example.txt

1. reviewed_book_embeddings = [
    [0.5, 1.2, 3.4],  # Embedding for Book 1
    [2.3, 0.8, 4.1],  # Embedding for Book 2
    [1.1, 2.5, 3.0]   # Embedding for Book 3
]

2. zip will produce: (it's "transposes" the data)
----------------------
[
    (0.5, 2.3, 1.1),  # First elements from each embedding
    (1.2, 0.8, 2.5),  # Second elements from each embedding
    (3.4, 4.1, 3.0)   # Third elements from each embedding
]

3. We calculate the average of each group
-----------------------------------------
avg_review_embedding = [
    (0.5 + 2.3 + 1.1) / 3,  # Average of the first elements
    (1.2 + 0.8 + 2.5) / 3,  # Average of the second elements
    (3.4 + 4.1 + 3.0) / 3   # Average of the third elements
]

4. avg_review_embedding = [1.3, 1.5, 3.5]


Vector Operators
---------------------------
Operator	Description	Added
--------    -----------------
+	        element-wise addition	
-	        element-wise subtraction	
*	        element-wise multiplication	0.5.0
||	        concatenate	0.7.0
<->	        Euclidean distance	# Calculates the straight-line distance between two vectors in a multi-dimensional space.
<#>        	negative inner product # Computes the inner product (dot product) of two vectors and negates the result.
<=>	        cosine distance	# Measures the cosine distance between two vectors, which indicates how similar they are in direction. 
                            It's useful for comparing vectors' orientations regardless of their length.
<+>	        taxicab distance	0.7.0 # Also known as Manhattan distance, it calculates the distance between two points by only moving along grid 
                                        lines (like moving in a city with square blocks).