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   <div class="section" id="module-apache_beam.transforms.enrichment_handlers.vertex_ai_feature_store">
 <span id="apache-beam-transforms-enrichment-handlers-vertex-ai-feature-store-module"></span><h1>apache_beam.transforms.enrichment_handlers.vertex_ai_feature_store module<a class="headerlink" href="#module-apache_beam.transforms.enrichment_handlers.vertex_ai_feature_store" title="Permalink to this headline">¶</a></h1>
 <dl class="class">
 <dt id="apache_beam.transforms.enrichment_handlers.vertex_ai_feature_store.VertexAIFeatureStoreEnrichmentHandler">
 <em class="property">class </em><code class="descclassname">apache_beam.transforms.enrichment_handlers.vertex_ai_feature_store.</code><code class="descname">VertexAIFeatureStoreEnrichmentHandler</code><span class="sig-paren">(</span><em>project: str</em>, <em>location: str</em>, <em>api_endpoint: str</em>, <em>feature_store_name: str</em>, <em>feature_view_name: str</em>, <em>row_key: str</em>, <em>*</em>, <em>exception_level: apache_beam.transforms.enrichment_handlers.utils.ExceptionLevel = &lt;ExceptionLevel.WARN: 1&gt;</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/transforms/enrichment_handlers/vertex_ai_feature_store.html#VertexAIFeatureStoreEnrichmentHandler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.transforms.enrichment_handlers.vertex_ai_feature_store.VertexAIFeatureStoreEnrichmentHandler" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="apache_beam.transforms.enrichment.html#apache_beam.transforms.enrichment.EnrichmentSourceHandler" title="apache_beam.transforms.enrichment.EnrichmentSourceHandler"><code class="xref py py-class docutils literal notranslate"><span class="pre">apache_beam.transforms.enrichment.EnrichmentSourceHandler</span></code></a></p>
 <p>Enrichment handler to interact with Vertex AI Feature Store.</p>
 <p>Use this handler with <a class="reference internal" href="apache_beam.transforms.enrichment.html#apache_beam.transforms.enrichment.Enrichment" title="apache_beam.transforms.enrichment.Enrichment"><code class="xref py py-class docutils literal notranslate"><span class="pre">apache_beam.transforms.enrichment.Enrichment</span></code></a>
 transform when the Vertex AI Feature Store is set up for
 Bigtable Online serving.</p>
 <p>With the Bigtable Online serving approach, the client fetches all the
 available features for an entity-id. The entity-id is extracted from the
 <cite>row_key</cite> field in the input <cite>beam.Row</cite> object. To filter the features to
 enrich, use the <cite>join_fn</cite> param in
 <a class="reference internal" href="apache_beam.transforms.enrichment.html#apache_beam.transforms.enrichment.Enrichment" title="apache_beam.transforms.enrichment.Enrichment"><code class="xref py py-class docutils literal notranslate"><span class="pre">apache_beam.transforms.enrichment.Enrichment</span></code></a>.</p>
 <dl class="docutils">
 <dt><strong>NOTE:</strong> The default severity to report exceptions is logging a warning. For</dt>
 <dd>this handler, Vertex AI client returns the same exception
 <cite>Requested entity was not found</cite> even though the feature store doesn’t
 exist. So make sure the feature store instance exists or set
 <cite>exception_level</cite> as <cite>ExceptionLevel.RAISE</cite>.</dd>
 </dl>
 <p>Initializes an instance of <cite>VertexAIFeatureStoreEnrichmentHandler</cite>.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
 <li><strong>project</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – The GCP project-id for the Vertex AI Feature Store.</li>
 <li><strong>location</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – The region for the Vertex AI Feature Store.</li>
 <li><strong>api_endpoint</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – The API endpoint for the Vertex AI Feature Store.</li>
 <li><strong>feature_store_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – The name of the Vertex AI Feature Store.</li>
 <li><strong>feature_view_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – The name of the feature view within the
 Feature Store.</li>
 <li><strong>row_key</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – The row key field name containing the unique id
 for the feature values.</li>
 <li><strong>exception_level</strong> – a <cite>enum.Enum</cite> value from
 <cite>apache_beam.transforms.enrichment_handlers.utils.ExceptionLevel</cite>
 to set the level when an empty row is returned from the BigTable query.
 Defaults to <cite>ExceptionLevel.WARN</cite>.</li>
 <li><strong>kwargs</strong> – Optional keyword arguments to configure the
 <cite>aiplatform.gapic.FeatureOnlineStoreServiceClient</cite>.</li>
 </ul>
 </td>
 </tr>
 </tbody>
 </table>
 <dl class="method">
 <dt id="apache_beam.transforms.enrichment_handlers.vertex_ai_feature_store.VertexAIFeatureStoreEnrichmentHandler.get_cache_key">
 <code class="descname">get_cache_key</code><span class="sig-paren">(</span><em>request: apache_beam.pvalue.Row</em><span class="sig-paren">)</span> &#x2192; str<a class="reference internal" href="_modules/apache_beam/transforms/enrichment_handlers/vertex_ai_feature_store.html#VertexAIFeatureStoreEnrichmentHandler.get_cache_key"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.transforms.enrichment_handlers.vertex_ai_feature_store.VertexAIFeatureStoreEnrichmentHandler.get_cache_key" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a string formatted with unique entity-id for the feature values.</p>
 </dd></dl>

