Today’s focus was on extracting and counting structural elements from code repositories. We built a component that parses Python and JavaScript projects, identifies their key elements, and represents them as nodes and relationships in a graph structure.
The system now tracks:
print(f"Nodes extracted:")
print(f" Files: {files}")
print(f" Classes: {classes}")
print(f" Functions: {functions}")
print(f" Variables: {variables}")
print(f" Packages: {packages}")
print(f" Docs: {docs}")
print(f" Total: {len(graph_data.nodes)}")
print(f"\nRelationships: {len(graph_data.relationships)}")
This output summarizes the architecture that Secrin now understands. Every file, class, and function becomes a node. Dependencies and references between them become edges. The result is a knowledge graph that represents the structure and logic of a codebase in a connected form.
This graph-based view lays the foundation for everything that follows. With it, we can:
- Visualize how code components depend on each other.
- Query relationships between files and functions.
- Build higher-level understanding of repositories for documentation, onboarding, and AI-based code reasoning.
The Grand Line of Open Source continues. ⚓️
Follow the project:
GitHub: https://github.com/SecrinLabs/secrin
Twitter (X): x.com/jenilsavani_
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