langchain.embeddings.gradient_ai.TinyAsyncGradientEmbeddingClient¶
class langchain.embeddings.gradient_ai.TinyAsyncGradientEmbeddingClient(access_token: Optional[str] = None, workspace_id: Optional[str] = None, host: str = 'https://api.gradient.ai/api', aiosession: Optional[ClientSession] = None)[source]¶
A helper tool to embed Gradient. Not part of Langchain’s or Gradients stable API.
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
mini_client = TinyAsyncGradientEmbeddingClient(
    workspace_id="12345614fc0_workspace",
    access_token="gradientai-access_token",
)
embeds = mini_client.embed(
    model="bge-large",
    text=["doc1", "doc2"]
)
# or
embeds = await mini_client.aembed(
    model="bge-large",
    text=["doc1", "doc2"]
)
Methods
__init__([access_token, workspace_id, host, ...])
aembed(model, texts)
call the embedding of model, async method
embed(model, texts)
call the embedding of model
__init__(access_token: Optional[str] = None, workspace_id: Optional[str] = None, host: str = 'https://api.gradient.ai/api', aiosession: Optional[ClientSession] = None) → None[source]¶
async aembed(model: str, texts: List[str]) → List[List[float]][source]¶
call the embedding of model, async method
Parameters
model (str) – to embedding model
texts (List[str]) – List of sentences to embed.
Returns