How can LlamaIndex be used for building knowledge graphs?

LlamaIndex can be used to build knowledge graphs by leveraging its document processing and indexing capabilities to extract structured relationships from unstructured data. A knowledge graph organizes information as entities (nodes) and their connections (edges), which LlamaIndex helps create by parsing text, identifying key concepts, and inferring relationships. For example, when processing a research paper, LlamaIndex can identify entities like “researchers,” “experiments,” and “findings,” then map how they relate through actions like “conducted” or “discovered.” This is achieved through integrations with language models (LLMs) that analyze text for semantic patterns and extract structured triplets (subject-predicate-object) from documents.

To structure the data, LlamaIndex uses its KnowledgeGraphIndex to convert extracted triplets into nodes and edges. Developers can configure parsers to split documents into chunks, then apply LLMs to generate relationships. For instance, a news article might be split into paragraphs, and each paragraph processed to identify entities like “Company A” and “CEO John Doe,” with edges like “founded by.” The graph can be stored in graph databases like Neo4j or as in-memory structures. LlamaIndex also allows customization—like defining which entity types or relationships to prioritize—ensuring the graph aligns with specific use cases, such as tracking product dependencies in technical documentation.

Once built, the knowledge graph enables advanced querying. Using LlamaIndex’s query engines, developers can ask complex questions like, “Which projects did Company A fund in 2023?” by traversing relationships in the graph. This is more efficient than keyword searches because it leverages the graph’s interconnected structure. For example, in a medical research graph, a query could trace connections between a drug and its side effects across multiple studies. LlamaIndex simplifies integration with applications by providing APIs to retrieve subgraphs or specific nodes, making it practical for tasks like recommendation systems or data visualization. The framework’s flexibility allows developers to refine the graph iteratively, adding new data sources or adjusting relationship extraction rules as needed.