51–60 of 117 posts
Graph DBs have been plagued with exploding complexity of queries as doing things like allowing recursion or counting paths isn't as trivial as it may sound. Do you have benchmarks and comparisons against other engines and query languages?
how did you get it 3 OOMs faster than neo4j?
What other papers did you get inspiration from?
Looks very interesting, but I've seen these kind of multi-paradigm databases like Gel, Helix and Surreal and I'm not sure that any of them quite hit the graph spot. Does Helix support much of the graph algorithm world? For things like GrapgRAG. Either way, I'd be all over it if there was a python SDK witch worked with the generated types!
I was thinking about intertwining Vector and Graph, because I have one specific usecase that required this combination. But I am not courageos or competent enough to build such a DB. So I am very excited to see this project and I am certainly going to use it. One question is what kind of hardware do you think this would require ? I am asking it because from what I understand Graph database performance is directly pro…
I was thinking about intertwining Vector and Graph, because I have one specific usecase that required this combination. But I am not courageos or competent enough to build such a DB. So I am very excited to see this project and I am certainly going to use it. One question is what kind of hardware do you think this would require ? I am asking it because from what I understand Graph database performance is directly pro…
With regard to the graph db, we mostly use our laptops to test it and haven't run into an issue with performance yet on any size dataset.
If you wanna chat DM me on X :)
I was thinking about intertwining Vector and Graph, because I have one specific usecase that required this combination. But I am not courageos or competent enough to build such a DB. So I am very excited to see this project and I am certainly going to use it. One question is what kind of hardware do you think this would require ? I am asking it because from what I understand Graph database performance is directly pro…
Neo4j supports vector indexes
Furthermore, the vectors is capped at 4k dimensions which although may be enough most of the time, is a problem for some of the users we've spoken to. Also, they don't allow pre filtering which is a problem for a few people we've spoken to including Zep AI. They are on the right track, but there are a lot of holes that we are hoping to fill :)
Edit: AND, it is super memory intensive. People have had problems using extremely small datasets and have had memory overflows.
Looks very interesting, but I've seen these kind of multi-paradigm databases like Gel, Helix and Surreal and I'm not sure that any of them quite hit the graph spot. Does Helix support much of the graph algorithm world? For things like GrapgRAG. Either way, I'd be all over it if there was a python SDK witch worked with the generated types!
I have been happily using Gel (formerly EdgeDB) for a few projects. I'm curious what you think it is missing in regards to hitting the "graph spot"?