Supercharge Search with LLMs: A Cheap and Fast Approach

This article demonstrates building a fast and cost-effective search service using Large Language Models (LLMs). The author deploys a FastAPI application calling a lightweight LLM (Qwen2-7B), leveraging Google Kubernetes Engine (GKE) Autopilot for automated cluster management to achieve structured parsing of search queries. Docker image building and deployment, combined with a Valkey caching mechanism, significantly improve performance and scalability. This approach avoids frequent calls to expensive cloud APIs, reducing costs and showcasing the potential of running LLMs on local infrastructure, offering a new perspective on building smarter and faster search engines.