Building a RAG System With Gemma, Hugging Face & Elasticsearch
Follow this step-by-step guide to build a Retrieval Augmented Generation (RAG) system using Gemma, Hugging Face, and Elasticsearch.
Introducing Elasticsearch vector database to Azure OpenAI Service On Your Data (preview)
Microsoft and Elastic partner to add Elasticsearch (preview) as an officially supported vector database and retrieval augmentation technology for Azure OpenAI On Your Data, enabling users to build chat experiences with advanced AI models grounded by enterprise data.
Avatar assisted & dialogue driven voice to RAG search
Create avatar-assisted voice search experience by integrating speech-to-text, semantic search, RAG and a synthesized avatar for responses.
How to build an Elastic search app with Streamlit, semantic search & NER
Learn how to develop a search application using machine learning models for named entity extraction (NER), semantic search and Streamlit.
RAG evaluation metrics: A journey through metrics
Explore RAG evaluation metrics like BLEU score, ROUGE score, PPL, BARTScore, and more. Discover how Elastic is evaluating RAG with UniEval.
Retrieval Augmented Generation (RAG) using Cohere Command model through Amazon Bedrock and domain data in Elasticsearch
Learn how to implement Retrieval Augmented Generation (RAG) using Cohere Command model via Amazon Bedrock & domain data in Elasticsearch.
Domain specific generative AI: pre-training, fine-tuning, and RAG
Explore strategies for integrating domain-specific knowledge into large language models (LLMs) through pre-training, fine-tuning, and RAG.
Retrieval Augmented Generation (RAG)
Learn about Retrieval Augmented Generation (RAG) and how it can help improve the quality of an LLM's generated responses by providing relevant source knowledge as context.
A conversational search experience for retail: Elasticsearch Relevance Engine with Google Cloud’s generative AI
This blog presents a new search experience for retailers using generative AI with Vertex AI and Elasticsearch.
Elasticsearch as a GenAI caching layer
Explore how integrating Elasticsearch as a caching layer optimizes Generative AI performance by reducing token costs and response times, demonstrated through real-world testing and practical examples.
How to Use Amazon Bedrock with Elasticsearch and Langchain
Learn to split workplace documents into passages, transform these passages into embeddings in Elasticsearch and integrate Amazon Bedrock LLM.
Generative AI architectures with transformers explained from the ground up
Here's how generative AI works from the ground up, including embeddings, transformer-encoder architecture, training/fine-tuning models & more.