GraphRAG combines knowledge graphs with retrieval-augmented generation for structured, relationship-aware retrieval. Articles cover GraphRAG architecture, BigQuery-based knowledge graph construction, and how graph-structured retrieval improves answer quality for questions involving entity relationships.
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Vector search treats every chunk as independent. GraphRAG models the relationships between entities, communities, and concepts. For corpus-spanning questions ("what's the relationship between X and Y"), graph wins.
All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.