feat: Add optimum-haystack (#199)

* feat: Add `optimum-haystack`

* Add license section

integrations/optimum.md

@@ -0,0 +1,83 @@
+---
+layout: integration
+name: Optimum
+description: High-performance inference using Hugging Face Optimum
+authors:
+  - name: deepset
+    socials:
+      github: deepset-ai
+      twitter: deepset_ai
+      linkedin: deepset-ai
+pypi: https://pypi.org/project/optimum-haystack
+repo: https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/optimum
+type: Model Provider
+report_issue: https://github.com/deepset-ai/haystack/issues
+logo: /logos/huggingface.png
+version: Haystack 2.0
+toc: true
+---
+
+### **Table of Contents**
+
+- [Overview](#overview)
+- [Installation](#installation)
+- [Usage](#usage)
+  - [Components](#components)
+- [License](#license)
+
+## Overview
+
+[Hugging Face Optimum](https://huggingface.co/docs/optimum/index) is an extension of the
+[Transformers](https://huggingface.co/docs/transformers/index) library that provides a set
+of performance optimization tools to train and run models on targeted hardware with maximum
+efficiency. Using Optimum, you can leverage the [ONNX Runtime](https://onnxruntime.ai/)
+to automatically export models from the [Hugging Face Model Hub](https://huggingface.co/docs/hub/en/models-the-hub) and deploy them in pipelines to achieve significant improvements in performance.
+
+## Installation
+
+```bash
+pip install optimum-haystack
+```
+
+## Usage
+
+### Components
+
+This integration introduces two components: [OptimumTextEmbedder](https://github.com/deepset-ai/haystack-core-integrations/blob/main/integrations/optimum/src/haystack_integrations/components/embedders/optimum/optimum_text_embedder.py) and [OptimumDocumentEmbedder](https://github.com/deepset-ai/haystack-core-integrations/blob/main/integrations/optimum/src/haystack_integrations/components/embedders/optimum/optimum_document_embedder.py).
+
+To create semantic embeddings for documents, use `OptimumDocumentEmbedder` in your indexing pipeline. For generating embeddings for queries, use `OptimumTextEmbedder`.
+
+Below is the example indexing pipeline with `InMemoryDocumentStore`, `OptimumDocumentEmbedder` and `DocumentWriter`:
+
+```python
+from haystack import Document, Pipeline
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+from haystack.components.writers import DocumentWriter
+from haystack_integrations.components.embedders.optimum import (
+    OptimumDocumentEmbedder,
+    OptimumEmbedderPooling,
+)
+
+
+document_store = InMemoryDocumentStore(embedding_similarity_function="cosine")
+
+documents = [Document(content="I enjoy programming in Python"),
+             Document(content="My city does not get snow in winter"),
+             Document(content="Japanese diet is well known for being good for your health"),
+             Document(content="Thomas is injured and can't play sports")]
+
+indexing_pipeline = Pipeline()
+indexing_pipeline.add_component("embedder", OptimumDocumentEmbedder(
+    model="intfloat/e5-base-v2",
+    normalize_embeddings=True,
+    pooling_mode=OptimumEmbedderPooling.MEAN,
+))
+indexing_pipeline.add_component("writer", DocumentWriter(document_store=document_store))
+indexing_pipeline.connect("embedder", "writer")
+
+indexing_pipeline.run({"embedder": {"documents": documents}})
+```
+
+## License
+
+`optimum-haystack` is distributed under the terms of the [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) license.