codebase-rag

Semantic code search lets you query indexed repositories using natural language. This Skill uses OpenAI embeddings and a Qdrant vector database to locate code implementations and answer where specific functionality resides within a project.

To set up semantic code search, you need a running Qdrant vector database instance, the codebase-rag CLI, and an OpenAI API key for generating embeddings. These components index repository code and execute similarity searches.

Yes, you can find feature implementations by querying the indexed codebase. The Skill searches for natural language questions like "how is user authentication implemented?" and returns relevant code snippets showing architectural patterns and locations.

Vector database integration for code search works by converting code snippets into OpenAI embeddings and storing them in Qdrant. When you search, the query is embedded and matched against stored vectors to retrieve similar code implementations.

Using embeddings for code search requires an OpenAI API key and a Qdrant instance, adding external dependencies. The effectiveness depends on how well the codebase is indexed, and semantic matches may not always capture exact variable or function names.

Semantic code search outperforms standard grep for understanding architecture by matching intent rather than exact strings. While grep finds literal text, semantic search with embeddings answers conceptual questions about how functionality is structured across files.