feat: support IndexRange and PartitionBlock seed selection strategy

src/data_designer/config/config_builder.py

@@ -30,7 +30,9 @@
 )
 from .seed import (
     DatastoreSeedDatasetReference,
+    IndexRange,
     LocalSeedDatasetReference,
+    PartitionBlock,
     SamplingStrategy,
     SeedConfig,
     SeedDatasetReference,
@@ -116,7 +118,11 @@ def from_config(cls, config: Union[dict, str, Path, BuilderConfig]) -> Self:
                     datastore_settings=builder_config.datastore_settings,
                 )
                 builder.set_seed_datastore_settings(builder_config.datastore_settings)
-            builder.with_seed_dataset(seed_dataset_reference, sampling_strategy=config.seed_config.sampling_strategy)
+            builder.with_seed_dataset(
+                seed_dataset_reference,
+                sampling_strategy=config.seed_config.sampling_strategy,
+                selection_strategy=config.seed_config.selection_strategy,
+            )
 
         return builder
 
@@ -545,6 +551,7 @@ def with_seed_dataset(
         dataset_reference: SeedDatasetReference,
         *,
         sampling_strategy: SamplingStrategy = SamplingStrategy.ORDERED,
+        selection_strategy: Optional[Union[IndexRange, PartitionBlock]] = None,
     ) -> Self:
         """Add a seed dataset to the current Data Designer configuration.
 
@@ -560,7 +567,11 @@ def with_seed_dataset(
         Returns:
             The current Data Designer config builder instance.
         """
-        self._seed_config = SeedConfig(dataset=dataset_reference.dataset, sampling_strategy=sampling_strategy)
+        self._seed_config = SeedConfig(
+            dataset=dataset_reference.dataset,
+            sampling_strategy=sampling_strategy,
+            selection_strategy=selection_strategy,
+        )
         self.set_seed_datastore_settings(
             dataset_reference.datastore_settings if hasattr(dataset_reference, "datastore_settings") else None
         )

src/data_designer/config/seed.py

@@ -3,8 +3,10 @@
 
 from abc import ABC
 from enum import Enum
+from typing import Optional, Union
 
-from pydantic import field_validator
+from pydantic import Field, field_validator, model_validator
+from typing_extensions import Self
 
 from .base import ConfigBase
 from .datastore import DatastoreSettings
@@ -16,9 +18,97 @@ class SamplingStrategy(str, Enum):
     SHUFFLE = "shuffle"
 
 
+class IndexRange(ConfigBase):
+    start: int = Field(ge=0, description="The start index of the index range (inclusive)")
+    end: int = Field(ge=0, description="The end index of the index range (inclusive)")
+
+    @model_validator(mode="after")
+    def _validate_index_range(self) -> Self:
+        if self.start > self.end:
+            raise ValueError("'start' index must be less than or equal to 'end' index")
+        return self
+
+    @property
+    def size(self) -> int:
+        return self.end - self.start + 1
+
+
+class PartitionBlock(ConfigBase):
+    index: int = Field(default=0, ge=0, description="The index of the partition to sample from")
+    num_partitions: int = Field(default=1, ge=1, description="The total number of partitions in the dataset")
+
+    @model_validator(mode="after")
+    def _validate_partition_block(self) -> Self:
+        if self.index >= self.num_partitions:
+            raise ValueError("'index' must be less than 'num_partitions'")
+        return self
+
+    def to_index_range(self, dataset_size: int) -> IndexRange:
+        partition_size = dataset_size // self.num_partitions
+        start = self.index * partition_size
+
+        # For the last partition, extend to the end of the dataset to include remainder rows
+        if self.index == self.num_partitions - 1:
+            end = dataset_size - 1
+        else:
+            end = ((self.index + 1) * partition_size) - 1
+        return IndexRange(start=start, end=end)
+
+
 class SeedConfig(ConfigBase):
+    """Configuration for sampling data from a seed dataset.
+
+    Args:
+        dataset: Path or identifier for the seed dataset.
+        sampling_strategy: Strategy for how to sample rows from the dataset.
+            - ORDERED: Read rows sequentially in their original order.
+            - SHUFFLE: Randomly shuffle rows before sampling. When used with
+              selection_strategy, shuffling occurs within the selected range/partition.
+        selection_strategy: Optional strategy to select a subset of the dataset.
+            - IndexRange: Select a specific range of indices (e.g., rows 100-200).
+            - PartitionBlock: Select a partition by splitting the dataset into N equal parts.
+              Partition indices are zero-based (index=0 is the first partition, index=1 is
+              the second, etc.).
+
+    Examples:
+        Read rows sequentially from start to end:
+            SeedConfig(dataset="my_data.parquet", sampling_strategy=SamplingStrategy.ORDERED)
+
+        Read rows in random order:
+            SeedConfig(dataset="my_data.parquet", sampling_strategy=SamplingStrategy.SHUFFLE)
+
+        Read specific index range (rows 100-199):
+            SeedConfig(
+                dataset="my_data.parquet",
+                sampling_strategy=SamplingStrategy.ORDERED,
+                selection_strategy=IndexRange(start=100, end=199)
+            )
+
+        Read random rows from a specific index range (shuffles within rows 100-199):
+            SeedConfig(
+                dataset="my_data.parquet",
+                sampling_strategy=SamplingStrategy.SHUFFLE,
+                selection_strategy=IndexRange(start=100, end=199)
+            )
+
+        Read from partition 2 (3rd partition, zero-based) of 5 partitions (20% of dataset):
+            SeedConfig(
+                dataset="my_data.parquet",
+                sampling_strategy=SamplingStrategy.ORDERED,
+                selection_strategy=PartitionBlock(index=2, num_partitions=5)
+            )
+
+        Read shuffled rows from partition 0 of 10 partitions (shuffles within the partition):
+            SeedConfig(
+                dataset="my_data.parquet",
+                sampling_strategy=SamplingStrategy.SHUFFLE,
+                selection_strategy=PartitionBlock(index=0, num_partitions=10)
+            )
+    """
+
     dataset: str
     sampling_strategy: SamplingStrategy = SamplingStrategy.ORDERED
+    selection_strategy: Optional[Union[IndexRange, PartitionBlock]] = None
 
