Building a web search engine from scratch with 3B neural embeddings
SMRTR summary
A developer built a web search engine from scratch using neural embeddings instead of keyword matching. The project leveraged 200 GPUs to generate 3 billion SBERT embeddings, with a cluster of crawlers ingesting 50K pages per second to reach 280 million indexed pages. The system understands natural language queries, delivers context-aware results from semantic chunking, and achieves 500ms query latency across 200 cores and 82TB of storage.
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