GradientEmbeddings#

class langchain_community.embeddings.gradient_ai.GradientEmbeddings[source]#

Bases: BaseModel, Embeddings

Gradient.ai Embedding models.

GradientLLM is a class to interact with Embedding Models on gradient.ai

To use, set the environment variable GRADIENT_ACCESS_TOKEN with your API token and GRADIENT_WORKSPACE_ID for your gradient workspace, or alternatively provide them as keywords to the constructor of this class.

Example

from langchain_community.embeddings import GradientEmbeddings
GradientEmbeddings(
    model="bge-large",
    gradient_workspace_id="12345614fc0_workspace",
    gradient_access_token="gradientai-access_token",
)

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 client: Any = None#: Gradient client.

param gradient_access_token: str | None = None#: gradient.ai API Token, which can be generated by going to https://auth.gradient.ai/select-workspace and selecting “Access tokens” under the profile drop-down.

param gradient_api_url: str = 'https://api.gradient.ai/api'#: Endpoint URL to use.

param gradient_workspace_id: str | None = None#: Underlying gradient.ai workspace_id.

param model: str [Required]#: Underlying gradient.ai model id.

param query_prompt_for_retrieval: str | None = None#: Query pre-prompt

async aembed_documents(texts: List[str]) → List[List[float]][source]#

Async call out to Gradient’s embedding endpoint.

Parameters:: texts (List[str]) – The list of texts to embed.
Returns:: List of embeddings, one for each text.
Return type:: List[List[float]]

async aembed_query(text: str) → List[float][source]#

Async call out to Gradient’s embedding endpoint.

Parameters:: text (str) – The text to embed.
Returns:: Embeddings for the text.
Return type:: List[float]

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

Call out to Gradient’s embedding endpoint.

Parameters:: texts (List[str]) – The list of texts to embed.
Returns:: List of embeddings, one for each text.
Return type:: List[List[float]]

embed_query(text: str) → List[float][source]#

Call out to Gradient’s embedding endpoint.

Parameters:: text (str) – The text to embed.
Returns:: Embeddings for the text.
Return type:: List[float]

Examples using GradientEmbeddings

Gradient