Add section on sparse embeddings to Fastembed page

integrations/fastembed.md

@@ -3,6 +3,11 @@ layout: integration
 name: FastEmbed
 description: Use the FastEmbed embedding models
 authors:
+  - name: deepset
+    socials:
+      github: deepset-ai
+      twitter: deepset_ai
+      linkedin: deepset-ai
     - name: Nicola Procopio
       socials:
         github: nickprock
@@ -24,14 +29,9 @@ toc: true
 ## 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
+- Light and fast: quantized model weights; ONNX Runtime for inference via Optimum.
+- Performant embedding models: list of [supported models](https://qdrant.github.io/fastembed/examples/Supported_Models/) - including multilingual models.
+- Support for sparse embedding models.
 
 
 ## Installation
@@ -42,14 +42,13 @@ 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`.
+The `fastembed-haystack` integrations provides the following components:
+- `FastembedTextEmbedder`: creates a dense embedding for text (used in query/RAG pipelines).
+- `FastembedDocumentEmbedder`: enriches documents with dense embeddings (used in indexing pipelines).
+- `FastembedSparseTextEmbedder`: creates a sparse embedding for text (used in query/RAG pipelines).
+- `FastembedSparseDocumentEmbedder`: enriches documents with sparse embeddings (used in indexing pipelines).
 
-### Example
-
-Below is the example indexing pipeline with `InMemoryDocumentStore`, `InMemoryEmbeddingRetriever`, `FastembedTextEmbedder` and  `FastembedDocumentEmbedder`:
+### Example with dense embeddings
 
 ```python
 from haystack import Document, Pipeline
@@ -81,6 +80,47 @@ query = "Who supports fastembed?"
 result = query_pipeline.run({"text_embedder": {"text": query}})
 ```
 
+### Example with sparse embeddings
+Currently, Sparse Embedding retrieval is only supported by `QdrantDocumentStore`.
+You can install the package as follows: 
+```bash
+pip install qdrant-haystack
+```
+
+```python
+from haystack import Document, Pipeline
+from haystack_integrations.components.retrievers.qdrant import QdrantSparseEmbeddingRetriever
+from haystack_integrations.document_stores.qdrant import QdrantDocumentStore
+from haystack_integrations.components.embedders.fastembed import FastembedDocumentEmbedder, FastembedTextEmbedder
+
+document_store = QdrantDocumentStore(
+    ":memory:",
+    recreate_index=True,
+    use_sparse_embeddings=True
+)
+
+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."),
+]
+
+sparse_document_embedder = FastembedSparseDocumentEmbedder()
+sparse_document_embedder.warm_up()
+documents_with_sparse_embeddings = sparse_document_embedder.run(documents)["documents"]
+document_store.write_documents(documents_with_sparse_embeddings)
+
+query_pipeline = Pipeline()
+query_pipeline.add_component("sparse_text_embedder", FastembedSparseTextEmbedder())
+query_pipeline.add_component("sparse_retriever", QdrantSparseEmbeddingRetriever(document_store=document_store))
+query_pipeline.connect("sparse_text_embedder.sparse_embedding", "retriever.query_sparse_embedding")
+
+query = "Who supports fastembed?"
+
+result = query_pipeline.run({"sparse_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.