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Merge pull request #216 from lmkerbey-mdb/DOCSP-24786
(DOCSP-24786) Create Atlas Search guides
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‎conf.py‎

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'.. |copy| unicode:: U+000A9',
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'.. |year| replace:: {0}'.format(datetime.date.today().year),
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'.. |ent-build| replace:: MongoDB Enterprise',
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'.. |fts| replace:: Atlas Search',
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])
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source_constants = {

‎snooty.toml‎

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guides-coll = "planets"
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full-java-version = "4.7.0"
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full-dotnet-version = "2.17.0"
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fts = "Atlas Search"

‎source/atlas/sample-data.txt‎

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.. procedure::
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:style: normal
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.. step:: Navigate to the {+db-deployments+} page for your project.
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.. step:: Click on your cluster name.
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.. figure:: /images/sample-data-cluster.png
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:figwidth: 600px
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:alt: sample-data-cluster
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.. step:: Click the :guilabel:`Collections` tab.
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.. figure:: /images/sample-data-tab.png
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:figwidth: 600px
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:alt: sample-data-tab
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.. include:: /includes/navigate_to_collections.rst
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.. step:: Click :guilabel:`Load Sample Dataset`.
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‎source/includes/create_index_sample_sales.rst‎

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.. procedure::
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:style: connected
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.. step:: In the right-hand pane, click ``sample_supplies``,
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then click the ``sales`` collection.
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.. step:: Click :guilabel:`Search Indexes` in the upper right
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of the collection preview pane.
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.. step:: Click
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:guilabel:`Create Search Index`.
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.. step:: Click the :guilabel:`Visual Editor` configuration
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method, then click :guilabel:`Next`.
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.. step:: Review the :guilabel:`Database and Collection` list to
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ensure that ``sample_supplies.sales`` is selected, then click
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:guilabel:`Next`.

‎source/includes/navigate_to_collections.rst‎

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.. step:: Navigate to the {+db-deployments+} page for your project.
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.. step:: Click on your cluster name.
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.. figure:: /images/sample-data-cluster.png
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:figwidth: 600px
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:alt: sample-data-cluster
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.. step:: Click
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the :guilabel:`Collections` tab.
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.. figure:: /images/sample-data-tab.png
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:figwidth: 600px
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:alt: sample-data-tab

‎source/index.txt‎

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.. guide:: /crud/update.txt
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.. guide:: /crud/delete.txt
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.. chapter:: Atlas Search
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:description: Learn how to create Atlas Search indexes
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and run queries to perform relevance-based search on your
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data sets.
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.. guide:: /search/dynamic.txt
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.. guide:: /search/static.txt
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.. guide:: /search/compound.txt
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.. guide:: /search/facet.txt
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..
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.. include:: /chapters/aggregation.rst

