# Display search results
for idx, docs_idx in enumerate(top_doc_idxs):
    print(f"Rank: {idx+1}")
    print(f"Document: {documents[docs_idx]}\n")
```

Here's a sample output:

```
Rank: 1
Document: COVID-19 symptoms can include: a high temperature or shivering (chills); a new, continuous cough; a loss or change to your sense of smell or taste; and many more

Rank: 2
Document: COVID-19 is a disease caused by a virus. The most common symptoms are fever, chills, and sore throat, but there are a range of others.

Rank: 3
Document: COVID-19 has the following symptom: Experiencing memory loss, poor judgment, and confusion.

Rank: 4
Document: COVID-19 has many symptoms.

Rank: 5
Document: COVID-19 symptoms are not nice
```

## Multilingual semantic search

The Embed endpoint also supports multilingual semantic search via `embed-v4.0` and previous `embed-multilingual-...` models. This means you can perform semantic search on texts in different languages.

Specifically, you can do both multilingual and cross-lingual searches using one single model.

Specifically, you can do both multilingual and cross-lingual searches using one single model.

```python PYTHON
### STEP 1: Embed the documents

documents = [
    "Remboursement des frais de voyage : Gérez facilement vos frais de voyage en les soumettant via notre outil financier. Les approbations sont rapides et simples.",
    "Travailler de l'étranger : Il est possible de travailler à distance depuis un autre pays. Il suffit de coordonner avec votre responsable et de vous assurer d'être disponible pendant les heures de travail.",
    "Avantages pour la santé et le bien-être : Nous nous soucions de votre bien-être et proposons des adhésions à des salles de sport, des cours de yoga sur site et une assurance santé complète.",
    "Fréquence des évaluations de performance : Nous organisons des bilans informels tous les trimestres et des évaluations formelles deux fois par an.",
]