grepai-quickstart

GrepAI enables semantic code search by indexing your codebase and allowing natural language queries. It uses Ollama to run local models, meaning your code remains private while you search for code based on meaning rather than exact text matches.

To install GrepAI for semantic code searching, follow the guided installation instructions which cover setting up GrepAI and Ollama, initializing your project, starting the indexing process, and executing your first semantic search query.

Semantic code search works by indexing your project files into vector embeddings and matching query intent. GrepAI uses Ollama to process these semantic queries locally, allowing you to find relevant code based on meaning rather than exact string matches.

Yes, you can use GrepAI for quick demonstrations. The quickstart provides an end-to-end workflow from installation to indexing and querying, designed to get a working semantic search setup running in minutes for team onboarding or stakeholder demos.

The best way to index a codebase for semantic queries is using the core GrepAI commands. The quickstart guides you through starting the indexing process, monitoring its progress, and executing semantic searches locally with Ollama.