1536-Dimensional Neural Embeddings

Our AI system leverages OpenAI's text-embedding-3-small model to transform content into a precise 1536-dimensional vector space, capturing semantic meaning beyond keywords:

// Simplified embedding generation
function generateEmbedding(text) {
  // Tokenize input text
  const tokens = tokenize(text);
  
  // Process through neural network
  const embeddings = transformer.encode(tokens);
  
  // Normalize vectors for comparison
  return normalize(embeddings);
}

This mathematically represents content context, enabling our AI to understand topic relationships and generate highly relevant SEO content.

Cosine Similarity Algorithm

We use cosine similarity to measure the angular distance between content vectors, allowing us to precisely identify semantic relationships:

// Cosine similarity calculation
function cosineSimilarity(vectorA, vectorB) {
  let dotProduct = 0;
  let magnitudeA = 0;
  let magnitudeB = 0;
  
  // Calculate dot product and magnitudes
  for (let i = 0; i < vectorA.length; i++) {
    dotProduct += vectorA[i] * vectorB[i];
    magnitudeA += vectorA[i] * vectorA[i];
    magnitudeB += vectorB[i] * vectorB[i];
  }
  
  // Return similarity score (0-1)
  return dotProduct / (Math.sqrt(magnitudeA) * 
                        Math.sqrt(magnitudeB));
}