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LangChain4j with Elasticsearch as the embedding store
LangChain4j (LangChain for Java) has Elasticsearch as an embedding store. Discover how to use it to build your RAG application in plain Java.
Comparing ELSER for retrieval relevance on the Hugging Face MTEB Leaderboard
This blog compares ELSER for retrieval relevance on the Hugging Face MTEB Leaderboard.
Testing your Java code with mocks and real Elasticsearch
Learn how to write your automated tests for Elasticsearch, using mocks and Testcontainers
How to ingest data from AWS S3 into Elastic Cloud - Part 1 : Elastic Serverless Forwarder
Learn how to ingest data from AWS S3 using Elastic Serverless Forwarder (ESF).
Automating traditional search with LLMs & Elastic Query DSL
Learn how to use LLMs to write Elastic Query DSL and query structured data with filters.
Quickly create RAG apps with Vertex AI Gemini models and Elasticsearch playground
Quickly create a RAG app with Vertex AI Gemini models and Elasticsearch playground
Vertex AI integration with Elasticsearch open inference API brings reranking to your RAG applications
Google Cloud customers can use Vertex AI embeddings and reranking models with Elasticsearch and take advantage of Vertex AI’s fully-managed, unified AI development platform for building generative AI apps.
Elasticsearch open inference API for Google AI Studio
Elasticsearch open inference API adds support for Google AI Studio
Adding AI summaries to your site with Elastic
How to add an AI summary box along with the search results to enrich your search experience.
Navigating an Elastic vector database
An overview of operating a modern Elastic vector database with practical code samples.
