# Integrating Databricks Genie API with API workflows

> Databricks Genie enables natural language interaction with your enterprise data, allowing you to query, analyze, and act on datasets without needing to write code.

Databricks Genie enables natural language interaction with your enterprise data, allowing you to query, analyze, and act on datasets without needing to write code.

This guide provides a practical walkthrough of how to interact with a Databricks Genie API to get information based on a question input. It performs the following main actions:

* **Initializes variables**—Sets up necessary parameters like tokens, URLs, and IDs.
* **Gets a token**—Makes an HTTP POST request to obtain an access token for authentication.
* **Starts a conversation**—Sends the user question to the Genie API to initiate a conversation.
* **Polls for message completion**—Enters a **Do While** loop to repeatedly check the status of the Genie message until it is COMPLETED.
* **Retrieves query results**—Once the message is complete, it fetches the final query results from the Genie API and provides the retrieved data as the workflow output.

  ![docs image](https://dev-assets.cms.uipath.com/assets/images/studio-web/studio-web-docs-image-615396-9194dcb0.webp)

## Prerequisites

First, you need to have a Genie space called "Item Restrictions" in your Databricks workspace.

Its purpose is to reference shipping restrictions for different products and provide answers regarding shipping details.

1. [Create an API workflow](https://docs.uipath.com/studio-web/automation-cloud/latest/user-guide/managing-api-workflows#creating-api-workflows).
2. Define workflow inputs. Open **Data manager** and add a new **Inputs** parameter with the following configuration:
   * **Name**—Question
   * **Type**—String
   * Mark it as **Required**.
3. Define workflow variables. Open **Data manager** > **Variables**, and add the following:
   * `token` (String)—Stores the authentication token required to access the Databricks Genie API.
   * `conversation_id` (String)—Tracks a specific conversation session with the Databricks Genie API. When you start a conversation, the API provides this ID, and you use it to refer to that ongoing conversation in subsequent requests.
   * `message_id` (String)—Refers to a specific message within a conversation. This ID is then used to retrieve the status and eventually the results of that particular message processing.
   * `attachment_id` (String)—Points to a specific attachment (which in this case holds the query result) associated with a message in the Genie API.
   * `url` (String)—Stores the base URL for the Databricks Genie API endpoint.
   * `space_id` (String)—Points to a specific space within Databricks Genie.
4. Add a **HTTP** activity to retrieve the access token and configure it as follows:
   * **Display name**—"Get token HTTP Request"
   * **Method**—POST
   * **Request URL**—`https://accounts.cloud.databricks.com/oidc/accounts/{account_id}/v1/token`
   * **Headers**—
     ```
     {
     "Authorization": "Basic <your_basic_token>",
     "Content-Type": "application/x-www-form-urlencoded"
     }
     ```
   * **Request body**—
     ```
     "grant_type=client_credentials&scope=all-apis"
     ```The context output name for this activity is `HTTP_Request_1`.
5. Assign the token retrieved from the previous activity to your `token` variable. Add an **Assign** activity and configure it as follows:
   * **To variable**—`token`
   * **Set value**—`$context.outputs.HTTP_Request_1.content.access_token`The context output name `HTTP_Request_1` may differ from the one used in your workflow.
6. Add a **HTTP** activity to start the conversation and configure it as follows:
   * **Display name**—"Start conversation HTTP Request"
   * **Method**—POST
   * **Request URL**—`https://{your-databricks-instance}/api/2.0/genie/spaces/{space_id}/start-conversation`, or build it with the Expression editor as
     ```
     $context.variables.url + "/api/2.0/genie/spaces/"+ $context.variables.space_id +"/start-conversation"
     ```
   * **Headers**—
     ```
     {"Authorization": "Bearer " + $context.variables.token}
     ```
   * **Request body**—
     ```
     {"content": $workflow.input.question}
     ```

