Multi Index Hierarchical Data Organization

via “hierarchical-index-construction-and-traversal”

This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.

Unique: Implements recursive document summarization to build multi-level hierarchies that enable top-down retrieval traversal, reducing embedding computations and improving efficiency for large collections — a structural approach to retrieval efficiency rather than algorithmic optimization

vs others: More efficient than flat indices for large collections because it reduces embeddings computed per query, and more effective than simple filtering because it uses semantic hierarchies rather than metadata-based pruning

via “multi-index hierarchical data organization”

Powerful data structures for data analysis, time series, and statistics

Unique: Stores MultiIndex as separate codes and levels arrays rather than materializing all tuples, reducing memory usage and enabling efficient partial indexing and cross-level operations without reconstructing the full index

vs others: More memory-efficient than storing explicit tuples for each row; enables pivot/unpivot operations that would require manual reshaping in NumPy or SQL

via “hierarchical and graph-based data indexing”