---
schema_version: "1"
id: 3300ba9ae92185d47e2a5ffa07883c5ea41715beae0db30e3c4ade042b5bcfe3
title: Configuring RAG Operations for Einstein AI Connector 1.2
collection: mulesoft
version: current
locale: en-us
portal_url: https://docs.salesforce.com/mulesoft/current/en-us/einstein-ai-connector/1.2/configuring-rag-operations.md
canonical_url: https://docs.mulesoft.com/einstein-ai-connector/1.2/configuring-rag-operations
source_site: docs.mulesoft.com
content_type: documentation
content_hash: 47f3b490846436876a94673736cfac7b42317ab1b60c4df908a48d888f49f8d6
description: MuleSoft Documentation Site
release: "1.2"
latest: false
---
---
product: Einstein AI Connector
version: 1.2
---

# Configuring RAG Operations for Einstein AI Connector 1.2

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

Retrieval-Augmented Generation (RAG) is a technique for enhancing AI-generated outputs by retrieving
relevant content, and using it to augment AI prompts with additional context. By grounding LLMs with
this additional information, they can provide more accurate and reliable responses.

## Configure the RAG Adhoc Load Document Operation

The **RAG adhoc load document** operation retrieves information based on a plain text prompt from an
in-memory embedding store.

To configure the **RAG adhoc load document** 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:

    - **Prompt**

      The prompt to send to the LLM and the embedding store to respond to.

    - **File Path**

      Contains the full file path for the document to ingest into the embedding store. Ensure the
      file path is accessible.

      You can also use a DataWeave expression for this field, for example:

      `mule.home "/apps/" app.name ++ "/customer-service.pdf"`

3.  In **Additional properties**, select the values for:

    - **Embedding name**

    - **File type**

      Type of document to ingest into the embedding store:

      - **Text**

      - **PDF**

      - **CSV**

      - URL

        A single URL pointing to web content to ingest.

    - **Option type**

      How to split the document prior to ingestion into the vector database

    - **Model name**

      Name of the API model that interacts with the LLM.

    - **Probability**

      Probability of the model API staying accurate

    - **Locale**

      Localization information, which can include the default locale, input locale(s), and expected
      output locales

This is the XML configuration for this operation:

```xml
<ms-einstein-ai:rag-adhoc-load-document
  doc:name="Rag adhoc load document"
  doc:id="edaea124-a8aa-4d4a-8f85-0f32ee4c9858"
  config-ref="Einstein_AI"
  prompt="#[payload.prompt]"
  filePath="#[payload.filePath]"
  optionType="PARAGRAPH"
/>
```

## See Also

- [Using Anypoint Code Builder to Configure a Connector](https://docs.mulesoft.com/connectors/introduction/intro-config-use-acb)

- [Using Studio to Configure a Connector](https://docs.mulesoft.com/connectors/introduction/intro-config-use-studio)

- [Einstein AI Connector Overview](https://docs.mulesoft.com/einstein-ai-connector/1.2/)

- [Einstein AI Connector Reference](https://docs.mulesoft.com/einstein-ai-connector/1.2/einstein-ai-connector-reference)

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