# Compute dot product similarity and display results
return_results(query_emb.int8, doc_emb.int8, documents)
```

```
Rank: 1
Score: 563583
Document: {'text': 'Joining Slack Channels: You will receive an invite via email. Be sure to join relevant channels to stay informed and engaged.'}

Rank: 2
Score: 508692
Document: {'text': 'Working from Abroad: Working remotely from another country is possible. Simply coordinate with your manager and ensure your availability during core hours.'}
```

### Further reading:

* [Documentation on embeddings compression levels](https://docs.cohere.com/docs/embeddings#compression-levels)

## Conclusion

In this tutorial, you learned about:

* How to embed documents for search
* How to embed queries
* How to perform semantic search
* How to perform multilingual semantic search
* How to change the embedding compression types

A high-performance and modern search system typically includes a reranking stage, which further boosts the search results.

In Part 5, you will learn how to [add reranking](/v2/docs/reranking-with-cohere) to a search system.