How This Helps Enrichment runs AI models against your dataset media to unlock advanced capabilities — caption-based search, object detection, face detection, and semantic similarity. Each enrichment operation produces a new dataset copy with the model outputs applied, leaving the original unchanged.
Prerequisites
A Visual Layer Cloud account with API access.
A valid JWT token. See Authentication .
A dataset in READY status with its ID (visible in the browser URL when viewing a dataset: https://app.visual-layer.com/dataset/<dataset_id>/data).
List Available Models Retrieve the models available for enrichment on a dataset.
GET /api/v1/enrichment/{dataset_id}/list_models Authorization: Bearer <jwt>
Example curl -H "Authorization: Bearer <jwt>" \ "https://app.visual-layer.com/api/v1/enrichment/<dataset_id>/list_models"
Response Returns an array of model objects. Each object includes:
Field Description idThe model ID to use when starting enrichment. nameThe display name of the model. typeThe enrichment type (for example, CAPTION_IMAGES, OBJECT_DETECTION). statusActive or Coming Soon. Only Active models can be applied.vendorThe model provider (VL or Nvidia). descriptionWhat the model does. dependenciesOther enrichment types that must be applied first, or null.
[ { "id" : "vl_image_captioner_v00" , "name" : "VL-Image-Captioner" , "type" : "CAPTION_IMAGES" , "status" : "Active" , "vendor" : "VL" , "description" : "Image captioning generates descriptive text that summarizes the content and context of the entire image input." , "dependencies" : null }, { "id" : "vl_object_detector_v00" , "name" : "VL-Object-Detector" , "type" : "OBJECT_DETECTION" , "status" : "Active" , "vendor" : "VL" , "description" : "Identifies and locates objects within images or videos by drawing bounding boxes and classifying each detected object." , "dependencies" : null }, { "id" : "vl_object_captioner_v00" , "name" : "VL-Object-Captioner" , "type" : "CAPTION_OBJECTS" , "status" : "Active" , "vendor" : "VL" , "description" : "Generates descriptive text summarizing detected objects and their interactions." , "dependencies" : [ "OBJECT_DETECTION" ] } ]
Check the dependencies field before selecting models. VL-Object-Captioner, for example, requires OBJECT_DETECTION to be applied first.
Check Applied Enrichments Retrieve the enrichment configuration currently applied to a dataset.
GET /api/v1/enrichment/{dataset_id}/context Authorization: Bearer <jwt>
Example curl -H "Authorization: Bearer <jwt>" \ "https://app.visual-layer.com/api/v1/enrichment/<dataset_id>/context"
Response { "enrichment_context_id" : "3666cf3e-af2b-4c6b-b617-c796520de8fd" , "status" : "NEW" , "dataset_enrichment_models" : { "enrichment_models" : {} } }
A status of NEW means no enrichments have been applied yet. An applied dataset shows the enrichment model IDs in dataset_enrichment_models.enrichment_models.
Start Enrichment Apply one or more AI models to a dataset. Enrichment creates a new dataset copy with the model outputs applied — the original dataset is not modified.
POST /api/v1/dataset/{dataset_id}/enrich_dataset Authorization: Bearer <jwt> Content-Type: application/json
Request Body { "enrichment_models" : { "<enrichment_type>" : "<model_id>" } }
Use the id values from list_models as the model IDs, and the corresponding type values as the keys.
Example curl -X POST \ -H "Authorization: Bearer <jwt>" \ -H "Content-Type: application/json" \ -d '{ "enrichment_models": { "CAPTION_IMAGES": "vl_image_captioner_v00", "OBJECT_DETECTION": "vl_object_detector_v00" } }' \ "https://app.visual-layer.com/api/v1/dataset/<dataset_id>/enrich_dataset"
Response { "dataset_id" : "f7a3c120-9913-11f1-b4ca-de29f6ee0a33" }
The response returns the dataset_id of the new enriched copy . Poll the status endpoint for this new dataset until status_new reaches READY before running search or export operations on it.
The source dataset enters READ ONLY status while enrichment runs. The enriched copy is a separate dataset — it has its own dataset_id and appears as a new entry in your dataset inventory.
Python Example import requests import time VL_BASE_URL = "https://app.visual-layer.com" JWT_TOKEN = "<your-jwt-token>" DATASET_ID = "<your-dataset-id>" headers = { "Authorization" : f "Bearer {JWT_TOKEN} " } # Step 1: List available models resp = requests.get( f " {VL_BASE_URL} /api/v1/enrichment/ {DATASET_ID} /list_models" , headers = headers, ) resp.raise_for_status() models = resp.json() active_models = [m for m in models if m[ "status" ] == "Active" ] print ( f "Available models: { [m[ 'name' ] for m in active_models] } " ) # Step 2: Start enrichment resp = requests.post( f " {VL_BASE_URL} /api/v1/dataset/ {DATASET_ID} /enrich_dataset" , headers = headers, json = { "enrichment_models" : { "CAPTION_IMAGES" : "vl_image_captioner_v00" , "OBJECT_DETECTION" : "vl_object_detector_v00" , } }, ) resp.raise_for_status() enriched_dataset_id = resp.json()[ "dataset_id" ] print ( f "Enriched dataset ID: { enriched_dataset_id } " ) # Step 3: Poll enriched dataset until READY while True : resp = requests.get( f " {VL_BASE_URL} /api/v1/dataset/ { enriched_dataset_id } " , headers = headers, ) resp.raise_for_status() data = resp.json() status = data.get( "status_new" ) progress = data.get( "progress" , 0 ) print ( f " Status: { status } ( { progress } %)" ) if status in ( "READY" , "ERROR" ): break time.sleep( 30 ) print ( f "Enriched dataset ready: { enriched_dataset_id } " )
Response Codes See Error Handling for the error response format and Python handling patterns.
HTTP Code Meaning 200 Request successful. 401 Unauthorized — check your JWT token. 404 Dataset not found or you do not have access. 409 Conflict — dataset is not in a state that allows enrichment. 500 Internal Server Error — contact support if this persists.
Retrieve Dataset Status Poll the enriched dataset until processing completes.
Semantic Search Use caption and semantic embeddings generated by enrichment for search.
