mulesoft · issuvarna1 · Apr 21, 2026
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+name: mulesoft-vectors-connector
+version: '1.1'
+display_version: 1.1 (Mule 4)
+title: MuleSoft Vectors Connector
+nav:
+- modules/ROOT/nav.adoc
+asciidoc:
+  attributes:
+    page-component-desc: Enables you to integrate AI into your data for smarter search and RAG
+    page-connector-type: Connector
+    page-connector-level: Select
+    page-exchange-group-id: com.mulesoft.connectors
+    page-exchange-asset-id: mule4-vectors-connector
+    page-runtime-version: 4.9.0
+    page-release-notes-page: release-notes::connector/mulesoft-vectors-connector-release-notes-mule-4.adoc
+    page-vendor-name: MuleSoft
+    page-vendor-title: MuleSoft
+    page-notice-banner-message: Standard support for Java 8 and 11 ends in August 2026 for 4.6 LTS. Plan your upgrade path for apps that are running on Java 8 or 11 accordingly.
@@ -0,0 +1,11 @@
+.xref:index.adoc[MuleSoft Vectors Connector]
+* xref:index.adoc[MuleSoft Vectors Connector Overview]
+* xref:mulesoft-vectors-connector-studio.adoc[Using Anypoint Studio to Configure MuleSoft Vectors Connector]
+* xref:mulesoft-vectors-connector-config-acb.adoc[Using Anypoint Code Builder to Configure MuleSoft Vectors Connector]
+* xref:mulesoft-vectors-additional-config.adoc[MuleSoft Vectors Connector Additional Configuration Information]
+ ** xref:configuring-embedding-operations.adoc[]
+ ** xref:configuring-store-operations.adoc[]
+ ** xref:configuring-transform-operations.adoc[]
+* xref:mulesoft-vectors-connector-xml-maven.adoc[MuleSoft Vectors Connector XML and Maven Support]
+* xref:mulesoft-vectors-connector-examples.adoc[MuleSoft Vectors Connector Examples]
+* xref:mulesoft-vectors-connector-reference.adoc[MuleSoft Vectors Connector Reference] 
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+= Configuring Embedding Operations
+
+Configure the *[Embedding] Generate from text* operation. 
+
+== Configure the Embedding Generate from Text Operation
+
+The *[Embedding] Generate from text* operation splits the text into chunks of the provided size and creates numeric vectors for each text chunk.
+
+The *[Embedding] Generate from text* operation can be followed by either the *[Store] Add* operation or the *[Store] Query* operation. Both operations can use the output payload of the *[Embedding] Generate from text* operation without any transformation. When the *[Embedding] Generate from text* operation is used before the:
+
+* *[Store] Add* operation: The text along with the generated embeddings can be ingested into a vector store.
+* *[Store] Query* operation: The text is first used to generate an embedding that is then used to perform a query against the vector store.
++
+NOTE: When generating an embedding from text for query purposes, don't provide any segmentation fields. Leave the *Max Segment Size (Characters)* and *Max Overlap Size (Characters)* fields blank.
+
+To configure the *[Embedding] Generate from text* operation:
+
+include::connectors::partial$t-configure-operations.adoc[]
+* *Input Texts*
++
+Enter the input list of texts to generate embeddings from.
+* *Max Segment Size (Characters)*
++
+Enter the segment size of the document to be split in. This can be left blank as it is an optional input.
+* *Max Overlap Size (Characters)*
++
+Enter the overlap size of the segments to fine-tune the similarity search. This can be left blank as it is an optional input.
+* *Model (Deployment) Name*
++
+Enter the embedding model (deployment) name.
+
+This is the XML for this operation:
+[source,xml,linenums, subs=attributes+]
+----
+<ms-vectors:embedding-generate-from-text
+  doc:name="[Embedding] Generate from text"
+  doc:id="92c7a561-7b99-4840-8ffb-f680c9e392dc"
+  config-ref="MuleSoft_Vectors_Connector_Embedding_config"
+  maxSegmentSizeInChar="3000"
+  maxOverlapSizeInChars="300"
+  embeddingModelName="sfdc_ai__DefaultOpenAITextEmbeddingAda_002">
+  <ms-vectors:text ><![CDATA[#[payload.text]]]></ms-vectors:text>
+</ms-vectors:embedding-generate-from-text>
+----
+
+=== Output Configuration
+
+This operation responds with a JSON payload. This is an example response:
+
+[source,json]
+----
+{
+    "embeddings": [
+      [-0.00683132, -0.0033572172, 0.02698761, -0.01291587, ...],
+      [-0.0047172513, -0.03481483, 0.02046227, -0.037395656, ...],
+      ...
+    ]
+    "text-segments": [
+        {
+            "metadata": {
+                "index": "0"
+            },
+            "text": "In the modern world, technological advancements have become .",
+        },
+        {
+            "metadata": {
+                "index": "1"
+            },
+            "text": "E-commerce giants like Amazon and Alibaba have redefined ..",
+        },
+        ...
+    ],
+    "dimension": 1536
+}
+----
+
+* `embeddings`: List of generated embeddings.
+** `list-item` (embedding)
+* `text-segments`: List of segments.
+** `list-item` (text-segment)
+*** `text`: Text segment.
+*** `metadata`: Metadata key-value pairs.
+***** `index`: Segment or chunk number for the uploaded data source.
+* `dimension`: Dimension of the embeddings.
+
+The operation also returns attributes that aren't within the main JSON payload, that include information about token usage, for example:
+
+[source,json]
+----
+{
+  "embeddingModelDimension": 1536,
+  "embeddingModelName": "sfdc_ai__DefaultOpenAITextEmbeddingAda_002",
+  "tokenUsage": {
+      "outputCount": 9,
+      "totalCount": 18,
+      "inputCount": 9
+  },
+  "additionalAttributes": {}
+}
+----
+
+* `embeddingModelDimension`: Dimension for the embedding model used.
+* `embeddingModelName`: Embedding model name used.
+* `tokenUsage`: Token usage metadata returned as attributes
+** `outputCount`: Number of tokens used to generate the output
+** `totalCount`: Total number of tokens used for input and output.
+** `inputCount`: Number of tokens used to process the input.
+
+== See Also
+
+* xref:index.adoc[MuleSoft Vectors Connector Overview]
+* xref:mulesoft-vectors-connector-reference.adoc[MuleSoft Vectors Connector Reference]
+* https://help.salesforce.com[Salesforce Help]