Breaking Language Barriers with Cross-Lingual Vector Search Python Notebook

[qn895]

No description provided.

[gitnotebooks]

Found 1 changed notebook. Review the changes at https://app.gitnotebooks.com/elastic/elasticsearch-labs/pull/491

[carlyrichmond]

Thanks for adding the code in a notebook @qn895! I've tweaked the connection settings to use API Key and endpoint instead of cloud id and the basic auth without credentials to show best connection practice.

Could you also add an example query to the end of the notebook as well from the piece for completeness? Once that's in it should be good to go in my view.

Hope that helps!

[qn895]

@carlyrichmond Just updated the notebook with more example queries, thank you for the feedback!

[qn895]

@carlyrichmond Hi Carly, just wanted to check with you if you're good with my latest updates for this notebook. Thanks!

[Copilot]

Pull Request Overview

This PR introduces a comprehensive Python Jupyter notebook demonstrating cross-lingual vector search capabilities using multilingual embedding models. The notebook shows how to overcome language barriers by enabling queries and information retrieval in any language from both single and multilingual datasets.

Key changes include:

• Complete implementation of multilingual COCO dataset processing and indexing
• Integration with Elasticsearch for vector search functionality
• Cross-lingual query examples demonstrating search capabilities across different languages

Comments suppressed due to low confidence (4)

supporting-blog-content/multilingual-embedding/multilingual_embedding.ipynb:1

• The count of documents is incorrect. len(bulk_data) includes both index operations and document data, so it counts twice the actual number of documents. Should be len(bulk_data) // 2 to get the correct document count.

{

supporting-blog-content/multilingual-embedding/multilingual_embedding.ipynb:1

• This creates a new Elasticsearch client without credentials, overriding the previously configured client with authentication. This will likely cause authentication failures. Should reuse the existing es client or remove this redundant initialization.

{

supporting-blog-content/multilingual-embedding/multilingual_embedding.ipynb:1

• Index name inconsistency: the destination index is 'coco_multilingual' but earlier the index was created as 'coco_multi'. This will cause the reindex operation to fail or create data in an unmapped index.

{

supporting-blog-content/multilingual-embedding/multilingual_embedding.ipynb:1

• Index name inconsistency: searching 'coco_multi' but the reindex operation in the previous cell targets 'coco_multilingual'. The index names should be consistent throughout the notebook.

{

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[carlyrichmond]

@qn895 I've fixed the notebook and added a prerequisites cell, so it's good to merge. Thanks so much for sharing!