BUILDING A WEB SEARCH ENGINE FROM SCRATCH IN TWO MONTHS WITH 3 BILLION NEURAL EMBEDDINGS
This article goes over how to build a web search engine from scratch. It covers how to generate 3 billion SBERT embeddings using 200 GPUs, crawl and index 280 million pages, optimize query latency, and implement a service mesh. The project involved developing custom solutions for crawling, data storage with RocksDB, a sharded HNSW index for vector search, and AI-powered features like quick answers and result summarization.