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Version: 2.28

Spacy

haystack_integrations.components.extractors.spacy.named_entity_extractor​

NamedEntityAnnotation​

Describes a single NER annotation.

Parameters:

  • entity (str) – Entity label.
  • start (int) – Start index of the entity in the document.
  • end (int) – End index of the entity in the document.
  • score (float | None) – Score calculated by the model.

SpacyNamedEntityExtractor​

Annotates named entities in a collection of documents.

The component can be used with any spaCy model that contains an NER component. Annotations are stored as metadata in the documents.

Usage example:

python
from haystack import Document

from haystack_integrations.components.extractors.spacy import SpacyNamedEntityExtractor

documents = [
    Document(content="I'm Merlin, the happy pig!"),
    Document(content="My name is Clara and I live in Berkeley, California."),
]
extractor = SpacyNamedEntityExtractor(model="en_core_web_sm")
results = extractor.run(documents=documents)["documents"]
annotations = [SpacyNamedEntityExtractor.get_stored_annotations(doc) for doc in results]
print(annotations)

init​

python
__init__(
    *,
    model: str,
    pipeline_kwargs: dict[str, Any] | None = None,
    device: ComponentDevice | None = None
) -> None

Create a Named Entity extractor component.

Parameters:

  • model (str) – Name of the spaCy model or a path to the model on the local disk.
  • pipeline_kwargs (dict[str, Any] | None) – Keyword arguments passed to the pipeline. The pipeline can override these arguments.
  • device (ComponentDevice | None) – The device on which the model is loaded. If None, the default device is automatically selected.

Raises:

  • ValueError – If the device represents multiple devices, which the spaCy backend does not support.

warm_up​

python
warm_up() -> None

Initialize the component.

Raises:

  • ComponentError – If the component fails to initialize successfully.

run​

python
run(documents: list[Document], batch_size: int = 1) -> dict[str, Any]

Annotate named entities in each document and store the annotations in the document's metadata.

Parameters:

  • documents (list[Document]) – Documents to process.
  • batch_size (int) – Batch size used for processing the documents.

Returns:

  • dict[str, Any] – Processed documents.

Raises:

  • ComponentError – If the model fails to process a document.

to_dict​

python
to_dict() -> dict[str, Any]

Serializes the component to a dictionary.

Returns:

  • dict[str, Any] – Dictionary with serialized data.

from_dict​

python
from_dict(data: dict[str, Any]) -> SpacyNamedEntityExtractor

Deserializes the component from a dictionary.

Parameters:

  • data (dict[str, Any]) – Dictionary to deserialize from.

Returns:

  • SpacyNamedEntityExtractor – Deserialized component.

initialized​

python
initialized: bool

Returns if the extractor is ready to annotate text.

get_stored_annotations​

python
get_stored_annotations(
    document: Document,
) -> list[NamedEntityAnnotation] | None

Returns the document's named entity annotations stored in its metadata, if any.

Parameters:

  • document (Document) – Document whose annotations are to be fetched.

Returns:

  • list[NamedEntityAnnotation] | None – The stored annotations.