# create\_asset\_builder\_template

`imerit_ango.sdk.SDK.`

## create\_asset\_builder\_template(project\_id, template)

Create templates to be used while uploading data via the [asset builder](/data/importing-assets/asset-builder.md).

### Parameters

* **project\_id:** string
  * The unique identifier for the project. You can find the project ID in [the user interface](/sdk/sdk-documentation.md#project-ids) or retrieve it using the [`list_projects`](/sdk/sdk-documentation/project-level-sdk-functions/list_projects.md) function.
* **template:** AssetBuilderTemplate

  An object defining the structure of the template, excluding the `_id` field. Includes:

  * **name:** string
    * The display name of the Asset Builder template.
  * **template:** string
    * Template HTML that will be filled in while uploading via [asset builder.](/data/importing-assets/asset-builder.md)
  * **external\_id\_column:** string
    * The column name from the input tabular data to be used as the [asset's externalId.](https://docs.imerit.net/core-concepts/assets#external-id) In each row, the value in the cell corresponding to this column will be used as the externalId for that row.
  * **data\_config:** Dict\[str, DataConfigItem]

    Dictionary mapping column names to their configuration. Each `DataConfigItem` contains:

    * **type**: string - The data type for this column. Must be one of:
      * <kbd>"text"</kbd>, <kbd>"link"</kbd>, <kbd>"image"</kbd>, <kbd>"audio"</kbd>, <kbd>"video"</kbd>, <kbd>"iframe"</kbd>, <kbd>"pdf"</kbd>
    * **storage**: string, *Optional, default None* - Storage configuration for this column. Only necessary when the type is one of: video, image, audio, or pdf.
    * **include\_in\_export**: string, *Optional, default False* - Whether to include this column in exports
  * **description:** string, *Optional, default ""*
    * A description of what the template does or what it is.
  * **batch\_column:** string, *Optional, default ""*
    * Batch column name from the input tabular data to be used as the [batch name](https://docs.imerit.net/core-concepts/batches) for that asset. In each row, the value in the cell corresponding to this column will be used as the batch name for that row.
  * **pre\_label\_config:** PreLabelConfig

    Dictionary containing pre-labeling configuration. Each item contains:

    * **cla**: PreLabelCla - Classification configuration with fields:
      * **schemaId**: string - Schema identifier
      * **title**: string - Display title
      * **multiple**: bool - Whether multiple selections are allowed
    * **value**: string - Column that will be used as the source of pre-label data

### Returns

* A dictionary containing the result of the operation.
* Including a `status` field indicating whether the request was successful and a `data` field containing the response payload with updated resources produced by the operation.

<details open>

<summary><strong>How to verify in Ango Hub?</strong></summary>

After successfully executing the `create_asset_builder_template` function, you can validate the changes directly in Ango Hub.

Navigate to: **Projects → \[Your Project] → Assets → Add Data → Asset Builder**

* Upload a CSV file and proceed to the **Rich Text Editor** step.
* Open the **Select Template** dropdown.
* Confirm that the newly created template appears in the list.

{% hint style="info" %}
Changes made via the SDK are reflected in Ango Hub in near real-time. If updates are not immediately visible, please refresh the page.
{% endhint %}

</details>

### Example

#### Basic Template (minimal required fields)

{% tabs %}
{% tab title="python" %}

```python
import os
from dotenv import load_dotenv
from imerit_ango.sdk import SDK
from imerit_ango.models.asset_builder_template import AssetBuilderTemplate, DataConfigItem

load_dotenv('variables.env')
api_key = os.getenv('API_KEY')
project_id = os.getenv('PROJECT_ID')

ango_sdk = SDK(api_key)

template = AssetBuilderTemplate(
    name="Simple Image Template",
    template="<img src='{{image_url}}' alt='Sample image' />",
    external_id_column="image_id",
    data_config={"image_id": DataConfigItem(type="text",),
                 "image_url": DataConfigItem(type="image", storage="<YOUR_STORAGE_INTEGRATION_ID>")}
)

sdk_response = ango_sdk.create_asset_builder_template(project_id=project_id, template=template)
template_id = sdk_response["data"]["_id"]
```

