#embeddings
4 articles
01
·RAG & Retrieval·★ MEMBER·PAPER·12 min read
Paper Walkthrough: WeMM-Embedding — Putting Text, Images and Video on One Ruler
A walkthrough of WeMM-Embedding (2B/4B/9B), Tencent's universal multimodal embedding family. The unified pair format, the <embedding> token, Matryoshka dimensions, two-stage training and distillation — explained from scratch, using only the numbers the paper reports.
02
·RAG & Retrieval·★ MEMBER·10 min read
Chunking Strategies — How You Split Decides What You Can Find
Most of a RAG system's quality is decided by how you split documents. The arithmetic of fixed-size chunks and overlap, structural and semantic splitting, parent-child chunks, and the two things that break every splitter: tables and equations.
03
·RAG & Retrieval·★ MEMBER·PAPER·10 min read
Recommenders and Embeddings — Same Math as RAG, Different Goal
What sits behind "recommended for you" is very nearly the same math as RAG's vector search. A from-zero tour: matrix factorization, two-tower models, and how the ANN stack is reused — plus why the evaluation and the failure modes end up completely different.
04
·RAG & Retrieval·★ MEMBER·8 min read
RAG Fundamentals and Design Patterns — Embeddings, Chunking, Reranking, and Evaluation from Scratch
Retrieval-Augmented Generation explained from zero: the core intuition, chunking strategies, hybrid search and reranking, and the evaluation design that matters most.