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 accurate and contextually appropriate responses to user queries. Here's more about it: https://memgraph.com/blog/precina-health-memgraph-graphrag-t...
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 think if you decide to try it out.
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.
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 me information about the TV show, it was a bit confused sometimes. In one run season had 13 episodes, while in the second run it had 9 :') But that was not stopping me from generating Cypher queries with the help of ChatGPT, creating a database and exploring the dataset. I think ChatGPT has a bright future in translating natural language into Cypher queries and in that way speed up the process of learning Cypher to raise graph database awareness.
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 modifications that happened to a file.
