[LangExtract](https://developers.googleblog.com/en/introducing-langextract-a-gemini-powered-information-extraction-library/) has got me curious, but I don't get what makes it different from a [spacy-llm/prodigy](https://prodi.gy/docs/large-language-models) setup. Is it just that I am spared the effort of chunking long input and/or constructing output JSON from entities and offsets by writing the corresponding python code myself?...

Ah, one more difference is that langextract is #OpenSource whereas prodigy is not (?). (On the other hand, prodigy has a better integration with a correction+training workflow.)

#llm #google #langextract #nlp #spacy #prodigy #ner