Skip to content
Merged
Show file tree
Hide file tree
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 2 additions & 0 deletions docs-website/docs/pipeline-components/extractors.mdx
Original file line number Diff line number Diff line change
Expand Up @@ -13,3 +13,5 @@ slug: "/extractors"
| [NamedEntityExtractor](extractors/namedentityextractor.mdx) | Extracts predefined entities out of a piece of text and writes them into documents' meta field. |
| [PresidioEntityExtractor](extractors/presidioentityextractor.mdx) | Detects PII in Documents and stores entities as structured metadata, without modifying the text. Powered by Microsoft Presidio. |
| [RegexTextExtractor](extractors/regextextextractor.mdx) | Extracts text from chat messages or strings using a regular expression pattern. |
| [SpacyNamedEntityExtractor](extractors/spacynamedentityextractor.mdx) | Extracts predefined entities out of a piece of text and writes them into documents' meta field. Uses a spaCy model. |
| [TransformersNamedEntityExtractor](extractors/transformersnamedentityextractor.mdx) | Extracts predefined entities out of a piece of text and writes them into documents' meta field. Uses a Hugging Face model. |
Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,10 @@ This component extracts predefined entities out of a piece of text and writes th

:::warning[Deprecated]

`NamedEntityExtractor` is deprecated and will be removed in Haystack 3.0. It has moved to the `transformers-haystack` package and was renamed to `TransformersNamedEntityExtractor`. See [TransformersNamedEntityExtractor](transformersnamedentityextractor.mdx) for the updated documentation.
`NamedEntityExtractor` is deprecated and will be removed in Haystack 3.0. It has moved to dedicated Core Integrations packages depending on the backend:

- Hugging Face backend: `transformers-haystack` package, renamed to `TransformersNamedEntityExtractor`. See [TransformersNamedEntityExtractor](transformersnamedentityextractor.mdx) for the updated documentation.
- spaCy backend: `spacy-haystack` package, renamed to `SpacyNamedEntityExtractor`. See [SpacyNamedEntityExtractor](spacynamedentityextractor.mdx) for the updated documentation.

:::

Expand Down Expand Up @@ -65,16 +68,16 @@ documents = [
Document(content="New York State is home to the Empire State Building."),
]

extractor.run(documents)
print(documents)
result = extractor.run(documents)
print(result["documents"])
```

Here is the example result:

