Since you did the hard work of parsing rich metadata already, it would be even cooler if your network visualization oriented nodes by some of this information. Here the 'hiveplot' idea (https://hiveplot.com/ ) is often even more useful than e.g. springloaded or UMAP based layouts; clustering into semantically-meaningful categories into axes (say, city or arrondissement? years open? cuisine? an explicit phylogeny from oldest culinary grandparents to youngest?) then choosing a coordinate to localize nodes on the axes (total node degree? prix? "les plus" tags?...) automatically compels us think about salient features of the data.

I agree the spatialization could be better. I used one of the algorithms in Gephi-lite directly. Do you have a favorite spatialization algorithm to recommend?

Yeah, they should have used UMAP or tSNE to cluster the data a bit

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so you mean how academics search for relevant publications on a topic, which is the direct inspiration for the original Google ranking algorithm, back in the happy days when the web was young and had not optimized itself around short-term money making.

I was clicking around with the embed, and eventually hit the "home/house" icon. That takes me to a Retnia credit/loading screen with no way to back out of it that I could see. Was forced to hard refresh the page. If there is a close button, it could be made more obvious

Thank you for the kind words!

Yes, Retina and Gephi are great. In fact I noticed a bug which they fixed immediatly while making the project.

The data is initially not at all structured, and the critics talk about a chef's CV in passing. For instance, take this example:

> At Grenat, Antoine Joannier and Neil Mahatsry are bathed in an ardent red glow, much like the pomegranate-toned walls of their space. After working together at La Brasserie Communale, where they first met, the duo is now firing on all cylinders in the heart of Marseille, where Antoine tends to guests seated around blonde wood tables, delivering dishes ignited by Neil behind the bar. From oysters to prime cuts of red meat, […]

I tried using NER models and the results were not great. Furthermore, these models do not extract relationships between entities (other models exist for that though). Haven't tried fine-tuning at all!

There is also a lot of variation in the ways of presenting a chef's prior restaurants, which makes this a good use-case for LLMs.

All credit goes to gephi-lite [1] and the sister project Retina [2].

[1] https://gephi.org/gephi-lite/ [2] https://ouestware.gitlab.io/retina/1.0.0-beta.4/

Yes I ended up using that. Libraries like outlines give that functionality to open models.

Local models hallucinated a lot more that gpt4o-mini, so I stayed with OpenAI. On top of that, I paid around 14€ for inference on ~200 examples on OVH and inference was much slower. I am planning on getting everything running on Mistral or Llama though.

I used sqlite everywhere so datasette was good for visualizing scraped and extracted data. Simon released structured generation for llm a few days after I did the project though, so I haven't tried yet.

It works on FF135.0.1 (aarch64) for me. Ad blocker?

Worked for me (FF 135.0.1 on Ubuntu)

The phrasing, possibly due to French->English translation, may cause a misleading reading.

It appears the author/poster is using LLMs (OpenAI and Claude, specifically) to extract entity and relationship data to create a knowledge graph of French Restaurants and Chefs.

https://github.com/theophilec/foudinge-scrub/blob/0a2701756f...

Yeah i read it that way to. :D

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Me too. Title might need a bit of adjusting. Neat concept though!

I do wish it expanded out past French cuisine. It has its place, but I am very much tired of French cooking being held above other locales/styles

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Learning is a perfectly fine end in itself.