HybridRAG
Next evolution in RAG systems that combines vector databases for semantic similarity with graph databases for relationship exploration and multi-hop reasoning.
About this tool
Overview
HybridRAG combines vector databases and graph databases, representing the next evolution in RAG (Retrieval-Augmented Generation) systems for Large Language Models.
Why Combine Both Technologies?
Vector databases find relevant entities or documents based on semantic similarity, then graph databases explore relationships between those entities and extract meaningful context.
Knowledge graphs model explicit entities, relationships, and permissions, while vectors capture semantic meaning across messy, unstructured content, making them complementary rather than competing technologies.
Real-World Applications
Cedars-Sinai's Alzheimer's Disease Knowledge Base uses HybridRAG combining Memgraph's graph database and a vector database, with the graph storing biomedical entities and relationships for multi-hop reasoning while the vector database enables semantic similarity searches.
The consensus in 2026 is that the future of advanced agent memory does not lie in choosing one or the other, but in a hybrid architecture.
Pricing
Concept/approach, implemented through various tools.
Loading more......
Information
Categories
Tags
Similar Products
6 result(s)