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Kacper Łukawski bc20106b7d Add "Practice datasets" section (#199)
* Add common datasets

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* Add tutorial on how to use Hugging Face datasets

* Add an information about Qdrant org at Hugging Face

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* Add CTA (Discord)

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* Add link to Discord

* List available datasets on top of the page

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* Fix some typos and improve intro

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Tutorials

These tutorials demonstrate different ways you can build vector search into your applications.

Tutorial Description Stack
Configure Optimal Use Configure Qdrant collections for best resource use. Qdrant
Separate Partitions Serve vectors for many independent users. Qdrant
Bulk Upload Vectors Upload a large scale dataset. Qdrant
Create Dataset Snapshots Turn a dataset into a snapshot by exporting it from a collection. Qdrant
Semantic Search for Beginners Create a simple search engine locally in minutes. Qdrant
Simple Neural Search Build and deploy a neural search that browses startup data. Qdrant, BERT, FastAPI
Aleph Alpha Search Build a multimodal search that combines text and image data. Qdrant, Aleph Alpha
Mighty Semantic Search Build a simple semantic search with an on-demand NLP service. Qdrant, Mighty
Asynchronous API Communicate with Qdrant server asynchronously with Python SDK. Qdrant, Python
Multitenancy with LlamaIndex Handle data coming from multiple users in LlamaIndex. Qdrant, Python, LlamaIndex
HuggingFace datasets Load a Hugging Face dataset to Qdrant Qdrant, Python, datasets
Troubleshooting Solutions to common errors and fixes Qdrant