leann-search

Semantic code search uses a vector index built from sentence embeddings to find conceptually similar code snippets across a codebase. It matches the underlying meaning of abstract functionalities and architectural patterns rather than relying on exact string matches.

You can find code related to abstract concepts like authentication by querying a vector index with natural language. Semantic search locates related code implementations by matching conceptual meaning, even when the exact terminology is not present in the codebase.

The best way to discover architectural patterns is using semantic search to query the codebase for specific implementations. It analyzes sentence embeddings to identify and retrieve code snippets that share conceptual similarities with your query.

Yes, you need LEANN to build and query vector indexes based on sentence embeddings. LEANN provides the required infrastructure to perform semantic search and retrieve conceptually similar code snippets from your codebase.

Semantic search provides a meaning-based alternative to grep by matching conceptual queries instead of exact strings. While grep finds literal text matches, semantic search retrieves code based on the underlying functionality and architectural patterns.

Yes, you can identify streaming implementation patterns by querying the vector index with conceptual descriptions. Semantic search finds code snippets that implement the desired provider architecture or pattern even if different terminology is used.