DatabricksEmbeddings

class langchain_databricks.embeddings.DatabricksEmbeddings

Bases: Embeddings, BaseModel

Databricks embedding model integration.

Setup:

Install langchain-databricks.

pip install -U langchain-databricks

If you are outside Databricks, set the Databricks workspace hostname and personal access token to environment variables:

export DATABRICKS_HOSTNAME="https://your-databricks-workspace"
export DATABRICKS_TOKEN="your-personal-access-token"

Key init args — completion params:

endpoint: str

Name of Databricks Model Serving endpoint to query.

target_uri: str

The target URI to use. Defaults to databricks.

query_params: Dict[str, str]

The parameters to use for queries.

documents_params: Dict[str, str]

The parameters to use for documents.

Instantiate:

Embed single text:

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

The target URI to use.

param endpoint: str [Required]

The endpoint to use.

param query_params: Dict[str, Any] = {}

The parameters to use for documents.

param target_uri: str = 'databricks'

The parameters to use for queries.

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_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