 </dd></dl>

 <dl class="class">
 <dt id="apache_beam.transforms.enrichment_handlers.vertex_ai_feature_store.VertexAIFeatureStoreLegacyEnrichmentHandler">
 <em class="property">class </em><code class="descclassname">apache_beam.transforms.enrichment_handlers.vertex_ai_feature_store.</code><code class="descname">VertexAIFeatureStoreLegacyEnrichmentHandler</code><span class="sig-paren">(</span><em>project: str, location: str, api_endpoint: str, feature_store_id: str, entity_type_id: str, feature_ids: List[str], row_key: str, *, exception_level: apache_beam.transforms.enrichment_handlers.utils.ExceptionLevel = &lt;ExceptionLevel.WARN: 1&gt;, **kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/transforms/enrichment_handlers/vertex_ai_feature_store.html#VertexAIFeatureStoreLegacyEnrichmentHandler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.transforms.enrichment_handlers.vertex_ai_feature_store.VertexAIFeatureStoreLegacyEnrichmentHandler" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="apache_beam.transforms.enrichment.html#apache_beam.transforms.enrichment.EnrichmentSourceHandler" title="apache_beam.transforms.enrichment.EnrichmentSourceHandler"><code class="xref py py-class docutils literal notranslate"><span class="pre">apache_beam.transforms.enrichment.EnrichmentSourceHandler</span></code></a></p>
 <p>Enrichment handler to interact with Vertex AI Feature Store (Legacy).</p>
 <p>Use this handler with <a class="reference internal" href="apache_beam.transforms.enrichment.html#apache_beam.transforms.enrichment.Enrichment" title="apache_beam.transforms.enrichment.Enrichment"><code class="xref py py-class docutils literal notranslate"><span class="pre">apache_beam.transforms.enrichment.Enrichment</span></code></a>
 transform for the Vertex AI Feature Store (Legacy).</p>
 <p>By default, it fetches all the features values for an entity-id. The
 entity-id is extracted from the <cite>row_key</cite> field in the input <cite>beam.Row</cite>
 object.You can specify the features names using <cite>feature_ids</cite> to fetch
 specific features.</p>
 <p>Initializes an instance of <cite>VertexAIFeatureStoreLegacyEnrichmentHandler</cite>.</p>
 <table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
 <li><strong>project</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – The GCP project for the Vertex AI Feature Store (Legacy).</li>
 <li><strong>location</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – The region for the Vertex AI Feature Store (Legacy).</li>
 <li><strong>api_endpoint</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – The API endpoint for the
 Vertex AI Feature Store (Legacy).</li>
 <li><strong>feature_store_id</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – The id of the Vertex AI Feature Store (Legacy).</li>
 <li><strong>entity_type_id</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – The entity type of the feature store.</li>
 <li><strong>feature_ids</strong> (<em>List</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>]</em>) – A list of feature-ids to fetch
 from the Feature Store.</li>
 <li><strong>row_key</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – The row key field name containing the entity id
 for the feature values.</li>
 <li><strong>exception_level</strong> – a <cite>enum.Enum</cite> value from
 <cite>apache_beam.transforms.enrichment_handlers.utils.ExceptionLevel</cite>
 to set the level when an empty row is returned from the BigTable query.
 Defaults to <cite>ExceptionLevel.WARN</cite>.</li>
 <li><strong>kwargs</strong> – Optional keyword arguments to configure the
 <cite>aiplatform.gapic.FeaturestoreOnlineServingServiceClient</cite>.</li>
 </ul>
 </td>
 </tr>
 </tbody>
 </table>
 <dl class="method">
 <dt id="apache_beam.transforms.enrichment_handlers.vertex_ai_feature_store.VertexAIFeatureStoreLegacyEnrichmentHandler.get_cache_key">
 <code class="descname">get_cache_key</code><span class="sig-paren">(</span><em>request: apache_beam.pvalue.Row</em><span class="sig-paren">)</span> &#x2192; str<a class="reference internal" href="_modules/apache_beam/transforms/enrichment_handlers/vertex_ai_feature_store.html#VertexAIFeatureStoreLegacyEnrichmentHandler.get_cache_key"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.transforms.enrichment_handlers.vertex_ai_feature_store.VertexAIFeatureStoreLegacyEnrichmentHandler.get_cache_key" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a string formatted with unique entity-id for the feature values.</p>
 </dd></dl>

