Shouldn’t it be possible since forever to put machine readable source information into PDF metadata. It’s more a problem of the tools and programs generating the PDFs.
We spend millions turning structured information into PDFs and billions to extract the same data from a printer rendering language
Catchall for 25 years :) (on domainfactory - df.eu) each company/service gets their own email prefix, so I can determine spam and also filter unsolicited emails.
My general point on GraphRAG is that it extracts and compresses the horizontal topic-clustering across many documents and makes that available for retrieval.
And that by creating the semantic network of entities, you can use patterns in the graph structure to answer questions that rely on information coming together from different documents. Think the detectives board connecting facts with strings from many different sources.
Feel free to ping me for a deeper discussion: michael at neo4j
RenTec was covered in much depth at the Acquired podcast.
Basically algorithms from signal processing applied to huge volumes of historical and current data to determine buy and sell signals. Originally developed for national defense.
Very secretive all external partners were bought out. Only hundred or so people benefited in the billions per person. Including Robert Mercer of Trump campaign financing and Cambridge Analytica fame.
Very interesting but also disheartening episode about smart people only caring about getting richer.
Quick 5 minute video on downloading and running Hugging Face language models in GGUF format (quantized by TheBloke) with Ollama on your local machine and checking GPU consumption with asitop (Apple Silicon Mac Top).
We spend millions turning structured information into PDFs and billions to extract the same data from a printer rendering language