Tag: embeddings
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...