 </dd></dl>

 </div>


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	<div class="section" id="module-apache_beam.transforms.enrichment_handlers.vertex_ai_feature_store">
	<span id="apache-beam-transforms-enrichment-handlers-vertex-ai-feature-store-module"></span><h1>apache_beam.transforms.enrichment_handlers.vertex_ai_feature_store module<a class="headerlink" href="#module-apache_beam.transforms.enrichment_handlers.vertex_ai_feature_store" title="Permalink to this headline">¶</a></h1>
	<dl class="class">
	<dt id="apache_beam.transforms.enrichment_handlers.vertex_ai_feature_store.VertexAIFeatureStoreEnrichmentHandler">
	<em class="property">class </em><code class="descclassname">apache_beam.transforms.enrichment_handlers.vertex_ai_feature_store.</code><code class="descname">VertexAIFeatureStoreEnrichmentHandler</code><span class="sig-paren">(</span><em>project: str</em>, <em>location: str</em>, <em>api_endpoint: str</em>, <em>feature_store_name: str</em>, <em>feature_view_name: str</em>, <em>row_key: str</em>, <em></em>, <em>exception_level: apache_beam.transforms.enrichment_handlers.utils.ExceptionLevel = <ExceptionLevel.WARN: 1></em>, <em>*kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/transforms/enrichment_handlers/vertex_ai_feature_store.html#VertexAIFeatureStoreEnrichmentHandler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.transforms.enrichment_handlers.vertex_ai_feature_store.VertexAIFeatureStoreEnrichmentHandler" title="Permalink to this definition">¶</a></dt>
	<dd><p>Bases: <a class="reference internal" href="apache_beam.transforms.enrichment.html#apache_beam.transforms.enrichment.EnrichmentSourceHandler" title="apache_beam.transforms.enrichment.EnrichmentSourceHandler"><code class="xref py py-class docutils literal notranslate"><span class="pre">apache_beam.transforms.enrichment.EnrichmentSourceHandler</span></code></a></p>
	<p>Enrichment handler to interact with Vertex AI Feature Store.</p>
	<p>Use this handler with <a class="reference internal" href="apache_beam.transforms.enrichment.html#apache_beam.transforms.enrichment.Enrichment" title="apache_beam.transforms.enrichment.Enrichment"><code class="xref py py-class docutils literal notranslate"><span class="pre">apache_beam.transforms.enrichment.Enrichment</span></code></a>
	transform when the Vertex AI Feature Store is set up for
	Bigtable Online serving.</p>
	<p>With the Bigtable Online serving approach, the client fetches all the
	available features for an entity-id. The entity-id is extracted from the
	<cite>row_key</cite> field in the input <cite>beam.Row</cite> object. To filter the features to
	enrich, use the <cite>join_fn</cite> param in
	<a class="reference internal" href="apache_beam.transforms.enrichment.html#apache_beam.transforms.enrichment.Enrichment" title="apache_beam.transforms.enrichment.Enrichment"><code class="xref py py-class docutils literal notranslate"><span class="pre">apache_beam.transforms.enrichment.Enrichment</span></code></a>.</p>
