Seems like this is now best done via functions, if you're using OpenAI's models? They call out "extracting structured data from text" as a key use case in their announcement.
https://openai.com/blog/function-calling-and-other-api-updat...
Does anybody here have experience with metadata extraction using LLMs? I've been thinking about it recently. and wonder if just making a big prompt and putting that into OpenGPT or even ChatGPT is really the way to go, or if there is a "cleverer" way. Maybe you could train specifically for certain fields, or use the LLM in a different way (like you can use the embeddings directly to do simularity search)?
Another idea was, if you have a lot of similar HTML documents, to not ask the LLM for the metadata, but to ask it for CSS selectors that contain the metadata fields - assuming it can deal with HTML and the data is verbatim in there. Then you should be able to get much more consistent results.
Try it out https://kadoa.com
