Retrieval paths
Dense + lexical
Implemented code-context retrieval paths. This preview shows a labelled local sample, not a retrieval-quality benchmark.
Validation notes Back to portfolio
Current source explorer with a bundled local sample. Symbols and call edges are parsed from the displayed source; no model answer is shown.Open full capture
Case study / Developer tooling prototype
CodeSage
Explores code through AST call graphs, dense and lexical retrieval, and an interface for inspecting repository relationships.
Developer tooling prototype
Solo project; AI-assisted development.
The idea
The problem
Understanding a repository requires definitions, calls, and relationships as well as matching text.
The implementation
What I built
Connected AST-derived relationships to dense/lexical search and inspectable repository context.
- Python
- FastAPI
- React
- AST
- Retrieval
Features
- Inspect AST call relationships
- Search code with dense and lexical retrieval
- Explore indexed repository context
Architecture
- Python source parsing and graph construction.
- Dense and lexical retrieval over indexed code.
- React graph interface and experimental training tools.
Engineering decisions
- Keep structural relationships alongside retrieved text.
- Expose selected context for inspection.
Validation and measured results
- Current source contains parsing, retrieval, graph inspection, and experimental training paths.
- No qualified H100 training, universal context-size, or model-accuracy result is claimed.
Limits and unfinished work
- Graph completeness depends on parser coverage and dynamic code behavior.
- Experimental training tooling does not establish trained model quality.
Evidence and sources
- Current CodeSage validation notes
Locally reviewed current implementation, distinct from older CodeSage variants. The repository was not publicly accessible on 14 October 2026.