Jeremy Morgan

Jeremy Morgan

@jeremymorgan.com

Developer, Tech Blogger, and Volunteer Firefighter. World's okayest developer "You don't need permission to be awesome" - @reverentgeek.com I came here to break builds and chew bubblegum. And I'm all out of bubble gum.

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A multimodal GraphRAG for storm damage assessment that models confirmed damage, negative evidence, unresolved conditions, and temporary repairs as distinct things. Most GraphRAG retrieves more facts. This one handles missing and contradictory ones. arxiv.org/abs/2610.28216

arxiv.orgA Multimodal Engineering GraphRAG Framework for Post-Wind-Event Building Assessment and Recovery InferencePost-event building assessment requires linking observed physical damage to its consequences for safety, habitability, functionality, and recovery. Yet post-disaster evidence is often heterogeneous,…

Vector search finds relevant information. Graph traversal connects it. Qdrant's hands-on GraphRAG tutorial combines both using Neo4j, with a reproducible Python implementation. A useful starting point for testing whether graph-enriched retrieval improves answers. qdrant.tech/documentatio...

qdrant.techGraphRAG with Qdrant and Neo4j - QdrantA step-by-step guide on implementing GraphRAG with Qdrant and Neo4j, combining vector search with knowledge graphs for enhanced retrieval.

Extracting entities is easy compared with defining what they mean. The latest Going Meta series explores ontologies, graph semantics, and knowledge representation. Worth following if you're building knowledge graphs that need to remain consistent beyond a prototype. neo4j.com/videos/going...

neo4j.comGoing Meta S04E01 – a Series on Semantics, Knowledge Graphs and All Things AISeason 04 Episode 1 of Going Meta – a Series on Semantics, Knowledge Graphs and All Things AI. Topic

Neo4j reports 45–81% fewer tokens in selected evaluations using graph-backed semantic layers across complex data catalogs. The numbers need independent validation, but the architecture is compelling: retrieve relevant schema relationships

neo4j.comConnected Intelligence: Accelerating Enterprise AI throughout the cloud ecosystem - Neo4j Graph Intelligence PlatformSee how Neo4j, GCP, AWS, and Microsoft deliver secure, zero-copy graph architectures that cut GenAI token costs up to 81% and accelerate enterprise AI readiness.

Neo4j just reduced graph embedding generation to a single API call. No elaborate ML pipeline required. The new Aura Graph Analytics APIs make structural similarity, clustering, and graph-based predictions much easier to explore. neo4j.com/blog/aura-gr...

neo4j.comGraph Embeddings, One Line of Code Away - Neo4j Graph Intelligence PlatformGenerate graph embeddings with one line of Python. The new Embeddings API in the GDS Python client is now in preview on Aura Graph Analytics.

FlexViz keeps charts interactive at 100M+ rows by answering every zoom and brush with lazy Polars aggregations and Rust kernels. Nothing raw ships to the browser. Apache 2.0, on PyPI, with a live demo and public benchmarks. Early and small. Worth a look. github.com/flex-analyti...

github.comGitHub - flex-analytics/flexviz: Interactive data exploration that is extremely fast and agent-nativeInteractive data exploration that is extremely fast and agent-native - flex-analytics/flexviz