aligned.exposed_model.partitioned.PartitionedModel

class documentation

class PartitionedModel(ExposedModel): (source)

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Returns an model that routes the inference request to a new model based on a partition key

```python @model_contract(

input_features=[MyFeature().name], exposed_model=partitioned_on(

"lang", partitions={

"no": openai_embedding("text-embedding-3-large"), "en": openai_embedding("text-embedding-ada-002"),

}, default_partition="no"

),

) class MyEmbedding:

my_entity = Int32().as_entity() name = String() lang = String() embedding = Embedding(1536) predicted_at = EventTimestamp()
embeddings = await store.model(MyEmbedding).predict_over({
"my_entity": [1, 2, 3], "name": ["Hello", "Hei", "Hola"], "lang": ["en", "no", "es"]

}).to_polars() ```

Method needed_configs Undocumented
Async Method needed_entities Undocumented
Async Method needed_features Undocumented
Async Method run_polars Undocumented
Class Variable default_partition Undocumented
Class Variable model_type Undocumented
Class Variable partition_key Undocumented
Class Variable partitions Undocumented
Property as_markdown Undocumented
def needed_configs(self) -> list[ConfigValue]: (source)

Undocumented

async def needed_entities(self, store: ModelFeatureStore) -> set[Feature]: (source)

Undocumented

async def needed_features(self, store: ModelFeatureStore) -> list[FeatureReference]: (source)

Undocumented

async def run_polars(self, values: RetrievalJob, store: ModelFeatureStore) -> pl.DataFrame: (source)

Undocumented

default_partition: str | None = (source)

Undocumented

model_type: str = (source)

Undocumented

partition_key: Feature = (source)

Undocumented

partitions: dict[str, ExposedModel] = (source)

Undocumented

@property
as_markdown: str = (source)

Undocumented