---
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title: Configuring Embedding Operations
collection: mulesoft
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locale: en-us
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canonical_url: https://docs.mulesoft.com/mulesoft-vectors-connector/1.0/configuring-embedding-operations
source_site: docs.mulesoft.com
content_type: documentation
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description: MuleSoft Documentation Site
release: "1.0"
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---
---
product: MuleSoft Vectors Connector
version: 1.0
---

# Configuring Embedding Operations

> For the full documentation index, see: https://docs.mulesoft.com/llms.txt

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:

1.  Select the operation on the Anypoint Code Builder or Studio canvas.

2.  In the **General** properties tab for the operation, enter these values:

    - **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:

```xml
<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:

```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:

```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

- [MuleSoft Vectors Connector Overview](https://docs.mulesoft.com/mulesoft-vectors-connector/1.0/)

- [MuleSoft Vectors Connector Reference](https://docs.mulesoft.com/mulesoft-vectors-connector/1.0/mulesoft-vectors-connector-reference)

- [Salesforce Help](https://help.salesforce.com)