Sample proposal: EvalPort ↔ azure-ai-evaluation interchange under scenarios/evaluate
Nobody has claimed this yet.
Assessment
- Difficulty
- 2/5
- Estimated time
- 1-3 hours
- Newbie friendliness
- 72/100
- Issue type
- Documentation
- Clarity
- Mostly clear
- Activity status
- Active
- Tech stack
- azure, jupyter-notebook, python
- Domain
- ai, documentation
Research direction
Start with scenarios/evaluate/README.md, the Supported_Evaluation_Targets/ and Supported_Evaluation_Metrics/ examples, and CONTRIBUTING.md for the notebook template. Use the existing azure-ai-evaluation adapter as the reference for the sample, and consider the work complete when a self-contained notebook demonstrates exporting a suite and result set and validates both.
Written by the indexing model from the issue text.
Description
Hi — I maintain EvalPort (Apache 2.0), an open JSON spec for portable LLM evaluation datasets: test suites, test cases, graders, and result sets that can move between eval frameworks without losing meaning.
I looked through scenarios/evaluate/ before opening this — the Supported_Evaluation_Targets/ and Supported_Evaluation_Metrics/ structure (and the comparison table in scenarios/evaluate/README.md) is a genuinely useful map of what azure-ai-evaluation supports, which is why I think this is worth a shot.
There's already a working, tested adapter for azure-ai-evaluation in EvalPort's adapters/ directory: azure-ai-evaluation-openeval-adapter. It's a small package with to_openeval() / from_openeval() / evaluation_result_to_openeval(), tested against the real azure-ai-evaluation package (21 tests, not mocks) — not a proposal, an existing reference implementation.
A minimal version of what a sample notebook would show:
from azure.ai.evaluation import F1ScoreEvaluator
from azure_ai_evaluation_openeval_adapter import to_openeval, evaluation_result_to_openeval
from openeval.validate import validate_suite, validate_result_set
# Your evaluate()-shaped data + evaluators, exported as a portable EvalPort suite
suite = to_openeval(
data="my_eval_data.jsonl",
evaluators={"f1": F1ScoreEvaluator()},
suite_id="my_eval_suite",
)
assert validate_suite(suite).valid
# ...run azure.ai.evaluation.evaluate() as usual, then export the result:
result_set = evaluation_result_to_openeval(result, suite_id="my_eval_suite")
assert validate_result_set(result_set).valid
Would a small notebook like this be a welcome addition under scenarios/evaluate/ (e.g. alongside Supported_Evaluation_Targets/)? It'd be scoped as one self-contained sample per your CONTRIBUTING.md guidance — happy to follow the README/notebook template if there's interest. No worries at all if this isn't a fit for the repo's current scope — just flagging since the adapter already exists and works.
Spec: https://github.com/adhabnr-ux/evalport/blob/main/spec/SPEC.md
- Dominant language
- Jupyter Notebook
- Stars
- 548
- Forks
- 318
- Avg merge
- 3d 15h
- Merged PRs (30d)
- 1
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
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- Open a pull request that references the issue number.
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