similar-code

To find semantically similar code across a repository, you query a code embedding model that ranks implementations by similarity score using vector search. It surfaces duplicates and reuse patterns by matching code semantics rather than exact text.

Vector-based code similarity search identifies duplicate code and patterns across a codebase using embeddings. It calculates semantic similarity to guide refactoring decisions and promote code consolidation across different modules.

Yes, you need a symbols.db index to exist at your project root to run a code duplication search. The vector search relies on this pre-built index to compute and retrieve ranked code similarity results.

You can search for code snippets with similar implementations by pasting your code snippet into the search query. The tool returns a ranked list containing the matching symbol, file path, and similarity score.

Yes, you can use vector search to consolidate code patterns in large development teams. It identifies semantically similar code across modules, enabling teams to detect duplicates and promote code reuse effectively.

The best way to detect code duplicates using embeddings is applying vector search to compute semantic similarity across your codebase. This approach surfaces nearby implementations and informs refactoring decisions based on ranked similarity scores.