Visual Layer Documentation: Visual Intelligence, At Scale

How This Helps

Snapshots capture the exact state of a dataset — media, metadata, enrichment models, clustering, tags, and labels — at a specific point in time. Use them to create checkpoints before risky operations, roll back to known-good states, or clone a dataset version for parallel experimentation.

Prerequisites

A Visual Layer Cloud account with API access.
A valid JWT token. See Authentication.
A dataset ID for a dataset in Ready status with snapshot support enabled. See Retrieve Dataset Status.

Snapshots are available only for datasets created with snapshot support. Older datasets created before this feature may not support snapshots. Contact support to enable snapshot support on an existing dataset.

List Snapshots

Retrieve all snapshots for a dataset, ordered newest first.

GET /api/v1/dataset/{dataset_id}/snapshot
Authorization: Bearer <jwt>

Path Parameters

Parameter	Type	Required	Description
`dataset_id`	UUID	Yes	The dataset to list snapshots for.

Example

curl -H "Authorization: Bearer <jwt>" \
  "https://app.visual-layer.com/api/v1/dataset/<dataset_id>/snapshot"

Response

[
  {
    "snapshot_id": "183163fa-e999-43da-b92f-964b445af9d7",
    "snapshot_name": "pre-enrichment-checkpoint",
    "source_dataset_name": "Product Images Q1",
    "created_at": "2026-03-29T12:21:33.090247",
    "created_by": "rachel@visual-layer.com",
    "n_images": 26,
    "n_videos": 0,
    "description": "Checkpoint before running VL-Image-Tagger enrichment",
    "reason": "manual",
    "training_model_id": null,
    "training_model_name": null,
    "is_current": true,
    "is_restoring": false
  },
  {
    "snapshot_id": "94b33dbd-f211-47b2-b7b8-ececa5fecac2",
    "snapshot_name": "Product Images Q1_snapshot_94b33dbd",
    "source_dataset_name": "Product Images Q1",
    "created_at": "2026-02-25T10:24:45.690903",
    "created_by": "rachel@visual-layer.com",
    "n_images": 26,
    "n_videos": 0,
    "description": "",
    "reason": "creation",
    "training_model_id": null,
    "training_model_name": null,
    "is_current": false,
    "is_restoring": false
  }
]

Snapshot Object Fields

Each snapshot in the response contains the following fields:

Field	Type	Description
`snapshot_id`	UUID	Unique identifier for the snapshot.
`snapshot_name`	string	Display name. Defaults to `{dataset_name}_snapshot_{snapshot_id}` if not set at creation.
`source_dataset_name`	string	Name of the dataset this snapshot belongs to.
`created_at`	datetime	ISO 8601 timestamp of when the snapshot was created.
`created_by`	string	Email address of the user who created the snapshot.
`n_images`	integer	Number of images in the dataset at the time of the snapshot.
`n_videos`	integer	Number of videos in the dataset at the time of the snapshot.
`description`	string	User-provided description, or empty string if none.
`reason`	string	How the snapshot was created: `manual`, `creation`, or `clone`.
`training_model_id`	UUID or null	Associated training model ID, if the snapshot was created during model training.
`training_model_name`	string or null	Display name of the associated training model.
`is_current`	boolean	`true` if this snapshot represents the active state of the dataset.
`is_restoring`	boolean	`true` if a restore operation is currently in progress for this snapshot.

The reason field indicates how the snapshot was created:

creation — Automatically created when the dataset was first indexed.
manual — Created by a user through the API or UI.
clone — Created as part of a clone operation.

Create a Snapshot

Save the current state of a dataset as a named checkpoint.

POST /api/v1/dataset/{dataset_id}/snapshot
Authorization: Bearer <jwt>
Content-Type: application/json

Path Parameters

Parameter	Type	Required	Description
`dataset_id`	UUID	Yes	The dataset to snapshot.

Request Body

Field	Type	Required	Description
`name`	string	No	Display name for the snapshot. Defaults to `{dataset_name}_snapshot_{snapshot_id}` if omitted.
`description`	string	No	Optional description to record why you created this checkpoint. Defaults to empty string.

Example

curl -X POST \
  -H "Authorization: Bearer <jwt>" \
  -H "Content-Type: application/json" \
  -d '{"name": "pre-enrichment-checkpoint", "description": "Before running VL-Image-Tagger"}' \
  "https://app.visual-layer.com/api/v1/dataset/<dataset_id>/snapshot"

Response

Returns the created snapshot object. See Snapshot Object Fields for the full schema.

