Neural Search Engine

The Challenge

Building a production-grade semantic search system that could handle millions of queries while maintaining sub-100ms latency required rethinking traditional search architectures.

Architecture Deep Dive

The system consists of three main components: an embedding service powered by a quantized Llama-3-70B model, a vector store built on FAISS with custom sharding, and a query orchestration layer handling caching and load balancing.

Results & Impact

The final system achieved 87ms p99 latency while handling 10,000 queries per second. Search relevance improved by 47% compared to the previous BM25-based system.