Neo4j Vector Index

Structured representations of entities and relationships - the foundation for GraphRAG, enterprise search, and neuro-symbolic AI.

Explore how knowledge graphs enhance RAG pipelines with structured relationships and reasoning.

Purpose-built databases for storing, indexing, and querying embedding vectors at scale — including managed options like Pinecone (Database + Nexus knowledge engine).

Understand how vector databases find similar items using high-dimensional embedding comparisons.

Labeled property graph model - nodes and edges with key-value properties, used by Neo4j and most graph databases.

The declarative query language for property graphs - pattern matching, traversals, and Neo4j queries.

Compare the two dominant knowledge graph models - RDF (W3C standard) and property graphs (Neo4j-style) - and when to use each.

Learn about the Resource Description Framework - the standard for representing knowledge as linked data.

Understand how ontologies define the vocabulary, relationships, and rules for a domain of knowledge.

Explore the Web Ontology Language for defining rich, machine-interpretable ontologies with reasoning capabilities.

Learn how organizations use knowledge graphs to connect data silos and power intelligent applications.

Discover how knowledge graphs and large language models complement each other - grounding LLMs with structured knowledge.

Practical guidelines for designing, building, and maintaining knowledge graphs in production.

Reference architecture for graph construction, community detection, hybrid retrieval, graph traversal, and query planning.

Reference architecture for grounding, entity resolution, graph traversal, hybrid reasoning, and structured retrieval with LLMs.

From RDF basics to enterprise deployments - a structured path to mastering knowledge graphs.