langchain_community.embeddings.databricks.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¶