Vector Search & Semantic Retrieval
Vector search is what makes “find me things like this” work: semantic search, recommendations, and retrieval for LLMs. These posts go from the high-level idea down to the engineering: what vector databases actually do, how to implement a graph-based ANN index from a research paper, and how to make that index fast.
Read them in order if the topic is new to you, or jump to the one that matches where you’re stuck.
| Date | Title | Read |
|---|---|---|
| How Google Search Actually Works (It's Not What You Think) Learn what vector databases are, how semantic search works, and why ANN, DiskANN, and Vamana matter for modern retrieval systems. | 6 min | |
| What I Got Wrong Implementing Graph-Based Vector Search A dev log about implementing a Vamana-style ANN index from the DiskANN paper, why my first version was slower than brute force, and what it taught me about reading algorithms with implementation in mind. | 9 min | |
| How I Made My Vector Search Engine 16x Faster Without Changing the Algorithm A performance story from sembed-engine: the search visited the same number of nodes and returned the same answers, but became much faster by changing what the CPU had to do for every distance calculation. | 9 min | |
| The Shape of Duplicate Detection Duplicate detection looks like a solved problem: use a hash set. A benchmark suite of 4050 measurements across finite batches and streaming workloads shows the fastest strategy can be 148x faster than a hash set, or 90,000x slower, depending on what you are deduplicating and what guarantees you need. | 16 min |