multimodal llms

Token-Efficient Long Video Understanding for Multimodal LLMs explained step by step

Introduction As large language models (LLMs) become increasingly multimodal—capable of reasoning across text, images, audio, and video—a key bottleneck remains: token inefficiency. Particularly in the realm of long video understanding, traditional tokenization methods lead to rapid input length explosion, making processing long videos infeasible without aggressive downsampling or truncation. In this post, we explore the…

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KNCMAP AI The Core of RAG Systems

The Core of RAG Systems: Embedding Models, Chunking, Vector Databases

In the age of large language models (LLMs), Retrieval-Augmented Generation (RAG) has emerged as one of the most powerful approaches for building intelligent applications. Whether you’re creating a chatbot, a document assistant, or an enterprise knowledge engine, three pillars make RAG work: embedding models, chunking, and vector databases. This article breaks down what they are,…

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