Hybrid search revisited: introducing the linear retriever in Elasticsearch!
Discover how the linear retriever enhances hybrid search by leveraging weighted scores and MinMax normalization for more precise and consistent rankings, and learn how to use it.
Creating judgement lists with Quepid
Learn how to create judgement lists in Quepid using a collaborative human rater process and use the benchmarks to tune your relevance.
Cracking the code on search quality: The role of judgment lists
Explore why a judgment list is essential, the different types of judgments, and the key factors that define search quality.
ES|QL, you know, for Search - Introducing scoring and semantic search
Elasticsearch 8.18 and 9.0 introduce several ES|QL enhancements: scoring, semantic search, expanded configuration for the match function, and a new KQL function.
Enhancing relevance with sparse vectors
Learn how to use sparse vectors in Elasticsearch to boost relevance and personalize search results with minimal complexity.
Generating filters and facets using ML
Exploring the pros and cons of automating the creation of filters and facets in a search experience using ML models vs the classical hard-coded approach.
How to automate synonyms and upload using our Synonyms API
Discover how LLMs can be used to identify and generate synonyms automatically, allowing terms to be programmatically loaded into the Elasticsearch synonym API.
Scaling late interaction models in Elasticsearch - part 2
This article explores techniques for making late interaction vectors ready for large-scale production workloads, such as reducing disk space usage and improving computation efficiency.
Searching complex documents with ColPali - part 1
The article introduces the ColPali model, a late-interaction model that simplifies the process of searching complex documents with images and tables, and discusses its implementation in Elasticsearch.
Unifying Elastic vector database and LLM functions for intelligent query
Leverage LLM functions for query parsing and Elasticsearch search templates to translate complex user requests into structured, schema-based searches for highly accurate results.
Elasticsesarch semantic search, leveled up: now with native match, knn and sparse_vector support
Semantic text search becomes even more powerful, with native support for match, knn and sparse_vector queries. This allows us to keep the simplicity of the semantic query while offering the flexibility of the Elasticsearch query DSL.
How to build autocomplete feature on search application automatically using LLM generated terms
Learn how to enhance your search application with an automated autocomplete feature in Elastic Cloud using LLM-generated terms for smarter, more dynamic suggestions.