langchain.embeddings.clarifai.ClarifaiEmbeddings

class langchain.embeddings.clarifai.ClarifaiEmbeddings(*, stub: Any = None, userDataObject: Any = None, model_id: Optional[str] = None, model_version_id: Optional[str] = None, app_id: Optional[str] = None, user_id: Optional[str] = None, pat: Optional[str] = None, api_base: str = 'https://api.clarifai.com')

Bases: BaseModel, Embeddings

Clarifai embedding models.

To use, you should have the clarifai python package installed, and the environment variable CLARIFAI_PAT set with your personal access token or pass it as a named parameter to the constructor.

Example

from langchain.embeddings import ClarifaiEmbeddings
clarifai = ClarifaiEmbeddings(
    model="embed-english-light-v2.0", clarifai_api_key="my-api-key"
)

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

param api_base: str = 'https://api.clarifai.com'

param app_id: Optional[str] = None

Clarifai application id to use.

param model_id: Optional[str] = None

Model id to use.

param model_version_id: Optional[str] = None

Model version id to use.

param pat: Optional[str] = None

Clarifai personal access token to use.

param stub: Any = None

Clarifai stub.

param userDataObject: Any = None

Clarifai user data object.

param user_id: Optional[str] = None

Clarifai user id to use.

embed_documents(texts: List[str]) → List[List[float]]

Call out to Clarifai’s embedding models.

Parameters

texts – The list of texts to embed.

Returns

List of embeddings, one for each text.

embed_query(text: str) → List[float]

Call out to Clarifai’s embedding models.

Parameters

text – The text to embed.

Returns

Embeddings for the text.

validator validate_environment  »  all fields

Validate that api key and python package exists in environment.

model Config

Bases: object

Configuration for this pydantic object.

extra = 'forbid'

Examples using ClarifaiEmbeddings

• Clarifai