chore: import upstream snapshot with attribution
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---
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title: "Spacy"
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id: integrations-spacy
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description: "Spacy integration for Haystack"
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slug: "/integrations-spacy"
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---
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## haystack_integrations.components.extractors.spacy.named_entity_extractor
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### NamedEntityAnnotation
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Describes a single NER annotation.
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**Parameters:**
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- **entity** (<code>str</code>) – Entity label.
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- **start** (<code>int</code>) – Start index of the entity in the document.
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- **end** (<code>int</code>) – End index of the entity in the document.
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- **score** (<code>float | None</code>) – Score calculated by the model.
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### SpacyNamedEntityExtractor
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Annotates named entities in a collection of documents.
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The component can be used with any [spaCy model](https://spacy.io/models) that contains
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an NER component. Annotations are stored as metadata in the documents.
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Usage example:
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```python
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from haystack import Document
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from haystack_integrations.components.extractors.spacy import SpacyNamedEntityExtractor
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documents = [
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Document(content="I'm Merlin, the happy pig!"),
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Document(content="My name is Clara and I live in Berkeley, California."),
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]
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extractor = SpacyNamedEntityExtractor(model="en_core_web_sm")
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results = extractor.run(documents=documents)["documents"]
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annotations = [SpacyNamedEntityExtractor.get_stored_annotations(doc) for doc in results]
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print(annotations)
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```
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#### __init__
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```python
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__init__(
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*,
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model: str,
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pipeline_kwargs: dict[str, Any] | None = None,
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device: ComponentDevice | None = None
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) -> None
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```
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Create a Named Entity extractor component.
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**Parameters:**
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- **model** (<code>str</code>) – Name of the spaCy model or a path to the model on
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the local disk.
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- **pipeline_kwargs** (<code>dict\[str, Any\] | None</code>) – Keyword arguments passed to the pipeline. The
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pipeline can override these arguments.
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- **device** (<code>ComponentDevice | None</code>) – The device on which the model is loaded. If `None`,
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the default device is automatically selected.
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**Raises:**
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- <code>ValueError</code> – If the device represents multiple devices, which the
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spaCy backend does not support.
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#### warm_up
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```python
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warm_up() -> None
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```
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Initialize the component.
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**Raises:**
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- <code>ComponentError</code> – If the component fails to initialize successfully.
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#### run
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```python
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run(documents: list[Document], batch_size: int = 1) -> dict[str, Any]
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```
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Annotate named entities in each document and store the annotations in the document's metadata.
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**Parameters:**
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- **documents** (<code>list\[Document\]</code>) – Documents to process.
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- **batch_size** (<code>int</code>) – Batch size used for processing the documents.
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**Returns:**
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- <code>dict\[str, Any\]</code> – Processed documents.
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**Raises:**
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- <code>ComponentError</code> – If the model fails to process a document.
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#### to_dict
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```python
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to_dict() -> dict[str, Any]
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```
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Serializes the component to a dictionary.
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**Returns:**
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- <code>dict\[str, Any\]</code> – Dictionary with serialized data.
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#### from_dict
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```python
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from_dict(data: dict[str, Any]) -> SpacyNamedEntityExtractor
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```
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Deserializes the component from a dictionary.
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**Parameters:**
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- **data** (<code>dict\[str, Any\]</code>) – Dictionary to deserialize from.
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**Returns:**
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- <code>SpacyNamedEntityExtractor</code> – Deserialized component.
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#### initialized
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```python
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initialized: bool
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```
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Returns if the extractor is ready to annotate text.
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#### get_stored_annotations
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```python
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get_stored_annotations(
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document: Document,
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) -> list[NamedEntityAnnotation] | None
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```
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Returns the document's named entity annotations stored in its metadata, if any.
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**Parameters:**
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- **document** (<code>Document</code>) – Document whose annotations are to be fetched.
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**Returns:**
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- <code>list\[NamedEntityAnnotation\] | None</code> – The stored annotations.
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