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| 1 | private cosineSimilarity(vecA: Embedding, vecB: Embedding): number { |
| 2 | const dotProduct = vecA.reduce((sum, value, index) => sum + value * vecB[index], 0); |
| 3 | const magnitudeA = Math.sqrt(vecA.reduce((sum, value) => sum + value ** 2, 0)); |
| 4 | const magnitudeB = Math.sqrt(vecB.reduce((sum, value) => sum + value ** 2, 0)); |
| 5 | |
| 6 | return magnitudeA > 0 && magnitudeB > 0 ? dotProduct / (magnitudeA * magnitudeB) : 0; |
| 7 | } |
| 8 | |
| 9 | findClosestEmbedding(queryEmbedding: Embedding): string | null { |
| 10 | let bestMatch: string | null = null; |
| 11 | let bestSimilarity = -Infinity; |
| 12 | |
| 13 | for (const [key, embedding] of this.embeddings.entries()) { |
| 14 | const similarity = this.cosineSimilarity(queryEmbedding, embedding); |
| 15 | if (similarity > bestSimilarity) { |
| 16 | bestSimilarity = similarity; |
| 17 | bestMatch = key; |
| 18 | } |
| 19 | } |
| 20 | |
| 21 | return bestMatch; |
| 22 | } |
| 23 | |
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