Seonglae ChoCode and natural language are not semantically similar - it is easier to semantic search on code bases if the code is first translated to natural language before generating embedding vectors.
Code Embedding Models
Track changes only with Merkle Tree. Find similar indexes using SimHash, generating a similarity hash representing the entire codebase. Large repositories take hours to generate indexes for semantic search → degrading agent performance. Codebases within an organization are very similar to each other (92% on average) → reuse existing indexes. Vector search with other indexes within the team → if sufficiently similar, copy and use existing index.