	<dl class="docutils">
	<dt><strong>NOTE:</strong> The default severity to report exceptions is logging a warning. For</dt>
	<dd>this handler, Vertex AI client returns the same exception
	<cite>Requested entity was not found</cite> even though the feature store doesn’t
	exist. So make sure the feature store instance exists or set
	<cite>exception_level</cite> as <cite>ExceptionLevel.RAISE</cite>.</dd>
	</dl>
	<p>Initializes an instance of <cite>VertexAIFeatureStoreEnrichmentHandler</cite>.</p>
	<table class="docutils field-list" frame="void" rules="none">
	<col class="field-name" />
	<col class="field-body" />
	<tbody valign="top">
	<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
	<li><strong>project</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – The GCP project-id for the Vertex AI Feature Store.</li>
	<li><strong>location</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – The region for the Vertex AI Feature Store.</li>
	<li><strong>api_endpoint</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – The API endpoint for the Vertex AI Feature Store.</li>
	<li><strong>feature_store_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – The name of the Vertex AI Feature Store.</li>
	<li><strong>feature_view_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – The name of the feature view within the
	Feature Store.</li>
	<li><strong>row_key</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – The row key field name containing the unique id
	for the feature values.</li>
	<li><strong>exception_level</strong> – a <cite>enum.Enum</cite> value from
	<cite>apache_beam.transforms.enrichment_handlers.utils.ExceptionLevel</cite>
	to set the level when an empty row is returned from the BigTable query.
	Defaults to <cite>ExceptionLevel.WARN</cite>.</li>
	<li><strong>kwargs</strong> – Optional keyword arguments to configure the
	<cite>aiplatform.gapic.FeatureOnlineStoreServiceClient</cite>.</li>
	</ul>
	</td>
	</tr>
	</tbody>
	</table>
	<dl class="method">
	<dt id="apache_beam.transforms.enrichment_handlers.vertex_ai_feature_store.VertexAIFeatureStoreEnrichmentHandler.get_cache_key">
	<code class="descname">get_cache_key</code><span class="sig-paren">(</span><em>request: apache_beam.pvalue.Row</em><span class="sig-paren">)</span> → str<a class="reference internal" href="_modules/apache_beam/transforms/enrichment_handlers/vertex_ai_feature_store.html#VertexAIFeatureStoreEnrichmentHandler.get_cache_key"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.transforms.enrichment_handlers.vertex_ai_feature_store.VertexAIFeatureStoreEnrichmentHandler.get_cache_key" title="Permalink to this definition">¶</a></dt>
	<dd><p>Returns a string formatted with unique entity-id for the feature values.</p>
	</dd></dl>

