Linear algebra patterns for similarity and projection tasks

                  import numpy as np

embeddings = np.array([
    [0.9, 0.1, 0.2],
    [0.1, 0.8, 0.3],
    [0.7, 0.2, 0.4],
])

query = np.array([0.8, 0.2, 0.1])

normalized_embeddings = embeddings / np.linalg.norm(embeddings, axis=1, keepdims=True)
normalized_query = query / np.linalg.norm(query)

cosine_similarity = normalized_embeddings @ normalized_query
ranked_indices = np.argsort(cosine_similarity)[::-1]

print(cosine_similarity)
print(ranked_indices)