# Rerank the documents
results = co.rerank(
    model="rerank-v4.0-pro",
    query=query,
    documents=faqs,
    top_n=1,
)

return_results(results, faqs)
```

```
Rank: 1
Score: 0.42232594
Document: {'text': 'Health and Wellness Benefits: We care about your well-being and offer gym memberships, on-site yoga classes, and comprehensive health insurance.'}

Rank: 2
Score: 0.00025118678
Document: {'text': 'Performance Reviews Frequency: We conduct informal check-ins every quarter and formal performance reviews twice a year.'}
```

## Conclusion

In this tutorial, you learned about:

* How to rerank lexical/semantic search results
* How to rerank semi-structured data
* How to rerank tabular data
* How to perform multilingual reranking

We have now seen two critical components of a powerful search system - [semantic search](/v2/docs/semantic-search-with-cohere), or dense retrieval (Part 4) and reranking (Part 5). These building blocks are essential for implementing RAG solutions.

In Part 6, you will learn how to [implement RAG](/v2/docs/rag-with-cohere).