Caching and resetting output

Extractor caches every LLM response so repeated calls with the same configuration reuse previous results.

Export the cache before you shut down and import it in a future session to avoid running the model on reports that have already been extracted:

extractor.export_cache("my_cache.pkl")
...
extractor.import_cache("my_cache.pkl")

If you want to start fresh, call reset() to clear cached feature values and token estimates. This is useful when you wish to re-run extract_features on the same DataFrame with a different feature model. reset returns the instance so you can immediately chain another call:

clean_df = extractor.reset().extract_features(feature_model=NewModel)

The returned DataFrame contains your newly extracted features and an empty cache ready for further runs.