Enables AI-powered medical information retrieval through FHIR clinical document search and GraphRAG-based exploration of medical entities and relationships. Combines vector search with knowledge graph queries for comprehensive healthcare data analysis.
Enables storage and retrieval of knowledge in a graph database format, allowing users to create, update, search, and delete entities and relationships in a Neo4j-powered knowledge graph through natural language.
Transforms code repositories and development documentation into a queryable Neo4j knowledge graph, enabling AI assistants to perform intelligent code analysis, dependency mapping, impact assessment, and automated documentation generation across 15+ programming languages.
Enables building and querying knowledge graphs by ingesting documents into Neo4j using Gemini for entity extraction, and exposes MCP tools for graph health, document ingestion, and knowledge base querying.
Enables building and exploring a knowledge graph from files (PDF, DOCX, code) with AI enrichment via Ollama or Claude. Supports pinning documents, linking entities, and traversing the graph.
