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 . 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.
Securely indexing large codebases · Cursor
By securely reusing a teammate's existing index, we cut time-to-first-query from hours to seconds on the largest repos.
https://cursor.com/blog/secure-codebase-indexing
Code search grep.app
Code Search | Grep by Vercel
Search for code, files, and paths across half a million public GitHub repositories.
https://grep.app/
Codebases are uniquely hard to search semantically
Cosine similarity in code vs. text.
https://www.greptile.com/blog/semantic
Generating similarities for code generation
Got it, so maybe a ‘hybrid’ approach? I.e. encode the code snippets as class/function/interface name + parameter_names + docstrings as a ‘syntactic’ embedding, and then use a code2seq or the like to generate embeddings based on their AST paths (and get the ‘semantic’ meaning as well). Then whatever the user prompts, I can generate an embedding based off of his prompt (whether a textual description or code) and see if I get some good similarity results for relevant coding snippets. Does this make...
https://community.openai.com/t/generating-similarities-for-code-generation/276894/9
