added fastembed integration tile (#188)

* added fastembed

* updated linkedin url

* Update integrations/fastembed.md

Co-authored-by: Bilge Yücel <[email protected]>

* Update integrations/fastembed.md

Co-authored-by: Bilge Yücel <[email protected]>

* Update integrations/fastembed.md

Co-authored-by: Bilge Yücel <[email protected]>

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Co-authored-by: Bilge Yücel <[email protected]>

integrations/fastembed.md

@@ -0,0 +1,86 @@
+---
+layout: integration
+name: FastEmbed
+description: Use the FastEmbed embedding models
+authors:
+    - name: Nicola Procopio
+      socials:
+        github: nickprock
+        linkedin: https://www.linkedin.com/in/nicolaprocopio
+pypi: https://pypi.org/project/fastembed-haystack/
+repo: https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/fastembed
+type: Model Provider
+report_issue: https://github.com/deepset-ai/haystack-core-integrations/issues
+# logo: /logos/your-logo.png
+version: Haystack 2.0
+toc: true
+---
+### **Table of Contents**
+- [Overview](#overview)
+- [Installation](#installation)
+- [Usage](#usage)
+- [License](#license)
+
+## Overview
+[FastEmbed](https://qdrant.github.io/fastembed/) is a lightweight, fast, Python library built for embedding generation.
+
+1. Light & Fast
+  * Quantized model weights
+  * ONNX Runtime for inference via Optimum
+
+2. Accuracy/Recall
+  * Better than OpenAI Ada-002
+  * Default is Flag Embedding, which is top of the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard)
+  * List of [supported models](https://qdrant.github.io/fastembed/examples/Supported_Models/) - including multilingual models
+
+
+## Installation
+
+```bash
+pip install fastembed-haystack
+```
+
+## Usage
+### Components
+
+You can use Fastembed models with two components: [FastembedTextEmbedder](https://github.com/deepset-ai/haystack-core-integrations/blob/main/integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/fastembed_text_embedder.py) and [FastembedDocumentEmbedder](https://github.com/deepset-ai/haystack-core-integrations/blob/main/integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/fastembed_document_embedder.py).
+
+To create semantic embeddings for documents, use `FastembedDocumentEmbedder` in your indexing pipeline. For generating embeddings for queries, use `FastembedTextEmbedder`.
+
+### Example
+
+Below is the example indexing pipeline with `InMemoryDocumentStore`, `InMemoryEmbeddingRetriever`, `FastembedTextEmbedder` and  `FastembedDocumentEmbedder`:
+
+```python
+from haystack import Document, Pipeline
+from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+from haystack_integrations.components.embedders.fastembed import FastembedDocumentEmbedder, FastembedTextEmbedder
+
+document_store = InMemoryDocumentStore(embedding_similarity_function="cosine")
+
+documents = [
+    Document(content="My name is Wolfgang and I live in Berlin"),
+    Document(content="I saw a black horse running"),
+    Document(content="Germany has many big cities"),
+    Document(content="fastembed is supported by and maintained by Qdrant."),
+]
+
+document_embedder = FastembedDocumentEmbedder()
+document_embedder.warm_up()
+documents_with_embeddings = document_embedder.run(documents)["documents"]
+document_store.write_documents(documents_with_embeddings)
+
+query_pipeline = Pipeline()
+query_pipeline.add_component("text_embedder", FastembedTextEmbedder())
+query_pipeline.add_component("retriever", InMemoryEmbeddingRetriever(document_store=document_store))
+query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
+
+query = "Who supports fastembed?"
+
+result = query_pipeline.run({"text_embedder": {"text": query}})
+```
+
+### License
+
+`fastembed-haystack` is distributed under the terms of the [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) license.