Category: Relevance

Articles tagged Relevance

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Understanding sparse vector embeddings with trained ML models
Elasticsearch Labs
Vector DatabaseRelevance

Understanding sparse vector embeddings with trained ML models

Learn about sparse vector embeddings, understand what they do/mean, how they differ from dense vector embeddings, and how to implement semantic search with them.

Dai Sugimori
How to search languages with compound words
Elasticsearch Labs
RelevanceBasics

How to search languages with compound words

Compound words present challenges in search engines during text analysis and tokenization, as they can obscure meaningful connections between word components. Tools like the Hyphenation Decompounder Token Filter help address these issues by deconstructing compound words.

Peter Straßer
Improve search results by calibrating model scoring in Elasticsearch
Elasticsearch Labs
Relevance

Improve search results by calibrating model scoring in Elasticsearch

Learn how to leverage annotated data to calibrate semantic model scoring for better search results

Quentin Herreros
Query rules retriever: ensuring business rules work seamlessly with semantic search
Elasticsearch Labs
Relevance

Query rules retriever: ensuring business rules work seamlessly with semantic search

Harness the power of Elasticsearch query rules combined with semantic search and rerankeras.

Kathleen DeRusso
Elasticsearch retrievers architecture and use-cases
Elasticsearch Labs
Relevance

Elasticsearch retrievers architecture and use-cases

Elasticsearch retrievers have gone through a significant revamp and are now generally available for all to use. Learn all about their c and use-cases.

Panagiotis Bailis
Reranking with an Elasticsearch-hosted cross-encoder from Hugging Face
Elasticsearch Labs
Relevance

Reranking with an Elasticsearch-hosted cross-encoder from Hugging Face

Learn how to use a model from Hugging Face to host and perform semantic-reranking in Elasticsearch.

Jeff Vestal
Elasticsearch Interval queries: why they are true positional queries & how to transition from Span
Elasticsearch Labs
Relevance

Elasticsearch Interval queries: why they are true positional queries & how to transition from Span

Explains how Interval queries are true positional queries and how to transition to them from Span queries.

Mayya Sharipova
What is semantic reranking and how to use it?
Elasticsearch Labs
ML ResearchRelevance

What is semantic reranking and how to use it?

Introducing the concept of semantic reranking. Learn about the trade-offs using semantic reranking in search and RAG pipelines.

Thomas Veasey
Personalized search with learning-to-rank (LTR)
Elasticsearch Labs
Relevance

Personalized search with learning-to-rank (LTR)

Learn how to train ranking models that improve search relevance for individual users and personalize search through learning-to-rank (LTR) in Elasticsearch.

Max Jakob
Looking back: Elastic's vector search improvements in Elasticsearch & Lucene
Elasticsearch Labs
Vector DatabaseRelevance

Looking back: Elastic's vector search improvements in Elasticsearch & Lucene

Looking back at Elastic's vector search innovations in Elasticsearch and Lucene.

Kathleen DeRusso
Elasticsearch query rules are now generally available
Elasticsearch Labs
Relevance

Elasticsearch query rules are now generally available

Introducing the general availability of query rules

Kathleen DeRusso
Introducing Learning To Rank (LTR) in Elasticsearch
Elasticsearch Labs
Relevance

Introducing Learning To Rank (LTR) in Elasticsearch

Discover how Learning To Rank (LTR) can help you to improve your search ranking and how to implement it in Elasticsearch.

Aurélien Foucret

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