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How to make a typed decision in Python without a server with RSI Jev

Call the Decider class in a script to score options for text or images.

Who it is forA Python developer building a batch job or pipeline that needs decisions on documents, chats or images.

The problemStanding up a separate server just to classify a set of records adds setup work for a one-off or scripted task.

The outcomeA script that returns a decision for each named question, with images passed in when needed.

What you need first

  • Python with pip and git installed
  • A GPU with enough memory for the chosen model, or an Apple silicon Mac for the MLX builds
  • A 96 GB GPU if you want to serve the 27B model

Steps

  1. Install the package with pip install "rsi-jev[fast,vision] @ git+https://github.com/Shanghua-Gao/RSI-Jev"
  2. Create a Python file that imports Decider with from rsijev import Decider
  3. Load the model with d = Decider("v6.1-vl-4b")
  4. Call d.decide(...) with the state text, a dictionary of typed questions and an images list, then run the script (inferred: with python <your-file>.py) (inferred, check the README)

Prompt for your agent

Using the Shanghua-Gao/RSI-Jev package, write a Python script that loads Decider with v6.1-vl-4b and calls decide() on each record in <input-file> with a noul question about <topic>. Output one line per record with the option probabilities. Verify that every record gets a result and that image inputs are passed only when present.

Caveat. The README example passes one image path, so check the Decider interface in the repo before running it on a large batch.

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