    Ensure the output is saved. You can retrieve `conversation_id` and `message_id` from the output of this activity. The context output name for this activity is `HTTP_Request_2`.
7. Enter a loop to check the status of the Genie message until COMPLETED. Add a **Do While** activity and set the **Condition** to `$context.outputs.HTTP_Request_3?.content?.status !== "COMPLETED"`, where `HTTP_Request_3` refers to the Get message HTTP Request activity. This means the loop continues as long as the message status is not COMPLETED.
8. Inside the **Do while** loop:
   1. Add a **HTTP** activity for getting the last message and configure it as follows:
      * **Display name**—"Get message HTTP Request"
      * **Method**—GET
      * **Request URL**—`https://{your-databricks-instance}/api/2.0/genie/spaces/{space_id}/conversations/{conversation_id}/messages/{message_id}`, or build it with the Expression editor as
        ```
        [ $context.variables.url+"/api/2.0/genie/spaces/"+$context.variables.space_id+"/conversations/", $context.outputs.HTTP_Request_2.content.conversation_id, "/messages/", $context.outputs.HTTP_Request_2.content.message_id ].join('')
        ```

    Where `HTTP_Request_2` refers to the Start conversation HTTP Request activity.
      * **Headers**—
        ```
        {"Authorization": "Bearer " + $context.variables.token}
        ```
      * **Request body**—
        ```
        {"content": $workflow.input.question}
        ```The context output name for this activity is `HTTP_Request_3`.
   2. Add an **If** activity immediately after the Get message HTTP Request acitvity, and set the **Condition** to `$context.outputs.HTTP_Request_3?.content?.status === "COMPLETED"`, where `HTTP_Request_3` refers to Get message HTTP Request activity.
   3. On the **Then** (true) branch, add an **Assign** activity and configure it as follows:
      * **To variable**—`conversation_id`
      * **Set value**—`$context.outputs.HTTP_Request_3.content.conversation_id`, where `HTTP_Request_3` refers to Get message HTTP Request activity.
   4. Add another **Assign** activity:
      * **To variable**—`message_id`
      * **Set value**—`$context.outputs.HTTP_Request_3.content.message_id`, where `HTTP_Request_3` refers to Get message HTTP Request activity.
   5. Add another **Assign** activity:
      * **To variable**—`attachment_id`
      * **Set value**—`$context.outputs.HTTP_Request_3.content.attachments[0].attachment_id`, where `HTTP_Request_3` refers to Get message HTTP Request activity.
   6. On the **Else** branch, add a **Wait** activity and set duration to one second. This instructs the workflow to wait for a short period before retrying.
9. Exit the loop and add a **HTTP** activity to execute the SQL query and configure it as follows:
   * **Display name**—"Execute SQL HTTP Request"
   * **Method**—GET
   * **Request URL**—`https://{your-databricks-instance}/api/2.0/genie/spaces/{space_id}/conversations/{conversation_id}/messages/{message_id}/attachments/{attachment_id}/query-result`, or build it with the Expression editor as
     ```
     $context.variables.url+"/api/2.0/genie/spaces/"+$context.variables.space_id+"/conversations/" + $context.variables.conversation_id + "/messages/"  + $context.variables.message_id + "/attachments/" +  $context.variables.attachment_id + "/query-result"
     ```
   * **Headers**—
     ```
     {"Authorization": "Bearer " + $context.variables.token}
     ```The context output name for this activity is `HTTP_Request_3`.
10. Define workflow outputs. Open **Data manager** and add the following **Inputs** parameters:
    * **Name**—row_count
    * **Type**—Number
    * Mark it as **Required**.and
    * **Name**—data_array
    * **Type**—Array
    * Mark it as **Required**.
11. Add a **Response** activity and configure it as follows:
    * **Response**—
      ```
      {
        "row_count": -75647404.57028332,
        "data_array": [
          [],
          [],
          [
            "ut et aute officia",
            "ex ut",
            "nisi non ut Lorem velit",
            "aliquip Duis consectetur irure id"
          ]
        ]
      }
      ```
12. Run the integration. Once deployed, the Workflow can be invoked with any natural language query. An example question could be : "Are there any restrictions to ship SKU ENB-SP-200 to Cuba?"

**Key take-aways:**
1. **Simple setup** – API Workflow provides a fast way to connect Genie without custom code.
2. **Consistent schema** – Genie responses are JSON-based, easy to parse into UiPath.
3. **Scalable integration** – Responses can be used in Apps, long-running workflows, or orchestrations.
4. **Rapid prototyping** – Setup for working automation takes minutes, allowing quick validation of use cases.