Extract the fact. Keep the trail back to the text.
LangExtract maps structured information back to its source. A useful starting point when “the model said so” is not enough.
TRACE IT.
CHECK IT.Ideas are better when shared.
An extraction tool you can inspect
LangExtract is a Python library in Google’s GitHub organization for extracting structured information with language models. Its documented source-grounding and visualization features help readers inspect where an extracted item appears in the input. The repository states that it is not an officially supported Google product.
A small experiment to start with
Use a short public-domain passage and ask for character names or locations, with a few examples. Compare each extracted span with the original. A highlighted source location helps verification, but it does not guarantee that every classification or inferred attribute is correct.
Choose your model deliberately
The documentation describes cloud and local model options. Check current provider support, credentials, cost, and data handling. For a community submission, include your prompt, examples, input, and one failure case rather than claiming that grounded output is automatically accurate.
Go to the source
Keep the idea moving.
Have a question, improvement, or your own experiment? Bring it to the repository.
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