FEAT Beam Search for OpenAIResponseTarget#1346
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Jun 5, 2026
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I am very much aware that this is less than ideal. However, since tools are stored on the class rather than supplied when a completion is wanted, and we want to run multiple different calls in parallel, I think this is the smallest possible change.
It might be better to have the tools be an extra argument to send_prompt_async() but that would have quite a blast radius.
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Description
Use the Lark grammar feature of the
OpenAIResponseTargetto create a beam search for PyRIT. This is a single turn attack, where a collection of candidate responses (the beams) are maintained. On each iteration, the model's response is allowed to extend a little for each beam. The beams are scored, with the worst performing ones discarded, and replaced with copies of higher scoring beams.Tests and Documentation
Have basic unit tests of the classes added, but since this requires features only currently in the
OpenAIResponseTargetthere didn't seem much point in mocking that. There is a notebook which runs everything E2E.