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Semantic code search allows you to query a codebase using natural language to find relevant snippets by concept. It leverages local embeddings to locate implementations without requiring exact string matches or complex regex patterns.

Yes, codebase exploration with natural language queries enables you to find code by concept. You can ask questions like where user authentication is handled and retrieve relevant code locations without prior knowledge of specific function names.

No, this semantic code search uses local embeddings via transformers.js, ensuring no remote API calls are made. This keeps your codebase exploration completely private and secure while maintaining fast search speeds.

The system uses auto-isolated indexes per repository, ensuring searches are scoped correctly to the active project. It also features live indexing to automatically keep the search index updated as your codebase changes.

Using natural language queries for semantic code search is the best way to find implementations by concept. It relies on local embeddings and includes adaptive performance throttling to maintain responsive search speeds across large codebases.

No, an internet connection is not required because the semantic search runs entirely on local embeddings. This offline capability guarantees privacy and allows uninterrupted codebase exploration in restricted environments.