Module traversal 

Graph Traversal Algorithms for GraphRAG

This module implements deterministic graph traversal algorithms that don’t require machine learning, following NLP best practices for knowledge graph exploration:

• BFS (Breadth-First Search): Level-by-level exploration for shortest paths
• DFS (Depth-First Search): Deep exploration for discovering all paths
• Ego-Network Extraction: K-hop neighborhoods around entities
• Multi-Source Path Finding: Simultaneous search from multiple entities
• Query-Focused Subgraph Extraction: Context-aware subgraph retrieval

These algorithms are essential for the query phase of GraphRAG, enabling:

• Efficient entity-centric retrieval
• Relationship path discovery
• Context-aware information gathering
• Multi-hop reasoning without neural networks

Structs

GraphTraversal

Graph traversal system implementing various search algorithms

TraversalConfig

Configuration for graph traversal algorithms

TraversalResult

Result of a graph traversal operation