codeindex

Semantic search across codebases works by embedding file skeletons, directory summaries, and commit messages, then ranking results by cosine similarity and recency. You query with a natural-language description to retrieve ranked file paths and context-ready snippets.

Yes, cross-repo search supports rapid discovery across multiple repositories. By using a scoped search, you can retrieve contextual results with file paths, skeletons, and directory summaries from large or unfamiliar codebases simultaneously.

Code discovery embeds commit messages and directory summaries to rank results by recency and relevance. This approach previews directory context alongside file paths and skeletons to guide inspection and rapid navigation.

The best way to locate code by function is using code-aware retrieval with natural-language queries. It understands meaning rather than relying on exact keywords or file names, returning ranked file paths and snippets for rapid navigation.

Yes, this semantic search approach is designed for integration with agents and tools requiring structured code discovery. It returns structured outputs including file paths, skeletons, and snippets for automated navigation across large repositories.

Semantic search is not ideal when you know the exact variable or file name you are looking for. It is designed for locating relevant code by meaning and context across large or unfamiliar codebases rather than exact string matching.