airCloset CTO Adds Semantic Search to 46-Repo Code Knowledge Graph

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Ryan, CTO at airCloset, has detailed how he solved the semantic search problem in a knowledge graph built from 46 production code repositories. Drawing on a prior internal project called db-graph — which made over 1,100 database tables searchable in natural language using AI-generated embeddings — he applied the same pattern to the code graph. By joining the two graphs, the code graph automatically inherited database context without requiring new annotations. For remaining boundaries such as APIs and UI pages, he opted to add targeted annotations only at boundary nodes rather than across all functions, making the approach feasible for large, established codebases. This selective annotation strategy allows AI agents to retrieve verified boundary-level intent, replacing unreliable inference with structured, searchable facts.

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