This is a super cool idea! I've sort of mused about an idea for general web search that's very similar to this concept, where you start with a set of trusted entities and then branch out from there, but choosing how you establish trust is really important. But this is a really clever application, well done!
Show HN: Knowledge graph of restaurants and chefs, built using LLMs
21–30 of 45 posts
Re: Show HN: Knowledge graph of restaurants and chefs, built using LLMs
#22Graph embed does not appear to work in FF 135. Loaded in Chrome though. Edit: Seems to be a me issue.
It works on FF135.0.1 (aarch64) for me. Ad blocker?
Tried without adblocker and turned off pihole. I did get it work on Zen Browser (FF engine). So my FF might have gotten borked. Console is giving me:
Failed to create WebGL context: WebGL creation failed: * tryANGLE (FEATURE_FAILURE_EGL_NO_CONFIG) * Exhausted GL driver options. (FEATURE_FAILURE_WEBGL_EXHAUSTED_DRIVERS)
Glad its workin for others!
Re: Show HN: Knowledge graph of restaurants and chefs, built using LLMs
#23The embedding is kind of weird. Like, there's no reason a "degree: 1" node should be so far away from its sibling. Example: https://imgur.com/a/7Cktyzp This makes the graph look more random/noisy/disorganized than it actually is.
Yeah, they should have used UMAP or tSNE to cluster the data a bit
Re: Show HN: Knowledge graph of restaurants and chefs, built using LLMs
#24Given the structured nature of the data, how does this compare to running a specialized classification model that looks for specific words in a review and uses those to assign Chefs to Restaurants? With some fine tuning, you might get more consistent results than feeding the reviews into a generative model.
Re: Show HN: Knowledge graph of restaurants and chefs, built using LLMs
#25Great project. I propose an improvement over this conventional kind of object-style graph. Instead, every single item should be a node or an edge. The objects are needless complexities that obscure pure graph relations. Like this: https://memelang.net/03/
Re: Show HN: Knowledge graph of restaurants and chefs, built using LLMs
#26Given the structured nature of the data, how does this compare to running a specialized classification model that looks for specific words in a review and uses those to assign Chefs to Restaurants? With some fine tuning, you might get more consistent results than feeding the reviews into a generative model.
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.
Re: Show HN: Knowledge graph of restaurants and chefs, built using LLMs
#27This looks great! I was just looking for a good web knowledge graph visualizer.
Re: Show HN: Knowledge graph of restaurants and chefs, built using LLMs
#28Nice! How'd the local models do vs gpt4o-mini? Did you spend much time playing with datasette?
Re: Show HN: Knowledge graph of restaurants and chefs, built using LLMs
#29The embedding is kind of weird. Like, there's no reason a "degree: 1" node should be so far away from its sibling. Example: https://imgur.com/a/7Cktyzp This makes the graph look more random/noisy/disorganized than it actually is.
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.
Re: Show HN: Knowledge graph of restaurants and chefs, built using LLMs
#30Nice! How'd the local models do vs gpt4o-mini? Did you spend much time playing with datasette?
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.