Structure-Grounded Knowledge Retrieval via Code Dependencies for Multi-Step Data Reasoning

This project represents a sophisticated approach to Retrieval-Augmented Generation (RAG) by moving from 'bag-of-words' similarity to 'graph-of-logic' retrieval. It targets the specific failure mode of LLMs in data science: losing track of data lineage and functional dependencies during complex analysis. While the reasoning is sound and the approach is a 'novel combination' of static analysis and RAG, the project currently lacks defensibility (0 stars, 3 forks, 5 days old). It functions primarily as a research artifact. The primary threat comes from frontier labs (OpenAI's Advanced Data Analysis) and IDE platforms (GitHub Copilot, Cursor). These entities have a structural advantage because they already possess the execution environment and can build the dependency graph natively within their sandboxes. Competitors like Microsoft's GraphRAG or open-source projects like LlamaIndex are already incorporating structural and graph-based retrieval, likely making this specific implementation a niche reference rather than a category leader. The 1-2 year displacement horizon reflects how quickly successful research techniques are absorbed into standard agentic frameworks.