```python
[Document(id=aec840d1b6c85609f4f16c3e222a5a25fd8c4c53bd981a40c1268ab9c72cee10, content: 'My name is Clara and I live in Berkeley, California.', meta: {'named_entities': [NamedEntityAnnotation(entity='PER', start=11, end=16, score=0.99641764), NamedEntityAnnotation(entity='LOC', start=31, end=39, score=0.996198), NamedEntityAnnotation(entity='LOC', start=41, end=51, score=0.9990196)]}),
Document(id=98f1dc5d0ccd9d9950cd191d1076db0f7af40c401dd7608f11c90cb3fc38c0c2, content: 'I'm Merlin, the happy pig!', meta: {'named_entities': [NamedEntityAnnotation(entity='PER', start=4, end=10, score=0.99054915)]}),
Document(id=44948ea0eec018b33aceaaedde4616eb9e93ce075e0090ec1613fc145f84b4a9, content: 'New York State is home to the Empire State Building.', meta: {'named_entities': [NamedEntityAnnotation(entity='LOC', start=0, end=14, score=0.9989541), NamedEntityAnnotation(entity='LOC', start=30, end=51, score=0.95746297)]})]
[Document(id=aec840d1b6c85609f4f16c3e222a5a25fd8c4c53bd981a40c1268ab9c72cee10, content: 'My name is Clara and I live in Berkeley, California.', meta: {'named_entities': [NamedEntityAnnotation(entity='PER', start=11, end=16, score=np.float32(0.99641764)), NamedEntityAnnotation(entity='LOC', start=31, end=39, score=np.float32(0.996198)), NamedEntityAnnotation(entity='LOC', start=41, end=51, score=np.float32(0.9990196))]}),
Document(id=98f1dc5d0ccd9d9950cd191d1076db0f7af40c401dd7608f11c90cb3fc38c0c2, content: 'I'm Merlin, the happy pig!', meta: {'named_entities': [NamedEntityAnnotation(entity='PER', start=4, end=10, score=np.float32(0.99054915))]}),
Document(id=44948ea0eec018b33aceaaedde4616eb9e93ce075e0090ec1613fc145f84b4a9, content: 'New York State is home to the Empire State Building.', meta: {'named_entities': [NamedEntityAnnotation(entity='LOC', start=0, end=14, score=np.float32(0.9989541)), NamedEntityAnnotation(entity='LOC', start=30, end=51, score=np.float32(0.9574631))]})]
```

### Get stored annotations
Expand All @@ -93,9 +96,11 @@ documents = [
Document(content="New York State is home to the Empire State Building."),
]

extractor.run(documents)
result = extractor.run(documents)

annotations = [NamedEntityExtractor.get_stored_annotations(doc) for doc in documents]
annotations = [
NamedEntityExtractor.get_stored_annotations(doc) for doc in result["documents"]
]
print(annotations)

# If a Document doesn't contain any annotations, this returns None.
Expand Down
Original file line number Diff line number Diff line change
@@ -0,0 +1,100 @@
---
title: "SpacyNamedEntityExtractor"
id: spacynamedentityextractor
slug: "/spacynamedentityextractor"
description: "This component extracts predefined entities out of a piece of text and writes them into documents’ meta field."
---

# SpacyNamedEntityExtractor

This component extracts predefined entities out of a piece of text and writes them into documents’ meta field.

<div className="key-value-table">

| | |
| --- | --- |
| **Most common position in a pipeline** | After the [PreProcessor](../preprocessors.mdx) in an indexing pipeline or after a [Retriever](../retrievers.mdx) in a query pipeline |
| **Mandatory init variables** | `model`: Name or path of the spaCy model to use |
| **Mandatory run variables** | `documents`: A list of documents |
| **Output variables** | `documents`: A list of documents |
| **API reference** | [Spacy](/reference/integrations-spacy) |
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/spacy |
| **Package name** | `spacy-haystack` |

</div>

## Overview

`SpacyNamedEntityExtractor` looks for entities, which are spans in the text. The extractor automatically recognizes and groups them depending on their class, such as people's names, organizations, locations, and other types. The exact classes are determined by the model that you initialize the component with.

`SpacyNamedEntityExtractor` takes a list of documents as input and returns a list of the same documents with their `meta` data enriched with `NamedEntityAnnotations`. A `NamedEntityAnnotation` consists of the type of the entity and the start and end of the span, for example: `NamedEntityAnnotation(entity='PERSON', start=11, end=16, score=None)`.

When the `SpacyNamedEntityExtractor` is initialized, you need to set a `model`. Optionally, you can set `pipeline_kwargs`, which are then passed on to the spaCy pipeline. You can additionally set the `device` that is used to run the component.

## Usage

Install the `spacy-haystack` package to use the `SpacyNamedEntityExtractor`:

```shell
pip install spacy-haystack
```

The component works with any [spaCy model](https://spacy.io/models) that contains an NER component.

`SpacyNamedEntityExtractor` accepts a list of `Documents` as its input. The extractor annotates the raw text in the documents and stores the annotations in the document's `meta` dictionary under the `named_entities` key.

```python
from haystack.dataclasses import Document
from haystack_integrations.components.extractors.spacy import (
SpacyNamedEntityExtractor,
)

extractor = SpacyNamedEntityExtractor(model="en_core_web_sm")

documents = [
Document(content="My name is Clara and I live in Berkeley, California."),
Document(content="I'm Merlin, the happy pig!"),
Document(content="New York State is home to the Empire State Building."),
]

result = extractor.run(documents)
print(result["documents"])
```

Here is the example result:

```python
[Document(id=aec840d1b6c85609f4f16c3e222a5a25fd8c4c53bd981a40c1268ab9c72cee10, content: 'My name is Clara and I live in Berkeley, California.', meta: {'named_entities': [NamedEntityAnnotation(entity='PERSON', start=11, end=16, score=None), NamedEntityAnnotation(entity='GPE', start=31, end=39, score=None), NamedEntityAnnotation(entity='GPE', start=41, end=51, score=None)]}),
Document(id=98f1dc5d0ccd9d9950cd191d1076db0f7af40c401dd7608f11c90cb3fc38c0c2, content: 'I'm Merlin, the happy pig!', meta: {'named_entities': [NamedEntityAnnotation(entity='PERSON', start=4, end=10, score=None)]}),
Document(id=44948ea0eec018b33aceaaedde4616eb9e93ce075e0090ec1613fc145f84b4a9, content: 'New York State is home to the Empire State Building.', meta: {'named_entities': [NamedEntityAnnotation(entity='GPE', start=0, end=14, score=None), NamedEntityAnnotation(entity='ORG', start=26, end=51, score=None)]})]
```

### Get stored annotations

This component includes the `get_stored_annotations` helper class method that allows you to retrieve the annotations stored in a `Document` transparently:

