The data loader module provides a standard way to feed evaluation data into tests. Use it to:
  • Build reusable input sources (adapters, files, generators)
  • Parameterize datasets with clear variant labeling
  • Preprocess inputs consistently (e.g., expand multi-turn data)

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Components

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DynamicDataLoader

Uses callables that return lists of EvaluationRow. Each callable becomes a labeled variant. 
from eval_protocol import DynamicDataLoader
from eval_protocol.models import EvaluationRow

def my_generator() -> list[EvaluationRow]:
    # Fetch or generate rows here (adapters, DB, etc.)
    return []

data_loader = DynamicDataLoader(
    generators=[my_generator],
)

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InlineDataLoader

Use when you have rows or raw messages inline. 
from eval_protocol import InlineDataLoader
from eval_protocol.models import EvaluationRow, Message

inline_rows = [
    EvaluationRow(messages=[
        Message(role="user", content="Hello"),
        Message(role="assistant", content="Hi there!"),
    ])
]

loader = InlineDataLoader(rows=inline_rows, id="demo", description="Two-turn chat")

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Preprocessing

All loaders support an optional preprocess_fn applied before returning rows. For example, expand multi-turn traces into multiple test cases: 
from eval_protocol import DynamicDataLoader, multi_turn_assistant_to_ground_truth

DynamicDataLoader(
    generators=[my_generator],
    preprocess_fn=multi_turn_assistant_to_ground_truth,
)

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Using with evaluation_test

from eval_protocol import evaluation_test, SingleTurnRolloutProcessor

@evaluation_test(
    data_loaders=data_loader,
    rollout_processor=SingleTurnRolloutProcessor(),
)
async def test_llm_judge(row: EvaluationRow) -> EvaluationRow:
    return await aha_judge(row)

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Metadata and Variants

Each loader emits one or more variants. For each variant, Eval Protocol stores metadata on every row under row.input_metadata.dataset_info:
  • data_loader_type: loader class (e.g., DynamicDataLoader)
  • data_loader_variant_id: callable name or inline id
  • data_loader_variant_description: docstring/description
  • data_loader_num_rows: original count before preprocessing
  • data_loader_num_rows_after_preprocessing: final count
This enables clear tracking of which inputs produced which results in the UI.

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Example with an Adapter

from eval_protocol import evaluation_test, aha_judge, DynamicDataLoader, SingleTurnRolloutProcessor
from eval_protocol.adapters.langfuse import create_langfuse_adapter

def langfuse_data_generator():
    adapter = create_langfuse_adapter()
    return adapter.get_evaluation_rows(limit=50, sample_size=10)

@evaluation_test(
    data_loaders=DynamicDataLoader(generators=[langfuse_data_generator]),
    rollout_processor=SingleTurnRolloutProcessor(),
)
async def test_llm_judge(row: EvaluationRow) -> EvaluationRow:
    return await aha_judge(row)

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API Reference

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DynamicDataLoader

class DynamicDataLoader(EvaluationDataLoader):
    generators: Sequence[Callable[[], list[EvaluationRow]]]

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InlineDataLoader

class InlineDataLoader(EvaluationDataLoader):
    rows: list[EvaluationRow] | None
    messages: Sequence[list[Message]] | None
    id: str
    description: str | None

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EvaluationDataLoader

class EvaluationDataLoader(ABC):
    preprocess_fn: Callable[[list[EvaluationRow]], list[EvaluationRow]] | None
    def variants(self) -> Sequence[DataLoaderVariant]: ...
    def load(self) -> list[DataLoaderResult]: ...

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Source Code

See the Python source for full details: eval_protocol/data_loader/models.py