# EntityAggregatorFilter **`class EntityAggregatorFilter(config)`** Bases: [`Filter`](squirro.lib.nlp.steps.filters.Filter.md#squirro.lib.nlp.steps.filters.Filter) The entity aggregator [`Filter`](squirro.lib.nlp.steps.filters.Filter.md#squirro.lib.nlp.steps.filters.Filter) creates a structured prediction field based on detected entities. This filter examines entities that have been detected in previous pipeline steps (typically from NER or entity extraction steps) and aggregates them into a structured prediction field with entity types as keys and unique entity names as values. **Input** - List[InputEntity]: Entities conforming to the InputEntity Pydantic model **Output** - PredictionOutput: Structured prediction field conforming to PredictionOutput model **Data Models:** - InputEntity: Defines the expected structure for input entities with type, name, and extracts - PredictionOutput: Defines the output structure as {entity_type: [unique_entity_names]} Parameters - `entities_field` ([`str`](https://docs.python.org/3.11/library/stdtypes.html#str)`, ``'entities'`) – Field containing detected entities - `output_field` ([`str`](https://docs.python.org/3.11/library/stdtypes.html#str)`, ``'prediction'`) – Field to write resulting prediction structure - `entity_types` ([`list`](https://docs.python.org/3.11/library/stdtypes.html#list)`, ``None`) – Optional list to filter which entity types to include. If None, all entity types will be used. **Example** ```json { "step": "filter", "type": "entity_aggregator", "entities_field": "entities", "output_field": "prediction", "entity_types": ["PERSON", "ORG", "GPE"] } ``` Methods SummaryMethods Documentation **`process_doc(doc)`** Process a document and generate prediction structure from entities Parameters `doc` – Document with entities field conforming to List[InputEntity] structure Returns Document with prediction field conforming to PredictionOutput structure