Composing a Search Engine

Exa's search engine complexity has increased due to diverse user needs and the rise of AI agents, prompting the development of Canon, a search pipeline orchestrator. Canon represents search processes as a Directed Acyclic Graph (DAG), allowing for automatic parallelism, durable execution, and enhanced introspectability by decomposing the search process into manageable nodes. This structure facilitates efficient query handling by parallelizing independent nodes, allowing for concurrency, caching, and error tracing, which streamlines debugging and ensures reliability amidst growing demands. The system is designed to accommodate the predominance of code written by agents, turning implicit assumptions into explicit constructs, and leveraging a robust type system to ensure correctness. Canon's runtime efficiently manages orchestration, handling tasks like memoization and cancellation, while providing comprehensive observability that records every decision made during a query process. This approach supports Exa's need to deliver complex search capabilities across diverse customer requirements while maintaining quality and correctness at scale.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.