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43 lines
2.2 KiB
Text
43 lines
2.2 KiB
Text
[[quickstart]]
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= Quick starts
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Use these quick starts to get hands-on experience with the {es} APIs.
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Unless otherwise noted, these examples will use queries written in <<query-dsl,Query DSL>> syntax.
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[discrete]
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[[quickstart-requirements]]
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== Requirements
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You'll need a running {es} cluster, together with {kib} to use the Dev Tools API Console.
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Run the following command in your terminal to set up a <<run-elasticsearch-locally,single-node local cluster in Docker>>:
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[source,sh]
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----
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curl -fsSL https://elastic.co/start-local | sh
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----
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// NOTCONSOLE
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Alternatively, refer to our <<elasticsearch-intro-deploy,other deployment options>>.
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[discrete]
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[[quickstart-list]]
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== Hands-on quick starts
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* <<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.
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* <<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.
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* <<esql-getting-started>>: Learn how to query and aggregate your data using {esql}.
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* <<semantic-search-semantic-text, Semantic search>>: Learn how to create embeddings for your data with `semantic_text` and query using the `semantic` query.
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** <<semantic-text-hybrid-search, Hybrid search with `semantic_text`>>: Learn how to combine semantic search with full-text search.
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* <<bring-your-own-vectors, Bring your own dense vector embeddings>>: Learn how to ingest dense vector embeddings into {es}.
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.Working in Python
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******************
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If you're interested in using {es} with Python, check out Elastic Search Labs:
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* 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].
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* 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.
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******************
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include::getting-started.asciidoc[]
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include::full-text-filtering-tutorial.asciidoc[]
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