Discover strategies for incorporating research papers in a software application, drawing our experiences with Elasticsearch and Lucene.

Background

Evaluate the paper as you would a software dependency

Get creative with testing

Focus on invariants

Compare to a reference implementation

Duel against an existing algorithm

Use the paper's terminology

You can email the author

To be continued

Elasticsearch & Lucene research papers: Implementation lessons

Implementing academic papers: Lessons learned from Elasticsearch and Lucene

This post explores how text embeddings and Elasticsearch’s new dense_vector type could be used to support similarity search.

What are text embeddings?

Word embeddings

Sentence embeddings

Comparison to traditional search approaches

Using embeddings for similarity search

Implementation details

Important limitations

Conclusions

Text similarity search is a type of search in which a user enters a short free-text query, and documents are ranked based on their similarity to the query. It can be useful in a variety of use cases, such as question-answering, article search, and image search.

What is text similarity search?

Text similarity search with vector fields

Author

Julie Tibshirani

Articles

Implementing academic papers: Lessons learned from Elasticsearch and Lucene

Text similarity search with vector fields