```python
from haystack.dataclasses import Document
from haystack_integrations.components.extractors.spacy import (
SpacyNamedEntityExtractor,
)

extractor = SpacyNamedEntityExtractor(model="en_core_web_sm")

documents = [
Document(content="My name is Clara and I live in Berkeley, California."),
Document(content="I'm Merlin, the happy pig!"),
Document(content="New York State is home to the Empire State Building."),
]

result = extractor.run(documents)

annotations = [
SpacyNamedEntityExtractor.get_stored_annotations(doc) for doc in result["documents"]
]
print(annotations)

# If a Document doesn't contain any annotations, this returns None.
new_doc = Document(content="In one of many possible worlds...")
assert SpacyNamedEntityExtractor.get_stored_annotations(new_doc) is None
```
Original file line number Diff line number Diff line change
Expand Up @@ -59,16 +59,16 @@ documents = [
Document(content="New York State is home to the Empire State Building."),
]

extractor.run(documents)
print(documents)
result = extractor.run(documents)
print(result["documents"])
```

Here is the example result:

```python
[Document(id=aec840d1b6c85609f4f16c3e222a5a25fd8c4c53bd981a40c1268ab9c72cee10, content: 'My name is Clara and I live in Berkeley, California.', meta: {'named_entities': [NamedEntityAnnotation(entity='PER', start=11, end=16, score=0.99641764), NamedEntityAnnotation(entity='LOC', start=31, end=39, score=0.996198), NamedEntityAnnotation(entity='LOC', start=41, end=51, score=0.9990196)]}),
Document(id=98f1dc5d0ccd9d9950cd191d1076db0f7af40c401dd7608f11c90cb3fc38c0c2, content: 'I'm Merlin, the happy pig!', meta: {'named_entities': [NamedEntityAnnotation(entity='PER', start=4, end=10, score=0.99054915)]}),
Document(id=44948ea0eec018b33aceaaedde4616eb9e93ce075e0090ec1613fc145f84b4a9, content: 'New York State is home to the Empire State Building.', meta: {'named_entities': [NamedEntityAnnotation(entity='LOC', start=0, end=14, score=0.9989541), NamedEntityAnnotation(entity='LOC', start=30, end=51, score=0.95746297)]})]
[Document(id=aec840d1b6c85609f4f16c3e222a5a25fd8c4c53bd981a40c1268ab9c72cee10, content: 'My name is Clara and I live in Berkeley, California.', meta: {'named_entities': [NamedEntityAnnotation(entity='PER', start=11, end=16, score=np.float32(0.99641764)), NamedEntityAnnotation(entity='LOC', start=31, end=39, score=np.float32(0.996198)), NamedEntityAnnotation(entity='LOC', start=41, end=51, score=np.float32(0.9990196))]}),
Document(id=98f1dc5d0ccd9d9950cd191d1076db0f7af40c401dd7608f11c90cb3fc38c0c2, content: 'I'm Merlin, the happy pig!', meta: {'named_entities': [NamedEntityAnnotation(entity='PER', start=4, end=10, score=np.float32(0.99054915))]}),
Document(id=44948ea0eec018b33aceaaedde4616eb9e93ce075e0090ec1613fc145f84b4a9, content: 'New York State is home to the Empire State Building.', meta: {'named_entities': [NamedEntityAnnotation(entity='LOC', start=0, end=14, score=np.float32(0.9989541)), NamedEntityAnnotation(entity='LOC', start=30, end=51, score=np.float32(0.9574631))]})]
```

### Get stored annotations
Expand All @@ -89,10 +89,11 @@ documents = [
Document(content="New York State is home to the Empire State Building."),
]

extractor.run(documents)
result = extractor.run(documents)

annotations = [
TransformersNamedEntityExtractor.get_stored_annotations(doc) for doc in documents
TransformersNamedEntityExtractor.get_stored_annotations(doc)
for doc in result["documents"]
]
print(annotations)

Expand Down
Original file line number Diff line number Diff line change
Expand Up @@ -13,3 +13,5 @@ slug: "/extractors"
| [NamedEntityExtractor](extractors/namedentityextractor.mdx) | Extracts predefined entities out of a piece of text and writes them into documents' meta field. |
| [PresidioEntityExtractor](extractors/presidioentityextractor.mdx) | Detects PII in Documents and stores entities as structured metadata, without modifying the text. Powered by Microsoft Presidio. |
| [RegexTextExtractor](extractors/regextextextractor.mdx) | Extracts text from chat messages or strings using a regular expression pattern. |
| [SpacyNamedEntityExtractor](extractors/spacynamedentityextractor.mdx) | Extracts predefined entities out of a piece of text and writes them into documents' meta field. Uses a spaCy model. |
| [TransformersNamedEntityExtractor](extractors/transformersnamedentityextractor.mdx) | Extracts predefined entities out of a piece of text and writes them into documents' meta field. Uses a Hugging Face model. |
Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,10 @@ This component extracts predefined entities out of a piece of text and writes th

