Optimized Search with Fast LLM/RAG and Vector DB
For most companies willing to adopt AI, search is one of the first problems to address. Whether for costumers or internally, many search systems relying on old designs, deliver poor results. While search technology has been around for hundreds of years (from library catalogs and indexing to Google), specialized LLM/RAG apps allow you to get more relevant results, much faster, as well as excellent summaries. Not just for text but to retrieve all kinds of documents in large repositories.
Overview
Join us for an upcoming webinar where we dive into the capabilities and techniques for implementing full-text search using SingleStore. As the volume of textual data continues to grow, efficient and powerful full-text search functionality has become essential for extracting meaningful insights and providing superior user experiences. This webinar will showcase how SingleStore can be leveraged to perform high-performance full-text searches, enabling you to handle large volumes of text data with ease.
During this session, you'll learn about the architecture and features of SingleStore that make it an ideal choice for full-text search applications. Discover best practices and practical tips for integrating full-text search into your applications, ensuring fast and accurate results.
Discussion topics
How to implement full-text search with SingleStore.
Key features and benefits of using SingleStore for text search.
Best practices for optimizing search performance and accuracy.
Real-world examples and use cases of full-text search applications.
Access to tools and resources to start your own full-text search projects.
Hands-on workshop for developers and AI professionals, featuring state-of-the-art technology, case studies, code-share, and live demos. Recording and GitHub material will be available to registrants who cannot attend the free 60-min session.