 
 class SeedDatasetReference(ABC, ConfigBase):

src/data_designer/engine/column_generators/generators/seed_dataset.py

@@ -7,12 +7,13 @@
 import duckdb
 import pandas as pd
 
-from data_designer.config.seed import SamplingStrategy
+from data_designer.config.seed import IndexRange, PartitionBlock, SamplingStrategy
 from data_designer.engine.column_generators.generators.base import (
     FromScratchColumnGenerator,
     GenerationStrategy,
     GeneratorMetadata,
 )
+from data_designer.engine.column_generators.utils.errors import SeedDatasetError
 from data_designer.engine.dataset_builders.multi_column_configs import SeedDatasetMultiColumnConfig
 from data_designer.engine.processing.utils import concat_datasets
 from data_designer.engine.resources.resource_provider import ResourceType
@@ -58,19 +59,67 @@ def _initialize(self) -> None:
         self._df_remaining = None
         self._dataset_uri = self.resource_provider.datastore.get_dataset_uri(self.config.dataset)
         self._seed_dataset_size = self.duckdb_conn.execute(f"SELECT COUNT(*) FROM '{self._dataset_uri}'").fetchone()[0]
+        self._index_range = self._resolve_index_range()
+
+    def _validate_selection_strategy(self) -> None:
+        err_msg = None
+        if self.config.selection_strategy is not None:
+            if (
+                isinstance(self.config.selection_strategy, IndexRange)
+                and self.config.selection_strategy.end >= self._seed_dataset_size
+            ):
+                err_msg = f"Selection strategy 'end' index {self.config.selection_strategy.end} is out of bounds for dataset size {self._seed_dataset_size}"
+            elif (
+                isinstance(self.config.selection_strategy, PartitionBlock)
+                and self.config.selection_strategy.num_partitions > self._seed_dataset_size
+            ):
+                err_msg = f"Selection strategy 'num_partitions' {self.config.selection_strategy.num_partitions} is out of bounds for dataset size {self._seed_dataset_size}"
+            if err_msg is not None:
+                raise SeedDatasetError(err_msg)
+
+    def _resolve_index_range(self) -> IndexRange | None:
+        self._validate_selection_strategy()
+        index_range = None
+        if self.config.selection_strategy is not None:
+            if isinstance(self.config.selection_strategy, IndexRange):
+                index_range = self.config.selection_strategy
+            elif isinstance(self.config.selection_strategy, PartitionBlock):
+                index_range = self.config.selection_strategy.to_index_range(self._seed_dataset_size)
+        return index_range
 
     def _reset_batch_reader(self, num_records: int) -> None:
         shuffle = self.config.sampling_strategy == SamplingStrategy.SHUFFLE
         shuffle_query = " ORDER BY RANDOM()" if shuffle else ""
-        self._batch_reader = self.duckdb_conn.query(f"SELECT * FROM '{self._dataset_uri}'{shuffle_query}").record_batch(
-            batch_size=num_records
-        )
+
+        if self._index_range is not None:
+            # Use LIMIT and OFFSET for efficient index range filtering
+            # IndexRange uses 0-based indexing [start, end] inclusive
+            # OFFSET skips the first 'start' rows (0-based)
+            # LIMIT takes 'end - start + 1' rows to include both start and end (inclusive)
+            offset_value = self._index_range.start
+            limit_value = self._index_range.end - self._index_range.start + 1
+            read_query = f"""
+                SELECT * FROM '{self._dataset_uri}'
+                LIMIT {limit_value} OFFSET {offset_value}
+            """
+
+            read_query = f"SELECT * FROM ({read_query}){shuffle_query}"
+        else:
+            read_query = f"SELECT * FROM '{self._dataset_uri}'{shuffle_query}"
+        self._batch_reader = self.duckdb_conn.query(read_query).record_batch(batch_size=num_records)
 