{
  "snapshot_id": "183163fa-e999-43da-b92f-964b445af9d7",
  "snapshot_name": "pre-enrichment-checkpoint",
  "source_dataset_name": "Product Images Q1",
  "created_at": "2026-03-29T12:21:33.090247",
  "created_by": "rachel@visual-layer.com",
  "n_images": 26,
  "n_videos": 0,
  "description": "Before running VL-Image-Tagger",
  "reason": "manual",
  "training_model_id": null,
  "training_model_name": null,
  "is_current": true,
  "is_restoring": false
}

The new snapshot becomes the current version of the dataset (is_current: true).

Snapshot creation is blocked while any of the following operations are running on the dataset: dataset creation, enrichment, media addition, re-indexing, label propagation, custom metadata import, or snapshot restore. Wait for the active operation to complete before creating a snapshot.

Update a Snapshot

Change the name or description of an existing snapshot. This does not affect the saved data in the snapshot.

PATCH /api/v1/dataset/{dataset_id}/snapshot/{snapshot_id}
Authorization: Bearer <jwt>
Content-Type: application/json

Path Parameters

Parameter	Type	Required	Description
`dataset_id`	UUID	Yes	The dataset the snapshot belongs to.
`snapshot_id`	UUID	Yes	The snapshot to update.

Request Body

Field	Type	Required	Description
`name`	string	No	New display name for the snapshot.
`description`	string	No	New description text.

Include at least one field. Both fields are optional individually.

Example

curl -X PATCH \
  -H "Authorization: Bearer <jwt>" \
  -H "Content-Type: application/json" \
  -d '{"name": "v1-baseline", "description": "Original dataset before any enrichment"}' \
  "https://app.visual-layer.com/api/v1/dataset/<dataset_id>/snapshot/<snapshot_id>"

Response

Returns the updated snapshot object. See Snapshot Object Fields for the full schema.

Restore a Snapshot

Revert a dataset to a previously saved snapshot state. This is an asynchronous operation — the dataset enters Read Only status while the restore processes.

POST /api/v1/dataset/{dataset_id}/snapshot/{snapshot_id}/restore
Authorization: Bearer <jwt>

Path Parameters

Parameter	Type	Required	Description
`dataset_id`	UUID	Yes	The dataset to restore into.
`snapshot_id`	UUID or `current`	Yes	The snapshot to restore from. Use the literal string `current` to restore the latest stable snapshot.

Example

# Restore a specific snapshot
curl -X POST \
  -H "Authorization: Bearer <jwt>" \
  "https://app.visual-layer.com/api/v1/dataset/<dataset_id>/snapshot/<snapshot_id>/restore"

# Restore the latest stable snapshot
curl -X POST \
  -H "Authorization: Bearer <jwt>" \
  "https://app.visual-layer.com/api/v1/dataset/<dataset_id>/snapshot/current/restore"

Response

Returns HTTP 202 Accepted with an empty body. The restore operation runs asynchronously. Track the restore progress using the Task Manager API. A SNAPSHOT_RESTORE task appears with the dataset ID. The dataset returns to Ready status once the restore completes.

Restore replaces all current dataset data — media, metadata, enrichment results, clustering, tags, and labels — with the saved state of the snapshot. This operation cannot be undone. Create a new snapshot of the current state before restoring if you want to preserve it.

State During Restore

While a restore is in progress:

The dataset status changes to Read Only.
The snapshot is_restoring field is true in the list response.
All data-modifying operations (enrichment, media addition, re-indexing) are blocked.
Read operations (search, explore, export) continue to work against the pre-restore data.

Clone a Snapshot

Create a new, independent dataset from a snapshot. The original dataset is unaffected. This is an asynchronous operation.

POST /api/v1/dataset/{dataset_id}/snapshot/{snapshot_id}/clone
Authorization: Bearer <jwt>

Path Parameters

Parameter	Type	Required	Description
`dataset_id`	UUID	Yes	The source dataset containing the snapshot.
`snapshot_id`	UUID	Yes	The snapshot to clone from.

