langchain_community.embeddings.mlflow.MlflowEmbeddings¶
class langchain_community.embeddings.mlflow.MlflowEmbeddings[source]¶
Bases: Embeddings, BaseModel
Wrapper around embeddings LLMs in MLflow.
To use, you should have the mlflow[genai] python package installed.
For more information, see https://mlflow.org/docs/latest/llms/deployments/server.html.
Example
from langchain_community.embeddings import MlflowEmbeddings
embeddings = MlflowEmbeddings(
    target_uri="http://localhost:5000",
    endpoint="embeddings",
)
param endpoint: str [Required]¶
The endpoint to use.
param target_uri: str [Required]¶
The target URI to use.
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¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include