We describe a method to retrieve images found on web pages with specified object class labels, using an analysis of text around the image and of image appearance. Our method determines whether an ...

The model specializes in code and “performs especially well for retrieval use cases on real-world code data.” The model is available to developers for $0.15 per million tokens.

A new study in Engineering introduces ERQA, a medical knowledge retrieval and QA framework driven by an enhanced large language model. It integrates a semantic vector database and a ...

vdr-2b-multi-v1 is a cutting-edge multilingual embedding model designed for visual document retrieval across various languages and domains. The model encodes document page screenshots into dense ...

Addressing this challenge requires a model capable of efficiently handling such diverse content. Introducing mcdse-2b-v1: A New Approach to Document Retrieval Meet mcdse-2b-v1, a new AI model that ...

The most common approach for document retrieval in RAG is to use “bi-encoders,” where an embedding model creates a fixed representation of each document and stores it in a vector database.