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1. **Word2Vec Embedding**:
   - Word2Vec typically creates embeddings in spaces ranging from 100 
+to 300 dimensions. Let's assume we are using a 300-dimensional model.
...
   - For 5 words, the total memory would be \( 5 \times 1200 \) bytes 
+= 6000 bytes (or 6 kilobytes).

So, in this hypothetical scenario, representing the phrase "And it cam
+e to pass..." using a 300-dimensional Word2Vec model would require ap
+proximately 6 kilobytes of memory.

#!/usr/bin/perl 


...

__END__

    (base) Merrills-Mac-mini:Documents mymac$ ./1.openai.pl 
Use of uninitialized value $embed_string in split at /Library/Perl/5.3
+0/AI/Embedding.pm line 141.
features must contain terms at /Library/Perl/5.30/Data/CosineSimilarit
+y.pm line 68.
(base) Merrills-Mac-mini:Documents mymac$