initial commit; Claude generated SEO descriptions

qdrant-landing/content/documentation/tutorials-basics/_index.md

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 title: Basics
+short_description: "Beginner Qdrant tutorials: spin up a cluster, ingest datasets, and build your first semantic search application in minutes."
+description: "Start with Qdrant fundamentals: create a collection, load datasets, generate embeddings, and run your first semantic search queries against a vector database."
 weight: 410
 is_empty: false
 aliases:

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

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 title: Hugging Face Dataset Ingestion
+short_description: "Load Hugging Face datasets directly into Qdrant collections, including precomputed embeddings, with batched uploads and streaming."
+description: "Step-by-step guide to ingest Hugging Face datasets into Qdrant collections using the Python SDK, with batching and streaming for large vector workloads."
 hideInSidebar: true
 aliases:
   - /documentation/tutorials/huggingface-datasets/

qdrant-landing/content/documentation/tutorials-basics/search-beginners-local.md

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 title: Semantic Search 101
+short_description: "Build a semantic search engine locally with Qdrant in five minutes using sentence-transformer embeddings and the Python SDK."
+description: "Tutorial: build a local semantic search engine with Qdrant in minutes by encoding text into embeddings and running vector queries with the Python client."
 hideInSidebar: true
 aliases:
   - /documentation/tutorials/mighty.md/

qdrant-landing/content/documentation/tutorials-basics/search-beginners.md

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 title: Semantic Search 101
+short_description: "Run your first semantic search on Qdrant Cloud: create a cluster, upload a small dataset, and query by meaning instead of keywords."
+description: "Step-by-step tutorial: spin up a Qdrant Cloud cluster, create a collection, upload sample data, and run semantic vector search queries in five minutes."
 weight: 4
 aliases:
   - /documentation/tutorials/mighty.md/