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‎.github/workflows/backport.yml‎

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name: Backport
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on:
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pull_request_target:
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types:
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- closed
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- labeled
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jobs:
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backport:
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name: Backport
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runs-on: ubuntu-latest
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# Only react to merged PRs for security reasons.
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# See https://docs.github.com/en/actions/using-workflows/events-that-trigger-workflows#pull_request_target.
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if: >
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github.event.pull_request.merged
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&& (
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github.event.action == 'closed'
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|| (
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github.event.action == 'labeled'
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&& contains(github.event.label.name, 'backport')
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)
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)
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steps:
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- uses: tibdex/backport@v2
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with:
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github_token: ${{ secrets.GITHUB_TOKEN }}

‎source/includes/indexes/indexes.py‎

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collection.create_search_index(index)
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# end-create-search-index
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# start-create-vector-search-index
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from pymongo.operations import SearchIndexModel
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search_index_model = SearchIndexModel(
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definition={
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"fields": [
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{
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"type": "vector",
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"numDimensions": <number of dimensions>,
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"path": "<field to index>",
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"similarity": "<select from euclidean, cosine, dotProduct>"
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}
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]
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},
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name="<index name>",
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type="vectorSearch",
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)
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collection.create_search_index(model=search_index_model)
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# end-create-vector-search-index
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# start-create-search-indexes
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index_one = {
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"definition": {
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"mappings": {
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"dynamic": True
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}
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},
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"name": "my_index",
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}
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index_two = {
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"definition": {
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search_idx = SearchIndexModel(
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definition ={
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"mappings": {
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"dynamic": True
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}
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},
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"name": "my_other_index",
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}
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name="my_index",
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)
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vector_idx = SearchIndexModel(
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definition={
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"fields": [
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{
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"type": "vector",
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"numDimensions": <number of dimensions>,
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"path": "<field to index>",
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"similarity": "<select from euclidean, cosine, dotProduct>"
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}
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]
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},
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name="my_vector_index",
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type="vectorSearch",
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)
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indexes = [index_one, index_two]
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indexes = [search_idx, vector_idx]
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collection.create_search_indexes(models=indexes)
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# end-create-search-indexes
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# end-list-search-indexes
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# start-update-search-indexes
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new_index = {
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"definition": {
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"mappings": {
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"dynamic": True
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}
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},
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"name": "my_new_index",
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new_index_definition = {
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"mappings": {
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"dynamic": False
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}
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}
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collection.update_search_index("my_index", new_index)
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# end-update-search-indexes
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# start-update-vector-search-indexes
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new_index_definition = {
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"fields": [
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{
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"type": "vector",
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"numDimensions": 1536,
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"path": "<field to index>",
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"similarity": "euclidean"
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},
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]
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}
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collection.update_search_index("my_vector_index", new_index_definition)
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# end-update-vector-search-indexes
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# start-delete-search-indexes
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collection.drop_index("my_index")
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# end-delete-search-indexes

‎source/includes/run-command.py‎

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# start-hello
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database = client.get_database("my_db")
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hello = database.command("hello")
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print(hello)
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# end-hello
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# start-readpref
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from pymongo.read_preferences import Secondary
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database = client.get_database("my_db")
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hello = database.command("hello", read_preference=Secondary())
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print(hello)
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# end-readpref
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# start-cursor-command
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database = client.get_database("sample_mflix")
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result = database.cursor_command("find", "movies", filter={"runtime": 11})
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print(result.to_list())
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# end-cursor-command
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# start-runcommand
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database = client.get_database("sample_mflix")
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result = database.command("dbStats")
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print(result)
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# end-runcommand

‎source/includes/troubleshooting/tls.rst‎

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- Downgrade Python to v3.9 or earlier
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- Upgrade {+mdb-server+} to v4.2 or later
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- Install {+driver-short+} with the :ref:`OCSP <pymongo-disable-ocsp>` option, which relies on PyOpenSSL
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- Install {+driver-short+} with the :ref:`OCSP <pymongo-disable-ocsp>` option, which relies on PyOpenSSL
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Unsafe Legacy Renegotiation Disabled
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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When using OpenSSL v3 or later, you might see an error similar to the following
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message:
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.. code-block:: python
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[SSL: UNSAFE_LEGACY_RENEGOTIATION_DISABLED] unsafe legacy renegotiation disabled
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These types of errors occur because of outdated or buggy SSL proxies that mistakenly
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enforce legacy `TLS renegotiation <https://www.ibm.com/docs/en/i/7.3?topic=settings-renegotiation>`__.
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To resolve this issue, perform the following steps:
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.. procedure::
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:style: normal
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.. step:: Check OpenSSL Version
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Run the following command to ensure that you have OpenSSL vv3.0.4 or
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later installed:
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.. code-block:: bash
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openssl version
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.. step:: Use the ``UnsafeLegacyServerConnect`` Option
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Create a configuration file that includes the
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``UnsafeLegacyServerConnect`` option. The following example shows how to set
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the ``UnsafeLegacyServerConnect`` option:
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.. code-block:: shell
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:emphasize-lines: 10
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openssl_conf = openssl_init
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[openssl_init]
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ssl_conf = ssl_sect
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[ssl_sect]
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system_default = system_default_sect
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[system_default_sect]
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Options = UnsafeLegacyServerConnect
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.. step:: Run Python with OpenSSL Configuration
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Run Python while setting the ``OPENSSL_CONF`` environment variable to use
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the OpenSSL configuration file you just created:
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.. code-block:: shell
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OPENSSL_CONF=/path/to/the/config/file/above.cnf python ...
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.. important::
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Because setting the ``UnsafeLegacyServerConnect`` option has
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`security implications <https://docs.openssl.org/3.0/man3/SSL_CTX_set_options/#patched-openssl-client-and-unpatched-server>`__,
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use this workaround as a last
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resort to address ``unsafe legacy renegotiation disabled`` errors.

