DOCUMENT OPS
Classify and tag contract clauses with a Hugging Face model before indexing
Splits each signed contract into clauses, classifies them with a Hugging Face model (indemnity, liability, termination, confidentiality), and stores per-clause tags alongside…
How it runs
The automated pipeline, trigger to output.
- TriggerNew signed contract in DropboxDropbox
- ActionExtract text and split into clausesDropbox
- ActionClassify each clause via Hugging Face modelHugging Face
- LogicRoute low-confidence clauses to unclassified bucket
- OutputWrite contract + labeled clause map to PostgresPostgres
What it does
Beyond top-level metadata, this workflow understands what's inside a contract. It segments the document into clauses, runs each through a Hugging Face classification model, and labels them — indemnification, limitation of liability, termination, auto-renewal, confidentiality, governing law — then stores the tagged clause map in Postgres next to the contract row.
When to use it
Use it when legal needs to answer clause-level questions across the portfolio: "which contracts cap liability?" or "which auto-renew?" It's the right fit for teams doing contract risk review or due diligence at scale, where plain metadata isn't enough.
How it works
- 1A new signed PDF in Dropbox triggers the run.
- 2The document text is extracted and split into discrete clauses.
- 3Each clause is classified by a Hugging Face inference model into a clause-type label with a confidence score.
- 4A logic step routes low-confidence clauses to an "unclassified" bucket so they don't pollute the index.
- 5The contract row plus its labeled clause array is written to Postgres, ready for clause-type queries.
Set it up
What you configure once, before turning it on.
- 1Connect DropboxFiles and folders.
- 2Connect Hugging FaceModels, datasets, spaces — the open-source hub.
- 3Connect PostgresAny Postgres URL — query, write, migrate.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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