:::warning[Deprecated]

`NamedEntityExtractor` is deprecated and will be removed in Haystack 3.0. It has moved to the `transformers-haystack` package and was renamed to `TransformersNamedEntityExtractor`. See [TransformersNamedEntityExtractor](transformersnamedentityextractor.mdx) for the updated documentation.
`NamedEntityExtractor` is deprecated and will be removed in Haystack 3.0. It has moved to dedicated Core Integrations packages depending on the backend:

- Hugging Face backend: `transformers-haystack` package, renamed to `TransformersNamedEntityExtractor`. See [TransformersNamedEntityExtractor](transformersnamedentityextractor.mdx) for the updated documentation.
- spaCy backend: `spacy-haystack` package, renamed to `SpacyNamedEntityExtractor`. See [SpacyNamedEntityExtractor](spacynamedentityextractor.mdx) for the updated documentation.

:::

Expand Down Expand Up @@ -65,16 +68,16 @@ documents = [
Document(content="New York State is home to the Empire State Building."),
]

extractor.run(documents)
print(documents)
result = extractor.run(documents)
print(result["documents"])
```

Here is the example result:

```python
[Document(id=aec840d1b6c85609f4f16c3e222a5a25fd8c4c53bd981a40c1268ab9c72cee10, content: 'My name is Clara and I live in Berkeley, California.', meta: {'named_entities': [NamedEntityAnnotation(entity='PER', start=11, end=16, score=0.99641764), NamedEntityAnnotation(entity='LOC', start=31, end=39, score=0.996198), NamedEntityAnnotation(entity='LOC', start=41, end=51, score=0.9990196)]}),
Document(id=98f1dc5d0ccd9d9950cd191d1076db0f7af40c401dd7608f11c90cb3fc38c0c2, content: 'I'm Merlin, the happy pig!', meta: {'named_entities': [NamedEntityAnnotation(entity='PER', start=4, end=10, score=0.99054915)]}),
Document(id=44948ea0eec018b33aceaaedde4616eb9e93ce075e0090ec1613fc145f84b4a9, content: 'New York State is home to the Empire State Building.', meta: {'named_entities': [NamedEntityAnnotation(entity='LOC', start=0, end=14, score=0.9989541), NamedEntityAnnotation(entity='LOC', start=30, end=51, score=0.95746297)]})]
[Document(id=aec840d1b6c85609f4f16c3e222a5a25fd8c4c53bd981a40c1268ab9c72cee10, content: 'My name is Clara and I live in Berkeley, California.', meta: {'named_entities': [NamedEntityAnnotation(entity='PER', start=11, end=16, score=np.float32(0.99641764)), NamedEntityAnnotation(entity='LOC', start=31, end=39, score=np.float32(0.996198)), NamedEntityAnnotation(entity='LOC', start=41, end=51, score=np.float32(0.9990196))]}),
Document(id=98f1dc5d0ccd9d9950cd191d1076db0f7af40c401dd7608f11c90cb3fc38c0c2, content: 'I'm Merlin, the happy pig!', meta: {'named_entities': [NamedEntityAnnotation(entity='PER', start=4, end=10, score=np.float32(0.99054915))]}),
Document(id=44948ea0eec018b33aceaaedde4616eb9e93ce075e0090ec1613fc145f84b4a9, content: 'New York State is home to the Empire State Building.', meta: {'named_entities': [NamedEntityAnnotation(entity='LOC', start=0, end=14, score=np.float32(0.9989541)), NamedEntityAnnotation(entity='LOC', start=30, end=51, score=np.float32(0.9574631))]})]
```

### Get stored annotations
Expand All @@ -93,9 +96,11 @@ documents = [
Document(content="New York State is home to the Empire State Building."),
]

extractor.run(documents)
result = extractor.run(documents)

annotations = [NamedEntityExtractor.get_stored_annotations(doc) for doc in documents]
annotations = [
NamedEntityExtractor.get_stored_annotations(doc) for doc in result["documents"]
]
print(annotations)

# If a Document doesn't contain any annotations, this returns None.
Expand Down
Loading
Loading