AI & ML

GraphRAG

An advanced RAG approach that uses knowledge graphs to capture entity relationships, enabling multi-hop reasoning over structured data.

GraphRAG extends traditional Retrieval-Augmented Generation by incorporating knowledge graphs into the retrieval pipeline. Instead of relying solely on vector similarity search, GraphRAG leverages entity relationships and graph traversal to find relevant information.

A knowledge graph represents information as nodes (entities) and edges (relationships). When a user asks a question, GraphRAG can traverse these relationships to perform multi-hop reasoning — connecting dots across multiple documents that a simple vector search might miss.

For example, if asked "What technologies does Company X use for their recommendation engine?", GraphRAG can follow relationships from Company X → Engineering Team → Projects → Tech Stack to surface a comprehensive answer.

GraphRAG is particularly valuable for enterprise use cases where data is highly interconnected: organizational knowledge, compliance documentation, and research corpora.