Add Elasticsearch semantic search getting started guide

solutions/search/get-started/quickstarts.md

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+---
+applies_to:
+  serverless: ga
+products:
+  - id: elasticsearch
+---
+
+# {{es}} quickstarts
+
+Our quickstarts reduce your time-to-value by offering a fast path to learn about search strategies.
+Each quickstart provides:
+
+- A highly opinionated, fast path to a specific use case
+- Sensible configuration defaults with minimal configuration required
+
+Follow the steps in these guides to get started quickly:
+
+- [](/solutions/search/get-started/semantic-search.md)
+
+For more advanced API examples, check out [](/solutions/search/api-quickstarts.md).
+
+## Related resources
+
+- [](/get-started/index.md): an introduction to Elastic
+- [](/manage-data/ingest.md): an overview of data ingestion methods

solutions/search/get-started/semantic-search.md

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+---
+navigation_title: Semantic search
+description: An introduction to semantic search in Elasticsearch.
+applies_to:
+  serverless: all
+  stack: all
+products:
+  - id: elasticsearch
+---
+# Get started with semantic search
+
+_Semantic search_ is a type of AI-powered search that enables you to use intuitive language in your queries.
+It returns results that match the meaning of a query, as opposed to literal keyword matches.
+For example, if you want to search for workplace guidelines on a second income, you could search for "side hustle", which is not a term you're likely to see in a formal HR document.
+
+Elastic offers an out-of-the-box Learned Sparse Encoder model ([ELSER](/explore-analyze/machine-learning/nlp/ml-nlp-elser.md)) that outperforms on a variety of data sets, such as financial data, weather records, and question-answer pairs.
+The model is built to provide great relevance across domains, without the need for additional fine tuning.
+For a summary of the use cases and implementation paths, go to [](/solutions/search/ai-search/ai-search.md).
+
+## Prerequisites
+
+::::{tab-set}
+:group: stack-serverless
+
+:::{tab-item} {{serverless-short}}
+:sync: serverless
+
+- An {{es-serverless}} project that is optimized for vectors. To learn more, refer to [](/solutions/search/serverless-elasticsearch-get-started.md).
+- If you want to add sample data, you must have a `developer` or `admin` [predefined role](/deploy-manage/users-roles/cloud-organization/user-roles.md#general-assign-user-roles-table) or an equivalent custom role.
+
+:::
+:::{tab-item} {{stack}}
+:sync: stack
+
+- An {{es}} cluster for storing and searching your data, and {{kib}} for visualizing and managing your data. This quickstart is available for all [Elastic deployment models](/deploy-manage/deploy.md). The quickest way to get started is by using [{{es-serverless}}](/solutions/search/serverless-elasticsearch-get-started.md).
+- If you want to add sample data, you must have authority to create an index, create documents, and view them. To use {{kib}}, you'll also need read authority for the **Discover**, **Dev Tools**, and **{{es}}** features at a minimum. For example, create a custom role that has `all` index privileges for the sample index ("semantic-index") and `read` authority for the specific {{kib}} features. To learn more, refer to [](/deploy-manage/users-roles/cluster-or-deployment-auth/user-roles.md).
+:::
+::::
+<!--
+TBD: It seems like semantic search fields exist in all, so what is the value of this "optimized for vectors" option?
+-->
+
+## Add data
+
+% TBD: What type of data is ideal for semantic search?
+
+::::{tab-set}
+:::{tab-item} {{serverless-short}}
+:sync: serverless
+There are some small data sets available for learning purposes.
+Go to **{{es}} > Home**, select the semantic search workflow, and click **Create a semantic optimized index**.
+
+Follow the instructions to install an {{es}} client and copy the code examples.
+Alternatively, try out the API requests in the [Console](/explore-analyze/query-filter/tools/console.md).
+:::
+:::{tab-item} {{stack}}
+:sync: stack
+There are some small data sets available for learning purposes.
+Go to **{{es}} > Home** and click **Create API index**.
+Select the semantic search workflow in the guided index flow.
+
+Follow the instructions to install an {{es}} client and copy the code examples.
+Alternatively, try out the API requests in the [Console](/explore-analyze/query-filter/tools/console.md).
+:::
+::::
+
+:::::{stepper}
+
+::::{step} Define a semantic text field
+
+You can implement semantic search with varying levels of complexity and customization.
+The recommended method is to use [semantic_text](elasticsearch://reference/elasticsearch/mapping-reference/semantic-text.md) fields in most cases.