‎source/index.txt‎

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/databases-collections
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/write-operations
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/read
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/run-command
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/indexes
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/aggregation
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/security
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Learn how you can retrieve data from MongoDB in the :ref:`pymongo-read` section.
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Run a Database Command
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----------------------
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Learn how to run a database command in the :ref:`pymongo-run-command` section.
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Optimize Queries with Indexes
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-----------------------------
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‎source/indexes/atlas-search-index.txt‎

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.. _pymongo-atlas-search-index:
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====================
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Atlas Search Indexes
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====================
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======================================
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Atlas Search and Vector Search Indexes
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======================================
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.. contents:: On this page
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:local:
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2020
Overview
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--------
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The Atlas Search feature enables you to perform full-text searches on
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collections hosted on MongoDB Atlas. The indexes specify the behavior of
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You can manage your :atlas:`Atlas Search </atlas-search>` and
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:atlas:`Atlas Vector Search </atlas-vector-search/vector-search-overview/>`
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indexes by using {+driver-short+}. The indexes specify the behavior of
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the search and which fields to index.
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To learn more about MongoDB Atlas Search, see the
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:atlas:`Atlas Search Indexes </atlas-search/atlas-search-overview/>`
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documentation.
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Atlas Search enables you to perform full-text searches on
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collections hosted on MongoDB Atlas. Atlas Search indexes specify the behavior of
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the search and which fields to index.
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Atlas Vector Search enables you to perform semantic searches on vector
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embeddings stored in MongoDB Atlas. Vector Search indexes define the
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indexes for the vector embeddings that you want to query and the boolean,
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date, objectId, numeric, string, or UUID values that you want to use to
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pre-filter your data.
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You can call the following methods on a collection to manage your Atlas Search
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indexes:
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and Vector Search indexes:
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- ``create_search_index()``
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- ``create_search_indexes()``
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You can use the `create_search_index() <{+api-root+}pymongo/collection.html#pymongo.collection.Collection.create_search_index>`__
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and the
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`create_search_indexes() <{+api-root+}pymongo/collection.html#pymongo.collection.Collection.create_search_indexes>`__
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methods to create Atlas Search indexes.
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methods to create Atlas Search indexes or Atlas Vector Search indexes.
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The following code example shows how to create a single index:
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The following code example shows how to create a single Atlas Search index:
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.. literalinclude:: /includes/indexes/indexes.py
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:language: python
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:start-after: start-create-search-index
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:end-before: end-create-search-index
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The following code example shows how to create multiple indexes:
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The following code example shows how to create a single Atlas Vector Search index
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by using the `SearchIndexModel <{+api-root+}pymongo/operations.html#pymongo.operations.SearchIndexModel>`__
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object:
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.. literalinclude:: /includes/indexes/indexes.py
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:language: python
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:start-after: start-create-vector-search-index
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:end-before: end-create-vector-search-index
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You can use the `create_search_indexes() <{+api-root+}pymongo/collection.html#pymongo.collection.Collection.create_search_indexes>`__
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method to create multiple indexes. These indexes can be Atlas Search or
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Vector Search indexes. The ``create_search_indexes()`` method takes a list of
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``SearchIndexModel`` objects that correspond to each index you want to create.
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The following code example shows how to create an Atlas Search index and an Atlas
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Vector Search index:
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.. literalinclude:: /includes/indexes/indexes.py
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:language: python
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You can use the
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`list_search_indexes() <{+api-root+}pymongo/collection.html#pymongo.collection.Collection.list_search_indexes>`__
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method to return the Atlas Search indexes of a collection.
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method to get information about the Atlas Search and Vector Search indexes
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of a collection.
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The following code example shows how to print a list of the search indexes of
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a collection:
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You can use the
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`update_search_index() <{+api-root+}pymongo/collection.html#pymongo.collection.Collection.update_search_index>`__
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method to update an Atlas Search index.
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method to update an Atlas Search or Vector Search index.
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The following code shows how to update a search index:
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The following code example shows how to update an Atlas Search index:
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.. literalinclude:: /includes/indexes/indexes.py
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:language: python
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:dedent:
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:start-after: start-update-search-indexes
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:end-before: end-update-search-indexes
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The following code example shows how to update an Atlas Vector Search index:
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.. literalinclude:: /includes/indexes/indexes.py
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:language: python
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:dedent:
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:start-after: start-update-vector-search-indexes
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:end-before: end-update-vector-search-indexes
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.. _pymongo-atlas-search-index-drop:
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Delete a Search Index
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---------------------
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You can use the
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`drop_search_index() <{+api-root+}pymongo/collection.html#pymongo.collection.Collection.drop_search_index>`__
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method to remove an Atlas Search index.
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method to remove an Atlas Search or Vector Search index.
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The following code shows how to delete a search index from a collection:
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