ai-ad-code-searcher

Semantic code search matches your natural language query against code across local projects, code libraries, and GitHub using embeddings and RAG. It returns ranked candidates with relevance scores, enabling you to find reference implementations without keyword-only matching.

Yes, semantic code search is language-agnostic. It uses AST-based parsing and functional descriptions to match code intent across Java, Python, JavaScript, and other languages, making it effective for polyglot repositories.

You need semantic embeddings of your codebase and natural language queries indexed across your sources. The search pipeline requires a multi-source index—local project, code library, GitHub—with AST parsing and relevance scoring configured for retrieval-augmented generation.

Code search locates function calls, API patterns, and dependency relationships by matching semantic intent. You provide a requirement or function name; the skill returns ranked candidates showing how similar code uses the API or calls related functions, with source attribution.

Semantic search understands functional context and intent, matching code with similar purpose even if syntax differs, while keyword search only finds exact term matches. Semantic matching is more effective for API discovery and cross-language lookups.

Yes, semantic code search enables repository navigation and call-graph tracing by returning ranked candidates with source attribution. You can identify related functions, follow dependency chains, and locate reference implementations across the codebase.