Neo4j Vector Search

A unified knowledge graph and vector database solution built on Redis that seamlessly integrates graph traversal and vector similarity search for building advanced GenAI applications with both relational reasoning and semantic search capabilities.

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.

Microsoft's approach to RAG that uses knowledge graphs to enhance retrieval. GraphRAG builds structured representations of documents enabling better context understanding and multi-hop reasoning for complex queries.

RAG architecture that combines knowledge graphs with vector databases, enabling multi-hop reasoning, relationship traversal, and structured knowledge representation for more accurate and explainable AI responses.

Official Neo4j package for building graph retrieval augmented generation (GraphRAG) applications in Python. Enables developers to create knowledge graphs and implement advanced retrieval methods including graph traversals, text-to-Cypher, and vector searches.

Vector search capabilities in Neo4j graph database using HNSW indexing. Enables combining knowledge graphs with semantic similarity search for hybrid retrieval that leverages both graph relationships and vector embeddings.