Fantastic webinar, very useful. On a long and only very slightly related tangent, protip: Before (and during!) long presentations drink tea with honey and a lemon slice. When we're nervous our throat tenses up and it's very easy to start coughing when talking constanly. The hot tea with honey helps with that, and the acidity from the lemon prevents your mouth from drying out. I used to have a terrible time doing these 50-minute presentations + Q&A. My throat would be shot even though I wasn't yelling or anything. Eventually I took a couple of vocal training lessons and it practically solved the issue in a week.
Very useful workshop. Really enjoyed watching. I'd like to know if text chunking is automated as part of the KG building phase. If not, what techniques can be used around chunking particularly in instances of large bodies of text.
Thanks for this - using this now with a small project. One question - once the index is upserted into Neo4j, how do you load it? I assume you don't have to rebuild it every time
While defining the schema, the node labels and relationships were defined. In addition to that, is there a way to define properties associated with each node label?
Its good learning stuff. I have an grpah db which i created. It has different nodes with different lables and relationships between them with properties. Those nodes and relationships and properties and lables generated with help of LLM propmpt and loaded into neo4j. I want to built RAG on top of it. What will be feasible solution as i have very random node lables and relationships.? Suggest some reference please reference
Hi sir ! I’m also LLM/KG enthusiasm, based on your expertise Is Ontology schema like RDF/OWL necessary when building Graph data base as RAG material for LLM output augmentation ?
feels like he never slept and started giving this presentation
maybe totally different time zone
Tomaz, thank you for sharing!
Fantastic webinar, very useful.
On a long and only very slightly related tangent, protip:
Before (and during!) long presentations drink tea with honey and a lemon slice. When we're nervous our throat tenses up and it's very easy to start coughing when talking constanly. The hot tea with honey helps with that, and the acidity from the lemon prevents your mouth from drying out. I used to have a terrible time doing these 50-minute presentations + Q&A. My throat would be shot even though I wasn't yelling or anything. Eventually I took a couple of vocal training lessons and it practically solved the issue in a week.
good tip!
This call was great, we used the LlamaIndex+Neo4j implementation as a basis for automatic knowledge graph generation in R2R
Jerry was a great moderator asking killer question to Tomaz
Very useful workshop. Really enjoyed watching. I'd like to know if text chunking is automated as part of the KG building phase. If not, what techniques can be used around chunking particularly in instances of large bodies of text.
How can I constrain the graph construction with .owl and RDF?
This also I want to ask, if RDF/OWL necessary for LLM augmentation performance
Thanks for this - using this now with a small project. One question - once the index is upserted into Neo4j, how do you load it? I assume you don't have to rebuild it every time
Great content, very useful. Where can we find the notebooks used here?
While defining the schema, the node labels and relationships were defined. In addition to that, is there a way to define properties associated with each node label?
where can we find the notebook used in the video? Please share!
He actually shows the notebook he used late in the video check after 45:25
Its good learning stuff. I have an grpah db which i created. It has different nodes with different lables and relationships between them with properties. Those nodes and relationships and properties and lables generated with help of LLM propmpt and loaded into neo4j. I want to built RAG on top of it. What will be feasible solution as i have very random node lables and relationships.? Suggest some reference please reference
Hi sir ! I’m also LLM/KG enthusiasm, based on your expertise Is Ontology schema like RDF/OWL necessary when building Graph data base as RAG material for LLM output augmentation ?
Watch the lecture with at least 1.5x speed to prevent yourself from sleeping...
great content but presentation skills....needs some work and energy!
the tune is reallyy dozy,however the code is easy to understand
Hangover recovery?😂
Not a fan of the way this was presented ... I was expecting a tutorial
🥱
Dude, you're slurring over words making it very hard to follow along