Stack

Have you struggled to build the Kibana visualizations you need using Lens and TSDB? Learn how to create complex visualizations using Kibana and Vega.

Explore data analysis with Elasticsearch, Python & Kibana. Learn about data loading, querying and creating Kibana dashboards with an example.

Learn how to use Node.js to keep your index current using an Azure Function App.

Elasticsearch adds a new vector search optimized profile to Elastic Cloud on Microsoft Azure.

Get started with custom GPTs using ChatGPT and Elasticsearch. Learn how to create custom GPTs that interact seamlessly with your Elasticsearch data.

Integrate Elasticsearch's search relevance with ChatGPT's question-answering capability to enhance your domain-specific knowledge base.

Learn how to implement a plugin and enable ChatGPT users to extend ChatGPT with any content indexed in Elasticsearch, using the Elastic documentation.

In this post we'll show how to easily ingest large documents and break them up into sentences via an ingest pipeline so that they can be text embedded along with nested vector support for searching large documents semantically. Generated image of a chonker.

Learn more about how to use Cohere embeddings with Elastic built search experiences!

“Elasticsearch integrates semantic reranking with Cohere’s Rerank models, with the inclusion of Rerank into our open Inference API.”

Explore how to quickly set up and ingest data into Elasticsearch for use as a vector database with Azure OpenAI On Your Data, enabling you to chat with your private data.

Elasticsearch open inference API adds support for Azure Open AI chat completions, providing full developer access to the Azure AI ecosystem

Learn how OpenAI chat completions and Elasticsearch can be used to summarize, translate or perform question & answering on any text.

Part 1: High-fidelity dense vector search in Elasticsearch. Learn how we achieve a 75% memory reduction without impacting search quality.

Learn about the improvements we've made to the inference performance of ELSER v2.

Learn about how we're reducing retrieval costs for ELSER v2.

How to export ES|QL queries as Pandas dataframes in Python

Learn how to execute and manage ES|QL queries in PHP. Follow this guide to map ES|QL results to a PHP object or custom class.

Learn how to perform ES|QL queries with the Java client. Follow this guide for step-by-step instructions, including examples.

Explore RAG evaluation metrics like BLEU score, ROUGE score, PPL, BARTScore, and more. Discover how Elastic is evaluating RAG with UniEval.

Learn more about exact and approximate kNN search in Elasticsearch, and when to use each one.

Learn how to implement image search with an example. This blog covers how to use vector search through image processing in Elasticsearch.

Elasticsearch adds a new vector search optimized profile for GCP.

Learn how to build a generative artificial intelligence (GAI) solution with Amazon SageMaker JumpStart, Elastic, and Hugging Face open source LLMs using the sample implementation provided in this post and a data set relevant to your business.

Taking Text Embeddings and Vector Similarity Search as the example task, this blog describes the process for getting up and running using deep learning models for Natural Language Processing, and demonstrates vector search capability in Elasticsearch

Optimizing scalar quantization for the vector database use case allows us to achieve significantly better performance for the same retrieval quality at high compression ratios.

Introducing the sparse vector query, powering sparse vector search in the future

Keep your Elasticsearch index updated with Python & Google Cloud Functions. Follow these steps to automatically update an index when new data is present.

Explore how the kNN query in Elasticsearch can be used and how it differs from top-level kNN search, including examples.

Elasticsearch 8.11 improves how it manages its indexing buffer, resulting in less segment merging.

Learn about the concepts related to vector databases, how they work and how to get the best out of lexical & AI search with Elastic’s vector database.

In this blog, we'll explore various approaches to retrieving information using Elasticsearch, focusing on lexical and semantic search.

With LocalAI, you can compute text embeddings in air-gapped systems. GPU support is available. Here's how to set up LocalAI to compute embeddings for your data.

Lucene 9.9 was released recently and brings major speedups to query evaluation

How we brought maximum-inner-product into Lucene

Deep learning has transformed how people retrieve information. We've created a retrieval model that works with a variety of text with streamlined processes to deploy it. Learn about the model's performance, its architecture, and how it was trained.

Bring your own transformer models into Elastic to use optimized embedding models and NLP, or integrate with third-party transformer modes such as OpenAI GPT-4 via APIs to leverage more accurate, business-specific content based on private data stores.

Elastic Learned Sparse Encoder is an AI model for high relevance semantic search across domains. As a sparse vector model, it expands the query with terms that don't exist in the query itself, delivering superior relevance without domain adaptation.

Improve the MAXSCORE algorithm to evaluate disjuntive queries more like a conjunctive query when possible, which helps evaluate even fewer hits

Sharing information among segment searches in multi-graph vector search allows us to achieve significant search speedups.

Learn more on how to link original context to a multi-vector document.

Here's how multilingual vector search works and how to use Elasticsearch with the multilingual E5 embedding model, including examples.

Learn about the evolution of the Elasticsearch .NET client and the transition from NEST to Elastic.Clients.Elasticsearch.

Elasticsearch Query Language (ES|QL) is GA. Simplify your data investigations with an innovative piped query language powered by a new query engine, all from a single unified UI.

Here's how to check for AI plagiarism using Elasticsearch, focusing on use cases with NLP models and Vector Search.

This blog offers practical strategies for tuning search relevance that can be complementary to semantic search.

How to implement and scale semantic search as a service for a search COE using Elastic Learned Sparse EncodeR (ELSER)

Discover this future of stateless Elasticsearch. Learn how we’re investing in building a new fully cloud native architecture to push the boundaries of scale and speed.

Learn about token pruning and how it boosts the performance of text expansion queries by making them more efficient without sacrificing recall.

In this blog, you'll learn how vector search has been integrated into Elasticsearch and the trade-offs that we made.

2023 has been another big year for Apache Lucene, this blog reviews major milestones of 2023

Here's how generative AI works from the ground up, including embeddings, transformer-encoder architecture, training/fine-tuning models & more.

Learn how to achieve hybrid search by combining keyword and vector search using Elasticsearch and the Elasticsearch Go client.

Learn how to perform traditional text queries in Elasticsearch using the Elasticsearch Go client through a practical example.

Learn how to perform vector search in Elasticsearch using the Elasticsearch Go client through a practical example.