Source code for langchain_community.document_loaders.csv_loader

import csv
from io import TextIOWrapper
from pathlib import Path
from typing import Any, Dict, Iterator, List, Optional, Sequence, Union

from langchain_core.documents import Document

from langchain_community.document_loaders.base import BaseLoader
from langchain_community.document_loaders.helpers import detect_file_encodings
from langchain_community.document_loaders.unstructured import (
    UnstructuredFileLoader,
    validate_unstructured_version,
)


[docs]class CSVLoader(BaseLoader): """Load a `CSV` file into a list of Documents. Each document represents one row of the CSV file. Every row is converted into a key/value pair and outputted to a new line in the document's page_content. The source for each document loaded from csv is set to the value of the `file_path` argument for all documents by default. You can override this by setting the `source_column` argument to the name of a column in the CSV file. The source of each document will then be set to the value of the column with the name specified in `source_column`. Output Example: .. code-block:: txt column1: value1 column2: value2 column3: value3 """
[docs] def __init__( self, file_path: Union[str, Path], source_column: Optional[str] = None, metadata_columns: Sequence[str] = (), csv_args: Optional[Dict] = None, encoding: Optional[str] = None, autodetect_encoding: bool = False, ): """ Args: file_path: The path to the CSV file. source_column: The name of the column in the CSV file to use as the source. Optional. Defaults to None. metadata_columns: A sequence of column names to use as metadata. Optional. csv_args: A dictionary of arguments to pass to the csv.DictReader. Optional. Defaults to None. encoding: The encoding of the CSV file. Optional. Defaults to None. autodetect_encoding: Whether to try to autodetect the file encoding. """ self.file_path = file_path self.source_column = source_column self.metadata_columns = metadata_columns self.encoding = encoding self.csv_args = csv_args or {} self.autodetect_encoding = autodetect_encoding
[docs] def lazy_load(self) -> Iterator[Document]: try: with open(self.file_path, newline="", encoding=self.encoding) as csvfile: yield from self.__read_file(csvfile) except UnicodeDecodeError as e: if self.autodetect_encoding: detected_encodings = detect_file_encodings(self.file_path) for encoding in detected_encodings: try: with open( self.file_path, newline="", encoding=encoding.encoding ) as csvfile: yield from self.__read_file(csvfile) break except UnicodeDecodeError: continue else: raise RuntimeError(f"Error loading {self.file_path}") from e except Exception as e: raise RuntimeError(f"Error loading {self.file_path}") from e
def __read_file(self, csvfile: TextIOWrapper) -> Iterator[Document]: csv_reader = csv.DictReader(csvfile, **self.csv_args) for i, row in enumerate(csv_reader): try: source = ( row[self.source_column] if self.source_column is not None else str(self.file_path) ) except KeyError: raise ValueError( f"Source column '{self.source_column}' not found in CSV file." ) content = "\n".join( f"""{k.strip() if k is not None else k}: {v.strip() if isinstance(v, str) else ','.join(map(str.strip, v)) if isinstance(v, list) else v}""" for k, v in row.items() if k not in self.metadata_columns ) metadata = {"source": source, "row": i} for col in self.metadata_columns: try: metadata[col] = row[col] except KeyError: raise ValueError(f"Metadata column '{col}' not found in CSV file.") yield Document(page_content=content, metadata=metadata)
[docs]class UnstructuredCSVLoader(UnstructuredFileLoader): """Load `CSV` files using `Unstructured`. Like other Unstructured loaders, UnstructuredCSVLoader can be used in both "single" and "elements" mode. If you use the loader in "elements" mode, the CSV file will be a single Unstructured Table element. If you use the loader in "elements" mode, an HTML representation of the table will be available in the "text_as_html" key in the document metadata. Examples -------- from langchain_community.document_loaders.csv_loader import UnstructuredCSVLoader loader = UnstructuredCSVLoader("stanley-cups.csv", mode="elements") docs = loader.load() """
[docs] def __init__( self, file_path: str, mode: str = "single", **unstructured_kwargs: Any ): """ Args: file_path: The path to the CSV file. mode: The mode to use when loading the CSV file. Optional. Defaults to "single". **unstructured_kwargs: Keyword arguments to pass to unstructured. """ validate_unstructured_version(min_unstructured_version="0.6.8") super().__init__(file_path=file_path, mode=mode, **unstructured_kwargs)
def _get_elements(self) -> List: from unstructured.partition.csv import partition_csv return partition_csv(filename=self.file_path, **self.unstructured_kwargs)