Show HN: Knowledge graph of restaurants and chefs, built using LLMs

Hi HN!

My latest side project is knowledge graph that maps the French culinary network using data extracted from restaurant reviews from LeFooding.com. The project uses LLMs to extract structured information from unstructured text.

Some technical aspects you may be interested in:

- Used structured generation to reliably parse unstructured text into a consistent schema

- Tested multiple models (Mistral-7B-v0.3, Llama3.2-3B, gpt4o-mini) for information extraction

- Created an interactive visualization using gephi-lite and Retina (WebGL)

- Built (with Claude) a simple Flask web app to clean and deduplicate the data

- Total cost for inferencing 2000 reviews with gpt4o-mini: less than 1€!

You can explore the visualization here: [Interactive Culinary Network](https://ouestware.gitlab.io/retina/1.0.0-beta.4/#/graph/?url...)

The code for the project is available on GitHub: - Main project: https://github.com/theophilec/foudinge - Data cleaning tool: https://github.com/theophilec/foudinge-scrub

Happy to get feedback!

Show HN: Knowledge graph of restaurants and chefs, built using LLMs
theophilecantelob.re

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

What's the use case for maintaining a list of restaurants that use LLMs?

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...

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.