     def _sample_records(self, num_records: int) -> pd.DataFrame:
         logger.info(f"🌱 Sampling {num_records} records from seed dataset")
         logger.info(f"  |-- seed dataset size: {self._seed_dataset_size} records")
         logger.info(f"  |-- sampling strategy: {self.config.sampling_strategy}")
-
+        if self._index_range is not None:
+            if isinstance(self.config.selection_strategy, IndexRange):
+                logger.info(f"  |-- selection: rows [{self._index_range.start} to {self._index_range.end}] inclusive")
+            else:
+                logger.info(
+                    f"  |-- selection: partition {self.config.selection_strategy.index + 1} of {self.config.selection_strategy.num_partitions}"
+                )
+            logger.info(f"  |-- seed dataset size after selection: {self._index_range.size} records")
         df_batch = pd.DataFrame()
         df_sample = pd.DataFrame() if self._df_remaining is None else self._df_remaining
         num_zero_record_responses = 0

src/data_designer/engine/column_generators/utils/errors.py

@@ -8,3 +8,6 @@ class PromptTemplateRenderError(DataDesignerError): ...
 
 
 class ExpressionTemplateRenderError(DataDesignerError): ...
+
+
+class SeedDatasetError(DataDesignerError): ...

src/data_designer/engine/dataset_builders/utils/config_compiler.py

@@ -36,6 +36,7 @@ def compile_dataset_builder_column_configs(config: DataDesignerConfig) -> list[D
                 columns=seed_column_configs,
                 dataset=config.seed_config.dataset,
                 sampling_strategy=config.seed_config.sampling_strategy,
+                selection_strategy=config.seed_config.selection_strategy,
             )
         )
 

src/data_designer/essentials/__init__.py

@@ -49,7 +49,7 @@
     UniformSamplerParams,
     UUIDSamplerParams,
 )
-from ..config.seed import DatastoreSeedDatasetReference, SamplingStrategy, SeedConfig
+from ..config.seed import DatastoreSeedDatasetReference, IndexRange, PartitionBlock, SamplingStrategy, SeedConfig
 from ..config.utils.code_lang import CodeLang
 from ..config.utils.misc import can_run_data_designer_locally
 from ..config.validator_params import (
@@ -89,6 +89,7 @@
     "DropColumnsProcessorConfig",
     "ExpressionColumnConfig",
     "GaussianSamplerParams",
+    "IndexRange",
     "ImageContext",
     "ImageFormat",
     "InferenceParameters",
@@ -104,6 +105,7 @@
     "ModalityContext",
     "ModalityDataType",
     "ModelConfig",
+    "PartitionBlock",
     "PersonSamplerParams",
     "PoissonSamplerParams",
     "ProcessorType",

tests/config/test_seed.py

@@ -0,0 +1,56 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+import pytest
+
+from data_designer.config.seed import IndexRange, PartitionBlock
+
+
+def test_index_range_validation():
+    with pytest.raises(ValueError, match="should be greater than or equal to 0"):
+        IndexRange(start=-1, end=10)
+
+    with pytest.raises(ValueError, match="should be greater than or equal to 0"):
+        IndexRange(start=0, end=-1)
+
+    with pytest.raises(ValueError, match="'start' index must be less than or equal to 'end' index"):
+        IndexRange(start=11, end=10)
+
+
+def test_index_range_size():
+    assert IndexRange(start=0, end=10).size == 11
+    assert IndexRange(start=1, end=10).size == 10
+    assert IndexRange(start=0, end=0).size == 1
+
+
+def test_partition_block_validation():
+    with pytest.raises(ValueError, match="should be greater than or equal to 0"):
+        PartitionBlock(index=-1, num_partitions=10)
+
+    with pytest.raises(ValueError, match="should be greater than or equal to 1"):
+        PartitionBlock(index=0, num_partitions=0)
+
+    with pytest.raises(ValueError, match="'index' must be less than 'num_partitions'"):
+        PartitionBlock(index=10, num_partitions=10)
+
+
+def test_partition_block_to_index_range():
+    index_range = PartitionBlock(index=0, num_partitions=10).to_index_range(101)
+    assert index_range.start == 0
+    assert index_range.end == 9
+    assert index_range.size == 10
+
+    index_range = PartitionBlock(index=1, num_partitions=10).to_index_range(105)
+    assert index_range.start == 10
+    assert index_range.end == 19
+    assert index_range.size == 10
+
+    index_range = PartitionBlock(index=2, num_partitions=10).to_index_range(105)
+    assert index_range.start == 20
+    assert index_range.end == 29
+    assert index_range.size == 10
+
+    index_range = PartitionBlock(index=9, num_partitions=10).to_index_range(105)
+    assert index_range.start == 90
+    assert index_range.end == 104
+    assert index_range.size == 15