Upsert

async upsert(items: VectorItem[]): Promise<SuccessResponseModel>

import { Client, IndexIVFModel, VectorItem } from 'cyborgdb';

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

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

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

// Prepare vector items
const vectorItems: VectorItem[] = [
    {
        id: 'doc1',
        vector: [0.1, 0.2, 0.3, /* ... 768 dimensions */],
        contents: 'This is the content of the first document',
        metadata: {
            title: 'Introduction to Machine Learning',
            author: 'Dr. Smith',
            category: 'education',
            tags: ['ml', 'ai', 'tutorial'],
            published_date: '2024-01-15'
        }
    },
    {
        id: 'doc2',
        vector: [0.4, 0.5, 0.6, /* ... 768 dimensions */],
        contents: 'This is the content of the second document',
        metadata: {
            title: 'Advanced Neural Networks',
            author: 'Dr. Jones',
            category: 'research',
            tags: ['neural-networks', 'deep-learning'],
            published_date: '2024-01-20'
        }
    }
];

// Upsert vectors
try {
    const result = await index.upsert(vectorItems);
    console.log('Upsert result:', result.message);
    // Output: "Upserted 2 vectors"
    
    console.log(`Successfully added ${vectorItems.length} vectors to the index`);
} catch (error) {
    console.error('Upsert failed:', error.message);
}

// Add initial vector
const initialVector: VectorItem = {
    id: 'updatable_doc',
    vector: [0.1, 0.2, 0.3, /* ... */],
    contents: 'Original content',
    metadata: { version: 1, status: 'draft' }
};

await index.upsert([initialVector]);

// Update the same vector with new data
const updatedVector: VectorItem = {
    id: 'updatable_doc', // Same ID - will update existing
    vector: [0.15, 0.25, 0.35, /* ... */], // Updated vector
    contents: 'Updated content with more information',
    metadata: { version: 2, status: 'published', updated_date: '2024-01-25' }
};

try {
    const updateResult = await index.upsert([updatedVector]);
    console.log('Update result:', updateResult.message);
    
    // Verify the update
    const retrievedVector = await index.get(['updatable_doc']);
    console.log('Updated vector:', retrievedVector[0]);
} catch (error) {
    console.error('Update failed:', error.message);
}

interface DocumentData {
    id: string;
    title: string;
    content: string;
    author: string;
    category: string;
    vector: number[];
}

async function processBatchDocuments(
    index: EncryptedIndex, 
    documents: DocumentData[]
) {
    // Convert documents to VectorItem format
    const vectorItems: VectorItem[] = documents.map(doc => ({
        id: doc.id,
        vector: doc.vector,
        contents: doc.content,
        metadata: {
            title: doc.title,
            author: doc.author,
            category: doc.category,
            word_count: doc.content.split(' ').length,
            char_count: doc.content.length,
            processed_date: new Date().toISOString()
        }
    }));
    
    try {
        console.log(`Processing batch of ${vectorItems.length} documents`);
        
        const result = await index.upsert(vectorItems);
        console.log('Batch upsert completed:', result.message);
        
        return {
            success: true,
            count: vectorItems.length,
            message: result.message
        };
    } catch (error) {
        console.error('Batch upsert failed:', error.message);
        return {
            success: false,
            error: error.message
        };
    }
}

// Usage
const documents: DocumentData[] = [
    {
        id: 'article_001',
        title: 'Getting Started with TypeScript',
        content: 'TypeScript is a powerful superset of JavaScript...',
        author: 'Jane Developer',
        category: 'programming',
        vector: [0.1, 0.2, /* ... */]
    },
    {
        id: 'article_002',
        title: 'React Best Practices',
        content: 'When building React applications, it is important...',
        author: 'John React',
        category: 'frontend',
        vector: [0.3, 0.4, /* ... */]
    }
];

const batchResult = await processBatchDocuments(index, documents);