katelatte

15 ms·

1.
by katelatte 2y ago
Is it possible to use Memgraph's vector search to build GraphRAG? How would that work?
2.
by katelatte 2y ago
I organize community calls for Memgraph community and recently a community member presented how he uses hypothetical answer generation as a crucial component to enhancing the effectiveness and reliability of the system, allowing for more ac
3.
by katelatte 2y ago
Suggestion: check out Memgraph for graph db storage - https://memgraph.com/ . I work at Memgraph as DX Engineer so feel free to ping me in case you have questions about it: https://memgraph.com/office-hours
4.
by katelatte 3y ago
I was collaborating on the latest feature in Memgraph Lab - GraphChat. To enable natural language querying in Memgraph Lab, we integrated the Lab backend with LangChain and powered this new feature with OpenAI LLM. Let me know what you thin
5.
Graph Database vs. Relational Database(memgraph.com)
2 points by katelatte 3y ago|1 comments
6.
by katelatte 3y ago
When I am presenting about graph databases, people often ask me about the differences between graph and relational databases so I decided to write a blog post about it.
7.
by katelatte 3y ago
Hi, author here. I wanted to play a bit with ChatGPT and see how it can help me in creating a graph database. It was really good in conversation about graph data modelling and I think this is where it shined. On the other hand, when giving
8.
How to Query Your Database with ChatGPT: Memgraph Edition(memgraph.com)
3 points by katelatte 3y ago|1 comments
9.
by katelatte 4y ago
Thanks for reporting! We will fix it asap
10.
by katelatte 4y ago
Memgraph does persist data. Snapshots are taken periodically during the entire runtime of Memgraph. When a snapshot is triggered, the whole data storage is written to the disk. There are also write-ahead logs that save all database modifica
11.
by katelatte 4y ago
While researching about NetworkX, I noticed sometimes projects become too big, and you can lose a lot of time on data import, instead on the actual graph analysis. You can see discussions on my previous posts at https://news.ycom
12.
by katelatte 4y ago
Thanks for creating this and helping others learn! Amazing effort :)
13.
by katelatte 4y ago
Well done on your work! It's nice to see new tools being developed in graph world, especially in Rust. I would just like to emphasize that there is a big difference between a graph database and graph algorithms library. This is a thing
14.
by katelatte 4y ago
Who ranks better was my word play, because of PageRank :') But yes, I totally agree with what you wrote and I will aim for more detailed comparisons in the next articles. And you are right, NetworkX is easy to use Python library, and I
15.
by katelatte 4y ago
I considered different ways of comparison here, and decided to go with a simple comparison on sample dataset, just to get a feel of it. I did consider doing it all in Python, but then it’s not fair towards Memgraph. Also, it depends on the
16.
by katelatte 4y ago
I forgot to mention: we do have biconnected components algorithm [1], and since all biconnected graphs are strongly connected, it can be useful. [1] https://memgraph.com/docs/mage/query-modules/cpp/biconn
17.
by katelatte 4y ago
We do have our own implementation of weakly connected components [1]. Currently, we only have NetworkX strongly connected components algorithm [2] as a part of the nxalg module (set of procedures) in MAGE (our graph algorithms library). I d
18.
by katelatte 4y ago
But again, thanks for sharing. This is also a valuable resource and reference for future comparisons.
19.
by katelatte 4y ago
Yeah, but this is not an official benchmark, it’s just a simple demo on sample dataset. It would take much more effort to create the whole benchmark to prove how much exactly Memgraph is faster and on what kind of workload.
20.
by katelatte 4y ago
Yes, I work for Memgraph, I am a developer there and I wrote this, and all of the previously published articles. I was comparing NetworkX to Memgraph algorithms, since that was the point of the whole article. I am mostly using Python in my
21.
by katelatte 4y ago
Yes, I agree. I read this comparison in performance, it's a really good resource, thanks for sharing. It all depends on what are your needs of course. But C++ implementation of graph-tool definitely wins the battle.
22.
Who ranks better? Memgraph vs. NetworkX PageRank(memgraph.com)
33 points by katelatte 4y ago|19 comments
23.
by katelatte 4y ago
I continued learning about NetworkX, and when it comes to issues with scaling and the need for persistence when working on applications in production, Memgraph saves the day. You can see the previous discussion at https://news.yc
24.
by katelatte 4y ago
I didn't compare it, since I haven't used Gephi, but I heard it's useful for large graphs. I don't like the user interface. It looks a bit outdated. I guess it's a good visualization tool, but I am not sure what it
25.
by katelatte 4y ago
Presenting the data and results of analytics is certainly not an easy task and it definitely depends on who is the audience. So, yeah, I agree with you about that. Regarding graph representation, I talked with a bunch of people on the confe
26.
by katelatte 4y ago
I heard about it, but I didn't try it. Does it offer data durability?
27.
by katelatte 4y ago
Lately I’ve been working on discovering what NetworkX can do. I learned it is a powerful and useful Python tool, but I also noticed challenges most NetworkX users are facing. Read more about it at my blog post. If you have any questions ju
28.
Data persistency, large-scale data analytics, visualizations-NetworkX challenges(memgraph.com)
28 points by katelatte 4y ago|9 comments
29.
by katelatte 4y ago
I like how you managed to add this feature in such elegant way. Also, I was waiting a long time for it, and I am glad it is finally here :)
30.
by katelatte 5y ago
I really like the visualizations! Nice work! Are you planning to do some interesting graph analysis on this dataset?

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