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# fiftyone.utils.twelvelabs

[TwelveLabs](https://twelvelabs.io) video understanding integration.

This module provides a [`fiftyone.core.models.Model`](fiftyone.core.models.md#fiftyone.core.models.Model) that wraps the
[TwelveLabs](https://docs.twelvelabs.io) video foundation models for video
dataset curation:

- **Marengo** generates 512-dimensional video embeddings (and matching text
  embeddings), enabling `compute_embeddings()`,
  `compute_visualization()`, and text-to-video
  `compute_similarity()` searches
- **Pegasus** generates natural-language captions/answers about a video

The models run server-side via the TwelveLabs API, so no local GPU is
required. Set your API key via the `TWELVELABS_API_KEY` environment variable
or pass `api_key=...` when loading the model.

Copyright 2017-2026, Voxel51, Inc.
<br/>
[voxel51.com](https://voxel51.com/)
<br/>
<br/>

**Classes:**

| [`TwelveLabsModelConfig`](#fiftyone.utils.twelvelabs.TwelveLabsModelConfig)(d)   | Configuration for running a [`TwelveLabsModel`](#fiftyone.utils.twelvelabs.TwelveLabsModel).    |
|----------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------|
| [`TwelveLabsModel`](#fiftyone.utils.twelvelabs.TwelveLabsModel)(config)          | Wrapper for running inference with [TwelveLabs](https://twelvelabs.io) video foundation models. |

### *class* fiftyone.utils.twelvelabs.TwelveLabsModelConfig(d)

Bases: [`Config`](fiftyone.core.config.md#fiftyone.core.config.Config), [`HasZooModel`](fiftyone.zoo.models.md#fiftyone.zoo.models.HasZooModel)

Configuration for running a [`TwelveLabsModel`](#fiftyone.utils.twelvelabs.TwelveLabsModel).

* **Parameters:**
  * **operation** ( *"embed"*) – the operation to perform when the model is
    applied. Supported values are `"embed"` (Marengo video
    embeddings) and `"caption"` (Pegasus video captions)
  * **api_key** (*None*) – the TwelveLabs API key to use. If not provided, the
    `TWELVELABS_API_KEY` environment variable is used
  * **embedding_model** ( *"marengo3.0"*) – the Marengo model to use for
    embeddings
  * **analysis_model** ( *"pegasus1.5"*) – the Pegasus model to use for captioning
  * **prompt** (*None*) – the prompt to use for `"caption"` operations. If not
    provided, a default captioning prompt is used
  * **max_tokens** (*512*) – the maximum number of tokens to generate for
    `"caption"` operations (must be >= 512)
  * **temperature** (*None*) – an optional sampling temperature for captioning

**Methods:**

| [`attributes`](#fiftyone.utils.twelvelabs.TwelveLabsModelConfig.attributes)()                                                 | Returns a list of class attributes to be serialized.                                                                   |
|-------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------|
| [`builder`](#fiftyone.utils.twelvelabs.TwelveLabsModelConfig.builder)()                                                       | Returns a ConfigBuilder instance for this class.                                                                       |
| [`copy`](#fiftyone.utils.twelvelabs.TwelveLabsModelConfig.copy)()                                                             | Returns a deep copy of the object.                                                                                     |
| [`custom_attributes`](#fiftyone.utils.twelvelabs.TwelveLabsModelConfig.custom_attributes)([dynamic, private])                 | Returns a customizable list of class attributes.                                                                       |
| [`default`](#fiftyone.utils.twelvelabs.TwelveLabsModelConfig.default)()                                                       | Returns the default config instance.                                                                                   |
| [`download_model_if_necessary`](#fiftyone.utils.twelvelabs.TwelveLabsModelConfig.download_model_if_necessary)()               | Downloads the published model specified by the config, if necessary.                                                   |
| [`from_dict`](#fiftyone.utils.twelvelabs.TwelveLabsModelConfig.from_dict)(d)                                                  | Constructs a Config object from a JSON dictionary.                                                                     |
| [`from_json`](#fiftyone.utils.twelvelabs.TwelveLabsModelConfig.from_json)(path, \*args, \*\*kwargs)                           | Constructs a Serializable object from a JSON file.                                                                     |
| [`from_kwargs`](#fiftyone.utils.twelvelabs.TwelveLabsModelConfig.from_kwargs)(\*\*kwargs)                                     | Constructs a Config object from keyword arguments.                                                                     |
| [`from_str`](#fiftyone.utils.twelvelabs.TwelveLabsModelConfig.from_str)(s, \*args, \*\*kwargs)                                | Constructs a Serializable object from a JSON string.                                                                   |
| [`get_class_name`](#fiftyone.utils.twelvelabs.TwelveLabsModelConfig.get_class_name)()                                         | Returns the fully-qualified class name string of this object.                                                          |
| [`init`](#fiftyone.utils.twelvelabs.TwelveLabsModelConfig.init)(d)                                                            | Initializes the published model config.                                                                                |
| [`load_default`](#fiftyone.utils.twelvelabs.TwelveLabsModelConfig.load_default)()                                             | Loads the default config instance from file.                                                                           |
| [`parse_array`](#fiftyone.utils.twelvelabs.TwelveLabsModelConfig.parse_array)(d, key[, default])                              | Parses a raw array attribute.                                                                                          |
| [`parse_bool`](#fiftyone.utils.twelvelabs.TwelveLabsModelConfig.parse_bool)(d, key[, default])                                | Parses a boolean value.                                                                                                |
| [`parse_categorical`](#fiftyone.utils.twelvelabs.TwelveLabsModelConfig.parse_categorical)(d, key, choices[, default])         | Parses a categorical JSON field, which must take a value from among the given choices.                                 |
| [`parse_dict`](#fiftyone.utils.twelvelabs.TwelveLabsModelConfig.parse_dict)(d, key[, default])                                | Parses a dictionary attribute.                                                                                         |
| [`parse_int`](#fiftyone.utils.twelvelabs.TwelveLabsModelConfig.parse_int)(d, key[, default])                                  | Parses an integer attribute.                                                                                           |
| [`parse_mutually_exclusive_fields`](#fiftyone.utils.twelvelabs.TwelveLabsModelConfig.parse_mutually_exclusive_fields)(fields) | Parses a mutually exclusive dictionary of pre-parsed fields, which must contain exactly one field with a truthy value. |
| [`parse_number`](#fiftyone.utils.twelvelabs.TwelveLabsModelConfig.parse_number)(d, key[, default])                            | Parses a number attribute.                                                                                             |
| [`parse_object`](#fiftyone.utils.twelvelabs.TwelveLabsModelConfig.parse_object)(d, key, cls[, default])                       | Parses an object attribute.                                                                                            |
| [`parse_object_array`](#fiftyone.utils.twelvelabs.TwelveLabsModelConfig.parse_object_array)(d, key, cls[, default])           | Parses an array of objects.                                                                                            |
| [`parse_object_dict`](#fiftyone.utils.twelvelabs.TwelveLabsModelConfig.parse_object_dict)(d, key, cls[, default])             | Parses a dictionary whose values are objects.                                                                          |
| [`parse_path`](#fiftyone.utils.twelvelabs.TwelveLabsModelConfig.parse_path)(d, key[, default])                                | Parses a path attribute.                                                                                               |
| [`parse_raw`](#fiftyone.utils.twelvelabs.TwelveLabsModelConfig.parse_raw)(d, key[, default])                                  | Parses a raw (arbitrary) JSON field.                                                                                   |
| [`parse_string`](#fiftyone.utils.twelvelabs.TwelveLabsModelConfig.parse_string)(d, key[, default])                            | Parses a string attribute.                                                                                             |
| [`serialize`](#fiftyone.utils.twelvelabs.TwelveLabsModelConfig.serialize)([reflective])                                       | Serializes the object into a dictionary.                                                                               |
| [`to_str`](#fiftyone.utils.twelvelabs.TwelveLabsModelConfig.to_str)([pretty_print])                                           | Returns a string representation of this object.                                                                        |
| [`validate_all_or_nothing_fields`](#fiftyone.utils.twelvelabs.TwelveLabsModelConfig.validate_all_or_nothing_fields)(fields)   | Validates a dictionary of pre-parsed fields checking that either all or none of the fields have a truthy value.        |
| [`write_json`](#fiftyone.utils.twelvelabs.TwelveLabsModelConfig.write_json)(path[, pretty_print])                             | Serializes the object and writes it to disk.                                                                           |

#### attributes()

Returns a list of class attributes to be serialized.

This method is called internally by `serialize()` to determine the
class attributes to serialize.

Subclasses can override this method, but, by default, all attributes in
vars(self) are returned, minus private attributes, i.e., those starting
with “_”. The order of the attributes in this list is preserved when
serializing objects, so a common pattern is for subclasses to override
this method if they want their JSON files to be organized in a
particular way.

* **Returns:**
  a list of class attributes to be serialized

#### *classmethod* builder()

Returns a ConfigBuilder instance for this class.

#### copy()

Returns a deep copy of the object.

* **Returns:**
  a Serializable instance

#### custom_attributes(dynamic=False, private=False)

Returns a customizable list of class attributes.

By default, all attributes in vars(self) are returned, minus private
attributes (those starting with “_”).

* **Parameters:**
  * **dynamic** – whether to include dynamic properties, e.g., those defined
    by getter/setter methods or the `@property` decorator. By
    default, this is False
  * **private** – whether to include private properties, i.e., those
    starting with “_”. By default, this is False
* **Returns:**
  a list of class attributes

#### *classmethod* default()

Returns the default config instance.

By default, this method instantiates the class from an empty
dictionary, which will only succeed if all attributes are optional.
Otherwise, subclasses should override this method to provide the
desired default configuration.

#### download_model_if_necessary()

Downloads the published model specified by the config, if necessary.

After this method is called, the `model_path` attribute will always
contain the path to the model on disk.

#### *classmethod* from_dict(d)

Constructs a Config object from a JSON dictionary.

Config subclass constructors accept JSON dictionaries, so this method
simply passes the dictionary to cls().

* **Parameters:**
  **d** – a dict of fields expected by cls
* **Returns:**
  an instance of cls

#### *classmethod* from_json(path, \*args, \*\*kwargs)

Constructs a Serializable object from a JSON file.

Subclasses may override this method, but, by default, this method
simply reads the JSON and calls from_dict(), which subclasses must
implement.

* **Parameters:**
  * **path** – the path to the JSON file on disk
  * **\*args** – optional positional arguments for `self.from_dict()`
  * **\*\*kwargs** – optional keyword arguments for `self.from_dict()`
* **Returns:**
  an instance of the Serializable class

#### *classmethod* from_kwargs(\*\*kwargs)

Constructs a Config object from keyword arguments.

* **Parameters:**
  **\*\*kwargs** – keyword arguments that define the fields expected by cls
* **Returns:**
  an instance of cls

#### *classmethod* from_str(s, \*args, \*\*kwargs)

Constructs a Serializable object from a JSON string.

Subclasses may override this method, but, by default, this method
simply parses the string and calls from_dict(), which subclasses must
implement.

* **Parameters:**
  * **s** – a JSON string representation of a Serializable object
  * **\*args** – optional positional arguments for `self.from_dict()`
  * **\*\*kwargs** – optional keyword arguments for `self.from_dict()`
* **Returns:**
  an instance of the Serializable class

#### *classmethod* get_class_name()

Returns the fully-qualified class name string of this object.

#### init(d)

Initializes the published model config.

This method should be called by `ModelConfig.__init__()`, and it
performs the following tasks:

- Parses the `model_name` and `model_path` parameters
- Populates any default parameters in the provided ModelConfig dict

* **Parameters:**
  **d** – a ModelConfig dict
* **Returns:**
  a ModelConfig dict with any default parameters populated

#### *classmethod* load_default()

Loads the default config instance from file.

Subclasses must implement this method if they intend to support
default instances.

#### *static* parse_array(d, key, default=<eta.core.config.NoDefault object>)

Parses a raw array attribute.

* **Parameters:**
  * **d** – a JSON dictionary
  * **key** – the key to parse
  * **default** – a default list to return if key is not present
* **Returns:**
  a list of raw (untouched) values
* **Raises:**
  [**ConfigError**](fiftyone.zoo.md#fiftyone.zoo.ConfigError) – if the field value was the wrong type or no default
      value was provided and the key was not found in the dictionary

#### *static* parse_bool(d, key, default=<eta.core.config.NoDefault object>)

Parses a boolean value.

* **Parameters:**
  * **d** – a JSON dictionary
  * **key** – the key to parse
  * **default** – a default bool to return if key is not present
* **Returns:**
  True/False
* **Raises:**
  [**ConfigError**](fiftyone.zoo.md#fiftyone.zoo.ConfigError) – if the field value was the wrong type or no default
      value was provided and the key was not found in the dictionary

#### *static* parse_categorical(d, key, choices, default=<eta.core.config.NoDefault object>)

Parses a categorical JSON field, which must take a value from among
the given choices.

* **Parameters:**
  * **d** – a JSON dictionary
  * **key** – the key to parse
  * **choices** – either an iterable of possible values or an enum-like
    class whose attributes define the possible values
  * **default** – a default value to return if key is not present
* **Returns:**
  the raw (untouched) value of the given field, which is equal to a
  value from `choices`
* **Raises:**
  [**ConfigError**](fiftyone.zoo.md#fiftyone.zoo.ConfigError) – if the key was present in the dictionary but its value
      was not an allowed choice, or if no default value was provided
      and the key was not found in the dictionary

#### *static* parse_dict(d, key, default=<eta.core.config.NoDefault object>)

Parses a dictionary attribute.

* **Parameters:**
  * **d** – a JSON dictionary
  * **key** – the key to parse
  * **default** – a default dict to return if key is not present
* **Returns:**
  a dictionary
* **Raises:**
  [**ConfigError**](fiftyone.zoo.md#fiftyone.zoo.ConfigError) – if the field value was the wrong type or no default
      value was provided and the key was not found in the dictionary

#### *static* parse_int(d, key, default=<eta.core.config.NoDefault object>)

Parses an integer attribute.

* **Parameters:**
  * **d** – a JSON dictionary
  * **key** – the key to parse
  * **default** – a default integer value to return if key is not present
* **Returns:**
  an int
* **Raises:**
  [**ConfigError**](fiftyone.zoo.md#fiftyone.zoo.ConfigError) – if the field value was the wrong type or no default
      value was provided and the key was not found in the dictionary

#### *static* parse_mutually_exclusive_fields(fields)

Parses a mutually exclusive dictionary of pre-parsed fields, which
must contain exactly one field with a truthy value.

* **Parameters:**
  **fields** – a dictionary of pre-parsed fields
* **Returns:**
  the (field, value) that was set
* **Raises:**
  [**ConfigError**](fiftyone.zoo.md#fiftyone.zoo.ConfigError) – if zero or more than one truthy value was found

#### *static* parse_number(d, key, default=<eta.core.config.NoDefault object>)

Parses a number attribute.

* **Parameters:**
  * **d** – a JSON dictionary
  * **key** – the key to parse
  * **default** – a default numeric value to return if key is not present
* **Returns:**
  a number (e.g. int, float)
* **Raises:**
  [**ConfigError**](fiftyone.zoo.md#fiftyone.zoo.ConfigError) – if the field value was the wrong type or no default
      value was provided and the key was not found in the dictionary

#### *static* parse_object(d, key, cls, default=<eta.core.config.NoDefault object>)

Parses an object attribute.

The value of d[key] can be either an instance of cls or a serialized
dict from an instance of cls.

* **Parameters:**
  * **d** – a JSON dictionary
  * **key** – the key to parse
  * **cls** – the class of d[key]
  * **default** – a default cls instance to return if key is not present
* **Returns:**
  an instance of cls
* **Raises:**
  [**ConfigError**](fiftyone.zoo.md#fiftyone.zoo.ConfigError) – if the field value was the wrong type or no default
      value was provided and the key was not found in the dictionary

#### *static* parse_object_array(d, key, cls, default=<eta.core.config.NoDefault object>)

Parses an array of objects.

The values in d[key] can be either instances of cls or serialized
dicts from instances of cls.

* **Parameters:**
  * **d** – a JSON dictionary
  * **key** – the key to parse
  * **cls** – the class of the elements of list d[key]
  * **default** – the default list to return if key is not present
* **Returns:**
  a list of cls instances
* **Raises:**
  [**ConfigError**](fiftyone.zoo.md#fiftyone.zoo.ConfigError) – if the field value was the wrong type or no default
      value was provided and the key was not found in the dictionary

#### *static* parse_object_dict(d, key, cls, default=<eta.core.config.NoDefault object>)

Parses a dictionary whose values are objects.

The values in d[key] can be either instances of cls or serialized
dicts from instances of cls.

* **Parameters:**
  * **d** – a JSON dictionary
  * **key** – the key to parse
  * **cls** – the class of the values of dictionary d[key]
  * **default** – the default dict of cls instances to return if key is not
    present
* **Returns:**
  a dictionary whose values are cls instances
* **Raises:**
  [**ConfigError**](fiftyone.zoo.md#fiftyone.zoo.ConfigError) – if the field value was the wrong type or no default
      value was provided and the key was not found in the dictionary

#### *static* parse_path(d, key, default=<eta.core.config.NoDefault object>)

Parses a path attribute.

The path is converted to an absolute path if necessary via
`os.path.abspath(os.path.expanduser(value))`.

* **Parameters:**
  * **d** – a JSON dictionary
  * **key** – the key to parse
  * **default** – a default string to return if key is not present
* **Returns:**
  a path string
* **Raises:**
  [**ConfigError**](fiftyone.zoo.md#fiftyone.zoo.ConfigError) – if the field value was the wrong type or no default
      value was provided and the key was not found in the dictionary

#### *static* parse_raw(d, key, default=<eta.core.config.NoDefault object>)

Parses a raw (arbitrary) JSON field.

* **Parameters:**
  * **d** – a JSON dictionary
  * **key** – the key to parse
  * **default** – a default value to return if key is not present
* **Returns:**
  the raw (untouched) value of the given field
* **Raises:**
  [**ConfigError**](fiftyone.zoo.md#fiftyone.zoo.ConfigError) – if no default value was provided and the key was not
      found in the dictionary

#### *static* parse_string(d, key, default=<eta.core.config.NoDefault object>)

Parses a string attribute.

* **Parameters:**
  * **d** – a JSON dictionary
  * **key** – the key to parse
  * **default** – a default string to return if key is not present
* **Returns:**
  a string
* **Raises:**
  [**ConfigError**](fiftyone.zoo.md#fiftyone.zoo.ConfigError) – if the field value was the wrong type or no default
      value was provided and the key was not found in the dictionary

#### serialize(reflective=False)

Serializes the object into a dictionary.

Serialization is applied recursively to all attributes in the object,
including element-wise serialization of lists and dictionary values.

* **Parameters:**
  **reflective** – whether to include reflective attributes when
  serializing the object. By default, this is False
* **Returns:**
  a JSON dictionary representation of the object

#### to_str(pretty_print=True, \*\*kwargs)

Returns a string representation of this object.

* **Parameters:**
  * **pretty_print** – whether to render the JSON in human readable format
    with newlines and indentations. By default, this is True
  * **\*\*kwargs** – optional keyword arguments for `self.serialize()`
* **Returns:**
  a string representation of the object

#### *static* validate_all_or_nothing_fields(fields)

Validates a dictionary of pre-parsed fields checking that either
all or none of the fields have a truthy value.

* **Parameters:**
  **fields** – a dictionary of pre-parsed fields
* **Raises:**
  [**ConfigError**](fiftyone.zoo.md#fiftyone.zoo.ConfigError) – if some values are truth and some are not

#### write_json(path, pretty_print=False, \*\*kwargs)

Serializes the object and writes it to disk.

* **Parameters:**
  * **path** – the output path
  * **pretty_print** – whether to render the JSON in human readable format
    with newlines and indentations. By default, this is False
  * **\*\*kwargs** – optional keyword arguments for `self.serialize()`

### *class* fiftyone.utils.twelvelabs.TwelveLabsModel(config)

Bases: [`Model`](fiftyone.core.models.md#fiftyone.core.models.Model), [`EmbeddingsMixin`](fiftyone.core.models.md#fiftyone.core.models.EmbeddingsMixin), [`PromptMixin`](fiftyone.core.models.md#fiftyone.core.models.PromptMixin)

Wrapper for running inference with [TwelveLabs](https://twelvelabs.io)
video foundation models.

Example usage:

```default
import fiftyone as fo
import fiftyone.zoo as foz
import fiftyone.brain as fob
from fiftyone.utils.twelvelabs import TwelveLabsModel, TwelveLabsModelConfig

dataset = foz.load_zoo_dataset("quickstart-video", max_samples=2)

#
# Video embeddings
#

# Load directly
model = TwelveLabsModel(TwelveLabsModelConfig({"operation": "embed"}))

# Load via zoo
# model = foz.load_zoo_model("twelvelabs-marengo3.0")

dataset.compute_embeddings(model, embeddings_field="twelvelabs")

# Text-to-video search
index = fob.compute_similarity(
    dataset,
    model=model,
    embeddings="twelvelabs",
    brain_key="tl_sim",
)

view = dataset.sort_by_similarity(
    "a person riding a bike",
    brain_key="tl_sim",
    k=10,
)

session = fo.launch_app(view)

#
# Video captions
#

# Load directly
model = TwelveLabsModel(TwelveLabsModelConfig({"operation": "caption"}))

# Load viz zoo
# model = foz.load_zoo_model("twelvelabs-pegasus1.5")

dataset.apply_model(model, label_field="caption")
```

* **Parameters:**
  **config** – a [`TwelveLabsModelConfig`](#fiftyone.utils.twelvelabs.TwelveLabsModelConfig)

**Attributes:**

| [`media_type`](#fiftyone.utils.twelvelabs.TwelveLabsModel.media_type)               | The media type processed by the model.                                                                                                                                     |
|-------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| [`has_embeddings`](#fiftyone.utils.twelvelabs.TwelveLabsModel.has_embeddings)       | Whether this model can generate embeddings.                                                                                                                                |
| [`can_embed_prompts`](#fiftyone.utils.twelvelabs.TwelveLabsModel.can_embed_prompts) | Whether this model can generate prompt embeddings.                                                                                                                         |
| [`ragged_batches`](#fiftyone.utils.twelvelabs.TwelveLabsModel.ragged_batches)       | True/False whether [`transforms()`](#fiftyone.utils.twelvelabs.TwelveLabsModel.transforms) may return tensors of different sizes.                                          |
| [`transforms`](#fiftyone.utils.twelvelabs.TwelveLabsModel.transforms)               | The preprocessing function that will/must be applied to each input before prediction, or `None` if no preprocessing is performed.                                          |
| [`preprocess`](#fiftyone.utils.twelvelabs.TwelveLabsModel.preprocess)               | Whether to apply [`transforms()`](#fiftyone.utils.twelvelabs.TwelveLabsModel.transforms) during inference (True) or to assume that they have already been applied (False). |
| [`has_logits`](#fiftyone.utils.twelvelabs.TwelveLabsModel.has_logits)               | Whether this model can generate logits for its predictions.                                                                                                                |

