DatabricksEmbeddings#

class langchain_community.embeddings.databricks.DatabricksEmbeddings[source]#

Bases: MlflowEmbeddings

Databricks embeddings.

To use, you should have the mlflow python package installed. For more information, see https://mlflow.org/docs/latest/llms/deployments.

Example

from langchain_community.embeddings import DatabricksEmbeddings

embeddings = DatabricksEmbeddings(
    target_uri="databricks",
    endpoint="embeddings",
)

param documents_params: Dict[str, str] = {}#

param endpoint: str [Required]#: The endpoint to use.

param query_params: Dict[str, str] = {}#: The parameters to use for documents.

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.

Parameters:: texts (List[str]) – List of text to embed.
Returns:: List of embeddings.
Return type:: List[List[float]]

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

Asynchronous Embed query text.

Parameters:: text (str) – Text to embed.
Returns:: Embedding.
Return type:: List[float]

embed(texts: List[str], params: Dict[str, str]) → List[List[float]]#

Parameters:

texts (List[str]) –
params (Dict[str, str]) –

Return type:

List[List[float]]

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

Embed search docs.

Parameters:: texts (List[str]) – List of text to embed.
Returns:: List of embeddings.
Return type:: List[List[float]]

embed_query(text: str) → List[float]#

Embed query text.

Parameters:: text (str) – Text to embed.
Returns:: Embedding.
Return type:: List[float]

Examples using DatabricksEmbeddings

Databricks