Custom Embeddings with the SDK

Encord enables the use of custom embeddings for images, image sequences, image groups, and individual video frames. Custom embeddings allow you to incorporate your own feature representations into Encord’s platform, enhancing capabilities like similarity search, visualizations, and data filtering. This flexibility supports more advanced workflows and deeper insights tailored to your specific use cases.

Support for videos (in their entirety) is coming soon.

Custom Embeddings Support

We currently support embeddings of dimensions ranging from 1 to 4096 for Index, and 1 to 2000 for Active, following on from our in-house clip Embeddings.

Import Custom Embeddings

To bring your custom embeddings into Encord, you first need to create a key in your metadata schema. After the key is in your schema, you can import your custom embeddings.

Step 1: Create a New Embedding Type

A key is required in your custom metadata schema for your embeddings. You can use any string as the key for your embeddings. We strongly recommend that you use a string that is meaningful.

If you do not include a key in your metadata schema, your imported embeddings are treated as strings.

Embedding key names can contain alphanumeric (a-z, A-Z, 0-1) characters, hyphens, and underscores.

Use add_embedding to add an embedding to your metadata schema.

Key	Description	Display Benefits
embedding	1 to 4096 for Index. 1 to 2000 for Active	Filtering by embeddings, similarity search, 2D scatter plot visualization (Coming Soon)


# Import dependencies
from encord import EncordUserClient
from encord.metadata_schema import MetadataSchema

SSH_PATH = "<file-path-to-ssh-private-key>"

# Authenticate with Encord using the path to your private key
user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key(
    ssh_private_key_path=SSH_PATH
)

# Create the schema
metadata_schema = user_client.metadata_schema()


# Add embedding fields
metadata_schema.add_embedding('my-test-active-embedding', size=512)
metadata_schema.add_embedding('my-test-index-embedding', size=<values-from-1-to-4096>)

# Save the schema
metadata_schema.save()

# Print the schema for verification
print(metadata_schema)

Step 2: Upload Embeddings

With the key in the custom metadata schema ready, we can now import our embeddings.

Custom embedding sizes are flexible and can be set anywhere between 1 and 4096.

You can import embeddings after you have added your data or during your data registration.

Your key frames (frames specified with or without embeddings) always appear in Index, regardless of what sampling rate you specify.

Embedding key names can contain alphanumeric (a-z, A-Z, 0-1) characters, hyphens, and underscores.

If config is not specified, the sampling_rate is 1 frame per second, and the keyframe_mode is frame.

Specifying a sampling_rate of 0 only imports the first frame and all keyframes of your video into Index.

Import while registering images

Import while importing images

This JSON file imports embeddings while registering your data with Index from a cloud integration.

{
  "images": [
    {
      "objectUrl": "file/path/to/images/file-name-01.file-extension",
      "title": "image-title.file-extension",
      "clientMetadata": {"metadata-1": "value", "metadata-2": "value"}
    },
  ],
  "skip_duplicate_urls": true
}

Update specific images

Import specific images

The custom embeddings format for images follows the same format as importing custom metadata.

# Import dependencies
from encord import EncordUserClient
from encord.http.bundle import Bundle

# Authentication
SSH_PATH = "<file-path-to-ssh-private-key>"

# Authenticate with Encord using the path to your private key
user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key(
    ssh_private_key_path=SSH_PATH,
)

# Define a dictionary with item UUIDs and their respective metadata updates
updates = {
    "<data-ID-1>": {"<my-embedding>": [1.0, 2.0, 3.0]},
    "<data-ID-2>": {"<my-embedding>": [1.0, 2.0, 3.0]}
}

# Use the Bundle context manager
with Bundle() as bundle:
    # Update the storage items based on the dictionary
    for item_uuid, metadata_update in updates.items():
        item = user_client.get_storage_item(item_uuid=item_uuid)

        # Make a copy of the current metadata and update it with the new metadata
        curr_metadata = item.client_metadata.copy()
        curr_metadata.update(metadata_update)

        # Update the item with the new metadata and bundle
        item.update(client_metadata=curr_metadata, bundle=bundle)

Import while registering videos

Import while importing videos

This JSON file imports embeddings while registering your data with Index from a cloud integration.

config is optional when importing your custom embeddings:

"config": {
    "sampling_rate": "<samples-per-second>",
    "keyframe_mode": "frame" or "seconds",
},

If config is not specified, the sampling_rate is 1 frame per second, and the keyframe_mode is frame.

Specifying a sampling_rate of 0 only imports the first frame and all keyframes of your video into Index.


