Make RAG systems 32x Memory Efficient!
This title could be clearer and more informative.Try out Clickbait Shieldfor free (5 uses left this month).
Binary quantization can make RAG systems 32x more memory efficient by converting float32 embeddings to binary vectors. The technique involves ingesting documents, generating binary embeddings, storing them in a vector database like Milvus, and using Hamming distance for retrieval. A complete implementation demonstrates querying
Table of contents
Pixeltable: Declarative Data Infrastructure for Multimodal AI AppsMake RAG systems 32x memory efficient!P.S. For those wanting to develop “Industry ML” expertise:1 Comment
Sort: