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enhance: make initial batch_size configurable #129

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11 changes: 10 additions & 1 deletion mostlyai/mock/core.py
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Original file line number Diff line number Diff line change
Expand Up @@ -250,6 +250,7 @@ async def _sample_table(
llm_config: LLMConfig,
config: MockConfig,
progress_callback: Callable[..., Awaitable[None]] | None = None,
batch_size = None,
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@cursor cursor Bot Feb 20, 2026

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Missing type annotation on batch_size parameter

Low Severity

The batch_size parameter in _sample_table is declared as batch_size = None without a type annotation, while every other function in the call chain (_sample_common, sample, _asample, _create_table_rows_generator) consistently annotates it as batch_size: int | None. This inconsistency reduces readability and breaks the typing contract across the codebase.

Fix in Cursor Fix in Web

) -> pd.DataFrame:
# provide a default progress callback if none is provided
if progress_callback is None:
Expand Down Expand Up @@ -282,6 +283,7 @@ async def default_progress_callback(**kwargs):
n_workers=n_workers,
llm_config=llm_config,
progress_callback=progress_callback,
batch_size=batch_size,
)
table_df = await _convert_table_rows_generator_to_df(
table_rows_generator=table_rows_generator,
Expand Down Expand Up @@ -443,6 +445,7 @@ def _create_table_prompt(
if n_rows is not None:
prompt += f"Number of data rows to {verb}: `{n_rows}`.\n\n"


if target_primary_key is not None:
prompt += f"Add prefix to all values of Target Table Primary Key. The prefix is 'B{batch_idx}-'."
prompt += " There is one exception: if primary keys are in existing data, don't add prefix to them."
Expand Down Expand Up @@ -823,8 +826,8 @@ async def _create_table_rows_generator(
n_workers: int,
llm_config: LLMConfig,
progress_callback: Callable[..., Awaitable[None]] | None = None,
batch_size: int | None = 20, # generate 20 root table rows at a time
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@cursor cursor Bot Feb 20, 2026

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Passing None overrides default batch_size of 20

High Severity

When sample() is called without specifying batch_size, the default None is explicitly passed through the entire call chain (sample → _asample → _sample_common → _sample_table → _create_table_rows_generator), overriding the default value of 20 in _create_table_rows_generator. For root tables without existing data or foreign keys, batch_size remains None, causing a TypeError at if ideal_batch_size < batch_size: when comparing int with None. This breaks the default (no batch_size argument) usage path entirely.

Additional Locations (2)

Fix in Cursor Fix in Web

) -> AsyncGenerator[dict]:
batch_size = 20 # generate 20 root table rows at a time

def supports_structured_outputs(model: str) -> bool:
model = model.removeprefix("litellm_proxy/")
Expand Down Expand Up @@ -1260,6 +1263,7 @@ async def _sample_common(
n_workers: int = 10,
return_type: Literal["auto", "dict"] = "auto",
progress_callback: Callable[..., Awaitable[None]] | None = None,
batch_size: int | None = None
):
tables: dict[str, TableConfig] = _harmonize_tables(tables, existing_data)
config = MockConfig(tables)
Expand Down Expand Up @@ -1295,6 +1299,7 @@ async def _sample_common(
llm_config=llm_config,
config=config,
progress_callback=progress_callback,
batch_size=batch_size
)
data[table_name] = df

Expand All @@ -1318,6 +1323,7 @@ def sample(
n_workers: int = 10,
return_type: Literal["auto", "dict"] = "auto",
progress_callback: Callable[..., Awaitable[None]] | None = None,
batch_size: int | None = None
) -> pd.DataFrame | dict[str, pd.DataFrame]:
"""
Generate synthetic data from scratch or enrich existing data with new columns.
Expand Down Expand Up @@ -1605,6 +1611,7 @@ def sample_common_sync(*args, **kwargs) -> pd.DataFrame | dict[str, pd.DataFrame
n_workers=n_workers,
return_type=return_type,
progress_callback=progress_callback,
batch_size=batch_size
)
return future.result()

Expand All @@ -1621,6 +1628,7 @@ async def _asample(
n_workers: int = 10,
return_type: Literal["auto", "dict"] = "auto",
progress_callback: Callable[..., Awaitable[None]] | None = None,
batch_size: int | None = None
) -> pd.DataFrame | dict[str, pd.DataFrame]:
return await _sample_common(
tables=tables,
Expand All @@ -1633,6 +1641,7 @@ async def _asample(
n_workers=n_workers,
return_type=return_type,
progress_callback=progress_callback,
batch_size=batch_size
)


Expand Down