Tag: embeddings
A Guide to LLM Embeddings
LLM embeddings are numerical representations of words, sentences, or other data that capture semantic meaning, enabling efficient text processing, similarity search, and retrieval in AI applications. They are generated through neural network transformations, particularly using self-attention mechanisms in transformer models...
AI-Ready Data: Automate Embeddings with Capella’s Vectorization Service
Couchbase Capella has launched a Private Preview for AI services! Check out this blog for an overview of how these services simplify the process of building cloud-native, scalable AI applications and AI agents. In our previous blog, we demonstrated how...
Plataforma única, Couchbase multiuso: Pesquisa vetorial, geoespacial, SQL++ e muito mais
Há casos de uso que são melhor atendidos por vários tipos de acesso a dados, incluindo SQL, pesquisa vetorial, consultas geoespaciais e acesso de valor-chave. Uma abordagem é combinar/encadear vários sistemas de dados para cada método de acesso. No entanto,...
Single Platform, Multi-Purpose Couchbase: Vector Search, Geospatial, SQL++, and More
There are use cases that are best served by multiple types of data access, including SQL, vector search, geospatial queries, and key-value access. One approach is to combine/chain together multiple data systems for each access method. However, the Couchbase approach...
What are Embedding Models? An Overview
What are embedding models? Embedding models are a type of machine learning model designed to represent data (such as text, images, or other forms of information) in a continuous, low-dimensional vector space. These embeddings capture semantic or contextual similarities between...
Preparing Datasets for Fine-Tuning ML Models: A Comprehensive Guide
Fine-tuning machine learning models starts with having well-prepared datasets. This guide will walk you through how to create these datasets, from gathering data to making instruction files. By the end, you’ll be equipped with practical knowledge and tools to prepare...
A Step-by-Step Guide to Preparing Data for Retrieval-Augmented Generation (RAG)
In today’s data-driven world, the ability to efficiently gather and prepare data is crucial for the success of any application. Whether you’re developing a chatbot, a recommendation system, or any AI-driven solution, the quality and structure of your data can...
What are Vector Embeddings?
Vector embeddings are a critical component in machine learning that convert “high-dimensional” information, such as text or images, into a structured vector space. This process enables the ability to process and identify related data more effectively by representing it as...