evals-viewer-io 0.1.0

Pydantic schemas and a writer for the evals-viewer on-disk format — the Python writer side of the evals-viewer framework.

evals-viewer-io

Pydantic schemas and a writer for the evals-viewer on-disk format. This is the Python writer side of the framework — it produces the JSON tree that @ideonate/evals-viewer-server reads and the Vue frontend @ideonate/evals-viewer-core renders.

Install

pip install evals-viewer-io

Requires Python 3.10+ and Pydantic 2.

What's in the box

Symbol	Purpose
RunMetadata, EvalSummary, CaseSummary, AggregateStats	Pydantic models matching the on-disk format
TokenUsage	Token / cost model with addition, from_pydantic_ai adapter, per-model breakdown
save_run_metadata, save_eval_results	Filesystem writers — given models and dicts, write JSON in the layout the viewer expects
compute_aggregates(cases)	Group case.scores[evaluator] across cases → {evaluator: {mean, min, max}}
compute_token_totals(cases)	Sum token usage / cost / per-model breakdown across cases
eval_run_dir (pytest fixture)	Optional fixture creating a fresh run directory under EVALS_RESULTS_DIR

Quickstart: minimal end-to-end

from evals_viewer_io import (
    RunMetadata, EvalSummary, CaseSummary, TokenUsage,
    compute_aggregates, compute_token_totals,
    save_eval_results,
)

# 1. Build per-case rows. The output_summary dict is a free-form bag of
#    fields the viewer can show in the eval-detail table; token fields
#    use the canonical input_tokens / output_tokens / cost_usd / usage_by_model.
cases = [
    CaseSummary(
        name="case_001",
        scores={"Accuracy": 0.9, "Coverage": 0.8},
        judge_reasons={"Accuracy": "All key facts present."},
        output_summary={
            "input_tokens": 1234,
            "output_tokens": 567,
            "cost_usd": 0.012,
        },
    ),
    CaseSummary(
        name="case_002",
        scores={"Accuracy": 0.7, "Coverage": 0.9},
        output_summary={"input_tokens": 980, "output_tokens": 440, "cost_usd": 0.009},
    ),
    CaseSummary(name="case_003", success=False, error="Timeout"),
]

# 2. Compute the per-eval aggregates and write the run.
summary = EvalSummary(
    timestamp="2026-04-07T10:30:00Z",
    aggregates=compute_aggregates(cases),
    cases=cases,
)

save_eval_results(
    results_dir="./tests/test-results/evals",
    run_id="2026-04-07_103000",
    eval_name="my_eval",
    summary=summary,
    outputs={
        "case_001": {"answer": "...", "input_tokens": 1234, "output_tokens": 567, "cost_usd": 0.012},
        "case_002": {"answer": "...", "input_tokens": 980, "output_tokens": 440, "cost_usd": 0.009},
    },
    run=RunMetadata(timestamp="2026-04-07T10:30:00Z", git_commit="abc1234"),
)

That writes:

tests/test-results/evals/2026-04-07_103000/
├── run.json
└── my_eval/
    ├── summary.json
    └── outputs/
        ├── case_001.json
        └── case_002.json

Open the viewer and the run shows up.

Token usage

TokenUsage is a normal Pydantic model with __add__ so you can sum across cases or across model calls:

from evals_viewer_io import TokenUsage

opus_call = TokenUsage(input_tokens=1200, output_tokens=300, cost_usd=0.018)
haiku_call = TokenUsage(input_tokens=800, output_tokens=200, cost_usd=0.0009)

# Per-model breakdown for one case
case_total = TokenUsage(
    input_tokens=opus_call.input_tokens + haiku_call.input_tokens,
    output_tokens=opus_call.output_tokens + haiku_call.output_tokens,
    cost_usd=(opus_call.cost_usd or 0) + (haiku_call.cost_usd or 0),
    usage_by_model={"opus": opus_call, "haiku": haiku_call},
)

# Or just use sum() across multiple cases:
total = sum([case1_usage, case2_usage, case3_usage])

The viewer reads input_tokens, output_tokens, cost_usd, and usage_by_model from both each case's full output JSON and from the per-case row in summary.json's output_summary.

Pydantic-AI adapter

If you use pydantic-ai, there's a one-liner to convert its Usage / RunUsage objects (which use request_tokens / response_tokens rather than input / output):

from evals_viewer_io import TokenUsage

usage = TokenUsage.from_pydantic_ai(result.usage(), cost_usd=my_cost_calc(result))

The adapter uses getattr so this package never imports pydantic-ai itself. Other frameworks (OpenAI SDK, Anthropic SDK, …) can be mapped just as easily — TokenUsage(input_tokens=resp.usage.prompt_tokens, output_tokens=resp.usage.completion_tokens) etc.

Cost is the caller's responsibility. Pricing tables go stale fast and don't belong in this package.

Aggregating tokens across cases

from evals_viewer_io import compute_token_totals

totals = compute_token_totals(cases)
print(totals.input_tokens, totals.output_tokens, totals.cost_usd)
print(totals.usage_by_model)  # per-model breakdown summed across all cases

The function reads input_tokens / output_tokens / cost_usd / usage_by_model from each case's output_summary. Cases that don't have those fields contribute zero.

pytest fixture

# tests/conftest.py
from evals_viewer_io.pytest import eval_run_dir  # noqa: F401

# tests/test_my_eval.py
def test_my_eval(eval_run_dir):
    # eval_run_dir is a pathlib.Path under EVALS_RESULTS_DIR (or a tmp dir),
    # and run.json has already been written.
    ...
    save_eval_results(
        results_dir=eval_run_dir.parent,
        run_id=eval_run_dir.name,
        eval_name="my_eval",
        summary=summary,
        outputs=outputs,
    )

Set EVALS_RESULTS_DIR=tests/test-results/evals (or wherever your project keeps them) so the run lands somewhere the viewer can find.

What this package deliberately does not do

This is intentionally a small package — schemas plus the smallest set of helpers that every consumer would need to write themselves. It does not include:

• Token field extraction from arbitrary model outputs. Different LLM SDKs name fields differently; the caller knows their own output schema.
• A pricing table. Costs are pricing × tokens; pricing changes weekly. You compute it, you pass it in via cost_usd.
• Pydantic→dict serialization. If your case output is a Pydantic model, call .model_dump() yourself before passing it to save_eval_results. Hiding that behind a wrapper would just suppress errors.
• Coupling to a specific eval framework like pydantic-evals or inspect_ai. The writer takes plain dicts. Frameworks can be added as adapters when there's demand.
• Schema versioning. The on-disk format is forward-compatible by design (extra="allow" everywhere). If a breaking change ever lands, that's the time for a schema_version field, not now.

On-disk contract

See docs/data-layout.md in the monorepo for the full directory tree and per-file schemas. The TL;DR:

{results_dir}/{run_id}/
├── run.json                       (RunMetadata)
└── {eval_name}/
    ├── summary.json               (EvalSummary: aggregates + per-case rows)
    ├── outputs/{case_name}.json   (full per-case output)
    ├── inputs/{case_name}.json    (optional; saved input fixture)
    └── case-scores/{case_name}.json (optional; per-question scores)

License

MIT