Introducing the Property Graph Index: A Powerful New Way to Build Knowledge Graphs with LLMs

LlamaIndex has introduced the Property Graph Index to enhance the flexibility, extensibility, and robustness of its knowledge graph capabilities. This new feature addresses the limitations of traditional knowledge graph representations, like knowledge triples, by using a labeled property graph representation, allowing for richer modeling, storage, and querying. The Property Graph Index supports categorizing nodes and relationships with associated metadata and enables complex queries using the Cypher graph query language. Users can combine several extraction methods—schema-guided, implicit, and free-form—to build knowledge graphs, while also utilizing various querying techniques such as keyword-based retrieval, vector similarity, and custom graph traversal. The Property Graph Index operates with a PropertyGraphStore abstraction for data storage and retrieval and is compatible with several backing stores, including Neo4j. This development is in collaboration with Neo4j, and users are encouraged to engage with the LlamaIndex community for support and to share their projects.