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…
In plain words
This project creates an interactive knowledge graph mapping the French culinary network by extracting structured data from restaurant reviews using large language models. It processes unstructured text from LeFooding.com into a consistent schema, tested across multiple models including open-source and commercial options. The result is visualized as an interactive network using WebGL technology, accessible through a web interface built with Flask. The project demonstrates cost-effective information extraction, processing 2,000 reviews for under one euro.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
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!
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