‎source/search/compound.txt‎

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:template: guide
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.. _guide-search-compound:
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===================================
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Querying with the Compound Operator
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===================================
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.. default-domain:: mongodb
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.. contents:: On this page
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:local:
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:backlinks: none
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:depth: 1
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:class: singlecol
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Overview
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--------
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.. short-description::
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Retrieve documents in MongoDB with a query on multiple fields.
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In this guide, you will learn how to use multiple search operators in
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a compound query using the aggregation pipeline.
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.. time:: 15
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What You'll Need
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----------------
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- A MongoDB account. See :ref:`guides-create-atlas-account`.
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- An Atlas cluster. See :ref:`guides-create-a-cluster`.
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- Sample datasets :ref:`loaded into your cluster <guides-load-sample-data>`.
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- A search index with default settings and dynamic indexing. See
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:ref:`guides-search-dynamic`.
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Procedure
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---------
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.. procedure::
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:style: normal
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.. include:: /includes/navigate_to_collections.rst
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.. step:: In the right-hand pane, click ``sample_supplies``,
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then click the ``sales`` collection.
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.. step:: Run a query on your index using the Aggregation Pipeline.
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.. procedure::
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:style: connected
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.. step:: Click
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the :guilabel:`Aggregation` tab.
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.. step:: If you do not see a pipeline with an initial
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stage window in the right pane, click
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:guilabel:`Create New` and select :guilabel:`Pipeline`.
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.. step:: In the stage window, click on the dropdown menu
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labeled :guilabel:`Select...` and select the
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:guilabel:`$search` stage.
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.. step:: Replace the placeholder code with the following
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``compound`` operator:
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.. code-block:: json
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{
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"compound": {
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"filter": [{
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"text": {
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"query": "Online",
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"path": "purchaseMethod"
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}
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}],
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"should": [{
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"text": {
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"query": "notepad",
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"path": "items"
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}
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}]
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}
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}
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.. step:: Review the results of the compound search in the
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right-hand pane. Your results should include the fields listed
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in the following sample document.
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.. note::
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The fields will not always appear in the same order
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for every returned document.
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.. code-block:: json
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:copyable: false
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items: Array
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storeLocation: "Denver"
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customer: Object
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couponUsed: false
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purchaseMethod: "Online"
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_id: ObjectID('5bd761dcae323e45a93cd06e')
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saleDate: 2013-11-22T18:49:45.212+00:00
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You can expand ``Array`` and ``Object`` fields to view their
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contents by clicking on them.
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Due to the ``filter`` operator, only records with a
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``purchaseMethod`` value of ``Online`` appear.
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Additionally, due to the ``should`` operator,
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records with an ``items`` value of ``notepad`` score
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higher than those without.
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You now have a ``$search`` aggregation stage with a compound
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search definition.
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Summary
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-------
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If you successfully completed the procedure in this guide, you have
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created an Atlas Search aggregation stage with a compound operator,
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and have seen how different clauses in the operator interact to
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focus your search results across multiple axes. In the next guide, you
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will learn how to group search results using facets.
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.. guide-next::

‎source/search/dynamic.txt‎

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:template: guide
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.. _guides-search-dynamic:
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=====================
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Build a Dynamic Index
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=====================
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.. default-domain:: mongodb
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.. contents:: On this page
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:local:
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:backlinks: none
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:depth: 1
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:class: singlecol
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Overview
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--------
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.. short-description::
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Retrieve documents in MongoDB with full-text search.
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In this guide, you will learn how to create a search index
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for relevance-based search as an alternative to document-based search.
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.. time:: 15
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What You'll Need
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----------------
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- A MongoDB account. See :ref:`guides-create-atlas-account`.
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- An Atlas cluster. See :ref:`guides-create-a-cluster`.
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- Sample datasets :ref:`loaded into your cluster <guides-load-sample-data>`.
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Procedure
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---------
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.. procedure::
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:style: normal
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.. include:: /includes/navigate_to_collections.rst
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.. step:: Create a dynamic search index.
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.. include:: /includes/create_index_sample_sales.rst
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.. step:: Click :guilabel:`Create Search Index`. Wait for the
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:guilabel:`Status` column to read :guilabel:`Active`.
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You now have a search index definition. This definition
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recursively indexes fields of
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:ref:`many common data types <bson-data-chart>` across indexed
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documents.
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.. step:: Click :guilabel:`QUERY`
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in the :guilabel:`Actions` column of your index definition.
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.. step:: Run a query on your index.
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.. procedure::
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:style: connected
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.. step:: Type ``notepad`` into the search bar, then click
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:guilabel:`Search`.
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.. step:: Review the results.
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Your results should include the fields listed in the following
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sample documents.
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.. code-block:: json
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:copyable: false
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SCORE: 0.20185701549053192
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salesDate: 2013-03-07T09:27:58.283+00:00
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items: Array
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storeLocation: "Austin"
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SCORE: 0.20185701549053192
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salesDate: 2013-05-05T19:03:06.358+00:00
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items: Array
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storeLocation: "London"
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SCORE: 0.20185701549053192
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salesDate: 2016-06-20T14:09:52.408+00:00
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items: Array
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storeLocation: "Denver"
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Each record also includes a :ref:`relevance score <scoring-ref>`.
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You can expand ``Array`` and ``Object`` fields to view their
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contents by clicking on them.
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.. step:: Click :guilabel:`Edit Query Syntax`. The modal window
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displays the query sent through the {+fts+} API.
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Summary
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-------
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If you successfully completed the procedure in this guide, you have
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created an Atlas Search index with dynamic field mappings and used it
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to perform a relevance-based query against the
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``sample_supplies.sales`` collection. In the next guide, you will learn
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how to perform a refined search by creating an index with static field
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mappings.
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.. guide-next::

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