Blogs
Developer insights and practical how-to articles from our experts to inspire and empower your search experience.
Building AI agents with AI SDK and Elastic
Do you keep hearing about AI agents, and aren't quite sure what they are or how to build one in TypeScript (or JavaScript)? Join me as I dive into what AI agents are, the possible use cases they can be used for, along with an example Travel Planner Agent built using AI SDK and Elasticsearch.
Anomaly detection population jobs: Spotify Wrapped, part 3
Anomaly detection can be a daunting task at first, but in this blog, we'll dive into it and figure out how the different jobs can help us find unusual patterns in our Spotify Wrapped data.
Configurable chunking settings for inference API endpoints
Elasticsearch open inference API extends support for configurable chunking for document ingestion with semantic text fields.
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.
Exploring GPU-accelerated vector search in Elasticsearch with NVIDIA: Chapter I
Powered by NVIDIA cuVS, the collaboration looks to provide developers with GPU-acceleration for vector search in Elasticsearch.
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.
How to optimize RAG retrieval in Elasticsearch with DeepEval
Learn about RAG retrieval and how to optimize the Elasticsearch retriever in a RAG pipeline using DeepEval.
How to ingest data to Elasticsearch through Airbyte
Using Airbyte to ingest data into Elasticsearch. We'll cover the prerequisites, Airbyte configuration, and the step-by-step integration.
Semantic text in Elasticsearch: Simpler, better, leaner, stronger
Our latest semantic_text iteration brings a host of improvements. In addition to streamlining representation in _source, benefits include reduced verbosity, more efficient disk utilization, and better integration with other Elasticsearch features. You can now use highlighting to retrieve the chunks most relevant to your query. And perhaps best of all, it is now a generally available (GA) feature!
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.
Building a multimodal RAG system with Elasticsearch: The story of Gotham City
Learn how to build a multimodal Retrieval-Augmented Generation (RAG) system that integrates text, audio, video, and image data to provide richer, contextualized information retrieval.
Beyond vectors: Intelligent hybrid search with LLM agents in Elasticsearch
Vector search alone does not account for time, space, or intention, limiting its effectiveness. Thankfully, a solution lies in combining Elastic's traditional search features with LLM agents.