I’d like to manually calculate the vectorSearchScore using embeddings. I have a cosine similarity function, that I think is correct, but it’s giving me very different results to the search scores returned by the vector search on mongodb. Is MongoDb using a proprietary algorithm or doing some kind of weighting/scaling, or is my function wrong?

The embeddings I’m using are raw data returned by OpenAi using the text-embedding-3-small model.

```
public static cosineSimilarity(A: number[], B: number[]): number {
let dotProduct = 0.0;
let normA = 0.0;
let normB = 0.0;
for (let i = 0; i < A.length; i++) {
dotProduct += A[i] * B[i];
normA += A[i] ** 2;
normB += B[i] ** 2;
}
normA = Math.sqrt(normA);
normB = Math.sqrt(normB);
if (normA === 0 || normB === 0) {
return 0;
} else {
return dotProduct / (normA * normB);
}
}
```