{
    "videos": [
        {
            "objectUrl": "<cloud-file-path-to-your-video-1>",
            "title": "<title-for-your-video-1>",
            "clientMetadata": {
                "$encord": {
                    "config": {
                        "sampling_rate": "<samples-per-second>",
                        "keyframe_mode": "frame" or "seconds",
                    },
                    "frames": {
                        "<frame-number-or-seconds>": {
                            "<my-embedding>": [1.0, 2.0, 3.0]
                        },
                        "<frame-number-or-seconds>": {
                            "<my-embedding>": [1.0, 2.0, 3.0]
                        }
                    }
                }
            }
        },
        {
            "objectUrl": "<cloud-file-path-to-your-video-2>",
            "title": "<title-for-your-vide-2>",
            "clientMetadata": {
                "$encord": {
                    "config": {
                        "sampling_rate": "<frames-per-seccond>",
                        "keyframe_mode": "frame" or "seconds",
                    },
                    "frames": {
                        "<frame-number-or-seconds>": {
                            "<my-embedding>": [1.0, 2.0, 3.0]
                        },
                        "<frame-number-or-seconds>": {
                            "<my-embedding>": [1.0, 2.0, 3.0]
                        }
                    }
                }
            }
        },
        {
            "objectUrl": "<cloud-path-to-your-video-3>",
            "title": "<title-for-your-video-3>",
            "clientMetadata": {
                "$encord": {
                    "config": {
                        "sampling_rate": "<frames-per-second>",
                        "keyframe_mode": "frame" or "seconds",
                    },
                    "frames": {
                        "<frame-number-or-seconds>": {
                            "<my-embedding>": [1.0, 2.0, 3.0]
                        },
                        "<frame-number-or-seconds>": {
                            "<my-embedding>": [1.0, 2.0, 3.0]
                        }
                    }
                }
            }
        }
    ],
    "skip_duplicate_urls": true
}

Update specific videos

# Import dependencies
from encord import EncordUserClient
from encord.http.bundle import Bundle
from encord.orm.storage import StorageFolder, StorageItem, StorageItemType, FoldersSortBy

# Authentication
SSH_PATH = "<file-path-to-ssh-private-key>"

# Authenticate with Encord using the path to your private key
user_client: EncordUserClient = EncordUserClient.create_with_ssh_private_key(
    ssh_private_key_path=SSH_PATH,
)

updates = {
    "<data-hash-1>": {
        "$encord": {
            "frames": {
                "<frame-number-1>": {
                    "<my-embedding>": [1.0, 2.0, 3.0],  # custom embedding ("embedding") with float values
                },
                "<frame-number-2>": {
                    "<my-embedding>": [1.0, 2.0, 3.0],  # custom embedding ("embedding") with float values
                }
            }
        }
    },
    "<data-hash-2>": {
        "$encord": {
            "config": {
                "sampling_rate": <samples-per-second>,  # VIDEO ONLY (optional default = 1 sample/second)
                "keyframe_mode": "frame" or "seconds",  # VIDEO ONLY (optional default = "frame")
            },
            "frames": {
                "<frame-number-1>": {
                    "<my-embedding>": [1.0, 2.0, 3.0],  # custom embedding ("embedding") with float values
                },
                "<frame-number-2>": {
                    "<my-embedding>": [1.0, 2.0, 3.0],  # custom embedding ("embedding") with float values
                }
            }
        }
    },
}

# Use the Bundle context manager
with Bundle() as bundle:
    # Update the storage items based on the dictionary
    for item_uuid, metadata_update in updates.items():
        item = user_client.get_storage_item(item_uuid=item_uuid)

        # Make a copy of the current metadata and update it with the new metadata
        curr_metadata = item.client_metadata.copy()
        curr_metadata.update(metadata_update)

        # Update the item with the new metadata and bundle
        item.update(client_metadata=curr_metadata, bundle=bundle)

Final Setup for Custom Embeddings

Once you import custom embeddings to your data (during cloud data registration or to existing data in Encord), there is a bit more setup required in Index and Active. These steps can only be performed from the UI.

Custom Embeddings in Index

Custom Embeddings in Active

Use Custom Embeddings

Custom embeddings in Index and Active allow you to:

Perform similarity searches on your data with custom embeddings
Adjust the similarity distance on similarity searches
View your data in a 2D embedding plot graph
Filter your data based on whether it has custom embeddings or not (Index only)

Get Started

General

Index

Ontologies

Projects

Labels

Datasets

DICOM

Active API & SDK

Custom Embeddings with the SDK

Custom Embeddings Support

Import Custom Embeddings

Step 1: Create a New Embedding Type

Step 2: Upload Embeddings

Import while importing images

Import specific images

Import while importing videos

Update specific videos

Final Setup for Custom Embeddings

Use Custom Embeddings

Get Started

General

Index

Ontologies

Projects

Labels

Datasets

DICOM

Active API & SDK

​Custom Embeddings Support

​Import Custom Embeddings

​Step 1: Create a New Embedding Type

​Step 2: Upload Embeddings

​Final Setup for Custom Embeddings

​Use Custom Embeddings

Custom Embeddings Support

Import Custom Embeddings

Step 1: Create a New Embedding Type

Step 2: Upload Embeddings

Final Setup for Custom Embeddings

Use Custom Embeddings