{% endtab %}
{% endtabs %}

<details>

<summary>Sample CSV File</summary>

```
image_id,image_url
IMG_001,https://picsum.photos/200/300
IMG_002,https://picsum.photos/200/300
```

</details>

#### Comprehensive Template (all optional fields)

{% tabs %}
{% tab title="python" %}

```python
import os
from dotenv import load_dotenv
from imerit_ango.sdk import SDK
from imerit_ango.models.asset_builder_template import AssetBuilderTemplate, DataConfigItem

load_dotenv("variables.env")
api_key = os.getenv("API_KEY")
project_id = os.getenv("PROJECT_ID")

ango_sdk = SDK(api_key)

# HTML template with placeholders
html_template = """
<div class="video-container">
    <video src='{{video_url}}' controls></video>
    <p>{{description}}</p>
    <a href='{{reference_link}}'>Reference</a>
    <div id="cla-portal-{{classification_schema_id}}">Content classification</div>
    <div id="cla-portal-{{quality_schema_id}}">Quality assessment</div>
</div>
"""

# Data configuration
data_config = {
    "video_id": DataConfigItem(type="text", include_in_export=True),
    "video_url": DataConfigItem(type="video", storage="<YOUR_STORAGE_ID>"),
    "description": DataConfigItem(type="text", include_in_export=True),
    "reference_link": DataConfigItem(type="link")
}

# Pre-label configuration
pre_label_config = {
    "classification_schema_id": {
        "cla": {
            "schemaId": "schema_123",
            "title": "Content Type",
            "multiple": False
        },
        "value": "content_type_column"
    },
    "quality_schema_id": {
        "cla": {
            "schemaId": "schema_456",
            "title": "Quality Issues",
            "multiple": True
        },
        "value": "quality_column"
    }
}

template = AssetBuilderTemplate(
    name="Video Classification Template",
    description="Template for video content classification with descriptions and quality assessment",
    template=html_template,
    external_id_column="video_id",
    data_config=data_config,
    pre_label_config=pre_label_config
)

sdk_response = ango_sdk.create_asset_builder_template(project_id=project_id, template=template)
```

{% endtab %}
{% endtabs %}

<details>

<summary>Sample CSV File</summary>

```
video_id,video_url,description,reference_link
VID_001,https://www.youtube.com/watch?v=dQw4w9WgXcQ&list=RDdQw4w9WgXcQ,Product demonstration showing key features and user interface walkthrough,https://www.youtube.com/watch?v=dQw4w9WgXcQ&list=RDdQw4w9WgXcQ
VID_002,https://www.youtube.com/watch?v=dQw4w9WgXcQ&list=RDdQw4w9WgXcQ,Basic tutorial covering getting started steps for new users,https://www.youtube.com/watch?v=dQw4w9WgXcQ&list=RDdQw4w9WgXcQ
```

