Image Data Enrichment

When you have a large catalog of products, manually adding rich detail to each product listing isn’t scalable. Image Data Enrichment is an AI-driven feature that analyzes product images alongside text to augment each product listing with highly accurate and relevant attributes. This makes every item more discoverable so you can increase conversion rates and revenue while decreasing search abandonment and lost opportunities.

By automatically and intelligently adding detail to your B2B or B2C product catalogs, you can surface more results for each query, including relevant products whose original listings don’t always match how customers search.

Before Image Data Enrichment

After Image Data Enrichment

With Image Data Enrichment, customers get more results for each query because richer text fields match more queries.

How it works

Image Data Enrichment combines computer vision and natural language processing to transform sparse product information into comprehensive, searchable content. It analyzes product images alongside existing text fields to generate key words and phrases that improve findability and help customers discover products through search.

As a result of this AI-driven analysis, Image Data Enrichment does the following:

• Expands keyword coverage
• Creates relevant synonyms
• Adds precise product categories

The result is a complete product profile that improves discoverability across B2B and B2C commerce platforms.

There are two ways to enable the feature:

• With Fusion’s LWAI Prediction index stage
• With the Prediction API

See Enabling Image Data Enrichment below for instructions.

When to use Image Data Enrichment

Image Data Enrichment provides measurable value when:

• Your catalog is image-rich
• Your catalog lacks rich text descriptions, keywords, categories, or synonyms
• Search recall is low despite good relevance tuning
• Manual tagging is expensive or infeasible

Image Data Enrichment is not recommended in any of these cases:

• Images are sparse or non-existent
• Your use case is knowledge management or site search
• Text field are already rich
• Compliance requirements prohibit AI-generated attributes

Examples

The following examples demonstrate how Image Data Enrichment processes real-world product catalogs and the specific enhancements it delivers.

B2C: 'futsal shoes'

Futsal is a popular indoor soccer variant. Imagine a customer searches your site for “futsal shoes”.

The original catalog listing on the left does not match. On the right, Image Data Enrichment has added details based on visual and contextual clues that suggest this shoe is designed for futsal.

Original result

Toque Rebound Pro

A blue indoor soccer shoe with side lacing and a gum outsole designed for indoor courts.

Keywords: blue, indoor, soccer, side lacing, gum outsole

Categories: indoor soccer footwear, soccer shoes, athletic indoor court shoes

Synonyms:

• indoor soccer shoe indoor court shoe
• traction grip

Image Data Enrichment result

Toque Rebound Pro

A blue indoor soccer shoe with side lacing and a gum outsole designed for indoor courts.

Keywords: blue upper, woven upper, side lacing, clean striking surface, gold embroidery, knit collar, gum rubber outsole, herringbone tread, soccer, futsal, indoor, indoor play

Synonyms:

• indoor soccer shoe indoor court shoe
• traction grip
• woven upper textured upper
• gum rubber outsole gum sole
• ball control touch control
• stability support

Categories: indoor soccer footwear, soccer shoes, athletic indoor court shoes, futsal shoes, indoor soccer footwear, futsal performance shoes, court traction footwear, technical soccer shoes, low profile indoor trainers, gum sole indoor shoes

B2B: 'din rail housing with hinged cover'

In this example, a customer searches for a “din rail housing with hinged cover”.

The original product listing on the left contains none of those terms, though a matching item is shown in the photo. The enriched product listing on the right adds much more detail derived from the specifications and the photo.

Original

Din rail mount enclosure

FlexView 40-Terminal Control Enclosure MRF-40TU-PC2035

Property	Value
Brand	ModuRail
Color	Gray (Light Gray)
Cover Color	Gray (Light Gray)
Flammability Rating	UL 94 V-0
Height	128 mm
Housing Color	Gray (Light Gray)
IP Rating	IP20
Length	48.3 mm
Material	Polycarbonate (PC)
Product	Accessories
Product Type	Enclosures for Industrial Automation 10

