Embedding

The practical value is that search is no longer limited to word match. The question “which customers are being lost?” can reach a document that uses the phrase “customer churn,” even if they share no words.

For Persian there is an important point: result quality depends heavily on the embedding model, and not all models support Persian equally. Normalizing Arabic and Persian characters, handling zero-width non-joiners, and digits is also necessary before embedding; otherwise a single phrase becomes several different vectors.