Neural Code Search
A semantic code search engine powered by transformer embeddings. Search your codebase using natural language queries.
Overview
Neural Code Search is a semantic search engine that understands code at a conceptual level. Instead of relying on keyword matching, it uses transformer-based embeddings to find code that matches the intent of your query.
Motivation
Traditional code search tools rely on exact text matching, which often fails when you’re looking for code that does something specific but you don’t know the exact function name or variable names used.
Technical Approach
The system uses a two-tower architecture:
- Query Encoder: Encodes natural language queries into dense vectors
- Code Encoder: Encodes code snippets into the same vector space
At search time, we compute the similarity between the query vector and all code vectors, returning the most similar results.
Results
In our evaluation on the CodeSearchNet benchmark:
- MRR@10: 0.72
- R@100: 0.89
Future Work
- Support for more programming languages
- Integration with IDE plugins
- Real-time indexing for large repositories