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How to route low-confidence fields to a human reviewer with Py Idp

Send fields below the confidence threshold to a Streamlit review screen and save the corrections.

Who it is forAn operations lead running document extraction at volume who needs people to check the uncertain fields before the data is used.

The problemAccepting every model output without review lets wrong amounts or dates reach downstream systems.

The outcomeLow-confidence documents are paused for review, and the saved corrections are stored for later use by the RL update step.

What you need first

  • Python with pip available
  • An API key for a hosted LLM backend such as OpenAI, or a local backend such as Ollama (the zero-config path runs on the in-tree mock backend without a key)
  • A sample PDF or text file to test with, such as invoice.pdf

Steps

  1. Run pip install py-idp and confirm a document runs through the pipeline with idp run invoice.pdf --schema Invoice --backend mock. (inferred, check the README)
  2. Start the Streamlit review app from the idp.hitl package. The README names the Streamlit review UI but does not give its launch command, so check the docs site for the exact command. (inferred, check the README)
  3. Open the review screen, correct any flagged fields, and save the corrections.
  4. Run idp rl-update to apply the saved corrections to the extraction policy.

Prompt for your agent

In the py-idp repo, run one document through the pipeline and find which fields fall below the confidence threshold. Use the Streamlit HITL review screen to correct them, then confirm the corrections are saved in storage. Report the list of corrected fields.

Caveat. The README names the Streamlit review UI but does not state its launch command, so the exact start step must be confirmed in the docs before use.

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