Semantic code search using transformer embeddings
A state-of-the-art code search engine that understands natural language queries and finds relevant code snippets across large codebases using neural embeddings.
Traditional code search relies on keyword matching, which fails when developers describe functionality in natural language. Finding relevant code in large repositories with millions of files becomes increasingly difficult.
Built a retrieval system using CodeBERT embeddings for semantic understanding. Implemented efficient approximate nearest neighbor search with FAISS for sub-second queries across million-file codebases. Developed a fine-tuning pipeline using contrastive learning on code-documentation pairs.
Achieved 89% MRR@10 on CodeSearchNet benchmark, outperforming BM25 baseline by 35%. Deployed internally, reducing average code discovery time by 60% for a team of 50+ developers.