POMA AI Achieves Best-in-Class RAG Chunking and Document Ingestion With 77% Token Reduction vs. Conventional Models

Out of the box,POMA PrimeCut uses 77% fewer tokens than conventional models. The figure rises to 83% when used in customized ...

How xMemory cuts token costs and context bloat in AI agents

When standard RAG pipelines retrieve redundant conversational data, long-term AI agents lose coherence and burn tokens.
Geeky Gadgets

How to use Embeddings in RAG with Llama-Index

In the realm of natural language processing (NLP), the concept of embeddings plays a pivotal role. It is a technique that converts words, sentences, or even entire documents into numerical vectors.
TMCnet

Breaking the 100M Token Limit: EverMind's MSA Architecture Achieves Efficient End-to-End Long-Term Memory for LLMs

This approach can be viewed as a memory plug-in for large models, providing a fresh perspective and direction for solving the ...

Google's Gemini Embedding 2 arrives with native multimodal support to cut costs and speed up your enterprise data stack

While previous embedding models were largely restricted to text, this new model natively integrates text, images, video, audio, and documents into a single numerical space — reducing latency by as muc ...