I seem to be becoming an aircraft geek in my middle age and have set up a local knowledge graph to build a complete conceptual history of flying machines.

I have the unsloth Q6 of Quen3-30B-A3B on my 4090 currently chugging through a list of about 10,000 things on Wikipedia, parsing the contents for content matching a basic schema, and populating a local neo4j graph database. But it’s also capturing each time any other context it thinks necessary to interpret the data it’s captured in each particular case and storing it in the main node for each entity. When it’s done, I’m going to have it go back across all the extra bits in one pass and augment/modify the original schema with its own important general concepts and then repopulate the database with the new information in each case, before running another Wikipedia pass to fill in any gaps it can find.

I know this is sort of an inefficient graphRAG, but it’s all human readable and the knowledge graph is interpretable. It’s really interesting to see the schema evolve. In testing, the basic schema was just model<-produced by<-manufacturer and engine_type<-has<-model and by the time it finished the second phase, models had designers, maiden flights, ICAO type designations, and it had invented the concepts of crashes (which had dates and locations), conflicts (likewise), operators and engineering challenges as augmentations to the schema. It’s just very cool to see it formalising an ontology in real time.

I think when it’s done for real - a couple of days - it should be able to answer questions like “how long does it take for engineering solutions to be implemented as a result of a fatal crash?” or “why don’t more sailplanes have turboprops?” and provide reasoned and referenced answers.

Next to it is a flutter app that I vibe coded with the instruct version of qwen3-30B to get real time look ups for local planes (I moved to an area near to an airport which is what caused all this) and the end point is that I can operate both through a smaller LLM running constantly and literally just get it to alert me whenever an interesting plane comes past - but where interesting is defined on much more rigourous terms then just being old or numerically rare. If I can get the database to fit, the whole thing can be mobile and my phone just tell me to look up when something interesting is around, wherever I am. And I should add that I don’t actually code much in Python or know the first damn thing about how to use Cypher for querying the database. The whole thing has been done locally and in languages I don’t speak. It’s great fun :)