On October 7, Perplexity open-sourced two ColBERT-style multimodal embedding models (0.6B and 9B, MIT license) that keep one 128-dim vector per token, score with MaxSim, and search PDF page images directly. The 9B scores 92.4% on MADQA (self-reported, with a Gemini 3.5 Flash agent); the cost is an index that grows with document length.
pplx-embed is the embedding family Perplexity has been releasing since February 2026, in three lines: v1 single-vector (0.6B/4B, API at $0.004/$0.03 per 1M tokens), context embeddings (v1 plus a 9B v2 preview), and v2-late multi-vector (0.6B/9B, searches PDF page images directly). They solve different problems; v2 does not replace v1.
Vectorize is Cloudflare's vector database. AI Search is the right starting point for a managed RAG pipeline; Vectorize is the better fit when you need control over chunking, embeddings, metadata filters, hybrid retrieval, reindexing, and fallback behavior.
Week 5 teaches embeddings: the meaning of a word can be represented by the words that tend to appear around it. It builds vectors from co-occurrence counts and compares them by cosine, then trains dense vectors of a few hundred dimensions with word2vec, and finally separates static vectors from ones that change with context. The same week's live lecture on computational social science has no public material.
Lecture 2 moves from word2vec's prediction task, objective, and gradients to count-based vectors and evaluation. Meaning becomes a high-dimensional position learned from context, not a label retrieved from a dictionary.