Hierarchical And Graph Based Data Indexing

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 data structure with query engine abstraction”

Interface between LLMs and your data

Unique: Supports 5+ index types with pluggable backends and a unified QueryEngine abstraction, enabling seamless switching between retrieval strategies (semantic, keyword, graph traversal, summarization) without rewriting application code. Implements automatic index persistence and lazy loading.

vs others: More flexible than LangChain's VectorStore abstraction by supporting multiple index types (graph, keyword, summary) with unified query interface; enables hybrid retrieval combining multiple strategies in a single query.

via “graph-based data retrieval”

MCP server: mcp-server-graphdb

Unique: Utilizes advanced graph traversal algorithms tailored for MCP integration, enabling efficient access to related data points.

vs others: More efficient for complex queries than traditional SQL databases due to its graph-based architecture.

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”

via “multi-strategy document indexing with pluggable index types”