+
+The following example creates a mapping for a single field:
+
+```console
+PUT /semantic-index/_mapping
+{
+  "properties": {
+    "text": {
+      "type": "semantic_text"
+    }
+  }
+}
+```
+
+::::
+
+::::{step} Add documents
+
+You can use the Elasticsearch bulk API to ingest an array of documents:
+
+```console
+POST /_bulk?pretty
+{ "index": { "_index": "semantic-index" } }
+{"text":"Yellowstone National Park is one of the largest national parks in the United States. It ranges from the Wyoming to Montana and Idaho, and contains an area of 2,219,791 acress across three different states. Its most famous for hosting the geyser Old Faithful and is centered on the Yellowstone Caldera, the largest super volcano on the American continent. Yellowstone is host to hundreds of species of animal, many of which are endangered or threatened. Most notably, it contains free-ranging herds of bison and elk, alongside bears, cougars and wolves. The national park receives over 4.5 million visitors annually and is a UNESCO World Heritage Site."}
+{ "index": { "_index": "semantic-index" } }
+{"text":"Yosemite National Park is a United States National Park, covering over 750,000 acres of land in California. A UNESCO World Heritage Site, the park is best known for its granite cliffs, waterfalls and giant sequoia trees. Yosemite hosts over four million visitors in most years, with a peak of five million visitors in 2016. The park is home to a diverse range of wildlife, including mule deer, black bears, and the endangered Sierra Nevada bighorn sheep. The park has 1,200 square miles of wilderness, and is a popular destination for rock climbers, with over 3,000 feet of vertical granite to climb. Its most famous and cliff is the El Capitan, a 3,000 feet monolith along its tallest face."}
+{ "index": { "_index": "semantic-index" } }
+{"text":"Rocky Mountain National Park  is one of the most popular national parks in the United States. It receives over 4.5 million visitors annually, and is known for its mountainous terrain, including Longs Peak, which is the highest peak in the park. The park is home to a variety of wildlife, including elk, mule deer, moose, and bighorn sheep. The park is also home to a variety of ecosystems, including montane, subalpine, and alpine tundra. The park is a popular destination for hiking, camping, and wildlife viewing, and is a UNESCO World Heritage Site."}
+```
+
+The bulk ingestion request might take longer than the default request timeout.
+If it times out, wait for the machine learning model loading to complete (typically 1-5 minutes) then retry it.
+
+<!--
+TBD: Describe where to look for the downloaded model in Trained Models?
+-->
+
+What just happened? The content was transformed into a sparse vector, which involves two main steps.
+First, the content is divided into smaller, manageable chunks to ensure that meaningful segments can be more effectively processed and searched. Then each chunk of text is transformed into a sparse vector representation using text expansion techniques.
+By default, `semantic_text` fields leverage ELSER to transform the content.
+
+% TBD: Confirm "Elser model" vs ".elser-2-elasticsearch" terminology.
+
+![Semantic search chunking](https://images.contentstack.io/v3/assets/bltefdd0b53724fa2ce/blt9bbe5e260012b15d/67ffffc8165067d96124b586/animated-gif-semantic-search-chunking.gif)
+
+::::
+::::{step} Explore the data
+To familiarize yourself with this data set, open [Discover](/explore-analyze/discover.md) from the navigation menu or by using the [global search field](/explore-analyze/find-and-organize/find-apps-and-objects.md).
+
+In **Discover**, you can click the expand icon ![double arrow icon to open a flyout with the document details](/explore-analyze/images/kibana-expand-icon-2.png "") to show details about any documents in the table.
+
+:::{image} /solutions/images/serverless-discover-semantic.png
+:screenshot:
+:alt: Discover table view with document expanded
+:::
+
+For more tips, check out [](/explore-analyze/discover/discover-get-started.md).
+::::
+:::::
+<!--
+TBD: When you view these documents in Discover they're shown as having "text" field type instead of "semantic_text" is this right?
+TBD: Should we call out that the KQL filters in Discover don't seem to work against semantic_text fields yet?
+-->
+
+## Test semantic search
+
+{{es}} provides a variety of query languages for interacting with your data.
+For an overview of their features and use cases, check out [](/explore-analyze/query-filter/languages.md).
+
+You can search data that is stored in `semantic_text` fields by using a specific subset of queries, including  `knn`, `match`, `semantic`, and `sparse_vector`. Refer to [Semantic text field type](elasticsearch://reference/elasticsearch/mapping-reference/semantic-text.md) for the complete list.
