heady-insight-graph

A knowledge graph maps nodes, edges, and properties to surface connections across memories, tasks, code, and conversations. It enables context-aware querying to discover patterns and emergent insights across multiple integrated data sources.

To build a knowledge graph, define a schema with node and edge types, configure an ingestion pipeline for your data sources, apply entity extraction rules, and run the workflow to populate the graph for querying and visualization.

Yes, you can query, explore, and visualize subgraphs to support knowledge retrieval and planning. Configurable rules and scoring help discover patterns and surface insights within the integrated knowledge graph.

The best way to ingest data is to configure your graph schema first, then run the ingestion workflow to integrate memories, tasks, code, and conversations, fueling the graph for connection and pattern discovery.

Yes, you need to define a graph schema specifying node types, edge types, and properties before extracting entities. This schema supports context-aware querying and structures the accumulated knowledge for accurate retrieval.

A graph-based approach may not suit contexts lacking relational data or complex interconnections. If your data consists of isolated records without meaningful cross-source patterns, standard relational or document storage may be more efficient.