Retrieval-Augmented Generation approach that combines graph databases with vector search for enhanced context retrieval. Uses graph structures to capture relationships between entities while leveraging vector embeddings for semantic search.
טוען......
בעמוד זה
GraphRAG
GraphRAG is an advanced Retrieval-Augmented Generation approach that combines graph databases with vector search capabilities. It leverages graph structures to capture relationships between entities while using vector embeddings for semantic similarity search.
By traversing the graph structure, GraphRAG can retrieve not just similar documents but also related entities and their connections, providing richer context for generation.
Several platforms now support GraphRAG:
Add vector search capabilities to existing graph databases (Neo4j, Spanner Graph)
Combine separate vector databases with graph databases
Platforms that natively support both graph and vector operations
In 2025, Google Spanner added functionality for using graphs with vector search specifically for GraphRAG use-cases, with "a lot more exciting capabilities lined up for 2026."
Implementation-dependent - varies by platform and architecture chosen.
גלו פריטים נוספים הקשורים לזה