+
+Let's try out two types of queries in two different languages.
+
+:::::{stepper}
+
+::::{step} Run a semantic query with Query DSL
+
+Open the **{{index-manage-app}}** page from the navigation menu or return to the [guided index flow](/solutions/search/serverless-elasticsearch-get-started.md#elasticsearch-follow-guided-index-flow) to find code examples for searching the sample data.
+
+:::{image} /solutions/images/serverless-index-management-semantic.png
+:screenshot:
+:alt: Index management semantic search workflow
+:::
+
+Try running some queries to check the accuracy and relevance of the search results.
+For example, click **Run in Console** and use some seach terms that you did not see when you explored the documents:
+
+```console
+POST /semantic-index/_search
+{
+  "retriever": {
+    "standard": {
+      "query": {
+        "semantic": {
+          "field": "text",
+          "query": "best park for rappelling"
+        }
+      }
+    }
+  }
+}
+```
+
+This is a [semantic](elasticsearch://reference/query-languages/query-dsl/query-dsl-semantic-query.md) query that is expressed in [Query Domain Specific Language](/explore-analyze/query-filter/languages/querydsl.md) (DSL), which is the primary query language for {{es}}.
+
+The query is translated automatically into a vector representation and runs against the contents of the semantic text field.
+The search results are sorted by a relevance score, which measures how well each document matches the query.
+
+```json
+{
+  "took": 22,
+  "timed_out": false,
+  "_shards": {
+    "total": 3,
+    "successful": 3,
+    "skipped": 0,
+    "failed": 0
+  },
+  "hits": {
+    "total": {
+      "value": 3,
+      "relation": "eq"
+    },
+    "max_score": 11.389743,
+    "hits": [
+      {
+        "_index": "semantic-index",
+        "_id": "Pp0MtJcBZjjo1YKoXkWH",
+        "_score": 11.389743,
+        "_source": {
+          "text": "Rocky Mountain National Park ..."
+  ...
+}
+```
+
+In this example, the document related to Rocky Mountain National park has the highest score.
+::::
+::::{step} Run a match query in ES|QL
+
+Another way to try out semantic search is by using the [match](elasticsearch://reference/query-languages/esql/functions-operators/search-functions.md#esql-match) search function in the [Elasticsearch Query Language](/explore-analyze/query-filter/languages/esql.md) (ES|QL).
+
+Go to **Discover** and select **Try ES|QL** from the application menu bar.
+
+:::{image} /solutions/images/serverless-discover-esql.png
+:screenshot:
+:alt: Run an ES|QL semantic query in Discover
+:::
+
+Copy the following query:
+
+```esql
+FROM semantic-index METADATA _score <1>
+  | WHERE text: "what's the biggest park?" <2>
+  | KEEP text, _score <3>
+  | SORT _score DESC <4>
+  | LIMIT 1000 <5>
+```
+
+1. The FROM source command returns a table of data. Each row in the table represents a document. The `METADATA` clause provides access to the query relevance score, which is a [metadata field](elasticsearch://reference/query-languages/esql/esql-metadata-fields.md).
+2. A simplified syntax for the `MATCH` search function, this command performs a semantic query on the specified field.
+3. The KEEP processing command affects the columns and their order in the results table.
+4. The results are sorted in descending order based on the `_score`.
+5. The maximum number of rows to return.
+
+In this example, the first row in the table is the document that had the highest relevance score for the query.
+
+To learn more, check out [](/explore-analyze/discover/try-esql.md) and [](/solutions/search/esql-for-search.md).
+::::
+:::::
+<!--
+TBD: Provide more information about how to interpret and filter the search results.
+TBD: Include the Elastic Open Web Crawler variation too or point to it in another guide?
+-->
+
+## Next steps
+
+Thanks for taking the time to try out semantic search in {{es-serverless}}.
+For a deeper dive, go to [](/solutions/search/semantic-search.md).
+
+If you want to extend this example, try an index with more fields.
+For example, if you have both a `text` field and a `semantic_text` field, you can combine the strengths of traditional keyword search and advanced semantic search.
+A [hybrid search](/solutions/search/hybrid-semantic-text.md) provides comprehensive search capabilities to find relevant information based on both the raw text and its underlying meaning.
+
+To learn about more options, such as vector and keyword search, go to [](/solutions/search/search-approaches.md).