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.

Aggregate billions of documents in milliseconds instead of minutes with Elastic. Learn more about how the new random_sampler aggregation gives you statistically robust results at a lower cost.

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

Learn how to update your Elasticsearch index automatically using Node.js and an Azure Function App. Follow these steps to ensure your index stays current.

Elasticsearch added a new vector search optimized profile to Elastic Cloud on Microsoft Azure. Get started and learn how to use it here.

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.

Learn how to chunk large documents using ingest pipelines and nested vectors in Elasticsearch for easy passage search in vector search.

Elasticsearch now supports Cohere embeddings! This blog explains how to use Cohere embeddings with Elastic-built search experiences.

“Learn about Cohere reranking, how to use Cohere's Rerank 3 model with the Elasticsearch open inference API and Elastic's roadmap for semantic reranking.”

Interact with Anthropic's Claude 3.5 Sonnet and other models to generate content and perform question & answering.

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

Azure OpenAI chat completions is available via the Elasticsearch inference API. Learn how to use this feature to answer questions.

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

This blog post presents an experimental project for querying Elasticsearch in natural language using ChatGPT.

Explore the cost, performance and benchmarking for running large-scale vector search in Elasticsearch, with a focus on high-fidelity dense vector search.

Learn about the improvements we've made to the inference performance of ELSER v2, achieving a 60% to 120% speed increase over ELSER v1.

Learn how we are reducing the retrieval costs of the Learned Sparse EncodeR (ELSER) v2.

Learn how to export ES|QL queries as Pandas dataframes in Python through practical examples.

Geospatial search in Elasticsearch Query Language (ES|QL). Elasticsearch has powerful geospatial search features, which are now coming to ES|QL for dramatically improved ease of use and OGC familiarity

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 export ES|QL queries as native Pandas dataframes in Python through practical examples.

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's vector search optimized profile for GCP is available. Learn more about it and how to use it in this blog.

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.

Using text embeddings and vector similarity search, this blog explains how to run deep learning models for NLP & showcases Elasticsearch's vector search capability.

In this blog post, we'll examine benchmark solutions to compare retrieval methods. We use a collection of data sets to benchmark BM25 against two dense models and illustrate the potential gain using fine-tuning strategies with one of those models.

In this blog we introduce hybrid retrieval and explore two concrete implementations in Elasticsearch. We explore improving Elastic Learned Sparse Encoder’s performance by combining it with BM25 using Reciprocal Rank Fusion and Weighted Sum of Scores.

In this first blog post, we will list and explain the differences between the primary building blocks available in the Elastic Stack to do information retrieval.

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.

Learn about the Elasticsearch sparse vector query, how it works, and how to effectively use it.

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.

Discover how Elasticsearch 8.11 improved its indexing buffer, resulting in less segment merging and faster ingestion.

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 brings major speedups to query evaluation. Here are the performance improvements observed in nightly benchmarks & optimization resources.

Explore how we brought maximum-inner-product into Lucene and the investigations undertaken to ensure its support.

Learn about the Elastic Learned Sparse Encoder (ELSER), its retrieval performance, architecture, and training process.

Explore the machine learning (ML) models supported in Elastic, the Eland library for loading models and how to apply transformers & NLP in Elastic.

Learn about the Elastic Learned Sparse Encoder (ELSER), an AI model for high relevance semantic search across domains.

Use Elasticsearch Query Language (ES|QL) to run statistical analysis on demographic data index in Elasticsearch

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

Learn about MAXSCORE, block-max MAXSCORE & WAND. Improve the MAXSCORE algorithm to evaluate disjunctive queries more like a conjunctive query.

Explore multi-graph vector search in Lucene and discover how sharing information between segment searches enhances search speed.

Learn about multi-vector documents in Elasticsearch, their use cases, and 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 now GA. Explore ES|QL's capabilities, learn about ES|QL in Kibana and discover future advancements.

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

Introducing the general availability of query rules

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

Elasticsearch is introducing a new type of vector that has 8-bit integer dimensions. This is 4x smaller than the current vector with 32-bit float dimensions, which can result in substantial space savings.

Learn how to implement and scale semantic search as a service for a search Center of Excellence (COE) using ELSER.

We discuss the data durability guarantees in stateless including how we fence new writes and deletes with a safety check which prevents stale nodes from acknowledging new writes or deletes

Learn about Elasticsearch stateless and explore the stateless architecture, which brings performance improvements and reduces costs.

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.

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

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.