langchain_community.embeddings.databricks.DatabricksEmbeddings¶
class langchain_community.embeddings.databricks.DatabricksEmbeddings[source]¶
Bases: MlflowEmbeddings
Wrapper around embeddings LLMs in Databricks.
To use, you should have the mlflow python package installed.
For more information, see https://mlflow.org/docs/latest/llms/deployments/databricks.html.
Example
from langchain_community.embeddings import DatabricksEmbeddings
embeddings = DatabricksEmbeddings(
    target_uri="databricks",
    endpoint="embeddings",
)
param endpoint: str [Required]¶
The endpoint to use.
param target_uri: str = 'databricks'¶
The target URI to use. Defaults to databricks.
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