Many AI code editors use semantic search to improve the quality of code retrieval and the performance of agent coding tasks. Cursor reports up to 23.5% higher accuracy in answering questions about the codebase and increased code retention by combining grep with semantic search.
Notably, Claude Code does not use semantic search in code retrieval. Let’s change that!
In this workshop, we’ll index our codebases on turbopuffer and give Claude Code (or any agent) a suite of code search tools – including semantic search, full-text search, and regex search. You’ll build a practical tool while learning how to maximize performance and recall on turbopuffer’s object-storage-native search database.
What you’ll learn:
- How to index code and other documents on turbopuffer for vector search, full-text search, regex search, and attribute filtering optimizing for cost and performance
- Tips for improving search performance and recall on turbopuffer
- How to build code search tools for agents that reduce token consumption and improve agent code quality
Bring your own codebase, or index the Linux kernel.
