* <<getting-started,Basics: Index and search data using {es} APIs>>. Learn about indices, documents, and mappings, and perform a basic search using the Query DSL.
* <<full-text-filter-tutorial, Basics: Full-text search and filtering>>. Learn about different options for querying data, including full-text search and filtering, using the Query DSL.
* <<aggregations-tutorial, Basics: Analyze ecommerce data with aggregations>>. Learn how to analyze data using different types of aggregations, including metrics, buckets, and pipelines.
* <<semantic-search-semantic-text, Semantic search>>: Learn how to create embeddings for your data with `semantic_text` and query using the `semantic` query.
** <<semantic-text-hybrid-search, Hybrid search with `semantic_text`>>: Learn how to combine semantic search with full-text search.
* <<bring-your-own-vectors, Bring your own dense vector embeddings>>: Learn how to ingest dense vector embeddings into {es}.
If you're interested in using {es} with Python, check out Elastic Search Labs:
* https://github.com/elastic/elasticsearch-labs[`elasticsearch-labs` repository]: Contains a range of Python https://github.com/elastic/elasticsearch-labs/tree/main/notebooks[notebooks] and https://github.com/elastic/elasticsearch-labs/tree/main/example-apps[example apps].
* https://www.elastic.co/search-labs/tutorials/search-tutorial/welcome[Tutorial]: This walks you through building a complete search solution with {es} from the ground up using Flask.