add Spacy integration (#513)

* add Spacy integration

* use logo in markdown file

* fix svg

* png

Author: anakin87

integrations/spacy.md

@@ -0,0 +1,115 @@
+---
+layout: integration
+name: spaCy
+description: Annotate named entities in your Haystack pipelines with spaCy models
+authors:
+    - name: deepset
+      socials:
+        github: deepset-ai
+        twitter: deepset_ai
+        linkedin: https://www.linkedin.com/company/deepset-ai/
+pypi: https://pypi.org/project/spacy-haystack
+repo: https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/spacy
+type: Custom Component
+report_issue: https://github.com/deepset-ai/haystack-core-integrations/issues
+logo: /logos/spacy.png
+version: Haystack 2.0
+toc: true
+---
+
+### **Table of Contents**
+
+- [Overview](#overview)
+- [Installation](#installation)
+- [Usage](#usage)
+  - [Components](#components)
+  - [Standalone](#standalone)
+  - [Pipeline](#pipeline)
+- [License](#license)
+
+## Overview
+
+[spaCy](https://spacy.io/) is a popular open-source library for Natural Language Processing in Python. The `spacy-haystack` integration provides the `SpacyNamedEntityExtractor`, which uses spaCy models to recognize named entities — such as people, organizations, and locations — and attach them to your documents.
+
+## Installation
+
+Install the `spacy-haystack` package:
+
+```bash
+pip install spacy-haystack
+```
+
+## Usage
+
+### Components
+
+This integration provides one component:
+
+- [`SpacyNamedEntityExtractor`](https://docs.haystack.deepset.ai/docs/spacynamedentityextractor): annotates named entities in documents using a spaCy model.
+
+When initializing it, you must set a `model`. Optionally, you can pass `pipeline_kwargs` (forwarded to the spaCy pipeline) and a `device` to run the model on.
+
+### Standalone
+
+The component works with any [spaCy model](https://spacy.io/models) that contains an NER component. `SpacyNamedEntityExtractor` accepts a list of `Documents`, annotates the text, and stores the result in each document's `meta` under the `named_entities` key. Use the `get_stored_annotations` helper to read the annotations back, and the span offsets to recover the entity text:
+
+```python
+from haystack 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="New York State is home to the Empire State Building."),
+]
+
+results = extractor.run(documents=documents)["documents"]
+
+for doc in results:
+    print(doc.content)
+    for ann in SpacyNamedEntityExtractor.get_stored_annotations(doc):
+        print(f"  {ann.entity}: {doc.content[ann.start:ann.end]}")
+
+# My name is Clara and I live in Berkeley, California.
+#   PERSON: Clara
+#   GPE: Berkeley
+#   GPE: California
+# New York State is home to the Empire State Building.
+#   GPE: New York State
+#   ORG: the Empire State Building
+```
+
+### Pipeline
+
+The most common place for the extractor is right after the preprocessing step of an indexing pipeline, so that the entities are stored alongside the documents you write to a Document Store:
+
+```python
+from haystack import Pipeline
+from haystack.components.converters import TextFileToDocument
+from haystack.components.preprocessors import DocumentSplitter
+from haystack.components.writers import DocumentWriter
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+from haystack_integrations.components.extractors.spacy import SpacyNamedEntityExtractor
+
+document_store = InMemoryDocumentStore()
+
+pipeline = Pipeline()
+pipeline.add_component("converter", TextFileToDocument())
+pipeline.add_component("splitter", DocumentSplitter(split_by="word", split_length=200))
+pipeline.add_component("extractor", SpacyNamedEntityExtractor(model="en_core_web_sm"))
+pipeline.add_component("writer", DocumentWriter(document_store=document_store))
+
+pipeline.connect("converter", "splitter")
+pipeline.connect("splitter", "extractor")
+pipeline.connect("extractor", "writer")
+
+pipeline.run({"converter": {"sources": ["document.txt"]}})
+
+# Each stored document now carries its named entities in meta["named_entities"].
+print(document_store.filter_documents()[0].meta["named_entities"])
+```
+
+## License
+
+`spacy-haystack` is distributed under the terms of the [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) license.