Source code for langchain_community.document_loaders.pyspark_dataframe

import itertools
import logging
import sys
from typing import TYPE_CHECKING, Any, Iterator, List, Optional, Tuple

from langchain_core.documents import Document

from langchain_community.document_loaders.base import BaseLoader

logger = logging.getLogger(__file__)

    from pyspark.sql import SparkSession

[docs]class PySparkDataFrameLoader(BaseLoader): """Load `PySpark` DataFrames."""
[docs] def __init__( self, spark_session: Optional["SparkSession"] = None, df: Optional[Any] = None, page_content_column: str = "text", fraction_of_memory: float = 0.1, ): """Initialize with a Spark DataFrame object. Args: spark_session: The SparkSession object. df: The Spark DataFrame object. page_content_column: The name of the column containing the page content. Defaults to "text". fraction_of_memory: The fraction of memory to use. Defaults to 0.1. """ try: from pyspark.sql import DataFrame, SparkSession except ImportError: raise ImportError( "pyspark is not installed. " "Please install it with `pip install pyspark`" ) self.spark = ( spark_session if spark_session else SparkSession.builder.getOrCreate() ) if not isinstance(df, DataFrame): raise ValueError( f"Expected data_frame to be a PySpark DataFrame, got {type(df)}" ) self.df = df self.page_content_column = page_content_column self.fraction_of_memory = fraction_of_memory self.num_rows, self.max_num_rows = self.get_num_rows() self.rdd_df = self.column_names = self.df.columns
[docs] def get_num_rows(self) -> Tuple[int, int]: """Gets the number of "feasible" rows for the DataFrame""" try: import psutil except ImportError as e: raise ImportError( "psutil not installed. Please install it with `pip install psutil`." ) from e row = self.df.limit(1).collect()[0] estimated_row_size = sys.getsizeof(row) mem_info = psutil.virtual_memory() available_memory = mem_info.available max_num_rows = int( (available_memory / estimated_row_size) * self.fraction_of_memory ) return min(max_num_rows, self.df.count()), max_num_rows
[docs] def lazy_load(self) -> Iterator[Document]: """A lazy loader for document content.""" for row in self.rdd_df.toLocalIterator(): metadata = {self.column_names[i]: row[i] for i in range(len(row))} text = metadata[self.page_content_column] metadata.pop(self.page_content_column) yield Document(page_content=text, metadata=metadata)
[docs] def load(self) -> List[Document]: """Load from the dataframe.""" if self.df.count() > self.max_num_rows: logger.warning( f"The number of DataFrame rows is {self.df.count()}, " f"but we will only include the amount " f"of rows that can reasonably fit in memory: {self.num_rows}." ) lazy_load_iterator = self.lazy_load() return list(itertools.islice(lazy_load_iterator, self.num_rows))