Example

curl -X POST \
  -H "Authorization: Bearer <jwt>" \
  "https://app.visual-layer.com/api/v1/dataset/<dataset_id>/snapshot/<snapshot_id>/clone"

Response

Returns HTTP 202 Accepted with an empty body. The clone operation runs asynchronously. The new dataset appears in the Dataset Inventory with the name {Original Dataset Name} (cloned). Track the clone progress using the Task Manager API. A SNAPSHOT_CLONE task appears for the new dataset.

The cloned dataset is fully independent. Changes to the clone do not affect the original dataset, and changes to the original do not affect the clone.

Delete a Snapshot

Remove a snapshot from the catalog. This is a soft delete — the snapshot is hidden from all API responses but does not affect the current dataset data or any datasets previously cloned from the snapshot.

DELETE /api/v1/dataset/{dataset_id}/snapshot/{snapshot_id}
Authorization: Bearer <jwt>

Path Parameters

Parameter	Type	Required	Description
`dataset_id`	UUID	Yes	The dataset the snapshot belongs to.
`snapshot_id`	UUID	Yes	The snapshot to delete.

Example

curl -X DELETE \
  -H "Authorization: Bearer <jwt>" \
  "https://app.visual-layer.com/api/v1/dataset/<dataset_id>/snapshot/<snapshot_id>"

Response

Returns HTTP 200 OK with an empty body.

The snapshot marked as the current version cannot be deleted. Create a new snapshot first to change the current version, then delete the old one.

Python Example

This example demonstrates the full snapshot lifecycle: listing existing snapshots, creating a checkpoint, verifying it, and cleaning up.

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}"}

# List existing snapshots
resp = requests.get(
    f"{VL_BASE_URL}/api/v1/dataset/{DATASET_ID}/snapshot",
    headers=headers,
)
resp.raise_for_status()
snapshots = resp.json()
print(f"Existing snapshots: {len(snapshots)}")
for s in snapshots:
    current = " (current)" if s["is_current"] else ""
    print(f"  {s['snapshot_name']}: {s['n_images']} images, {s['reason']}{current}")

# Create a checkpoint before enrichment
resp = requests.post(
    f"{VL_BASE_URL}/api/v1/dataset/{DATASET_ID}/snapshot",
    headers=headers,
    json={
        "name": "pre-enrichment-checkpoint",
        "description": "Before running VL-Image-Tagger enrichment",
    },
)
resp.raise_for_status()
snapshot = resp.json()
snapshot_id = snapshot["snapshot_id"]
print(f"\nCreated snapshot: {snapshot_id}")
print(f"  Name: {snapshot['snapshot_name']}")
print(f"  Images: {snapshot['n_images']}, Videos: {snapshot['n_videos']}")

# Update the snapshot description
resp = requests.patch(
    f"{VL_BASE_URL}/api/v1/dataset/{DATASET_ID}/snapshot/{snapshot_id}",
    headers=headers,
    json={"description": "Baseline before VL-Image-Tagger v2 enrichment run"},
)
resp.raise_for_status()
print(f"  Updated description: {resp.json()['description']}")

# Restore a previous snapshot (async — returns 202)
previous_id = snapshots[-1]["snapshot_id"]  # oldest snapshot
resp = requests.post(
    f"{VL_BASE_URL}/api/v1/dataset/{DATASET_ID}/snapshot/{previous_id}/restore",
    headers=headers,
)
if resp.status_code == 202:
    print(f"\nRestore initiated for snapshot {previous_id}")
    print("Track progress in Task Manager (task_type=SNAPSHOT_RESTORE)")

# Poll task manager for restore completion
while True:
    resp = requests.get(
        f"{VL_BASE_URL}/api/v1/tasks",
        headers=headers,
        params={"dataset_id": DATASET_ID, "task_type": "SNAPSHOT_RESTORE"},
    )
    resp.raise_for_status()
    tasks = resp.json()["tasks"]
    latest = tasks[0] if tasks else None
    if latest and latest["status"] in ("COMPLETED", "FAILED"):
        print(f"  Restore {latest['status'].lower()}")
        break
    time.sleep(5)

Use Cases

Checkpoint Before Enrichment

Create a snapshot before running a new enrichment model. If the results are unsatisfactory, restore the snapshot to undo the enrichment and try a different model.