</details>

#### Template with Batch Processing

{% tabs %}
{% tab title="python" %}

```python
import os
from dotenv import load_dotenv
from imerit_ango.sdk import SDK
from imerit_ango.models.asset_builder_template import AssetBuilderTemplate, DataConfigItem

load_dotenv("variables.env")
api_key = os.getenv("API_KEY")
project_id = os.getenv("PROJECT_ID")

ango_sdk = SDK(api_key)

html_template = """
<div>
    <img src='{{image_url}}' alt='{{image_title}}' />
    <p>{{image_description}}</p>
    <iframe title='{{metadata_url}}' sandbox="" referrerpolicy="no-referrer" src='{{metadata_url}}' width="100%" height="200"></iframe>
    <div id="cla-portal-{{category_schema_id}}">Image Category</div>
    <div id="cla-portal-{{sentiment_schema_id}}">Sentiment Analysis</div>
</div>
"""

data_config = {"image_id": DataConfigItem(type="text", include_in_export=True),
               "image_url": DataConfigItem(type="image", storage="<YOUR_STORAGE_INTEGRATION_ID>"),
               "image_title": DataConfigItem(type="text"),
               "image_description": DataConfigItem(type="text", include_in_export=True),
               "metadata_url": DataConfigItem(type="iframe"),
               "batch_group": DataConfigItem(type="text")}

pre_label_config = {
    "category_schema_id": {
        "cla": {
            "schemaId": "cat_202",
            "title": "Image Category",
            "multiple": False,
        },
        "value": "category_column"
    },
    "sentiment_schema_id": {
        "cla": {
            "schemaId": "sent_303",
            "title": "Sentiment",
            "multiple": False,
        },
        "value": "sentiment_column"
    }
}

template = AssetBuilderTemplate(
    name="Batch Image Processing Template",
    template=html_template,
    external_id_column="image_id",
    data_config=data_config,
    description="Template for batch processing of images with categorization and sentiment analysis",
    batch_column="batch_group",
    pre_label_config=pre_label_config
)

sdk_response = ango_sdk.create_asset_builder_template(project_id=project_id, template=template)
```

{% endtab %}
{% endtabs %}

<details>

<summary>Sample CSV File</summary>

```
image_id,image_url,image_title,image_description,metadata_url,batch_group
IMG_001,https://picsum.photos/200/300,Sample Product Photo,High-quality product image for e-commerce catalog,https://www.youtube.com/watch?v=dQw4w9WgXcQ&list=RDdQw4w9WgXcQ,batch_alpha
IMG_002,https://picsum.photos/200/300,Nature Landscape,Beautiful mountain landscape during golden hour,https://www.youtube.com/watch?v=dQw4w9WgXcQ&list=RDdQw4w9WgXcQ,batch_alpha
IMG_003,https://picsum.photos/200/300,Urban Architecture,Modern city building with glass facades,https://www.youtube.com/watch?v=dQw4w9WgXcQ&list=RDdQw4w9WgXcQ,batch_beta
```

</details>

#### Usage with [upload\_files\_with\_asset\_builder](/sdk/sdk-documentation/project-level-sdk-functions/upload_files_with_asset_builder.md)

{% tabs %}
{% tab title="python" %}

```python
import os
from dotenv import load_dotenv
from imerit_ango.sdk import SDK
from imerit_ango.models.asset_builder_template import AssetBuilderTemplate, DataConfigItem

load_dotenv("variables.env")
api_key = os.getenv("API_KEY")
project_id = os.getenv("PROJECT_ID")

ango_sdk = SDK(api_key)

# Step 1: Create a simple template
template = AssetBuilderTemplate(
    name="Basic Image Template",
    template="<img src='{{image_url}}'/>",
    external_id_column="id",
    data_config={"id": DataConfigItem(type="text"),
                 "image_url": DataConfigItem(type="image", storage="Public")}
)

sdk_response = ango_sdk.create_asset_builder_template(project_id=project_id, template=template)
template_id = sdk_response["data"]["_id"]

# Step 2: Use the template_id to upload files
ango_sdk.upload_files_with_asset_builder(
    project_id=project_id,
    template_id=template_id,
    input_file_path="data.csv"
)
```

{% endtab %}
{% endtabs %}

<details>

<summary>Sample CSV File</summary>

```
id,image_url
IMG_001,https://picsum.photos/200/300
IMG_002,https://picsum.photos/300/400
IMG_003,https://picsum.photos/250/350
```

</details>

{% hint style="info" %}
**See also**

[get\_asset\_builder\_templates](/sdk/sdk-documentation/project-level-sdk-functions/get_asset_builder_templates.md), [upload\_files\_with\_asset\_builder](/sdk/sdk-documentation/project-level-sdk-functions/upload_files_with_asset_builder.md), [Asset Builder](/data/importing-assets/asset-builder.md)
{% endhint %}


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.imerit.net/sdk/sdk-documentation/project-level-sdk-functions/create_asset_builder_template.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.