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twelvelabs

__all__ = ['TwelveLabsEmbeddingDriver'] module-attribute

TwelveLabsEmbeddingDriver

Bases: BaseEmbeddingDriver

TwelveLabs Marengo Embedding Driver.

Generates multimodal embeddings with TwelveLabs' Marengo model. Marengo maps text, images, and video into the same 512-dimensional vector space, so text queries can be matched against visual content stored in a vector store.

Attributes:

Name Type Description
model str

TwelveLabs Marengo model name. Defaults to marengo3.0.
api_key str | None

TwelveLabs API key. Defaults to the TWELVELABS_API_KEY environment variable.
client TwelveLabs

Optionally provide a custom twelvelabs.TwelveLabs client.
Source code in griptape/drivers/embedding/twelvelabs_embedding_driver.py 
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@define
class TwelveLabsEmbeddingDriver(BaseEmbeddingDriver):
    """TwelveLabs Marengo Embedding Driver.

    Generates multimodal embeddings with TwelveLabs' Marengo model. Marengo maps
    text, images, and video into the same 512-dimensional vector space, so text
    queries can be matched against visual content stored in a vector store.

    Attributes:
        model: TwelveLabs Marengo model name. Defaults to `marengo3.0`.
        api_key: TwelveLabs API key. Defaults to the `TWELVELABS_API_KEY` environment variable.
        client: Optionally provide a custom `twelvelabs.TwelveLabs` client.
    """

    DEFAULT_MODEL = "marengo3.0"

    model: str = field(default=DEFAULT_MODEL, kw_only=True, metadata={"serializable": True})
    api_key: str | None = field(default=None, kw_only=True, metadata={"serializable": False})
    _client: TwelveLabs | None = field(default=None, kw_only=True, alias="client", metadata={"serializable": False})

    @lazy_property()
    def client(self) -> TwelveLabs:
        return import_optional_dependency("twelvelabs").TwelveLabs(api_key=self.api_key)

    def try_embed_artifact(self, artifact: TextArtifact | ImageArtifact, **kwargs) -> list[float]:
        if isinstance(artifact, TextArtifact):
            return self.try_embed_chunk(artifact.value, **kwargs)
        response = self.client.embed.create(
            model_name=self.model,
            image_file=(artifact.name, artifact.value, artifact.mime_type),
        )
        return self._extract_vector(response.image_embedding)

    def try_embed_chunk(self, chunk: str, **kwargs) -> list[float]:
        response = self.client.embed.create(model_name=self.model, text=chunk)
        return self._extract_vector(response.text_embedding)

    def _extract_vector(self, result: Any) -> list[float]:
        if result is None or not result.segments:
            raise ValueError("TwelveLabs returned no embedding segments.")
        vector = result.segments[0].float_
        if vector is None:
            raise ValueError("TwelveLabs returned an empty embedding.")
        return vector

DEFAULT_MODEL = 'marengo3.0' class-attribute instance-attribute

_client = field(default=None, kw_only=True, alias='client', metadata={'serializable': False}) class-attribute instance-attribute

api_key = field(default=None, kw_only=True, metadata={'serializable': False}) class-attribute instance-attribute

model = field(default=DEFAULT_MODEL, kw_only=True, metadata={'serializable': True}) class-attribute instance-attribute

_extract_vector(result)

Source code in griptape/drivers/embedding/twelvelabs_embedding_driver.py 
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def _extract_vector(self, result: Any) -> list[float]:
    if result is None or not result.segments:
        raise ValueError("TwelveLabs returned no embedding segments.")
    vector = result.segments[0].float_
    if vector is None:
        raise ValueError("TwelveLabs returned an empty embedding.")
    return vector

client()

Source code in griptape/drivers/embedding/twelvelabs_embedding_driver.py 
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@lazy_property()
def client(self) -> TwelveLabs:
    return import_optional_dependency("twelvelabs").TwelveLabs(api_key=self.api_key)

try_embed_artifact(artifact, **kwargs)

Source code in griptape/drivers/embedding/twelvelabs_embedding_driver.py 
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def try_embed_artifact(self, artifact: TextArtifact | ImageArtifact, **kwargs) -> list[float]:
    if isinstance(artifact, TextArtifact):
        return self.try_embed_chunk(artifact.value, **kwargs)
    response = self.client.embed.create(
        model_name=self.model,
        image_file=(artifact.name, artifact.value, artifact.mime_type),
    )
    return self._extract_vector(response.image_embedding)

try_embed_chunk(chunk, **kwargs)

Source code in griptape/drivers/embedding/twelvelabs_embedding_driver.py 
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def try_embed_chunk(self, chunk: str, **kwargs) -> list[float]:
    response = self.client.embed.create(model_name=self.model, text=chunk)
    return self._extract_vector(response.text_embedding)