**Methods:**

| [`predict`](#fiftyone.utils.twelvelabs.TwelveLabsModel.predict)(arg)                   | Generates a caption for the given video.                                                           |
|----------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|
| [`embed`](#fiftyone.utils.twelvelabs.TwelveLabsModel.embed)(arg)                       | Generates a Marengo embedding for the given video.                                                 |
| [`get_embeddings`](#fiftyone.utils.twelvelabs.TwelveLabsModel.get_embeddings)()        | Returns the embeddings generated by the last forward pass of the model.                            |
| [`embed_prompt`](#fiftyone.utils.twelvelabs.TwelveLabsModel.embed_prompt)(arg)         | Generates a Marengo text embedding for the given prompt.                                           |
| [`embed_all`](#fiftyone.utils.twelvelabs.TwelveLabsModel.embed_all)(args)              | Generates embeddings for the given iterable of data.                                               |
| [`embed_prompts`](#fiftyone.utils.twelvelabs.TwelveLabsModel.embed_prompts)(args)      | Generates embeddings for the given prompts.                                                        |
| [`from_config`](#fiftyone.utils.twelvelabs.TwelveLabsModel.from_config)(config)        | Instantiates a Configurable class from a <cls>Config instance.                                     |
| [`from_dict`](#fiftyone.utils.twelvelabs.TwelveLabsModel.from_dict)(d)                 | Instantiates a Configurable class from a <cls>Config dict.                                         |
| [`from_json`](#fiftyone.utils.twelvelabs.TwelveLabsModel.from_json)(json_path)         | Instantiates a Configurable class from a <cls>Config JSON file.                                    |
| [`from_kwargs`](#fiftyone.utils.twelvelabs.TwelveLabsModel.from_kwargs)(\*\*kwargs)    | Instantiates a Configurable class from keyword arguments defining the attributes of a <cls>Config. |
| [`parse`](#fiftyone.utils.twelvelabs.TwelveLabsModel.parse)(class_name[, module_name]) | Parses a Configurable subclass name string.                                                        |
| [`predict_all`](#fiftyone.utils.twelvelabs.TwelveLabsModel.predict_all)(args)          | Performs prediction on the given iterable of data.                                                 |
| [`validate`](#fiftyone.utils.twelvelabs.TwelveLabsModel.validate)(config)              | Validates that the given config is an instance of <cls>Config.                                     |

#### *property* media_type

The media type processed by the model.

Supported values are “image” and “video”.

#### *property* has_embeddings

Whether this model can generate embeddings.

This method returns `False` by default. Models that can generate
embeddings should override this via implementing the
`EmbeddingsMixin` interface.

#### *property* can_embed_prompts

Whether this model can generate prompt embeddings.

This method returns `False` by default. Models that can generate
prompt embeddings should override this via implementing the
`PromptMixin` interface.

#### *property* ragged_batches

True/False whether [`transforms()`](#fiftyone.utils.twelvelabs.TwelveLabsModel.transforms) may return tensors of
different sizes. If True, then passing ragged lists of data to
[`predict_all()`](#fiftyone.utils.twelvelabs.TwelveLabsModel.predict_all) is not allowed.

#### *property* transforms

The preprocessing function that will/must be applied to each input
before prediction, or `None` if no preprocessing is performed.

#### *property* preprocess

Whether to apply [`transforms()`](#fiftyone.utils.twelvelabs.TwelveLabsModel.transforms) during inference (True) or to
assume that they have already been applied (False).

#### predict(arg)

Generates a caption for the given video.

* **Parameters:**
  **arg** – an active `eta.core.video.FFmpegVideoReader`
* **Returns:**
  a [`fiftyone.core.labels.Classification`](fiftyone.core.labels.md#fiftyone.core.labels.Classification)

#### embed(arg)

Generates a Marengo embedding for the given video.

* **Parameters:**
  **arg** – an active `eta.core.video.FFmpegVideoReader`
* **Returns:**
  a 512-dimensional 1D numpy array

#### get_embeddings()

Returns the embeddings generated by the last forward pass of the
model.

By convention, this method should always return an array whose first
axis represents batch size (which will always be 1 when [`predict()`](#fiftyone.utils.twelvelabs.TwelveLabsModel.predict)
was last used).

* **Returns:**
  a numpy array containing the embedding(s)

#### embed_prompt(arg)

Generates a Marengo text embedding for the given prompt.

