Add "Practice datasets" section (#199)

* Add common datasets

* Add instruction for titles

* Add tutorial on how to use Hugging Face datasets

* Add an information about Qdrant org at Hugging Face

* Add links to Hugging Face datasets

* Add preview images

* Add CTA (Discord)

* Fix typo

* Add link to Discord

* List available datasets on top of the page

* Fix some typos and improve intro

* Fix some typos and improve intro

* Add fixed snapshot links

* Add Wolt food dataset

* Add API calls to restore the snapshots

* Adjust the weight of the section

* Reorder tutorials

qdrant-landing/content/documentation/tutorials/huggingface-datasets.md

@@ -0,0 +1,115 @@
+---
+title: Load Hugging Face dataset
+weight: 19
+---
+
+# Loading a dataset from Hugging Face hub
+
+[Hugging Face](https://huggingface.co/) provides a platform for sharing and using ML models and
+datasets. [Qdrant](https://huggingface.co/Qdrant) also publishes datasets along with the
+embeddings that you can use to practice with Qdrant and build your applications based on semantic
+search. **Please [let us know](https://qdrant.to/discord) if you'd like to see a specific dataset!**
+
+## arxiv-titles-instructorxl-embeddings
+
+[This dataset](https://huggingface.co/datasets/Qdrant/arxiv-titles-instructorxl-embeddings) contains 
+embeddings generated from the paper titles only. Each vector has a payload with the title used to 
+create it, along with the DOI (Digital Object Identifier).
+
+```json
+{
+    "title": "Nash Social Welfare for Indivisible Items under Separable, Piecewise-Linear Concave Utilities",
+    "DOI": "1612.05191"
+}
+```
+
+You can find a detailed description of the dataset in the [Practice Datasets](/documentation/datasets/#journal-article-titles) 
+section. If you prefer loading the dataset from a Qdrant snapshot, it also linked there.
+
+Loading the dataset is as simple as using the `load_dataset` function from the `datasets` library:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("Qdrant/arxiv-titles-instructorxl-embeddings")
+```
+
+<aside role="status">The dataset has over 16 GB, so it might take a while to download.</aside>
+
+The dataset contains 2,250,000 vectors. This is how you can check the list of the features in the dataset:
+
+```python
+dataset.features
+```
+
+### Streaming the dataset
+
+Dataset streaming lets you work with a dataset without downloading it. The data is streamed as 
+you iterate over the dataset. You can read more about it in the [Hugging Face 
+documentation](https://huggingface.co/docs/datasets/stream).
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset(
+    "Qdrant/arxiv-titles-instructorxl-embeddings", split="train", streaming=True
+)
+```
+
+### Loading the dataset into Qdrant
+
+You can load the dataset into Qdrant using the [Python SDK](https://github.com/qdrant/qdrant-client).
+The embeddings are already precomputed, so you can store them in a collection, that we're going
+to create in a second:
+
+```python
+from qdrant_client import QdrantClient, models
+
+client = QdrantClient("http://localhost:6333")
+
+client.create_collection(
+    collection_name="arxiv-titles-instructorxl-embeddings",
+    vectors_config=models.VectorParams(
+        size=768,
+        distance=models.Distance.COSINE,
+    ),
+)
+```
+
+It is always a good idea to use batching, while loading a large dataset, so let's do that.
+We are going to need a helper function to split the dataset into batches:
+
+```python
+from itertools import islice
+
+def batched(iterable, n):
+    iterator = iter(iterable)
+    while batch := list(islice(iterator, n)):
+        yield batch
+```
+
+If you are a happy user of Python 3.12+, you can use the [`batched` function from the `itertools`
+](https://docs.python.org/3/library/itertools.html#itertools.batched) package instead.
+
+No matter what Python version you are using, you can use the `upsert` method to load the dataset,
+batch by batch, into Qdrant:
+
+```python
+batch_size = 100
+
+for batch in batched(dataset, batch_size):
+    ids = [point.pop("id") for point in batch]
+    vectors = [point.pop("vector") for point in batch]
+
+    client.upsert(
+        collection_name="arxiv-titles-instructorxl-embeddings",
+        points=models.Batch(
+            ids=ids,
+            vectors=vectors,
+            payloads=batch,
+        ),
+    )
+```
+
+Your collection is ready to be used for search! Please [let us know using Discord](https://qdrant.to/discord) 
+if you would like to see more datasets published on Hugging Face hub.