Information Retrieval on AI VOID

Multimodal RAG: Enhancing Knowledge with Diverse Sources

Fri, 20 Mar 2026 00:00:00 +0000

Introduction to Multimodal RAG

Welcome back, intrepid AI explorers! In previous chapters, we’ve journeyed through the fascinating world of multimodal AI, learning how to integrate diverse data types like text, images, audio, and video, and how Large Language Models (LLMs) can act as powerful reasoning engines. We’ve seen how these systems can understand and process information far beyond what a single modality can offer.

However, even the most advanced LLMs have limitations. They can “hallucinate” (generate factually incorrect but convincing text), struggle with truly up-to-date information, or lack specific domain knowledge. This is where Retrieval Augmented Generation (RAG) swoops in to save the day! Traditionally, RAG has focused on augmenting LLMs with relevant textual information retrieved from a knowledge base. But what if our knowledge base isn’t just text? What if it’s a rich tapestry of images, videos, and audio clips?

Chapter 14: Implementing Semantic Search for Documents

Tue, 17 Feb 2026 00:00:00 +0000

Introduction to Semantic Document Search

Welcome back, intrepid learner! In our previous chapters, you’ve mastered the fundamentals of vector embeddings and USearch, and even explored how ScyllaDB provides a robust platform for storing and querying these high-dimensional vectors. Now, it’s time to bring these concepts to life with a practical, real-world application: semantic document search.

Imagine a search engine that doesn’t just match keywords but truly understands the meaning behind your query. That’s the power of semantic search! Instead of searching for exact terms, we’ll transform both documents and user queries into numerical vectors (embeddings) and then find documents whose embeddings are “closest” to the query embedding in the vector space. This allows us to retrieve relevant results even if they don’t contain any of the exact words from the query.

Chapter 22: Project: Developing a Semantic Search Engine with Embeddings

Sat, 17 Jan 2026 00:00:00 +0000

Chapter 22: Project: Developing a Semantic Search Engine with Embeddings

Welcome to an exciting hands-on project that brings together several concepts we’ve explored: embeddings, natural language processing, and practical application! In this chapter, you’ll learn how to build a semantic search engine from the ground up. Unlike traditional keyword-based search that relies on exact word matches, semantic search understands the meaning and context of your query, providing far more relevant results.

USearch & ScyllaDB Vector Search Practical Field Guide

Tue, 17 Feb 2026 00:00:00 +0000

Welcome to the World of Ultra-Fast Vector Search!

Are you ready to dive into one of the most exciting areas in modern AI and data management? This guide is your comprehensive pathway to mastering USearch – an incredibly efficient open-source vector search library – and integrating it seamlessly with ScyllaDB, a real-time, high-performance NoSQL database. Together, they form a powerhouse for building scalable, lightning-fast AI applications.

What is USearch and ScyllaDB Vector Search?

Imagine you have millions of items – perhaps images, documents, or user queries – and you want to find others that are “similar” in meaning or content, not just by exact keyword matches. This is where vector search shines!