rawq: Local CLI Tool for AI Agent Semantic Code Search

What rawq does

rawq is a local CLI tool that helps AI agents find relevant code without reading entire files. It uses semantic search to guide AI agents to specific code parts instead of whole files, letting them use read/grep on the right files. The tool is not RAG nor a replacement for read/grep.

Technical implementation

rawq uses a 33MB local model by default that works via ONNX runtime for semantic search, combined with BM25 lexical search via tantivy. The tool supports tree sitter code chunking for 16 languages and works on any GPU thanks to ONNX with DirectML, CUDA, or CoreML, with automatic CPU fallback.

Performance and features

In testing, AI agents that were using rawq consumed 4x fewer tokens and completed tasks 2x faster compared to blind read/grep tools. rawq also has a map command that shows the structure of a codebase.

Development details

The tool is written in Rust, fully open-source, and released under MIT license. It's available on GitHub at https://github.com/auyelbekov/rawq.