Several tech companies have introduced embedding quantization, a new technique to quantize embeddings for faster retrieval and reduced storage size. Benefits include up to 45x faster retrieval, 32x lower costs, and maintaining high performance levels. This technique aims to enhance knowledge retrieval systems like RAG applications.
AI leaders: Boost your knowledge retrieval (RAG) systems with Embedding Quantization! 🔍 Embeddings represent data efficiently for search & analysis 💰 Quantization reduces storage size & cost ⚡️ Faster apps with whole number math https://t.co/dVyl5M08bV
huggingface 🤝 mixedbreadai Check out embedding quantization. It brings you 25x faster retrieval & 32x lower costs. Imagine the efficiency - like a whole bakery in a bread box! 🍞💡 Open-source, with up to 99.3% performance maintained. Dive in: https://t.co/cVjSxRTgdE
Introducing embedding quantization!💥 A new technique to quantize embeddings to achieve up to 45x faster retrieval while keeping 96% accuracy on open Embedding Models. This will help scale RAG Application! 🚀 TL;DR: 📝 🔥 Binary quantization: 32x less storage & up to 45x faster… https://t.co/SehXaJ4IJ4
Embedding Quantization is here! 25x speedup in retrieval; 32x reduction in memory usage; 4x reduction in disk space; 99.3% preservation of performance🤯 The sky is the limit. Read about it here: https://t.co/1r1ojcxWdr More info in 🧵
In this hands-on tutorial, we show you how to generate embeddings, store them in a Vertex AI Vector Search Index, and implement RAG. https://t.co/eRokzJLNZV #SoftwareDevelopment #AIEngineering #LargeLanguageModels #LLMs