How We're Building the Next Generation of Search with Semantic Search Technology

This post details Exa's foundational infrastructure for next-generation search, emphasizing the shift from keyword-based search to an embedding-based neural search algorithm. It highlights the three core components: crawling (distributed network, custom HTML parser, S3 storage), preprocessing (training specialized transformer models to generate document embeddings), and serving (building a custom vector database). The post introduces the 'Exacluster' (144 H200 GPUs) as a testament to the 'Bitter Lesson' philosophy, enabling extensive model training. It also outlines two product offerings: Exa Search (low latency) and Exa Websets (high-computation, long-latency queries), with a future vision of merging them into a 'GPT-5 search model'. The post implicitly contributes to the academic publication search by describing the underlying technology that enhances its semantic search capabilities and overall performance.