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jmanhype/jev-dspy-lab

Reproducible calibration and selective-risk benchmarks for Jev decisions inside DSPy workflows. Runs offline for confidence gating, latency and cost modelling without live API calls.

jmanhype/jev-dspy-lab on GitHub

Stars
7
Forks
0
Language
Python
License
MIT
Last push
20 Sep 2026

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How it uses Jev

for case in cases:
    request = case["request"]
    questions = {name: builder(question) for name, question in request["questions"].items()}
    recorder.system_one(
        request["document"],
        questions,
        model=case["model"],
    )
return recorder

View in src/jev_dspy_lab/live.py

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