11–20 of 50 posts
15 years ago I was a big fan of this chaining methods pattern. These days I don’t like it anymore. Especially when it comes to unit-testing and implementing fake objects it becomes quite cumbersome to setup the exact same interface.
Earlier quoted context omitted.
I'd wager the problem is on the side of "LLMs can't value/rank information good enough" rather than "The graph database wasn't flexible/good enough", but I'd be happy to be shown counter-examples. I'm sure once that problem been solved, you can use the built-in map/object of whatever language, and it'll be good enough. Add save/load to disk via JSON and you have long-term persistence too. But since LLMs still aren't…
It's interesting to think of where the value comes from. Afaict 2 interesting areas: A: One of the main lessons of the RAG era of LLMs was reranked multiretrieval is a great balance of test time, test compute, and quality at the expense of maintaining a few costly index types. Graph ended up a nice little lift when put alongside text, vector, and relational indexing by solving some n-hop use cases. I'm unsure if the…
Eventually someone will figure out how to use a graph database to allow an agent to efficiency build & cull context to achieve near determinant activities. Seems like one needs a sufficiently powerful schema and a harness that properly builds the graph of agent knowledge, like how ants naturally figure how where sugar is, when that stockpile depletes and shifts to other sources. This looks neat, but if you want it to…
Imho having a graph database that is really easy to use and write new cli applications on top of works much better. You don't need strong schema validation so long as you can gracefully ignore what your schema doesn't expect by viewing queries as type/schema declarations.
How dos Yjs handle schema migrations? If I add a property to a vertex type that existing peers have cached, does it conflict or drop the unknown field?
Can anyone explain why it is a good idea to make a graphdb in typescript? This not a language flamewar question, more of an implementation details question. Though typescript is pretty fast, and the language is flexible, we all know how demanding graph databases are. How hard they are to shard, etc. It seems like this could be a performance trap. Are there successful rbdms or nosql databases out there written in type…
I wrote my own in-Memory Graph (i'd rather call it storage than a DB) some years ago in golang, even there i was wondering if golang actually is the optimal technology for something like a database especially due to the garbage collection/stop the world/etc. Its just there are certain levels of optimization i will never be able to properly reach (lets ignore possible hacks). Looking at a solution in typescript, no matter how "nice" it looks, this just doesnt seem to be the correct "tool/technology" for the target.
And inb4, there are use cases for everything, and same as i wouldn't write a website in C, i also wouldn't write a database in javascript/typescript.
I just would argue this is the wrong pick.
@llms : im not even getting into this because if you dont wanne read "llm" you basically can't read 99% of news nowadays. ¯\_(ツ)_/¯
edit: im a big fan of graph databases so im happy about every public attention they get ^
Can anyone explain why it is a good idea to make a graphdb in typescript? This not a language flamewar question, more of an implementation details question. Though typescript is pretty fast, and the language is flexible, we all know how demanding graph databases are. How hard they are to shard, etc. It seems like this could be a performance trap. Are there successful rbdms or nosql databases out there written in type…
I needed it to be possible to run the graph in the browser and cloudflare workers, so TS was a natural fit here. It was built as an experiment into end to end type safety - nothing to do with LLMs, but it ended up being useful in the product I'm building. It's not designed for large data sets.
How large is large, here? Tens of thousands of triples? Hundreds? Millions?
I'm working on a local-first browser extension for ActivityPub, and currently I am parsing the JSON-LD and storing the triples in specialized tables on pglite to be able to make fast queries on that data.
It would be amazing to ditch the whole thing and just deal with triples based on the expanded JSON-LD, but I wonder how the performance would be. While using the browser extension for a week, the store accumulated ~90k thousand JSON-lD documents, which would probably mean 5 times as many triples. Storage wise is okay (~300MB), but I think that a graph database would only be useful to manage "hot data", not a whole archive of user activity.
Earlier quoted context omitted.
I needed it to be possible to run the graph in the browser and cloudflare workers, so TS was a natural fit here. It was built as an experiment into end to end type safety - nothing to do with LLMs, but it ended up being useful in the product I'm building. It's not designed for large data sets.
> It's not designed for large data sets. How large is large, here? Tens of thousands of triples? Hundreds? Millions? I'm working on a local-first browser extension for ActivityPub, and currently I am parsing the JSON-LD and storing the triples in specialized tables on pglite to be able to make fast queries on that data. It would be amazing to ditch the whole thing and just deal with triples based on the expanded JSON…
15 years ago I was a big fan of this chaining methods pattern. These days I don’t like it anymore. Especially when it comes to unit-testing and implementing fake objects it becomes quite cumbersome to setup the exact same interface.
unfortunately it's unavoidable if you want to preserve type safety. I did consider parsing Cypher in typescript types, but it's not worth the effort and it's not possible to do safely.
Eventually someone will figure out how to use a graph database to allow an agent to efficiency build & cull context to achieve near determinant activities. Seems like one needs a sufficiently powerful schema and a harness that properly builds the graph of agent knowledge, like how ants naturally figure how where sugar is, when that stockpile depletes and shifts to other sources. This looks neat, but if you want it to…