qmd-search

Semantic code search across large codebases minimizes token usage by retrieving only relevant 800-token chunks instead of entire files. This hybrid approach combines BM25 keyword and vector search to return concise code segments for efficient developer workflows.

Finding specific code segments by meaning is achieved through semantic code search, which leverages vector search to understand intent rather than relying on exact keyword matches. This enables developers to discover relevant code chunks across multiple collections efficiently.

You can search for specific files or code segments within a targeted collection by utilizing the Skill's targeted retrieval feature. It supports searching across multiple collections to fetch relevant 800-token chunks, optimizing LLM context and minimizing token consumption.

Hybrid ranking improves code discovery by combining BM25 keyword search with vector search, delivering optimal results that standard keyword search alone cannot achieve. This ensures high-quality semantic and keyword-based searching across codebases.

To locate code responsible for a specific function like a scoring formula calculation, you can use semantic search to find relevant code chunks based on the function's meaning. This retrieves concise 800-token segments, avoiding the need to read thousands of lines.