Embeddings

An embedding turns text into a vector of numbers, so texts with similar meaning end up close together.

• 'Reset my password' and 'forgot login' get nearby vectors.
• Embed questions and documents with the same model.
• Normalised vectors make the dot product equal cosine similarity.

import numpy as np
v = np.array([3.0, 4.0])
print((v / np.linalg.norm(v)).tolist())

[0.6, 0.8]

Remember: Same model for questions and documents, always.

Common mistake: Switching embedding models without re-embedding the old documents.