This enables text-to-video similarity searches, since Marengo embeds
text and video into a shared space.

* **Parameters:**
  **arg** – the text prompt
* **Returns:**
  a 512-dimensional 1D numpy array

#### embed_all(args)

Generates embeddings for the given iterable of data.

Subclasses can override this method to increase efficiency, but, by
default, this method simply iterates over the data and applies
[`embed()`](#fiftyone.utils.twelvelabs.TwelveLabsModel.embed) to each.

* **Parameters:**
  **args** – an iterable of data. See [`predict_all()`](#fiftyone.utils.twelvelabs.TwelveLabsModel.predict_all) for details
* **Returns:**
  a numpy array containing the embeddings stacked along axis 0

#### embed_prompts(args)

Generates embeddings for the given prompts.

Subclasses can override this method to increase efficiency, but, by
default, this method simply iterates over the data and applies
[`embed_prompt()`](#fiftyone.utils.twelvelabs.TwelveLabsModel.embed_prompt) to each.

* **Parameters:**
  **args** – an iterable of prompts
* **Returns:**
  a numpy array containing the embeddings stacked along axis 0

#### *classmethod* from_config(config)

Instantiates a Configurable class from a <cls>Config instance.

#### *classmethod* from_dict(d)

Instantiates a Configurable class from a <cls>Config dict.

* **Parameters:**
  **d** – a dict to construct a <cls>Config
* **Returns:**
  an instance of cls

#### *classmethod* from_json(json_path)

Instantiates a Configurable class from a <cls>Config JSON file.

* **Parameters:**
  **json_path** – path to a JSON file for type <cls>Config
* **Returns:**
  an instance of cls

#### *classmethod* from_kwargs(\*\*kwargs)

Instantiates a Configurable class from keyword arguments defining
the attributes of a <cls>Config.

* **Parameters:**
  **\*\*kwargs** – keyword arguments that define the fields of a
  <cls>Config dict
* **Returns:**
  an instance of cls

#### *property* has_logits

Whether this model can generate logits for its predictions.

This method returns `False` by default. Models that can generate
logits should override this via implementing the
`LogitsMixin` interface.

#### *static* parse(class_name, module_name=None)

Parses a Configurable subclass name string.

Assumes both the Configurable class and the Config class are defined
in the same module. The module containing the classes will be loaded
if necessary.

* **Parameters:**
  * **class_name** – a string containing the name of the Configurable class,
    e.g. “ClassName”, or a fully-qualified class name, e.g.
    “eta.core.config.ClassName”
  * **module_name** – a string containing the fully-qualified module name,
    e.g. “eta.core.config”, or None if class_name includes the
    module name. Set module_name = \_\_name_\_ to load a class from
    the calling module
* **Returns:**
  the Configurable class
  config_cls: the Config class associated with cls
* **Return type:**
  [cls](fiftyone.brain.internal.core.elasticsearch.md#fiftyone.brain.internal.core.elasticsearch.ElasticsearchSimilarityConfig.cls)

#### predict_all(args)

Performs prediction on the given iterable of data.

Image models should support, at minimum, processing `args` values
that are either lists of uint8 numpy arrays (HWC) or numpy array
tensors (NHWC).

Video models should support, at minimum, processing `args` values
that are lists of `eta.core.video.VideoReader` instances.

Subclasses can override this method to increase efficiency, but, by
default, this method simply iterates over the data and applies
[`predict()`](#fiftyone.utils.twelvelabs.TwelveLabsModel.predict) to each.

* **Parameters:**
  **args** – an iterable of data
* **Returns:**
  a list of [`fiftyone.core.labels.Label`](fiftyone.core.labels.md#fiftyone.core.labels.Label) instances or a list
  of dicts of [`fiftyone.core.labels.Label`](fiftyone.core.labels.md#fiftyone.core.labels.Label) instances
  containing the predictions

#### *classmethod* validate(config)

Validates that the given config is an instance of <cls>Config.

* **Raises:**
  **ConfigurableError** – if config is not an instance of <cls>Config