Semantic Code Search with Qdrant

Finding relevant code in large projects can be challenging when developers only know the task they want to perform, not the exact function names. This project implements a semantic code search system using vector embeddings and Qdrant, a high-performance vector database. Code files are chunked into logical units (functions, classes, blocks) and embedded with FastEmbed, generating vector representations that capture their meaning. These embeddings, along with metadata like file path, language, and repository, are stored in Qdrant. Users can search using natural language queries, which are converted into embeddings to find code chunks with similar semantics. The system supports multiple languages, offers a CLI and Gradio web interface, and allows metadata-based filtering for precise results. This solution drastically reduces the time and effort needed to locate relevant code, making code navigation and reuse smarter and faster.