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]