langchain/libs/community/langchain_community/embeddings/databricks.py

from __future__ import annotations

from typing import Iterator, List
from urllib.parse import urlparse

from langchain_community.embeddings.mlflow import MlflowEmbeddings


def _chunk(texts: List[str], size: int) -> Iterator[List[str]]:
    for i in range(0, len(texts), size):
        yield texts[i : i + size]


class DatabricksEmbeddings(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:
        .. code-block:: python

            from langchain_community.embeddings import DatabricksEmbeddings

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

    target_uri: str = "databricks"
    """The target URI to use. Defaults to ``databricks``."""

    @property
    def _mlflow_extras(self) -> str:
        return ""

    def _validate_uri(self) -> None:
        if self.target_uri == "databricks":
            return

        if urlparse(self.target_uri).scheme != "databricks":
            raise ValueError(
                "Invalid target URI. The target URI must be a valid databricks URI."
            )