# 1. Create checkpoint
curl -X POST -H "Authorization: Bearer <jwt>" \
  -H "Content-Type: application/json" \
  -d '{"name": "pre-captioning", "description": "Before VL-Image-Captioner run"}' \
  "https://app.visual-layer.com/api/v1/dataset/<dataset_id>/snapshot"

# 2. Run enrichment (separate API call)
# ...

# 3. If results are not satisfactory, restore the checkpoint
curl -X POST -H "Authorization: Bearer <jwt>" \
  "https://app.visual-layer.com/api/v1/dataset/<dataset_id>/snapshot/<snapshot_id>/restore"

A/B Test Enrichment Models

Clone a snapshot to create two identical datasets, then apply different enrichment models to each for comparison.

# 1. Create a snapshot of the baseline dataset
curl -X POST -H "Authorization: Bearer <jwt>" \
  -H "Content-Type: application/json" \
  -d '{"name": "baseline-for-comparison"}' \
  "https://app.visual-layer.com/api/v1/dataset/<dataset_id>/snapshot"

# 2. Clone the snapshot to a new dataset
curl -X POST -H "Authorization: Bearer <jwt>" \
  "https://app.visual-layer.com/api/v1/dataset/<dataset_id>/snapshot/<snapshot_id>/clone"

# 3. Run Model A on the original, Model B on the clone, and compare results

Audit Trail

List all snapshots to see the history of dataset checkpoints and understand how the dataset evolved over time.

curl -H "Authorization: Bearer <jwt>" \
  "https://app.visual-layer.com/api/v1/dataset/<dataset_id>/snapshot"

The reason field distinguishes between the automatic creation snapshot, manual checkpoints, and cloned versions.

Operation Constraints

Keep the following constraints in mind when working with the Snapshot API:

The dataset must be in Ready status to create a snapshot. Restore and clone also accept Partial Index and Error statuses.
Snapshot creation is blocked while another operation is running (enrichment, media addition, re-indexing, label propagation, custom metadata import, or snapshot restore).
Restore is blocked while any major operation is running, including other restore or clone operations.
Clone operations run on the new dataset, so they do not block operations on the source dataset.
The current version snapshot cannot be deleted. Create a new snapshot to change the current version first.
Restore and clone are asynchronous. The dataset enters Read Only status during restore. Track progress through the Task Manager API.

Response Codes

See Error Handling for the error response format and Python handling patterns.

HTTP Code	Meaning
200	Request successful (list, create, update, delete).
202	Accepted — async operation started (restore, clone).
403	Forbidden — snapshot feature not enabled for this dataset, or insufficient permissions.
404	Dataset or snapshot not found.
409	Conflict — dataset not in valid status, conflicting operation running, or attempting to delete the current version.
500	Internal Server Error — contact support if this persists.

Dataset Snapshots (UI)

Manage snapshots from the Visual Layer interface.

Task Manager

Track snapshot restore and clone operations programmatically.

Enrichment

Run AI models on your dataset — create a snapshot first as a safety net.

Export Dataset

Export a specific version of your dataset after restoring a snapshot.

How This Helps

​Prerequisites

​List Snapshots

​Path Parameters

​Example

​Response

​Snapshot Object Fields

​Create a Snapshot

​Path Parameters

​Request Body

​Example

​Response

​Update a Snapshot

​Path Parameters

​Request Body

​Example

​Response

​Restore a Snapshot

​Path Parameters

​Example

​Response

​State During Restore

​Clone a Snapshot

​Path Parameters

​Example

​Response

​Delete a Snapshot

​Path Parameters

​Example

​Response

​Python Example

​Use Cases

​Checkpoint Before Enrichment

​A/B Test Enrichment Models

​Audit Trail

​Operation Constraints

​Response Codes

​Related Resources

Dataset Snapshots (UI)

Task Manager

Enrichment

Export Dataset

Prerequisites

List Snapshots

Path Parameters

Example

Response

Snapshot Object Fields

Create a Snapshot

Path Parameters

Request Body

Example

Response

Update a Snapshot

Path Parameters

Request Body

Example

Response

Restore a Snapshot

Path Parameters

Example

Response

State During Restore

Clone a Snapshot

Path Parameters

Example

Response

Delete a Snapshot

Path Parameters

Example

Response

Python Example

Use Cases

Checkpoint Before Enrichment

A/B Test Enrichment Models

Audit Trail

Operation Constraints

Response Codes

Related Resources