Image Data Enrichment result

Din rail mount enclosure

FlexView 40-Terminal Control Enclosure MRF-40TU-PC2035

Property	Value
Brand	ModuRail
Color	Gray (Light Gray)
Cover Color	Gray (Light Gray)
Flammability Rating	UL 94 V-0
Height	128 mm
Housing Color	Gray (Light Gray)
IP Rating	IP20
Length	48.3 mm
Material	Polycarbonate (PC)
Product	Accessories
Product Type	Enclosures for Industrial Automation 10

Keywords:

• control enclosure
• terminal enclosure
• DIN rail box
• automation accessory
• electrical housing
• modular enclosure
• wiring enclosure
• control cabinet insert

Synonyms:

• control enclosure control box, control housing, control casing, control unit shell
• terminal enclosure terminal housing, terminal casing, connection housing, terminal unit shell
• DIN rail box rail mounted box, rail installable housing, rail mount case, rail mounted enclosure
• automation accessory automation component, automation hardware, control system accessory, automation module
• electrical housing electrical casing, electrical enclosure unit, power housing, equipment housing
• modular enclosure modular housing, modular casing, expandable enclosure, modular shell
• wiring enclosure wiring housing, cable enclosure, connection box, wiring casing
• control cabinet insert cabinet module, cabinet insert unit, panel insert, cabinet component

Categories:

• industrial control enclosures
• DIN rail mounting accessories
• terminal block housings
• automation panel components
• electrical junction enclosures
• modular control boxes
• low voltage distribution enclosures
• equipment protection housings

Benefits

For your team

• Higher conversions and revenue
• Faster adaptation to the latest search trends
• Improved performance against key business metrics

For your customers

• More (and more relevant) results for every search
• Easier product discoverability
• Higher satisfaction

Enabling Image Data Enrichment

You can enable this feature in Fusion or by using the Prediction API. Before you get started, ensure your product catalog images are publicly available with a URL. Alternatively, you can provide base64-encoded images.

Using Fusion

In Fusion, you enable Image Data Enrichment by configuring the LWAI Prediction stage in your index pipeline.

You might also need to configure the Field Mapping index pipeline stage and the Query Fields query pipeline stage to generate and display metadata for images.

It is essential to review your Image Data Enrichment results. Plan to test different combinations of “images only” or “images with text fields” to find the balance that produces the best results.

Configure the LWAI Prediction index pipeline stage

1. In Fusion, open the index pipeline you want to use for Image Data Enrichment.

2. Click Add a new pipeline stage and select LWAI Prediction.

3. In the Label field, enter a unique identifier for this stage. For example, LWAI Image Metadata Enrichment.

4. In the Condition field, enter what the document must contain for the stage to select it. For example, doc.hasField("image_url_t") means the document must have the image’s web address stored as a text field to be included in the stage.

5. In the Account Name field, select the Lucidworks AI API account name defined in Lucidworks AI Gateway.

6. In the Use Case field, select or enter image-metadata-enrichment.

7. In the Model field, you must enter a model that can scan multiple types of data, including images, videos, or PDFs. For multi-modal use cases such as image-metadata-enrichment, this example uses the gemini-2.5-flash-lite model.

8. In the Input context variable variable field, enter the name of the field that contains the image URL. For example, <doc.image_url_t>.

9. In the Destination field name and context output field, enter the name that will be used as both the field name in the document where the prediction is written and the context variable that contains the prediction. For example, image_enrichment.

10. In the Use Case Configuration section, you can add parameters and values to send to Lucidworks AI. These useCaseConfig parameters are only included in the stage processing when they are present in the incoming document. The parameters can also include the type of metadata returned and the maximum number of those metadata elements.

    This example specifies to submit the image title, and for the stage to focus on documents already containing certain categories that are located in the United States. The example parameters also specify the stage generate up to three keywords, five synonyms, and three subcategories.

    

11. In the Model Configuration section, you can add parameters and values to send to Lucidworks AI. For example, you can specify parameters such as region to refine the search in the stage.

12. In the API Key field, enter the secret value specified in the external model.

13. Click Save.

Configure the Field Mapping index pipeline stage

You may also need to set some parameters in the Field Mapping index pipeline stage.

1. Click the Field Mapping stage and scroll to the Field Translations section.

   For example, if the source field names are extremely long, you can use the Field Translations section to shorten them for your target index fields.

