
    ids: string[],
    include: string[] = ["vector", "contents", "metadata"]
): Promise<Array<GetResultItem>>

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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"

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Returns

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

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Exceptions

  • Throws if the API request fails due to network connectivity issues.
  • Throws if authentication fails (invalid API key).
  • Throws if the encryption key is invalid for the specified index.
  • Throws if there are internal server errors preventing the retrieval.
  • Throws if the ids parameter is null, undefined, or empty.
  • Throws if the include parameter contains invalid field names.

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Example Usage

import { Client, IndexIVFModel } from 'cyborgdb';

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

// Create and populate index
const indexKey = crypto.getRandomValues(new Uint8Array(32));
const config: IndexIVFModel = {
    dimension: 768,
    nLists: 1024,
    metric: 'cosine'
};

const index = await client.createIndex('my-vectors', indexKey, config);

// Add some vectors first
await index.upsert([
    { 
        id: 'doc1', 
        vector: [0.1, 0.2, 0.3, /* ... 768 dimensions */], 
        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, /* ... 768 dimensions */], 
        contents: 'This is the second document content',
        metadata: { title: 'Document 2', category: 'tutorial', date: '2024-01-16' }
    }
]);

// Retrieve vectors with all fields
try {
    const vectors = await index.get(['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) {
    console.error('Failed to retrieve vectors:', error.message);
}

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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, /* ... */],
        "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, /* ... */],
        "contents": "Another document content",
        "metadata": {
            "title": "Document 2", 
            "category": "tutorial",
            "date": "2024-01-16"
        }
    }
]

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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)