langchain.embeddings.mlflow_gateway.MlflowAIGatewayEmbeddings¶
class langchain.embeddings.mlflow_gateway.MlflowAIGatewayEmbeddings[source]¶
Bases: Embeddings, BaseModel
Wrapper around embeddings LLMs in the MLflow AI Gateway.
To use, you should have the mlflow[gateway] python package installed.
For more information, see https://mlflow.org/docs/latest/gateway/index.html.
Example
from langchain.embeddings import MlflowAIGatewayEmbeddings
embeddings = MlflowAIGatewayEmbeddings(
    gateway_uri="<your-mlflow-ai-gateway-uri>",
    route="<your-mlflow-ai-gateway-embeddings-route>"
)
param gateway_uri: Optional[str] = None¶
The URI for the MLflow AI Gateway API.
param route: str [Required]¶
The route to use for the MLflow AI Gateway API.
async aembed_documents(texts: List[str]) → List[List[float]]¶
Asynchronous Embed search docs.
async aembed_query(text: str) → List[float]¶
Asynchronous Embed query text.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