leann

You can run local semantic code search on large monorepos offline by building a memory-efficient anchor-based index. This approach uses an anchor-graph storage model with lazy reconstruction to enable self-hosted retrieval without cloud dependencies.

Memory-efficient RAG indexing for large codebases is achieved using an anchor-based approach with selective embedding storage. It dramatically reduces storage requirements by leveraging an anchor-graph model with lazy recomputation while preserving fast retrieval.

Yes, you can use graph-based backends like HNSW or DiskANN for offline code retrieval. These backends support the anchor-graph storage model, enabling production-grade indexing and fast semantic retrieval in memory-constrained environments.

Anchor-based code search supports incremental delta updates for new commits. This allows the index to incorporate changes efficiently without requiring a full rebuild, maintaining up-to-date retrieval for continuous self-hosted workflows.

Lazy reconstruction reduces storage in local code search indexes by selectively storing embeddings using an anchor-graph model. Instead of keeping all embeddings in memory, it recomputes them on demand, which dramatically lowers storage overhead while supporting fast retrieval.

Local semantic code search is highly suitable for memory-constrained and air-gapped environments. It targets edge deployments and self-hosted workflows, utilizing graph-based selective embedding storage and disk-based backends to maintain retrieval capabilities offline.