   

2. When you enter all of the parameter and field values, click Save.

Run the datasource job

Run a datasource job that uses the configured index pipeline to add enriched data to your index. When the datasource job finishes, you can see the image metadata data fields in the Query Workbench.

Configure the Query Fields stage

1. In your query pipeline, select the Query Fields stage.

2. Scroll to the Return Fields section and enter the image metadata fields to include in the query results. The fields need to include, but do not have to be limited to, the fields specified in the indexing stage. This example includes the fields specified in indexing field example.

   

Review the query results

Review the results in the Query Workbench, which should include:

• The image link under the title or the image URL to view the image.

• The title, locale specified as the US in the stage field, and the calculated score of the item.

• The metadata generated based on the categories field in the stage. For this example, the categories requested were bath, kitchen, home, and industrial. The keywords, subcategories, and synonyms now reflect image metadata related to those categories.

  

Using the Prediction API

If you’re not using Fusion, you can enable Image Data Enrichment by making calls to the Prediction API and incorporating the response into your data source.

Example requests

The Prediction API requests below use image metadata enrichment parameters.

Batch with text

curl --request POST \
  --url https://APPLICATION_ID.applications.lucidworks.com/ai/prediction/image-metadata-enrichment/MODEL_ID \
  --header 'Authorization: Bearer ACCESS_TOKEN' \
  --header 'Content-type: application/json' \
  --data '{
  "batch": [
      {
        "text": "https://i.postimg.cc/XYZ12345/leather-messenger-bag.png"
      }
    ],
    "useCaseConfig": {
      "title": "leather-messenger-bag",
      "categories": [
        "bags",
        "leather",
        "professional",
        "business"
      ],
      "maxKeywords": 3,
      "maxSynonyms": 2,
      "maxSubcategories": 2,
      "locale": "en-US"
    }
  }'

curl --request POST \
  --url https://APPLICATION_ID.applications.lucidworks.com/ai/prediction/image-metadata-enrichment/MODEL_ID \
  --header 'Authorization: Bearer ACCESS_TOKEN' \
  --header 'Content-type: application/json' \
  --data '{
    "batch": [
        {
            "imageLink": "https://i.postimg.cc/XYZ12345/leather-messenger-bag.png"
        }
      ],
    "useCaseConfig": {
      "title": "leather-messenger-bag",
      "categories": [
        "bags",
        "leather",
        "professional",
        "business"
      ],
      "maxKeywords": 3,
      "maxSynonyms": 2,
      "maxSubcategories": 2,
      "locale": "en-US"
    }
  }'

encoded=$(base64 -i blushing_happymushroom.png -o - | tr -d '\n');
curl --request POST \
  --url https://APPLICATION_ID.applications.lucidworks.com/ai/prediction/image-metadata-enrichment/MODEL_ID \
  --header 'Authorization: Bearer ACCESS_TOKEN' \
  --header 'Content-type: application/json' \
  --data '{
    "batch": [
        {
            "image": "$encoded"
        }
    ],
    "useCaseConfig": {
      "title": "leather-messenger-bag",
      "categories": [
        "bags",
        "leather",
        "professional",
        "business"
      ],
      "maxKeywords": 3,
      "maxSynonyms": 2,
      "maxSubcategories": 2,
      "locale": "en-US"
    }
  }'

Example response

The response for all of the above examples looks like this:

{
    "predictions":[
    {
        "tokensUsed": {
            "promptTokens": 1614,
            "completionTokens": 61,
            "totalTokens": 1675
        },
        "imageMetadata": {
          "keywords": [
            "leather",
            "messenger",
            "work bag"
          ],
          "subcategories": [
            "professional bags",
            "office accessories"
          ],
          "synonyms": [
            "briefcase",
            "shoulder bag",
            "business bag",
            "laptop bag",
            "work satchel"
          ],
          "locale": "en-US"
        },
        "response": "```yaml\nkeywords:\n  - leather\n  - messenger\n  - work bag\nsubcategories:\n  - professional bags\n  - office accessories\nsynonyms:\n  - briefcase\n  - shoulder bag\n  - business bag\n  - laptop bag\n  - work satchel\n```"
        }
    ]
}