KDB.AI

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KDB.AI is a multi-modal vector database designed to power scalable, real-time AI applications. It integrates temporal and semantic relevance into workflows, enabling advanced capabilities such as dynamic hybrid search, personalization, and Retrieval Augmented Generation (RAG). Built for speed and flexibility, KDB.AI supports high-performance, time-based, multi-modal data queries, making it ideal for enterprise AI solutions. Key Features and Functionality: - Dynamic Hybrid Search: Combines similarity, exact, and literal search within a single query to maintain result relevance as content evolves. - Mixed Search: Leverages hybrid, semantic, keyword, and temporal search to execute queries faster and achieve more accurate results. - Multimodal RAG: Seamlessly connects with Large Language Models (LLMs) to enhance and personalize search outcomes. - Zero Embedding: Performs searches 17 times faster with 12 times less memory than HNSW, eliminating the complexity of embeddings in environments with rapidly changing temporal data. - CPU Centric: Delivers all the advantages of KDB.AI using CPUs, providing a performant alternative to AI processing. - Killer Compression: Reduces memory and on-disk storage by 100 times for slow-changing time-based datasets, accelerating search by 10 times. Primary Value and Solutions Provided: KDB.AI addresses the challenges of managing and analyzing vast amounts of structured and unstructured data in real-time. By integrating temporal and semantic context into AI workflows, it enables businesses to perform advanced searches, detect patterns and anomalies, and make data-driven decisions more efficiently. Its support for multi-modal data and seamless integration with popular LLMs allow organizations to enhance and personalize search outcomes, optimize costs, and build high-performance, scalable AI applications.