pulse-ai-utils/supabase/vector-helpers.js

Utility functions and helpers for AI-powered applications

"use strict";
/**
 * Helper functions for working with pgvector data in Supabase
 */
Object.defineProperty(exports, "__esModule", { value: true });
exports.parseVectorString = parseVectorString;
exports.formatVectorForInsert = formatVectorForInsert;
exports.getEmbeddingArray = getEmbeddingArray;
exports.cosineSimilarity = cosineSimilarity;
exports.prepareContentForBulkInsert = prepareContentForBulkInsert;
exports.transformSupabaseResponse = transformSupabaseResponse;
/**
 * Parse a pgvector string representation to a JavaScript array
 * Handles the string format returned by Supabase: "[0.1,0.2,0.3,...]"
 */
function parseVectorString(vectorString) {
    if (!vectorString || typeof vectorString !== 'string') {
        return [];
    }
    try {
        // Remove brackets and split by comma
        const cleanString = vectorString.replace(/^\[|\]$/g, '');
        return cleanString.split(',').map(val => parseFloat(val.trim()));
    }
    catch (error) {
        console.error('Error parsing vector string:', error);
        return [];
    }
}
/**
 * Convert a JavaScript array to pgvector format for insertion
 * This handles the proper formatting for Supabase/PostgreSQL
 */
function formatVectorForInsert(vector) {
    return JSON.stringify(vector);
}
/**
 * Safely get embedding from Supabase response
 * Handles both array and string formats
 */
function getEmbeddingArray(data) {
    if (!data?.embedding)
        return null;
    // If it's already an array, return it
    if (Array.isArray(data.embedding)) {
        return data.embedding;
    }
    // If it's a string, parse it
    if (typeof data.embedding === 'string') {
        return parseVectorString(data.embedding);
    }
    // If it's an object (from JSONB cast), extract the array
    if (typeof data.embedding === 'object' && data.embedding !== null) {
        // Handle JSONB format that might come from our SQL functions
        if (Array.isArray(data.embedding)) {
            return data.embedding;
        }
    }
    return null;
}
/**
 * Calculate cosine similarity between two vectors
 * Useful for client-side similarity calculations
 */
function cosineSimilarity(vec1, vec2) {
    if (!vec1 || !vec2 || vec1.length !== vec2.length) {
        throw new Error('Vectors must have the same length');
    }
    let dotProduct = 0;
    let norm1 = 0;
    let norm2 = 0;
    for (let i = 0; i < vec1.length; i++) {
        dotProduct += vec1[i] * vec2[i];
        norm1 += vec1[i] * vec1[i];
        norm2 += vec2[i] * vec2[i];
    }
    const denominator = Math.sqrt(norm1) * Math.sqrt(norm2);
    return denominator === 0 ? 0 : dotProduct / denominator;
}
/**
 * Batch process content with embeddings
 * Ensures proper formatting for bulk operations
 */
function prepareContentForBulkInsert(documents) {
    return documents.map(doc => ({
        ...doc,
        embedding: doc.embedding ? formatVectorForInsert(doc.embedding) : null
    }));
}
/**
 * Transform Supabase response to ensure embeddings are arrays
 * Use this after fetching data with embedding fields
 */
function transformSupabaseResponse(data) {
    return data.map(item => ({
        ...item,
        embedding: item.embedding ? getEmbeddingArray(item) : null
    }));
}
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