	</dd></dl>

	<dl class="class">
	<dt id="apache_beam.transforms.enrichment_handlers.vertex_ai_feature_store.VertexAIFeatureStoreLegacyEnrichmentHandler">
	<em class="property">class </em><code class="descclassname">apache_beam.transforms.enrichment_handlers.vertex_ai_feature_store.</code><code class="descname">VertexAIFeatureStoreLegacyEnrichmentHandler</code><span class="sig-paren">(</span><em>project: str, location: str, api_endpoint: str, feature_store_id: str, entity_type_id: str, feature_ids: List[str], row_key: str, , exception_level: apache_beam.transforms.enrichment_handlers.utils.ExceptionLevel = <ExceptionLevel.WARN: 1>, *kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/apache_beam/transforms/enrichment_handlers/vertex_ai_feature_store.html#VertexAIFeatureStoreLegacyEnrichmentHandler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.transforms.enrichment_handlers.vertex_ai_feature_store.VertexAIFeatureStoreLegacyEnrichmentHandler" title="Permalink to this definition">¶</a></dt>
	<dd><p>Bases: <a class="reference internal" href="apache_beam.transforms.enrichment.html#apache_beam.transforms.enrichment.EnrichmentSourceHandler" title="apache_beam.transforms.enrichment.EnrichmentSourceHandler"><code class="xref py py-class docutils literal notranslate"><span class="pre">apache_beam.transforms.enrichment.EnrichmentSourceHandler</span></code></a></p>
	<p>Enrichment handler to interact with Vertex AI Feature Store (Legacy).</p>
	<p>Use this handler with <a class="reference internal" href="apache_beam.transforms.enrichment.html#apache_beam.transforms.enrichment.Enrichment" title="apache_beam.transforms.enrichment.Enrichment"><code class="xref py py-class docutils literal notranslate"><span class="pre">apache_beam.transforms.enrichment.Enrichment</span></code></a>
	transform for the Vertex AI Feature Store (Legacy).</p>
	<p>By default, it fetches all the features values for an entity-id. The
	entity-id is extracted from the <cite>row_key</cite> field in the input <cite>beam.Row</cite>
	object.You can specify the features names using <cite>feature_ids</cite> to fetch
	specific features.</p>
	<p>Initializes an instance of <cite>VertexAIFeatureStoreLegacyEnrichmentHandler</cite>.</p>
	<table class="docutils field-list" frame="void" rules="none">
	<col class="field-name" />
	<col class="field-body" />
	<tbody valign="top">
	<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
	<li><strong>project</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – The GCP project for the Vertex AI Feature Store (Legacy).</li>
	<li><strong>location</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – The region for the Vertex AI Feature Store (Legacy).</li>
	<li><strong>api_endpoint</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – The API endpoint for the
	Vertex AI Feature Store (Legacy).</li>
	<li><strong>feature_store_id</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – The id of the Vertex AI Feature Store (Legacy).</li>
	<li><strong>entity_type_id</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – The entity type of the feature store.</li>
	<li><strong>feature_ids</strong> (<em>List</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>]</em>) – A list of feature-ids to fetch
	from the Feature Store.</li>
	<li><strong>row_key</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – The row key field name containing the entity id
	for the feature values.</li>
	<li><strong>exception_level</strong> – a <cite>enum.Enum</cite> value from
	<cite>apache_beam.transforms.enrichment_handlers.utils.ExceptionLevel</cite>
	to set the level when an empty row is returned from the BigTable query.
	Defaults to <cite>ExceptionLevel.WARN</cite>.</li>
	<li><strong>kwargs</strong> – Optional keyword arguments to configure the
	<cite>aiplatform.gapic.FeaturestoreOnlineServingServiceClient</cite>.</li>
	</ul>
	</td>
	</tr>
	</tbody>
	</table>
	<dl class="method">
	<dt id="apache_beam.transforms.enrichment_handlers.vertex_ai_feature_store.VertexAIFeatureStoreLegacyEnrichmentHandler.get_cache_key">
	<code class="descname">get_cache_key</code><span class="sig-paren">(</span><em>request: apache_beam.pvalue.Row</em><span class="sig-paren">)</span> → str<a class="reference internal" href="_modules/apache_beam/transforms/enrichment_handlers/vertex_ai_feature_store.html#VertexAIFeatureStoreLegacyEnrichmentHandler.get_cache_key"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#apache_beam.transforms.enrichment_handlers.vertex_ai_feature_store.VertexAIFeatureStoreLegacyEnrichmentHandler.get_cache_key" title="Permalink to this definition">¶</a></dt>
	<dd><p>Returns a string formatted with unique entity-id for the feature values.</p>
	</dd></dl>

	</dd></dl>

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