Anyone have experience loading RDF data into Llama index? I can use GPTVectorStoreIndex to index the raw RDF (or ttl or JSON-LD) file and that works quickly but doesn't give the best results. The KnowledgeGraphIndex takes super long and takes a lot of resources and I don't know that the results are that much better. Also, when I visualize the knowledge graph that KnowledgeGraphIndex creates it seems like it only took some of the entities in the KG as nodes in the LLM. Any help/guidance would be greatly appreciated.
Am I right in assuming that this is the recommended way to load a pre-made knowledge graph into Llama index, and that the KnowledgeGraphIndex is the recommended way of CREATING a KG with llama using unstructured data?
It looks like that notebook is focused on building a KG using unstructured data, is that correct? I am trying to use structured data to create a KG (using RDFLib/python, not Llama) and then use that RDF data as context info for Llama. I believe the correct approach is then to just use the RDFReader and ignore the KGIndex functionality in Llama entirely, is that correct?
I think one reason for the poor performance on the initial index approach is that our default csv reader is not great. I'm pretty sure it just splits each row into a document/node 😅
