Hello World Walkthrough

This tutorial mirrors examples/01_hello_world.py.

Run It

uv run python examples/01_hello_world.py

Output:

{'model_id': 'demo-model', 'slice_id': 'arithmetic', 'metric_id': 'exact_match', 'source': 'synthetic', 'prompt_variant_id': 'qa-default', 'mean': 1.0, 'count': 1}

What To Notice

• ArithmeticDatasetProvider.scan(...) receives both slice_spec and query
• the benchmark declares semantic dimensions on the slice
• prompt selection is explicit through prompt_variant_ids=["qa-default"]
• the read side is aggregation-first through result.aggregate(...)

Full Script

"""Minimal benchmark-first Themis run."""

from pathlib import Path

from themis import ( BenchmarkSpec, DatasetQuerySpec, ExecutionPolicySpec, InferenceGridSpec, InferenceParamsSpec, ModelSpec, Orchestrator, PluginRegistry, ProjectSpec, PromptMessage, PromptVariantSpec, ScoreSpec, SliceSpec, StorageSpec, ) from themis.contracts.protocols import InferenceResult from themis.records import InferenceRecord, MetricScore from themis.specs import DatasetSpec, GenerationSpec from themis.types.enums import CompressionCodec, DatasetSource, PromptRole

class ArithmeticDatasetProvider: def scan(self, slice_spec, query): del slice_spec, query return [{"item_id": "item-1", "question": "2 + 2", "answer": "4"}]

class DemoEngine: def infer(self, trial, context, runtime): del trial, runtime return InferenceResult( inference=InferenceRecord( spec_hash=f"inf_{context['item_id']}", raw_text=str(context["answer"]), ) )

class ExactMatchMetric: def score(self, trial, candidate, context): del trial actual = candidate.inference.raw_text if candidate.inference else "" return MetricScore( metric_id="exact_match", value=float(actual == context["answer"]), )

def main() -> None: registry = PluginRegistry() registry.register_inference_engine("demo", DemoEngine()) registry.register_metric("exact_match", ExactMatchMetric())

project = ProjectSpec(
    project_name="hello-world-benchmark",
    researcher_id="examples",
    global_seed=7,
    storage=StorageSpec(
        root_dir=str(Path(".cache/themis-examples/01-hello-world-benchmark-first")),
        compression=CompressionCodec.NONE,
    ),
    execution_policy=ExecutionPolicySpec(),
)
benchmark = BenchmarkSpec(
    benchmark_id="hello-world",
    models=[ModelSpec(model_id="demo-model", provider="demo")],
    slices=[
        SliceSpec(
            slice_id="arithmetic",
            dataset=DatasetSpec(source=DatasetSource.MEMORY),
            dataset_query=DatasetQuerySpec.subset(1, seed=7),
            dimensions={"source": "synthetic", "format": "qa"},
            prompt_variant_ids=["qa-default"],
            generation=GenerationSpec(),
            scores=[ScoreSpec(name="default", metrics=["exact_match"])],
        )
    ],
    prompt_variants=[
        PromptVariantSpec(
            id="qa-default",
            family="qa",
            messages=[
                PromptMessage(
                    role=PromptRole.USER,
                    content="Question: {item.question}",
                )
            ],
        )
    ],
    inference_grid=InferenceGridSpec(params=[InferenceParamsSpec(max_tokens=32)]),
)

orchestrator = Orchestrator.from_project_spec(
    project,
    registry=registry,
    dataset_provider=ArithmeticDatasetProvider(),
)
result = orchestrator.run_benchmark(benchmark)

for row in result.aggregate(
    group_by=["model_id", "slice_id", "metric_id", "source", "prompt_variant_id"]
):
    print(row)

if name == "main": main()