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Accessing machine learning models in Elastic

In this post we'll introduce multilingual vector search. We'll use the Microsoft E5 multilingual embedding model, which has state-of-the-art performance in zero-shot and multilingual settings. We'll walk through how multilingual embeddings work in general and then how to use E5 in Elasticsearch.

Why multilingual embeddings?

Let's consider a few examples

Example 1

Example 2

Example 3a

Example 3b

Multilingual E5 embedding model

Elasticsearch for multilingual vector search with E5

Setup

Indexing

Search

Conclusion

Multilingual vector search with the E5 embedding model

Author

Josh Devins

Articles

Accessing machine learning models in Elastic

Multilingual vector search with the E5 embedding model